Textbook of Psychiatric Epidemiology

Textbook in Psychiatric Epidemiology

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

Textbook in Psychiatric Epidemiology Edited by

Ming T. Tsuang Center for Behavioral Genomics, Department of Psychiatry, University of California, Harvard Institute of Psychiatric Epidemiology & Genetics, Harvard School of Public Health, Boston, USA

Mauricio Tohen Department of Psychiatry, University of Texas Health Science Centre at San Antonio, USA

Peter B. Jones Department of Psychiatry, University of Cambridge, UK

THIRD EDITION

A John Wiley & Sons, Ltd., Publication

This edition first published 2011, © 2011 John Wiley & Sons, Ltd. Wiley-Blackwell is an imprint of John Wiley & Sons, formed by the merger of Wiley’s global Scientific, Technical and Medical business with Blackwell Publishing. Registered office: John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK Other Editorial Offices: 9600 Garsington Road, Oxford, OX4 2DQ, UK 111 River Street, Hoboken, NJ 07030-5774, USA For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com/wiley-blackwell The right of the author to be identified as the author of this work has been asserted in accordance with the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. Designations used by companies to distinguish their products are often claimed as trademarks. All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners. The publisher is not associated with any product or vendor mentioned in this book. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought. The contents of this work are intended to further general scientific research, understanding, and discussion only and are not intended and should not be relied upon as recommending or promoting a specific method, diagnosis, or treatment by physicians for any particular patient. The publisher and the author make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of fitness for a particular purpose. In view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of medicines, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each medicine, equipment, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. Readers should consult with a specialist where appropriate. The fact that an organization or Website is referred to in this work as a citation and/or a potential source of further information does not mean that the author or the publisher endorses the information the organization or Website may provide or recommendations it may make. Further, readers should be aware that Internet Websites listed in this work may have changed or disappeared between when this work was written and when it is read. No warranty may be created or extended by any promotional statements for this work. Neither the publisher nor the author shall be liable for any damages arising herefrom. Library of Congress Cataloguing-in-Publication Data Textbook of psychiatric epidemiology / [edited by] Ming T. Tsuang, Mauricio Tohen, Peter B. Jones. – 3rd ed. p. ; cm. Rev. ed. of: Textbook in psychiatric epidemiology / edited by Ming T. Tsuang, Mauricio Tohen. 2nd ed. c2002. Includes bibliographical references and index. ISBN 978-0-470-69467-1 (cloth) 1. Psychiatric epidemiology. I. Tsuang, Ming T., 1931- II. Tohen, Mauricio, 1951- III. Jones, Peter B. (Peter Brian), 1960IV. Textbook in psychiatric epidemiology. [DNLM: 1. Epidemiologic Methods. 2. Mental Disorders–epidemiology. 3. Mental Disorders–diagnosis. WM 140] RC455.2.E64T49 2011 362.2 0422–dc22 2010046396 ISBN: 978-0-470-69467-1 A catalogue record for this book is available from the British Library. This book is published in the following electronic formats: ePDF: 978-0-470-97672-2; Wiley Online Library: 978-0-470-97673-9; ePub: 978-0-470-97740-8. Set in 9/12pt Sabon by Laserwords Private Limited, Chennai, India First

2011

Contents

List of Contributors, xi 1 Introduction to epidemiologic research methods, 1 Glyn Lewis 1.1 What is epidemiology? 1 1.2 Causation in medicine, 2 1.3 Causal inference, 6 1.4 The future for psychiatric epidemiology, 7 References, 7 2 Analysis of categorical data: The odds ratio as a measure of association and beyond, 9 Garrett M. Fitzmaurice and Caitlin Ravichandran 2.1 Introduction, 9 2.2 Inference for a single proportion, 10 2.3 Analysis of 2 × 2 contingency tables, 11 2.4 Analysis of sets of 2 × 2 contingency tables, 16 2.5 Logistic regression, 18 2.6 Advanced topics, 25 2.7 Concluding remarks, 29 2.8 Further reading, 29 References, 29 3 Genetic epidemiology, 31 Stephen V. Faraone, Stephen J. Glatt and Ming T. Tsuang 3.1 Introduction, 31 3.2 The chain of psychiatric genetic research, 31 3.3 Psychiatric genetics and psychiatric epidemiology, 44 Acknowledgements, 45

References, 45 Further reading, 47 4 Examining gene–environment interplay in psychiatric disorders, 53 Judith Allardyce and Jim van Os 4.1 Introduction, 53 4.2 The process of genetic epidemiology, 54 4.3 Gene–environment interplay takes different forms, 55 4.4 Gene–environment correlation, 55 4.5 Gene–environment interaction, 58 4.6 Measurement of genotype, environmental exposure and pathological phenotype, 58 4.7 Models of GxE, 60 4.8 Which scale should we use to measure GxE? 61 4.9 Study designs for the detection of GxE, 62 4.10 Threats to the validity of epidemiological GxE studies, 65 4.11 Epigenetic mechanisms, 67 References, 67 5 Reliability, 73 Patrick E. Shrout 5.1 Introduction, 73 5.2 The reliability coefficient, 73 5.3 Designs for estimating reliability, 74 5.4 Statistical remedies for low reliability, 76 5.5 Reliability theory and binary judgements, 77 5.6 Reliability statistics: General, 78 5.7 Other reliability statistics, 82 5.8 Summary and conclusions, 83 References, 83

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CONTENTS

6 Moderators and mediators: Towards the genetic and environmental bases of psychiatric disorders, 87 Helena Chmura Kraemer 6.1 Introduction, 87 6.2 Current methodological barriers, 89 6.3 Moderation, mediation and other ways in which risk factors ‘work together’, 92 6.4 Extensions, 94 6.5 Beyond moderators and mediators, 95 References, 96 7 Validity: Definitions and applications to psychiatric research, 99 Jill M. Goldstein, Sara Cherkerzian and John C. Simpson 7.1 Introduction, 99 7.2 Validity of a construct, 99 7.3 Validity of the relationships between variables, 110 7.4 Summary, 112 Acknowledgements, 113 References, 113 8 Use of register data for psychiatric epidemiology in the Nordic countries, 117 Jouko Miettunen, Jaana Suvisaari, Jari Haukka and Matti Isohanni 8.1 Introduction, 117 8.2 Registers for use in psychiatric research, 118 8.3 Register research in Denmark, 122 8.4 Register research in Finland, 123 8.5 Register research in Norway, 124 8.6 Register research in Sweden, 125 8.7 Discussion, 126 Acknowledgements, 127 References, 127 9 An introduction to mental health services research, 133 ´ Anna Fernandez, Alejandra Pinto-Meza, Antoni Serrano-Blanco, Jordi Alonso and Josep Maria Haro 9.1 Introduction, 133 9.2 What is mental health services research? 134 9.3 A framework for mental health services research, 135 vi

9.4 Key concepts in mental health services research, 137 9.5 Examples of mental health services research studies, 141 9.6 Conclusion, 152 References, 152 10 The pharmacoepidemiology of psychiatric medications, 155 Philip S. Wang, Alan M. Brookhart, Christine Ulbricht and Sebastian Schneeweiss 10.1 Introduction, 155 10.2 Overview of psychopharmacoepidemiology, 156 10.3 Sources of data, 157 10.4 Examples of recent psychopharmacoepidemiologic studies, 159 10.5 Conclusions, 162 Acknowledgements, 163 References, 163 11 Peering into the future of psychiatric epidemiology, 167 Michaeline Bresnahan, Ezra Susser, Dana March and Bruce Link 11.1 Introduction, 167 11.2 Levels of causation: A historical overview, 167 11.3 Levels of causation, 169 11.4 Causation over (life) time, 172 11.5 Examples, 174 11.6 Framing the future, 178 References, 179 12 Studying the natural history of psychopathology, 183 William W. Eaton 12.1 Introduction, 183 12.2 Onset, 183 12.3 Course, 188 12.4 Outcome, 191 12.5 Methodological concepts for studying the natural history of psychopathology, 192 12.6 Conclusion, 195 Acknowledgements, 195 References, 195

CONTENTS

13 Symptom scales and diagnostic schedules in adult psychiatry, 199 Jane M. Murphy 13.1 Introduction, 199 13.2 North American instruments for epidemiological research, 202 13.3 North American instruments for psychiatric services and primary care, 205 13.4 European instruments for psychiatric services and primary care, 206 13.5 European instruments for epidemiological research, 208 13.6 Summary, 210 Acknowledgements, 212 References, 212 14 The National Comorbidity Survey (NCS) and its extensions, 221 Ronald C. Kessler 14.1 Introduction, 221 14.2 The baseline NCS, 221 14.3 The NCS follow-up survey (NCS-2), 227 14.4 The NCS replication survey (NCS-R), 229 14.5 The NCS-R adolescent supplement (NCS-A), 233 14.6 The WHO WMH Surveys, 234 14.7 Overview, 236 Acknowledgements, 237 References, 237 15 Experimental epidemiology, 243 John R. Geddes 15.1 Introduction, 243 15.2 Limitations of non-randomised evidence, 243 15.3 RCTs: The translation of the experimental design into the real world, 245 15.4 Importance and control of systematic error or bias, 245 15.5 Importance and control of random error and noise, 248 15.6 Reporting the results of clinical trials—the CONSORT statement, 248 15.7 Different clinical questions will prioritise control of different threats to validity and confidence, 248

15.8 The classification of RCTs, 251 15.9 Effectiveness trials in schizophrenia, 255 15.10 Department of Veterans Affairs co-operative study on the cost-effectiveness of Olanzapine (Rosenheck), 255 15.11 The clinical antipsychotic trials of intervention effectiveness (CATIE) study, 257 15.12 Cost utility of the latest antipsychotic drugs in schizophrenia study (CUtLASS 1), 257 15.13 European first-episode schizophrenia trial (EUFEST), 258 15.14 The size and cost of experimental studies in psychiatry, 259 15.15 Clinical trials in the future, 259 References, 259 16 Epidemiology of Schizophrenia, 263 William W. Eaton, Chuan-Yu Chen and Evelyn J. Bromet 16.1 Introduction, 263 16.2 Methods, 263 16.3 The burden of schizophrenia, 264 16.4 Natural history, 265 16.5 Demographic correlates, 268 16.6 Social risk factors, 269 16.7 Biological risk factors, 272 16.8 Prevention, 279 16.9 Discussion, 280 References, 280 17 Epidemiology of depressive disorders, 289 Deborah S. Hasin, Miriam C. Fenton and Myrna M. Weissman 17.1 Introduction, 289 17.2 Major depression, 290 17.3 Dysthymia, 302 17.4 Summary, 304 Appendix 17.A Measurement of major depression in the NLAES and NESARC, 304 References, 305 18 Epidemiology of anxiety disorders, 311 Ewald Horwath, Felicia Gould and Myrna M. Weissman 18.1 Introduction, 311 vii

CONTENTS

18.2 18.3 18.4 18.5 18.6 18.7 18.8

Anxiety disorders, 311 Panic disorder, 313 Agoraphobia, 316 Social phobia, 317 Generalised anxiety disorder, 318 Obsessive–compulsive disorder, 319 Anxiety and affective disorders and mass disasters, 320 18.9 Future developments, 323 Acknowledgements, 323 References, 323 Further reading, 326 19 Epidemiology of bipolar disorder in adults and children, 329 Kathleen R. Merikangas and Mauricio Tohen 19.1 Introduction, 329 19.2 Epidemiology of bipolar disorder, 329 19.3 Patterns of comorbidity of bipolar disorder, 333 19.4 Risk Factors, 334 19.5 Future directions, 336 19.6 Summary, 338 References, 338 20 Epidemiology of eating disorders, 343 Tracey D. Wade, Anna Keski-Rahkonen and James I. Hudson 20.1 Introduction, 343 20.2 Case definition, 343 20.3 Major prevalence studies, 345 20.4 Incidence studies, 351 20.5 Comorbidity, 351 20.6 Mortality from eating disorders, 352 20.7 Risk factors, 352 20.8 Future directions, 355 References, 356 21 Epidemiology of alcohol use, abuse and dependence, 361 Deborah A. Dawson, Ralph W. Hingson and Bridget F. Grant 21.1 Introduction, 361 21.2 Population estimates of per capita consumption, 361 21.3 Survey-based estimates of the prevalence of drinking, 362 21.4 Alcohol-related mortality and morbidity, 365 viii

21.5 Alcohol and injury, 365 21.6 Alcohol and chronic disease, 366 21.7 Diagnostic classification of alcohol use disorders, 367 21.8 Population estimates, prevalence, incidence and natural course of alcohol use disorders, 368 21.9 Comorbidity of DSM-IV alcohol use disorders and other psychiatric disorders, 371 21.10 Summary, 374 Acknowledgements, 375 References, 375 22 Epidemiology of illicit drug use disorders, 381 Wilson M. Compton, Marsha F. Lopez, Kevin P. Conway and Yonette F. Thomas 22.1 Introduction, 381 22.2 Drug consumption, 381 22.3 Definitions, 384 22.4 Rates of DSM-IV abuse and dependence, 384 22.5 Global rates of drug use disorders, 387 22.6 Comorbidities with psychiatric conditions, 388 22.7 Genetic epidemiology, 391 22.8 Future opportunities, 391 22.9 Conclusions, 394 22.10 Disclaimer, 394 References, 394 23 The epidemiology of personality disorders: Findings, methods and concepts, 401 Michael J. Lyons, Beth A. Jerskey and Margo R. Genderson 23.1 Introduction, 401 23.2 Substantive findings, 402 23.3 Course, prognosis and developmental issues, 404 23.4 Treated prevalence, 406 23.5 Prevalence of specific personality disorders, 407 23.6 Antisocial personality disorder, 410 23.7 Conceptual issues, 419 23.8 Models of personality disorder, 419 23.9 Methodological issues, 422 23.10 Future directions, 427 References, 428

CONTENTS

24 The epidemiology of depression and anxiety in children and adolescents, 435 Kathleen Ries Merikangas and Erin F. Nakamura 24.1 Introduction, 435 24.2 Magnitude of depression and anxiety in children and adolescents, 435 24.3 Correlates and risk factors, 438 24.4 Service patterns and impact, 442 24.5 Summary, 443 References, 443 25 Epidemiology of attention deficit hyperactivity disorder, 449 Stephen V. Faraone 25.1 Introduction, 449 25.2 Prevalence of ADHD, 450 25.3 Pharmacoeconomics of ADHD, 451 25.4 Comorbid psychiatric disorders, 451 25.5 Demographic risk factors, 452 25.6 Genetic risk factors, 453 25.7 Environmental risk factors for ADHD, 457 25.8 Summary and conclusions, 460 25.9 Future directions, 460 Acknowledgements, 461 References, 461 26 The epidemiology of autism, 469 Gregory S. Liptak 26.1 Introduction, 469 26.2 Background, 469 26.3 Definition and diagnosis, 469 26.4 Natural history, 472 26.5 Prevalence, 473 26.6 Risk factors, 473 26.7 Genetic factors, 476 26.8 Public health impact, 476 26.9 Associations and causal factors, 477 26.10 Future directions, 477 26.11 Summary, 478 References, 478 27 Mental illness, women, mothers and their children, 483 Kathryn M. Abel and Vera A. Morgan 27.1 Introduction, 483

27.2 The epidemiology of mental illness in women of reproductive age, 484 27.3 Fertility and fecundity in women with mental illness, 487 27.4 Maternal mental illness at the time of conception and during pregnancy, 488 27.5 Gene–environment interactions and offspring outcomes, 493 27.6 Obstetric complications and risk of adult onset mental disorder in offspring, 493 27.7 Parental condition, 496 27.8 Motherhood and perinatal mental illness, 500 27.9 Designing studies examining the relationship between maternal mental illness and outcomes for their children, 504 27.10 Conclusions, 507 References, 507 Further reading, 515 28 Epidemiology of suicide and attempted suicide, 517 Dianne Currier and Maria A. Oquendo 28.1 Introduction, 517 28.2 Definitions, 517 28.3 Prevalence of suicide and attempted suicide, 518 28.4 Risk factors for suicide and attempted suicide, 520 28.5 Protective factors, 526 28.6 Conclusions, 526 Acknowledgements, 526 References, 526 29 Epidemiology and geriatric psychiatry, 535 Celia F. Hybels and Dan G. Blazer 29.1 Introduction, 535 29.2 Issues of case identification, 535 29.3 The distribution of cases, 536 29.4 Aetiological studies, 544 29.5 Outcome studies, 547 29.6 Historical trends in the epidemiology of psychiatric disorders in late life, 549 29.7 Use of health care services, 550 References, 550 ix

CONTENTS

30 Recent epidemiological studies of psychiatric disorders in Japan, 559 Masayoshi Kawai, Kenji J. Tsuchiya and Nori Takei 30.1 Introduction, 559 30.2 Schizophrenia, 560 30.3 Affective disorders, 566 30.4 Autism and autism spectrum disorder, 569 30.5 Summary, 572 References, 573 31 Epidemiology of migration and serious mental illness: The example of migrants to Europe, 579 Monica Charalambides, Craig Morgan and Robin M. Murray 31.1 Introduction, 579 31.2 Defining the constructs, 579 31.3 High rates of psychosis in migrants: A genuine finding or methodological artefact?, 581 31.4 Possible explanations, 584 31.5 Biological considerations, 585 31.6 Cannabis use, 586 31.7 Adverse social experiences, 586 31.8 Mechanisms, 589 31.9 Implications, 590 References, 591 32 Epidemiology of migration substance use disorder in Latin American populations and migration to the United States, 595 Mar´ıa Elena Medina-Mora, Guilherme Borges, Tania Real and Jorge Villatoro 32.1 Introduction, 595

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32.2 Definitions: What do we understand by migration?, 595 32.3 Countries of origin: Social, political and other reasons that trigger migration, 597 32.4 Living conditions of migrants in the United States, 599 32.5 Alcohol and drug use in countries of origin and receiving communities, 600 32.6 Dependence and treatment rates, 604 32.7 The process of migrating, 606 32.8 Migration, substance use and access to services, 608 32.9 Returning migrants and families left behind, 611 32.10 Conclusions, 611 References, 611 33 Early detection and intervention as approaches for preventing schizophrenia, 617 Ming T. Tsuang, William S. Stone, Margo Genderson and Michael Lyons 33.1 Introduction, 617 33.2 Modelling genetic and phenotypic heterogeneity, 618 33.3 Defining a syndrome of liability using cognitive and clinical characteristics of relatives, 620 33.4 Gene-based vs. genome-based research, 624 33.5 Future directions, 626 33.6 Clinical implications, 627 Acknowledgements, 627 References, 628 Index, 633

List of Contributors

Kathryn M. Abel

Monica Charalambides

Centre for Women’s Mental Health, 3rd Floor East, Jean McFarlane Bdg, University of Manchester, Oxford Road, Manchester M13 9PL, UK

Institute of Psychiatry, Psychological Medicine, King’s Institute, De Crespigny Park, Denmark Hill, London SE5 8AF, UK

Judith Allardyce

Chuan-Yu Chen

Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, South Limburg Mental Health Research and Teaching Network, EURON, SEARCH, Maastricht University Medical Centre, PO BOX 616 (VIJV), 6200 MD Maastricht, The Netherlands

National Health Research Institutes, Institute of Population Health Sciences, Division of Mental Health and Addiction Medicine, Taiwan

Jordi Alonso Carrer del Doctor Aiguader, 88, Edifici PRBB, E-08003 Barcelona, Spain

Dan G. Blazer Department of Psychiatry and Behavioral Sciences, Center for the Study of Aging and Human Development, Box 3003, Duke University Medical Center, Durham NC 27710, USA

Guilherme Borges Ramon de la Fuente National Institute of Psychiatry, Calzada Mexico Xochimilco 101, DF 14370, Mexico

Michaeline Bresnahan Mailman School of Public Health, Columbia University, Department of Epidemiology, 600 West 168th Street, New York NY 10032, USA

Evelyn J. Bromet Dept of Psychiatry, SUNY Stony Brook University, PutnamHall – South Campus, Stony Brook NY 11794-8790, USA

Alan M. Brookhart Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Harvard Medical School, Boston MA 02115, USA

Sara Cherkerzian Departments of Psychiatry and Medicine at Brigham and Women’s Hospital (BWH), Harvard Medical School, Boston, MA, USA

Wilson M. Compton Division of Epidemiology, Services, and Prevention Research, National Institute on Drug Abuse, 6001 Executive Blvd., MSC 9589, Bethesda MD 20892-9589, USA

Kevin P. Conway Division of Epidemiology, Services, and Prevention Research, National Institute on Drug Abuse, 6001 Executive Blvd., MSC 9589, Bethesda MD 20892-9589, USA

Dianne Currier Division of Molecular Imaging and Neuropathology, Department of Psychiatry, Columbia University, New York, NY 10032, USA

Deborah A. Dawson Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Suite 514, Willco Building, 6000 Executive Boulevard, MSC 7003, Bethesda MD 20892-7003, USA

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LIST OF CONTRIBUTORS

William W. Eaton Dept of Mental Health, Bloomberg School of Public Health, John Hopkins School of Hygiene and Public Health, John Hopkins University, 615 N. Wolfe Street, Baltimore MD 21205, USA

Stephen V. Faraone Center for NeuroPsychiatric Genetics, SUNY Upstate Medical University, Weiskotten Hall 3285, Syracuse NY 13210, USA

Miriam C. Fenton New York State Psychiatric Institute 1051 Riverside Drive, New York, NY 10032, USA

´ Anna Fernandez Fundac´ıo Sant Joan de D´eu Research and Development Unit, Dr. Antoni Pujadas, 42., 08830 Sant Boi de Llobregat, Barcelona, Spain

Garrett Fitzmaurice Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, MA, USA

Institutes of Health, NIAAA/LEB, 5635 Fishers Lane, Bethesda MD 20892-9304, USA

Josep Maria Haro Fundacı´o Sant Joan de D´eu Research and Development Unit, Dr. Antoni Pujadas, 42., 08830 Sant Boi de Llobregat, Barcelona, Spain

Deborah S. Hasin Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York NY 10032, USA

Jari Haukka National Institute for Health and Welfare, Department of Mental Health and Substance Abuse Services, P.O. Box 30, FI 00271 Helsinki, Finland

Ralph W. Hingson Division of Epidemiology and Prevention Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, NIAAA/LEB, 5635 Fishers Lane, Bethesda MD 20892-9304, USA

Ewald Horwath John R. Geddes Oxford University, Department of Psychiatry, Warneford Hospital, Oxford OX3 7JX, UK

Margo R. Genderson Department of Psychology, Boston University, Boston, MA 02215, USA

Stephen J. Glatt Departments of Psychiatry and Behavioral Sciences and Neuroscience and Physiology, Medical Genetics Research Center, SUNY Upstate Medical University, NY, USA

Jill M. Goldstein Departments of Psychiatry and Medicine at Brigham and Women’s Hospital (BWH), Harvard Medical School, Boston MA02115, USA

Department of Psychiatry, Epidemiology and Public Health, Miller School of Medicine, University of Miami, MHHC, Suite 3100, 1695 NW 9th Ave, Miami FL 33136, USA

James I. Hudson Psychiatric Epidemiology Research Program, Harvard Medical School/McLean Hospital, 115 Mill Street, Belmont MA 02478, USA

Celia F. Hybels Department of Psychiatry and Behavioral Sciences, Center for the Study of Aging and Human Development, Duke University Medical Center, Box 3003, Durham NC 27710, USA

Matti Isohanni Department of Psychiatry, University of Oulu, Finland

Felicia Gould Department of Psychiatry & Behavioral Sciences, Miller School of Medicine, University of Miami, 3225 Aviation Ave., Suite 303, Miami FL 33133, USA

Bridget F. Grant Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National

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Beth A. Jerskey Instructor of Psychiatry and Human Behavior (Research), Alpert Medical School of Brown University, Providence RI 02912, USA

Peter B. Jones Department of Psychiatry, University of Cambridge, Box 189, Addenbrooke’s Hospital, Cambridge CB2 2QQ, UK

LIST OF CONTRIBUTORS

Masayoshi Kawai

Kathleen R. Merikangas

Research Centre for Child Mental Development, Hamamatsu University School of Medicine 1-20-1, Handayama, Higashi-Ku, Hamamatsu 431–3192, Japan

Genetic Epidemiology Research Branch, Intramural Research Program, NIMH, Porter Neuroscience Research Center, 35 Convent Dr., MSC 3720, Bethesda MD 20892-3720, USA

Anna Keski-Rahkonen

Jouko Miettunen

Academy of Finland, University of Helsinki, Helsinki, Finland

Department of Psychiatry, University of Oulu, P.O.Box 5000, FI-90014 Helsinki, Finland

Ronald C. Kessler

Craig Morgan

Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston MA 02115, USA

Institute of Psychiatry, Psychological Medicine, King’s Institute, De Crespigny Park, Denmark Hill, London SE5 8AF, UK

Helena Chmura Kraemer

Vera A. Morgan

Department of Psychiatry, Stanford University, Palo Alto, Stanford CA 94305, USA

Neuropsychiatric Epidemiology Research Unit, School of Psychiatry & Clinical Neurosciences, The University of Western Australia, Medical Research Foundation Building, 50 Murray Street, Perth, WA 6000, Australia

Glyn Lewis Academic Unit of Psychiatry,University of Bristol, Cotham House, Cotham Hill, Bristol BS6 6JL, UK

Jane M. Murphy Bruce Link Mailman School of Public Health, Columbia University, Department of Epidemiology, 600 West 168th Street, New York NY 10032, USA

Greg S. Liptak Center for Development, Behavior and Genetics, SUNY Upstate Medical University, Center for Children’s’ Health Policy, Syracuse NY 13210, USA

Professor, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Room 215, 5 Longfellow Place, Boston MA 02114, USA

Robin M. Murray Institute of Psychiatry, Psychological Medicine, King’s Institute, De Crespigny Park, Denmark Hill, London SE5 8AF, UK

Erin F. Nakamura Marsha F. Lopez Division of Epidemiology, Services, and Prevention Research, National Institute on Drug Abuse, 6001 Executive Blvd., MSC 9589, Bethesda, MD 20892-9589, USA

Michael J. Lyons Department of Psychology, Boston University, Harvard Institute of Psychiatric Epidemiology and Genetics, 64 Cummington Street, Boston MA 02215, USA

Dana March Mailman School of Public Health, Columbia University, Department of Epidemiology, 600 West 168th Street, New York NY 10032, USA

Mar´ıa Elena Medina-Mora Ramon de la Fuente National Institute of Psychiatry, Calzada Mexico Xochimilco 101, DF 14370, Mexico

Genetic Epidemiology Research Branch, Intramural Research Program, NIMH, Porter Neuroscience Research Center, 35 Convent Dr., MSC 3720, Bethesda MD 20892-3720, USA

Maria A. Oquendo Division of Molecular Imaging and Neuropathology, Department of Psychiatry, Columbia University, NY, USA

Alejandra Pinto-Meza Fundac´ıo Sant Joan de D´eu Research and Development Unit, Dr. Antoni Pujadas, 42., 08830 Sant Boi de Llobregat, Barcelona, Spain

Antoni Pujadas Fundaci o Sant Joan de Deu Research and Development Unit, Dr. Antoni Pujadas, 42., 08830 Sant Boi de Llobregat, Barcelona, Spain

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LIST OF CONTRIBUTORS

Caitlin Ravichandran

Yonette F. Thomas

Laboratory for Psychiatric Biostatistics, McLean Hospital, 115 Mill St, Belmont MA 02478, USA

Howard University, Office of the Vice President for Research and Compliance (OVPRC), C.B. Powell Building, Suite 137, 525 Bryant Street, N.W., Washington DC 20059, USA

Tania Real Ramon de la Fuente National Institute of Psychiatry, Calzada Mexico Xochimilco 101, DF 14370, Mexico

Mauricio Tohen

Rebeca Robles

Division of Mood and Anxiety Disorders, University of Texas Health Science Center at San Antonio, 7526 Louis Pasteur Drive, San Antonio TX 78229-3900, USA

Ramon de la Fuente National Institute of Psychiatry, Calzada Mexico Xochimilco 101, DF 14370, Mexico

Ming T. Tsuang

Sebastian Schneeweiss Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Harvard Medical School, Boston MA02115, USA

Antoni Serrano-Blanco Fundac´ıo Sant Joan de D´eu Research and Development Unit, Dr. Antoni Pujadas, 42., 08830 Sant Boi de Llobregat, Barcelona, Spain

Patrick E. Shrout Department of Psychology, New York University, 6 Washington Place, Rm 455, New York NY 1003, USA

Center for Behavioral Genomics, Department of Psychiatry, University of California, San Diego, 9500 Gilman Drive, La Jolla CA 92039, USA

Kenji J. Tsuchiya Research Centre for Child Mental Development, Hamamatsu University School of Medicine 1-20-1, Handayama, Higashi-Ku, Hamamatsu 431–3192, Japan

Christine Ulbricht Division of Services and Intervention Research, National Institute of Mental Health, 6001 Executive Blvd, Room 7151, MSC 9629, Bethesda MD 20892-9663, USA

Jim van Os John Simpson Department of Psychiatry at VA Boston Healthcare System, Harvard Medical School Department of Psychiatry, Boston MA 02215, USA

William S. Stone

Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, South Limburg Mental Health Research and Teaching Network, Maastricht University Medical Centre, PO Box 616, Vijverdal, 6200 MD, Maastricht, The Netherlands

Harvard Institute of Psychiatric Epidemiology and Genetics, Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA

Jorge Villatoro

Ezra Susser

Tracey D. Wade

Mailman School of Public Health, Columbia University, Department of Epidemiology, 600 West 168th Street, New York NY 10032, USA

School of Psychology, Flinders University, GPO Box 2100, Adelaide SA 5001, Australia

Jaana Suvisaari

Division of Services and Intervention Research, National Institute of Mental Health, 6001 Executive Blvd, Room 7151, MSC 9629, Bethesda, MD 20892-9663, USA

Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, P.O.Box 30, FI 0027 Helsinki, Finland

Nori Takei Research Centre of Child Mental Development, Hamamatsu University School of Medicine 1-20-1, Handayama, Higashi-Ku, Hamamatsu 431–3192, Japan

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Ramon de la Fuente National Institute of Psychiatry, Calzada Mexico Xochimilco 101, DF 14370, Mexico

Philip S. Wang

Myrna M. Weissman Department of Psychiatry, Columbia University,College of Physicians and Surgeons, New York State Psychiatric Institute, 1051 Riverside Drive, New York NY 10032, USA

1

Introduction to epidemiologic research methods Glyn Lewis Academic Unit of Psychiatry, University of Bristol, Bristol, UK

1.1 What is epidemiology? Epidemiology, according to Last’s Dictionary of Epidemiology, is ‘The study of the distribution and determinants of health-related states or events in specified populations and the application of this study to control of health problems’ [1]. Wikipedia states ‘Epidemiology is the study of factors affecting the health and illness of populations, and serves as the foundation of interventions made in the interest of public health and preventive medicine’. Rothman and Greenland [2] after observing ‘there seem to be more definitions of epidemiology than epidemiologists’ fulfil their own observation by creating a new definition: ‘the ultimate goal of most epidemiologic research is the elaboration of causes that can explain patterns of disease occurrence’ [2], thereby narrowing the focus of the subject on aetiology. John Snow is usually credited with creating epidemiology as a result of his work in the 1840s associating cholera with contaminated water from the River Thames in London [3]. It was only in the second half of the twentieth century that epidemiological methods began to be consistently applied to the whole range of health problems. Before that time, most of the focus was on infectious disease, though there were exceptions, such as pellagra [4]. Rothman coined the term ‘modern epidemiology’ [5] to reflect the increasing understanding of population based research after the second world war and the increase in its application. The Framingham Heart Study was started in 1949 and Bradford Hill,

amongst his other contributions, conducted the first randomised controlled trial (RCT) in medicine in 1948 [4]. This postwar era is the most important from the perspective of psychiatry. In this period the terms ‘chronic disease epidemiology’ or ‘risk factor epidemiology’ have been used to describe the extension of epidemiological methods to noninfectious disease. It is during this period that, in the main, psychiatric epidemiology has developed, often learning from epidemiologists studying heart disease and cancer. Epidemiologists get involved in studies with a variety of uses [6] including straightforward description, as well as the studies of aetiology that Rothman and Greenland mention in their definition. However, most definitions of epidemiology appear, at least at first sight, to leave out RCTs and systematic reviews yet many epidemiologists also carry out such studies. The use of the term clinical epidemiology [5] reflects this broadening of epidemiological methods into the care of patients, the validity of diagnostic tests and clinical decision making [7]. Epidemiologists have been at the heart of the evidence-based medicine movement [8] and thinking about how research findings are best transferred to clinical practice. And finally, ‘genetic epidemiology’ [6] is the creation of a marriage between epidemiology and genetics. It is designed to exploit molecular genetics and the technological advances that have enabled rapid characterisation of a person’s genetic makeup. Epidemiology has increased its scope and remit within medicine and psychiatric epidemiology is

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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a reflection of these imperialistic tendencies. At times, it is difficult to decide where epidemiology ends and ‘other’ clinical research begins; it is a matter of emphasis. Epidemiologists tend to be more oriented towards the study of common conditions of public health importance and are more interested in making inferences about whole populations. In epidemiology, there is more emphasis on establishing causal relationships than understanding the mechanisms that might underpin those relationships. Even though, when possible, epidemiological methods are also needed for investigating mechanisms. This concern with causation has led epidemiologists to emphasise the importance of RCTs to evaluate treatments and to summarise evidence using systematic reviews. So perhaps, those definitions of epidemiology quoted above are sufficient and adequately cover the remit and scope of the discipline.

the collection of DNA in epidemiological studies, which is now useful thanks to the massive technological progress in genetics. But for other areas of neuroscience, this is still mostly a challenge for the future. There are examples of population-based studies of brain imaging [9] and neuroendocrinology [10] but epidemiologists will need further assistance from scientists engaged in imaging, psychology and other areas of basic science in order to develop quicker and easier tests to use in population studies and improve understanding of the neuroscientific basis of psychiatric disorder. We should also not forget about social science. Social scientists will also help in understanding the social context of psychiatric disorder, for example the ideas about social capital have been influential [11] though the advances in social science appear less rapid than those in neuroscience.

1.1.1 Psychiatric epidemiology

1.2 Causation in medicine

Psychiatric epidemiology is simply the epidemiology of psychiatric disorders – no more, no less. The principles and practice are the same when studying psychiatric disorder as they are when studying other medical conditions. Understanding the epidemiological principles and methods developed for physical disease will inform our epidemiological study of psychiatric disorder. Good psychiatric practice requires attention paid to biological, psychological and social factors. The same can be said for psychiatric epidemiology. When studying aetiology or evaluating treatments, epidemiological research is testing hypotheses about cause or treatment based upon a theory relating biological, psychological or social factors to illness or recovery. Understanding the mechanisms underlying disease and treatment is therefore critical in interpreting data from epidemiological and clinical studies. However, it is important to acknowledge that epidemiology is often limited in investigating mechanisms as epidemiological studies often involve measurements that are remote from the mechanisms that are likely to be important. This is an especial problem in psychiatry as it is difficult to carry out intensive biological and psychological assessments in the context of large scale epidemiological studies. A recent exception is

One of the most important functions of epidemiology, as suggested by Rothman and Greenland [2] is to investigate factors that might cause disease and treatments or interventions that might cause recovery. Causal inference is the label for a process of reasoning that provides some structure to this difficult and often rather subjective task. ‘Risk factor’ is often used by epidemiologists, in part, to show that there is always some doubt about causal relationships. However, we are only really interested in ‘risk factors’ if they are causal. The first issue to address, then, is what is meant by ‘cause’. Cause is a word, that is used in everyday language but in medicine it is important that this word is defined and understood in a way that distinguishes it from its usual use in language. Rothman [5] has provided one of the most reasoned and influential approaches towards thinking about cause in medicine. He defines cause (of disease) as ‘an event, condition or characteristic that plays an essential role in producing an occurrence of a disease’. In other words, that a particular occurrence of disease would not have occurred without that event/condition/characteristic having happened first. Rothman has also argued that causes have to occur before outcomes. This is a sine qua non of any causal

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INTRODUCTION TO EPIDEMIOLOGIC RESEARCH METHODS

relationship and so any consideration of cause has to include this criterion. Rothman [5] emphasises that causation implies a comparison. Smoking one pack of cigarettes a day is not a cause if it is compared to two packs, but is a cause when compared to a person who smokes no cigarettes. This comparison is usually measured in epidemiology by calculating an index of association, such as an odds ratio, between a possible causal factor and the disease or outcome of interest. For example, smoking cannabis regularly doubles the risk of schizophrenia compared to people who do not smoke cannabis [12] (though whether this is a causal relationship is still uncertain). In everyday talk, people often think of causes as though they have a one-to-one relationship with an outcome. The smashed china was caused by the ball kicked by your son. This approach is also attuned to the deterministic model common in basic science, in which, for example, a neurotransmitter acts on a receptor, that is coupled with a G protein that in turn activates a signal transduction pathway. However, the model of causation in clinical medicine has increasingly regarded causes as neither necessary nor sufficient for the majority of non-infectious medical conditions. Smoking cigarettes increases the risk of lung cancer, but many people who smoke do not develop lung cancer and some people develop lung cancer without smoking. It is possible to think of some exceptions to this rule, but in the main these are single gene disorders with high penetrance such as Huntington’s disease. In infectious disease, the infectious agent is necessary, but not always sufficient for the clinical disease. Nevertheless, for most non-infectious disease, there has grown a consensus that causal factors are likely to be neither necessary nor sufficient. This has also encouraged use of the term ‘risk factor’ in epidemiology as the causal factors that are identified in human populations increase the risk of disease but do not confer any certainty about future events. At first sight there appears to be a conflict between the deterministic models used in biological science and the more probabilistic models that seem to apply to disease in human populations. There are two ways in which this apparent conflict has been resolved. First, that most diseases have multiple causes and this would seem particularly true for psychiatric

disorder. The evidence from heart disease and cancer provides ample evidence that this can be the case. The other suggestion, again made by Rothman [13], is the idea of multiple sufficient causes for a single disease, and that each of these sufficient causes are in turn multifactorial and with overlapping sets of causal factors. If we accept this model, it is possible to understand that in a circumstance of partial knowledge, each element of those sufficient causes will appear neither necessary nor sufficient. This is an important argument that enables us to link epidemiology to the underlying mechanisms that underpin the associations that epidemiologists will observe in human populations.

1.2.1 Alternative explanations Epidemiological studies estimate the association between a possible causal factor and a disease or a treatment and recovery. In human populations, this is the only approach that is feasible. We also have to understand that the tight experimental controls that can occur in basic science and in experimental animals are impossible in epidemiological studies of human populations. Participants in epidemiological studies or clinical trials will change their behaviour, change their treatment, and may refuse to continue to take part in a study. On occasions, these changes will be influenced by the study itself, public health campaigns or changes to health policy. There will always be difficulties therefore, in interpreting data from epidemiological studies. There are no perfect studies in epidemiology and this leads to more emphasis upon interpretation of any finding of association. It also implies that single studies are rarely sufficient, on their own, to draw conclusions. It is common for RCTs to be described as the ‘gold standard’ but this ignores the difficulties in interpreting even that most rigorous of the designs at our availability. Patients drop out of RCTs, stop taking their medication, start taking non-trial medication or make other changes to their lifestyle and health care use, sometimes as a result of the randomised intervention. RCTs might reduce the controversy surrounding interventions but they do not eliminate them [14, 15]. If this is epidemiological gold, it has less lustre than its counterpart in government vaults. 3

CHAPTER 1

One approach towards causal inference is therefore to consider the alternative explanations for an association, apart from causation and it is usual to consider at least these four alternatives: sampling variation and chance, confounding, bias and reverse causality.

1.2.1.1 Sampling variation and chance Epidemiologists have been at the forefront of considering statistical issues in relation to medical research. There is marked variation within human populations and so sampling variation is usually important to consider. It is difficult to imagine the days when medical journals did not include any statistical tests, but at least in the United Kingdom, Bradford Hill’s series of articles in The Lancet in the 1930s were very influential in introducing statistical tests into medicine [16]. Many studies are completed and many statistical tests are carried out, even within a single study. Every article in a scientific journal will usually contain dozens of statistical tests. Type 1 errors in which results are statistically significant by chance are therefore common. Statistical tests can be very useful when an analysis was planned as part of a hypothesis driven investigation. However, carrying out repeated tests during exploratory analyses or ‘data mining’ can lead to results that will often be due to chance. Results from exploratory analyses are best thought of as ‘hypothesis generating’ that require replication. It is particularly difficult when unscrupulous investigators report such analyses as though they were testing a priori hypotheses. In the light of these concerns, the conventional 5% threshold for statistical significance is almost certainly too high [17], and for most decisions, one needs much better statistical evidence. Type 2 errors, in which non-significant findings are interpreted as reflecting no association, are very common in the psychiatric literature given the relatively small size of many studies. Confidence intervals can help you decide upon the accuracy with which an association is estimated and help to decide if the investigators have excluded an important result. This is a common circumstance in treatment research in psychiatry [18].

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1.2.1.2 Confounding Factors of aetiological importance are not randomly allocated in human populations. In RCTs of sufficient size there should be a complete balance between the groups in confounding factors, including those that the investigator does not know about. In observational studies, however, confounding can occur. For example, cannabis users will differ in many ways from people who do not use cannabis. In the Swedish conscript study, cannabis users were more likely to live in cities, were more sociable and were more likely to get into trouble with the police [19]. It is possible that these other characteristics could alter risk of subsequent psychosis. These ‘other characteristics’ are potential confounding variables. A confounder is defined as an independent risk factor (or protective factor) for the outcome at each level of the exposure, that is also associated with the exposure. A confounding factor can lead to a spurious association or can eliminate a real association between exposure and disease. In the case of cannabis and psychosis, there is good evidence that confounding occurs [12]. In other words, much of the increased risk of psychosis in cannabis users can be attributed to their other characteristics. Statistical adjustment for confounders accounted for about half, but only half, of the observed association.

1.2.1.3 Bias Bias is another epidemiological term that is borrowed from normal every day use. In epidemiology, bias refers to the possibility that the estimate of association that is obtained is not the ‘true’ association that would pertain if one could carry out a perfect study. It can be contrasted with confounding, that is, a real explanation for an association that would be present even if your study had perfectly estimated the association in the population. In contrast, bias is introduced by the investigator or is a consequence of the investigation. The distinction between confounding and bias can be illustrated using the example given above of the link between cannabis and schizophrenia. Even if the measurement of cannabis and schizophrenia were

INTRODUCTION TO EPIDEMIOLOGIC RESEARCH METHODS

done perfectly and everyone in a study was followed up, confounding would still exist and have to be considered. Bias will only be introduced as one departs from this utopian state. There are two main types of bias: selection and measurement bias. Selection bias is to do with the selection of subjects for the study while measurement or information bias is concerned with bias in measurement, diagnosis and ascertainment of outcome and confounders. There are more comprehensive classifications of bias [7], but in the main these two types are the most important to consider. Selection bias is often described in relation to case–control studies that are very susceptible to this bias. It occurs when the cases and controls in a case–control study are drawn from different populations that differ with respect to the exposure variable. In case–control studies, controls estimate the frequency of exposure in the population from which the cases were drawn. If the control were to become diseased the control should be in the sampling frame for the cases. Case–control studies are therefore population based studies and it is this aspect of case–control design, that is often overlooked. For example, Mulvany and colleagues [20] carried out a case–control study in which people with schizophrenia (the cases) were selected from a hospital in Dublin who had birth records in the local maternity hospitals. The controls were the next birth in that hospital. There was no way of knowing whether the controls were still resident in Dublin when adult so might not have been in the population ‘at risk’ of being cases in the study. Some of the controls will have moved away from Dublin. This mismatch could lead to selection bias. This study reported that people of higher socioeconomic status were more likely to develop schizophrenia but this might have been because wealthier people were less likely to move away between birth and adulthood. This result is the opposite of the findings from a cohort study [21] and a case–control study with less risk of selection bias [22] that both found that people of lower socioeconomic status were at increased risk of schizophrenia. On balance, the Mulvany study does not support the idea that higher social classes

are at risk of developing schizophrenia; if anything, the reverse is the case. Selection bias can also be used to describe the bias introduced by partial follow-up in cohort studies and RCTs. Cohort studies are relatively insensitive to the selection of participants in the cohort, for example the British doctors’ cohort of Doll and Hill [23] has produced some robust and reproducible findings even though British doctors are a highly selected group. Likewise, Framingham is far from a representative town. However, bias is more likely to be introduced by differential drop out from the cohort than from the initial selection of the subjects in the cohort, at least in this kind of design. Many cohort studies have quite marked attrition, particularly for longer term follow-up and statistical methods for dealing with such missing data (see www.missingdata.org.uk) are designed to reduce this form of bias. Measurement or information bias occurs when measurement of exposure or ascertainment of disease is influenced by knowledge of the exposure (longitudinal designs or cross-sectional designs) or of the outcome (case–control and cross-sectional designs). Recall bias can be a problem if the presence of disease influences the measurement of exposure, as might occur in case-control studies and cross-sectional surveys. People with an illness, or their relatives, are likely to be more aware of past events that might be relevant to illness. The mental state of people with psychiatric disorder might increase or reduce the chance that past events are remembered. For example, people with depression have well-documented information processing biases that make it more likely that negative events are recalled [24]. There are many examples of studies that ask people with depression to record negative adverse experiences [25]. The strong associations that have been observed between depression and these measures may be partly as a result of such a recall bias. It is always difficult or impossible to estimate the likely influence of bias on results. The high chance of recall bias when measuring factors of potential aetiological importance in psychiatry is a powerful argument for using longitudinal designs to study causation. Using data sources gathered before the onset of disease will

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also reduce measurement bias. Other strategies to reduce measurement bias include using structured questionnaires and restricting retrospective inquiry to events that are unlikely to be forgotten. Bias can also be introduced by the researcher who is interviewing the participant, so-called observer bias. If possible, this source of bias can be eliminated by using self-administered questionnaires. However, there are occasions when participants might find the questions in self-administered form difficult to understand or when they might be misinterpreted. This seems particularly likely when asking about psychotic symptoms [26]. Many assessments of psychiatric disorder are semistructured and rely upon ‘cross-examination’ of the participant. There has been a vigorous debate comparing the validity and reliability of self-reported and semistructured interviews in assessing psychiatric disorder [27]. One has to balance the danger that questions can be misinterpreted with the risk that the observer can influence findings according to preconceived views. The balance of these arguments differs according to the diagnoses that are being studied. For most depression and anxiety disorders where insight is retained, self-reported information would seem to be an advantage. In contrast, for psychotic disorders the cross-examination style of semistructured interviews would seem necessary.

1.2.1.4 Reverse causality Finally, the disease may cause the exposure. This might occur in case–control studies and crosssectional surveys because data on exposure is usually collected retrospectively. In contrast, longitudinal studies should ensure that exposures occur before the onset of disease. Many biological aspects of psychiatric disorder are studied using case–control methods. For example, in imaging studies the abnormalities described in people with schizophrenia could result from the illness rather than being a marker of possible causes. Studies of first episode psychosis [28] go some way to address this possibility, but longitudinal studies are required in order to establish abnormalities that are present before the onset of psychosis.

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1.3 Causal inference A number of criteria have been suggested that might encourage a conclusion that exposures have a causal role in disease [13, 29, 30]. These usually require evidence from a variety of sources and one would expect a number of different studies using different approaches all to produce consistent results before coming to a conclusion about causality. The criteria usually suggested include: 1 Timing. The cause has to occur before the disease. 2 Strength of relationship measured by relative risk. Large relative risks are more likely to be causal. A relative risk below about 1.5 should be treated with more caution. 3 Consistency of findings across studies. One would want a variety of different studies in different populations and with different strengths and weaknesses in the design all to produce the same results. 4 Dose–response relation. Does the evidence support a ‘dose–response’ relation in that the more exposure to a risk factor the more likely the disease. 5 Biological plausibility. Is the relationship biologically plausible and underpinned by a reasonable mechanism? One advantage of epidemiology is that it can work in isolation of knowledge of mechanisms. For example, John Snow argued that contaminated water led to cholera many decades before the cholera Vibrio was identified or the molecular basis of that disease was established. This should be especially useful for psychiatric epidemiology given the complexity of brain structure and function and the limits of our basic neuroscientific knowledge. None of the criteria listed above are essential, except perhaps for the issue of timing – causes have to occur before the onset of disease. These criteria are a guide, but often the final conclusion relies upon a matter of judgement. One important principle to consider is whether the evidence is good enough to justify any policy decisions that might be taken. For example, if cannabis

INTRODUCTION TO EPIDEMIOLOGIC RESEARCH METHODS

had a causal relationship to schizophrenia then the main policy implication would be to carry out a public health campaign to alert young people to the possible dangers. The amount of evidence required to justify this would be less than that needed to justify a more expensive or risky intervention. For example, suggestions to recommend widespread use of cholesterol-lowering agents has to take account of the greater financial cost and potential for adverse effects. The strength of evidence required for such an intervention would be greater than that needed for a publicity campaign.

1.4 The future for psychiatric epidemiology Studying the causes of psychiatric disorder in human populations has to be carried out using epidemiological methods. Basic science experiments can often suggest likely causal mechanisms and generate hypotheses about the risk factors for psychiatric disorder but cannot support that such mechanisms are operating in humans. Small-scale experimental studies in humans can illustrate if these mechanisms are occurring in humans with disease but they cannot argue if they are causing the disease in human populations. For example, the work of Meaney and others [31] has suggested possible influences on stress reactivity based upon work on experimental animals. Small-scale experimental work on humans can investigate possible mechanisms further. However, it is only by studying humans in population-based studies that allow us to infer whether the kind of stresses that exist in human life could lead to permanent changes in hypothalamo–pituitary–adrenal axis responsivity and thus lead to human disease. The future of psychiatric epidemiology will rest upon advances in neuroscience and will increasingly need to measure psychological and biological processes in population based studies. Likewise, epidemiology can generate hypotheses that will need to be investigated by basic scientists and in smaller scale experimental studies in humans. This approach is often described as ‘translational medicine’ [32] and epidemiology will remain one of its key building

blocks if this vision is to be realised and the benefits of medical research to human health will be achieved.

References [1] Last, J. (2001) A Dictionary of Epidemiology, 4th edn, Oxford University Press, New York. [2] Rothman, K. and Greenland, S. (1998) Modern Epidemiology, 2nd edn, Lippincott, Williams & Wilkins, Philadelphia. [3] Snow, J. (1936) On the Mode of Communication of Cholera, 2nd edn, The Commonwealth Fund, New York. [4] Shepherd, M. (1978) Epidemiology and clinical psychiatry. Br. J. Psychiatry, 133, 289–298. [5] Rothman, K. (1987) Modern Epidemiology, Little Brown, Boston. [6] Morris, J.N. (1975) Uses of Epidemiology, 3rd edn, Churchill Livingstone, Edinburgh. [7] Sackett, D.L. and Holland, W.W. (1975) Controversy in the detection of disease. Lancet, 2 (7930), 357–359. [8] Evidence-Based Medicine Working Group (1992) Evidence-based medicine. a new approach to teaching the practice of medicine. J. Am. Med. Assoc., 268 (17), 2420–2425. [9] Tanskanen, P., Ridler, K., Murray, G.K. et al. (2008) Morphometric brain abnormalities in schizophrenia in a population-based sample: relationship to duration of illness. Schizophr. Bull., 36, 766–777. [10] Strickland, P.L., Deakin, W.J.F., Percival, C. et al. (2002) Bio-social origins of depression in the community. Br. J. Psychiatry, 180, 168–173. [11] Putnam, R.D. (1993) The prosperous community: social capital and public life. Am. Prospect, 13, 35–42. [12] Moore, T., Zammit, S., Lingford-Hughes, A. et al. (2007) Systematic review of cannabis use and risk of developing psychotic or affective outcomes. Lancet, 370, 319–328. [13] Rothman, K.J. and Greenland, S. (2005) Causation and causal inference in epidemiology. Am. J. Public Health, 95 (Suppl. 1), S144–S150. [14] Gotszche, P.C. and Olsen, O. (2000) Is screening for breast cancer with mammography justifiable? Lancet, 355, 129–134. [15] Marshall, M. and Creed, F. (2000) Assertive community treatment – is it the future of community care in the UK? Int. Rev. Psychiatry, 12, 191–196. [16] Doll, R. (1992) Sir Austin Bradford Hill and the progress of medical science. Br. Med. J., 305 (19–26), 1521–1526.

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CHAPTER 1 [17] Sterne, J.A. and Davey, S.G. (2001) Sifting the evidence – what’s wrong with significance tests? Br. Med. J., 322 (7280), 226–231. [18] Hotopf, M., Lewis, G. and Normand, C. (1997) Putting trials on trial: the costs and consequences of small trials in depression. J. Epidemiol. Community Health, 51, 354–358. [19] Zammitt, S., Allebeck, A., Dalman, C. et al. (2002) Self-reported cannabis use as a risk factor for schizophrenia: further analysis of the 1969 Swedish conscript cohort. Br. Med. J., 325, 1199–1201. [20] Mulvany, F., O’Callaghan, E., Takei, N. et al. (2001) Effect of social class at birth on risk and presentation of schizophrenia: case–control study. Br. Med. J., 323 (7326), 1398–1401. [21] Wicks, S., Hjern, A., Gunnell, D. et al. (2005) Social adversity in childhood and the risk of developing psychosis: a national cohort study. Am. J. Psychiatry, 162 (9), 1652–1657. [22] Harrison, G., Gunnell, D., Glazebrook, C. et al. (2001) Association between schizophrenia and social inequality at birth: case–control study. Br. J. Psychiatry, 179, 346–350. [23] Doll, R., Peto, R., Boreham, J. et al. (2000) Smoking and dementia in male British doctors: prospective study. Br. Med. J., 320 (7242), 1097–1102. [24] Mathews, A. and MacLeod, C. (2005) Cognitive vulnerability to emotional disorders. Annu. Rev. Clin. Psychol., 1, 167–195.

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[25] Paykel ES. (2001) The evolution of life events research in psychiatry. J. Affect. Disord., 62 (3), 141–149. [26] Horwood, J., Salvi, G., Thomas, K. et al. (2008) IQ and non-clinical psychotic symptoms in 12-year-olds: results from the ALSPAC birth cohort. Br. J. Psychiatry, 193 (3), 185–191. [27] Wittchen, H.U., Ustun, T.B. and Kessler, R.C. (1999) Diagnosing mental disorders in the community. A difference that matters? Psychol. Med., 29 (5), 1021–1027. [28] Steen, R.G., Mull, C., McClure, R. et al. (2006) Brain volume in first-episode schizophrenia: systematic review and meta-analysis of magnetic resonance imaging studies. Br. J. Psychiatry, 188, 510–518. [29] Hill, A.B. (1965) The environment and disease: association or causation? J. R. Soc. Med., 58, 295–300. [30] Susser, M. (1991) What is a cause and how do we know one? A grammar for pragmatic epidemiology. Am. J. Epidemiol., 133 (7), 635–648. [31] Meaney, M.J. (2001) Maternal care, gene expression, and the transmission of individual differences in stress reactivity across generations. Annu. Rev. Neurosci., 24, 1161–1192. [32] Sung, N.S., Crowley, W.F.Jr., Genel, M. et al. (2003) Central challenges facing the national clinical research enterprise. J. Am. Med. Assoc., 289 (10), 1278–1287.

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Analysis of categorical data: The odds ratio as a measure of association and beyond Garrett M. Fitzmaurice1,2,3 and Caitlin Ravichandran1,2 1 Laboratory

for Psychiatric Biostatistics, McLean Hospital, Belmont, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA 3 Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA 2

2.1 Introduction In this chapter we present an overview of many of the statistical methods commonly used for the analysis of categorical ‘outcome’ data in psychiatric studies. A categorical variable is defined as one that takes on a finite number of levels or categories (e.g. ‘success’ and ‘failure’ in the case of a dichotomous or binary variable). For example, consider the data in Table 2.1 which are from a study of rates and predictors of recovery in patients with first-episode major affective disorders with psychosis [1]. In this study investigators obtained information on candidate predictors of recovery at the time of first hospitalisation (e.g. Axis I comorbidity) and then followed patients for 2 years to determine which patients experienced syndromal and functional recovery. In this simple illustration of one comparison of interest, the categorical outcome has two levels, ‘recovered’ or ‘not recovered’. Table 2.1 is commonly referred to as a 2 × 2 contingency table. Much of the statistical theory underlying the analysis of categorical data is more easily formulated for 2 × 2 contingency tables. Indeed, methods for the analysis of 2 × 2 contingency tables provide the cornerstone for many of the advanced statistical methods required for more complicated problems. These include

Table 2.1 Illustrative data from a study of recovery in patients with first-episode major affective disorders with psychosis. Comorbidity

No Axis I Axis I Total

Recovery

Total

Not recovered

Recovered

65 48 113

50 18 68

115 66 181

extensions for analysing outcomes with more than two levels (e.g. ‘not recovered’, ‘partially recovered’ and ‘recovered’), which may or may not be ordered; the former are referred to as ordinal variables, the latter are referred to as nominal variables. In addition, there can be more than two levels of the experimental treatment or exposure variable (e.g. no Axis I comorbidity, one Axis I comorbidity, two or more Axis I comorbidities) and other factors or covariates (e.g. age, gender, health status before treatment) that influence the outcome variable. Some of the most widely used probability distributions for categorical outcomes include the Bernoulli, binomial, hypergeometric and multinomial distributions. Throughout this chapter we assume the reader has very little prior knowledge of these probability distributions. The chapter is

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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organised as follows. We begin with a discussion of inference for a single probability or proportion. This is followed by a description of methods for analysing 2 × 2 contingency tables; the extensions to R × C contingency tables (i.e. contingency tables with R rows and C columns) are mentioned but not discussed in great detail. We discuss measures of association for 2 × 2 tables that quantify departures from independence. In particular, we focus on the odds ratio (OR) as a measure of association. We also discuss the analysis of sets of 2 × 2 tables, and describe the Cochran–Mantel–Haenszel test. Finally, we present an overview of regression models for categorical data, focusing extensively on logistic regression models for binary outcomes. The logistic regression model is first introduced for the simple case where there is only a single predictor or covariate. This model is compared and contrasted with the classical linear regression model. Later the generalisations to more than one predictor variable are considered. A major emphasis of this chapter is placed on how logistic regression is used in practice and how the logistic regression coefficients should be interpreted. An example, based on data from the first-episode major affective disorders with psychosis study, is used to illustrate and reinforce the main concepts. Finally, at the end of the chapter, we introduce some advanced topics, including extensions of logistic regression to matched study designs; exact logistic regression, which is appropriate for small sample sizes or sparse data; multinomial regression models for nominal and ordinal outcomes; and applications of logistic regression models to so-called ‘clustered’ categorical data, when the outcomes are not independent.

2.2 Inference for a single proportion In this section we discuss inference for a single proportion or probability. In order to motivate the methods, we consider data from the first-episode major affective disorders with psychosis study. One of the goals of this study was to estimate the probability that patients with first-episode major affective disorders with psychosis achieve functional recovery after 2 years. The outcome for each patient can 10

be denoted ⎧ ⎪ ⎨ 1 Yi = ⎪ ⎩ 0

if the patient achieves functional recovery, if the patient does not achieve functional recovery

for i = 1, . . . , n patients. The binary outcomes for the n patients are assumed to be independent of each other. The probability of success (e.g. ‘recovered’) is denoted by p = pr(Yi = 1) and the probability of failure (e.g. ‘not recovered’) by 1 − p = pr(Yi = 0). The distribution of the number of successes among  the n patients, Y = ni=1 Yi can be used to form test statistics and a confidence interval for p. Counts of the number of successes, Y, have a binomial probability distribution   n y pr(Y = y) = p (1 − p)n−y , y   n where the binomial coefficient, , is the number y of ways y ‘successes’ can be obtained in n trials. The probability of success can be estimated using the sample proportion of successes, p = Yn . In large samples (say, n > 30, and with the expected number of successes np ≥ 5 and the expected number of failures n(1 − p) ≥ 5), p has an approximate normal distribution with mean p and variance p(1−p) n . A 95% confidence interval for p is given by

p(1 − p) p ± 1.96 . n The above confidence interval for p, known as a Wald confidence interval, is commonly used and easy to compute, but has been criticised for its poor performance; for example these confidence limits cover the true value of p less than 95% of the time on average. Two alternatives with more favourable properties are the Wilson confidence interval, which is based on the score test, and the Jeffreys interval, which can be derived using Bayesian statistical theory. Both can be calculated using popular statistical software, and the Wilson interval also has the closed-form expression:  z21−α/2 p(1 2 ˆ ˆ − p) +

z1−α/2 4n pˆ + ± z1−α/2 2n n , (2.1) z21−α/2 1+ n

ANALYSIS OF CATEGORICAL DATA: THE ODDS RATIO AS A MEASURE OF ASSOCIATION AND BEYOND

where z1−α/2 = 1.96 for a 95% confidence interval. For more information on the performance of these and other intervals, see for example Brown, Cai and DasGupta [2]. When sample sizes are relatively small (say, n < 30), an exact confidence interval can be obtained that is based directly on the binomial distribution for Y. Finally, hypothesis tests for p equalling a specified value, say po , can be conducted using either large sample theory for the approximate normal distribution of p or via exact methods based on the binomial distribution for Y.

2.2.1 Example Using data from the first-episode major affective disorders with psychosis study presented in Table 2.1 and the methods for inference for a single proportion, we can estimate the proportion of patients who achieve functional recovery 2 years after first hospitalisation. The estimated proportion is the total number of patients who recovered (Y = 68) divided by the total number of patients (n = 181), which equals 0.376. Ninety-five per cent confidence intervals for this estimate are (i) Wald: (0.305, 0.446), (ii) Wilson: (0.308, 0.448), (iii) Jeffreys: (0.308, 0.448) and (iv) exact: (0.305, 0.451). Note that there is close agreement among the four confidence intervals; this is to be expected when, as with these data, n is relatively large and both n p ≥ 5 and n(1 − p) ≥ 5.

2.3 Analysis of 2 × 2 contingency tables In many settings we are interested in the effect of treatments or exposures on a binary outcome. When the treatment or exposure has only two levels the data can be summarised in a 2 × 2 contingency table. Data in the form of a 2 × 2 contingency table can arise from many different types of study designs [3]. For example, consider a clinical trial comparing the probability of remission between patients with depression assigned to a novel treatment or standard treatment. The question of scientific interest is: ‘How does treatment affect the probability of remission?’ Similarly, for the first-episode major affective disorders with psychosis study (Table 2.1), the presence of Axis I comorbidities (the exposure) was determined

at baseline, and we are interested in the number of patients with and without Axis I comorbidities who recover. The question of scientific interest is: ‘How does the presence of comorbidities affect recovery?’ Both are examples of prospective study designs. However, data in the form of 2 × 2 tables also arise from other types of study designs. Consider, for example, the data from a retrospective case–control study of psychiatric disorders and occurrence of elderly suicides [4] presented in Table 2.2. The number of suicide cases and controls (non-cases) are fixed by design (with 85 cases and 153 controls) and the prevalence of psychiatric disorders is then ascertained on each subject in the study. In this retrospective case–control study design, the prevalence of psychiatric disorder is considered a random variable. Case–control studies are commonly used when the outcome is rare and/or when it is not ethical to randomise patients to the ‘exposure’ in a prospective study. In this particular case–control study one question of scientific interest is: ‘Does prevalence of substance use disorders vary among the cases and controls?’ The third type of study design in which data in the form of a 2 × 2 table arise is the so-called double dichotomy, cross-sectional or prevalence study. In this study design a fixed number (n) of subjects are randomly selected and each subject is crossclassified on the basis of the two variables (the row and column variables) of scientific interest. Table 2.3 displays data from a prevalence study of neuropsychiatric symptoms and mild cognitive impairment (MCI) in the elderly [5], where only the total number of subjects, n = 1969, is fixed by design. Table 2.3 contains data on the presence of delusions for the 1909 subjects with neuropsychiatric data available. In this example, the question of scientific interest is: ‘Are delusions and cognitive status related?’

Table 2.2 Substance use disorders (SUDs) and occurrence of elderly suicides. SUD

Yes No Total

Status

Total

Case

Control

23 62 85

1 152 153

24 214 238

11

CHAPTER 2 Table 2.3 Data from the study of neuropsychiatric symptoms and MCI. Delusions

Yes No Total

Cognitive status MCI

Normal cognitive ageing

11 308 319

6 1584 1590

Total

17 1892 1909

Finally, although not shown here, data in the form of a 2 × 2 table can arise when the total sample size, n, is not fixed in advance. For example, an exit poll conducted at an election station might set out to record the political party preferences (e.g. Democrat or Republican) and opinions about mental health parity legislation (in favour or against) of all respondents who agree to participate in the poll; here, the total number of individuals who agree to participate, n, is random. Suppose we let Xi denote the row variable (e.g. treatment or exposure) and Yi denote the column variable (e.g. outcome variable) for each one of these types of study designs, where both Xi and Yi are binary (taking values 0 or 1). Then, the data in a general 2 × 2 contingency table can be represented as in Table 2.4. In Table 2.4 njk is the count of the number of subjects with X = j and Y = k; njk is referred to as a cell count. For example, n11 is the number of subjects with X = 1 and Y = 1. Also, in Table 2.4 the marginal row counts are nj+ = nj0 + nj1 (the

Table 2.4 General representation of counts in a 2 × 2 contingency table. Y 0 1 Total

0

n00

n01

Table 2.5 Probabilities in a 2 × 2 contingency table, with only n fixed.

Y

n0+

X

12

number of subjects with X = j), and the marginal column counts are n+k = n0k + n1k (number of subjects with Y = k). In each study, different marginal totals are fixed by design. As a result, the counts in the tables have different distributions. For example, for the case–control study, n+0 and n+1 are fixed by design, and the numbers of exposed subjects in each column (n10 and n01 ) have independent binomial distributions. However, for all of these different types of study designs, the question of scientific interest can be formulated in a similar way: ‘Are X and Y associated or are they independent?’ For ease of explanation, we focus on data arising from a cross-sectional study design. For the cross-sectional design, we can write the probabilities for the 2 × 2 table as in Table 2.5. The probabilities in Table 2.5 are pjk = pr[(Xi = j), (Yi = k)], and the marginal probabilities are pj+ = pj0 + pj1 = pr[X = j] and p+k = p0k + p1k = pr[Y = k]. For the cross-sectional design, all of these probabilities can be estimated from the data at hand. For prospective studies with the number of exposures fixed by design, and for case-control studies, they are not all estimable. For prospective studies with the number of exposures fixed by design, only the two conditional row probabilities pr(Yi = 1|Xi = 0) = p01 /p0+ and pr(Yi = 1|Xi = 1) = p11 /p1+ can be estimated. Similarly, for case–control studies, only the two conditional column probabilities pr(Xi = 1|Yi = 0) = p10 /p+0 and pr(Xi = 1|Yi = 1) = p11 /p+1 can be estimated. For example, using data from the study

0

1

Total

0

p00

p01

p0+

X 1

n10

n11

n1+

1

p10

p11

p1+

Total

n+ 0

n+1

n

Total

p+0

p+1

1

ANALYSIS OF CATEGORICAL DATA: THE ODDS RATIO AS A MEASURE OF ASSOCIATION AND BEYOND

of psychiatric disorders and occurrence of elderly suicides, the probability of a substance use diagnosis can be estimated for suicides and non-suicides, but the probability of suicide cannot be estimated for elderly with and without substance use diagnoses.

That is, the OR for Y associated with X is equal to the OR for X associated with Y, pr(Yi = 1|Xi = 1)  pr(Yi = 1|Xi = 0) OR = pr(Yi = 0|Xi = 1) pr(Yi = 0|Xi = 0) pr(Xi = 1|Yi = 1)  pr(Xi = 1|Yi = 0) = . pr(Xi = 0|Yi = 1) pr(Xi = 0|Yi = 0)

2.3.1 The odds ratio as a measure of association

It is this property, unique to the OR, that accounts for its widespread use for assessing the association between exposure and disease in case–control studies. In addition, in ‘rare disease’ settings, the OR is a close approximation to another measure of association called the relative risk (RR). The RR is defined as pr(Yi = 1|Xi = 1) RR = , pr(Yi = 1|Xi = 0)

To determine whether Xi and Yi are associated, it becomes necessary to formulate measures of association that quantify any departure from independence. The most commonly used measure of association is the odds ratio (OR), also known as the cross-product ratio (for reasons that will soon become apparent). The OR is a measure of association based on a comparison of ‘odds’. The odds is simply another metric for expressing risk or probability. Specifically, if p is p the probability of success, then 1−p is referred to as the odds of success. For example, if the probability of success is 0.8 then the odds of success is 4 (or 0.8 0.2 ) to 1. That is, the probability of success is four times as large as the probability of failure. In a prospective study with the number of exposures fixed by design, the OR measures association by comparing the odds of Y in the two exposure groups defined by X. Specifically, the OR for Y associated with X is OR =

pr(Yi = 1|Xi = 1)  pr(Yi = 1|Xi = 0) . pr(Yi = 0|Xi = 1) pr(Yi = 0|Xi = 0)

The null value for the OR is 1 because it corresponds to pr(Yi = 1|Xi = 1) = pr(Yi = 1|Xi = 0) and implies that Y and X are independent. Quite often, the log of the OR is used as a measure of association, since log(OR) = 0 under the assumption of no association between Y and X. When OR > 1, then pr(Yi = 1|Xi = 1) > pr (Yi = 1|Xi = 0); similarly, when OR < 1, then pr(Yi = 1|Xi = 1) < pr(Yi = 1|Xi = 0). Note that the OR expresses association in relative (or multiplicative) terms in the sense that the odds of success in one group (e.g. unexposed group) is multiplied by OR to obtain the corresponding odds in the other group (e.g. exposed group). An appealing property of the OR is that it is symmetric in the roles of Y and X in the sense that reversing the roles of Y and X yields the same OR.

and also expresses association in relative or multiplicative terms. Unlike the OR, however, the RR is not symmetric in Y and X. The relationship between the OR and the RR will be discussed in greater detail in Section 2.5.1. Finally, we note that a simple computational formula for the OR arises from the following equivalent expression, OR =

p00 p11 . p10 p01

This expression helps to explain why the OR is sometimes referred to as the ‘cross-product ratio’. It is usually of interest to obtain a point estimate and confidence interval for the OR, and to test the null hypothesis that the OR equals 1. For all four designs, the OR can be estimated as  = n00 n11 . OR (2.2) n10 n01  is approximately normally disBecause the log(OR) tributed, and because it will always result in nonnegative estimates of the OR, it is preferable to obtain a confidence interval for log(OR), and then exponentiate the endpoints. That is, a 95% confidence interval for log(OR) is given by   ± 1.96 Var[log(   log(OR) OR)], (2.3)   = 1 + 1 + 1 + 1 . Then, where Var[log( OR)] n00 n10 n01 n11 a 95% confidence interval for the OR is obtained by exponentiating the endpoints of this interval,     ± 1.96 Var[log(   exp log(OR) OR)] .

13

CHAPTER 2

Finally, suppose it is of interest to construct a test for no association (independence). There are three commonly used test statistics. The Wald test statistic for the null hypothesis, H0 : log(OR) = 0, is given by: Z= 

 log(OR)   Var(log( OR))

,

(2.4)

which, in large samples, has an approximate standard normal distribution, denoted by N(0, 1), under the null hypothesis of no association. Alternatively, the likelihood ratio test (LRT) statistic can be used. This is simply twice the difference in the log-likelihood under the alternative (association) and null (independence) hypotheses. Remarkably, for any of the four types of study designs considered, the LRT statistic reduces to G2 = 2

1  1 

 Ojk log

j=0 k=0

Ojk Ejk

 ,

(2.5)

where Ojk = njk is the ‘observed’ count in the 2 × 2 table and Ejk = E(njk |H0 ) = nj+ n+k /n is the ‘estimated expected’ count (under the assumption of independence). In addition, for any of the four study designs, the score test statistic reduces to X2 =

1  1  (Ojk − Ejk )2 j=0 k=0

Ejk

,

(2.6)

which is also known as the Pearson chi-square test for a 2 × 2 table. In large samples, both the likelihood ratio and the Pearson chi-square statistics have approximate chi-square distributions with 1 degree of freedom. Similarly, in large samples, the Wald test statistic has an approximate standard normal distribution or, equivalently, the squared Wald test statistic has an approximate chi-square distribution with 1 degree of freedom. If the sample size, n, is relatively small, these asymptotic (or very large sample) approximations cannot be relied upon. In particular, a rule-ofthumb in statistical folklore is that the asymptotic approximations cannot be relied upon if one (or say 25%) of the cells in the 2 × 2 table have estimated expected counts (Ejk ) less than 5. When at least

14

one Ejk is less than 5, and it is of interest to make inferences about the OR, a common technique is to fix both margins of the 2 × 2 table and use so-called ‘exact’ tests and confidence intervals. That is, for a prospective study (where the row margins are fixed), we further condition on the column margins; for a case–control study (where the column margins are fixed), we further condition on the row margins; or for a cross-sectional design (where n is fixed), we condition on both row and column margins. In all of these cases it can be shown that the counts in the resulting table with fixed margins have a non-central hypergeometric distribution. Under the null hypothesis H0 : OR = 1, the non-central hypergeometric distribution becomes a central hypergeometric distribution, which forms the basis of Fisher’s exact test of no association in a 2 × 2 contingency table (see, for example [6]). This test is appropriate in small samples; the non-central hypergeometric can also be used to obtain an estimate of the OR that has better small sample properties than the usual OR estimate given in Equation 2.2. One potential drawback with exact methods, however, is that they can be ‘conservative’ in the sense that the true significance level of an exact test is often far smaller than the nominal level (e.g. 0.05), thereby making it more difficult to reject the null hypothesis of independence.

2.3.2 Examples Returning to the data from the first-episode major affective disorders with psychosis study in Table 2.1, the scientific interest is in the association between Axis I comorbidity and 2-year functional recovery. Using formulas from this section (Equations 2.2–2.6), the estimated OR comparing odds of recovery between patients with and without Axis I Comorbidity is 0.49, with 95% confidence interval (0.25, 0.94). We would estimate that patients with Axis I comorbidity have about one half the odds of 2-year functional recovery as patients without Axis I comorbidity. Performing a test of no association, we would obtain a Wald test statistic of Z = −2.15 with a p-value of 0.03, a LRT statistic of G2 = 4.81 with a p-value of 0.03, or a score (Pearson chi-square) test statistic of χ2 = 4.70 with

ANALYSIS OF CATEGORICAL DATA: THE ODDS RATIO AS A MEASURE OF ASSOCIATION AND BEYOND

a p-value of 0.03. At the conventional α = 0.05 significance level, we would conclude there is a significant association between Axis I comorbidity and 2-year functional recovery among patients with first-episode major affective disorders with psychosis from any of these large-sample test statistics. For the data from the study of neuropsychiatric symptoms and MCI in Table 2.3, exact methods are more appropriate than large-sample methods due to small expected cell counts. For this example, the estimated OR comparing odds of MCI between elderly persons with and without delusions is 9.43, and the p-value for Fisher’s exact test is <0.001. We conclude that elderly persons with delusions have increased odds of MCI.

2.3.3 Analysis of R × C contingency tables In this section we briefly outline methods for the analysis of contingency table data when there are more than two rows and/or columns. Data often arise in the form of R × C contingency tables, where the number of rows R and/or the number of columns C is greater than 2. For example, in Table 2.2 substance use disorders could be divided into alcohol use disorder only and drug use disorders. In another example, using data from the first-episode major affective disorders with psychosis study, we may be interested in rates of 2-year functional recovery among patients with different types of onset. Suppose we again let Xi denote the row variable and Yi denote the column variable. The notation for an R × C table is a straightforward extension of the 2 × 2 table. In particular, let njk be the number of subjects with X = j and Y = k, and pjk = pr[(Xi = j), (Yi = k)], for j = 1, . . . , R and k = 1, . . . , C. For an R × C table, the question of scientific interest is once again: ‘Are X and Y associated or are they independent?’ To study departures from independence, it is possible to define sets of (R − 1)(C − 1) nonredundant ORs for an R × C table. For example, one such set of OR are those that are relative to the last row and column of the table, that is OR(j, R : k, C) =

pjk pRC , pjC pRk

(2.7)

for j = 1, . . . , R − 1, and k = 1, . . . , C − 1. The OR in Equation 2.7 is the OR conditional on rows j and R and columns k and C. If the rows and columns are independent, then all of these ORs equal 1. The estimate of this OR is  R : k, C) = OR(j,

njk nRC , njC nRk

and confidence intervals or hypothesis tests can be formed based on the large sample normality of the estimator of the log OR. Alternatively, the likelihood ratio or the Pearson chi-square tests can be used to test for independence of X and Y, and their form is identical to Equations 2.5 and 2.6, except that the sum is now over j = 1, . . . , R, and k = 1, . . . , C. If the sample size is relatively small (and at least 25% of the cells in the R × C table have estimated expected counts Ejk less than 5), then the asymptotic chi-square approximations for these test statistics cannot be relied upon. In that case Fisher’s exact test can be used to perform a test for no association in an R × C table. Finally, if the rows and columns are ordered, and scores are assigned to the rows and columns, the score test statistic for no association is simply a function of the correlation coefficient between the scores assigned to the rows and columns. On the other hand, if only one of the variables is ordered, and scores are assigned to the ordinal variable, then the score statistic is a function of the one-way analysis of variance (ANOVA) test statistic, where the ordinal variable is treated as the outcome, and the unordered variable is a factor or group variable. Both large sample (based on an asymptotic chi-square distribution) and exact p-values (based on the central hypergeometric) can be calculated for these score statistics.

2.3.4 Example The LRT or Pearson chi-square test can be used to test for independence between type of onset of firstepisode affective disorder with psychosis and 2-year functional recovery. For the data in Table 2.6, the LRT statistic is G2 = 0.29, and the Pearson chisquare statistic is χ2 = 0.29, both with p-values of 0.87. Either test provides no evidence of an

15

CHAPTER 2 Table 2.6 Additional data from the study of recovery in patients with first-episode major affective disorders with psychosis. Onset

Acute Subacute Chronic Total

Recovery

Total

Not recovered

Recovered

45 28 33 106

30 17 18 65

75 45 51 171

association between type of onset and two-year functional recovery.

2.4 Analysis of sets of 2 × 2 contingency tables When there are three or more categorical variables, we can form a ‘multidimensional contingency table’. In this setting all of the variables could be random, or some margins of the table could be fixed by design. One particular type of multidimensional contingency table results from a set of J 2 × 2 tables. For example, consider the data introduced earlier on the 181 patients with first-episode major affective disorders with psychosis (see Table 2.1). In Table 2.7 these patients are now cross-classified by sex (W), in addition to Axis I comorbidity (X), and recovery (Y). The 2 × 2 tables of (X, Y) at each level of W are referred to as partial or conditional tables; for example they express the relationship between comorbidity and recovery controlling for sex. The (Comorbidity, Recovery) table formed by combining (or collapsing over) the partial tables is referred to as the marginal table (Table 2.1). Similarly, the ORs in the partial tables are called partial odds ratios, and the OR in the marginal table is called the marginal odds ratio. When the ORs in the partial tables differ,

there is said to be interaction between W and (X, Y). On the other hand, when the partial ORs are the same, but the common partial OR is different from the marginal OR, there is said to be confounding. Confounding occurs when two variables are associated with a third in a way that obscures their relationship. In particular, W (Sex) can potentially confound the relationship between X (Comorbidity) and Y (Recovery) when W is related to both X and Y. In Table 2.7, there are two 2 × 2 tables. Suppose, in general, that there are J 2 × 2 tables, with notation as in Table 2.8. Let njk denote the number of subjects with (W = j, X = k, Y = ). Suppose it is of interest to test for no partial association, that is H0 : no association between Y and X given W = j, j = 1, . . . , J. For each of the 2 × 2 contingency tables, Cochran [7] and Mantel and Haenszel [8] proposed a test statistic based on conditioning upon both margins. Earlier, we discussed how this is valid for a single 2 × 2 table regardless of the type of study design; this result also generalises to J 2 × 2 tables. Thus, the following Cochran–Mantel–Haenszel test is valid for any design, including both prospective and case–control studies. Conditional on both margins of the jth table, the data follow a (central) hypergeometric distribution under the null hypothesis of no association, leading to the Cochran–Mantel–Haenszel test for H0 : no association between Y and X, given W, J Z=

Sex (W)

Male Female

16



J j=1

− Ej ]

,

Vj

Table 2.8 The jth 2 × 2 contingency table, j = 1, . . . , J.

Y 0

Table 2.7

j=1 [nj11

1

Total

Illustration of two 2 × 2 contingency tables. Comorbidity (X)

No Axis I Axis I No Axis I Axis I

0

nj00

nj01

nj0+

1

nj10

nj11

nj1+

Total

nj+0

nj+1

nj++

Recovery (Y) Not recovered

Recovered

35 31 30 17

27 10 23 8

X

ANALYSIS OF CATEGORICAL DATA: THE ODDS RATIO AS A MEASURE OF ASSOCIATION AND BEYOND

where Ej =

nj1+ nj+1 nj++

is the mean of the hypergeo-

metric distribution and Vj =

nj1+ nj0+ nj+1 nj+0 n2 (n −1) j++ j++

is the

variance. The Cochran–Mantel–Haenszel statistic has an approximate N(0, 1) distribution under the null hypothesis provided that the number of tables is large, say J > 30, and/or the sample size in each table, nj++ , is large. An estimate of the adjusted OR is given by  (Adjusted) OR ⎛ ⎞ ⎛ ⎞ J J    =⎝ nj00 nj11 /nj++ ⎠ ⎝ nj01 nj10 /nj++ ⎠ . j=1

j=1

Among the available tests for interaction (homogeneity of the ORs) is the Breslow–Day test [3], which has an approximate chi-square distribution with (J − 1) degrees of freedom. Like the Cochran–Mantel–Haenszel test, this test is based on conditioning on both margins. However, unlike the Cochran–Mantel–Haenszel test, the Breslow–Day test requires that the sample size in each partial table is large even if the number of tables is large. The calculation of the Breslow–Day test statistic is more complex than other calculations presented in the chapter, but this test is readily available within most popular statistical software.

2.4.1 Example The Cochran–Mantel–Haenszel test statistic for the data in Table 2.7 for the test that the common OR equals one is Z = −2.12, with a p-value of 0.03; the estimate of the adjusted OR is 0.49 (with 95% confidence interval: 0.26, 0.95). From the Cochran–Mantel–Haenszel test and estimate of the adjusted OR, we conclude that, adjusting for sex, patients with Axis I comorbidity have significantly lower odds of recovery than patients without Axis I comorbidity. Finally, we note that the Breslow–Day test statistic for homogeneity of the ORs is χ21 = 0.32 with a p-value of 0.57. From the Breslow–Day test, we would conclude we have no evidence that the association between Axis I comorbidity and recovery differs between males and females (i.e. there is no interaction between comorbidity and sex).

2.4.2 Matched pair study design A matched pair study design is an example of a case when the number of partial tables (J) is large, and the sample size for each partial table (nj++ ) is small. The matched pair design has become increasingly popular in epidemiologic studies. In a matched case–control study, a case is selected, and then a control is matched to the case on factors that could be confounders of the association between the exposure and outcome variables. Then, as in the usual case–control study, investigators determine the exposure status (exposed, not exposed) of all subjects. For example, the data in Table 2.9, reported in Everitt [9], arose from a study designed to test the hypothesis that complications during pregnancy and birth, a known risk factor for the development of schizophrenia, are more prevalent in schizophrenics with a low age of onset (prior to age 16) compared to those with a later age of onset (after age 21). In this study, 36 subjects with low age of onset schizophrenia (cases) were matched one-to-one to 36 controls with later age of onset schizophrenia; the cases and controls were pair-matched on sex, race and socioeconomic status. Alternatively, in a matched prospective study, individuals are matched on exposure status. For example, individuals could be matched by sex, race and socioeconomic status, and then assigned to two different treatments and followed over time to determine whether the patients respond to the treatments. In these study designs, there are J 2 × 2 tables with one matched pair each. That is, the total sample size for each 2 × 2 table is 2. Even though each 2 × 2 table has only two subjects, assuming the number of matched pairs J is large, the Cochran–Mantel–Haenszel test can be used. In this case, the Cochran–Mantel–Haenszel test reduces to a test specific to matched paris, McNemar’s test: χ2 =

[n10 − n01 ]2 , n10 + n01

which has an approximate chi-square distribution with 1 degree of freedom for large J, where n10 is the number of matched pairs in which the case is exposed and the control is unexposed (or the exposed subject is a success and the unexposed subject is a failure) and n01 is the number of matched pairs in which the case is unexposed and the control 17

CHAPTER 2 Table 2.9 Complications during pregnancy and birth for 36 matched pair cases and controls. Controls

Cases

Absent Present

Absent

Present

23 9

4 0

is exposed (or the exposed subject is a failure and the unexposed subject is a success). An exact test based on the binomial distribution can be used when J (and particularly n10 + n01 ) is small. Matching one case (or exposed individual) to one control (or unexposed individual) is desirable because it maximises the power of the study for a given total sample size. However, when the number of cases is limited but a greater number of controls are available (e.g. in a rare disease setting), study designs matching one case to multiple controls are common. Because the total number of subjects nj++ can vary across partial tables, the Cochran–Mantel–Haenszel test can accommodate an arbitrary number of controls for each case. In addition, conditional logistic regression, which will be discussed in Section 2.6, also accommodates matched designs other than matched pairs and offers many of the advantages of logistic regression to matched studies.

2.4.3 Example McNemar’s test can be used to test for an association between birth complications and age of onset of schizophrenia using the data from Table 2.9. For this example, n10 is the number of pairs for which the case with earlier onset schizophrenia experienced birth complications but the control did not and equals 9, and n01 is the number of pairs for which the control experienced birth complications but the case with earlier onset schizophrenia did not and equals 4. If the large-sample test were applied, the test statistic would be χ2 = 1.92, and the p-value would be 0.17. Because n10 + n01 is small, exact methods are appropriate in this case, and would result in a p-value of 0.27. Therefore, we would conclude there is no evidence of an association between birth complications and age of onset of schizophrenia from this study. 18

2.5 Logistic regression In this section we consider how the relationships in multi-way contingency tables, and more complicated designs, can be explored using regression methods known as logistic regression. Logistic regression is one of the most widely used methods for the analysis of binary data. It is used to examine and describe the relationship between a binary response variable Yi (e.g. 1 = ‘success’ or 0 = ‘failure’) and one or more covariates for i = 1, . . . , n independent subjects. The covariates can be continuous or categorical (e.g. indicator variables). Denoting the two possible outcomes for Yi by 0 and 1, the probability distribution of the response variable is the Bernoulli distribution with probability of success pi . In common with linear regression, the primary objective of logistic regression is to model the mean of the response variable, given a set of covariates. Recall that with a binary response, the mean of Yi is simply the probability that Yi takes on the value 1, pi . However, what distinguishes logistic regression from linear regression is that the response variable is binary rather than continuous in nature. This has a number of consequences for modelling the mean of the response variable. For ease of exposition, we will first consider the simple case where there is only a single predictor variable, say xi . Generalisations to more than one predictor variable will be considered later. Since linear models play such an important and dominant role in applied statistics, it may at first seem natural to assume a linear model relating the mean of Yi to xi , pr[Yi = 1|xi ] = pi = β0 + β1 xi

(2.8)

However, expressing pi as a linear function is problematic since it violates the restriction that probabilities must lie within the range from 0 to 1. As a result, for sufficiently large or small values of xi , the linear model given by Equation 2.8 will yield probabilities outside of the permissible range. A further difficulty with the linear model for the probabilities is that we often expect a nonlinear relationship between pi and xi . For example, a 0.2 unit increase in pi might be considered more ‘extreme’ when pi = 0.1 than when pi = 0.5. In terms of ratios, the change from pi = 0.1 to pi = 0.3 represents a threefold or

ANALYSIS OF CATEGORICAL DATA: THE ODDS RATIO AS A MEASURE OF ASSOCIATION AND BEYOND

g(pi ) = β0 + β1 xi .

(2.9)

However, the most commonly used in practice are 1 Logit or logistic function: g(pi ) = log[pi /(1 − pi )] 2 Probit or inverse normal function: g(pi ) = −1 (pi ), where  is the standardised normal cumulative distribution function 3 Complementary log–log function: g(pi ) = log [− log(1 − pi )]. We note that all of these transformations are very closely related when 0.2 < pi < 0.8, and in a sense only differ in the degree of ‘tail-stretching’ outside of this range. Indeed, for most practical purposes it is not possible to discriminate between a data analysis that is based on, for example, the logit and probit functions. To discriminate empirically between probit and logistic regression would, in general, require very large numbers of observations. However, the logit function does have a number of distinct advantages over the probit and complementary log–log functions which probably account for its more widespread use in practice. Later in this chapter we will consider some of the advantages of the logit or logistic function. When the logit or logistic function is adopted, the resulting model logit(pi ) = log[pi /(1 − pi )] = β0 + β1 xi ,

p

then 1−pi is the odds of success. Consequently, logisi tic regression assumes a linear relationship between the log odds of success and xi . Note that this simple model can be expressed equivalently in terms of pi , pi =

exp(β0 + β1 xi ) . 1 + exp(β0 + β1 xi )

(2.11)

We must emphasise that Equations 2.10 and 2.11 are completely equivalent ways of expressing the logistic regression model. Expression 2.10 describes p how the log odds, log( 1−pi ), has a linear relationship i with xi , while expression 2.11 describes how pi has an S-shaped relationship with increasing values of β1 xi ; although, in general, this relationship is approximately linear within the range 0.2 < pi < 0.8 (see Figure 2.1 for a plot of pi versus xi when β0 = 0.5 and β1 = 0.9). Observe that the expression on the right of (Equation 2.11) cannot yield a value that is either negative or greater than 1. That is, the logistic transformation ensures that the predicted probabilities are restricted to the range from 0 to 1.

1.0

0.8 Probability of Success

200% increase, whereas the change from pi = 0.5 to pi = 0.7 represents only a 40% increase. In a sense, the units of measurement for a probability or proportion are often not considered to be constant over the range from 0 to 1. The linear probability model given by Equation 2.8 simply does not take this into consideration when relating pi to xi . To circumvent these problems, a nonlinear transformation is usually applied to pi and the transformed probabilities are related linearly to xi . In particular, a transformation of pi , say g(pi ), is chosen so that it maps the range of pi from (0, 1) to (−∞, ∞). Since there are many possible transformations, g(pi ), that achieve this goal, this leads to an extensive choice of models that are all of the form

0.6

0.4

0.2

0.0 −4

(2.10)

−2

0

2

4

x

is known as the logistic regression model. Recall from Section 2.3.1 that if pi is the probability of success,

Fig 2.1

Plot of logistic response function.

19

CHAPTER 2

and xi = 1. Let pi (xi = j), denote the probability of success when xi = j, for j = 0, 1. Then,

Finally, note that 1 − pi = so that the odds,

1 , 1 + exp(β0 + β1 xi ) pi 1−pi ,

β1 = (β0 + β1 ) − β0 = logit[pi (xi = 1)] − logit[pi (xi = 0)]   pi (xi = 1) × [1 − pi (xi = 0)] = log pi (xi = 0) × [1 − pi (xi = 1)]

is simply exp(β0 + β1 xi ).

2.5.1 Interpretation of logistic regression coefficients Next we consider the interpretation of the logistic regression coefficients, β0 and β1 , in Equation 2.10. In simple linear regression, recall that the interpretation of the slope of the regression is in terms of changes in the mean of Yi for a single unit change in xi . Similarly, the logistic regression slope, β1 , in Equation 2.10 has interpretation as the change in the log odds of success for a single unit change in xi . Equivalently, a single unit change in xi increases or decreases the odds of success multiplicatively by a factor of exp(β1 ). Also, recall that the intercept in simple linear regression has interpretation as the mean value of the response variable when xi is equal to 0. Similarly, the logistic regression intercept β0 , has interpretation as the log odds of success when xi = 0. Note that, for case–control studies, the intercept β0 cannot be validly estimated since it is determined by the proportions of ‘successes’ (Y = 1) and ‘failures’ (Y = 0) selected by the study design. However, in many studies, there is far less scientific interest in the intercept than in the slope. For the special case where xi is dichotomous, taking values of 0 and 1, the logistic regression slope, β1 , has a simple and very attractive interpretation. Consider the two possible values for pi when xi = 0

which is the log of the OR (or cross-product ratio) in the 2 × 2 table of the cross-classification of Yi and xi (see Table 2.10). Thus, exp(β1 ) has interpretation as the OR of the response for the two possible values of the covariate. The OR has many appealing properties that probably account for the widespread use of logistic regression in many areas of application. First, as was noted earlier, the OR does not change when rows and columns of the 2 × 2 table are interchanged. This implies that it is not necessary to distinguish which variable is the response and predictor variable in order to estimate the OR. Furthermore, as noted in the previous sections, a very appealing feature of the OR, exp(β1 ), is that it is equally valid regardless of whether the study design is prospective, crosssectional or retrospective. That is, logistic regression provides an estimate of the same association between Yi and xi in all three study designs. Finally, in psychiatric studies where Yi typically denotes the presence or absence of a disease or disorder, the OR is often interpreted as an approximation to the RR p(x =1) of disease, p(xi =0) . When the disease is rare, and pi i is reasonably close to 0 in both of the risk groups (often known as the ‘rare disease’ assumption), the OR provides a close approximation to the RR. Retrospective designs are especially common in psychiatry

Table 2.10 Cross-classification probabilities for logistic regression of Y on x.

Y 1

1

p(x = 1) =

0

exp(β0 + β1) 1 + exp(β0 + β1)

1 − p(x = 1) =

1 1 + exp(β0 + β1)

Total

1.0

x 0

20

p(x = 0) =

exp(β0) 1 + exp(β0)

1 − p(x = 0) =

1 1 + exp(β0)

1.0

ANALYSIS OF CATEGORICAL DATA: THE ODDS RATIO AS A MEASURE OF ASSOCIATION AND BEYOND

where the possible outcomes of interest are very rare. Although the RR cannot be estimated from a retrospective study, the OR can be used to provide an approximation to the RR. Extra care is necessary when interpreting the OR as an approximation to the RR in prospective studies. In many prospective studies the binary event is relatively common (say greater than 10%) and the ‘rare disease’ assumption no longer holds; in these settings, the OR can be a very poor and unreliable approximation to the RR and should not be given such an interpretation.

2.5.2 Hypothesis testing and confidence intervals for logistic regression parameters Often, we are interested in testing for an association between the predictor in our logistic regression model and the outcome, or, equivalently, testing H0 : β1 = 0. As for 2 × 2 table methods, Wald, likelihood ratio and score statistics can be used for this test. A Wald test statistic can be obtained using the result that the estimate of β1 divided by its standard error (s.e.) approximately follows a N(0, 1) distribution in large samples. A LRT statistic can be obtained by comparing the log likelihood for the full model with the predictor included to the log likelihood for a reduced model including only the intercept β0 ; the former is at least as large as the latter. In large samples, twice the difference between the maximised log likelihoods for the full and reduced models approximately follows a chi-square distribution with 1 degree of freedom. Two-sided Wald confidence limits for β1 can be obtained using the result that βˆ 1 follows an approximate normal distribution; the confidence limits are given by the formula βˆ 1 ± zα/2 ∗ s .e.(βˆ 1 ). Just as we ˆ can exponentiate β1 to get an estimate of the OR comparing the odds of disease for a unit change in x1 , we can exponentiate the lower and upper limits of the confidence interval for β1 to get a confidence interval for the OR. Estimates of β1 (or, alternatively, its associated OR), its standard error and the log likelihood for the model are available from the output from logistic regression routines from popular statistical software. Test statistics and p-values for tests that β1 = 0 and Wald 95% confidence intervals are often also included automatically. Likelihood ratio and score test statistics can sometimes be requested.

Although Wald tests and confidence intervals are standard output from software for fitting logistic regression, we caution the reader that in certain circumstances the performance of Wald tests (and confidence intervals) can be somewhat irregular and lead to misleading conclusions. As a result, we recommend that LRTs (and confidence intervals) be used whenever possible.

2.5.3 Example: Logistic regression with a single binary covariate We now return to the Table 2.1 data from the first-episode major affective disorders with psychosis study and show that we can obtain identical results using large-sample methods for 2 × 2 contingency tables (as reported in Section 2.3.2) and logistic regression. Recall that our interest is in the association between Axis I comorbidity and 2-year functional recovery in this group of patients. Using logistic regression, we fit the model: logit[pr(Recoveryi = 1)] = β0 + β1 ∗ Comorbidityi , (2.12) where Recoveryi is an indicator variable coded 1 if the ith subject recovered and 0 otherwise, and Comorbidityi is an indicator variable coded 1 if the ith subject had Axis I Comorbidity and 0 otherwise. The following are the results: βˆ

ˆ s.e.(β)

Z

p > |Z| 95% CI

−0.2624 0.1881 −1.39 0.163 −0.6310, 0.1063 Comorbidity −0.7185 0.3343 −2.15 0.032 −1.3737, −0.0632 Intercept

The estimate of the OR comparing the odds of recovery in patients with and without Axis I comorbidities is exp(−0.7185) = 0.49, and the 95% confidence interval for the OR is exp(−1.3737, −0.0632) = (0.25, 0.94). The Wald test statistic for no association (or, equivalently, for H0 : β1 = 0 or H0 : OR = 1), which appears in the table, is Z = −2.15, with an accompanying p-value of 0.03. We can obtain the LRT statistic for no association by fitting the model with the intercept as 21

CHAPTER 2

the only covariate, which has a log-likelihood of −119.8066, and comparing it to the log likelihood from the model with both the intercept and comorbidity as covariates, −117.4037. The LRT statistic is χ21 = 2 ∗ [−117.4037 − (−119.8066)] = 4.81. The associated p-value, which can be obtained using statistical software or estimated from chi-square distribution tables, is 0.03. These results and their interpretation are identical to those obtained using methods for 2 × 2 contingency tables and reported in Section 2.3.2.

2.5.4 Multiple logistic regression So far, we have only considered the simple case where there is a single covariate xi . Next, we consider the extensions of Equations 2.10 and 2.11 to the case where there are two or more covariates. Recall that, in Section 2.4, we applied methods for stratified contingency tables to the first-episode major affective disorders with psychosis study data to test that the OR comparing patients with and without comorbidities adjusted for sex equals 1. Methods for stratified contingency tables are useful when adjusting for a small number of categorical covariates. However, multiple logistic regression has important advantages over stratified contingency table methods when the number of categorical covariates is larger or when we want to adjust for quantitative covariates. For example, using the first-episode data, we may want to test that the OR adjusted for both sex and age equals 1 and to obtain an estimate of the adjusted OR without classifying age into arbitrary categories. When there are many covariates, the logistic regression model becomes, log[pi /(1 − pi )] = β0 + β1 xi1 + β2 xi2 + · · · + βK xiK ,

2.5.5 Example: Multiple logistic regression To obtain an estimate of the OR for comorbidity adjusted for sex and age and to test that the adjusted OR equals one, we fit the following multiple logistic regression model to the first-episode major affective disorders with psychosis data: logit[pr(Recoveryi = 1)] = β0 + β1 ∗ Comorbidityi + β2 ∗ Malei + β3 ∗ Agei , (2.14) where Malei is an indicator variable coded 1 if the ith subject is male and 0 if the ith subject is female and Agei is the age of the ith subject in decades. The following results are obtained:

(2.13)

where xi1 , xi2 , . . . , xiK are the K covariates. The logistic regression coefficients in Equation 2.13 have the following interpretations. The logistic regression intercept, β0 , now has interpretation as the log odds of success when all covariates equal 0, that is when xi1 = xi2 = · · · = xiK = 0. Each of the logistic regression slopes, βk (for k = 1, . . . , K), has interpretation as the change in the log odds of success for a 22

single unit change in xik given that all of the other covariates remain constant. Note that the appealing property of logistic regression that the same OR can be estimated from either a prospective or retrospective study design readily generalises when xik is quantitative rather than dichotomous, and also when there are two or more predictor variables. Methods for hypothesis testing and constructing confidence intervals also generalise easily from the predictor in a simple logistic regression model (β1 ) to a predictor in a multiple logistic regression model βk . Expressions for Wald test statistics and confidence intervals for (βk ) can be obtained by substituting βk for β1 in the relevant portions of Section 2.5.2. LRTs of βk = 0 can be constructed by comparing the fit of the full model with βk included to the fit of a reduced model with all covariates except βk included. Twice the difference between the maximised log likelihood for the full model and the maximised log likelihood for the reduced model still approximately follows a chi-square distribution with one degree of freedom.

βˆ

ˆ s.e.(β)

Z

p > |Z| 95% CI

−1.4019 0.4955 −2.83 0.005 −2.3730, −0.4307 Comorbidity −0.4845 0.3496 −1.39 0.166 −1.1697, 0.2008 Male 0.0049 0.3243 0.01 0.988 −0.6307, 0.6404 Age 0.3107 0.1094 2.84 0.004 0.0963, 0.5250 Intercept

ANALYSIS OF CATEGORICAL DATA: THE ODDS RATIO AS A MEASURE OF ASSOCIATION AND BEYOND

The estimate of the OR for comorbidity adjusted for sex and age is exp(−0.4845) = 0.62, and its 95% confidence interval is exp(−1.1697, 0.2008) = (0.31, 1.22). Holding sex and age constant, we estimate that the odds of two-year functional recovery is 38% lower for patients with Axis I comorbidity when compared to patients without Axis I comorbidity. However, note from the 95% confidence interval that our data are consistent with odds of recovery up to 22% higher for patients with Axis I comorbidity. In addition, the Wald test statistic for testing that the adjusted OR equals one is Z = −1.39 with an associated p-value of 0.17, and the LRT statistic is χ21 = 1.95 with an associated p-value of 0.16. Using either test we conclude there is no association between Axis I comorbidity and 2-year functional recovery after adjusting for sex and age. We can also use the results from the multiple logistic regression to obtain estimates and test statistics for the other covariates in the model. The estimated OR comparing odds of recovery in males versus females is 1.00 (95% confidence interval: 0.53, 1.90), and we conclude from the Wald test that there is no evidence of an association between sex and recovery after adjusting for Axis I comorbidity and age (Z = 0.01, p = 0.99). On the other hand, the estimated OR comparing odds of recovery for a 10-year age increase is 1.36 (95% confidence interval: 1.10, 1.69). Adjusting for Axis I comorbidity and sex, the odds of two-year functional recovery increases with age (Z = 2.84, p = 0.004); for every decade age increase, we estimate that the odds of recovery is 36% higher.

2.5.6 Categorical predictors with more than two levels in logistic regression Section 2.3.3 presented contingency table methods that could be used to test for independence with predictors or outcomes with more than two categories. This section describes how logistic regression accommodates predictors with more than two categories, either with or without adjustment for additional covariates. (A later section describes extensions of logistic regression that accommodate outcomes with more than two categories.) For K unordered categories, a test for independence can be obtained by adding K − 1 indicator or ‘dummy’ variables as

covariates in the regression, where the kth indicator variable is coded 1 for subjects in the kth category and 0 for all other subjects (so that subjects in the remaining ‘reference’ category are coded 0 for all K − 1 indicator variables). A LRT for no association can be conducted by comparing the log likelihood for the model containing the predictor to the log likelihood for the model with the K − 1 indicator variables corresponding to the predictor removed; the LRT statistic follows a chi-square distribution with K − 1 degrees of freedom. Wald and score hypothesis tests are also available. However, when a predictor has three or more categories, the Wald test of no association is sometimes not available from standard logistic regression output and must be requested. For ordered categories, a test for independence can be conducted by assigning scores to each level of the predictor and then using the score as a covariate in the regression model. For example, the scores 1, 2 and 3 could be assigned to the categories mild, moderate and severe. The Z statistic for the covariate then corresponds to a test for no association, and interpretation of the corresponding regression parameter is similar to the interpretation of a regression parameter for a quantitative predictor. For example, the OR for the severity predictor would compare the odds of the outcome for a one category increase in severity, either moderate versus mild or severe versus moderate. This approach is most appropriate when the association between the score and outcome is approximately linear.

2.5.7 Example: Logistic regression with a three-level predictor In Section 2.3.4, we performed tests for independence between type of onset of first-episode affective disorder with psychosis (categorised as chronic, subacute or acute) and 2-year functional recovery. Equivalent tests can be performed using logistic regression by fitting the model: logit[pr(Recoveryi = 1)] = β0 + β1 ∗ Subacutei + β2 ∗ Acutei ,

(2.15)

where Subacutei is an indicator variable coded 1 if the ith subject had subacute onset and 0 otherwise, Acutei is an indicator variable coded 1 if the ith 23

CHAPTER 2

subject had acute onset and 0 otherwise, and chronic onset is the reference category. The following results are obtained:

we would use the model: logit[pr(Recoveryi = 1)] = β0 + β1 ∗ Comorbidityi + β2 ∗ Malei + β3 ∗ Comorbidityi ∗ Malei

βˆ

ˆ s.e.(β)

p > |Z|

95% CI

Intercept −0.6061 0.2930 −2.07

0.039

Subacute

0.1071 0.4247

0.25

0.801

Acute

0.2007 0.3761

0.53

0.594

−1.1804, −0.0318 −0.7253, 0.9396 −0.5364, 0.9377

Z

Exponentiating the parameters for subacute and acute onset provides estimates of the ORs comparing odds of recovery for subacute and acute onset respectively versus chronic onset, and the Z statistics for these two parameters are for separate tests that these ORs are equal to 1. However, our primary interest is in the overall test for independence between onset and recovery. The log likelihood for this model is −113.421, and the log likelihood for the model with an intercept only is −113.565. The resulting LRT statistic for independence is χ22 = 0.29 (i.e. twice the difference in log likelihoods), with an associated p-value of 0.87. These results are identical to the LRT results from Section 2.3.4, and our conclusions are the same; that is, there is no association between type of onset and 2-year functional recovery. In this case, the Wald test statistic is also χ22 = 0.29 with a p-value of 0.87.

2.5.8 Interactions in logistic regression In Section 2.4, we introduced a test for interaction using methods for contingency tables. Recall that an interaction between two predictor variables is present when the OR for one predictor differs according to the value of the other predictor. For example, for the data from Table 2.7, we would state that there is an interaction between comorbidity and sex if the OR comparing odds of recovery with and without comorbidity differs between males and females. We can allow for interaction in logistic regression models by multiplying the covariates for the predictors involved in the interaction and adding them as additional covariates to the regression model. For example, to test for an interaction between comorbidity and sex, 24

(2.16) For this model, exp(β1 ) is the OR comparing odds of recovery in female patients with and without comorbidity, and exp(β1 + β3 ) is the OR comparing odds of recovery in male patients with and without comorbidity. These two ORs are equal if and only if β3 = 0; therefore, the test of H0 : β3 = 0 is a test of no interaction between comorbidity and sex. Using logistic regression, tests for interaction are also straightforward for quantitative predictors, categorical predictors with more than two levels, and interactions among more than two predictors.

2.5.9 Example: Logistic regression with interaction Fitting the model from Equation 2.16 to the data from Table 2.7 results in the following output: βˆ

ˆ s.e.(β)

Z

p > |Z| 95% CI

−0.2657 0.2771 −0.96 0.338 −0.8089, 0.2775 Comorbidity −0.4881 0.5105 −0.96 0.339 −1.4887, 0.5125 Male 0.0062 0.3774 0.02 0.987 −0.7335, 0.7459 Comorbidity −0.3838 0.6771 −0.57 0.571 −1.7110, * Male 0.9433 Intercept

The estimated OR comparing odds of 2-year functional recovery for patients with and without Axis I comorbidity is exp(−0.4881) = 0.61 for females and exp(−0.4881 − 0.3838) = 0.42 for males. Note that we can calculate a confidence interval for the OR for females but not for males from the information in the output; because the OR for males is calculated by summing two parameter estimates, the covariance between the two parameter estimates would be required in order to calculate the confidence interval. The Wald test statistic for no interaction is Z = −0.57, with an associated p-value of 0.57. The LRT statistic (obtained by comparing the loglikelihood for this model to the log-likelihood for

ANALYSIS OF CATEGORICAL DATA: THE ODDS RATIO AS A MEASURE OF ASSOCIATION AND BEYOND

the model with covariates for comorbidity and sex but not their interaction) is 0.32 with an associated p-value of 0.57. We conclude that there is no interaction between Axis I comorbidity and sex; that is, the OR comparing the odds of functional recovery for patients with and without Axis I comorbidity is the same for males and females. These results agree with the Breslow–Day test results from Section 2.4.1.

2.5.10 Goodness-of-fit When a multiple logistic regression model has been used to draw conclusions from a study, we should check the fit of the model to the study data. One way to check the fit of a model is to use statistical tests for goodness-of-fit; in the absence of significant evidence of poor fit from these test statistics, we conclude the fit of our model is adequate. The deviance (based on the likelihood ratio statistic) or the Pearson chi-square can be used as a goodness-of-fit statistic if, at each observed covariate pattern, the data can be grouped. That is, if there are ni subjects with the same covariate values (and hence the same Bernoulli distribution), they can be treated as a binomial sample and a test of goodness-of-fit can be based on the comparison of the observed and expected (or predicted) counts in each covariate pattern. Alternatively, if the covariates are quantitative rather than categorical, Hosmer and Lemeshow [10] proposed a goodness-of-fit statistic similar to the Pearson chi-square, which can be calculated after grouping individuals on the basis of having similar values of the predicted probability pi . Evidence of poor fit can reflect a variety of problems with our model, such as an inappropriate choice of transformation function, failure to include important interaction terms, or inappropriate assumption of linearity for quantitative or ordered categorical covariates, and is an indication that we should revisit the assumptions made during the modelling process.

2.6 Advanced topics In this section we briefly review a number of more advanced topics that can be considered extensions of the standard logistic regression model. Many of these methods have been somewhat slow to move into

the mainstream of psychiatric research. However, with their recent implementation in widely available statistical software, these methods are starting to be more widely applied.

2.6.1 Conditional logistic regression The previous section showed that logistic regression can be used to perform analyses similar to those using contingency table methods but with more complex extensions and applications. This section introduces a related technique known as conditional logistic regression, which extends many of the benefits of logistic regression to studies with matched designs. In matched study designs individuals are stratified on the basis of variables thought to be related to the outcome variable of interest. For example, age and years of education are two variables commonly used to define strata in many psychiatric studies. The conditional logistic regression model used to analyse matched data is log[pij /(1 − pij )] = αi0 + β1 xij1 + β2 xij2 + · · · + βK xijK .

(2.17)

Note that this is similar to the standard logistic regression model, except that the probability of success and the predictors are now indexed by i and j instead of i alone to indicate that they apply to the jth individual from the ith strata (e.g. matched pair). Note also that the common intercept β0 in standard logistic regression has been replaced in Equation 2.17 by a stratum-specific intercept αi0 . Parameter estimates from conditional logistic regression can be interpreted in a similar way as parameter estimates from standard (or unconditional) logistic regression, and conditional logistic regression offers the same capabilities as standard logistic regression with a few exceptions. One is that the stratum-specific intercepts cannot be estimated (and will not be included in conditional logistic regression output). This is because the method of estimation (discussed later) effectively eliminates them to ensure that the β’s are estimated without any bias. This is usually not a concern since, as for standard logistic regression, these intercepts are generally not of scientific interest. Second, because the model includes stratum-specific intercepts, the β’s now have 25

CHAPTER 2

stratum-specific interpretations in terms of changes in the log odds of success for within-stratum changes in the covariates. For example, β1 has interpretation in terms of changes in the log odds of success for a single unit change in xij1 within the ith stratum (i.e. comparing two individuals within the same stratum that happen to differ by one unit in the covariate). Third, the associations between any variables used for matching (or any other covariates that are constant within strata) and the outcome cannot be quantified. This is because the method of estimation is based entirely on variation within a stratum; conditional logistic regression cannot be used to estimate the effect of a covariate that varies only between strata (but not within a stratum). Returning to the example from Section 2.4.2, a study examining the association between birth complications and age of onset of psychosis that matches on sex, conditional logistic regression cannot quantify the association between sex and age of onset of psychosis because, by study design, sex does not vary within each stratum. However, it is still possible to test for interactions between variables used for matching and other predictors. Next we consider estimation of the model parameters. One approach to fitting this model would be to attempt to estimate all of the parameters, including the stratum-specific intercepts. However, for matched designs, the number of strata grows as the sample size increases, which means that the number of parameters would be large relative to the sample size no matter how big a sample was collected. For example, in a matched-pair design with n pairs (i.e. two subjects in each stratum), such an analysis would require the estimation of n + K parameters from a sample of only 2n observations. It should not be surprising that this proliferation of stratum-specific intercepts causes problems for estimation; it also causes problems with the properties of standard maximum likelihood estimates of the model parameters. To avoid these problems, conditional logistic regression maximises a likelihood for the conditional distribution (and hence the term ‘conditional’ logistic regression) that eliminates these stratum-specific intercepts and bases estimation of the associations between the predictors and outcome entirely on information from within the strata. 26

2.6.2 Exact logistic regression Like many methods for contingency tables, logistic regression as traditionally implemented (i.e. maximum likelihood logistic regression) relies on large sample theory for the validity of its results. Maximum likelihood logistic regression can perform poorly when the sample size is small, the probability of success is near one or zero, or we have an insufficient number of successes or failures for certain combinations of our covariates. Error messages from statistical software, very large or small parameter estimates, or very wide confidence intervals can sometimes alert us to these problems, though logistic regression can still have poor performance due to sparse data even when the problem is not evident from the distribution of individual covariates or from an examination of the results [11]. Exact logistic regression [12] is a method for fitting logistic regression models that produces valid estimates, test statistics and confidence intervals even for small datasets or sparse data. For example, exact logistic regression was used to study psychiatric and social predictors of attempted suicides in a sample of Indian women [13]. In this study, the total number of participants was fairly large (2494), but the number of suicide attempts was relatively small (19). As a result, very small numbers of successes (suicide attempts) were observed for some predictors, and exact logistic regression was an appropriate analysis strategy. The relationship of exact logistic regression to maximum likelihood logistic regression is similar in some ways to the relationship of Fisher’s exact test to large-sample methods for R × C contingency tables. However, whereas Fisher’s exact test conditions on the row and column totals in order to derive the distribution of the test statistic, exact logistic regression conditions on the so-called sufficient statistics for the remaining parameters in the model when estimating each parameter. The sufficient statistics for the parameters are determined by the number of successes for different values of the corresponding covariates. Like other exact methods, exact logistic regression guarantees that tests conducted at significance level α have a type I error rate less than or equal to α, and that 95% confidence intervals have at least 95% coverage even for small sample sizes and sparse data.

ANALYSIS OF CATEGORICAL DATA: THE ODDS RATIO AS A MEASURE OF ASSOCIATION AND BEYOND

It can be implemented by several popular statistical software packages, and parameter estimates and confidence intervals have an interpretation identical to those for maximum likelihood logistic regression. Some disadvantages are that it may be overly conservative in settings when maximum likelihood logistic regression performs adequately and that it can be computationally intensive, especially when quantitative covariates or a large number of categorical covariates are included in the model. In principle, exact logistic regression can be applied in settings with multiple covariates; however, greater care is required when attempting to fit complex models. When feasible, exact logistic regression is an attractive alternative to maximum likelihood logistic regression in small sample and sparse data settings.

2.6.3 Multinomial regression models A major focus of this chapter has been on logistic regression modelling of a binary outcome. For some applications, however, the outcome variable of interest is categorical with more than two levels. For example, in a study of trauma in a high-risk AfricanAmerican sample [14], response to trauma was categorised as currently ill (current psychiatric disorder), recovered (past history of one or more psychiatric disorders) or resilient (no history of psychiatric disorder). Predictors of response to severe trauma in this population were examined using polytomous logistic regression. In this section we introduce multinomial models for categorical outcomes with more than two levels by first considering the case of such a nominal categorical variable. Suppose the outcome for individual i is categorical with J levels and let Yi equal 1 with probability pi1 , equal 2 with probability pi2 , and so on. In general, Yi equals j with probability pij , j = 1, . . . , J. We can introduce some additional notation that will make the extension of logistic regression to this setting more transparent. Suppose we let Yij equal 1 if Yi = j, and equal 0 otherwise, for j = 1, . . . , J. Then, pij = pr[Yi = j|xi1 , . . . , xiK ] = pr[Yij = 1|xi1 , . . . , xiK ]. When J > 2, the extension of the logistic regression model known as polytomous (or multinomial) logistic regression is appropriate. In polytomous logistic regression, we form J − 1

non-redundant logits:   pr[Yi = j|xi1 , . . . , xiK ] log pr[Yi = J|xi1 , . . . , xiK ]   pr[Yij = 1|xi1 , . . . , xiK ] = log pr[YiJ = 1|xi1 , . . . , xiK ]   pij = log = βj0 + βj1 xi1 + · · · + βjK xiK piJ j = 1, . . . , J − 1, where the regression parameters, βj0 , βj1 , . . . , βjK , can be different for each level j. In this notation, the last category J is referred to as the ‘reference’ category. It can also be shown that, for j = 1, . . . , J − 1, pij =

exp[βj0 + βj1 xi1 + · · · + βjK xiK ] J−1 1 + j=1 exp[βj0 + βj1 xi1 + · · · + βjK xiK ]

Note that this polytomous logistic regression model is more appropriate when the categorical variable is nominal. In other settings, the categorical outcome is ordinal. For example, in a study of predictors of remission in patients over age 60 treated for depression, the outcome was categorised as no remission, partial remission or full remission [15]. For ordinal outcomes a variety of regression models can be used, including mean score models and models of a logistic regression form. The logistic models for ordinal data include the continuation odds model, the adjacent category logit and the cumulative logit models [16]. Here, we briefly discuss the cumulative logistic proportional odds model, one of the most widely used regression models for ordinal data. To formulate an ordinal response model, we form logits of the cumulative probabilities. Recall, pij = pr[Yij = 1|xi1 , . . . , xiK ]. We define the cumulative probabilities as Fij = pr[Yi ≤ j|xi1 , . . . , xiK ] = pi1 + . . . + pij . In the previous example, Fi2 is the probability of a response of (i) no remission or (ii) partial remission. The logit of Fij ,     pi1 + · · · + pij Fij = log logit(Fij ) = log 1 − Fij pi,j+1 + . . . + piJ is often referred to as the ‘cumulative’ logit, and these cumulative logits can be related to covariates in the following proportional odds model, logit(Fij ) = αj + β1 xi1 + · · · + βK xiK . 27

CHAPTER 2

Note that the original multinomial probabilities can be expressed in terms of the cumulative probabilities via pij = Fij − Fi,j−1 . Inferences about the ‘cumulative logits’ or ‘cumulative’ log ORs can be made similarly to inferences for standard logistic regression. For example, with remission category as the outcome, if xi1 is an indicator for comorbid dysthymia (an important predictor in the Hybels et al. [15] study), exp(β1 ) is the OR comparing the odds of full or partial remission versus no remission. A property of the proportional odds model is that exp(β1 ) is also the OR comparing the odds of full remission versus partial or no remission for patients with and without comorbid dysthymia.

2.6.4 Clustered categorical data In the previous sections we have considered regression models for a single categorical outcome. However, multivariate categorical response data commonly arise in a number of applications in psychiatry. That is, two or more measurements of the response are often obtained in a block or cluster and the categorical responses within a cluster are expected to be positively correlated. When this occurs, the responses from any pair of members of the same cluster are expected to be more closely related than the responses from a pair belonging to different clusters. Some common examples where data arise in clusters include repeated measures or longitudinal studies and studies on families, communities or other naturally occurring groups. For example, in a study of the familial association between rheumatic fever and obsessive–compulsive spectrum disorders, each cluster consisted of first-degree relatives of either a case with rheumatic fever or a control [17]. The important aspect of all of these studies is that the categorical responses within a cluster (e.g. the presence of obsessive–compulsive spectrum disorders in firstdegree relatives) cannot be regarded as independent of one another. There may be many reasons for the correlation among cluster members. For example, when the cluster is comprised of all the siblings within a family the correlation among siblings may be due to shared (or at least similar) genetic, environmental and social conditions. In a longitudinal study, where 28

the responses within a cluster represent measurements taken at different occasions, the categorical responses are expected to be positively correlated simply because they have been obtained on the same individual (or cluster). Whatever the underlying reasons for the correlation, failure to account for it in the analysis can lead to invalid inferences. That is, the standard application of logistic regression (or any methods that assume independent observations) in this setting is no longer appropriate. For the remainder of this section we focus on the case of clustered binary data; however, the methods we discuss apply more broadly to clustered categorical data. There are two general approaches for handling the analysis of clustered binary data. The first is to consider models for the joint distribution of the cluster of binary responses that explicitly account for the within-cluster correlation. There is an extensive statistical literature on this topic and the interested reader is referred to a review article by Pendergast et al. [18]. For the most part, these models can be computationally demanding and have only recently been implemented in commercially available statistical software. An alternative approach is to simply ignore the correlation among members of a cluster. That is, the analysis proceeds naively as though the binary responses within a cluster can be regarded as independent observations, but later a correction is applied to ensure that valid standard errors are obtained. Note that in this ‘naive’ approach that ignores the within-cluster correlation the estimated logistic regression coefficients are valid, but their nominal standard errors are not. However, valid standard errors can be readily obtained using the well known empirical variance estimator, first proposed by Huber [19]. Thus, the analysis proceeds in two stages. In the first stage, the correlation among binary responses within a cluster is simply ignored and standard logistic regression is used to obtain estimates of the logistic regression coefficients. In the second stage, valid standard errors for the estimated logistic regression coefficients are obtained using an alternative, but widely implemented, variance estimator that properly accounts for the correlation among the binary responses. The chief advantage of this approach is that it can be readily implemented using standard statistical software for logistic regression.

ANALYSIS OF CATEGORICAL DATA: THE ODDS RATIO AS A MEASURE OF ASSOCIATION AND BEYOND

2.7 Concluding remarks This chapter presents an overview of statistical methods for categorical (and primarily binary) outcomes, with an emphasis on the OR and its applications in psychiatry. The OR is a particularly useful measure of association because it can be estimated from a number of common study designs. Contingency table methods provide intuitive ways of examining the associations among a limited number of categorical variables from these study designs. Logistic regression offers many of the same advantages of contingency table methods but also provides an efficient method for incorporating an arbitrary number of categorical and quantitative covariates. Logistic regression also provides a framework for extensions to more complex study designs often encountered in psychiatry.

[6] [7] [8]

[9]

[10]

[11]

[12]

2.8 Further reading A very comprehensive description of statistical methods for analysing categorical data can be found in the classic textbook by Agresti [20], and a thorough overview of logistic regression methods with an emphasis on applications can be found in the text Applied Logistic Regression by Hosmer and Lemeshow [21].

References

[13]

[14]

[15]

[16] [17]

[1] Tohen, M., Hennen, J., Zarate, C.M. et al. (2000) Two-year syndromal and functional recovery in 219 cases of first-episode major affective disorder with psychotic features. Am. J. Psychiatry, 157, 220–228. [2] Brown, L.D., Cai, T.T. and DasGupta, A. (2001) Interval estimation for a binomial proportion. Statist. Sci., 16, 101–133. [3] Breslow, N.E. and Day, N.E. (1980) Statistical Methods in Cancer Research, The Analysis of Case–Control Studies, Vol. 1, World Health Organization, Lyon. [4] Wærn, M., Runeson, B.S., Allebeck, P. et al. (2002) Mental disorder in elderly suicides: a case–control study. Am J. Psychiatry, 159, 450–455. [5] Geda, Y.E., Roberts, R.O., Knopman, D.S. et al. (2008) Prevalence of neuropsychiatric symptoms in mild cognitive impairment and normal cognitive

[18]

[19]

[20] [21]

aging: population-based study. Arch. Gen. Psychiatry, 65, 1193–1198. Fisher, R.A. (1934) Statistical Methods for Research Workers, Oliver and Boyd, Edinburgh. Cochran, W.G. (1954) Some methods of strengthening the common χ2 tests. Biometrics, 10, 417–451. Mantel, N. and Haenszel, W. (1959) Statistical aspects of the analysis of data from retrospective studies of disease. J. Natl. Cancer Inst., 22, 719–748. Everitt, B.S. (1992) Some aspects of the analysis of categorical data, in A Handbook for Data Analysis in the Behavioral Sciences, Statistical Issues, Vol. 2 (eds G. Keren and C. Lewis), Lawrence Erlbaum Associates, Hillsdale, NJ, pp. 321–348. Hosmer, D.W. and Lemeshow, S. (1980) A goodnessof-fit test for the multiple logistic regression model. Commun. Stat., A10, 1043–1069. King, E.N. and Ryan, T.P. (2002) A preliminary investigation of maximum likelihood logistic regression versus exact logistic regression. Am. Stat., 56, 163–170. Mehta, C.R. and Patel, N.R. (1995) Exact logistic regression: theory and examples. Stat. Med., 14, 2143–2160. Maselko, J. and Patel, V. (2008) Why women attempt suicide: the role of mental illness and social disadvantage in a community cohort study in India. J. Epidemiol. Community Health, 62, 817–822. Alim, T.N., Feder, A., Graves, R.E. et al. (2008) Trauma, resilience, and recovery in a high-risk African-American population. Am. J. Psychiatry, 165, 1566–1575. Hybels, C.F., Blazer, D.G. and Steffens, D.C. (2005) Predictors of partial remission in older patients treated for major depression: the role of comorbid dysthymia. Am. J. Geriatr. Psychiatry, 13, 713–721. McCullagh, P. and Nelder, J. (1989) Generalized Linear Models, 2nd edn, Chapman & Hall, London. Hounie, A.G., Pauls, D.L., do Rosario-Campos, M.C. et al. (2007) Obsessive-compulsive spectrum disorders and rheumatic fever: a family study. Biol. Psychiatry, 61, 266–272. Pendergast, J.F., Gange, S.J., Newton, M.A. et al. (1996) A survey of methods for analyzing clustered binary response data. Int. Stat. Rev., 64, 89–118. Huber, P.J. (1967) The behavior of maximum likelihood estimates under nonstandard conditions, in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Vol. 1, Berkeley, CA: University of California Press, pp. 221–233. Agresti, A. (2002) Categorical Data Analysis, 2nd edn, John Wiley & Sons, Inc., New York. Hosmer, D.W. and Lemeshow, S. (2000) Applied Logistic Regression, 2nd edn, John Wiley & Sons, Inc., New York.

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3

Genetic epidemiology Stephen V. Faraone,1 Stephen J. Glatt1 and Ming T. Tsuang2,3 1 Departments

of Psychiatry and Behavioral Sciences and Neuroscience and Physiology, Medical Genetics Research Center, SUNY Upstate Medical University, NY, USA 2 Center for Behavioral Genomics, Department of Psychiatry, University of California, San Diego, CA, USA 3 Veterans Affairs San Diego Healthcare System, San Diego, CA, USA

3.1 Introduction Epidemiologists usually concern themselves with describing the distribution and determinants of disease as a function of exposure to some environmental variable. This leads naturally to the goal of finding environmental risk factors that cause illness. In contrast, geneticists focuses on genetic mechanisms and, in experimental studies, may even seek to strictly control the environment and eliminate environmental variance. Thus, epidemiologic research often treats genetic determinants as noise and environmental agents as the signal; genetic studies reverse the roles of genes and environment. Psychiatric genetics adopts the position of genetic epidemiology, which has been defined as, ‘a science that deals with aetiology, distribution and control of disease in groups of relatives and with inherited causes of disease in populations’ [1]. Genetic epidemiologists examine the distribution of illness within families with the goal of finding genetic and environmental causes of illness. Thus, psychiatric genetics considers both environmental and genetic risk factors – and their interaction – to be on an equal footing. This paradigm extends the epidemiologist’s concept of ‘exposure’ to genes and family relationships. Most psychiatric genetic research is predicated on an assumption that the pathway from genotype to phenotype

cannot be understood without reference to environmental agents that trigger illness in susceptible individuals. The debate ascribing the risk for psychiatric disorders to either nature or nurture has been laid to rest, as most of these conditions are now understood to arise from the combination of both. Current work seeks to define genetic and environmental risk factors, the magnitude of their contributions and how they interact. The methods of psychiatric genetic epidemiology have established the familial and heritable nature of various psychiatric disorders over the last several decades; they are now poised to unravel their underlying mechanisms in the years to come.

3.2 The chain of psychiatric genetic research Work in psychiatric genetics follows a series of progressive questions (Table 3.1). This chain of genetic epidemiologic research [2], is as follows: First, we ask ‘Is the disorder familial? Does it run in families?’ Second, ‘What is the relative magnitude of genetic and environmental contributions to disease aetiology and expression?’

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

31

CHAPTER 3 Table 3.1

Chain of psychiatric genetic research.

Questions

Methods

Is disorder familial? What are the relative contributions of genes and environment? What is mode of transmission? Where is the gene (or genes) located? What is the risk-conferring variants?

Family study Twin and adoption studies

Third, ‘How is disease transmitted from generation to generation?’ Fourth, ‘If genes mediate transmission, where are they located?’ Fifth, ‘What are the risk-conferring variants of the genes and what is the mechanism of disease?’ In modern practice, some questions are pursued before those ‘earlier’ in the chain have been addressed. This is due to the fact that ‘later’ questions are presumed to elucidate more specific risk factors than earlier ones, and because the technology for pursuing these later questions has advanced rapidly, making their wide-scale implementation highly feasible. It is a fallacy that the newest methods can obviate earlier ones, as there is no method devised to date that has been found capable of explaining all sources of variation in the liability towards a complex phenotype such as those in the realm of psychiatry. Thus, we start our introduction to the field of psychiatric genetic epidemiology with a review of its oldest methods and conclude by reviewing the latest molecular genetic techniques, with the understanding that each method is but one essential instrument in the genetic epidemiologist’s toolbox.

3.2.1 Is the disorder familial? This question can be addressed more easily than some others in genetic epidemiology, which is why it is often asked first; another reason for its primacy in the chain of research is that it provides the most fundamental direction for subsequent genetic epidemiologic studies. If a disorder shows familial transmission, follow-up with other methods is warranted, whereas if no familial resemblance for the trait is observed, investigations of the disorder would proceed in a different direction (e.g. environmental surveys). 32

Segregation analysis Linkage analysis Association analysis

Observations of disorders ‘running in families’ may come from clinicians who often treat patients from the same family. Once familiality is informally established in a clinical setting, it remains to be confirmed with a rigorous research design, known as the family study method.

3.2.1.1 Selection of probands A family study should use the blind case–control paradigm. The cases and controls used in genetic studies are known as probands. We select probands with the disorder from a source, that is ‘enriched’ with the diagnosis of interest. For example, patients in psychiatric clinics are more likely to have bipolar disorder than patients in a family practice clinic. Furthermore, patients in a bipolar speciality clinic are more likely to have bipolar disorder than patients in a general psychiatric clinic. Selection from clinics instead of the general population is useful because, to achieve an adequate number of cases from the general population we would need to screen many individuals. This is costly and of dubious benefit. Also, multiple stages of ascertainment increase the probabilities of ill probands being ‘true cases’ and of unaffected or ‘control’ probands not having the disorder under study. The positive predictive power of a diagnosis (the proportion of those with the disorder among all patients receiving the diagnosis) increases as the base rate of the disorder being diagnosed increases [3]. Thus, multiple-stage ascertainment increases positive predictive power by using clinic status to increase the proportion of ‘true cases’ in the sample. This method of increasing positive predictive power will increase the false-negative rate. In this context, false negatives are those who have a disorder but are (i) not referred to a clinic or (ii) referred but do not

GENETIC EPIDEMIOLOGY

receive a clinical diagnosis. The generalisability of results will be limited to the degree that these false negatives differ from the probands. Treatment is the most notable factor that differentiates these groups. Multi-stage screening of controls decreases the probability of misclassifying someone with the disorder as a control. Since screened controls are selected for absence of the disorder of interest, they are not representative of the general population, but they are very effective for projects seeking to delineate factors that differentiate controls from cases [4]. Furthermore, unscreened controls frequently have rates of psychopathology and its correlates that are above the population expectation [5–7], thereby obscuring the effects of the variable of interest. Controls should be screened only for the disorder being studied, not for other psychiatric disorders or conditions. When controls are screened for additional disorders, the results can spuriously indicate a familial relationship between the disorder used to select cases and the disorders screened from controls [8]. The selection of controls should satisfy the comparability principles required for meaningful inferences in case–control epidemiological studies [9–12]. It is usually not possible to establish a primary study base with a geographically defined population because the clinics from which probands are selected may serve a broad geographic region that is difficult to delineate. The usual approach establishes a secondary study base defined by the ascertainment source. This limits generalisability and does not produce a representative sample from a geographical population. Nevertheless, it allows for meaningful case–control comparisons if the controls are individuals who could have been cases had they developed the disorder of interest [9–12]. When sampling from a clinic, this requires that, if the control subjects had needed treatment for the disorder, they would have been referred to the clinics that provided the case probands. Instead of establishing a secondary study base, one could match cases and controls on ‘relevant’ variables. One problem here is defining what is and is not ‘relevant’. Age, sex and socioeconomic status are usually considered. Matching should be used cautiously to avoid the ‘matching fallacy’ [13] and ‘overmatching’ [9, 14] because matching on specific variables often unmatches on others [13].

This creates unusual samples, reduces statistical efficiency and biases estimates [12]. These problems are worse when the matching variable is strongly associated with the disorder under study.

3.2.1.2 Assessment of disorder among relatives After selecting case and control probands, the family study compares rates of illness among relatives of cases to rates among relatives of controls. Care must be taken to assess as many relatives as possible. Because psychiatric disorders affect emotions, thinking and interpersonal relationships, nonparticipation may not be random with respect to illness status: ill family members may be more likely to refuse participation than others. If a disorder has a genetic aetiology, then relatives of ill probands should carry a greater risk for the illness than relatives of controls and the risk to relatives of probands should increase with the amount of genes they share in common. First-degree relatives – such as parents, siblings and children – share 50% of their genes, on average, with the proband. They should be at greater risk for the disorder than second-degree relatives (grandparents, uncles, aunts, nephews, nieces and half-siblings) because second-degree relatives share only 25% of their genes with the proband. Family studies rarely have the resources to diagnose second- or third-degree relatives. Table 3.2 displays the familial pattern of risk found in the families of schizophrenic probands, which is similar to that for other psychiatric disorders. These risk figures show that first-degree relatives are at highest risk, followed by second- and then third-degree relatives. In Table 3.2, it is clear that risk for disorder increases with the amount of shared genes; however, the increase in risk is not linear with the increase in biological similarity. Rather, it is exponential, with the individuals most similar to an affected proband (monozygotic (MZ) twins, who are 100% genetically identical) at more than double the risk incurred by individuals with only half their genes in common with an affected proband. These results underscore the complexity of the genetic bases for psychiatric disorders, and imply that gene–gene interactions (epistasis) as well as environmental factors must contribute to their aetiologies. 33

CHAPTER 3 Table 3.2 Rates of schizophrenia among relatives of schizophrenic patients. Type of relative First-degree relatives Parents Children Both parents schizophrenic Brothers and sisters Neither parent schizophrenic One parent schizophrenic Fraternal twins of opposite sex Fraternal twins of same sex Identical twins

Per cent at risk 4.4 12.3 36.6 8.5 8.2 13.8 5.6 12.0 57.7

Second-degree relatives Uncles and aunts Nephews and nieces Grandchildren Half brothers/sisters

2.0 2.2 2.8 3.2

Third-degree relatives First cousins General population

2.9 0.86

Based on Slater and Cowie [15] with the exception of twin data from Shields and Slater [16]. Adapted, with permission from Tsuang et al. [17].

3.2.1.3 Family study vs. family history methods The family history method assesses diagnoses of family members by interviewing only one or several informants per family. In contrast, the family study method determines diagnoses by interviewing each family member [2]. Several excellent structured psychiatric interviews are available but only one was designed specifically for genetic studies: the Diagnostic Interview for Genetic Studies (DIGS; [18, 19]. The main advantage of the family history method is low cost: interviewing a few family members is less costly than interviewing all family members. However, family history data underestimate true rates of many psychiatric disorders. Ideally, diagnoses of subjects should use three sources of information: direct interviews with the subject, family history interviews with informants who are familiar with the subject and medical records when available. All sources of information about a given individual may then be combined into a consensus diagnosis [20, 21]. The direct interview and medical record usually provide more useful information than the family history 34

assessment. In fact, two studies find that diagnoses based on direct interviews alone closely approximated best-estimate diagnoses [20, 21]. However, a diagnosis based only on medical records is often a suitable proxy to the best-estimate diagnosis [20]. The choice between the family history and family study methods requires a tradeoff between data quality and the expense of data collection. The family history method is the method of choice when there are not sufficient data to justify the expense of a family study. It is a good choice for pilot studies. After the family history method demonstrates familiality, the family study is the tool of choice for examining the details of familial transmission and developing reliable estimates of familial risk. When using the family history method, the following should be considered: 1 use the Family Interview for Genetic Studies or some other semistructured method for eliciting the family history; 2 because the family history method has low sensitivity, use less stringent diagnostic criteria than used for direct interviews; 3 use multiple informants for each person to be diagnosed; 4 seek out informants who have had substantial contact with the person to be diagnosed; 5 the method is most valid when the person being diagnosed is ill at the time of interviewing the informant. These ‘rules of thumb’ provide a rough guide for planning a family history study.

3.2.1.4 Caveats We must be cautious in concluding a disorder is caused by genes after we observe that it is familial. Disorders can ‘run in families’ for non-genetic reasons such as shared environmental adversity, viral transmission and social learning. Because the culture and environment shared by family members tends to increase as the degree of relationship decreases, the pattern of risk due to environmental factors may mimic the pattern expected for genetic relationships. Thus, a finding of familial transmission cannot be

GENETIC EPIDEMIOLOGY

unambiguously interpreted. Although family studies are indispensable for establishing the familiality of disorders they cannot establish whether genes or environment mediate that transmission.

3.2.2 What are the relative contributions of genes and environment? Genes, environment and their interaction: these are the ingredients of the pathophysiological brew that engenders psychopathology. To assay these ingredients and determine their relative proportions, we turn to twin and adoption studies [2].

3.2.2.1 Twin studies Identical or MZ twins inherit identical chromosomes and thereby have 100% of their genes in common. In contrast, like siblings, dizygotic (DZ) twins share 50% of their genes. MZ and DZ twins are markedly different with regard to their genetic similarity, but, if twin pairs are reared in the same household then the degree of environmental similarity between MZ twins should be no different than that between DZ twins. The astute reader will note that our comments regarding genetic similarity are facts of inheritance, but our comments about the environment are assumptions. The correctness of these assumptions is key to the valid use of the twin method. Since MZ twins are genetic copies of one another, any differences between a pair of MZ twins are assumed to be due primarily to environmental influences. In contrast, differences between DZ twins could be due to either genetic or environmental influences. Thus, comparing the co-occurrence of a psychiatric disorder in the two types of twins provides information about the relative contributions of genes and environment to the disorder. The co-occurrence of a disorder in both twins is called concordance; if one twin has the disorder and the other does not, the twins are discordant for the disorder. Because we assume the same environmental similarity for both types of twins, a higher concordance rate for MZ compared with DZ twins indicates the influence of genes. We can use pairwise or probandwise concordance rates, depending on the method of sampling the twins. The pairwise concordance rate

is defined as the proportion of twin pairs in which both twins are ill. To compute this, count the number of twin pairs concordant for the disorder and divide the result by the total number of pairs. Use this method when the probability of sampling any specific ill individual is so low that two ill co-twins are never independently sampled as probands. When the sampling probability is higher, use the probandwise concordance rate. Probandwise concordance is the proportion of proband twins that have an ill co-twin. Thus, it is the number of concordant pairs plus the number of concordant pairs in which both the twins are probands, divided by the total number of pairs. Twin data can estimate the ‘heritability’ of a disorder. Heritability measures the degree to which genes influence variability in the manifestation of the disorder (the phenotype). We divide phenotypic variability (Vp ) into two parts: genetic variability (Vg ) and environmental variability (Ve ). This partitioning of phenotypic variability assumes that genetic and environmental factors are statistically independent (i.e. Vp = Vg + Ve ). Heritability in the broad sense (h2 ) is the ratio of genetic and phenotypic variances (i.e. h2 = Vg /Vp ). As these formulas show, a heritability of one indicates that variability in the phenotype is due to genes. A heritability of zero attributes all phenotypic variation to environmental factors. When estimating heritability, diagnostic unreliability increases the estimate of environmental influence. Heritability estimates are context-dependent, and this is reflected by the fact that the heritability estimate accounts for main effects of genetic factors but also gene–environment interactions. The details of methods for calculating heritability are beyond the scope of this chapter. Smith [22] and Plomin et al. [23] provide information about the calculation and interpretation of this measure. If we have data from parents and siblings of twins or indices of the environment, then specialised methods can provide information about gene–environment interaction and gene–environment correlation. An excellent reference for these methods is the book by Neale and Cardon [24]. A major assumption, and often-cited challenge to twin studies, is the ‘equal-environments assumption’. A basic tenet underlying the partitioning of genetic and environmental variance is the assumption that MZ twin-pairs reared together have the same degree 35

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of exposure to similar environmental factors that reared-together DZ twin-pairs have. However, this will be wrong if many more MZ twin-pairs than DZ twin-pairs are treated identically and exposed to the same events. Serious violation of the equalenvironments assumption could result in increased phenotypic similarity among MZ twin-pairs relative to DZ twin-pairs, that is due to environmental – not genetic – similarities between MZ twin-pairs. Thus, a portion of the variance in a phenotype that should be attributed to environmental factors would inadvertently be ascribed to genes, and heritability estimates will be artificially inflated. When MZ twins are reared apart, we have a unique – but rare – opportunity to study the relative importance of genes and environment without having to assume environments are equal. Since MZ twins reared apart do not share a common environment, any phenotypic similarity must be due to genetic factors. However, MZ twins with psychiatric illness are rare, and cases of such twins reared apart are even rarer. Thus, this design cannot be routinely used. A second twin study design uses the children of discordant MZ twins. The logic of this design is straightforward. If a disorder is caused by a genotype combining with environmental factors, then the well member of a discordant MZ twin pair should carry the genotype. Presumably, they did not develop the disorder because they had not been exposed to a relevant environmental cause. If so, then the children of the well twin should have the same risk for the disorder as the children of the ill twin.

3.2.2.2 Adoption studies Children adopted at an early age have a genetic relationship to their biological parents and an environmental relationship to their adopted parents. Thus, adoption studies can determine if biological or adoptive relationships account for the familial transmission of disorders. If genes are important, then the familial transmission of illness should occur in the biological, but not the adoptive family. If culture, social learning or other sources of environmental transmission cause illness, then familial transmission of illness should occur in the adoptive, but not the biological family. 36

There are three major adoption study designs. The parent-as-proband design compares rates of illness in the adopted offspring of parents with and without the disorder. If genetic factors mediate the disorder then rates of illness should be greater in the adopted away children of ill parents compared with the adopted children of well parents. The adoptee-as-proband design starts with ill and well adoptees and examines rates of illness in both biological and adoptive relatives. If the biological relatives of ill adoptees have higher rates of illness than the adoptive relatives of ill adoptees, then a genetic hypothesis is supported. In contrast, if the adoptive relatives show higher rates of illness then an environmental hypothesis gains support. The third design is the cross-fostering design. This approach compares rates of illness for two groups of adoptees: one has well biological parents and is raised by ill adoptive parents and the other has ill biological parents and is raised by well adoptive parents. Higher rates of illness in the former group of adoptees compared with the latter group imply a non-genetic mode of illness transmission. Although they are difficult to execute, adoption studies have provided extensive data for both mood disorders [25–29] and schizophrenia [30–32]. Taken as a group, these studies support the hypothesis that the familial transmission of these disorders is due to genetic, not environmental factors. Adoption studies must be viewed with caution due to potential methodological problems that cloud their interpretation. Adoptees and their families are not representative of the general population. This limits generalisability. Adoptees are at greater risk for psychiatric illness compared with non-adopted children [33, 34]. Although the reasons for this are not clear, this increased risk for psychiatric disorders requires use of an adoptee control group. For example, in the adoptee-as-proband design, the relatives of ill adoptees must be compared with the relatives of well adoptees. Some other control group cannot be used, even if it is matched to the ill adoptee group on demographic measures. It may be difficult to find a sample of adoptees who were all separated from their parents at birth. If the child has lived with a parent for even a short period of time prior to adoption, the biological relationship will have been ‘contaminated’ by environmental

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factors. Some might even argue that the child’s contact with the mother immediately after birth creates a residue of environmental influence that affects subsequent psychopathology. Kety et al. [31, 32] presented a compelling design that deals with this problem. Their method requires a sample of biological paternal half-siblings of ill and well adoptees. Paternal half siblings share a common father yet have different mothers so do not share prenatal, perinatal or neonatal environmental exposure to the same mother. This design rules out confounding by in utero influences, birth traumas and early mothering. In the work of Kety and colleagues, the biological paternal half-siblings of schizophrenic adoptees were at greater risk for schizophrenia than the biological paternal halfsiblings of control adoptees, which bolsters the hypothesis that schizophrenia is caused, at least in part, by genetic factors. There are some environmental correlates of the biological parents that cannot be handled by the paternal half sibling design. Children born to fathers of the lowest social class may share toxic environmental factors such as poor pre- and perinatal care, poor nutrition and an adverse social environment; these may confound the genetic parent–child relationship. Despite these potential confounds and the difficulty of ascertaining appropriate cases and controls, the adoption study remains a valuable tool for disentangling genetic and environmental contributions to the familial aggregation of psychiatric disorders. The problems we note serve to underscore a basic tenet of psychiatric genetic research: any assertion that a disorder is caused by genetic factors must refer not to a single study, but to a series of studies using different paradigms.

3.2.3 What is the mode of transmission? After demonstrating that a disorder is influenced by genetic factors, the next logical task is to determine the mechanism of transmission from parent to child [2]. This information is useful from two perspectives. Showing that the transmission of a disorder corresponds to a known mode of transmission provides clues for subsequent research. For example, if the transmission is clearly due to a single gene, the next step might be linkage analysis, which uses family

psychiatric data and samples of DNA to find mutant genes. If environmental factors are implicated then a search for such factors would be warranted. Moreover, the mode of transmission has implications for genetic counselling. Genetic counselling is the process whereby clinical professionals inform people about either their probability of developing a genetic disorder or that of children they are planning to conceive. Ideally, in the absence of data implicating a particular genetic polymorphism(s), such counselling should be based on risk figures from a known model of genetic transmission. This model can be applied to an individual’s pedigree to determine that individual’s risk for a disorder. Morton et al. [35] demonstrated that the degree of risk predicted depends on the model of transmission. They also found that clinically important errors in risk prediction were made when they used the wrong genetic model to make predictions. A model of familial transmission translates assumptions about genetic and environmental causes into mathematical equations. These equations predict the distribution of a disorder expected in pedigrees or twin pairs. If the pattern of disorder predicted by the model is close to what we observe we say that the model fits the data. In contrast, if the predicted pattern of disorder differs from what is observed we reject the model and seek another. The term segregation analysis is used to describe analyses that assess the mode of disease transmission. The methods we discuss in this section require a good deal of mathematical and statistical expertise to be understood and correctly implemented. In the short space of this chapter we cannot present these mathematical details but instead provide an overview of the different classes of methods used to test hypotheses about genetic and environmental transmission. Several excellent texts, review articles and computer program documentation provide a detailed guide to these methods [1, 36–38].

3.2.3.1 Mathematical modelling of genetic and environmental transmission A genetic model comprises two major components. First, we must describe how the disorder is transmitted. For example, if we believe the disorder is due to a single dominant gene, our model must include the 37

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frequency of the gene in the population. It must also require that the transmission of the gene from parent to child follows the laws of genetic transmission. For example, if a mother carries one pathogenic gene the probability that she transmits this gene to a child must be 50%. Genetic models can specify environmental effects in several ways. In a single gene model we specify the penetrance of each genotype. Penetrance is the probability that each genotype causes disease. If we believe that disease occurs when an environmental event occurs in someone carrying the pathogenic gene, then our model should allow some gene carriers to be well. If other causes for the disease exist, then penetrance exceeds zero for those who do not carry the hypothetical disease gene. Genetic modelling requires a procedure for determining whether model predictions adequately describe the pattern of illness observed in families. One modelling approach attempts to predict rates of illness in various classes of relatives. The pedigree data is reduced to a table of numbers indicating the rate of illness in these classes (e.g. mothers, fathers, brothers, sisters, sons, daughters and more distant relatives). The mathematical model chooses values for the model parameters (e.g. gene frequency and penetrance) that most accurately reproduce the observed rates. The observed and predicted rates can then be compared with a chi-square test to determine if deviations between predicted and observed rates are large enough to warrant rejecting the model. Modelling rates of illness does not capitalise on all the information available in pedigree data. By lumping all families together within one data table, we cannot directly model the transmission of genes from one generation to the next. In contrast, pedigree analysis computes the likelihood of the pattern of illness in each family. For this approach, the analysis uses the status of each person and their relationship to others in the pedigree who are and are not affected. An algorithm then computes the probability, or likelihood, that the assumed model is correct given the pedigree data and the value of model parameters. Those parameter values yielding the most likely model are used as final estimates. With this approach we establish the model’s goodness of fit with a likelihood ratio chi-square test.

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3.2.3.2 Types of transmission models Single gene Mendelian transmission is only one of many mechanisms used to describe family and twin data. We find it useful to classify the genetic mechanisms into three types of models: single major gene, oligogenic and multifactorial polygenic (MFP). The word ‘major’ indicates that one gene can account for most of the genetic transmission of a disorder. Other genes and environmental conditions may play minor roles in modifying the expression of the disease or determining its age of onset. In contrast, an oligogenic model assumes that the combined actions of several genes cause illness. These genes may combine in an additive fashion such that the probability of illness is a function of the number of pathogenic genes. Alternatively, the mechanism may be interactive. For example, three abnormal alleles at different chromosomal locations may be needed for disease to occur. The MFP model proposes that a large, unspecified number of genes and environmental factors combine in an additive fashion to cause disease. The difference between oligogenic and polygenic models is one of degree. The former contain ‘several’ genes (e.g. less than 10) whereas the latter include a ‘large number’ of genes (e.g. [39]). Geneticists originally developed polygenic models to describe quantitative traits such as height and intelligence. By specifying a ‘large number’ of genes, these models could explain how discrete genes could cause traits that were continuously distributed in the population. Since many diseases are qualitative categories – not quantitative dimensions – geneticists developed the concept of liability [40]. Liability describes an unobservable trait: the predisposition to onset with disease. As liability increases so does the probability of disease onset. Alternatively, we might assume that disease occurs when one’s liability crosses a specific threshold. More than one threshold may be placed along the liability continuum, representing varying degrees of severity. Individuals above an upper threshold will develop a severe form of the disorder. Those below the lower threshold may have minor problems or be unaffected, while those whose liability falls between the two thresholds would have an intermediate form of the disorder. The mixed model posits that both MFP and single gene

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components may be involved in disease aetiology. Statistical analysis of the mixed model can determine if either component alone can provide an adequate fit to the data, or if the null hypothesis of no single gene effect and no MFP effect fits best.

3.2.4 Where is the gene (or genes) located? Eventually, psychiatric genetic research leads to questions such as, ‘Where is the gene located?’ This stage of inquiry requires molecular geneticists to provide the methods for tracking the inheritance of these disorders through families [2]. The search for disease genes faces formidable obstacles. Paramount among these is the number of potential disease genes. Each of us has over 20 000 genes. Moreover, only 10% of our chromosomal material (DNA) contains the coding sequences (i.e. instructions) for these genes. The average gene is made up of 3000 bp (the building blocks of DNA). But the entire set of chromosomes (the genome) contains 3 billion bp. Thus, searching for disease genes might seem as difficult as looking for a needle in a haystack. Fortunately, geneticists and statisticians have solved this genetic needle-in-a-haystack problem. Today, there is no question that molecular genetic and statistical technologies can find genes that cause genetic disorders. The list of diseases with known disease genes grows each year. Examples include Huntington’s disease, Alzheimer’s disease, cystic fibrosis, Duchenne’s muscular dystrophy, myotonic dystrophy and familial colon cancer.

3.2.4.1 Background for linkage analysis Linkage analysis is made possible by the ‘crossing over’, which takes place between two homologous chromosomes during meiosis, the process whereby gametes are created. Genetic transmission occurs because we inherit one member of each pair of chromosomes from our mother and one from our father. These inherited chromosomes are not identical to any of the original parental chromosomes. During meiosis, the original chromosomes in a pair cross over each other and exchange portions of their DNA. After multiple crossovers, the resulting

two chromosomes each consist of a new and unique combination of genes. When meiosis is complete, each gamete will contain one chromosome from each of the newly formed pairs. The probability that two genes on the same chromosome recombine during meiosis is a function of their physical distance from one another. This relationship is not linear because recombination events do not occur randomly across the genome, but instead occur at hotspots, leaving other tightly bound segments of the genome relatively intact and heritable as ‘blocks’. We say that two loci on the same chromosome are ‘linked’ when they are so close to one another that crossing over rarely occurs between them. Closely linked genes usually remain together on the same chromosome after meiosis is complete. The greater the distance between loci on the same chromosome, the more likely it is that they will recombine. This biological fact makes it possible to locate genes that are risk factors for disease. Genes on the same chromosome that are very far apart from one another are transmitted independently, as are genes on different chromosomes.

3.2.4.2 Statistical methods for linkage analysis The statistical methods of linkage analysis are beyond the scope of the present chapter, and so are only reviewed in brief. Linkage analysis capitalises on both the occurrence of crossing over and the availability of polymorphic genetic markers. It computes a statistic indicating the probability that the cosegregation of genetic markers and disease within pedigrees exceeds that expected from chance alone. Thus, linkage analysis assesses the association of disease and marker within families.

3.2.4.3 The affected relative pair method The affected relative pair method of linkage analysis evolved from the affected sib-pair method [41, 42]. The original ‘identity by descent’ affected sib-pair method worked with pairs of ill siblings having parents with four different alleles at the marker locus. That is, the father carried two versions of the gene and the mother carried two versions that differed from the father’s. For example, the father might

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have allele A and B and the mother might have C and D. Under the null hypothesis of no linkage, the distribution of alleles shared by siblings at the locus is well defined. For example, consider any of the alleles, say A. The probability that the father transmitted it to the first child is 0.50. The probability of transmitting it to the second child is also 0.50. Therefore, the probability that he transmits A to both children is 0.50 times 0.50 or 0.25. Now assume that the marker locus is close to a disease locus and that the two children have the disease. Because both have the disease gene, both share a segment of DNA that contains the gene and surrounding loci. The size of this segment is not fixed (it depends upon where crossovers occurred). However, the probability that the marker locus is on this segment increases with the proximity of the marker to the disease locus. If the marker and disease loci are contiguous, then children who inherited the disease gene should also have inherited the same allele at the marker locus. We say that the shared marker allele is ‘identical by descent’ to indicate that the alleles observed in the children are copies of the same parental allele. A statistical test was developed to determine whether the observed distribution of marker alleles differed from what was expected in the absence of linkage. The method was later generalised to the case in which the parental marker alleles were not all unique. In this situation we can determine if alleles are ‘identical by state’ but not if they are ‘identical by descent’. Identity by state means that the two alleles are the same but we cannot be certain if they are copies of the same parental gene. For example, suppose the father has allele A and B and the marker locus and the mother has A and C. If two of their children each have allele A at the marker locus then we cannot determine if both received it from the father, both from the mother or one from the mother and the other from the father. The loss of identity by descent information reduces statistical power [43]. The affected relative pair method is a general form of the sibling pair method. It allows all ill relative pairs to be included in the analysis. The major advantage of the affected relative pair method is that it can detect linkage with no a priori knowledge of the genetic and environmental parameters that mediate familial transmission. However, by eliminating 40

information about the mode of inheritance, the method sacrifices some statistical power.

3.2.4.4 The lod score method The lod score method requires knowledge of the mode of inheritance. Although it is possible to estimate the mode of inheritance and test for linkage simultaneously, the usual practice is to test for linkage under an assumed genetic model. We do so by estimating the recombination fraction: the probability that the disease and marker genes will recombine during meiosis. The lower the probability, the greater the likelihood of linkage. Most methods compute a maximum likelihood estimate of the recombination fraction. Then a likelihood ratio test compares the odds of the data occurring given that estimate with the odds of the data if the true recombination fraction is 0.5 (this is our null hypothesis because unlinked loci recombine with a probability of 0.5). This likelihood ratio is an odds ratio comparing the probability that linkage is present with the probability of no linkage. Since we usually examine the base-10 logarithm of the odds ratio, the test statistic is known as the lod score (log of the odds ratio). Lod scores greater than 3 are considered to be evidence in favour of linkage, while lod scores less than −2 constitute evidence against linkage. Thus, a linkage analysis will support the hypothesis of linkage if the odds favouring linkage are 1000 to 1 (i.e. log (1000/1) = 3). The main drawback of the lod score method is that we must specify parameters that describe the mode of genetic transmission. However, there is a way around this problem. Greenberg [44] showed that if we analyse our data several times under different modes of inheritance, the lod score will be greatest for the model closest to the true mode of inheritance. For example, we might choose to examine two dominant models and two recessive models. We might also vary the assumed frequency of the gene in the population. So far, we have been discussing linkage analyses that involve only two loci: one marker locus and one disease locus. Since a disease gene will be surrounded by many potential marker loci, these ‘two point’ analyses will not have optimal power to detect linkage and locate the gene. Multipoint analyses use several markers simultaneously during the linkage

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analysis. Multipoint mapping improves statistical power by using all available marker information in the area of the putative disease locus [45]. Lander [46] proposed ‘interval mapping’ which assesses linkage, not to a single marker, but to an interval flanked by a pair of markers. Xu et al. [47] evaluated interval mapping with statistical simulations. Compared with single point analyses, interval mapping was much more powerful, requiring 30% fewer families to detect linkage. Moreover, interval mapping was more robust to misclassification of penetrances, diagnoses and phenocopies. Although these considerations favour a multipoint approach, the method must be used with caution. Risch and Guiffra [48] showed that, if the mode of transmission is complex, multipoint analyses can spuriously reject linkage. However, they also show that this problem is mitigated when using high estimates of the disease allele frequency. The lod score method has been generalised for the detection of linkage heterogeneity. That is, we can test the null hypothesis that all families are linked in favour of an alternative hypothesis that only a proportion are linked. The lod score criterion of three (LOD3) may not be appropriate for complex genetic diseases like schizophrenia. The LOD3 criterion was originally designed by Morton [49] for Mendelian diseases for which it was reasonable to compute a prior probability of linkage. For non-Mendelian diseases the prior probability is unknown [50, 51]. Morton also assumed that the test was carried out sequentially as pedigrees were collected. Thus, LOD3 does not apply to analyses of fixed sample sizes [50]. The LOD3 criterion must also be adjusted for the effects of testing multiple markers. This includes both the assessment of linkage at multiple loci and the use of multiple markers to assess linkage at a single locus [50, 52, 53]. Guidelines for interpreting linkage results for complex genetic disorders have adjusted the usual 5% probability of false positives since linkage analysis typically consists of multiple statistical tests. Lander and Kruglyak [54] proposed three levels of statistical significance for use in the interpretation of genomewide linkage results. Suggestive linkage would occur by chance once during a genome-wide scan. For the lod score method, the p-value would be less than 0.0017 and for the sib-pair method, p < 0.0007.

Significant linkage refers to a chance event of 0.05 times during a genome-wide scan. For this level of significance, they defined the lod score p < 0.000049 and for the sib-pair method, p < 0.00002. Confirmed linkage refers to the finding of a significant linkage in an initial scan, and independently confirmed in another sample. The use of computer simulation methods to determine the appropriate lod score criterion was demonstrated by Weeks et al. [55]. Briefly, the procedure is as follows. First, the linkage analysis is performed on real data by maximising the lod score over genetic models and phenotype definitions. After a high lod score is found, a second analysis is performed on the same pedigrees. The only difference between the two analyses is that the first analysis uses the real marker data and the second uses simulated marker data. In the second analysis the markers are simulated under the assumption that the disease and marker are not linked. In the simulation, marker genotypes are assigned to subjects whose parents were not studied based on the marker gene frequencies used in the first analysis and the assumption of Hardy–Weinberg equilibrium. Marker genotypes are assigned to other pedigree members by simulating Mendelian laws of transmission on the pedigree. The simulation step is replicated many times and, for each replicate, we record the maximum lod score attained. This provides us with the distribution of maximum lod scores expected under the null hypothesis of no linkage. To set a Type-I error rate of α, we choose the lod score corresponding to the 1 – α point on the cumulative maximum lod score distribution computed by the simulation. To accurately estimate probabilities in the upper tail of the maximum lod score distribution, many replications are necessary.

3.2.5 What are the risk-conferring variants of the genes and what is the mechanism of disease? 3.2.5.1 Association studies Crossing over during meiosis shuffles the parental genes so that the chromosomes we receive from our fathers and mothers are not identical to any of their original chromosomes. Through the generations, 41

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genes are constantly shifting from one chromosome to another. As a result, we should expect no association between alleles of genes on the same chromosome. For example, assume that locus 1 can have allele a or A and locus 2 can have allele b or B. If the two loci are on the same chromosome then the probability that any chromosome contains the pair Ab, P(Ab), should be equal to the probability of A, P(A), times the probability of b, P(b). That is, P(Ab) = P(A) × P(b). Similarly, P(AB) = P(A) × P(b), and so on. Put simply, if we know that a chromosome contains allele A at locus 1, this tells us nothing about the probability of locus 2 containing allele B or b. This random distribution of alleles at different loci on the same chromosome is only partially true. It is an empirical fact that some loci are associated with one another so that P(Ab) = P(A) × P(b) [39]. For example, it may be that chromosomes with allele A at locus 1 are more likely to have allele b at locus 2 than we would expect by chance (i.e. than we would expect from the frequency of allele b in the population). Now, assume that locus 1 is a disease locus and that A is a dominant pathogenic allele. Also assume that locus 2 is a DNA marker locus. If the two loci are associated as indicated above, then people with the disease should be more likely to have marker allele b than people without the disease. This nonrandom association of alleles at different loci is called linkage disequilibrium. One cause of linkage disequilibrium is the fact that the reshuffling of genes on chromosomes depends on genetic distance. If two genes are very close to one another, then they will rarely be separated by crossing over and will usually be transmitted together. Thus, due to very close linkage, the alleles at two loci will tend to be transmitted together. We say ‘tend to’ because eventually crossing over will separate them. Fortunately, the reshuffling of linked genes can take many thousands of years. This means that, theoretically, we should be able to detect associations between diseases and DNA markers if the marker locus is very close to the disease locus. Compared with a linkage study, the design and analysis of an association study is straightforward. We do not require pedigrees with multiple ill members. Samples of unrelated patients and controls will suffice (though family-based association study designs exist, and have their advantages). Instead of a complex 42

linkage analysis, all we need do is compare the rates of marker alleles (or genotypes) in patients and controls with standard statistical tests [39]. Genes within a linked region are candidates for involvement in the phenotype based on their chromosomal location or position (i.e. they are ‘positional candidate genes’). Within a linked region or even in the absence of linkage evidence, a gene may also be a candidate if there is some compelling reason to suspect that the gene influences risk for a given disorder. Association of candidate genes can be evaluated in an independent sample of cases with the disorder and matched control subjects (i.e. in a ‘case–control’ study), or in small family units, where the transmission of variant and normal forms of the gene from parents to offspring can be monitored. In a case–control association study, we simply count the number of each type of allele of a gene, that is found in cases and compare these counts with the allele distribution seen in the control group; this process can also be performed for genotypes. A statistical test is then used to determine if the distribution of alleles observed among cases differs from that seen among controls. If it is different, then we have found evidence for a genetic association with the disorder, where the allele that is over-represented in the group of cases is considered the risk allele. The degree of over-representation of the risk allele in cases relative to controls can be used to derive an odds ratio, which gives a numeric indication of the probability of an affected individual possessing the allele compared the probability of an unaffected individual possessing the allele. Association studies can be performed for alleles or genotypes. In addition, a disorder can be tested for association with a haplotype, which is a pattern of alleles across several markers on the same chromosome (for a description of the International HapMap Project, which is dedicated to cataloguing the haplotype structure of the entire human genome, see http://hapmap.ncbi.nlm .nih.gov/. If linkage disequilibrium, or unusually tight linkage, occurs between the markers in a haplotype, they will typically be inherited together, as no recombination will occur between them. This concept is particularly useful for family-based association studies. In family-based studies, we can use analogous statistics to determine if any difference from the expected equal inheritance of

GENETIC EPIDEMIOLOGY

risk and normal alleles of a gene (or haplotypes within or across several genes) is detected in affected probands who could have received either allele from their parent. In a family-based study, the odds ratio estimates the haplotype relative risk, which represents the increase in the probability of the affected offspring receiving the risk allele (which is presumed to be on the same haplotype as the marker allele) relative to the normal allele. If the odds ratio, relative risk or other effect size attributed to a polymorphism is large enough to attain statistical significance, there are four possible explanations for the result: (i) there is a true association with a causative risk allele; (ii) the associated polymorphism is in linkage disequilibrium (i.e. is in close proximity and usually co-inherited) with the causal variant; (iii) there is some confounding factor that introduces a systematic bias (e.g. population stratification, or background genetic differences between case and control groups) or (iv) the result is due to chance or random error. A disadvantage of association studies is that the DNA marker must either be in the disease gene itself or very close to it. This is in contrast with the linkage method which can detect linkage over relatively large distances. However, unlike linkage analysis, association analysis can detect genes having only a small effect on the susceptibility to illness. Candidate gene association studies are limited by the method used to choose candidates. For a gene to become a positional candidate gene, it must map to a chromosomal region that has been observed to show linkage to the disorder. However, genes with a small but reliable effect on risk may not generate a linkage signal, and thus may never come under study. Selecting genes for association analysis based on their theoretical involvement in the disease process is risky as well. Since our understanding of the biological basis of most psychiatric disorders is far from comprehensive, the pursuit of candidate genes typically progresses incrementally through genes that are expressed within systems widely implicated in the disorder. This is clearly not an optimal process, as the prior probability of selecting the right candidate gene (out of ∼25 000 human genes) and the right polymorphism (out of more than 10 000 000 in the human genome) for analysis is remote. The recent advancement of laboratory and statistical methods

for genome-wide association analysis should allow for a more unbiased examination of association patterns throughout the genome and help resolve this dilemma in coming years [56]. Another limitation of association studies is that they are notoriously difficult to replicate, perhaps owing to their propensity towards false-positive results [57]. The problem of false positive results is exacerbated by the fact that close linkage is not the only cause of disease-marker associations. As discussed above, the frequencies of DNA marker alleles may vary among ethnic groups, cohorts of different ages or other isolated segments of a population. Thus, if case and control groups are not drawn from the same populations and carefully matched on all relevant factors, spurious differences in allele frequencies between groups will emerge due to the population admixture alone [58]. Because it may be difficult to find patient and control groups that are suitably matched for ancestry, genomic control methods have been advocated to account for any imprecision in matching. These methods genotype ancestry-informative markers (i.e. those that differ in frequency across ancestral groups) in addition to those of hypothesised importance in the study, and use the frequencies of these markers in cases and controls to derive an adjustment factor that can be applied to the results pertaining to the hypothesised risk locus. Several investigators have developed tests of linkage disequilibrium that use the parents of ill individuals as controls [59–63], which also circumvents the problems associated with ancestral mis-matching between cases and controls. The transmission disequilibrium test (TDT) uses families having at least one affected offspring and one parent who is heterozygous for the DNA marker to be tested [61]. The TDT compares the number of times heterozygous parents transmit the associated marker allele to affected offspring with the number of times they transmit the other marker allele. If these probabilities differ from what is expected by chance, then we can conclude that linkage disequilibrium exists. Although the TDT solves the problem of ethnicity matching, it still faces the problem of false positives and must be cautiously interpreted in the absence of a credible candidate gene. 43

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Family-based association tests (FBATs) have been developed as extensions to the TDT model, whereby parents or siblings of patients are used as controls. Since each parent transmits only one allele to a child, the allele, that is not transmitted to the child is used as the control allele. The statistical test involves comparisons of the transmitted versus the non-transmitted allele. Because both alleles come from the same parent, there are no differences in ethnicity.

3.3 Psychiatric genetics and psychiatric epidemiology As this chapter shows, psychiatric genetics is a multidisciplinary endeavour. In combines the methodological talents of the epidemiologist, the mathematical proficiency of the statistician and the laboratory wizardry of the molecular geneticist. Although we now look towards molecular genetics and neuroscience to clarify the aetiological and pathophysiological details of psychiatric illness, these are unlikely to succeed without a continued partnership with epidemiology. Many complexities plague psychiatric genetics and many solutions have been proposed in the above referenced articles and others. We have summarised them in 10 key points as follows: 1 Use standardised diagnostic criteria. 2 Define diagnoses that will be included as affected cases before the data collection. 3 Use assessment and diagnostic procedures that minimise false positive diagnoses. 4 Ascertain pedigrees and collect data in a manner that can be reproduced by other investigators;. 5 Collect detailed clinical and demographic data to allow comparisons with other samples and the derivation of quantitative traits. 6 Maintain complete blindness between the psychiatric diagnoses and marker statuses of all subjects. 7 Implement procedures to facilitate the follow-up of pedigree members. 8 Implement procedures to minimise laboratory errors. 9 Use a threshold of statistical significance that takes into account the data analytic issues unique to complex non-Mendelian disorders. 44

10 Allow other investigators access to complete pedigree and clinical information relevant to any publications of linkage results. Psychiatric genetic researchers have a powerful toolbox of methods at their disposal for determining the genetic and environmental causes of mental illnesses. These methods span a wide spectrum, from clinical and behavioural genetic methods to molecular biological assays, reflecting the present status of psychiatric genetics as truly a multidisciplinary field. In addition, new methods such as transcriptomics (i.e. the analysis of gene transcription rates via mRNA quantification) and proteomics (i.e. the analysis of gene translation rates by protein quantification) are pushing the boundaries of what is considered ‘psychiatric genetics’. In a strict sense, these are not genetic techniques and thus may fall under the larger rubric of molecular psychiatry, or even biological psychiatry. However, these techniques examine gene products whose expression is influenced by both genetic and environmental factors, and in this sense, examining such molecules is entirely consistent with the approach of genetic epidemiology, which is to identify both genetic and environmental causes of disease. Despite this progress, the major contributions of psychiatric genetic research to the diagnosis, treatment, prediction and prevention of psychiatric disorders currently remain unrealised. As we have acknowledged throughout the chapter, several limitations of genetic research and its impact on clinical practice must be acknowledged and overcome. Most importantly, the aetiologic heterogeneity of psychiatric disorders must be embraced as the rule rather than the exception. Identifying phenotypic factors that differentiate genetic subtypes will allow future genetic research to derive more reasonable and reliable estimates of familial risk and heritability that are based on the particular features of the affected family and its members. It is nevertheless exciting to visualise the contributions to clinical psychiatry and genetic counselling that await: reduced uncertainty in formulating primary and differential diagnoses; individually tailored pharmacotherapy and disease management; early identification and intervention, leading to better prognosis and ultimately, effective prevention programmes. As technologies improve,

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experimental capacity increases, and computational methods become more efficient, it is expected that the rate of discovery of risk genes for psychiatric disorders will also accelerate. The identification of specific genetic risk factors for psychiatric disorders will then facilitate the identification and quantification of environmental risk factors that interact with these genes to produce illness. A thorough understanding of these determinants of mental illness will allow the considerable promise of the psychiatric genetic approach to be fulfilled.

Acknowledgements Preparation of this article was supported in part by National Institutes of Health GrantsR01DA012846, R01DA018662, R01MH065562, R01MH071912, R21MH075027 and R01MH081861 to Dr. M.T. Tsuang, P50MH081755 and R01MH085521to Dr. S.J. Glatt, and R01DA018659, R01HD053586, R01MH066877, R01MH081803, R13MH059126 and U01MH085518 to Dr. S.V. Faraone, as well as a NARSAD Young Investigator Award to Dr. S.J. Glatt.

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Further reading Thompson, W.D., Orvaschel, H., Prusoff, B.A. and Kidd, K.K. (1982) An evaluation of the family history method for ascertaining psychiatric disorders. Arch. Gen. Psychiatry, 39, 53–58. Mendlewicz, J., Fleiss, J.L., Cataldo, M. and Rainer, J.D. (1975) Accuracy of the family history method in affective illness. Arch. Gen. Psychiatry, 32, 309–314. LaBuda, M.C., Gottesman, I.I. and Pauls, D.L. (1993) Usefulness of twin studies for exploring the etiology of childhood and adolescent psychiatric disorders. Am. J. Med. Genet. Neuropsychiatr. Genet., 48, 47–59. Orvaschel, H., Thompson, W.D., Belanger, A. et al. (1982) Comparison of the family history method to direct interview: factors affecting the diagnosis of depression. J. Affect. Disord., 4, 49–59. Andreasen, N.C. (1986) The family history approach to diagnosis: how useful is it?. Arch. Gen. Psychiatry, 43, 421–429. Gottesman, I.I. and Bertelsen, A. (1989) Confirming unexpressed genotypes for schizophrenia. risks in the offspring

of Fischer’s Danish identical and fraternal discordant twins. Arch. Gen. Psychiatry, 46, 867–872. Silverman, J.M., Breitner, J.C.S., Mohs, R.C. and Davis, K.L. (1986) Reliability of the family history method in genetic studies of Alzheimer’s disease and related dementias. Am. J. Psychiatry, 143 (10), 1279–1282. Ott, J. (1983) Linkage analysis and family classification under heterogeneity. Ann. Hum. Genet., 47, 311–320. Risch, N. (1988) A new statistical test for linkage heterogeneity. Am.J. Hum. Genet., 42, 353–364. Ott, J. (1991) Analysis of Human Genetic Linkage, The Johns Hopkins University Press, Baltimore. Kosten, T.A., Anton, S.F. and Rounsaville, B.J. (1992) Ascertaining psychiatric diagnoses with the family history method in a substance abuse population. J. Psychiatr. Res., 26 (2), 135–147. Elston, R.C. and Namboodiri, K.K. (1977) Family studies of schizophrenia. Bull. Int. Stat. Inst., 47, 683– 697. McGue, M., Gottesman, I.I. and Rao, D.C. (1985) Resolving genetic models for the transmission of schizophrenia. Genet. Epidemiol., 2, 99–110. Risch, N. and Baron, M. (1984) Segregation analysis of schizophrenia and related disorders. Am.J. Hum. Genet., 36, 1039–1059. Faraone, S.V., Kremen, W.S. and Tsuang, M.T. (1990) Genetic transmission of major affective disorders: quantitative models and linkage analyses. Psychol. Bull., 108 (1), 109–127. Goldin, L.R. (1990) The increase in type I error rates in linkage studies when multiple analyses are carried out on the same data: a simulation study. Am. J. Hum. Genet., 47 (3), A180 (abstract). Ott, J. (1990) Genetic linkage and complex diseases: a comment. Genet. Epidemiol., 7, 35–36. Faraone, S.V. and Tsuang, M.T. (1985) Quantitative models of the genetic transmission of schizophrenia. Psychol. Bull., 98, 41–66. Pauls, D.L. and Leckman, J.F. (1986) The inheritance of Gilles De La Tourette’s syndrome and associated behaviors. Evidence for autosomal dominant transmission. N. Engl. J. Med., 315, 993–997. Egeland, J.A., Gerhard, D.S., Pauls, D.L. et al. (1987) Bipolar affective disorders linked to DNA markers on chromosome 11. Nature, 325, 783–787. Kelsoe, J.R., Ginns, E.I., Egeland, J.A. et al. (1989) Reevaluation of the linkage relationship between chromosome 11p loci and the gene for bipolar affective disorder in the old order Amish. Nature, 342, 238–243. Freimer, N.B., Sandkuiji, L.A. and Blower, S.M. (1993) Incorrect specification of marker allele frequencies: effects on linkage analysis. Am. J. Hum. Genet., 52, 1102–1110. Cavalli-Sforza, L.L. and King, M.-C. (1986) Detecting linkage for genetically heterogeneous diseases and detecting

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CHAPTER 3 heterogeneity with linkage data. Am. J. Hum. Genet., 38, 599–616. Ott, J. (1986) The number of families required to detect or exclude linkage heterogeneity. Am. J. Hum. Genet., 39, 159–165. Clerget-Darpoux, F., Babron, M.-C. and Bona¨ıti-Pelli´e, C. (1987) Power and robustness of the linkage homogeneity test in genetic analysis of common disorders. J. Psychiatr. Res., 21 (4), 625–630. Martinez, M.M. and Goldin, L.R. (1989) The detection of linkage and heterogeneity in nuclear families for complex disorders: one versus two major loci. Am. J. Hum. Genet., 44, 552–559. Martinez, M.M. and Goldin, L.R. (1990) Power of the linkage test for a heterogeneous disorder due two independent inherited causes: a simulation study. Genet. Epidemiol., 7, 219–230. Goldin, L.R. and Gershon, E.S. (1988) Power of the affected-sib-pair method for heterogeneous disorders. Genet. Epidemiol., 5, 35–42. Risch, N. (1990) Linkage strategies for genetically complex traits. II. The power of affected relative pairs. Am. J. Hum. Genet., 46, 229–241. McGue, M., Gottesman, I.I. and Rao, D.C. (1983) The transmission of schizophrenia under a multifactorial threshold model. Am. J. Hum. Genet., 35, 1161–1178. Risch, N. (1990) Linkage strategies for genetically complex traits. I. Multilocus models. Am. J. Hum. Genet., 46, 222–228. Risch, N. (1990) Linkage strategies for genetically complex traits. III. The effect of marker polymorphism on analysis of affected relative pairs. Am. J. Hum. Genet., 46, 242–253. Chen, W.J., Faraone, S.V. and Tsuang, M.T. (1992) Linkage studies of schizophrenia: a simulation study of statistical power. Genet. Epidemiol., 9, 123–139. Goldin, L.R. and Martinez, M.M. (1989) The detection of linkage and heterogeneity in nuclear families when unaffected individuals are considered unknown, in Multipoint Mapping and Linkage Based Upon Affected Pedigree Members (eds R.C. Elston, M.A. Spence, S.E. Hodge and J.W. MacCluer), Alan R. Liss, Inc., New York, pp. 195–200. Levinson, D.F. (1993) Linkage information in small family structures: comparison of pedigrees with three to five affected members. Psychiatr. Genet., 3 (1), 45–57. Levinson, D.F. (1993) Power to detect linkage with heterogeneity in samples of small nuclear families. Am. J. Med. Genet., Neuropsychiatr. Genet., 48 (2), 94–102. Boehnke, M. (1986) Estimating the power of a proposed linkage study: a practical computer simulation approach. Am. J. Hum. Genet., 39, 513–527.

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Ploughman, L.M. and Boehnke, M. (1989) Estimating the power of a proposed linkage study for a complex genetic trait. Am. J. Hum. Genet., 44, 543–551. Ott, J. (1989) Computer-simulation methods in human linkage analysis. Proc. Natl. Acad. Sci. USA, 86, 4175–4178. Kendler, K.S., Silberg, J.L., Neale, M.C. et al. (1991) The family history method: whose psychiatric history is measured? Am. J. Psychiatry, 148 (11), 1501–1504. Schweber, M.A. (1985) A possible unitary genetic hypothesis for Alzheimer’s disease and Down’s syndrome. Ann. N. Y. Acad. Sci., 450, 223–238. Korenberg, J., West, R. and Pulst, S. (1988) The amyloid protein precursor gene maps to chromosome 21 subbands q21.15-q21.1. Neurology, 38, 265. Goate, A., Chartier-Harlin, M-C., Mullan, M. et al. (1991) Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer’s disease. Nature, 349, 704–706. Chartier-Harlin, M-C., Crawford, F., Houlden, H. et al. (1991) Early-onset Alzheimer’s disease caused by mutations at codon 717 of the β-amyloid precursor protein gene. Nature, 353, 844–846. Naruse, S., Igarashi, S., Kobayashi, H. et al. (1991) Missense mutation Val-lle in exon 17 of amyloid precursor protein gene in Japanese familial Alzheimer’s disease. Lancet, 337, 978–979. van Duijn, C.M., Hendriks, L., Cruts, M. et al. (1991) Amyloid precursor protein gene mutation in early-onset Alzheimer’s disease. Lancet, 337, 978. Hsu, Y-P.P., Weyler, W., Chen, S. et al. (1988) Structural features of human monoamine oxidase A elucidated from cDNA and peptide sequences. J. Neurochem., 51, 1321–1324. Hsu, Y-P.P., Powell, J.F. and Chen, S. (1988) Molecular genetic studies of MAO genes, in Progress in Catecholamine Research: Part A. Basic and Peripheral Mechanisms (eds A. Dalstrom, H. Belmaker and M. Sandler), Alan Liss, New York, pp. 89–95. Bach, A.W.J., Lan, N.C., Johnson, D.L. et al. (1988) cDNA cloning of human liver monoamine oxidase A and B: molecular basis of differences in enzymatic properties. Proc. Natl. Acad. Sci., 85, 4934–4938. Kobayashi, K., Kurosawa, Y., Fujita, K. and Nagatsu, T. (1989) Human dopamine-beta hydroxylase gene: two mRNA types having different 3’-terminal regions are produced through alternative polyadenylation. Nucleic Acids Res., 17, 1089–1102. Lamouroux, A., Vigny, A., Faucon, B.N. et al. (1987) The primary structure of human dopamine-beta-hydroxylase: insights into the relationship between the soluble and the membrane-bound forms of the enzyme. Eur. Mol. Biol. Organ. J., 6 (13), 3931–3937.

GENETIC EPIDEMIOLOGY Grima, B., Lamouroux, A., Boni, C. et al. (1987) A single human gene encoding multiple tyrosine hydroxylases with different predicted functional characteristics. Nature, 326, 707–711. Bunzow, J.R., Van, T.H., Grandy, D.K. et al. (1988) Cloning and expression of a rat D2 dopamine receptor cDNA. Nature, 336 (6201), 783–787. Grandy, D.K., Litt, M., Allen, L. et al. (1989) The human dopamine D2 receptor gene is located on chromosome 11 at q22-q23 and identifies a TaqI RFLP. Am. J. Hum. Genet., 45, 778–785. Zander, K.J., Fischer, B., Zimmer, R. and Ackenheil, M. (1981) Long-term neuroleptic treatment of chronic schizophrenic patients: clinical and biochemical effects of withdrawal. Psychopharmacology, 73, 43–47. Sunahara, R.K., Niznik, H.B., Weiner, D.M. et al. (1990) Human dopamine D1 receptor encoded by an intronless gene on chromosome 5. Nature, 347, 80–83. Dearry, A., Gingrich, J.A., Falardeau, P. et al. (1990) Molecular cloning and expression of the gene for a human D1 dopamine receptor. Nature, 347, 72–76. Blum, K., Noble, E.P., Sheridan, P.J. et al. (1990) Allelic association of human dopamine D2 receptor gene in alcoholism. J. Am. Med. Assoc., 263 (15), 2055–2060. Gelernter, J., O’Malley, S., Risch, N. et al. (1991) No association between an allele at the D2 dopamine receptor gene (DRD2) and alcoholism. J. Am. Med. Assoc., 266 (13), 1801–1807. Nothen, M.M., Erdmann, J., Korner, J. et al. (1992) Lack of association between dopamine D1 and D2 receptor genes and bipolar affective disorder. Am. J. Psychiatry, 149 (2), 199–201. Schwartz, X.L. and Moises, H.W. (1993) No association between schizophrenia and homozygosity at the D3 dopamine receptor gene. Am. J. Med. Genet., Neuropsychiatr.Genet., 48 (2), 83–86. Conneally, P.M. (1991) Association between the D2 dopamine receptor gene and alcoholism. A continuing controversy. Arch. Gen. Psychiatry, 48, 757–759. Kidd, K.K. (1993) Associations of disease with genetic markers: Deja vu all over again. Am. J. Med. Genet., Neuropsychiatr.Genet., 48 (2), 71–73. Pato, C.N., Macciardi, F., Pato, M.T. et al. (1993) Review of the putative association of dopamine D2 receptor and alcoholism: a meta-analysis. Am. J. Med. Genet., Neuropsychiatr.Genet., 48 (2), 78–82. Crowe, R.R. (1993) Candidate genes in psychiatry: an epidemiological perspective. Am. J. Med. Genet., Neuropsychiatr.Genet., 48 (2), 74–77. Botstein, D., White, R.L., Skolnick, M. and Davis, R.W. (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am. J. Hum. Genet., 32 (3), 314–331.

Jeffreys, A.J., Wilson, V. and Thein, S.L. (1985) Hypervariable ‘minisatellite’ regions in human DNA. Nature, 314, 67–73. Marshall, E. (1999) Genomics: drug firms to create public database of genetic mutations. Science, 284, 406–407. Schork, N.J., Fallin, D. and Lanchbury, S. (2000) Single nucleotide polymorphisms and the future of genetic epidemiology. Clin. Genet., 58, 250–264. Wang, J. (2000) From DNA biosensors to gene chips. Nucleic Acids Res., 28, 3011–3016. Sobell, J.L., Heston, L.L. and Sommer, S.S. (1992) Delineation of genetic predisposition to multifactorial disease: a general approach on the threshold of feasibility. Genomics, 12, 1–6. Sommer, S.S., Lind, T.J., Heston, L.J. and Sobell, J.L. (1993) Dopamine D4 receptor variants in unrelated schizophrenic cases and controls. Am. J. Med. Genet., Neuropsychiatr. Genet., 48 (2), 90–93. Gejman, P.V. and Gelernter, J. (1993) Mutational analysis of candidate genes in psychiatric disorders. Am. J. Med. Genet., Neuropsychiatr. Genet., 48(4), 184–191. Murphy, K.C., Jones, A.L. and Owen, M.J. (1999) High rates of schizophrenia in velo-cardio-facial syndrome. Arch. Gen. Psychiatry, 56, 940–945. Buckley, P., O’Callaghan, E., Larkin, C. and Waddington, J.L. (1992) Schizophrenia research: the problem of controls. Biol. Psychiatry, 32, 215–217. Risch, S.C., Lewine, R.J., Jewart, R.D. et al. (1990) Ensuring the normalcy of ‘normal’ volunteers. Am. J. Psychiatry, 147 (5), 682–683. Thaker, G.K., Moran, M., Lahti, A. et al. (1990) Psychiatric morbidity in research volunteers. Arch. Gen. Psychiatry, 47, 980. Weinberg, W. (1925) Methoden und technik der statistik ¨ mit besonderer berucksichtigun der sozialbiologie, in Handbuch der Sozialen Hygiene und Gesundheitsfursorge 1. Grundlagen und Methoden (eds A. ¨ Gottstein, A. Schlossmann and L. Teleky), Verlag von Julius Springer, Berlin, pp. 71–148. Fisher, R.A. (1934) The effect of methods of ascertainment upon the estimation of frequencies. Ann. Eugen., 6, 13–25. McGue, M. and Gottesman, I.I. (1989) Genetic linkage in schizophrenia: perspectives from genetic epidemiology. Schizophr. Bull., 15, 453–464. McGue, M. and Gottesman, I.I. (1991) The genetic epidemiology of schizophrenia and the design of linkage studies. Eur. Arch. Psychiatry Clin. Neurosci., 240, 174–181. Pulver, A.E. and Bale, S.J. (1989) Availability of schizophrenic patients and their families for genetic linkage studies: findings from the Maryland epidemiology sample. Genet. Epidemiol., 6, 671–680.

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CHAPTER 3 Weissman, M.M., Merikangas, K.R., John, K. et al. (1986) Family-genetic studies of psychiatric disorders. Developing technologies. Arch. Gen. Psychiatry, 43 (11), 1104–1116. Bonney, G.E. (1984) On the statistical determination of major gene mechanisms in continuous human traits: regressive models. Am. J. Human Genet., 18, 731–749. Bonney, G.E. (1986) Regressive logistic models for familial disease and other binary traits. Biometrics, 42, 611–625. Bonney, G.E. (1987) Logistic regression for dependent binary observations. Biometrics, 43, 951–973. Borecki, I.B., Lathrop, G.M., Bonney, G.E. et al. (1990) Combined segregation and linkage of genetic hemochromatosis using affection status, serum iron, and HLA. Am. J. Hum. Genet., 47, 542–550. Chase, G.A. and Kramer, M. (1986) The abridged census method as an estimator of lifetime risk. Psychol. Med., 16, 865–871. ¨ Stromgren, E. (1935) Zum ersatz des Weinbergschen ‘abgekurzten verfahrens’ zugleich ein beitrag zur Frage von der Erblichkeit des Erkrankungsalters bei der Schizophrenie. Z. Gesamte Neurol. Psychiatr., 153, 784–797. ¨ Larsson, T. and Sjogren, T. (1954) A methodological, psychiatric and statistical study of a large Swedish rural population. Acta Psychiatr. Neurol. Scand., 89, 40–54. Risch, N. (1983) Estimating morbidity risks with variable age of onset: review of methods and a maximum likelihood approach. Biometrics, 39, 929–939. ¨ Stromgren, E. (1938) Beitrage zur psychiatrischen erblehre auf grund von Untersuchungen an einer Inselbevolkerung. Acta Psychiatr. Neurol. Scand., 19 (Suppl), 1–257. Thompson, W.D. and Weissman, M.M. (1981) Quantifying lifetime risk of psychiatric disorder. J. Psychiatr. Res., 16, 113–126. Cupples, L.A., Risch, N., Farrer, L.A. and Myers, R.H. (1991) Estimation of morbid risk and age at onset with missing information. Am. J. Hum. Genet., 49, 76–87. Chen, W.J., Faraone, S.V. and Tsuang, M.T. (1992) Estimating age at onset distributions: a review of methods and issues. Psychiatr. Genet., 2, 219–238. Heimbuch, R.C., Matthysse, S. and Kidd, K.K. (1980) Estimating age-of-onset distributions for disorders with variable onset. Am. J. Hum. Genet., 32, 564–574. Baron, M., Risch, N. and Mendlewicz, J. (1983) Age at onset in bipolar-related major affective illness: clinical genetic implications. J. Psychiatr. Res., 17, 5–18. Chen, W.J., Faraone, S.V., Orav, E.J. and Tsuang, M.T. (1993) Estimating age at onset distributions: The bias from prevalent cases and its impact on risk estimation. Genet. Epidemiol., 10, 43–60. Sturt, E. (1985) Estimating morbidity risks with variable age of onset (correspondence). Biometrics, 41, 311–313.

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Lee, E.L. (1980) Statistical Methods for Survival Data Analysis, Lifetime Learning, Belmont. Faraone, S.V., Biederman, J., Krifcher, B. et al. (1993) Evidence for independent transmission in families for Attention Deficit Hyperactivity Disorder (ADHD) and learning disability: results from a family-genetic study of ADHD. Am. J. Psychiatry, 150, 891–895. Kaplan, E.L. and Meier, P. (1958) Nonparametric estimation from incomplete observations. Am. Stat. Assoc. J., 53, 457–481. Breslow, N. and Crowley, J. (1974) A large sample study of the life table and product limit estimates under random censorship. Ann. Stat., 2, 437–453. Lewis, S.W., Reveley, A.M., Reveley, M.A. et al. (1987) The familial/sporadic distinction as a strategy in schizophrenia research. Br. J. Psychiatry, 151, 306–313. Kendler, K.S. and Hays, P. (1982) Familial and sporadic schizophrenia: a symptomatic, prognostic and EEG comparison. Am. J. Psychiatry, 139, 1557–1562. Lyons, M.J., Faraone, S.V., Kremen, W.S. and Tsuang, M.T. (1989) Familial and sporadic schizophrenia: a simulation study of statistical power. Schizophr. Res., 2, 345–353. Kendler, K.S. (1987) Sporadic versus familial classification given etiologic heterogeneity: sensitivity, specificity, and positive and negative predictive power. Genet. Epidemiol., 4, 313–330. Eaves, L.J., Kendler, K.S. and Schulz, S.C. (1986) The familial sporadic classification: Its power for the resolution of genetic and environmental etiological factors. J. Psychiatr. Res., 20, 115–130. Lyons, M.J., Kremen, W.S., Tsuang, M.T. and Faraone, S.V. (1989) Investigating putative genetic and environmental forms of schizophrenia: Methods and findings. Int. Rev. Psychiatry, 1, 259–276. Erlenmeyer-Kimling, L. (1975) A prospective study of children at risk for schizophrenia: methodological considerations and some preliminary findings, in Life History Research in Psychopathology (eds R. Wirt, G. Winokur and M. Ross), University of Minnesota Press, Minneapolis, pp. 22–46. Mednick, S.A., Mura, E., Schulsinger, F. and Mednick, B. (1971) Perinatal conditions and infant development in children with schizophrenic parents. Soc. Biol. (Suppl. 18), 103. Fish, B., Marcus, J., Hans, S.L. et al. (1992) Infants at risk for schizophrenia: sequelae of a genetic neurointegrative defect. A review and replication analysis of pandysmaturation in the Jerusalem infant development study. Arch. Gen. Psychiatry, 49, 221–235. Biederman, J., Rosenbaum, J.F., Bolduc, E.A. et al. (1991) A high risk study of young children of parents with panic disorder and agoraphobia with and without comorbid major depression. Psychiatry Res., 37, 333–348.

GENETIC EPIDEMIOLOGY Orvaschel, H. (1990) Early onset psychiatric disorder in high risk children and increased familial morbidity. J. Am. Acad. Child Adolesc. Psychiatry, 29 (2), 184–188. Tsuang, M.T., Faraone, S.V. and Lyons, M.J. (1993) Advances in psychiatric genetics, in International Review of Psychiatry, vol. 1 (eds J.A. Costae Silva, C.C. Nadelson, N.C. Andreasen and M. Sato), American Psychiatric Press, Washington, DC, pp. 395–440. Tsuang, M.T., Gilbertson, M.W. and Faraone, S.V. (1991) Genetic transmission of negative and positive symptoms in the biological relatives of schizophrenics, in Positive vs. Negative Schizophrenia (eds A. Marneros, M.T. Tsuang and N. Andreasen), Springer-Verlag, New York, pp. 265–291. Morton, N.E., Rao, D.C. and Lalouel, J.-M. (1983) Methods in Genetic Epidemiology, Karger, New York. Sorant, A.J.M. and Elston, R.C. (1989) Segregation analysis of a truncated (censored) trait with logistic P.D.F. (REGTL version 1.0), in Statistical Analysis for Genetic Epidemiology (eds J.E. Bailey-Wilson and R.C. Elston), Department of Biometry and Genetics, LSU Medical Center, New Orleans. Morton, N.E. and MacLean, C.J. (1974) Analysis of family resemblance. III. Complex segregation analysis of quantitative traits. Am. J. Hum. Genet., 26, 489–503. Iselius, L. and Morton, N.E. (1991) Transmission probabilities are not correctly implemented in the computer program POINTER. Am. J. Hum. Genet., 49 (459), 459. Sorant, A.J.M. and Elston, R.C. (1989) A subroutine package for function maximization (A users guide to MAXFUN version 5.0), in Statistical Analysis for Genetic Epidemiology (eds J.E. Bailey-Wilson and R.C. Elston), Department of Biometry and Genetics, LSU Medical Center, New Orleans. Akaike, H. (1974) A new look at statistical model identification. IEEE Trans. Autom. Control, AC-19 (6), 716–723. DeLisi, L.E., Dauphinais, I.D. and Hauser, P. (1989) Gender differences in the brain: are they relevant to the pathogenesis of schizophrenia? Comp. Psychiatry, 30 (3), 197–208. Goldstein, J.M., Tsuang, M.T. and Faraone, S.V. (1989) Gender and schizophrenia: implications for understanding the heterogeneity of the illness. Psychiatry Res., 28 (3), 243–253. Faraone, S.V., Biederman, J., Keenan, K. and Tsuang, M.T. (1991) A family-genetic study of girls with DSM-III attention deficit disorder. Am. J. Psychiatry, 148 (1), 112–117. Pauls, D.L. (1979) Sex effect on the risk of mental retardation. Behav. Genet., 9 (4), 289–295.

Harris, T., Surtees, P. and Bancroft, J. (1991) Is sex necessarily a risk factor to depression? Br. J. Psychiatry, 158, 708–712. Cloninger, C.R., Christiansen, K.O., Reich, T. and Gottesman, I.I. (1978) Implications of sex differences in the prevalences of antisocial personality, alcoholism, and criminality for familial transmission. Arch. Gen. Psychiatry, 35, 941–951. Berney, T.P. (1989) Fragile X syndrome and disorders of the sex chromosome. Curr. Opin. Psychiatry, 2, 593–598. Khoury, M.J., Beaty, T.H. and Cohen, B.H. (1993) Fundamentals of Genetic Epidemiology, Oxford University Press, New York. Ottman, R. (1990) An epidemiologic approach to gene–environment interaction. Genet. Epidemiol., 7, 177–185. Fischer, M. (1971) Psychosis in the offspring of schizophrenic monozygotic twins and their normal co-twins. Br. J. Psychiatry, 118, 43–52. Merikangas, K.R., Spence, A. and Kupfer, D.J. (1989) Linkage studies of bipolar disorder: methodologic and analytic issues. Report of MacArthur foundation workshop on linkage and clinical features in affective disorders. Arch. Gen. Psychiatry, 46, 1137–1141. Ott, J. (1990) Invited editorial: cutting a Gordian knot in the linkage analysis of complex human traits. Am. J. Hum. Genet., 46, 219–221. Risch, N. (1990) Genetic linkage and complex diseases, with special reference to psychiatric disorders. Genet. Epidemiol., 7, 3–7. Weeks, D.E., Brzustowicz, L., Squires-Wheeler, E. et al. (1990) Report of a workshop on genetic linkage studies in schizophrenia. Schizophr. Bull., 16 (4), 673–686. Pato, C.N., Lander, E.S. and Schulz, S.C. (1989) Prospects for the genetic analysis of schizophrenia. Schizophr. Bull., 15 (3), 365–372. Faraone, S.V. and Santangelo, S. (1992) Methods in genetic epidemiology, in Research Designs and Methods in Psychiatry (eds M. Fava and J.F. Rosenbaum), Elsevier, Amsterdam, pp. 87–105. Andreasen, N.C., Endicott, J., Spitzer, R.L. and Winokur, G. (1977) The family history method using diagnostic criteria. Reliability and validity. Arch. Gen. Psychiatry, 34, 1229–1235. NIMH Genetics Initiative (1992) Family Interview for Genetic Studies, National Institute of Mental Health, Rockville.

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4

Examining gene–environment interplay in psychiatric disorders Judith Allardyce1 and Jim van Os1,2 1 Maastricht University Medical Centre, School of Mental Health and Neuroscience, Department of Psychiatry and Neuropsychology, South Limburg Mental Health Research and Teaching Network, Maastricht, The Netherlands 2 King’s College, King’s Health Partners, Department of Psychosis Studies, Institute of Psychiatry, London, UK

4.1 Introduction Epidemiologists have traditionally studied the distribution and determinants of health related states as a function of environmental exposures; for example stressful life events, pregnancy and birth complications, minority status, urban environments and cannabis use. The aim has been to identify environmental risk factors that are causal and potentially modifiable. In contrast, classical psychiatric genetics has tended to focus on gene mapping to identify single susceptibility or candidate genes for a particular disorder, or on statistical modelling techniques that aim to quantify heritability. Genetic methodologies assume that genetic determinants are true signals while environmental effects are noise, which should be controlled for where possible, while studies examining only environmental risk factors, do not take account of the possibility that exposure and outcome may share a genetic liability. Over the past few decades, it has become increasingly clear that the identification of specific genes and environmental risk factors will be greatly aided by integrating the respective fields of epidemiology and genetics into the new discipline of genetic epidemiology, which considers the joint actions of genetic and environmental factors in causing disease, within human populations and

the pattern of inheritance in families. Family studies have been the basic approach used by genetic epidemiologists but classical epidemiological designs such as case–control and cohort studies are considered useful in many situations [1–4]. Genetic epidemiology considers both environmental and genetic risk factors – and their interactions – with potential parity, by extending the conceptualisation of epidemiological exposures to include genetic factors and family relationships [5, 6]. This joint approach aims to unravel the pathway from genotype to phenotype by trying to understand how an individual’s genetic makeup modifies their susceptibility to environmental exposures or modifies their level (dose) of causal environmental exposures and explores ways in which the physical and social structure of the environment may exacerbate genetic risk. In actual fact the best evidence that there are non-genetic causal factors in the expression of psychiatric disorders comes from the classical twin studies which demonstrate concordance rates for monozygotic twins of between 50 and 70% for schizophrenia or mood disorders, though of course it is also quite probable that (partial) genetic mechanisms such as epigenetic effects, stochastic factors or mitochondrial inheritance could contribute to this discordance, conflating the ‘environmental’ contribution estimated [7–9]. Similarly, standard

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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heritability scores are confounded, in that they do not discriminate between ‘purely’ genetic determinants and gene–environment correlation (genetic mediation of the level of environmental exposures) and interaction (genetic mediation of the susceptibility of a causal environmental exposure). The high proportion of monozygotic pairs that remain unaffected, certainly points towards an environmental influence in the aetiology of these disorders [10, 11].

4.2 The process of genetic epidemiology The identification of causal factors in disease onset generally follows a series of logical steps or questions. This is the process or chain of genetic epidemiological research [2]. First, there is a systematic analysis of the rates of disease, comparing and contrasting rates in populations over time, between place and within different subgroups, for example; in people who have migrated, or between people of different social economic class and age/gender variations can all provide clues to putative genetic and environmental casual factors. The second question asks whether the disorder tends to run in families (do the relatives of patients with a disorder have higher rates of a specified disorder, greater than would be expected by chance alone?) Once a familial pattern is identified the risk to relatives is examined for correlation to closeness (degree) of the relationship, that is first degree relatives (parents, siblings and children) who share 50% of their genes on average with the patient should have a higher risk for the disorder, than second degree relatives who share on average only 25% of their genes with the affected person. The family study is a robust design, in many cases providing the initial clue that a disorder has a genetic component; however we have to be cautious in our interpretation of such familiarity as there are always alternative non-genetic explanations for family clustering, such as shared environment, social learning and viral causations, to name but a few. The third question asks what type of genetic transmission is occurring, is the familiar pattern compatible with one or more major genes or more suggestive of an oligogenic, polygenetic transmission 54

or shared environmental factors? Single major gene models propose that one gene accounts for most of the genetic transmission while other genes and environmental exposures play only a minor role in modifying the expression of the disorder or determining its age of onset. Oligogenic models assume that a few genes (e.g., less than 10) combine to cause the disorder. This joint effect can be additive, that is the likelihood of developing the disorder is simply the linear function of a number of genes or alternatively the mechanism may be interactive or a combination of the two models. The multifactorial–polygenetic model posits that an unspecified number of genes, perhaps running into the hundreds plus environmental factors act additively or interactively to increase risk of a disorder. Associated with the polygenetic model of disease is the concept of genetic liability, which is a latent trait that predisposes an individual to a particular disorder; the functional form of this may be a continuous linear association or a threshold form when disease occurs once a liability threshold has been crossed. The polygenetic model assumes that liability is normally distributed in the population. The fourth question asks where the genes for the disorder are located. This uses linkage analyses, a methodology examining family pedigrees (multiple case families) or affected sib pairs, using polymorphic markers of known chromosomal location in blood samples taken from the patent and their family. Linkage analyses establish whether markers are transmitted through the pedigree in a manner that parallels disease transmission (cotransmission/cosegregation) so that the general chromosomal location of the susceptibility gene can be identified. This process of gene mapping often begins by examining an array of widely spaced markers positioned across the whole genome, the search region narrowing down as information accrues and is later augmented with fine tuning techniques such as linkage disequilibrium (LD) mapping, which exploits the fact that common variants which are located close together on a chromosome (<= 10 000 bp) are likely to be transmitted together, that is are in LD. Therefore if one variant increases the risk of a common disorder (i.e., a direct association), genetic variants in LD with it will also be associated (indirectly) with the disorder. It remains unclear how effective linkage studies have been in

EXAMINING GENE–ENVIRONMENT INTERPLAY IN PSYCHIATRIC DISORDERS

identifying candidate genes for psychiatric disorders. Statistically significant linkage has been found but replication has been inconsistent presumably due to gene variants having small and heterogeneous effects. However, there is some support for its effectiveness in autistic spectrum disorder and bipolar disorder [12, 13]. If the polygenetic model is a good approximation to the genetic architecture of common psychiatric disorders (this is not fully accepted) , then it is likely the genes involved will be of small effect, therefore requiring very large numbers of affected families to make such studies practical [14]. The fifth question therefore, asks how strongly the identified genes are related to the disorder. Association studies have the potential to identify causal genes of small effect in realistically sized samples even in unrelated subjects. By comparing genotypes at candidate loci in cases and controls (family and population-based case–control studies) the strength of the relationship can be estimated. The main problem in designing association studies is that there is still no clearly defined understanding of the pathogenesis of major psychiatric disorders and so myriad potential genes could theoretically be involved. Candidate gene hypotheses for most psychiatric disorders have been extrapolated from the pharmacological actions of drugs used in their management, focusing particularly on functional polymorphisms implicated in the dopaminergic and serotonergic pathways [15–17]. Association studies are susceptible to type 1 errors, as low prior probability from multiple testing of multiple single gene variants makes it difficult to sort out true, from false positive associations [18, 19] a problem that will be even more problematic in genome-wide association studies (GWASs), which investigate a million genetic markers or more, at the same time [20]. Such major methodological problems have resulted in poor reproducibility of findings from association studies, as samples vary in their demographic profiles, confounder backgrounds and comorbidity characteristics. This is especially problematic if the genetic variant only increases risk of disorder under certain specific environmental conditions, that is: if there is gene–environment interaction (GxE). If this is the case, harnessing such heterogeneity in the sense of exploiting any GxE may aid the identification of susceptibility genes. The final

question in the epidemiological process therefore asks about the character of the genetic effect and includes examining interplay with environmental factors and measuring the frequency of high risk variations within the population. (See Text Box 4.1.)

4.3 Gene–environment interplay takes different forms There are a variety of biologically plausible mechanisms by which genes and the environment can co-influence onset of disease. These are: gene– environmental correlation (GEr) reflecting genetically influenced differences in exposure to particular environmental factors, GxE (co-action) referring to genetic influenced differences in susceptibility to particular environments and epigenetic mechanisms.

4.4 Gene–environment correlation Genetic expression has traditionally been considered a process taking place entirely within the constraints of the physiological milieu of an individual and the associated corollary of this conceptualisation is that any association between the individual and their external environment will be unidirectional, that is genes will have no influence on environmental exposures. The last few decades has seen a revision of this view, as it has became clear that some genes have an impact on their external environmental setting through behaviours which are, at least partly genetically determined, that is bidirectional relationship between genes and environment is probable [21]. Twin studies suggest genetic factors operate through pathways external to the person, influencing exposure to a range of environmental risk factors such as stressful life events, poor social support and marital relationship quality [22–24]. That individuals have an active role in the selection, modification and construction of their environmental milieu is now widely accepted by behavioural geneticists and evolutionary biologists. Some environmental exposures may indeed be heritable, a phenomenon referred to in evolutionary biology as the extended phenotype [25]. It has been shown in animal behavioural studies that the way animals modify their physical environment, 55

CHAPTER 4

Box 4.1 The process to determine the joint gene–environmental interplay influencing the risk for psychiatric disorders.

• generation of hypotheses about the relative contribution of putative genetic and environmental risk factors

Descriptive Epidemiology

• family history studies • twin & adoption studies

Determine whether there is familiar aggregation

• What is the genetic model of transmission? • single gene • few genes • poly gene

Segregation analyses

Locate risk gene (genes) main effects / interaction using Linkage & association studies

methods employed to measure genetic exposure

Study Design

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• Proxy (surrogate) indicators of genetic risk • family history • intermediate phenotypes • sibling correlations • Molecular genotyping • Candidate gene approach • GEWIS

• Cohort • (nested case control) • Case Control • Family member as control • General population control • Family Study • Sib pair analysis • case-parent trios • multi generational pedigrees

EXAMINING GENE–ENVIRONMENT INTERPLAY IN PSYCHIATRIC DISORDERS

for example how they build burrows, dams and webs is not randomly distributed but in part, involves heritable traits. Furthermore in social animals such as rodents, the provision and development of different parental nurturing behaviours (e.g., feeding routines) is partly predicted by variation in their genetics. Furthermore, selection experiments find genes to be associated with aggressive traits which lead the rodent to experience a different social milieu, compared to that of their less aggressive counterparts [24]. GEr can be both causal (Figure 4.1) and noncausal (Figure 4.2) [26]. Non-causal GEr occurs when the health outcome and the environmental factor under study share a genetic liability, but the environmental factor does not have a real causal relationship to the outcome, that is the observed environmental health outcome association is confounded by genotype. Behavioural contamination may also result in GEr when genetic/individual factors influence how a person feels or perceives a situation, which will be reflected on how they report environmental exposures. This information bias will be especially problematic when environmental exposure is collected retrospectively. Causal GEr occurs when there is (at least in part) a genetically mediated variation in causal environmental exposure profiles, that is part of a causal pathway. Genotype is unlikely to code directly for specific environmental exposures, but is more likely to act indirectly, through factors such as behaviour and personality; most studies to date have looked at dimensions of neurotism, extraversion and openness to experience, with shared genetic influences evident in studies examining neurotism scores and parenting and neurotism and stressful life events [26, 27].

Gene

O

BS

ER

VE

D

AS

SO

CI

AT

IO

N

LEVEL OF CAUSAL ENVIRONMENTAL FACTOR

PSYCHIATRIC DISORDER

Fig 4.1 Casual: gene–environment correlation.

Gene

True causal relationship

ENVIRONMENTAL FACTOR CONFOUNDED ASSOCIATION

Fig 4.2

PSYCHIATRIC DISORDER

Non-casual gene–environment correlation.

Three different GEr mechanisms have been described. Passive GEr occurs as most children inherit their genetic characteristics and early rearing environment from the same biological parents, for example parental maltreatment is associated with childhood conduct disorder; however, antisocial behaviour has been demonstrated to be moderately heritable and parents with such traits are more likely to abuse their children, therefore the maltreatment may be indicative of genetic risk transmitted by the parent to the child rather than a true environmental risk factor for conduct disorder [21]. Evocative (reactive) GEr refers to the impact an individual’s behaviour has on their social environment, especially the reaction of others to their behaviour, such that a shy person and a gregarious person evoke different responses in the same social setting. An association between marital difficulties and depression has been demonstrated; however, this relationship may reflect the conflicts that arise from interacting with someone who is depressed, rather than marital difficulties causing depression per se [21]. Selective (active) GEr occurs when an individual’s genetically mediated personality traits and behaviours influence the environmental ‘niches’ they select to live their life in. Kenneth and Baker [28] have recently carried out a systematic review of human studies examining the heritability (the proportion of individual differences for a trait in a particular population that results from inter-individual genetic differences) of environmental exposures [28]. They identified 55 independent papers reporting on general and specific stressful life events, parenting, family environment, social 57

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support, peer interactions and marital quality and found weighted heritability estimates ranging from 7 to 39%. This review suggests that all aspects of the environment studied in the literature to date, have a significant genetic influence but with small to moderate effect sizes. The review suggests that stressful life events which are more dependent on an individual’s own behaviour (e.g., divorce) are more heritable than events that are out with the control of the individual (e.g., death of a loved one). It found parenting behaviours reflecting positive emotional quality of the parent-child relationship were more heritable than parenting behaviour related to discipline styles. Importantly, it seems that the genetic makeup from all parties in a dynamic relationship contribute equally to the quality of the environmental experience. For example, in the child–parent relationship the genes from the parent seem to impact as much as the genes in the child, therefore it is possible that risk of psychiatric disorders may be mediated in part through self selection into pathogenic environment.

4.5 Gene–environment interaction GxE is defined as genetically moderated variation in the susceptibility to environmental exposures (Figure 4.3). There is biological plausibility and face validity in suspecting GxE to be relatively common in nature [29, 30]. From evolutionary theory, we know that it is gene–environmental interplay that drives an organism’s adaptation and evolution over subsequent generations. Furthermore, GxE mechanisms could help explain the ubiquitous heterogeneity of susceptibility to environmental hazards and opportunities seen between individuals [31]. Biological GxE is an explanatory model for causal mechanism and will require experimental validation as each element along the causal pathways emerges [32]. Some commentators have highlighted the distinction between biological and statistical interaction [33] emphasising that it is a core biological construct, best understood by the iterative delineation of specific mechanisms along causal pathways [34]. While this makes theoretical sense, in human epidemiological studies the 58

Gene Di

Direct effect may or may not be present

re

ct

ef

fe

SUSCEPTIBILITY ENVIRONMENTAL FACTOR

ct

PSYCHIATRIC DISORDER

Fig 4.3 Gene–environment interaction.

only way biological synergism can be demonstrated is via inference from statistical interactions. The current conceptual model for multi-factorial disease is the sufficient-component cause framework. This posits that a given disorder can have more than one sufficient causal mechanism, and every causal mechanism involves the joint action of multiple component causes, therefore any identified risk factor, be it genetic or environmental, will be neither necessary nor sufficient to produce the disorder. Inherent in this model is the concept of interaction. If a component cause is not sufficient, then it must coparticipate with other factors to produce disease [34]. Such joint effect mechanisms, sometimes known as sufficient causal pies, do not characterise a specific disease model or require component factors to act at the same time. It is also probable that specific component causes play a role in more than one sufficient causal mechanism. Biological synergism/interaction is therefore defined when two risk factors are components of the same sufficient cause [35]. If the two factors are part of different sufficient causes, their combined effects on risk will simply be the sum of their effects (additive).

4.6 Measurement of genotype, environmental exposure and pathological phenotype 4.6.1 Measurement of genetic variants There are three different approaches to measuring the genetic component in GxE studies; (i) The hypothesis-driven molecular candidate gene approach; (ii) Molecular whole genomic scanning approaches, which have no a priori hypothesis

EXAMINING GENE–ENVIRONMENT INTERPLAY IN PSYCHIATRIC DISORDERS

but hunt for genes; (iii) Omnibus approaches: using surrogate (proxy) variables of genetic risk. The first two approaches (to date) have used DNA array, though expression arrays (messenger RNA and non coding RNA) and proteome analysis may be utilised in the future. The common DNA sequence markers used in GxE studies are SNPs (single nucleotide polymorphisms) CNVs (copy number variants) and SSRs (simple sequence repeats). While genome–environment wide interaction studies (GEWISs) are being proposed, to date population based GxE studies have used the candidate gene approach. The selection of the candidate genes should have a biologically plausible hypothesis. Information regarding the function of candidate genes and its validated SNPs can be found in genetic information websites [35]. Neale and Sham [36] suggest selecting gene variants using criteria based on their function in order of priority: amino acid change, promoter polymorphism; association with potential splice sites, synonymous coding regions variation and non coding (intronic) variants. In addition, they suggest using multiple markers spanning the length of the candidate gene to ensure all its regions are investigated [36]. However, to avoid redundancy when selecting multiple SNPs, LD can be exploited (i.e., dependence of loci usually in close proximity on the gene). This can be measured by inferring haplotypes (specific set of alleles at linked genetic loci). If a haplotype is identified then it is often possible to identify a single ‘tag’ marker, which will capture most of the variation across that region of the gene [37] so reducing the number of markers required for full coverage. Molecular genotyping is prone to genotyping errors, which can lead to loss of statistical power, therefore it is important to quality control genetic analyses [38]. Omnibus approaches using proxy or surrogate measures of genetic exposure such as family history or intermediate (endo)phenotypes (heritable biomarker, lying along the causal pathway from gene to disorder, but at a more proximal position to the gene, than the manifest symptoms) [39] are useful approaches as they potentially allow the combined genetic contribution to be quantified, including all unspecified GxG and GxE, which may contribute to differential susceptibility for

environmental exposures. The drawback of omnibus models is non-differential information bias from false negative misclassification of the genetic risk.

4.6.2 Measurement of environmental exposures Ideally the environmental exposures used in GxE should be an evidenced causal factor. This is a problem for GxE studies in psychiatric disorders as very few of the environmental risk factors for common psychiatric disorders meet this criterion. The environmental exposures should be measured accurately and if possible be multifaceted and include: timing (specific developmental stages), duration (discrete interval dosing or continuous exposures) and intensity (e.g., cannabis can be ingested in different forms with different tetrahydrocannabinol levels), in order to avoid misclassification which can significantly bias main effect estimates as well as interaction effects. To date, studies have tended to employ dichotomous environmental exposures, for example urban versus non-urban environment. However, the categorisation of continuous exposures, results in substantial loss of information and power, therefore it is advantageous to use the continuous measure. Possible exceptions are when there is a priori evidence to support a possible threshold model of association between the exposure and disorder. For example, the relationship between stressful life events and increased risk of depression, only holds for severe contextual threats [32, 40]. Methodologies which capture the cumulative nature of many of the ongoing chronic environmental exposures for example momentary assessment technologies, are likely to be useful [41]. Specific methodological issues around the measurement of environmental exposures are discussed in the study design section. Psychiatric epidemiological studies in the 1980– 1990s focused primarily on environmental exposures at the level of the individual (e.g., obstetric complications, an individual’s cannabis use). Over the next decade, researchers will try and characterise environmental exposures not just at the individual (and molecular) level but across a hierarchy of systemic complexity, including the micro shared environments (e.g., shared by families); neighbourhood composition and macro societal levels factors [42, 43]. 59

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4.6.3 Measurement of the pathological phenotype (disorder) Psychiatric disorders are categorical constructs and therefore it is attractive to treat them as dichotomous outcomes in GxE analyses. However, psychopathology can also be conceptualised as continuous latent variables (dimensions), the merits of which have been highly debated for depression and psychotic disorders [44, 45]. In future studies, it may be appropriate to use a continuous pathological outcome measure which prevents substantial loss of variability and power if there is no evidence of a bimodal distribution in the psychopathological outcome in the population under study. Another approach to the measurement of pathological outcome that may yield more consistent estimates of GxE is the use of phenotypes that are more proximal to the underlying pathophysiological mechanisms, such as intermediate (endo)phenotypes. For example, while initial report on the role of the hypothesised interaction between variations in the gene encoding the serotonin transporter and life stress in depression generated much interest, recent meta-analysis of this association concluded that there was no evidence to support GxE [46]. A possible explanation for this finding may be that behavioural measures of life events and depression introduce more error than proximal biological measures such as amygdala activation in response to experimental emotional stimuli, where meta-analysis of moderation by the same serotonin transporter polymorphism does suggest GxE [47].

of E and G according to whether the factor is present or absent. • r(E) the risk in the population exposed to the environmental factor only. • r(G) the risk in the population exposed to the genetic influence only. • r(GE) the risk in the population exposed to both the genotype and environmental risk factor. • r(00) the risk in the population exposed to neither exposure. If no interaction of G and E is present (that is, conditional independence) then the effect of G on the disease risk is the same across strata defined by E; however if the risk of disease varies across strata of E, then interaction is said to exist. Under strict statistical definitions whether or not an interaction exists will depend on the scale of the measurement. If risks are measured on the additive scale, so the effect of a risk factor is expressed as a risk difference (attributable risk), for example r(G) is 0.25 and r(00) is 0.10, then the effect of G is 0.25 – 0.10 = 0.15, the effect of G can then be expressed as r(G) – r(00), the effect associated with E as r(E) – r(00) and the effect associated with the joint GE exposure as r(GE) – r(00). The expected patterns of risk difference and relative risk with and without interaction would therefore be [48, 49] Model of joint action No interaction

4.7 Models of GxE To illustrate how GxE are modelled we will use the (overly) simple situation where all the three variables are dichotomous, and assume that the genetic and environmental factors increase the risk for disorder rather than reduce it. The underlying principles would be the same for protective effects, though some statistical extensions may be necessary. E is an environmental risk factor, G is a single gene variant risk factor, disease risk is the proportion of individuals who develop the pathology denoted as r. This situation results in four possible combinations 60

Synergistic action

Risk pattern r(GE) − r(G) = r(E) − r(00) r(GE) − r(G) > r(E) − r(00)

Relative risk (RR) pattern RR (GE) = RR (G) + RR (E) − 1 RR (GE) > RR (G) + RR (E) − 1

Risks can also be measured on a multiplicative scale where the effect of the risk is measured as a ratio (relative risk) rather than a risk difference. For example, if r(G) is 0.25 and r(00) is 0.10, then the effect of G is 0.25/0.10 = 2.5, the effect of G can then be expressed as r(G)/r(00), the effect associated with E as r(E)/r(00) and the effect associated with the joint GE exposure as r(GE)/r(00). Using this scale the expected patterns of risk and relative risk with and without interaction would be:

EXAMINING GENE–ENVIRONMENT INTERPLAY IN PSYCHIATRIC DISORDERS

Model of joint action No interaction Synergistic action

Risk pattern r(GE)/r(G) = r(E)/r(00) r(GE)/r(G) > r(E)/r(00)

Relative risk (RR) pattern RR (GE) = RR (G) × RR (E) RR (GE) > RR (G) × RR (E)

4.8 Which scale should we use to measure GxE ? A given data set can be tested to see how well it conforms to an additive or multiplicative model. However, the question of which scale should be adopted has been heavily contested, though the theoretical literature on sufficient cause models seems to be converging on a consensus advocating use of a fixed reference definition based on the additive scale. This argument integrates the idea that two causal factors from different causal pies (mechanisms) will generally have an additive relationship, whereas component causes from the same mechanism will have a relationship which is super-additive [34]. It is therefore possible that our previous emphasis on multiplicative models has meant missing biological relevant interactions [50]. Further support for use of the additive scale when examining interactions, comes from theoretical work regarding the concept of biological parallelism. It is possible that within the group of individuals exposed to both G and E there may be a subgroup of individuals who would contract the disorder if they were exposed to just one of the risk factors G or E, that is the risk factors act in parallel (|G E|) This conceptualisation of potentially competing risk factors does not fit neatly into the sufficient causal framework, however it has been proposed that it could be accommodated by modifying the model allowing one component of a sufficient cause to be non-definite, containing either G alone, E alone, synergistic GxE or parallel |G E|. Considering parallelism potentially allows the extent of interaction to be quantified, by estimating the proportional size if the subgroup with GxE synergism from the group of individuals who are exposed to both G and E [51]. The actual amount of interaction or parallelism cannot be directly measured in individuals exposed to both

G and E. However, it has been demonstrated that the amount of synergism exceeding parallelism equates to the statistical additive interaction [52]. In practise the amount of interaction has been approximated using contingency tables suggested by Darroch [51]. Approximation of synergy (true interaction effect) |synergism| |x1|

|x2| |parallelism|

r(GE) – r(G) r(G) – R

R(GE) – r(E)

r(E) – r(00)

r(GE) – r(00)

Take, for example, the Finish Adoption study [53]. Diagnosis of maternal schizophrenia was used as a proxy marker for G , while E was the level of communication deviance and thought disorder in the adopted into family and the disease outcome was broadly defined schizophrenia spectrum disorder. The risk of schizophrenia spectrum disorder was around 4% in the group of individuals who were exposed to neither exposure and for those exposed to G alone. The r (E) was 34% and the r (GE) was 62%, therefore filling in the risks for Darroch’s table, (x1 and x2 are unmeasured parameters). Approximation of synergy (true interaction effect) in the Finnish Adoption Study |synergism| |x1|

|x2| |parallelism|

0.58 0

0.28

0.30

0.58

It follows that |x2| must lie somewhere between 0 and 0.30. Therefore |synergism| must be between 0.28 and 0.58. That is, between 45% (0.28/0.62) and 94% (0.58/0.62) of the patients with schizophrenia spectrum disorder exposed to both communication deviance in the family (E) and an affected mother (G) seems to be attributable to GxE. It is possible that the optimum choice of measurement scale may depend on the goal of the investigation. Additive models have certainly been shown to be of greater relevance when assessing the public health impact of interactions as interventions at the population level need to be understood in the context of the prevalence of all other causal factors [49], while some purport that the multiplicative scale is apposite for aetiological research. If the environmental risks have very low variance, 61

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that is they are pervasive, multiplicative GxE models would not be helpful even if the biological reality is that the effect of the genotype is contingent on the environmental exposure. For example, genetically moderated susceptibility to malaria in regions where the infection is endemic would not be demonstrated by epidemiological GxE studies on the multiplicative scale [54]. However, in the absence of specific pathophysiological models of the disorder being studied, the scale chosen to measure interactions will at some level be arbitrary [48]. This concern notwithstanding, some researchers have postulated that the multiplicative scale may be more appropriate when certain causative pathways are being investigated, for example a multistage model of disease progression may best be described on the multiplicative scale, as the onset of the disorder will only occur after a number of iterative stages are completed, that is the stages are independent of each other. For example, genetic factors may influence risk for the first stage of the disorder, and first stage only, whereas an environmental factor may influence risk for a second stage, and second stage only, with the disorder only developing after there has been transition from stage 1 to stage 2 [55]. Under this hypothesis the combined effect of G and E will be equal to the product of the individual effects. Therefore if the magnitude of G effect on the disorder is 10 and the effect size of E equals 2, then the effect of their combined exposure will be 20. Inherent in the use of the multiplicative scale is the idea that the causal factors are independent from each other. However, the current definition of the sufficient component cause framework is deliberately abstract and independent of any specific disease model. It considers component causes acting at different times as biologically interacting (that is they are not considered Independent) if both are necessary components of a specific casual mechanism, so the additive scale still fits. It is important to note that the presence or absence of interaction on the additive scale does not indicate any specific causal model. It is recommended when reporting interactions in scientific journals that sufficient information should be provided to allow the interested reader to interpret the interaction on the additive scale if the multiplicative scale has been used, therefore allowing interpretation in a sufficient causal framework [56]. 62

This can be done by presenting the direct effects of the genetic and environmental risk factor and their joint effect, relative to the group of individuals exposed to neither factor [57]. Another approach is to present the full multiplicative model, including the direct effects and product term so allowing the recalculation of the joint effect on the additive scale [58].

4.9 Study designs for the detection of GxE There are several different study designs that potentially allow GxE to be detected, each with its own strengths and potential problems [59, 60].

4.9.1 Cohort design The cohort study has been the design of choice for common disorders; however rarer disorders and those with late or very wide ranges of age of onset may require sample sizes which are too large to be practically viable. In this design DNA samples and environmental exposure information can be obtained from an initially healthy sample, that is followed up prospectively. As the assessment of the environmental exposure occurs prior to onset of the disorder it is relatively free of information (recall) bias. High rates of follow up are however necessary to reduce selection bias, this can be difficult if the disorder has a long incubation period requiring years or even decades of observation. It is also necessary to try and ensure DNA samples come from a high proportion of both cases and controls, as differential take up can lead to selection bias. Information about ethnic background or genomic control methods [61] should be considered if population stratification is a potential problem due to migration. A nested case–control approach can be used to compare cases with those individuals who did not develop the disorder, and analysed using the group of individuals who have neither exposure to the environmental risk factor or the high risk gene variant as the reference group, so estimating odds ratios. Measured confounding variables can be adjusted for by stratification procedures or by using multivariable modelling such as logistic regression, Poisson regression or Cox’s proportional hazard models [62, 63].

EXAMINING GENE–ENVIRONMENT INTERPLAY IN PSYCHIATRIC DISORDERS

4.9.2 Case–control design Generally, case–control studies are more economical than cohort studies. Further, they are potentially powerful methods for the investigation of rarer disorders. Selection bias, particularly due to the selection of controls who may not be representative of the population at risk, is a major limitation. Further, if cases are collected from a clinical setting then the sample is enriched with individuals who are help-seeking. This is especially problematic when investigating less severe disorders as many people will not bother engaging with services for mild disability. Information on environmental exposure is often collected retrospectively, which may result in information (recall) bias, therefore it is preferable if the estimation of previous exposure comes from multiple sources or contemporaneous records. A high and non-differential take up rate for DNA analyses is required if we wish unbiased estimates of genetic main effects and interactions; however, biased main effect estimates for the environmental factor [64] and biased genetic main effects [65] may result in relatively unbiased interaction parameters. If there is an ethnic differential between cases and controls then population stratification could result in spurious gene variant associations. The controls can be unrelated individuals or relatives of the cases. The use of unrelated controls can be analysed by using the group of individuals who have neither exposure to the environmental risk factor or the high-risk gene variant as the reference comparison and estimating odds ratios, controlling for measured confounding variables by stratification procedures. Multivariable methods such as logistic regression, recent traditionally measured interaction terms on the multiplicative scale, but more recently extensions have been developed to assess interactions on the additive scale [66, 67]. When relatives are used as controls, detection of interactions may be more efficient as we are enriching the sample with the high risk gene, However, if the risk variant has a high frequency in the family controls there will be a loss of contrast, which will reduce the study power, such that in the most extreme case, monozygotic twins, testing for GxE will more likely reflect main effects rather than the interaction effect. Each case is matched to one or more unaffected

relative and conditional logistic regression models are used to estimate the GxE. The main threat to the validity of findings from such studies is the problem that both genes and environment are generally shared by family members so correlation on unmatched risk factors within the matched case control pair is likely. Furthermore, gene–environment correlation is built into the design reducing its power to detect GxE. Twin studies share the same disadvantages [68, 69]. Few candidate/susceptibility genes have been replicated in psychiatric disorders to date. When there is no candidate gene known GxE can be measured indirectly using family-based approaches: (i) Case–control studies using both relatives and unrelated (population based) controls. The analytical strategy is to compare the odds ratio for the effect of the environmental factor, in the cases with relative controls, to the odds ratio estimated from the case and non-related controls. The premise is that if there is GxE operating you would expect to find higher odds ratios when relatives are used as controls as compared to the analyses using population-based controls, while you would expect equivalence of the risk across control groups if there was no GxE interaction [59]. Of course it is quite possible that the ‘family effect’ could result from a shared environmental factor which has been unmeasured. (ii) The use of proxy (surrogate) measures of genetic liability such as family history or confirmed intermediate (endo)phenotypes (heritable biomarker, lying along the causal pathway from gene to disorder, but at a more proximal position to the gene, than the manifest symptoms), is also possible.

4.9.3 Case only design When a genotype is independent of an environmental exposure and the disorder is rare, then within the population GxE can be tested in cases only [70]. In this case–case design, the prevalence of the exposure in the genotype-positive cases is expected to be the same as the prevalence of the exposure in the cases without the high risk genetic variant. Thus, statistically significant departures from equal prevalence are indicative of an interaction between genotype and environmental exposure. However, independence of genotype and environmental exposure is rare and gene–environment correlation is generally the rule 63

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rather than the exception. Violation of this assumption of independence has been shown to produce grossly inflated type 1 errors [71]. Furthermore, this method only allows an estimation of interaction not the main effect of the genotype and environment. Simulation studies demonstrate that GxE can be subsumed into main effect of the genotype; therefore this design fails to provide a comprehensive test of the causal mechanism and should only be used with great caution.

however multi-level analytical models are currently being developed to deal with these factors [75]. Multi-generational pedigrees may be useful in order to indirectly test the hypothesis that there has been a change in the penetrance of a known high risk variant over time due to changes in environmental factors, however this approach is most useful when the high risk gene variant is highly penetrant, which is required to allow familiar aggregation to be adequately detected.

4.9.4 Family designs

4.9.5 Gene–environment wide interaction studies (GEWIS)

Sib-pair analyses are linkage techniques based on the simple premise that pairs of phenotypically concordant siblings (the affected sib-pair design) will demonstrate excess sharing of commonly inherited genomic segments, while phenotypically discordant siblings (the unaffected sib pair design) , will tend to have lower proportions of shared variants. By estimating the degree of inter-pair genetic similarity (at the region of interest, or across the genome) should help us identify the chromosomal location of candidate genes. This is achieved by estimating the sharing pattern, that is the number of alleles at a given locus that are the same (identical by descent, IBD). The expected sharing pattern in siblings approximates to z0 = 25% for no identical allele – z1 = 50% for one identical allele and z2 = 25% for two identical alleles. Departure from this pattern suggests linkage and statistical significance can be estimated within the likelihood framework [72, 73]. Sib-pair studies can be extended to include GxE by using stratification or extensions of common multivariable models [74]. Case parent trio design has been used to test candidate gene associations including testing GxE interactions. This model uses the genotypes on all three members of the trio but only the environmental exposure from the case (i.e., a partial case control design). The basic premise of the design is to stratify the genetic relative risk estimates from the caseparent trio, by environmental exposure status of the case. If there is no GxE interaction the two genetic relative risks would be expected to be the same, however if an interaction is present their ratio will be an estimation of the interactive relative risk. Currently stratified analyses are used to control for known within family variables which may influence the risk, 64

The candidate (susceptibility) gene approach to the identification of genetic determinants of common psychiatric disorders is impeded by: • Lack of a definitive allelic architecture model for the disorders: the polygenetic model is generally considered the best approximation; however, this has been strongly contested [76]. • Substantial gaps in pathophysiological understanding of the disorders. If the multifactorial (polygenetic) model is a good approximation to the allelic architecture of common psychiatric disorders, GWAS will provide a potentially unbiased method to search the genome for causative variants of small effect [77]. However, we should bear in mind that if substantial allelic heterogeneity is present, due to rare variants or epigenetic phenomena (i.e., low allelic identity) this method will be less successful as each genetic variant will arise from an independent haplotype (set of genetic markers in DL) background, so cancelling out each other’s signal [78, 79]. Experience from GWAS in non-psychiatric conditions suggests that for some disorders as many as 30 000 cases and similar numbers of controls will be required to robustly identify highrisk genetic variants [77]. Such large-scale studies have led to the formation of consortia to coordinate the development of such methodology and carry out the studies in psychiatry [80]. To date, GWAS methods have only been used to detect main (direct) effects of single or linked (haplotypes) markers. GWAS SNPs cover more than 4/5th of the SNPs known to HapMap (http://www

EXAMINING GENE–ENVIRONMENT INTERPLAY IN PSYCHIATRIC DISORDERS

.hapmap.org) CNVs are also detected but with less reliability using current technology. However, in complex multi-factorial diseases, scanning for main effects might miss important genetic variants, especially in subgroups of individuals with specific environmental exposure interactions. Furthermore, GxE with opposite effects in groups with different exposure profiles, that is crossing interaction will not be identified, as no direct main effect will be found. Therefore, to be clinically relevant GWAS will have to be placed in an epidemiological and public health context. One way of doing this is to enrich GWAS with environmental information – a technique known as GEWIS. No GEWIS study has yet been done, due to considerable methodological and logistic challenges, however a number of analytical approaches have been proposed which attempt to deal with the substantial problems of prior probability errors which will occur when estimating main effects on 1 000 000 or more markers, which is even more likely with concomitant estimation of E exposures and GxE. GEWIS studies will therefore require new statistical approaches as the current log linear regression methods do not effectively test the global null hypothesis of a genetic variant not being associated with the disorder in any of E strata. Extensions of the interaction methods beyond the currently employed simple departures from additive (or multiplicative) joint effects will be required, most likely based on multivariate latent variable modelling techniques that can deal with ‘mega-variate’ data [81–83].

4.10 Threats to the validity of epidemiological GxE studies The study designs discussed in this chapter are (observational) epidemiological in nature, therefore the genotype, environmental risk exposure and the pathology are studied as they occur within the population and as such their findings need to be interpreted, in the light of potential bias (systematic errors) and confounding (mixing the effect of extraneous variables with the effects of the exposures under study). Such issues can be reviewed in any comprehensive textbook of epidemiology [84]. There are however specific threats to the validity of

GxE epidemiological studies including unmeasured GEr, population stratification effects and sample size and power issues, which we will discuss here.

4.10.1 Confounding by gene–environment correlation GxE analyses are based on the assumption that the environmental and genetic exposures are independent. However, it is clear a number of the exposures we conceptualise as ‘environmental’ have a significant genetic contribution, for example stressful life events and substance use both have demonstrable gene–family-environment covariance [85]. Case only , partial case–control and family methods are sensitive to even statistically non-significant GEr, while case–control and cohort designs are slightly more robust [86, 87]. A number of strategies can be used, preferably in combination to reduce GEr confounding. 1 Careful selection and measurement of environmental exposures. The retrospective measurement of environmental exposures using self report questionnaires are prone to recall bias, as variation in recall is likely to be behaviourally mediated by an individual’s personality, potentially leading to non causal GEr. Therefore in case–control studies that rely on retrospective measures, multiple informant sources and contemporaneous records should be sought. 2 Careful selection and measurement of proxy (surrogate) measures of genetic risk. Proxy measures of genetic liability such as family history or intermediate phenotypes can be used in omnibus (assumes a single unified interaction between the unidentified genes) models for GxE, with care. These are clearly related to genetic liability. However, while dimensions of personality and life events, such as adversity, are in part genetically determined, it would be invalid to substitute such surrogates as genetic risk markers as they are influenced by both genetic and environmental factors. 3 Stratification analyses are undertaken to determine whether the relationship between environmental exposures is modified by the genotype variants. That is, stratification by genotype subgroup. 65

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This can also be performed using genotype-specific disease – exposure odds ratios; interstrata differences are compared using likelihood ratio tests of homogeneity. Theoretically GEr can be controlled for by enforcing independence of G and E in loglinear models; however, such tests generally have low power to detect meaningful departures from independence [88]. 4 Compare prevalence of exposure in relatives as compared to non-related controls. These analyses are based on the premise that rates will be higher in relative controls as compared to population controls if there is a genetic effect uncontrolled for shared environmental influences, suggesting GEr. However, if there is no difference in exposure prevalence across the two control groups, GEr is less likely. Of course the most effective way to control for GEr in GxE studies is to use an experimental paradigm. While it would be unethical to allocate individuals to adverse environmental exposures, it may be possible to develop ‘harmless’ analogues of environmental adversity. An elegant example of this approach used standard emotional stimuli as a surrogate for vulnerability to stress in an functional magnetic resonance imaging study [89]. GxE strategies have been shown to be effective in the assessment of pharmacological interventions (pharmacogenetics) [90].

4.10.2 Population stratification When unrelated controls are used in case–control and cohort-designed GxE, the studies are potentially susceptible to confounding by ethnicity (migration), which in the genetic literature is termed population stratification confounding. Population stratification describes the gradients observed in gene frequencies, within broad ethnic groupings, that is there are potentially unmeasured genetic subpopulations. If these subgroups are unequally distributed between cases and controls, biased estimates of effect could be possible if subpopulation genetic variations are correlated with unmeasured environmental risk factor. Potentially more problematic, is the possibility of statistical over-dispersion (i.e., greater variability observed in the data than would be expected based on a given simple statistical model) 66

when subpopulations are present. Simulation studies suggest that even small amounts of stratification have significant consequences in large samples [91], though some commentators have refuted this claim [92]. While family-based designs overcome the potential of confounding by population stratification, they are subject to other problems as discussed above. Population stratification can, however, be effectively handled using genomic control approaches [61, 93].

4.10.3 Power, sample size and issues of multiple comparisons Power and sample size calculations are critical in the design of GxE studies and will vary according to the design used. However, it should be stressed that sample size is only one factor in determining power. The statistical power of any GxE study will also depend on the strength of the interaction effect, the variability of the environmental exposure, the frequency of the genetic variant in the population and the accuracy (reliability and validity) of the measured gene, environment and disorder (or pathological outcome) [94]. Uher [32] has demonstrated the impact of these factors in simulation studies. Strong effects were evident in the presence of variation in the reliability of environmental and pathological measures. A decrease of just 0.2 in both measures equated to losing half of the sample size [32]. Therefore, smaller studies with high quality accurate discriminative measures of environmental and pathological outcomes may be advantageous in many circumstances [95]. Simulation studies clearly show how the frequency and variability of the environmental exposures influence study power. One potential approach to handling exposure variation effects is to sample subjects from both extremes of the exposure distribution (very low and very high doses). Simulation studies suggest this effectively increases the power for detecting GxE, especially when the exposure is measured on a quantitative scale, though this approach requires further simulation testing [96]. Sample size calculations estimate the minimum number of subjects necessary to provide sufficient power to detect a GxE, if it is truly present. GxE as a higher order effect, require larger sample sizes as compared to designs which only measure main

EXAMINING GENE–ENVIRONMENT INTERPLAY IN PSYCHIATRIC DISORDERS

effects. A number of different statistical methods have been described that estimate the sample size requirements to detect GxE [97–99]. How to handle multiple comparisons is a major problem facing genetic epidemiology generally and GxE investigators in particular. The problem of potential type 1 errors (false positives) has traditionally been handled by making corrections for multiple testing, based on the probability of making at least 1 spurious positive inference; however such techniques are conservative as they do not take account of linkage between multiple genetic markers. This has lead to the development of rates based on the false discovery rate (FDR). Many such rates have recently been published and preserve greater power to detect true positive interactions. There is no consensus on how best to handle issues of multiple testing, with other techniques based on permutation methods and sequential multiple-decision procedures currently being developed [35]. While false positive findings in GxE will occur, perhaps a much commoner problem is actually type 2 errors, that is accepting the null hypothesis as a result of inherent low power in our statistical models [99, 100].

4.11 Epigenetic mechanisms As well as genes influencing the exposure and susceptibility to environmental exposures, through GEr and GxE, the reverse association is also possible, and has been postulated in a number of psychiatric disorders, including depression, schizophrenia, substance dependence and developmental disorders. Epigenetic mechanisms occur when environmental factors impact on DNA sequencing (causing de novo mutations) or through changes in DNA methylation and chromatin structure (causing altered gene expression through epimutations) both globally and at the promoters of candidate gene sites. For example, epigenetic chromatin remodelling of the brain derived neurotrophic factor promoter site (BDNF) is associated with neuronal activity, seizures, chronic stress, cocaine addiction and Rett’s syndrome: remodelling at the reelin promoter may play a role in mouse models of schizophrenia [101]. Although research on DNA methylation as an epigenetic mechanism underlying GxE is only in its early

stages the preliminary results are promising. Animal studies have shown early maternal behaviour predict the offspring’s stress sensitivity through altered DNA methylation in some key neuronal receptor genes that are involved in the stress response [102]. Environmentally induced epigenetic mechanisms may explain a range of epidemiological findings including the age of onset curves, monozygote discordance and the gender differences observed in psychiatric disorders. Methodologies designed to investigate such epigenetic processes are being developed currently and are likely to further elucidate the gene–environment interplay in psychiatric disorders [103].

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5

Reliability Patrick E. Shrout Department of Psychology, New York University, NY, USA

5.1 Introduction In psychiatric epidemiology, assessment of mental conditions and of risks for psychiatric disorder relies heavily on information provided by patients (or survey respondents) and by informants who are close to the patient/respondent. How good is the information provided by these people, and how good are the assessment inferences that we make on the basis of this information? The quality of the assessment in psychiatry and epidemiology is typically characterised by the reliability and validity of the measure. Reliability is the degree to which a measurement is reproducible and not affected by transient assessment noise. Validity is the degree to which the measurement is useful. Although validity is the ultimate criterion by which to judge a measure, we know that a measure will not be useful if it is dominated by measurement noise. This means that reliability is a necessary condition for validity, but it is not sufficient to guarantee validity. Even though reliability is only an intermediate step towards quality measurement, it is often methodologically interesting because it is a problem that can usually be fixed. Reliability can be improved by structuring and standardising the assessment procedure, by improving the training of both the respondents and those carrying out the assessment and by averaging replicate measurements. If problems of unreliability are not addressed, then subsequent problems of validity are intractable. This is why reliability was given so much attention in developing the Versions III and IV of the Diagnostic and Statistical Manual of the American Psychiatric Association [1, 2].

Epidemiologists must attend both to the reliability of diagnostic measures and risk measures. Two features of psychiatric epidemiology make reliability more of an enduring problem in this field than in others. One feature is the previously mentioned dependence on information provided by respondents or informants. Respondent reports present many opportunities for noise to enter the recorded data: the understanding of the question, the recall and reporting of the answer and the coding and entry of the data. The other feature is the epidemiologists’ search for novel populations and risk groups that might provide clues to the aetiology of mental disorders. New populations require new assessments of reliability, since populations vary in language, literacy and cultural expression of disorders. As we will see, the variability of the trait under study in the new population also affects the reliability of measures.

5.2 The reliability coefficient Consider a single measurement procedure. Respondents are sampled from a specific population, measured in some way and assigned a numerical value, that is represented by the variable X. If the characteristic being measured is qualitative, such as having a certain diagnosis, then the variable X might be defined to be binary, that is X = 1 if the respondent has the characteristic, and X = 0 otherwise. If the characteristic is quantitative, such as severity of illness or exposure, then X might be defined to take some well-specified numerical score. 2 The variance of X, σX , is a population parameter that describes how much the measurements differ from person to person in the population being

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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CHAPTER 5 2 studied. In some populations σX might be relatively small, while in other populations the variance might be large. Small variance implies that the measurement distinction is subtle in the population, while large variation implies the opposite. In populations with small overall variation in X, any measurement error may be quite serious. According to classic reliability theory, it is useful to 2 2 decompose σX into two components, σX = σE2 + σT2 , 2 where σE is variance due to measurement noise and σT2 is variance due to systematic differences between persons being measured. We will discuss how these two components are estimated later. This decomposition implies that random measurement noise increases the total measurement variation. If measurement errors can be eliminated, then the error variance, σE2 , goes to zero and the total variance shrinks to σT2 . If errors dominate the measurement, 2 then the majority of σX may be attributable to σE2 , even if there is systematic variation between persons, that is of interest. In its purest form, the reliability coefficient, RX , is 2 a ratio of the population parameters, σT2 and σX :

RX =

σT2 2 σX

=

σT2 [σT2

+ σE2 ]

(5.1)

RX varies from zero (X is due entirely to unsystematic stochastic processes) to unity (X is due entirely to systematic individual differences). It can be thought 2 of as the proportion of σX that represents genuine, replicable differences in subjects. It turns out to be a useful quantity in statistical analyses as well. For example, it can be shown that the correlation between X and another variable Y will get smaller as the reliability coefficient of either variable gets smaller [3, 4]. Knowledge of reliability can also be used to adjust for bias [5] and to obtain more powerful tests of group differences [6]. How do we evaluate different values of RX ? If we know that a measure truly has a reliability of 0.50, then we know that only half its variance is systematic. That may not be what we hope for, but it might be good enough for some preliminary studies. For more definitive studies, we should aim to have reliability above 0.80. To provide some interpretive guidelines, Shrout [7] recommends the following characterisations of reliability values: (0.00,0.10), virtually no reliability; (0.11,0.40), slight; (0.41,0.60), fair; 74

(0.61,0.80), moderate; (0.81,1.0), substantial reliability.1 For a complete development of RX and its implications, see Lord and Novick [8] or Dunn [9].

5.3 Designs for estimating reliability To estimate RX we need to define what is meant by systematic variation of X. Classical psychometric theory defines this hypothetically. Suppose that a subject is selected and is measured once to produce the score X1 . Now suppose that it is possible to make the measurement over and over again without affecting the subject, and without recall of the previous Xj values (where j indexes each replicate measure). Classical measurement theory defines the systematic part of X to be the average of all of these hypothetically infinite measurements of the selected subject. This systematic component of the measurement is written as T = E(X), which is interpreted as the expected average of the many replications of X. Note that if the measurement were height or weight, then it would actually be possible to take many repeated measurements of this sort. In psychiatric epidemiology, reliability is estimated by approaching the hypothetical ideal with approximately replicate measurements. If there is virtually no variation across replications of X, then we infer that σE2 is small in magnitude, and that reliability is very good. If variation across replications is observed, then the magnitude of the within-subject variation is compared to that of the between subject variation using the definition of RX in Equation 5.1 above. The most common replication design calls for making the X measurement at two points in time (the test–retest design). Variation in the X values across replications and across respondents is used to 2 estimate σE2 , σT2 and σX , and these can be used to estimate RX . The formal equations for these estimates are presented in a later section. Although theoretically and intuitively appealing, the test-retest design falls short of the hypothetical ideal in several ways. On one hand, the second measurement is often affected by systematic 1 In setting standards for reliability, however, we must be aware that estimates of reliability may be smaller than the actual reliability because of systematic bias, which is discussed later.

RELIABILITY

biological, psychological and social changes in the respondent. These systematic changes make the estimate of σE2 appear larger than it would have been at a single measurement instance. When legitimate change is included with error, the estimate of the reliability of the first assessment is too small. On the other hand, if the respondents remember their original responses, and then try to be ‘good’ by reporting the same thing, then the reliability estimate may be too large. Methodologists who address these opposing biases recommend that the second assessments be carried out after a long enough period to reduce memory artefacts, but promptly enough to reduce the probability of systematic changes. Recommendations of how long the period should be are more products of opinion than science, but 2 weeks often seems to work well. Test–retest designs can be used with the whole range of measures made in psychiatric epidemiology. Interviews, questionnaires, ratings and physical measurements can all be repeated after an appropriate time. It is not always necessary, however, to wait to obtain a replicate measurement. When the measurement is a judgement, such as the Global Assessment Scale [10], it is possible to have two independent ratings made at the same time. Moreover, time can be frozen by video-recording the structured interview so that ratings can be obtained from those viewing the recording. Although these alternatives to traditional test–retest designs overcome the confounding of unreliability with genuine growth or development, they bring with them their own problems. These have been discussed by several authors, including [11]. Insofar as the respondent’s idiosyncratic responses contribute to unreliability, then estimates based on a single recorded interview may underestimate the level of random variation in the actual ratings obtained in the field. For this reason, inter-rater reliability studies using recorded interviews are expected to overestimate true reliability. When the measurement procedure under study is a questionnaire that includes several items pertaining to a single underlying psychological trait or symptom dimension, it is also possible to obtain some information about reliability within a single assessment occasion. The items that relate to the same underlying concept are considered to be replications of each other. The degree to which the patterns of

responses suggest that they are empirically related is used as evidence of reliability. This inference is made on the basis of the internal consistency of the questionnaire responses. The most widely used measure of reliability based on internal consistency is coefficient alpha [12]. An alternate measure is McDonald’s omega [13]. Internal consistency measures of reliability are affected by some biases that make them underestimate actual reliability [14], and others that make them overestimate reliability [15]. They will underestimate reliability if the items within the set are not close replications of each other. For example, a scale of depression symptoms may contain some items on mood, others on psychophysiological complaints and yet others on cognitive beliefs. Although these are all expected to be related to depression, they are not exact replications of each other. To the degree that the correlations among the items is due to the different item content rather than error, the overall reliability estimate will be smaller than it should be. Reliability may be overestimated by the internal consistency design if the whole interview is affected by irrelevant global response patterns, such as mood or response biases. For instance, some respondents may perceive that acknowledging symptoms is socially undesirable, and may systematically under report more bizarre problems. Others may fall into a pattern of denying everything. These so-called response biases inflate internal consistency reliability estimates. They are often addressed by mixing the items across many conceptual domains, editing the items so that half are keyed as a symptom when the respondent says ‘no’ and half are keyed the opposite way. Scales of Yea-saying, and Need-forapproval are also sometimes constructed to identify those respondents who are susceptible to response biases. The validity of these scales, however, is a subject of open discussion. Given the possibility of opposing biases, how should we evaluate internal consistency results? If the results appear to indicate high reliability, look for response artifacts that might have inflated the estimate. If provisions have been taken to address response biases, then the high level of reliability might be real. If the results indicate that there is low reliability, then look to see if the items included within the internal consistency analysis are heterogeneous 75

CHAPTER 5

in content. It is possible that a set of items that are heterogeneous might have adequate test–retest reliability even though the internal consistency estimate is low. Because researchers often seem unaware of the ambiguity of reliability results based on calculations of Cronbach’s alpha from one administration of a measure, a number of psychometric experts have recommended that reliability be measured other ways (e.g. [14, 16]). It is always helpful to incorporate multiple designs into a reliability program. By systematically studying the kinds of replication, one can gain an insight into sources of measurement variation. This is what is recommended by Cronbach and his colleagues [17] in their comprehensive extension of classical test theory known as Generalisability Theory. This theory encompasses both reliability and validity by asking about the extent to which a measurement procedure works in different populations, at different times, with different raters, who may have different training. This broad perspective easily included designs such as those on the Diagnostic Interview Schedule (DIS) [18] that compared results from interviews done by ‘lay’ interviewers to those done by mental health professionals. To the extent that the trained lay interviewers performed like the professionals, the results might be interpreted as test–retest reliability of the DIS. If the level of training actually made a difference, then the results might be interpreted as the validity of using lay interviewers (assuming that the professionals are the ideal interviewers for this structured measure). From the generalisability perspective, it is neither a reliability or validity study, but rather a study of the generalisability of DIS results across time and interviewer-type (see [19]). The flexibility of Generalisability Theory was illustrated by Cranford et al. [20], who used this approach be used to estimate reliability of changes in affect over days in a diary study. These reliability procedures have great utility for epidemiological studies of the course and temporal correlates of pathology.

5.3.1 The effect of population variance on reliability In all of the reliability designs reviewed above we assumed that respondents were sampled from the population, that is to be studied. By randomly sampling 76

from the population, we can obtain an unbiased esti2 mate of σX . Note that any bias, that is introduced in 2 estimating σX can have serious effects on the estimate of RX . Epidemiologists should be especially sensitive to the fact that samples of patients should not be used in a reliability study if the ultimate survey is to be carried out in the general population. Relative to the variance in community surveys, the variance of most psychiatric measures will be too large in treated samples. The bias is usually concentrated in the σT2 term 2 of σX = σT2 + σE2 and thus the reliability often appears to be better in the treated population than in a community population. When the reliability study sample has been constructed using stratified samples of cases and non-cases, then it is often possible to undo the bias through weighting (e.g. [21]).

5.4 Statistical remedies for low reliability If an investigator discovers that a quantitative measure is not sufficiently reproducible, there are several remedies that have been mentioned briefly before. The measure itself can be changed, the training of those administering it can be improved, or perhaps some special instructions can be developed for the respondents that improve the purity of the measurement outcome. These are examples of procedural remedies that are often effective. There is also a statistical remedy: Obtain several independent replicate measurements and average their results. The idea is simple: averages of replicate measures are by definition more systematic than the individual measures themselves, so the reliability of the sum or average of items or ratings will be consistently higher than that of the components. The degree to which reliability is expected to improve in the composites is described mathematically by Spearman [22] and Brown [23]. Let the sum of k ratings or items (X1 , X2 , X3 , . . ., Xk ) be called W(k). Then the expected reliability of W(k) can be written as a function of k and the reliability of the typical measurement, RX , according to the Spearman–Brown formula: RW(k) =

kRX . 1 + (k − 1)RX

(5.2)

Equation 5.2 is based on assumptions about the comparability of the measurements that are averaged

RELIABILITY

or summed into W(k), not on the form or distribution of the individual measurements. Because the result is not limited by the distribution of the X measures, the formula is even useful in calculating the expected reliability of a scale composed of k binary (0,1) items as well as scales composed of quantitative ratings or items. Note that averaging measures only is a remedy for low reliability if there is some evidence of replicability. It is clear that RW will be zero if RX is zero, regardless of the magnitude of k. The Spearman–Brown formula is especially useful for internal consistency reliability studies. When multiple items are available as replicate measures, it is usually the reliability of the scale score (the sum or average of items) that is of interest. While we could use the internal consistency design to calculate the average item reliability, and then use that result in Equation 5.2 to calculate the expected scale reliability, these steps are combined when one uses certain estimation formulas, such as the classic coefficient alpha of Cronbach [12]. The relationship described in the Spearman–Brown formula can also be used in studies of interrater reliability to determine how many independent ratings need to be averaged to obtain an ideal level of reliability, say CR . If the obtained level of reliability for a single rater is RX , then the number of raters that are needed to produce an averaged-rater reliability of CR is k=

CR (1 − RX ) . RX (1 − CR )

(5.3)

For example, if each rater only has a reliability of RX = 0.40 and one wants a reliability of CR = 0.75, then Equation 5.3 gives k = 4.5. This means that averages of four raters would be expected to have less than 0.75 reliability, while averages of five raters would exceed the target reliability of 0.75.

5.5 Reliability theory and binary judgements The reliability theory just reviewed does not make strong assumptions about the kind of measurement embodied in X, and indeed many of the results hold for binary variables such as ones that might represent specific psychiatric diagnoses (e.g. X = 1 when the respondent is thought to have current

major depression, X = 0 otherwise). Kraemer [24] has shown explicitly how the results work with binary judgements. From her mathematical analysis of the problem it can be seen that the systematic component of X that I have called T = E(X), will end up as a proportion falling between the extremes of 0 and 1. It represents the expected proportion of diagnosticians who would give the diagnosis to the respondent being evaluated. If T is close to 1, then most diagnosticians would say that the respondent is a case, and if T is close to 0, then most would say that the respondent is not a case. Note that while X itself is binary, T is quantitative in the range (0,1). Because averages are quantitative (at least as n gets large), the psychometric results from the Spearman–Brown formula are applicable only when the composite of interest is quantitative. This is often the case when X represents binary items in a symptom scale. Of interest is the count of symptoms, which is closely related to the average of symptom items. However, if we really want a binary variable as the outcome, then the Spearman–Brown result does not apply. For example, diagnoses of several independent judges are sometimes combined into a ‘consensus diagnosis’, that is itself binary. If the consensus rule is one that requires that all judges make the diagnosis before the diagnosis is applied, the result might be less reliable than some of the individual diagnosticians (see [25]). The total consensus rule is as weak as the least reliable diagnostician, because each has veto power regarding whether the consensual diagnosis is made. Many of the classic psychometric results depend on the assumed symmetry of errors. Because T is defined as an average, by definition about half the errors go in one direction and half in the other. For diagnoses, however, the errors that attract attention are those that seem to cause clinically relevant discrepancies. For example, if we know that a certain set of presentation facts are viewed by 90% of trained clinicians as indicating schizophrenia, then the clinically relevant discrepancies are those diagnosticians who argue that the diagnosis of schizophrenia is inappropriate. Persons who insist that schizophrenia should be diagnosed with more than 90% certainty are not usually considered in practical terms to be outliers. The interest in the asymmetry of errors in diagnoses prompts some researchers to decompose 77

CHAPTER 5

interrater discrepancies into ones that are consistent with problems of sensitivity and specificity. From this perspective it can be shown that the reliability coefficient of Equation 5.1 is a function of both kinds of errors. If we focus on one kind of error only, such as sensitivity, the classic relation between reliability and validity no longer holds necessarily. There are some examples in which different levels of reliability are consistent with the same level of sensitivity. (One usually finds that the assumed specificity or prevalence varies with the reliability in examples such as this.; see [26].) When asymmetric errors are of central interest, the results reviewed in this chapter may not be totally applicable. The role of asymmetric errors in binary ratings is only one special aspect of such data. Another is the relation of the expected mean of a binary variable and the expected variance of that variable. For variables that are normally distributed the mean contains no information about the variance of the variable, but for variables that are binomial (a very common distribution for binary variables), the variance is necessarily small for variables with means near 0 or 1. This fact has implications in the interpretation of Equation (5.1), the definition of the reliability coefficient. If the prevalence of a diagnosis is low in a population, then σT2 will be small. If the level of error variance is held constant, but σT2 is made smaller, then RX will be smaller. One way to interpret this result is that the level of error must be reduced to study disorders that have smaller base rates in the population. Any randomly false positive diagnosis makes the diagnostic system seem unreliable for rare disorders. In this case the diagnostic system is unreliable because the precious few true positives are swamped by the random false positives. Nevertheless, the fact that reliability is empirically related to prevalence has caused some commentators to question the utility of reliability measures in binary variables [27–29]. Others of us have argued that dropping the statistic because of the challenge of measuring rare disorders is misguided [7, 30] because the reliability statistic is useful in describing the effects of measurement error on statistical analyses. Kraemer [31] lucidly reviewed the rationale of reliability studies and showed how the challenge of establishing reliability of categorical data is affected by various features of the measurement situation and the design of the reliability study. 78

In the next section I present a survey of reliability statistics that can be used to evaluate data from reliability studies. One of these statistics is Cohen’s kappa [32]. It is especially designed for categorical outcomes, but it shares with the quantitative statistics its interpretation as estimators of the reliability coefficient in Equation 5.1 Although the special features of binary data require a careful consideration of the effects of errors in epidemiological analyses, the general concerns for the concept of reliability as reviewed in the preceding sections are usually relevant for multivariate analyses that treat binary distinctions as dummy variables.

5.6 Reliability statistics: General As we have seen, the reliability coefficient of Equation 5.1 is defined in terms of variances: variances of systematic person characteristics σT2 , and variances of measurements across replications for a single person, σE2 . There are several ways to estimate the variance ratio shown in Equation 5.1 [9], but one direct method is simply to estimate the separate variance components and then combine them in the form of Equation 5.1 Estimates of this sort are called intraclass correlations. Intraclass correlation is not a single statistic, but rather a family of statistics that can be used for estimating reliability. In this section we will review several versions that can be used with a wide variety of variables. We focus here on the easiest part of reliability analysis, ‘point estimation’ of the statistic that summarises the reliability results. Although it is important, we do not have the space to present the methods that must be used to estimate 95% confidence intervals for the study results. The form of the interval estimators depends on the nature and distribution of the data, and new methods are being actively developed in the literature. For reviews of methods for confidence intervals see Shrout [7], Dunn [9] and Blackman and Koval [33]. It is important to note, however, that estimates of reliability are often less precise than we would like [34], and that this fact is made clear by the use of confidence intervals. The intraclass correlation point estimates are derived from information summarised in the analysis of variance (ANOVA) of the data from the reliability

RELIABILITY

study. The ANOVA treats each subject as a level of the SUBJECTS factor. Usually subjects are considered to be a random factor, because they are selected to be representative of a population of interest. If the replicate measurements of the subjects are systematically obtained using a certain set of k raters or measuring devices, then the ANOVA might involve a two-way SUBJECTS by MEASURES design. If, on the other hand, the replicate measurements of each subject are obtained by randomly sampling k measures, then the analysis would use a one-way ANOVA.

Table 5.1 Hypothetical data on functioning of 10 probands by three of their relatives.

5.6.1 One-way ANOVA analyses

subjects’ mean ratings are larger than the disagreements among relatives regarding the subjects’ scores. The reliability estimate for the one-way ANOVA is calculated using the first formula in Table 5.3A. This form of the intraclass correlation was called ICR (1,1) by Shrout and Fleiss [36], and we retain that designation. To illustrate the calculation with the numerical example from Table 5.1, we find,

Table 5.1 illustrates data that might be collected in reliability study of relative informants. Each of N = 10 probands is rated by k = 3 distinct relatives. Between-subject variation can be estimated using all k ratings, and within-subject variation is used to estimate the magnitude of the error variation. When the relationships of the relatives vary from proband to probands (e.g. siblings for one proband, parents for another, cousins for a third), these data do not have a data analytic structure for informant. If there had been such a structure, we might have considered a proband-by-relationship two-way ANOVA. In our analysis we will assume that the informants are essentially a random sample of possible informants for a given respondent. Table 5.2 shows the layout of the one-way ANOVA, along with the numerical estimates obtained from the data in Table 5.1. The actual computation of the ANOVA results can be obtained from standard computer software, such as SPSS RELIABILITY [35]. The numerical example illustrates a pattern in which the between-subjects (probands) mean squares is substantially larger than the withinsubjects mean squares. Consistent with an informal examination of the hypothetical data in Table 5.1, this pattern suggests that the differences between

Proband

Relative 1

Relative 2

Relative 3

29 23 19 6 13 0 10 5 31 15

32 33 17 10 20 0 11 1 26 17

17 28 18 5 20 2 15 15 19 18

1 2 3 4 5 6 7 8 9 10

ICR(1, 1) = (251.0 − 22.2)/(251.0 + 2 ∗ 22.2) = 0.77 This result describes the reliability of a single randomly selected informant. About 77% of the variance of a single informant’s ratings is attributable to systematic differences between subjects. Although the stability of the result might be questioned because of the limited sample size, the result is encouraging that this rating, in this population appears to be made fairly reliably by a single informant. Suppose that it is possible to obtain three informant ratings for each subject in the survey. How much more reliable would the average of the three ratings be than an individual informant? The answer can be calculated using the Spearman–Brown formula (Equation 5.2), with k = 3 and RX = 0.77. Alternatively, one can use the formula for ICR(1,k)

Table 5.2 Analysis of variance when replications are nested within subjects: one way ANOVA. Source of variation Between subjects Within subjects

df

Sums of squares

Mean squares

Table 5.1 Example: MS on df

n−1 n(k − 1)

BSS WSS

BMS = BSS/(n − 1) WMS = WSS/[n(k − 1)]

BMS = 251.0 on 9 df WMS = 22.2 on 20 df

79

CHAPTER 5 Table 5.3

Versions of intraclass correlation statistics useful for various reliability designs.

Type of reliability study design

Raters fixed or random? Version of intraclass correlationa

(A) Reliability of single rater Nested: n subjects rated by k different raters

Random

ICR(1,1) =

BMS − WMS . BMS + (k − 1)WMS

Subject by rater crossed design

Random

ICR(2,1) =

TMS − EMS . TMS + (k − 1)EMS + k(JMS − EMS)/n

Subject by rater crossed design

Fixed

ICR(3,1) =

TMS − EMS . TMS + (k − 1)EMS

Nested: n subjects rated by k different raters

Random

ICR(1,k) =

BMS − WMS . BMS

Subject by rater crossed design

Random

Subject by rater crossed design

Fixed

(B) Reliability of the average of k ratings

TMS − EMS . TMS + (JMS − EMS)/n TMS − EMS . ICR(3,k) = TMS ICR(2,k) =

a BMS

and WMS refer to between-subject and within-subject mean squares from a one way ANOVA. TMS, JMS and EMS refer to between-subjects (targets), between measures (judges) and error mean squares from two way ANOVA based on n target-subjects and k raters.

shown in Table 5.3B. This formula is obtained by algebraically combining the expression for ICR(1,1) with the Spearman–Brown formula. In this case the answer is ICR(1,k) = 0.91. About 91% of the variance of the average of three randomly chosen informants is attributable to systematic differences between subjects.

5.6.2 Two-way ANOVA analyses Table 5.4 illustrates data that might be collected in a reliability study of two professional raters or interviewers. As a result of the interview by Interviewer 1 we have both a binary diagnosis (disorder present [X = 1] vs. disorder absent [X = 0]) and a quantitative score such as a total functioning score (called Z in the table). Replicate scores and diagnoses are obtained by a second interviewer, called Interviewer 2. The hypothetical data on X1, X2, Z1 and Z2 are shown for 17 respondents. The layout of the two-way ANOVA is shown in Table 5.5, along with numerical results from the Table 5.4 examples. Only two interviewers were used in the reliability study illustrated in Table 5.4, but we might consider the two to be a random sample from all possible interviewers from the study. If so, then they must not be selected on the basis of their special skills as interviewers, but rather should be selected to be 80

Table 5.4 Hypothetical data on assessment of depression and functioning. X1 and X2 represent test–retest diagnoses of major depression (X = 1, present; X = 0, not present), and Z1 and Z2 represent ratings of adaptive functioning. Respondent 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

X1

X2

Z1

Z2

0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0

1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0

17 17 26 24 19 22 17 23 19 18 21 13 21 22 15 20 21

11 15 25 22 14 16 18 19 16 12 18 11 23 17 12 18 20

representative. When interviewers who are employed in the reliability study represent the population of potential interviewers, we say that they are random effects. In some cases we are interested in the ratings of specific interviewers rather than a population of

RELIABILITY Table 5.5 Analysis of variance when replications have structure: two way ANOVA. Source of variation

df

Sums of squares

Mean squares

Table 5.4 Examples: Mean square on df

Between subjects (targets)

n−1

TSS

TMS = TSS/(n − 1)

Variable X: 0.254 on 16 df Variable Z: 25.7 on 16 df

Between measures (judges)

(k − 1)

JSS

JMS = JSS/(k − 1)

Variable X: 0.029 on 1 df Variable Z: 67.8 on 1 df

(n − 1)(k − 1)

ESS

Residual (error)

interviewers. Suppose Interviewer 1 is a doctoral candidate who carried out her own data collection, and that Interviewer 2 is a colleague who is hired to document that the ratings are systematic. In this case we simply wish to describe the quality of data collected by the doctoral candidate, and we say that the interviewers are fixed effects. Depending on whether the raters are considered to be random or fixed, we use different versions of the intraclass correlation to estimate reliability. When we wish to estimate the reliability of a randomly sampled interviewer, we use the expression for ICR(2,1) shown in of Table 5.3A. This intraclass correlation is not only a function of the betweensubjects mean squares and the error mean squares, but also the between-measure (judge) mean squares. If different raters are more or less liberal in assigning high scores, then the final variability of the ratings will be affected. ICR(2,1) takes this extra variation into account in estimating reliability. In the two examples of Table 5.4, one reveals a large between-measure effect and the other does not. From the numbers in Table 5.4 it can be seen that the Z2 ratings are usually smaller than the Z1 ratings. Rater 2 seems to believe that most subjects are functioning somewhat worse than perceived by Rater 1. Even with this rater difference, the reliability of Z is higher than the reliability of X, according to the data in Table 5.4. The ICR(2,1) for Z is calculated as (25.7 − 2.67)/(25.7 + (1) ∗ 2.67 + 2 ∗ (67.8 − 2.67)/17) = 0.64 The ICR(2,1) for X is calculated as (0.254 − 0.092)/(0.254 + (1) ∗ 0.092 + 2 ∗ (0.029 − 0.092)/17) = 0.48

EMS =

ESS (n − 1)(k − 1)

Variable X: 0.092 on 16 df Variable Z: 2.67 on 16 df

For the rating of adaptive functioning we could consider averaging both individual Z ratings to obtain a more reliable score. We can use either the Spearman–Brown formula, or the expression ICR(2,k) to calculate the reliability of the mean of two such ratings. In this case, the result is 0.78 rather than 0.64. Although the reliability of the binary X variable is worse than that of the quantitative Z variable, it would not usually be meaningful to rely on an average diagnosis instead of a truly binary rating. For this reason the ICR(2,k) form of the intraclass correlation would not be applied to X in Table 5.4. The calculations carried out so far have assumed that the two sets of ratings in Table 5.4 are representative of a host of possible interviewers. Now we turn our attention to the situation in which the two raters can be considered to be fixed. In this case we can either ignore systematic rater differences in mean ratings, or we can adjust for them. The expression for ICR(3,1) in Table 5.3A is appropriate when we wish to describe the reliability of a single fixed rater. Unlike ICR(2,1), this version of the intraclass correlation is not affected by the between-rater mean squares. On average, ICR(3,1) will be larger in magnitude than ICR(2,1). By fixing the raters to certain persons, the extraneous variation due to sampling of raters is eliminated and the resulting reliability is usually higher. This effect is especially obvious for Z, which had a large between-rater effect. The ICR(3,1) for Z is calculated as (25.7 − 2.67)/(25.7 + (1) ∗ 2.67) = 0.81 ICR(3,1) for X is not much different than ICR(2,1), as the rater effects were small: (0.254 − 0.092)/(0.254 + (1) ∗ 0.092) = 0.47 81

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5.6.3 The reliability of the average of k fixed measures: Cronbach’s alpha Just as ICR(1,1) and ICR(2,1) can be used in the Spearman–Brown formula to determine how much reliability improves by using an average score, so can ICR(3,1) be used when an average measurement is of interest. In this case the reliability of the averaged measurement can be computed directly using ICR(3,k) from Table 5.3. For the quantitative Z variable, the reliability of the average is expected to be 0.90. One common application of ICR(3,k) is to internal consistency analyses of psychometric scales. Items in self-report questionnaires are usually fixed in that the same items are used with all respondents. Suppose that n subjects are administered k scale items, and the results are analysed using the two-way ANOVA layout of Table 5.5. The estimate of the reliability of the sum or average of the k fixed items can be computed using ICR(3,k). The result is identical to Cronbach’s alpha [12], which we discussed in the first section. Alpha is computed directly by computer programs such as SPSS RELIABILITY [35].

5.7 Other reliability statistics 5.7.1 Cohen’s kappa When binary data such as that for variable X in Table 5.4 are collected, reliability can be estimated directly using Cohen’s kappa [32]. Fleiss and Cohen [37] showed that kappa is conceptually equivalent to ICR(2,1) in Table 5.3. It can be calculated simply using the entries of a 2 × 2 table showing the diagnostic agreement. In general, this agreement table might be laid out as follows:

Rater 2: + Rater 2: − Total

Rater 1: +

Rater 1: −

Total

a c a+c

B D b+d

a+b c+d n

Cohen [32] pointed out that while cells a and d represent agreement, it is not sufficient to evaluate reliability by reporting the overall proportion of agreement, Po = (a + d)/n. This statistic may be 82

large even if raters assigned diagnoses by flipping coins or rolling dice. His kappa statistic adjusts for simple chance mechanisms: kappa =

Po − Pc 1 − Pc

where Po is the observed agreement, [(a + d)/n] and Pc is the expected agreement due to chance: Pc = [(a + c)(a + b) + (b + d)(c + d)]/n2 . When computing kappa by hand, it is sometimes more convenient to use the following equivalent expression, kappa =

ad − bc . ad − bc + n(b + c)/2

When the X data in Table 5.4 are tabulated into a 2 × 2 table like that shown above, we get a = 2, b = 2, c = 1 and d = 12. The observed agreement, Po = 0.82, but the expected agreement by chance is Pc = 0.67. Using either of the expressions for kappa, we find the reliability to be 0.46. As expected, this is quite close to the value of 0.48 obtained using ICR(2,1). One advantage of calculating the reliability of binary judgements using kappa instead of intraclass correlation methods is that the expressions for kappa’s standard error and confidence bounds are explicitly suited to binary data. Kappa can also be generalised to describe the overall reliability of classifications into multiple categories. Fleiss et al. [38] provides an overview of many forms of kappa, and Donner and his colleagues [39–44] have done much to describe the sampling variation of kappa statistics.

5.7.2 Product moment correlation If the reliability study yields two measurements, and if the raters are considered to be fixed (rather than representative of a pool of raters), then reliability can be estimated by computing the product moment correlation between the two measures. This is the usual correlation statistic built into most computer programs and calculators. When the ratings are quantitative, the correlation is known as the Pearson correlation, and when the ratings are binary it is known as the phi coefficient. Regardless of what they

RELIABILITY

are called, they are comparable to the ICR(3,1) version of the intraclass correlation described above. For the Z variables the Pearson correlation is rP = 0.83 and for the X variables in Table 5.4 the phi coefficient is rP = 0.47. These are very close to the ICR(3,1) values of 0.81 and 0.47 obtained on the same data.

5.7.3 Item response theory statistics Investigators who have a set of survey questions that are known to reflect an underlying dimension, such as severity of distress or impairment, often report estimates of Cronbach’s alpha as a summary of measurement quality. An alternate approach is to focus on the relation of each item response pattern to the underlying dimension. When items are clearly phrased and related to the underlying (latent) dimension, the probability of endorsing an item category will be systematically related to the latent dimension (see, for example [45]). Of special interest are the slope and location of each item, indicating the relevance and severity of each item with regard to the latent dimension. Item response theory (IRT) analyses are especially useful for comparing the measurement equivalence of items across different groups (e.g. [46]). Some argue that IRT analyses should supplant traditional reliability analyses (e.g. [45]).

5.8 Summary and conclusions Unreliability is a measurement problem that can often be rectified by improving interview procedures, or by using statistical sums or averages of replicate measures. Determining the extent to which unreliability is a problem, however, can be challenging. There are various designs for estimating reliability, but virtually all have some biases and shortcomings. Studies of sampling variability of reliability statistics [9, 39, 47] have suggested that sample sizes in pilot studies are often not adequate to give stable estimates about the reliability of key measurement procedures. It is important that reliability studies be considered critically in search for ways to improve measurement procedures. Specifically, if the reliability of a measure appears to be very good, ask whether there are biases in the reliability design that might bias the results optimistically. Were the respondents

sampled in the same way in the reliability study that they will be in the field study? Was the respondent given the chance to be inconsistent, or did the replication make use of archived information? If serious biases are not found, and the reliability study produced stable estimates, then one can put the issue of reliability behind you, at least for the population at hand. If the reliability of a measure appears to be poor, one should also look for biases in the reliability design. How similar were the replications? Could the poor reliability results be an artifact of legitimate changes over time, heterogeneous items within a scale, or artificially different measurement conditions? Was the sample size large enough to be sure that reliability is in fact bad? Be especially suspicious if you have evidence of validity of a measure that is purported to be unreliable. Rather than dismissing a measure with apparently poor reliability, ask whether it can be improved to eliminate noise.

References [1] American Psychiatric Association (1980) Diagnostic and Statistical Manual of Mental Disorders, 3rd edn, American Psychiatric Association, Washington, DC. [2] American Psychiatric Association (1994) Diagnostic and Statistical Manual of Mental Disorders, 4th edn, American Psychiatric Association, Washington, DC. [3] Cochran, W.G. (1968) Errors in measurement in statistics. Technometrics, 10, 637–666. [4] Snedecor, G.W. and Cochran, W.G. (1967) Statistical Methods, 6th edn, Iowa State University Press, Ames. [5] Bollen, K.A. (1989) Structural Equations with Latent Variables, John Wiley & Sons, Inc., New York. [6] Borm, G.F., Munneke, M., Lemmers, O. et al. (2007) An efficient test for the analysis of dichotomized variables when the reliability is known. Stat. Med., 26, 3498–3510. [7] Shrout, P.E. (1998) Measurement reliability and agreement in psychiatry. Stat. Methods Med. Res., 7, 301–317. [8] Lord, F.M. and Novick, M.R. (1968) Statistical Theories of Mental Test Scores, Addison-Wesley, Reading. [9] Dunn, G. (1989) Design and Analysis of Reliability Studies, Oxford University Press, New York. [10] Endicott, J., Spitzer, R.L., Fleiss, J.L. et al. (1976) The global assessment scale: a procedure for measuring overall severity of psychiatric disturbance. Arch. Gen. Psychiatry, 33, 766–771.

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CHAPTER 5 [11] Spitzer, R.L. (1983) Psychiatric diagnosis: are clinicians still necessary? Compr. Psychiatry, 24, 399–411. [12] Cronbach, L.J. (1951) Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334. [13] McDonald, R.P. (1999) Test Theory: A Unified Treatment, Erlbaum, Mahwah. [14] Sijtsma, K. (2009) On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika, 74 (1), 107–120. [15] Raykov, T. (1997) Scale reliability, Cronbach’s coefficient alpha, and violations of essential tauequivalence with fixed congeneric components. Multivariate Behav. Res., 32, 329–353. [16] Kraemer, H.C., Shrout, P.E. and Rubio-Stipec, M. (2007) Developing the diagnostic and statistical manual V: what will ‘statistical’ mean in DSM-5. Soc. Psychiatry Psychiatr. Epidemiol., 42 (4), 259–267. [17] Cronbach, L.J., Gleser, G.C., Nanda, H. et al. (1972) The Dependability of Behavioral Measurements: Theory of Generalizability for Scores and Profiles, John Wiley & Sons, Inc., New York. [18] Anthony, J.C., Folstein, M.,Romanoski, A.J. et al. (1985) Comparison of the lay Diagnostic Interview Schedule and a standardized psychiatric diagnosis. Arch. Gen. Psychiatry, 42, 667–675. [19] Brennan, R.L. (2001) Generalizability Theory, Springer, New York. [20] Cranford, J.A., Shrout, P.E., Iida, M. et al. (2006) A procedure for evaluating sensitivity to within-person change: can mood measures in diary studies detect change reliably? Pers. Soc. Psychol. Bull., 32 (7), 917–929. [21] Jannarone, R.J., Macera, C.A. and Garrison, C.Z. (1987) Evaluating interrater agreement through ‘casecontrol’ sampling. Biometrics, 43, 433–437. [22] Spearman, C. (1910) Correlation calculated from faulty data. Br. J. Psychol., 3, 271–295. [23] Brown, W. (1910) Some experimental results in the correlation of mental abilities. Br. J. Psychol., 3, 296–322. [24] Kraemer, H.C. (1979) Ramifications of a population model for kappa as a coefficient of reliability. Psychometrika, 44, 461–472. [25] Fleiss, J.L. and Shrout, P.E. (1989) Reliability considerations in planning diagnostic validity studies, in The Validity of Psychiatric Diagnoses (ed. L. Robbins), Guilford Press, New York, pp. 279–291. [26] Carey, G. and Gottesman, I.I. (1978) Reliability and validity in binary ratings: areas of common misunderstanding in diagnosis and symptom ratings. Arch. Gen. Psychiatry, 35, 1454–1459.

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[27] Grove, W.M., Andreason, N.C., McDonald-Scott, P. et al. (1981) Reliability studies of psychiatric diagnosis: theory and practice. Arch. Gen. Psychiatry, 38, 408–413. [28] Guggenmoos-Holzmann, I. (1993) How reliable are chance-corrected measures of agreement? Stat. Med., 12, 2191–2205. [29] Spitznagel, E.L. and Helzer, J.E. (1985) A proposed solution to the base rate problem in the kappa statistic. Arch. Gen. Psychiatry, 42, 725–728. [30] Shrout, P.E., Spitzer, R.L. and Fleiss, J.L. (1987) Quantification of agreement in psychiatric diagnosis revisited. Arch. Gen. Psychiatry, 44, 172–177. [31] Kraemer, H.C. (1992) Measurement of reliability for categorical data in medical research. Stat. Methods Med. Res., 1, 183–199. [32] Cohen, J. (1960) A coefficient of agreement for nominal scales. Educ. Psychol. Meas., 20, 37–46. [33] Blackman, N.J.-M. and Koval, J.J. (2000) Interval estimation for Cohen’s kappa as a measure of agreement. Stat. Med., 19, 723–741. [34] Walter, S.D., Eliasziw, M. and Donner, A. (1998) Sample size and optimal designs for reliability studies. Stat. Med., 17, 101–110. [35] SPSS Inc. (2009) SPSS for Windows (Version 16), SPSS Inc., Chicago. [36] Shrout, P.E. and Fleiss, J.L. (1979) Intraclass correlations: uses in assessing rater reliability. Psychol. Bull., 86, 420–428. [37] Fleiss, J.L. and Cohen, J. (1973) The equivalence of weighted kappa and the intraclass coefficient as measures of reliability. Educ. Psychol. Meas., 33, 613–619. [38] Fleiss, J.L., Levin, B. and Paik, M.C. (2003) Statistical Methods for Rates and Proportions, 3rd edn, John Wiley & Sons, Inc., New York. [39] Donner, A. (1998) Sample size requirements for the comparison of two or more coefficients of interobserver agreement. Stat. Med., 17, 1157–1168. [40] Donner, A. and Eliasziw, M. (1992) A goodnessof-fit approach to inference procedures for the kappa statistic: confidence interval construction, significancetesting and sample size estimation. Stat. Med., 11, 1511–1519. [41] Donner, A. and Eliasziw, M. (1994) Statistical implications of the choice between a dichotomous or continuous trait in studies of interobserver agreement. Biometrics, 50, 550–555. [42] Donner, A. and Eliasziw, M. (1997) A hierarchical approach to inferences concerning interobserver agreement for multinomial data. Stat. Med., 16, 1097–1106.

RELIABILITY [43] Donner, A., Eliasziw, M. and Klar, N. (1996) Testing the homogeneity of kappa statistics. Biometrics, 52, 176–183. [44] Donner, A., Shoukri, M.M., Klar, N. et al. (2000) Testing the equality of two dependent kappa statistics. Stat. Med., 19, 373–387. [45] Embretson, S.E. and Reise, S.P. (2000) Item Response Theory for Psychologists, Erlbaum, Mahwah.

[46] Gregorich, S.E. (2006) Do self-report instruments allow meaningful comparisons across diverse population groups? Med. Care, 44 (11), S78–S94. [47] Cantor, A.B. (1996) Sample-size calculations for Cohen’s kappa. Psychol. Methods, 1, 150–155.

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6

Moderators and mediators: Towards the genetic and environmental bases of psychiatric disorders Helena Chmura Kraemer Department of Psychiatry, Stanford University, CA, USA and University of Pittsburgh, Pittsburgh, PA, USA

6.1 Introduction The terms ‘moderator’ and ‘mediator’ have been around for at least 50 years, but until 1986, the terms were used inconsistently and idiosyncratically. As a result, the constructs of moderation/mediation played little role in biomedical research. In 1986 [1] Baron and Kenny proposed conceptual definitions to distinguish moderators from mediators, giving each term a specific distinct meaning. According to those conceptual definitions, when M (moderator or mediator), T (target variable) and O (outcome variable) are three variables measured on the individual subjects in a population: • M moderates the effect of T on O if M helps explain on whom or under what conditions T leads to O. • M mediates the effect of T on O if M helps to explain why or how T leads to O. Baron and Kenny also proposed a statistical method to apply those definitions, based on a linear model, assuming that the outcome is determined by a linear function of T and M: Linear Model: β0 + β1 T + β2 M + β3 TM

To show moderation Baron and Kenny required only that it be shown that β3 = 0. That is problematic, for a demonstration that T moderates M also demonstrates that M moderates T, leaving the direction of moderation ambiguous. To show mediation, they required that the interaction effect be assumed to be zero. That too is problematic, for if interaction exists in the population but is assumed zero in the model, that effect is remapped partially into the other coefficients, biasing them, and partially into the error, reducing power. Moreover, users were encouraged to fit both models to one set of data, first proving a non-zero interaction to show moderation, and then illogically assuming a zero interaction to show mediation, leading to a conclusion that one variable can both moderate and mediate another in its effect on the outcome. Added to such problems intrinsic to the approach, many users began to refer to any interactive relationship as ‘moderation’, to use the term ‘mediator’ as synonymous with ‘cause’, or to refer to a variable as a ‘moderator’ or a ‘mediator’ without specifying what the target and/or the outcome was. These are all misuses of the Baron and Kenny definitions. Around 2000, a subgroup of the MacArthur Network on Psychopathology in Development, modified

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the Baron and Kenny criteria [2, 3], retaining their conceptual definitions, but attempting to set criteria to resolve and clarify the incongruities and ambiguities [4]. This approach, the so-called MacArthur approach, is discussed here. The MacArthur reformulation clarified the importance of the moderator/mediator concept to all biomedical research, particularly psychiatry. Moderators of treatment on outcome in randomised clinical trials (RCTs) identify subpopulations that respond differently to treatments, facilitating personalisation in the form of targeting of treatment choices to those most benefited [3, 5–7]. Mediators of treatments in RCTs suggest ways in which a treatment might be made more effective or more cost-effective, thus encouraging personalisation in the form of tailoring treatment to individual needs [5]. Similarly in risk research, there may be chains of mediators leading to onset of a disorder (some perhaps causal), and even multiple such chains in subpopulations defined by moderators of those risk factors on that disorder [8, 9]. In the present discussion the moderator/mediator approach specifically directed to understanding the genetic influences on disorders will be discussed. Consider the simple situation in which G represents the presence (G = 1) or absence (G = 0) of a certain genotype, and E represents some binary factor (E = 1 and 0) such as presence/absence of an environmental factor, a gene expression or its effect on the individual, or an event, all during the early lifetime of an individual, where both G and E are risk factors for the disorder D. D represents whether the individual experiences the onset of a disorder (D+) or not (D−) at a certain time point (prevalence) or during a certain time span (incidence) in later lifetime. The complete distribution of (G, E, D) in the population is described in Table 6.1 There is nothing in the moderator/mediator approach that requires that the three variables be binary, but the basic concepts are clearest in this simple situation and thereafter easily expanded to more general situations. To motivate this discussion, consider first an extreme special case: Suppose that the only individuals who have D+ are those with both G = 1 and E = 1 (P11 = 1, Pij = 0 otherwise). If G and E were simultaneously studied, in the population of interest, the risk difference (RD) between those with both G 88

Table 6.1 G and E binary risk factors for the disorder D, with G (genotype) temporally preceding E (environmental risk factor, genetic expression, event), preceding D. G

E

Probability of (G, E) in the population

Probability of D+

1 1 0 0

1 0 1 0

pq1 p(1 − q1 ) (1 − p)q2 (1 − p)(1 − q2 )

P11 P10 P01 P00

and E = 1 and others would be a perfect 1.00. It would take a very small sample from the population to detect what the situation is. However, if one studied only the genotype, the probability of the disorder in the G = 1 subgroup is q1 , and that in the G = 0 subgroup is 0, a RD now of q1 < 1. If, as often happens, E = 1 is rare (q1 near zero), that represents considerable attenuation of the effect size. Moreover, the disorder itself cannot be observed; only a diagnosis of the disorder, which has a certain sensitivity (SED ) and specificity (SPD ) for that disorder, that is almost never perfect. Then when only G is considered, the RD is an even more attenuated q1 (SED + SPD − 1). To detect such an attenuated association, the sample size may have to be very large, and even if such association were found ‘statistically significant’, the effect size might well appear trivial. A great deal of attention has been focused on improving the reliability of measurement of G, E and especially of D for psychiatric disorders, but until recently, little attention has been paid to the fact that if genes and environment ‘work together’ in their effect on a disorder, studying genes in absence of the environment, (or environment in absence of genes), may conceal the crucial role that both genes and environment play in the aetiology of disorders. In what follows, the sample is assumed to be a representative one from the population of interest. G, E and D are binary, as reliably measured as possible. Their errors of measurement are independent of each other, an assumption that can be guaranteed in design by ‘blinding’ each assessment to the others. Based on these assumptions, several methodological problems are discussed, that are barriers to resolving such issues, necessary to clarifying the MacArthur moderator/mediator approach

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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for considering the genetic/environmental bases of psychiatric disorders. Then moderation and mediation as well as other important ways in which multiple factors can ‘work together’ to explain an outcome are defined. The concept is then expanded from one binary variable at each of three time points, to one variable measured at any level at each of three time points, to multiple variables at each of three time points, to multiple variables at multiple time points. The goal is to suggest how, using these principles, one might ‘piece together’ a very complex picture of what might lead to a disorder, thus to suggest how complex disorders might be prevented or successfully treated.

6.2 Current methodological barriers 6.2.1 Case–control studies Case–control studies are one of the most common approaches to examining the genetic bases of a disorder. It is very difficult in population-based studies to generate a sufficient number of cases of the disorder (D+) in a prospective study, when the disorder of interest is, as it often is, quite rare. A favoured alternative has long been to do a retrospective case–control study, in which N1 subjects are sampled from among those who have already had onset of the disorder of interest, and N2 subjects from among those without that disorder. In a case–control study, clearly genotype can be as reliably and accurately measured on the total sample after onset of the disorder as it might have been prospectively before the onset of the disorder. Accurate measurement of environment, gene expression, or events prior to onset of the disorder, however, is very difficult. Memory is flawed, records are often incomplete, and, most important, recall is often coloured by the subject’s knowledge that they do or do not have the disorder in question at the time of assessment. Moreover, Berkson’s Fallacy has been known since the 1950s [10, 11]: The samples of ‘cases’ and ‘controls’ may not be representative of the cases or the controls that would have resulted in prospective study of a population for a variety of reasons. What one sees in a case–control study may

well misrepresent what would have been seen in a prospective study of the same risk factors and outcomes in the same population. In short, while the first studies to explore associations between risk factors and outcomes may well be case–control studies, accepting inferences from such studies as ‘scientific truth’ is risky. Instead, such studies should be used to generate strong hypotheses to be tested in subsequent prospective studies, and to yield the information necessary to design powerful and cost-effective such prospective studies. The type of study design necessary to understanding gene moderation or mediation corresponds to the third type of study design discussed by Fitzmaurice and Ravichandran (Chapter 2), in which a random number of subjects are sampled from a population and both binary characteristics are measured on each subject.

6.2.2 Statistical significance necessary but not sufficient Concepts related to statistical hypothesis testing, such as ‘significance level’, ‘p-values’, ‘power’, came into common use in biomedical research about midtwentieth century. In recent years, however, there is a growing realisation of the limitations of statistical hypothesis testing [12–18]. One prominent epidemiological journal actually banned the use of ‘p-values’ for a time, while psychology [14, 19] and medicine [20–22] tried to deal with the problem by urging that every p-value be accompanied by a clinically interpretable effect size and its confidence interval. In the way such testing is commonly done (testing null hypotheses of randomness), ‘statistically significant’ generally means that the data are sufficient to demonstrate a non-random association, a comment on the data, not on the strength of association. Any non-null association, no matter how trivial, can be shown to be ‘statistically significant’ provided the sample size is large enough. The crucial issue is whether the strength of association is enough to warrant further interest in that association. In dealing with how risk factors ‘work together’ in the MacArthur approach, effect sizes are decomposed to understand the contributions of various risk factors. In essence, this approach assumes that, when there is rationale and justification for suspecting an 89

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association between G and D, most, if not all, effect sizes are non-null, although many, perhaps most, may be trivial. Certainly in all research into the risk factors (genetic or otherwise) for a disorder, it is necessary to establish statistical significance to warrant drawing any conclusions. It is not sufficient to stop there: a clinically interpretable effect size is necessary.

6.2.3 Odds ratio is not a clinically interpretable effect size The most common effect size used in epidemiological and genetic research is the odds ratio. If the probability of D+ in the high risk group is Q1 and that in the low risk group is Q0 , the odds of D+ in the two groups are Q1 /(1 − Q1 ) and Q0 /(1 − Q0 ). The odds ratio (OR) is the ratio of those two odds. The odds ratio was originally introduced as the likelihood ratio test statistic to test the null hypothesis of random differences between the two groups (Q1 = Q0 ), and remains an excellent indicator of non-randomness [23]. OR = 1 means that Q1 = Q0 ; OR = 1 indicates non-random association. Use of the odds ratio to test for non-random association, for example using logistic regression analysis, is both common and recommended. However, there are a number of arguments against the odds ratio as an interpretable effect size [23–27] all converging on one conclusion: odds ratio should not be used as an effect size, but only as an indicator of nonrandomness. Consider three questions: If not the odds ratio, then what? Why the odds ratio? Why not the odds ratio? If not the odds ratio, then what? What has been shown is that, once one excludes the odds ratio from consideration, all the other common measures of 2 × 2 association correspond to one or another of the weighted kappa coefficients [25]. Which weighted kappa is appropriate in any given situation depends on the relative clinical importance of false positives to false negatives (which determines the weight in the weighted kappa). Thus among commonly used measures of 2 × 2 association, odds ratio is an outlier. For the purpose of the present discussion, one commonly used such weighted kappa will be used,

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the RD = Q1 − Q0 , where Q1 and Q0 are the incidence/prevalence of the disorder in the two groups compared. This is not to say that RD is the only appropriate choice, but this effect size is a reasonable choice in many clinical situations. Moreover RD is easily translated to ‘number needed to take’ (NNT = 1/RD), an effect size easily interpretable for clinical or public policy decision-making [28–31]. Suppose one could magically transfer subjects in the high risk group (Q1 ) to the low risk group (Q0 ). How many high risk patients would have to be transferred to hope to prevent one case of D+? The answer to that question is NNT = 1/(Q1 − Q0 ). If NNT = 1, every high risk subject has the disorder and every low risk subject does not: As soon as one patient is transferred, one case may be prevented. If, on the other hand, one needs to transfer 3 or 30 or 3000 or even more high risk subjects to prevent one case, the clinical importance of the risk factor that defines ‘high risk’ becomes progressively weaker. The choice between odds ratio and RD (or NNT) is of no concern when association is random. In that case OR = 1, RD = 0 and NNT is infinite. Also there is consistency when the probability of being in the high risk group equals the probability of having D+ in the population, for then √ √ RD = ( OR − 1)/( OR + 1) = 1/NNT Thus, under this condition, OR = 4 corresponds to RD = 1/3 (NNT =√3). Otherwise, NNT is √ always greater than ( OR + 1)/( OR − 1) [31]. Thus OR = 4 may correspond to NNT = 3, or to NNT = 30, 300, 3000, . . ., which makes interpretation for public health purposes impossible. Why the odds ratio? If the magnitude of the odds ratio is so difficult to interpret for public health purposes, what arguments have been given supporting its use (other than as an indication of non-randomness)? Epidemiologists often suggest that this is the statistic recommended by biostatisticians, and biostatisticians suggest that this is the statistic demanded by epidemiologists. If either claim is true, such recommendations in absence of a sound scientific basis are questionable. The most common reason given is ‘because this is what we’ve always used’ or ‘this is what everyone

MODERATORS AND MEDIATORS: TOWARDS THE GENETIC AND ENVIRONMENTAL BASES OF PSYCHIATRIC DISORDERS

uses’, that is that it is the most commonly used measure of 2 × 2 association (Section 6.3.1). This claim is true, but again, leaves the scientific basis for such common use unclear. Another reason is that, unlike many measures of 2 × 2 association, the odds ratio is symmetric in the roles of Y and X (see Section 6.3.1), that reversing the roles of Y and X yields the same odds ratio. However, the weighted kappa, placing equal weight on false positives and false negatives, and the phi coefficient have the same property, but generally yield very different conclusions. Similarly, the fact that the odds ratio approximates another measure of association, the relative risk, but that claim is true only for a very low prevalence situation (a ‘rare disease’). In any 2 × 2 table there are four relative risks, and the odds ratio is always larger than the largest one. Another, less often articulated reason is that the odds ratio is often big when most other effect sizes indicate a trivial effect, often stated as a claim that the odds ratio is more sensitive to deviations from randomness. As noted above, this is often true but leaves the question unanswered as to whether the odds ratio is conveying the right or wrong message. Often it is pointed out how easy odds ratio is to compute, most particularly that it can be estimated equally well with a prospective naturalistic or stratified sample or from an unbiased case–control sample [32]. However, if message conveyed by the odds ratio is wrong, ease of computation is not a valid scientific reason for its use. In short, scientific support for the use of the odds ratio as an interpretable effect size seems to be lacking, although it must again be emphasised that it remains the index of choice in testing null hypotheses of randomness, and is very convenient for use in multivariate modelling, for example in the logistic regression model (Chapter 2). Why not the odds ratio? The fundamental problem with the odds ratio lies in the fact that it is a ratio, very sensitive to the magnitude, and to the error of estimation, of a denominator that often approaches zero. For example, suppose that underlying the categorical diagnoses D+ and D−, there is a dimensional diagnosis D [33], which is normally distributed with equal variances in the two groups, where a

categorical diagnosis is obtained by dichotomising D at some cut-point. The effect size differentiating the two groups is δ = (μ1 − μ0 )/σ, with μ1 and μ0 the means of the dimensional diagnoses in the high and low risk groups, and σ their common standard deviation. Then where () is the standard normal distribution function, and the cut-point c is measured in σ-units from the point halfway between the two means (μ1 + μ2 )/2, the odds ratio would be: OR =

[1 − (c − δ)](c) . (c − δ)[1 − (c)]

This odds ratio is shown in Figure 6.1 for various cut-points c and for various values of δ. Clearly if δ = 0, OR = 1, the null value, regardless of the cut-point c (and accordingly RD = 0 and NNT is infinite). If δ = 0, OR takes on its minimal value halfway the two means at which √ between √ point RD = ( OR − 1)/( OR + 1). For cut-points above and below this midpoint, OR monotonically increases to infinity (and RD monotonically decreases to 0). The crucial fact is that when δ > 0, one can get an odds ratio, as large as one can possibly desire, simply by dichotomising far enough in the tails of the distribution. From another perspective, one of the most important research uses of an effect size is power computation in planning a hypothesis-testing study. However, as is well known, power computations cannot be done using odds ratio as the effect size. For example, in testing the simple hypothesis OR = 1 versus OR = 4 at the 5% level of significance, there is no sample size large enough to assure at least 80% power whenever OR = 4. This is because OR = 4 may mean that Q1 = 2/3 and Q0 = 1/3, in which case the necessary sample size per group would be 34, or it may mean that Q1 = 0.004 and Q0 = 0.001, in which case the necessary sample size would be 4294 per group (and even larger for smaller values of Q1 and Q0 having OR = 4). Generally to do power computations, users switch to other effect sizes such as RD. For example, OR = 4 corresponds at best to RD = 1/3. With 34 subjects per group, one has at least 80% power to detect any Q1 and Q0 pair with RD = 1/3.

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CHAPTER 6 40.00 35.00 30.00 = 0.0 Odds ratio

25.00

= 0.2 = 0.4

20.00

= 0.6 = 0.8

15.00

= 1.0 10.00 5.00 0.00 −4.0 −3.0 −2.0 −1.0 0.0

1.0

2.0

3.0

4.0

cutpoint

Fig 6.1 Values of odds ratio obtained by dichotomising a hypothetical dimensional diagnosis having normal distributions with equal variance in the high risk and low risk subpopulation, where the effect size comparing those distributions is δ, the standardised mean difference between the two means. The cutpoints are measured in standard deviation units from the point halfway between the means of the two distributions.

There are many other such arguments that raise questions about the value of the odds ratio as an interpretable effect size, and few, other than those based on custom or convenience, supporting its use as such.

the association of two variables (or more) to a third. Interaction may exist with or without correlation; correlation may exist with or without interaction. That now sets the basis for consideration of moderation and mediation.

6.2.4 Correlation versus interaction A common source of confusion is that between two risk factors being correlated and two risk factors interacting. In Table 6.1, G and E are correlated if q1 = q2 and then G and E are correlated regardless of which outcome is being considered in that population. On the other hand, G and E interact in their effect on a specific outcome D if the effect size relating E to D is different for those with G = 1 than for those with G = 0, or equivalently if the effect size relating G to D is different for those with E = 1 than for those with E = 0, that is if P11 − P10 − P01 + P00 = 0. G and E may interact with respect to one choice of D, but not with another. Thus ‘correlation’ refers to the relationship between two variables, ‘interaction’ to 92

6.3 Moderation, mediation and other ways in which risk factors ‘work together’ 6.3.1 G moderates E in its effect on D In general, to show that M moderates the effect of T on O, one must show that in the population of interest: 1 M must precede T which must precede O. 2 M and T are uncorrelated. 3 The effect size relating T to O is different depending on M. As already specified, G precedes E precedes D (satisfying criterion (1)). Note that here G means

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genotype, which is fixed at the fusion of the gametes, and which therefore precedes all E and D, as opposed to gene expression which can vary across the lifespan and which might be considered here as one possibility for E. To show that G moderates E on D, G and E must be shown to be uncorrelated (q1 = q2 ). Since the effect size relating E to O if G = 1 is P11 − P10 and that relating E to D if G = 0 is P01 − P00 , criterion 3 is satisfied if P11 − P10 − P01 + P00 = 0 (a non-zero ‘interaction effect’ between G and E on D). For example, several studies [34, 35] have shown that a certain set of genes moderate the effects of repeated rhythmic vestibular stimulation (tossing) on young EL mice on later occurrence of epileptic seizures. Indeed ‘susceptibility’ genes may often be those genes that moderate the effect of environmental insults on the subsequent onset of disorders.

6.3.2 Mediation: E mediates the effect of G on D In general, to show that M mediates the effect of T on O, one must show that in the population of interest: 1 T must precede M which must precede O. 2 T and M are correlated. 3 The effect size of T on O can be explained in part by the effect of T on M. Since E is temporally between G and D (criterion (1)), E mediates G on D, only if G and E are correlated (q1 = q2 ) (criterion (2)). The probability of D+ when G = 1 is q1 P11 + (1 − q1 )P10 , and when G = 0 is q2 P01 + (1 − q2 )P00 . The overall RD associated with G is then: RD = q1 P11 + (1 − q1 )P10 − q2 P01 − (1 − q2 )P00 . Let q∗ = (q1 + q2 )/2 Then:

and

q = (q1 − q2 )/2.

RD = (P10 − P00 ) + q(P11 − P10 + P01 − P00 ) + q ∗ (P11 − P01 − P10 + P00 ) The first bracket indicates the effect size of G on D in the absence of E (E = 0). The second term indicates how much is contributed to the effect size by the ‘main effect’ of E on D (P11 − P10 + P01 − P00 )

provided G and E are correlated (q = 0) and the third term how much by the ‘interaction’ (P11 − P01 − P10 + P00 ) between G and E on D. Thus the latter two terms indicate how much of the effect of G on D may be explained by E. Only if P11 = P10 and P01 = P00 , in which case there is neither a main nor an interactive effect of E on D, does E not ‘matter’ to the outcome. For example, the phenylketonuria (PKU) gene (G) is mediated in its effect on PKU-related retardation (D) by the PKU enzyme and its effects (E). This is important, since one can manipulate the PKU enzyme effects by dietary control to prevent PKU-related retardation. In general, all links in a causal chain leading to an outcome are mediators, but not all mediators are links in causal chains. Thus mediators suggest possible causal links; they do not prove they exist. There are other ways in which two factors can ‘work together’ in their effect on a third, that are also important.

6.3.3 Independent risk factors If two factors (perhaps G and E as above, but perhaps two choices for G or two choices for E) are uncorrelated, but one does not moderate the other, either because of lack of time precedence or absence of interaction, such factors are called ‘independent risk factors’ for the outcome. Independent risk factors lie on separate paths leading to the outcome. For example, gender and age are independent risk factors for many disorders (e.g. major depression, eating disorders).

6.3.4 Proxies If two factors, G and E, are correlated with G preceding E, but mediation does not exist because P10 = P11 and P01 = P00 , then E is said to be ‘proxy to’ G. In such cases E considered alone, may be a risk factor for D, but when G and E are considered together, only G matters. For example, G might be gender, E might be ‘excellent ball-throwing ability at age 10’ and D+ might be the onset of depression during the teenage years. Girls are more likely to develop depression during the teenage years, as well as more likely to be 93

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the attenuating effects described above. Moreover, including many measures of the same construct places additional measurement burden on both the subjects and assessors, which often leads to decreased reliability [36] of these measures, and to drop-out from prospective studies, both of which decrease the scientific value of a study while increasing its costs and difficulties

poor ball-throwers. If G were here ignored, it might well be that E would be identified as a risk factor for subsequent onset of depression. However, when G and E here are considered together, among boys as well as among girls, ball-throwing ability is unlikely to be correlated with D. If so, ball-throwing ability (E) is proxy to gender (G) for onset of depression (D), that is it is probably not worth while teaching girls how to throw a ball better in order to prevent depression! Here, we’ve taken a silly example, but often proxies are taken all too seriously. A similar situation occurs with two Gs or two Es, that is where there is no temporal precedence between the risk factors. If when considered together, only one of two such variables appears to be associated with D, the other is proxy to the variable that matters. Proxies are often found when there is one strong risk factor, but correlates of that risk factor are also simultaneously considered.

6.3.6 Summary The principles above can be applied to any two risk factors (M, T) for an outcome (O) of interest in the population as summarised in Table 6.2.

6.4 Extensions 6.4.1 One risk factor at each of two time points measured at any level

6.3.5 Overlapping risk factors

The principles here discussed for one binary risk factor at each of two time points are easily extended. With binary G and E, but with an outcome that may be binary, categorical, interval, or even, in some cases, multivariate, the principles in Table 6.2 can be directly used replacing RD with the non-parametric effect size area under the curve [37]. When either or both of G and E are ordinal, a linear model might be considered, as suggested by Baron and Kenny, for example multiple linear regression. If the outcome is time to onset, Cox Proportional Hazards Model might be considered. In all such models, interaction is indicated by a non-zero β3 . Both risk factors matter if any two of the three

Finally, two risk factors that satisfy criteria (2) and (3) for mediation, but where there is no time precedence, are ‘overlapping risk factors’ for D. Such factors might be closely linked genes, or might be multiple measures of the same underlying construct. In such cases, it would be far better to combine such measures a priori, to choose the best among such measures, or to select one measure that best represents the common construct of interest, than to include multiple measures of the same underlying construct. Including multiple overlapping measures does not add useful information to the data; it only increases the impact of errors of measurement in

Table 6.2 M and T are risk factors for an outcome O. In absence of temporal precedence between M and T, the labels M and T are arbitrarily assigned to the risk factors. Otherwise, if M and T are uncorrelated, M is assigned to the earlier risk factor, T to the later. If M and T are correlated, T is assigned to the earlier risk factor, M to the later. With reference to O:

Time precedence?

Correlation?

Analytic criterion

Possible action?

M moderates T M and T independent

Yes Yes No Yes Yes/no No No

No No No Yes Yes Yes Yes

Interactive effect No interactive effect Both M and T matter Both M and T matter. Only T matters Only M matters Both M and T matter

Stratify on M – – – Set aside M Set aside T Combine M, T

M mediates T M is proxy to T T is proxy to M M and T overlapping

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regression coefficients are non-zero. Only T matters when both β2 and β3 are zero; only M matters when both β1 and β3 are zero. The effect sizes in such cases are functions of the standardised regression coefficients [37].

6.4.2 Multiple risk factors at two time points Moreover, the process can be extended to the consideration of multiple measures at two time points. First, one would examine all the pairs of risk factors in the set of risk factors measured at each time point, identifying and removing proxies, combining or otherwise removing overlapping variables. By this process the set of variables measured at each time is reduced to a smaller set of independent risk factors for the outcome. Then one would examine whether any variables in the earlier set moderate any of the variables in the later set. If so, the sample would have to be stratified on those moderators, for identification of a moderator of treatment response often means that the mediation patterns will differ in strata defined by moderators. Finally, within each moderator-defined stratum, one would seek variables in the later set that mediate variables in the first set on the outcome. What may result are one or more short mediational chains leading to the outcome of interest.

6.4.3 Multiple risk factors at multiple time points Finally the process can be extended even to include consideration of, not merely two time points, but multiple time points prior to the outcome by applying the above process to Times 1 and 2, then to Times 2 and 3, and so on. For example, in considering the risk factors for psychiatric symptoms for third graders, Essex et al. [2] considered variables measured (i) in infancy, (ii) in the preschool period and (iii) in kindergarten and first grade, all to predict a third grade outcome. This process has been compared to piecing together a jigsaw puzzle, by first discarding irrelevant or extraneous pieces (proxies and overlapping risk factors), sorting pieces that belong to different ‘pictures’ (moderators), then fitting pairs of pieces together systematically (mediators and independent risk factors) to begin to see the ‘whole picture(s)’.

6.5 Beyond moderators and mediators Einstein is quoted as defining insanity as doing the same thing over and over again and expecting different results, a comment that applies as much to choice of methods as to other activities. While it is generally conceded that psychiatric disorders are ‘complex’, the methods commonly used to investigate such disorders have often been selected for their simplicity and ease of use, often despite evidence that they cannot resolve complex problems: • Sampling: Case–control studies are notoriously subject to sampling and measurement biases. These as well as cross-sectional studies cannot be use to establish time precedence and thus have limited utility in identification of risk factors, or of how risk factors ‘work together’. Case–control and cross-sectional studies are easy; the prospective cohort studies necessary to risk research are difficult, but essential. • Terminology: There are terms in common use (beyond ‘moderators’ and ‘mediators’) that are questionable as they are currently applied. A ‘confounder’ is defined [38] (p. 35) as ‘A variable that can cause or prevent the outcome of interest, is not an intermediate variable (mediator), and is associated with the factor under investigation’. If the causal paths to an outcome were known, there would be little point to study of risk factors. The causal references in the definition aside, a ‘confounder’ may be proxy to the factor under investigation or that factor proxy to the confounder, or the two might be overlapping. It makes a difference whether the ‘confounder’ should be set aside and the risk factor under investigation retained or vice versa. Efforts to ‘control for’ or ‘adjust for’ certain ‘confounders’ are often motivated by the desire to estimate the specific causal effect of a selected risk factor of interest. However, causation cannot be inferred simply from correlation, and if one risk factor moderates or mediates another, even if causal, their effects cannot be separated. The phrase ‘independent risk factor’ is often applied to a risk factor that adds to the predictive value for the outcome after another risk factor 95

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is considered. Thus the term might be applied to overlapping risk factors, to a moderator, to a mediator, as well as to what in the MacArthur model is more narrowly defined as an independent risk factor. The usual use of the term seems vague and can be misleading. Thus the MacArthur approach proposes the definition in Table 6.2, which requires both that the factors be independent of each other, and that their effects on the outcome be independent. • Analysis: Entering multiple risk factors into multiple regression models omitting interactions is easy; one need merely enter all the data into a computer program and interpret what results. Carefully examining every pair-wise association and taking the correct action for each pair, as suggested in the MacArthur methods, is challenging. Including interactions in such models requires appropriate centring [39], larger sample sizes, and careful and thoughtful interpretations, and is difficult. However, omitting interactions that exist in the population both biases results and reduces power to detect associations. Ignoring interactions that might signal moderation effects is particularly troublesome. Since the paths leading to the disorder may differ in the subgroups defined by a moderator of subsequent risk factors on the disorder, problems associated with Simpson’s paradox may be quite prevalent [40–43]. In brief, if the risk factors in subpopulations defined by a moderator differ, the correlation obtained by ‘muddling’ the subpopulations mixes within group associations (which differ and are meaningful) with between group associations (which may be irrelevant). The associations one observes may be misleading. Moreover, even in absence of interactions in the population, the inclusion of proxies or overlapping variables in regression analyses induce problems associated with multicollinearity, again introducing bias and reducing power. The motivation in developing the MacArthur approach: to examine whether the methods in common use may actually be slowing progress in risk research. Whether the MacArthur approach will lead to more rapid gains in understanding the aetiology of psychiatric disorders remains to be seen.

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References [1] Baron, R.M. and Kenny, D.A. (1986) The moderatormediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol., 51, 1173–1182. [2] Kraemer, H.C., Stice, E., Kazdin, A. and Kupfer, D. (2001) How do risk factors work together to produce an outcome? Mediators, moderators, independent, overlapping and proxy risk factors. Am. J. Psychiatry, 158, 848–856. [3] Kraemer, H.C., Wilson, G.T., Fairburn, C.G. et al. (2002) Mediators and moderators of treatment effects in randomized clinical trials. Arch. Gen. Psychiatry, 59, 877–883. [4] Kraemer, H.C., Kiernan, M., Essex, M.J. et al. (2008) How and why criteria defining moderators and mediators differ between the Baron & Kenny and MacArthur approaches. Health Psychol., 27 (2), S101–S108. [5] King, A.C., Ahn, D.F., Atienza, A.A. et al. (2008) Exploring refinements in targeted behavioral medical intervention to advance public health. Ann. Behav. Med., 35 (3), 251–260. [6] Kraemer, H.C., Frank, E. and Kupfer, D.J. (2006) Moderators of treatment outcomes: clinical, research, and policy importance. J. Am. Med. Assoc., 296 (10), 1–4. [7] Owens, E.B., Hinshaw, S.P., Kraemer, H.C. et al. (2003) What treatment for whom for ADHD: moderators of treatment response in the MTA. J. Consult. Clin. Psychol., 71 (3), 540–552. [8] Boyce, W.T., Essex, M.J., Alkon, A. et al. (2006) Early father involvement moderates biobehavioral susceptibility to mental health problems in middle childhood. J. Am. Acad. Child Adolesc. Psychiatry, 45 (12), 1510–1520. [9] Essex, M.J., Kraemer, H.C., Armstong, J.M. et al. (2006) Exploring risk factors for the emergence of children’s mental health problems. Arch. Gen. Psychiatry., 63, 1246–1256. [10] Berkson, J. (1946) Limitations of the application of fourfold table analysis to hospital data. Biometrics Bull., 2, 47–53. [11] Berkson, J. (1955) The statistical study of association between smoking and lung cancer. Proc. Staff Meet. Mayo Clin., 30, 56–60. [12] Abelson, R.P. (1997) On the surprising longevity of flogged horses: why there is a case for the significance test. Psychol. Sci., 8 (1), 12–15. [13] Borenstein, M. (1997) Hypothesis testing and effect size estimation in clinical trials. Ann. Allergy Asthma Immunol., 78, 5–16.

MODERATORS AND MEDIATORS: TOWARDS THE GENETIC AND ENVIRONMENTAL BASES OF PSYCHIATRIC DISORDERS [14] Borenstein, M. (1998) The shift from significance testing to effect size estimation, in Research and Methods, Comprehensive Clinical Psychology, vol. 3 (eds A.S. Bellak and M. Hersen), Elsevier Science Publishing Company, Burlington, MD, pp. 319–349. [15] Cohen, J. (1995) The earth is round (p < .05). Am. Psychol., 49, 997–1003. [16] Dar, R., Serlin, R.C. and Omer, H. (1994) Misuse of statistical tests in three decades of psychotherapy research. J. Consult. Clin. Res., 62, 75–82. [17] Hunter, J.E. (1997) Needed: a ban on the significance test. Psychol. Sci., 8 (1), 3–7. [18] Shrout, P.E. (1997) Should significance tests be banned? Introduction to a special section exploring the pros and cons. Psychol. Sci., 8 (1), 1–2. [19] Wilkinson, L., The_Task_Force_on_Statistical_Inference (1999) Statistical methods in psychology journals: guidelines and explanations. Am. Psychol., 54, 594–604. [20] Altman, D.G., Schulz, K.F., Hoher, D. et al. (2001) The revised CONSORT statement for reporting randomized trials: explanation and elaboration. Ann. Int. Med., 134 (8), 663–694. [21] Begg, C., Cho, M., Eastwood, S. et al. (1999) Improving the quality of reporting of randomized controlled trials: the CONSORT statement. J. Am. Med. Assoc., 276, 637–639. [22] Rennie, D. (1996) How to report randomized controlled trials: the CONSORT Statement. J. Am. Med. Assoc., 276 (8), 649. [23] Kraemer, H.C. (2007) Correlation coefficients in medical research: from product moment correlation to the odds ratio. Stat. Methods Med. Res., 15 (6), 525–545. [24] Kraemer, H.C. (2004) Reconsidering the odds ratio as a measure of 2X2 Association in a population. Stat. Med., 23 (2), 257–270. [25] Kraemer, H.C., Kazdin, A.E., Offord, D.R. et al. (1999) Measuring the potency of a risk factor for clinical or policy significance. Psychol. Methods, 4 (3), 257–271. [26] Newcombe, R.G. (2006) A deficiency of the odds ratio as a measure of effect size. Stat. Med., 25, 4235–4240. [27] Sackett, D.L. (1996) Down with odds ratios!. Evid. Based Med., 1, 164–166. [28] Altman, D.G. (1998) Confidence intervals for the number needed to treat. Br. Med. J., 317 (7168), 1309–1312.

[29] Altman, D.G. and Andersen, K. (1999) Calculating the number needed to treat for trials where the outcome is time to an event. Br. Med. J., 319, 1492–1495. [30] Cook, R.J. and Sackett, D.L. (1995) The number needed to treat: a clinically useful measure of treatment effect. Br. Med. J. 310, 452–454. [31] Kraemer, H.C. and Kupfer, D.J. (2006) Size of treatment effects and their importance to clinical research and practice. Biol. Psychiatry, 59 (11), 990–996. [32] Cornfield, J. (1956) A statistical problem arising from retrospective studies, in Proceedings of the Third Berkeley Symposium, vol. 4 (ed J. Neyman), University of California Press, Berekely, CA, p. 135. [33] Helzer, J.E., Kraemer, H.C. and Krueger, R.F. (eds) (2008) Dimensional Approaches in Diagnostic Classification: Refining the Research Agenda for DSM-5, American Psychiatric Association, Arlington, VA. [34] Poderycki, M.J., Simoes, J.M., Todorova, M.A. et al. (1998) Environmental influences on epilepsy gene mapping in EL mice. J. Neurogenet., 12 (2), 67–85. [35] Todorova, M.T., Mantis, J.G., Le, M. et al. (2006) Genetic and environmental interactions determine seizure susceptibility in epileptic EL mice. Genes Brain Behav., 5 (7), 518–527. [36] Thiemann, S., Csernansky, J.G. and Berger, P.A. (1987) Rating scales in research: the case of negative symptoms. Psychiatry Res., 20, 47–55. [37] Kraemer, H.C. (2008) Toward non-parametric and clinically meaningful moderators and mediators. Stat. Med., 27, 1679–1692. [38] Last, J.M. (1995) A Dictionary of Epidemiology, Oxford University Press, New York. [39] Kraemer, H.C. and Blasey, C. (2004) Centring in regression analysis: a strategy to prevent errors in statistical inference. Int. J. Methods Psychiatr. Res., 13 (3), 141–151. [40] Hand, D.J. (1979) Psychiatric examples of Simpson’s paradox. Br. J. Psychiatry, 135, 90–96. [41] Kraemer, H.C. (1978) Individual and ecological correlation in a general context: investigation of testosterone and orgasmic frequency in the human male. Behav. Sci., 23, 67–72. [42] Samuels, M.L. (1951) Simpson’s Paradox and related phenomena. J. Am. Stat. Assoc., 88, 81–88. [43] Wagner, C.H. (1982) Simpson’s paradox in real life. Am. Stat., 36, 46–48.

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Validity: Definitions and applications to psychiatric research Jill M. Goldstein,1,2 Sara Cherkerzian1,2 and John C. Simpson3 1 Departments of Psychiatry and Medicine at Brigham and Women’s Hospital (BWH), Harvard Medical School, Boston, MA, USA 2 Connors Center for Women’s Health and Gender Biology, Department of Medicine, Brigham & Women’s Hospital, Boston, MA, USA 3 Department of Psychiatry at VA Boston Healthcare System, Harvard Medical School, Boston, MA, USA

7.1 Introduction Measurement is a process of linking unobservable theoretical concepts to empirical indicators [1]. There are two basic properties of measurement that ensure the strength of this linkage: reliability and validity. In this chapter, we discuss the concept and usage of validity. Reliability was discussed fully in a previous chapter, but, for convenience, we define it here simply as the reproducibility of an empirical measure (e.g. internal consistency of the items in a scale, reproducibility of a measurement on different occasions or agreement between raters). For an empirical indicator to be valid it must first be reliable, but indicators can be reliable without also being valid. There are a number of ways to assess validity, not all of which are used for every measure of interest. In fact, validity has a number of meanings in different contexts and is perhaps one of the most overused words in the scientific literature. In this chapter, we discuss validity as it applies to the measurement of a construct, that is the process of ‘construct validity’. We also discuss validity as it applies to relationships between constructs, that is to the ‘internal

validity’ and ‘external validity’ of a presumed causal relationship. We provide examples of how validity is applied and statistically evaluated in psychiatric research. Finally, we discuss the future of the process of validating psychiatric disorders, given new genetic and brain imaging technologies that will allow for new aetiological discoveries to be incorporated into our concepts of how we define psychiatric disorders.

7.2 Validity of a construct An essential feature of scientific research is often the measurement of abstract concepts and relationships between abstract concepts. Validity can be defined as the extent to which an empirical indicator of a concept actually represents the concept of interest [2–4]. For example, if one used a particular symptom checklist to measure ‘major depressive disorder’ (MDD), validity asks the question, how accurate is this empirical indicator for diagnosing MDD? Thus, validity refers to the questions ‘for what purpose is the indicator being used?’ (e.g. to diagnose MDD) and ‘how accurate is it for that purpose?’ In fact, an indicator

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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(e.g. an instrument such as a test, a rating or an interview) can be valid for one purpose, but not for another [5]. Thus, one validates the instrument in relation to its intended purpose [1, 4, 5]. If an instrument is to be scientifically useful, it must be both reliable (i.e. result in consistent findings over repeated measurements) and valid (i.e. represent the concept it is intended to represent). Unlike reliability, validity is an unending process [4] in which one attempts to capture the essence of the concept of interest as accurately as possible. It therefore involves a theoretical understanding of the concept of interest in order to measure it accurately. It also involves an assessment of the empirical relationships between an instrument and criteria chosen to evaluate whether the instrument assesses what it is intended to assess. There are three basic ways in which validity is assessed: content validity, criterion validity and construct validity (see Table 7.1).

7.2.1 Content validity For every abstract concept, there is a universe of items that one might sample in order to measure the concept operationally. Content validity involves the adequacy with which one samples the domain of items [4]. Content validity is ensured by the procedures used to construct items for a test [4]. One must first specify the universe of items that one hypothesises will accurately measure the concept of interest. Second, items are then sampled from this domain. If certain kinds of items are central to understanding the concept, one may decide to oversample these types. Finally, selected items are put into a testable form [1]. For example, if one were interested in measuring (diagnosing) ‘schizophrenia’, one would choose, among other things, items such as bizarre delusions or other types of delusions, various kinds of hallucinations, formal thought disorder and flat affect. An instrument would then be constructed in order to assess these items. Different types of diagnostic instruments have been constructed that are based on certain assumptions about how to acquire accurate assessments of the items. For example, the Diagnostic Interview Schedule (DIS) [6] was designed to allow lay interviewers to assess symptom items in a dichotomous form, that is 100

as present or absent, and was wholly dependent on the patient’s response to each item. That is, there was an assumption that clinical judgement was unnecessary to assess symptomatology. In contrast, the Schedule for Affective Disorders and Schizophrenia (SADS) [7] was designed to allow for clinical questioning to assess symptom items. Clinical/diagnostic knowledge was required in order to use the SADS instrument. In addition, ratings of SADS items consisted of a severity scale rather than present versus absent, as in the DIS. As one can see, these two instruments are based on different assumptions regarding how to assess a similar domain of symptom items. One can then assess the content validity of these two approaches, even though the evaluation of content validity alone would provide an incomplete assessment of the validity of these instruments. There are two standards by which content validity is assessed: the representativeness of the collection of items chosen and the type of test construction used to measure the concept. There are, however, no statistical means of assessing content validity. Essentially, content validity is dependent on appeals to reason regarding the accuracy of the content sampled, or a consensus among experts, and the adequacy with which the items are put into a testable form [2, 4].

7.2.2 Examples of assessment of content validity Streiner [8] recommended the use of a ‘content validity matrix’ as a means of ensuring that items in a scale are appropriately tapping the intended domains. In such a matrix, each column represents a distinct domain within the general domain of interest, and each row represents a single item. As a means of improving reliability, each domain is represented by several items (i.e. in terms of the content validity matrix, each column should have check marks in several rows). On the other hand, to minimise ambiguity of interpretation, each item should tap only one domain (i.e. each row should have only a single check mark). As an example of the relevance of domains and items to content validity, we can make use of a study by Schwartz et al. [9] who devised the Social Adjustment Interview Schedule to investigate outcome in schizophrenia. Although this study was from the mid

VALIDITY: DEFINITIONS AND APPLICATIONS TO PSYCHIATRIC RESEARCH Table 7.1 Three methods to assess validity: content, criterion and construct validity. Method

Description

Measurement

Key differences between measures

Content

Content validity: accuracy with which one samples the domain of items to measure the concept of interest operationally

Three steps: • Specify the universe of items • Sample items from the domain • Establish testable form

No statistical means of assessing

For a categorical criterion a qualitative rating is evaluated using methods such as sensitivity, specificity, and receiver operating characteristic (ROC) analysis Empirical relationship between the instrument under study and the criterion is statistically estimated by a correlation if continuous data are used The strength of the correlation often interpreted as strength of the measure’s validity

Dependent on empirical results

Three steps: • Understand theoretical relationships between related concepts • Estimate empirical relationships between operational measures • Interpret empirical evidence within theoretical context • Relate findings from other studies for coherence and consistency

Content and criterion validity used alone are limited in contributing to understanding the relationship between the theoretical (unobserved) concept and the empirical measure used to indicate it

Two standards: • Representativeness of the collection of chosen items • Adequacy of the testable form or construction used to measure the concept Criterion

Criterion, or predictive, validity measures that which is external to the measurement of the concept itself, the criterion Four forms: • Post-dictive • Concurrent • Prospective • Discriminant Post-dictive validity: correlates events/behaviours that occurred in the past Concurrent validity: correlates a measure and some criterion at the same point in time Prospective validity: correlates a measure with future criteria Discriminant validity: assesses whether the measure of interest is uncorrelated with expected events or behaviours, that is, specificity

Construct

Construct validity: extent to which one’s measure is related to other theoretically related and measured concepts

Content and criterion validity: part of the process of assessing construct validity

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1970s, it illustrates an important point with regard to assessing content validity and is still relevant today with regard to domains of social adjustment. Within the general domain of social adjustment, the authors conceptually identified eight role areas (i.e. domains) and devised multiple questions (i.e. items) within each role area to address performance and subjective feelings. The different domains included work role (18 items), household role (15 items), marital role (nine items) and social and leisure roles (54 items). Typical items within the work domain included the questions ‘Are you employed now?’ and ‘Are you confident about your ability to do the job?’ Within the marital domain, typical items included ‘In general, how do you and your spouse get along?’ and ‘Have you been able to talk about feelings and problems with your spouse recently?’ There would probably be little disagreement about content validity in this example. In other words, most would agree that these four questions comprise two sets of items, that the first two items are related to work roles, whereas the latter two concern marital roles, and furthermore that there is little if any overlap between the content of these specific items. Not all applications of content validity will be as straightforward, particularly if the concepts being measured are abstract, that is not directly observable. For example, Cloninger [10] devised an 80-item self-report inventory called the Tridimensional Personality Questionnaire (TPQ) to investigate three hypothesised dimensions of personality: harm avoidance, novelty seeking and reward dependence (an instrument that is still used today). Cloninger’s approach to content validity is apparent in his description of how the items were devised (p. 580): ‘To quantify behavioural variation on each dimension separately, questions were specified that were theoretically expected to involve minimal interaction among the dimensions. In practice, this meant that questions were chosen to evaluate the behaviours that were thought to be characteristic of individuals deviant on one dimension and average on the others’. As evidence that this standard was achieved, Cloninger reported that the intercorrelations among the three major TPQ scales (calculated using the Pearson product-moment correlation coefficient) were ‘negligible or weak’ and low relative to the reported index of internal consistency (Cronbach’s 102

α coefficient; see Chapter 5 for a discussion in the context of reliability). However, the interpretation of these results is complicated because weak intercorrelations were expected in some cases for theoretical reasons (e.g. a weak negative correlation between novelty seeking and harm avoidance). A somewhat different perspective was presented by Takeuchi et al. [11], who translated the TPQ into Japanese and replicated [10] study using a large sample of Japanese university students. Like [10, 11] reported negligible or weak intercorrelations between the three major scales. However, they also reported results from a factor analysis that were not completely consistent with the theoretical model. Factor analysis is a multivariate statistical procedure that is used to explain covariation among a set of observed variables in terms of a reduced number of unobserved, latent variables; for example see [12], for an introductory explanation. Within the framework of Streiner’s content validity matrix [8], for example, if each derived factor was considered to define a separate domain (i.e. column) in the matrix, then the harm avoidance, novelty seeking and reward dependence items should have loaded on different factors. While this was by and large the result for harm avoidance and reward dependence, ‘the novelty seeking scale showed a scattering factor structure, with several equivocal items loaded on two or more factors; reduction or reorganisation of items might be required here’ [11, p. 277]. On the other hand, all reported items had factor loadings above the cutoff of 0.4 on only one of the six factors, and this was consistent with the use of Streiner’s ideal content validity matrix (1993) of only one check mark per item in the matrix.

7.2.3 Criterion validity The second type of validity is referred to as criterion validity (or predictive validity). It is concerned with measuring something that is external to the measurement of the concept itself, called the criterion [2, 4]. For example, one dimension of predictive criterion validity for psychiatric diagnoses is to relate them to predictions of outcome. (Examples of this are discussed in detail later in this chapter.) Unlike content validity, which essentially depends on a consensus among experts, predictive validity is dependent on

VALIDITY: DEFINITIONS AND APPLICATIONS TO PSYCHIATRIC RESEARCH

empirical results. Predictive validity refers to the empirical relationships between the instrument under study and external events or behaviours that can occur at three points in time: before, during or after the instrument is used. In many studies, the empirical relationship is statistically estimated by a correlation if continuous data are used. Post-dictive validity refers to correlating events/ behaviours that have occurred in the past with the instrument one is presently using. These assessments are referred to as retrospective. For example, one might have a specific prediction about the early developmental history of patients, with a particular diagnosis that is being currently assessed with an instrument. Post-dictive validity entails correlating early history information with the diagnostic assessment currently obtained using the instrument under study. Concurrent validity refers to correlating a measure and some criterion at the same point in time. This involves what are known as cross-sectional assessments. Thus, for example if there were a laboratory test for diagnosing MDD, one could correlate the instrument used to diagnose the disorder with a laboratory test taken when the patient was interviewed. The form of predictive validity most commonly referred to correlates a measure with a criterion that is assessed at some future point in time. This form of validity entails prospective assessments. A common use of predictive validity in psychiatry is to assess outcomes of a specific diagnostic group under study, under the assumption that certain diagnostic groups have worse or better outcomes than others (see examples below). A fourth form of criterion validity is referred to as discriminant validity. Discriminant validity assesses whether certain external criteria (i.e. events or behaviours) are uncorrelated with the measure of interest compared with other criteria that are hypothesised to be related to the measure of interest. That is, is the measure of interest uncorrelated with events or behaviour with which one expects it would be independent? This has also been referred to as assessing the specificity of the relationship between the measure of the concept of interest and the external criteria chosen to relate to the concept. It is important to mention here that criterion validity is often assessed using correlations (when continuous data are involved). The strength of the

correlation is often interpreted as the strength of the validity of the measure. However, the strength of the correlation depends not only on the variability and other characteristics of the measure of interest, including its reliability, but also on the choice, measurement and reliability of the criterion.

7.2.4 Examples of criterion validity For examples of applications of criterion validity, we turn to two studies in the psychiatric literature from the 1990s. The first study [13] provides a fairly typical example of the use of correlational techniques. At issue was whether a self-report instrument can be used in populations of patients with schizophrenia to obtain valid ratings of depression. To examine this question, the authors compared self-report ratings obtained using the Beck Depression Inventory (BDI) with ratings of the Calgary Depression Scale (CDS), a semistructured interview designed to assess depression in schizophrenia patients. In this study, the CDS is the criterion because it makes use of informed judgements by trained clinicians, which form the current ‘gold standard’ for identifying depression in clinical populations. BDI and CDS scores were compared by calculating the Pearson product-moment correlation coefficient (e.g. see [14]), after creating scatterplots to examine the joint distribution of BDI and CDS scores as well as identifying any outliers. The latter step was essential because the presence of even a single outlier (i.e. an extreme and atypical value) could easily distort the product-moment correlation (e.g. see [15]). Another important methodologic step employed by Addington et al. [13] was to compare correlations between the BDI and CDS in clinically distinct subgroups of patients with schizophrenia: inpatients vs. outpatients, and (within these subgroups) patients who either did or did not require assistance in completing the self-report instrument. In this particular study, the correlation between the BDI and CDS was stronger among inpatients than outpatients, regardless of whether the patients required assistance (r = 0.84 vs. r = 0.96). However, the substantially greater percentage of inpatients requiring assistance (34% of inpatients vs. 12% of the outpatients) led the authors to conclude that ‘depressed affect can be assessed in patients with 103

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schizophrenia by both self-report and structured interview, but the BDI poses difficulties with use with inpatients’ [13, p. 561]. For our purposes, however, the substantive findings of this study were less important than the fact that this study admirably illustrated the critical importance of selecting and describing validation samples that are clinically meaningful in the context of the measurement instrument of interest [8]. In particular, users of such instruments need to be aware that published validation studies might have used ‘samples of convenience’ (e.g. university students) that do not approximate the clinical population the user has in mind and that the results of such studies do not necessarily generalise to other samples. Our second example of criterion validity in psychiatric research [16] also illustrates the critical importance of the validation sample. In this study, the validity of using a questionnaire (the Center for Epidemiologic Studies Depression Scale or CES-D) [17] as a case identification tool in studies of mood disorders among Native Americans was investigated. CES-D scores were compared with DSM-III-R diagnoses [18] based on a structured psychiatric interview (the Lifetime Version of the SADS [7]). The authors had concerns about the cross-cultural applicability not only of the screening instrument but also of the criterion itself (e.g. DSM-III-R diagnoses of affective disorders). For purposes of the study, however, it was assumed that DSM-III-R diagnoses would be relevant among Native Americans. Although the CES-D, like the BDI in the above example, yields a numerical score, its proposed use as a screening instrument for depression was for the purpose of identifying not the degree of depression, but the presence of a particular clinical syndrome, namely, DSM-III-R major depression. The criterion was therefore a categorical (i.e. qualitative) rating rather than a numerical (i.e. quantitative) rating, making it inappropriate to use correlational procedures. Instead, to evaluate the validity of the instrument for case identification, the authors employed statistical methods that have been expressly developed for qualitative data, including sensitivity, specificity and receiver operating characteristic (ROC) analysis. Sensitivity and specificity are both calculated using data that have been summarised in a 2 × 2 table of 104

Table 7.2 Schematic representation of the calculation of indices of criterion validity and predictive valuea. Criterion (gold standard)b

Test result

Positive Negative Total

Present

Absent

Total

a c a+c

b d b+d

a+b c+d N

a a,

b, c, d and N are frequencies (e.g. numbers of persons rated). b The Gold Standard is assumed too represent the ‘true’ value and thus to be free of error.

frequencies (see Tables 7.2 and 7.3 for definitions and computational formulas). In the example at hand, a 2 × 2 table was used to cross-classify the numbers of screened persons with and without the criterion (e.g. a DSM-III-R diagnosis of major depression) who either did or did not score above the cut-off for depression in the screening instrument, the CES-D. (ROC analysis was used to determine the optimal cut-off value for the CES-D.) As an illustrative finding, the sensitivity for DSM-III-R major depression was 100% (i.e. all three persons in the sample with a diagnosis of major depression scored above the cut-off on the CES-D). The corresponding value of specificity was 82% (i.e. 82% of those persons in the sample who did not have diagnoses of major depression scored below the CES-D cut-off for depression). It follows directly from the reported specificity value of 82% that 18% (100−82%) of the persons in the sample with no psychiatric diagnoses or with DSM-III-R diagnoses other than major depression scored above the CES-D cut-off and would have been classified as depressed by that screening instrument. Whether or not this degree of misclassification error (or invalidity) is considered to be an unacceptably high ‘false-positive rate’ depends on the proposed use of the instrument and on the comparable ‘operating characteristics’ of alternative instruments. For example, a higher CES-D cut-off value could be expected to decrease the false-positive rate (via increased specificity), but at the expense of sensitivity. In this particular study, a higher CES-D cut-off actually increased specificity without decreasing sensitivity, but this was probably attributable to the small number of cases with DSM-III-R diagnoses of

VALIDITY: DEFINITIONS AND APPLICATIONS TO PSYCHIATRIC RESEARCH Table 7.3 Statistical indices for evaluating qualitative data in the assessment of validity. Term

Definition

Formulaa

Concepts

Sensitivity

Proportion of those a test correctly identifies as having the disease (or characteristic) of interest

a/(a + c)

Sensitivity and specificity of a test are theoretically independent of disease/exposure prevalence as both conditioned on the bottom, or ‘true’, totals of Table 7.2

Specificity

Proportion of those a test correctly identifies as not having the disease (or characteristic) of interest Probability of misclassifying a true positive as a negative Probability of misclassifying a true negative as a positive Proportion of true positives among individuals who test positive

d/(b + d)

False negative rate False positive rate Positive predictive value (PPV)

Negative predictive value (NPV) Prevalence a Refer

Proportion of true negatives among individuals who test negative Proportion of true positives in the population

1 − (a/(a + c)) or 1 − sensitivity 1 − (d/(b + d)) or 1 − specificity a/(a + b)

In addition to the dependence of the PPV and NPV on the sensitivity/specificity of a test, they are also a function of the disease/exposure prevalence

d/(c + d)

(a + c)/N

to Table 7.2.

major depression. In most studies there is a systematic trade-off between sensitivity and specificity, and for that reason both of these indices of criterion validity must be considered together in determining whether a particular instrument is more valid than the available alternatives. ROC analysis provides a useful framework for making such comparisons (e.g. see [19]). In the present example, the non-negligible false-positive rate was consistent with the investigators’ concerns (based on previous research by a number of researchers using other samples) that the CES-D might be reflecting symptoms of not only major depression but also increased levels of anxiety, demoralisation or even physical ill health [16]. The study by Somervell et al. [16] also illustrates the difference between criterion validity and the related, but nevertheless distinct, concept of predictive value. Positive predictive value is literally the predictive value of a positive rating, that is the probability of having the criterion of interest given a positive rating on the instrument under investigation.

(Formulas for calculating positive predictive value, and the related index, negative predictive value, are given in Table 7.3.) Since the criterion (e.g. DSMIII-R major depression) is frequently of more direct clinical importance than the rating (e.g. a particular CES-D score), positive and negative predictive values are often more clinically meaningful than sensitivity and specificity. For example, most clinicians would probably be more interested in the usefulness of the CES-D for predicting major depression than the other way around. However, positive predictive value is a joint function of sensitivity, specificity and prevalence, such that low prevalence values can severely constrain the values of positive predictive value that can be realistically attained, even with very high sensitivity and specificity values [20, 21]. (Negative predictive value is similarly constrained by high prevalence values.) In the study by Somervell et al. [16], the prevalence of major depression can be estimated from the rate of major depression in the sample as 3/120 = 0.025. 105

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Using a cut-off value of 16 on the CES-D, the reported specificity value of 82.1% therefore corresponds to a positive predictive value of 0.125. In other words, even though sensitivity was perfect (100%) and specificity was very high, only one of every eight persons who scored above the CES-D cut-off of 16 would be expected actually to have major depression. Even increasing the CES-D cutoff to improve specificity would not dramatically change this result. Again, this is due to the constraint imposed by the low estimated prevalence of major depression in the study population. (With the CES-D cut-off set at 28, the reported specificity value of 96.6% corresponds to a positive predictive value of 0.429.) In conclusion, this example shows that even though an instrument may have excellent criterion validity as assessed using standard indices (namely, sensitivity and specificity), the actual predictive value of the instrument could be much more limited, depending on the prevalence of the disorder of interest, which in turn may vary with the composition of the validation sample.

within the theoretical context in which the concept of interest is embedded. In addition, findings from other studies must be related to one’s current findings regarding the measure and the concept it is intended to indicate. The theoretical context allows one to make theoretical predictions that then lead to empirical tests using the operational measure of the concept of interest. One study cannot wholly validate a measure of a concept. Construct validity requires a pattern of consistent findings across studies involving different samples and different settings. Cronbach and Meehl [2] refer to the theoretical context as the nomologic network. The use of the nomologic network requires relating theoretical constructs to each other, theoretical constructs to empirical indicators, and empirical indicators to each other. The construct is not reduced to the empirical indicators; it is combined with other constructs in the nomological net that allow for predictions using the empirical indicators [2, p. 290]. An ideal example of Cronbach and Meehl’s 1955 [2] framework for assessing construct validity is how we measure and ultimately understand psychiatric diagnoses.

7.2.5 Construct validity Of the three basic types of validity, construct validity involves the most complex process. Content validity and criterion validity used alone are limited in contributing to understanding the relationship between the theoretical (unobserved) concept and the empirical measure used to indicate it. In fact, content and criterion validity are considered part of the process of assessing construct validity. As first pointed out by Cronbach and Meehl [2], construct validity is essential for all abstract concepts, since there is no criterion or entire content of a domain that is wholly adequate to define the concept of interest. Construct validity is thus defined in a theoretical context. It is the extent to which one’s measure of interest is related to other theoretically related concepts that are also measured [4]. There are three steps to assessing construct validity [1]. First, one must have an understanding of the theoretical relationships between related concepts. Second, one must estimate the empirical relationships between operational measures of these concepts. Finally, the empirical evidence must be interpreted

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7.2.6 Application of construct validity to psychiatric diagnosis In psychiatry, there are no known laboratory tests for wholly identifying a psychiatric case. Thus, in 1972, Robins and Guze established five criteria that became standards for validating a diagnosis. The first criterion of Robins and Guze [22] consisted of establishing the clinical description of the disorder. This involved specifying the phenomenology or symptomatology, premorbid history, age at onset, sociodemographic distribution and precipitating factors. The clinical description criterion thus involves issues of content validity. For example, what is the domain of symptoms chosen to represent the diagnosis? ‘On the face of it’, do these symptoms reasonably represent the domain of interest? Furthermore, how would one construct an instrument to assess these symptoms? The clinical description criterion also involves criterion validity. For example, post-dictive validity would be relating premorbid history, age at onset or precipitating factors to the empirical measure of the diagnosis.

VALIDITY: DEFINITIONS AND APPLICATIONS TO PSYCHIATRIC RESEARCH

The second Robins and Guze criterion referred to the relationship of the diagnostic measure to laboratory tests. As mentioned earlier, this is a form of concurrent validity. Laboratory tests could include chemical, physiological, neuropathological, genetic, brain imaging and/or psychological tests. In psychiatry, however, at present there are no laboratory ‘gold standards’ for validating diagnoses. The third criterion involved the use of family history to contribute to validation (in the era prior to the discovery of the genome). The assumption behind the use of family history was that many psychiatric disorders run in families. Thus, an increased prevalence of the same disorder in family members could be used as an indicator that the diagnosis was a valid entity. Family history can be thought of as a concurrent validator (in reference to ill relatives who are currently alive) or as a postdictive validator (in reference to relatives who were ill but who are now deceased). (Incorporation of genetic information in the molecular genetics era today is discussed in Section 7.2.7.) The fourth criterion, commonly thought of as predictive validity in psychiatric research, related the diagnosis of interest to outcomes, including treatment response. The assumption behind using this criterion was that individuals with the same diagnosis will have similar outcomes. Furthermore, it is sometimes assumed that certain diagnostic groups have particularly poor or good outcomes compared with other diagnostic groups. However, the use of outcome as a validating criterion is problematic because many psychiatric disorders have heterogeneous outcomes. This validating criterion will remain controversial unless more definitive knowledge regarding the specific outcomes of diagnostic groups can be elaborated. The final criterion for validating a diagnosis involved assessing the specificity of the other criteria for a particular diagnosis. This can be referred to as discriminant validity. Although different diagnoses may share, for example, certain symptoms, laboratory test results or outcomes, it is the role of discriminant validity to specify how a particular disorder is differentiated from other disorders. If it cannot be differentiated from other disorders, this becomes support for rejecting the validity of this particular diagnosis as a separate entity.

7.2.7 The future of validating psychiatric disorders: Towards DSM-5 and beyond The current American classification schema (the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, Text Revision (DSM-IV), American Psychiatric Association [23]) as it was originally conceived, has an uncertain future given that it was derived from clinical consensus to address primarily the need for diagnostic reliability rather than validity [24], and has not yet been reoriented towards state-of-the-art investigations of the aetiology of psychiatric disorders. The shift in perspective to inclusion of aetiologic information is, in part, the result of a disappointing lack of identifying unequivocal, consistently replicated susceptibility genes for these disorders (as currently defined in the DSM), despite high heritability estimates and the development of powerful research tools such as genome-wide association studies [25, 26]. This may not be surprising given that the organisation of the DSM classification is not based on the pathogenesis of disorders, but rather on operationalised sets of categorical criteria based on signs and symptoms from clinical observation and research [24, 27, 28]. As pointed out by Steven Hyman, Without genotypes, objective tests, clues to pathogenesis and even adequate family and longitudinal studies, it was not possible to establish a true empirical base for valid diagnoses in DSM-III, DSM-II-R, DSM-IV and DSM-IV-TR [24, p. xiii]. Furthermore, Hyman [24] and other investigators have voiced concerns over how the DSM defines the thresholds for categorising disorders by delineating arbitrary cut-off points for normally distributed variables, such as behaviour traits, and for continuous measures such as severity and chronicity of the illness. Hyman [29] notes that many patients do not fit precisely into these categories and hence the DSM has relied extensively on the catch-all term ‘not otherwise specified (NOS)’. In fact, the NOS diagnoses are more commonly used than a number of the specifically named disorders. The DSM, now

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under revision, is expected to address the validity of the diagnostic system by including experimental criterion sets aimed at incorporating new genetic and neurobiological findings in its fifth edition [29]. This is a shift in approach from the use of categories based on clinical syndromes and levels of functioning [30] to biologically valid phenotypes that can potentially address questions concerning illness aetiology and clinical treatment. In general, psychiatric disorders are likely to be characterised by complex multifactorial and polygenetic aetiologies marked by the interaction of numerous genes with each other and a wide range of environmental risk factors, resulting in varying phenotypic expression from normal to clinical relevance [31]. Although we know there are high heritability estimates for many psychiatric disorders, single genes with sufficiently large effects are not likely to generate most disease phenotypes. Instead, the genetic contribution to psychiatric disorders can be viewed as the combined effect of a number of different genes, each with a small or moderate effect on disease liability [25]. Individually, each gene may have only a slight effect on the phenotype such that close relatives may share several susceptibility variants, although one relative may develop the disorder and another may not [25]. Environmental factors also play a significant role in the aetiology of psychiatric disorders, for example as epigenetic factors (i.e. exogenous exposures that influence the expression of genes). As illustrated by research findings on schizophrenia and MDD, environmental influences have included early foetal or neonatal events, such as exposure to obstetric complications [32–35], viruses [36–39], poor nutrition [40, 41] social conditions such as living in urban compared with rural regions [42, 43], and migration [44–47]. One example of environmental factors that has influenced the expression of genetic polymorphisms is the increased risk for schizophrenia in individuals who both smoke cannabis and have a functional polymorphism in the catecholO-methyltransferase (COMT) gene, a gene responsible for metabolism of dopamine [48]. Another example is the role of the serotonin transporter gene-linked polymorphic region (5-HTTLPR, 5-HTT (5-hydroxytryptamine transporter) gene-linked polymorphic region), which appears to increase the risk 108

for MDD only among those carrying the short ‘s’ allele and in the context of stressful life events, such as early childhood trauma [49]. Given the complexity of finding genetic causes of psychiatric disorders per se, there has been a surge of research focused on using intermediate phenotypes or traits in genetic modelling of disorders, called endophenotypes [50]. Endophenotypes are quantitative or continuous traits found more commonly in psychiatrically ill individuals and their unaffected family members (i.e. family members not meeting the same psychiatric diagnostic criteria) than in the healthy population. Endophenotypes are hypothesised to underlie or precede disease onset or the expression of a clinical phenotype (i.e. as measured on a continuum of the aetiologic pathway to the clinical phenotype), and are assumed to be strongly associated with the expression of genes that underlie the disorder [30, 50, 51]. The rationale behind using endophenotypes in molecular genetics research is that (i) Traits represent more elementary phenomena of decreased complexity than the clinical phenotype and thus will likely have stronger associations with specific functions of genes and hence be more genetically informative (i.e. the phenotype will segregate with the susceptibility locus) [26, 30], and (ii) by including unaffected family members, endophenotypes may afford the investigator greater power to detect linkage than a categorical diagnostic approach [26]. While the endophenotype approach is now widely used, the identification of quantitative endophenotypic traits of the clinical phenotypes remains controversial [52], given that it is still unclear how informative they are in contrast to the DSM categories [26]. Thus criteria for evaluating the validity and utility of endophenotypic markers for research in psychiatric genetics have been proposed by several investigative teams, including Gottesman and Gould [30], Skuse [53], Doyle et al. [54] and Waldman [55]. Based on the guidelines set forth in the psychiatric literature, Bearden and Freimer [51], have proposed a set of criteria viewed as both necessary and sufficient. 1 Endophenotypes should be familial, with at least moderate heritability, and should be detectable in those with the mental illness associated with the phenotype as well as in unaffected family members.

VALIDITY: DEFINITIONS AND APPLICATIONS TO PSYCHIATRIC RESEARCH

2 Endophenotypes should be part of the casual chain in the relationship between genes and the DSM diagnosis rather than an effect or sequelae of the disorder. 3 Endophenotypes should be reliable (internal consistency), and have test–retest reliability (at least within a particular clinical state, and preferably across clinical states in illnesses with an episodic pattern), sound psychometric properties (e.g. can discriminate across a broad range of individual differences) and good concurrent validity (convergent and divergent validity) with respect to hypothesised endophenotypes. 4 Endophenotypic traits should exhibit a continuous distribution (ideally, normally distributed) within the general population. 5 The endophenotype should be associated with an increased risk for a particular DSM diagnosis. Illness-specificity is desirable but not required. The authors also add that an ‘optimally informative candidate endophenotype should: (i) relate to reasonably well-characterised neural systems models, and (ii) involve homologies of expression across species (to enable development of animal models).’ [51, p. 309] The use of quantitative endophenotypic traits for understanding the genetic nature of psychiatric disorders fits well with the fact that there is a high comorbidity of psychiatric illnesses not only among psychiatric illnesses but with general medical disorders [56, 57]. In fact, only 10–20% of lifetime diagnoses are single disorders [58]. For example, Tsuang and et al. [59] point out that many genetic studies in the late 1990s failed to show linkage to schizophrenia based on a DSM diagnosis of schizophrenia alone, but found stronger linkage when the phenotype was broadened to include additional psychotic disorders (e.g. [60, 61]). As another example, chromosome 13q [62–64], 4p [65], 22q [62, 63] and 18p [62, 66] have been implicated as promising genomic regions for schizophrenia and bipolar disorder. Thus, for an understanding of the nature of schizophrenia and other psychotic disorders, these examples illustrate the relevance of traits that are shared across psychotic disorders and must be distinguished from the traits that may be specific for the disorders themselves.

In psychiatry, genetic studies using endophenotypes have been met with moderate success, including the development of animal models based on these traits [30, 67]. Endophenotypes in psychiatry have been described for several disorders including schizophrenia, mood disorders, Alzheimer’s disease and personality disorders. Schizotaxia, for example, is a clinical condition that indicates a predisposition or liability, to schizophrenia. A concept first termed by Meehl in 1962 [68] and which has subsequently been reformulated to reflect current research [69], schizotaxia is a more subtle brain disorder than schizophrenia, marked by negative symptoms and neuropsychologic impairment. A number of non-psychotic, first-degree relatives of persons with schizophrenia exhibit clinical and neurobiological abnormalities that are also manifest in patients with schizophrenia [70]. Family studies indicate that schizotaxia is present in about 20–50% of non-psychotic adult relatives of persons with schizophrenia [71, 72], with about 10% of relatives developing psychosis and another 10% developing schizotypal personality disorder [73]. Schizotaxia may well express the aetiologic mechanisms that underpin schizophrenia more clearly than the clinical symptoms of the disorder [70]. For example, Tsuang et al. [69, 74] conducted a validation study of schizotaxia based on the treatment of nonpsychotic, adult first-degree relatives of patients with schizophrenia using the antipsychotic medication risperidone, a drug which has been found to ameliorate negative symptoms and neuropsychologic abnormalities in persons with schizophrenia. The authors hypothesised that if schizotaxia is biologically related to schizophrenia, the negative symptoms and neuropsychologic abnormalities in the schizotaxic relatives would improve with risperidone treatment. In this study, all study subjects exhibited moderate levels of negative symptoms and neuropsychological deficits at baseline, and after a 6-week, open-label course of risperidone, these symptoms and cognitive deficits improved in five of the six relatives supporting the authors’ hypothesis in terms of predictive validity. Though the findings were only preliminary, they suggested common aetiologic elements between the two disorders [69]. The authors later published a study of the concurrent validity of schizotaxia in a group of 27 adult first-degree 109

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relatives of patients with schizophrenia [75]. Of these subjects, eight individuals met criteria for schizotaxia and were compared with 19 control subjects who were free of DSM-IV psychiatric diagnoses. The authors found that in contrast to those without schizotaxia, the schizotaxia group exhibited significantly lower levels of functioning and had a lifetime substance abuse diagnosis rate (50%) similar to that among persons with schizophrenia. Findings in this study provided further validation of schizotaxia as a psychiatrically relevant, familially-related condition closely associated with and aetiologically related to schizophrenia. In summary, new approaches to validating psychiatric diagnoses are being developed to incorporate aetiologic information with regard to the psychiatric disorder rather than relying on symptom and functioning information alone. The focus here on endophenotypes illustrates this trend and reflects the expressed need to identify characteristics that are likely related to specific genetic traits and other biomarkers for psychiatric illnesses. However, the search for traits associated with the underlying biomarkers for the illness is still in its infancy. Given the new genetic methodologies and biomedical imaging technologies, there is a realistic hope that future classification systems beyond DSM-5 will include specific biomarkers underlying these illnesses that may help tailor specific treatments to affected individuals in a reliable and valid manner.

7.3 Validity of the relationships between variables We now turn to another use of the term validity in psychiatric as well as other fields of research that refers to the ‘internal and external validity’ of a study. Internal and external validity are essential properties of how we assess empirical research and thus are important to discuss here in this chapter. Internal and external validity are discussed thoroughly by Cook and Campbell [76] in relation to quasiexperimental design studies. They are also discussed in basic textbooks on epidemiology [77, 78]. Internal validity refers to the extent to which a relationship found to be statistically significant is a causal relationship. Internal validity is an empirical 110

issue. That is, do the empirical measures used to assess concepts of interest relate to each other in a causal way? It is also a theoretical issue in that the presumed causal association between variables must be coherent with other empirical evidence and theory. In epidemiology, there are five ‘criteria of judgement’ that are used to aid in establishing a causal relationship [78]: (i) the temporal (time) sequence of variables, (ii) the consistency of associations on replication, (iii) the strength of the association, (iv) the specificity of association and (v) the coherency of the explanation of the association. The time sequence refers to the temporal order of the variables of interest. The consistency of the association refers to its reliability. The strength of the association is measured empirically using relative risk, correlational or nonparametric statistics. Specificity refers to what we previously discussed as discriminant validity. Finally, the coherence criterion refers to a more theoretical question of whether the explanation of the association between the variables of interest ‘fits’ with pre-existing theory and evidence. These five criteria are then used to make judgements regarding whether the empirical association between variables has internal validity or causal plausibility. The causal plausibility of a relationship may in part be dependent on the type of study design used to assess one’s variables of interest. In a controlled experimental study, one may specifically manipulate the time order of variables and experimentally control for confounding factors that may be threats to internal invalidity. However, many epidemiologic studies are not experimental, but rather are observational and what has been called quasi-experimental [76]. In these types of studies, it may be more difficult to establish the internal validity of the relationship between variables. There are a number of threats to internal validity that may arise in using non-experimental designs. They are discussed in detail by Cook and Campbell [76, p. 5159] and briefly described here. Suppose that in a treatment study one found that treatment ‘a’ was significantly better for a specific diagnostic group than treatment ‘b’, as measured by pre- and post-treatment measurements of symptomatology. However, suppose there was no random assignment to treatment; thus the study was not an experimental design. The following threats to the

VALIDITY: DEFINITIONS AND APPLICATIONS TO PSYCHIATRIC RESEARCH

‘internal validity’ of the effect of treatment ‘a’ may be operating and should be addressed. In general, threats to internal validity have to do with the possibility of differential effects of events on the treatment versus the control groups that are not due to the treatment of interest per se (see Table 7.4). History effects refer to the influence of events outside of the control of the study that may differentially affect the outcomes of the groups being studied but have little relationship to the treatment of interest. Maturation involves the differential development of participants in each group that is not due to treatment effects. Testing and instrumentation effects refer, respectively, to the number of times a test is given resulting in differential learning effects and changes in instrumentation over time that differentially affects one’s groups unrelated to treatment effects. Statistical regression artefacts are especially difficult for which to control. They can occur if the groups at pretreatment time are not equivalent, that is, do not come from the same population. In a nonrandomised study, one attempts to match groups on

certain pretreatment variables. However, the matching variables may be unreliable themselves, resulting in unmatched groups at pretreatment assessment time. Respondents with high scores on unreliable pretreatment variables may score lower at posttreatment time, and the reverse may be true for respondent with low scores on unreliable pretreatment variables. The expected direction of the change in unreliable scores from pre- to post-treatment is always towards the population mean [76]. This is referred to as regression to the mean. Thus, the change in one’s treatment groups would not be due to treatment, but rather to these regression artifacts. One way to control for these artefacts is to ensure that pretreatment matching variables are as reliable as possible. It is often difficult to match one’s groups completely, and therefore using experimental designs in treatment studies is preferable, although not always possible. A classic example of how regression artefacts can adversely affect results was the WestinghouseOhio University study of Head Start (preschool

Table 7.4 External and internal validity: definition and threats to validity.

Internal validity

Definition

Threats

The validity of the inferences drawn as they pertain to the subjects in the study

Differential effects of events on the exposed and unexposed groups that are not due to exposure • History: influence of events outside study control that may differentially affect outcomes and have little relationship to exposure • Maturation: differential development of participants in each group not due to exposure • Testing: number of times a test is given resulting in differential learning effects • Instrumental: changes in instrumentation over time that differentially affects groups (unrelated to exposure) Statistical regression artifacts • Regression to the mean: tendency for high values of continuous variables to decrease to the mean and low values to increase to the mean with repeated measurement • Selection: intra-individual variability at baseline • Mortality: differential group drop-out or refusal

External validity

Validity of the inferences drawn as they pertain to persons outside the study population • Generalisable to and across persons, time periods and settings

Interaction effects with exposure. Threats to external validity: • Selection • Setting • History

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education) [79]. In this study, the cases and controls were undermatched for socioeconomic status resulting in making Head Start look damaging to children. This occurred because controls were selected from a more able population than Head Starters. That is, the pretreatment or pretest matching variable, socioeconomic status, which includes educational status, was unreliable. When cognitive measures were assessed post-Head Start, the control group’s cognitive scores regressed to their population mean, which were higher than those in the Head Start group. The population means of the two groups were different, because the controls were originally selected from a population that was educationally and cognitively more advanced than the Head Start group [79]. When controls were appropriately selected for comparison with the Head Start children, the Head Start programme was shown to have a significant impact on the cognitive functioning of the children who experienced the programme. Selection effects are related to regression artefacts. Selection becomes a threat to internal validity when the characteristics of one’s groups are different, and this results in differential changes from preto post-treatment assessment between groups. For example, mortality can result in selection artefacts. Mortality effects refer to the differential drop-out or refusal rates between the groups that may affect the group’s post-treatment mean. For example, if the more severely ill patients dropped out of treatment ‘a’, then post-treatment assessment of symptoms among the treatment ‘a’ group may look better due to the differential drop-out of severely ill patients in that group rather than to effects of treatment ‘a’ on symptomatology. Other threats to internal validity discussed by Cook and Campbell [76, pp. 53–55] include differential social influences on the groups being compared. For example, communication between patients in the treatment and control groups about the treatment of interest may result in rivalry between the groups, ‘resentful demoralisation’ of the group receiving a less desirable treatment, or imitation of one group by the other. The external validity of a significant result refers to the extent to which a finding is generalisable to and across persons, time periods and settings [76]. Random sampling of one’s groups from the population 112

of interest contributes to the ability to ‘generalise to’ the population of interest. Generalising across populations refers to the identification of those populations to which the findings can be applied. That is, it refers to the extent of the generalisation of findings to other populations aside from those that were directly studied or subpopulations among those studied. For example, most readers would be cautious about generalising across males and females from a study of health services utilisation based solely on a sample of males. The threats to external validity can be thought of as interaction effects with the treatment of interest (see Table 7.4) [76]. For example, differences in treatment response between the sexes or socioeconomic statuses will lower the generalisability across the population as a whole. There are three types of interaction effects that are threats to external validity: interactions of selection, setting and history with treatment [76, pp. 73–74]. For example, selection interactions, or systematic recruitment artefacts, may result in findings being attributable only to those recruited into the study. The same can be said for interactions of treatment with setting and history. For example, using a university setting may limit one’s generalisability across other settings. Conducting the treatment study during a particular historical period may not allow generalisability to future time periods. To minimise both of these threats, multiple studies would need to be implemented using different populations at different historical time periods.

7.4 Summary In summary, validity can have different meanings depending on the context in which it is used. It is applied to the measurement of concepts, called construct validity, as in the case of ‘validating psychiatric diagnoses’, and to the relationship between operational measures, called the internal and external validity of a presumed causal relationship. As applied to construct validity, it is an unending process in which one attempts to measure a concept of interest as accurately as possible. Validity involves a theoretical understanding of the concept as well as an empirical assessment of the criteria chosen to operationalise the concept. This chapter discusses

VALIDITY: DEFINITIONS AND APPLICATIONS TO PSYCHIATRIC RESEARCH

three basic ways in which validity is assessed: content validity, criteria validity and construct validity. Content and criterion validity can be thought of as part of the process of assessing construct validity. One study cannot wholly validate a measure of a concept. It requires a pattern of consistent findings across studies involving different samples and different settings. An ideal example of this is how the field has approached identifying psychiatric diagnoses from the inception of the Diagnostic and Statistical Manual of Mental Disorders to current notions of understanding psychiatric diagnoses by incorporating aetiological information in future versions of how we operationalise our diagnostic classifications. The other way in which validity has been discussed in this chapter refers to the ‘internal and external validity’ of empirical relationships between operational measures of the concepts of interest. Internal validity refers to the extent that a statistically significant relationship is a causal one. There are a number of ways in which causal plausibility is assessed, for example the five criteria of judgement used in epidemiological studies [78]. In addition, causal plausibility is dependent on the type of study design employed. As discussed, quasi-experimental designs are open to a number of threats to internal validity, including regression artefacts, history and selection effects. Experimental study designs, in which one manipulates the time order of variables and controls for confounding factors, are less vulnerable to threats to internal validity. Finally, external validity refers to the extent that one can generalise the study findings to and across persons, time periods and settings. To minimise threats to external validity, multiple studies are needed in which the study populations, the historical time periods and the setting are varied.

Acknowledgements This chapter was written while Dr. Goldstein was supported by NIMH RO1 MH56956 and NIMH-ORWH P50 MH082679. Drs. Goldstein and Cherkerzian are also supported by the Connors Center for Women’s Health and Gender Biology at Brigham & Women’s Hospital. The authors would like to thank Lisa Cushman-Daly for help in manuscript preparation.

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8

Use of register data for psychiatric epidemiology in the Nordic countries Jouko Miettunen,1 Jaana Suvisaari,2 Jari Haukka2,3 and Matti Isohanni1 1

Department of Psychiatry, University of Oulu, Finland Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, Helsinki, Finland 3 Department of Public Health, University of Helsinki, Finland 2

8.1 Introduction The four largest Nordic countries, Denmark (population 5.5M), Finland (5.3M), Norway (4.8M) and Sweden (9.3M), have great similarities and interconnections in their history and social structures. Their population has remained stable in spite of bouts of emigration and more recently immigration. The national and local levels of administration and the documentation associated with these have been well developed for hundreds of years, and relevant data have been computerised ever since this became possible. Records have been kept in the Nordic Countries for a long time. For instance, in Finland and Sweden, parishes used to keep a record of births and deaths as early as the sixteenth century for purposes of recruitment and taxation. In the seventeenth century these records were used by central government for its own survey and planning purposes [1]. Nowadays, management, organisation, planning, evaluation, control and protection of individuals as well as the identification, selection and enumeration of cases have been listed as good reasons to collect administrative health and welfare data [2]. Similar

registers are also available in non-Nordic countries, but they are not based on the whole population and it is not possible to link data between different registers. At the beginning such registers included only aggregate data, but later individual register data have been available as well. The data in different registers can be linked within countries using personal identification codes in all the Nordic countries since 1960s, and since nearly all administrative registers operate on this basis, vast linkage possibilities exist. The main registers used in psychiatric research are case registers [3] and administrative health and welfare registers [4]. Case registers are usually kept locally and include all referrals to psychiatric services in a particular community, for example as in the Stockholm County In-patient Register [5]. It is only in the Nordic countries that nationwide case registers are to be found, for example the Norwegian Case Register of Mental Disorders and the Danish Psychiatric Central Register. Case registers have been used in health system planning, for instance [6, 7]. Administrative registers (e.g. the Finnish Hospital Discharge Register, FHDR) are ones which are maintained nationally mainly for administrative purposes,

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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although they are also employed for scientific purposes. Routinely collected administrative data can be used to study such matters as the incidence and prevalence of diseases, treatment outcomes and service utilisation. By linking different registers it is also possible to study risk factors for mental disorders. Register studies have notable strengths regarding statistical power and representativeness, and registers have enabled the examination of issues that would have been difficult to study otherwise, for example due to rare exposure events and/or disorders and high drop-out rates. Register studies offer internationally unique possibilities for psychiatric research. Many epidemiological studies have high drop-out rates, and this may affect the results and their interpretation [8]. Attrition is also a major problem in epidemiological and cross-sectional research related to psychiatric disorders, especially severe mental disorders. However, unlike the situation in most countries, it is possible in the Nordic region to compare participants and non-participants using information collected from registers and to estimate the effect of attrition [9]. The registers concerned can be used for statistical and scientific purposes even without specifically asking the subjects for their consent, – whereas the obtaining of informed consent for large study samples would be impossible for practical reasons. Thus registers of this kind provide an excellent basis for efforts to improve health, welfare and the health care and social welfare services [2, 7]. Where previous reviews of the use of Nordic health care registers have focused on specific topics or on one country [2, 10–13], the first and main aim of the current work is to describe the registers used in psychiatric research and to discuss issues related to register-based research. Second, we will also briefly review a selection of studies in psychiatric epidemiology produced in Denmark, Finland, Norway and Sweden that have made use of such registers.

8.2 Registers for use in psychiatric research The Nordic countries have quite similar sets of administrative registers of relevance to health care;

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Table 8.1 Starting years of various nationwide health care registers in Nordic countries. Register Hospital discharge register Causes of death register Disability pension register Prescription register Medical birth register Cancer register

Denmark Finland Norway Sweden 1969a

1967b

1990c

1965d

1970e

1969

1951

1952

1996

1962

1967

1971

1994 1973 1987

1994 1987 1953

2004 1967 1953

2005 1973 1958

a Attempted

suicide since 1989. Full coverage since 1972. c The data are not identifiable to person. Personally identifiable data are now being gathered and when finished will be available from 1 March 2007. d Full coverage since 1987. e Suicides. b

although their availability and commencement dates vary somewhat between the countries. The coverage of selected health registers in the four countries is summarised in Table 8.1, while Table 8.2 gives links to the web sites of selected maintainers of registers in the Nordic countries. Most of the information on such pages is also in English. In addition, Denmark and Finland have register centres, which have collected information and links related to register-based research. In the following paragraphs we will briefly describe the different types of registers and give more detailed information on their content, especially in Finland, although their content is quite similar in Denmark, Norway and Sweden. Nationwide, representative samples for study purposes can be obtained in Finland from the Central Population Register, also called the Population Information System, which is maintained by the Population Register Centre and local registry offices throughout the country [1]. Similar agencies exist in the other Nordic countries. The data registered for individual persons include name and personal identity code, address, nationality and mother tongue, marital status, dates of birth and death and information on emigration and immigration. The information is mainly updated by the authorities, but

USE OF REGISTER DATA FOR PSYCHIATRIC EPIDEMIOLOGY IN THE NORDIC COUNTRIES Table 8.2 Selected organisations maintaining health registers in the Nordic countries, their internet addresses and examples of their registers. Register maintainers by country Denmark National Centre for Register-based Research Descriptions and addresses of various health registers Centre for Suicide Research Suicide attempts National Board of Health Causes of deaths, cancer register, psychiatric case register National Social Appeals Board Disability pension register Central Office of Civil Registration Civil Registration System Statistics Denmark Medical birth register

Internet address www.ncrr.au.dk www.selvmordsforskning.dk www.sst.dk www.ast.dk www.cpr.dk www.dst.dk

Finland Finnish Information Centre for Register Research Descriptions and addresses of various health registers National Institute for Welfare and Health Medical birth register, hospital discharge register Finnish Centre for Pensions Finnish Employment Register and Pensions Register Social Insurance Institution Disability pensions, social benefits (e.g. unemployment), medication reimbursement register Statistics Finland Cause of death register

www.rekisteritutkimus.fi www.thl.fi www.etk.fi www.kela.fi

www.stat.fi

Norway Norwegian Institute of Public Health Norwegian prescription database, Medical Birth Registry Norwegian Labour and Welfare Service Register of employers and employees, disability pensions, unemployment Cancer Registry of Norway Statistics Norway Norwegian Patient Register (statistics)

www.fhi.no www.nav.no

www.kreftregisteret.no www.ssb.no

Sweden National Board of Health and Welfare (Centre for Epidemiology) Patient register, causes of death register, medical birth register, medicine register, cancer register Social Insurance Agency Disability pensions Quality registers Statistics Sweden Multigeneration register

www.socialstyrelsen.se

www.forsakringskassan.se www.kvalitetsregister.se www.scb.se

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change-of-address information has to be provided by the individuals themselves. Information on people who have emigrated or are deceased is also kept in the register, emigrants being moved to the category of people who are absent from the country, and deceased individuals provided with the date of death. The population information system together with the availability of other registers has allowed the Nordic countries to replace questionnairebased population censuses with register-based censuses. Denmark pioneered this by completing its first register-based census in 1981. Linked, cross-sectional data files (such as those required in a census) can in principle be constructed from a continuously updated population register and a number of other registers as often as needed – on a weekly or annual basis as appropriate.

8.2.1 Hospital discharge registers The most commonly used health care registers are the hospital discharge registers. The FHDR, maintained by the National Institute for Health and Welfare, for instance, cover periods of treatment received in all public and private hospitals in Finland since the early 1970s. Data on the beginning and end of each in-patient stay, together with the primary diagnosis and up to three subsidiary diagnoses and a hospital identification code, are listed. The number of erroneous personal IDs in the Finnish administrative registers is negligible and the quality of the FHDR data has been improving continuously [14]. The Hospital Discharge Register (and the Register of Causes of Death) use the ICD classification and include complete diagnostic codes. Similar hospital discharge registers exist in all the Nordic countries, and information on outpatient treatments has also been included since the 1990s, first in Denmark and then in Sweden, but the coverage of the outpatient treatment data varies greatly. The validity and reliability of an FHDR diagnosis of schizophrenia or schizophrenia spectrum psychosis (ICD-9 295) have been investigated in several studies, revealing a good concordance in general between clinical and research diagnoses for any psychosis [15–18]. Clinical diagnoses have been found by Isohanni [15] and Moilanen et al. [19] to be conservative, however, with over 40% of cases with a 120

research diagnosis of schizophrenia having a register diagnosis of non-schizophrenic psychosis. Taiminen et al. [20] found a poorer validity for schizophrenia diagnoses, the kappa value between clinical diagnoses and the best-estimate research diagnoses being only 0.44 for schizophrenic disorders. The reliability of hospital diagnoses of schizophrenia has also been investigated in the other Nordic countries and has been found acceptable [21–23]. In a twin study Kieseppa¨ et al. [24] validated bipolar disorder diagnoses in the FHDR, founding 92% accuracy for both bipolar I disorder and the manic type of schizoaffective disorder. The reliability of other psychiatric diagnoses has not been investigated. Several studies have investigated the reliability of diagnoses of other medical conditions [25, 26]. When investigating less severe disorders it is important to remember that the registers cover only patients treated in hospital and will underestimate the true incidence and prevalence figures.

8.2.2 Medication data Antidepressants and antipsychotics are among the most widely used drugs at the population level. Although clinical trials are the primary source of information about the efficacy and effectiveness of drug treatment, they suffer from certain flaws [27]. The characteristics of the samples studied are usually not the same as in the population that will ultimately use the tested drug, and even in large-scale trials the samples are not large enough to detect rare adverse effects. While randomisation guarantees certain aspects of correct inference in clinical trials, selective and often massive drop-out and/or drop-in during a long-term trial (e.g. over 70% in the famous CATIE schizophrenia trial [28]) can considerably complicate the interpretation of the results and their application to ‘real-world’ situations. This means that large-scale observational register linkage studies could provide invaluable information on drug treatment. These phase IV or post-marketing surveillance studies nevertheless require high-quality register data on prescriptions and on community and hospital care. Such studies are especially urgently required for antipsychotic drugs, as these are usually taken continuously for a very long period of time, often decades. Prescription registers and other sources

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of administrative data have become an important source of information for carrying out pharmacoepidemiological studies, yielding data that can be used to study the pattern of medication in large populations and to estimate individual exposure for assessments of the effectiveness and safety of drug treatment. There are two types of medication registers, prescription registers and medication reimbursement registers. All the Nordic countries have prescription databases [29, 30], and these are fairly similar in content. We will introduce the Finnish prescription register in more detail here. This contains information on all medications purchased in accordance with a doctor’s prescription, but before the year 2006 there was a 10 Euros cost threshold for basic reimbursement which meant that the register information was incomplete for very cheap medicines. The latter limitation means that the register information may not be complete for very cheap medicines. The prescription data available from the state-controlled Finnish Social Insurance Institution (SII) includes the generic name of the drug and its Anatomical Therapeutic Chemical (ATC) classification system code, the brand name that was bought, the formulation and package, the amount, the date when the drug was purchased, the prescribing practice (primary vs. secondary health care) and the prescribing physician’s area of specialisation. The validity of the prescription database by comparison with patient-reported medication data has been studied in Nordic countries by Glintborg et al. [31], Haukka et al. [32] and Haapea et al. [33], for instance, and has been found to be good for antipsychotics and antidepressives but slightly poorer for sedatives and hypnotics. It should be noted that prescription databases do not include data on drugs used in hospitals, nor all the drugs used in daily care at hospitals or nursing homes. The other register that contains information on medication supplied in Finland, the Medication Reimbursement Register maintained by the Finnish Social Insurance Institution, contains data on the diagnoses of persons receiving special reimbursements for outpatient medication for chronic diseases. Persons having ‘severe psychotic and other severe mental disorders’ are entitled to free antipsychotic

and antidepressive medication. Unfortunately, the registration of diagnostic codes in the Medication Reimbursement Register is not complete, and it often contains only the first three digits of the ICD diagnostic codes, or else the ICD code may be missing entirely in older data sets.

8.2.3 Cause of death registers Cause of death registers are among the oldest registers in all the Nordic Countries. The Finnish Causes of Death Register (FCDR), maintained by Statistics Finland, provides data on dates and causes of death and also stores death certificates. Statistics Finland has stored death certificates since 1936, but the data are available on the combined electronic file only since 1969. A large validation study came to the conclusion that none of the personal identification codes in the CDR was incomplete [14]. The register includes the personal identification number of each deceased person, sex, age, place of residence and principal, underlying and contributory causes of death. The routine validation of death certificates means that the accuracy of Nordic cause of death registers is good by international standards [34, 35]. Also the autopsy rate is very high, being found in one Finnish study, for instance, to have been 31% for all deceased persons aged 1 year or more [35]. Cause of death registers have been used in psychiatric research, for studying such topics as on mortality due to various somatic disorders, and especially suicides [36]. When studying suicidal behaviour it is also possible to include data on suicide attempts, which may be included in hospital discharge registers as external causes of hospitalisation [37].

8.2.4 Other registers There are also several other nationwide registers available in the Nordic countries, some of which include data collected more for the purposes of research than administration. The Nordic countries have some unique biobanks. The Finnish Maternity Cohort, started in 1983, for example contains currently approximately 1.5M serum samples from about 750 000 pregnant women (∼98% of all pregnancies during that period). These 121

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samples can be used for scientific research and linked to data from other sources, including personal identification numbers, numbers of pregnancies and deliveries and places of residence [38]. Denmark has been storing dried blood spot samples from all newborn infants since 1982 as a part of a neonatal screening programme, and this biobank has been regulated by specific legislation since 1993, granting it a unique position among biological specimen banks. Specimens from this source can also be used for research purposes, and have been used to investigate prenatal and neonatal infections and their association with schizophrenia, for example [39]. There are some specific registers in Sweden, such as the Multigeneration Register and Quality Registers. The Multigeneration Register provides information on all the people who have been resident in Sweden since 1960 who were born in 1932 or later and on their biological parents. This makes it possible to trace all first-degree relatives and second-degree relatives of these people who were alive in 1947 or later. The Quality Registers (www.kvalitetsregister.se) collect data on particular areas of health care, for example costs and outcomes, in order to motivate improvements. Data are being collected in 2009 on the treatment of eating disorders and substance dependence. There is a strong history of twin studies in Nordic countries [40, 41], also these have utilised registers. For instance, in Finland multiple births since the 1950s can be identified through the use of family member links added in the early 1970s for all persons in the database of the Population Register Centre [41]. There exists also specific twin registers [40, 42, 43]. Also in adoptive family studies registers have played an important role [44]. Both the Danish [45] and Finnish [46] adoptive family studies of schizophrenia have utilised several registers in finding individuals with schizophrenia who have adopted away a child (the Finnish study) or individuals who have been adopted and have developed schizophrenia (the Danish study), and their biological and adoptive relatives as well as control adoptees. There are also several social welfare registers which have been used in psychiatric research, for example for case finding purposes or for studying the outcomes of psychiatric disorders. These include registers of (disability) pensions, social benefits, sick 122

leave, unemployment, working periods, incomes and housing. Crime registers have commonly been used in forensic psychiatry, and others such as medical birth registers, birth defect registers and cancer registers have been used in psychiatric research. The Finnish and Swedish conscript registers have been also used for research purposes [46, 47]. Conscripts undergo a statutory medical examination, but males with known severe handicaps or chronic diseases are generally not accepted for conscription. The examination usually takes place at 17–19 years of age and consists of a health examination and an assessment of intellectual performance. The Finnish and Swedish school and education registers have also been used in psychiatric research [48].

8.3 Register research in Denmark Denmark has been a pioneer of register-based research, being the first country to abandon questionnaire-based censuses entirely, in 1981, basing all its censuses on registers only. The use of registers for psychiatric research purposes in Denmark has mainly amounted to epidemiological studies of schizophrenia, mania, depression and suicide. In their milestone work on the effects of family history and place and season of birth on the risk of schizophrenia, Mortensen et al. [49] were able to show that although the family history is a strong risk factor, other more common risk factors such as place and season of birth may play a more prominent role at the population level. They estimated that the population-attributable fraction (PAF) of having a parent or sibling with schizophrenia was 5.5%, whereas that of place birth was 34.6%. The work concerned involved linking the Danish Civil Registration System with the Danish Central Psychiatric Register. Later, the same group showed that the more urban the area of upbringing was, the higher was the risk of developing schizophrenia in adult life [50]. High-quality research from Denmark has also been published on other risk factors for schizophrenia, for example advanced paternal age [51], autoimmunity [52], prenatal infections [39, 53] and prenatal maternal stress [54].

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The Danes have also carried out intensive investigations into the outcomes of children of parents with severe mental disorders, finding, for example that their mortality risk is elevated perinatally and during the first year of life [55, 56], and also in adolescence and young adulthood [57]. The power of large-scale register linkage was also shown in a study of the association of measles, mumps and rubella vaccination with autism [58], the material for which was obtained by linking data from the Danish Civil Registration System, Danish Central Psychiatric Register, vaccination data reported by general practitioners to the National Board of Health, the National Hospital Registry and the Danish Medical Birth Registry. The overall coverage was 537 303 children (82.0% vaccinated) and over 2M person-years, and the outcome was a body of strong evidence against the hypothesis that MMR vaccination causes autism. One example of a Danish pharmacoepidemiological register linkage study used a population of 2.1M individuals aged 50 years and over to study the association between increased use of antidepressants and decreasing suicide rates [59]. The authors were able to show that only a small portion of the individuals concerned were receiving treatment with antidepressants at the time of their death, and they concluded that active treatment with antidepressants seems to account for only 10% of the decline in the suicide rate. Another study in which the Danish Civil Registration System was linked with the Danish Central Psychiatric Register found no support for the hypothesis that depression independently increases the risk of cancer [60]. Because suicide mortality in Denmark was very high in the 1980s, this subject has been studied extensively. Nordentoft et al. [61] showed that natural and unnatural mortality remained high 10 years after an attempted suicide. A nationwide study showed that people with mental disorders are also run a risk of death by homicide and other unnatural causes [62]. Risk factors for suicide are different for psychiatric patients, the accent being on high incomes and postgraduate employment, which was not the case in the general population [63]. Frequent changes of residence in childhood were associated with an increased risk of suicide in a study that used the Danish Civil Registration System combined with the Central Psychiatric Register [64].

8.4 Register research in Finland The versatile registers available in Finland have been made use of in many studies, including cases of international scientific collaboration. Some analyses have led to substantial new findings of major clinical relevance. Several reports in the early 1990s [65] suggested that the incidence of schizophrenia was declining. A Finnish study combined information from the hospital discharge register, pension register and medication reimbursement register and carried out an age-period-cohort analysis of changes in the incidence of schizophrenia among birth cohorts born between 1954 and 1965 [66]. The incidence had declined, and the effects of period and cohort on the change were both significant. While the effect of period reflects the operation of related confounding factors such as changes in diagnostic criteria, the cohort effect suggests that the intensity or frequency of one or more risk factors for schizophrenia may have decreased in these birth cohorts. A recent Finnish study found a high lifetime prevalence of psychotic disorders (DSM-IV) in Finland, 3.1%, which rose to 3.5% when the non-responder group and their register diagnoses were included. Registers were the most important and reliable screening method, the kappa value for psychotic disorders being 0.80 for the Hospital Discharge Register, while the CIDI interview section on psychotic symptoms was able to identify only 27% of the persons with psychotic disorders, due to considerable under-reporting of psychotic episodes and symptoms [67]. Researchers in Finland have found marked regional variation in the incidence and prevalence of schizophrenia, but negligible urban–rural variation [68, 69]. The Finnish Adoptive Family Study of Schizophrenia used registers to follow-up adoptees. The main finding of the study was that adoptees at high genetic risk are significantly more sensitive to adverse vs. ‘healthy’ rearing patterns in adoptive families than are adoptees at low genetic risk [70]. Studies of Finnish twin cohorts have also utilised various registers in studies of various psychiatric disorders [41]. Finnish twin studies based on national registers have found over 80% heritability for schizophrenia [71] and bipolar disorder [72]. Tiihonen et al. [73], who studied the relation between antidepressant treatment and the risk of 123

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suicide and overall mortality through a nationwide computerised database, observed a substantially lower mortality rate when receiving a selective serotonin re-uptake inhibitor. Current use of medication among the subjects who had used an antidepressant at some time was associated with a markedly decreased risk of completed suicide and mortality as compared with no current use of medication. The lower mortality was attributable to a decrease in cardiovascular and cerebrovascular deaths during selective serotonin reuptake inhibitor use. Tiihonen et al. [74] also studied the association between prescribed antipsychotic drugs and outcome in cases of schizophrenia or schizoaffective disorders in the community, using national central registers and a series of 2230 adults hospitalised in Finland. Initial use of clozapine, a perphenazine depot and olanzapine had the lowest rates of discontinuation associated with them, while that for oral haloperidol was higher, but the first-mentioned drugs carried the lowest risk of rehospitalisation. Mortality was markedly higher in patients not taking antipsychotics, and the risk of suicide was also high in these cases. In a recent study, Tiihonen et al. [75] found that among second-generation antipsychotic drugs clozapine was associated with a substantially lower mortality than any other antipsychotics. In the scientifically valuable birth cohort setting it is possible to pool register data with clinical and observational data; whereas most large epidemiological, genetic or imaging studies are based on clinical case series, which are not representative. The aim of the Northern Finland 1966 Birth Cohort have been to analyse the developmental pathways of schizophrenic psychoses from the fetal period to adulthood, especially with respect to risk factors and outcomes, including genome-wide analyses and brain morphology [76]. The cases for the cohort have been obtained from the hospital discharge register. One aim has been to determine whether adult-onset schizophrenia is associated with abnormalities during pregnancy, delivery or the neonatal period [77]. Both low and high birth weight were more common among the schizophrenic subjects. The same cohort data have also been used to study register-based outcomes, for example by Miettunen et al. [78], who studied work periods and disability pensions from registers and found that almost half of the patients 124

with schizophrenic psychoses had not been pensioned off after an average follow-up of 10 years. One example of a study analysing psychiatric comorbidity in somatic illness is the comparison of the incidence and severity of depression in stroke patients and those chiefly responsible for taking care of them in four districts of Finland, two with and two without after-discharge intervention programmes [79]. In this case a population-based stroke register was used. Fewer patients in the districts with active programmes were depressed than in the control districts. Another example of register linkage was the work by Gissler et al. [80] to determine rates of suicide associated with pregnancy by the type of pregnancy. Information on suicides was linked with the Finnish birth, abortion and hospital discharge registers to find out how many women who committed suicide had had a completed pregnancy during their last year of life. Given a mean annual suicide rate of 11.3 per 100 000, the rate associated with birth was significantly lower (5.9) and those associated with miscarriage (18.1) and induced abortion (34.7) were significantly higher.

8.5 Register research in Norway Early Norwegian studies using the Norwegian Psychiatric Case Register were focused on topics such as admission rates for schizophrenia [81]. Hansen et al. [82] studied total mortality among people admitted to psychiatric hospitals and concluded that mortality among psychiatric patients is still unsatisfactorily high, and that men constitute a special high-risk group. Later they also studied cause-specific mortality among psychiatric patients after deinstitutionalisation and found especially that there were more cardiovascular deaths and unnatural deaths among such cases in both genders, but especially among men. Strand and Kunst [83] studied suicide mortality using registry data on 613 807 Norwegians born in 1955–1965. Suicide mortality was higher among women with a high childhood socioeconomic position than among those with a low childhood socioeconomic position. They suggested downward mobility and failure to meet the high demands set by the well-educated parents, psychological distress, mental disorder, gender differences and

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social networks and norms as possible mechanisms for this finding. Tellnes et al. [84] analysed persons in Norway with long-term sickness certification at the end of 1990, based on data recorded by the National Insurance Administration. In cases of long-term sickness certification, mental disorders had a prevalence of 3.1 per 1000 employed persons respectively. The work demonstrates the possibility of using data from registers to provide information on the epidemiology of long-term sickness certification. The authors concluded, however, that it is necessary to further improve the validity of the data. Hagen et al. [85] linked the genetic data used in the Nord-Trondelag Health Study with antipsychotic medication data from the Norwegian prescription database. The Val158Met polymorphism in the COMT gene had no major impact on the number of individuals who had been prescribed antipsychotic medication, but the subjects with the Met/Met genotype were receiving the highest median daily doses of antipsychotics. Bramness et al. [86], who studied the muscle relaxant carisoprodol and its use and abuse on the basis of the Prescription Database, concluded that this drug was widely used and that the skewedness in its use indicated that it is a potential object of abuse. The Prescription Database has also been used when studying trends in the use of selective serotonin re-uptake inhibitors [87]. The Norwegian Twin Registers have also commonly been used for research in psychiatry [40]. The first Norwegian twin study based on the Norwegian Twin Registers was by Kringlen [88], who studied genetic factors of psychoses, an important finding was that problems in sampling techniques in earlier studies of schizophrenia resulted in overestimation of the genetic factors.

8.6 Register research in Sweden Sweden took a step towards promoting the use of health care registers for research purposes by founding the Swedish Centre for Epidemiology in 1992, and outstanding epidemiological research based on Swedish registers has been published during the past 15 years. In particular, there is a strong tradition of risk factor research in Sweden, work which has included several landmark studies.

Although investigations carried out in many countries had suggested that the incidence and prevalence of schizophrenia is higher in large cities than in rural areas, it was assumed for a long time that this had been caused by geographical drift of persons with a higher risk of schizophrenia from rural to urban areas. Lewis et al. [89] nevertheless showed in a 1992 follow-up of the 1969–1970 conscripts that it was urban upbringing, not urban residence that increased the risk of schizophrenia. Another Swedish study showed that the effect of urban place of birth was not related to obstetric complications or socioeconomic status in childhood [90]. Another landmark study was the longitudinal follow-up of a Swedish conscript cohort, which suggested that cannabis use in adolescence or young adulthood is a risk factor for schizophrenia [91]. A later follow-up of the same cohort showed that there was a linear trend in the frequency of cannabis use and the risk of schizophrenia, with a 3.1-fold higher risk of schizophrenia among those who had used cannabis over 50 times compared to those who had never used it [92]. Although there have been several investigations into cannabis and psychosis, this Swedish study is still the only one which has been able to use schizophrenia diagnosis as the outcome, due to its large sample size and register-based outcome assessment. Swedish research groups have actively used the Swedish birth register to investigate prenatal and perinatal risk factors for severe mental disorders. Hultman et al. [93] and Dalman et al. [94] showed that several specific obstetric complications increase the risk of schizophrenia and to some extent also the risk of affective and reactive psychoses. Other Swedish studies on childhood risk factors for psychotic disorders have found that serious viral infections of the central nervous system [95] and poor school performance both increase the risk of nonaffective psychosis [96]. A recent Swedish family study combined the multigeneration register and the hospital discharge register to investigate whether schizophrenia and bipolar disorder share a common genetic risk [97]. In the end 64% heritability was reported for schizophrenia and 59% heritability for bipolar disorder, with a 0.60 genetic correlation between the two disorders [97]. One key study concerning mortality in cases of 125

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psychiatric disorders was that published by Allebeck and Wistedt [5], which showed that persons with schizophrenia have an increased risk of mortality from all causes of death and from suicide in particular. A recent Swedish follow-up study compared the risk of death by suicide after a suicide attempt in different psychiatric disorders, and found the highest risks of suicide to exist among patients with schizophrenia and unipolar depressive and bipolar disorders [98]. Swedish Twin Registry has been used, for example in investigation of heritability of major depression. Kendler et al. [99] found, for the first time, that the heritability of liability to major depression was significantly higher in women (42%) than men (29%).

8.7 Discussion 8.7.1 Main findings The Nordic registers are unique nationwide registers that have been used in numerous high-quality investigations. Nationwide hospital registers have been used to study admission rates and also, in combination with interview data, to assess the incidence and prevalence of certain disorders. Hospital registers have also commonly been used for case finding, especially in more severe disorders such as schizophrenia. Registers from early life, such as those of births, have been used as sources for exposure variables in connection with various register or interview outcomes (e.g. suicides), and registers have enabled hypotheses to be tested which otherwise would have been difficult to study reliably, for example due to the rarity of the events or disorders concerned.

8.7.2 Methodological and administrative challenges In principle (but not always in practice) it is easy to obtain, analyse and publish data from registers. In a real-world setting, however, the use of register data includes both practical and methodological challenges [2]. Register data are usually collected for administrative or clinical purposes, and not for scientific study. The data are often superficial, and exposure and outcome definitions may be imprecise. 126

The variables used may reflect events that are easy to categorise, but not usually the complicated measures needed for analysing psychological and qualitative events as required for psychiatric research. Information on family and social environments is scarce, for example and while we can obtain information on the medication prescribed for the patient, we have no information on why the doctor chose this particular medication. Thus unidentified confounding factors are one major limitation on register-based research. Most registers include only subjects with severe psychiatric disorders that require hospitalisation or medical treatment, which may cause bias. The usefulness of health care registers for investigating a disease depends on the extent to which medical care is provided at different levels and in different units [100]. Most people with chronic psychotic disorders receive hospital care at some point in their illness [67], and thus psychotic disorders can be studied using a hospital discharge register. Most persons with depressive disorders or a cluster A personality disorder, for example [101] receive outpatient treatment or are not treated at all, and the cases treated in hospital represent only the tip of the iceberg. This explains why a substantial proportion of studies using registers are concerned with schizophrenia and other psychotic disorders. Byrne et al. [4] have reviewed studies of the validity of administrative registers and have concluded that these most often concern hospital discharge registers and that relatively little high-quality work exists on the topic. At best we may have longitudinal data collected over a period of decades which cover the entire lifespan of certain individuals. This may allow the exact time of exposure to be defined, but analysing the complex trajectories and pathways between exposure (e.g. genetic disposition or childhood adversity) and outcome (e.g. mental disorder) is challenging and full of potential mediating and confounding factors and effect modifications. The major advantages of register-based data are minimal attrition and the possibility to achieve high power when studying topics which would otherwise be unapproachable. In practice, there are still plenty of methodological problems involved in analysing possible causal relationships in the context of observational studies based on large register data sets, for instance. Modelling techniques have been developed further in recent years,

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however, and, given certain assumptions, marginal structural models can partly resolve the problem of causal inference in observational studies [102].

8.7.3 The Nordic countries: An epidemiologist’s paradise? This metaphor contained in this title is sometimes presented by non-Nordic scientists – without the question mark. In real world, hard work, data processing, methodological skills and teamwork are needed for successful register-based studies. Nordic registers at best are population-based and achieve high levels of ascertainment. They are ideal for epidemiological purposes, as the biases common to many epidemiological surveys (such as information bias) are minimal, and loss to follow-up occurs only through emigration or death. Explanatory variables and outcome data are collected prospectively and the number of cases can be large, so that it is possible to investigate rare exposure events, for example specific birth complications, with rare outcomes, for example schizophrenia. The authors are most familiar with the Finnish register system; we acknowledge that our aim to review all Nordic countries in a balanced way may not have been successful. The current review did not include Iceland (population of 0.3M), which also has nationwide registers that have been used in psychiatric research. The current focus is on the anonymous Icelandic Healthcare Database, which has been constructed by a private company, deCODE Genetics. This database has opened up unique possibilities for modelling disease risk as a function of genetic and environmental factors and has resulted in the identification of risk genes for several disorders, including schizophrenia [103, 104]. For administrative and also ethical reasons, some registers are not yet available for research purposes or for linkage to other register data. The introduction of new registers will nevertheless make it possible to study many topics more reliably. More extensive outpatient registers, for instance, will make it possible to identify more subjects with less severe psychiatric disorders. Until recent years, outpatient registers have often been kept locally, or else nationwide registers have limited coverage. The accumulating medication data should be used in the

future to carry out observational, population-level, phase IV efficacy, effectiveness and safety studies on psychoactive drugs. Register data could also be more commonly used together with data collected from interviews. In the future, increasing international collaboration and the combining of different registers within and between countries would give further possibilities for studying novel topics.

Acknowledgements This work has been supported by the Academy of Finland (#125 853, J.M.; #129 434, JS; #110 143, M.I.), NARSAD: Brain and Behavior Research Fund (J.M., J.S., M.I.), and the Sigrid Jus´elius Foundation (J.S., M.I.).

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a psychiatric inpatient register. Nord. Psykiatr. Tidsskr., 43, 229–234. ¨ ¨ ¨ Loffler, W., Hafner, H., Fatkenheur, B. et al. (1994) Validation of Danish case register diagnosis for schizophrenia. Acta Psychiatr. Scand., 90, 196–203. Dalman, C., Broms, J., Cullberg, J. et al. (2002) Young cases of schizophrenia identified in a national inpatient register. Soc. Psychiatry Psychiatr. Epidemiol., 37, 527–531. ¨ T., Partonen, T., Kaprio, J. et al. (2000) Kieseppa, Accuracy of register- and record-based bipolar I disorder diagnoses in Finland – a study of twins. Acta Neuropsychiatr., 12, 106–109. ¨ J., Sundstrom, ¨ Ingelsson, E., Arnlov, J. et al. (2005) The validity of a diagnosis of heart failure in a hospital discharge register. Eur. J. Heart Fail., 7, 787–791. Elo, S.L. and Karlberg, I.H. (2009) Validity and utilization of epidemiological data: a study of ischaemic heart disease and coronary risk factors in a local population. Public Health, 123, 52–57. Flay, B.R. (1986) Efficacy and effectiveness trials (and other phases of research) in the development of health promotion programs. Prev. Med., 15, 451–474. Lieberman, J.L., Stroup, T.S., McEvoy, J.P. et al. (2005) Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. N. Engl. J. Med., 353, 1209–1223. Gaist, D., Sørensen, H.T. and Hallas, J. (1997) The Danish prescription registries. Dan. Med. Bull., 44, 445–448. Furu, K. (2008) Establishment of the nationwide Norwegian prescription database (NorPD) – new opportunities for research in pharmacoepidemiology in Norway. Nor. Epidemiol., 18, 129–136. Glintborg, B., Hillestrom, P.R., Olsen, L.H. et al. (2007) Are patients reliable when self-reporting medication use? Validation of structured drug interviews and home visits by drug analysis and prescription data in acutely hospitalized patients. J. Clin. Pharmacol., 47, 1440–1449. Haukka, J., Suvisaari, J., Tuulio-Henriksson, A. et al. (2007) High concordance between selfreported medication and official prescription database information. Eur. J. Clin. Pharmacol., 63, 1069–1074. Haapea, M., Miettunen, J., Lindeman, S. et al. (2010) Concordance between self-reported and pharmacy data on medication use in the Northern Finland 1966 Birth Cohort. Int. J. Methods Psychiatr. Res., 19, 88–96.

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CHAPTER 8 [61] Nordentoft, M., Breum, L., Munck, L.K. et al. (1993) High mortality by natural and unnatural causes: a 10 year follow up study of patients admitted to a poisoning treatment centre after suicide attempts. Br. Med. J., 306, 1637–1641. [62] Hiroeh, U., Appleby, L., Mortensen, P.B. et al. (2001) Death by homicide, suicide, and other unnatural causes in people with mental illness: a population-based study. Lancet, 358, 2110–2112. [63] Agerbo, E. (2007) High income, employment, postgraduate education, and marriage: a suicidal cocktail among psychiatric patients. Arch. Gen. Psychiatry, 64, 1377–1384. [64] Qin, P., Mortensen, P.B. and Pedersen, C.B. (2009) Frequent change of residence and risk of attempted and completed suicide among children and adolescents. Arch. Gen. Psychiatry, 66, 628–632. [65] Munk-Jørgensen, P. and Mortensen, P.B. (1992) Incidence and other aspects of the epidemiology of schizophrenia in Denmark, 1971–1987. Br. J. Psychiatry, 161, 489–495. [66] Suvisaari, J.M., Haukka, J.K., Tanskanen, A.J. et al. (1999) Decline in the incidence of schizophrenia in Finnish cohorts born from 1954 to 1965. Arch. Gen. Psychiatry, 56, 733–740. ¨ a, ¨ J., Suvisaari, J., Saarni, S.I. et al. (2007) Life[67] Peral time prevalence of psychotic and bipolar I disorders in a general population. Arch. Gen. Psychiatry, 64, 19–28. [68] Haukka, J., Suvisaari, J., Varilo, T. et al. (2001) Regional variation in the incidence of schizophrenia in Finland: a study of birth cohorts born from 1950 to 1969. Psychol. Med., 31, 1045–1053. ¨ a, ¨ J., Saarni, S., Ostamo, A. et al. (2008) [69] Peral Geographic variation and sociodemographic characteristics of psychotic disorders in Finland. Schizophr. Res., 106, 337–347. [70] Tienari, P., Wynne, L.C., Sorri, A. et al. (2004) Genotype-environment interaction in schizophrenia-spectrum disorder. Long-term follow-up study of Finnish adoptees. Br. J. Psychiatry, 184, 216–222. ¨ J. et al. (1998) [71] Cannon, T.D., Kaprio, J., Lonnqvist, The genetic epidemiology of schizophrenia in a Finnish twin cohort. A population-based modeling study. Arch. Gen. Psychiatry, 55, 67–74. ¨ T., Partonen, T., Haukka, J. et al. (2004) [72] Kieseppa, High concordance of bipolar I disorder in a nationwide sample of twins. Am. J. Psychiatry, 161, 1814–1821. ¨ [73] Tiihonen, J., Lonnqvist, J., Wahlbeck, K. et al. (2006) Antidepressants and the risk of suicide, attempted suicide, and overall mortality in a nationwide cohort. Arch. Gen. Psychiatry, 63, 1358–1367.

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¨ [74] Tiihonen, J., Wahlbeck, K., Lonnqvist, J. et al. (2006) Effectiveness of antipsychotic treatments in a nationwide cohort of patients in community care after first hospitalisation due to schizophrenia and schizoaffective disorder: observational followup study. Br. Med. J., 333, 224. ¨ [75] Tiihonen, J., Lonnqvist, J., Wahlbeck, K. et al. (2009) 11-year follow-up of mortality in patients with schizophrenia: a population-based cohort study (FIN11 study). Lancet, 374, 620–627. ¨ [76] Isohanni, M., Miettunen, J., Maki, P. et al. (2006) Developmental pathways of schizophrenia from gestation to the course of illness. The Northern Finland 1966 Birth Cohort Study. World Psychiatry, 5, 168–171. [77] Moilanen, K., Jokelainen, J., Jones, P.B. et al. (2010) Deviant intrauterine growth and risk of schizophrenia: A 34-year follow-up of the Northern Finland 1966 Birth Cohort. Schizophr. Res., 124, 223–230. [78] Miettunen, J., Lauronen, E., Veijola, J. et al. (2007) Socio-demographic and clinical predictors of occupational status in schizophrenic psychoses – followup within the Northern Finland 1966 Birth Cohort. Psychiatry Res., 150, 217–225. [79] Kotila, M., Numminen, H., Waltimo, O. et al. (1998) Depression after stroke: results of the FINNSTROKE Study. Stroke, 29, 368–372. ¨ [80] Gissler, M., Hemminki, E. and Lonnqvist, J. (1996) Suicides after pregnancy in Finland, 1987–1994: register linkage study. Br. Med. J., 313, 1431–1434. [81] Ødegard, Ø. (1971) Hospitalized psychoses in Norway: time trends 1926–1965. Soc. Psychiatry, 6, 53–58. [82] Hansen, V., Arnesen, E. and Jacobsen, B.K. (1997) Total mortality in people admitted to a psychiatric hospital. Br. J. Psychiatry, 170, 186–190. [83] Strand, B.H. and Kunst, A. (2006) Childhood socioeconomic status and suicide mortality in early adulthood among Norwegian men and women. A prospective study of Norwegians born between 1955 and 1965 followed for suicide from 1990 to 2001. Soc. Sci. Med., 63, 2825–2834. [84] Tellnes, G., Mathisen, S., Skau, I. et al. (1992) Who is long-term sick-listed in Norway? From the project Evaluation of the follow-up of long-term sick-listed. Tidsskr. Nor. Laegeforen., 112, 2773–2778. [85] Hagen, K., Stovner, L.J., Skorpen, F. et al. (2008) COMT genotypes and use of antipsychotic medication: linking population-based prescription database to the HUNT study. Pharmacoepidemiol. Drug Saf., 17, 372–377. [86] Bramness, J.G., Furu, K., Engeland, A. et al. (2007) Carisoprodol use and abuse in Norway: a pharmacoepidemiological study. Br. J. Clin. Pharmacol., 64, 210–218.

USE OF REGISTER DATA FOR PSYCHIATRIC EPIDEMIOLOGY IN THE NORDIC COUNTRIES [87] Bramness, J.G., Hausken, A.M., Sakshaug, S. et al. (2005) Prescription of selective serotonin reuptake inhibitors 1990–2004. Tidsskr. Nor. Laegeforen., 125, 2470–2473. [88] Kringlen, E. (1968) An epidemiological-clinical twin study on schizophrenia. J. Psychiatr. Res., 6 (Suppl. 1), 49–63. [89] Lewis, G., David, A., Andreasson, S. et al. (1992) Schizophrenia and city life. Lancet, 340, 137–140. [90] Harrison, G., Fouskakis, D., Rasmussen, F. et al. (2003) Association between psychotic disorder and urban place of birth is not mediated by obstetric complications or childhood socio-economic position: a cohort study. Psychol. Med., 33, 723–731. ¨ [91] Andreasson, S., Allebeck, P., Engstrom, A. et al. (1987) Cannabis use and schizophrenia: a longitudinal study of Swedish conscripts. Lancet, 8574, 1483–1486. [92] Zammit, S., Allebeck, P., Andreasson, S. et al. (2002) Self reported cannabis use as a risk factor for schizophrenia in Swedish conscripts of 1969: historical cohort study. Br. Med. J., 325, 1199. [93] Hultman, C.M., Sparen, P., Takei, N. et al. (1999) Prenatal and neonatal risk factors for schizophrenia, affective psychosis, and reactive psychosis of early onset: case-control study. Br. Med. J., 318, 421–426. [94] Dalman, C., Allebeck, P., Cullberg, J. et al. (1999) Obstetric complications and the risk of schizophrenia. A longitudinal study of a national birth cohort. Arch. Gen. Psychiatry, 56, 234–240. [95] Dalman, C., Allebeck, P., Gunnell, D. et al. (2008) Infections in the CNS during childhood and the risk of subsequent psychotic illness: a cohort study of more than one million Swedish subjects. Am. J. Psychiatry, 165, 59–65.

[96] MacCabe, J.H., Lambe, M.P., Cnattingius, S. et al. (2008) Scholastic achievement at age 16 and risk of schizophrenia and other psychoses: a national cohort study. Psychol. Med., 38, 1133–1140. ¨ [97] Lichtenstein, P., Yip, B.H., Bjork, C. et al. (2009) Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a populationbased study. Lancet, 373, 234–239. ˚ ¨ N., Lichtenstein, P. et al. [98] Tidemalm, D., Langstr om, (2008) Risk of suicide after suicide attempt according to coexisting psychiatric disorder: Swedish cohort study with long-term follow-up. Br. Med. J., 337, a2205. [99] Kendler, K.S., Gatz, M., Gardner, C.O. et al. (2006) A Swedish national twin study of lifetime major depression. Am. J. Psychiatry, 163, 109–114. [100] Wigertz, A. and Westerling, R. (2001) Measures of prevalence: which healthcare registers are applicable?. Scand. J. Public Health, 29, 55–62. [101] Isohanni, M. and Tienari, P. (2005) Cluster a personality disorders: unanswered questions about epidemiological, evolutionary and genetic aspects, in Personality Disorders (eds M. Maj, H.S. Akiskal, J.E. Mezzich and A. Okasha), John Wiley & Sons, Inc., New York, pp. 87–89. ´ [102] Hernan, M.A., Cole, S.R., Margolick, J. et al. (2005) Structural accelerated failure time models for survival analysis in studies with time-varying treatments. Pharmacoepidemiol. Drug Saf., 14, 477–491. [103] Stefansson, H., Sigurdsson, E., Steinthorsdottir, V. et al. (2002) Neuregulin 1 and susceptibility to schizophrenia. Am. J. Hum. Gen., 71, 877–892. [104] Stefansson, H., Rujescu, D., Cichon, S. et al. (2008) Large recurrent microdeletions associated with schizophrenia. Nature, 455, 232–236.

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An introduction to mental health services research 1 ´ Anna Fernandez, Alejandra Pinto-Meza,2 Antoni Serrano-Blanco,3 Jordi Alonso4 and Josep Maria Haro5 1

Research and Development Unit, Sant Joan de D´eu-SSM, Fundacio´ Sant Joan de D´eu, ´ y Promocion ´ de Barcelona, Spain, Red de Investigaciones en Actividades de Prevencion la Salud (REDIAPP) 2 Research and Development Unit, Sant Joan de D´eu-SSM, Fundacio ´ Sant Joan de D´eu, ´ y Promocion ´ de Barcelona, Spain, Red de Investigaciones en Actividades de Prevencion la Salud (REDIAPP) 3 Research and Development Unit, Sant Joan de D´eu-SSM, Fundacio ´ Sant Joan de D´eu, ´ y Promocion ´ de Barcelona, Spain, Red de Investigaciones en Actividades de Prevencion la Salud (REDIAPP) 4 Head, Health Services Research Unit (IMIM-Hospital del Mar), CIBER Epidemiolog´ıa ´ y Salud Publica (CIBERESP), Spain, Master’s Program in Public Health (UPF-UAB), Carrer del Doctor Aiguader, Barcelona, Spain 5 Research and Development Unit, Sant Joan de D´eu-SSM, Fundacio ´ Sant Joan de D´eu, Barcelona, Spain, CIBER Salud Mental (CIBERSAM)

9.1 Introduction Health Services Research (HSR) entered the public health arena in the 1970s. Two events, in particular, heralded its appearance. The first was the publication, in 1973, of an essay by Barbara Starfield entitled ‘Health Services Research: a working model’ [1]. The second one was the publication of the widely cited Lalonde Report in 1974 [2] in which, for the first time, the term ‘determinants of health’ was used. In this report, four determinants of the health and well-being of populations were identified: human biology, environment, lifestyles and healthcare systems. In the Lalonde Report the influence of human biology, environment and lifestyles were emphasised, but this did not lead to a decrease of attention to health care. On the contrary, this report called for a change in the healthcare system from a limited

focus on ‘cures’ to illness prevention and health promotion. The inclusion of health services as one of the determinants of health, and the development of a framework for its study, promoted the creation of institutes and departments to study the performance and the effectiveness of health services. During the 1980s HSR received support mainly from three groups [3]: (i) payers who thought that HSR could help them to contain increasing costs; (ii) clinicians who felt that HSR would serve to provide them with evidence against those who argued that health services had little effect on the health of individuals and (iii) public/users who called for a more important role in the public health arena. In the case of mental health, the transformation that mental healthcare was undergoing, that is the implementation of deinstitutionalisation in the 1960s and 1970s, and the steady rise of community support

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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system (CSS) programmes in the 1980s [4], delayed the development of a specific framework for mental health services research (Mental HSR) until the end of the 1990s. It was not until 1998 that Tansella and Thornicroft published their paper ‘A Conceptual Framework for Mental Health Services: the matrix model’, which aimed to review the major concepts, and the applications of HSR to mental health services [5]. Nevertheless, although this could be considered the first systematisation, it is important to mention the work of Stein and Test (1980) in the United States [6], and Hoult and Reynolds (1983) in Australia [7] who were pioneers in the evaluation of the effectiveness of the new forms of mental healthcare (specifically the CSS) for people suffering from mental illness. The chapter is organised into four sections. In the first section, various definitions of Mental HSR are presented and the general challenges faced by Mental HSR are discussed. In the second section, we describe and discuss the conceptual framework for Mental HSR developed by Tansella and Thornicroft. In the third section, we examine the key concepts in Mental HSR. Finally, we present some examples of Mental HSR.

9.2 What is mental health services research? Different definitions have been proposed for HSR that, generally, could be applied to the specific area of mental health services. One of the first definitions was proposed in 1979 by the Institute of Medicine. In the revised and expanded version of 1995 they defined HSR as [8]: ( . . . ) a multidisciplinary field of inquiry, both basic and applied, that examines the use, costs, quality, accessibility and delivery, organization, financing and outcomes of health care services to increase knowledge and understanding of the structure, processes and effects of health services for individuals and populations. The Agency for Healthcare Research and Quality defined HSR as the field of scientific investigation that [9]: 134

( . . . ) examines how people get access to health care, how much care costs, and what happens to patients as a result of this care. The main goals of health services research are to identify the most effective ways to organize, manage, finance, and deliver high quality care; reduce medical errors; and improve patient safety. A further definition, such as the one proposed by the Academy of Health, states that HSR is [10]: ( . . . ) the multidisciplinary field of scientific investigation that studies how social factors, financing systems, organizational structures and processes, health technologies, and personal behaviors affect access to health care, the quality and cost of health care, and ultimately our health and well-being. Its research domains are individuals, families, organizations, institutions, communities, and populations. On the other hand, Mental HSR has been defined as [11]: ( . . . ) the area of research that aims to maximize the quality of mental health care received by patients in their communities, as well as the quality of their lives. It examines treatment through the lenses of public health, public policy and the economics of mental health care Taking into account the above definitions, a major characteristic of Mental HSR is its multidisciplinary nature. Mental HSR uses concepts and methods from fields of knowledge such as medicine, epidemiology, sociology, economics and psychology. The use of multiple methods allows the use of different approaches depending on the problems being addressed. For example, qualitative methods are increasingly being applied, especially with the inclusion of service users and their relatives in the design and implementation of HSR studies [12]. The Mental HSR field of study is broad, covering the micro level (patient-based evaluation) to macro analysis (how health and social policies influence the outcomes of health services). As such, Mental HSR operates in the continuum from the patient–physician encounter to the wider

AN INTRODUCTION TO MENTAL HEALTH SERVICES RESEARCH

community and environmental context in which these encounters happen. The approach to many problems is not local but systemic, taking into account the influence of medical and non-medical factors on populations’ health. Finally, especially when economic aspects are taken into account, the approach is also relevant. Sometimes the focus is on individual health service costs, but in mental health there is a need to include a broader view of the costs of care, including indirect costs. The societal perspective allows the analysis of the impact of specific services not only on patients’ wellbeing, but also on their overall quality of life and integration into society. Sustainability of services requires the evaluation of overall costs and care benefits. The context in which HSR and Mental HSR have developed suffers from internal and external difficulties. General and mental health services are exposed to external pressures which some have called ‘environmental turbulence’ [3]. General and Mental HSR have to deal with the ‘turbulence’ originating from: (i) government, which can influence HSR through financial controls or political strategies; (ii) local opinions, local politicians and consumer organisations; (iii) healthcare organisations with their staff, internal politics and norms and conditions of services and (iv) the medical–industrial complex, which can promote new technologies primarily for commercial interest. On the other hand, both general and Mental HSR have to deal with the specific internal characteristics of the healthcare arena. These are: (i) the complexity of healthcare, with different occupational groups involved in the provision of healthcare, often with competing interests; (ii) the continuous healthcare changes and (iii) the effect of employees in healthcare organisations, especially medical doctors, who have considerable autonomy and influence over how resources are used. Finally, complexity comes from the fact that there are no two identical patients, which complicates the implementation of standardised processes. Somehow, this internal and external turbulence is common in all areas of HSR. Mental HSR has some additional difficulties, which arise from: (i) the still common use of non-standardised outcome measures; (ii) the complexity of mental health treatments, which often include social components and (iii) the

incapacity, or possible incapacity, of some patients to provide consent [12].

9.3 A framework for mental health services research In order to deal with this internal and external turbulence, Tansella and Thornicroft developed, in 1998, a conceptual framework for Mental HSR [5, 13]. This conceptual framework is a map which allows the organisation of the field and clearly states the objective of the studies to be conducted. One of their concerns was to avoid studies with limited use. For example, general descriptions of mental health services which were difficult to use, in particular, contexts; or conversely, specific descriptions of mental health services, which were difficult to extrapolate. Tansella and Thornicroft built a matrix with two dimensions, one geographical and one temporal. The geographical dimension is composed of three geographical levels: (i) country; (ii) local and (iii) patients. The temporal dimension considers three phases: (i) inputs; (ii) processes and (iii) outcomes. Combining the two dimensions, they constructed a 3 × 3 matrix that reflect the crucial issues for Mental HSR (see Table 9.1). The geographical dimension has three levels: • Country/regional level: which is also known as the macro level. At the macro level, mental health laws are established and policies are formulated. The domains studied in this level are related to: (i) the social, political and legal forces that shape policies; (ii) economic issues, such as public expenditure on mental health services or the methods to allocate health expenditure which consider variations in psychiatric morbidity and (iii) professional education and development, such as professional training and accreditation or setting standards of care. • The local level: By local level, Tansella and Thornicroft refer to the catchment area in which mental health services are set up. In their characterisation, they have in mind how most developed countries organise mental health services. Typically, areas with between 50 000 and 250 000 residents are defined and a given number of services are 135

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assigned to cover the needs of the population of the area. The local level is usually seen as the best perspective to study the components of the mental health system, how they are organised and integrated with general healthcare and social services. Moreover, at this level, assessment of required services is carried out. • The patient level: Here the focus is on the individual patient or small groups of patients sharing some common traits, needs or problem. Traditionally, this level is considered the clinicians’ domain. Nevertheless, in the matrix model, the influence of higher levels (country/region and local levels) on clinical work is considered. The temporal dimension has three phases: • The input phase: inputs are defined as the resources devoted to the mental health system. According to the authors, inputs could be divided into ‘visible’ and ‘invisible’. At the local level, the ‘visible’ inputs are basically composed of staff and facilities. The ‘invisible’ inputs activate the visible inputs and potentiate their effective performance. For instance, coordination between primary care and mental health professionals is an ‘invisible’ input. Invisible inputs also include such elements as experience, qualification and staff training. In traditional HSRthe input phase is also named ‘structure’. Table 9.1

• The process phase: in this phase the focus is on activities developed to provide mental health services. For instance, we could study the appropriateness of treatments provided for mental health problems. • The outcome phase: outcomes are changes in functioning, morbidity, mortality and quality of life, both at the individual and country-aggregated level. The outcomes could be seen as the complex result of resources and treatment received, which, as we have seen, could be considered as inputs and process variables. The model by Tansella and Thornicroft has many similarities to the model proposed by Starfield 25 years previously. She also divided the health services components into structure, processes and outcomes, which basically correspond to the three levels of the temporal dimension of the matrix. Moreover, Starfield also emphasised that the study of the interrelation between the patient and the health professional had to take into account the social context in which the encounter takes place. In recent revisions of her work, the individual–country dimension has also been incorporated [14]. The use of the matrix model may assist mental health services researchers in considering different factors that could help them answer complex questions. In the fourth section of this chapter we will provide some examples of the application of the matrix model.

The mental health matrix, with some examples. Temporal dimension

Geographical dimension

Input

Process

Outcome

Country

Mental health policies Expenditure on services

Compulsory treatment rates Bed occupancy rates

National suicide rates Burden of disease Primary prevention

Local (catchment area)

Population needs assessment

Pathways to care

Better access to services

Coordination between sectors

Patterns to service use

Secondary and tertiary prevention

Patient needs assessment Patients’ and/or relatives demands

Treatment appropriateness Continuity of care

Symptom reduction Increase in the quality of life

Patient

Adapted from: Tansella M and Thornicroft G (1998) A conceptual framework for mental health services: the matrix model. Psychological Medicine, 28, 503–508 [5].

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9.4 Key concepts in mental health services research Once we have established the framework for Mental HSR, and have discussed the two dimensions that must be taken into account when dealing with complex questions, we will briefly review some of the key concepts in Mental HSR.

9.4.1 Need Need is one of the main drivers of health service use. In layman’s terms it may mean the existence of a health problem [15], but a definition must be more complex than this. According to The Dictionary of Epidemiology [16] the term ‘need’ has both ‘a precise and all-but-undefinable meaning in the public health context’. The fact is that when using the word ‘need’, there are implied value judgements that define what, and when, a health status can be defined as a health problem. For instance, in the case of mental health, before psychiatric deinstitutionalisation, the needs of outpatient treatment for people affected by schizophrenia were not considered, whereas since deinstitutionalisation, and in the context of the subsequent progressive sensitisation of citizens, their needs for communitarian treatment have been taken into account. From an economist’s point of view, ‘need’ can be defined as ‘the minimum amount of resources needed to exhaust an individual’s capacity to benefit’. A relatively simple definition from a health economics point of view, provided by Davis, states that ‘need is a subjective feeling state that initiates the process of choosing among medical resources’ [15]. Other authors, from a sociological standpoint, have distinguished four approaches to define need [15, 17, 18]: • Normative need: those needs ‘objectively’ defined by professionals. • Felt need: those needs ‘subjectively’ defined by individuals. • Expressed need: defined by the actions carried out by individuals. That is, for instance, seeking care for a health problem. • Comparative need: derived from examining the services provided in one area to one population

and using this information as the basis to determine the sort of services required in another area with a similar population. From a Mental HSR approach, a mental health need is defined as: ‘the requirement of individuals to enable them to achieve, maintain, or restore an acceptable level of social independence or quality of life, as defined by particular care agency or authority’ [17].

9.4.2 Want, demand and supply Need is related to other key concepts: want, demand and supply. The four terms are in some sense overlapped, and sometimes they are used loosely. Simply put, want is understood to mean what the individuals would like but may not act upon, demand refers to the expressed want (some authors will say to the expressed need) and supply refers to the services/treatment/kinds of care that are available [15, 18]. As a goal, mental health systems try to increase the overlap between need, demand and supply. Additionally, some authors have argued for the importance of differentiating between unmet and met needs [15], according to whether people are receiving effective services or care, or not. Moreover, others have pointed out the importance of the existence of treatment to determine whether something is a need. That is, to say, if no treatment exists for an illness, one could argue that, rather than a need for this treatment, there is a want.

9.4.3 Efficacy, effectiveness and efficiency The study of the efficacy, effectiveness and efficiency of mental health services is among the key issues in Mental HSR. The concepts refer to the effects of an intervention. Efficacy is assessed by answering the question ‘Can it work?’ That is, does a given intervention causes more good than harm to specifically diagnosed patients who are adequately treated and who totally comply with the full treatment? In other words, efficacy tries to assess whether an intervention (be it a drug, a surgical procedure or an organisational arrangement) works in ideal 137

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conditions. Typically, randomised clinical trials are designed to evaluate the efficacy of interventions. On the other hand, effectiveness is measured by answering the question ‘does it work?’ That is, in everyday conditions, will the treatment work? Everyday conditions can depart from the ideal for a number of reasons, such as incomplete diagnostic efforts, comorbidity and insufficient compliance by the provider and/or the patient. Assessing effectiveness is important for more accurate planning and evaluation of services provision. Lastly, efficiency takes into account the relationship between costs and effects. Two different types of efficiency are defined: (i) Production efficiency refers to achieving a given level of output at minimum cost, that is if two interventions obtain the same results, the intervention with lower costs will be more efficient. (ii) Allocative efficiency refers to maximising the results, in this case on population health, with a given amount of resources. With a health budget, maximum allocative efficiency will be achieved if resources are devoted to the interventions that produce the maximum improvement in health [19]. There are three ways of estimating efficiency depending on the way outcome is measured: costeffectiveness, cost-utility and cost-benefit. All three take into account costs in monetary units (which could be direct, such as the costs of treatments, or indirect, for example productivity losses associated with illness) but differ in the unit of outcomes: in cost-effectiveness analysis the consequences of the intervention (outcomes) are measured in the most appropriate natural effects or physical units, such as ‘reduction in psychotic symptomatology’ or ‘cases adequately detected’. In cost-utility analyses, the outcomes are measured in health state preference scores or utilities. The most common measure used in cost-utility analysis is the quality-adjusted life-year (QALY). Finally, cost-benefit analysis measures the consequences, the outputs, in monetary terms, for instance, applying a monetary value to the illness status or life [20].

9.4.4 Appropriateness of care Most of the concepts discussed above deal with the results, outputs or outcomes of health services. When

138

interested in evaluating the process of providing services, adequacy or appropriateness of care is important. Appropriateness tries to assess whether the particular patient receives adequate treatment, in a timely manner, from the appropriate professional, in the right setting. According to Shape and Faden [21], the concept of appropriateness has to be considered from at least three different perspectives: (i) the clinical point of view, that is is there enough evidence about a procedure in terms of potential benefits and harm?; (ii) the perspective of the individual patient; that is when studying appropriateness, the values and ‘nonclinical’ benefits and harm to the patients and their interests have to be incorporated. In other words, from a patient’s point of view, an intervention will be considered as adequate when the patient has participated in the decision-making process and has freely accepted it once informed and, finally, (iii) from the societal point of view, that is in an era of escalating healthcare costs and contained financing, procedures should also be cost-effective. The relationship of needs with effectiveness and adequacy can be understood through the following example. Imagine an epidemiological research study designed to assess whether the citizens of a region with mental health needs are receiving appropriate interventions. Following the steps suggested by Spasoff [15] and Muir Gray [22], we should proceed as follows: 1 We should estimate the number of people in need (as a proxy we can use the prevalence of people with mental disorders). 2 We should measure the actual level of health service utilisation by people with the problem (that is how many people with mental disorders are using health services for their emotional problems?). 3 We should determine, from evidence-based literature, which interventions are beneficial (effective and/or cost-effective) for their problems. 4 We should try to assess whether recommendations from literature are consistent with the kind of care that they are receiving. This type of approach is illustrated in Table 9.2. In cell (a) there is the number of cases for which the intervention is indicated and who is actually receiving

AN INTRODUCTION TO MENTAL HEALTH SERVICES RESEARCH Table 9.2 The relationship between needs for treatment and appropriateness of care.

Intervention is indicated Intervention is not indicated Total

Receiving recommended intervention

Not receiving recommended intervention

Not receiving any intervention

Total

(a) Met need/adequately treated (d) Inappropriate treatment Total treated

(b) Inappropriate treatment

(c) Unmet need

Total need for intervention

(e) Inappropriate treated

(f) Appropriate non-treatment Not treated

No need for intervention Total cases of problem

Adapted and modified from: Spasoff RA (1999) Epidemiologic Methods for Health Policy, Oxford University Press, New York, p. 111 [15].

it. These could be considered as patients whose needs have been met. Cell (b) represents the number of people for whom the intervention is needed but they are receiving an intervention which does not meet minimum quality standards. In cell (c) are those patients who are not receiving any treatment, despite their need for it. These are the cases with unmet need. Cells (d and e) indicate misuse of resources such as cases where people are receiving treatment for which the intervention is not indicated. In other words, there are cases of inappropriate treatments. Lastly, cell (f) shows those cases without a need for intervention who are, appropriately, not treated. Nevertheless, this approach has some limitations which should be acknowledged. If we use ‘normative’ needs, assuming that anyone with a psychiatric diagnosis is in need, we could be overestimating the number of people with unmet needs. Moreover, it is important to bear Tansella’s and Thornicroft’s matrix in mind, and try to describe the various factors that would explain why people are not expressing their needs or receiving the required treatment. Of course, unmet need could also simply be due to the fact that effective treatments are not being supplied in a particular country/area, are not considered to be cost-effective, or a lack of conclusive evidence exists regarding effective treatments for a problem. Another limitation of this approach is that it does not consider the patient’s perspective. A person could be diagnosed but does not feel disabled enough to seek care or, conversely, a person could be in need of some kind of mental health care that does not meet diagnostic criteria.

9.4.5 Small area variations (SAV) Related to the study of appropriateness, another important issue for Mental HSR is the study of small area variations (SAVs). This concept refers to the large differences in the rates of use of medical services between geographical regions. Such variations can be detected between countries, provinces or regions [23]. The study of SAVs is important because it could indicate poor access to health services or underuse of resources in some areas. It could also show iatrogenic consequences of overuse. Briefly, the steps in analysing SAVs are: 1 Determination of numerator. For instance, number of emergency psychiatric consultations during a month. 2 Determination of denominators. For instance, health regions. 3 Adjustment for age and gender. 4 Use of statistical test to control random fluctuations. Different hypotheses have been put forward to explain what causes SAVs. Among the most commonly cited are the following: 1 The uncertainty hypothesis: according to this hypothesis, formulated by the first time by Wennberg [24], variability is low when there is clinical consensus (and/or scientific evidence) about which is the best procedure. When there is uncertainty about the best therapeutic option, health professionals act for the best according

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to their own criteria. In these cases of high uncertainty, factors related to health-system provisions play an important role in explaining SAV. 2 Enthusiasm hypothesis: this hypothesis suggests that the inappropriate use of a procedure is equal in areas with high and low use of services. Nevertheless, in areas with high use of services, there are few clinicians who are enthusiastic about a procedure being responsible for the variability [25]. 3 Patient practice variations hypothesis: states that differences in morbidity explain SAV. Variables related to demand (i.e. the patient) such as socioeconomic level, studies, ethnicity, health status and beliefs are the main source of variability [26].

9.4.6 Factors associated with access to health care Different models have been proposed to understand why people access health care. One of the most used is the Behavioral Model and Access to Medical Care by Ronald M. Andersen [27]. Figure 9.1 depicts the components and their interrelation. This model suggests that people’s use of health services is a result of a combination of factors related to the environment, their predisposition to use these services, along with factors that may enable or impede use, and their need for health. It also includes feedback loops. For instance, outcomes may, in turn, affect perception of need and health behaviour. The first component of the model, the environment, refers explicitly to the national health policy, the resources devoted to health and their organisation. For instance, in a country with a national health system with universal coverage, higher access to healthcare than in a country with a private health system would be expected. With respect to external environment, the influences of political and economical components are also taken into account. The second component, population characteristics, covers three distinct factors: • Predisposing characteristics include: demographic characteristics such as age and gender; social structure (education, occupation and ethnicity); social networks, interactions and networks and 140

the health beliefs that comprise the attitudes, values and knowledge that people have about both health and health services. In the case of Mental HSR, the stigma associated with mental disorders is also one of the key elements that could explain lower use. • Enabling resources refer to the community and personal facilities that people have. For instance, income, health insurance, a regular source of care and perceived social support, are just some of the enabling factors. • The perceived need for care, as discussed above. The third component of the model is the use of health services per se, traditionally the main outcome. Additionally, other personal health practices, such as diet, exercise or self-care are recognised as interacting with the formal use of services. The inclusion of other outcomes (fourth component) such as: perceived health status, evaluated health status and consumer/user/patient satisfaction allows research to include other outcomes that could be important to health policy. Thus, Andersen suggests some additional measures such as ‘effective access’, which is achieved when utilisation studies show that use improves health status or consumer satisfaction with services, and ‘efficient access’ which is established when the level of health status or satisfaction increases relative to the amount of health care services consumed.

9.4.7 Equity The International Society for Equity in Health (ISEqH) defines equity in health as: ‘the absence of potentially remediable, systematic differences on one or more aspects of health across socially, economically, demographically or geographically defined population groups or subgroups’ [28]. Investigations related to mental HSR and equity will explore, for instance, whether people with equivalent needs receive equal treatment (horizontal equity) or whether those with greater mental health needs receive preferential treatment (vertical equity).

AN INTRODUCTION TO MENTAL HEALTH SERVICES RESEARCH

ENVIRONMENT Health care system

External environment

POPULATION CHARACTERISTICS Predisposing characteristics

Enabling resources

Need

HEALTH BEHAVIOUR Personal health practices

Use of Health Services

OUTCOMES (satisfaction with treatment, perceived or/and evaluated health status, quality of life…)

Fig 9.1 Behavioral Model and Access to Medical Care by Ronald M. Andersen. Adapted from: Andersen RM (1995) Revisiting the behavioral model and access to medical care: does it matter? Journal of Health and Social Behavior, 36, 1–10 [27].

Using his model of access to care, Andersen defines equitable access as occurring when demographic and need variables account for most of the variance in utilisation, whereas inequitable access occurs when social structure (for instance ethnicity), health beliefs or enabling resources (income) determine who gets medical care [27].

9.5 Examples of mental health services research studies In this section we describe and discuss several studies in the area of Mental HSR to provide a more applied perspective of the concepts outlined in the first part of the chapter. Mental HSR is a multidisciplinary area of knowledge and, as such, it implies the use of different methodologies which depend on the main aim of the study. In this second part we present studies based both on administrative data and on primary data collection, including some examples of qualitative studies.

9.5.1 Administrative data Deinstitutionalisation radically changed how mental health care attempts to meet patients’ wants and needs. No longer does the state hospital try to meet these multiple wants and needs; a great number of alternative community-based settings and alternative inpatient settings have sprung up since deinstitutionalisation [4]. In fact the principles of psychiatric reform emphasised the need to focus on community care, with no more admissions to state psychiatric hospitals and with in-patient care provided in small wards in general hospitals. To determine whether this objective was met, we can use available case registers and study patterns of service use. The paper by Tansella and colleagues [29] is an example of this. In this paper, they describe the development of a community-based mental health service in South Verona (Italy), the patterns of care provided by this new service, and its costs since its set-up in 1978. Using the South-Verona Psychiatric Case Register they were able to show that, between 1979 and 2003, 141

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hospital care consistently decreased, whereas outpatient care, home visits and day-hospital increased. Specifically, hospital rates decreased from almost 350 patients per 100 000 adult South Verona residents in 1979, to just 50 patients per 100 000 South Verona residents in 2003. On the other hand, outpatient/community care increased from nearly 25 patients per 100 000 residents in 1979 to more than 250 per 100 000 in 2003. Twenty-five years after the reform (from 1978 to 2003) there was a 29% decrease of inpatient admissions, with a 56% decrease in compulsory admissions. The mean number of occupied beds per day decreased over time, falling by 81% between 1977 and 2003. Figure 9.2 shows the patterns of inpatient admissions from 1977 to 2004. This study could be seen as evidence of the achievement of one of the main objectives of psychiatric reform. These kinds of studies could be useful in monitoring and evaluating the implementation of a programme or a new policy. One of the main studies in Mental HSR is the World Health Organization (WHO) Mental Health Atlas. Following Thornicroft and Tansella’s matrix, this study is an example of a country-input study, as it is comparing resources devoted to mental health (inputs) in different countries that are grouped into wide regions. This project was initiated in 2000 with the objectives of collecting, compiling and disseminating global information on mental health resources and services in each country [30]. With this information, WHO aims to show both

public and professionals the inadequacies of existing resources and services devoted to mental health, and the large inequities in their distribution at national and global level. In 2005 this information was updated in a second edition of the Atlas. Information was obtained from the Ministry of Health of each country and triangulated with results of an exhaustive literature search and with other kind of documents submitted and collected by WHO Regional Offices staff. Information was also checked with experts and members of the World Psychiatric Association. The 192 WHO member states and the 11 associated members are represented in the Atlas. This represents nearly 99% of the world’s population. As an example, Table 9.3 shows a comparison of the median number of different mental health professionals per 100 000 inhabitants, according to WHO Regions. As can be observed, there is a large variation in the number of professionals from region to region. For instance, there are nearly 1800 psychiatrists for 702 million people in the African Region, compared with more than 89 000 psychiatrists for 879 million people in the European Region. It points out not just the lack of resources but also the high inequities in resource distribution. Such information has potential value for planning mental health services both at national and international level. Moreover, as information is updated, comparisons and changes in resources devoted to mental health can be monitored, indicating whether

600

Compulsory

500

To state mental hospital (voluntary)

400 300

To public care

200

To private care

100

TOTAL

0 1977

1979

1983

1987

1991

1995

1999

2003

Fig 9.2 Patterns of in-patient admissions from 1977 to 2003 in South Verona (ratios per 100 000 residents). Own elaboration with data obtained from: Tansella M, Amaddeo F, Burti L, Lasalvia A and Ruggeri M (2006) Evaluating a communitybased mental health service focusing on severe mental illness. The Verona experience. Acta Psychiatrica Scandinavica, 113, 90–94 [29].

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AN INTRODUCTION TO MENTAL HEALTH SERVICES RESEARCH Table 9.3 Median number of mental health professionals by WHO regions.

Psychiatrists Psychiatric nurses Neurologists Neurosurgeons Psychologists working in mental health Social workers working in mental health

Africa

Americas

Eastern Mediterranean

Europe

South-East Asia

Western Pacific

World

0.04 0.20 0.02 0.01 0.05

2.00 2.60 0.70 0.40 2.80

0.95 1.25 0.30 0.20 0.60

9.80 24.8 4.00 1.00 3.10

0.20 0.10 0.05 0.03 0.03

0.32 0.50 0.00 0.00 0.03

1.20 2.00 0.30 0.20 0.60

0.05

1.00

0.40

1.50

0.04

0.05

0.40

Own elaboration with data obtained from: World Health Organization (2005) Mental Health Atlas, World Health Organization, Geneva [30].

specific policies aimed at improving resources have been effective. For instance, comparisons of data collected in 2001 and updated in 2004 show an increase in the quantity of mental health professionals in the world, the number of psychologists and social workers showing the greatest increases (with increases in median of 0.2 points and 0.1 points per 100 000 inhabitants respectively). There were no major changes in the median number of other professionals. Comparisons between large regions are interesting from a macro/international standpoint. Nevertheless, it would be interesting to complete and compare these results with data obtained at a meso-level, that is with data gathered in municipalities, health areas or districts, as it may diverge from data aggregated at higher levels (i.e. countries). The meso-level comparison of mental health service availability is related to the study of SAV in medical procedures. One of the major difficulties when comparing availability of services in different areas (even within the same countries) is the different names that services are given. Moreover, the name they receive may or may not describe its main activity, which can make comparisons difficult. To deal with this barrier, in 1994 a group of investigators named the European Psychiatric Care Assessment Team (EPCAT) group began to work towards the establishment of a standardised methodology for the description and assessment of the care received by people suffering from mental disorders. They developed the European Service Mapping Schedule (ESMS). The ESMS is an instrument that serves three purposes: (i) to compile the adult mental health services of a catchment

area; (ii) to describe and compare the structures and types of mental health services between catchment areas and (iii) to measure and compare the levels of provision of major types of mental health services between catchment areas. The ESMS uses atheoretical descriptors based on the main types of care: (i) residential care; (ii) day care and (iii) outpatient and communiy care. By choosing these terms, the ESMS avoid using culturally laden words (such as rehabilitation) or common names designing different types of care (day-centre). Moreover, each type of care is divided according to whether patients stays overnight at the service, receives care in a day-care facility or has face-to-face contact with the professional. Secondary and tertiary subdivisions are made on the basis of other characteristics such as: intensity, time of stay and mobility [31]. Graphically, the ESMS can be seen as a ‘service tree’ (Figure 9.3). Salvador-Carulla et al. [32] used the ESMS to make a meso-level comparison of mental health service availability and use in Chile and Spain. They selected small areas (catchment areas) with marked differences regarding organisation and provision of services. The areas selected in Spain were: Gava` (Catalonia, in the north-east), Granada-Norte (Andalusia, South) and Rochapea (Navarre, North). The three areas differed in the socioeconomic, distribution and organisation models for their mental health services. It is also important to note that in Spain the responsibilities of the National Health System and Social Services have been gradually transferred to each of the 17 autonomous regions that comprise Spain. The three small Spanish areas selected are from different autonomous regions, with 143

144 Hospital

Non-acute

Daily support

24-h support

Indefinite stay

Daily support

24-h support

Time limited

Daily support

24-h support

Indefinite stay

Daily support

24-h support

Time limited

Acute

Non-hospital

Non-acute

Non-hospital

Hospital

Generic acute

Day & structured activity

Work

Work

Social support

Other structured activity

Work related activity

Low intensity

Social support

Other structured activity

Work related activity

High intensity 24 h

Limited hours

24 h

Moderate intensity Low intensity

Low intensity

Moderate intensity

High intensity

Non-mobile

Mobile High intensity

Self-help & non-professional

Continuing care

Limited hours

Non-mobile

Mobile

Emergency care

Out-patient & community

Fig 9.3 The ESMS service tree. Modified from: Johnson S, Kuhlmann R and the EPCAT group (2000) The European Service Mapping Schedule (ESMS): development of an instrument for the description and classification of mental health services. Acta Psychiatrica Scandinavica, 102, 14–23 [31].

Secure

Residential

Mental Health Services

AN INTRODUCTION TO MENTAL HEALTH SERVICES RESEARCH

different mental health services and objectives. The Chilean areas were: Concepcion and Talcahuano. On the one hand, the organisation of services in Concepcion is more traditional (dating from the 1960s). On the other hand, provision of mental health services in Talcahuano was reorganised during the 1990s. Briefly, the procedure for data collection for the ESMS began in each area with a face-to-face interview with the head of the community mental-health centre and the reference hospital setting. A map of the services and the main local administrative data source were identified. Figures 9.4–9.7 show the utilisation rates of the main types of care in the five small health areas per 100 000 inhabitants. This study showed that there were differences in the use of residential and day-care facilities between Spanish and Chilean areas. However, if we look data in detail, the rate of continuous outpatient care in Chilean areas was closer to that of the Rochapea area than the other two Spanish areas. This could be related to the greater availability of these kinds of services in these areas which could have an impact on demand as well as the clinical pattern. This study also showed the lack of availability of day-care services and acute care. It demonstrated that patterns of hospital residential care in Chile and Spain were more similar than expected. In fact, the poorest Spanish studied area (Granada) was very similar to the Chilean ones. Combining data from the WHO Mental Health Atlas with meso-level data offers a more accurate picture of the use of mental health services. Another example of the use of the ESMS could be found in the study by Pirkola et al. in Finland [33],

which aimed to investigate the relation between suicide risk and different ways of organising mental health services in the 428 municipalities that make up Finland. Each of these municipalities has nearly 5000 inhabitants. The provision of mental health care has been transferred to these municipalities, so management structure and procedures vary widely among them. Again, following the mental health matrix, this study could be seen as an example of meso-level comparison, but in this case the authors compare outcomes (suicide) rather than inputs. The authors obtained ESMS data by means of interviews with the 20 mainland Finnish hospital districts, and from health care and social-care officers. Data on suicide was obtained from Statistics Finland. Findings from this study suggested that, after controlling for socioeconomic factors, those municipalities with a predominance of outpatient services had a low suicide rate (relative risk (RR) 0.94, 95% CI 0.90–0.98). In spite of the cross-sectional design of the study that precluded causal implications, results were consistent with results of a meta-analysis that suggested that patients treated by community mental-health teams are less likely to kill themselves. Studies made with administrative data have some advantages: they are readily available, normally they are inexpensive to acquire, they are computer-based and typically have a big sample size. Nevertheless, when compared with studies using primary data, some limitations have to been acknowledged. The main disadvantage is that, in most cases, sociodemographic information is scarce. Moreover, with administrative data, the study of unmet needs from the general population can not

30 25 20 15 10 5 0

Rochapea Gavà Granada Norte Concepcion Hospital acute

Hospital non-acute:total

Nonhospital: total

Talchuano

Fig 9.4 Comparison of mental health services in five small areas. Residential care (beds occupied per month per 100 000 population). Own elaboration with data obtained from Salvador-Carulla L, Sladivia S, Mart´ınez-Leal R, Vicente B, Garc´ıaAlonso C, Grandon P and Haro JM (2008) Meso-level comparison of mental health services availability and use in Chile and Spain. Psychiatric Services, 59, 421–428 [32].

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120.00 100.00

Rochapea

80.00

Gavà

60.00

Granada Norte

40.00

Concepcion Talchuano

20.00 0.00 Day-care (users per months per 100 000 population)

Fig 9.5 Comparison of mental health services in five small areas. Day care (day and structured activities). Own elaboration with data obtained from Salvador-Carulla L, Sladivia S, Mart´ınez-Leal R, Vicente B, Garc´ıa-Alonso C, Grandon P and Haro JM (2008) Meso-level comparison of mental health services availability and use in Chile and Spain. Psychiatric Services, 59, 421–428 [32].

250

Rochapea Gavà Granada Norte Concepcion Talchuano

200 150 100 50 0 Emergency

Fig 9.6 Comparison of mental health services in five small areas. Outpatient and ambulatory care I (contacts per month per 100 000 population). Own elaboration with data obtained from Salvador-Carulla L, Sladivia S, Mart´ınez-Leal R, Vicente B, Garc´ıa-Alonso C, Grandon P and Haro JM (2008) Meso-level comparison of mental health services availability and use in Chile and Spain. Psychiatric Services, 59, 421–428 [32].

3000 2500

Rochapea

2000

Gavà

1500

Granada Norte

1000

Concepcion

50 0 0

Talchuano Continuing care

Fig 9.7 Comparison of mental health services in five small areas. Outpatient and ambulatory care II (services users per month per 100 000 population). Own elaboration with data obtained from Salvador-Carulla L, Sladivia S, Mart´ınez-Leal R, Vicente B, Garc´ıa-Alonso C, Grandon P and Haro JM (2008) Meso-level comparison of mental health services availability and use in Chile and Spain. Psychiatric Services, 59, 421–428 [32].

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be studied. Epidemiological studies could deal with these disadvantages, helping, with the information obtained, to document service use and unmet need for treatment.

9.5.2 Studies using primary data collection One of the most important epidemiological initiatives for Mental HSR is the World Mental Health (WMH) Survey Initiative. This project, sponsored by WHO [34], aims to obtain cross-national information on the prevalence and correlates of mental, substance and behavioural disorders in all WHO Regions. To date, 28 countries are participating in this study. Using data from the WMH surveys Wang et al. published a paper in 2007 examining frequency, types and adequacy of mental health service use in 17 countries in which surveys were completed at the time of their study [35]. The main strength of this initiative is the use of common methodology in all the countries. Briefly, face-to-face household interviews were carried out in population representative samples in the participating countries, providing a total sample of nearly 85 000 respondents from lowincome, middle-income and high-income countries. Presence of lifetime, 12-month and current mental disorders were assessed with the CIDI 3.0, a structured diagnostic interview which can be administered by trained lay interviewers. The CIDI 3.0 can provide diagnosis of mental disorders based on criteria from the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSMIV) or the International Classification of Diseases (ICD-10). The CIDI 3.0 has been proven to generally show good agreement when compared to clinical diagnosis [36]. Disorders included in this study were agoraphobia, generalised anxiety disorders, panic disorder, post-traumatic stress disorder, social phobia, specific phobia, bipolar disorder type I and II, dysthymia, major depressive disorder and substanceuse disorders. Mental disorders were classified as serious, moderate or mild depending on specific criteria regarding functioning, disability and clinical aspects. Services received in the previous 12 months were assessed by asking respondents if they had ever seen any type of professional, either as an outpatient or inpatient, for problems with emotions, nerves,

mental health or use of alcohol or drugs. Included were mental health professionals (e.g. psychiatrist, psychologist), general medical professionals (e.g. family doctor, occupational therapist) and other non-health professionals such as religious counsellors or traditional healers. Examples of these types of providers were presented in a Respondent Booklet used as a visual recall aid and varied somewhat across countries depending on local circumstances. Follow-up questions asked about age at first and most recent contacts as well as number and duration of visits in the past 12 months. They also estimated the proportion of participants who potentially could have received minimally adequate treatment according to evidence-based guidelines. Treatment was considered adequate if the participant received medication for at least 1 month plus at least four visits to any type of medical doctor, or did not receive medication but had made at least eight visits to any type of professional. The proportion of respondents having made any use of health services by severity of mental disorders is shown in Figure 9.8. As can be seen, there is a relation between disorder severity and the probability of any use of health services in all the countries except in China. In general, the more severe the disorder, the greater the probability of health service use. On the other hand, the proportion of respondents using 12-month services for their emotional problems is lower in low-income countries than in developed countries. This may indicate a serious equity issue. Another problem highlighted in these results is the higher number of people with unmet need for treatment, even among those with the most severe disorders. This problem is worse in developing countries, but even in developed countries only half of those with serious mental disorders receive any kind of treatment. It is also interesting to note that there is a proportion of participants without mental disorders who use services for emotional problems. In theory, this use could be considered as an inappropriate and inefficient use of services. However, they could be affected by disorders not assessed in the survey, or in maintenance treatment for a disorder which occurred in the past. A limitation of the WMH consortium’s approach is that they are using ‘normative needs’ (i.e. they considered that people affected by mental disorders 147

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Belgium USA Spain New Zealand Israel Italy Netherlands France Germany Colombia South-Africa Mexico Ukraine Japan Nigeria Lebanon China 0

10

20

30

40 severe

50 moderate

60 mild

70

80

90

100

none

Fig 9.8 Use of mental health services by severity of mental disorders and country. Own elaboration with data extracted from Wang PS et al. (2007) Use of mental health services for anxiety, mood and substance disorders in 17 countries in the WHO world mental health surveys. The Lancet, 370, 841–850 [35].

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are objectively in need of treatment), and not a ‘subjective’ approach (based on what people feel they need). So the data should be interpreted with caution. The subjective approach in the assessment of needs is exemplified by the work analysing patient needs using the Camberwell Assessment of Need (CAN) inventory. For example, Ochoa et al. [37] conducted a study which evaluated 231 people with schizophrenia living in the city of Barcelona and its surroundings. The CAN instrument is useful in helping professionals to design treatment plans for their individual patients, but also in studying the performance of mental health services. The CAN evaluates the presence of need in 22 areas: accommodation, food, house upkeep, self-care, daytime activities, physical health, psychotic symptoms, information, psychological distress, risk to self, risk to others, alcohol, drugs, company, intimate relationships, sexual expression, child care, education, telephone, transport, money and benefits. For each of these areas, the CAN determines, if a need is detected, whether it is met, who provides the care (formal or informal care) and whether the help provided is appropriate. The questionnaire is completed independently by the staff and the patient. This double assessment allows the comparison of normative needs with felt needs. Briefly, this study pointed out that staff detected more needs than patients did (staff mean = 6.6 (SD = 3.17) vs. patients mean = 5.36 (SD = 2.71); p < 0.0001). The most frequent detected needs by patients were: psychotic symptoms, house upkeep, food and information. Staff detected needs in the areas of psychotic symptoms, company, daytime activities, house upkeep, food and information. With regard to who gave the required help, results showed that patients received more informal than formal help (75% of participants with met needs received informal help while, on the other hand, less than 50% received formal help). Regarding unmet needs, they also found that staff rated more areas as unmet needs than patients did (staff mean = 1.38 (SD = 1.75) vs. patients mean = 1.82(SD = 1.98); p < 0.0001). Most frequent unmet need expressed by patients included: companionship, intimate relationship, sexual expression and daytime activities. The same areas were detected by staff. It is important to note that in most of the unmet areas, the participant

reported that they received help; although this was not considered sufficient to meet their need. So far we have reviewed examples describing or comparing data. These kinds of studies are usual in Mental HSR and are useful for analysing and planning the needs of a given community. But Mental HSR is also interested in assessing the performance of programs or interventions focused on mental or emotional problems. One example of such studies is the UK 700 case management trial [38]. This study was carried out in four centres, three in London and one in Manchester, which obtained, in 1993, funding from the National Health Service (NHS) for a randomised, controlled trial of intensive case management (ICM). Investigators aimed to investigate the cost-effectiveness of ICM (case-load size 10–15) compared with standard case management (SCM) (case-load size 30–35) for patients with severe psychosis. A total of 708 patients with psychosis and a history of repeated hospital admissions were randomly allocated to ICM or SCM and assessed at baseline, 12 and 24 months by researchers independent of those providing clinical care. They did not find any differences in terms of days in hospital for psychiatric problems over 24 months, or in the scores of the Comprehensive Psychiatric Rating Scale, in the Quality of Life, in the assessment of unmet needs, in the mean Disability Assessment Schedule total score or in patient satisfaction. Nor did they find differences between ICM and CSM in the total 2-year costs of care per patient. As neither form of case management was better than the other, the authors conclude that formal cost-effectiveness analyses were not required. This study had a clear policy implication: it contradicted the policy of advocating ICM for patients with severe psychosis, as their study showed no beneficial effects of ICM on costs, clinical outcomes or cost-effectiveness. Another example is the paper by Bellon et al. [39] carried out in a primary care setting aiming to assess the effectiveness of general practitioner intervention to reduce frequent-attendee consultation. This study was carried out by a multidisciplinary team formed by general practitioners, statisticians and psychiatrists. The interest of this study from a Mental HSR standpoint is that, typically, frequentattendee consultations are sought by people affected by emotional problems or mental disorders. The 149

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authors designed a randomised, controlled trial with frequent attendees divided into an intervention group (N = 66) and two control groups (CG1, N = 71; and CG2, patients who consulted the same general practitioners (GPs) as the intervention group, N = 72). A total of six GPs participated in the study. GPs on the control groups were blind to which patients were selected to be acting as controls. They used two different control groups, CG1 absolutely na¨ıve to the intervention, and CG2 formed by those GPs also in the intervention group aiming to study if intervention was interiorised. The setting was a primary health care centre in southern Spain. Authors identified the sample of frequentattendees with reference to mean annual consultation rates (before intervention) at the health centre, stratified by sex and age. Frequent attendees were considered to be those patients who had an annual rate of consultation at least twice as high as the sexand-age-related mean for the health centre; that is, nearly the 90th percentile of the overall distribution. The intervention aiming to reduce frequentattendee visits was called by the investigators, the ‘seven hypotheses + team’ intervention. The three GPs in the intervention group underwent an interactive workshop training session (15 hours). Briefly, this intervention encourages GPs to select, from a list of seven possible hypotheses, a reason why the patient is a frequent attendee: biological, psychological, social, family, cultural, administrative–organisational or related to the doctor–patient relationship. After this, GPs share their analysis with other GPs regarding the hypothesis and the plans derived from it (this is the team component of the intervention). The frequent-attendees’ mean consultations by group at baseline and 1-year after intervention with GPs are detailed in Figure 9.9. At the end of the follow-up it was observed that the intervention group had significantly fewer visits than control group 1 (p < 0.001) and control group 2 (p < 0.001). Moreover, CG2 (those patients whose GPs form part of the control and intervention groups) also showed a reduction between visits at baseline and 1 year later (p < 0.001). All the results were adjusted by covariates such as chronic diseases and self-reported health, provider-use interface variables (such as traveling time to the health centre 150

and satisfaction with the GP), sociodemographic and psychosocial variables. Pending further evidence, the intervention showed a significant and relevant reduction in frequent-attendee consultations. This study could be seen as an example of a patientprocess study.

9.5.3 Qualitative studies The use of qualitative methodologies in the Mental HSR is relatively recent. According to the review made by Murphy et al. in 1998 [40], qualitative methods could be particularly useful in order to understand the findings of outcome studies in HSR. Qualitative research could provide the information that both policy makers and clinicians need to translate the findings of research into interventions and changes in policies and health services. In this sense, qualitative methods are very close to the field of implementation research, an emerging science that could be defined as ‘the systematic study of how a specific set of strategies are used to successfully integrate evidence-based public health interventions within specific settings’ [41, 42]. For instance, Hysong et al. conducted a qualitative study in which 102 employees involved in the implementation of clinical guidelines in different centres were interviewed. They were asked about specific strategies for its implementation. Results showed that in those centres where strategies were adapted to the local context, implementation was successful [43]. Additionally, other areas where qualitative methodology has shown particular strengths are: (i) in the identification of natural solutions to problems; (ii) in the studies about processes, focused on the functioning of a programme or a team and aiming to understand its internal organisation and, (iii) in comparative analyses, for instance, between different ways of coordinating services. Studies aiming to describe problems of coordination among professionals could be seen as examples of research at the local level, focusing on ‘invisible’ inputs. For instance, Calderon and colleagues [44] carried out a study to find out what family doctors and psychiatrists thought about their collaboration in the healthcare of patients with depression. A total of 29 family doctors and 13 psychiatrists participated in four discussion groups (two for family doctors and

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25

visits

20 Intervention Group (IG) Control Group 1 (CG1) Control Group 2 (CG2)

15 10 5 0 Baseline

1 year

Fig 9.9 Frequent attendees’ mean consultations by group at baseline and one year after intervention with GPs. Own ´ JA; Rodr´ıguez-Bayon ´ A, de Dios Luna J and Torres-Gonzalez ´ elaboration with data obtained from Bellon F (2008) Successful GP intervention with frequent attenders in primary care: randomised controlled trial. British Journal of General Practice, 58, 324–330 [39].

two for psychiatrists). In these groups, they related their experiences of treating patients with depression. Results showed that the perceptions and attitudes of the two types of professionals were different. They had diverse views on the patients, the health context and their own expectations. For instance, family doctors often found that patients with depression consulted on another type of health problem which made the diagnosis of depression more difficult. For them, previous knowledge of the patient was a great facilitator in a correct diagnosis of depression. Family doctors did not feel skilled enough to deal with mental disorders. When asked about referral to a psychiatrist, family doctors explained that referral did not only depend on the severity of the problem or its course. Other factors, such as the relationship with the patient, the previous experience with the psychiatrist and the knowledge that the family doctor had about the functioning of the Outpatient Mental Health Center were also critical in deciding which patients to refer. On the other hand, psychiatrists usually attended the patients when they were referred by a primary doctor. Psychiatrists felt that a diagnosis of depression by a family doctor was sometimes inappropriate, since family doctors could label a person with a depression diagnosis who is having social problems, or who suffers from personality, or even psychotic, disorders. Psychiatrists did not know much about the primary health care context, their relationship with the patient is

also conditioned by their relationship with family doctors. Moreover, their expectations were focused on treating serious mental disorders and not on mental problems of lesser severity. Among the ideas that both types of professionals shared, they found the lack of resources, the progressive psychiatrisation of sadness and the low tolerance of frustration in citizens. This study showed that, independently of the macro aspects which are out of clinicians’ control, in order to improve treatment of depression in public health, family doctors and psychiatrists needed to share the same knowledge and to adopt a patient-centred approach. Finally, the role of users and their relatives in the Mental HSR is progressively increasing. Relatives have been involved in mental HSR since it was first set up. For instance, the National Alliance on Mental Illness (NAMI), a non-profit organisation formed by consumers, relatives and friends of people with mental problems, has been fighting against stigma since its inception in 1979 and they have now included research among their aims. In Europe it is also worth mentioning the efforts of the European Federation of Associations of Families of People with Mental Disorders (EUFAMI) that have had, since 2004, a Research Advisory Group aiming to initiate different research projects in cooperation with investigators. On the other hand, initiatives such as the Patient Programmed Expert [45] or the Best Practice Guidelines for consumer-delivered services [46] could be seen 151

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as an example of user involvement in Mental HSR. Using a narrative approach, research performed in collaboration with patients’ organisations gives us information about how patients experience their illness and how they perceive stigma and their relationship with mental health services. An example of this kind of research is that carried out by ADEMM, the mental health users association of Catalonia (Spain), focused on the relation between users and professionals in the field of mental healthcare. This research is interesting because it was done by mental health patients with the collaboration of the Catalan Department of Health and the methodological supervision of a psychosocial research centre. This is an example of how patients could be empowered by means of research [47].

9.6 Conclusion In this chapter we have presented a basic description of the major issues in the field of mental HSR. We have used the well-known Mental Health Matrix by Tansella and Thornicroft as a way of organising this field of knowledge. The major concepts identified include the need for care, appropriateness, effectiveness and equity. We have shown how these concepts are dealt with in some examples. Each of them deserves further attention and could easily provide enough information for its own handbook. As a conclusion, Mental HSR could be important for providers, administrators and the public, for at least the following four reasons: (i) to guarantee resource efficiency and avoid waste; (ii) to help to establish priorities in limited resource environments; (iii) to reduce mental health inequities and, (iv) to provide a base of evidence for mental health planning [48].

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[3] Black, N. (1997) Health services research: saviour or chimera? Lancet, 349, 1834–1836. [4] Anthony, W.A. (1993) Recovery form mental illness: the guiding vision of the mental health services system in the 1990s. Psychosoc. Rehabil. J., 16, 11–23. [5] Tansella, M. and Thornicroft, G. (1998) A conceptual framework for mental health services: the matrix model. Psychol. Med., 28, 503–508. [6] Stein, L.I. and Test, M.A. (1980) Alternative to mental hospital treatment I. Conceptual model treatment program, and clinical evaluation. Arch. Gen. Psychiatry, 37, 392–397. [7] Hoult, J. and Reynolds, I. (1983) Psychiatric Hospital Versus Community Treatment: A Controlled Study. New South Wales Department of Health, Canberra. [8] Institute of Medicine (1995) Committee on Health Services Research: Training and Work Force Issues, Health Services Research: Workforce and Educational Issues, National Academy Press, Washington, DC. [9] Helping the Nation With Health Services Research. Fact Sheet. AHRQ Publication No. 02-P014, March 2002. Agency for Healthcare Research and Quality, Rockville, MD. Available from http://www.ahrq.gov/ news/focus/scenarios.htm (accesed 7 October 2010).May 2008. [10] Lohr, K.N. and Steinwachs, D.M. (2002) Health services research: an evolving definition of the field. Health Serv. Res., 37, 7–9. [11] Busch, A.B. (2006) Recent advances in mental health services research: introduction. Harv. Rev. Psychiatry, 14, 183–184. [12] Thornicroft, G. and Rose, D. (2005) Health services research: is there anything to learn from mental health? J. Health Serv. Res. Policy, 10, 1–2. [13] Thornicroft, G. and Tansella, M. (1999) The Mental Health Matrix: A Manual to Improve Services, Cambridge University Press, Cambridge. [14] Aday, L.A. (2001) Establishment of a conceptual base for health services research. J. Health Serv. Res. Policy, 6, 183–185. [15] Spasoff, R.A. (1999) Epidemiologic Methods for Health Policy, Oxford University Press, New York. [16] Last, JM. (ed.) (1995) A Dictionary of Epidemiology, 3rd edn, Oxford University Press, New York. [17] Thornicroft, G. (2001) Measuring Mental Health Needs, 2nd edn, Royal College of Psychiatrists, London. [18] Asadi-Lari, M., Packham, C. and Gray, D. (2003) Need for redefining needs. Health Qual. Life Outcomes, 1, 34. [19] Aday, L.A., Begley, A.C., Lairson, D.R. and Skater, C.H. (1993) Evaluating the Medical

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[35] Wang, P.S., Aguilar-Gaxiola, S., Alonso, J. et al. (2007) Use of mental health services for anxiety, mood, and substance disorders in 17 countries in the WHO world mental health surveys. Lancet, 370, 841–850. [36] Haro, J.M., Arbabzadeh-Bouchez, S., Brugha, T.S. et al. (2006) Concordance of the Composite International Diagnostic Interview Version 3.0 (CIDI 3.0) with standardized clinical assessments in the WHO World Mental Health surveys. Int. J. Methods Psychiatr. Res., 15, 167–180. [37] Ochoa, S., Haro, J.M., Autonell, J. et al. (2003) Met and unmet needs of schizophrenia patients in a Spanish sample. Schizophr. Bull., 29, 201–210. [38] UK 700 Group (2000) Cost-effectiveness of intensive v. standard case management for severe psychotic illness. UK 700 case management trial. Br. J. Psychiatry, 176, 537–543. ´ J.A., Rodr´ıguez-Bayon, ´ A., de Dios Luna, J. [39] Bellon, ´ and Torres-Gonzalez, F. (2008) Successful GP intervention with frequent attenders in primary care: randomised controlled trial. Br. J. Gen. Pract., 58, 324–330. [40] Murphy, E., Dingwall, R., Greatbatch, D. et al. (1998) Qualitative research methods in health technology assessment: a review of the literature. Health Technol. Assess., 2 (16), 1–294. [41] Proctor, E.K., Landsverk, J., Aarons, G. et al. (2009) Implementation research in mental health services: an emerging science with conceptual, methodological, and training challenges. Admin. Policy Ment. Health Ment. Health Serv. Res., 36, 24–34. [42] Tansella, M. and Thornicroft, G. (2009) Implementation science: understanding the translation of evidence into practice. Br. J. Psychiatry, 195, 283–285. [43] Hysong, S.J., Best, R.G. and Pugh, J.A. (2006) Clinical practice guideline implementation strategy patterns in veterans affairs primary care clinics. Health Serv. Res., 42, 84–103. ´ ´ [44] Calderon-G omez, C., Retolaza Balsategui, A., Bacigalupe de la Hera, A. et al. (2009) Family doctors and psychiatrists and the patient with depression: the need to re-adjust health care approaches and organizational dynamics. Aten. Primaria, 41, 33–40. [45] Davidson, L. (2005) Recovery, self-management and the expert patient-camping the culture of mental health from a UK perspective. J. Ment. Health, 14, 25–35. [46] Kloos, B. (2005) Creating new possibilities for promoting liberation, well-being and recovery: learning from experiences of psychiatric consumers/survivors, in Community Psychology. In Pursuit of Liberation and Well-Being (eds G. Nelson and

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10

The pharmacoepidemiology of psychiatric medications Philip S. Wang,1 Alan M. Brookhart,2 Christine Ulbricht1 and Sebastian Schneeweiss2 1 Division

of Services and Intervention Research, National Institute of Mental Health, Bethesda, MD, USA 2 Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

10.1 Introduction The need for rigorous pharmacoepidemiologic studies of the use, risks and benefits of psychotropic medications has grown considerably in recent years. Medications representing the major classes of modern psychotherapeutic drugs first became available over half a century ago. Such agents (and classes) included imipramine (a tricyclic antidepressant), chlorpromazine (a neuroleptic antipsychotic), chlordiazepoxide (a benzodiazepine anxiolytic) and lithium (a mood stabiliser) [1]. Except for lithium, each of these agents led to the development of other medications that tended to have very similar mechanisms of action. Newer drugs within each class began to emerge by the 1980s, including fluoxetine (a selective-serotonin reuptake inhibiting (SSRI) antidepressant), clozapine (an ‘atypical’ antipsychotic), buspirone (a non-sedating anxiolytic), as well as valproate and carbamazepine (antiepileptics with mood-stabilising properties). In spite of more than half a century of use of many psychotherapeutic drugs, empirical data concerning their utilisation, safety, effectiveness and cost-effectiveness in real-world patient populations is often lacking. Data from randomied controlled trials (RCTs) that were conducted to establish the basic efficacy and safety of medications as well as register them with regulatory bodies, are

often the only information to guide treatment decisions. Unfortunately, such RCT data may not be generalisable to the way medications are used, and the benefits and risks that result from such use, under typical practice conditions. For example, earlier efficacy trials suggested the newer second-generation of antipsychotics emerging in the 1980s were potentially superior to older first-generation neuroleptics at treating negative symptoms of schizophrenia, avoiding adverse effects like extrapyramidal symptoms, and in terms of their economic value [2]. Such results led to heavy promotion of the second-generation antipsychotics and a rapid increase in their use. By the second half of the 1990s, second-generation antipsychotics comprised the majority of antipsychotic use in the United States, many years before results of large comparative effectiveness trials became available [3, 4]. Other examples of rapid diffusion of practice in the absence of data on safety or benefits include the standing regimens of multiple concurrent antipsychotics that were being given to over one in six patients with schizophrenia spectrum disorders by the late 1990s [3]. In fact, results from a recent trial of clozapine plus risperidone suggest that this particular combination may not be superior to clozapine alone [5]. Such rapid adoption and diffusion of psychotropic regimens before data are available on how

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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medications are being used, their safety, and their benefits in typical practice can lead to substantial increases in health care expenditures. Spending on public programs such as Medicare and Medicaid in the United States has tripled over the past 30 years, rising from 1.3% of gross domestic product in 1975 to 4% in 2007 and projected to continue increasing to 12% by 2050 under current policies [6]. Spending on psychotropic medications can comprise a large proportion of these increases, with expenditures for just second-generation antipsychotics making up nearly a third of all drug costs for some Medicaid programmes [7]. Unfortunately, without clear data on the safety, benefits and cost-effectiveness of such regimens, purchasers of health care can be uncertain if such costs are justified. In a recent analysis of Medicaid prior authorisation policies used to control drug costs, there was no consistent relationship between the application of these policies and overall spending on atypical antipsychotic medications [7]. Such findings suggest Medicaid programmes do not have sufficient data on how these medications are being used in their patient populations and what outcomes their patients are experiencing to know if use should be increased or decreased. The lack of such data can also leave policy makers unable to respond to new challenges such as drug advisories from regulators. In 2005, the United States Food and Drug Administration (FDA) issued a warning of increased mortality among elderly patients with dementia taking second-generation antipsychotics. Over the next year no state Medicaid programme modified its prior authorisation policy to respond to this warning [7]. Likewise, an analysis of Medicaid prior authorisation policies for antidepressants prescribed to children found that states made few and variable changes after an FDA advisory in 2003 warning of increased risks of treatment-emergent suicidality [8]. Again, such results suggest that Medicaid benefit managers do not have sufficient information on the use, risks and benefits of psychotropic medications to inform their decisions. Finally, psychopharmacoepidemiology is increasingly being called upon to help intervene and improve upon the poor quality of regimens and outcomes that patients currently experience on the basis of 156

their psychotropic medication use. As will be covered below, even while the use and expenditures for psychotropic regimens have risen substantially, many people with mental disorders experience unmet needs for effective treatment as well as poor health and functioning. Although the United States spends the greatest percentage of gross domestic product on health care, recent data from the World Health Organization’s World Mental Health Survey indicates the United States lags behind other developed nations in terms of the rate of receiving effective treatment [9]. Similarly, analyses of temporal trends in the United States have shown that even though use of mental health treatments increased 65% in the last decade, the prevalence of mental disorders and suicidality failed to decline [10–12]. For this reason, psychopharmacoepidemiologists have begun to focus on evaluating interventions, policies, delivery system redesigns and even means of financing mental health treatments, in order to improve the care and outcomes that patients experience. The remainder of this chapter provides a brief overview of potential data sources for investigations, examples of recent psychopharmacoepidemiologic studies, and some suggestions for future developments.

10.2 Overview of psychopharmacoepidemiology 10.2.1 A brief history Both the parent field of pharmacoepidemiology and the narrower field of psychopharmacoepidemiology are relatively young disciplines [13]. Both arose out of needs revealed by the thalidomide catastrophe in the 1960s. Thalidomide, a psychotropic medication originally marketed for sedation and hypnosis, was found to be associated with deformed extremities among children who had been exposed to it in utero [14]. This public health crisis involving a marketed psychotropic drug led to important policy changes and the establishment of systems for reporting unexpected hazards from approved medications throughout the world [15]. The Kefauver–Harris Amendments passed in the United States established the FDA’s current regulatory requirements for drug approvals [16]. In a step that directly led to the

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establishment of the field of pharmacoepidemiology, these amendments required that drugs that were approved and being marketed also receive review. From these origins in the 1960s, pharmacoepidemiologic studies have grown in frequency, scope and impact. Post-marketing pharmacoepidemiologic studies have sometimes been required by the FDA as a precondition at the time of approval [17]. Uncovering serious unanticipated adverse events from psychotropic drugs has continued to be a major focus of such studies in subsequent decades. During the 1980s, the antidepressant nomifensine was marketed in Europe and showed promise for treatment-resistant depression, but was found to cause fatal haemolytic anaemia [18]. The ultra shortacting benzodiazepine triazolam was frequently used as a hypnotic when it was found to be associated with anterograde amnesia [19]. Results such as these have had a significant public health impact, in part by leading to the withdrawal of hazardous agents as well as new recommendations and regulations to ensure the safe and effective use of psychotropic drugs that remain. Important advances in the data and study designs available to psychopharmacoepidemiologists have allowed for an expansion of the discipline’s role. Large administrative databases with accurate drug exposure information on hundreds of thousands or even millions of patients have been developed. These large automated databases have in turn allowed psychopharmacoepidemiologists to efficiently study rare adverse effects, those occurring after long lag periods, and adverse effects with high background rates. Novel designs such as the case cross-over and case-time-control designs have allowed investigators to conduct new types of studies, such as those identifying transient effects from intermittent drug exposures [20–22]. New methodologies for conducting quasi-experimental and simulation studies have also become available and made it possible for psychopharmacoepidemiologists to evaluate the impact of things like psychotropic drug policies and even hypothetical psychotropic drug regimens [23–25]. Methodologic advances in the analysis of pharmacoepidemiologic data have allowed investigators to more effectively deal with or at least quantify threats to the validity of observational studies, such as the common problem of confounding by

indication (‘channelling bias’, as might occur if certain drugs regimens are preferentially prescribed to patients with particular conditions) [26]. For example, investigators can minimise this type of bias by employing such analytic procedures as propensity score matching (which controls for differences in the characteristics of patients given different drug regimens), restriction, instrumental variable techniques and adjustment for unmeasured confounders using external sources of information [27–31]. For these reasons, psychopharmacoepidemiologic studies conducted after drug approval have become an indispensable complement to the clinical trials performed before drug approval for registration purposes. Pharmacoepidemiologic studies may be the only type of study that can detect certain outcomes, such as those that are rare or occur only after long delays. Because of their larger sample sizes, pharmacoepidemiologic studies can allow investigators to estimate drug effects with much greater precision or in particular subgroups. Psychopharmacoepidemiologists can examine how psychiatric drugs are used and their effects in populations that are often excluded from clinical trials, including patients with comorbid psychiatric and general medical disorders, the elderly, children or pregnant patients. Psychopharmacoepidemiology makes it possible to evaluate psychiatric drug regimens that are typically used in the real world but may not be studied in clinical trials for practical or ethical reasons, including long-term exposures, cotreatment with other medications, no treatment and even overdosages.

10.3 Sources of data The strengths as well as limitations of psychopharmacoepidemiologic studies for answering particular questions often depend critically upon the underlying data sources being employed. In general, answering psychopharmacoepidemiologic questions often requires information on a number of patients, that is orders of magnitude greater than the hundreds usually studied in clinical trials prior to drug approval. This very large number of patients on whom information is needed has in turn made it essential to employ secondary data collected for other purposes whenever possible. 157

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Beyond these general considerations, characteristics of individual data sources may make them more or less suitable for answering specific psychopharmacoepidemiologic questions [32]. Factors that may favour using one data source over another include: whether data on the drug exposure of interest is available, from time periods of interest, and common enough to adequately power analyses; the level of detail and accuracy of these exposure data; whether there are adequate numbers of and accurate data on outcomes of interest; whether there is information needed to control for confounding and other biases and the representativeness of the study population to other populations of interest. Below are brief descriptions of some data sources typically used in psychopharmacoepidemiologic studies, including some of their strengths and weaknesses for answering specific study questions.

10.3.1 Large governmental administrative databases The establishment of governmental entitlement programmes, such as Medicaid in the mid-1960s, created an important source of data for psychopharmacoepidemiologists [33]. Databases from specific state Medicaid programmes (e.g. New Jersey and Tennessee) as well as collections of states (e.g. the Computerized On-line Medical Pharmaceutical Analysis and Surveillance System [COMPASS] consortium) have been employed successfully in pharmacoepidemiologic studies. Because Medicaid databases contain information on large numbers of psychiatric patients due to the poverty and disability associated with mental illness, they are often ideal data sources for studies of psychotropic medications. The indigent status of recipients also reduces out-of-pocket health care expenditures and contributes to the high level of completeness of Medicaid data for information on use of medications and other services [34]. Disadvantages of Medicaid data can include their lack of information on inpatient drug utilisation, limited generalisability for certain investigations and questions about the completeness and validity of recorded diagnoses [35]. Other large governmental administrative databases collected for insurance purposes also exist, including data collected by the 158

US Veteran’s Administration and provincial governments in Canada (e.g. the British Columbia Pharmacare programme). These data sources may offer specific advantage in studies due to their inclusion of subjects with a wider range of socioeconomic and other characteristics. However, like all databases collected for administrative purposes, questions persist concerning the accuracy of their clinical information, especially on mental disorders.

10.3.2 Data from health maintenance organizations The number of people in the United States receiving their pharmacy benefits through health maintenance organizations (HMOs) has increased substantially over the past decade. This has allowed HMO databases to become an important datasource for psychopharmacoepidemiologic studies. Prescription claims databases from health plans such as Group Health Cooperative, the Kaiser Permanente Medical Care Program, United Health Care, Fallon Health Plan and Harvard Pilgrim Health Care have all been employed in psychopharmacoepidemiologic studies. Data from such plans have also been successfully used in concert, through consortia like the HMO Research Network and the Vaccine Safety Datalink programmes. An important advantage of data from HMOs is that the clinical information collected for billing purposes can often be supplemented with more complete or accurate information from review of patients’ primary medical records. HMO databases provide an ideal means to study psychotropic medication use in primary care, the setting in which the majority of mental health care is delivered in the United States. However, because HMO membership often requires employment, HMO databases may not include use of psychiatric medications by those with serious mental illness. In addition, patient turnover can be high, hampering longitudinal studies.

10.3.3 Large-scale surveys Data for psychopharmacoepidemiologic studies can also be obtained from large surveys of medication and other health services use. Surveys administered in multiple years in the United States include the

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annual National Ambulatory Medical Care Survey (NAMCS), which samples a nationally representative group of visits to physicians in office-based practices and records the prescriptions for medications given to patients. Advantages of data from such surveys include the ability to generate nationally representative estimates concerning psychiatric medication use. Disadvantages include the surveys’ high costs, the possibility that patients may not have filled or consumed prescribed medications, the lack of longitudinal follow-up and the lack of completeness and detail regarding clinical conditions. Psychiatric epidemiologic surveys such as the National Comorbidity Survey (NCS) in the early 1990s and its replication (NCS-R) a decade later contain detailed information on mental disorders and also assessed the use of psychotropic medications among respondents. Because similar survey methods and data collection instruments were employed in the NCS and NCS-R, analyses of temporal trends in use of medications as well as mental disorders is possible. The same methodology and instruments as the NCS/NCS-R have also been employed in population-based surveys being conducted throughout the world as part of the WHO World Mental Health Survey Initiative (www.hcp.med.harvard.edu/wmh/), making it now possible to conduct cross-national analyses as well. Potential limitations in all of these surveys include the frequent lack of detail concerning medication regimens and the fact that they are based on respondents’ recall and subject to information biases.

10.3.4 Practice-based networks Practice-based networks are designed to provide information on the patterns and outcomes of health services use in typical practice settings. One such network – the General Practice Research Database (GPRD) in the United Kingdom – contains the computerised medical records from hundreds of general practices in the United Kingdom and has been used extensively in psychopharmacoepidemiologic studies. Other practice-based networks in the United States include the family practice Ambulatory Sentinel Practice Network (ASPN), the Pediatric Research in Office Settings (PROS) and the American Psychiatric Association’s Practice Research Network (PRN) of psychiatrists. More recently, the National

Institute of Mental Health (NIMH) established practice-based research networks for the study of schizophrenia, depression and bipolar disorder. These NIMH networks have mainly been used to rapidly recruit large numbers of typical patients for large practical clinical trials such as the Clinical Antipsychotic Trial of Intervention Effectiveness (CATIE) trial in schizophrenia, Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial in depression and Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) trial in bipolar disorder [4, 36–38]. However because they reflect real-world practice, the NIMH networks have also been used successfully in pharmacoepidemiologic studies [39, 40]. General strengths of data from practice-based networks include their more accurate clinical information and the potential to develop representative estimates; disadvantages include the high costs of maintaining networks and the uncertainty over whether patients actually consumed prescribed medications.

10.4 Examples of recent psychopharmacoepidemiologic studies From its origins investigating adverse effects from psychotropic medications, the field of psychopharmacoepidemiology has broadened to now include a wider variety of study types. The following section describes a few recent psychopharmacoepidemiologic studies. It is not intended to be a comprehensive review of the large body of work that has been conducted but instead a brief presentation of examples of the range of studies now possible.

10.4.1 Uncovering adverse effects and unanticipated benefits of psychiatric medications Identifying adverse effects from psychiatric medications that were not observed in RCTs conducted for registration purposes continues to be a major reason for conducting psychopharmacoepidemiologic studies. One question that has received considerable attention recently has been whether the antidepressant medications used to treat depression 159

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can paradoxically incite or exacerbate suicidal thoughts and behaviours. In March 2004, the FDA first issued a public health advisory that the use of 10 newer antidepressants may be associated with the development of suicidal thoughts and behaviours in children and adults [41]. In October 2004, the FDA extended this advisory to a ‘black box’ warning of these potential risks for suicidality that covered all antidepressant agents and all patient age groups [42]. At the heart of these warnings were data from meta-analyses that the FDA had conducted on available RCTs of antidepressant medications. The clinical implications of these findings and warning were complicated by the fact that antidepressant medications remain a therapeutic mainstay for depression and are quite beneficial for some patients. For this reason, it has been imperative to further identify particular vulnerable subgroups or regimens associated with risk so that they can be avoided. Some psychopharmacoepidemiologic analyses have examined whether users of particular classes (e.g. SSRIs, serotonin norepinephrine reuptake inhibitors (SNRIs), tricyclic amines) or individual agents are especially hazardous but have generally found only small or no differences in completed suicides and suicide attempts [43–45]. However, other psychopharmacoepidemiologic studies attempting to uncover whether particular age groups may be at greater or lower risk have reported that the hazards of treatment emergent suicide attempts are elevated among children but not adults [46]. This apparent effect modification by age was also observed in re-analyses conducted by the FDA of available RCT data and led to the FDA’s 2007 modification of its earlier warnings to now only cover potential treatment of emergent suicidality among children and adolescents treated with antidepressants [47]. Another psychotropic drug safety issue receiving considerable attention recently concerns the frequent use of antipsychotic drugs to treat behavioural symptoms and agitation in dementia patients. In 2005, the FDA issued an advisory warning that atypical antipsychotics increased the risk of death compared to placebo in short-term RCTs conducted among elderly dementia patients [48]. ‘Black box’ warnings were added to the labels of all atypical antipsychotics describing these risks and advising that the atypical antipsychotics are not approved for use in elderly 160

patients with dementia. There was insufficient trial data on the mortality associated with conventional antipsychotic use by elderly dementia patients, so the FDA did not include these agents in its advisories. However, recent pharmacoepidemiologic studies of elderly patients initiating antipsychotic treatments began to raise questions about this omission of conventional agents from the FDA’s warnings. One investigation found that patients prescribed conventional agents had a 37% greater, dosedependent risk of short-term mortality than those prescribed atypical antipsychotics [49]. Subsequent psychopharmacoepidemiologic studies performed in other populations have also found elevated risks of short-term mortality among elderly initiating conventional as opposed to atypical antipsychotics and allowed the FDA in June 2008 to include the conventional agents in its earlier warnings of mortality risks among dementia patients [50–52]. Another role for psychopharmacoepidemiology related to its focus on identifying adverse effects, is uncovering unanticipated benefits from psychiatric medications. While similar designs and analytic methods are employed in both types of investigations, results from studies of unanticipated benefits can be more difficult to interpret than those of adverse effects because of the often greater possibilities of residual confounding by indication. One topic that has received renewed attention has been whether the mood stabilising medication lithium may offer unique protection against suicidality among patients with bipolar disorders. A recent psychopharmacoepidemiologic study of this question found that lithium was superior to divalproex, gabapentin and carbamazepine in protecting patients against suicide attempts, although the latter two comparisons did not reach statistical significance [53]. However, as the authors point out, the observational nature of such studies and the possibility that choice of mood stabiliser may be related to clinical severity or suicidality risk complicates interpretation of such findings. Adjudicating between the possibilities of true benefits versus confounding by indication may ultimately required randomised data, such as that from the ongoing NIMH-supported Use of Moderate-Dose Lithium for the Treatment of Bipolar Disorder (LiTMUS) trial of moderate dose lithium added to usual care.

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10.4.2 Descriptive analyses of the use and quality of psychiatric medication use Traditionally, pharmaceutical companies have examined how their products are used and by whom, largely for marketing purposes. More recently psychopharmacoepidemiologists have been shedding light on the use and quality of psychotropic medication use for a variety of other stakeholders. Such studies have revealed the continuing rise in psychiatric medication use by children and adolescents, particularly the use of atypical antipsychotics [54]. Although these medications are given for a variety of clinical reasons, a rapid rise in the diagnosis of bipolar disorder among children and adolescents may be behind much of the increase in youth atypical antipsychotic use [55]. The accuracy of such diagnoses being made in community practice as well as the effectiveness and safety of these prescribed regimens are areas of active investigation. In addition to identifying how psychotropic medications are being used and by whom, psychopharmacoepidemiologists are engaged in studying the quality of mental health care. Recent nationally representative data have shown that the majority of people in the United States with mental disorders during the prior year receive either no treatment or treatment that fails to meet minimal standards for adequacy [56]. Although comparably large unmet needs for effective mental health treatment have been observed worldwide, it is notable that the United States fails to achieve better despite spending by far the greatest proportion of its gross domestic product on health care [9]. More focused psychopharmacoepidemiologic studies have identified other problematic aspects of regimens that could render many to be ineffective and/or harmful to patients. For example, one analysis of antidepressant treatments given to elderly patients with depression found that nearly half were suboptimal because they involved either potentially hazardous (i.e. highly anticholinergic agents or excessively high dosages) or low-intensity regimens (i.e. low dosages, short durations or lack of follow-up) [57]. Descriptive psychopharmacoepidemiologic studies uncovering such patterns and potentially modifiable determinants of poor quality psychotropic medication use are often a necessary first step to design and target the types of interventions covered below.

10.4.3 Pharmacoeconomic analyses With the rising expenditures on pharmaceuticals, it has become increasingly important to shed light on not only the outcomes from their use but also their value. Weighing the relative costs and benefits from psychiatric medication use has required conducting formal economic studies. Although such economic evaluations have usually accompanied clinical trials, advances in the decision sciences and simulation modelling have allowed investigators to employ a wider range of data, including pharmacoepidemiologic. This in turn has allowed the field to answer questions concerning the cost-effectiveness of a wider array of interventions and in a wider range of populations than just those involved in clinical trials. This expanding role and capacity to conduct pharmacoeconomic evaluations is illustrated by the recent body of work to enhance the treatment of depression, particularly the widespread poor quality pharmacotherapy in primary care settings. Economic analyses of trials of primary care quality improvement interventions had shown that they are a good value from a societal perspective, with cost-effectiveness ratios below the $50 000 per quality adjusted life year (QALY) benchmark used to judge whether interventions are worth investing in [58–60]. Unfortunately, widespread uptake of these interventions has not occurred, in part because the employers that purchase much of US health care do not know what their return-on-investment would be from enhanced depression care programs for specifically depressed workers. To shed light on this, the costs and benefits of enhanced depression care for workers from both the societal and employer-purchaser perspectives, were estimated in a state-transition Markov model [61]. Results from this economic analysis indicated that improving the quality of depression treatment for workers was not only a good value from society’s point of view, but also potentially costsaving to employer-purchasers due to the recovery of lost work productivity.

10.4.4 Studying interventions to optimise psychiatric medication use In addition to conducting descriptive studies of psychotropic medication use and analytic studies of 161

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the outcomes from such use, psychopharmacoepidemiologists have increasingly become engaged in evaluating interventions to actually improve use and outcomes. For example, based upon the favourable results from the economic models described above, an actual randomised effectiveness trial of an enhanced depression treatment programme was conducted among workers to experimentally assess the intervention’s effects on clinical as well as work productivity outcomes. Results of the trial showed that by 12 months, the intervention had significantly improved both clinical as well as workplace outcomes compared to usual depression care. The financial value of the latter to employers in terms of recovered hiring-training and salary costs suggested that many employers would experience a positive return on investment from improved treatment of depressed workers. Other interventions could have potentially profound effects on the use, quality and outcomes from psychiatric medication use, but may not be amenable to study with experimental designs. Fortunately, methodological developments in quasi-experimental methods like the econometric technique known as instrumental variables analysis have helped investigators to produce unbiased estimates of the impacts of interventions on outcomes using epidemiological data [23, 31]. These methods in turn have opened the door for psychopharmacoepidemiologists to study the ‘natural’ experimentation, that is occurring with mental health policies, delivery system redesigns and financing of mental health care. For example, one recent analysis evaluated whether increases in patient cost-sharing enacted by the Canadian province of British Columbia curtailed already generally underused antidepressant medications by seniors with depression [62]. Introducing a $10–$25 copayment for prescriptions was associated with a significant drop in the frequency of antidepressant initiation. Subsequent replacement of these copayments with a more stringent income-based deductible policy then led to a significant reduction in the rate of increase in antidepressant initiation. While introducing these new forms of medication cost sharing did appear to have the potential to reduce use of antidepressant therapy by seniors, the clinical consequences of such reduced use still need to be clarified. 162

10.5 Conclusions As this chapter has attempted to illustrate, the data sources, capacities and roles for psychopharmacoepidemiology have all expanded. Given the centrality of psychotropic medications in the current treatment of mental disorders, psychopharmacoepidemiologic studies remain essential to ensuring that such use is safe, effective and cost-beneficial. If history is any guide, investigators can anticipate a steady stream of new hypotheses concerning unanticipated effects of both established and new psychotropic medications. Likewise, tracking how psychiatric medications are used, by whom, and the outcomes from use is imperative to identify unmet needs for effective treatment and new intervention targets. And as constraints on health care resources and reliance on psychotropic medications increase, so too will the need for evaluations of the relative value obtained from psychopharmacologic regimens. Psychopharmacoepidemiologists should anticipate a growing need to evaluate a wide range of interventions that could have important impacts on patients’ use of psychotropic medications and their clinical outcomes. To meet all of these future demands, advances will also be needed in the data, methods and resources available for conducting psychopharmacoepidemiologic studies. Recognising the need for new data sources and analyses, the Food and Drug Administration Amendments Act (FDAAA) of 2007 calls for a marked expansion in the current system of monitoring the performance of approved medications [63]. Part of this new capacity for conducting active surveillance will include the Sentinel Initiative, a national electronic system of linked healthcare datasources for monitoring medical product safety. The FDAAA legislation sets as targets that data on 25 million patients and 100 million patients be accessible by 1 July 2010 and 2012, respectively. Parallel methodologic and analytic developments will also be needed to ensure that queries of these expanded datasources can be implemented and yield valid answers. Another important potential future role that psychopharmacoepidemiology could play is in facilitating health care reform. Experts and opinion leaders have emphasised the need for rigorous data on the comparative effectiveness of medical treatments to both inform practice decisions and improve health

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care quality, outcomes and value [38]. Bodies such as the Congressional Budget Office and Institute of Medicine have joined in these calls for new research shedding light on the relative benefits, risks and costs of medical therapies [64, 65] and US Congressional legislation [66] has been introduced that would establish an independent, non-governmental Healthcare Comparative Effectiveness Research Institute. Generating some of this data will certainly involve conducting large comparative effectiveness trials. However the costs, time required and other challenges of conducting large practical clinical trials make it clear that additional means will also be needed. As covered in this chapter, the range of data sources, study methods and analytic approaches now available to psychopharmacoepidemiologists leave them well poised to answer questions regarding the comparative effectiveness, safety and value of psychotropic medications in the future.

Acknowledgements The views and opinions expressed are those of the authors and should not be construed to represent the views of any sponsoring organisation, agencies or the US Government. The views expressed do not necessarily represent the views of the National Institute of Mental Health, the National Institutes of Health, the Department of Health and Human Services or the United States government.

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[54] Olfson, M., Blanco, C., Liu, L. et al. (2006) National trends in outpatient treatment of children and adolescents with antipsychotic drugs. Arch. Gen. Psychiatry, 63, 679–685. [55] Moreno, C., Laje, G., Blanco, C. et al. (2007) National trends in the outpatient diagnosis and treatment of bipolar disorder in youth. Arch. Gen. Psychiatry, 64, 1032–1039. [56] Wang, P.S., Lane, M., Olfson, M. et al. (2005) Twelvemonth use of mental health services in the United States. Arch. Gen. Psychiatry, 62, 629–640. [57] Wang, P.S., Schneeweiss, S., Brookhart, M.A. et al. (2005) Suboptimal antidepressant use in the elderly. J. Clin. Psychopharmacol., 25, 118–126. [58] Simon, G.E., Katon, W.J., VonKorff, M. et al. (2001a) Cost-effectiveness of a collaborative care program for primary care patients with persistent depression. Am. J. Psychiatry, 158, 1638–1644. [59] Simon, G.E., Manning, W.G., Katzelnick, D.J. et al. (2001) Cost-effectiveness of systematic depression treatment for high utilizers of general medical care. Arch Gen. Psychiatry, 58, 181–187. [60] Schoenbaum, M., Unutzer, J., Sherbourne, C. et al. (2001) Cost-effectiveness of practice-initiated quality improvement for depression: results of a randomized controlled trial. J. Am. Med. Assoc., 286, 1325–1330. [61] Wang, P.S., Patrick, A.R., Avorn, J. et al. (2006) The costs and benefits of enhanced depression care to employers. Arch. Gen. Psychiatry, 63, 1345–1353. [62] Wang, P.S., Patrick, A.R., Dormuth, C. et al. (2008) The impact of cost-sharing on antidepressant use among older adults in British Columbia. Psychiatr. Serv., 59, 377–383. [63] US Food and Drug Administration Amendments Act of 2007. Public Law 110-85, September (2007). Title IX, Section 905. [64] Clancy, C.M. (2006) Getting to ‘smart’ health care. Health Aff., 25 (6), w589–w592. [65] Wilensky, G.R. (2006) Developing a center for comparative effectiveness information. Health Aff., 25, w572–w585. [66] US Senate (2008) Comparative Effectiveness Research Act of 2008. United States Senate Bill S. 3408, July 31.

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Peering into the future of psychiatric epidemiology Michaeline Bresnahan,1,2 Ezra Susser,1,2 Dana March1,2 and Bruce Link1,2 1 Department of Epidemiology, Mailman School of Public Health, Columbia University, NY, USA 2 New York State Psychiatric Institute, NY, USA

11.1 Introduction Epidemiology has already contributed a great deal to psychiatric research. The discipline has been used extensively for studying the frequency of mental disorders in communities across the world, establishing the enormous burden of illness associated with these disorders and identifying their causes and consequences. In the past decade, the extension of epidemiologic risk factor methods to genetic studies has further opened a new and exciting realm for the use of epidemiology in psychiatric research [1]. Yet we have utilised only one small part of the potential contributions of epidemiology. In this chapter, we describe uses of epidemiology that are rapidly emerging but not fully established. Peering into the future, we anticipate that these applications will be increasingly adapted for psychiatric research in the coming decades. Among the salient developments are that epidemiologists increasingly focus on studying multiple levels of causation, the trajectory of health and illness over the life course and the interplay of genes and environment [2]. We discuss the first two, and interweave the third (interplay of genes and environment) into our examples insofar as possible.

11.2 Levels of causation: A historical overview The history of epidemiology has been marked by dramatic transitions in thinking, occurring in response to new public health challenges and/or scientific breakthroughs [3]. These can be used to demarcate historical eras, characterised by prevailing causal paradigms [4], which are tied inherently to culturally and historically bound styles of thinking, and shift through the exchange of ideas and debate [5, 6]. Below we trace the shifts in thinking about levels of causation over successive eras and paradigms in epidemiology. The crucible for the development of epidemiology was the Industrial Revolution in England in the early nineteenth century. In this early ‘sanitary era’, epidemiologists adopted a very broad view of causation, with the focus mainly on societal factors. A dominant view was that the social transformation associated with industrialisation had led to a concentration of human waste and other decaying organic matter in the new urban areas. At the societal level, the thinking of the Sanitarians was valid enough; the societal transformation that they witnessed was indeed the underlying force behind the change in the health of

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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England at that time. It motivated one of the most effective public health reforms ever enacted, the Public Health Act of 1848, an effort that culminated in the building of sewage and water systems throughout the newly industrialised towns and cities of England. Despite its evident successes, sanitary epidemiology had fatal flaws. While the Sanitarians focused on explaining patterns of disease in populations, they lacked good explanations as to how societal factors led to disease in individuals. The theory of ‘miasma’, a kind of polluting vapour that emerged from the accumulation of decaying waste, prevailed as the main explanation of disease causation. Often, the miasma theory was a plausible, albeit incorrect, explanation for patterns of disease. For example, William Farr used miasma theory to explain the relation between elevation and mortality from cholera in London during the 1848 epidemic (William Farr Cholera in England 1848–1849); while the miasma theory could explain the higher mortality rates at lower elevations, the real explanation lay in the contaminated water consumed by the population residing at lower elevations. As such, the miasma theory was contested. For instance, the epidemiologist John Snow inferred the presence of microorganisms causing cholera as early as the 1840s [7]. The epidemiology of the sanitary era was brought to a close by the development of a new science, microbiology, which provided an explanation for disease at the individual level, and quickly supplanted the miasma theory of disease causation [8], though not without debate (e.g. [9, 10]). Towards the end of the nineteenth century, Robert Koch and others made a series of stunning discoveries that demonstrated beyond any doubt that microbes played a crucial role in some of the most important diseases of the time. This ushered in the ‘infectious disease era’, in which epidemiology was actually redefined in some instances as the science of infectious diseases. In this period, epidemiologists primarily sought to identify microbial agents and their mode of transmission [11]. However, infectious disease transmission is an inherently social process. As such, the societal level of thinking remained important, but only within the narrow framework of the ways in which social factors influenced epidemic transmission [12]. With notable exceptions (see later), few continued to focus on the implications of societal 168

transformation for public health, and their ways of thinking were relegated to the periphery of mainstream epidemiology [6]. The next transition, to the ‘chronic disease era’, was largely motivated by the changing health profile of developed countries in the mid-twentieth century. Infectious diseases were declining rapidly, whereas apparently non-infectious ‘chronic diseases’ such as cardiovascular disease and cancer were increasing at an alarming rate. Infectious disease methods could not address the challenges presented by these frightening new causes of morbidity and mortality. Within a short period after World War II the discipline was again redefined and its methodology transformed. The signal event was the demonstration that smoking was a ‘cause’ – a major ‘risk factor’ – for lung cancer, using cohort and case–control designs developed for the purpose [13]. For cardiovascular disease, the notion of the risk factor was arguably even more important; many factors, such as serum cholesterol, hypertension, diet and exercise seemed to bear on the risk of disease, even though demonstrating causality per se presented an ongoing challenge [14]. Subsequently the notion of the risk factor became common parlance among epidemiologists, statisticians, clinicians and indeed the population at large. Cohort and case–control study designs became standard methods for investigating risk factors, especially individual exposures or lifestyles, in chronic diseases that likely had many causes. What is most important for the present argument is that these designs brought the discipline to focus still further on the individual as opposed to the societal level influences on disease. As we shall explain below, the risk factor designs are individual level studies par excellence, and their very strength lies in isolating the individual level risk factor from all others. Risk factor methods still predominate in teaching and research (e.g. [15]), but the field is rapidly changing once more. In the introduction, we noted several trends in epidemiology. Here we will take up two in greater depth: multilevel causation, and causation over the life course. Investigators have taken up the challenge of thinking about multiple levels of causation [16–19]. Epidemiologists are not dispensing with risk factor investigations (nor should they), but rather, are subsuming them under a broader framework, and it is a framework that we believe to

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be especially well suited to psychiatric research [1]. Thus, we now think systematically not only about risk factors, but also about the impact of family, community, society, and of gene, cell and tissue. What is motivating the latest transition? Although the question is far beyond the scope of this chapter, it is worth noting that the acquired immune deficiency sundrome (AIDS) pandemic had an enormous impact [6, 20]; its multiple and often interrelated causes necessitate epidemiologic methods for dealing with sociopolitical, behavioural and molecular complexities. Epidemiology and public health were faced with the greatest challenge in their short history, a virtual holocaust, as human immunodeficiency virus swept through Africa and other developing regions. It is a challenge that simply could not be met using risk factor methods alone. The war on AIDS requires research and intervention on every level: political leadership, deep social change, individual behaviour change and molecular genetics.

11.3 Levels of causation We now turn to introducing the concept of levels of causation, which is coming to the fore in epidemiology. Before doing so, we should note that exceptional, forward-thinking individuals have systematically considered causation at multiple levels throughout the history of epidemiology [2]. Historical eras are demarcated by prevailing paradigms but these are not the exclusive method in any given era. Nonetheless, it is only recently that this kind of thinking has received sustained attention from the field and has been used as a foundation for training in epidemiology. The idea of expanding the scope of psychiatric epidemiology ‘up’ to social contexts and ‘down’ to biological mechanisms is immediately appealing for several reasons. It allows the possibility of integrating disparate orientations into an organic whole. A combined undertaking takes greater advantage of advances in understanding across levels of research and disciplines. In addition it releases us from prejudice that the ‘real causes’ reside at any one level, to conceive disease causation as occurring at many levels. Once we are able to specify the potential relevance of any particular level

of analysis the idea of excluding that level raises the spectre of incompleteness, missed opportunity, model misspecification and confounding. Conceptualising disease causation in this way does not mean that every study or even any study has to include many levels. Integrated understanding may be achieved through a series of studies with a much more limited purview. It does mean that every study has to begin by asking the question: what level/s of organisation are most relevant to the question at hand? Then the research is designed accordingly.

11.3.1 Individual level Why are some people within a population more likely to develop disease than others? This is the question posed by risk factor investigations, which are conducted at the individual level. An individual level observational study, whether it is cohort or case–control, is designed to see whether variation in the disorder among individuals within the population reflects variation in their exposure histories. It does not require venturing down to the level of the cell, where we might ask which cells are affected by the exposure and in what ways, nor up to the level of the society, where we might ask which societies are organised in such a way that their members are exposed. Imagine that you posit a relation between exposure to sunlight and the risk of seasonal affective disorder. This model is appropriately conceptualised and investigated at the individual level. Individuals with more exposure to sunlight are hypothesised to be less vulnerable to this disorder, within the population of interest. To examine this hypothesis, it is sufficient to collect data on sunlight exposure and seasonal affective disorder for individuals within the population. The effects of sunlight exposure on cells, and the effects of social organisation on sunlight exposure, are related topics but are not directly addressed by either the hypothesis or the study design. Thus, the risk factor investigation is at once important, useful and incomplete. We will mention three important limitations on what can be revealed about determinants of disease using individual level designs. This discussion will provide a link to the next section on the contextual level, where we will see that these limitations can be partially overcome by research on other levels of causation. 169

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First, not all risk factors of interest will vary between individuals within the study population. A factor, that is universal in the study population, even if it participates in causing disease, cannot be readily examined in this framework. This can arise for exposures that are effectively mandated by government policy (e.g. vaccines) or by cultural norms (e.g. circumcision) in a given society. A small number of people may not follow the mandate, however; they tend to differ from the rest of the population in important ways making them unsuitable as an unexposed comparison group. A second limitation is that individual level risk factor designs are not well suited to discover the causes of an increase (or a decrease) in disease incidence in a population. A noticeable increase in the incidence of a disease is often what motivates an investigation. Generally the most parsimonious and useful explanation for a change in incidence is found at the societal level, albeit a societal change that brought about an increase (or decrease) in the population prevalence of an individual risk factor. An individual level study is ill equipped to identify the pivotal event, societal change. Consider the example of autism. Studies suggest that the prevalence of autism has increased markedly in developed societies over the last two decades. Hypothesised explanations include environmental exposures, potential toxins we encounter in our environment that are a byproduct of modern living (e.g. air and water pollution, plastics, food additives, products made from synthetic materials). This latent variable – an increasing multiplex exposure consisting of >80 000 synthetic chemicals in the environment and counting – is ubiquitous, and could contribute to the time trend. Beyond the measurement and identifiability challenge, isolating a subset of exposures causing an individual case may not explain the trend if each subset of component causes is individually rare. A third limitation is that the effect of an individual level determinant on the risk of disease is context dependent – even at the purely individual level of analysis. Under the paradigm of risk factor epidemiology, disease causation requires the participation of multiple risk factors, and individual cases may result from different constellations of risk factors, so that many different constellations may be ‘sufficient’ to cause disease. For the risk factors comprising any one 170

sufficient constellation, the impact of each risk factor upon the disease risk will vary, depending upon the relative frequency of the other risk factors within the constellation, in the population being investigated. Generally, in studies within a given population, the common risk factors of a sufficient constellation tend to appear less ‘influential’ in disease causation than the rare risk factors of the same constellation [1]. This occurs in spite of their joint contribution to disease occurrence in a given case. Suppose that congenital neural tube defects (spina bifida, anencephaly) are caused by a combination of two risk factors: a genetic defect that increases the need for folate, and low folate in the maternal diet. (This causal model is realistic albeit simplified for exposition.) When the genetic defect is common and a low folate maternal diet is uncommon, in a crude analysis, the effect of the genetic defect on the risk of disease will appear to be much less than that of low folate diet. On the other hand, when the genetic defect is uncommon and a low folate maternal diet is common, the effect of the genetic defect will appear to be greater than that of low folate diet. The more common risk factor thus carries a lower relative risk, and will be more difficult to detect. Yet, it may be precisely the common risk factors that carry the most implications for disease prevention. Thus it is in part for this reason that some of the causes that would be important for prevention are common, and will be of small effect when evaluated in an individual level analysis. Effects of common risk factors tend to be among the most controversial of epidemiological findings. A corollary result is that the magnitude of effect attached to a given risk factor can be expected to vary across populations due to variation in the prevalence of causal cofactors. Hence, we should not expect identical findings when we conduct the same study in two populations with somewhat different constellations of risk factors. The findings may be similar in populations with similar risk factors, supporting the pursuit of ‘replication’ of findings, but there should be some variation.

11.3.2 Contextual level Why do some populations have higher rates of disease than others? To identify determinants that

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explain differences in rates between populations, or in the same population over time, we often turn to studies at the level of the social context. A social context may be any combination of individuals who are connected in some meaningful way, such as a family, a community or a society. Thus, we move ‘up’ from the individual level to higher levels, in order to gain access to causal determinants that may not be identifiable in individual level studies. As implied earlier, these include determinants that are invariant within a population and therefore obscured or even invisible at the individual level, as well as those determinants that are not defined in individuals but in the relationships and contexts that surround them. The core idea in reasoning about contexts is that properties emerge as we move up from the individual to these higher levels of organisation. For example, most of us are accustomed to thinking about the emergent properties of neighbourhoods, and intuitively understand their meaning. In New York City, Harlem, Greenwich Village and Chinatown are examples of neighbourhoods with particular attributes, although the individuals living in each of them are by no means homogeneous. Living in one or another of these neighbourhoods will have a large influence on many dimensions of life, for example the cost and quality of housing, the type of recreation available (e.g. parks, gymnasiums, cinemas, museums), the presence of noxious facilities (e.g. sewage treatment plants, power plants), the quality of schooling for children and the amount and type of police surveillance. Residents will also be affected by the perceptions of other people about these neighbourhoods. In these and other ways, the emergent properties of the three neighbourhoods will shape the experiences of people who live there. The same can be said of emergent properties of nations, regions of the country, cities, schools, work places, families and dyadic relationships. The critical issue for epidemiologists is to identify which are most central to health and then to measure those properties so as to test causal explanations that involve them. The societal determinants of health may appear remote from the occurrence of a specific disease in an individual, and yet be of great consequence as a causal determinant. Consider the hypothetical example of sunlight and seasonal affective disorder, which we previously used to illustrate the individual

level of investigation. We could now elaborate our causal model by positing a relation between rates of seasonal affective disorder among women and societal determinants of women’s work and leisure activities. Let us propose that societies which severely restrict women’s access to outdoor occupations and recreations will have higher rates of seasonal affective disorder among women. This model is appropriately conceptualised at the societal level because the crucial determinant of health is societal constraints on women, and the outcome is the rate of disorder in the population. To examine the hypothesis, we might choose to compare several populations with different societal constraints on women, but similar geographic and climatic conditions, with respect to both pattern of sunlight exposure and rate of seasonal affective disorder among women. Note that while the risk factor investigation would provide the more ‘proximal’ causal mechanism, the societal level investigation might be more likely to indicate an effective intervention. Unless the societal barriers can be reduced, individual women may find it difficult to change their work and leisure patterns. When we turn attention from the individual to the contextual level we encounter great opportunities and enormous challenges. The opportunities arise from the fact that the full scope of contextual level influence (family, neighbourhood, school, work group, country) has barely been explored. Our fundamental understanding of context is also constantly evolving. Social contexts entirely supported by virtual medium mean that physical contact may be optional or entirely nonexistent, geographic proximity not always relevant. Both relational and physical-distances and boundaries are important in defining the ‘level’ affecting health [21]. While there are exemplary studies that indicate the importance of contexts for health outcomes [22–26], we are still in the early stages of development in putting together social, physical, cultural and other contexts with health outcomes. These opportunities exist in part because the conceptual and measurement work needed to capture variation in contexts like these is still early in its development (e.g. [27, 28]). Current practice in collecting data for epidemiologic research has, perhaps, slowed our progress. The standard approach is to sample and collect data on individuals; data are 171

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provided either through self reports or lab based measures. As useful as this approach is, it does not give us direct access to information about contexts. Often, we only learn about context indirectly through what people tell us about contexts, or what their biological measurements may reveal about contexts; however, few fine examples of direct measurements of social context exist (e.g. [24]). Our attention is drawn towards individual level processes, and away from the potential importance of processes at the contextual level. Consequently concepts and measurements at the contextual level do not come into the purview of the scientist on a regular basis when this approach is used. The best way to think about conceptual level causation is not yet entirely clear, and competing proposals have generated some excitement. Link and Phelan [29] propose thinking of contexts as units that vary in the power they possess to secure health enhancing living conditions – the capacity to secure good things for health and avoid bad things. The example of neighbourhood suggests some possibilities along these lines in that well-heeled neighbourhoods can resist noise, pollution and crime in ways that neighbourhoods that possess less social and political power cannot. Similarly, in a unionised workplace the union can negotiate for safe work conditions and better health care opportunities. Social capital (e.g. [30]), social stratification (e.g. [31]), social cohesion (e.g. [32]), social fragmentation (e.g. [33]), ethnic density (e.g. [34–37]), inequality (e.g. [38]) may be the most commonly investigated contextual features in relation to health – the literature for the first two being the most extensive. We are reminded, however, that careful measurement of context is as crucial as careful measurement of disease outcomes [39].

11.3.3 Combining individual and contextual levels Thinking about both individual and contextual levels at the same time frees us to ask different questions than we would thinking at either level alone. Previously, we were limited to two essential questions: Why do some people in a population develop disease and not others? Why are the rates of disease higher/lower in some populations than others? We can now ask about the interplay between determinants at different levels. 172

Studies of neighbourhood social isolation and schizophrenia provide an example from contemporary research. Following on early findings from the landmark ecologic studies of Faris and Dunham [40], Hare [41] demonstrated that in the city of Bristol, the incidence rate of schizophrenia was associated with neighbourhood social isolation, measured by the proportion of people living alone. He proposed two explanations (not mutually exclusive): individuals might migrate to these neighbourhoods, or, the social context of these neighbourhood might foster the development of schizophrenia. van Os et al. [42] took up this line of enquiry, in a study in Holland, using a multilevel analysis that well reflects the emerging era of epidemiology. They too found an effect of neighbourhood social isolation, measured by the proportion single and the proportion divorced, on the risk of schizophrenia. They also found an effect of marital status at the individual level. The neighbourhood effects were not explained, however, by the individual effects of marital status, indicating that the measure of neighbourhood social isolation tapped some emergent property of the neighbourhood. Furthermore, in their study neighbourhood interacted with individual risk factors in the following manner: being single and living in a neighbourhood with a lower proportion of single persons more than doubled the risk of schizophrenia over being single and living in a neighbourhood with a higher proportion single persons. A plausible interpretation is that one is more at risk – perhaps one feels more alone – as a single person when living in a neighbourhood comprised of married people.

11.4 Causation over (life) time Increasingly epidemiologists are adopting a life course perspective on disease causation. The significance of gestational and early life experience with respect to adult health outcomes has come into sharper focus over the last few decades [43–46]. There has been a fundamental shift thinking about the evolution of disease over the life course [47, 48]. Simultaneously, and perhaps encouraged by this fresh perspective, there has been an exponential development of new and existing resources explicitly designed for life course studies. The linkage of birth

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and disease/death registry data, and the expansion of birth cohort research have provided the basis for these developments [49]. The impact of a life course perspective in psychiatry is reflected in the way we conceive the development of psychopathology, and conceive of the pathologies themselves. We are learning that adult mental disorders typically do not arise de novo in adulthood. Most often they are preceded by symptoms or frank disorders in childhood and/or adolescence. Oppositional defiant disorder, for example has been found to predate multiple adult disorders [50, 51] possibly reflecting a liability to adult mental illness per se rather than a one-to-one liability for a specific disorder in adulthood. Just as a life course perspective has changed how we think about mental disorders as outcomes, it reframes our investigations of causes by lengthening the causal time frame to include possible causes all along the life course. Models of causation over long periods include accumulating risk, chains of risk and critical and/or sensitive periods [52]. The least intuitive causal sequence is based on latent effects of gestational exposures. Gestation is a privileged period of rapid growth; within the gestational period, timing of exposure measured in weeks or days may represent the difference between a life-changing effect and no effect whatsoever. And the consequence may manifest decades later. The classic example is diethylstilbestrol (DES) [53]: maternal exposure to DES during pregnancy resulted in diseases in offspring in adulthood. Other critical and susceptible phases most certainly exist. Adolescence may be another such period [54–56]. Research on the latent effects of gestational exposures on psychiatric disorders in adulthood exemplify the informative potential of the lengthened time frame. A relation of famine during gestation to risk of schizophrenia during adulthood emerged from studies of the Dutch Hunger Winter, and the Great Famine in China following the Great Leap Forward of 1958. In each of three studies, exposure to famine during early gestation was associated with a twofold increased risk of schizophrenia in adulthood [57]. One hypothesis is that micronutrient deficiency during gestation was responsible for the increased risk of schizophrenia in offspring. Some attention has focused specifically on folate deficiency because of

its crucial role in DNA repair and methylation. De novo mutations associated with folate deficiency are one possible explanation for the increased rate of schizophrenia. Changes in DNA methylation due to folate deficiency are another possible explanation. These explanations are not mutually exclusive; however, we will focus on DNA methylation in order to introduce epigenetics, which we envision as part of the future of epidemiology. Epigenetic effects change the potential for gene expression without changing the DNA coding sequence. They include DNA methylation, histone acetylation and other processes which alter the accessibility of DNA for transcription [58, 59]. Epigenetic effects are mitotically heritable. Animal studies have shown that in utero exposures to micronutrients can have epigenetic effects that alter the phenotype of the offspring. Among the bestknown examples is an experiment in which micronutrients (including folate) in the one carbon pathway were administered prenatally to Agouti mice dams, resulting in altered DNA methylation as well as phenotype among offspring. (e.g. [60]) Notably, epigenetic effects are probabilistic (e.g. shift the per cent of DNA methylated) and are thought to be potentially reversible in at least some instances. In one of the first human studies of epigenetic effects of prenatal nutrition, it was found that after early prenatal exposure to the Dutch Hunger Winter, there was an alteration of imprinting on insulin-like growth factor 2, an epigenetic effect, still evident 60 years after birth [61]. At the same time, studies of post-mortem brain tissue have implicated epigenetic effects as potentially related to schizophrenia [62, 63]. This is still a young field and these findings on prenatal famine and on schizophrenia are still very preliminary. We use them here to indicate that the future of psychiatric epidemiology will almost certainly include studies of epigenetic effects. By providing a concrete mechanism by which early environmental exposures can modify gene expression and physiology, the study of epigenetic effects has the potential to explain latent effects of in utero exposures over the life course, and to bring together social and biological explanations for psychiatric outcomes (e.g. [64]). A further development in our thinking about the causal time frame emerging from the life course 173

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framework is the widening perspective on transgenerational effects beyond transmission through genes (DNA sequence) and culture. Mechanisms for intergenerational epigenetic effects are now being articulated [59, 65, 66]. An example of behavioural transmission of an epigenetic effect can be found in studies in mice where it has been shown that the transmission of nurturing behaviour is achieved during the early postnatal period; this maternal care influences gene expression and development of the stress response [67]. A big challenge in epigenetics will be how to establish these mechanisms in human studies [68]. It is certain that psychiatric epidemiology will participate in these developments. When we test hypotheses in life course framework, we are confronted with a series of challenges – some particular to life course epidemiology, and others simply exaggerated by the length and breadth of lifetime studies (e.g. confounding, multiple measurements, missing data). Methods for reducing the complexity in informative models and analyses are being developed as the opportunities to examine these hypotheses increase [69].

11.5 Examples Rethinking existing epidemiologic research and outstanding questions in the field of psychiatry within multilevel and longitudinal frameworks illustrates the relevance of this approaches when they are explicitly applied. Thinking about these issues in terms of levels of causation, and over time often adds intellectual interest and rigour, and opens new perspectives on intervention. The examples which follow, some drawn from our own research, show how multilevel reasoning and life course frame evolved from a research question or finding, and contributed to a new line of investigation.

11.5.1 Parental age The archetypal parental-age related disorder is Down’s syndrome. Increased risk of Down’s syndrome in offspring among older mothers was noted in 1933 [70]. Whether paternal age is related to increased risk is debated. Recent analyses and reanalyses hoping to resolve the issue have supported a small to negligible effect of paternal age [71, 72]. 174

The father’s age has, however, been related to a broad range of other outcomes including fetal death [73], congenital syndromes (Apert’s) [74] and neurocognitive deficits in childhood [75], and most relevant here autism [76] and schizophrenia [77, 78]. While the mechanism has not been established, the layering of risk across levels is elegantly illustrated in this example, and the direction of future research following up these findings brings us to the edge of current research technologies. One hypothesis to explain the excess risk of schizophrenia associated with older fathers is mutagenesis. Mutations in the paternal germline increase with age [79, 80]. In genome-wide scans, copy number variation in networks controlling neurodevelopment have been associated with schizophrenia [81]. Investigators have not yet established that these or similar mutations are more common in individuals with schizophrenia whose fathers were relatively older when they were born. Even if the causal process involves genetic mutations, the determinants of age at parenting arise in part from societal, family and partner relationships. Contextual influences on the distribution of age at child bearing are channelled through social norms for transitions into adult roles, educational and economic participation of women and economic conditions. When interventions are warranted, they may consist of policies that reinforce the value and feasibility of ‘on-time’ parenthood for both men and women (family policies, work policies, health care policies).

11.5.2 Neighbourhood and ethnic density A rapidly growing body of work has demonstrated markedly elevated rates of schizophrenia in migrant and ethnic minority populations [82–84]. Such findings do not appear attributable to selective migration, nor to elevated background rates in countries of origin. In particular, observed elevations in rates of schizophrenia in ethnic minority populations have catalysed a contemporary emphasis on the social patterning of schizophrenia, and of the risk and protective factors that influence it. One especially compelling example is neighbourhood ethnic density. In their classic analysis of schizophrenia in Chicago neighbourhoods, Faris and

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Dunham found that rates of schizophrenia among blacks decreased as the percentage of black residents increased in neighbourhoods [40]. Recent studies in London [85] and The Hague [35], which measured ethnic density around the time of illness onset, have reported similar results. In both studies, an interaction between individuals and neighbourhoods was found, and the protective effect of (own) ethnic density persisted even in the most deprived neighbourhoods. The mechanism(s) by which ethnic density might operate to attenuate rates of schizophrenia remain elusive. Neighbourhood ethnic density also seems to be protective against other outcomes, such as psychological distress [86], admissions to psychiatric hospitals [87] and suicide [88]. Some have posited that ethnic minorities living in neighbourhoods with higher percentages of other ethnic minorities are subjected less to discrimination, which has been associated with rates of schizophrenia in ecological studies (e.g. [35]). Others have suggested that ethnic minority dense neighbourhoods are likely to have greater social cohesion than neighbourhoods in which majority ethnicity constitute the greatest proportion of residents. Typically, ethnic density is measured using administrative (e.g. census) data. Recent work in the United Kingdom indicates that perceived ethnic density and measured ethnic density are moderately correlated, and that the impact differs by ethnic group [34]. Further investigation at the neighbourhood and individual levels across a range of contexts and ethnic groups is required to better understand the protective properties of this social phenomenon, and perhaps to harness its salutary effects.

11.5.3 Alcohol: Genes, culture and health The association between a genotype and disease can be modified by context. Genetic susceptibility to alcohol dependence is associated with genes coding for enzymes involved in the metabolism of alcohol in the liver. In Asian populations, an allele coding for one of these enzymes, aldehyde dehydrogenase 2 (ALDH2*2), has repeatedly been shown to decrease alcohol consumption [89], and decrease the risk of alcohol dependence [90, 91]. The mechanism by which the allele reduces the risk of alcoholism

involves an aversive reaction to alcohol consumption caused by a high concentration of acetaldehyde in the blood following consumption. The aversive symptoms can be very unpleasant, including intense flushing, palpitations and headache. Individuals who are homozygous for ALDH2 protective alleles (ALDH2*2*2) have such a strong aversive reaction that they drink very little if at all [89]. This accounts for the fact that none were found in large samples of male alcoholics in Japan [90]. Individuals who are heterozygotes for this allele have a weaker and more variable aversive reaction. Consequently the biological effects of homozygous ALDH2*2 are so strong that they are little affected by cultural factors, whereas the effects of being heterozygous ALDH2*2 allow for an interaction of culture with the genotype. This was put forward as one possible explanation of observed changes in the proportion of ALDH2 heterozygotes in samples of male alcoholics in Japan [90]. The protective effect of the heterozygous genotype may have become weaker as the strength of the social pressures for heavy drinking increased. The ALDH2 alleles have also been used to provide evidence on the health effects of alcohol consumption. A method referred to as ‘Mendelian randomisation’ is increasingly employed in epidemiology to provide complementary evidence as to whether an observed association between an environmental exposure and a disease is causal (see [92–94] for more detailed discussion). Often an exposure is associated with a cluster of potential confounders (e.g. high alcohol intake may be associated with cigarette smoking, poor diet and other unhealthy habits) and it is difficult to disentangle their effects. This problem can be overcome to some degree by examining a genetic variant that is related to the exposure but not the confounders. This condition appeared to be met in a Japanese study in which ALDH2*2*2 was strongly related to (reduced) acohol intake but not to some potential confounders such as cigarette smoking [95]. ALDH2*2*2 was related to reduced levels of high-density lipoprotein cholesterol and increased risk of myocardial infarction [95], providing some supportive evidence for a protective effect of alcohol use that has been reported from observational studies (e.g. [96–98]). Again, these relationships will vary across different contexts 175

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according to the frequency of both the genetic variant and of alcohol use. For instance, in many Asian societies, women consume much less alcohol than men and consequently the relation of ALDH2*2*2 to the health effects of alcohol is harder to detect among women.

11.5.4 Course and outcome of schizophrenia in developing and developed countries In studies of schizophrenia in the twentieth century, the course and outcome were found to be on average more benign in developing than developed countries [99, 100]. Thinking only in terms of individual level influences on course and outcome, these findings were counterintuitive. It had been shown that within populations, modern treatments (e.g. medication, family interventions) reduce the risk of relapse in patients with schizophrenia [101]. And yet, in developed countries where individuals had greater access to those treatments, the mean outcome was comparatively worse. To explain this difference in mean outcome across settings, researchers had to consider societal level processes. Speculation concentrated on three dimensions of context: family relationships, informal economies and segregation of the mentally ill. The overarching theme of most theories was that developing country settings offered more opportunities for individuals with mental illness to maintain family, work and community roles. Recently some investigators have challenged whether this difference in course and outcome is valid [102]. Our view is that the original findings represent the best work on this topic in the twentieth century and were valid in that historical context. Nonetheless, the world of today is dramatically different, and one should not expect therefore to see the same patterns of course and outcome today across these same countries. Massive urbanisation and the growth of megacities represent but one of the salient sociocultural changes that have taken hold in low- and middle-income countries. We do not know the implications of these sociocultural changes for either the incidence or the course of schizophrenia but this is surely an important topic for the future of psychiatric epidemiology. 176

11.5.5 BirthWeight and psychiatric outcomes Relationships between birthweight and psychiatric outcomes have been postulated since the midtwentieth century. Given the ready availability of birthweight data in many locales, this would seem to be one of the simplest relationships to establish (or refute), but in fact, has turned out to be among the most difficult. This question has still not been resolved, for example for schizophrenia and affective disorders, despite the availability of registries which link birthweight and psychiatric treatment outcomes for many millions of persons. Therefore the experience may be instructive. The central issues can be illustrated with the relation of birthweight to IQ. Reports have suggested that birthweight may be related to IQ, well into the normal birth weight range [103, 104]. Studies of the relationship between birthweight and IQ are shadowed; however, by the powerful and potentially confounding influence of family social environment. Removing the influence of family social environment is extremely difficult in individual level studies: controlling for parental attributes, and other measured family factors, does not fully capture the complex influence of family environment. The aspects of family social environment that potentially confound these results are generally shared by siblings, and therefore, are better conceptualised as family level rather than individual level variables. So we are dealing with, a family level variable (social environment) as a potential confounder of an individual level association (of birth weight and IQ). Once the cross-level nature of the confounding is recognised, it becomes possible to design studies so as to tightly control it. Sib-pair designs, examining individual level effects within families, offer a potential solution to this problem. Matte and colleagues used this strategy to examine the association of birthweight and IQ in a large cohort born 1959–1966 in the United States. Comparing individuals within same-sex sibships, they demonstrated that for boys, the increase in childhood IQ with birthweight extends well into the normal birthweight range [105]. Under this design, the birthweight effect could not be confounded by family environment, as siblings within the same

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family share this environment. Although the effect was modest, the ramifications on a population level were potentially important. But this was not the end of the story. Some large contemporary studies of the birthweight-IQ relationship within sibling pairs have found minimal or no association (e.g. [106–108]). Although the most recent large study (probably the best study so far) did fine one, also there may be different causes of birthweight variation within versus between families [109, 110]. The question remains open as to whether these conflicting findings reflect historical change or geographic variation in the relationship. This example indicates still another way in which explicit thinking about multiple levels can be useful, that is in the control of confounding. Causal determinants at one level can be confounders of findings at another level. Consequently, a clear conceptual framework that includes multiples levels of causation makes it much easier to find ways to control confounding, which is especially important for relatively small effects.

11.5.6 Violence and mental illness There are many individual level risk factors for violent behaviours and severe mental illness is one of them [111]. At the same time, it is clear that the societal context exerts a powerful influence on violent behaviour. This was demonstrated, for example in an innovative study of Chicago neighbourhoods, where collective efficacy (similar to social cohesion) of the neighbourhood was inversely related to the rate of violent crime [24]. Consistent with this are findings from two studies by Link and colleagues, one in New York City and the other in Israel, using similar measures of violence [112, 113]. They found modestly higher rates of violence among the mentally ill in both study populations; however, people with mental illness in Israel had rates of violence comparable to members of the public in New York City. In light of these relationships, what do we do about higher rates of violence amongst people with schizophrenia and other severe mental illnesses? One answer is: we find out more about what predicts violence in samples of people who have been hospitalised for mental illnesses and we develop risk assessment tools to select out violent people for more thorough intervention and control. Individual risk

factors do seem to play a role in the increased rates of violence that people with mental illness exhibit. Some investigators emphasise comorbid substance abuse [114], while others emphasise the nature of psychotic symptoms [113]. Such an approach is a reasonable and important one. But let us see how it can be enhanced by reasoning at a contextual level. Once we accept the possibility that context matters for violent behaviours we can begin to reason about the connection between mental illnesses and violent behaviours with a different frame of reference. Our vision is then shifted to thinking about the policies we implement and the structural arrangements these impose on people who develop serious mental illnesses. Currently, the most striking feature of policy towards individuals with schizophrenia in the United States is the scarcity of evidence-based treatments and the insufficient provision of even the most basic care such as shelter. Due in large part to the scarcity of supported housing, a very large number of mentally ill persons are presently residing in jails and prisons and municipal shelters. In these facilities, violent norms are well documented, and in such environments, mentally ill men and women are likely to adopt more violent behaviours. Moreover, those who can obtain supported housing generally are located in neighbourhoods which have low social cohesion and high rates of violence; again these neighbourhood characteristics can affect the behaviours of all residents including those who are mentally ill. To a large degree, these issues also pertain to individuals with other severe mental illnesses. It may very well be, then, that policies shaped by irrational stigmatisation and fear of people with schizophrenia and other severe mental illnesses, have the ironic effect of contributing to high rates of violence in this group. The stigmatisation of mental illness no doubt contributes a great deal to the policy of scarce services and supported housing, as it would be inconceivable for a developed society to impose the appalling conditions of prisons and shelters on individuals with less stigmatised illnesses (e.g. diabetes). In addition, the strong societal fear that people with mental illnesses will be dangerous, a fear, that is entirely out of proportion to the real risk that people with these problems actually pose, breeds the ‘not in my back yard’ (NIMBY) syndrome, 177

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ensuring that the available housing for people with mental illnesses will be mainly located in neighbourhoods that do not have the clout to exclude this feared group from their midst. Should these considerations change our viewpoint about policies to reduce violence among individuals with mental illness? Perhaps the most effective intervention of all would be to make adequate care available including supported housing in safe neighbourhoods. This policy would, at the same time, tend to reduce substance abuse and psychotic symptoms, which are among the important risk factors for violence that have been identified among mentally ill individuals. In addition, it might behoove us to address the antecedents of current policy, and advocate for change societal attitudes towards mental illness.

11.6 Framing the future While epidemiologists wrestle with the application of the methods and concepts just described, methodologists are also working on developing the next frontier – dynamic modelling. One approach to modelling dynamic systems is agent-based modelling [115]. Agent based modelling is a bottom up approach: the models assign individuals and environments characteristics, allow them to interact, and observe the emergence of higher level dynamics from these lower level interactions [116]. It is anticipated that these simulations will be of particular value in the development of health interventions. The more immediate future relates to the developments in multilevel and life course frameworks – and our ability to manage these complexities. The possibilities for expansion both up and down are enormous, indeed endless. Take expansion up to contexts – there is the global context, the national context, the neighbourhood context, the peer group context, the work context, the family context, even the context of a relationship with just one other person. Moreover, there isn’t just one facet to each of these contexts but rather a multitude of facets just as there are many, many characteristics of individuals. Similarly, biological determinants exist at many different levels of organisation – molecule, cell, tissue, organ, system. Over and above the 178

complexity brought about by considering multiple levels, we are in danger of being overwhelmed with information at several levels. We are now in the era of whole genome sequencing; we must manage this information. Health and disease are emergent properties of individuals; the result of a dynamic process. Placing biological determinants in the hierarchy of causation helps to remind us that individual and higher contextual level processes will influence biological phenomena. As a consequence, we are multiplying complexities. This appealing expansion brings home two very critical points about epidemiological inquiry. First, we choose our focus. Because we cannot conceptualise, let alone accurately measure all influences, at all levels, we are forced to choose a focus whether we want to or not – whether we know it or not. Second, because we cannot include all variables at all levels, our statistical analyses are always mis-specified by leaving out variables that would be included in a fully comprehensive model. This principle would apply even if we narrowed our focus to include only the individual level of analysis – it certainly applies when we expand our focus to include the cell and the society. Whatever choice we make, much will be left out, and the gap cannot be filled by any statistical analysis of the data collected. The practical significance of the foregoing considerations is that to approach epidemiological questions wisely, we need to have causal explanations that involve multiple levels and the interconnections between those levels. This will require theory and conceptualisation of what is salient for disease causation at the various levels. Thus, the era of multilevel inquiry will require the creative construction of rigorous causal explanations and the careful conceptualisation and measurement of the variables implied by those explanations. We cannot hope to succeed by simply adding measures at other levels of analysis to the kinds of statistical manipulations used during the individually focused era of risk factor epidemiology. The data of census tracts may seem to offer a measure of the social context, but we cannot solely rely on what the census gathers, nor can we limit our assessment of contexts to the arbitrarily constructed boundaries of census tracts. We must also keep the longitudinal perspective in mind, even when we are not conducting life course

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studies. Causation is rarely immediate; identifying the relevant causal factors will require a deeper understanding of how we develop liability to mental disorders, and mindfulness of a pathogenic trajectory over the life course, and perhaps over the life course of parent, children and grandchildren. Classical epidemiology before the primacy of multivariate methods is replete with examples of strategic inquiry focused on evaluating explanations for disease causation. Clever tests help us decide whether a causal explanation is consistent with observed facts or inconsistent with those facts. These examples from classical epidemiology tell us we need two things together: causal explanations and informative tests of those causal explanations. We need to bring this aspect of classical epidemiology to the new focus on multiple levels of inquiry.

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CHAPTER 11 [88] Neeleman, J. and Wessely, S. (1999) Ethnic minority suicide: a small area geographical study in south London. Psychol. Med., 29 (2), 429–436. [89] Higuchi, S., Matsushita, S., Muramaysu, T. et al. (1996) Alcohol and aldehyde dehydrogenase genotypes and drinking behavior in Japanese. Alcohol. Clin. Exp. Res., 20, 493–497. [90] Higuchi, S., Matsushita, S., Imazeki, H. et al. (1994) Aldehyde dehydrogenase genotypes in Japanese alcoholics. Lancet, 343, 741–742. [91] Goedde, H.W., Agarwal, D.P., Fritze, G. et al. (1992) Distribution of ADH2 and ALDH2 genotypes in different populations. Hum. Genet., 88 (3), 344–346. [92] Smith, G.D. and Ebrahim, S. (2003) ’Mendelian randomisation’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol., 32, 1–22. [93] Smith, G.D. and Ebrahim, S. (2004) Mendelian randomization: prospects, potentials, and limitations. Int. J. Epidemiol., 33, 30–42. [94] Ebrahim, S. and Smith, G.D. (2008) Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology? Hum. Genet., 123 (1), 15–33. [95] Takagi, S., Iwai, N., Yamauchi, R. et al. (2002) Aldehyde dehydrogenase 2 gene is a risk factor for myocardial infarction in Japanese men. Hypertens. Res., 25 (5), 677–681. [96] Gaziano, J.M., Gaziano, T.A., Glynn, R.J. et al. (2000) Light-to-moderate alcohol consumption and mortality in the Physicians’ Health Study enrollment cohort. J. Am. Coll. Cardiol., 35 (1), 96–105. [97] Mukamal, K.J., Conigrave, K.M., Mittlemen, M.A. et al. (2003) Roles of drinking pattern and type of alcohol consumed in coronary heart disease in men. N. Engl. J. Med., 348, 109–118. [98] Djouss´e, L., Lee, I.M., Buring, J.E. et al. (2009) Alcohol consumption and risk of cardiovascular disease and death in women: potential mediating mechanisms. Circulation, 120 (3), 237–244. [99] Jablensky, A., Sartorious, N., Ernberg, G. et al. (1992) Schizophrenia: manifestations, incidence and course in different cultures. Psychol. Med. Monogr. Suppl., 20, 1–97. [100] Harrison, G., Hopper, K., Craig, T. et al. (2001) Recovery from psychotic illness: a 15- and 25-year international follow-up study. Br. J. Psychiatry., 178, 506–517. [101] Wyatt, R.J. and Henter, I. (2001) Rationale for the study of early intervention. Schizophr. Res., 51 (1), 69–76. [102] Cohen, A., Patel, V., Thara, R. et al. (2008) Questioning an Axiom: better prognosis for Schizophrenia in the developing world? Schizophr. Bull., 34 (2), 229–244.

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[103] Breslau, N., Chilcoat, H., DelDotto, J. et al. (1996) Low birth weight and neurocognitive status at six years of age. Biol. Psychiatry., 40, 389–397. [104] Richards, M., Hardy, R., Kuh, D. et al. (2001) Birth weight and cognitive function in the British 1946 birth cohort: longitudinal population based study. Br. Med. J., 322, 199–203. [105] Matte, T.D., Bresnahan, M., Begg, M.D. et al. (2001) Influence of variation in birth weight within normal range and within sibships on IQ at age 7 years: cohort study. Br. Med. J., 323 (7308), 310–314. [106] Lawlor, D.A., Bor, W., O’Callaghan, M.J. et al. (2005) Intrauterine growth and intelligence within sibling pairs: findings from the Mater-University study of pregnancy and its outcomes. J. Epidemiol. Community Health, 59, 279–282. [107] Lawlor, D.A., Clark, H., Smith, G.D. et al. (2006) Intrauterine growth and intelligence within sibling pairs: findings from the aberdeen children of the 1950s cohort. Pediatrics, 117, e894–e902. [108] Yang, S., Lynch, J., Susser, E.S. et al. (2008) Birth weight and cognitive ability in childhood among siblings and nonsiblings. Pediatrics, 122, e350–e358. [109] Susser, E., Eide, M.G. and Begg, M. (2010) Invited commentary: The use of sibship studies to detect familial confounding. Am. J. Epidemiol., 172 (5), 537–539. [110] Tambs, K. (2010) Birth weight standardized to gestational age and intelligence in young adulthood: a register-based birth cohort study of male siblings. Am. J. Epidemiol., 172 (5), 530–536. [111] Stueve, A. and Link, B.G. (1997) Violence and psychiatric disorders: results from an epidemiological study of young adults in Israel. Psychiatr Q., 68 (4), 327–342. [112] Link, B.G., Andrews, H. and Cullen, F.T. (1992) The violent and illegal behavior of mental patients reconsidered. Am. Sociol. Rev., 57, 2750292. [113] Link, B.G., Monahan, J., Stueve, A. et al. (1999) Real in their consequences: a sociological approach to understanding the association between psychotic symptoms and violence. Am. Sociol. Rev., 64, 316–332. [114] Steadman, H.J., Mulvey, E.P., Monahan, J. et al. (1998) Violence by people discharged from acute psychiatric inpatient facilities and by others in the same neighborhoods. Arch. Gen. Psychiatry, 55, 1–9. [115] Bonabeau, E. (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. U.S.A., 99 (3), 7280–7287. [116] Auchicloss, A.H. and Diez Roux, A.V. (2008) A new tool for epidemiology: the usefulness of dynamicagent models in understanding place effects on health. Am. J. Epidemiol., 168, 1–8.

12

Studying the natural history of psychopathology William W. Eaton Department of Mental Health, Bloomberg School of Public Health, John Hopkins University, Baltimore, MD, USA

12.1 Introduction The natural history of psychopathology is a description, at the level of the population, of the ebbing and flowing of psychopathology from its earliest appearance to its final outcome. This chapter provides a conceptual framework for studies of the natural history of psychopathology and illustrates details of the framework with examples from research in the field of psychiatric epidemiology. Three major aspects of the natural history of psychopathology are onset, course and outcome. Onset of psychiatric disorders can occur very early in life, and the study of outcome ends with death. The ebbing and flowing sometimes occurs rapidly, as in the crescendo of fear involved in a panic attack; but for the most part the course is much more languid, operating over days, weeks, months, years and decades. Since a large proportion of individuals with mental disorders do not seek treatment, and those that do seek treatment presumably represent the most severe cases, the natural history of psychopathology is best studied with a population-based sample, in which individuals are selected from the entire general population without regard to whether they have received treatment of not. This avoids the well-known Berkson bias [1]. The combination of population-based samples and languid evolution of psychopathology favours the approach of life course epidemiology [2].

This chapter reviews concepts and methods for the study of the natural history of psychopathology. It is not a review of findings of studies on natural history. A comprehensive review would be cumbersome and uninformative because there is so much variation in methodologic quality of studies of natural course. If methodological standards are set high for such a review (for example by including only population-based studies with diagnostic information on an adequate number of subjects), there are very few studies that would be included. On the other hand, if methodological standards are set low for such a review (for example by including small studies of clinic samples and studies without diagnostic information), there would be a confusing morass of numerous studies with results so mixed and contradictory that the review would be of dubious value. This situation shows the state of the art in this area, indicating that we are still at the beginning stages of learning about the natural history of psychopathology.

12.2 Onset Signs and symptoms which might be related to psychiatric disorders are widespread in the population, not always reflecting the presence of psychiatric disorder. This high frequency makes the evolution from a normal deviation to a pathologic process difficult

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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to define and discern. The absence of firm data on the validity of the classification system enjoins us to be careful about operationally defining disease onset. It is particularly difficult to establish the validity of a threshold for the presence versus the absence of disorder, because, from the clinical standpoint, subtle differences in a given clinician’s approach to treatment may suggest quite varied thresholds; from the epidemiologic standpoint, subtle differences in threshold may produce widely varying prevalences. A simple definition is that onset occurs when the individual first enters treatment. A related definition is that onset occurs when a symptom is noticeable by a clinician. Another definition is the point when the symptom is first noticed by the individual. With the operational criteria of the Diagnostic and Statistical Manual (DSM), it is possible to conceive of onset as the time when full criteria are met for the first time in the life. This definition has been used in studies of incidence (e.g. [3, 4]). But it omits that part of the pathological process that takes place prior to meeting full criteria for disorder – the prodrome, as described below. Since the aetiological process may be extended in time, and the operation of aetiological factors distant, the definitions above, although capable of being operationalised, lack an explicit relationship to the pathological process. Pathology occurs when the sociobiologic dynamics have become abnormal and signifies a distinct change in the relationship among variables, the new influence of variables that were not important beforehand or a new metabolism of some sort. Onset is that point in time when the aetiological process becomes irretrievably pathological, that is, the point when it is certain that the full criteria for disorder will eventually be met. This point of irreversibility is difficult to observe. Focus on population indicators for the force of morbidity leads to explicit consideration of the idea of a continuous line of development toward manifestation of disease with an as-yet-unknown point of irreversibility. At present we can only hypothesise where the disease begins, so that even the use of the word ‘symptom’ is problematic in the strict medical sense, since we cannot ascribe the complaint to the disease with perfect accuracy. Studying the natural history of psychopathology may, in the end, lead to the

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conclusion that the disease concept is inappropriate or not useful, suggesting a shift to a more explicitly developmental framework [5, 6], with emphasis on normally distributed characteristics, and continuities in development, rather than rare dichotomies and discontinuities, which the disease model entails. One way of thinking about the development toward disease is to focus on the increase in severity or intensity of symptoms. An individual could have all the symptoms required for diagnosis but none of them in sufficient intensity or severity as to meet the threshold for case definition. The underlying logic of this concept is that the relatively high frequency of symptoms at a mild level of intensity in the general population makes it difficult to distinguish normal and subcriterial complaints from manifestations of disease. For many chronic disorders, including psychiatric disorders, it may be inappropriate to regard the symptom as ever having been absent (for example, deviant personality traits on axis II of the DSM). This type of progression toward disorder is termed intensification and leads the researcher to consider whether a crucial level of intensity exists at which the development toward disorder becomes irreversible. Figure 12.1 is an adaptation of a diagram used by Lilienfeld and Stolley [7, Figure 6.2], to visualise incidence as a time-oriented rate. The adaptation shows several distinct forms that onset can take when the disorder is defined by different levels of intensity or severity of symptoms. Compare cases No. 3 and No. 5, for example, which in the original diagram are situations of uncomplicated incidence. The bottom part of the figure shows how intensity represented by the vertical width of the bars, might be different for these two new cases. It also shows how there might be intensifications occurring that are stronger in magnitude than that associated with incidence, which would not be recorded as new cases (bottom two ‘cases’ in grey). Since the intensification of symptoms represents the force of morbidity in the population, use of a simple dichotomous measure of incidence will be misleading, unless the threshold of intensity is precisely where the pathologic process begins. A second conceptual approach toward disease development is the occurrence of new groups of symptoms where none existed. This involves the

STUDYING THE NATURAL HISTORY OF PSYCHOPATHOLOGY Wave 1

Wave 2 Existing chronic case

1 2

Remitted case 4

New case New case, not discovered New case 5 3

For bottom of figure, let = Threshold of intensity for defining onset 3

New case with sudden onset New case with gradual onset

5

Sudden onset but not a new case Existing chronic, not new, case with intensification

1

Fig 12.1 Dichotomous view of onset (top) compared to symptom intensification (bottom).

.0

R

.3

.2

=0

R

=0

R

=0

.5

.4

R

=0

R

=0

So

m

at

ic

sy

m

pt

om

s

Depressed mood

gradual acquisition of symptoms so that clusters are formed that increasingly approach the constellation required to meet specified definitions for diagnosis. ‘Present’ can be defined as occurrence either at the non-severe or at the severe level: thus, decisions made about the process of symptom intensification complicate this idea which focuses on symptom acquisition. This leads the researcher to consider the order in which symptoms occur over the natural history of the disease and, in particular, whether one symptom is more important than others in accelerating the process. Conceptualising the force of morbidity as time to a single dichotomous event (i.e. traditional concepts of incidence) is not flexible enough to deal with dimensional constructs, as shown in Figure 12.1. It is also not flexible enough to deal with changes through time in the covariation of indicators, which can be an important aspect of the force of morbidity. Emergence is defined to be the development of new covariation of a group of symptoms to each other. Figure 12.2 shows a simplified view of this developmental phenomenon for the example of the depression syndrome. The vertical axis represents the intensity of mood disturbance, and the diagonal axis, slanting backwards from lower left to upper right, the intensity of somatic disturbance. Time is represented by the horizontal axis, passing from left to right. At some early stage of development, the correlation

Precursors Prodrome 5

10

15 20 Time (years)

Disorder

25

Fig 12.2 Acquisition of symptoms to covariation threshold of onset.

of mood to somatic disturbance is pictured as being 0.0 (round circle representing cross-sectional scatter plot with correlation equal to 0.0). Gradually the mood comes to be associated with the somatic disturbance, shown by the evolution of the circle into an ellipse. At this point, the normal and the abnormal have not split, and the disorder is not inevitable. At this stage both mood and somatic disturbance predict imperfectly to later onset of major depressive disorder. Later, a group begins to emerge for whom mood and somatic disturbance are highly correlated. Finally, there emerges a group with very high covariation of mood and somatic disturbance, and a second normal group where little covariation remains. An

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increase in covariation can occur without an increase in mean levels of either mood or somatic disturbance. But presumably there is a sharp increase in impairment associated with some threshold of covariation. At some stage in the development of the covariation and impairment, a threshold for disorder might be set. These concepts allow the study of the progression of disease independently of case definition.

12.2.1 Prodromes and precursors

Cumulative percent with onset

The prodrome is the period prior to meeting fullblown criteria of disorder, when some signs or symptoms are nevertheless present. The prodome is defined only for those who eventually are diagnosed as cases, and can only be observed with complete certainty in a retrospective fashion. The speed of onset is the length of the prodromal period and can be measured in simple units of time (e.g. months or years). The presence of signs or symptoms below the criterion level may help to identify individuals at heightened risk for developing the full-blown disorder, who might be considered targets of prevention. Given the widespread prevalence of individual signs and symptoms of mental disorders in the general population, it is likely that many individuals with signs and symptoms of disorder will not go on to develop the full-blown criteria. In this situation the signs and symptoms are not quite prodromal, in the strict sense of the word, but it unacceptably imprecise to refer to them as risk factors. Signs and symptoms from a diagnostic cluster that precede

disorder, but do not predict the onset of disorder with certainty, are referred to here as precursor signs and symptoms. At the present state of our knowledge of the onset of mental disorders, there are few or no signs and symptoms that predict onset with certainty, but precursor signs and symptoms may be helpful in identifying groups at higher risk for onset than the general population. Converting what is known about precursors into true prodromes is an important topic of research for epidemiologists interested in longitudinal research and in prevention. An illustration of these issues is presented in Figure 12.3 and Table 12.1. Figure 12.3 shows two cumulative distributions for depressive disorder. The distribution on the right focuses on the age at which the individual first meets full criteria for DSM-III depressive disorder. For this distribution, onset must occur during the 1-year follow-up period of the Epidemiologic Catchment Area (ECA) Program, that is, a prospective design. The population at risk includes those who had never met criteria for the diagnosis at the beginning of the follow-up period. Thus, the at-risk group includes those with no symptoms, as well as those with some symptoms of disorder, but not meeting full DSM-III criteria. The distribution on the left focuses on the age at which the depression syndrome first occurred, as reported by the new cases. The dotted lines mark the quintiles. The area between the two curves gives a rough outline of the prodromal period. Depressive disorder has onset in young adulthood (Figure 12.3). Twenty per cent of cases meet criteria

100 80 60

Onset of Problem Onset of Disorder

40 20 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 Age in years

Fig 12.3 DIS/DSM-III major depressive disorder prodromal period for new cases epidemiologic catchment area program. Eaton et al. [8] Am J Psychiatry.

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STUDYING THE NATURAL HISTORY OF PSYCHOPATHOLOGY Table 12.1 Relative and attributable risk for depressive disorder due to selected precursors epidemiologic catchment area programme. Precursor

Sad mood for two weeks Weight problems Sleep problems Fatigue Thoughts of death Depression syndrome

Precursor Precursor Precursor relative prevalence attributable risk (%) risk (%) 7.0 3.0 7.6 4.0 6.8 5.7

6.6 10.4 13.6 7.9 12.1 0.5

28 17 47 19 41 2

Dysphoria

63 43

Anhedonia

56

Appetite Sleep

62

Slow/restless

38 47

Tired Worthless

35

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54

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49 0

5 10 15 20 25 30 35 Duration of Prodrome in Years

Adapted from [8].

for diagnosis for the first time before the age of 27 years and 50% before they are 40. Twenty per cent have their first depressive episode before the age of 17 and 50% before the age of 25. The prodromal period is about 15 years long. Symptoms associated with onset of depressive disorder, defined above as precursors, are associated with accelerated onset of the disorder. Table 12.1 shows the prevalence of the precursor, its relative risk in predicting onset of depressive disorder during the one year of follow-up in the ECA Program, and the attributable risk that can be estimated with the prevalence and the relative risk. The standard formula for attributable risk can be applied here (e.g. [9]) and is useful because it might prioritise precursors for screening or other prevention programmes, but this use of the term is conceptually different from other uses because of the limited duration of the follow-up. Therefore, the duration of the follow-up is used to qualify the attributable risk. Sleep problems have the highest relative risk (RR = 7.6), as well as high prevalence (13.6%): if there exists a single aetiologic pathway connecting sleep problems to depression, its elimination would reduce the occurrence of depressive disorder by 47%. The occurrence of depression syndrome (sad mood or anhedonia and two or more other symptoms) also has high relative risk (RR = 5.7), but the prevalence of depression syndrome is so low (0.5%) that the precursor attributable risk is only 2%. This formulation has been applied to depression previously [10, 11], and is applicable to most disorders. Many mental disorders have long prodromal periods, as shown in Figure 12.3 for depressive disorder.

Fig 12.4 Duration of prodrome by symptom group baltimore ECA followup. Adapted from Eaton et al. [12], Arch Gen Psychiatry.

The symptomatic picture of the prodromal period is efficiently summarised with a horizontal box plot, as shown in Figure 12.4, in this case for depression [12]. As required for prodromes, only new cases, from the Baltimore ECA Follow-up, are included. The boxes show the durations of time that symptoms in the DSM-IV symptom groups have endured prior to onset. The median time is designated by the vertical line inside the box, and the quartiles are designated by the ends of the boxes. Most symptom groups have prodromes lasting 1 or 2 years, but for dysphoria and suicidal ideation, there is much heterogeneity, with over half the prodromes being more than 5 years long.

12.2.2 Population measures of onset Incidence is the rate at which new cases develop in the population. It is essential to distinguish first incidence from total incidence. The numerator for first incidence is composed of those individuals who have had an occurrence of the disorder for the first time in their lives during a specified time period; the denominator excludes all persons who start the period with any prior history of the disorder. The numerator for total incidence includes all individuals who have a new occurrence of the disorder during the time period under investigation whether or not it is the initial episode of their lives or a recurrent episode. The denominator for total incidence excludes only persons who are active cases at the 187

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beginning of the follow-up period. The distinction itself is commonly assumed by epidemiologists, but there does not appear to be consensus on the terminology. Most definitions of the incidence numerator include a concept such as new cases [13], illness commencing [14], cases that come into being [15] or persons who develop a disease [16] or have onset [17]. Sartwell and Last [18] imply total incidence when they state the necessity of allowing for an individual being counted more than once, if the condition is one for which this is possible (e.g. accidents or colds). Kleinbaum et al. [9] hint at the distinction between first and total incidence, but are not explicit on the issue. Morris [19] defines incidence as equivalent to our first incidence and attack rate as equivalent to our total incidence. Lilienfeld and Lilienfeld [13] also occasionally equate incidence with attack rate. Except for the latter text, in none of these definitions is it explicit whether or not an individual who is healthy now, but has had episodes of the disorder over the life course, qualifies for a new onset. First incidence corresponds to the most common use of the term ‘incidence’, but since the usage is by no means universal, the prefix is recommended. The preference for first or total incidence in aetiological studies depends on hypotheses and assumptions about the way causes and outcomes important to the disease ebb and flow. If the disease is recurrent and the causal factors vary in strength over time, then it might be important to study risk factors not only for first but for subsequent episodes (total incidence). For example, one might consider the effects of changing levels of stress on the occurrence of episodes of neurotic illness [20] or of schizophrenia [21]. For a disorder with a presumed fixed progression from some fixed starting point, such as dementia, the first occurrence might be the most important episode to focus on, and first incidence is the appropriate rate. In the field of psychiatric epidemiology, there are a range of disorders with both types of causal structures operating, which leads to discussion of the two distinct types of incidence. The two types of incidence are functionally related to different measures of prevalence. Kramer et al. [22] have shown that lifetime prevalence (i.e. the proportion of the population who have ever had an occurrence of a disorder) is a function of first incidence and mortality in affected and unaffected 188

populations. Point prevalence (i.e. the proportion of persons in a defined population at a given time who manifest the disorder) is linked to total incidence by the queuing formula P = I*D [9, 23]: that is, point prevalence is equal to the total incidence multiplied by the average duration of episodes. Incidence data on specific psychiatric disorders are expensive to gather. A minority of individuals, not necessarily representative of those with disorder, receive treatment, and therefore a field survey is required. Many of the disorders are rare and many well individuals have to be evaluated, at two distinct points in time to estimate the incidence rate. The number of prospective studies with sufficiently large samples to estimate rates of incidence is small. If 5000 person-years of observation is set as the minimum requirement, there are only a handful of studies that cover a range of disorders. These include the ECA study in the United States [3, 4], the Stirling County study in Canada [24], the Traunstein study in Germany [25], the Lundby Study in Sweden [26], the Baltimore ECA Follow-up [12], the Netherlands Mental Health Survey and Incidence Study (NEMESIS) study [27], and, soon, the Follow-up of the National Comorbidity Survey [28]. Comparison of results between these studies is important because the numerators are so small that the findings from any one study are statistically volatile. Analysis of the onset of alcohol abuse or dependence in the ECA cohort [3, 4] shows sharply declining incidence after young adulthood and a slight rise at the beginning of the seventh decade. The rise in age-specific incidence rates in the elderly is caused by only five individuals who had onset in that age range. A similar curve from the Lundby study has the same shape [29], with the rise after age 60 based on only three individuals who had incidence in that age range. These results suggest aetiological clues and have implications for prevention efforts. The results of each study might not be convincing, but the replication of the identical pattern is credible.

12.3 Course 12.3.1 Remission Careful definition of terms is essential for studying the course of psychopathology [30]. Conceptualising

STUDYING THE NATURAL HISTORY OF PSYCHOPATHOLOGY

and measuring the ebb and flow of psychopathology after onset necessitates focus on duration, measured by units of time, and on recurrence, which is measured in a manner similar to incidence. Remission is a point in time after onset when signs and symptoms diminish sharply. After the first onset has occurred, it is useful to have a measure of level of symptomatology that defines remission unambiguously. Only after setting a threshold for remission can the duration of the episode be studied [31]. The definition of remission has all the complexities of the definition of onset. But as well as a threshold for the presence and absence of signs and symptoms, defined by both intensity and breadth, the definition of remission requires that a threshold of a minimum time period be set, before which a remission does not occur. For example, a remission may be defined as a continuous period of three months or more during which the individual is not meeting full criteria for disorder; or, a stricter definition might be three months during which the individual has no symptoms of the disorder at all. The measure of remission will be most useful if it uses the diagnostic criteria as a comparison or standard value, because that will facilitate meaningful comparison of qualities of remission between disorders. As an example, an operational measure of completeness of remission is proposed to describe that point between episodes, that is most free of signs and symptoms. It requires that thresholds be established for the intensity of signs and symptoms, as in, for example, the SCAN (rating scale one value of 1 versus 2 or 3; [32]. The measure of completeness of remission can be used even if the threshold levels are set differently in different research studies. The measure below takes advantage of the SCAN definitions to set thresholds of symptom intensity, and sets three months as the minimum time period during which the individual must fail to meet complete diagnostic criteria in order for a remission to be defined and measurable. The proposed levels of completeness of remission are the following: • Level 1: No signs and symptoms present. • Level 2: At least one sign or symptom present, but none above the threshold of intensity. • Level 3: One and only one sign or symptom present above the threshold of intensity; other

signs and symptoms may or may not be present below the threshold of intensity. • Level 4: More than one sign or symptom present above the threshold. • Level 5: Full criteria for disorder are present continuously, that is remission does not occur (‘continuously’ is defined as having no gaps greater than three months). The speed of remission is defined similarly to the speed of onset and the prodromal period. It is the time from the point at which the disorder is at its symptom peak to the beginning of the remission. The symptom peak is best defined similarly to the concept of acquisition, discussed above: the point in time where the highest number of signs and symptoms are above the threshold of intensity. The speed of remission can be measured in standard units of time (e.g. weeks and months). A relapse occurs if the individual meets criteria for disorder after a remission. Relapse requires careful work on terminology and operational definition, as with remission [33]. The speed of relapse is the time required to move from the state of remission to the symptom peak. As with other duration measures, the metric for speed of relapse is standard units of time. Recurrence is the risk for relapse and is analogous to the incidence in expressing a dynamic or timeoriented risk for onset, as discussed above regarding attack rate. The rate of recurrence can be estimated similarly to incidence, with the risk set for recurrence being comprised of all those not currently meeting criteria for disorder. The natural course is advantageously displayed in quasi-continuous fashion, as in Figure 12.5. Here the horizontal dimension is time, measured in yearly increments, and the vertical dimension is an ordinal measure of the frequency of panic attacks during the year. The graph shows every bit of data obtained on the course for the 33 new cases in the Baltimore ECA Follow-up [34]. The points in the graph are randomly jittered so that the course for each individual can be observed. The graph displays the great heterogeneity of the natural history, without reducing information, as would occur with the calculation of remission or recurrence rates. Individuals representing certain typical types of natural course can be identified: the quick and enduring recovery for case A; the stable 189

Frequency of Panic Attacks 2 4 6 8

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Fig 12.5 Frequency of attacks after onset of panic disorder Baltimore ECA followup.

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24% 8% 60% “Poor” 12% outcome

10% Schubweis

chronic case B; the gradual recovery in case C and case D, who crosses the threshold of panic attacks back and forth repeatedly. These data can serve as the basis for random effects models, which estimate a slope for each individual. The average of all the individual slopes is shown in Figure 12.5 as a solid thick line declining from ordinal frequency Level 4 to level zero over the 14 years of follow-up. Many of the concepts discussed above present a simplistic point of view by not taking the longterm course into account. For example, incidence, remission and relapse are all dichotomous outcomes that can be measured with only two waves of observations. One wave defines the sample at risk, which comprises the denominator, and the second wave estimates the numerator. These approaches involve severe reductions in the complexity of data, such as that displayed in Figure 12.6. Attempts have been made to categorise or quantify the entire course of psychopathology for a given disorder – what might be termed the career of psychopathology. For example, Ciompi [35], after observing a first onset sample for an average of 35 years, proposed eight categories for the course of schizophrenia that combine the three dichotomies of onset (acute vs. insidious), course (stable vs. episodic) and outcome (good vs. bad). A visual description of these categories, adapted from Ciompi, is shown in Figure 12.6. These figures stimulate questions as to the nature of the course. For example, what is the ultimate outcome? Is the course steadily, progressively deteriorative

6%

Fig 12.6 Typologies of course from Ciompic ollow-up.

or progressively ameliorative [36]. Is the rate of remission related to the speed of onset? Is the risk for recurrence related to the duration of the episode or to the speed of onset? Answers to these questions would be important for clinical treatment, but not much is known because of the difficulties of conducting research on the natural history of psychopathology. Risk factors may have differential effects on incidence, duration and recurrence, and it is informative to combine the study of all three indicators for any given risk factor. For example, prevalence studies uniformly show that both female gender and lower socioeconomic status have been associated with higher prevalence of major depressive disorder. Analysis from the Baltimore ECA cohort showed that the gender difference existed only for incidence, not

STUDYING THE NATURAL HISTORY OF PSYCHOPATHOLOGY

prevalence for one disorder, given the presence of another [39]. Studies of natural history focus on risk, either through retrospective recall of the timing of one disorder versus the other, or through a true prospective design. For example, in the ECA data, the risk for onset of DSM-III major depressive disorder is 3.4 times higher if the individual has had a panic attack than if the person has not suffered a panic attack [40]. Many mental disorders have their peak periods of onset in adolescence and young adulthood (e.g. depressive disorder, panic disorder, alcohol disorder, substance use disorder and schizophrenia), while many important chronic physical conditions have peak onset in middle age or later (e.g. heart diseases, cancers, type 2 diabetes and strokes). Therefore, physical illness is another type of comorbidity and a possible consequence of psychopathology. In followups based on psychiatric case registers, the systems of registration are usually based on the structure of the treatment systems, which tend to separate psychiatry from other areas of medicine. Thus, only highly specialised registration systems, such as the Oxford Record Linkage Study [41], or the use of two or more illness-based registers, such as the Danish Psychiatric and Cancer registration systems [42], is effective. In population-based follow-ups, such as the Baltimore ECA Follow-up, a difficulty has been anticipating the range of potential consequences. Table 12.2 shows the range of consequences of depression for selected physical conditions and symptoms. Each relative risk in the table was the result of a separate analysis that compared depressive disorder to other forms of psychopathology, and which adjusted for other known risk factors for the physical condition. For many conditions, depressive disorder was the only nontrivial predictor from

for duration of episodes, nor for the risk for recurrence [12]. In contrast, the SES difference is very weak for incidence, but much stronger for persistence of disorder [37].

12.4 Outcome Outcome refers to the consequences of the psychopathology. These consequences can be immediate, such as impairment and disability resulting from the disorder. The focus here is on important and pernicious consequences of disorder that occur afterward and that are not included in the defining phenomena of the disorder, that is future psychopathology of other types and physical illness (comorbidity), overall functioning and death.

12.4.1 Comorbidity Comorbidity is the occurrence of two or more disorders in one individual [38]. There has been increasing interest in narrowly defined disorders since the introduction of the DSM-III. Since psychopathology does not always fit into the DSM categories and is highly overlapping, the increased splitting of disorders has led to increasing interest in psychiatric comorbidity: the occurrence of two or more disorders in the same individual. The disorders can occur simultaneously in the same individual, or they can occur at different points in time – so-called lifetime comorbidity. Comorbidity over the lifetime presumably expresses a genetic diathesis, an early and enduring risk factor or a long-standing environmental cause. Observed patterns of differential comorbidity will contribute, eventually, to improved nosology. Cross-sectional study of comorbidity focuses on the increase in

Table 12.2 Depressive disorder as outcome and predictor of medical conditions over a 13 year follow up of the Baltimore ECA cohort. Predict medical condition Condition Type 2 diabetes Heart attack Cancer Stroke Arthritis

Predict depressive disorder

At risk

New cases

Relative risk

At risk

New cases

Relative risk

1715 1551 2017 1705 1332

89 64 203 95 270

2.2 4.5 1.0 2.7 1.3

1633 1633 1633 1633 1633

71 71 71 71 71

1.1 1.7 0.6 8.4 1.0

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the range of psychopathology. Consistent with the developmental approach taken above, the effects of psychopathology below the threshold of diagnosis were also important for some physical conditions (not shown). The sizes of the relative risks are large enough to place depressive disorder on a par with other risk factors such as high cholesterol for heart attack, family history for breast cancer, hypertension for stroke and obesity for type 2 diabetes. Since depressive disorder is mostly not treated, in spite of the availability of effective treatments, and since it is relatively easy to screen for it, these data have implications for the practice of preventive medicine in the primary care setting. The table also shows the consequences of the medical conditions for later depression, mostly not important, except for stroke, which raises risk for depression significantly.

12.4.2 Functioning Functioning is the ability to deal with the normal demands of everyday life. Persons with psychopathology are often less able to function effectively than the general population. The term as used here includes the World Health Organization definition of disability [43]. Impairment and disability resulting from a given disorder such as schizophrenia is widely variable [44, 45], and most of the costs associated with psychiatric problems come from the reduced functioning, not from the signs and symptoms themselves. The conversion of psychopathology to impairment and disability is thus an important area of study. A growing number of longitudinal studies show that psychopathology has strong consequences for disability, comparable to, or greater than, consequences of chronic physical conditions [46–49].

12.4.3 Mortality Mortality, or the rate of death in the population, is usually higher in individuals with psychopathology than in the general population. Increased mortality is associated with schizophrenia (e.g. [50]), mood disorders (e.g. [24, 51–53]), anxiety disorders [54], cognitive impairment [55] and substance use disorders [56, 57]. Recent data from the public mental health sector estimate a life expectancy reduced by 192

25 years for those with severe mental disorders as compared to the general population [58]. For some disorders the increased mortality is associated with the signs and symptoms of the disorder itself, as is the situation for suicide with depression. But the risk for suicide is also high for disorders where the connection is less obvious, as in the controversy over panic and suicide [54, 59], and suicide in schizophrenia [60]. The rate of accidental death is also sometimes higher among persons with psychopathology. Other causes of death related to psychopathology are more subtle still. For example, it may be the case that individuals with psychopathology are less likely to engage in illness prevention and health promotion behaviours, such as curtailment of smoking or lowering of cholesterol intake, due to preoccupation with psychopathology or less effective functioning generally. Finally, the mortality rate is raised due to the association with physical conditions which raise risk for death, as discussed above and in Table 12.2.

12.5 Methodological concepts for studying the natural history of psychopathology Measuring onset, course and outcome in the context of population benefits from a prospective approach. The traditional design for natural history is the cohort study in which a population of individuals are observed prospectively over years, decades or even a lifetime [61–63]. The minimum design requirement is two waves of data collection. For example, to estimate incidence, the lifetime history of psychopathology is determined at the first wave in order to exclude individuals who have already met the criteria for caseness. At the second wave, those who have become new cases form the numerator of the incidence rate, and those who were never cases at wave 1 form the risk set, or denominator.

12.5.1 Attrition Major sources of error in cohort studies are due to attrition, censoring and recall. Attrition is the loss of subjects in longitudinal research usually due to one of three causes: individual mobility outside the study area or to an unknown residence, death and refusal to

STUDYING THE NATURAL HISTORY OF PSYCHOPATHOLOGY

participate after some threshold of response burden is reached. In field surveys such as the ECA, attrition after even so short a period as one year can be large enough to threaten the credibility of results. The ECA attrition in one year of follow-up was mostly due to refusal (about 15%) and partly to failure to locate individuals (about 5%). Since the time period was short, there was relatively little attrition due to mortality (less than 1%). In the Baltimore ECA Follow-up, in which the follow-up interview was 13 years after the baseline, the proportions shifted: nearly 25% had died, 12% could not be located and 8% refused [55]. Attrition can bias results. In the 1 year follow-up of the ECA, older white women and younger black males had about twice the rate of attrition than other respondents, and these differences in attrition were larger than differences related to baseline psychopathology [64]. In the Baltimore ECA Follow-up, older persons were more likely to die, but there were also biases connected to psychopathology, such as the tendency for those with cognitive impairment to die, and for those with antisocial characteristics not to be located [55, 65]. Attrition forestalls studying the effect of psychopathology during the interval between baseline and follow-up: For example, there may be a tendency for those with new episodes of disorder to move to another location (e.g. a young person might move to another city to live with parents during recovery). Since both the episode of psychopathology and the attrition occur between waves of interviews, attrition eliminates the possibility of studying this tendency. In population-based psychiatric case registers, attrition is likely to have different causes and a different structure. In a survey study, persons with psychosis may be more likely to refuse to be interviewed, and more likely to change address and be lost to follow-up after the passage of time. For a psychiatric case register, refusal is less likely to be important if the level of psychopathology is such as to need or even require treatment, such as might be argued is the case for psychosis. For disorders such as depression, where treatment is often not sought, register data may be severely biased by attrition. For registers of limited geographic spread, mobility will be important; for case registers that cover an entire country, such as in Denmark or Israel, mobility will be much

less important. The upshot of these comparisons is that population-based psychiatric case registers are a useful source of information on the natural history of severe mental disorders such as psychosis.

12.5.2 Censoring Censoring is the bias that results from the fact that the period of observation is limited in time. The extreme version of censoring is the cross-sectional study. It is possible to approximate measures of incidence, remission and recurrence using data gathered at one point in time, but this requires assumptions that are not generally tenable. Age of onset can be determined in a cross-sectional sample, for example by asking each respondent who meets lifetime criteria for disorder when the symptoms began. Even if the recall is accurate (discussed below), episodes of individuals who have onsets after the data collection is complete will be omitted, and this will lead to a downward bias in the estimate of age of onset. The problems of censoring are less severe with a cohort study, but exist nevertheless in any study that begins after birth and ends before all members of the cohort have died. In estimating the duration of an episode of psychopathology, for example, there will always be a small portion of the cohort who are in an episode at the time the data collection concludes, making it impossible to estimate the average duration of the episode in the cohort. Since the mean is highly influenced by observations on the tail of the distribution, the bias in the mean can be strong.

12.5.3 Prevalence bias Many individuals with mental disorders do not experience a recurrence, and those that do have recurrent episodes represent more chronic and severe cases. For this reason the natural history is best studied by prospective follow up of a sample of individuals with first lifetime onsets – that is from the first episode forward. This approach avoids the well-known ‘clinician’s illusion’ [66]. The problems of attrition, censoring and prevalence bias are illustrated in Figure 16.2 (in Chapter 16 of this book) with data from the Danish Psychiatric Case Register on hospital admissions during the period 1973–1988. In contrast to the display 193

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of course in Figure 12.6, this method requires a dichotomous indicator for presence or absence of disorder. The cohort begins with the first episode in the individual’s lifetime wherein the diagnosis of schizophrenia was given. The figure shows survival curves for the 1st, 5th, 10th and 15th episodes. Each curve shows the percentage of individuals who remain outside the hospital (vertical axis) according to time since discharge (horizontal axis). Relapse from the first episode tends to occur in the first few years after discharge; by the fifth year, almost threequarters of the cohort have had a second episode of hospitalisation. In any given curve, the manner of presentation is immune from the censoring bias, since it correctly portrays the lack of information for the individuals who have not suffered a relapse by the end of the follow-up in 1988. But curves for those with more episodes reveal the effects of prevalence bias since they are only computed for individuals suffering 4 or more, 9 or more and 14 or more relapses, respectively. Survival in the community is less likely for these cohorts because they represent an increasingly severe subsample of the first admission cohort. Imagine a clinician making an inference from his/her daily experience about the chronicity of a disorder – the clinician sees the most chronic cases 15 times as often as the cases with only one episode. These data show that prevalence bias can generate a falsely pessimistic view of the chronicity and severity of psychopathology.

12.5.4 Recall Recall bias is the error in measurement due to inaccuracies in the respondent’s memory of events. The cross-sectional approach is compromised because it relies on the respondent’s autobiographical memory to recall the time of the onset, which may be quite distant from the time of the data collection. It is likely that those with more recent onsets are less likely to forget the occurrence of the disorder, which biases the onset distribution toward later onset. If the disorder tends to occur early in life, as many mental disorders do, the tendency to forget distant episodes can generate findings with nonsensical data on lifetime prevalence [67, 68], and also possibly suggesting an upward trend in the occurrence of the 194

disorder [69], as in the suggestion of an ‘age of melancholy’ [70]. Simulation models suggest it takes only a small difference in recall to produce the appearance of strong upward trends in occurrence [71]. It is likely that those with severe cases of disorder are less likely to forget the occurrence of disorder; if severity is associated with earlier onset, this bias would be toward earlier onset. The study of risk factors will be further complicated because individuals may not remember the order of occurrence of the risk factor and the onset. Thus, retrospective data from a cross-sectional approach include a mixture of biases that are sometimes undecipherable. The same mistakes in recall can occur in the cross-sectional or prospective design. But in the prospective design, the mistakes made by an individual are likely to be smaller than in the cross-sectional design, because the time of data collection is closer to the present for the individual, especially at the second or later waves where new onsets are determined. The effects of error are complex in the prospective design, because the biases can concatenate in so many different ways. For example, in the East Baltimore ECA panel cohort, there were 2622 individuals who had never in their lifetimes met criteria for diagnosis of panic disorder by the time of the interview at Wave 1; 20 of these met criteria at Wave 2, giving a cumulative annual incidence rate of about 7 per 1000 per year [4]. There were 40 individuals at Wave 1 who met criteria for past or present diagnosis; of these, 20 reported never having experienced a panic attack at Wave 2. These 20 might be labelled ‘reverse incidence’. They represent half (20/40) of those meeting criteria for diagnosis at Wave 1; they match exactly the number (20) of incident cases. This phenomenon is not unique to the ECA surveys. The existence of reverse incidence is due to forgetting and, while disquieting, does not negate the existence of the 20 cases in the numerator of the incidence rate. It does suggest that forgetting of episodes occurs, a tendency that would bias prevalence rates downward; and, probably, bias incidence rates upward. The upward bias in incidence would occur because cases that belong in the numerator of the attack rate would be mixed in with the numerator of the first incidence rate. Lack of blind measurement is an important problem in estimation as regards outcome. The dependence of outcome on initial state is a central focus

STUDYING THE NATURAL HISTORY OF PSYCHOPATHOLOGY

of research on natural history, but it may be difficult to measure outcome independently of initial state. If the respondent or the interviewer remembers the initial measurement session, the results of that session are likely to bias measurement of outcome. For example, an interviewer may probe more persistently for the occurrence of panic attacks if it is known that they have occurred in the recent, or even distant, past. Impairment and disability are likely to be rated downward if it is known that the individual once met the criteria for diagnosis of schizophrenia, even if no signs and symptoms are present at the time of the follow-up. Thus, bias due to lack of blindness is likely to overestimate the relationship of early indicators of psychopathology to later outcomes. Random error has counterintuitive pernicious effects in prospective research on the natural history of disorder. Indeed, in the context of estimating incidence in field surveys, the concept of random error is not very useful. If by random error is meant an equiprobable response, then it is straightforward to show that, for a sample, the bias resulting is moderately upward for prevalence and strongly upward for incidence. The rates of false-positive and falsenegative answers to a given question will depend on the question and will not be equiprobable, in general; but many other types of errors in the survey process – mistakes in data entry, for example – will have an equiprobable character to them. Thus, the tendency is for seemingly random errors to bias the incidence and recurrence rates upward.

four or more waves of analysis, with continuous and categorical constructs not directly observable, have been developed [75–77]. Inverse probability weighting techniques allow inference to the baseline sample in a cohort study, even in the presence of attrition [78].

12.6 Conclusion Studying the natural history of psychopathology in the general population requires large resources of effort and expense because of the combination of population-based sampling, long-term commitment and intensity of measurement. Most data on natural history are based on clinical samples, which are not representative of the population of persons with mental disorders. There are few benchmark estimates for the incidence of most major mental disorders that have been replicated and for which there is a consensus among investigators. The estimates for parameters of long-term course of disorders are widely varying. Thus, there is plenty of progress to be made!

Acknowledgements This work was supported by NIDA grant DA026652 and NIMH grant MH53188.

References 12.5.5 Statistical Innovations There has been an explosion of statistical techniques over the last several decades which address many of the problems of prospective studies. Problems of censoring are addressed with the family of techniques called survival analysis (e.g. [72]). The development of covariation over time can be studied with secondorder generalised estimating equations [73]. Risk factors at different stages of the disease may be differentially related to disease progression only above or below the threshold set by the diagnosis. In this situation, the diagnostic threshold might be reconsidered. Statistical techniques to locate a threshold have been developed [74]. Latent growth mixture models, which are statistical techniques suitable for

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CHAPTER 12 [68] Parker, G. (1987) Are the lifetime prevalence estimates in the ECA Study accurate? Psychol. Med., 17, 275–282. [69] Klerman, G.L. and Weissman, M.M. (1989) Increasing rates of depression. J. Am. Med. Assoc., 261, 2229–2235. [70] Hagnell, O., Lanke, J., Rorsman, B. et al. (1982) Are we entering an age of melancholy? Depressive illnesses in a prospective epidemiological study over 25 years: the Lundby study, Sweden. Psycholo. Med., 12, 279–289. [71] Giuffra, L. and Risch, N. (1994) Diminished recall and the cohort effect of major depression: a simulation study. Psycholo. Med., 24, 375–383. [72] Lawless, J.F. (1982) Statistical Models and Methods for Lifetime Data, John Wiley & Sons, Inc., New York. [73] Zeger, S.L. and Liang, K.-Y. (1986) Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42 (1), 121–130. [74] Scharfstein, D., Liang, K., Eaton, W. and Chen, L.-S. (2001) The quadratic cumulative odds regression

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[75]

[76] [77]

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model for scored ordinal outcomes: application to alcohol dependence. Biostatistics, 2, 2473–2483. Muthen, B. (2001) Second-generation structural equation modeling with a combination of categorical and continuous latent variables: new opportunities for latent class/latent growth modeling, in New Methods for the Analysis of Change (eds L.L. Collins and A. Sayer), American Psychological Association, Washington, DC, pp. 291–322. Bollen, K.A. (1989) Structural Equations with Latent Variables, John Wiley & Sons, Inc., New York. McArdle, J.J. and Hamagami, F. (1992) Modeling incomplete longitudinal and cross-sectional data using latent growth structural models. Exp. Aging. Res., 18, 145–166. Robins, J.M., Rotnickski, A. and Zhao, L.P. (1994) Analysis of semiparametric regression models for repeated outcomes under the presence of missing data. J. Am. Stat. Assoc., 90, 106–121.

13

Symptom scales and diagnostic schedules in adult psychiatry Jane M. Murphy Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School; Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA

13.1 Introduction Psychiatric ‘scales’ concern dimensions of psychopathology while ‘schedules’ deal with categories of psychiatric disorders. A scale queries a set of inter-related symptoms that constitute a continuum from a few to many symptoms. It concerns a quantitative gradient based on symptoms representing a qualitative theme. Many scales reflect influence from psychometric theory and survey methodology. Psychologists and sociologists have been more prominent as their designers than psychiatrists. Schedules are based on the syndromes that define diagnostic categories as described in the Diagnostic and Statistical Manuals (DSMs) of the American Psychiatric Association [1–3] and the recent versions of the International Classification of Diseases (ICD) of the World Health Organization [4, 5]. A syndrome is a pattern of symptoms made up of ‘essential features’, ‘associated symptoms’, ‘duration’ and frequently also ‘disability’. Depending on the completeness of the pattern, the syndrome is considered to be present or absent thereby reflecting dichotomous measurement. Psychiatrists have played active roles in the construction of schedules. Most scales used thus far in psychiatric epidemiology deal with anxiety and/or depression. Each question is asked of each subject, and categories of response refer to the presence or absence of a symptom, its frequency of occurrence or the degree to which it is bothersome. Responses are given numerical values that are added together to form a score, the

range of which has a ‘cutting-point’ that allows cases to be separated from non-cases. The distribution of scale scores in a general population shows marked skewness. The majority of people report that they do not have these symptoms, or only a few; and a minority of people report that they suffer from several to many. In other words, the score distributions for psychiatric scales are not normally distributed as are height, weight, IQ and some social attitudes. Diagnostic schedules are more comprehensive in psychiatric coverage in that they deal with psychotic disorders and substance abuse as well as depression and anxiety. The schedules differ in terms of whether they focus on the clinical status at the time of the interview or on the subject’s history of psychiatric disorders. The schedules designed for epidemiological research use the ‘lifetime’ approach and are highly structured so that clinical judgement need not be applied during the course of the interview. Most of them have ‘modules’ for the separate diagnoses thereby allowing the researcher to be selective. Often, the module opens with one or two ‘stem’ questions about the ‘essential features’ of that diagnosis. If the subject responds negatively to the ‘stem’, the module for some diagnoses can be skipped. The schedules contain careful instructions to the interviewers about ‘skip-outs’. Even if a given module is started, skipouts occur as it becomes clear that the subject will not meet the criteria for that diagnosis. Most scales and schedules are known by an acronym standing for the full name of the instrument, and sometimes by the name of the designer.

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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CHAPTER 13 Table 13.1 Scales and schedulesa. BDI CES-D CIDI CIS CIS-R CMI CSI DIS DPAX Eysenck GHQ HOS HSCL ISPI MMPI MSS NSA PERI PHQ-G PHQ-S PRIME-MD PSE PSS SADS SCAN SCID SCL-90 SF-36 UM-CIDI WMH-CIDI Zung 22IS

Beck Depression Inventory [109] Center for Epidemiologic Studies Depression Scale [48] Composite International Diagnostic Interview [68] Clinical Interview Schedule [139] Clinical Interview Schedule Revised [191] Cornell Medical Index [89] Cornell Selectee Index [15] Diagnostic Interview Schedule [58] Depression and Anxiety Schedule [37, 38] Eysenck Personality Inventory [13, 14] General Health Questionnaire [138] Health Opinion Survey [20] Hopkins Symptom Checklist [95, 96] Iowa Structured Psychiatric Interview [52] Minnesota Multiphasic Personality Inventory [12] Mental Status Schedule [113] Neuropsychiatric Screening Adjunct of the US Army [16] Psychiatric Epidemiological Research Instrument [48] Personal Health Questionnaire (Goldberg and Simpson, in Rizzo et al. [149]b Patient Health Questionnaire [132]a Primary Care Evaluation of Mental Disorders [131] Present State Examination [54, 150] Psychiatric Status Schedule [114] Schedule for Affective Disorders and Schizophrenia [55] Schedules for Clinical Assessment in Neuropsychiatry [79] Structured Clinical Interview for DSM-III-R [81, 126] Symptom Checklist 90 Items [99] Short-Form Health Survey 36 items [135] University of Michigan CIDI [69] World Mental Health CIDI [78] Zung Depression Scale [110] Twenty-Two Item Scale [21]

a The

references shown in Table 13.1 are those in which the instrument is most fully described. instruments developed by Goldberg and Simpson and by Spitzer et al., have different names but the same acronym. For purposes here, the former is called PHQ-G and the latter PHQ-S.

b The

Table 13.1 gives a list of acronyms and names of the instruments discussed in this chapter. The selection is intended to provide a historical overview since many of the instruments used today are outgrowths or revisions of earlier ones. The selection is not, however, exhaustive. Specifically excluded are rating scales for use by observers rather than for asking direct questions. The Hamilton Rating Scale for Depression [6] and the Brief Psychiatric Rating Scale [7] are examples of well-known instruments excluded on this basis. Most of the symptom scales were developed as paper/pencil questionnaires since they were intended for use in the armed forces or in clinical settings. For epidemiological research, the face-to-face interview 200

has been the typical mode of data-gathering although increasingly telephone interviews have been used. Experimentation is going on regarding the use of computers either as the mode of inquiry or as an interview aid [8]. Figure 13.1 gives a general overview of the field and introduces the instruments in the context of their development. The dates shown on the vertical axis refer to the first publication that describes how the instrument was constructed and gives evidence about reliability and validity. The separate parts of the figure refer to North America, the United Kingdom and the World Health Organization. Each of these parts is divided into the types of settings where an instrument

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Fig 13.1 Time, place, and purpose of instruments developed for adult psychiatry.

was developed: population-based epidemiology, psychiatric facilities and primary care. The scales are shown in white letters and the schedules in black letters. Solid lines that end in an arrow show the evolution of an instrument as carried out by a given group held together by one or two leaders. Dotted lines that end in an arrow reflect that an instrument developed by one group of researchers influenced a subsequent instrument designed by a different group. Almost without exception, instruments designed for one setting have been used in other settings. Further, each new instrument has been influenced to some degree by prior instruments, and there are several lines of influence that flow across the geographic boundaries. For epidemiological research, the instruments have been used in both ‘single-stage’ studies, where a given instrument is administered to each individual

of a population sample, and ‘two-stage’ designs that typically involve a scale at the first stage followed by a more complete psychiatric work-up by means of a schedule for a subset of subjects, a large portion of whom gave evidence of a psychiatric history at the first stage. In addition to describing the instruments themselves, the studies in which they have been used will also be mentioned. This means that most of the major investigations in psychiatric epidemiology will be noted, except for one of the most long-lasting studies, the Lundby Study in Sweden [9–11]. The Lundby data derive from face-to-face interviews carried out by psychiatrists who followed an outline of questions. But a schedule based on the questions has not been published. While it is not the intent of this chapter to cover personality assessment per se, two instruments 201

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designed for that purpose need to be mentioned because of their influence on psychiatric instruments. One is the Minnesota Multiphasic Personality Inventory (MMPI) which was designed in the United States during the 1930s [12] and the other is the Eysenck Personality Inventory [13, 14] which was created in the United Kingdom after the World War II. Many of the psychiatric instruments have borrowed questions from one or another of these inventories. Recently, the broad dimensions of the Eysenck Inventory, ‘Introversion’ and ‘Extraversion’, have contributed to discussions about possible future directions for psychiatric measurement.

13.2 North American instruments for epidemiological research During the World War II two scales were designed to screen for psychopathology among Army recruits: The Cornell Selectee Index (CSI) [15] and the US Army’s Neuropsychiatric Screening Adjunct (NSA) [16], both of which, but especially the NSA, stood up well to extensive psychometric testing. After the War, the CSI and NSA were administered to the same individuals [17]. The correlation was very high, and recommendations were made for the development of a new psychiatric instrument that would incorporate the best features of each and would be appropriate for general use. Unfortunately such an instrument was not developed. The CSI was transformed into a clinical instrument while the NSA was never changed, updated or used again as an independent instrument. However, the NSA strongly influenced the subsequent instruments for population-based epidemiology. The two World Wars indicated that psychiatric disorders were much more common than shown in mental hospital statistics. To investigate the question of ‘how much?’, two epidemiological studies of general populations were undertaken: the Stirling County Study [18] conducted in Atlantic Canada, and the Midtown Manhattan Study [19] in New York City. Each used a rather long structured interview administered by lay interviewers. In addition, each study produced a shorter screening instrument. From the Stirling Study came the Health Opinion Survey (HOS) [20] and from the Midtown Study 202

the Twenty-Two Item Scale (22IS) [21]. Through empirical testing, the composition of each involved numbers of questions from the Army’s NSA which concentrated on the type of general anxiety that focused on ‘nervousness’ and involved the autonomic expressions of fearfulness as indicated in ‘pounding heart’, ‘cold sweats’ and other features of the ‘body’s alarm system’ being activated and which will be called here ‘autonomic anxiety’. There was lesser coverage of depression which was in line with the fact that, at that time, anxiety was thought of as the hallmark of neurotic disorders and depression was mainly considered a psychotic disorder. During the 1960s and 1970s, the HOS and 22IS, despite being presented as screening instruments, were used as the main source of data-gathering in several other epidemiologic studies [22–28]. In addition, they were adapted for studies of US national samples [29–31]. These national sample studies were not, however, epidemiological in the usual sense because they did not estimate prevalence but rather focused on the proportions of the samples who answered individual questions in particular ways. Both the Stirling and Midtown Studies reported that, counting all types of psychiatric disorders together, prevalence was much higher than expected being approximately 20% in each study. The Stirling Study has continued with repeated cross-sectional surveys and cohort follow-up to provide a 40-year epidemiological perspective which is based on both face-to-face interviews with subjects and interviews by psychiatrists with the subjects’ general physicians [32–36]. Using a longer schedule that included the HOS, a computerised algorithm was designed for the longitudinal research. Both the longer schedule and the algorithm were given the acronym ‘DPAX’. This acronym was selected to emphasise that the procedures focused specifically on depression, represented by ‘DP’ and anxiety, represented by ‘AX’ [37]. It has steps for ‘essential features’, ‘associated symptoms’, ‘duration’ and ‘impairment’. Two versions of the schedule and algorithm (DPAX-1 and DPAX-2) were constructed to accommodate historical changes in the colloquial vernacular by which the mood of depression and the sensations of anxiety were recognized (for example, the idiom of ‘being in poor spirits’ became outmoded while ‘feeling low and hopeless’ was

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easily understood) [38]. Using these methods, it was found that these disorders exhibited quite steady prevalence, tended to be chronic with low incidence, and that depression carried a significant mortality risk [39, 40]. The next instrument to appear on the US scene was the Center for Epidemiologic Studies Depression Scale (CES-D) [41]. Designed at the National Institute of Mental Health (NIMH), it deals exclusively with depression and has considerable face validity for that syndrome in contrast to what were perceived as the ambiguities of autonomic anxiety. It was better accepted by clinical psychiatrists than its predecessors. In part this was based on the profession’s increasing appreciation of the importance of overt symptoms in contrast to unconscious anxiety and intrapsychic features. The CES-D was first used in an epidemiologic study in Missouri and Maryland [42, 43] and now has been used in many studies, both clinical and epidemiological, where assessment of the current level of depressed mood is needed [44–47]. The Psychiatric Epidemiologic Research Instrument (PERI) [48] was developed by Dohrenwend and colleagues to produce empirically distinct and reliable scales among different ethnic and racial groups. It is much broader than earlier instruments and has scales for ‘false beliefs and perceptions’, ‘manic characteristics’, ‘suicide ideation’ and so on. It also contains items from the NSA, HOS and 22IS which are described as ‘non-specific psychological distress’ [49] or ‘demoralisation’ [50]. The Dohrenwend group distinguish between ‘demoralisation’ as a dimension and diagnosable categories of psychiatric disorder. One of the main studies using the PERI is a large two-stage investigation named the Israeli Study of Psychiatric Disorder and Social Status [51]. At about the same time as the PERI, another broad-range instrument, the first of the diagnostic schedules developed in the United States, was presented as the Iowa Structured Psychiatric Interview (ISPI) [52] and used in the Iowa 500 Study [53], which involved follow-up of mental hospital patients and normal controls. It was designed to be acceptable to people who did not suffer from a psychiatric disorder. Influenced by the British Present State Examination (PSE) [54], it focused on psychiatric categories but begins with a core of 20 screening

questions for depression, mania, schizophrenia and neurosis. In the body of the interview, attention is given to the duration and history of the symptoms. Encouragement about the feasibility of using a diagnostic schedule in a full-scale general population study came from a group of researchers who trained lay interviewers to administer a clinical instrument, the Schedule for Affective Disorders and Schizophrenia (SADS) [55, 56]. The demonstration that a long instrument dealing with psychotic as well as milder disorders could be used successfully in a community study was the beginning of a new trend in which epidemiologic research would be focused on multiple and discrete categories of psychiatric disorders [57]. The first epidemiologic instrument designed with this new goal in mind was the Diagnostic Interview Schedule (DIS) which implemented the criteria outlined in DSM-III [58]. The DIS was used in the NIMH Epidemiologic Catchment Area (ECA) program which had been mandated by President Carter’s Commission on Mental Health [59] in order to provide an up-to-date and comprehensive overview of the prevalence of psychiatric disorders in the United States. Most of the earlier studies had drawn samples of 1000–1500 subjects, but the ECA’s sample consisted of more than 20,000 subjects from five mental health catchment areas in different parts of the United States [60, 61]. The DIS has gone through several revisions mainly based on changes in DSM criteria. The original DIS dealt with schizophrenia, mania, depression, panic, phobias, obsessive–compulsive, somatisation, alcohol and drug abuse as well as antisocial personality, thus forecasting that larger and larger numbers of diagnoses would be involved in instruments of this kind. Many reports on the different categories of disorder have been published and the overall annual prevalence was reported as 20%, a rate that continued to be higher than expected [62]. The Baltimore portion of the ECA has become the Baltimore ECA Follow-up Study with two periods of re-interviews that, combined, cover nearly a quarter of a century [63, 64]. The DIS has also been used in different countries [65, 66]. One of the largest of these was carried out in Canada, the Edmonton Psychiatric Epidemiology Study [67] which reported comparable current prevalence. The DIS module for depression illustrates the more complex approach of a diagnostic schedule. 203

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The first question, which is a ‘stem’, concerns both the ‘essential features’ and ‘duration’: ‘Over your lifetime, have you ever had 2 weeks or more when you felt sad, blue or depressed or when you lost all interest and pleasure in things you usually enjoyed?’. This is followed by questions dealing with disturbances of appetite, sleep, energy, psychomotor activity, loss of interest in sex, disturbances of concentration and of self-worth, as well as preoccupation with death. A probing system is used to rule out instances in which the symptom might have been caused by physical illness or injury or due to taking drugs or alcohol. The module is terminated at this point if the subject did not report at least three associated symptoms. If such were reported, however, the remainder of the module deals with whether the symptoms clustered together in time and whether the episode occasioned seeing a doctor, taking medication or being impaired. The next instrument used in a North American study was an adaptation of the World Health Organization’s Composite International Diagnostic Interview (CIDI) [68]. The adaptation was named the University of Michigan Composite International Diagnostic Interview (UM-CIDI) [69] and was used in the National Comorbidity Survey (NCS) [70]. This study grew out of evidence given in the ECA that many people had more than one type of psychiatric disorder, with many of the comorbid disorders involving drugs and alcohol [71]. Unlike the earlier US national sample studies, the NCS was the first to use a diagnostic schedule. Many of the diagnostic-specific prevalence rates were somewhat higher than in the ECA and the overall annual rate was 29%. The UM-CIDI was then used in the Ontario Mental Health Survey, the first province-wide sample in Canada with 19% being its overall annual rate [72, 73]. The modifications introduced in the UM-CIDI focused on strategies for increasing comprehension of the questions and motivating accurate reporting using principles of survey methodology. For example, the DIS stem question for depression involved both dysphoria and anhedonia. In the UMCIDI, these two features were presented in separate questions. The ‘stem’ questions from all modules were brought to the beginning of the interview rather than being scattered throughout as the first question in each separate module. This change was designed 204

to discourage subjects from giving a falsely negative response because they had learned by experience that a positive response led to further questions. The UM-CIDI also involved a ‘commitment’ question for motivating accurate retrieval of autobiographical memory. It is possible that some of these adjustments related to the higher prevalence compared to other North American studies. With some further modification based on comparison to validating clinical interviews, the UM-CIDI was used 10 years later in the National Comorbidity Follow-up Study which involved re-interviews with members of the original sample. It indicated that the risk of chronicity and recurrence was related in a graded way to the severity of the initial disorder [74]. The same instrument was also used in the National Comorbidity Survey Replication (NCS-R), which involved a new national sample also selected 10 years after the first NCS [75]. This study indicated that while overall prevalence remained steady, the proportion of people receiving treatment increased [76]. The most recent study in North America is the largest to date involving a national sample of Canada numbering over 30,000 subjects, the Canadian Community Health Survey [77]. This investigation used the modules for diagnosing a major depressive disorder as well as for diagnosing the anxiety disorders from the version of the CIDI known as World Mental Health Composite International Diagnostic Interview (WMH-CIDI) [78]. While this recent Canadian study did not provide an overall rate, the other North American studies taken together suggest annual rates that cluster around 20% with the NCS being somewhat higher. Taking all of such studies together, current rates for depression, as an example, cluster around 5%. Throughout the more than 50 years of accumulating such information, questions have been raised about validity because the resultant prevalence rates were perceived as unrealistically high. It has been suggested that these surveys must be identifying normal and transient reactions to stressful life events rather than clinical disorders. If clinicians were to examine the subjects, it was thought that they would be able to differentiate between normal and pathological reactions and would identify smaller numbers and therefore produce lower prevalence rates. However, two recent studies indicated the opposite. Both

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involved a design whereby community subjects were selected for a clinical interview based on the results of a lay-administered schedule. One came from the Baltimore ECA Project Follow-up Study which used the WHO’s Schedules for Clinical Assessment in Neuropsychiatry (SCAN) [79] to assess the DIS [80]. The other came from the last survey in the Stirling County Study where the American Structured Clinical Interview for DSM-III-R (SCID) [81] was used as the standard to assess both the DIS and DPAX-2 [82]. In each, the clinicians identified a much larger number of cases than did the lay-interview methods with specificity being high but sensitivity low. The clinicians rarely negated a case identified in the lay interviews but they identified many additional cases. Such information raises new questions and may re-direct validation efforts towards greater scrutiny of how clinical skills and judgements are applied in a structured clinical interview. Another methodologic issue relates to the use of a lifetime-orientation in diagnostic schedules such as the DIS and CIDI [83]. They have consistently indicated an association between increasing age and low prevalence. This finding has led to two different interpretations. One is that the higher rates among younger people indicate that depression is increasing over time [84, 85]. The other interpretation is that reliance on recollections over the whole lifetime has led to faulty recall among older people [86–88]. The use of retrospective reconstruction remains an active methodological issue.

13.3 North American instruments for psychiatric services and primary care The developers of the wartime Cornell Selectee Index (CSI) were interested in the relationship between emotional problems and medical conditions. Thus they adapted the original instrument for use in clinical settings. Questions about physical conditions were included, and it was re-named the Cornell Medical Index (CMI) [89]. Many of the psychiatric questions were cast as ‘Do you usually feel ...?’, a feature that probably contributed to the instrument’s performance in forecasting subsequent psychiatric and psychosomatic problems [90]. The

CMI was widely used in medical settings and several epidemiological investigations [91–94]. The first post-war scale specifically for use in psychiatric clinics was the Hopkins Symptom Checklist (HSCL) which was developed to monitor the effectiveness of psychotherapy [95, 96]. Although borrowing items from the CMI, the response pattern was changed from ‘Yes/No’ to four categories for the degree to which the symptom bothered the patient, and the time frame was specified as the recent week. The HSCL was improved over many years largely based on factor analytic studies but including tests of internal consistency, test-retest reliability and correspondence with psychiatrists’ assessments [97, 98]. The patients tested were often described as ‘anxious neurotics’ but the ultimate version, an instrument known as the Symptom Checklist 90-items (SCL-90), covers a much wider range of psychopathology with factors for depression, anxiety, obsessive–compulsive symptoms, hostility, paranoid ideation and ‘psychoticism’ [99]. Versions of the HSCL were used in the early drug trials when psychotropic medications were first being developed [100–102] as well as in epidemiological studies [103, 104]. The evolution of the HSCL led to a 25-item version consisting of the factors for anxiety and depression for use in primary care [105]. A diagnostic algorithm based on DSM-III was developed for it and applied in a national sample [106]. Algorithmic assessment did not, however, become the standard procedure, and most HSCL studies continue to use a simple score and a ‘cutting point’. Because of the simplicity of the HSCL language, it has been a good candidate for translation into other languages [107], and recently the HSCL-25 was used for the first stage of a two-stage Norwegian investigation [108]. Some years after the launching of the HSCL, the Beck Depression Inventory (BDI) [109] and the Zung Depression Scale [110] were constructed for psychiatric settings. These instruments reflect the growing interest in depression as antidepressant medications were developed and marketed. The BDI has been used extensively for monitoring of cases in treatment for depression. Along with the Hamilton Rating Scale, the BDI is the best known to psychiatric residents of any of the psychiatric scales. In recent years, the programme of national screening for depression in the United States has drawn heavily on both the BDI 205

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and the Zung [111]. A version of the BDI has also been prepared for use in primary care [112]. In the mid-1960s, diagnostic schedules began to be developed for research in North American clinical settings under the leadership of Robert Spitzer. A step-by-step development of diagnostic schedules began with the Mental Status Schedule (MSS) [113]. Next was the Psychiatric Status Schedule (PSS) [114] which was used in an important study that came to be known as the US/UK Diagnostic Project that explored reasons for differences in diagnostic practices in the two countries [115]. In addition, Spitzer and Endicott [116, 117] created for the PSS a system of differential diagnosis performed by a computerised set of algorithms. The computer programs were named DIAGNO. Then followed the Schedule for Affective Disorders and Schizophrenia (SADS) [55] which played a special role in the developments leading to DSM-III. As Chair of the American Psychiatric Association Task Force that produced DSM-III, Spitzer’s experience in instrument development and in designing criteria for diagnosis contributed significantly to the work of the Task Force. The most important study in which the SADS has been used, however, is the Psychobiology of Depression Study [118–123]. This study has emphasised that depression is often chronic with its episodic features appearing as symptom florescences on top of a chronic base, as in ‘double depression’ [124]. This led Judd [125] to say that ‘the most recent and important paradigm shift is the acceptance of unipolar Major Depression as primarily a chronic rather than an acute illness’. Most of the diagnostic schedules, including the SADS, continue to inquire about depression as an ‘episodic’ illness, but awareness of its chronic nature will probably be reflected in future schedules. After the third DSM was revised, the Structured Clinical Interview for DSM-III-R (SCID) was produced and has been assessed through field trials, and a version for non-patients was created [81, 126]. Later, a version was designed to be congruent with DSM-IV [127]. The SCID has become the most commonly-used schedule in US clinical studies [128–130]. Spitzer and colleagues have also prepared an instrument named Primary Care Evaluation of 206

Mental Disorder (PRIME-MD) as a guide for general physicians to evaluate psychiatric disorders often seen in their practices [131]. A revision named Patient Health Questionnaire (PHQ-S) was subsequently presented, which is entirely self-administered [132]. The Medical Outcomes Study (MOS) was designed to provide information about the functional impairment of patients treated in different types of clinical settings [133, 134]. The instrument developed for it was named the MOS 36-Item Short-Form Health Survey (SF-36) [135]. It assesses disability associated with both physical and emotional health and its use led to the finding that depression is comparable to or worse than eight major chronic medical conditions in terms of markers such as missing work, staying in bed and other features of poor functioning. The SF-36 was created mainly by factor analytic techniques. It is a multi-item scale concerned with eight health concepts such as limitations in physical and social activities, bodily pain, psychological distress, vitality and so on [136]. The appearance of the SF-36 was timely in that it reflects the growing recognition of the importance of impairment in psychiatric measurement.

13.4 European instruments for psychiatric services and primary care After the World War II, National Health Insurance was established in the United Kingdom. Because nearly complete population registration was involved, epidemiologic estimates could be provided through medical services as illustrated in the London General Practice Study [137]. For this study, physician diagnoses as well as patient responses to the American CMI were utilised. The CMI yielded a high test-retest coefficient over 1 year (0.87), a result which probably derives from its use of the word ‘usually’ in describing the frequency of symptoms and to its predictive capacity. The test results suggested to the London group that the CMI measures stable personality traits rather than the types of psychiatric episodes of concern in general practices. The General Health Questionnaire (GHQ) was designed by Goldberg [138] to overcome this feature of the CMI and to be a more appropriate instrument

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in primary care. Thus the GHQ asks if the person has the symptom ‘more than usual’. The intent is to identify the kinds of changes from a person’s usual state that lead to consultation with a general physician. Excellent validity results were achieved when the GHQ was compared to a clinician-administered structured interview named the Clinical Interview Schedule (CIS) that is congruent with the intent of the GHQ [139]. The original publication of the GHQ emphasised that it measures ‘general’ psychopathology of a non-psychotic type. In light of growing interest in diagnosis, Goldberg and Hillier [140] developed a scaled version intended to distinguish between the syndromes of anxiety and depression. Factor analysis identified four domains: ‘anxiety and insomnia’ and ‘severe depression’ as well as ‘social dysfunction’ and ‘general illness’. The ‘anxiety and insomnia’ factor indicates that GHQ anxiety is more cognitive than pertains in any of the earlier scales. Typical GHQ questions deal with ‘being under strain’, ‘everything getting on top of me’, and ‘having difficulty sleeping’ in contrast to the autonomic expressions of fear. The ‘severe depression’ factor reveals that death and suicide are more extensively and explicitly covered than in earlier scales. Also distinctive is that the GHQ factor called ‘social dysfunction’ elicits impairment in everyday activities better than almost any other scale. The GHQ has been used in several studies in the United States, the first of which compared the GHQ and HSCL and found them to show a correlation coefficient of 0.78 [141]. The GHQ was also compared to DIS depression [142] and in one study the GHQ-28, HSCL-25 and CES-D were simultaneously compared to the DIS [143]. The three scales were indistinguishable (sensitivity from 0.65 to 0.69 and specificity from 0.78 to 0.84) indicating that each performed similarly, and none perfectly. The use of the GHQ around the world in both epidemiologic and clinical studies far exceeds that of the other short scales [144–148]. Recently, Goldberg and Simpson developed the Personal Health Questionnaire (PHQ-G), a 10-item instrument designed to gather information specifically about depression according to ICD-10 [149]. For research in psychiatric clinics rather than primary care, the Present State Examination (PSE) was

developed over several years by Wing and co-workers [54, 150]. The first publication appeared about the same time as the first of the schedules designed by Spitzer’s group in the United States, thus suggesting that the need for such instrumentation was beginning to be widely recognised. The original purpose of the PSE was to provide a guide for ‘cross-examining’ a patient for evidence of schizophrenia. The word ‘Present’ in the name of the Examination refers to the fact that the inquiry focuses on the ‘current clinical state’ as exhibited in the recent month. Prior experimentation suggested that recall of subjective experiences over a longer period of time was often faulty. Unlike most of the instruments described thus far, the results of the PSE reflect the decisions of the interviewer rather than the report given by the subject. The schedule consists of pre-formulated questions, but the responses of the subject are not used in analysis. Rather, diagnosis is based on the interviewer’s evaluation of the subject’s responses as guided by a glossary of differential definitions. The interviewer decides if the symptoms are sufficiently severe to warrant contributing evidence to a syndrome. In the late 1960s, the seventh revision of the PSE was employed in the US/UK Diagnostic Project [115], which demonstrated that many of the differences in diagnosis disappeared when structured interviews were employed. In addition to the PSE, this project used Spitzer’s PSS. One of the nosological issues explored was the question whether anxiety and depression could be differentiated. The PSE definition of anxiety involves a syndrome in which autonomic hyperactivity and motor tension are well represented while the PSS definition was more cognitive, like that of the GHQ, with a focus on anxious mood, worry and feeling under strain. Zubin and Fleiss [151] found that the syndromes of anxiety and depression were better discriminated by the PSE than by the PSS. This suggests that the autonomic indicators play an important role in the distinction despite the fact that the two syndromes are often found to be comorbid. PSE-8 was used in the International Pilot Study of Schizophrenia [152] which contributed evidence that schizophrenia seems to be found in most parts of the world. Shortly thereafter, the ninth revision was published along with a description of a computer program named CATEGO that had been developed for standardised analysis [54, 150]. Then, in order 207

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to use the PSE in population-based epidemiology, an Index of Definition was constructed to differentiate between cases and non-cases [153]. PSE-9 was translated into more than 40 languages, and it has been used extensively in clinical research and in several single and two-stage epidemiological studies [154–157]. Throughout this phase, it continued to focus on psychoses and neuroses and to exclude substance abuse and personality disorders. Not long after the US President’s Commission on Mental Illness that led to the DIS and ECA, the WHO Division of Mental Health and the US Alcohol, Drug Abuse, and Mental Health Administration (ADAMHA) joined forces in order to carry out a worldwide review of diagnoses and classification of psychiatric disorders. In 1982 a WHO-ADAMHA Task Force was formed to develop diagnostic interviews that would implement the definitions embodied in ICD-10 as well as the criteria employed in DSM-III and the principles of the PSE-CATEGO system. One goal was to develop a schedule for studying clinical samples, the product of which consisted of a series of schedules, the overall name being Schedules for Clinical Assessment in Neuropsychiatry (SCAN) [79]. SCAN provides a comprehensive procedure for clinical examination appropriate for use throughout the world. It incorporates the 10th version of the PSE, and it is suggested that other schedules for personality and disability assessment also be used [158, 159].

13.5 European instruments for epidemiological research In addition to developing the SCAN, the WHOADAMHA Task Force was charged with preparing an epidemiological instrument that could be administered by lay interviewers and used throughout the world. The CIDI is the product of this work [68]. It was intended to bring together the best features of the DIS and the PSE. Like the DIS, the CIDI does not allow variation in order or changes in the way the questions are asked but it contains 35 PSE items that could be transformed into close-ended questions. Because it is highly structured and does not allow interviewers to interpret responses it is much more similar to the DIS than the PSE. In fact, PSE items dealing with delusion were not incorporated because they required clinical judgement. 208

The CIDI went through numerous field trials and a variety of special topics were investigated. These included analysis of comparability with the PSE [160]; issues of recall and dating symptoms [161]; appropriateness and feasibility for cross-cultural investigations [162–164], as well as reliability and validity [165–167]. A computerised version, CIDI-AUTO, was created and tested [168], as were also a short form (CIDI-SF) [169], and a screening version (CIDI-S) [170]. While both the DIS and CIDI were going through phases of change and improvement, the diagnostic criteria on which they were built were not static. For example, the definition of generalised anxiety disorder changed from involving the autonomic indicators, such as had been prominent in the early scales, to being more cognitive, as in the GHQ and PSS. Based on clinical studies, the definition came to focus on ‘feeling miserable’, worrying, being tense, high-strung and sleepless. The typical indicators of ‘bodily alarm’ came to reside only in panic and phobic disorders rather than in a generalised form of autonomic anxiety. Quite aside from UM-CIDI being used in North America, the standard CIDI was used in a primary care study conducted in 15 different sites around the world [171], a two-stage investigation in Norway [172], in the Australian National Survey of Mental Health and Well-Being [173] and in the Netherlands Mental Health Survey and Incident Study (NEMESIS) [174]. Another version of the CIDI was mentioned as having been used in the recent national sample study in Canada: the World Mental Health CIDI. It was mainly constructed, however, for the World Mental Health Initiative [78]. The questions about diagnoses were based on criteria represented in DSM-IV and ICD-10. In addition to diagnoses, there were sections for functional impairment, treatment, consequences, risk factors and sociodemographic variables. Several innovations were introduced, among them are mechanisms for including dimensional as well as categorical assessment, subthreshold disorders, maintaining standard wording of questions along with culturally suggested clarifications. The World Mental Health Initiative is the outgrowth of the Global Burden of Disease, which indicated that, by defining ‘burden’ as a combination

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of reduced quality of life (disability) and reduced quantity of life (death), the toll taken by mental disorders was brought to the fore [175]. The goal of the new initiative was to provide empirical evidence about the prevalence of psychiatric disorders in many countries around the world. A report based on using the WMH-CIDI in 14 countries indicates that while the rates varied more than in North America, everywhere the more seriously ill had the greatest likelihood of receiving treatment [176]. In addition, the WMH-CIDI has been used in the European Study of the Epidemiology of Mental Disorders (ESEMeD) [177] and, as mentioned earlier, in Canada. Based on the amount of comorbidity seen in epidemiological studies that were using the CIDI, questions began to be asked about whether a small number of broad categories might have nosological advantages over many discrete categories. Reanalysis of CIDI data using factor analysis indicated that the diagnoses of social phobia, simple phobia, agoraphobia and panic disorder loaded on a factor with the suggested name of ‘fear’. On the other hand, generalised anxiety disorder, as defined by this time with an emphasis on cognitive worry, affiliated with major depressive episode and dysthymia in a factor named ‘distress’ [178–180]. If alcohol, drug and anti-social diagnoses were added, they loaded on a factor of ‘externalisation’, while ‘fear’ and ‘distress’ were sufficiently correlated to suggest an ‘internalisation’ factor. These factor analytic results have been interpreted as possibly dividing psychiatric disorders in a more meaningful way than multiple categories. This idea has brought considerable discussion about dimensional versus categorical measurement [181, 182]. Contributing to the view that these more comprehensive groupings relate to the core of psychopathology is evidence that generalised anxiety disorder and major depression share the same genetic liability [183, 184]. The CIDI has not, however, been used in the United Kingdom. Studying large samples of the population by means of a structured instrument does not have as long a history in the United Kingdom as in North America. Until recently only a few household surveys had been carried out and they tended to focus on segments of the population

such as women [154, 185] or residents of special housing areas [186, 187]. In the 1990s, however, a very large investigation was conducted, named the National Psychiatric Morbidity Surveys of Great Britain [188–190]. The decision not to use the CIDI was motivated primarily due to its length and reliance on complex questions about the subject’s whole life. Instead, the earlier instrument Clinical Interview Schedule (CIS) developed by Goldberg and co-workers [139] was improved and named the Clinical Interview Schedule – Revised (CIS-R) [191]. The original CIS was intended to be used by psychiatrists for identifying the common disorders that are found in primary care and community settings [192]. The schedule consisted of two halves, the first based on self-report about the frequency, duration and intensity of symptoms with the second based on the psychiatrist’s observations of ‘manifest abnormalities’. When used as a validating standard for the GHQ, the CIS did not give a diagnosis but rather a rating of severity along with a ‘cutting point’ to separate cases from non-cases. The CIS approach was thus congruent with the GHQ focus on identifying ‘general’ non-psychotic disorder. The CIS-R can be administered by lay interviewers but the emphasis on ‘general’ neurosis is maintained. It uses a ‘cutting point’ to identify cases but additional analytic routines allow the application of diagnostic designations for generalised anxiety disorder, depressive episode, phobias, obsessive–compulsive disorders, panic disorder and non-specific neurotic disorder according to ICD-10. To avoid long-term recall, the time frame is the previous week, but subjects are asked to give the date of onset of key symptoms. The CIS-R is described as a ‘bottom-up’ schedule that gathers information about the basic phenomena to which classification algorithms can be subsequently applied. This contrasts to the ‘top-down’ instruments like the DIS and CIDI that build the classification rules into the questions. The rationale for the CIS-R approach relates to the objective of conducting subsequent surveys for comparison over time when the specific criteria may be modified. In addition, a screening instrument for psychosis was developed for the survey, and those who scored positively were later interviewed with the SCAN. Like most of the instruments designed before 1980, the CIS-R 209

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itself does not include substance abuse. A separate schedule was therefore developed for that purpose. It is unknown whether researchers in other countries will use the CIS-R. There is growing evidence, however, that the broad dimensional approach it embodies is being given increased attention. In addition, numerous substantive reports have been produced from the data gathered in the United Kingdom [193–195].

13.6 Summary For the first half of the period reviewed, psychiatrists stood aside and viewed with skepticism the developments described here. The reasons for their distance were multiple and complex including the influence of psychodynamic psychiatry, doubt that asking questions was enough, and belief that non-psychiatrists were unable to interpret answers accurately or to perceive the nuances of facial expression and body movement that are necessary for an adequate psychiatric work-up. Undoubtedly visual information about appearance and comportment contributes to a psychiatric assessment. The time may come when the process of observation will achieve sufficient standardisation to be useful in epidemiological research. However, that approach is not yet on the horizon. The time may come when biological markers will have been identified and proven sufficiently accurate and efficient to be used in large-scale studies, but that approach is also not yet on the horizon. The question/answer interaction (by paper/pencil, face-toface interview, telephone or computer) remains the most useful mode of gathering data for psychiatric epidemiology. Major advances in the question/answer approach were achieved when clinical criteria, such as now exist in the DSM and the ICD, became available. Because schedules like the DIS and CIDI were designed to implement the criteria, a foundation was laid for comparability across studies and for covering the range of diagnoses that involve psychotic, substance abuse and personality disorders as well as the earlier focus on neurotic disorders. There were also some losses. Psychometric principles tended to be ignored as did also the principles of 210

survey methodology. At the present time, one of the main questions is whether the categories of disorder embodied in the DSM have maximal utility or if dimensional measures may be superior. The concept of a ‘psychiatric syndrome’ is at the heart of the existing classification systems. By its nature categorical, the syndrome is either complete enough to say that it is present or sufficiently incomplete to warrant saying it is not. There are numerous aspects of syndrome recognition, however, that draw on dimensional models. This can be illustrated by reference to the existing scales, all of which are dimensional. All of the ones reviewed here refer to the ‘essential features’ and ‘associated symptoms’ – at least of depression or anxiety. Further, a dimension for what is ‘essential’ could be constructed separately from what is ‘associated’ so that the requirements for exhibiting key symptomatology could be met. ‘Duration’ and ‘disability’ are also by their nature dimensional. In addition to the questions about how a categorical approach might be improved by using dimensions for its component parts, questions have arisen about the value of using much broader dimensions. In so far as the main goal of epidemiology will continue to be the estimation of prevalence and incidence, much research would be required to find a single and adequate ‘cutting point’ on dimensions as broad as ‘introversion’ and ‘extraversion’. On the other hand, one can envisage the productive epidemiologic use of such ‘middle range’ factors as ‘fear’ and ‘distress’ which to a reasonable degree translate as anxiety and depression. It should not be ignored, however, that the focus on discrete categories as identified through the schedules has provided re-conceptualisations that appear to be useful. One is the change from viewing depression as an episodic disorder to seeing it as a chronic one which is subject to fluctuations in intensity. The other is that generalised anxiety may have a more cognitive manifestation than appeared to be the case in the early years although it may also be true that there are two forms of generalised anxiety, one more articulated through the autonomic system and the other more through mental processes. A feature of the diagnostic schedules that needs further thought concerns the use of lifetime recall. There is evidence of international tension on this point. The

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schedules developed in the United Kingdom (PSE and CIS-R) focus on the current clinical state. The US and WHO schedules (DIS and CIDI) elicit information about lifetime experiences. The lifetime approach in psychiatric epidemiology appeared about the same time as an upsurge of genetic research, in which lifetime population norms were needed for family studies. With the changing face of genetic research towards molecular studies, the rationale for lifetime rates may recede. Important steps forward in reliability might thus be achieved if assessment of the current clinical state becomes the first order of inquiry. Reliability is fostered when the subject comprehends the interview situation and is well motivated to give accurate answers. Both psychometric theory and survey experience suggest that the best ways to reduce misunderstanding on the part of the subject and variability on the part of interviewers is to provide clear instructions and use simple language [196, 197]. Scrutiny of diagnostic schedules to reduce complexity may also be a useful step towards increasing reliability. A major challenge that lies ahead for both lay and clinician interviews has to do with validity. New questions have been raised by the fact that the use of well-recognised clinical interviews in the Baltimore and Stirling Studies gave considerably higher rates of depression than did lay interviews. The clinical approach did not invalidate the lay results in the sense of denying them, as was expected, but rather the clinicians indicated that the lay-administered schedules missed numerous cases. Many of the questions asked by clinicians were the same as those asked by lay interviewers. This raises the problem of whether use of a question-oriented ‘gold standard’ provides an adequate test of validity. Campbell and Fiske [198] emphasise that validity depends upon using independent and different information: ‘Reliability is the agreement between two efforts to measure the same trait through maximally similar methods. Validity is represented in the agreement between two attempts to measure the same trait by maximally different methods’. Given the fact that both the clinical and lay interviews involved similar questions, the differences must result from features other than the questions asked. What is ‘maximally different’ about them? Do clinicians ask the questions in a different manner? Do

subjects hear the questions of a clinician in a different way? Do clinicians interpret the same response to the same question in ways that are distinctively different? Evidence suggests that schedules like SCID and SCAN have achieved reliability when applied in clinical settings. In such settings, it is a matter of determining what kind of diagnosis is pertinent rather than whether the person is a case or not. In community settings, the situation may be sufficiently different to warrant a different approach to validity. Over and above investigation of interpretative differences when the same questions are asked, efforts at validation need to seek materials that are genuinely dissimilar from the question/answer format. Such material may reside in the ‘lead standard’ which Spitzer [199] defined as involving ‘Longitudinal assessment by a panel of clinical Experts who have access to All available Data’. Such a standard has been employed to assess certain CIDI diagnoses in a clinic-based study [168]. A group of psychiatrists (the ‘panel of experts’) who had known the patients over considerable time (an approximation of ‘all available data’) provided consensus diagnoses that agreed well with the CIDI results. Given the fact that many people in the community do not seek treatment for a psychiatric illness, another application of the standard would be to focus on the ‘Longitudinal assessment’ part of the definition. For example, it may be possible to use prospective evidence about the course and outcome of illness identified by structured lay interviews to confirm or reject the diagnosis. From the limited amount of longitudinal follow-up data available at the present time, the evidence about chronicity and risk for recurrence and other adversities does not support the view that the epidemiologic studies have identified transient and normal reactions to life stress. Rather, a degree of predictive validity seems already to have been achieved. Despite the fact that the schedules take much longer to administer than the scales, the samples used in epidemiologic research have become larger and larger over time, making it possible to achieve adequate numbers for the rarer disorders. At the same time, the cost of conducting such surveys has increased. Because of cost, two-stage designs may become more popular in the future. However, a note of caution is in order. It is now known that those 211

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who are psychiatrically ill are more likely to refuse than are others and thus it becomes increasingly compelling to avoid subject attrition. Incompleteness of data is one of the serious problems faced by psychiatric epidemiology at the present time, and two-stage designs give two opportunities for refusal in contrast to one in single-stage investigations [70, 200]. This review of the use of scales and schedules to estimate prevalence among adults in the general population indicates that the ‘unknowns’ observed at the end of the World War II have to some extent become ‘knowns’. It is now clear that prevalence is higher in many countries than originally estimated and that many of those who suffer from a psychiatric disorder do not receive treatment for it. Whether prevalence and incidence are increasing remains a question but the two studies (NCS-R and Stirling) that have thus far compared a sample drawn earlier with one drawn latter suggest more stability than change. Research using the scales and schedules described here has shown that psychiatric disorders are common, diverse in character, often comorbid, widely distributed, probably more steady than fluctuating in rate, and heavily burdensome.

Acknowledgements This chapter is based on a course taught at the Harvard School of Public Health. From 1987 to 1999, the course was titled ‘Psychiatric Screening and Diagnostic Tests’ after which the title was changed to ‘Psychiatric Diagnosis in Clinic and Community Populations’. The chapter also draws on a report prepared for the National Institute of Mental Health under contract 80M014280101D titled ‘Psychiatric Instrument Development for Primary Care Research: Patient Self-Report Questionnaire’, 1981. In addition, the chapter draws on materials from the Stirling County Study through NIMH Grant R01 MH39576-25.

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problems in primary care. Arch. Gen. Psychiatry, 50, 819–824. ˚ Sandanger, I., Nygard, J.F., Ingebrigtsen, G. et al. (1999) Prevalence, incidence rate and age at onset of psychiatric disorders in Norway. Soc. Psychiatry Psychiatr. Epidemiol., 34, 570–579. Andrews, G., Henderson, S. and Hall, W. (2001) Prevalence, comorbidity, disability and service utilisation: overview of the Australian National Mental Health Survey. Br. J. Psychiatry, 178, 145–153. Spijker, J., de Graaf, R., Bilj, R. et al. (2002) Duration of major depressive episodes in the general population: results of The Netherlands Mental Health Survey and Incidence Study (NEMESIS). Br. J. Psychiatry, 181, 208–213. Murray, C.J.L. and Lopez, A.D. (1996) The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries, and Risk Factors in 1990 and Projected to 2020, The Harvard School of Public Health on behalf of the World Health Organization and the World Bank, Boston. WHO World Mental Health Survey Consortium (2004) Prevalence, severity, and unmet need for treatment of mental disorders in the World Health Organization World Mental Health Surveys. J. Am. Med. Assoc., 291, 2581–2590. ESEMeD/MHEDEA 2000 Investigators (2004) Prevalence of mental disorders in Europe: results of the European study of the epidemiology of mental disorders (MSEMeD) project. Acta Psychiatr. Scand., 109 (Suppl. 420), 21–27. Krueger, R.F. (1999) The structure of common mental disorders. Arch. Gen. Psychiatry, 56, 921–926. Vollebergh, W.A.M., Iedema, J., de Graaf, R. et al. (2001) The structure and stability of common mental disorders: the NEMESIS Study. Arch. Gen. Psychiatry, 58, 597–603. Slade, T. and Watson, D. (2006) The structure of common DSM-IV and ICD-10 mental disorders in the Australian general population. Psychol. Med., 36, 1593–1600. Goldberg, D.P. (2000) Plato versus Aristotle: categorical and dimensional models for common mental disorders. Compr. Psychiatry., 41 (Suppl. 1), 8–13. Brugha, T.S. (2002) The end of the beginning: a requiem for the categorization of mental disorder? Psychol. Med., 32, 1149–1154. Kendler, K.S., Neale, M.C., Kessler, R.C. et al. (1992) Major depression and generalized anxiety disorder: same genes, (partly) different environments? Arch. Gen. Psychiatry, 49, 716–722. Kendler, K.S., Prescott, C.A., Myers, J. et al. (2003) The structure of genetic and environmental risk factors for common psychiatric and substance use

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disorders in men and women. Arch. Gen. Psychiatry, 60, 929–937. Surtees, P.G., Dean, C., Ingham, J.G. et al. (1983) Psychiatric disorder among women from an Edinburgh community: associations with demographic factors. Br. J. Psychiatry, 142, 238–246. Taylor, L. and Chave, S. (1964) Mental Health and Environment, Longman Green, London. Hare, E.H. and Shaw, G.K. (1965) Mental Health on a New Housing Estate: A Comparative Study of Health in Two Districts in Croydon, Oxford University Press, Oxford. Meltzer, H., Gill, B., Petticrew, M. et al. (1995) Morbidity in Great Britain: the Prevalence of Psychiatric Morbidity Among Adults Living in Private Households, Her Majesty’s Stationery Office (HMSO), London. Jenkins, R., Bebbington, P., Brugha, T. et al. (1997a) The National Psychiatric Morbidity Survey of Great Britain – strategy and methods. Psychol. Med., 27, 765–774. Jenkins, R., Lewis, G., Bebbington, P. et al. (1997b) The National Psychiatric Morbidity Surveys of Great Britain – initial findings from the Household Survey. Psychol. Med., 27, 775–789. Lewis, G., Pelosi, A.J., Araya, R. et al. (1992) Measuring psychiatric disorder in the community: a standardized assessment for use by lay interviewers. Psychol. Med., 22, 465–486. Lewis, G. and Williams, P. (1989) Clinical judgment and the standardized interview in psychiatry. Psychol. Med., 19, 971–979.

[193] Lewis, G., Bebbington, P., Brugha, T. et al. (1998) Socioeconomic status, standard of living, and neurotic disorder. Lancet, 352, 605–609. [194] Paykel, E.S., Abbott, R., Jenkins, R. et al. (2000) Urban-rural mental health differences in Great Britain: findings from the National Morbidity Survey. Psychol. Med., 30, 269–280. [195] Weich, S., Lewis, G. and Jenkins, S.P. (2001) Income inequality and the prevalence of common mental disorders in Britain. Br. J. Psychiatry, 178, 222–237. [196] Choi, I.C. and Comstock, G.W. (1975) Interviewer effect on responses to a questionnaire relating to mood. Am. J. Epidemiol., 101, 81–92. [197] Nunnally, J.C. and Bernstein, I.H. (1994) Psychometric Theory, 3rd edn, McGraw-Hill, New York. [198] Campbell, D.T. and Fiske, D.W. (1959) Convergent and discriminant validation by the multitraitmultimethod matrix. Psychol. Bull., 56, 81–105. [199] Spitzer, R.L. (1983) Psychiatric diagnosis: are clinicians still necessary? in Psychotherapy Research: Where Are We and Where Should We Go? (eds J.B.W. Williams and R.L. Spitzer), Guilford Press, New York, pp. 273–292. [200] Horton, N.J., Laird, N.M., Murphy, J.M. et al. (2001) Multiple informants: mortality associated with psychiatric disorders in the Stirling County Study. Am. J. Epidemiol., 154, 649–656.

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14

The National Comorbidity Survey (NCS) and its extensions Ronald C. Kessler Department of Health Care Policy, Harvard Medical School, Boston, MA, USA

14.1 Introduction This chapter presents an overview of the research program associated with the US National Comorbidity Survey (NCS) and its extensions. The baseline NCS, which was fielded in the autumn of 1990 and completed in the Spring of 1992, was the first nationally representative mental health survey in the United States to use a fully structured research diagnostic interview to assess the prevalence and correlates of Diagnostic and Statistical Manual of Mental Disorders, 3rd edition revised (DSM-III-R) disorders. The baseline NCS respondents were re-interviewed in 2001–2003 (NCS-2) in order to study patterns and predictors of the course of mental and substance use disorders and to evaluate the effects of primary mental disorders in predicting the onset and course of secondary substance disorders. A National Comorbidity Survey Replication (NCS-R) was also carried out in conjunction with NCS-2 in a new national sample of 9282 respondents. The goals of the NCS-R were to study trends in a wide range of variables assessed in the baseline NCS and to obtain more information about a number of topics not covered in the baseline NCS. A survey of over 10 000 adolescents (NCS-A) was being carried out in parallel with the NCS-R and NCS-2 surveys. The goal of the NCS-A was to produce nationally representative data on the prevalences and correlates of mental disorders among youth. The NCS-R, finally, was replicated in a number of countries around the world as part of the World Health Organization (WHO) World Mental Health (WMH) Survey Initiative [1]. This chapter

presents a brief overview of each of these phases in the evolution of the NCS research programme.

14.2 The baseline NCS 14.2.1 Background and design The need for a national survey on patterns and predictors of psychiatric disorders was noted nearly two decades ago in the report of the President’s Commission on Mental Health and Illness [2]. Such a survey could not be undertaken at that time, though, due to the absence of a structured research diagnostic interview capable of generating reliable psychiatric diagnoses in general population samples. Recognising this need, the National Institute of Mental Health (NIMH) funded the development of the Diagnostic Interview Schedule (DIS) [3], a research diagnostic interview that can be used by trained interviewers who are not clinicians. The DIS was first used in the Epidemiologic Catchment Area (ECA) Study, a landmark series of surveys that interviewed over 20 000 respondents in five local community samples. The ECA was the main source of data in the United States on the prevalence of psychiatric disorders and utilisation of services for these disorders during the decade between the early 1980s and the early 1990s [4–6]. The baseline NCS was designed to take the next step beyond the ECA [7] by carrying out a nationally representative survey of mental disorders. This was done by administering a face-to-face structured diagnostic

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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interview to a widely dispersed sample that was representative of all people living in households in the continental United States. The 8098 NCS respondents were selected from over 1000 neighbourhoods in over 170 counties distributed over 34 states. The NCS diagnostic interview was a modification of the Composite International Diagnostic Interview (CIDI) [8], a state-of-the-art structured diagnostic interview based on the DIS. We deleted diagnoses known to have low prevalences in the ECA (e.g. obsessive–compulsive disorder, somatisation disorder). We also modified the CIDI in several ways based on extensive pilot tests [9, 10]. The most important of these modifications involved the diagnostic stem questions. Almost all CIDI diagnostic sections begin with a small number of questions that assess core features of the disorder. If these questions are answered positively, the respondent is asked a detailed series of follow-up questions about the disorder. If the stem questions are answered negative, in comparison, the respondent is skipped to the next section. Our pilot work showed clearly that respondents quickly catch on to this stem–branch logic and sometimes deny stem questions in order to get through the interview more quickly. We addressed this problem by moving the diagnostic stem questions for all disorders into a separate lifetime review section that was administered before any other sections of the CIDI. We prefaced the administration of the lifetime review section with a preamble designed to motivate serious and honest responding [11]. A field experiment that randomised pilot test respondents to receive the CIDI either with or without this lifetime review section showed that use of this section resulted in a statistically significant and substantively important increase in the estimated prevalences of most DSM-III-R disorders. A separate clinical validity study showed that this increase was due to a decrease in false negative diagnostic evaluations rather than to an increase in false positives [10]. Another NCS innovation was the use of a twophase clinical reinterview design for complex cases. WHO CIDI field trials showed that most CIDI diagnoses have good inter-rater reliability, test–retest reliability and validity in comparison to blind clinician reinterviews in non-patient samples [12]. An important exception to this general pattern, however, is non-affective psychosis, which is diagnosed 222

with low reliability and validity in structured interviews like the CIDI. Based on this fact, and given the great public health importance of non-affective psychosis, the NCS included clinical reinterviews with respondents who reported any evidence of schizophrenia or other non-affective psychoses. These reinterviews were administered by experienced clinicians using an adapted version of the Structured Clinical Interview for DSM-III-R (SCID) [13], an instrument with demonstrated reliability in the diagnosis of schizophrenia [14]. The NCS diagnoses of schizophrenia and other nonaffective psychoses are based on these clinical reinterviews rather than on the CIDI interviews [15]. As described below, this reliance on clinical reinterviews for diagnosis of complex cases was expanded in NCS-R. A final noteworthy NCS innovation was the systematic evaluation of the relationship between survey non-response and diagnosis. Based on a concern that non-respondents might have considerably higher rates of some mental disorders than respondents, we carried out a systematic non-respondent survey in which a random subsample of non-respondents was contacted by specially trained refusal conversion interviewers and asked to complete a 10-minute screening interview. The screening interview was completed either face-to-face or over the telephone by approximately one-third of the non-respondents who were selected into this special subsample. Propensity score weighting that made use of the information about diagnostic stem question profiles obtained in these screening interviews was then used to adjust the sample for the under-representation of these initial refusers [16]. Analysis of response bias showed, interestingly, that failure to adjust for differential non-response led most importantly to an underestimation of the prevalence of anxiety disorders [17]. This occurred because anxious people were more reluctant than other people to allow a stranger into their homes, while they were willing to complete the screening once the option of telephone administration was offered.

14.2.2 Illustrative findings The number of NCS analyses is much too large to summarise in a single chapter. As a result, we present just a sample of results here in order to give the

THE NATIONAL COMORBIDITY SURVEY (NCS) AND ITS EXTENSIONS

reader a flavour of the kinds of analyses carried out. A complete list of NCS publications can be found at the NCS web site: www.hcp.med.harvard.edu/ncs.

14.2.2.1 Lifetime and recent prevalence of DSM disorders As reported in more detail elsewhere [7], the NCS found that DSM-III-R disorders are more prevalent than previously thought to be the case. The results in Table 14.1 show prevalence estimates for the 14 lifetime and 12-month disorders assessed in the core NCS interview. Lifetime prevalence is the proportion of the sample who ever experienced a disorder, while 12-month prevalence is the proportion who

experienced the disorder at some time in the 12 months prior to the interview. The prevalence estimates in Table 14.1 are presented without exclusions for DSM-III-R hierarchy rules. The most common disorders are major depression and alcohol dependence. The next most common are social and simple phobias. As a group, substance use disorders and anxiety disorders are somewhat more prevalent than affective disorders, with approximately one in every four respondents reporting a lifetime substance use disorder and a similar number a lifetime anxiety disorder. Approximately one in every five respondents reported a lifetime affective disorder. Anxiety disorders, as a group, were

Table 14.1 Lifetime and 12-month prevalence of DSM-III-R disorders. Male Lifetime

Female 12-mo

Lifetime

Total 12-mo

Lifetime

12-mo

%

(SE)a

%

(SE)

%

(SE)

%

(SE)

%

(SE)

%

(SE)

12.7 1.6 4.8 14.7

(0.9) (0.3) (0.4) (0.8)

7.7 1.4 2.1 8.5

(0.8) (0.3) (0.3) (0.8)

21.3 1.7 8.0 23.9

(0.9) (0.3) (0.6) (0.9)

12.9 1.3 3.0 14.1

(0.8) (0.3) (0.4) (0.9)

17.1 1.6 6.4 19.3

(0.7) (0.3) (0.4) (0.7)

10.3 1.3 2.5 11.3

(0.6) (0.2) (0.2) (0.7)

3.6 2.0 11.1 6.7 3.5 19.2

(0.5) (0.3) (0.8) (0.5) (0.4) (0.9)

2.0 1.3 6.6 4.4 1.7 11.8

(0.3) (0.3) (0.4) (0.5) (0.3) (0.6)

6.6 5.0 15.5 15.7 7.0 30.5

(0.5) (1.4) (1.0) (1.1) (0.6) (1.2)

4.3 3.2 9.1 13.2 3.8 22.6

(0.4) (0.4) (0.7) (0.9) (0.4) (0.1)

5.1 3.5 13.3 11.3 5.3 24.9

(0.3) (0.3) (0.7) (0.6) (0.4) (0.8)

3.1 2.3 7.9 8.8 2.8 17.2

(0.3) (0.3) (0.4) (0.5) (0.3) (0.7)

12.5 20.1 5.4 9.2 35.4

(0.8) (1.0) (0.5) (0.7) (1.2)

3.4 10.7 1.3 3.8 16.1

(0.4) (0.9) (0.2) (0.4) (0.7)

6.4 8.2 3.5 5.9 17.9

(0.6) (0.7) (0.4) (0.5) (1.1)

1.6 3.7 0.3 1.9 6.6

(0.2) (0.4) (0.1) (0.3) (0.4)

9.4 14.1 4.4 7.5 26.6

(0.5) (0.7) (0.3) (0.4) (1.0)

2.5 7.2 0.8 2.8 11.3

(0.2) (0.5) (0.1) (0.3) (0.5)

4.8 0.3 48.7

(0.5) (0.1) (0.2)

– 0.2 27.7

– (0.1) (0.9)

1.0 0.7 47.3

(0.2) (0.2) (1.5)

– 0.4 31.2

– (0.1) (1.3)

2.8 0.5 48.0

(0.2) (0.1) (1.1)

– 0.3 29.5

– (0.1) (1.0)

Mood disorders Major depression Mania Dysthymia Any mood disorder Anxiety disorders Generalised anxiety disorder Panic disorder Social phobia Simple phobia Agoraphobia without panic Any anxiety disorder Substance use disorders Alcohol abuse Alcohol dependence Drug abuse Drug dependence Any substance use disorder Other disorders Antisocial personality (ASP)a Non-affective psychosisb Any NCS disorder a ASP

was only assessed on a lifetime basis. psychosis = schizophrenia, schizophreniform disorder, schizoaffective disorder, delusional disorder and atypical psychosis. Standard errors are reported in parentheses.

b Non-affective

223

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considerably more likely to occur in the 12 months prior to interview than either substance use disorders or affective disorders, suggesting that anxiety disorders are more chronic than affective or substance disorders. The prevalence of other NCS disorders is considerably lower. As shown in the last row of Table 14.1, 48.0% of the sample reported at least one lifetime disorder and 29.5% at least one disorder in the 12 months prior to the interview. While there is no meaningful sex difference in these overall prevalences, there are sex differences in prevalences of specific disorders. Consistent with previous research, men were much more likely to have substance use disorders and ASPD than women, while women were much more likely to have anxiety disorders and affective disorders than men (with the exception of mania, for which there is no sex difference). The data also show, consistent with a trend found in the ECA [18], that women in the household population are more likely than men to have nonaffective psychosis. There was a good deal of scepticism about these results when they were first published. The main criticism was that the NCS prevalence estimates were higher than those found in the ECA and other epidemiological surveys based on the ECA methodology. However, clinical reappraisal studies in which clinicians blindly reinterviewed a sample of NCS respondents subsequently showed that the NCS estimates are accurate [10], suggesting that the ECA estimates are biased downwards. A later reanalysis of the ECA data found that ECA estimates can be adjusted for reporting bias to approximate the NCS estimates [19]. Methodological studies suggest that the life review section, mentioned earlier, is largely responsible for the more accurate estimates in the NCS than the ECA [10].

14.2.2.2 Age at onset The NCS collected retrospective data on the ages of first onset of each lifetime disorder. Consistent with previous evidence [20], simple and social phobia were found to have much earlier ages at onset than the other disorders [21] – with simple phobia often beginning during middle or late childhood and social phobia during late childhood or early adolescence. Substance abuse was found to have a typical age of 224

onset during the late teens or early 20s. A substantial proportion of people with lifetime major depression and dysthymia also reported that their first episode occurred during the late teens or 20s. Some other disorders had later ages at onset, but the most striking overall impression from the data as a whole was that most psychiatric disorders have first onsets quite early in life.

14.2.2.3 Comorbidity The ECA Study was the first survey to document that comorbidity is widespread not only among patients but also in the general population [6, 22]. Over 54% of ECA respondents with a lifetime history of at least one DSM-III disorder were found to have a second diagnosis. Fifty-two per cent of persons with a lifetime history of alcohol abuse or dependence received a second diagnosis and 75% of persons with lifetime drug abuse or dependence had a second diagnosis. Respondents with a lifetime history of at least one mental disorder in the ECA had a 2.3 relative-odds of having a lifetime history of alcohol abuse or dependence and a relative-odds of 4.5 of some other drug use disorder compared to respondents with no lifetime mental disorder. Very similar patterns were found in the NCS. Fifty-six per cent of NCS respondents with a lifetime history of at least one DSM-III-R disorder also had one or more other disorders [7]. Fifty-two per cent of respondents with lifetime alcohol abuse or dependence also had a lifetime mental disorder, while 36% had a lifetime drug use disorder. Fifty-nine per cent of the respondents with a lifetime history of drug abuse or dependence also had a lifetime mental disorder and 71% had a lifetime alcohol use disorder. More detailed analyses showed that lifetime comorbidities of specific pairs of disorders are very similar in the ECA and NCS surveys [23]. In both surveys, virtually all the odds-ratios (ORs) between each pair of lifetime disorders is greater than 1.0. This means that there is a positive association between the lifetime occurrences of almost all ECA and NCS disorders, demonstrating that comorbidity of psychiatric disorders is truly pervasive in the general population. There is considerable variation in the sizes of the ORs. This variation is systematic and quite consistent across the two surveys.

THE NATIONAL COMORBIDITY SURVEY (NCS) AND ITS EXTENSIONS

14.2.2.4 Pure and comorbid lifetime disorders It is of interest to look beyond simple two-variable associations for broader patterns of comorbidity among multiple disorders. The 48% of persons in the NCS who had a lifetime history of at least one DSM-III-R disorder was found to be made up of 21% with exactly one, 13% with exactly two and 14% with three or more disorders. Thinking of disorders as the unit of analysis, we found that only 21% of all lifetime disorders occurred to the subsample of respondents who had no lifetime comorbidity. The other 79% occurred to respondents with lifetime comorbidity. The vast majority of lifetime disorders, then, were comorbid disorders [7]. Furthermore, we found that over 50% of all lifetime disorders occurred to the 14% of the population with a history of three or more disorders. This highly comorbid segment of the population also accounted for close to 60% of all 12-month disorders and close to 90% of severe 12-month disorders. These results show that while psychiatric disorders are widespread in the general population, the major burden of psychopathology is concentrated among people with high comorbidity.

14.2.2.5 Primary and secondary disorders Given the importance of comorbidity, a question arises at to which disorders in comorbid sets have the earliest ages at onset. The results in Table 14.2 show that there was considerable variation across disorders in the NCS in the probability of being the first lifetime disorder. Simple phobia, social phobia, alcohol abuse and conduct disorder were the only disorders considered in the NCS where the majority of lifetime cases were temporally primary. In general, anxiety disorders were most likely to be temporally primary, with 82.8% of NCS respondents having one or more anxiety disorders reporting that one of these was their first lifetime disorder compared to 71.1% of those with conduct disorder, 43.8% of those with an affective disorder and 48.1% of those with a substance use disorder. Results in the third column of Table 14.2 show the per cent of overall respondents who reported each disorder as temporally primary. Anxiety disorders, again, were more likely to be temporally primary (45.3% of all lifetime cases)

than either affective disorders (16.4%), substance use disorders (24.5%) or other disorders (19.5%). Information about age at onset was used to study the time-lagged effects of earlier disorders in predicting the subsequent onset of secondary disorders using a discrete-time survival analysis approach. This work showed clearly that early-onset anxiety disorders are the most important primary disorders in terms of predicting later disorders [24]. Interestingly, while most of these effects are only associated with active disorders, there are others that are also associated with remitted disorders. For example, respondents with a history of early-onset panic attacks have an elevated risk of secondary major depression throughout the majority of their adulthood even if their panic attacks occurred exclusively many years in the past [25]. Results such as this suggest that some early-onset anxiety disorders are risk markers rather than direct causes of secondary disorders.

14.2.2.6 The Societal costs of mental disorders Epidemiologists have traditionally been much more concerned with the causes than with the consequences of the illnesses they study. However, the rise of cost-effectiveness analysis as a tool for allocating health care resources has led to a dramatic increase in research on the adverse consequences of untreated chronic conditions and the benefits of treatment [26]. The NCS analyses consequently included an investigation of the adverse consequences of mental disorders. Consistent with the Rand Medical Outcome Study [27], we found that mental disorders have adverse effects on role functioning that equal or exceed the effects of most chronic physical conditions [28]. Data from clinical trials on the reversibility of these role impairments, when combined with NCS data on the costs of workrelated role impairments to employers, suggest that the cost savings due to increased work productivity might well make it cost-effective for employers to develop aggressive screening, outreach and treatment programmes for employees with some mental disorders [11]. This is an issue that is being examined in much more detail in the NCS-R and the other WMH surveys [29, 30]. NCS analyses also found that the early age at onset of mental disorders led them to have much 225

CHAPTER 14 Table 14.2

Percent and distribution of temporally primary NCS/DSM-III-R disorders. Percent temporally primary among those having the disorder

Distribution of temporally primary disorder

%

(SE)

%

(SE)

41.1 37.7 20.2 43.8

(2.7) (3.1) (6.0) (2.4)

13.4 4.8 0.7 16.4

(0.9) (0.5) (0.2) (0.9)

37.0 23.3 63.1 67.6 45.2 52.1 82.8

(2.9) (3.2) (2.0) (2.7) (4.0) (3.0) (1.3)

3.6 1.6 16.0 14.5 5.9 7.5 45.3

(0.4) (0.2) (0.9) (1.0) (0.7) (0.7) (1.4)

57.0 36.8 39.7 20.8 48.1

(2.3) (3.1) (3.0) (2.5) (1.6)

10.2 9.9 3.4 3.0 24.5

(0.6) (0.6) (0.3) (0.3) (1.0)

71.1 14.0 28.8

(2.0) (1.8) (5.6)

17.7 1.4 0.4

(1.0) (0.2) (0.1)

Mood disorders Major depression Dysthymia Mania Any mood disorder Anxiety disorders Generalised anxiety disorder Panic disorder Social phobia Simple phobia Agoraphobia Posttraumatic stress disorder Any anxiety disorder Substance use disorders Alcohol abuse Alcohol dependence Drug abuse Drug dependence Any substance use disorder Other disorders Conduct disorder Adult antisocial behaviour Nonaffective psychosis

SE, standard error; NCS, National Comorbidity Survey. All disorders are operationalised using DSM-III-R criteria ignoring diagnostic hierarchy rules.

greater effects than physical disorders on critical life course transitions such as educational attainment, teen childbearing, the timing and stability of marriage and early career decisions [31–33]. These adverse effects typically occur as part of a cascade of events as a result of the onset of serious early-onset mental disorders. People with this complex pile-up of emotional and psychosocial difficulties typically do not seek professional mental health treatment until at least a decade after the onset of their first mental disorder. It is consequently of great importance to develop aggressive outreach and treatment programmes for young people with mental disorders. This is a topic of central importance in the NCS-A survey. 226

14.2.2.7 Treatment Although only 4 out of every 10 NCS respondents with a lifetime history of at least one DSM-III-R disorder reported ever obtaining professional treatment, a survival analysis that compared age at onset with time to treatment suggested that the vast majority of people with persistent mental illness eventually seek treatment [34]. Delays in initial help-seeking, however, are pervasive, with the median time between first lifetime onset of a mental illness and first treatment contact greater than a decade. Importantly, delays in seeking treatment are inversely related to age at onset, with child and adolescent onsets

THE NATIONAL COMORBIDITY SURVEY (NCS) AND ITS EXTENSIONS

being associated with the lowest probabilities of ever seeking treatment. This is a critical finding because early-onset disorders are the ones most likely to promote comorbidity and adverse life course consequences. On a more positive note, analysis of retrospective NCS data suggests that rates of treatment-seeking increased over the four decades of historical time retrospectively assessed in the NCS. This presumably reflects a combination of increases in access to care, in awareness that mental illness is treatable and in attitudes conducive to seeking care.

14.2.2.8 Primary prevention of secondary disorders One question suggested by the NCS results is whether early treatment of pure child-onset or adolescentonset mental disorders would result in a reduction in the percentage of people who go on to develop comorbid mental disorders and, if so, whether it would also lead to a reduction in the persistence and adverse social consequences of primary mental disorders. We do not know the answer because no large-scale controlled study has ever attempted to screen and treat a representative sample of children or adolescents with mental disorders and then follow them over time to document the long-term effects of treatment. Given the high prevalences and enormous personal and societal costs of mental disorders, such an investigation should be undertaken. An issue of special interest in the current social policy arena is the prevention of adolescent substance disorder. Current federal policy on substance abuse prevention emphasises a combination of strategies that focus on reduction in access to drugs and unproven school-based primary prevention programmes, such as DARE, that ignore the fact that the majority of adolescent substance abusers have a primary mental disorder [35, 36]. Policy simulations based on the NCS data suggest that a more cost-effective strategy would be to develop outreach and treatment programmes for youngsters with early-onset mental disorders that predispose them to substance abuse. In addition to sharply reducing the proportion of youth who become substance abusers, such an effort could have a powerful preventive effect on subsequent adult serious mental disorder.

14.3 The NCS follow-up survey (NCS-2) 14.3.1 Design and rationale The NCS-2 was designed with the explicit purpose of providing an epidemiological foundation for early intervention programmes of the sort just described. While the baseline NCS simulations suggested that early primary mental disorders are important predictors of the subsequent onset and course of secondary mental and substance disorders, these results are based on retrospective reports about age at onset. The 10-year follow-up data in the NCS-2 were designed to determine whether these retrospective results hold up prospectively. This was done using a life chart approach to assess onset and course of disorders during the decade between the baseline NCS and the NCS-2. The life chart method, pioneered by Freedman and her colleague [37], provides respondents with a paper calendar covering the recall period that includes notations of important historical events in an effort to create memory anchors. Respondents are also asked to include personal memory anchors in the calendar to further enhance the accuracy of dating. Life charting was facilitated in the NCS-2 by the use of laptop computerised interviews that included a customised preloaded data file for each respondent based on baseline NCS reports. Respondents with a history of a particular disorder as of the baseline NCS were asked to chart the course of that disorder during the decade since the baseline NCS, while respondents with no history of the disorder as of the baseline NCS were asked about subsequent onsets and, if onsets occurred, about the course of the disorder after the time of onset. A similar procedure was used by Eaton and his associates in a 13-year follow-up of the Baltimore ECA sample [38]. In addition to charting the course of mental disorders, the NCS-2 charted major role transitions in education, marriage, childbearing and work that might play a part in influencing the onset and course of mental and substance disorders. Major stressor events and difficulties were also charted using a structured version of the Brown and Harris (1978) Life Events and Difficulties system [39]. Charting was done separately for each year 227

CHAPTER 14

across the decade between the two interviews and for each month in the 12 months prior to the NCS-2 interview.

14.3.2 Illustrative findings 14.3.2.1 Primary and secondary disorders As noted above, the NCS analyses investigated the distinction between temporally primary and secondary disorders by using retrospective age of onset reports. We were able to replicate and extend these analyses in the NCS-2 panel data using prospective information about age of onset. A good example of this line of analysis concerns the relationship between major depressive episode (MDE) and generalised anxiety disorder (GAD). Although MDE and GAD are known to be highly comorbid and to share most, if not all, of their genetic determinants [40], little prospective research has examined whether these two disorders predict the subsequent first onset or persistence of the other or the extent to which other predictors explain the time-lagged associations between GAD and MDE. An analysis of these issues in the NCS-2 showed that while baseline MDE significantly predicted subsequent GAD onset but not persistence, baseline GAD significantly predicted not only subsequent MDE onset but also the persistence of MDE [41]. We also found that the associations of each disorder with the subsequent onset of the other attenuated with time since onset of the temporally primary disorder, but remained significant for over a decade after this onset. We also found that baseline risk factors of onset and persistence varied somewhat between the two disorders. These results argue against the view of some that the two disorders are merely different manifestations of a single underlying internalising syndrome or that GAD is merely a prodrome, residual or severity marker of MDE.

14.3.2.2 Targeted risk factors for disorder onset and progression Another kind of prospective analysis carried out in the NCS-2 panel focused on baseline (NCS) predictors of the subsequent onset and progression of various other disorders. A good illustration of this work concerns substance disorders, where data 228

were obtained in both the NCS and NCS-2 on use, abuse and dependence. It was possible to study patterns and prospective predictors of the first onset of substance use, of the transition from use to abuse, of the transition from abuse to dependence and of the predictors of persistence versus recovery from abuse and dependence in ways that replicated earlier analyses in the NCS that used retrospective age of onset reports to mimic prospective data [42]. These analyses showed clearly that many of the previously documented risk factors for substance dependence are, in fact, risk factors only for one or two transitions. For example, the well-known finding that women have lower rates of alcohol and drug dependence than men was shown to be largely due to lower rates of ever starting to use among women than men, with very little evidence that women differ from men in the probability of progressing from use to abuse or from abuse to dependence.

14.3.2.3 Persistence of disorders and syndromes The NCS-2 was also used to study patterns and correlates of disorder persistence. One of the most fascinating of these studies focused on suicidality, including suicidal ideation, plans and attempts. Substantial persistence of suicidality was found over the decade between the two interviews, with over onethird of respondents who had a baseline history of suicide ideation continuing to have suicide ideation at some time over the intervening decade [43]. Indeed, the strongest predictors of later suicidality were measures of baseline suicidality. Nonetheless, a number of additional baseline predictors were found both of new first onsets of suicidality and of persistence of suicidality that have important implications for targeting interventions. Importantly, we found that even though mental disorders are powerful predictors of suicidality and that the vast majority of suicidal people have a pre-existing mental disorder, the main impact of mental disorders is in predicting the onset of suicide ideation, while other factors determine the transition from ideation to plans and attempts [44].

14.3.2.4 Disorder progression An important line of investigation in the NCS-2 panel has focused on disorder progression, with a special

THE NATIONAL COMORBIDITY SURVEY (NCS) AND ITS EXTENSIONS

emphasis on severity. This work was motivated by the fact that several restrictive definitions have been proposed to narrow the number of people qualifying for treatment of mental disorders. For example, a number of health plans restrict mental health coverage to the subset of DSM disorders that they consider to be ‘biologically-based’. A team of researchers from the American Psychiatric Association has argued that disorders currently classified as mild in the DSM-IV system should be excluded altogether from future diagnostic systems [45, 46]. This suggestion has important implications not only for the definition of current unmet need for treatment but also for current research and consideration of future treatment needs. Research shows that many syndromes currently defined as mental disorders are extremes on continua that appear not to have meaningful thresholds [47, 48]. These results are important in at least two ways. First, exploration of the full continua rather than the currently established diagnostic thresholds might yield greater power in studies of genetic and environmental risk factors [49]. Second, development of early interventions to prevent progression along a given severity continuum might reduce the prevalence of serious cases [50]. Removal of current mild cases from the DSM system would undercut both of these advantages as well as distort the reality that mental disorders, like physical disorders, vary widely in seriousness [51, 52]. In an effort to investigate this issue empirically, we examined the associations of baseline NCS 12-month illness severity with clinically significant outcomes assessed in a decade later in the NCS-2. Twelvemonth baseline NCS disorders were disaggregated into 3.2% severe, 3.2% serious, 8.7% moderate and 16.0% mild. All four categories were associated with significantly elevated risk of the NCS-2 outcomes compared to baseline non-cases, with ORs of any outcome ranging monotonically from 2.4 (95% CI: 1.6–3.4) to 15.1 (95% CI: 10.0–22.9) for mild to severe cases (Table 14.3). ORs comparing mild to moderate cases were generally non-significant. The existence of graded relationship between mental illness severity and later clinical outcomes has important implications for the decision whether or not to retain mild cases in the DSM. Retention of these cases would help represent the fact that mental disorders, like physical disorders, vary in severity and that

decisions about treating mild cases should include recognition that treatment of mild cases might prevent a substantial proportion of future serious cases.

14.4 The NCS replication survey (NCS-R) 14.4.1 Design and rationale As noted above, the NCS-R was carried out to study trends in a wide range of variables assessed in the baseline NCS and to obtain more information about a number of topics either not covered in the baseline NCS or covered in less depth than we currently desire. A new sample of 9282 adult respondents was interviewed in the same nationally representative sampling segments as the baseline NCS. There was also an update of new segments to adjust for population shifts over the decade between the two surveys. The NCS-R interview repeated many of the questions assessed in the baseline NCS for purposes of trending. New questions were also asked to expand old topics as well as to add new topics of investigation. The recruitment procedures and materials were identical to the baseline NCS. As in the baseline, interviews were carried out face-to-face in the homes of respondents. A complication in studying trends is that diagnoses in the baseline NCS were based on DSM-III-R [54] criteria, while diagnoses in the NCS-R were based on DSM-IV [55] criteria. The CIDI was used in both surveys, but it proved to be impossible to revise the version of CIDI used in the NCS-R to repeat all the DSM-III-R questions from the baseline survey as well as include the new questions needed to operationalise the new DSM-IV criteria. As in the baseline NCS, clinical reappraisal interviews in the NCS-R documented good concordance and conservative prevalence estimates compared with blinded clinician diagnoses [10, 56]. Because DSM-III-R and DSM-IV criteria differ too greatly to justify direct comparisons of prevalence, trend analysis was based on a re-calibration of both surveys to a common summary severity rating developed in the NCS-R and then imputed to the NCS. This severity rating is described in detail elsewhere [57]. In brief, a serious 12-month disorder 229

230

29.7∗

OR

23.8 9.7 10.1∗ 3.0 3.0∗ 2.9 2.7∗ 1.0 1.0 152.1∗

% (16.9–52.1) (4.8–21.3) (1.7–5.4) (1.5–4.9) –

(95% CI)

Hospitalisation

5.6∗

OR

6.1 1.7 1.5 1.4 1.3 1.5 1.3 1.0 1.0 17.0∗

% (2.2–14.4) (0.5–4.3) (0.4–3.6) (0.4–3.2) –

(95% CI)

Work disability

11.7∗

OR

8.0 5.0 6.1∗ 2.2 2.9∗ 1.6 2.0 0.7 1.0 40.4∗

% (4.5–30.4) (3.0–12.5) (1.2–7.4) (0.8–4.9) –

(95% CI)

Suicide attempt

15.4∗

OR

28.9 22.1 10.6∗ 13.2 5.6∗ 6.1 2.6∗ 2.5 1.0 194.0∗

%

SMI

(9.9–24.0) (6.0–18.5) (3.7–8.4) (1.8–3.8) –

(95% CI)

15.1∗

OR 42.4 30.8 8.8∗ 16.4 3.8∗ 9.9 2.4∗ 4.5 1.0 202.8∗

%

(95% CI) (10.0–22.9) (5.7–13.6) (2.7–5.5) (1.6–3.4) –

Any

The associations (odds-ratios) between baseline (1990–2002) NCS/DSM-III-R illness severity and NCS-2 (2000–2002) outcomes (n = 4375)a.

in the % columns are unadjusted prevalences of the NCS-2 outcomes in sub-samples defined by baseline 12-month NCS/DSM-III-R disorder severity. Entries in the OR and (95% CI) columns are odds-ratios and design-corrected 95% confidence intervals obtained by exponentiating multiple logistic regression coefficients in equations that simultaneously included dummy variables for the baseline disorder severity categories and controls for age and sex to predict the NCS-2 outcomes. This table appeared previously in Kessler, R.C., Merikangas, K.R., Berglund, P., Eaton, W.W., Koretz, D., Walters, E.E. (2003). Mild disorders should not be eliminated from the DSM-5. Archives of General Psychiatry 60 (11), 1117–1122 [53]. © 2003 American Medical Association. All rights reserved. Used with permission. ∗ Significant to the 0.05 level, two-sided test.

a Entries

Severe Serious Moderate Mild Non-cases χ2 4

Table 14.3

THE NATIONAL COMORBIDITY SURVEY (NCS) AND ITS EXTENSIONS

was defined as either: meeting 12-month criteria for schizophrenia, any other non-affective psychosis, bipolar I or II disorder or substance dependence with a physiological dependence syndrome; making a suicide attempt or having a suicide plan in conjunction with any NCS-R/DSM-IV disorder; reporting severe role functioning in two or more areas of life from among the four assessed (family, friends, work, household maintenance); or reporting functional impairment associated with a mental disorder at a level consistent with a Global Assessment of Functioning (GAF) [58] score of 50 or less. Respondents whose disorder did not meet criteria for being serious were classified moderate or mild based on responses to the disorder-specific Sheehan Disability Scales [59]. The imputation of severity scores to NCS cases was based on logistic regression equations estimated in the NCS-R that used symptom measures available in both surveys to predict: (i) serious disorder vs. all other respondents; (ii) serious–moderate disorder vs. all other respondents and (iii) any disorder vs. no disorder. Prediction accuracy was good in all three equations (AUC = 0.73 for serious, 0.84 for serious–moderate and 0.78 for any disorder). The coefficients in these equations were used to generate predicted probabilities for each NCS and NCS-R respondent for each nested outcome, which, in turn, were used to impute discrete scores on the severity scale.

14.4.2 Illustrative findings 14.4.2.1 Trends in the prevalence of DSM disorders Twelve-month prevalence estimates of DSM-IV disorders did not differ significantly across surveys, with a (29.4% estimated prevalence of any 12-month disorder in the NCS (1990–1992) and a 30.5% estimate in the NCS-R (2001–2003; p = 0.52). No significant change was found either in serious (5.3 vs. 6.3%, p = 0.27), moderate (12.3 vs. 13.5%, p = 0.30) or mild (11.8 vs. 10.8%, p = 0.37) disorders [60]. No statistically significant interactions were found between time and sociodemographics, suggesting that the overall lack of significant trend is not due to opposite-sign trends in major subsets of the population. We also looked at trends in

12-month suicidality and found no significant changes either in suicidal ideation (2.8–3.3%), plans (0.7–1.0%), gestures (0.3−0.2%) or attempts (0.4–0.6%) [61].

14.4.2.2 Trends in treatment Prevalence of 12-month treatment for emotional problems, in comparison, was found to change dramatically in the decade between the two surveys, from 12.2% in the NCS to 20.1% in the NCS-R [60] (Table 14.4). The association between severity and treatment was positive and significant (p < 0.001), although substantively modest in the pooled data (with a Pearson’s Contingency Coefficient (C) of 0.14), and did not differ significantly over time. Only a minority of respondents with serious disorders received treatment (24.3% in the NCS and 40.5% in the NCS-R). Approximately half of patients who received treatment had none of the disorders considered here. Trends in sector-specific treatment (psychiatric treatment, other speciality mental health treatment, general medical treatment, human services treatment, complementary-alternative medical treatment) were similar to overall trends in two respects. First, severity was significantly related to treatment in each sector (p < 0.001). Second, these associations did not change over time (p = 0.40–0.98). A significant difference in treatment trends was found across sectors (p < 0.001). General medical treatment increased most dramatically (from 3.9 to 10.0%), psychiatrist treatment (from 2.4 to 5.2%) and other mental health treatment (from 5.3 to 8.4%) less dramatically, human services treatment only modestly (from 2.6 to 3.5%) and complementary–alternative medical treatment decreased (from 3.3 to 2.7%). A distributional shift in treatment occurred because of these within-sector differences. Most significantly, general medical treatment changed from 31.5 to 49.6% of all treatment. This distributional increase, importantly, did not vary by severity, which means that more and more people with mental disorders of all severity levels are seeing a general medical doctor for treatment. This trend has important implications for treatment quality, as the NCS-R showed clearly that treatment quality is lower for patients treated in general medical than speciality settings [62]. 231

CHAPTER 14 Table 14.4 Twelve-month treatment of DSM-IV disorders by severity and sector among NCS (n = 5388) and NCS-R

(n = 4319) respondents ages 18–54. Anya

PSYa

OMHa

GMa

HSa

CAMa

NCS (1990–1992)b

Serious Moderate Mild Any None Total

%

(SE)

%

(SE)

%

(SE)

%

(SE)

%

(SE)

%

(SE)

24.3 25.4 13.3 20.3 8.8 12.2

(3.8) (2.4) (2.4) (1.5) (0.7) (0.6)

7.3 5.8 2.5 4.8 1.4 2.4

(2.2) (1.2) (1.2) (0.8) (0.3) (0.3)

11.4 13.6 4.9 9.7 3.5 5.3

(2.5) (1.6) (1.3) (1.0) (0.4) (0.3)

8.2 8.6 4.3 6.8 2.6 3.9

(3.0) (1.4) (1.4) (1.0) (0.4) (0.4)

4.5 5.5 3.0 4.3 1.9 2.6

(1.9) (1.1) (1.2) (0.7) (0.3) (0.3)

8.4 7.1 3.0 5.7 2.3 3.3

(1.9) (1.2) (0.8) (0.7) (0.3) (0.3)

22.1 19.5 11.8 17.3 6.8 10.0

(3.5) (2.4) (2.9) (1.3) (0.6) (0.5)

6.5 5.5 3.9 5.1 2.7 3.5

(1.6) (1.2) (1.5) (0.8) (0.4) (0.3)

6.2 4.6 2.9 4.3 1.9 2.7

(1.5) (1.0) (0.9) (0.6) (0.3) (0.3)

NCS-R (2001–2003)b Serious Moderate Mild Any None Total

40.5 37.2 23.0 32.9 14.5 20.1

(4.7) (3.0) (3.8) (2.0) (0.9) (0.8)

14.4 13.0 5.1 10.5 2.9 5.2

(3.3) (1.6) (1.3) (1.0) (0.4) (0.3)

19.4 15.8 9.0 14.1 5.9 8.4

(3.5) (1.8) (2.2) (1.3) (0.6) (0.5)

NCS-R : NCSc

Serious Moderate Mild Any None Total

RR

(SE)

RR

(SE)

RR

(SE)

RR

(SE)

RR

(SE)

RR

(SE)

1.7 1.5∗ 1.7∗ 1.6∗ 1.6∗ 1.6∗

(0.4) (0.2) (0.4) (0.2) (0.2) (0.1)

2.0 2.3∗ 2.2 2.2∗ 2.0∗ 2.2∗

(0.8) (0.6) (1.1) (0.4) (0.5) (0.3)

1.7 1.2 1.8 1.5∗ 1.7∗ 1.6∗

(0.5) (0.2) (0.6) (0.2) (0.3) (0.2)

2.9 2.3∗ 2.8 2.6∗ 2.6∗ 2.6∗

(1.3) (0.5) (1.0) (0.4) (0.5) (0.3)

1.5 1.0 1.3 1.2 1.4 1.3

(0.7) (0.3) (0.6) (0.2) (0.3) (0.2)

0.7 0.6 1.0 0.8 0.9 0.8

(0.2) (0.2) (0.4) (0.1) (0.2) (0.1)

χ2

(p)

χ2

(p)

χ2

(p)

χ2

(p)

χ2

(p)

χ2

(p)

194.6 56.8 0.5

(0.000) (0.000) (0.928)

112.2 34.5 0.2

(0.000) (0.000) (0.975)

118.1 22.7 3.0

(0.000) (0.000) (0.399)

105.3 72.4 0.3

(0.000) (0.000) (0.958)

23.0 3.3 0.9

(0.000) (0.069) (0.825)

82.9 3.3 1.2

(0.000) (0.067) (0.759)

Statistical significanced

Severity (S) Time (T) T×S

a Any, Any treatment; PSY, Psychiatrist; OMH, Other mental health specialist; GM, General medical; HS, Human services; CAM,

Complementary-alternative medicine. b %, Proportion of respondents in the total sample who received either any treatment or treatment in the treatment sector indicated in the

column heading. SE, Design-based multiply imputed standard error of the % estimate. c RR, Risk Ratio, the proportional increase in prevalence in NCS-R compared to NCS. For example, a RR of 1.5 corresponds to the NCS-R

prevalence being 50% higher than the NCS prevalence. Note that RR does not always equal the ratio of the % estimates in Parts I and II. This is because the Multiple Imputation method calculates % and RR as means of these estimates in pseudo-samples. The mean of a within-pseudo-sample ratio does not necessarily equal the ratio of the within-pseudo-sample means of the % estimates. d The significance tests for severity (S) evaluate the significance of differences in treatment proportions across the four categories of the severity variable pooled across the two surveys. Each severity χ2 test has 3 degrees of freedom (serious, moderate and mild vs. none). The significance tests for time (T) evaluates the significance of differences in treatment proportions in the two surveys controlling for differences in severity. Each time χ2 test has 1 degree of freedom (1990–1992 vs. 2001–2003). The significance tests for interactions between time and severity (T × S) evaluate the significance of differential change across the two surveys depending on severity. Each T × S χ2 test has 3 degrees of freedom. Adapted from a table previously published in Kessler, R.C., Demler, O., Frank, R.G., Olfson, M., Pincus, H.A., Walters, E.E., Wang, P.S., Wells, K.B., Zaslavsky, A.M. (2005). Prevalence and treatment of mental disorders, 1990 to 2003. New England Journal of Medicine 352 (24), 2519 [60]. © 2005 Massachusetts Medical Society, All rights reserved. Used with permission. ∗ Significant at the 0.05 level, two-sided test.

232

THE NATIONAL COMORBIDITY SURVEY (NCS) AND ITS EXTENSIONS

14.5 The NCS-R adolescent supplement (NCS-A) 14.5.1 Design and rationale The NCS-A was designed to provide basic descriptive psychiatric epidemiological information on adolescents comparable to the information on adults obtained in the baseline NCS [63]. In addition, the NCS-A interview schedule included a detailed risk factor battery to study modifiable determinants of the onset and course of child and adolescent mental disorders. Furthermore, as a nationally representative sample of schools was selected to help recruit the NCS-A sample (described below), the survey included considerable detail on school and neighbourhood environmental factors that might be important determinants of early detection, outreach and treatment of child and adolescent mental disorders. A number of important design decisions arose in planning NCS-A that deviated from the model used in the adult NCS-2 and NCS-R surveys [64, 65]. One of these concerned sampling. Because adolescents only reside in a small proportion of all households, a critical design decision concerned the sampling scheme. The scheme we settled on used a dual-frame approach, in which a representative sample of all schools in the country and a representative sample of all households in the country were both used to select adolescents for interview. The school sample was a probability sample of the schools in the communities used in the NCS-2 and NCS-R samples. A probability sample of students in the eligible age range (12–17) was selected in each sample school. The household sample was based on a random selection of one adolescent in each household contacted for the NCS-2 and NCS-R adult surveys. Information was recorded for each household sample respondent regarding whether or not they still attend school and, if so, the name of their school. This information was used to weight the data to adjust for the under-sampling of school dropouts and of students who, along with their parents, agreed to participate in the survey as part of the household sample while the principal of the school they attend did not agree to include the school in the school sample. This dual frame approach was facilitated by the fact that the

adult NCS-2 and NCS-R surveys were carried out in parallel with the adolescent survey. Dual-frame sampling is much more efficient than other sample designs in a situation of this sort. Another critical design decision concerned instrumentation. A number of research diagnostic interviews exist to assess mental disorders among children and adolescents [66–68]. We were unable to achieve consensus among our advisors in selecting one of these instruments based on the simultaneous consideration of accuracy and ease of implementation. As a result, we elected to use a modified version of the NCS-2 and NCS-R diagnostic interview, the CIDI, in the adolescent survey. This decision was based, in part, on the fact that the CIDI was previously used successfully in a German adolescent sample [69] as well as among 15–17-year-olds in the baseline NCS. An additional consideration was that the same interview staff that administers the NCS-2 and NCS-R adult interviews also administered the adolescent interviews. We reasoned that it would be much easier for these interviewers if we maintained relative consistency in the instrument across samples rather than use a totally different instrument for the adolescents than the adults. The CIDI was expanded for the NCS-A to include new sections on child and adolescent disorders derived from the DIS. These include oppositional-defiant disorder, conduct disorder, attention-deficit/hyperactivity disorder and separation anxiety disorder. We also modified existing CIDI diagnostic sections that have different criteria for adolescents than adults. In addition, the risk factor battery was expanded to include a more detailed assessment of childhood adversity, while the interview questions on treatment for emotional disorders were revised to blend relevant questions from the NCS-R with questions in another instrument designed for use with children and adolescents [70]. Once all these modifications were complete, revisions in question wording were made to improve comprehension among adolescent respondents. This work made use of recently developed cognitive interviewing methods to gain insights into areas of confusion in the instrument and into ways that these confusions might be resolved with modified questions [9, 10]. Finally, a self-administered informant version of the instrument was developed to 233

CHAPTER 14

obtain information from the parents of respondents. A clinical reappraisal study showed that these modifications resulted in the diagnoses based on the CIDI having very good concordance with independent diagnoses based on blinded clinical reappraisal interviews [71].

14.5.2 Illustrative findings As the NCS-A analyses are only now beginning, no substantive results can be reported here other than to note that preliminary analyses show that, consistent with previous epidemiological surveys, the estimated prevalence of mental disorders among youth is both quite high and quite widely distributed throughout the population. We anticipate that published reports of these analyses will begin to appear in 2009–2010. The NCS web site will post information about these reports as they become available (www.hcp.med.harvard.edu/ncs).

14.6 The WHO WMH Surveys 14.6.1 Design and rationale The WMH Survey Initiative is an outgrowth of the WHO Global Burden of Disease (GBD) study [72, 73], an investigation of the comparative prevalence and societal costs of diseases throughout the world. The first phase of the GBD study concluded that mental disorders are among the most burdensome of all diseases in the world today and that major depression will become the single most burdensome disease in the world within the next two decades. These striking conclusions are based on a unique combination of characteristics shared by depression and many other mental disorders: that they are very common diseases; that they typically have much earlier ages of onset than most chronic physical diseases; that they have high rates of chronicity in conjunction with high risks of impairment and disablement and that they have low rates of treatment. It might be hoped that these results would influence health policy planners throughout the world to move mental disorders up in their priority list for prevention and treatment initiatives. However, this has not happened as yet. At least one reason for this is that the 234

first phase of the GBD study relied entirely on panels of clinical experts to estimate comparative levels of disease-specific impairment and disablement. The validity of these ratings can be called into question, undercutting the persuasive power of the GBD results concerning the importance of mental disorders. The WMH Initiative was designed to address this limitation by carrying out a series of parallel community epidemiological surveys based on the interview schedule developed for the NCS-R in countries throughout the world in order to obtain objective estimates of the prevalences, impairments and patterns of treatment for mental disorders. Over two dozen countries from all regions of the world are participating in WMH, with a combined sample size in excess of 250 000 respondents [1]. To date, surveys have been conducted in Australia, Belgium, Brazil, Bulgaria, China, Colombia, France, Germany, India, Iraq, Israel, Italy, Japan, Lebanon, Mexico, The Netherlands, New Zealand, Nigeria, Northern Ireland, Peru, Portugal, Romania, South Africa, Spain, Turkey, Ukraine and the United States. Surveys are pending in Nepal, Saudi Arabia and Spain. Because of their emphasis on comparative disease burden, the WMH surveys, including the NCS-R, differ from previous CIDI surveys in a number of important respects that were developed in the NCSR. First, while the focus of almost all previous CIDI surveys was on lifetime disorders, the WMH surveys are equally interested in past year and current (at the time of interview) disorders. All previous versions of CIDI, including the version used in the baseline NCS, provided only superficial information on recent disorders by focusing on lifetime symptoms and asking only one question – ‘How recently have you had (the disorder)?’ – to learn about recency. This made it impossible to characterise the persistence of disorders over the recent past or to know whether respondents with a lifetime disorder meet full criteria during the recent past. The CIDI was modified to correct these problems for use in the NCS-R and WMH surveys by obtaining information about current symptoms and persistence of symptoms over the past year. Second, the WMH surveys were designed to focus on recent prevalence to address a question raised by critics of the baseline NCS concerning the clinical significance of community cases [74]. These critics hypothesised that a substantial proportion

THE NATIONAL COMORBIDITY SURVEY (NCS) AND ITS EXTENSIONS

of community cases of mental disorders are not clinically significant. We addressed this concern by administering structured versions of standard clinical severity measures to all NCS-R and WMH respondents with recent CIDI disorders. Included here are such measures as a structured version of the Inventory of Depressive Symptomatology to assess the severity of recent depression [75], a structured version of the Panic Disorder Severity Scale to assess the severity of panic [76] and a structured version of the Yale–Brown Obsessive–Compulsive Scale to assess the severity of OCD [77, 78]. Our goal was to use standard clinical severity scales such as these to provide a heretofore missing crosswalk between the findings in our epidemiological surveys and the findings in clinical studies. Third, related to the issue of clinical significance is the issue of impairment. The original version of the CIDI asked only one dichotomous disorder-specific role impairment question for all disorders: ‘Did (the disorder) ever interfere a lot with your life or activities?’ No questions about impairment were asked independent of disorders. This was inadequate for the purposes of the WMH surveys. We consequently expanded the assessment of impairment in the CIDI to include more detailed disorder-specific questions about both lifetime and 12-month role impairments. All WMH surveys, including the NCS-R, also include the WHO Disability Assessment Schedule [79] to assess overall role impairment and disablement independent of particular disorders. Importantly, in order to provide comparative information on the impairments of mental and physical disorders, a checklist of chronic physical disorders was included in the NCS-R and the other WMH surveys. The problem of under-reporting due to some people with chronic conditions not being aware of their disorders was dealt with for symptom-based condition by using symptom screening questions for a random subsample of physical diseases for each WMH respondent. The random subsampling strategy is required because comprehensive screening for all possible physical disorders would be too time-consuming for a one-session survey devoted to mental disorders. However, by taking care to screen randomly to select a separate representative subsample of physical disorders for each respondent, we will guarantee that

data will be collected for a representative subsample of people with each chronic disorder for purposes of comparative assessment of within-disorder role impairments.

14.6.2 Illustrative results 14.6.2.1 Disorder prevalence Consistent with the results of the NCS-R, the WMH results found that mental disorders are commonly occurring in the vast majority of the countries studied [80]. Comparative prevalence estimates were also quite similar across countries, with phobias virtually always the most common anxiety disorder and major depression the most common overall disorder. Comorbidity among these disorders was also found to be high in most countries, with distinct clusters of internalising and externalising disorders.

14.6.2.2 Relative impairments of mental and physical disorders Comparative analyses in the WMH data found that the mean levels of self-reported impairment associated with mental disorders are significantly higher than those associated with the vast majority of the commonly occurring chronic physical disorders assessed in the surveys [81]. This general pattern was true in all regions of the world and to both developed and developing countries.

14.6.2.3 Treatment Despite these higher impairments, only a minority of people with even seriously impairing mental disorders were found in the WMH surveys to receive treatment [82]. This was true even in developed countries although the pattern was found to be more pronounced in developing countries. Treatment rates were considerably lower in every country for mental disorders than for physical disorders associated with comparable levels of impairment [81].

14.6.2.4 Diagnostic criteria for DSM and International Classification of Diseases disorders The WMH data have been used to investigate a number of issues raised in debates over appropriate 235

CHAPTER 14

diagnostic criteria for DSM and International Classification of Diseases (ICD) disorders. For example, the WMH data were used to investigate the implications of the suggestion that the six-month minimum duration requirement for a diagnosis of GAD be reduced to 1 month [83]. Results showed that symptom severity, persistence, comorbidity and impairment of GAD were all quite similar for cases defined with a 1–5 month duration requirement compared to a 6-month minimum duration requirement. These results showed that the current DSM and ICD 6-month requirement excludes a large number of people with clinically significant short recurrent episodes of a GAD-like syndrome.

14.6.2.5 Cross-national correlates The WMH data have also been used to examine cross-national variation in correlates of mental illness. One of the most interesting of these investigations focused on gender differences. Epidemiological surveys consistently find significantly higher levels of anxiety and mood disorders among women than men [84, 85] and significantly higher levels of externalising and substance use disorders among men than women [86, 87]. Similar patterns are found in the WMH data. Although a number of biological, psychosocial and biopsychosocial hypotheses have been proposed to account for these findings [88–90], evidence that gender differences in both depression [91, 92] and substance use [93, 94] have been narrowing in recent years in a number of countries has led to a special interest in the ‘sex roles’ hypothesis. The latter hypothesis holds that gender differences in the prevalence of mental disorders are due to differences in the typical stressors, coping resources and opportunity structures for expressing psychological distress that are made available to women and men in different countries at different points in history [95, 96]. No rigorous test of the sex roles hypothesis has ever been carried out before the WMH surveys. We did this by using administrative data collected by the World Bank, the United States and WHO on time–space variation in diverse indicators of the positions of women relative to men in countries around the world to generate an index of sex

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role inequality. Index scores were then merged with the WMH survey data. We showed that gender differences in both depression and substance disorders have become significantly smaller across successive cohorts in countries where sex role inequality has decreased over time [97]. We also found that point-in-time cross-national variation in the strength of association between sex and mental disorders is significantly related to traditionality of gender roles.

14.7 Overview Descriptive studies like the NCS and WMH surveys are of more importance in psychiatric epidemiology than in other branches of epidemiology due to the fact that psychiatric epidemiology has traditionally been hampered by difficulties in conceptualising and measuring disorders. The baseline NCS was important mainly because it helped resolve these difficulties by providing accurate descriptive data on the prevalence and correlates of mental disorders. However, we have to remember that the ultimate goals of epidemiology are to understand and control disease by empirically studying associations between variation in exposure to disease-causing agents external to the individual, variation in the resistance of individuals exposed to the disease-causing agents and variation in resistance resources in the environments of exposed individuals. Although these investigations are initially carried out by examining natural variations of the sort assessed in the NCS surveys, we have to move beyond this initial step to develop hypotheses that can be tested provisionally in naturalistic quasiexperimental situations with matching or statistical controls used to approximate the conditions of an experiment. If the hypotheses stand up to these preliminary tests, they then need to be evaluated in interventions aimed at preventing the onset or altering the course of the disorders. This perspective on the role of surveys like the NCS and WMH surveys suggests that they should be seen as a necessary step in the evolution of epidemiological research on mental disorders that provide a firm descriptive foundation for further analytic and experimental epidemiological research.

THE NATIONAL COMORBIDITY SURVEY (NCS) AND ITS EXTENSIONS

Acknowledgements The baseline National Comorbidity Survey (NCS) was supported by NIMH grants MH46376, MH49098 and MH52861, with supplemental support from the W.T. Grant Foundation (Grant 90135190) and an NIMH Career Scientist award to R.C.K. (MH00507). The NCS-2 was funded by National Institute of Drug Abuse (NIDA) grant DA12058, with supplemental support from NIMH. The National Comorbidity Survey Replication (NCS-R) and National Comborbidity Replication Adolescent Supplement (NCS-A) are supported by the National Institute of Mental Health (U01MH60220) with supplemental support from the National Institute on Drug Abuse, the Substance Abuse and Mental Health Services Administration (SAMHSA), the Robert Wood Johnson Foundation (RWJF; Grant 044780) and the John W. Alden Trust. Collaborating NCS-R investigators include Ronald C. Kessler (Principal Investigator, Harvard Medical School), Kathleen Merikangas (Co-Principal Investigator, NIMH), James Anthony (Michigan State University), William Eaton (The Johns Hopkins University), Meyer Glantz (NIDA), Doreen Koretz (Harvard University), Jane McLeod (Indiana University), Mark Olfson (New York State Psychiatric Institute, College of Physicians and Surgeons of Columbia University), Harold Pincus (University of Pittsburgh), Greg Simon (Group Health Cooperative), Michael Von Korff (Group Health Cooperative), Philip S. Wang (NIMH), Kenneth Wells (UCLA), Elaine Wethington (Cornell University) and Hans-Ulrich Wittchen (Max Planck Institute of Psychiatry; Technical University of Dresden). The views and opinions expressed in this report are those of the authors and should not be construed to represent the views of any of the sponsoring organisations, agencies or US Government. The NCS-R is carried out in conjunction with the World Health Organization World Mental Health (WMH) Survey Initiative. We thank the staff of the WMH Data Collection and Data Analysis Coordination Centres for assistance with instrumentation, fieldwork and consultation on data analysis. These activities were supported by the National Institute

of Mental Health (R01 MH070884), the John D. and Catherine T. MacArthur Foundation, the Pfizer Foundation, the US Public Health Service (R13MH066849, R01-MH069864 and R01 DA016558), the Fogarty International Center (FIRCA R03-TW006481), the Pan American Health Organization, Eli Lilly and Company, Ortho-McNeil Pharmaceutical, Inc., GlaxoSmithKline and Bristol-Myers Squibb. A complete list of WMH publications can be found at http://www.hcp.med.harvard.edu/wmh/. A complete list of publications from the NCS, NCS-2 and NCS-R can be found at www.hcp.med.harvard.edu/ncs. Information about the WMH surveys can be found at www.hcp.med.harvard.edu/wmh/. Address comments to R.C Kessler, Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA 02115.

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Experimental epidemiology John R. Geddes Department of Psychiatry, Warneford Hospital, Oxford, UK

15.1 Introduction The investigation of the relation between cause and effect in psychiatric research is the same as in any other area of clinical science. When possible, the experiment, in which the exposure is controlled, produces the most convincing evidence of causal association. The most commonly used experimental design for assessing the effects of treatments is the randomised controlled trial (RCT). This chapter will deal with some of the evolving trends in our understanding and classification of clinical trials. Before considering the design of RCTs in more detail, the limitations of non-randomised evidence will be considered because RCTs can also be vulnerable to similar problems and need careful design to preserve the advantages of randomisation. We will then consider the main threats to the validity and success of RCTs and the main strategies for dealing with them. Finally, we will examine some of the practical implications of the issues discussed in the chapter.

15.2 Limitations of non-randomised evidence The main problem with non-randomised evidence is that it is unclear to what extent any observed association is causal. First of all, when the exposure is not under the control of the investigator, even in a prospective trial, it can be hard to time the relationship of the putative cause to the outcome. There are two main problems: • Did the exposure occur before the outcome? In an observational study it is often unclear if a putative

risk factor predisposes to the outcome of interest, if the outcome causes the putative risk factor (i.e. reverse causation), or if the putative risk factor and outcome are both caused by a third factor. This is because measurement of both the exposure and outcome are subject to imprecision and bias. An effective way of determining the temporal relationship between exposure and outcome is by controlling the exposure. By manipulating the exposure in a prospective study, it is possible to determine exactly when it is administered to the participant. • Have alternative explanations been excluded? This is a particular problem in risk factor research in that there are usually alternative explanations for any observed association. It is usually unclear if the observed association is simply due to an additional association between the risk factor, the outcome and a third variable which is related to both the exposure and the outcome. This is known as confounding and is a particular problem where there is an inter-relationship between numerous causal factors. When considering the effect of a particular medicine it may be unclear if any difference in outcomes between patients who take the drug and those who do not is due to the drug or to other clinical factors which are related both to the clinical choice of the drug and the outcome (or prognosis) of the condition. For example, in an observational study comparing suicidal behaviour in patients who were prescribed a selective serotonin re-uptake inhibitor (SSRI) and those who were prescribed another drug, is an increased rate of suicide due to the SSRI or because SSRIs were

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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more likely to be prescribed to patients who were at clinically increased risk of suicide because they were safer in overdose than alternatives? There are seven main ways of trying to deal with confounding. 1 Exclusion – participants with exposures to known confounders are excluded from the study. 2 Stratification of input – participants are stratified according to one or more known confounder. 3 Stratification of analysis – the potential effect of confounding is adjusted for in the analysis. 4 Matching – each case and control is matched according to their exposure to one or more known confounders. 5 Standardisation – rates of outcomes are compared to standardised rates from a reference population. 6 Regression analysis – the potential effect of confounding is adjusted for in the analysis using regression techniques. 7 Randomisation – participants are randomly allocated to the exposure of interest or a control intervention. When done properly, randomisation has the key effect of preventing the allocation of exposure of control from being influenced by any other factors. All other factors – both confounders and non-confounders will, on average, be equally distributed between groups. Methods 1–6 can, to some extent, control for known confounders if (and it is a substantial if!) they can be both measured and quantified accurately. Of course, this is time consuming, expensive and sometimes impossible – but, in principal, can be done for many known confounders. In observational studies of drug therapies, propensity score matching is increasingly used. In this technique the conditional probability of being treated one way or another, given the person’s clinical characteristics, is used to balance the comparison groups using matching, stratification or regression [1]. However, despite the increasing sophistication of observational techniques, random allocation of the exposure is the only way of dealing with unknown confounders. This unique ability to control the allocation of

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exposures, dealing with confounding and subsequent measurement of specific, investigator-specified outcomes is the key strength of the randomised experiment and the approach has become standard inside the laboratory. The convincing nature of evidence from a properly conducted randomised experiment is the reason why this design is seen as the gold standard for assessing causation in medicine. Clearly, there are many exposures where it is either impossible or unethical to allocate patients to exposures randomly. For example, generally, people either smoke or do not smoke through choice (at least initially) but it is not usually regarded as ethical to allocate them to smoking by random. Within psychiatry, observational evidence has identified a possible causal association between events occurring perinatally and subsequent development of schizophrenia. However, one cannot allocate mothers to perinatal incidents. This means that uncertainty about the attribution of cause and effect in observational studies is inevitable. Even if a very clear estimate of the exposure dose can be obtained it is rarely possible to adjust for confounders (even known confounders) to the same degree of exactness as one can determine the exposure dose. Further, it is of course impossible to either control, measure or assess the effect of unknown confounders. Despite the limitations of observational evidence, large scale, well-controlled non-randomised studies of routinely collected observational data will remain a necessary tool in the evaluation of comparative treatment effects because of the difficulties and expense of conducting large RCTs [2]. Non-randomised studies also have the advantages of being generally cheaper to achieve a given sample size and they do not suffer from the inevitable selection biases of RCTs. Given their specific strength and weaknesses, it is reassuring when the treatment effects estimated in RCTs and observational studies are consistent. For example, estimates of the magnititude of the reduction in suicidal risk associated with long-term lithium therapy is consistent across randomised and non-randomised evidence, with the non-randomised evidence playing a key role in view of the small and heterogeneous nature of the RCTs [3, 4]. Box 15.1 summarises the advantages and disadvantages of RCTs.

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Box 15.1 The Advantages and Disadvantages of RCTs Advantages • Most efficient design for investigating causality because we can ensure that the ‘cause’ precedes the ‘effect’. • We can ensure that possible confounding factors do not confuse the results. • We can ensure that treatments are compared efficiently. • Randomisation facilitates statistical analysis. Disadvantages • Can take a long time and be very expensive. • Not suitable for very rare diseases or diseases with a long latency. • Ethical problems. • Generalisability – RCTs often screen out vulnerable groups such as the very young, very old and pregnant women (or at risk).

Even when it is both feasible and ethical to conduct a randomised trial, there are major difficulties in using experimental approaches in the real world where one has much less control over the environment or the precise allocation of a known amount of exposure and measurement of outcome than in the laboratory. The rationale for conducting expensive and difficult randomised trials in clinical populations is to produce solid and reliable evidence for clinical decision-making. Inevitably, however, compromises must be made in the design of all trials to achieve the best balance between the adherence to the optimal designs used in the laboratory and the degree to which the results can be applied in the real world of clinical practice.

15.3 RCTs: The translation of the experimental design into the real world In the laboratory, it is possible to control most of the aspects of the participant (in animal studies),

environment, exposure and outcome measurement. All known sources of bias can be controlled and therefore one can have great confidence in the observed results. Part of the translation and development of the design of randomised trials has been the application of methods derived in highly controlled environments and their application in situations where control is far less possible. The main validity threats in this translation from lab to bedside are the introduction of random and systematic errors. The standard design of a clinical trial is to administer a known exposure and measure an outcome in a reliable manner in an environment, that is standard and consistent across all participants. The aim is both to reduce unnecessary ‘noise’ – or random error – but also to reduce bias or systematic error. The optimal design of the randomised control trial must consider both systematic and random error and limit their effects to as great an extent as possible.

15.4 Importance and control of systematic error or bias Systematic error, or bias, seriously undermines the internal validity of the trial and produces bias in the estimation of the treatment effect – or, simply, the wrong result. The methodological development of randomised trials has, to a large extent, been concerned with identifying those aspects of the design of the randomised trial which are most important to reduce the impact of bias. As above, it can be challenging to protect against all forms of bias in a trial while at the same time making the trial both feasible and clinically applicable. Therefore, if we could know which aspects of the trial are most important, then the design can prioritise methods which limit these. Other aspects that are less critical to validity could be loosened to some extent, to make the design feasible in the real world without seriously invalidating or biasing the results of the study.

15.4.1 Randomisation with allocation concealment Randomisation with allocation concealment prevents the treatment being assigned by the clinician on

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the basis of any baseline clinical characteristic, prognostic factor or preference. Without randomisation and allocation concealment, there is a serious potential for selection bias. Note that this does simply refer to the process by which participants are allocated to treatments randomly. Perhaps more important is that the investigator and participant are completely unaware of the treatment allocation prior to randomisation. So, a pregenerated list of random allocations in which the next allocation is known will not suffice because it is possible for the investigator to select patients based on this knowledge. Most of the empirical studies show that randomisation is the single most important way of avoiding bias. In principle, randomisation is quite a simple procedure but in practice maintaining adequate concealment of allocation is crucial and can be challenging. Why is allocation concealment so important? The main reason is that if a patient or clinician knows which treatment they will receive prior to participating or prior to randomisation then they may choose not to participate in the trial. Further, if the investigator has control over the allocated treatment then they may choose to tamper with the allocation and select a treatment allocation that fits with their preference. These two mechanisms will lead to bias because the patients being treated with one treatment or another will differ systematically from each other. Therefore, the allocation has to be concealed from both patient and clinician until they have entered the trial and been allocated. Patient and clinician may remain blind to the treatment allocation following randomisation, but this is a separate matter to allocation concealment and will not always be possible. How might adequate allocation concealment be achieved? The best way is to ensure that the allocation schedule is held remotely and cannot be accessed by either the patient or clinician. Modern trials tend to have remote, independent randomisation services. The patient consents to randomisation and the investigator then phones the randomisation service. The phone call is logged and the patient is registered as a participants into the trial. Then, and only then, is the patient randomly allocated to the treatment or comparator. The allocation needs to be completely concealed if pregenerated – for example it could be

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help by an independent pharmacist in a remote site, or – more preferable – the allocation is only generated when the patient has consented to the trial and has been entered into the trial database. The importance of allocation concealment cannot be overstated – allowing treatment to be chosen by randomisation can be difficult for both patient and clinician. The lengths that investigators will go to trying to identify the next treatment allocation have been well documented and include steaming open envelopes and using X-ray equipment! [5].

15.4.2 Blinding (or masking) of treatment allocation Blinding refers to maintaining concealment of allocation following randomisation. The aim of blinding is: 1 to prevent participants being treated in different ways depending on which treatment they have been allocated to (performance bias) and 2 to prevent the trial outcomes being measured differently depending on which treatment they have been allocated to (ascertainment bias). The allocation may be concealed from the participant, investigator, outcome assessor, statistician, authors, journal reviewers, and so on, and there is sometimes uncertainty about who is masked when the trial is simply referred to as double-blind [6]. It is important to discriminate between allocation concealment and blinding – the former is always essential, while it is often impossible to mask the identity of the intervention from all who are involved in a trial – although it will often be possible to mask some parts of the trial (such as assessment of outcome). Particularly in RCTs with subjective outcomes, some methods of blinding should always be considered because there is evidence that absence of blinding leads to biased estimates of treatment effect [7]. There is also some uncertainty about whether the success of blinding should be checked during the trial. Current consensus is that this should not be done routinely because it may focus attention on the issue, although there may be specific situations in which it is useful [8].

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15.4.3 Maximising follow-up Any participant who does not complete the trial is a source of uncertainty in the results. How do we know what happened to them? If the numbers of trial withdrawals and the reasons for the withdrawals are similar in the two arms of the trial then this may not bias the trial (although it is likely to increase random error, see below). If however, there are differential total, or specific, drop-outs in the arms of the trials then attrition bias may be introduced – especially if drop-out from the trial is related to one or other of the trial treatments.

15.4.4 Accounting for participants following randomisation and analysis by allocated treatment group The potential for introducing bias by excluding certain participants has long been recognised [9]. An analysis in which all randomised participants are included, irrespective of the duration of participation and in their randomly allocated groups, is least likely to introduce bias and is also most likely to produce a clinically relevant results [10]. This has often been termed an intention-to-treat analysis although this term has recently fallen from favour because it is frequently misused and, hence, its meaning has become uncertain [8]. For example, sometimes participants are removed from the analysis (often patients with

no follow-up data following randomisation, or participants who did not receive the allocated treatment) and this is called this a modified intention-to-treat analysis. The key issue is to decide on how trial withdrawals will be dealt with in the analysis prior to the analysis in a detailed analysis plan.

15.4.5 Empirical evidence of bias in RCTs A major advance in our understanding of RCTs over the past two decades has been gained from empirical study of specific design features on the estimated treatment effect (Figure 15.1). Much of the empirical analysis of the design of clinical trials has consisted of the comparison of the estimates of effect obtained from trials in which there is greater control of bias with trials where there is lesser control. The assumption here is that less well controlled trials will, on average, produce larger (and more biased) estimates of treatment effects than more tightly controlled trials. In a review of 250 RCTs from 33 meta-analyses, Schulz et al. compared the treatment effects from studies according to several design characteristics [11]. They found that the treatment effect was 30–41% larger in trials without adequate concealment of treatment allocation. In a further analysis, inadequate or unclear allocation concealment was again found to be associated with larger (and, presumably, biased) estimates of treatment effects but

Intervention

Outcome

Control

Outcome

Patients

Selection bias

Performance bias

Attrition bias

Detection bias

Fig 15.1 Specific sources of bias in a randomised controlled trial.

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lack of blinding was only significantly associated with larger treatment effects when subjective outcomes were used – which is often the case in psychiatry [7].

15.5 Importance and control of random error and noise A fundamental issue in clinical trial design is the required sample size. The power of a trial is determined by the sample size, the anticipated difference between the interventions (or treatment effect), the chance of detecting a significant difference when there is no real difference between the treatments (α, which represents the risk of a false positive result, the type 1 error). The aim of achieving a sufficient sample size is therefore to provide sufficient statistical power to be reasonably sure of detecting a real treatment effect while, at the same time, not producing a false positive result. There are standard methods of calculating sample size and these are not considered further here [12, 13]. Achieving a sufficient sample size is often considered to be the main way of reducing random error. This is often considered to be less of a problem than selection bias because it essentially produces clean noise that limits the power of the trial and the precision of the results, rather than producing biased results. However, it is essential to consider its impact because a trial needs to be able to detect the treatment signal above the inevitable noise of the uncontrolled environment. A useful way on considering the issues is in terms of the ratio of signal to noise. Failure to control the signal to noise ratio is an important potential reason for a trial to fail to demonstrate a treatment effect even for an intervention of known effectiveness [14]. David Sackett observed that he relationship between our confidence in the results of a trial, signal to noise ratio and sample size is: Confidence =

Signal  × Sample Noise

It will immediately be seen that relying in increased sample size alone is inefficient: confidence increases as a function of the square root of sample size. In other word, to double confidence in the results, the sample size will need to increase by a factor of 4. A more efficient strategy is to maximise the signal 248

to noise ratio. Figure 15.2 shows some potential strategies for maximising signal and sample size, and minimising noise. There are certain conclusions that can be drawn from this relationship. First, a highly controlled trial is likely to maximise signal to noise ratio and will therefore minimise the required sample size for a given degree of confidence. Second, a trial conducted in a relatively uncontrolled clinical environment will tend to have a low signal to noise ratio and numbers will be crucially important. Lastly, a trial with a low signal:noise ratio and minimal sample size is at high risk of failing to pick up a treatment effect. The term assay sensitivity has also been used to denote similar issues.

15.6 Reporting the results of clinical trials—the CONSORT statement Bias may often occur in the reporting of randomised trials and this can lead to misinterpretation. The Consolidated Standards of Reporting Trials (CONSORT) Group have developed the CONSORT Statement which is an evidence-based set of recommendations for the reporting of RCTs [15] (see Table 15.1 and the flow-diagram shown in Figure 15.3). The CONSORT statement offers a standard way for investigators to report trials (http://www.consortstatement.org/). CONSORT is soundly based on the results of empirical studies into the relationship between specific design features and bias. Journals increasingly require articles reporting RCTs to conform to the CONSORT criteria and it is useful to use the checklist when designing a trial because it focuses specifically on areas which are susceptible to bias.

15.7 Different clinical questions will prioritise control of different threats to validity and confidence The design features that protect against bias and the relationship between confidence, signal, noise and sample size begins to make explicit some of the decisions that need to be made in the design of any RCT. In some situations, it might be undesirable to have strict eligibility criteria because it will reduce the

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• Baseline risk in patients • Responsiveness of patients to treatment • Potency of experimental intervention • Completeness of ascertainment of outcome

•Reduce variation between patients by inclusion criteria •Reduce variation between arms by stratification or minimisation •Increase compliance •Reduce misclassification •Improve precision of outcome assessment

• Use as main strategy only as last resort – to increase confidence by 2x, need to 4x sample size – however, • Reduce risk of failing to achieve sample size by making procedures simple, providing support, reducing barriers, • Increase compliance • Reduce misclassification • Improve precision of outcome assessment

Fig 15.2 Relationship between confidence, signal to noise ratio and sample size in randomised controlled trials.

external validity, or clinical applicability, of the trial. It may be difficult and expensive to blind treatment assignment from participants. It may be impossible to recruit large numbers of patients. The appropriate design for each trial will depend on the primary clinical question that the trial is designed to answer. For example, in drug development, several phases are conventionally recognised: • Phase I: Clinical pharmacology in healthy volunteers (first in man). • Phase II: Clinical pharmacology in patients to establish preliminary efficacy and safety and dose. Frequently subdivided into Phase IIa (to estimate clinically effective dosage) and IIb (preliminary test of efficacy). • Phase III: Formal therapeutic trials to provide pivotal evidence of efficacy and safety. • Phase IV: Post licensing studies to establish broader efficacy and safety.

There is often a degree of overlap between these phases and it is increasingly common for a trial to be conducted to meet the needs of multiple phases of development, particularly Phase II and III. For example, a trial may randomise patients between placebo, multiple doses of the investigational drug and an active comparator. In general, however, the designs of the study will vary between phases. RCTs will often be the primary research design in phases II and III and an important part of phase IV. Phase II trials will tend to be smaller and more tightly controlled – and the concern is less with external validity than with maximising the sensitivity of the design to pick up treatment effects. Phase III trials are sometimes called pivotal as they are aimed at the regulatory authorities who will principally require a high level of internal validity and a reasonable degree of external validity. Phase IV trials are usually more concerned with external validity and, to achieve this, may relax some of the design features 249

CHAPTER 15 Table 15.1

CONSORT 2010 checklist of information to include when reporting a randomised triala.

Section/topic

Item no

Checklist item

1a 1b

Identification as a randomised trial in the title Structured summary of trial design, methods, results and conclusions (for specific guidance see CONSORT for abstracts)

2a

Scientific background and explanation of rationale

2b

Specific objectives or hypotheses

3a

Description of trial design (such as parallel, factorial) including allocation ratio Important changes to methods after trial commencement (such as eligibility criteria), with reasons Eligibility criteria for participants Settings and locations where the data were collected The interventions for each group with sufficient details to allow replication, including how and when they were actually administered Completely defined pre-specified primary and secondary outcome measures, including how and when they were assessed Any changes to trial outcomes after the trial commenced, with reasons How sample size was determined When applicable, explanation of any interim analyses and stopping guidelines

Title and abstract

Introduction Background and objectives Methods Trial design

3b Participants Interventions

4a 4b 5

Outcomes

6a 6b

Sample size

7a 7b

Randomisation: Sequence generation

8a 8b

Allocation concealment mechanism

9

Implementation

10

Blinding

11a

Statistical methods

11b 12a 12b

Method used to generate the random allocation sequence Type of randomisation; details of any restriction (such as blocking and block size) Mechanism used to implement the random allocation sequence (such as sequentially numbered containers), describing any steps taken to conceal the sequence until interventions were assigned Who generated the random allocation sequence, who enrolled participants and who assigned participants to interventions If done, who was blinded after assignment to interventions (for example, participants, care providers, those assessing outcomes) and how If relevant, description of the similarity of interventions Statistical methods used to compare groups for primary and secondary outcomes Methods for additional analyses, such as subgroup analyses and adjusted analyses

Results Participant flow (a diagram is strongly recommended)

13a

13b

250

For each group, the numbers of participants who were randomly assigned, received intended treatment and were analysed for the primary outcome For each group, losses and exclusions after randomisation, together with reasons

Reported on page no

EXPERIMENTAL EPIDEMIOLOGY Table 15.1 (cont.) Section/topic

Item no

Checklist item

Recruitment Baseline data

14a 14b 15

Numbers analysed

16

Outcomes and estimation

17a

Dates defining the periods of recruitment and follow-up Why the trial ended or was stopped A table showing baseline demographic and clinical characteristics for each group For each group, number of participants (denominator) included in each analysis and whether the analysis was by original assigned groups For each primary and secondary outcome, results for each group and the estimated effect size and its precision (such as 95% confidence interval) For binary outcomes, presentation of both absolute and relative effect sizes is recommended Results of any other analyses performed, including subgroup analyses and adjusted analyses, distinguishing pre-specified from exploratory All important harms or unintended effects in each group (for specific guidance see CONSORT for harms)

17b Ancillary analyses

18

Harms

19

Reported on page no

Discussion Limitations

20

Generalisability Interpretation

21 22

Trial limitations, addressing sources of potential bias, imprecision and, if relevant, multiplicity of analyses Generalisability (external validity, applicability) of the trial findings Interpretation consistent with results, balancing benefits and harms and considering other relevant evidence

Other information Registration Protocol Funding

23 24 25

Registration number and name of trial registry Where the full trial protocol can be accessed, if available Sources of funding and other support (such as supply of drugs), role of funders

a We strongly recommend reading this statement in conjunction with the CONSORT 2010 Explanation and Elaboration for important clarifications on all the items. If relevant, we also recommend reading CONSORT extensions for cluster randomised trials, non-inferiority and equivalence trials, non-pharmacological treatments, herbal interventions and pragmatic trials. Additional extensions are forthcoming: for those and for up to date references relevant to this checklist, see www.consort-statement.org.

protecting against bias and may risk a lower signal to noise ratio.

15.8 The classification of RCTs RCTs are often classified according to the extent to which they maximise internal or external validity. Trials are usually optimally designed to answer specific research questions and a corollary of this is that different questions will require different designs. Variation in the primary design objectives of a trial should be borne in mind when considering

the results of apparently heterogeneous results from a set of trials. Tightly controlled trials with high levels of internal validity can tell us whether an intervention can have an effect although it will remain unclear if the intervention will have an effect in the real world [16]. Efficacy trials (sometimes call explanatory [17]) have the objective of assessing if a drug can work; the design is therefore driven by the need to establish if a drug can work under ideal, highly controlled, circumstances [18, 19]. The major goals in the design of efficacy trials is to make sure that any treatment effect is likely to be picked up and to reduce the 251

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Enrolment

Assessed for eligibility (n = )

Excluded (n = ) Not meeting inclusion criteria (n = ) Declined to participate (n = ) Other reasons (n = )

Randomized (n = )

Allocation Allocated to intervention (n = ) Received allocated intervention (n = ) Did not receive allocated intervention (give reasons) (n = )

Allocated to intervention (n = ) Received allocated intervention (n = ) Did not receive allocated intervention (give reasons) (n = )

Follow-Up Lost to follow-up (give reasons) (n = ) Discontinued intervention (give reasons) (n = )

Lost to follow-up (give reasons) (n = ) Discontinued intervention (give reasons) (n = )

Analysis Analysed (n = ) Excluded from analysis (give reasons) (n = )

Analysed (n = ) Excluded from analysis (give reasons) (n = )

Fig 15.3 CONSORT flow chart for randomised clinical trials.

background ‘noise’ caused particularly by interrater variation in the assessment of outcomes and the presence of comorbidity. The aim is to optimise the signalto-noise ratio. To reduce variability and noise, these trials use trained personnel with high interrater reliability, specialised settings and highly selected compliant participants. They use rating scales of symptoms that are highly sensitive to drug effects, rather than clinical events, as the study primary outcomes [18]. A potential antipsychotic drug is usually compared both to placebo and to a known antipsychotic; this procedure helps to confirm assay sensitivity, or that the study design and procedures were adequate to detect

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a difference between a drug known to be effective and placebo. The practical implications of the above can be illustrated by considering the design an efficacy trial of an antipsychotic in schizophrenia. In such a trial, usually sponsored by the pharmaceutical industry, trained researchers will recruit patients experiencing an acute episode of schizophrenia. The trial will have extensive eligibility criteria to ensure that participants meet rigorous diagnostic and severity of illness criteria using standardised and comprehensive assessment procedures. Patients with coexisting medical illnesses, substance use disorders or additional

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psychiatric diagnoses will be excluded both to reduce the likelihood of serious adverse events and limit the variance in outcomes (thus maintaining the signal : noise ratio). Following random allocation of treatments, trial treatment and procedures will be conducted under double-blind conditions, to reduce the chances of the patient and investigators from knowing the treatment assignments. Duration will be fairly short – typical efficacy trials in schizophrenia will be of 6–8 weeks’ duration. Outcome assessments during follow-up will be by trained personnel who will conduct frequent assessments of symptoms and side effects using comprehensive and psychometrically validated instruments. The primary outcome in these trials is typically improvement on a symptom rating scale, such as the Positive and Negative Symptom Schedule. Response, or improvement by 30% or some other prespecified amount on a symptom rating scale, is a common secondary outcome. Known side effects and common laboratory parameters will be measured systematically, while other adverse events are reported spontaneously if and when they occur. There are extensive quality assurance procedures to ensure that the trial is conducted according to Good Clinical Practice guidelines including on-site monitoring of all data. Efficacy trials meet the requirements of drug licensing and regulatory agencies although the designs are rather inefficient typically with high levels of data redundancy. The stereotyped designs, while passing the scrutiny of the regulator, tend to stifle innovation in clinical trial design. Efficacy trials do not reveal much about how a new drug works in real world settings, with patients who may have medical and psychiatric comorbidities and who may take other medications. They do not tell us about longer-term safety issues or about the effects of drug therapies on mortality, ability to work or other issues that are important to patients. Nonetheless, given an effective drug, these designs work reasonably well for drug developers who want to complete trials rapidly, because the reduced time to market and profitability offset the high costs of using many highly trained and geographically dispersed research centres. By contrast, practical (also called pragmatic or effectiveness trials) have the objective of providing independent evidence to inform decision-makers

about clinical and policy choices related to the risks and benefits of approved treatments. The main rationale for an effectiveness trial is to assess the effect of a treatment in unselected patients in the real world. Researchers design practical trials to provide high-quality evidence, with high internal and, crucially, high external validity, regarding the everyday effectiveness of clinically relevant alternative interventions [20]. To realise this objective practical trials recruit heterogeneous patients and collect data on a broad range of meaningful health outcomes in a range of health care settings intended to represent usual treatment [20]. Practical trials use broad patient inclusion criteria and minimal exclusion criteria to enhance external validity and thus enhance the relevance of the results for clinicians and patients in typical treatment settings [21]. Practical trials compare treatments about which there is clinical uncertainty about the outcome at the individual patient level, and use randomisation to protect against selection biases [22]. Not all practical trials conceal the post-randomisation treatment assignment from patients and study clinicians, but for subjective outcomes determined by raters’ blinding should be considered [7]. Again, there is a trade-off between choosing a design that is likely to minimise bias and one that is likely to be feasible in non-specialist clinical sites. The training and personnel requirements of rater blinding places burdens on sites that make it less likely that typical clinical sites, rather than research sites, can participate in these trials. In addition, measures of service utilisation and quality of life will be measured to allow estimation of cost-effectiveness. This may conflict with the desire to limit participant and researcher burden. The purposive inclusion of heterogeneity will increase the variance in the sample which will inevitably decrease the signal : noise ratio and risk failing to detect a true treatment effect. To some extent, practical trials can be conceptualised as hybrids of efficacy and large simple trials with the main trade-offs being in internal validity and the potential for a low signalto-noise ratio. A further category of trial is the large simple trial that focuses narrowly on clearly defined, objective, patient-orientated and clinically relevant outcome [23, 24]. A typical large simple trial

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outcome, mortality, is discrete and meaningful. In designing a large simple trial, the desire to collect information on a wide array of outcomes is resisted and resources are used to enrol large numbers of participants to provide sufficient power to detect relatively small but clinically important differences. Large simple trials are conducted at typical treatment settings with usual clinical personnel. Study procedures are simple so that the need for specialised research training and interference with routine clinical care is minimised. Inclusion criteria are broad and exclusion criteria are minimal. The key criterion for study entry is uncertainty about which treatment option is best for the individual participant [25]. A good example of a large simple trial was ISIS-2 [26]. ISIS-2 was a 2 × 2 factorial trial investigating the effect of streptokinase vs. placebo, and aspirin vs. placebo, in the prevention of vascular death following myocardial infarction. The trial was designed to be both large to detect differences in a hard, objective outcome (17 187 participants were randomised) and simple to allow widespread participation (patients were recruited from 417 hospitals). The primary outcome was chosen both because of its clear importance but also because it could be estimated efficiently and reliably in routine clinical practice and with very limited bias. Because this is a relatively uncommon outcome, even in this high risk group, the sample size required to determine an effect of the treatments on it was very large. Conventional approaches to clinical trial design used in efficacy trials would render such a trial unfeasibly expensive. The required sample size obviously means that either the trial is extremely expensive or that procedures are massively simplified to allow efficient large-scale recruitment of patients. Although the trial procedures are highly simplified, procedures aimed at reducing bias and optimising signal : noise ratio (within the constraints of the aims of the trial are retained. Thus, a large simple trial is likely to blind participants and investigators and adopt highly centralised 24-hour (to allow worldwide participation) adaptive and real-time randomisation to protect against subversion of randomisation. Randomisation will often use adaptive minimisation [27] to prevent chance occurrence of baseline imbalances of important prognostic factors. This approach has been used successfully in many 254

areas of medicine where it is possible to identify a clear unequivocal hard clinical outcome and it has been possible to recruit large numbers of patients. It has been particularly useful in RCTs of preventive strategies of fatal outcomes in high risk patients but as it has become possible to recruit larger samples of participants, it has become possible to look at patients or populations at lower risk and to explore the possibility of stratification of treatment effect by baseline severity of illness. In psychiatry, to date it has been harder to design and conduct large simple trials. Two of the main problems have been the limited validity and reliability of psychiatric diagnoses and the absence of simple, unequivocal objectives that capture the complexity of the disorder. Nonetheless, there has been some interest in the potential of this approach in psychiatry [28]. Bipolar Affective disorder: Lithium Anticonvulsant Comparative Evaluation (BALANCE) is a recent example of trial which adopted highly simplified procedures and achieved an adequately large sample size to produce unequivocal results on a small number of clinically relevant outcomes in patients with bipolar disorder, type 1 [29]. BALANCE demonstrates some of the difficulties of conducting large simple trials. BALANCE was an open trial with no blinding of investigators or participants. This introduced the possibility of both ascertainment bias (bias in the estimation of outcome) and performance bias (different treatment of participants in the treatment groups other than in investigational treatment allocation). The decision to conduct the trial without blinding was taken because of the difficulties (and hence expense) in conducting a double-blind trial with lithium, a drug with a narrow therapeutic index [30]. Two main approaches to dealing with the potential bias were taken in the design and analysis. First, patients with a strong preference (either their own or their treating clinician’s) for a particular treatment were excluded at both trial entry and following an active, open, run-in. The possibility of bias affecting the results was estimated by comparing the treatment effect on the primary outcome (time to first new treatment for mood episode) with a more objective outcome that was less open to manipulation to the investigator (admission to hospital). In fact, the treatment effect on the two outcomes was highly consistent indicating a low chance of bias [29].

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15.9 Effectiveness trials in schizophrenia Table 15.2 summarises the typical features of trials designed to answer different questions in the clinical area of schizophrenia. The industry-sponsored RCTs of atypical antipsychotics demonstrated that these drugs were more effective than placebo in acute schizophrenia, but failed adequately to address the relative effectiveness and cost-effectiveness compared to the older (and cheaper) conventional antipsychotic drugs. Although there can be little doubt that these trials were a major advance over previous trial in many ways, as a group, they had several methodological limitations which limited confidence in the results [31, 32]: • Patient selection: The majority of participants in the trials had already been exposed to treatment with conventional agents and had, on average, not responded well: patients who had done well would not have been randomised. While this was perfectly ethical and clinically reasonable at the time, trials randomising such selected patients could not provide a fair comparison of conventional and atypical agents but would indicate that treatment with the new agents was not inferior and may be superior to re-exposure to conventional drugs. • Comparator and comparator dose: Haloperidol, a relatively pure D2-antagonist, was the standard comparator used in the trials. At the time of the trials, it was already known that the dose of haloperidol required to produce optimal blockade of D2 receptors and clinical response was lower than had previously been thought [33–36]. Consequently, the higher doses used in the trials would be likely to have reduced tolerability without increasing efficacy. • Duration: Trials were 6 weeks duration, which is a reasonable length of time to assess initial treatment response in an acute episode, but it is difficult to extrapolate the results to the long-term treatment of schizophrenia. • Patient retention: The trials had high drop-out rates often >50%, which were differential between the treatment arms. Higher drop-out rates in the comparator arms meant that these patients had

shorter exposure to study treatment and less time to experience symptom reduction. • Analysis: Analysis was by modified intent-to-treat analysis in the presence of substantial drop-out using simple last observation carried forward, which is prone to bias. These methodological issues limited the confidence in the results of these trials, especially in view of the tendency to be more enthusiastic about the advantages of second generation antipsychotics (SGAs) an the evidence supported [37, 38]. To attempt to answer some of the remaining uncertainties about the relative efficacy of first and SGAs, several independent trials were conducted. It is important to recognise that these trials were not designed to answer the same questions as the industry trials and they have their own strengths and weaknesses [19].

15.10 Department of Veterans Affairs co-operative study on the cost-effectiveness of Olanzapine (Rosenheck) This was a double-blind, randomised controlled trial comparing olanzapine (5–20 mg/day; 159 patients) and haloperidol (5–20 mg/day) in combination with prophylactic benztropine (1–4 mg/day) (150 patients) in the treatment of schizophrenia over 12 months [39]. This trial found ‘no statistically or clinically significant advantages of olanzapine for schizophrenia on measures of compliance, symptoms or overall quality of life, nor did it find evidence of reduced inpatient or total cost’. Olanzapine caused fewer extrapyramidal symptoms (EPS) but more weight gain than haloperidol. Olanzapine led to small but statistically significant improvements in measures of memory and motor function. The authors concluded that olanzapine did not demonstrate advantages compared with haloperidol (in combination with prophylactic benztropine) in compliance, symptoms, extrapyramidal symptoms or overall quality of life and its benefits in reducing akathisia and improving cognition must be balanced with the problems of weight gain and higher cost [39]. 255

CHAPTER 15 Table 15.2

Comparing effectiveness and efficacy trials. Efficacy trials

Practical trials

Large simple trials

Goal

To achieve regulatory approval to market drug

To inform decision-makers about clinical and policy choices

To compare treatment options to examine small but potentially important differences

Specific aims

To establish short-term efficacy and safety of a new drug

To examine relative benefits and risks of available treatments

To determine comparative longer-term safety or effectiveness

Primary outcome

Improvement of target symptoms

A discrete and clinically meaningful outcome

A discrete and clinically meaningful outcome

Secondary outcomes

Safety measures; response rates

Often many health-related outcomes, including health economic measures Post-market

Post-market

–

Timing

Before a drug is marketed

Funding

Drug maker

Varies; drug manufacturer, government agency, foundation

Varies; drug manufacturer, government agency, foundation

Diagnosis

Diagnosis by structured interview

Clinical diagnosis or structured interview

Clinical diagnosis or structured interview

Sample size

A few hundred

Hundreds to thousands

Thousands

Comparisons

Placebo and an active comparator

One or more active comparators; clinical equipoise

One or more active comparators; clinical equipoise

Dosing

Fixed dosing

Flexible dosing in clinically used range

Flexible dosing in clinically used range

Blinding

Double-blinded

Open label, single or double blinded

Open label, single or double blinded

Duration

6 weeks

1 year or longer

1 year or longer for primary outcome

Research sites

Experienced research sites capable of specialised procedures: number variable but usually <100 Strictly defined research protocol with frequent research assessments and procedures

Sites are typical treatment settings, may need capacity to measure many outcomes: often hundreds of sites

Sites are typical treatment settings: often hundreds of sites

Protocol designed to mimic usual decision-making for antipsychotic drugs; many research procedures may be included Included except for specific contraindications to examine effects in typical patient populations

Protocol very similar to usual practice; minimal additional assessments or procedures

Protocol

Comorbid medical and psychiatric conditions

Excluded to isolate the effects of the drug

Adjunctive and concomitant treatments

Excluded except specific drugs for limited time

Allowed as in usual practice; some exclusions depending on study aims

Included except for specific contraindications to examine effects in typical patient populations Allowed as in usual practice

Typical features of different clinical trial designs used to investigate the efficacy and safety of antipsychotic drugs. (modified from Geddes et al. [29, 31]).

256

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15.10.1 Methodological aspects This study had several advantages over other independent trials. It was conducted reasonably soon after the introduction of the newer agents (recruiting between June 1998 and June 2000), which possibly helped recruitment, and the doses of drugs used were clinically representative. There have been two main criticisms of this trial – neither of which, in our view, substantially limit the robustness of the results. The first concern is that all patients allocated to haloperidol were also treated with benztropine, an anticholinergic agent. The concern is that this does not reflect established clinical practice (many reserve anticholinergics for patients who suffer EPS rather than using them routinely) and also potentially reduces the rate of EPS in haloperidol treated patients, hence obscuring the inherent advantage of olanzapine. The second concern is that the sample size was smaller than originally planned and this limits the confidence in the results.

15.11 The clinical antipsychotic trials of intervention effectiveness (CATIE) study CATIE was a large US National Institute of Mental Health-funded double-blind RCT comparing the efficacy and safety of several atypical antipsychotics and perphenazine over 18 months [40]. CATIE was conducted between January 2001 and December 2004 at 57 clinical sites in the United States and included patients aged between 18 and 65 years with a diagnosis of schizophrenia according to the DSM-IV. Patients were randomly allocated to perphenazine, olanzapine, quetiapine, risperidone or ziprasidone. CATIE was designed a practical trial with a very pragmatic primary outcome in discontinuation of treatment for any cause chosen because it is a simple composite measure of effectiveness and tolerability from both the patient’s and the clinician’s point of view. There were range of secondary outcomes included discontinuation due to inadequate efficacy or intolerability, adverse events, neurocognitive functioning, quality of life and conventional efficacy outcomes including the Positive and Negative Symptom Schedule and the Clinical Global Impressions scale.

Although olanzapine was statistically superior to risperidone and quetiapine in terms of overall dropouts, the difference was small and balanced by findings that olanzapine was associated with more weight gain and other adverse metabolic effects than the other medications. Otherwise, there were rather few differences between treatments.

15.11.1 Methodological aspects CATIE is substantially the largest and most comprehensive direct comparison of antipsychotic medications. As CATIE was double-blinded with good allocation concealment, the internal validity of the trial is high. Inevitably, CATIE has a number of limitations that would limit generalizability, including a restricted range of possible doses (constrained due to double-blind design) and the use of multiple comparisons and outcomes diminished statistical power.

15.12 Cost utility of the latest antipsychotic drugs in schizophrenia study (CUtLASS 1) CUtLASS 1 was a pragmatic, open RCT comparing first generation antipsychotics (FGAs) and SGAs in 227 patients with schizophrenia from 14 community mental health services in the United Kingdom who required a change of treatment [41]. The primary outcome was the Quality of Life Scale (QLS) with secondary outcomes including symptoms, adverse effects, participant satisfaction and costs of care over one year. Treatment was open but outcome assessment was masked to treatment allocation. CUtLASS 1 found evidence that treatment with SGAs did not lead to a clinically significant degree of improved quality of life – or benefits on any of the secondary outcomes – compared to conventional agents.

15.12.1 Methodological aspects CUtLASS 1 has a substantially different design than other trials in that it treated atypicals and conventional drugs pragmatically as classes. Clinicians could choose which drug to use out of these groups. Of the 227 participants in the trial, 109 were randomised to receive SGA and, of those patients, 50 257

CHAPTER 15

were prescribed olanzapine, 22 to risperidone, 23 to quetiapine and 13 to amisulpride. Of the 118 patients randomised to the FGA arm, 58 were prescribed sulpiride (1 of 11 FGAs prescribed in the study). The results were, perhaps, therefore more applicable to olanzapine and sulpiride than to some of the other antipsychotic drugs and this illustrates how an open design with clinical flexibility can lead to potential performance bias. On the other hand, it can be argued that the choice of drug following random allocation simply reflected the reality of clinical decision-making in the trial sites at the time the trial was being conducted. The relatively small sample size, combined with the substantial heterogeneity and potential for a low signal : noise ratio meant that power to detect differences between arms was low. The difference between the trial arms was not statistically significant and, taken alone, this would be consistent with a true lack of difference between the arms of the trial or that the trial lacked sufficient power and precision to detect a true difference. However, a clearer interpretation of the results is possible because the primary hypothesis of the trial was that the SGAs were at least five points superior to FGAs on the QLS. CUtLASS 1 estimated the difference in QLS score was −1.7, a non-significant difference in favour of FGAs. The 95% confidence interval around this estimate was (−4.5 to 1.1), which indicates that the largest QLS benefit in favour of the SGAs consistent with the trial data at the 95% level of confidence would be 1.1 – unlikely to be clinically significant. Further, the confidence interval clearly excludes a five point difference in favour of the SGAs. The pragmatic design of did not, therefore, prevent CUtLASS 1 from producing a clear-cut result.

15.13 European first-episode schizophrenia trial (EUFEST) European First-Episode Schizophrenia Trial (EUFEST) was an open-label RCT comparing haloperidol, amisulpride, olanzapine, quetiapine or ziprasidone in patients aged 18–40 who were within 2 years of diagnosis of schizophrenia, schizophreniform or schizoaffective disorder according to DSM-IV from 50 sites in Europe and Israel [42]. The primary outcome was all-cause discontinuation at 258

12 months and a number of secondary outcomes of efficacy and safety were also measured. All atypical drugs were superior to haloperidol on the primary outcome but there was no difference on other key outcomes such as symptomatic status or hospital admission. EPS were more common with haloperidol and weight gain was more common with olanzapine.

15.13.1 Methodological aspects A key feature of the EUFEST trial was that it was conducted as an open trial and so participants and investigators knew which drug they had been allocated and treatment and outcome assessment were not conducted blind to treatment assignment. Open trials are susceptible to performance bias (patients in the trial groups are treated differently depending on the allocated treatment) and ascertainment bias (assessment of outcome is biased by knowledge of the treatment assignment). These biases are likely to be substantial if clinicians and patients have preferences about the treatments at the beginning of the trial. In EUFEST, the coordinators (and presumably the investigators) were not all in equipoise at the beginning of the trial; the authors report that 11 (34%) site coordinators expected haloperidol to lead to the worst outcome, and 21 (66%) of them thought that it would be no worse than the atypical drugs. These prior expectations would make it more likely that patients allocated to haloperidol would be switched off allocated treatment earlier, leading to a higher rate of discontinuation – and this was the primary outcome. The survival plots show that almost half of haloperidol allocated patients were switched to another therapy within the first month postrandomisation – the most common reason given was insufficient efficacy. Further, the trial report suggests that the discontinuation rate in the haloperidol arm was non-significantly higher in the sites which believed that haloperidol was the worst option. This potential (and probable) bias is one of the main reasons that, despite the apparent clear-cut result in favour of the SGA over haloperidol, the EUFEST report states ‘it cannot be concluded that second-generation antipsychotic drugs are more efficacious than is haloperidol in the treatment of these patients’. The other reason for this statement was that there was no difference between the drugs in terms of

EXPERIMENTAL EPIDEMIOLOGY

symptomatic improvement on the Positive and Negative Symptom Scale (PANSS) (although there was a significant difference in global status measured by the Clinical Global Impressions scale). In retrospect, the open-label design of EUFEST, though pragmatic, led to great doubt about the validity of the primary analysis. The striking absence of any difference in efficacy between drugs as measured by the PANSS is at odds with the observed (and probably biased) differences in discontinuation rates.

15.14 The size and cost of experimental studies in psychiatry Large pragmatic trials are very expensive and it is unlikely that they will ever be done to answer more than a handful of important clinical questions. Hence the importance of large-scale observational studies for many clinical questions of the safety and efficacy of treatments [2]. There needs to be great selection and prioritisation of the research questions which need a pragmatic trial or the complexity and cost of randomised trials need to be radically reduced to increase the number of trials that can be done within a fixed budget. One clear route is to develop ways of integrating trials with routine clinical practice and to create efficient systems will increase our ability to do large numbers of trials, keeping costs low and driving up the internal validity of the studies. The first generation of independent randomised trials conducted in the United States (including CATIE) should be considered as proof of concept for the next generation of more efficient trials [43]. These trials were very expensive because they required the development of infrastructures. We must build on the experience of conducting these trials to allow a gradual reduction in cost-per-randomised-patient to allow increasing numbers of large scale randomised trials in the future. Expertise in randomised trials, as in any other form of research is hard won – and it cannot be assumed that setting up a research network of people who have little experience or aptitude for clinical research will generate the same positive outcomes as building on practical expertise. Effective trial networks need to be developed around a trial but need

effective integration between policymakers, patients, research funder, researchers and the administrators of the research networks. Unfortunately each of these stakeholders often has their own particular set of priorities, which only partially overlap.

15.15 Clinical trials in the future One common conclusion from most reviews the priorities for research in mental health is an increase in the number and quality of randomised clinical trials. This includes early stage clinical trials at the point of translation from basic science treatment discovery, through to large-scale pragmatic trials of the effect of healthcare policy. There clearly has to be an increase in the efficiency with which these trials are conducted. Advances in information technology need to be harnessed to ensure that accurate timely outcomes are captured routinely using robust and validated systems.

References [1] D’Agostino, R.B. (1998) Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat. Med., 17 (19), 2265–2281. [2] Lauer, M.S. and Collins, F.S. (2010) Using science to improve the nation’s health system: NIH’s commitment to comparative effectiveness research. J. Am. Med. Assoc., 303 (21), 2182–2183. [3] Goodwin, F.K., Fireman, B., Simon, G.E. et al. (2003) Suicide risk in bipolar disorder during treatment with lithium and divalproex. J. Am. Med. Assoc., 290 (11), 1467–1473. [4] Cipriani, A., Pretty, H., Hawton, K. et al. (2005) Lithium in the prevention of suicidal behavior and all-cause mortality in patients with mood disorders: a systematic review of randomized trials. Am. J. Psychiatry., 162 (10), 1805–1819. [5] Schulz, K.F. (1995) Subverting randomisation in controlled trials. J. Am. Med. Assoc., 274, 1456–1458. [6] Schulz, K.F. and Grimes, D.A. (2002) Blinding in randomised trials: hiding who got what. Lancet, 359 (9307), 696–700. [7] Wood, L., Egger, M., Gluud, L.L. et al. (2008) Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: meta-epidemiological study. Br. Med. J., 336 (7644), 601–605.

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[24] Yusuf, S., Collins, R. and Peto, R. (1984) Why do we need some large, simple randomized trials? Stat. Med., 3 (4), 409–422. [25] Peto, R. and Baigent, C. (1998) Trials: the next 50 years. Large scale randomised evidence of moderate benefits. Br. Med. J., 317, 1170–1171. [26] Second International Study of Infarct Survival Collaborative Group (1988) Randomised trial of intravenous streptokinase, oral aspirin, both, or neither among 17,187 cases of suspected acute myocardial infarction: ISIS-2. Lancet, 2, (8607) 349–360. [27] Treasure, T. and MacRae, K.D. (1998) Minimisation: the platinum standard for trials? Randomisation doesn’t guarantee similarity of groups; minimisation does [editorial]. Br. Med. J., 317 (7155), 362– 363. [28] Geddes, J.R., Rendell, J.M. and Goodwin, G.M. (2002) BALANCE: a large simple trial of maintenance treatment for bipolar disorder. World Psychiatry, 1, 48–51. [29] Geddes, J.R., Goodwin, G.M., Rendell, J. et al. (2010a) Lithium plus valproate combination therapy versus monotherapy for relapse prevention in bipolar I disorder (BALANCE): a randomised open-label trial. Lancet, 375 (9712), 385–395. [30] Rendell, J.M., Juszczak, E., Hainsworth, J. et al. (2004) Developing the BALANCE trial – the role of the pilot study and start-up phase. Bipolar Disord., 6 (1), 26–31, Aavailable from: ISI:000188809800003. [31] Geddes, J.R., Stroup, T.S. and Lieberman, J.A. (2011) Comparative efficacy and effectiveness in the drug treatment of schizophrenia, in Schizophrenia (eds P.J. Harrison and D.R. Weinberger), in press. [32] Geddes, J.R., Freemantle, N., Harrison, P.J. et al. (2000) Atypical antipsychotics in the treatment of schizophrenia: systematic review and meta-regression analysis. Br. Med. J., 321, 1371–1376. [33] Van Putten, T., Marder, S.R., May, P.R. et al. (1985) Plasma levels of haloperidol and clinical response. Psychopharmacol. Bull., 21 (1), 69–72. [34] Van Putten, T., Marder, S.R. and Mintz, J. (1987) The therapeutic index of haloperidol in newly admitted schizophrenic patients. Psychopharmacol. Bull., 23 (1), 201–205. [35] Van Putten, T., Marder, S.R. and Mintz, J. (1990a) A controlled dose comparison of haloperidol in newly admitted schizophrenic patients. Arch. Gen. Psychiatry, 47 (8), 754–758. [36] Van Putten, T., Marder, S.R., Mintz, J. et al. (1992) Haloperidol plasma levels and clinical response: a therapeutic window relationship. Am. J. Psychiatry, 149 (4), 500–505. [37] Cipriani, A. and Geddes, J.R. (2003) Comparison of systematic and narrative reviews: the example of the

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life of second- vs first-generation antipsychotic drugs in schizophrenia: cost utility of the latest antipsychotic drugs in schizophrenia study (CUtLASS 1). Arch. Gen. Psychiatry, 63 (10), 1079–1087. [42] Kahn, R.S., Fleischhacker, W.W., Boter, H. et al. (2008) Effectiveness of antipsychotic drugs in firstepisode schizophrenia and schizophreniform disorder: an open randomised clinical trial. Lancet, 371 (9618), 1085–1097. [43] Geddes, J.R., Furukawa, T.A., Cipriani, A. et al. (2007) Depressive disorder needs an evidence base commensurate with its public health importance. Can. J. Psychiatry, 52 (9), 543–544.

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16

Epidemiology of Schizophrenia William W. Eaton,1 Chuan-Yu Chen,2 and Evelyn J. Bromet3 1

Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA 2 Division of Mental Health and Addiction Medicine, National Health Research Institutes, Institute of Population Health Sciences, Taiwan 3 Department of Psychiatry, SUNY Stony Brook University, NY, USA

16.1 Introduction Psychotic disorders, particularly schizophrenia, are debilitating and persistent. They are associated with poor physical health, early mortality and substantial impairment in functioning. Knowledge about the epidemiology of schizophrenia has expanded over the last three decades with greater specificity in diagnosis, increased attention to genetic risk factors and renewed attention to gene–environment interactions. This chapter focuses on research on schizophrenia, integrating suggestions from several key studies and reviews (e.g. [1–4]) and focusing in particular on the recent advances in knowledge. The chapter begins with descriptive aspects quantifying the level of occurrence and the natural history. Following that is material on sociodemographic correlates, which are presumed to derive from the characteristics of the natural history. The final sections consider risk factors, divided in a rough and arbitrary way into social risk factors and biological risk factors.

16.2 Methods 16.2.1 Case identification Since the classic United States–United Kingdom studies [5] research on the epidemiology of schizophrenia

has had to take a hard look at the method of formulating the diagnosis. The early studies relied on clinical diagnoses that were formulated without specific operational criteria, but they were typically based on long-term observation in clinical settings. Clinical diagnoses continue to form the basis of epidemiological studies based on case registries. Clinical cohort studies are usually based on diagnoses derived from semi-structured examinations, such as the Present State Examination (PSE) [6], Schedules for Clinical Assessment in Neuropsychiatry (SCAN) [7], or Structured Clinical Instrument for Diagnosis (SCID) [8]. These instruments were designed for administration by mental health professionals, and require them to probe when necessary and to use clinical judgement about the significance of the symptoms described by the respondent or patient. The most reliable studies integrate information from semi-structured interviews with comments in the medical record, and interviews with significant others. The final diagnosis is either made by the examiner or by a consensus committee. Given the shifts in symptom presentation, particularly in the early stages of psychosis, longitudinal consensus diagnosis is optimal. Since the late 1970s, several structured psychiatric diagnostic assessment schedules were developed for use by non-clinical (or lay) interviewers. These include the Diagnostic Interview Schedule (DIS) [9] administered in the Epidemiologic Catchment Area

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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(ECA) surveys and the Composite International Diagnostic Interview (CIDI) [10] used in studies such as the National Comorbidity Survey. With structured instruments, the judgement as to the presence or absence of a sign or symptom is left to the respondent and thus requires a degree of insight and understanding of the questions being asked. Since lack of insight is a common feature of schizophrenia, structured interviews alone are not adequate for ascertaining the diagnosis. Indeed, sensitivity varies widely (21 ∼71%), suggesting the capability of survey diagnostic instruments to identify a schizophrenia case in the community is generally less satisfactory in comparison with psychiatrists’ examination [11]. Thus, recent epidemiological studies have often applied structured instruments as stage 1 of a two-stage design, with clinicians reinterviewing high and low risk respondents with the semi-structured instruments described above [12].

16.2.2 Case-finding Two common methods of finding cases in epidemiologic studies of schizophrenia are community surveys and case registries. Community surveys of representative population samples are ideally suited to case finding for reasonably prevalent conditions. For rare disorders like schizophrenia, however, in addition to measurement problems, the cost of case finding can be prohibitive since it requires an enormous population survey, a two-stage study design or a multisampling approach involving clinical settings. For example, if the prevalence rate of schizophrenia is about 1/2 of 1%, as discussed below, then to make an estimate of the rate in three age groups by two gender groups, with 30 in the numerator of each of the resulting six cells, would require that 36 000 individuals be surveyed. If the survey is to be done with a medically trained interviewer, the expense is prohibitive. An incidence study requires that the population be monitored over time, an even more daunting task. The register method of case-finding relies on treatment facilities to organise their psychiatric admissions or outpatient files in such a way as to eliminate duplication between facilities. For registers that cover a known geographic or administrative area, the census of population forms the denominator 264

for the prevalence rate. Register systems have less difficulty following the population through time, and are able to estimate incidence as well as prevalence. Registers rely on the diagnoses of treating clinicians, which have less reliability and validity than a research examination like the SCAN or PSE. Furthermore, surveys have shown that some 10–50% of persons with schizophrenia may be omitted from the numerator because they never seek treatment [13]. Since those seeking treatment may be different from persons with the disorder not seeking treatment, register data are subject to Berkson’s bias – that is they are more likely to have comorbid disorders and more likely to be severe cases than those not in treatment [14]. The most credible data on the epidemiology of schizophrenia come from registers including inpatient and outpatient facilities for an entire nation, in which the diagnosis is typically made carefully according to the standards of the International Classification of Diseases, and in which treatment for schizophrenia in particular, and health conditions, in general, is free.

16.3 The burden of schizophrenia 16.3.1 Prevalence The point prevalence of schizophrenia is the proportion of the population at a point in time that has the disorder. The point prevalence of schizophrenia is about five per thousand population. The estimate depends on the age distribution of the population – if persons too young to be at risk are included in the denominator, for example the estimates will be lower. Table 16.1 presents a selection of findings from areas in which credible estimates of both prevalence and incidence are available [15]. The range in prevalence in Table 16.1 is from 2.7/1000 to 8.3/1000. We note that this range would not be much affected if several dozen other studies, available from prior reviews, were included [13, 16, 17]. Other prevalence estimates include period prevalence, which measures the proportion of individuals with the disorder during a certain period of time (e.g. one year), and lifetime prevalence, which is the proportion of a population at a point in time that either has the disorder or has had it over their lifetime up to the time of the estimation – presumably

EPIDEMIOLOGY OF SCHIZOPHRENIA Table 16.1 Prevalence and incidence of schizophrenia per 1000 population. Area

Denmark Baltimore, Maryland, USA Camberwell, England Ireland Portogruaro, Italy Hampstead, England

Date

1977 1972 1963 1963 1963 1971 1973 1986 1982–1989 1989 1991–1995 1991–1995

Author

Age

Nielsen Munk-Jorgensen Wing Warthen Wing Hailey Walsh WHO de Salvia et al. de Salvia et al. Jeffreys et al. McNaught et al.

15+ All All All 15+ All 15+ 15–54 – – – –

Prevalence

Incidence

Type

Rate

Lifetime Annual One year Annual One year Annual Point Annual One year Annual Point Annual

2.7 – 7 – 4.4 – 8.3 – 2.7 – 5.1 –

– 0.12 – 0.7 – 0.11 – 0.22 – 0.19 – 0.21

Selected from reviews by Eaton [13, 16], with additions of Jeffreys et al. [18], McNaught et al. [19], de Salvia et al. [20].

yielding the largest rate for a non-fatal disorder like schizophrenia. Recent systematic reviews of these prevalence estimates from 132 general populationbased studies published between January 1965 and December 2002 indicate that the median values per 1000 persons (10–90% range) for the point, one-year and lifetime prevalence rates of schizophrenia were 4.6 (1.9–10.0), 3.3 (1.3–8.2) and 4.0 (1.6–12.1), respectively [21, 22].

16.3.2 Incidence The incidence of schizophrenia is about 0.20/1000/ year. The incidence rates included in Table 16.1 were all estimated for 1 year, making the comparison somewhat tighter. The range in annual incidence in Table 16.1 is from 0.11/1000/year to 0.70/1000/year, and this range would not be much affected if several dozens of other studies, reviewed elsewhere, were included [22, 23]. The presentation of prevalence and incidence figures from the same areas in juxtaposition shows that the point prevalence is usually more than 10 times the annual incidence, consistent with the chronic nature of the disorder. Figure 16.1 shows the incidence rates of schizophrenia in studies including the World Health Organization (WHO) incidence research (dark bars). The WHO studies attempted to utilise similar case finding and assessment methods, and the variation in rates is indeed somewhat smaller [24]. One inference from the WHO findings drawn by

some investigators is that there is little variability in the rates of schizophrenia around the world. However, others have questioned whether the case finding and diagnostic methods imposed an artificial and inherently biased uniformity on the resulting findings. Regarding the specific rates, Figure 16.1 shows that the lowest estimate occurred in Vancouver (0.04/1000/year) [25], and the highest in Madras [26] (0.58/1000/year). Based on their review, McGrath and colleagues [22] found that the median (10–90% range) estimate was 0.15 (0.077–0.43)1000/year. Moreover, the estimates were homogeneous across countries with differing economic status, defined by per capita gross national product [27]. Further trend analyses revealed the median incident rates derived from earlier studies (1947–1976), 0.18/1000/year, was slightly higher than the corresponding estimates from studies conducted in 1977–1983 and 1984–1995 (0.13 and 0.14/1000/year). There is also some evidence that the incidence of schizophrenia has been declining over time, while the incidence estimates for affective or non-affective psychoses have changed very little [28].

16.4 Natural history 16.4.1 Onset Natural history includes three aspects of onset, course and outcome. 265

CHAPTER 16 Bristol Buckingham Camberwell Dublin Hampstead Ireland (3 Countries) London UK Manchester New Zealand Nottingham (WHO) Nottingham (SIN) Nottingham (AESOP) Oxfordshire Salford

EU

Aarhus Bavaria Cantabria Croatia Finland Germany Groningen Helsinki Ireland Italy Lundby Mannheim Moscow Netherlands-Hague Portogruaro Sweden

NA

Brazil Jamaica Honolulu Saskatchewan Surinam Trinidad Vancouver

Asia

Beijing Chandigarh R Chandigarh U Madras Nagasaki

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Annual Incidence per 1000

Fig 16.1 Incidence of schizophrenia in selected studies published after 1985. Criteria: study focus is the general population of a defined geographic area; diagnosis is made by a psychiatrist; case finding includes inpatient and outpatient services; greater than 25 000 person years of risk in age group studied.

The onset of schizophrenia is varied. In the classic long-term followup of Ciompi, about 50% had an acute onset, and 50% a long prodrome. The intensive study of prodrome by Hafner and colleagues [29] suggests onset of negative symptoms tends to occur about five years before the initial psychotic episode, with onset of positive symptoms much closer to the first hospitalisation.

16.4.2 Course The symptomatic course of schizophrenia is varied also. In Ciompi’s study [30], about half had an undulating course, with partial or full remissions followed by recurrences, in an unpredictable pattern. 266

About one-third had relatively chronic, unremitting course with poor outcome. A small minority in that study had a steady pattern of recovery with good outcome. Followup studies which are not strictly prospective, such as the study by Ciompi, can be deceptive, because there is a tendency to focus on a residue of survivors who were able to be located (less than 40% of the original cohort in this case), making the disorder appear more chronic than it actually is. Figure 16.2 shows data on time to rehospitalisation for a cohort with schizophrenia in Denmark [31]. The proportion remaining in the community without rehospitalisation is shown on the vertical axis, and time is on the horizontal axis. After the initial hospitalisation, about 25% were not

EPIDEMIOLOGY OF SCHIZOPHRENIA

100 1st discharge n = 8697

90

5th discharge n = 3016 80

10th discharge n = 1231 15th discharge n = 567

Discharged

70 60 50 40 30

1

20 10 15 0

1

2

10 3

4

5

6

5

7 8 9 10 11 12 13 14 15 16 Years of follow-up

Fig 16.2 Days without rehospitalisation in Denmark after 1, 5, 10 and 15 hospital discharges for schizophrenia. Mortensen and Eaton [31].

rehospitalised even after 15 years. For that subgroup of the cohort with 10 hospitalisations, more than 90% were rehospitalised within 3 years following the 10th episode. While it could be that the occurrence of episodes are reinforcing the illness (so-called schubweis (stepwise) process), or that hospitalisation itself is damaging [32], it seems more likely that the cohort is sorting itself into those with a tendency for more versus less chronicity of disorder. This process may lead clinicians and others to overestimate the chronicity of the disorder, since they see individuals in the bottom curve of Figure 16.2 about 15 times as often as individuals in the top curve [33]. For this reason, the natural history of schizophrenia is best studied with cohorts of first onsets [34]. There is variation in the course of schizophrenia around the world, with better prognosis in so-called ‘developing’ countries. Table 16.2 shows a summary of data from the WHO study on this issue [35], with the right most columns extracted by us from the publication of Leff and colleagues [36]. Those in developing countries are less likely to have been chronically psychotic over the period of followup, and more likely to have no residual symptoms after five years, than those in the developed countries. Over a period of 15 years after the first treated

episode, the percentage of ‘never psychotic’ in the 2 years preceding assessment was 37% in developed countries compared to 53% in developing counties [37] This result remains to be explained. It could be that individuals meeting criteria for schizophrenia in developing countries include a subset destined for better prognosis because of the risk factor structure in those countries – more deaths of compromised fetuses, for example; or a cause connected to good prognosis, such as a parasite which is rare in developed countries. Another interpretation is that the environment of recovery in the developed world is more pernicious, involving harsher economic competition, a greater degree of stigma and smaller family networks who can share the burden of care for the person with schizophrenia.

16.4.3 Outcome Many indicators exist to measure outcomes of schizophrenia, including recovery, functional impairment, quality of life and symptom remission, among others. The search for predictors of the outcome for schizophrenia remains an active research focus. Study findings are difficult to compare because of differences in the source of the samples 267

CHAPTER 16 Table 16.2 WHO followup of schizophrenia after 5 years. Sample size

Per cent with no symptoms

Per cent with chronic psychosis

64 50 66 65 51

5 6 17 6 3

14 40 21 23 23

73 91 68

42 11 34

10 21 10

Developed countries London, England Aarhus, Denmark Moscow, Russia Prague, Czechoslovakia Washington, DC USA Developing countries Agra, India Cali, Colombia Ibadan, Nigeria Leff et al. [36].

(type of institution; chronicity prior to baseline assessment), the length of the follow-up and the specific measures employed. These are especially important issues to take into account in crossnational or societal comparisons [37–39]. Overall, the outcome of schizophrenia appears to be heterogeneous, although the majority of cases experience multiple episodes and considerable impairment throughout their lives. In a meta-analysis of 320 studies conducted between 1895 and 1992, 40% of patients were considered improved after (an average across studies of) five years of followup [40]. In a later review of 37 first episode studies from 1966 through 2003, representing 4100 patients, Menezes and colleagues [41] reported that ∼42% of individuals had ‘good’ outcomes and 27% had poor outcomes after approximately 3 years of follow-up. Interestingly, one of the predictors of poor outcome was the representativeness of the sample: the more representative the sample, the poorer the outcome. In a review of 13 prospective studies of the course of schizophrenia in first episode cohorts, short-term outcome by about 2 years was consistently predicted by negative symptoms during first hospitalisation as well as a more gradual onset [34]. In a recent review of first episode research, we found that the relatively consistent predictors of poorer outcome were family history of schizophrenia, insidious onset, poor premorbid functioning, severity of negative symptoms and severity and duration of untreated psychosis [42].

268

16.4.4 Mortality Death is the final outcome state, and the mortality rate among individuals suffering from schizophrenia is elevated compared with the general population. A systematic review reported that the standardised mortality ratios (SMRs) for all-cause mortality of schizophrenia was 2.58, and the excess mortality seems to have increased in recent decades [43]. In addition, the country-specific variation in causes of death requires far more attention [38]. In general, the majority of schizophrenia patients in industrial countries died of unnatural causes (e.g. suicide) [42], whereas in non-industrialised countries the deaths were primarily due to natural causes. In addition, individuals with schizophrenia have higher rates of cigarette smoking than the general population and few socio-economic resources, putting them at greater risk for cancer and cardiovascular disease than the general population.

16.5 Demographic correlates 16.5.1 Age and sex The force of morbidity of schizophrenia peaks in young adulthood. Figure 16.3 shows estimates of incidence by age and sex for Denmark in 1970– 1982 [43]. The peak incidence for males and females is in the decade 15–24. The peak for young adults is more marked for males, and females have a second

EPIDEMIOLOGY OF SCHIZOPHRENIA

90 80

Males

Rate per 100,000

70

Females

60 50 40 30 20 10 0 16

22

28

34

40

46

52

58

64

70

Age at First Diagnosis Fig 16.3 Incidence of schizophrenia among all births in Denmark by age and sex born 1934–1954 (age 51–71 at diagnosis) and 1955–1990 (age 15–50 at diagnosis). Adapted from Thorup et al. [44].

small peak in the years 55–64. The sex-dependent shape of the curve is consistent with recent research in United Kingdom (Figure 16.3) [45]. The figure suggests that males have a higher lifetime risk of schizophrenia, and a meta-analysis reported that males have a 30–40% higher lifetime risk of developing schizophrenia [46], as reported also by McGrath and colleagues [22, 47]. Studies before 1990 reported relatively equal rates of cumulative lifetime incidence for males and females, and there is some epidemiologic evidence that the relative rates have shifted in the last 50 years to produce this male–female difference [48].

16.5.2 Marital status Being unmarried is a well established risk factor for schizophrenia. A recent study by Agerbo et al. [49] also showed that compared to the general population, individuals with schizophrenia are more likely to be single as long as 20 years prior to diagnosis, where the relative odds is about 4 (Figure 16.4a). The relative odds of being single peak at the time of admission, at more than 15, and remain high for decades afterward. The effect is greater for males, possibly because their earlier onset occurs during the years of formation of marriage.

16.5.3 Socioeconomic position There is a long literature on the relationship of low socioeconomic position to risk for schizophrenia [50–53]. Longitudinal studies suggest that the association is due to the effects of insidious onset on the ability of the individual to compete in the job market [54, 55]. A similar pattern was found for unemployment in the study by Agerbo et al. [49]: That is, individuals who eventually are diagnosed with schizophrenia were more likely to be unemployed than others many years earlier than the first diagnosis of schizophrenia, and many years afterward (Figure 16.4b). Recent studies from Scandinavia suggest that, if anything, the parents of individuals diagnosed with schizophrenia are likely to come from a higher, not lower, social position [56].

16.6 Social risk factors 16.6.1 Ethnicity Ethnic status is a relatively easy to identify characteristic of an individual which indicates a shared history with others. Markers of ethnic status include race, country of origin and religion. Country of origin has proven to be a consistent risk factor for schizophrenia in the United Kingdom and the Netherlands.

269

CHAPTER 16 20 Single Women Single Men Women single, Not re-adm Men single, Not re-adm

18 16

Odds ratio

14 12 10 8 6 4 2 −20

0 −15 −10 −5 0 5 10 15 20 Years before and after the first admission

25

(a)

6 Unemployed

5

Discharged, Not re-admitted Reference

Odds ratio

4

3

2

1

−20

0 −15 −10 −5 0 5 10 15 20 Years before and after the first admission with schizophrenia (b)

25

Fig 16.4 (a) The odds ratios of being single for individuals with schizophrenia versus healthy controls. The rates are adjusted for age, gender, calendar year and labour market affiliation; (b) The odds ratios of being unemployed >1% of year for individuals with schizophrenia versus controls with reference to fully or self-employed individuals. The rates are adjusted for age, gender, calendar year and marital status. Dotted lines are 95% confidence bands. Agerbo et al. [49].

In the United Kingdom, various reviews show that those immigrating from Africa or the Caribbean, and their second generation offspring, have rates of schizophrenia up to 10 times higher than those in the general population [57–61], and the overall estimated migrant/native born rate ratio for schizophrenia falls between 4 and 5 [47]. Recent results from the Aetiology of and Ethnicity in 270

Schizophrenia and Other Psychoses (ÆSOP) study confirmed that in comparison with white British, minority status was consistently associated with higher incidence of schizophrenia, especially for African-Caribbean (incidence rate ratio (IRR): 9.1) and Black African (IRR: 5.8). Most importantly, the ethnicity-minority excess remained significant when current socioeconomic status was taken into

EPIDEMIOLOGY OF SCHIZOPHRENIA

account [61–63], especially for those who are non-UK born. Since immigrant groups who do not have black skin do not have higher rates, and since the second generation is also affected, it is unlikely that this finding is only attributable to the stresses of immigration. Since rates in the countries of origin are not elevated, it is also unlikely to be attributable to genetic differences. One possible cause is the psychological conditions associated with being Black in England, or being from Surinam in Holland. It could be discrimination, or a more subtle form of difficulty associated with planning one’s life when the future is as uncertain as it is for racial groups at the structural bottom of society [64]. Another possibility is that obstetric complications are more common in the second generation after immigration because the fetus is better-nourished than the mother had been, leading to disproportion between the head of the fetus and the pelvis of the mother, and resultant higher rate of birth complications [65]. A third hypothesis is that the decreased amount of sunlight in northern climates like the United Kingdom puts darker-skinned people at higher risk due to decreased production of vitamin D [66, 67].

16.6.2 Urban residence In the 1930s, Faris and Dunham showed that, while the addresses of first admissions for manic depressive illness were distributed more or less randomly throughout Chicago, rates of hospital admission for schizophrenia were about five times higher in the centre of the city than in the periphery [68]. This finding, and other similar findings [69], were interpreted as due to the selection into the city of individuals who would develop schizophrenia. But later studies from Europe were strictly prospective, with the cohort defined in late adolescence, well prior to onset [70], or even at birth [71]. The relative risk is about two to four times higher for those born in urban areas. The difficulty is identifying the plausible biological process associated with urban residence. It does not appear to be differences in obstetric complications in urban and rural areas [72], but it could conceivably be connected to differences during infancy such as breastfeeding [73]. It could include differences in the physical environment, such

as the higher concentration of lead in the soil and air in cities; differences in the cultural environment, such as the expectation to leave the family of origin and define a new life plan [64], crowding which might permit spread of infections [74, 75], or a host of other factors, including possible interaction with genetic risk and cognitive social capital [76].

16.6.3 Modernisation Data more typical of epidemiologic research are available beginning in the nineteenth century, with statistics from asylums. Torrey and Miller [77] collected data from four separate areas; England, Ireland, Atlantic Canada and the United States. In each of these regions there was an increase in the number, and the proportion, of individuals in asylums, from less than 1/1000 to more than 5/1000 (as it happens, the current estimate for the prevalence of schizophrenia, discussed above). Most of these individuals are presumed to have been psychotic, and possibly a majority or at least a substantial proportion would be diagnosed today with schizophrenia. Figure 16.5 shows the data they displayed for the United States, with additions by us. The data produced by Torrey and Miller exaggerate the trend for the United States somewhat because the data collection ended with the beginning of the era of neuroleptics and deinstitutionalisation. We have added data points from the National Reporting System of the National Institute of Mental Health, and it is clear that the trend is downward after 1960. Although the more recent downward trend has been the subject of some discussion [78–81], it seems likely to be explained by the combination of diagnostic narrowing and deinstitionalisation [81]. Nevertheless, even with the new, later data points, there would appear to be an upward trend over two centuries, with a doubling or quadrupling of the prevalence. Likewise, adding to the figure the carefully collected data from the classic study of Goldhamer and Marshall also suggests an upward trend (contrary to the conclusion of the authors) [82].

16.6.4 Substance use Hallucinogenic drugs such as LSD or cannabis, which are more prevalent in urban areas and in modern 271

CHAPTER 16 Insanity in the United States

Insane Persons per 1000 Population

4 3.5 3 2.5 2 1.5 1 0.5

72 19 79 19 86 19 93

65

19

19

51 58 19

44

19

19

30 37 19

23

19

16

19

09

19

02

19

95

19

88

18

81

18

74

18

67

18

60

18

44

18

18

18

07

0

Year United States

Masschusetts

United States

Fig 16.5 Modernisation and schizophrenia. Sources: United States, 1840–1955, from Torrey, E.F. and Milller, J. (2001) The invisible plague: the rise of mental illness from 1750 to the present. Rutgers University Press, New Brunswick, NJ; Massachusetts, 1844–1930, from Goldhamer and Marshall as organized in Eaton, 2001 [83]; United States, 1969–1994, from Witkin, M.J., Atay, J.E., Manderscheid, R.W. et al. Highlights of organized mental health services in 1994 and major national and state trends, pp. 143–175 in Manderscheid, R.W., Henderson, M.J. Mental Health, United States, 1998, US Department of Health and Human Services, Rockville, MD.

times, have been found to increase the risk for schizophrenia. There are numerous case control studies showing that persons with schizophrenia are more likely to have taken, or be using, cannabis [84]. The most compelling evidence comes from prospective studies in Sweden, the Netherlands, New Zealand and Israel, showing higher risk of subsequent onset of schizophrenia, ranging from 2 to as high as 25 [85–88]. Moore and colleagues [89] reviewed 35 longitudinal population-based studies, pointing out that excess risk of psychotic outcome associated with ever use of cannabis was moderate (odds ratio (OR) = 1.4, 95% CI: 1.2–1.7), but the risk was substantially higher for frequent marijuana users (OR = 2.1) [89]. It could be that individuals in the prodromal phase of schizophrenia are responding to initial, mild symptoms of schizophrenia by using drugs, even though these studies have attempted to control for premorbid conditions. On the other 272

hand, it could be that cannabis and other hallucinogens precipitate, or even cause, psychotic symptoms, which evolve into schizophrenia. Indeed, in addition to triggering psychosis, the consumption of cannabis can also exert detrimental effects on recovery [90]. A follow-up study of first-onset schizophrenia cases and healthy controls revealed that cannabis use was associated with a pronounced grey matter volume loss and a significant lateral ventricle enlargement over a period of 5 years [91].

16.7 Biological risk factors 16.7.1 Genes It has been speculated for more than a century, and known for about half a century, that the vulnerability to schizophrenia is partially inherited.

EPIDEMIOLOGY OF SCHIZOPHRENIA

There have been 17 twin studies, beginning with Luxenberger in 1928 and continuing through Cardno in 1999 [92, 93], suggesting that the heritability is above 50% – that is, well more than half the variation in the vulnerability is inherited. Since there may be a tendency for parents and others to treat monozygotic twins more similarly than dizygotic twins (‘common environment’), it is the five separate adoption studies, including analysis and re-analysis of the most influential study in Denmark [92], and with the addition of a recent study from the population-based register in Sweden (Table 16.3) that clinches the genetic argument. Along the way there were more than 10 family studies also showing a strong tendency for schizophrenia to run in families [94]. The recurrence risk compares the morbidity risk in a given family member of a person with schizophrenia to the risk in the general population. The monozygotic twin recurrence risk for schizophrenia is about 50 [94]. For first degree relatives (parents, offspring and full siblings, which share half their genes), the recurrence risk is about 9–10; and the recurrence risk for second-degree relatives, such as nephews and nieces, is about two to three, and lower still for cousins whose relationship is yet more distant (Table 16.3). Table 16.3 Recurrence risks for schizophrenia in biological and adoptive relatives. Relationship

Recurrence risk

First degree relatives Father and mother Father only Mother only Full sibling

89.0 10.7 10.3 8.6

Second degree relatives Half-sibling Nephews and nieces Grandchildren

2.5 2.7 3.0

Third degree relatives First cousin

2.3

Adoptive relatives Adopted away offspringa Adopted away biological siblinga Non-biological sibling in adoptive familya

13.7 7.6 1.3

Unrelated Mating partner

8.6

a Drawn

from Lichtenstein et al. [95], and with data from Lichtenstein et al. [96].

An aspect of the family studies is that if a single gene were responsible for the inherited vulnerability to schizophrenia, it would show up in such studies; and a dominant or recessive mode of transmission would be an obvious outcome, if it existed. These studies thus have ruled out simple Mendelian models for schizophrenia and the interest has turned to more complex models. Genetic methodologies have evolved quickly as the Human Genome Project has been completed [97]. Genome-wide linkage studies (GWLS) take advantage of data on families, and the imperfect tendency of genes to remain physically close to one another during meiosis, to provide a logical search procedure for areas of the genome that might include risk genes for schizophrenia. The focus is on areas of the genome, not specific genes, and an advantage is that the entire genome can be studied with several hundred such markers in any given study, without an a priori hypothesis. But after more than a decade of these studies in schizophrenia, the results have been disappointing. Each study generates lots of positive findings, but they generally are not confirmed in later studies. A meta-analysis of 20 GWLS studies in 2003 [98] suggested 12 separate areas that might include a gene with increased risk for schizophrenia. A later more inclusive meta-analysis of 32 GWLS studies from 1995 through 2008 revealed amazing failure to replicate and yielded only two areas on chromosomes 2 and 5, respectively, that contained ‘suggestive’ evidence for linkage [99]. Candidate gene studies focus on a specific gene and test its association with risk for schizophrenia in cases and controls, like any other risk factor might be tested. These studies tend to err on the side of false positive results, and are hard to summarise because the case and control samples, sample sizes and ascertainment procedures differ. Although these studies began with tests of a few a priori candidate genes in any given study, new genomic technologies allow cost-effective assays for as many as one million single nucleotide polymorphisms (SNPs) in a single individual, providing new challenges for inferential statistics. This situation is being addressed by massive meta-analyses assisted by public datasets available on the World Wide Web. A recent meta-analysis included data from 1179 genetic association studies, with median sample size of cases of over 3500, and 273

CHAPTER 16

Months filled in with black have significantly higher risk for schizophrenia Investigator

Year

Sample 12

1

2

3

4

5

6

7

8

9

10

11

Northern Hemisphere Tramer

1929

3100

Petersen

1934

3467

de Sauvage Nolting

1934

2589

Huntington

1938

10420

Laestadius

1949

2232

de Sauvage Nolting

1951

2090

Norris and Chowning

1962

3617

Hare and Price

1968

3596

Dalen

1968

16238

Hare et al.

1974

5139

Odegard

1974

19740

Videbechet al.

1974

7427

Parker and Balza

1977

3508

Shimura et al.

1977

7960

Torrey et al.

1977

53584

O’Hare et al.

1980

4855

Watson et al.

1984

3556

Kendell and Kemp

1985

2653

Hafner et al.

1987

2020

Bourgeois et al.

1990

3944

Torrey et al.

1991

43814

Rodrigo et al.

1992

2892

Fig 16.6 Season of birth and schizophrenia months filled in with black have significantly higher risk for schizophrenia. Drawn from Torrey et al. [111], with additions of studies by Mortensen et al. [112]; only studies with sample sizes larger than 1500 are included.

274

EPIDEMIOLOGY OF SCHIZOPHRENIA

Torrey et al.

1993

30467

Aschauer et al.

1994

2450

Kim et al.

1994

1606

Tam and Sewell

1995

3346

Chen et al.

1996

3749

Torrey et al.

1997

71278

Mortensen et al.

1999

2669

Dalen and Roche

1975

2947

Parker and Neilson

1976

2256

McGrath et al.

1999

8027

Southern Hemisphere

Fig 16.6 (cont.)

results for 3608 SNPs [100]. Twenty-four variants in 16 genes were identified as nominally significant. Five of the 16 genes had been identified earlier in a meta-analysis of linkage studies [98]. It was concluded that four genes (DRD1, DTNBP1, MTHFR and TPH1) had a ‘strong’ degree of epidemiological credibility [100]. Now there is intense interest in the functioning of those genes in the body and the brain (e.g. [101, 102]). Current advances in the genetics of schizophrenia include developments that take advantage of the new genomic assay technologies to collect evidence about variable numbers of mistakes in copying a gene more than once, or in deleting a gene, over the entire genome of an individual. These studies of copy number variations may reveal a genetic basis not related to any particular gene [103]. As well, combinations of genes, and SNPs within one gene, can be studied in haplotype analyses [104]. There even exists the possibility to sequence the entire array of base pairs on a chromosome or a portion of it. The study of the interaction of genes with environments is likely to provide fruitful avenues of exploration [105]. There will also be the development of a variety of new target outcomes related to, but not identical with, the

phenotype – so-called ‘endophenotypes’ [106]. The array of positive associations can be organised by the functions of the genes, guiding future search procedures [107]. None of these new developments has succeeded in identifying a specific genetic basis for schizophrenia helpful in prediction, prevention or treatment, but it seems likely that this will occur in the next decade.

16.7.2 Winter birth A number of studies have found that winter birth is associated with a small (on the order of a 10% increase for those born in the winter versus summer) but reliably replicated increased risk of schizophrenia (Figure 16.6). This risk factor is interesting in part because it is indisputably not genetic in origin. There have been methodological challenges to the finding [108] based on the way the beginning of the calendar year interacts with the shape of the onset curve for schizophrenia, but later studies have adjusted for the methodological difficulties and still find an effect. Moreover, the effect exists in the southern hemisphere, with more births during the southern hemisphere winter season, which does not 275

CHAPTER 16

coincide with the beginning of the calendar year. Recently, carefully implemented meta-analysis on >120 000 schizophrenia patients in the Northern Hemisphere demonstrated that winter/spring-related population attributable risk for schizophrenia was roughly 3.3%. Furthermore, this relationship was heterogeneous by latitudes, with the strongest relative risks appearing in areas located in latitude bands of 40–50◦ , that is, away from the Equator [109] One explanation for the excess in winter births is that conception is more likely to take place in the summer, possibly due to access to private places out of doors, with exposure to unknown elements. Another explanation is that the mother is passing through the second trimester of her pregnancy in the height of the flu season, and that infections during that period raise risk for schizophrenia in the offspring. Only a few studies have been done on the equator, where there is little fluctuation in temperature. In one of these, the risk for schizophrenia was higher when the second trimester occurred in the rainy season, during which influenza is more prevalent [110].

16.7.3 Parental age The role of advanced parental age in relation to a higher risk of schizophrenia was first proposed in the mid-twentieth century, and has gained extensive scientific attention in recent years. Based upon the family background data of 1000 patients in the Ontario hospital, Canada, Gregory [113] reported that schizophrenic patients’ parents were, on average, 2–3 years older than those of the general population. However, subsequent investigations have shown inconsistent findings [114, 115], and it also has been argued that observed maternal age-associated higher risk in schizophrenia might be largely confounded by raised paternal age [116]. Recently, several population-based epidemiological studies in Demark, Israel, Sweden and the United States have provided stronger evidence as to the role of paternal age in schizophrenia [117–122]. For example, using population-based birth cohort data in Israel, Malaspina and colleagues [117] found that the relative risk of schizophrenia rose monotonically in each 5-year group of paternal age, with a maximum relative risk of 2.96 (95% CI: 1.60–5.47) in the group aged 55 or above in comparison with 276

the age of 20–24. Additionally, once paternal age is statistically adjusted, maternal age no longer is a significant predictor of schizophrenia. The evidence from one nested case–control study indicates that the paternal age-related excess in the risk of schizophrenia is generally greater in females [121]. Several hypotheses have been posited to explain the underlying mechanisms linking advancing paternal age to schizophrenia. Unlike females, in which all the germline cell divisions are completed before birth, males have germline cell divisions throughout their reproductive period. Due to the results of accumulation of mutagens, reduced fidelity of DNA replication and inefficiency of repair mechanism, males with advancing age are at a higher chance to produce sperm with mutations (i.e. de novo mutations) [123, 124]. If de novo mutations explain the link between advancing paternal age and schizophrenia, the observed association is presumably stronger in sporadic cases rather than familial ones, since de novo mutations largely involve one single base substitution [123]. Another possible explanation is that certain schizophrenia-related vulnerabilities or risk factors (e.g. genetic traits, personality or lower socioeconomic status) might impair an individual’s social functioning, and consequently delay the age at marriage and fatherhood [115, 116]. A third hypothesis is that offspring of older fathers may experience more stressful life events, such as loss of father in early childhood, as compared to their peers with younger fathers [125].

16.7.4 Pregnancy and birth complications In the last two decades there have been many studies, as well as meta-analyses, reporting a relative odds of about two for those with one or another sort of birth complication [126–129]. Later analyses have begun to specify the individual type of birth complication, with the hope of elucidating the causal mechanism. Figure 16.7 selects results in which the 95% confidence interval has 0.90 or larger as its lower bound (i.e. ‘significant’ or nearly so), along with the number of studies in the left side of the figure, from a recent meta-analysis [129]. This structure facilitates assessment of consistency across studies, as well as strength and significance. For example, the relative odds for pre-eclampsia is not large (1.36), and does not meet

EPIDEMIOLOGY OF SCHIZOPHRENIA Diabetes in pregnancy Placental abruption Birth weight < 2000g Emergency Cesarean section Congenital malformations Uterine atony Rhesus variables Asphyxia Bleeding in pregnancy Birth weight < 2500g Head circumference < 32cm Smoking in pregnancy Preeclampsia Gestational age < 37 weeks Small for gestational age Induction of labor Forceps delivery or vacuum extraction Birth length < 49 cm 0.1

0.4

1.0

3.0

10.0

20.0

50.0

Relative Odds

Fig 16.7 Pregnancy and birth complications in meta-analytic review. Cannon et al. [129].

conventional levels of statistical significance, but the estimate is based on six studies. The complications variously suggest as a possible cause malnutrition, extreme prematurity, hypoxia, ischaemia and various sorts of in utero stress [130–132]. For example, evidence from retrospective cohort studies indicates that individuals born in periods of famine were twice as likely to develop schizophrenia compared with controls [133, 134]. Khashan and colleagues [135] found that mothers who experienced severe adverse life events, defined as death or serious illness of mother’s close relatives at or shortly before pregnancy, had close to a 70% increased risk of schizophrenia in their offspring; this observed elevated risk was especially salient in women without a family history of schizophrenia and who experienced the stressor during the first trimester [135]. Several mechanisms were posited to explain this elevated risk of schizophrenia associated with prenatal exposure to stress, such as stress hormone-related harmful effects on fetal brain developmental, fatal hypoxia induced by uterine vasoconstriction, as well as fetal nutritional deprivation [136].The influence of maternal stress exposure and obstetric complications on the increased likelihood of schizophrenia in adulthood may be mediated through conferring a latent vulnerability that may

impact on neuromaturational processes and stressors encountered in later life [137].

16.7.5 Minor physical anomalies Minor physical anomalies (MPAs) are structural deviations observed in various parts of the body (e.g. global head, eyes, ears, mouths, hands and feet), and they appear to be increased in individuals with schizophrenia and their siblings as compared to the rest of population [138–141]. A recent meta-analysis summarised 13 studies [142]. In one clinical comparison between schizophrenia patients with patients’ siblings and normal subjects using the modified Waldrop scale [143], Ismail and colleagues [139] found that the highest occurrence of MPAs tended to occur in the patient sample, followed by their siblings and normal subjects accordingly, and the significant odds ratios of MPAs with schizophrenia range from 31 for the feature of eyes’ heterochromia, to 3.1 in those with hands’ curved fifth finger. Similar evidence has been shown in one prospective population-based study in Demark, which suggests three or more MPAs in childhood might be associated with an estimated three to four times higher risk to develop schizophrenic spectrum disorders in adulthood [141]. 277

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Although there has been argument about the measurement issues of MPAs (e.g. the content validity of Waldrop scale) [144], MPAs-associated higher risk in schizophrenia was consistently reported even when other measurement instruments, or a revised Waldrop scale with additional items, were applied [138, 139, 141]. One possible explanation for MPAs-related excess in schizophrenia is that MPAs may be the manifestation of prenatal developmental disruption occurring in the first or second trimesters of pregnancy, a critical period of brain development. For example, since both MPAs and the central nervous system have embryonic origins from the ectoderm, it is very likely that the presence of MPAs may be an externally observed sign of abnormal brain development.

16.7.6 Childhood developmental abnormalities Earlier work on high risk (HR) groups has shown that offspring of schizophrenic parents were more likely to have lower intelligence, poor attentional skills, thought disorder, poor social adjustment and more psychiatric symptoms as compared to the offspring of controls [145–147]. Although several concerns have been raised regarding the generalisability of HR findings to non-familial forms of schizophrenia, recent longitudinal studies conducted in the United Kingdom, Sweden, Finland and New Zealand have provided evidence that individuals with schizophrenia differ from their peers even in early childhood on a variety of developmental markers, such as the age of attaining developmental milestones [148–150], levels of cognitive functioning [151, 152], educational achievement [148, 153–155], neurological and motor development [156–158], social competence [153–159] and psychological disturbances [159]. It is noteworthy that there seems to be no common causal paths that link these developmental markers with schizophrenia [145]. Indeed, individuals who later develop schizophrenia or related disorders may have already experienced a general or pandevelopmental impairment in early childhood. For example, a prospective study of the 1972–1973 birth cohort in New Zealand found that patients with schizophrenia may have suffered a significant deficit 278

in neuromotor, language and cognitive development in the first decade of their lives [160]. In addition, children who later have diagnoses of schizophreniform disorders were more likely to experience higher levels of emotional problems and peer rejection.

16.7.7 Infections A series of ecological studies suggest that persons whose mothers were in their second trimester of pregnancy during a flu epidemic have a higher risk for schizophrenia [161–163] Infection during pregnancy as a risk factor is consistent with the neurodevelopmental theory of schizophrenia [164–165]. Later studies, which are more convincing, include individual assessment of infection, either via comparison of antibodies in adults with schizophrenia versus normal individuals [166], or, even more compelling, prospective studies in which the infection can be determined to have occurred during the pregnancy [167]. There is consistent evidence that individuals with antibodies to Toxoplasma gondii have a higher prevalence of schizophrenia [168]. A recent meta-analysis reported that the combined odds ratio of Toxoplasma gondii antibodies was estimated to be 2.79 for schizophrenia in different clinical phases, not too much different from that for first-episode schizophrenia (i.e. 2.54: [168]). One study suggested a relative risk of 5.2 for individuals with documented infection by the rubella virus during fetal development [169]. Another prospective study found a higher risk for psychosis in individuals whose mothers had higher levels of antibodies to herpes simplex virus [170, 171]. A study in Brazil compared individuals who had meningitis during the 1971–1974 epidemic, with their sibs who did not have meningitis. The study found that the prevalence of psychosis, and schizophrenia specifically, was five times higher in those who had meningitis. The finding is intriguing because the average age of infection with meningitis was 26 months, that is, much later than prenatal infection [172]. If this finding is replicated it will have important implications for the neurodevelopmental theory of schizophrenia. Findings on season of birth, obstetric complications, MPAs and an array of developmental problems are consistent with the neurodevelopmental theory of schizophrenia, in which causes may be traced

EPIDEMIOLOGY OF SCHIZOPHRENIA

to a defect in early brain development [164, 165]. Meta-analyses of brain volumes show consistent evidence of differences in brain structures in persons with diagnosis of schizophrenia at, or prior to, their onset [173, 174]. In addition, a small twin study suggests that the major differences are not explained by genetic factors, but rather by common environment – in this case presumably the womb or other birth processes associated with the mother [175]. These findings, when put alongside the cumulative knowledge about genetics of the disorder, are consistent with a two-factor vulnerability model, in which polygenic or oligogenic forces combine with possibly independent factors related to foetal or other early injuries to produce high risk for schizophrenia [176]. The ultimate aetiopathic process, however, is almost certain to be more complex than this [137].

16.7.8 Immune function Related to infectious processes is the relatively small but consistent literature which indicates that persons with schizophrenia have unusual resistance from, or susceptibility to, autoimmune diseases. Studies have consistently shown that individuals with schizophrenia are less likely to have rheumatoid arthritis [177]. There are at least eight credible studies with relative odds ranging from less than 0.1 to 0.5 protective effect. While it could be that medications for schizophrenia are protective for rheumatoid arthritis in some unknown way, two of the studies were conducted prior to the era in which neuroleptic medications were available. It could be that other physiologic consequences of schizophrenia are protective, or it could be that a single gene raises risk for the one disorder and protects for the other. A single small study suggests that mothers of offspring with schizophrenia have a lower risk for rheumatoid arthritis, but its size and quality are not convincing [178]. Later studies have been less convincing [179]. It is intriguing, in this regard, that case–control studies have shown that persons taking non-steroidal anti-inflammatory medications, which primarily treat arthritis, are protected from dementia, which has certain similarities to schizophrenia [180, 181].

Other autoimmune disorders have been linked to higher risk for schizophrenia [179, 182, 183], including thyroid disorders [184], type 1 diabetes [179, 183] and coeliac disease [185]. Currently the evidence is strongest for thyroid disorders and coeliac disease. In a study from the Danish population registers, persons whose parents had coeliac disease were three times as likely to be diagnosed later with schizophrenia [186]. Subjects in the Clinical Anti-Psychotic Trials of Intervention Effectiveness trial had higher than expected levels of antibodies to tissue transglutaminase, the self antigen associated with coeliac disease [187]. Coeliac disease is an immune reaction to wheat gluten. One possibility explaining all the autoimmune results is that the increased permeability of the intestine brought about by coeliac disease permits entry into the blood stream of a variety of antigens, which then generate antibody responses which are not capable of distinguishing self from non-self, and go on to pass through the blood–brain barrier, or trigger an immune reaction that passes through the blood–brain barrier. It is also possible that the wheat gluten molecule breaks apart, with sections resembling endogenous brain chemicals [188]. The results linking schizophrenia to autoimmune disease are paralleled by the clinical and laboratory study of autoimmune processes in schizophrenia. There are apparently abnormalities of the immune system in schizophrenia, but it is not clear that they were not produced as a consequence of schizophrenia or its treatment [189, 190]. It is possible that a single weakness in the immune system in schizophrenics explains both the data on infections and the results on autoimmune disorders, but this remains to be proven [191]. Meanwhile, there are ongoing clinical trials of anti-inflammatory [192] and antibiotic [193] agents for schizophrenia. There are also recent credible findings linking schizophrenia to genes in the major histocompatibility complex on the short arm of chromosome 6 [194].

16.8 Prevention Retrospective clinical history revealed that schizophrenia’s symptom disturbance and functional disability may appear in 1 to 2 years before the 279

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onset, and researchers identified a syndrome with a substantial risk for subsequent risk of full-blown schizophrenia – the prodromal risk syndrome [195, 196]. A series of study showed the conversion rate over one-year follow-up ranged from 20 to 60%. Recent results of the North American Prodrome Longitudinal Study (NAPLS) on approximately 300 prospectively identified clinical HR youth indicate that the conversion rate was 35%, with a declining rate during the 21/2-year follow-up [197]. Some researchers have proposed the inclusion of prodromal risk syndrome in the Diagnostic and Statistical Manual of Mental Disorders [198]. On the other hand, ethical concerns have been raised about this issue as well particularly because of the support of the pharmaceutical industry in promoting antipsychotic medication treatment in this prepsychotic population. A recent study found that adolescents who met criteria for prodromal schizophrenia may benefit from medication treatment with other psychiatric medications, in this case antidepressants [199, 200]. However, it is unclear what symptoms the treating clinician had targeted (anxiety, depression, etc.), and whether early treatment will simply forestall, rather than prevent, the onset of schizophrenia. Studies of the combination of risk factors will facilitate prospective studies of HR individuals, even in advance of the prodrome. For example, Mortensen et al. [112] have studied the combined effects of season of birth, urbanisation of birthplace and family history of schizophrenia. In these studies the HR is defined not simply due to family history, as in earlier HR studies. Combination of risk factors will raise the positive predictive value of the risk formulation to the point where it may be ethically feasible to approach the individual, identify the risk, observe cautiously to determine if the prodrome is beginning and then possibly begin treatment efforts to protect them from the catastrophic effects of the first episode of schizophrenia. Studies such as these have begun, albeit very cautiously [201–204].

16.9 Discussion What has been accomplished over the last several decades, and what are the prospects for future 280

progress? Even as late as 40 years ago, the epidemiology of schizophrenia was nearly a blank page. There was even argument about the value of the concept itself. The only risk factors which seemed strong and consistent were the conditions of lower social class life and family history of schizophrenia. Since that time, there has been considerable progress delineating a more or less consistent picture of the descriptive epidemiology and the natural history of schizophrenia. Research in analytic epidemiology has generated a series of heretofore unsuspected risk factors, as described above. Since the first edition of this book, the literature on risk factors has gained considerable consistency and credibility: for example the current review includes 10 meta-analyses and eight systematic reviews of the literature, of which only two were available for the first edition. Virtually all of the risk factors have now been incorporated into various hypotheses about the aetiopathic process of schizophrenia (which vary in the degree to which they can be tested). These developments are healthy. Epidemiological research has built a strong knowledge base over the past quarter century, which has contributed to initial efforts at prevention. These efforts are in their infancy, but they were unthinkable when the first edition was published.

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Epidemiology of depressive disorders Deborah S. Hasin,1,2,3 Miriam C. Fenton1,2 and Myrna M. Weissman1,2,3 1 Mailman

School of Public Health, Department of Epidemiology, Columbia University, NY, USA 2 New York State Psychiatric Institute NYSPI, NY, USA 3 College of Physicians and Surgeons, Department of Psychiatry, Columbia University, NY, USA

17.1 Introduction The epidemiological study of mood symptoms spans many decades. Community surveys of the 1950s and 1960s paid close attention to psychosocial variables, and documented significant levels of functional impairment caused by psychiatric symptoms. However, these studies defined mental health and illness along a general continuum and therefore did not establish rates of specific psychiatric disorders or of mood disorders [1]. They also assumed that the aetiology of mental illness was fully psychosocial, not anticipating the developments in genetics, neuroscience and psychopharmacology of psychiatric disorders that emerged later. For these reasons, we will limit our review to epidemiologic studies conducted since the 1980s. To aid in interpretation, we will include only studies that adhered to certain design and methodological standards. These include the following: 1 Diagnoses were based on diagnostic nomenclatures of the American Psychiatric Association (APA) or the World Health Organization (WHO). APA criteria that served as the basis for most of the studies included the Diagnostic and Statistical Manual of Mental Disorders, third edition (DSMIII) [2], DSM-III-R (revised) [3] and DSM-IV [4]. WHO criteria (the International Classification of

2

3

4 5

6

Diseases, 10th edition (ICD-10), 1992 [5] were used in a few of the studies, as noted within this chapter. Studies used a weighted and nationally or regionally representative sample, with a sample size (N) of at least 1000. For studies not focused on a single stage of life, a broad range of adult ages (generally age 18+) were included. Mental disorders were assessed with standardised diagnostic interviews. Rates were reported in lifetime and current (last 12 months) timeframes, accompanied by an indicator of statistical precision such as confidence intervals. An explicit response rate was provided.

We will focus on two categories of mood disorder: major depression, either major depressive disorder (MDD) or major depressive episodes (MDEs) and dysthymia. Throughout the paper, the terminology we use (MDD or MDE) will be consistent with the specific diagnosis used in the studies. Readers interested in epidemiological data prior to 1980 are referred to earlier reviews [1]. The studies included in this chapter were conducted in many countries, giving a broad, international picture of the epidemiology of MDD. Many of the studies were part of two major

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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international initiatives [6]. First, in the 1980s, the Cross-National Collaborative Group conducted studies across America, Asia, Australasia, Europe and the Middle East to determine estimates of mental health prevalence. The study in the United States, the Epidemiological Catchment Area (ECA) Study, was revolutionary in its sample size (20 000) and use of modern diagnostic criteria. The ECA therefore provided the first reliable and accurate estimates of prevalence of mental health disorders in the United States [7]. However it was geared towards treatment needs and only conducted in five sites in the United States. The second set of studies were conducted by the International Consortium of Psychiatric Epidemiology (ICPE) in the 1990s and included countries in the Americas, Europe and Asia [8]. The ICPE study in the United States was the National Comorbidity Study (NCS) which was then followed up by the National Comorbidity Replication Study (NCS-R). These were first surveys of the US general population and therefore provided the first generalisable estimates of mental health prevalence in the United States [9–11]. Most of these studies had sample sizes ranging from 1500 to about 10 000. However, two additional studies from the United States deserve mention due to their sampling and unprecedented sample sizes (each over 42 000). One is the National Longitudinal Alcohol Epidemiologic Survey (NLAES), conducted in 1992 The NLAES provided information on MDE. The second study is the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), conducted in 2001–2002, which provided information on MDD. These two studies provide a wealth of information on the epidemiology of affective and other psychiatric disorders. More information on the assessment methodology of the NLAES and NESARC is found in Appendix 17.A.

17.2 Major depression 17.2.1 Definition The DSM-IV diagnosis of MDE requires a persistent period of at least 2 weeks during which dysphoric mood or loss of interest or pleasure occurred, as

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well as at least four other symptoms (e.g. significant weight change or appetite disturbance, sleep disturbance, psychomotor agitation or retardation, fatigue or loss of energy, feelings of worthlessness, inappropriate guilt, impaired concentration and suicidal ideation or behaviour [4]. DSM-IV criteria require that the symptoms cause clinically significant distress or impairment in social, occupational or other important areas of functioning and not to be caused by the direct physiological effects of a substance (e.g. a drug of abuse, a medication) or a general medical condition (e.g. hypothyroidism). Uncomplicated bereavement is also ruled out as a MDD. DSM-Ill and DSM-III-R criteria for major depression are similar. MDD is diagnosed in DSM-IV among individuals without a history of bipolar disorders (manic, mixed or hypomanic episodes). In some epidemiologic studies of major depression, bipolar disorders were not assessed. In this case, MDE was diagnosed rather than MDD. These studies indicate either a current disorder meeting full criteria for MDD, or a lifetime history of such disorders, but without the assumption that a lifetime bipolar history has been ruled out. All other things being equal, prevalence estimates of major depression from such studies should be slightly higher than studies that have ruled out bipolar cases of depressive disorders.

17.2.2 Measure of occurrence – prevalence and incidence In this chapter, we present information on prevalence and incidence of major depression. Prevalence refers to the total number of cases in a given population at a specific time. We present prevalence using the two most commonly used timeframes in psychiatric epidemiology: past 12 months, and lifetime. In contrast, incidence indicates the rate of new cases within a population at risk (i.e. they never previously had the disorder) per given number at risk, most often per 100. Incidence is given within a defined time period, most often 12 months. To obtain accurate incidence rates, studies must include large sample sizes and assessments of the participants at two or more points in time. Since such studies are more expensive and time consuming than

EPIDEMIOLOGY OF DEPRESSIVE DISORDERS

cross-sectional (one-time) surveys, far more studies have reported prevalence than incidence.

17.2.3 Prevalence 17.2.3.1 Prevalence by country Table 17.1 shows the lifetime and 12-month prevalence per 100 of major depression, as well as the sample sizes and diagnostic criteria used in each study. These studies include general population surveys conducted in Africa, the Americas, Asia, Australasia (Australia and New Zealand), Europe and the Middle East. All studies used DSM-III, DSM-III-R or DSM-IV criteria.

17.2.3.2 Lifetime prevalence As shown in Table 17.1, lifetime prevalence tended to be highest in Europe (median = 12.8), the Americas (median = 9.5) and Australasia (12.6). Lifetime prevalence appeared somewhat lower in the Middle East (median = 8.1) and was considerably lower in Asian countries (median = 3.5). By country, the highest lifetime prevalence rates were reported in one US study [10, 15], Brazil [8] and New Zealand [31, 32], whereas the lowest lifetime prevalence rates were reported in Kunming, China [30] and Japan [8]. Taken as a whole, Table 17.1 indicates that major depression is not a rare disorder in the general population, even in the countries with lower lifetime prevalence.

17.2.3.3 Past 12 month prevalence Past 12-month prevalence followed a slightly different distribution internationally than lifetime prevalence, with the highest rates reported in Australasia (6.0) and the Americas (median = 5.6), followed by Europe (median = 3.9) and the Middle East (median = 4.7). Similar to the findings for lifetime diagnoses, past year prevalence was lowest for the Asian countries (median = 1.5). South Africa most resembled the Middle East and Europe with a past year prevalence of 4.9. The highest 12month prevalence was 10.3 from the United States NCS, [10, 15], followed by the Canadian sample

(8.7) and then the American sample (8.2) of the Joint Canadian/United States Survey of Health (JCUSH). As the NCS and JCUSH used the same diagnostic procedure, the similarity in high rates is not unexpected. The next highest past year prevalence was reported in Australia (6.6) and Mexico (6.2). While 12-month rates were lower than lifetime rates, as expected, they also indicate that major depression is not a rare disorder in the general population. The most comparable surveys, in terms of location (United States), sample design and size and diagnostic procedures (see Appendix 17.A) were the NLAES and NESARC. These two surveys produced similar lifetime and past year prevalence estimates to other studies conducted in North America (with the exception of the NCS and JCUSH; see below for discussion of the measures used in these studies). Rates of major depression in the Americas other than in the United States were similar to rates obtained in the United States. Prevalence estimates in European countries ranged considerably. For example, lifetime prevalence in the Netherlands was about double that of the Czech Republic (15.7 compared to 7.8). Interestingly, last 12-month prevalence of major depression in the Czech Republic was very low compared to the rest of Europe, whereas lifetime prevalence was comparable. This may indicate the sensitivity of epidemiological studies to the economic and political state of the country. For example, the Czech study included individuals born prior to 1970 who endured war and instability in their lifetimes, but may have benefited from peace and some degree of increasing prosperity since the late 1990s. The most representative European study in terms of sample size and geographical location is the European Study of the Epidemiology of Mental Disorders, which provided data on over 20 000 people from six European countries. Unfortunately, prevalence estimates for individual countries were not provided, but the overall estimates are intermediate between estimates from other European countries [34]. The lowest prevalence was reported in the Asian region, particularly in Japan and China. A significant question is whether these cross-national differences are related to true cross-cultural variation in risk

291

CHAPTER 17 Table 17.1 Prevalence rates from epidemiological community surveys of psychiatric disorders using DSM-III, DSM-III-R and DSM-IV criteria. Place

Field dates

N

Age range (years)

Past year prevalence

Lifetime prevalence

References

Africa South Africa

2002–2004

4 351

18+

4.9

–

[12]

The Americas ECAa NHANES NCSb NLAES NCS-R NESARC JCUSH (U.S.) Edmonton, Canada Canada Canadaa JCUSH (Canada) Puerto Ricoa Chileb Brazilb Mexicob Mexico

1978–1983 1988–1994 1990–1992 1991–1992 2001–2002 2001–2002 2003 1983–1986 1994 1990–1991 2003 1984 1992–1999 1994–1996 1995 1999–2001

18 572 8 449 8 098 42 862 9 090 43 093 5 183 3 258 16 989 6 902 3 505 1 551 2 978 1 464 1 734 1 602

18+ 15–40 15–54 18+ 18+ 18+ 18+ 18+ 15+ 18+ 18+ 17–64 15+ 18+ 18–54 18+

2.6 – 10.3 3.3 6.6 5.3 8.7 – 5.6 4.3 8.2 – 5.6 5.8 4.5 6.1

4.4 9.5 17.1 9.9 16.2 13.2 – 8.6 – 8.3 – 4.6 9.0 12.6 8.1 12.8

[7, 13] [14] [10, 15] [16] [9] [17] [18] [19] [20, 21] [8] [18] [22] [8] [8] [8] [23]

Asia Seoul, Korea Hong Kong Japanb Korea China Kunming, China

1984 1984–1986 1997–1999 2001 2001–2002 2005–2006

5 100 7 229 1 029 6 275 5 201 5 033

18–65

–

20+ 18–64 18–80 15+

1.2 1.7 1.8 1.1

3.4 3.7 3.0 4.3 3.6 2.0

[24–26] [27] [8] [28] [29] [30]

1986 1998

1 498 10 641

18+ 18+

5.3 6.6

12.6 –

[31, 32] [33]

Europe Netherlandsb Czech Republicb Six countriesc

1996 1998–1999 2001–2003

7 076 1 564 21 425

18–64 18–79 18+

5.9 2.0 3.9

15.7 7.8 12.8

[8] [8] [34]

Middle east Turkeyb Israel

1996 2003–2004

6 095 4 859

18–54 21+

3.5 5.9

6.3 9.8

[8] [35]

Australasia New Zealanda Australia

a These

studies were part of the Cross-National Collaborative Group. studies were part of the International Consortium of Psychiatric Epidemiology initiative. c This study was conducted in Belgium, France, Germany, Italy the Netherlands and Spain. b These

292

EPIDEMIOLOGY OF DEPRESSIVE DISORDERS

factors or to methodological differences across studies. See the Section 17.2.7.5 for further discussion.

17.2.3.4 Measurement differences and prevalence At the time of the initial publication of NCS results, the substantially higher rates of depressive disorders in the NCS (particularly current disorders) drew considerable attention and a number of efforts to explain the findings. The explanations largely focused on differences between the diagnostic interview used in the NCS and the assessment procedures used other surveys [10, 36]. Since other surveys conducted at the same time (e.g. the NLAES) and since (e.g. the NESARC) used methods that differed from the NCS but were similar to each other and produced findings that were more similar to each other than to the NCS, measurement differences between the NCS and other surveys appear a reasonable explanation for the high rates found by the NCS. This explanation is further supported by the very high prevalence estimates from the Canadian JCUSH, which used the same diagnostic interview as the NCS.

confirm that DSM-IV and ICD-10 produce similar prevalence estimates of major depression in other general population samples, but the Australian findings suggest that DSM-IV and ICD-10 estimates are comparable [37].

17.2.4 Incidence Incidence data can be particularly valuable in studying risk factors in order to understand disease aetiology. Psychiatric epidemiological studies have rarely provided such data because large, prospectively observed samples are required to generate accurate incidence estimates. Our main sources of information about the incidence of psychiatric disorders include the ECA and NESARC studies, with a few smaller international studies as well.

17.2.4.1 Incidence in the ECA Several investigators have reported on incidence data from the ECA study [38–40]. Annual incidence of first onset major depression was 1.6 per 100 across four of the five sites [38].

17.2.3.5 ICD-10 compared to DSM-IV All the studies in Table 17.1 used DSM-III, DSMIII R or DSM-IV criteria, and DSM criteria have been the norm in psychiatric epidemiological surveys conducted since the 1980s. However, for clinical purposes and health statistics in countries outside the United States, ICD criteria are more commonly used. Given the widespread use of ICD criteria outside the United States, a comparison between DSM and ICD rates is of interest. To our knowledge, only one study, the Australian National Survey of Mental Health and Wellbeing (ANSMHW) measured depression using both DSM-IV and ICD-10 criteria. This study used a different timeframe, past month, from the two used throughout the rest of this chapter, but the results are instructive nevertheless. The ANSMHW indicated that overall past month prevalence of major depression measured by DSMIV (3.2%) was very similar to past month prevalence measured by the ICD-10 (3.3%). Furthermore, no differences in past-month prevalence emerged when the data were stratified by age and gender. Additional research from different surveys is needed to

17.2.4.2 Incidence in the NESARC The size of the NESARC and the fact that a 3-year follow up interview was conducted with 34 653 of the original 43 093 respondents provides an unprecedented opportunity to study incidence rates and risk factors for major depression and other psychiatric disorders [41]. Among the 28 859 NESARC respondents at risk for lifetime first incidence of MDD at Wave 1, the weighted estimate of first incident MDD was 1.51% (s.e. 0.08). Of all mood disorders, incidence of MDD was highest (bipolar 1: 0.53, bipolar 2: 0.21). Rates of first incident MDD were similar to rates of any anxiety disorder (1.57), lower than rates of alcohol dependence (1.7) and higher than rates of any drug disorder (0.31). Risk factors for first-incident cases of MDD included female gender, age 18–55, low income and unmarried status [41]. A history of other disorders that contributed to risk for first-incident MDD included dysthymia (odds ratio, OR = 4.7), all anxiety disorders (ORs 1.9–2.2), and schizotypal (OR = 2.6), borderline (OR = 3.6) and narcissistic (OR = 1.8) personality disorders. The 293

CHAPTER 17

incidence rate for MDD reported in the NESARC (1.5) rate was virtually identical to the rate found in the ECA (1.6) [38], suggesting generalisability of this finding, at least in the United States.

17.2.4.3 Incidence outside the United States Outside the United States 1-year incidence rates have been estimated from two prospective surveys that included 1 to 3 year follow ups [42, 43]. These two studies measured major depression with DSM-III criteria. Results indicate that in Edmonton, Canada 1-year incidence of major depression was 2.8 and in the Netherlands it was 2.7. These estimates are higher than those found in the United States. The reasons for the higher incidence rates are unclear, as they may include true cross-national or methodological differences.

17.2.5 Subtypes of major depression 17.2.5.1 Depression with psychotic features Several ECA studies found evidence supporting the validity of major depression with psychotic features. In one ECA study, 14% of MDD were accompanied by psychotic features and that these cases, when compared with non-psychotic depression, had a more severe course, as reflected in increased risk of relapse, persistence over 1 year, suicide attempts, hospitalisation, comorbidity and financial dependency [44]. A more recent study in five European countries reported that current prevalence of depression with psychotic features was 0.4, which comprised 18.5% of all respondents screening positive for major depression [45]. The findings of the proportion of cases of major depression with psychotic features are remarkably similar, and are consistent with reports from clinical samples. The findings provide epidemiological support for the validity of depression accompanied by psychotic features to be a subtype of major depression.

17.2.5.2 Depression with ‘atypical’ features Several decades ago, clinicians considered ‘typical’ features of major depression to include insomnia and lack of appetite. Thus, patients presenting with excess 294

sleep and increased eating patterns were considered ‘atypical’. ECA data [46] indicated that compared to MDD without atypical features, MDD with atypical features (defined as overeating and oversleeping) was associated with a younger age of onset, more psychomotor slowing, and more comorbid panic disorder, drug abuse or dependence and somatization disorder. These differences could not be explained by differences in demographic characteristics or symptom severity. Most recently, among NCS respondents who met DSM-III criteria for MDD, 39% could be classified as having atypical depression. In this study, atypical depression was defined as meeting all the DSM-III criteria for MDD plus hyperphagia and hypersomnia [47]. Together, the evidence from epidemiological and treatment studies suggests that MDD with atypical features constitutes a distinct and valid subtype of MDD. Therefore while questions remain as to its exact nature [48], atypical depression is considered a valid subtype with distinct diagnostic characteristics as laid out in the DSM-IV [48]. NESARC analyses currently underway suggest that atypical depression may be emerging as the most common subtype of depressive disorders in the general population (C. Blanco et al., unpublished findings).

17.2.5.3 Empirically derived subtypes Using latent class analysis, a recent study of NESARC data suggested three subtypes of depression, termed ‘severely depressed’, ‘psychosomatic’ and ‘cognitiveemotional’ [49]. Individuals classified as severely depressed were less likely to live in an urban area than a rural area (OR: 0.7, 95% CI: 0.6, 0.9) and more to have never been married than married or cohabiting (OR: 1.5, 95% CI: 1.2, 1.8). Individuals classified as psychosomatic were less likely to be between 18 and 29 than between 30 and 44 (OR: 0.7, 95% CI: 0.5, 0.7), and less likely to be Hispanic than white (OR: 0.4, 95% CI: 0.2, 0.7). Cognitive-emotional depressed and severely depressed respondents were less likely to earn over $70 000 than to earn under $19 999 per year (OR: 0.7, 95% CI: 0.5, 0.98; OR: 0.8, 95% CI: 0.6, 0.97 respectively). Compared to non-depressed individuals, those with any of the three subtypes were more likely to be male, American Indian, have less than a high school education,

EPIDEMIOLOGY OF DEPRESSIVE DISORDERS

have experienced negative life events in the past 12 months, have a family history of major depression and to meet criteria for any lifetime mood, anxiety and personality disorder [49]. The utility of this subtyping scheme for major depression remains to be demonstrated in further research, but the study serves as an example of this type of research using an epidemiologic dataset.

17.2.6.2 Disability in the NESARC

Depression can cause substantial impairment in overall functioning. Studies have highlighted the extensive disability caused by depression, comparable in magnitude to that associated with a number of other chronic medical disorders.

The NESARC also provides information on disability, albeit in a different from, the SF12v2. This is a short measure of impairment in current functioning. In the NESARC, current major depression diagnoses were significantly associated with social and other types of disability [17]. Further, among respondents whose worst episode of major depression occurred in the 12 months prior to the interview, the number of depressive symptoms was strongly and significantly (p < 0.001) correlated with level of disability. Thus, the Global Burden of Disease Study and NESARC findings are consistent in demonstrating empirically that major depression is associated with disability, even though they are based on considerably different measures of disability and functioning.

17.2.6.1 Global burden of disease study

17.2.7 Risk factors for major depression

The Global Burden of Disease Study conducted by WHO in 1990 [50] investigated the worldwide prevalence and disability due to mental disorders, including unipolar major depression and panic disorder. This study developed a measurement index, the Disability-Adjusted Life Years (DALYs) to compare the burden of disease from premature mortality and years lived with disability across various types of disease and injury in global populations. The DALYs consists of two classes, the years of life lost (YLL to premature mortality) and the years of life lived with disability (YLD, adjusted for severity of disability). Using these two measures, depression was found to be one of the most disabling diseases in the world and ranked as the fourth most disabling disease (DALY = 50.8) after lower respiratory infections, diarrhoeal diseases and perinatal diseases [51]. Of note, DALY scores for disease rankings decreased relatively slowly with intervals ranging from approximately two to six points. The exception to this was a decrease of more than 40 DALY points between the third leading cause of disability, perinatal disorders and the fourth, unipolar depressive disorders. These studies were followed up in 2001, and results indicate that unipolar depressive disorders were ranked as the third most disabling disease in high-income countries, representing 5.6% of all DALYs [52]. Furthermore, depression was ranked as the world leading cause of YLD.

17.2.7.1 Gender

17.2.6 Depression as a disabling disease: Shifting paradigms

One of the most consistent findings in psychiatric epidemiology is the gender difference in prevalence and incidence of major depressive disorder. With very few exceptions [53, 54], women are at higher risk for unipolar major depression than men. Consistently higher rates of major depression in women were reported in general population studies using DSM-III and DSM-III-R (Table 17.2). The ratios of rates of major depression in females to males were about 2 : 1, with a range from 0.8 : 1 in China to 2.7 : 1 in the ECA. Earlier efforts to explain the consistent gender difference [55, 57] indicated that the greater risk among women is not accounted for by a tendency for women to report distress or to seek help more readily than men. Greater risk among women appears to be specific to unipolar depressive disorders, as the incidence and prevalence rates of bipolar disorder are similar in men and women [41, 58–60]. Simply relying upon prevalence information would leave open the possibility that the apparent greater risk among women could be explained by more persistent or recurrent courses of major depression among women, accounting for more active cases at any one point in time. The greater risk for lifetime disorders among women could also be explained by better memory or greater willingness of women to 295

CHAPTER 17 Table 17.2 gender.

Lifetime prevalence of major depression by

Place The Americas U.S. ECA NHANES NCS NLAES NESARC Edmonton, Canada Puerto Rico Mexico Asia Seoul, Korea Hong Kong Korea China Kunming, China Australasia New Zealand

Female Male Sex ratios References female/male

17.2.7.2 Age 7.0 2.6 12.6 6.3 21.3 12.7 11.0 8.6 17.1 9.0 11.4 5.9 5.5 3.5 15.9 9.0

2.7 2.0 1.7 1.3 1.9 1.9 1.6 1.8

[7, 55] [14] [10, 11, 56] [16] [17] [19] [22] [23]

4.1 2.4 5.9 3.2 2.4

2.4 1.3 2.6 3.9 1.5

1.6 1.8 2.3 0.8 1.6

[22, 24–26] [27, 29] [28] [29] [30]

16.3

8.8

1.9

[31, 32]

report past episodes than men. However, both of these explanations are inconsistent with incidence data. This includes incidence from four sites of the ECA, in which the annual incidence rate was almost twice as great in women as in men [57]. Similarly, NESARC data indicates that males were significantly less likely than females to report one year major depression incidence (OR: 0.5, 99% CI: 0.4, 0.8) [41]. Taken together, the ECA and NESARC findings suggest that the higher prevalence rates in women reflect a true increased risk of major depression in the UnitedStates. The reports of a higher risk of major depression in women are consistent across cultures. The elevated rates for women appear in studies with a variety of sampling and measurement methods. While some studies conducted in the late 1980s suggested a decreasing sex difference in rates among those persons born after World War II [61], the gender difference did not entirely disappear. Furthermore, although depression was the leading cause of YLD in the Global Burden of Disease study, the burden of all depression was 50% higher for women than men [52]. Although the increased risk of major 296

depression in women is a firmly established and widely accepted finding, the reason for the increased risk among women remains unclear.

A number of studies from the 1980s and 1990s (e.g. the ECA [62], and the 1992 Cross-National Collaborative Group [63]) indicated that the risk of current and lifetime major depression was highest in the youngest age groups. Further, a review of studies conducted prior to the late 1980s also found evidence for higher prevalence of major depression in the youngest cohorts, that is those born after World War II [61]. This gave rise to questions about whether the age difference was attributable to artifact, true age effects or to some specific risk factor affecting the birth cohort born after World War II (the ‘baby boom’ cohort) [61]. Most [64, 65] although not all [66] artifactual explanations were unsupported, but other substantive explanations, for example biological, cultural and economic factors, remained unclear. Comparing the age distributions of major depression in the NLAES and the NESARC offers additional information on the birth cohort effect extended into the present millennium. In the 1991–1992 NLAES, the largest US sample from the 1990s, prevalence of MDE in the prior 12 months among those aged 18–29, 30–44, 45–64 and 65+ was 5.99, 3.86, 1.80 and 0.55, respectively [16]. For the same age groups, lifetime prevalence was 10.38, 9.48, 6.79 and 1.50. Thus, the risk for MDE was strikingly higher in the younger age groups, for both current and lifetime MDE. In contrast, NESARC data for the same age groups [17] indicated 12-month prevalence of 6.39, 5.52, 5.62 and 2.69, and lifetime prevalence of 12.02, 14.03, 15.91 and 8.91. Thus, in the NESARC, the difference in 12-month prevalence between the youngest group and those aged 30–65 was slight compared to the difference in the corresponding age groups in the NLAES. Further, in contrast to NLAES, NESARC lifetime prevalence was highest among middle-aged respondents (i.e. those between the ages of 30 and 64). The findings suggest that the higher risk among the youngest respondents may ultimately emerge as a specific birth cohort effect. Identifying the reasons for this would

EPIDEMIOLOGY OF DEPRESSIVE DISORDERS

provide important information about the aetiology of major depression. International studies also provide prevalence estimates of major depression in young adults. Past-year prevalence was 7.0% among 6193 respondents aged 19–24 in Zurich, Switzerland [67, 68], while a slightly lower rate, 5.2% was found among 3021 respondents aged 14–25 in Germany [8]. International studies suggest that the US pattern of higher risk among younger respondents does not always apply elsewhere. For example, in Australia, rates of DSM-IV major depression increased with age and peaked in the 45–54 age group, with respondents aged 45–54 significantly more likely than 18–24 year olds to report past month major depression (OR: 1.9, 95% CI: 1.2, 3.1) [37]. Furthermore, no significant age differences in major depression were found in Brazil, Chile, Canada, the Czech Republic, Japan, Mexico [8] or China [29, 30].

17.2.7.3 Geriatric depression In the ECA, although MDD decreased with age, specific depressive symptoms were more common among older respondents [69]. While the prevalence of major depression has been estimated between 1 and 5% of the elderly, a much greater proportion of the older community dwelling population, 12–20%, suffer from depressive symptoms [69]. A prospective cohort study indicates a relationship between depressive symptoms and physical decline in the elderly [70]. Consistent with prevalence studies, incidence data from the NESARC indicates that respondents over the age of 55 were significantly less likely to report 1-year MDD incidence that the younger age groups [41]. In studies outside the United States, findings are similar. For example, in Australia, respondents older than 64 were significantly less likely than those 18–24 to report past month major depression (OR: 0.5, 95% CI: 0.2, 0.9) [37]. The prognosis of depressive disorders in the community dwelling elderly aged 65 and older has been studied using the Geriatric Mental StateAutomated Geriatric Examination for Computer Assisted Taxonomy (GMS-AGECAT) as well as psychiatrist-confirmed DSM-IV criteria for depressive disorders [71]. At 3-year follow-up, 30.2% of

depressed subjects had died, 34.9% had persistent or relapsed depression and 10.4% had recovered completely. Degree of physical illness, bereavement and positive family history were associated with poorer outcome.

17.2.7.4 Adolescent depression Among adolescents, estimated lifetime prevalence of DSM-IV major depressive disorder ranged from 4 to 17% [72, 36]. A large study in this age group was the 1999 British Child and Adolescent Mental Health Survey, which investigated the prevalence of DSMIV disorders among 10 438 children aged 5–15 in the United Kingdom [73]. The prevalence of current major depression among boys and girls was 0.9 and 1.0%, respectively. Prevalence increased with age, from 0.1% among 5–7-year-olds to 2.5% among 13–15-year-olds. Predictors of recurrence of major depressive disorder were investigated in a US community sample with adolescent-onset major depressive disorder, followed up at ages 19–23. Risk factors for recurrence included female gender, multiple depressive disorder episodes in adolescence, heavier family loading for recurrent major depression and borderline personality symptoms [74]. Data from the British Child and Adolescent Mental survey indicate that among children with at least one psychiatric diagnosis, those with depression were most likely to have a comorbid diagnosis, with 66% reporting at least one other disorder [73]. Comorbidity can be disabling in adolescents. Among 1507 community adolescents aged 14–18 with single and comorbid forms of depression, anxiety, substance use and disruptive behaviour was compared on six clinical outcome measures [75]. Adolescents with comorbid disorders had poorer academic performance, higher mental health treatment and higher risk for suicide attempts.

17.2.7.5 Race and ethnicity In the ECA study, the risk for major depression did not differ consistently between African-American and white subjects [76]. At the Los Angeles site of the ECA, lifetime prevalence of major depression was lower among Hispanics than others [77], while incidence was higher [40]. Prevalence of major 297

CHAPTER 17

depression in Hispanics in Puerto Rico was similar to their prevalence in the ECA [22, 63]. In the NCS, blacks had lower risk for pure major depression (i.e. major depression without psychiatric comorbidity) than whites, but similar risk for comorbid major depression [10]. Hispanics were at higher risk for comorbid major depression than whites, but the risk for pure depression was similar [10]. The reason for these differences is not clear. In the NLAES, the prevalence of MDE was substantially lower rate among blacks (12-month, 2.55; lifetime, 5.62) than among other respondents (12-month, 3.43; lifetime, 10.29). Further information on African-Americans was provided by the 2001–2003 National Survey of American Life, a study conducted on the wellbeing of African-Americans that included 5191 blacks and a comparison group of 891 whites. This study also showed highest lifetime prevalence of MDD among whites (17.9%) with lower prevalence among African-Americans (10.4%) (p < 0.001) [78]. In the NESARC [17], controlling for many other factors, black respondents (N = 8245) also had significantly lower lifetime prevalence of major depression than whites. A methodological explanation of the lower rates in blacks is that they were over-represented in inpatient treatment or other institutions and therefore not included in the surveys. Studies have been inconsistent on whether this is a meaningful source of bias [79, 80] and the methodology of the NLAES and NESARC was designed to minimise such bias in any case [81, 82]. The consistency of lower risk for major depression among blacks in two large, nationally representative studies conducted 10 years apart supports their validity, and suggests that an explanation for this could aid in improving the general understanding of major depression. In the NESARC, Hispanic and Asian/Pacific Islander respondents also had significantly lower lifetime prevalence compared to whites. The only NESARC race/ethnic group with significantly higher lifetime prevalence of MDD compared to whites was Native Americans. This group had very high lifetime prevalence (19.17) [17]. Concerning incidence, NESARC race/ethnic groups did not differ, [41], perhaps because the incidence rate was low and thus statistical power to detect race/ethnic differences was limited. 298

In general, prevalence of major depression was far lower in Asian than Western countries (Table 17.1). For example, NESARC lifetime prevalence (13.2%) [17] contrasts sharply with findings from Japan (3.0) [8] and China (2.0) [30]. Among Asians, depression may take on a more somatic and less psychological form [83], with depression reported in terms of cognitive impairment and somatic symptoms not fully assessed in Western measures of MDD [29]. A United States–China collaborative study in the 1970s showed that Chinese psychiatrists diagnosed a third of their patients with ‘neurasthenia’, considered a disorder of the neurological system characterised by weariness, irascibleness, poor concentration and unstable and depressed mood. Interviews of Chinese neurasthenic patients using a highly reliable American diagnostic interview [84]indicated that 87% of the patients met DSM·III criteria for MDD [85]. This cross-cultural research suggests that culturally mediated values and views of symptoms may influence the expression as well as the diagnosis of psychiatric disorder. Although the use of standard interview techniques and methodologies across cultures may mitigate these effects to some extent, the sharply lower rates of depression in China and Japan as compared to Western countries suggest that cultural factors may play an important role in the expression of major depression.

17.2.7.6 Socioeconomic and employment status In the ECA, socioeconomic status was not associated with major depressive disorder, although risk of major depression was higher among the unemployed, including homemakers. The causal direction of this association is unclear. Certainly, job loss and inability to find a job can contribute to psychological, social and economic stress, which may predispose to depression. On the other hand, depressed individuals may be impaired in their ability to find or hold a job. The finding of an increased risk for depression among homemakers in the ECA is consistent with a smaller study (N = 800) of women conducted in Camberwell, United Kingdom [86] that found elevated rates of depression among homemakers with small children compared to others. In the NCS, lower education and being unemployed or a homemaker

EPIDEMIOLOGY OF DEPRESSIVE DISORDERS

were significant risk factors for major depression, even after controlling for other variables associated with depression (e.g. age, sex, marital status). In the NESARC, prevalence of lifetime major depression was significantly higher among respondents earning less than $20 000/year than those earning more than $70 000, although other income groups did not differ significantly [17]. Incidence was also higher among those in the lowest income level compared to the highest (OR: 2.1, 95% CI: 1.1, 3.7) [41]. However, no other income levels affected incidence, perhaps because income questions are sensitive survey items [87, 88]. Concerning housewives, the NESARC revealed no differences in past year major depression between female full time homemakers and those reporting full time paid employment (OR: 1.0, 95% CI: 0.8, 1.1; D. Hasin, unpublished), controlling for other relevant demographic characteristics. In Australia, compared to those working full or part-time, risk for past month MDE was higher among those unemployed on a short-term (OR: 2.7, 95% CI: 1.6, 4.5) or long-term basis (OR: 3.5, 95% CI: 1.4, 8.3) [37]. The Canadian and Netherlands studies found significantly lower risk for 12-month MDE among those with higher income (p < 0.05) [8]. However, studies from Brazil, Chile, the Czech Republic, Japan, Mexico and Turkey reported no difference in 12-month MDE by education (p > 0.05) [8]. Since all the non-US studies with no effect of socioeconomic indicators (except the Turkish study) had small sample sizes, the lack of effect for unemployment may have resulted true differences or from low statistical power, leaving the question unresolved.

17.2.7.7 Urban–rural residence An environmental hypothesis about the aetiology of major depression involves living in an urban or suburban area compared to a rural area. The ECA study found significant differences between urban and rural rates of major depression at the Durham, NC and at the St. Louis, MO sites, although the relationship was different in these two sites. In Durham, the 12-month prevalence of major depression twice as high in the urban compared to the rural sample, while in St. Louis, major depression was more

prevalent in the rural sample. These sites differed in that the Durham rural area was remote and isolated, while in St. Louis the large urban centre was more transitionally connected by suburban sprawl to the rural area. In Taiwan, small-town samples showed trends toward higher major depression rates than ‘rural village’ or ‘metropolitan Taipei’ samples [89]. The Puerto Rico study found trends toward higher prevalence rates among urban as compared to rural residents [22]. Neither the NCS nor the NESARC [17, 45] found urban–rural differences in the prevalence or incidence of major depression. Thus, findings from the ECA have not been replicated in more recent studies. Similarly, studies carried out in Canada, the Czech Republic, Germany and Turkey found no urban–rural differences in past year rates of MDE (p > 0.05) [8]. Thus, the urban–rural hypothesis has not been strongly supported by the recent literature.

17.2.7.8 Marital status Marital status was associated with major depression in all North American studies listed in Table 17.1. Married and never-married persons had a significantly lower risk of depression compared to separated, divorced or widowed individuals, even after controlling for sex, age and other variables associated with depression. Persons who were currently separated or divorced had a risk for major depression two or three times higher than those with another marital status. The NESARC was consistent with this; compared with married respondents, individuals who are separated, divorced or widowed had twice the odds (OR 2.2, 99% CI: 1.4, 3.4) of major depression incidence [41]. In Australia, separated, widowed or divorced respondents had over twice the odds (OR 2.5, 95% CI: 1.5, 4.0) of past month MDE compared to married respondents [37]. Similarly, in Canada, Chile and the Netherlands, unmarried respondents were significantly more likely to report past year MDE than married respondents (p < 0.05) [8]. The few exceptions to increased risk for formerly married respondents included the studies from Brazil, the Czech Republic, Japan, Mexico and Turkey [8]. Causal inferences regarding the nature and direction of the association between rates of 299

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major depression and separation and divorce are problematic. Episodes of major depression are often followed by marital maladjustment, which can persist for years after the acute depressive episode [90, 91]. On the other hand, the stresses of separation and divorce may predispose to the onset of depression. Incidence data can help sort out the causal direction. In the NESARC [45], the risk for incidence of major depression was significantly increased among respondents who were separated, divorced or widowed at the Wave 1 interview, compared to those who were married (adjusted OR 2.2). In contrast, those who had never been married did not have elevated risk for incident major depression compared to the married respondents. The incidence findings suggest that the ending of a marriage can be an aetiological risk factor for MDD.

17.2.7.9 Psychiatric comorbidity ECA data indicated associations between major depression and several other psychiatric disorders, including drug and alcohol disorders [92], and panic disorder [93]. Incidence data from the ECA [40] showed that dysthymic disorder predicted a fivefold increase and schizophrenia predicted a threefold increase in the risk for first-onset major depression. Among NCS respondents, comorbidity was very common [36], and a lifetime history of major depression was associated with having one or more anxiety disorders [94, 95]. Comorbidity with major depression was strongest for panic disorder, generalised anxiety and social phobia [96–98]. A comprehensive analysis of comorbidity data from the NESARC [17] showed that for 12-month as well as lifetime diagnoses, all major Axis I and all Axis II disorders were associated with major depression. However, the strength of the associations varied by disorder. Alcohol and drug abuse showed the weakest associations (OR < 2.0) with MDD, while alcohol, nicotine or and drug dependence showed stronger associations (ORs 1.9–3.7). Associations of major depression with anxiety disorders were the strongest (ORs 2.5–8.6). Personality disorders were also strongly associated with major depression (ORs 2.5–4.2). Associations between unipolar and bipolar disorders were not assessed because the categories are mutually exclusive. 300

Wave 2 incidence data from the NESARC indicate that risk of incident major depression was significantly higher for respondents with the following 12-month disorders at Wave 1: alcohol dependence (OR = 1.2), dysthymia (OR = 4.7), panic disorder (OR = 1.9), social phobia (OR = 2.0), specific phobia (OR = 2.0) and generalised anxiety disorder (OR = 2.2). Additionally, individuals were significantly more likely to report incident major depression if they had met criteria for schizotypal (OR = 2.6), borderline (OR = 3.6) or narcissistic (OR = 1.8) personality disorders [40].

17.2.7.10 Family history/genetics Due to the difference in the nature of family-based and genetic research, we include studies in this section that do not meet the inclusion criteria stated at the beginning of the chapter. Family studies show a two to threefold increase in risk of major depression among first-degree relatives of individuals with major depression compared to relatives of normal controls [99–103]. A meta-analysis of five large and methologically robust family studies were consistent in their findings, indicating the influence of a family history of depressive disorders on susceptibility to major depression (aggregate OR: 2.8, 95% CI: 2.3, 3.5) [104]. A recent family study extended the family design to three generations [105]. Psychopathology was compared between three groups of respondents: those with no parental or grandparental depression, those with parental depression only, and those with parental and grandparental depression [105]. Results indicated that individuals with both parental and grandparental depression were at the highest risk of developing psychopathology and mood disorders in general. The data showed a similar trend when major depression was examined specifically in the grandchildren, although the results did not reach significance, possibly because a large number of the grandchildren were prepubertal and major depression is uncommon before puberty [105]. Familial aggregation of a disorder is consistent with either genetic or environmental aetiology, and family studies cannot determine whether the risk is genetic or environmental. Twin studies can do so, by comparing the concordance of disorders between monozygotic twins, whose DNA is identical, and

EPIDEMIOLOGY OF DEPRESSIVE DISORDERS

dizygotic twins, who share their environments but on average, only half their DNA. Greater concordance in monozygotic than dizygotic twins indicates genetic contribution to the aetiology of a disease or trait. Several twin studies indicate a moderate genetic contribution to the risk for major depression [104, 106, 107]. Based on the evidence from twin/behavioural genetics studies, molecular genetics studies, including a large genome wide association (GWA) study, are now underway to identify the specific genetic variants giving rise to the increased risk for major depression within families. Preliminary data suggests that DNA sequence variations in one or more genes in chromosome 15q [108, 109], 17p12 and 8p22–p21.3 [109] may increase vulnerability to major depression. There is also evidence that a polymorphism of the serotonin transporter gene moderates the influence of stressful life events on depression [110]. Numerous studies have replicated the finding that the serotonin transporter promoter, 5-HTTLPR, interacts with environment (stressful life events) in the risk for major depression. Among those exposed to stressful life events, the s allele confers greater risk for major depression than the l allele [110–116]. Recent studies have focused on a subtype of major depression, early onset recurrent cases with onset less than age 30. Common variants in some genes including FLJ12484, RHCG, DKFZp547K1113, VPS33B, SV2B, SLCO3A1, RGMA, MCTP2 and particularly NTRK3 may be associated with early onset of major depression [117]. However, whether this subtyping turns out to be necessary awaits the results of the GWA studies.

17.2.8 Secular trends Epidemiologic studies that use similar measures and sample designs, conducted on successive samples, offer the opportunity to indicate important changes over time in the prevalence rates of conditions such as major depression. In an analysis directly comparing prevalence of MDE between the NLAES and NESARC (thus, comparison between 1991–1992 and 2001–2002), the prevalence of MDE in the last 12 months increased significantly, from 3.3 to 7.1% [118]. (Note that this estimate for MDE in the NESARC is higher than the estimate in

Table 17.1, which shows MDD. The lower prevalence in Table 17.1 is due to exclusion of bipolar cases). When stratified analyses were performed of the differences in prevalence between the NLAES and the NESARC, significant increases found for black and white men and women of all age groups, and for Hispanic women of all age groups. No significant increases were found among Hispanic men of any age group. Given the subjective and objective difficulties and disability associated with major depression, identifying the reasons for the continuing increase in prevalence, and whether it will continue into the future, is an important research priority.

17.2.9 Major depression – summary In contrast to the amount of knowledge available on the epidemiology of major depression in the 1980s, over 30 general population studies have now been conducted that used standardised diagnostic assessments, with sample sizes over 1000. Taken as a whole, the lifetime and 12-month prevalence of major depression varies by period, location and respondent characteristics. The large amount of information now available has supplied the research field with information about some consistent aspects of major depression, and some aspects that may be changing. the prevalence of major depression is higher in Western than in Asian countries, and a birth cohort increase for the ‘baby boom’ generation born following World War II appears confirmed by the most recent prevalence data from NESARC showing that baby boomers are at somewhat higher risk for a lifetime major depression diagnosis than the younger birth cohorts of adults included in the survey. The epidemiological studies provided data on a number of risk factors for major depression. Women are at clearly increased risk factor for major depression in most prevalence and incidence studies, a gender difference that remains to be explained. Another consistent finding is that formerly married individuals are at higher risk of major depression. NESARC incidence data suggests that becoming unmarried is a risk factor for subsequent development of major depression among those who have not previously experienced the disorder. While earlier studies suggested that psychiatric comorbidity with major depression occurred, the 301

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extent to which this could be shown for individual disorders was limited by the sample sizes of the studies conducted prior to 2001–2002. Using the NESARC, major depression was shown to be comorbid with all other Axis I and Axis II disorders studied, with varying levels of association found for different disorders. Incidence data from the ECA and the NESARC shows that several psychiatric disorders increase the risk for first-time onset of major depression. The extent of comorbidity of other psychiatric disorders with major depression remains to be explained. In all, many aspects of the epidemiology of major depression were formerly uncertain due to small samples, methodological inconsistencies between studies or too few studies to examine consistencies. However, many aspects of major depression are now sufficiently well established that studies investigating the causes of these features are warranted.

17.3 Dysthymia 17.3.1 Definition The DSM-IV diagnosis of dysthymic disorder (dysthymia) in adults requires a depressed mood most of the day, for more days than not, as indicated either by subjective account or observation of others, for at least 2 years. In addition, at least two other symptoms must be present while depressed (e.g. poor appetite or overeating, insomnia or hypersomnia, low energy or fatigue, low self esteem, poor concentration or difficulty making decisions or feeling of hopelessness [4]. The symptoms are required to cause clinically significant distress or impairment in social, occupational or other important areas of functioning and not to be caused by the direct physiological effects of a substance (e.g. a drug of abuse, a medication) or a general medical condition (e.g. hypothyroidism). These symptoms and the depression mood must not have been absent for more than 2 months during the 2-year period. DSM-Ill, DSMIII-R and DSM-IV criteria for MDD are similar [2, 3]. Dysthymia is diagnosed in DSM-IV among individuals without a history of bipolar disorders (manic, mixed or hypomanic episodes and cyclothymic disorder) and no history of MDD during the first 2 years of the affective disturbance. 302

17.3.2 Prevalence Much less is known about the epidemiology of dysthymia than major depression. Earlier studies mainly focused on rates of lifetime prevalence [19, 119, 120], as assessed by the Diagnosis Interview Schedule (DIS) [121]. Concerns have been raised regarding the test-retest reliability and validity of the DIS diagnoses of dysthymia in accurately assessing dysthymia, with one study indicating a sensitivity of only 20% [122]. However, data from the NESARC which measures dysthymia using the Alcohol Use Disorder and Associated Disabilities Interview Schedule (AUDADIS) reported fairly good test–retest reliability (kappa = 0.58) [123]. Table 17.3 shows the lifetime prevalence rates per 100 of dysthymia based on general population surveys in North America, Europe and Asia. Findings from US studies vary. The most representative study, the NESARC, reported a prevalence of 4.9 which was between prevalence found in the ECA (3.0) and NHANES (6.1). Estimates of prevalence in Europe were similar to those obtained in the United States (4.1). The lowest prevalence was reported in Asia (median = 2.2) and Israel (0.9). These estimates appear to differ from North America to a greater degree than was found for prevalence of MDD.

17.3.3 Risk factors The study of risk factors for dysthymic disorder is somewhat complicated by the unresolved question of whether it is a mood disorder distinct from MDD. Clinical observation of depressed patients over time suggests that a condition analogous to dysthymic disorder may precede or follow episodes of MDD. Incidence data from the ECA and NESARC indicates that individuals with dysthymic disorder, when compared with controls, are at a greater than fourfold increased risk for MDD [35, 40, 126, 127]. However, even individuals with dysthymic disorder that has not developed into major depression have significantly elevated medical and psychiatric treatment utilisation and suicidal behaviour compared to individuals with no psychiatric disorder [7]. Therefore although the boundary between dysthymic disorder and MDD remains unclear, the evidence that uncomplicated dysthymic disorder predicts psychosocial morbidity

EPIDEMIOLOGY OF DEPRESSIVE DISORDERS Table 17.3 Lifetime prevalence of dysthymic disorder. Place

Female

Male

Total

Sex ratios female/male

References

The Americas US ECA NHANES NESARC Puerto Rico Edmonton, Canada

4.1 4.5 6.2 7.6 5.2

2.2 7.7 3.5 1.6 2.2

3.0 6.1 4.9 4.7 3.7

1.9 0.6 1.8 4.8 2.4

[7, 124] [14] [125] [22] [19]

Asia Seoul Hong Kong Korea

2.8 2.8 0.9

1.6 1.1 0.1

2.2 3.9 0.5

1.7 2.5 9

[24–26] [27] [28]

Europe Six countriesa

5.6

2.6

4.1

2.2

[34, 126]

–

–

0.9

–

[35]

Middle East Israel a This

study was conducted in Belgium, France, Germany, Italy the Netherlands and Spain.

and first onset MDD suggests that dysthymia should remain as a separate diagnostic category and not be combined with MDD.

17.3.3.1 Gender All general population studies reporting separate male and female rates of dysthymic disorder found increased risk among women. The ratio of female to male rates of dysthymic disorder ranged from 1 : 7 in Seoul and 1 : 9 in the ECA study to a high of 4 : 8 in Puerto Rico and 9 : 0 in Korea.

17.3.3.2 Age Unlike MDD, in which the highest prevalence was often found in younger groups, the prevalence of dysthymic disorder in the ECA tended to increase in the 30–65 age group, and then drop dramatically in those over 65 years of age. South Korea and Edmonton, Canada also found an increasing prevalence with age. As in the ECA, the Edmonton study showed a steep drop in prevalence over age 65 years, but Korea did not sample those over 65. It is not clear why the age effects are different for MDD and dysthymic disorder (see Section 17.2.7.2). The variations by age may reflect true differences

between these disorders, but an understanding of these differences in complicated by the high comorbidity between MDD and dysthymic disorder and by the fact that these are comparisons between the chronic disorder of dysthymia and MDD, which may range in duration from a brief to a chronic condition.

17.3.3.3 Race/ethnicity The ECA study showed no significant differences in rates of dysthymic disorder when comparing African-Americans and whites. However, rates of dysthymic disorder were higher among Hispanics than those of African-Americans or whiles, consistent with the higher rate in Puerto Rico [22]. As with MDD, rates of dysthymic disorder were markedly lower in Taiwan than in the West, while the prevalence in Korea was comparable with that in the West (see Section 17.2.7.5). NESARC data focused on Hispanic groups, indicated that US-born non-Latino whites were significantly less likely than US-born Puerto Ricans to report lifetime dysthymia (OR: 0.6, 95% CI: 0.4, 0.9). Similarly, foreignborn non-Latino whites were significantly less likely than island-born Puerto Ricans to report lifetime dysthymia prevalence (OR: 0.3, 95% CI: 0.1, 0.6) [128]. Thus, both the ECA and the NESARC suggest increased risk for dysthymia among Hispanic groups. 303

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17.3.3.4 Marital status Dysthymic disorder was more prevalent among unmarried than married persons under the age of 65 years in the ECA study [7]. In Edmonton, Canada, divorced or widowed persons had higher rates than married persons, who had higher rates than the never married. As is the case with MDD (see Section 17.2.7.8), the direction of causality in these associations between age and prevalence of dysthymic disorder is not clear.

17.3.3.5 Urban–rural Puerto Rico [22] showed a significantly higher prevalence of dysthymic disorder among urban than rural dwellers. In Taiwan, urban and rural rates were similar, but the small town rates were somewhat higher, as was the case for MDD.

17.3.3.6 Comorbidity Estimates from the ECA indicate that dysthymia is associated with higher rates of alcohol and drug disorders than major depression. For example, compared with respondents with major depression, respondents with dysthymia reported more alcohol and drug use disorders [92]. The most recent data from the NESARC indicate that dysthymia is significantly associated with any personality disorder, particularly paranoid, obsessive-compulsive and avoidant personality disorders [129].

17.4 Summary Dysthymic disorder, a milder but more chronic form of depression than MDD, shares some risk factors with MDD, including female gender and unmarried status. Although almost half of the persons with dysthymic disorder also had episodes of MDD, individuals with uncomplicated dysthymic disorder, when compared with individuals with no psychiatric disorder had substantial evidence of morbidity such as treatment seeking and suicide attempts. Additional studies are warranted to clarify the relationship of MDD to dysthymia, and whether the aetiology of dysthymia among individuals without a history of MDD is different from the aetiology 304

among those who have also experienced episodies of MDD.

Appendix 17.A Measurement of major depression in the NLAES and NESARC Due to the size and informativeness of the NLAES and NESARC, we provide some information on the diagnostic instrument used in these studies. This was the NIAAA AUDADIS [130], a structured diagnostic interview. In the NESARC, consistent with DSM-IV, a MDE was diagnosed when at least 2 weeks of persistent depressed mood and/or anhedonia were present, accompanied by at least four more of the nine DSM-IV depression symptoms. Episodes meeting these criteria that were due to medical illness, heavy substance use (i.e. substanceinduced depressive disorders) or bereavement were ruled out. Lifetime DSM-IV MDD was defined as having at least one MDE over the life course without history of manic, mixed or hypomanic episodes (i.e. excluding bipolar 1 and bipolar 2 disorders). Among respondents with lifetime MDD thus defined, respondents with at least one MDE in the year preceding the interview were classified with 12-month MDD. The version of the AUDADIS used in the NLAES was very similar, except that bipolar disorders were not evaluated, and thus the type of major depression in the NLAES was MDE. Psychometric studies indicate that the AUDADIS diagnoses of MDD and MDE have good testretest reliability and validity. In the NESARC, test–retest reliability was good for MDD (kappa = 0.65–0.74) [17]. Furthermore, clinical reappraisal of MDD diagnoses showed that AUDADIS measures and psychiatrists’ diagnoses agreed well (kappa = 0.64–0.68) [131]. In the NESARC, validity of the AUDADIS assessment of MDD was further assessed with the Mental Component Summary, Social Functioning, Role Emotional function and Mental Health components of the Short Form-12v2 (SF-12v2), a reliable and valid impairment measure in population surveys [132]. Linear regression analyses of NESARC data indicated highly significant associations (p < 0.00001) between AUDADIS diagnosis of MDD and SF-12v257 scores [133].

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Epidemiology of anxiety disorders Ewald Horwath,1 Felicia Gould2 and Myrna M. Weissman3 1 Department

of Psychiatry & Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, USA 2 Department of Psychiatry, Case Western Reserve University School of Medicine at MetroHealth, Cleveland, OH, USA 3 College of Physicians and Surgeons of Columbia University and Division Epidemiology, New York State Psychiatric Institute, NY, USA

18.1 Introduction The Epidemiological Catchment Area (ECA) study was the first epidemiological survey to employ lay interviewers and a standardised assessment tool, the Diagnostic Interview Schedule (DIS), to collect data on psychiatric signs and symptoms and to develop estimates of the prevalence of specific mental disorders based on Diagnostic and Statistical Manual of Mental Disorders (DSM)-III diagnostic criteria. Since the publication of the ECA results, a number of other community studies have employed standardised assessments and modern diagnostic criteria. The National Comorbidity Survey (NCS) [1], National Comorbidity Survey Replication (NCS-R) [2], National Latino and Asian American Study (NLAAS) [3], The National Survey of American Life (NSAL) [3], National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) [4] and the European Study of the Epidemiology of Mental Disorders (ESEMeD) [5] all have made substantial contributions to our current understanding of mental illness and its prevalence world-wide.

We will limit our review to epidemiological studies of the 1980s, 1990s and early twenty-first century that used standardised interview instruments, operationalised diagnostic criteria and reported data on widely agreed upon diagnostic categories, such as those in DSM-III, [6], DSM-III-R, [7], DSM-IV [8] and the International Classification of Diseases, ninth revision (ICD-9) [9]. We will focus on five categories of anxiety disorders: panic disorder (PD), agoraphobia, social phobia, generalised anxiety disorder (GAD) and obsessive–compulsive disorder (OCD).

18.2 Anxiety disorders Anxiety has been recognised as a symptom ever since the writings of Freud. However, anxiety states began to be subdivided into distinct disorders such as panic, phobias and GAD somewhat more recently, beginning with the DSM-III. Our focus in this chapter is on the more recent epidemiologic studies in which anxiety disorders are subdivided on the basis of DSM-III,

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

311

CHAPTER 18 Table 18.1 Epidemiological community surveys of psychiatric disorders using DSM-III, DSM-III-R or DSM-IV diagnostic criteria. Place USA-NCS(DSM-III-R) USA-NCS-R(DSM-IV) USA-NLAAS(DSM-IV) USA-NSAL(DSM-IV) USA-NESARC (DSM-IV) USA - ECA New Haven Baltimore St. Louis Durham, NC Los Angeles Edmonton, Canada Puerto Rico Florence, Italy Seoul, Korea Taiwan Urban Small towns Rural villages New Zealand Zurich, Switzerland Europe–ESEMeD Multinational – WHOWMHSI

n

Age (Years)

Investigator

8,098 9,090 2,554 9,090 43,093 18,572 5,034 3,481 3,004 3,921 3,132 3,258 1,551 1,110 5,100 11,004 5,005 3,004 2,995 1,498 6,193 21,425 85,052

15–54 18–60 18–65 18–60 18–65 18+

Kessler et al. [10] Kessler et al. [11] Alegria et al. [12] Jackson et al. [3] Kessler et al. [13, 14] Weissman et al. [15, 16]

18+ 17–64 15+ 18–65 18+

Bland et al. [17] Canino et al. [18] Faravelli et al. [19, 20] Lee et al. [21–23] Hwu et al. [24]

18+ 19–24 19–65 65–65

Joyce et al. [25, 26] Angst, Dobler, Mikola [27, 28] Alonso et al. [29] Kessler et al. [30]

This table was adapted from Textbook in Psychiatric Epidemiology (2nd ed.), Tsuang M.T., Tohen, M., Zahner, G.E.P. (Eds.): New York, N.Y., John Wiley & Sons, Inc., 2002.

DSM-III-R, DSM-IV or ICD-9 criteria. Epidemiological community surveys that assess psychiatric disorders using these criteria are listed in Table 18.1. Anxiety disorders are among the most prevalent psychiatric disorders based on community studies using modern diagnostic criteria. In the United States the National Community Survey Replication (NCS-R) reported a lifetime prevalence of any anxiety disorder of 28.8% based on a sample of 9282 English-speaking respondents aged 18 years and older [13]. The ESEMeD included data from more than 21 400 participants and involved the efforts of both the World Health Organization (WHO) and other investigators. They obtained data in six European nations, including Belgium, France, Germany, Italy, the Netherlands and Spain using the World Mental Health-Composite International Diagnostic Interview (WMH-CIDI). Methods in the ESEMeD study were generally comparable to those used in the NCS-R. The results indicate that the lifetime 312

prevalence of any anxiety disorder in the European nations that were surveyed was 13.6%, with 17.5% lifetime prevalence in women and 9.5% in men. The 12-month prevalence of any anxiety disorder was 6.4%, with 8.7% in women and 3.8% in men (ESEMeD/MHEDEA 200 Investigators, 2004 [31]). The WHO WMH Survey Initiative collected data via face-to-face surveys in 17 countries in Africa, Asia, the Americas, Europe and the Middle East, with a total of 85 052 respondents [30, 32]. The results indicated that projected lifetime risk for any mental disorder ranged from 47.4% in the United States to 12% in Nigeria, with an interquartile range (IQR) of 18.1–36.1%. Although biases with respect to underreporting and cohort effects were noted, anxiety disorders emerged as the most prevalent psychiatric disorders in 10 countries, with an IQR of 4.8–31.0%, underscoring the world-wide impact of anxiety disorders [30, 32].

EPIDEMIOLOGY OF ANXIETY DISORDERS

NSAL was conducted between 2 February 2001 and 30 June 2003 [33]. Like the NCS-R it is one of three studies included in the NIMH Collaborative Psychiatric Epidemiology Surveys initiative. The sample included 6082 individuals, of which 3570 were African-American, 891 non-Hispanic whites and 1621 blacks of Caribbean decent. The CIDI was used to evaluate a range of DSM-IV disorders. As in the other studies, anxiety disorders were found to be common and impairing conditions within each of the racial/ethnic study groups. The NSAL found that GAD, PD and social anxiety disorder (SAD) had significantly higher prevalence rates among whites when compared to African American and Caribbean black subjects. In spite of the lower prevalence rates, African Americans and Caribbean blacks who did meet criteria for an anxiety disorder reported greater levels of functional impairment than whites. In contrast to the higher rates of GAD, PD and SAD among whites, post-traumatic stress disorder (PTSD) was more prevalent among African Americans and Caribbean blacks than among whites. The third study commissioned by the NIMH Collaborative Psychiatric Epidemiology Surveys initiative is the NLAAS [12, 34], which was conducted between May 2001 and November 2003, and included 2554 Latino and 2095 Asian respondents. The NLAAS is the first epidemiological study of psychiatric disorders and service use that targeted Latinos and Asian Americans and selected respondents via a national sampling frame [12]. In the NLAAS Latino sample, the lifetime prevalence estimate of any CIDI/DSM-IV anxiety disorder was 15.7%, significantly lower than the lifetime risk of 25.7% for any anxiety disorder found in the NCS-R data on non-Latino white subjects. When compared to NCS-R non-Latino whites, the NLAAS Latino subjects had a significantly lower lifetime prevalence of GAD, PD, PTSD and social phobia, consistent with existing literature. The only exception to this pattern was agoraphobia without PD, for which there was no significant difference [35]. Among ethnic subgroups in the NLAAS, Puerto Ricans had the highest lifetime prevalence of any psychiatric disorder, when compared to Cuban, Mexican and other Latino subjects, with rates of 21.7, 14.4, 15.5 and 14.1%, respectively [35]. Prior community studies of Latinos have reported on the

‘immigrant paradox’, whereby psychiatric disorders among US-born Latinos are more prevalent than among Latino immigrants. When the NLAAS sample was disaggregated into ethnic subgroups by immigration status, some interesting and more complex findings emerged. For anxiety disorders, the immigrant paradox was consistently observed only among Mexican subjects. For Puerto Rican, Cuban and other Latino subjects, rates of anxiety disorders were similar for immigrants as compared to US-born Latino subjects [35]. The authors hypothesised that the contrasting findings for Mexican subjects may be explained by differences in immigration experience, intergenerational conflict and ages of immigration. Whatever the explanation, these findings provide an important insight into the large subgroup variability in prevalence rates within the US Latino population based on ethnic subgroup and nativity. These differences may be masked in studies that use the common practice of aggregating Latino ethnic subgroups into a single group.

18.3 Panic disorder 18.3.1 Definition The key feature of PD in DSM-IV is the occurrence of four or more panic attacks within a 4-week period, followed by a persistent fear of having another attack. These attacks cannot be precipitated only by exposure to a feared situation, cannot be due to a physical disorder, and must be accompanied by at least four of the following symptoms: dyspnoea, palpitations, chest pain, smothering or choking, dizziness, feelings of unreality, paraesthesias, hot and cold flushes, sweating, faintness, trembling or shaking, nausea or abdominal distress [8]. PD can be diagnosed as a primary disorder with or without agoraphobia.

18.3.2 Rates Table 18.2 shows prevalence rates of PD from community studies using DSM III, DSM-III-R or DSM-IV diagnostic criteria. For studies using DSM-III, the six-month prevalence of PD ranged from 0.6/100 in New Haven, CT, to 1.1/100 in 313

CHAPTER 18 Table 18.2 Prevalence rates per 100 of panic disorder using DSM-III, DSM-III-R or DSM-IV diagnostic criteria. Place

Rate/100* 6-month

USA-NCS (DSM-III-R) USA-NCS-R (DSM-IV) USA-ECA (5 site) ECA: New Haven, CT Baltimore, MD St Louis, MO Piedmont, NC Los Angeles, CA Zurich survey Edmonton, Canada Puerto Rico New Zealand Florence, Italy Korea Taiwan Urban Small towns Rural

1-year 2.3 2.7

Lifetime 3.5 4.7 1.6

0.6 1.0 0.9 0.7 0.9 3.1 0.7 1.1

1.2 1.7 2.2 1.4 1.7 0.20 0.34 0.13

This table was adapted from Textbook in Psychiatric Epidemiology (2nd ed.), Tsuang M.T., Tohen, M., Zahner, G.E.P. (Eds.): New York, N.Y., John Wiley & Sons, Inc., 2002.

Puerto Rico, representing a remarkable level of consistency across sites. The annual prevalence rate of 3.1/100 from the Zurich survey was based upon a definition of panic that only approximated that of DSM-III. The NCS-R reported a one-year prevalence of 2.7/100 for DSM-IV PD, which is only slightly higher than the 1-year prevalence of 2.3/100 estimated by the NCS for DSM-III-R PD. Lifetime rates of DSM-III PD showed good agreement with one another cross-nationally, with prevalence varying from 1.2/100 in Edmonton, Canada, to 2.2/100 in New Zealand. The exception to this narrow range of lifetime rates was Taiwan, where PD occurred at rates from 0.13/100 in rural areas to 0.34/100 in small towns. The only study that reported on lifetime DSM-III-R PD was the NCS, which found a rate of 3.5/100, considerably higher than the lifetime rates based upon DSM-III. This may be due to the broadening of the concept of PD in DSM-III-R as compared to DSM-III, or to the differences in memory probes used in the UM-CIDI, 314

as compared to those used in the DIS. Lifetime DSM-IV PD was estimated at the somewhat higher rate of 4.7/100 based on data from the NCS-R.

18.3.3 Risk factors 18.3.3.1 Gender Comparing lifetime prevalence rates, all of the studies reporting on PD showed higher rates for women than for men. With the exception of Puerto Rico and Taiwan, the higher lifetime risk for women was statistically significant in all of the community studies. In an analysis of the NCS data, Eaton and colleagues [36] found uniformly higher rates of panic attacks and PD for women compared to men within every age group. Of interest, Keyl and Eaton [37] analysed incidence rates from the ECA study and found a twofold increase in the odds of incident PD in women compared to men. This finding is analogous to the increased incidence rates for major depression in women compared to men, and suggests that for both PD and major depression the higher rates in women reflect a true increased risk for new onset panic and depression rather than a greater tendency to seek treatment or have longer episodes of illness.

18.3.3.2 Age In both the NCS and the ECA data, a bimodal distribution of age of onset was reported [36, 38]. The NCS found an early mode for PD in the 15–24 year age range for both men and women, and a later mode in the 45–54 range [36]. In the ECA and Edmonton studies, older persons (65 and over) had the lowest lifetime prevalence rates of PD. This pattern was quite different for Hispanics in the ECA and in the Puerto Rican study. In Puerto Rico and in Hispanic women in the ECA, the lifetime prevalence of PD tended to increase with age. For Hispanic men in the ECA, the lifetime rate dropped with each age group, reaching 0 in the group over 65 years of age. The NCS reported no significant ethnic differences for young adults, but did find lower rates in nonwhite compared to white older age groups. The reason for these differences is not clear.

EPIDEMIOLOGY OF ANXIETY DISORDERS

18.3.3.3 Race/Ethnicity In the ECA study, there were no significant differences in prevalence rates between African-American, Hispanic and white groups [39–41]. Similarly, the NCS found no main effects of race/ethnicity, but did report an age by race/ethnicity interaction effect (see above). The NCS-R study found that non-Hispanic Blacks had a lower risk for PD relative to Hispanic and non-Hispanic Whites [42, 43]. Comparisons of other studies are more remarkable for the crosscultural similarities in rates of PD, with the exception of the Taiwan study, which had the lowest rates of panic. The prevalence rates of PD in Korea were comparable to those in the West, while Taiwan’s were much lower. In a cross national study of independently conducted community surveys in 10 different countries (United States, Canada, Puerto Rico, France, West Germany, Italy, Lebanon, Taiwan, Korea, New Zealand), lifetime prevalence rates for DIS/DSM-III PD were found to range from 1.4/100 to 2.9 per 100, with a much lower prevalence rate in Taiwan of 0.4/100 [44]. These findings underscore the relative consistency of PD in its prevalence and distribution across diverse cultures. Women had higher prevalence rates than men in all countries and age of onset occurred in the early to middle 20s [44]. Consistent with previous epidemiological studies, across all countries included in the study, PD was found to be strongly associated with increased risk of major depression and agoraphobia.

18.3.4 Comorbid psychiatric disorder Approximately 50–60% of individuals with a lifetime history of major depression also have a lifetime history of one or more anxiety disorders [45–47]. Among respondents of the NCS, 56% of those with a history of at least one disorder were found to have two or more disorders [10]. The majority of those with anxiety disorder had comorbid major depression. Comorbidity with major depression was greatest among PD cases (56–73%), followed by generalised anxiety (62–67%) and social phobia (15–21%) [48]. The NCS found strong associations between the lifetime prevalence of panic and major depressive

episode. Those with panic attacks had a 6.2 times higher odds ratio for major depression when compared to those without panic attacks, and those with PD had a 6.8 times higher odds ratio for depression than those without PD [45, 46]. Analyses of the NCS have shown that comorbid lifetime and 12-month panic and major depression is associated with greater symptom severity and impairment, a worse course of disease, and help seeking from health care professionals [49]. Comorbidity (both 12-month and lifetime) was strongly linked to impairment as measured by help seeking from health care professionals, taking medication, perceived role impairment, suicide attempts, recent active disorder, frequency of lifetime depressive disorders or panic attacks. In those with comorbid panic and depression, the average number of lost work-days was 4.5 days, four times higher than in those with a single disorder and 20 times higher than in those with no disorder. In addition to greater impairment, comorbidity is associated with a more severe course of disease. Respondents with panic attacks and comorbid lifetime depression were found to experience a significantly higher number of physiological symptoms (mean = 9.1) during a panic attack than subjects with panic without comorbid lifetime major depression (mean = 7.9) [49]. A relationship between comorbidity and number of DSM-III-R Criterion A depressive symptoms was also found (7.1 symptoms in those with panic compared to 6.5 in those without panic, p < 0.001). Comorbid psychiatric disorders are quite common and disabling in adolescent populations. One study evaluated a community sample of 1507 adolescents, aged 14–18, with and without comorbid forms of depression, anxiety, substance use and disruptive behaviour, on six clinical outcome measures [50]. Adolescents with comorbid disorders had worse academic performance, higher mental health treatment usage and a higher rate of past suicide attempts when compared to those with a single non-comorbid disorder. According to the 2001–2002 NESARC, a random US household survey of 49 093 respondents, comorbid substance use and mood and anxiety disorders are quite common in the general US population. The NESARC findings indicate that approximately 20% 315

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of persons with a current substance use disorder experience a mood or anxiety disorder at the same time or within the past year. Similarly, approximately 20% of those with a current mood or anxiety disorder experience a concurrent substance use disorder. With the first wave of NESARC respondents, the 12-month prevalence of any DSM-IV anxiety disorder was 11.08% (95% CI: 10.43–11.73%). Substance-use disorder without a comorbid psychiatric condition was rare. The NESARC study included a longitudinal follow-up, which consisted of a second wave of interviews conducted in 2004–2005, resulting in 34 653 respondents. However, the results regarding anxiety disorders have not been published yet.

18.4 Agoraphobia 18.4.1 Definition DSM-III agoraphobia is defined as a fear and avoidance of being in places or situations from which escape might be difficult or in which help might not be available in the event of sudden incapacitation [6]. As a result of such fears, the agoraphobic person avoids travel outside the home or requires the accompaniment when away from home. Moderate cases may cause some constriction in lifestyle, while severe cases of agoraphobia may result in the person being completely housebound or unable to leave home unaccompanied. DSM-III-R revised the diagnosis of agoraphobia to a condition accompanying PD (PD with agoraphobia). Although the diagnosis of agoraphobia without history of PD was retained, this category emphasised the avoidance behaviour as a response to the sudden development of anxiety or somatic symptoms [6]. In DSM-IV, the criteria for agoraphobia without history of PD specify that the agoraphobic avoidance behaviour must occur specifically in response to incapacitating panic-like symptoms or limited-symptom attacks rather than full panic attacks [7].

Table 18.3 Prevalence rates per 100 of agoraphobia using DSM-III, DSM-III-R or DSM-IV diagnostic criteria. Place

Rate/100* 6-month

USA-NCS (DSM-III-R) USA-NCS-R (DSM-IV, without panic) USA-ECA (4 site) ECA: New Haven, CT Baltimore, MD St Louis, MO Piedmont, NC Los Angeles, CA Puerto Rico Zurich Survey New Zealand Florence, Italy Edmonton, Canada Korea Taiwan Urban Small towns Rural

1-year

Lifetime

2.8 0.8

5.3 1.4 5.6

2.8 5.8 2.7 5.4 3.2 3.9 2.5

6.9 3.8 1.3 2.9 2.7 1.1 1.5 1.3

This table was adapted from Textbook in Psychiatric Epidemiology (2nd ed.), Tsuang M.T., Tohen, M., Zahner, G.E.P. (Eds.): New York, N.Y., John Wiley & Sons, Inc., 2002.

Comparable 6-month and 1-year prevalence rates were found in Zurich and Puerto Rico. Lifetime rates of agoraphobia showed considerable variation, from a low of 1.1/100 in urban Taiwan to a high of 6.9/100 in Puerto Rico. Some of this variation may have been due to the use of a translated DIS [51]. If one considers only the studies carried out in primarily English speaking countries, the lifetime prevalence rates vary over a narrower range, from 2.9/100 in Edmonton, Canada, to 5.6/100 in the ECA data from four sites. In spite of the changes in the diagnostic definition between DSM-III and DSM-III-R, the lifetime rates from the ECA and NCS studies (5.6 vs. 5.3/100, respectively) show remarkable consistency.

18.4.2 Rates

18.4.3 Risk factors

Table 18.3 shows prevalence rates of agoraphobia from community studies using DSM criteria. In the ECA study, 6-month prevalence rates ranged from 2.7/100 in St. Louis to 5.7/100 in Baltimore.

Lifetime rates of agoraphobia were significantly higher for women than for men in each of the community studies. This is consistent with gender differences found for PD and major depression.

316

EPIDEMIOLOGY OF ANXIETY DISORDERS

In the ECA study, lifetime prevalence of agoraphobia was higher among African-Americans than among whites or Hispanics. The effects of race/ethnicity and gender combined to produce a considerable range in lifetime prevalence, from a low of 2.9/100 in white males to a high of 12/100 in African-American women [51]. In the NCS, current agoraphobia (past month) was associated with an increased risk in African-Americans compared to whites, and in homemakers compared to those working outside the home [52]. Current agoraphobia was inversely related to income and education in a bivariate analysis of the NCS data [52]. Similarly, the NSAL reported higher lifetime prevalence estimates of agoraphobia without PD among African-Americans and Caribbean blacks than among whites, and lifetime prevalence of agoraphobia was inversely related to education and poverty level [32].

18.4.4 Relationship between panic attacks, panic disorder and agoraphobia In the DSM-IV-TR [53] agoraphobia can only be coded as PD with agoraphobia or agoraphobia without history of PD, but not as a disorder independent of PD, panic attacks or panic-like symptoms. An important factor in this change was the observation by Klein and others [1] that, in clinic settings, agoraphobia rarely occurs without preceding PD, spontaneous panic attacks or limited symptom attacks. In spite of the progress that has been made in our understanding of the relationship between panic and agoraphobia, the boundary between panic attack and PD and the relationship of agoraphobia with each are not well understood. Findings from the NCS-R have shed important light on these issues. The NCS-R reported lifetime prevalence estimates of DSM-IV PD (4.7%) and PD with agoraphobia (1.1%) that are similar to the DSM-III-R rates reported in the NCS [54, 55]. However, the NCS-R used a much more detailed set of stem questions probing for panic attacks in the DSM-IV version of the CIDI than in the DSM-II-R version. As a result, the NCSR reported a lifetime prevalence of panic attacks (28.3%), much higher than that of the NCS (7.3%), presumably because the NCS underestimated rates of panic attacks. Many subjects in the NCS-R reported

isolated panic attacks, but not PD, largely because they had numerous cued attacks but not recurrent uncued panic attacks.

18.5 Social phobia 18.5.1 Definition The central feature of DSM-IV social phobia is a persistent, irrational fear accompanied by a compelling desire to avoid situations in which a person may act in a humiliating or embarrassing way while under the scrutiny of others [7]. The phobic situation may be avoided or endured with intense anxiety, and the avoidant behaviour must either interfere with occupational or social functioning or cause marked distress [6]. Common social phobias involve fears of speaking or eating in public, urinating in public lavatories, writing in front of others or saying foolish things in social situations.

18.5.2 Rates Table 18.4 shows the lifetime prevalence of social phobia from studies using DSM-III, DSM-III-R or

Table 18.4 Lifetime prevalence rates/100 of social phobia using DSM-III, DSM-III-R or DSM-IV diagnostic criteria. Community survey USA-NCS (DSM-III-R) USA-NCS-R (DSM-IV) USA-ECA (4 sites) Baltimore, MD St Louis, MO Durham, NC Los Angeles, CA Edmonton, Canada Puerto Rico New Zealand Florence, Italy Korea Taiwan Urban Small towns Rural

Rates/100 13.3 12.1 2.4 3.1 1.9 3.2 1.8 1.7 1.6 3.9 1.0 0.6 0.6 0.5 0.4

This table was adapted from Textbook in Psychiatric Epidemiology (2nd ed.), Tsuang M.T., Tohen, M., Zahner, G.E.P. (Eds.): New York, N.Y., John Wiley & Sons, Inc., 2002.

317

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DSM-IV criteria. Lifetime rates of DSM-III social phobia varied considerably, with a low of 0.4/100 in rural Taiwan and a high of 3.9/100 in New Zealand. It is not clear whether these contrasting rates reflect true cross-cultural differences or are due to differences in methodology or translation of the DIS. The lifetime prevalence rates of social phobia vary over a somewhat narrower range, from 1.7/100 in Edmonton, Canada, to 3.9/100 in New Zealand, when comparing rates from English speaking countries. The rate of lifetime DSM-III-R social phobia from the NCS was considerably higher (13.3/100) than in any of the DSM-III studies. Magee and colleagues [52] attributed the higher prevalence to differences between the DIS and UM-CIDI. The UMCIDI uses a stem question based on the broader DSM-III-R criteria, allowing either avoidance of a feared situation or endurance with intense anxiety, and it also asks about six specific social-phobic fears (compared to three in the DIS), including the high prevalence fears of using a public toilet, writing in front of others or talking to people and sounding foolish or having nothing to say. Follow-up research using the WMH-CIDI and DSM-IV criteria in the NCS-R yielded lower estimates of lifetime social phobia (12.1/100). Another important disparity between SAD and social phobia estimates was found between the NCS-R data and the ESEMeD study. The NCS-R lifetime and 12-month prevalence rates were 12.1 and 6.8%, respectively. In contrast, the ESEMeD study reported a 2.8% lifetime prevalence and 12-month prevalence of 1.6% for SAD and social phobia [13].

18.5.3 Risk factors In an analysis of the ECA data from four sites (the New Haven site used a version of the DIS which did not include social phobia items), Schneier et al. [56] found that lifetime prevalence rates of social phobia were highest among women and persons who were younger (ages 18–29 years), less educated, single and of lower socioeconomic class. In the NCS, higher rates were found in women, those with less education or income, the never married, students and those who live with their parents [52]. A significantly higher prevalence of lifetime social phobia was also found among women in Korea and urban Taiwan, 318

while no significant gender differences were found in Edmonton, Puerto Rico or small town or rural areas of Taiwan. In an analysis of NCS-R and NLAAS data, Alegria et al. [34] found significantly higher lifetime prevalence of social phobia among NCS-R non-Latino white subjects (14.3%) than among NLAAS Latino subjects (7.5%). For the NCS-R non-Latino whites, the lifetime prevalence of social phobia was significantly higher for US-born subjects (16.9%) than for immigrants (8.8%). For the NLAAS Latino subjects, significantly higher lifetime prevalence of social phobia was found only among US-born Mexican subjects (10.0%) when compared to Mexican immigrants (4.7%). For the Puerto Rican, Cuban and other Latino subgroups, no significant differences were found in prevalence rates based on nativity.

18.6 Generalised anxiety disorder 18.6.1 Definition The DSM-IV-TR criteria for GAD require the presence of excessive anxiety and worry that are difficult to control, accompanied by three symptoms from the following six: restlessness, fatigue, difficulty concentrating, irritability, muscle tension and sleep disturbance. The anxiety must be clinically significant. The anxious mood must continue for more days than not for at least 6 months, and the diagnosis is not made if phobias, PD, PTSD or OCD are present, or if the disturbance is due to another physical or mental disorder, such as hyperthyroidism, major depression or schizophrenia. Further, the anxiety cannot be due to the direct effects of a substance [54]. By this definition, GAD is treated primarily as a residual category after the exclusion of the other major anxiety disorders.

18.6.2 Rates Table 18.5 shows the prevalence of GAD from community studies using DSM-III, DSM-III-R or DSM-IV criteria. In the ECA study, hierarchical diagnostic exclusion of PD and major depression yielded the 1-year prevalence of 2.7/100, while dropping the exclusions resulted in a rate of 3.8/100 [57]. Lifetime prevalence of GAD in the ECA study was

EPIDEMIOLOGY OF ANXIETY DISORDERS Table 18.5 Prevalence rates per 100 of generalized anxiety disorder using DSM-III, DSM-III-R or DSM-IV diagnostic criteria. Place

Rate/100* 6-month

USA-NCS (DSM-III-R) USA-NCS-R (DSM-IV) USA-ECA 3 sites (no exclusions) (no panic no MDD) USA-ECA 3 sites (no panic, no MDD) Durham St. Louis Los Angeles Zurich Survey Florence, Italy Florence, Italy (DSM-III-R) Taiwan Urban Small towns Rural Korea

1-year

Lifetime

3.1 3.1

5.1 5.7

3.8 2.7

6.6 6.6 4.1 5.2 5.4 3.9

3.7 10.5 7.8 3.6

This table was adapted from Textbook in Psychiatric Epidemiology (2nd ed.), Tsuang M.T., Tohen, M., Zahner, G.E.P. (Eds.): New York, N.Y., John Wiley & Sons, Inc., 2002.

quite consistent across three study sites, varying from 4.1/100 in Los Angeles to 6.6/100 in Durham and St. Louis. In spite of differences in diagnostic criteria, the ECA and NCS rates of GAD were quite similar. Lifetime prevalence varied considerably more in the Taiwan study, from 3.7/100 in Taipei to 10.5/100 in small town areas of Taiwan. The Florence study provides an interesting example of the effects of requiring the longer 6-month duration of DSM-III-R. For DSM-III, the lifetime rate was 5.4/100, while the narrower DSM-III-R definition resulted in the lower rate of 3.9/100. In the NCS, which used a different interview, the UM-CIDI, the changes in criteria did not yield changes in prevalence.

18.6.3 Risk factors Based upon data combined from three ECA study sites, the 1-year prevalence of GAD, with or without

diagnostic exclusions, was significantly higher in females, in African-Americans and in persons under 30 years of age, but the differences were significant for age only without diagnostic exclusions and for race only when panic and depression were excluded [57]. The Taiwan study reported significantly higher rates for women than for men, but no gender differences were found in Korea. In the NSAL, 12-month prevalence of GAD was significantly higher for whites than for AfricanAmericans and Caribbean blacks. In a multivariate Cox proportional hazards regression model, the risk for onset of GAD remained significantly higher for whites when compared to African-Americans, for women when compared to men, and for those 18–59 years of age when compared to those over 60 years of age. Similarly, the NLAAS found significantly higher lifetime prevalence of GAD among non-Latino whites when compared to Latino subjects, with rates of 8.6 and 4.1%, respectively. There were no significant differences in prevalence of GAD when comparing ethnic subgroups or when comparing immigrant to US-born Latino subjects. The 2001–2002 NESARC study examined gender differences in the development of GAD in association with comorbid substance abuse [58]. The 12-month prevalence rates of GAD were 1.2% for men and 2.7% for women, while the lifetime prevalence rates were 2.8 and 5.3%, respectively. Men with GAD were found to be at higher risk for comorbid substance use when compared to women, while the women were more likely to endorse a higher number of criteria for GAD than the men. Men and women did not differ in mean age of onset, nor with respect to the median number of episodes and the median duration of their longest episode [58].

18.7 Obsessive–compulsive disorder 18.7.1 Definition DSM-IV-TR OCD requires the presence of obsessions or compulsions that are sources of significant distress or impairment and are not due to another mental disorder. Obsessions are defined as recurrent, 319

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persistent thoughts, images or impulses that are experienced as senseless and repugnant. Compulsions are excessively repetitive, stereotyped behaviours, such as repeatedly checking locked doors or gas jets or washing hands [54].

18.7.2 Rates Table 18.6 shows prevalence rates of OCD from community studies using DSM-III, DSM-III-R or DSM-IV criteria. Six-month prevalence of OCD varied from 0.7/100 in Los Angeles to 2.1/100 in Piedmont, North Carolina. Lifetime prevalence of OCD varied from 0.3/100 in rural Taiwan to 3.2/100 in Puerto Rico. The studies in English language sites showed excellent agreement, with lifetime prevalence of 2.6/100 in the ECA and 3.0/100 in Edmonton, Canada. Most remarkable about these rates is that they contradict the previous traditional view of OCD as a rare disorder on the basis of published clinical reports.

Table 18.6 Prevalence rates per 100 of obsessive compulsive disorder using DSM-III or DSM-IV criteria. Place

Rate/100* 6-month

NCS-R- US ECA: New Haven, CT Baltimore, MD St Louis, MO Piedmont, NC Los Angeles, CA Puerto Rico Edmonton, Canada ECA - 5 sites Florence, Italy Korea Edmonton, Canada Puerto Rico Taiwan Urban Small towns Rural

1-year 1.0

Lifetime 1.6

1.4 2.0 1.3 2.1 0.7 1.8 1.6 2.6 0.7 2.1 3.2 3.2 0.94 0.54 0.30

This table was adapted from Textbook in Psychiatric Epidemiology (2nd ed.), Tsuang M.T., Tohen, M., Zahner, G.E.P. (Eds.): New York, N.Y., John Wiley & Sons, Inc., 2002.

320

18.7.3 Risk factors As with other anxiety disorders, prevalence rates of OCD were higher among women than men in the ECA study. However, when gender comparisons were controlled for marital status, employment status, job status, ethnicity and age, there were no remaining differences in prevalence rates for women compared to men [59].

18.8 Anxiety and affective disorders and mass disasters Mass disasters, including natural disasters and terrorist attacks, are widely believed to place the population affected by them at an increased risk for psychiatric illness. However, the type of disaster and the timing of any subsequent study pose significant challenges to the investigation of these phenomena [60, 61]. Further, cultural differences and inconsistent methodology result in considerable variability in the types of mental health problems and the prevalence rates reported in studies [62]. Although the occurrence of PTSD may be of particular interest following a mass disaster, most of the large community epidemiological studies, such as the ECA, NCS and NCS-R, did not assess for PTSD. The exceptions are the NSAL and ESEMeD, which did assess for PTSD [29, 32]. In the NSAL, the overall rate of 12-month PTSD was 3.7%, with higher rates among women than men, higher rates among younger cohorts when compared to those greater than 60 years of age, and higher rates among those below the poverty line than those above the poverty line. Estimating lifetime risk for PTSD, the NSAL found significantly higher rates among Caribbean blacks (8.4%) and African-Americans (9.1%) than among whites (6.8%) [32]. In the ESEMeD, the 12-month prevalence of PTSD was estimated as 1.1%, with a significantly higher rate among women (1.7%) than among men (0.5%). The ESEMeD also assessed subjects for potentially traumatic events (PTEs). Among individuals exposed to at least one PTE, the following six events were found to be the most significantly associated with PTSD (p < 0.001): being raped (odds ratio OR = 8.9), being beaten up by a spouse or romantic

EPIDEMIOLOGY OF ANXIETY DISORDERS

partner (OR = 7.3), experiencing an undisclosed private event (OR = 5.5), having a child with a serious illness (OR = 5.1), being beaten up by a caregiver (OR = 4.5) or being stalked (OR = 4.2) [29]. The NSAL and ESEMeD provide the only available baseline community prevalence rates that are not linked to mass disasters. The following discussion will highlight available epidemiological findings from investigations of the aftermaths of the 11 September 2001 terrorist attacks, Hurricane Katrina and the South-east Asia tsunami in southern Thailand.

18.8.1 September 11 terrorist attacks On 11 September 2001, New York City (NYC) and Washington, DC, were the targets of the largest terrorist attacks in US history. This mass exposure to traumatic events placed thousands at risk for psychiatric disorder [63, 64]. Schlenger et al. [63] examined psychological symptoms in the US population using a web-based, national representative epidemiological survey for 1–2 months following the 11 September attacks. Their sample included 2273 people from NYC and Washington, DC, other major US cities and a sample of US residents dwelling outside a major metropolitan area. Their survey instrument was the PTSD Checklist (PCL), a self-report measure that rates each of 17 PTSD symptoms on a five-point scale. The PCL has been shown to have excellent sensitivity and specificity when compared to a PTSD diagnosis based on the Structured Clinical Interview for DSM-II-R (SCID). When defined as a PCL score of 50 or higher, the prevalence of probable PTSD was 11.2% in the NYC area, 2.7% in Washington, DC, 3.6% in other major metropolitan areas and 4% among US residents in non-metropolitan areas. The risk of probable PTSD was associated with proximity to the NYC terrorist site, which resulted in by far the greatest casualties and received the greatest media coverage. Person, Tracy and Galea [60] examined the prevalence of probable major depression and risk factors for depression in the 6-month period after the 11 September attacks. Their sample included 2700 people representative of NYC metropolitan residents. The prevalence of probable major depression in the 6 months following the attacks was 9.4%. Probable major depression was associated with being

directly affected by the attacks, having had a panic attack prior to the attack, multiple life stressors and previous exposure to traumatic events.

18.8.2 Hurricane Katrina Galea et al. [65, 66] examined the impact of exposure to hurricane-related stressors after Hurricane Katrina in a sample of 1043 English-speaking residents of the affected areas using a telephone survey to screen for 30-day DSM-IV anxiety and/or mood disorder. The majority of the sample endorsed exposure to at least one hurricane-related stressor. Hurricane-related stressors significantly predicted anxiety and/or mood disorders. The highest risk for physical illness/injury was associated with living in the New Orleans metropolitan area, with higher risk for mood and anxiety disorders and hurricane-related stressors in this area as well. The findings also indicated that the impact of Hurricane Katrina was widespread and comparable for different sociodemographic groups. McLeish and Del Ben [67] evaluated symptoms of depression and PTSD in an outpatient sample of 156 psychiatric patients following Hurricane Katrina. Psychiatric patients are a particularly vulnerable patient population that has been traditionally excluded from post-disaster investigations. Patients were administered standardised self-report measures of depression and PTSD and were interviewed on their exposure to hurricane-related events. Depression scores were significantly higher 1 month after the hurricane, but PTSD scores remained stable.

18.8.3 Tsunami in southern Thailand On 26 December 2004, a massive undersea earthquake near Indonesia caused a tsunami that devastated the coasts of Indonesia, Sri Lanka, India and Thailand, causing an estimated 200 000 deaths and making it one of the deadliest natural disasters in human history [62]. To date only two investigations have been conducted to ascertain the impact of the tsunami on the mental health of affected Asian populations. van Griensven et al. [62] conducted a multistage, cluster, population-based mental health survey between 15 and 22 February 2005 and administered follow-up measures in September 2005. The 321

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study collected data on random samples of people displaced by the tsunami (n = 371) and nondisplaced people (n = 322) living in the Phang Nga province in Thailand, as well as 368 non-displaced people in Krabi and Phuket, also in Thailand. Follow-up data was obtained on 371 displaced and 322 nondisplaced people in Phang Na. Standardised outcome measures included the Medical Outcomes Study-36 Short Form Health Survey (SF-36), the Harvard Trauma Questionnaire and the Hopkins Checklist-25 to detect symptoms of anxiety and depression. Two months after the tsunami, the study found symptoms of PTSD, depression or anxiety in 7.3, 20.4 and 29.6%, of the survivors in southern Thailand, respectively. Although raw prevalence rates were higher among those who were displaced than those who were not displaced, a multivariate analysis of the prevalence of symptoms of PTSD, anxiety and depression found no significant differences between the displaced and the non-displaced. However, those who had a family member who died or was missing, those who had injury to self or a family member, and those who lost their home or livelihood because of the tsunami had a higher prevalence for symptoms of PTSD, anxiety and depression than those who had not experienced these events. Nine months after the tsunami, the follow-up assessment revealed that overall symptoms had decreased among the displaced and nondisplaced persons from the higher 2-month levels, but the decrease was significant only for the group of displaced persons. Thienkrua et al. [68] conducted an investigation of trauma experiences and the prevalence of symptoms of depression and PTSD among children that were affected by the tsunami in southern Thailand from Phang Nga, Krabi and Phuket. They assessed 371 children between 15 and 22 February 2005, and follow-up measures were administered between 7 and 12 September 2005. Their sample was composed of children who were displaced by the tsunami, non-displaced children from areas affected by the tsunami and non-displaced children from nonaffected areas. The prevalence rates for PTSD symptoms were 13% for displaced children, 11% for non-displaced children and 6% for children from unaffected areas. Children in the Phang Nga sample were assessed again 9 months later, but there was no significant change in their symptoms. 322

18.8.4 Summary In summary, although these these post-disaster studies did not specifically evaluate prevalence based on DSM-IV diagnostic criteria, exposure to a mass traumatic event does appear to be a strong environmental risk factor for subsequent psychopathology, and the degree of risk appears to be associated with the nature and severity of the impact of the trauma. In particular, those who experienced the loss of a family member, their home or livelihood, and those who had injury to themselves or a family member had significantly more symptoms of PTSD, anxiety and depression than those who had not experienced these events. Emerging data from post-disaster investigations suggest that the acute manifestations of psychopathology may appear in the one to two months after the event. Thus, the timing of post-disaster assessment is an important factor in future investigations [68–70]. Additional studies of the long-term and developmental effects of massive disasters are clearly needed.

18.8.5 Limitations of epidemiologic studies of anxiety disorders Kessler et al. [14] have pointed out the following important limitations in the NCS-R study. First, homeless, institutionalised and non-Englishspeaking populations were underrepresented in the NCS-R sample, thus reducing prevalence estimates. Second, NCS-R estimates are likely conservative given the well-known bias against reporting embarrassing behaviours [14, 44, 45, 71]. Third, a lay-administered interview, the WMH-CIDI [72], was used. Regarding estimates of lifetime prevalence [9], have pointed out the following additional limitations in the NCS-R study. First, the method used to estimate lifetime risk was based on the assumption that the risk of first onset in a given year of life is constant among people who differ in age at the time of interview. This assumption is likely to be incorrect because recent studies have shown higher prevalence rates among younger cohorts. Second, age at onset may be recalled incorrectly, with a likely increase in incorrect recall and failure to recall lifetime disorders with age.

EPIDEMIOLOGY OF ANXIETY DISORDERS

18.9 Future developments A number of major epidemiological studies have examined the prevalence and nature of anxiety disorders. However, more research is needed on the long-term and developmental impact of natural and mass disasters on the occurrence of PTSD and other anxiety disorders, especially in places where psychiatric care is limited or nonexistent. While important steps have been taken, the answers to major questions remain elusive. For example, although women have been found to have consistently higher rates of PD, GAD and social phobia, no satisfactory explanation has been found for this difference. Similarly, recent studies have found higher rates of GAD and social phobia among whites than among AfricanAmericans, Caribbean blacks or Latinos. Among subgroups of Latinos, higher rates of anxiety disorders were found among US-born ethnic Mexicans when compared to Mexican immigrants, but no such differences were found for the Puerto Rican, Cuban or other Latino subgroups. These findings will require replication and additional study to better understand the causes of these differences. Substantial questions also remain regarding the comorbidity between psychiatric disorders, such as the nature of the association between anxiety disorders, depressive disorders and substance abuse. The strength of epidemiologic community studies lies in their ability to provide data on rates and risks of anxiety disorders in community samples independent of treatment seeking. When variations by race, ethnicity and geography and over time are ascertained, they may provide important clues regarding aetiology. These aetiological clues can then be followed-up with studies of different design, such as biological and family studies, that will improve our understanding of the complex interactions between biological and psychosocial variables in the aetiology and course of these disorders.

Acknowledgements This manuscript was updated from: Horwath E, Cohen RS, Weissman MM: Epidemiology of Mood and Anxiety Disorders, in: Tsuang MT, Tohen M,

Zahner GEP (eds): (2002) Textbook in Psychiatric Epidemiology, 2nd edition, John Wiley & Sons, Inc., New York, pp. 389–426 [73].

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Weissman, M.M., Leaf, P.J., Holzer, C.E.III and Merikangas, K.R. (1985) The epidemiology of anxiety disorders: a highlight of recent evidence. Psychopharmacol. Bull., 21 (3), 538–541. Weissman, M.M., Myers, J.K., Tischler, G.L. et al. (1985) Psychiatric disorders (DSM-III) and cognitive impairment among the elderly in a US urban community. Acta Psychiatr. Scand., 71 (4), 366–379. Weissman, M.M., Wickramaratne, P., Adams, P.B. et al. (1993) The relationship between panic disorder and major depression. A new family study. Arch. Gen. Psychiatry, 50 (10), 767–780. Wittchen, H.U., Zhao, S., Kessler, R.C. and Eaton, W.W. (1994) DSM-III-R generalized anxiety disorder in the National Comorbidity Survey. Arch. Gen. Psychiatry, 51 (5), 355–364. Young, A. and Breslau, N. (2007) Troublesome memories: reflections on the future. J. Anxiety Disord., 21 (2), 230–232.

19

Epidemiology of bipolar disorder in adults and children Kathleen R. Merikangas1 and Mauricio Tohen2 1

Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, Bethesda, MD, USA 2 Division of Mood and Anxiety Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

19.1 Introduction During the past decade the results of numerous international epidemiological surveys using contemporary diagnostic criteria and appropriate diagnostic instruments have strengthened the evidence base on the magnitude, correlates and consequences of bipolar disorder (BPD) in representative samples of populations across the globe. This work has highlighted the dramatic personal and societal impact of BPD. The estimated disability-adjusted life years of BPD outrank all cancers and primary neurologic disorders such as epilepsy and Alzheimer’s disease, primarily because of its early onset and chronicity across the life span [1]. The aims of this chapter are: (i) to summarise the magnitude of BPD from community surveys of adults and youth; (ii) to describe the patterns of comorbidity of BPD in the general population; (iii) to summarise the risk factors and correlates of BPD in community surveys and (iv) to discuss future directions in epidemiologic research on BPD.

19.2 Epidemiology of bipolar disorder 19.2.1 Adults Comprehensive summaries of international studies of the prevalence of BPD over the past 25 years

have been provided in the two earlier editions of this textbook [2, 3], as well as several subsequent published reviews [4–9]. The aggregate cross-study estimate of the lifetime prevalence of BPD is about 1.0%. Only one of the reviews of the epidemiology of BPD includes bipolar II (BPII) and bipolar spectrum disorders [4]. As expected, it was found that the median rates increase with successively less restrictive definitions of BPD; the median lifetime prevalence rate of BPII was 1.2%, and of bipolar spectrum was 2.9%. The only systematic difference that has been found to explain the variation in rates is the actual diagnostic interview employed to generate the criteria for BPD [8]. This chapter will provide an update of this work based on subsequent community surveys that have ascertained BPD as defined by the Diagnostic and Statistical Manual DSM-IV criteria. A summary of the 12-month and lifetime prevalence rates of BPD as defined by DSM-IV criteria is presented in Table 19.1. The aggregate cross-study estimate of the lifetime prevalence of BPD is 1.2%, with a range of 0.0% in Nigeria [10] to 3.3% in the United States [11]. Despite these outliers, the prevalence rates of bipolar I disorder are highly consistent across studies. The lifetime prevalence rates cluster at about 1.0%, whereas the average 12 month prevalence rate is only slightly lower with a median of 0.6%, with a range from 0 to 1.8%. The prevalence of BPII was only assessed in a few studies, but tend to be lower than those of BPI, a counter-intuitive finding [12–14]. Two recent studies

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

329

330

Study

Lee et al. [17] Jacobi et al. [18] Alhasnawi et al. [19] Levinson et al. [20] Kawakami et al. [21] Karam et al. [22] Medina-Mora et al. [23] Baxter et al. [24] Gureje et al. [10] Angst et al. [25] Grant et al. [11] Ford et al. [26] Merikangas et al. [12]

China Germany Iraq Israel Japan Lebanon Mexico New Zealand Nigeria Switzerland United States

18–70 18–65 ≥18 ≥21 ≥20 ≥18 18–65 16–64 ≥18 40 ≥12 ≥55 ≥18

Age

5201 4181 4332 4859 1664 2857 5826 12 992 4984 591 43 093 6082 9282

WMH-CIDI/DSM-IV M-CIDI/DSM-IV WMH-CIDI/DSM-IV WMH-CIDI/DSM-IV WMH-CIDI/DSM-IV CIDI 3.0/DSM-IV WMH-CIDI/DSM-IV CIDI 3.0/DSM-IV WMH-CIDI/DSM-IV SPIKE/DSM-IV NESARC/DSM-IV WMH-CIDI/DSM-IV CIDI/DSM-IV

Sample size Method

Rates of DSM-IV bipolar disorders in community samples of adults.

Location

Table 19.1

Bipolar I/Bipolar II Any bipolar Bipolar disorder Bipolar disorder Bipolar I/Bipolar II Bipolar disorder Bipolar I/Bipolar II Bipolar disorder Bipolar I/Bipolar II Bipolar I, II Bipolar I Bipolar I/Bipolar II Bipolar I Bipolar II Subthreshold BPD

Diagnosis

– 0.8 – – – 2.6 – – – – 3.2 – 0.8 0.9 2.6

– 1.2 – – – 2.3 – – – – 3.4 – 1.1 1.3 2.1

0.1 1.0 0.2 0.7 – 2.4 1.9 – 0.0 2.6 3.3 0.8 1.0 1.1 2.4

– 0.6 – – – – – – – – 1.8 – – – –

1.1 – – – – – – – – 2.2 – – – –

– 0.8 0.2 0.1 0.1 – – 1.8 0.0 – 2.0 0.4 0.6 0.8

Total

Male Female

Male Female

Total

12-month prevalence (%)

Lifetime prevalence (%)

EPIDEMIOLOGY OF BIPOLAR DISORDER IN ADULTS AND CHILDREN

also reported on prevalence of BPD defined by the International Classification of Diseases ICD-10 criteria. The lifetime prevalence of ICD-10 BP-I was 1.8 in Ethiopia [15], and the 12-month prevalence in Ireland was 0.2. [16]. With increased interest in evaluating the validity of the thresholds of mania and its core components, investigators are beginning to test different thresholds in general community samples. Application of the concept of subthreshold bipolarity to the Zurich Cohort Study demonstrated the enormous consequences of varying definitions of diagnostic criteria for symptoms, duration and impairment. The broader criteria yielded rates of 5.3% for BPII disorder, 3.2% for minor BPD and 3.3% for hypomania [27]. Similar rates emerged from a reanalysis of the Epidemiologic Catchment Area study by Judd et al. [28], who reported that 5.1% of the population met criteria for lifetime subthreshold mania/hypomania [28]. Likewise, expansion of the definition of hypomania in the National Comorbidity Study Replication (NCS-R) study yielded a lifetime prevalence rate of 4.5%. [12, 29]. Evidence for the validity of the expanded definition was provided by the clinical significance, severity and impairment associated with subthreshold BPD. Of particular interest, the severity of symptoms of depression and mania associated with subthreshold BPD suggested that the latter category did tap clinically significant manifestations of BPD that were comparable to people seeking treatment for these conditions in outpatient settings [29]. One major contribution of epidemiological research is the information on service patterns from the general population perspective. The studies reviewed earlier in this chapter indicate that about 60% of those with BPI in US community samples receive mental health treatment. Though more variable, more than half of those with BP in other countries receive treatment as well. This finding is alarming as it suggests that close to half of individuals suffering from this devastating condition are not being properly assessed or treated. The finding also suggests that those suffering with BPD in the general population are not properly

represented in mental health treatment facilities, thereby limiting the generalisability of research conducted in these settings.

19.2.2 Youth There is still a striking lack of information on the magnitude of BPD in youth. Even though there is an increasing number of population-based surveys of children and adolescents, many of these studies do not include assessment of the symptoms of mania because it is believed to be so rare [31, 32]. To date, there is also a lack of data on the prevalence of BPD in youth in a nationally representative sample of US youth. The results of the existing cross-sectional or short-term prospective studies of youth that applied DSM-IV criteria are shown in Table 19.2. The 12month prevalence rates of mania range from 0.8 to 1.9%, and hypomania from 0.4 to 0.9% to age 18 [32–34]. A recent population survey of adolescents in Mexico City [32] reported the highest prevalence rate of BPD in youth to date, with a 1-year prevalence of 2.5% with BPD. The most valid information on the prevalence and patterns of onset of BPD can be derived from prospective follow-up studies of youth through adulthood. Studies in New York State [38], North Carolina [42] and Oregon [43] in the United States, and in Munich, Germany [14] and Dunedin, New Zealand [44] have monitored prevalence of mental disorders through early adulthood. The lower half of Table 19.2 shows the prevalence of BPD in these studies. The results of these studies converge in estimating the prevalence of BPD at between 1.4 and 2.1%, which approximates cross-sectional prevalence rates of BPD in adult samples. Prospective studies of child and adolescent samples from population surveys are also the best source of incidence rates of BPD. Lewinsohn et al. [45] found that the incidence of BPD peaks at age 14 in both males and females and decreases gradually thereafter [45]. By age 21, the rate of BPD rose to 2% in the prospective cohort studies of youth who were followed for several years [36, 45]. There are also a growing number of studies that evaluate the

331

CHAPTER 19 Table 19.2 Design

Rates of bipolar disorders in community samples of children and adolescents.

Authors

N

Age

Mania

Hypomania

Bipolar

760

13–18

1.9% (6 monthso)

0.9% (6 monthso)

–

1015

9, 11, 13

–

0.1%

0.2%

4175

11–17

0.4% (12 months)

0.8% (12 months)

–

3005

12–17

–

–

2.5% (12-mo)

Early Developmental Study of Psychopathology Wittchen et al. [14]

3021

14–24

–

0.4% (12 monthso)

1.3% (12 mo)

Dunedin Longitudinal Study Cannon et al. [36] New York Longitudinal Study Velez et al. [37] Cohen et al. [38] Pine et al. [39] Oregon Adolescent Depression Project Lewinsohn et al. [40, 41]

980

26

2.0% (12 monthso)

0.4% (LT) –

1.4% (LT) –

776

9–18

–

–

760 716

11–20 17–26

Cross-sectional Dutch Adolescent Study Verhulst et al. [34] Smoky Mountains Study Costello et al. [33] Teen Health 2000 Roberts et al. [35] Mexican Adolescent Mental Health Survey Benjet et al. [32] Longitudinal (into adulthood)

– 1709 1507 865

14–18 15–19 24

incidence of first onset mania in clinical samples of youth. Incidence rates from these studies range from 1.7 to 2.2 per 100 000 per year, with a weighted average of 1.4% [46]. Although estimates of the average age of onset of BPD from clinical samples was believed to occur in the third decade of life, retrospective estimates from the population surveys reveal that the average first onset of manic episodes occurs in the late teens to early 20s [12]. Emerging evidence from prospective studies of adolescents converges in demonstrating that the first onset of BPD generally begins in adolescence (possibly pre-adolescence) or early adulthood, with a mean age of onset of 18 years [47]. Waraich et al. [8] showed that there was remarkable stability in the lifetime prevalence of BPD across adulthood, thereby demonstrating the chronicity of this condition across the lifespan [8]. 332

2.0% (T1 or T2) 1.4% – 0.9% (LT) 1.0% (LT) 2.1% (LT)

Studies of offspring at risk for BPD through the presence of parental BPD confirm the onset of BPD in adolescence with the first onset of BPD beginning with major depression followed by the subsequent onset of hypomanic or manic episodes [48, 49]. Treatment rates in children have also been reported from prospective surveys. Newman et al. [44] found that approximately half of those youth in New Zealand with a 12-month episode of mania had received treatment [44]. Moreno et al. [50] examined data from the National Ambulatory Medical Care Survey to show that most visits by both youth and adult patients with a diagnosis of BPD included the prescription of at least one psychotropic medication, with use of mood stabilisers (generally anticonvulsants) in approximately two thirds of the visits, and antidepressants in approximately one third of the visits [50].

EPIDEMIOLOGY OF BIPOLAR DISORDER IN ADULTS AND CHILDREN

19.3 Patterns of comorbidity of bipolar disorder 19.3.1 Mental disorders Recent epidemiologic surveys have highlighted the striking magnitude of comorbidity between BPD and other Axis I DSM-IV disorders. Several population surveys confirm the strong link between anxiety disorders and BPD described in clinical samples (NCS R; National Epidemiologic Survey on Alcohol and Related Conditions [NESARC]; European Study of the Epidemiology of Mental Disorders [ESEMeD]). As shown in Table 19.3, data from the NCS-R revealed that more than 90% of those with lifetime BPI or BPII disorder also meet criteria for another lifetime disorder, and that 70% of those with bipolar spectrum disorders have a history of three or more Table 19.3

disorders [12]. The disorders that are most strongly associated with BPD are anxiety disorders and substance use disorders. The NCS-R study revealed that more than 80% of those with BPD also have a lifetime history of DSM-IV anxiety disorders, particularly panic attacks (e.g. 70%) and social phobia (e.g. 50%) [12]. Prospective studies of community samples provide valuable information on the temporal patterns of association between BPD and comorbid conditions. Follow-up studies of children have shown that BPD is associated with multiple other disorders including attention-deficit/hyperactivity disorder (ADHD) [45, 50–52], anxiety disorders and/or oppositional defiant disorder (ODD) [52] and conduct disorder [45]. An 8-year follow up study of a population sample of youth from New York State revealed that childhood anxiety disorders and depression, and to

Comorbidity of bipolar disorder in the NCS-R.

Associations between bipolar disorder with anxiety and substance use disorders Bipolar disorder Disorder Anxiety disorders Agoraphobia without panic Panic disorder Panic attacks Post traumatic stress disorder Generalised anxiety disorder Specific phobia Social phobia Obsessive–compulsive disorder Separation anxiety disorder Any anxiety disorder Substance use disorders Alcohol abuse Alcohol dependence Drug abuse Drug dependence Any substance Any disorder Any disorder Exactly one disorder Exactly two disorders Three or more disorders

%a

(se)

ORb

(95% CI)

5.7 20.1 61.9 24.2 29.6 35.5 37.8 13.6 35.4 74.9

(1.3) (2.0) (2.0) (2.6) (2.5) (2.8) (3.1) (3.1) (2.0) (2.8)

5.3∗ 5.8∗ 4.3∗ 4.7∗ 6.1∗ 4.0∗ 4.6∗ 10.2∗ 5.4∗ 6.5∗

(3.0–9.3) (4.4–7.7) (3.5–5.2) (3.3–6.8) (4.6–8.1) (3.1–5.2) (3.5–5.9) (4.6–22.9) (4.6–6.5) (4.7–9.0)

39.1 23.2 28.8 14.0 42.3

(2.6) (1.9) (2.7) (1.8) (2.7)

4.3∗ 5.7∗ 4.5∗ 5.2∗ 4.2∗

(3.3–5.5) (4.3–7.6) (3.3–5.9) (3.7–7.2) (3.3–5.5)

92.3 12.7 9.4 70.1

(2.2) (2.0) (1.7) (2.5)

13.1∗ 4.8∗ 5.6∗ 26.4∗

(6.7–25.5) (2.2–10.4) (2.5–12.5) (13.7–50.8)

a Mean

(SE) prevalence of the comorbid disorder in respondents with bipolar disorder. Based on logistic regression models with one DSM-IV/CIDI disorder at a time as a predictor of lifetime bipolar disorder, controlling for age at interview (5-year intervals), sex and race/ethnicity. ∗ Significant at the p = 0.05 level, two-sided test. b

333

CHAPTER 19

a lesser extent disruptive behaviour disorders, were significantly associated with the development of BPD in early adulthood [53, 54]. Recent results of a high risk study of BPD confirm the anxiety–bipolar link. Duffy et al. [48, 49] found that rates of anxiety disorders and sleep disturbances were significantly elevated among offspring of bipolar probands compared to those of controls [48, 49]. The latter work suggests that anxiety disorders may constitute an early form of expression of the developmental pathway of BPD. Future studies should attempt to distinguish whether anxiety disorders represent manifestations of the same aetiological factors or independently elevate the risk for development of BPD. For example, one possible explanation for comorbidity in high risk samples could be parental concordance for these disorders, such as paternal BPD and maternal anxiety disorder. The strong association between BPD with substance use disorders has also been widely described in both community and clinical samples. Retrospective research has shown that the onset of BPD generally precedes that of the substance use disorder. Merikangas et al. [55] used data from a 20-year prospective cohort study to demonstrate the dramatic increase in risk of alcohol dependence associated with symptoms of mania and BPD in early adulthood [55]. Furthermore in a first episode sample recruited from McLean Hospital in Belmont MA, Tohen et al. [56, 57] found that the prevalence of substance use disorder was 18% compared to a multiple episode cohort [58] recruited from the same institution that had a substance use disorder comorbidity prevalence of over 50%. This suggests that BPD can be considered a risk factor for the development of substance use disorders. Therefore, there appears to be a need to provide educational therapy to all patients suffering from BPD with the aim of preventing comorbid substance use disorders [59].

19.3.2 Non-psychiatric medical disorders Clinical studies have also noted the importance of evaluation of physical disorders in people with BPD [60, 61]. In the McLean/Harvard first episode cohort Tohen et al. [57] found a prevalence of comorbid medical conditions of 31% in individuals who had not been exposed to any psychotropic 334

medication for more than 90 days. Recent community studies have begun to include assessment of medical conditions as well as mental disorder [62]. Evaluation of physical–mental comorbidity in World Mental Health countries showed that heart disease, hypertension and back/neck pain are associated with BPD in both high and low income countries, whereas associations with arthritis, asthma and cancer are limited to high income countries. In contrast, severe headaches/migraine are more strongly associated with BPD in low income countries [63]. Several studies of population-based samples and the recent results of the NCS-R have also confirmed the strong association between migraine and bipolar symptoms/disorder [64, 65]. Health information collected in the NHANES data showed that those with BPD were more likely to rate themselves as in fair or poor health than those without affective disorders; however, other subtypes of mood disorders including major depression and dysthymia tended to have even stronger associations with poor health than BPD [66]. In the same study, associations emerged between all mood disorder subtypes with hypertension, but asthma was only significantly associated with major depression [66]. Although there is scant information on medical comorbidity in children with BPD, some studies of children have reported links between BPD with diabetes and cardiovascular diseases [67]. There are also are several studies of systematic samples, such as the Veteran’s Administration [68] and health insurance claims data bases [69], that provide strong evidence that people with BPD have high rates of physical disorders. The study of health care claims by Carney and Jones [69] found that nearly every medical disorder was more common among those with BPD; however, the extremely large data base, lack of correction for multiple comparisons and failure to conduct multivariate analysis reduced the ability of this study to address the specificity of these associations.

19.4 Risk Factors The finding of equal rates of BPD in men and women from epidemiological surveys was confirmed in all of the recent US population surveys [11, 12, 66].

EPIDEMIOLOGY OF BIPOLAR DISORDER IN ADULTS AND CHILDREN

Although there is consistent evidence for an equal sex ratio for BPI disorder, some private practice-based clinical studies have suggested that more women manifest the BPII subtype [70]. The lack of a sex difference suggests a possible sources of bias in clinical samples which tend to have a greater proportion of women in psychiatric care for BPD [71]. Studies of youth also confirm the lack of sex differences in the rates of BPD and its components during adolescence [46]. However, caution should be exercised in drawing conclusions regarding the lack of sex differences in prevalence rates because there may be differential manifestations of BPD in males and females. Whereas in clinical samples males may be more likely to exhibit mania, females are more likely to present with depression [72]. Although many early studies of treated samples suggest that BPD was more common in upper socioeconomic classes, the most recent US epidemiological studies have consistently found that there are higher rates among those with lower income and education [11, 12, 66]. Likewise, rates of BPD are greater among those who were separated, divorced or widowed compared to those who are married or never married in all of the recent US population surveys. In contrast, a comparison of rates of BPD in high income and low income countries from the World Mental Health Survey showed that BPD was more common in high income than in low income countries (1.4% vs. 0.7%), as was disability associated with BPD [63]. Moreover, people from high income countries were nearly three-times more frequently likely to enter treatment than their low income counterparts. No ethnic or racial differences in the rates of BPD have been reported in recent population surveys of the United States including the National Health and Nutrition Examination Survey (NHANES), NCS-R and NESARC. However, there are only a limited number of studies that can truly distinguish ethnic differences because of the inclusion of sufficiently large multiethnic samples. The large sample size of the NESARC study enabled inclusion of several distinct ethnic subgroups in the US population. This study found that Native Americans reported higher rates of BPI disorder [11] than the other subgroups included in the survey. Another study that examined cultural subgroups is the New Zealand Mental Health Survey [24] that yielded higher rates of BPD

among the Maori (3.4%) and Pacific people (2.7%) compared to European and other whites (1.9%).

19.4.1 Other risk factors Most of the research from community surveys has focused on demographic correlates of BPD. However, there is emerging research from prospective studies that provides information on risk factors that may elevate the risk for development of BPD. Risk factors for the development of mental disorders in children have been divided into those that characterise or affect the individual child, the child’s parents or family or both and the broader environment. There is a dearth of research on links between nondemographic risk factors and BPD from prospective research. Most of the knowledge on risk factors and correlates can be derived from either clinical samples or high risk studies of offspring of parents with BPD [48, 49, 73]. This is an obvious gap that should be addressed in future studies.

19.4.1.1 Family history/genetics A family history of BPD is one of the strongest and most consistent risk factors for the development of BPD. Controlled family studies of BPD yield an average 10-fold increased risk of BPD among adult relatives of probands with BPD compared to relatives of controls [74], as well as a 3.5-fold increased risk of BPD among relatives of probands with non-bipolar major depression. Results of a small number of twin studies yield an aggregate estimate of threefold greater risk among monozygotic compared to dizygotic twins indicating that a significant proportion of the familiality of BPD can be attributed to genetic factors [75]. However, there is a remarkable lack of twin studies of BPD defined by modern diagnostic criteria [74]. Existing twin studies yield an average concordance rates for monozygotic twins of 40% compared to 5% for dizygotic twins, thereby suggesting a complex mode of inheritance of this condition [76]. Table 19.4 presents a summary of the risk ratios for mood disorders derived from controlled family studies and population based twin studies [74, 76]. Despite the strong evidence for familial and genetic factors underlying BPD, there is still a lack of information on susceptibility genes that have been 335

CHAPTER 19 Table 19.4 Family and twin studies of mood disorders among relatives of bipolar (BP) and major depressive disorder (MDD) probands. Relatives BP Family studies Bipolar disorder (weighted average)

%N

Major depression (weighted average)

%N

MDD

Cases

Controls

RR

Cases

Control

RR

5.5 1492 4.4 1534

0.6 1373 0.7 1894

9.2 – 6.3 –

13.7 919 13.7 1529

7.1 1373 6.8 1894

1.9 – 2.0 –

Co-twins BP Twin studies Bipolar disorder (weighted average)

%

consistently shown to have significant predictive value for the development of BPD. Although there have been many studies designed to identify candidate genes underlying BPD through either linkage (segregating within family) or association (differences between cases and controls), there are still no replicated genetic markers for BPD. The results of recent genome-wide association studies did not identify any of the candidate genes found in earlier studies, but it is anticipated that combined results of several large studies now underway may yield more presumptive evidence for susceptibility genes in the next few years [76]. Irrespective of whether the family history represents increased genetic or environmental risk, or more likely elements of both, it is one of the most important predictors of the development of BPDs in particular and mood and anxiety disorders in general youth.

19.4.1.2 Studies of offspring of bipolar probands The potential contribution of the family study can be enhanced by inclusion of a high-risk component, where individuals with a high probability for developing a specific disorder are compared to controls and followed over time. This design permits identification of the components and processes underlying disorders, early patterns of expression of these disorders, determinants of disorder progression, order of onset of comorbid disorders and the longitudinal 336

MZ 67%

DZ 16%

MDD RR 2.5

MZ 50%

DZ 38%

RR 1.3

course and stability of symptoms and disorders. Following early studies by Meyer et al. [77] there are a growing number of studies of offspring of parents with BPD [48, 49, 77–80]. Controlled studies of offspring of parents with BPD have revealed an increased risk of a range of disorders including depression, anxiety disorders and attention deficit hyperactivity disorder, suggesting a lack of specificity of early manifestations of bipolarity [81]. Rates of mania and BPD are generally low due to the young age of adolescent offspring in these studies; however, children of bipolar parents show greater specificity of transmission of affective disorders than do children of parents with unipolar depression [82]. The increased rates of ADHD that have been reported in some studies have been interpreted as evidence that symptoms of ADHD may be manifestations of a common underlying diathesis with BPD [51, 83–86]. The prospective design of many of these studies will enable investigators to evaluate the prognostic significance of the symptoms and syndromes manifested by these children across development.

19.5 Future directions This review has demonstrated substantial progress in the application of the tools of descriptive epidemiology in both US and in international

EPIDEMIOLOGY OF BIPOLAR DISORDER IN ADULTS AND CHILDREN

settings. Population-based studies that incorporate sophisticated methods of sampling, assessment and analysis have provided important information on the prevalence, correlates and service patterns for mental disorders using contemporary diagnostic criteria, and the tremendous impact of mental disorders is finally receiving well-deserved recognition. Most importantly, there has been tremendous growth of international collaborations in the application of common methods that permit investigation of cultural and regional differences in prevalence and risk factors for mental disorders. Recent efforts such as the proposal of a common global nomenclature to define the course and outcome in BPD as proposed by a task force under the auspices of the International Society for Bipolar Disorders [87] should facilitate outcome studies across geographies. There is a growing consensus that descriptive epidemiology has achieved a level of maturity that warrants a shift to the application of the tools of analytic epidemiology [88, 89]. Contemporary issues concerning BPD that warrant further study include: the diagnostic components, thresholds and boundaries of BPD; better integration of adult and child epidemiology of BPD and further investigation of the patterns of comorbidity between BPD with other mental and physical disorders.

19.5.1 Diagnostic spectrum of bipolar disorder There is growing recognition that BPD has a spectrum of expression that is substantially more common than the 1% BPI prevalence traditionally found in population surveys. The studies reviewed in this paper suggest a lack of clear boundaries between BPI and BPII. Many studies failed to assess BPD at all because of the belief that it is too rare, or that its assessment is unreliable. There is growing evidence from population surveys that a substantial proportion of people characterised with major depression may actually manifest subclinical bipolarity [90, 91]. The most compelling evidence is derived from prospective studies that demonstrate the increased incidence of episodes of mania and hypomania among those with subclinical hypomania. Although expansion of diagnostic thresholds would naturally lead to increased prevalence rates of BPD, Angst notes that it would not lead to an

increase in affective disorders in general because the cases shift from non-bipolar to BPD [92]. These findings have important implications for both treatment and course prediction of BPD and warrant further investigation in population-based studies. Several epidemiological studies are now collecting information on the components of BPD independently rather than requiring a shift in mood for a pre-specified duration before eliciting information about activity levels, sleep, attention and cognition. In addition, following the lead of Angst [93], several community surveys have begun to collect independent information on severity, impairment, duration and recurrence without restriction [90, 92].

19.5.2 Integration of research in child and adult epidemiology Epidemiologic studies of adults and children have generally proceeded independently in part because of differences in diagnostic methods and measures, and the requisite inclusion of informant reports regarding child disorders [73]. Youngstrom et al. [93] addressed the similarities and differences in manifestations of BPD in adults and children [93]. Since the samples of children have been identified in a range of speciality clinics that may not be representative of the general population of children, future research will require systematic sampling and longitudinal follow up to address the issue of continuity and discontinuity of the early manifestations of bipolar illness. There is sparse information on the symptoms of BPD from community surveys that can address the possible sampling bias in these clinical samples of youth (with the exception of prospective studies such as that of Lewinsohn [40, 41]). The most informative studies on the evolution of BPD are the prospective studies that followed youth into early adulthood as described above. The cumulative lifetime prevalence of mental disorders derived from these long-term follow-up studies [40, 41, 94–97] tend to be even greater than the retrospective estimates of lifetime prevalence reported in adult psychiatric epidemiology [98, 99]. Despite the contribution of these studies, however, the numbers of youth who develop BPD remains small and future studies of larger and more representative samples are clearly necessary. 337

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19.5.3 Comorbidity of bipolar disorder with mental and physical disorders The consistent finding that the majority of people with BPD also suffer from other mental disorders, both concurrently and across the lifetime, confirms that comorbidity is a real phenomenon rather than an artifact of sampling bias. The finding in the NCS-R study that about 90% of those with lifetime BPI or BPII disorder also meet criteria for another lifetime disorder, and that 70% of those with bipolar spectrum disorders have a history of three or more disorders [12] raises serious questions about the extent to which BPD is a distinct category. Although there is growing evidence that substance use disorders tend to be a consequence of BPD, the emerging evidence from prospective and high risk studies of children suggest that anxiety and behaviour dysregulation may be early forms of manifestation of BPD described above provide preliminary evidence regarding potential mechanisms for comorbidity and suggest that comorbidity could comprise an important source of heterogeneity of BPD. Prospective studies of at risk youth could also provide insight on these important issues. Additional research on possible explanations for comorbidity with physical disorders, particularly migraine and cardiovascular disease, is also highlighted by recent findings in epidemiological research.

19.6 Summary This chapter provides a comprehensive review of the magnitude of BPD in adults (DSM-IV) and children in community surveys across the world. Despite some outliers, the prevalence rates of BPI disorder are highly consistent across studies. The lifetime prevalence rates cluster at about 1.0%, whereas the average 12-month prevalence rate is only slightly lower with a median of 0.6%, with a range from 0 to 1.8%. Evidence from population surveys also reveals that BPD may exist as a spectrum with a lifetime prevalence as high as 5–6% of adults in the community. Integration of evidence from studies of children and adolescents reveals that the onset of BPD occurs before age 18 in about half of those who will eventually develop BPD, and by age 30 in 338

nearly all adults with this condition. Comorbidity is pervasive among both adolescents and adults with BPD in the general population, suggesting disturbances in multiple systems of emotion, cognition and behaviour. Evidence suggests that substance use disorders are a consequence of mania thereby providing an important target for prevention. BPD also tends to co-occur with a range of physical disorders, but the causes for this association are not well understood. Future epidemiologic studies of BPD should incorporate a broader spectrum of assessment, examine continuities and discontinuities between mania and depression across the life span, and investigate explanations for the pervasive comorbidity between BPD and other physical and mental disorders.

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20

Epidemiology of eating disorders Tracey D. Wade,1 Anna Keski-Rahkonen,2 and James I. Hudson3,4 1 School

of Psychology, Flinders University, Adelaide, SA, Australia Academy of Finland, University of Helsinki, Helsinki, Finland 3 Psychiatric Epidemiology Research Program, McLean Hospital, Belmont, MA 4 Harvard Medical School, Boston, MA, USA 2

20.1 Introduction Behaviours thought to be consistent with eating disorders have been observed in the literature from medieval times [1]. In the late nineteenth century anorexia nervosa (AN) was described as a specific disorder by Gull and Las`egue and bulimia nervosa (BN) was described in a form recognisable as the contemporary disorder by Janet [2], but it was not until 1979 that BN was identified as specific disorder by Russell [3]. Binge eating disorder (BED) was first described in 1959 by Stunkard [4] as an abnormal pattern of eating associated with obesity. However within both the Diagnostic and Statistical Manual of Mental Disorders (DSM) classification system and the International Statistical Classification of Diseases and Related Health Problems (ICD), eating disorders have a relatively recent history, with AN and BN first being explicitly described in the third edition of the DSM in 1980. Between DSM-III and DSM-III-R some small diagnostic changes were introduced. Between DSM-III-R and DSM-IV, both AN and BN were moved from the ‘Disorders Usually First Diagnosed in Infancy, Childhood or Adolescence’ section to the ‘Eating Disorders’ section, and BED was introduced as a research category, resulting in a sharp increase of research activity on the prevalence and treatment of this disorder [5]. Given that only minor differences exist between current DSM and

ICD criteria, the focus of the current chapter is the DSM system given the dominance of this system with respect to epidemiological studies. The classification of eating disorders remains a vigorously debated topic, with many suggestions for ongoing refinement, and thus this field represents a vital and developing area of enquiry that has implications for our understanding of the epidemiology of eating disorders.

20.2 Case definition The core features of eating disorders have been described as including the following [6]: disturbance in body image, over- or under-control of eating and extreme behaviours to control weight and shape. These features are not necessarily present across all eating disorders and it should be noted that weight concerns are more likely to be a defining feature for females than males [6]. The two eating disorders currently recognised in the DSM nomenclature are AN and BN. In addition, atypical eating disorders, called eating disorder not otherwise specified (EDNOS), are also recognised and include eating disorders of clinical severity that do not conform to the diagnostic criteria for AN or BN. The diagnostic criteria for AN currently includes the following: (i) refusal to maintain body weight at or above 85% of that expected; (ii) intense fear of

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gaining weight and becoming fat; (iii) self-evaluation is unduly influenced by weight and shape or disturbance in way that body is experienced or denial of seriousness of low body weight; and (iv) amenorrhoea for three consecutive cycles. The criteria for BN include the following: (i) binge eating that includes eating a large amount of food in a 2-hour period of time and experiencing a sense of loss of control; (ii) recurrent use of at least one inappropriate compensatory behaviour including self-induced vomiting, laxatives, excessive exercise, fasting (and under-dosing insulin if the person has Type 1 diabetes); (iii) experiencing binge eating at least twice a week for 3 months; (iv) and selfevaluation is unduly influenced by weight and shape. The working criteria for BED – technically classified as a form of EDNOS, but the only one for which there are operational criteria – include binge episodes occurring at least twice a week for 6-month period that are characterised by three or more of the following: (i) eating much more rapidly than normal; (ii) eating until feeling uncomfortably full; (iii) eating large amounts when not physically hungry; (iv) eating alone as embarrassed by how much one is eating; and (v) feeling disgusted with oneself/depressed/or very guilty after overeating. In addition, marked distress regarding binge eating is present and the binge eating is not associated with regular use of compensatory behaviours. Currently, disturbance in body image has not been included in these criteria, but research suggests that inclusion of extreme weight and shape concerns can differentiate women with BED [7], with women having high weight and shape concerns reporting significantly higher levels of eating pathology and impairment than women without these concerns. One of the major goals of DSM-5 is to increase validity of diagnoses given that rigid adoption of DSM criteria has hindered investigation of aetiology and therefore prevention and treatment [8]. Amongst the changes mooted for DSM-5 are removal of the amenorrhoea criterion for AN [9, 10], requiring the minimum frequency of behaviours related to BN to once a week rather than twice a week [10], elevation of BED from a proposed disorder with research criteria to an official diagnosis [5, 10] and provision of research criteria for purging disorder [11, 12]. 344

There are four issues that are driving the perceived need for changes in the DSM-5. The first is that a very large proportion, perhaps the majority of children, adolescents and adults seeking treatment, are classified as EDNOS (even when cases of BED are not considered), where this group can be considered to have a commensurate clinical severity of impairment to AN and BN [13–15]. Given that not otherwise specified (NOS) diagnoses in the DSM tend to be neglected in terms of recognition and research [16], the current state of classification would appear to be unsatisfactory given that the disorders affecting perhaps the majority of the clinical population are characterised less well than AN, BN or BED. This imbalance of information is reflected in the empirical investigations of eating disorders, including studies of epidemiology and treatment. Second, there is much movement between eating disorder categories amongst clinical populations. For example, of those patients who had not remitted over a 30-month period, only 49, 37 and 10% of people with AN, BN and EDNOS retained the same eating disorder whereas the remainder crossed over to other eating disorder categories [17]. Given this considerable diagnostic flux, it has been suggested that there may be common biological and psychological processes underpinning different eating disorder categories, thus casting some doubt on the validity on our current classification system. Third, the current diagnostic categories are inconsistently supported by empirical investigations, which typically involve latent class or profile analyses and taxometric analyses. The latent class or profile analyses conducted can be categorised into three groups. The first contains studies limited to investigation of people experiencing bulimic symptoms, where two have suggested the existence of two classes characterised not so much by eating disorder symptoms but by degree of comorbidity [18, 19], one suggested classes grouped by frequency of binge eating and vomiting [20], and the other identified a BN subtype along with a bingeing only and a purging only disorder [21]. The second group contains studies of populations exhibiting both bulimic and anorexic syndromes. The first of these [22] distinguished four distinct classes, where two resembled restricting AN, in addition to a mixed group typified by multiple methods of purging, and a

EPIDEMIOLOGY OF EATING DISORDERS

BN class. In contrast, the second identified a six-class solution: AN, BN, BED, a shape and weight preoccupied class, a low weight with binge eating class, and a low weight without binge eating class [23]. A third study, limited to an EDNOS group [24], indicated three subsyndromal disorders and two obese groups, one of which resembled BED. In the final group are three studies of community populations including both non-affected and affected people. All of these studies identified a healthy class, and the two interview-based studies [25, 26] identified only one eating disorder class where all clinically eating disorders were clustered. In contrast, the study utilising a self-report questionnaire and limiting investigation to bulimic behaviour found separate classes for purging, binge eating and BN [27]. Such inconsistent findings highlight the need for further investigation of the validity of our current classification system. Finally, the current classification system accounts poorly for the eating disturbances observed in children [28], where symptomatic expression of disordered eating varies with developmental capacities and chronicity, thus rendering some aspects of the AN and BN diagnostic criteria difficult to apply to children. In addition, the both the DSM and ICD systems do not currently include the variety of disorders found in children, including selective eating, food avoidance emotional disorders, food intake related phobias and pervasive refusal syndrome. Given the dearth of studies examining any eating disorders in children, only adult studies of prevalence and incidence are reviewed in the following section.

20.3 Major prevalence studies The prevalence studies reviewed in the current section and presented in Table 20.1 include only those that: (i) have utilised diagnostic interviews as opposed to self-report questionnaires or medical notes, (ii) adopt a population-based selection strategy and (iii) include a population considered to be largely past the period of risk for developing AN or BN [29, 30] and well past the median age of onset for BED [30], defined as including at least people aged up to 24 years. Among those included are those that used a two-stage approach, where the first stage utilises a screening self-report questionnaire followed by a

second stage interview. Case registry studies have not been included given that recent research indicates that approximately one third to one half of AN, BN and BED cases in the population have sought treatment [30, 31]. Neither have studies been included if they relied completely on self-report questionnaires to infer lifetime presence of eating disorders as it is generally acknowledged that this form of assessment does not permit the most reliable and valid delineation of eating disorders [32]. Indeed, it has been argued that even some interviews schedules, especially those that are brief and incorporate skips rules as part of the assessment procedure, are poorly suited to assessing a population where the disorder can be characterised as being ego-syntonic in nature, or typified by a complex and varied psychopathology, that is difficult to elicit and define [12, 33]. For example, the widely used Composite International Diagnostic Interview (CIDI) [34] has only two stem questions for disorders related to AN (‘Was there ever a time in your life when you had a great deal of concern about or strongly feared being too fat or overweight?’ and ‘Did you ever have this strong worry or fear at a time when you really weighed less than most other people?’), one stem question for disorders related to binge eating, and no stem questions related to purging. This latter omission can potentially miss a number of people who have some sort of purging disorder, as shown by the substantial prevalence of this disorder (5.3%) found in an Australian study of female twins [35]. The prevalence studies should also be interpreted in the context of two further criticisms of populationbased interviews studies. First, the test–retest reliability of lifetime diagnoses is generally quite modest, with kappas ranging from 0.27 to 0.70 over a 6-year period [36]. Recall for major depression is one of the more reliably recalled diagnoses where around 74% of people report lifetime depression at two different assessments [36]. The reliability of lifetime BN has been found to be similar to that of major depression [37], where the presence of salient behavioural markers such as vomiting and laxatives, and increased severity of symptoms, increases reliability of recall [37, 38]. Hence eating disorders that meet diagnostic criteria but are at the lower end of severity of symptomatology may not be captured in assessment of lifetime psychopathology. Second, many published 345

346

Source

Nationally representative sample: New Zealand

State-wide representative sample: South Australia

Oakley Browne et al. [41]

Hay et al. [42]

All women in two areas of Padova, Italy

Population based Twin Registry in Sweden

Favaro et al. [43]

Bulik et al. [44]

European countries

Nationally representative sample: New Zealand

Bushnell et al. [40]

1935–1958 birth cohort

18–25

≥15

4419 females

≥16

15 515 male

15 891 female

934 female

1493 males

1554 female

3016 males

1498 female

N

18–44

Age (years)

Participants

CIDI (adapted)

SCID

EDE diagnostic questions

Long-form only assessed ED CIDI

(1) DIS – lay interviewers (2) DIS – experts

Assessment

DSM-IV

DSM-IV

DSM-IVb

DSM-IV

DSM-III/ DSM-III-R

Diagnostic criteria

Methods

AN AN partial

AN AN partial

AN AN partial BN BN partial BED Purging disorder

BN BED EDNOS

BN BED EDNOS

AN BN

AN BN

0.4 0.1

1.8 1.2

2.0 2.6 4.6 2.1 0.6 1.1

0.1 0.5

1.0 2.0

1.7 1.6

Lifetime

Prevalence (%)

BN (DSM-III) BN (DSM-III-R)

Disorder

Eating disorder prevalence studies – population and interview based where some proportion of the population is ≥24 years.

Australia/New Zealand

Study

Table 20.1

0.3 0.7 0.1 – 0.1 –

0.3 1.5 1.0

1.3 3.0 2.2

Pointa

347

Representative sample: Austria

Representative sample of household population: Belgium, France, Germany, Italy, the Netherlands and Spain

Kinzl et al. [46, 47]

Preti et al. [48]

Population based Twin Registry in Virginia

Nationally representative sample

Kendler et al. [49], Walters et al. (1995) [50]

Garfinkel et al. [51]

North American Countries

Population based Twin Registry: Finland

Keski-Rahkonen et al. [31, 45]

15–65

17–55

≥18

15–85

22–28

4285 female

2163 females

1742 males

2397 females

1000 male

1000 female

2881 female

CIDI

SCID

CIDI

Four questions asked over telephone (EDE)

(1) Self-report – height, weight, purging, three EDI sub-scales (2) SCID

DSM-III-R

DSM-III-R

DSM-IV

DSM-IV

DSM-IV

AN Threshold AN BN Threshold BN

BN Partial-syndrome BN AN Partial-syndrome AN

AN BN BED Any binge eating

AN BN BED Any binge eating

BN BED partial BED EDNOS

BN BED

AN Broad AN BN Broad BN

0 0.01 0.07 0.3

0 0.1 0.3 1.2

0.6 1.4 1.1 2.1

1.6 3.7

1.5 4.2

0.01 0.3 0.6 0.8

0.5 0.8 4.2 9.4

1.5 1.2

1.0 0.9 1.9 3.0

2.2 2.0 1.7 2.3

348

Representative sample of urban dwellers: Montreal

Recruitment from schools representing population diversity

National Comorbidity Survey – Replication (representative sample of US household population)

Gauvin et al. (2009) [53]

Striegel-Moore et al. [54]

Hudson et al. [30]

Source

Randomly selected sample of girls from nine high schools

(cont.)

Lewinsohn et al. [52]

Study

Table 20.1

985 white females

19–24

≥18

1501 female

20–40

1760 female

1061 black females

538 females assessed in 24th year

3831 male

N

8–24

Age (years)

Participants

CIDI

(1) Telephone interview: SIED (2) EDE + SCID

EDE-Q used on in a 20-min telephone call

Wave 1: K-SADS Waves 2 and 3: LIFE

Assessment

DSM-IV

AN BN BED

DSM-IV

AN BN BED Any binge eating

AN BN BED

AN BN BED EDNOS Purging disorder

AN Partial-syndrome AN BN Partial-syndrome BN

AN (no amenorrhea) Threshold AN BN Threshold BN

Disorder

0.9 1.5 3.5 4.9

0 0.4 1.4

1.5 2.3 2.7

2.8 2.5

1.4 2.4

0.2 0.1 0.3

0.1

Lifetime

Prevalence (%)

DSM-IV

DSM-IV

Diagnostic criteria

Methods

0 0.5 1.6 2.5

0 0.6 3.4 14.6 0.5

0.3

0

Pointa

349

National Latino and Asian American Study – Latinos

National Latino and Asian American Study – Asians

Alegria et al. [56]

Nicdao et al. [57]

≥18

≥18

≥16

998 males

1097 females

1127 males

1427 females

563 males

607 females

CIDI

CIDI

CIDI

DSM-IV

DSM-IV

DSM-IV

AN BN Any binge eating

AN BN Any binge eating

AN Partial AN BN BED Any binge eating

AN Partial AN BN BED Any binge eating

AN BN Any binge eating

AN BN Any binge eating

AN BN BED Any binge eating

0.1 0.7 3.9

0.1 0.2 2.1

0 0.5 2.0

0.03 0.03 0.7 0.6 2.2 0.03 0.03 1.3 1.6 5.4 0.1 1.4 4.7

0.02 0.02 0.9 1.2 3.3

0.1 0.3 1.8

0 1.0 2.6

0 0.1 0.8 1.7

0.1 0.1 1.9 2.3 5.8

0.2 1.0 4.1

0.1 1.9 5.8

0.3 0.5 2.0 4.0

b The

studies using the CIDI, the 12-month prevalence was measured instead of point prevalence. Oxford criteria [51] were used, where behaviour was required to have occurred at least weekly for a three-month period prior to interview. DIS, Diagnostic Interview Schedule; SCID, Structured Clinical Interview for DSM-IV; AN, anorexia nervosa; BN, bulimia nervosa; BED, binge eating disorder; EDNOS, eating disorder otherwise not specified; EDE, Eating Disorder Examination; CID, Composite International Diagnostic Interview; LIFE, Longitudinal Interval Follow-up Evaluation; SIED, Screening Interview for Eating Disorders.

a For

National Survey of American Life – Blacks

Taylor et al. [55]

1220 male

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investigations use lay-administered semistructured interviews, such as the CIDI, which has previously been shown to underdiagnose eating disorders [39] and therefore such studies may identify the lower bound of true prevalence estimates [30].

20.3.1 Studies from Australia and New Zealand In this group both of the lifetime prevalence studies come from New Zealand [40, 41], where a lifetime prevalence of approximately 1% for AN and 2% for BN was found. Males had a 10-fold lower prevalence of AN and a fourfold lower prevalence of BN. The point prevalence study [42] which used a comprehensive eating disorder interview, the Eating Disorder Examination (EDE) [58] but adopted a requirement for behaviour to occur at least weekly, estimated a 4 : 1 ratio of EDNOS to BN in females and an 8 : 1 ratio in males. Apart from BED, this EDNOS category contained people who reported weight and shape concerns of moderate importance in addition to regular extreme weight control behaviours. In this group, 15 people could be considered to have purging disorder, a point prevalence of 0.5%, the same as that estimated in a Canadian study [39].

20.3.2 Studies from Europe The European studies present a wealth of information about lifetime prevalence for EDNOS. Across the two studies that used the SCID the prevalence of AN was between 2 and 2.2% [31, 43], whereas the study using the CIDI found a slightly lower rate of 1.0 and 1.8% [44, 48]. The prevalence of BN varies markedly across two of the studies, with estimates ranging from 0.9 to 4.6% [43, 45, 48]. Across the studies levels of EDNOS were similar to those for the full syndrome eating disorders, with the Italian study [43] estimating EDNOS at 6.4%: this included partial AN (lacking amenorrhoea or despite significant weight loss the individual’s weight remains in the normal range), partial BN (subjective rather than objective binge eating or binge eating or purging occurring at least twice a month but less than twice a week), BED and purging disorder. Two

350

studies [31, 44] examined subclinical forms of AN (without amenorrhoea or weight loss of at least 15% leading to a body mass index (BMI) ≤ 19) finding respective prevalence levels of 2.0 and 1.2%, and a further study examined sub-clinical levels of BN [45], where binge eating or purging failed to meet DSM criteria for BN but occurred at least once a week over the 3-month period, estimated at 0.6%. Kinzl and colleagues [46, 47] also found a much higher point prevalence of EDNOS to BN, an almost 30-fold greater prevalence. The largest proportion of this group experienced objective binge episodes without a sense of loss of control.

20.3.3 Studies from North America The one lifetime prevalence study from Canada [51] is of interest as it includes subclinical disorders for both AN and BN, showing subclinical AN to be twice as high as AN across both females and males, where 75% of the female cases lacked only the amenorrhea criterion. Subclinical BN, defined as engaging in binge eating less than twice a week or lacking over concern with weight and shape, is also twice as prevalent as BN. The majority of prevalence studies come from the United States, which on the whole focus less on EDNOS with the exception of BED and binge eating. Across studies of Caucasian women [30, 47, 49, 51, 52], the lifetime prevalence of AN, BN and BED is between 0.9–1.6, 1.5–2.8 and 2.7–3.5% respectively. The commensurate figures for males are 0.3, 0.5 and 2.0% [30]. Partial syndrome AN and BN for Caucasian women is around similar levels as the full syndrome presentations, ranging from 2.4–3.7 to 2.5–4.2% respectively. Recent studies using the CIDI permit a direct comparison between different racial and ethnic groups, namely non-Hispanic Caucasians, Blacks, Latinos and Asians [30: see also Appendix Table 3 in supplemental material for this article available online at http://www.hcp.med.harvard.edu/ncs//eating.php, 55–57]. The lifetime prevalence for eating disorders for women across the three latter groups was very similar: 0.12–0.14 (AN), 1.42–1.91% (BN), 2.31–2.67% (BED). AN prevalence was much lower than for non-Hispanic Caucasian women

EPIDEMIOLOGY OF EATING DISORDERS

but the BEDs were of a similar prevalence. These findings support the suggestion that race/ethnicity differentially confers risk for restricting disorders, placing non-Hispanic Caucasian women at higher risk [6, 52]. A point prevalence study [46] shows EDNOS forms of eating disorders to be substantially higher than AN or BN, with a 30-fold greater prevalence, where EDNOS (defined as bingeing or purging at sub-clinical levels, namely one to seven episodes in the previous month) accounted for 18.5% of the population compared to 0.6% for AN and BN. A variety of North American studies of point prevalence suggest that binge eating is the most common form of disordered eating across all cultural groups [30, 54–56].

20.3.4 Summary of prevalence studies The lifetime prevalence of AN across the studies from Australia/New Zealand, Europe, North America, and utilising DSM-IV criteria ranges from 0.9 to 2.2%. However, prevalence of restricting disorders for non-Caucasian women are 6–16 times lower than this. Addition of partial AN syndromes increases the prevalence to between 3.0 and 4.6%. Typically, delineation of these partial syndromes requires waiving the requirement for amenorrhoea, and thus if the changes in DSM-5 incorporate criteria for AN that does not include amenorrhoea, this will clearly make a major difference in cited prevalence rates for this disorder in future studies. Lifetime BN ranges from 1.5 to 4.6%, with the addition of partial BN syndromes increasing this to between 4 and 6.7%. Lifetime BED is less studied, but ranges from 0.6 to 3.5%, and only one estimate of lifetime purging disorder exists, suggesting a prevalence of 1.1%. The studies consistently show that men are substantially less likely to experience lifetime eating disorders, where AN ranges from 0.1 to 0.4% and BN has been estimated at 0.5%. Overall, incorporation of EDNOS suggests that between 8.7 and 15.9% of women will suffer from a clinically significant eating disorder in their lifetime. It should be noted that all studies were carried out in western developed countries and that the prevalence of eating disorders is likely to be considerably less

in geographic areas where there is less sociocultural emphasis on thinness – this concept will be explored later in the chapter.

20.4 Incidence studies Overall the incidence studies of eating disorders are hard to interpret with respect to time trends. Such studies typically use psychiatric case registers, medical records from hospitals in circumscribed areas, registrations by general practitioners or medical records of health care providers in a community. In the most recent review of 13 studies of AN [59] across Sweden, Switzerland, the United States, the United Kingdom, Netherlands and Denmark, incidence of AN per 100 000 population ranged from 0.10 (1931–1940) to 12.0 (1980–1989), raising the unanswerable question of whether there has been an increase in true incidence of AN (i.e. incidence in the community), or whether there has been an increased demand for care. A subsequent study [60] found that there was no increase of primary care AN incidence over 1988–2000. A recent study conducted independent of healthcare settings in Finland estimated that the incidence of AN at age 10–24 years was 270 per 100 000 females per year and 15.7 per 100 000 males per year [61]. Fewer studies exist of the incidence of BN, with five reported in the literature across the United Kingdom, United States and Netherlands. No clear pattern of results emerges: one recent study suggested an increase in the incidence of primary care BN in the United Kingdom between 1988 and 1996 with a decline thereafter [62], another study suggested a decrease in primary care BN in the United States between 1982 and 2002 [62]. The incidence of BN in the unselected general population in Finland was 100 per 100 000 women per year [45].

20.5 Comorbidity Lifetime eating disorders commonly co-occur with other lifetime psychopathology. In recent population-based studies from the United States [30] and Europe [48], eating disorders co-occurred significantly with almost all of the core DSM-IV Axis I

351

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mood, anxiety, impulse control and substance abuse disorders. These results are consistent with those reported in previous community- and populationbased studies for AN [63], BN [49, 51, 64, 65], BED (or regular binge eating without compensatory behaviours) [66, 67], as well as in previous studies of clinical populations for AN, BN and BED [30, 67, 68]. The cause for the high levels of comorbidity is not known, although the co-occurrence of eating disorders with mood disorders may be caused in part by common familial [69, 70] or genetic factors [71, 72].

20.6 Mortality from eating disorders The traditionally held view is AN is frequently fatal, with a mortality approaching that of substance use disorders [73]. A meta-analysis of follow-up studies of inpatient populations reveal that AN is associated with 12 times the annual death rate due to all causes of death for females aged 15–24 years in the general population [74], or a crude mortality rate of 5% [74–76]. More recent studies have focused on mortality from AN using nationwide death registers or a systematic combination of death records and inpatient registers in geographically defined areas. A population-based study of all patients presenting with AN in Rochester MN, in 1935–1989, found an estimated survival of 93% 30 years after the initial diagnosis of AN, which did not differ from an expected population survival rate of 94%; the standardised mortality ratio was 0.71 (95% confidence interval (CI): 0.42–1.09) [77]. Also, a review of all deaths recorded in the United States over 1986–1990 revealed a very low rate of deaths directly due to AN: the annual death rate was 6.73 per 100 000, or an average of 145 deaths per year in the entire United States [78]. Of deaths due to AN, 79% occurred among women; their mean age at death was 69 years. Further, a register study of females born in Sweden between 1968 and 1977 found a mortality rate of 1.2% for adolescent inpatients with AN at 9–14 year follow-up [79]. In a nationwide Swedish study [80], higher mortality (4.4%) was found among female patients hospitalised due to AN in 1977–1981 compared 352

to those hospitalised in 1987–1991 (all-cause mortality 1.2%, AN-related mortality 0.8% versus 0.4% in the general population) [79]. The hazard ratio of death for the 1977–1981 relative to the 1987–1991 cohort was 3.7. The dramatic decrease in the mortality coincided with the introduction of specialised care units for eating disorders in Sweden. In summary, severe AN that results in inpatient care is associated with a clearly elevated risk of death. It is possible that studies that include communitybased cases of AN generally report lower mortality rates than clinically based studies, but more research is needed. BN does not appear to be associated with elevated mortality: a meta-analysis of 43 follow-up studies of BN cohorts yielded a non-significant overall aggregate standardised mortality ratio of 1.6 (95% CI: 0.8–2.7) [81]. Keel and Mitchell [82] reviewed 88 follow-up studies with 2194 bulimic subjects and reported only seven deaths, two of which were by suicide (crude mortality rate due to suicide = 0.1%). No deaths due to suicide have been reported in subsequent long-term outcome studies of BN [83].

20.7 Risk factors A full delineation of identified risk factors for eating disorders is beyond the scope of the current chapter and a comprehensive reviews can be found elsewhere [6, 84]. We have elected to review here three broad areas of risk factors, including sociodemographic, sociocultural and genetic.

20.7.1 Sociodemographic 20.7.1.1 Age An early study of eating disorder patients referred to a treatment clinic [29] found that the mean (standard deviation) age of onset of lowest weight for AN was 23.3 years (7.7), and the age of onset for binge eating for BN was 18.1 years (3.8). These ages were slightly lower for patients who had BN with a history of AN. Community based epidemiological studies that have included women up to the age of 25 yield lower estimates for the onset of AN, including 16.5 years (3.1) [31]; 18.9 years (0.8) [30]; 18.1 years (2.2) [43]

EPIDEMIOLOGY OF EATING DISORDERS

and between 16.5 and 18.2 years depending on the presence or absence of bulimic behaviours comorbid or consequent to the AN, with the lowest age of onset associated with restricting AN [35]. With respect to eating disorders involving binge eating, there is an almost linear increase in onset for bulimic disorders between the ages of 14 and 25 years [33], with a mean onset between 17.0 years (2.9) [43] and 19.7 years (1.3) for BN [30] and 25.4 years (1.2) for BED [30]. The mean age of onset of self-induced vomiting in the absence of binge episodes was 21.0 years (5.09) [35]. After age 25 cumulative lifetime prevalence for BED continues to climb but at a far lesser rate [30]. Late onset cases of AN are not unknown, but are considered to be relatively rare [85]. Overall, the data suggest that the greatest time of risk for developing an eating disorder occurs between the adolescent years and the mid-20s, representing a period of great psychological and neurobiological developmental change and accompanying challenges.

20.7.1.2 Sex As shown in Table 20.1, both prevalence and incidence studies consistently show males to have a lower risk of developing an eating disorder, where males are about 8–46 times less likely to develop AN (without the requirement of amenorrhea), three to nine times less likely to develop BN, and two to six times less likely to develop BED. The reasons for this differential are unknown, with hypotheses centred on sociocultural pressures which have historically been more pronounced for women than men, and differences in the way that genetic risk factors work [86–88]. However, it is possible that biological factors may also play a role [89].

20.7.1.3 Urbanisation Three studies exist that address whether urbanisation increases risk for eating disorders. In the Netherlands, a nationwide study that registered all eating disorder cases detected by primary care physicians during 1985–1989, the incidence of BN was lowest in rural areas, intermediate in urbanised areas, and highest in large cities (6.6, 19.9 and 37.9 respectively per 100 000 females per year); no rural-urban differences for AN were found [90]. The incidence of

BN appears to be associated with the degree of urbanisation in a dose–response fashion, where incidence is almost 2.5 times higher in urbanised than rural areas, and 5 times higher in larger cities than rural areas [90]. In Italy, the degree of urbanisation was significantly associated with higher lifetime prevalence of BN, and also of AN and BED [43]. There was a higher lifetime prevalence of any eating disorder in the urban area compared to suburban areas (12.8% versus 9.1%). Overall, the evidence to date is consistent with the suggestion that increasing urbanisation is associated with increased risk of eating disorders, with the strongest evidence existing for BN.

20.7.1.4 Socioeconomic status While a widespread perception exists that eating disorders are over-represented in the upper socioeconomic groups, especially in families where there is AN [92], the weight of the evidence from community cases consistently finds no relationship between eating disorders and socioeconomic status (SES). A British survey of 722 students found that more disturbed eating attitudes were significantly associated with having only one parent employed compared to two parents [93]. A survey of over 17 000 adolescents in the United States found that parental SES was associated with adolescent body dissatisfaction but that there was no association between dieting and SES when BMI was controlled [94]. Further, SES was not significantly associated with eating disorder behaviours or low weight. In recent research from the United States from the National Comorbidity Survey – Replication (NCS-R) [30] which included a representative sample of the population, there was no consistent association between SES (indicated by educational level) and eating disorder status. A recent meta-analysis [95] indicated that associations with SES differences were greatest when examined with non-clinical or normative constructs such as dietary restraint and body dissatisfaction and weakest when clinical forms of eating disturbance were examined. Longitudinal research indicates that low parental education and poverty are predictive of obesity [96] and that both obesity and weight dissatisfaction are associated with economic problems in the family [97], but no relationships exist between SES indicators and low body weight or bulimic 353

CHAPTER 20

behaviours [96, 98]. In summary, it appears that SES does not influence the development of eating pathology.

20.7.1.5 Race/ethnicity As summarised above, the prevalence of eating disorders appears to be similar across different racial and ethnic groups studied to date (Caucasians, blacks, Latinos and Asians) with the exception of eating disorders that involve restriction and low weight, where the prevalence is substantially lower in nonCaucasian women.

20.7.2 Sociocultural The potential impact of sociocultural influences on eating disorders represents a complex area of evidence to interpret, where such evidence comes from a number of different types of observations [6]. The first observation relates to the greater number of female than male cases of eating disorders. As discussed above, a sociocultural explanation for this phenomenon represent only one such possible explanation. The second observation relates to the possibility that the incidence of AN, BN and BED has increased over the second half of the twentieth century as the ideal body size for women decreased [30, 49, 59]. However, given the limited power to detect such a trend and the conflicting findings and interpretations, this evidence cannot be considered definitive. The third observation comes from a body of literature that shows variables related to thin-ideal internalisation to predict the growth of risk for eating disorders [99]. However, about one-third of the variance of this measure is likely to be influenced by genetic risk factors [100], indicating that it is not a pure measure of environment, thus confounding the explanatory meaning of the relationships between this variable and eating behaviour. The fourth source of evidence relates to crosscultural studies that can inform our understanding of the impact of increasing westernisation of culture and consequent exposure to the thin ideal. For example, Anne Becker’s work in Fiji [101] over 1989–1998, corresponding to a time of rapid social change in that country including increased exposure to Western ideas, values and media images, shows 354

significant changes between cohorts with respect to an increased interest in changing body shape and a decreased level of body satisfaction. However, few studies have incorporated methodology that can inform specificity of this hypothesised risk factor in influencing growth of clinical eating disorders [84, 102, 103]. Two studies exist that are suggestive of the importance of sociocultural risk factors for AN. The first study examined the population of Curac¸ao, a Caribbean island that has its origins in plantation slavery and has become more affluent over recent years with current classification by the World Bank as a high-income country [104]. Incidence rates of AN ascertained from community health and service providers on the island was found to be lower than westernised countries at 1.82 per 100 000 personyears but the incidence rate increased to 9.08 in the mixed and white population, with no cases amongst blacks. AN incidence was associated with higher education, greater earnings and being more likely to have travelled overseas. Second, data examining first-time admissions for AN in the Czech Republic over 1981–2005, a period representing transition to modern western culture, suggests an increase in admissions over this time for females aged 10–39 years [105]. While access to inpatient facilities did not increase over this time period, admission rates increased significantly from 4.5 in 1994 to 7.5 in 1999, followed by a non-significant decrease. However, the impact of any change of referral practices over that period is unknown. Overall, more unambiguous evidence supporting the role of culture in the causation of eating disorders is required before any firm conclusions can be made.

20.7.3 Genetic factors The role of genetics is important in explaining why all young women exposed to western sociocultural pressures do not have eating disorders. In particular, interactions between genetic and environmental risk factors would provide an elegant explanation of who is most at risk in which type of environments. The evidence supporting the influence of genes on the development of eating behaviour comes from family and twin studies. Family studies, while unable to differentiate between risk associated with shared

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environment and genetic factors, suggest a 7- to 12fold increase in the prevalence of AN or BN, and a twofold or greater increase in the prevalence of BED, in relatives of eating disordered probands compared to the families of controls [106–108]. Twin studies in the area of eating disorders have proliferated over the last 20 years, thus a thorough review is not possible in this chapter, but can be found elsewhere [109]. Twin studies can be broadly divided into three areas: those that investigate the heritability of eating disorders (i.e. AN, BN and BED), the diagnostic criteria for eating disorders, and disordered eating (behaviours and attitudes) not included in the diagnostic criteria. Heritability estimates for AN range from 31 to 76% [44, 110] but it should be noted that 95% CI range from 0 to 95. Six studies that examine the heritability of BN [49, 65, 71, 111–113] all come from a Caucasian Virginian population in the United States, and five of these examined the same population of women. Heritability estimates range from 28 to 83%, again with wide 95% CI, ranging from 0 to 100. Estimates of heritability of BED of 39 and 57% have come from a twin study of binge eating without compensatory behaviours in Norway and a family study (using methods to separate the contribution of genetic vs. shared environment effects) in the United States, respectively [114]. Eight studies across Norway, Finland, the United States and Australia have examined diagnostic criteria for eating disorders, including binge episodes, self-induced vomiting, intentional weight loss and the degree to which self-evaluation is unduly influenced by weight and shape [77, 100, 112, 115–119]. Heritability estimates for the behaviours range from 8 to 82%, and importantly no estimates have 95% CI containing zero. The cognitive component has been shown to be consistently mainly impacted by the environment, although the best measures of this diagnostic criterion do include 15 to 25% of genetic variance [100, 119]. A number of studies from the United States, United Kingdom, Australia, Finland, Japan and the Netherlands, for example [87, 88, 120–124] of disordered eating and attitudes consistently implicate genetic risk. Attitudinal measures in this group tend to show a large contribution of shared and unique environment but the influence of the environment has been

shown to decrease while heritability increases over childhood to adolescence [121]. Some of these twin studies have also examined an overlap of genetic risk factors between eating disorders and a variety of psychiatric and psychological phenotypes. Overlap has been shown between AN and major depression [72], BN and major depression [71] and drug use disorders and BN [113], as well as BN and phobia and panic disorder [111]. Family studies have further implicated shared risk which may be genetic or environmental between AN and obsessive–compulsive personality disorder [125], obsessive–compulsive spectrum disorders [126] and high personal standards (a dimension of perfectionism), a need for order, and reward dependence [127]. Shared risk between BN and mood disorders [69, 128], and novelty seeking and neuroticism [129] has also been shown. Finally, shared risk between BED and mood disorders [70, 108] and anxiety disorders [110] has been reported. To date, no genes contributing to ED have been identified. There have been both two large linkage studies of eating disorders, with the most recent yet to publish any results [130], and the other, a large collaborative study funded by the Price Foundation, has examined the genetics of AN and BN, for example [131] with suggestive respective linkages associated with areas on chromosomes 1 and 10. There exist a multitude of association studies [132] characterised by small samples that present contradictory findings. Of future interest are studies that can combine examination of risk of specific genes and environments, with the purpose of informing gene–environment interactions.

20.8 Future directions There are three future directions that need to be pursued. First, future work is urgently required to further develop valid and reliable diagnostic criteria for eating disorders, supported by appropriate assessment tools. In particular, the meaningful categorisation of EDNOS is an important endeavour, which will impact significantly on the prevalence estimates of eating disorders. In turn, this could impact on, and perhaps clarify, our understanding of time trends and risk factors for eating disorders, and 355

CHAPTER 20

provide a richer field of enquiry for the examination of interactions between sociodemographic, sociocultural and genetic risk factors. Second, there is a need to acquire data on the prevalence of eating disorders using population-based studies in less-studied populations, including developing countries, and different racial and ethnic groups in developed countries, so we can explore whether current categories are even applicable to capturing disturbed eating behaviour of clinical relevance in these groups. Such studies will help us to further explore the validity of diagnostic groupings. Finally, further population-based studies of epidemiology would be significantly enhanced if they could be linked with the testing of candidate genes and environments that may interact to increase the likelihood of development of an eating disorder. Such studies would have the potential to provide a significant step forward in our understanding of eating disorders.

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eating disorders in a community population of young adult women. Int. J. Eat. Disord., 33, 1–9. Stice, E. (2002) Risk and maintenance factors for eating pathology: a meta-analytic review. Psychol. Bull., 128, 825–848. Wilksch, S.M., Wade, T.D. (2009) An investigation of temperament endophenotype candidates for early emergence of the core cognitive component of eating disorders. Psychol. Med., 39, 811–821. Becker, A.E., Gilman, S.E. and Burwell, R.A. (2005) Changes in prevalence of overweight and in body image among Fijian women between 1989 and 1998. Obes. Res., 13, 110–117. Becker, A. (2004) New global perspectives on eating disorders. Cult. Med. Psychiatry, 28, 433–437. Becker, A. (2007) Culture and eating disorders classification. Int. J. Eat. Disord., 40, S111–S116. Hoek, H.W., van Harten, P.N., Hermans, J.M.E. et al. (2005) The incidence of anorexia nervosa on Curacao. Am. J. Psychiatry, 162, 748–752. Pavlova, B., Uher, R., Dragomirecka, E. and Papezova, H.. (2010) Trends in hospital admissions for eating disorders in a country undergoing a sociocultural transition, the Czech Republic 1981–2005. Soc. Psychiatry Psychiatr. Epidemiol., 45, 541–550. Klump, K.L., Kaye, W.H. and Strober, M. (2001) The evolving genetic foundations of eating disorders. Psychiatr. Clin. North Am., 24, 215–225. Hudson, J.I., Lalonde, J.K., Berry, J.M. et al. (2006) Binge eating disorder as a distinct familial phenotype in obese individuals. Arch. Gen. Psychiatry, 63, 313–319. Lilenfeld, L.R., Ringham, R., Kalarchian, M.A. et al. (2008) A family history study of binge-eating disorder. Compr. Psychiatry, 49, 247–254. Wade, T.D. (2010) Genetic influences on eating and the eating disorders, in Oxford Handbook of Eating Disorders (ed. W.S. Agras), Oxford University Press, New York, pp 103–122. Klump, K.L., Miller, K., Keel, P. et al. (2001) Genetic and environmental influences on anorexia nervosa syndromes in a population-based twin sample. Psychol. Med., 31, 737–740. Kendler, K.S., Walters, E.E., Neale, M.C. et al. (1995) The structure of the genetic and environmental risk factors for six major psychiatric disorders in women: phobia, generalized anxiety disorder, panic disorder, bulimia, major depression, and alcoholism. Arch. Gen. Psychiatry, 52, 374–383. Bulik, C.M., Sullivan, P.F. and Kendler, K.S. (1998) Heritability of binge-eating and broadly defined bulimia nervosa. Biol. Psychiatry, 44, 1210–1218. Baker, J.H., Mazzeo, S.E. and Kendler, K.S. (2007) Association between broadly defined bulimia nervosa and drug use disorders: common genetic and

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environmental influences. Int. J. Eat. Disord., 40, 673–678. Javaras, K.N., Laird, N.M., Reichborn-Kjennerud, T. et al. (2008) Familiality and heritability of binge eating disorder: results of a case-control family study and a twin study. Int. J. Eat. Disord., 41, 174–179. Sullivan, P.F., Bulik, C.M. and Kendler, K.S. (1998) Genetic epidemiology of binging and vomiting. Br. J. Psychiatry, 173, 75–79. Bulik, C.M., Sullivan, P.F. and Kendler, K.S. (2003) Genetic and environmental contributions to obesity and binge eating. Int. J. Eat. Disord., 33, 293–298. Wade, T.D., Treloar, S.A. and Martin, N.G. (2008) Shared and unique risk factors between lifetime purging and objective binge eating: a twin study. Psychol. Med., 38, 1455–1464. Reichborn-Kjennerud, T., Bulik, C.M., Kendler, K.S. et al. (2004) Undue influence of weight on selfevaluation: a population-based twin study of gender differences. Int. J. Eat. Disord., 35, 123–132. Wade, T.D. and Bulik, C.M. (2007) Shared genetic and environmental risk factors between undue influence of body shape and weight on self evaluation and dimensions of perfectionism. Psychol. Med., 37, 635–644. Rutherford, J., McGuffin, P., Katz, R.J. et al. (1993) Genetic influences on eating attitudes in a normal female twin population. Psychol. Med., 23, 425–436. Klump, K.S., Burt, A., McGue, M. et al. (2007) Changes in genetic and environmental influences on disordered eating across adolescence. A longitudinal twin study. Arch. Gen. Psychiatry, 64, 1409–1415. Wade, T.D., Martin, N.G., Neale, M.C. et al. (1999) The structure of genetic and environmental risk factors for three measures of disordered eating. Psychol. Med., 29, 925–934. Wade, T.D., Martin, N.G. and Tiggemann, M. (1998) Genetic and environmental risk factors for the weight and shape concern characteristic of bulimia nervosa. Psychol. Med., 28, 761–771.

[124] Kamakura, T., Ando, J., Ono, Y. et al. (2003) A twin study of genetic and environmental influences on psychological traits of eating disorders in a Japanese female sample. Twin Res., 6, 292–296. [125] Lilenfeld, L.R., Kaye, W.H., Greeno, C.G. et al. (1998) A controlled family study of anorexia nervosa and bulimia nervosa: psychiatric disorders in firstdegree relatives and effects of proband comorbidity. Arch. Gen. Psychiatry, 55, 603–610. [126] Bellodi, L.M.C., Cavallini, M.C., Bertelli, S. et al. (2001) Morbidity risk for obsessive-compulsive spectrum disorders in first-degree relatives of patients with eating disorders. Am. J. Psychiatry, 158, 563–569. [127] Wade, T.D., Tiggemann, M., Bulik, C.M. et al. (2008) Shared temperament risk factors for anorexia nervosa: a twin study. Psychosom. Med., 70, 239–244. [128] Hudson, J.I., Laird, N.M., Betensky, R.A. et al. (2001) Multivariate logistic regression for familial aggregation of two disorders: II. Analysis of studies of eating and mood disorders. Am. J. Epidemiol., 153, 506–514. [129] Wade, T.D., Bulik, C.M., Prescott, C. et al. (2004) Sex influences on shared risk factors for bulimia nervosa and other psychiatric disorders. Arch. Gen. Psychiatry, 61, 251–256. [130] Kaye, W.H., Bulik, C.M., Plotnicov, K. et al. (2008) The genetics of anorexia nervosa collaborative study: methods and sample description. Int. J. Eat. Disord., 41, 289–300. [131] Devlin, B., Bacanu, S., Klump, K.L. et al. (2002) Linkage analysis of anorexia nervosa incorporating behavioural covariates. Hum. Mol. Genet., 11, 689–696. [132] Slof-Op’t, L.M.C.T., van Furth, E.F., Meulenbelt, I. et al. (2005) Eating disorders: from twin studies to candidate genes and beyond. Twin Res. Hum. Genet., 8, 467–482.

21

Epidemiology of alcohol use, abuse and dependence Deborah A. Dawson,1 Ralph W. Hingson2 and Bridget F. Grant1 1 Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA 2 Division of Epidemiology and Prevention Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA

21.1 Introduction The role of alcohol in society is a complex one. With a long history of both formal and informal production, alcoholic beverages provide a substantial employment base and numerous tax revenues and are used for multiple and diverse social and cultural purposes. Despite its economic and social entrenchment, alcohol is also a substance whose use is associated with physical, psychological and social harm. Babor et al. [1] identified three mechanisms of alcohol-related harm: toxicity, intoxication and dependence. Toxicity, associated with chronic heavy drinking (i.e. a high volume of consumption), can cause tissue and organ damage. This is the mechanism through which alcohol is associated with chronic conditions such as liver cirrhosis, heart disease, pancreatitis and cancer. Intoxication, the result of heavy per-occasion consumption which may or may be frequent enough to result in a large volume of ethanol intake, impairs judgment and psychomotor skills and is the mechanism through which alcohol is associated with violence, injury and social harm. Dependence, through its contribution to sustained/increasing

levels of consumption, affects the likelihood of both chronic and acute alcohol-related harm, while itself representing the most well-known consequence of excessive alcohol use. This chapter summarises what is currently known about the epidemiology of alcohol use and alcohol-related harm, including the distribution of use and harm within the general population and among important population subgroups, trends in use and harm, and the associations between patterns of alcohol use and various types of harm.

21.2 Population estimates of per capita consumption Alcohol sales data are the gold standard with which other sources of information on alcohol consumption are compared for completeness of coverage. Used extensively in aggregate level analyses linking consumption and alcohol-related outcomes at the population level and in the estimation of per capita consumption, sales data are not without limitations [2]. They exclude unrecorded

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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consumption, for example alcohol purchased abroad and illegal and home-brewed alcoholic beverages, and are not available for all locales. Per-capita consumption estimates for US states that do not provide sales data generally utilise beverage industry information on production and shipments [3–5]. Sales data, which reflect beverage alcohol, must be converted to ethanol (pure alcohol) based on the assumed ethanol content of different alcoholic beverages. Ethanol content varies widely across brands and among subtypes of major beverages and has changed over time, as well. These sources of variation may affect apparent trends over time in ethanol consumption [6, 7]. Despite these limitations, alcohol sales data are generally accepted as the best source of information on trends in volume of alcohol consumption. Based on the most recently available data for 2005, US per capita consumption of all types of alcoholic beverages in the population 14 years and older was 8.5 litres (2.24 gallons). Beer accounted for the major share (4.5 litres or 1.19 gallons per capita), followed by distilled spirits (2.6 litres or 0.70 gallons) and wine (1.4 litres or 0.36 gallons). Per capita consumption in 2005 was almost 5% higher than in 1997 and 1998 (8.1 litres or 2.14 gallons), but well below the peak consumption years of 1980 and 1981 (10.4 litres or 2.76 gallons). The increase since 1998 reflects rising consumption of wine (20%) and spirits (13%). Beer sales continued to decline, but less sharply than between 1980 and 1998 [8]. Compared to other countries in a compilation based on 2003 data originally published in litres [9], the US per capita consumption of 8.3 litres (2.19 gallons) ranked 20th. Most of the countries with higher per capita consumption were European but included Australia, New Zealand and South Korea. The top three were Luxembourg (15.5 litres or 4.09 gallons), France (14.8 litres or 3.91 gallons) and Ireland (13.5 litres or 3.57 gallons). Most high consumption countries showed trends over time in per capita consumption similar to those for the United States [1, Figure 3.1].

21.3 Survey-based estimates of the prevalence of drinking Beverage alcohol sales data do not describe the prevalence of drinking or specific drinking patterns and 362

cannot be used to link drinking and alcohol-related outcomes at the individual level. Rather, information on drinking practices and their consequences are typically derived from survey data. Survey measurement of alcohol consumption is extremely challenging because of variation in the quantity and frequency of drinking, drink sizes and types of beverages consumed, not only across individuals but across drinking occasions [1]. Measurement approaches include prospective techniques (e.g. daily drinking diaries) and exact recall of all alcohol consumed in the past day or week, but most survey data are based on retrospective reporting of drinking patterns during a specified reference period, for example past year or past 30 days. Differences among survey estimates for comparable populations and time periods may reflect the techniques used for capturing variation in drinking quantity, whether consumption is queried for all types of alcohol combined or on a beverage-specific basis, whether the questions are posed in terms actual drink sizes or standard drinks of a fixed size, and the extent to which ethanol content is ascertained and factored into estimates – in addition to mode of administration and response rate. The goal of surveys is to distinguish drinkers and abstainers (lifetime and/or current) and to collect information that can yield analytic measures of alcohol consumption that are correlated with alcohol-related outcomes. Typically, these include volume of ethanol intake and frequency of heavy episodic drinking, generally defined in the United States as five or more (5+) drinks in a single day but sometimes defined as 5+ drinks for men and 4+ drinks for women [2, 10–15]. Survey-based sources of information on alcohol consumption in the United States include both periodic and individual national surveys whose main focus is alcohol, for example the Wave 1 (2001–2002) and Wave 2 (2004–2005) National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) and the periodic National Alcohol Surveys (NAS) conducted approximately every 5 years, and annual surveys such as the National Survey on Drug Use and Health (NSDUH), the National Behavioural Risk Factor Surveillance System (BRFSS) and the National Health Interview Survey (NHIS), which contain more limited data on alcohol use. A long-time series of data on drinking

EPIDEMIOLOGY OF ALCOHOL USE, ABUSE AND DEPENDENCE

among US 8th, 10th and 12th graders is provided by the Monitoring the Future (MTF) Study, and the Harvard University College Alcohol Studies (CASs) have yielded information at several points in time in the drinking practices of students in 4-year colleges and universities in the United States. [16, Appendix D]. Data from the 2007 NSDUH indicated that within the past 30 days, an estimated 126.8 million individuals 12 years of age and older had consumed at least one alcoholic drink, an estimated 57.8 million had consumed five or more (5+) drinks at least once, and an estimated 17.0 million had consumed 5+ drinks at least five times. The prevalence of these behaviours has remained fairly stable between 2002 and 2007 [16]. Data from the 1984, 1990 and 1995 NAS revealed a small but significant decline over that period in the percentage of US adults 18 and older who had consumed alcohol in the past year (from 69.4 to 64.6%), but the decline in the percentage who had consumed 5+ drinks at least once in the past year (from 30.0 to 27.6%) was not statistically significant [17]. According to the 2001–2002 Wave 1 NESARC, 65.4% of US adults 18 and older were past-year drinkers. This included 40.5% who were light drinkers (an average of ≤3 drinks per week), 14.0% who were moderate volume drinkers (>3 but no more than 7 drinks per week for women and >3 but no more than 14 drinks per week for men), and 10.3% who were heavy volume drinkers (>7 drinks per week for women and >14 drinks per week for men). In addition, 17.3% each were former drinkers and lifetime abstainers who had never consumed any alcohol [18]. The prevalence of past-year drinking peaked at ages 25–44, then declined steadily with age (Table 21.1). Drinking was more common among men than women and among non-Hispanic whites compared to other race-ethnic groups. The prevalence of past-year drinking was highest among the never married and increased with education. Individuals living in urban areas were considerably more likely than those in rural areas to be past-year drinkers, and the prevalence of drinking was lowest in the South and highest in the Northeast. Differentials with respect to the prevalence of heavy episodic drinking (HED, 5+ drinks on any

day for men and 4+ drinks on any day for women) showed somewhat different patterns. There was a sharp linear decline with age, and the gender differential more strongly favoured males. The race–ethnic distribution revealed equally high rates of HED among whites, Native Americans and Hispanics. Although the prevalence of HED was lowest among individuals with less than a high school education, as was the case for any past-year drinking, there was no variation at higher levels of education. HED was equally prevalent in urban and rural areas and was more prevalent in the Midwest than in other regions. Similar differentials in the prevalence of drinking and HED have been reported across a broad range of surveys [16, 18–20]. Data from the MTF Study have shown a steady downward progression in overall alcohol use among US secondary school students during recent years. The proportions of 8th, 10th and 12th graders who reported past-year alcohol use declined from 45.4, 63.4 and 72.7%, respectively, in 1993 to 31.8, 56.3 and 66.4% in 2007. Lifetime and 30-day use showed similar declines. The prevalence of having been drunk increased up through the end of the 1990s and then started to decline. In 2007, the proportion of students who had been drunk in the past year varied from 12.6% of 8th graders to 46.1% of 12th graders. The proportion who had been drunk in the past 30 days varied from 5.5 to 28.7% [21]. The Youth Risk Behaviour Surveillance System (YRBSS), which has monitored drinking patterns among high school students (grades 9–12) from 1991 to 2007 [22], likewise reported declines in lifetime drinking (from 81.6 to 75.0%), drinking during the past 30 days (from 50.8 to 44.7%), and drinking 5+ drinks at least once during the past 30 days (from 31.3 to 26.0%, with a peak at 33.4% in 1997). YRBSS data for 2007 revealed a greater prevalence of drinking and HED for male than female students and for white and Hispanic compared to black students. According to MTF data, between 1993 and 2006 the prevalence of any drinking in the past 30 days declined from approximately 70 to 65% for college students 1–4 years post high school while remaining stable at about 60% for their non-college age peers. Over that same period, the prevalence of drinking 5+ drinks in the past two weeks remained fairly stable at approximately 40 and 35%, respectively, for the 363

CHAPTER 21 Table 21.1 Prevalence of past-year drinking, heavy episodic drinkinga and DSM-IV alcohol use disorders among U.S. adults 18 years of age and older, by selected characteristics: Data from the 2001–2002 Wave 1 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC).

Total

Past-year drinking

Past-year heavy episodic drinking

Any alcohol use disorder

Alcohol abuse only

Alcohol dependence

65.4 (0.6)

23.6 (0.5)

8.5 (0.2)

4.7 (0.2)

3.8 (0.1)

70.8 (1.0) 72.9 (0.7) 64.3 (0.7) 45.1 (0.9)

40.7 (1.1) 31.0 (0.7) 17.1 (0.5) 4.5 (0.3)

18.4 (0.8) 10.4 (0.4) 5.4 (0.3) 1.5 (0.2)

6.7 (0.5) 6.2 (0.3) 3.5 (0.3) 1.2 (0.1)

11.6 (0.6) 4.2 (0.2) 1.9 (0.2) 0.2 (0.1)

71.8 (0.6) 59.6 (0.8)

30.8 (0.6) 17.1 (0.5)

12.4 (0.4) 4.9 (0.2)

6.9 (0.3) 2.6 (0.2)

5.4 (0.2) 2.3 (0.1)

69.5 (0.7) 53.2 (0.8) 58.2 (2.6) 48.4 (2.1) 59.9 (0.9)

25.5 (0.5) 14.8 (0.6) 25.5 (2.2) 11.9 (1.0) 24.9 (1.0)

8.9 (0.3) 6.9 (0.4) 12.1 (1.6) 4.5 (0.6) 7.9 (0.6)

5.1 (0.2) 3.3 (0.3) 5.7 (1.0) 2.1 (0.5) 4.0 (0.3)

3.8 (0.2) 3.6 (0.3) 6.3 (1.2) 2.4 (0.4) 4.0 (0.4)

66.3 (0.7) 56.8 (0.7) 70.1 (0.8)

20.5 (0.5) 19.2 (0.6) 36.5 (0.9)

6.0 (0.2) 8.1 (0.4) 15.9 (0.6)

4.0 (0.2) 4.4 (0.3) 6.9 (0.4)

2.1 (0.1) 3.7 (0.3) 9.0 (0.4)

46.4 (0.8) 60.9 (0.7) 70.9 (0.7) 76.2 (0.7)

18.5 (0.7) 22.8 (0.6) 27.7 (0.7) 22.9 (0.6)

7.0 (0.5) 8.3 (0.4) 10.4 (0.4) 7.2 (0.3)

3.1 (0.3) 4.5 (0.3) 5.6 (0.3) 4.6 (0.3)

4.0 (0.3) 3.7 (0.2) 4.8 (0.2) 2.6 (0.2)

67.2 (07) 58.4 (0.9)

23.7 (0.5) 23.2 (0.7)

8.4 (0.3) 8.8 (0.4)

4.6 (0.2) 4.8 (0.3)

3.8 (0.2) 4.0 (0.3)

70.9 (1.8) 69.9 (0.8) 59.0 (0.9) 66.1 (1.3)

23.8 (1.1) 27.7 (1.1) 20.9 (0.6) 23.6 (1.0)

7.8 (0.6) 10.6 (0.7) 7.3 (0.3) 8.8 (0.5)

4.3 (0.5) 5.9 (0.4) 4.1 (0.2) 4.5 (0.3)

3.5 (0.3) 4.6 (0.4) 3.1 (0.2) 4.3 (0.3)

Age group 18–24 25–44 45–64 65+ Sex Male Female Race–ethnicity White Black Native American Asian Hispanic Marital status Married/cohabiting Widowed/divorced/separated Never married Education
5+ drinks for men or 4+ drinks for women on at least one day in the past year. Note: Figures in parentheses are standard errors of estimates. Source: 2001–2002 National Epidemiologic Survey on Alcohol and Related Conditions.

364

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two groups [23]. The series of Harvard University CAS surveys conducted in 1993, 1997, 1999 and 2001 likewise revealed no change in the proportion of students drinking 5+/4+ drinks in the past 2 weeks, slightly less than 45%, but it indicated an increase in the frequency with which they drank this amount [24].

to alcohol from 1992 to 2000, accounting for nearly 8% of ED visits. Of the 2 1/4 million annual trauma centre admissions, over 40% have been linked to injuries under the influence of alcohol [31].

21.4 Alcohol-related mortality and morbidity

In a review of alcohol and injury research, Lunetta and Smith [32] described direct biological mechanisms through which alcohol can increase the risk of injury, for example by slowing the decision-making process, reducing visual acuity, increasing reaction time, depressing the cough reflex (increasing the risk of choking and aspiration), impairing postural control, balance, and gait, reducing the capacity to swim and resist cold temperatures and increasing the odds of falling asleep while smoking and/or of failing to hear a smoke alarm or fire alarm [32]. By increasing confidence, inhibiting self-control and impairing assessment of risk, alcohol also may indirectly increase the risk of injury and the likelihood that conflicts will escalate into violence [33]. A case crossover study in which patients served as their own controls showed a ninefold increase in the odds of injury among patients who reported consuming five to six drinks during a 6-hour period before the injury, and a 17-fold increase among patients consuming seven or more drinks [34]. A recent case–control emergency department study found significantly elevated odds of injury in 10 out of 11 body regions and for all types of injuries among women who consumed 4+ drinks and men who consumed 5+ drinks during the preceding 24 hours, controlling for age [35]. Unintentional injuries are the leading cause of death in the United States for persons 1–44 years of age, and intentional injury is the second leading cause for persons aged 8–34 [26]. Injury deaths are attributed to alcohol if the decedent had a blood alcohol level of 0.10% or higher. From 2001 to 2005, the annual average alcohol-attributable deaths for major types of unintentional injuries were 13 818 for motor vehicle crashes, 5532 for falls, 818 for drownings and 5416 for poisonings, including 307 alcohol poisonings. There were also 7787 alcoholattributable homicides and 7235 alcohol-attributable suicides. In a review of over 300 medical examiner studies published in scientific peer review journals in

Excessive alcohol consumption is the third leading preventable cause of death in the United States, following tobacco use and poor diet/physical inactivity [25]. Between 2001 and 2005, an annual average of 79 646 deaths was attributable to excess alcohol consumption, 43 731 from acute causes (injuries and poisonings) and 35 915 from chronic diseases (Table 21.2). Because alcohol-attributable injuries and poisonings occur at younger ages, they contribute to nearly twice as many years of preventable life lost as alcohol-attributable chronic diseases, 1 557 079 vs. 800 465 [26]. Disability adjusted life years (DALYs), which represent the sum of life years lost plus a weighted fraction of years lived with disability as a result of a given condition, represent another leading measure of the health burden of specific diseases or risk factors. A recent study found that alcohol use was the fifth leading source of DALYs for men, and the 11th leading source for women [27]. The role of alcohol in generating poor health also may be inferred from health care utilisation. Data from the National Hospital Discharge Study for 2003 [28] showed that 424 000 hospital discharge episodes for person aged 15 and older had a principal (i.e. first-listed) alcohol-related diagnosis. Approximately 1.6 million, between 5 and 6% of all hospital discharges in 2003, had any alcohol-related diagnosis, including alcoholic psychoses, alcohol dependence syndrome (ADS), non-dependent abuse of alcohol and chronic liver disease and cirrhosis. A separate study found that alcohol abuse disorders were principally responsible for almost 210 000 hospitalisations and listed as concomitant conditions for nearly 1.1 million additional hospital stays [29]. McDonald et al. [30] estimated that 7.6 million emergency department visits annually were attributable

21.5 Alcohol and injury

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366

800 Drivers/Motorcycle Riders

700

Illegal per se BAC Level = 0.08

Median BAC = 0.16

600 500 400 300 200 100 0 0. 0 0. 1 0 0. 3 0 0. 5 0 0. 7 0 0. 9 1 0. 1 1 0. 3 1 0. 5 1 0. 7 1 0. 9 2 0. 1 2 0. 3 2 0. 5 2 0. 7 2 0. 9 3 0. 1 3 0. 3 3 0. 5 3 0. 7 3 0. 9 4 0. 1 0. 43 45 +

the United States from 1975 to 1995 [36] alcohol was a factor in 40% of motor vehicle deaths, 39% of other unintentional injury deaths, 47% of homicides and 29% of suicides [26]. Thus, alcohol is a, if not the, leading contributor to unintentional and intentional injuries. Traffic crashes are the predominant type of alcohol-attributable injury death. A review of over 100 laboratory studies found that impairment in the skills needed to safely operate a motor vehicle begins with any departure from 0% blood alcohol content (BAC), and that virtually all drivers exhibit impairment on some critical driving measure at 0.08% BAC [37]. Deficits include reduced peripheral vision, poor recovery from glare, poor performance in complex visual tracking, and reduced divided attention performance. Driver simulation and road course studies have revealed poor parking performance and poor driving performance at low speeds, and roadside observational studies have identified increased deterioration of speeding and breaking performance [38]. In 2007, 12 998 people were killed in an alcoholimpaired driving crash, that is a crash that involved at least one motor vehicle operator with a BAC of 0.08% or higher. A comparison of alcohol test results for drivers in single vehicle fatal crashes vs. those stopped at random in roadside surveys found that each 0.02% increase in BAC nearly doubled a driver’s risk of being in a single vehicle fatal crash, with a greater increases in risk among drivers under the age of 21. For all age and gender groups, the likelihood of being a fatally injured driver was a least nine times greater at BACs of 0.05–0.099% than at zero BAC [39]. Zador et al. [40] found that drivers with BACs between 0.08 and 0.099% had at least 11 times greater a risk of dying in a single vehicle crash than non-drinking drivers, rising to an almost 52-fold increase among male drivers ages 16–20. Figure 21.1 provides the distribution of blood alcohol concentrations among drivers and motorcyclists who died in traffic crashes in 2007 [41]. An analysis linking data from the Fatal Accident Reporting System and the National Mortality Follow–Back Survey explored the issue of alcohol dependence in alcohol-related crashes. Fatally injured drivers with BACs of 0.15% or higher were much more likely than zero BAC drivers to have been

BAC (g/dL)

Fig 21.1 Distribution of blood alcohol concentration values among drivers and motorcycle riders involved in fatal crashes who had positive BAC values, 2007. Source: National Highway Traffic Safety Administration, National Center for Statistics and Analysis [41]. 2007 Traffic Safety Annual Assessment – Alcohol-Impaired Driving Fatalities. Pub. No. DOT HS 811 016, Washington, DC, August 2008. http://www-nrd.nhtsa.dot.gov/Pubs/811016.PDF.

classified by informants (usually spouses, parents, children or siblings of decedents) as ‘problem drinkers’, 31% vs. 1% [42]. In the 1992 National Longitudinal Alcohol Epidemiologic Survey (NLAES), the 13% of respondents 18 and older who had been classified with alcohol dependence at some point in their lives represented 65% of those who had ever been in a motor vehicle crash after having too much to drink (based on self-report) and 72% of those who had been in alcohol–related crashes during the past year [38]. Further analyses of the NLAES revealed that the younger people were when they began drinking, the greater their likelihood of driving after drinking too much and of being in motor vehicle crashes because of drinking [43]. In addition, alcohol dependence and early initiation of drinking were each independently associated with experiencing unintentional injuries under the influence of alcohol [44] and with physical fights after drinking [45].

21.6 Alcohol and chronic disease About 3 in 10 US adults drink at levels that elevate their risk for physical, mental and social

EPIDEMIOLOGY OF ALCOHOL USE, ABUSE AND DEPENDENCE

problems [46]. These heavy drinkers have increased risks of hypertension, gastrointestinal bleeding, sleep disorder, major depression, hemorrhagic stroke, cirrhosis of the liver and several cancers [47]. Men are considered heavy or at-risk drinkers if they consume five or more (5+) drinks on any day or an average of 15 or more drinks per week. For women, the respective thresholds are four or more (4+) on any day or seven or more drinks per week. Of US adults 18 and older, 16% exceed both daily and weekly alcohol limits, and almost half of these meet the criteria for either alcohol abuse or dependence [46]. The prolonged intervals between alcohol use and the onset of chronic diseases make it difficult to identify the exact patterns of consumption that increase their risk; however, a recent review found that daily consumption of 5+ drinks was independently associated with the development of cirrhosis and an increased risk of death from cardiovascular disease and strokes [48]. Another review found elevated risks for a variety of diseases among females who consumed 40 or more grams of alcohol daily and men who consumed 60 or more grams daily. The diseases included hypertension, coronary heart disease (women), stroke, liver cirrhosis, oral cancer, esophageal cancer, liver cancer, breast cancer (women), major depression and epilepsy [49]. A recent prospective analysis of adult drinkers based on the 1988–2002 NHIS revealed that men who consumed 5+ drinks on drinking days had significantly elevated rates of all-cause mortality, cardiovascular mortality, cancer mortality and other causes of death compared with those who had one drink [50]. A prospective analysis of data from Waves 1 and 2 of the NESARC found that daily or near daily consumption of 5+ drinks for men and 4+ drinks for women was associated with a significantly increased likelihood of developing liver disease over the course of a 3-year follow-up interval, in addition to increased risks of initiating smoking and developing alcohol abuse and dependence, drug abuse and dependence and nicotine dependence [51]. Perhaps no area of alcohol epidemiology is more contentious than the debate surrounding the u-shaped or j-shaped curve that is purported to reflect the benefits of moderate drinking for coronary heart disease, cardiovascular disease and all-cause mortality. Cardioprotective effects have been shown

in prospective studies and meta-analyses [52–58], and plausible mechanisms have been articulated for such an effect [59–61]. However, past research has been faulted for systematic failure to distinguish former drinkers, who may have stopped drinking because of health problems, from lifetime abstainers in the specification of the reference category. A meta-analysis that excluded studies with this type of error found no reduced risk among moderate drinkers [62], but was itself criticised for its selection of error-free studies [63–65], and a recent study that clearly distinguished former drinkers from lifetime abstainers found a protective effect of drinking for coronary heart disease and cardiovascular disease mortality, although for women only [66]. Thus, evidence for a protective effect of moderate drinking remains open to divergent interpretations and continues to be an area of intense scientific debate.

21.7 Diagnostic classification of alcohol use disorders The most recent classification of mental disorders, the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) [67], has its roots in the ADS developed by Edwards and Gross [68] in 1976. The syndrome was defined by the following seven elements: (i) narrowing of the drinking repertoire; (ii) salience of drink-seeking behaviour; (iii) increased tolerance to alcohol; (iv) repeated withdrawal symptoms; (v) relief or avoidance of withdrawal by further drinking; (vi) subjective awareness of compulsion to drink and (vii) rapid reinstatement of symptoms after a period of abstinence. The ADS was described as existing in degrees of severity, with the emphasis squarely placed on dimensionality. Not all components of the syndrome needed always to be present in the same intensity. More important, the unidimensional ADS was kept theoretically distinct from other alcohol-related disabilities in what Edwards and his colleagues [69] termed the biaxial concept. Other alcohol-related and biological problems were arrayed along a separate distinct and single dimension reflecting the DSM concept of alcohol abuse and the International Classification of Diseases (ICD) concept of harmful use. 367

CHAPTER 21

In 1994 the DSM criteria were revised to incorporate the ADS model into the classification of alcohol use disorders (AUDs) [67], with a diagnosis of DSM-IV alcohol dependence requiring at least three of the following seven criteria within a 1-year period: (i) tolerance, (ii) withdrawal, (iii) drinking in larger amounts or longer than intended, (iv) a persistent desire to reduce consumption or unsuccessful efforts to cut down or control alcohol use, (v) spending a great deal of time obtaining, using or recovering from the effects of alcohol, (vi) giving up or reducing social, occupational or recreational activities because of alcohol use and (vii) continued use despite knowledge of physical or psychological problems caused or exacerbated by alcohol. Consistent with the ADS model, the DSM-IV adhered to the biaxial concept by including a distinct category of alcohol abuse defined in terms of four criteria reflecting legal, interpersonal and social problems and hazardous use, at least one of which was required to achieve a diagnosis. Despite their ADS theoretical underpinnings, diagnoses derived from the DSM-IV are clearly categorical, failing to capture the severity and dimensional aspects of the syndrome as originally postulated by Edwards and Gross. However, the results of several recent studies [70–75] have shown that DSMIV abuse and dependence criteria are arrayed along a single continuum of severity. These results call into question the validity of DSM-IV dependence criteria as distinct from abuse criteria, with some dependence criteria well represented among the mildest criteria and some abuse criteria tapping the more severe range of the continuum. These findings challenge the common interpretation of DSM-IV alcohol abuse as a milder disorder than alcohol dependence and can be used to inform future revisions of the DSM by empirically refining the structure of AUD classification.

21.8 Population estimates, prevalence, incidence and natural course of alcohol use disorders Recent US estimates of the overall prevalence of AUD indicate that approximately 18.6 million individuals aged 12 and older had an AUD in 2007. This figure has not changed significantly since 2002 [15, p. 276]. The most recent national estimates for DSM-IV 368

alcohol abuse and dependence [67] come from the 2001–2002 Wave 1 NESARC, which found that 4.7% of US adults aged 18 and older had past-year alcohol abuse (no history of dependence) and 3.8% had alcohol dependence, with or without abuse. The overall rate of past-year AUD was 8.5%. Lifetime rates were 30.3% (any AUD), 17.8% (abuse only) and 12.5% (dependence) [76, 77]. As shown in Table 21.1, the prevalence of AUD varied substantially across population subgroups. Although the prevalence of abuse and dependence both declined with age, the difference between the 18–24 year and 25–44 year age groups was significant for dependence only. Dependence was more prevalent than abuse in the youngest age group, whereas the opposite was true at ages 25 and older. Men were more than twice as likely as women to have an AUD. In terms of race–ethnicity, white and Native Americans had equally high rates of abuse, but the prevalence of dependence was higher among Native Americans than whites. Asians had the lowest rates of each disorder, with intermediate rates for blacks and Hispanics. For all race–ethnic groups other than Whites, rates of abuse and dependence were of about the same magnitude, but Whites had a significantly higher prevalence of abuse than dependence. These differentials match those reported by the NSDUH for the US population 12 years of age and older in the years 2003–2005, which found the highest rates of any AUD at ages 18–25 and among Native Americans and in which men were more than twice as likely as women to have a past-year AUD [19, 78]. The NESARC-based rates of both abuse and dependence (Table 21.1) were lowest among individuals who were married or cohabiting and highest among those who had never married. Among the latter, the prevalence of dependence exceeded that of abuse, whereas abuse was almost twice as common among married and cohabiting persons. In terms of education, adults who had attended college but not completed 4+ years, including current college students, had the highest rates of both abuse and dependence. Whereas the lowest rates of abuse were found among individuals who had not graduated from college, the lowest rates of dependence were found among college graduates. The prevalence of abuse and dependence did not differ for persons living in urban as compared to rural areas. AUD rates

EPIDEMIOLOGY OF ALCOHOL USE, ABUSE AND DEPENDENCE

were slightly higher in the Midwest than in other regions. When simultaneously controlling for all the sociodemographic factors shown in Table 21.1, in addition to personal income, differences with respect to education and most regional differences were no longer significant; however, rates of both abuse and dependence continued to be significantly increased among the young, the unmarried and men [76]. The prevalence rates for dependence shown in Table 21.1 include dependence with and without abuse, but there is evidence that these may represent heterogeneous phenotypes. Of the 3.8% of US adults who had past-year dependence in the Wave 1 NESARC, 2.5% had dependence with abuse and 1.3% had dependence without abuse. Thus, overall about one third of the individuals with dependence did not have abuse, but the extent to which dependence occurred in the absence of abuse varied by age, sex and race and was particularly high among minority women. When lifetime alcohol dependence was considered, about 14% of the individuals with lifetime dependence had never had abuse. Thus, screening instruments for dependence that require a positive screen for abuse before asking dependence symptoms run the risk of failing to identify a substantial proportion of dependent cases, and this failure may also lead to misrepresentation of gender and racial differentials in the prevalence of alcohol dependence [79]. Trends in the prevalence of abuse and dependence are difficult to interpret because of changes over time in the criteria for those disorders. For example, the estimates derived from the Epidemiologic Catchment Area (ECA) study [80] utilised DSM-III criteria for AUD [81] and the National Comorbidity Survey (NCS) estimates [82] were based on DSM-III-R criteria [83]. Other differences also confound trend estimation, for example the inclusion of institutionalised individuals in the ECA and the use of a population aged 15 and older in the NCS. For the 10-year period between 1991–1992 and 2001–2002, though, comparisons are possible, because the 1991–1992 NLAES ascertained its estimates of AUD with the same measurement instrument [84] that was used in the Wave 1 2001–2002 NESARC. Comparison of NLAES and NESARC data revealed that over the 10-year time interval between the two surveys, the prevalence of abuse

Table 21.2 Average number of alcohol-attributable deaths per year among individuals with moderate to heavy alcohol consumption, United States 2001–2005.

Total for all causes Subtotal for chronic causes Acute pancreatitis Alcohol abuse Alcohol cardiomyopathy Alcohol dependence syndrome Alcohol polyneuropathy Alcohol-induced chronic pancreatitis Alcoholic gastritis Alcoholic liver disease Alcoholic myopathy Alcoholic psychosis Breast cancer (females only) Cholelithiases Chronic hepatitis Chronic pancreatitis Degeneration of nervous system due to alcohol Epilepsy

Fetal alcohol syndrome Fetus and newborn affected by maternal use of alcohol Gastrooesophageal haemorrhage Hypertension

Male

Female

Total

57 429

22 217

79 646

25 269

10 646

35 915

366

329

695

1868 389

514 59

2382 448

3037

820

3857

1

0

1

248

63

311

17

4

21

8938

3281

12 219

1

0

1

568

183

751

0

355

355

–7 2 118

–4 2 112

–11 4 229

77

14

91

102

88

191

2

1

3

0

1

1

16

13

29

753

610

1363

369

CHAPTER 21 Table 21.2

Table 21.2 (cont.)

(cont.) Male

Ischaemic heart disease Laryngeal cancer Liver cancer Liver cirrhosis, unspecified Low birth weight, prematurity, IUGR, deatha Oesophageal cancer Oesophageal varices Oropharyngeal cancer Portal hypertension Prostate cancer (males only) Psoriasis Spontaneous abortion (females only) Stroke, haemorrhagic Stroke, ischaemic Superventricular cardiac dysrthymia Subtotal for acute causes Air-space transport Alcohol poisoning Aspiration Child maltreatment Drowning Excessive blood alcohol level Fall injuries Fire injuries Firearm injuries Homicide Hypothermia Motor-vehicle non-traffic crashes

370

Female

Total

609

277

886

207 598 4134

30 187 2921

237 786 7055

108

52

160

426

52

478

53

20

74

320

56

376

26

14

40

232

0

232

<1 0

<1 <1

<1 <1

1472

303

1775

495 85

181 102

676 187

32 159

11 572

43 731

104

21

125

292

78

370

109 96

95 72

204 168

716 0

152 0

868 0

2888 692 108 6174 182 147

2644 466 15 1613 87 36

5532 1158 123 7787 269 183

Male Motor-vehicle traffic crashes Occupational and machine injuries Other road vehicle crashes Poisoning (not alcohol) Suicide Suicide by and exposure to alcohol Water transport

Female

Total

10 802

3016

13 819

130

7

138

165

45

210

3669

1747

5416

5778 22

1457 9

7235 31

87

11

98

a

Surgeon General warns against alcohol use during pregnancy. Note: Numbers may not sum exactly to totals due to rounding. <1 indicates that there was less than 1 but greater than 0 deaths from a particular cause that were attributable to alcohol consumption. Negative numbers indicate lives saved. IUGR, intrauterine growth retardation. Centers for Disease Control and Prevention [26], Alcohol-Related Disease Impact (ARDI) System, http:// apps.nccd.cdc.gov/ardi/Homepage.aspx, September 2010.

increased significantly, from 3.0 to 4.7%, whereas the prevalence of dependence declined significantly, from 4.4 to 3.8%. Despite the overall decrease in dependence, its prevalence increased among young Asian males and young black females [77]. Grucza et al. [85] also used data from the NLAES and NESARC in a repeated cross-sectional analysis that compared the lifetime prevalence of dependence across cohorts, controlling for age. They found that while men compared at similar ages showed few cross-cohort effects, women born in 1954–1963 had a 50% greater likelihood of lifetime alcohol dependence among drinkers than those born in 1944– 1953. By race–ethnicity, this increase was observed among white and Hispanic but not black women. Past-year prevalence rates for AUD reflect incident cases (those occurring for the first time during the past year), chronic cases (those continuing from an earlier time period) and recurrent cases (those that were incident in an earlier interval but had previously remitted). Because prevalence reflects

EPIDEMIOLOGY OF ALCOHOL USE, ABUSE AND DEPENDENCE

factors that are associated with remission in addition to factors associated with the development of a disorder, correlates of AUD prevalence are not necessarily the same as correlates of AUD incidence. Although most incidence data for general population samples are retrospectively derived and thus susceptible to measurement error in dating the onset of AUD, the longitudinal design of the NESARC permitted prospective assessment of the incidence of DSM-IV AUD in the U.S. adult population. The NESARC 12-month incidence rates for DSM-IV alcohol abuse (considered without respect to the DSM-IV hierarchy that precludes a diagnosis of abuse in the presence of concurrent or prior dependence) and dependence were 1.03 and 1.72%, respectively. Because individuals may have been incident for both abuse and dependence, the rates cannot be summed to represent the incidence of any AUD, which was independently estimated as 1.66% [86]. This figure is quite similar to the 1-year AUD incidence rate of 1.79 from the earlier ECA study [80]. As for the prevalence of AUD, the incidence rates of abuse and dependence decreased with age and were more than twice as high for men as women. The incidence of abuse and dependence did not vary among whites, blacks and Hispanics, but incidence rates for both disorders were higher for widowed/divorced/separated and never married individuals than for those who were married or cohabiting. There were no differentials in incidence with respect to education, urbanicity or region of residence. When simultaneously considering all the above sociodemographic factors, with the addition of family income, the associations of incidence with age, sex and marital status remained statistically significant for both alcohol abuse and dependence. Data on the resolution of AUDs are usually based on treatment samples, but data from the general population reveal that the majority of individuals with AUDs never receive treatment for those disorders. According to the Wave 1 NESARC, only 24.1% of individuals with lifetime DSM-IV alcohol dependence had ever received treatment, including participation in 12-step or other self/mutual help programs. Among those with lifetime abuse, the proportion ever treated was even lower, 7.0% [76]. Thus, data from general population surveys may provide a far different picture of the natural course of AUD than

what might be inferred from clinical samples. A recent study examined the course of alcohol dependence in the general adult population by looking at the past-year diagnostic status of individuals who had been classified with alcohol dependence prior to that point, using retrospective data from the Wave 1 NESARC. In the past year, nearly half (47.7%) of those previously diagnosed with dependence were in full remission, including 18.2% who were abstainers and 29.5% who were drinking without reporting any AUD symptoms. In addition, 27.3% were in partial remission. Only 25.0% were still dependent. The proportion remaining dependent decreased steadily over time, from 64.9% of those whose onset of dependence had occurred less than 5 years earlier to 6.9% among those whose onset of dependence had occurred 20 or more years earlier [87]. When the individuals in full remission were followed over the 3 years between the Wave 1 and Wave 2 NESARC interviews, 26% had experienced the recurrence of one or more AUD symptoms, but only 5% had experienced enough symptoms to be classified with a recurrent episode of dependence. Rates of relapse were lowest among the abstainers [88].

21.9 Comorbidity of DSM-IV alcohol use disorders and other psychiatric disorders Over the last three decades, there has been increasing interest in the relationship between AUD and other types of psychopathology, the presence of which has been among the elements used to distinguish alcoholic subtypes in many modern alcohol typologies such as Type 1/Type 2 [89–92] and Type A/Type B [93]. Despite an extensive literature demonstrating high levels of psychiatric comorbidity among individuals in treatment for substance use or mental disorders [94–100], it is widely recognised that treatment samples may overstate comorbidity due to an increased likelihood of treatment seeking in the presence of multiple disorders [101]. Early studies of lifetime comorbidity in the general population based on the ECA survey revealed that lifetime AUD was associated with a more than doubled likelihood of any lifetime mental disorder and a more than sevenfold increase in the likelihood of any drug use 371

CHAPTER 21

disorder [102, 103]; likewise, data from the NCS indicated two to fourfold increases in the odds of lifetime mood and anxiety disorders among men and women with lifetime alcohol dependence [104]. However, these estimates did not adjust for potential confounders, nor did they consider the issue of temporality, that is of which disorder occurred first or whether the disorders occurred during the same time period. Thus, whereas there is available information regarding the magnitude and patterns of comorbidity from prior research, there is considerably less information that permits inferences regarding the mechanisms underlying comorbidity. There are several possible explanations for comorbidity, including the direct causal mechanism whereby the secondary disorder is elicited directly by the primary disorder [105]. The indirect causal model of comorbidity posits that the secondary effects of the primary disorder give rise to the secondary disorder (e.g. alcohol dependence resulting from the self-medication for major depression) or, alternatively, that the development of risk factors for the comorbid disorder could arise as a consequence of the primary disorder. Another explanation of the associations between AUD and other psychiatric disorders posits that both disorders may share common aetiological factors. The identification of specific mechanisms underlying comorbidity between AUD and other psychopathology would have far-reaching implications for both prevention and treatment strategies. However, the support for a specific mechanism of comorbidity has until recently been hindered by two major methodological limitations. First, the diagnosis of some comorbid conditions, especially mood and anxiety disorders, is complicated by the fact that many symptoms of those disorders resemble symptoms of intoxication and/or withdrawal from alcohol or other substances. The challenge is to differentiate between intoxication and withdrawal symptoms and the symptoms of the mood and anxiety and other psychiatric disorders. The DSMIV was the first nomenclature to provide clear and specific guidelines for making the distinction between these substance-induced disorders (those occurring only during periods of substance use or remitting shortly thereafter) and true or independent 372

anxiety and mood disorders (those where either the full mood or anxiety syndrome is established before substance use or the mood or anxiety syndrome persists for more than 4 weeks after the cessation of intoxication or withdrawal). Second, our understanding of mechanisms underlying the comorbidity of AUD with other specific psychiatric disorders has been hindered by failure to consider the substantial impact of comorbidity among these other psychiatric disorders. Tests of association controlling for only sociodemographic characteristics, as have been presented in prior research, cannot yield information on the unique relationships of other disorders to AUD, which requires additional statistical adjustment for all other psychiatric disorders that are present. The 2001–2002 NESARC was the first national survey to distinguish between DSM-IV substanceinduced and independent disorders to determine if mood, anxiety and AUDs were associated even when substance-induced disorders were ruled out [106]. NESARC analyses were also the first to control for other psychiatric disorders when assessing the association between AUD and specific psychiatric disorders. The associations between 12-month DSM-IV alcohol abuse and dependence and other substance use disorders (SUDs) and independent mood and anxiety disorders are shown in Table 21.3. Controlling for sociodemographic characteristics alone, there were significant associations of alcohol abuse with drug use disorders and nicotine dependence, but not with mood and anxiety disorders. Consistent with past research, alcohol dependence was significantly associated with all disorders examined: drug abuse (odds ratio (OR) = 5.8) and dependence (OR = 18.7), nicotine dependence (OR = 4.9), mood disorders (OR = 2.1–4.0) and anxiety disorders (OR = 2.3–3.6). When sociodemographics and psychiatric comorbidity were both controlled, the observed odds ratios were reduced. Alcohol abuse remained significantly associated with drug abuse (OR = 2.5) and nicotine dependence (OR = 1.8). The OR for the associations of alcohol dependence with drug use and nicotine disorders were reduced but remained strong, suggesting unique factors leading to the disorder-specific associations. That is, the specific factors underlying the alcohol dependence-nicotine dependence association are not necessarily the same

EPIDEMIOLOGY OF ALCOHOL USE, ABUSE AND DEPENDENCE Table 21.3 Adjusted odds ratios 12-month DSM-IV alcohol use disorders and other substance use, mood and anxiety disorders controlling for sociodemographic characteristics and comorbid psychiatric disorders. Comorbid disorder

Adjusted odds ratios (99% confidence intervals) Sociodemographic characteristicsa

Any drug use disorder Any drug abuse Any drug dependence Nicotine dependence Any mood disorder Major depressive disorder Bipolar I Bipolar II Dysthymia Any anxiety disorder Any panic disorder Panic with agoraphobia Panic without agoraphobia Social phobia Specific phobia Generalised anxiety

Alcohol abuse

Alcohol dependence

Sociodemographic characteristicsb and other psychiatric disordersb Alcohol abuse Alcohol dependence

2.8 (2.02–3.77)c 3.2 (2.21–4.58)c 1.6 (0.89–3.04) 2.0 (1.62–2.44)c 1.2 (0.98–1.59) 1.3 (0.98–1.81) 1.1 (0.70–1.73) 0.9 (0.37–2.03) 1.0 (0.49–2.11) 1.2 (0.93–1.46) 1.0 (0.63–1.70) 1.5 (0.64–3.62) 0.9 (0.46–1.60) 0.9 (0.59–1.47) 1.2 (0.89–1.60) 1.0 (0.56–1.71)

9.8 (7.19–13.24)c 5.8 (3.99–8.37)c 18.7 (10.74–32.51)c 4.9 (4.01–5.93)c 3.2 (2.51–4.00)c 2.1 (1.57–2.95)c 4.0 (2.71–5.96)c 3.1 (1.92–5.09)c 2.2 (1.10–4.24)c 2.7 (2.15–3.31)c 3.5 (2.42–5.18)c 3.6 (1.74–7.41)c 3.4 (2.18–5.25)c 2.3 (1.50–3.46)c 2.3 (1.79–3.00)c 3.0 (2.00–4.61)c

2.1 (1.48–2.90)c 2.5 (1.72–3.72)c 1.2 (0.61–2.19) 1.8 (1.48–2.90)c 1.0 (0.79–1.36) 1.2 (0.89–1.68) 0.8 (0.49–1.32) 0.7 (0.30–1.65) 0.9 (0.42–1.87) 1.1 (0.82–1.36) 0.8 (0.47–1.42) 1.2 (0.47–3.12) 0.7 (0.35–1.33) 0.8 (0.49–1.34) 1.1 (0.78–1.45) 0.8 (0.41–1.44)

5.0 (3.59–6.94)c 4.1 (2.74–6.03)c 7.5 (4.16–13.64)c 3.4 (2.87–4.19)c 1.7 (1.23–2.29)c 1.3 (0.92–1.93) 1.9 (1.13–3.01)c 2.0 (1.22–3.41)c 0.9 (0.42–1.76) 1.5 (1.16–1.99)c 1.4 (0.89–2.20) 1.0 (0.47–2.32) 1.5 (0.91–2.57) 1.0 (0.59–1.67) 1.3 (0.94–1.71) 1.0 (0.62–16.8)

a Odds

ratios adjusted for age, race/ethnicity, sex, education, income, marital status, urbanicity and geographic region. Odds ratios adjusted for age, race/ethnicity, sex, education, income, marital status, urbanicity, geographic region and other psychiatric disorders. c Odds ratio is significant. Source: 2001–2002 National Epidemiologic Survey on Alcohol and Related Conditions. b

as those underlying the alcohol dependence-illicit drug use disorder associations. With the controls for additional comorbidity included, associations between alcohol dependence and specific mood and anxiety disorders remained significant only for bipolar I and bipolar II disorders and specific phobia, and these were considerably reduced. Thus, while some unique disorder-specific associations were found, much of the association of alcohol dependence with other affective and anxiety disorders appears due to factors common to these other disorders [106]. Data from the second wave of the NESARC, conducted in 2004–2005, yielded new information on the associations between baseline psychopathology and the 1-year incidence of additional disorders [86]. When considered without respect to the DSM-IV [67] hierarchy of abuse and dependence, and after controlling for sociodemographic characteristics and other baseline psychiatric disorders, baseline alcohol abuse predicted incident alcohol dependence (OR = 3.9), and vice versa (OR = 3.4). In addition,

baseline drug abuse predicted incident alcohol abuse (OR = 3.0) and borderline personality disorder (PD) predicted incident alcohol dependence (OR = 2.4). In contrast, the odds of incident alcohol abuse were reduced by baseline bipolar II disorder (OR = 0.2), and the odds of incident alcohol dependence were reduced by baseline dependent PD (OR = 0.1). Prior to adjusting for other psychopathology, baseline AUD was associated with a slightly greater than twofold increase in the incidence of bipolar I disorder, panic disorder, social phobia and generalised anxiety disorder (GAD); however, none of these associations remained statistically significant after controlling for other baseline psychopathology. Thus, as was the case for cross-sectional comorbidity, other common underlying factors appear to mediate most of the prospective associations of alcohol dependence with affective and anxiety disorders. Despite progress in overcoming the two major methodological limitations of comorbidity research by differentiating clearly between substance-induced and independent 373

CHAPTER 21

mood and anxiety disorders and accounting for the confounding effects of additional psychiatric disorders, no consistent pattern has yet emerged to fully support any of the aetiological models reviewed previously. Beyond its implications for aetiology, comorbidity may affect clinical presentation and prognosis for recovery. Comorbid depression and anxiety disorders have been associated with poorer alcohol treatment response, reduced likelihood of remission from alcohol dependence and increased rates of relapse in a limited number of studies of treatment samples [107–109]; however, comorbid mood and anxiety disorders generally have failed to show a significant association with the course of alcohol dependence in studies of general population samples [87, 110]. As the population studies controlled for more measures of baseline AUD severity, this suggests that any adverse effect of comorbidity on the prognosis for alcohol dependence may be mediated through greater initial AUD severity among individuals with comorbid conditions. Among the more numerous studies addressing the impact of comorbid alcohol dependence on the clinical presentation and course of affective and anxiety disorders, findings are also mixed. Several studies of individuals with comorbid major depressive disorder (MDD) and alcohol dependence found that remission from alcohol dependence predicted recovery from MDD [111–113]. However, another study found that the presence of comorbid alcohol dependence was associated with neither the severity of presenting symptoms of affective disorder nor with the rate of remission from affective disorder [114]. Similarly, a study that compared MDD patients with and without concurrent alcoholism found that the two groups did not vary in terms of time to remission or relapse, although the former did maintain lower levels of psychosocial functioning over the course of follow-up [115]. Two comprehensive reviews of the impact of comorbid SUD on the course of bipolar disorder [116, 117] describe evidence that comorbid SUD are associated with delayed recovery and shorter time to relapse, poorer treatment outcome and reduced compliance with medication regimens, increased likelihood of rapid cycling, more psychiatric hospitalisations, reduced quality of life and more 374

depressive and manic symptoms. Most of the studies cited in these reviews failed to distinguish the effects of alcohol and drug use disorders; however, a recent study of individuals with bipolar disorder who were followed after a first hospitalisation for mania found that those with comorbid AUD whose onset preceded the onset of bipolar disorder were more likely to recover, and did so more rapidly, than either those without comorbid AUD or those with comorbid AUD whose onset followed that of bipolar disorder. In terms of impact on anxiety disorders, a 12-year prospective study reported that the presence of a comorbid SUD decreased the likelihood of recovery from GAD and increased its risk of recurrence [118]. There was no reported association of SUD with recovery from panic disorder or social phobia, and other anxiety disorders were too rare to permit analysis. Two reviews of schizophrenia and comorbid SUD [119, 120] concluded that schizophrenics with SUD had higher relapse rates, lower levels of treatment compliance and response to medication, higher rates of hospitalisation and longer admissions, higher medical costs and increased risks for violence and suicidality. Interestingly, a number of recent studies (see review in [121]) have shown that schizophrenics with SUD have executive cognitive performance, that is generally as good as or better than that of non-addicted schizophrenics, although those with comorbid SUD demonstrated reduced context sensitivity in one recent study [121]. Speculative explanations for the general lack of increased cognitive impairment associated with comorbid SUD have included drug-induced increases in dopaminergic activity that benefit cognitive functioning and the fact that most treatment subjects had been abstinent for enough time prior to cognitive assessment to ameliorate the effects of substance use [121].

21.10 Summary Future advances in understanding the initiation and continuation of alcohol use and AUD will substantially benefit from a lifespan perspective. Clearly, more longitudinal investigations are needed to elucidate the mechanisms underlying the transitions from alcohol use to dependence and comorbidity

EPIDEMIOLOGY OF ALCOHOL USE, ABUSE AND DEPENDENCE

of AUD and other psychopathology, transitions whose occurrence and correlates are likely to vary across the life span. Further, although large-scale epidemiological surveys have provided important information about the prevalence, incidence and risk of excessive alcohol use and comorbidity of AUD and psychiatric disorders among groups of individuals, they tell us little about the aggregation of and transitions of these conditions within families or the degree to which comorbidity may be heritable. This gap in our knowledge highlights the future key role of genetic epidemiology to identify patterns of disorder occurrence that will further elucidate the mechanisms of comorbidity. Future work in this area promises to increase our understanding of factors relevant to the onset and continuation of alcohol use, AUDs and their comorbidity that reflect genetic risk, environmental exposure and the complex ways in which genes interact with one another and with the environment.

Acknowledgements This research was supported in part by the Intramural Program of the National Institutes of Health, National Institute on Alcohol Abuse and Alcoholism. The views and opinions expressed in this paper are those of the authors and should not be construed to represent the views of any of the sponsoring organisations, agencies or the US Government.

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22

Epidemiology of illicit drug use disorders Wilson M. Compton,1 Marsha F. Lopez,1 Kevin P. Conway1 and Yonette F. Thomas2 1 Division

of Epidemiology, Services and Prevention Research, National Institute on Drug Abuse, Bethesda, MD, USA 2 Howard University, Washington, DC, USA

22.1 Introduction Drug use disorders are environmentally mediated psychiatric conditions in that sufficient exposure to an external factor (i.e. the drug itself) is necessary for the expression of the condition. Thus, this chapter begins with review of data concerning use of drugs. The epidemiology of drug use disorders is then described including brief summaries of drug use disorders internationally, overlap with other psychiatric conditions (i.e. comorbidity), and genetic epidemiology. A key concept for the chapter is that the trajectory of drug use disorders is not linear but has multiple reciprocal pathways over time between non-use, use, problematic use, and addiction. Each of these pathways can be seen as having predictors and outcomes, including traditional sociodemographic, psychiatric, and genetic factors the combination of which may be unique to each pathway.

22.2 Drug consumption Any complete review of the epidemiology of drug use disorders includes an understanding of the extent, distribution and patterns of consumption of the various drugs of abuse. Multiple sources of information help to illustrate the distribution of illicit drugs of abuse across multiple dimensions – spatial, temporal

and social. In the United States, annual surveys of drug abuse in household and student populations serve as the primary source of information about the distribution of illicit drug use in the population according to age, race/ethnicity, gender, other demographic factors (employment, marriage, etc.) and region of the country. For example, the nationally representative secondary student survey, the Monitoring the Future Study (MTF), has been conducted annually since 1975 [1], the National Survey on Drug Use and Health (NSDUH) (previously called the National Household Survey on Drug Abuse, [2]) has been conducted with household respondents ages 12 and older since 1972 (conducted every few years at first and then annually since 1988), and the Youth Risk Behaviour Survey (YRBS) has been conducted biennially since 1991 (both national and state/local samples, [3]). In addition to these standard yearly surveys, a rich array of specialised populations are also surveyed on a more intermittent basis: including adolescents (e.g. National Longitudinal Survey of Youth (NLSY79, [4]), National Longitudinal Study of Adolescent Health (Add Health, [5]), adults (e.g. the National Longitudinal Alcohol Epidemiologic Survey (NLAES, [6])) and National Epidemiologic Survey on Alcohol and Related Conditions (NESARC, [7]) and incarcerated persons (e.g. [8, 9]). There are also numerous local area epidemiology surveys and studies implemented using both cross-sectional and longitudinal designs. Thus,

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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Fig 22.1 Per cent of US students reporting any illicit drug use in past year, by grade [1].

rich data exist to inform researches about the social distribution of drugs of abuse among consumers. From these sources of information, we have documented that the landscape of drug use shifts over time among secondary students. As seen in Figure 22.1, following a peak level of use of any illicit drug at least once in the prior 12 months in 1979, rates diminished through the 1980s reaching a nadir in 1991. Rates increased until about 1997 and have subsequently levelled off or, in some cases, declined. This overall trend is most typical for marijuana because, as the most commonly used drug, changes in its prevalence tend to drive the trends for the ‘any’ illicit drug index. Although the major national surveys document, in the most general sense, similar trends in drug use across the last decades, important distinctions between the surveys and discrepancies in their findings demonstrate certain principles in epidemiology (as in all of science): namely, that multiple methods and multiple approaches to a problem may be needed to obtain a full view. However, one major issue is the sampling frame. Whereas MTF studies only youth who attend school and are in grades 8, 10 or 12 [1], the NSDUH includes the civilian population aged 12 and older housed in residences and non-institutional group quarters [2]. Among youth, MTF routinely yields higher estimates of tobacco, alcohol and illicit drug use than the NSDUH, a difference attributed largely to collection in school rather than in the household. In addition, methodological differences in the way that questions are asked of respondents may play a role in the differences between them [10]. 382

What are we to make of these differences? How does a clinician or a scientist make sense of the inconsistencies? In fact, it is just such inconsistencies that argue for the utility of the different sources of information. The weaknesses of school-based methods are that these surveys can only adequately cover a certain age range and only school-attending youth. For household surveys, coverage of the full range of ages is possible, but some of the populations of most interest are not included, such as the homeless or incarcerated. Additional studies are needed of these other populations for a complete picture of drug use. Despite variance in the absolute rates found in the major surveys, systematic monitoring of drug use in the United States indicates that illicit drug use is very common and typically begins during adolescence. The 2009 NSDUH data indicate that approximately 47.1% – an estimated 119 million individuals in the United States ages 12 and older – have tried any illicit drug at least once in their lives, 41.5% have used marijuana and 30.1% have used other illicit drugs [2]. Reflecting the emergence of substance use in adolescence, the 2009 MTF found that 19.9% have tried an illicit drug by 8th grade, 36.0% by 10th grade and 46.7% by 12th grade [1]. Marijuana is far and away the most commonly used illicit drug, with 15.7% of 8th graders, 32.3% of 10th graders and 42.0% of 12th graders reporting having ever tried marijuana [1]. A nearly universal finding is that drug use increases from adolescence to young adulthood then gradually declines [1]. Thus, adolescence marks a period of risk for onset of drug use.

EPIDEMIOLOGY OF ILLICIT DRUG USE DISORDERS Table 22.1 2009 past year illicit drug use among 12th graders in the Monitoring The Future study [1]. Drug

Prevalence (%)

Marijuana/ Hashish Vicodin∗ Amphetamines∗ Tranquilizers∗ Cough Medicine∗ Salvia Adderall∗ Sedatives∗ OxyContin∗ Hallucinogens MDMA (Ecstasy) ∗ Non-medical

Drug

Prevalence (%)

32.8

Cocaine (any form)

3.4

9.7 6.6 6.3 5.9

Inhalants Cocaine Powder Ritalin∗ LSD

3.4 3.0 2.1 1.9

5.7 5.4 5.2 4.9 4.7 4.3

Provigil∗ Ketamine Steroids Crack Methamphetamine Heroin

1.8 1.7 1.5 1.3 1.2 0.7

use.

Another key finding derived from surveillance studies is that the number of illicit users of prescription drugs has been increasing in recent years [11, 12]. In particular, the past few years have seen a marked increase in the use of opioid medications (such as oxycodone and hydrocodone) but an even greater increase in problems associated with such use [13]. As seen in Table 22.1, in 2009, among 12th graders, marijuana was the most commonly used illicit substance (32.8% past year prevalence). Examining the list of commonly abused substances shows that prescription agents (as well as the overthe-counter dextromethorphan containing cough medications) are prominent. Much attention to this epidemic of prescription drug abuse (e.g. [11, 12]) is due to the recognition of this problem among teens in the United States. Other concerning changes in recent years have included an increase in marijuana abuse and dependence (especially among younger black and Hispanic persons) possibly related to increases in marijuana potency [14]; major shifts in the epidemiology of methamphetamine abuse, especially in the west and rural parts of the United States [15–17]; increased availability of high-purity heroin and a rise in heroin use via smoking and other non-injection routes [18], and a decline in the use of lysergic acid diethylamide (LSD, [1]). Rates of drug use can be examined among different subgroups as well, including variance in rates

across geography, gender, race/ethnicity and other demographic groupings (socioeconomic status, education, marital status, etc.). Significant variation in rates of illicit drug use is seen across nearly all of these categories, indicating the role of social factors in the expression of drug using behaviours. In summary, drug use may be seen as varying across populations and time (i.e. in terms of cohort, period and population effects). There are multiple ways to understand the epidemiology of drug abuse. Key to the issue is an understanding of the transitions in drug use trajectories. While a classic pathway is from use to problems to addiction to remission, such a linear system does not fully describe the multiple complex trajectories that are possible. As an alternative, the trajectories may be considered as a series of potential bidirectional transitions (Figure 22.2), in which each transition can be examined and may be understood as having its own description (including associated risk and protective factors). This implies the potential for worsening, remission and/or recurrence at each stage from use to problematic use and addiction. The prototypical pattern is for progression from non-use to use to problematic use to addiction, followed by recovery (non-use) and relapse cycles (back to use to problems to addiction). Each transition has a measurable probability. The speed and probability of transition can be calculated in terms of incidence and prevalence. In addition, each transition can be thought of as having an associated set of risk and

Non-Use

Addiction

Use

Problems Fig 22.2 Transitions in drug use trajectories. Each arrow represents a transition.

383

CHAPTER 22

protective factors – which may or may not overlap with one another. For example, conduct disorder (and the related antisocial personality disorder) are associated with every transition. By contrast, sex is associated with exposures to use but not with progression from use to problems (or addiction). That is, given a specific level of exposure, boys are no more likely to progress to problems or addiction, than girls.

22.3 Definitions The next key issue is that despite concerning data regarding high rates of illicit drug use, a major limitation of the existing research is that the terms misuse, abuse, dependence and addiction are applied in idiosyncratic ways. In this chapter, drug abuse is generally defined as any intentional use of intoxicants (including prescription medications used outside of a physician’s prescription for a bona fide medical condition other than accidental misuse). It should be noted that this use of the term ‘abuse’ is distinct from the abuse diagnosis specified in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV, [19]). On the other hand, ‘addiction’ is used in this chapter as a synonym to the standard DSM-IV diagnosis of dependence. DSM-IV defines substance dependence as three or more symptoms from a list of seven (tolerance, withdrawal, using more than intended, inability to control use, spending excessive time using or obtaining the substance, limiting important activities because of the substance and continued use despite physical and psychological problems caused or exacerbated by use [19]), occurring within a 1-year period. DSM-IV substance abuse is defined as a residual category for persons who do not meet criteria for substance dependence, but have at least one of four social or legal problems resulting from substance use: failure to fulfil major role obligations, recurrent hazardous substance use, recurrent legal problems and continued substance use despite social or interpersonal problems. Although persons can abuse or be dependent on several substances at the same time according to DSM-IV, many researchers group subjects into ‘any illicit drug use disorder’ category. 384

New approaches to diagnosis include consideration of illicit drug addiction as a single dimension of behaviour [20–24]. This approach is in contrast to the categorical approach in the DSM system and depends in large part on the assessment of the relative severity of a case rather than its presence/absence. Future work on the diagnostic approaches to addiction may include just such an approach [25, 26]. Furthermore, what is not completely clear is how much illicit drug addiction is a distinct illness as opposed to part of a broader cluster with a shared aetiology. Twin studies comparing the degree of similarity of behaviour of monozygous and dizygous twin pairs show that several externalising disorders are linked. As the specific genetic causes are defined for addiction and other related externalising conditions, perhaps these questions will be addressed.

22.4 Rates of DSM-IV abuse and dependence Although extensive data on use of drugs in the US population have been available on an ongoing basis for adults and adolescents [1, 2], epidemiological data on the prevalence, correlates, disability, treatment and comorbidity of drug use disorders among adults is less often collected. Yet, abuse and dependence on illicit substances are widespread in the general population and are associated with substantial societal, personal and economic costs [27–30]. National epidemiological surveys [6, 31–33] and numerous clinical studies [34–38] consistently indicate that drug use disorders have strong associations with alcohol use disorders and mood, anxiety and personality disorders. Further, Axis I and II comorbidity with drug use disorders has been associated with underachievement, decreased work productivity, poor health, neuropsychological impairment, human immunodeficiency virus infection, hepatitis, social dysfunction, violence, incarceration, poverty, homelessness, lower probability of recovery and poor treatment outcome and poor quality of life [39–43]. The risk of suicide is also increased for persons with comorbid disorders; especially for individuals with drug use disorders and bipolar disorder [44]. A recent publication about the epidemiology of drug use disorders was published in 2007 [7],

EPIDEMIOLOGY OF ILLICIT DRUG USE DISORDERS

following similar studies conducted and published periodically over the decades. In one earlier study, the 1990–1992 National Comorbidity Study [32], DSM-III-R[45], criteria were used to assess drug use disorders. In another earlier study, the National Institute on Alcohol Abuse and Alcoholism’s 1991–1992 NLAES [6], DSM-IV [19] criteria were used, but assessments of disorders comorbid with drug use disorders were limited to major depression and dysthymia. Although NSDUH began to collect prevalence data on DSM-IV drug use disorders in 2000, data on disability and specific psychiatric comorbidity are not collected, and only 12-month but not lifetime, disorders are assessed [2]. As seen in Table 22.2, 12-month rates of DSM drug abuse reported in large-scale epidemiological surveys conducted since the early 1980s have remained generally stable regardless of whether DSM-III [46], DSM-III-R [45] or DSM-IV [19] was used: DSM-III 0.9% [33], DSM-III-R 0.3–0.8% [32, 47–49] and DSM-IV 0.9–1.4% [2, 6, 7, 51]. Rates of 12-month drug dependence were also generally consistent across the different epidemiological studies: DSM-III 1.2% [33], DSM-III-R

0.6–2.8% [32, 47–49] and DSM-IV 0.5–2.0% [2, 6, 7, 50]. On the other hand, lifetime prevalence rates of abuse were lower for DSM-III (2.6%, [33]) compared with DSM-III-R (1.5–4.4%, [32, 47, 48]) and DSM-IV (3.1–7.9%, [6, 7]). In contrast, the lifetime prevalence estimates for DSM-IV drug dependence (2.6–2.9%, [6, 7]) were lower than those derived from surveys using DSM-III (3.5%, [33]) and DSM-III-R (1.9–7.5%, [32, 47, 48]) criteria. Recent results [7] indicate that in 2001–2002, 2.0% of adult Americans living in households experienced a DSM-IV drug use disorder in the prior 12 months (1.4% abuse, 0.6% dependence) while 10.3% reported a DSM-IV drug use disorder at some time during their lives (7.7% abuse, 2.6% dependence). Drug abuse and dependence were associated with significant disability and early onset. Thus, drug use disorders continue to be a widespread and substantial public health problem in the United States. As seen in Table 22.3, rates of drug abuse and dependence were significantly greater among men than among women, a finding consistent with previous epidemiologic surveys [2, 31–33, 47–51].

Table 22.2 Illicit drug abuse and dependence in major epidemiological studies. Citation

Regier et al. [33] Kessler et al. [32] Bijl et al. [47] Kringlen et al. [48] Vicente et al. [49] Jenkins et al. [51] Grant [6] Andrews et al. [50] Compton et al. [7] SAMHSA [2]

Population

Study

Diagnostic system

Drug abuse

Drug dependence

12-month

Lifetime

12-month

Lifetime

US population US households Dutch 18–64 yo Oslo adults

ECA

DSM-III

0.9%

2.6%

1.2%

3.5%

NCS

DSM-III-R

0.8% (0.1)

4.4% (0.3)

2.8% (0.3)

7.5% (0.4)

NEMESIS

DSM-III-R

0.5% (0.1)

1.5% (0.1)

0.8% (0.1)

1.8% (0.2)

NPES

DSM-III-R

0.2% (0.1)

1.5% (0.3)

0.6% (0.2)

1.9% (0.3)

Chilean adults Britons 16+ yo US adults Australian households US adults

CPPS

DSM-III-R

0.3% (0.2)a

–

1.3% (0.4)a

–

NSPM

Non-standard

–

–

2.2%

–

NLAES ANMHS

DSM-IV DSM-IV

1.1% (0.07) 1.0% (0.1)

3.1% (0.11) –

0.5% (0.04) 2.0% (0.2)

2.9% (0.1) –

NESARC

DSM-IV

1.4% (0.1)

7.7% (0.2)

0.6% (0.1)

2.6% (0.1)

NSDUH

DSM-IV

0.9%

US 12+ yo

–

1.9%

–

a Six-month

prevalence. Prevalence (SE, when available). yo, years old

385

386 0.7 (0.48–1.12) 0.6 (0.42–0.93) 0.5 (0.30–0.69) 1.0

1.1 (0.79–1.52) 1.0

3.4 (1.54–7.61) 2.2 (1.02–4.82) 1.2 (0.52–2.94) 1.0

1.4 (0.96–2.05) 1.3 (1.03–1.74) 1.0

1.0 2.6 (1.82–3.67) 2.0 (1.48–2.68)

37.2 (15.57–88.82) 20.5 (8.74–48.26) 7.9 (3.20–19.44) 1.0

1.0 0.9 (0.66–1.26) 2.1 (1.12–4.09) 0.5 (0.18–1.20) 0.4 (0.29–0.63)

2.6 (2.01–3.33) 1.0

Any drug use disorder OR (CI)a

Significant odds ratios are highlighted in boldface.

a CI = 99% confidence interval.

Northeast Midwest South West

Region

Urban Rural

Urbanicity

$0–19 999 $20 000–34 999 $35 000–69 999 $70 000+

Personal Income

Less than high school High school Some college or higher

Education

Married/cohabiting Widowed/separated/divorced Never married

Marital Status

18–29 30–44 45–64 65+

Age

White Black Native American Asian Hispanic

Race-ethnicity

Sex Male Female

Characteristic

0.7 (0.43–1.08) 0.5 (0.33–0.80) 0.4 (0.25–0.59) 1.0

1.2 (0.77–1.74) 1.0

2.9 (1.14–70.8) 2.0 (0.78–4.98) 1.2 (0.44–3.32) 1.0

1.1 (0.74–1.71) 1.4 (1.00–1.92) 1.0

1.0 2.1 (1.35–3.35) 1.9 (1.34–2.56)

35.7 (12.78–99.53) 19.7 (6.85–56.81) 6.6 (2.18–19.68) 1.0

1.0 0.9 (0.64–1.33) 1.4 (0.59–3.10) 0.4 (0.14–1.35) 0.4 (0.25–0.55)

2.6 (1.87–3.47) 1.0

Any drug abuse OR (CI)

12-month

0.9 (0.46–1.84) 1.0 (0.50–1.95) 0.7 (0.35–1.49) 1.0

1.0 (0.59–1.67) 1.0

5.8 (1.50–22.17) 3.3 (0.80–13.91) 1.5 (0.35–5.98) 1.0

1.9 (1.01–3.54) 1.2 (0.75–1.94) 1.0

1.0 3.6 (1.96–6.59) 2.3 (1.12–4.63)

35.9 (8.01–160.78) 20.6 (4.67–90.56) 10.7 (2.29–50.07) 1.0

1.0 0.9 (0.54–1.55) 3.8 (1.56–9.11) 0.6 (0.16–2.12) 0.6 (0.27–1.47)

2.5 (1.60–3.90) 1.0

Any drug dependence OR (CI)

0.6 (0.45–0.75) 0.6 (0.49–0.82) 0.5 (0.42–0.67) 1.0

1.1 (0.91–1.31) 1.0

1.4 (1.09–1.80) 1.2 (0.95–1.47) 1.1 (0.86–1.33) 1.0

1.1 (0.89–1.31) 1.0 (0.90–1.16) 1.0

1.0 1.7 (1.44–1.95) 1.1 (0.97–1.29)

33.3 (19.93–55.59) 35.0 (21.27–57.51) 18.3 (11.27–29.70) 1.0

1.0 0.7 (0.58–0.78) 1.6 (1.10–2.22) 0.2 (0.15–0.33) 0.4 (0.30–0.48)

2.3 (1.98–2.55) 1.0

Any drug use disorder OR (CI)

0.6 (0.50–0.82) 0.7 (0.56–0.92) 0.6 (0.50–0.77) 1.0

1.1 (0.90–1.27) 1.0

1.2 (0.89–1.51) 1.1 (0.84–1.36) 1.0 (0.80–1.28) 1.0

1.0 (0.78–1.24) 1.0 (0.87–1.17) 1.0

1.0 1.5 (1.28–1.81) 1.1 (0.93–1.29)

28.6 (16.65–49.27) 30.9 (17.90–53.24) 17.5 (10.27–29.69) 1.0

1.0 0.7 (0.56–0.78) 1.3 (0.81–1.93) 0.2 (0.15–0.37) 0.4 (0.31–0.51)

2.2 (1.93–2.56) 1.0

Any drug abuse OR (CI)

Lifetime

0.5 (0.36–0.75) 0.5 (0.39–0.74) 0.4 (0.28–0.59) 1.0

1.1 (0.81–1.57) 1.0

2.4 (1.45–4.11) 1.7 (1.04–2.80) 1.4 (0.82–2.26) 1.0

1.3 (0.93–1.81) 1.1 (0.81–1.37) 1.0

1.0 1.9 (1.48–2.48) 1.1 (0.83–1.56)

35.7 (11.65–109.51) 34.9 (12.08–100.85) 16.6 (5.80–47.61) 1.0

1.0 0.8 (0.58–1.02) 2.1 (1.32–3.38) 0.2 (0.10–0.57) 0.4 (0.27–0.63)

1.9 (1.52–2.41) 1.0

Any drug dependence OR (CI)

Table 22.3 Adjusted odds ratios (ORs) of 12-month and lifetime DSM-IV drug use disorders by sociodemographic characteristics in the NESARC study [7].

EPIDEMIOLOGY OF ILLICIT DRUG USE DISORDERS

Regarding other sociodemographic correlates [7], male sex, Native American ethnicity, younger age, having never married, having a low income and residing in the West were factors generally associated with greater risk of DSM-IV drug abuse and dependence on a 12-month and lifetime basis. Other correlates were found to be specific for abuse and dependence and for each time period examined. That is, rates of 12-month drug dependence, but not drug abuse, were significantly greater among those who were widowed/separated/divorced, and increased among those with lower education for 12-month drug abuse and drug dependence. These findings highlight the importance of disaggregating drug abuse from drug dependence and lifetime from current disorders. In particular, identification of risk factors for currently active cases of drug abuse and dependence may be especially critical to planning prevention and intervention programs. In this light, noteworthy in the Compton et al. study [7] were the high rates of drug use disorders among Native Americans in both the lifetime and 12-month time frames. This finding is consistent with regional studies of Native Americans [52, 53] and highlights the acute needs of this population. Consistent with prior surveys [2, 6, 54, 55], Compton et al. [7] found that prevalences of DSMIV 12-month drug disorders were generally greater among individuals with lower socioeconomic status, in terms of lower education or income levels. These findings are of great interest to epidemiologists because the correlates represent potentially modifiable conditions. Those residing in the West also had higher rates of drug disorders. The long-established Mexico-based polydrug trafficking organisations could be responsible for the increased rates in the West relative to other regions of the country. Further, detailed analyses of the NESARC data and other similar data are needed to examine the reasons for these sociodemographic differentials within the context of the multifactorial nature of drug use disorders and with particular emphasis on drug availability, features of the neighbourhood and work environments, age at onset, psychiatric comorbidity and genetic predisposition towards both drug taking and drug abuse and dependence [56–63]. Younger age is consistently associated with drug use disorders [2, 7, 54, 55, 64]. In the Compton

et al. [7] study, the rates among 30–44-year-olds were to be expected – that is are known to have increased independently among cohorts that went through adolescence during the drug epidemic in the United States beginning in the early 1970s. These results indicate a potential for increases in rates for older cohorts as the current baby-boomer generation ages [65, 66]. The Compton et al. [7] study also found that onsets of drug abuse and drug dependence typically occurred during late adolescence or early adulthood, with onsets later in life as rare occurrences. The implications are that adolescence is a particularly vulnerable period for the onset of drug use disorders and should be a target for aetiological and prevention research.

22.5 Global rates of drug use disorders As with the distribution across the United States, the global distribution of drug use and disorders varies greatly. Similarly, the burden associated with alcohol, tobacco and illicit drug use varies across the world [67]. Cross-national epidemiological data is somewhat limited, often comparisons of nationspecific can not be made because of differences in protocol, study design or comparable diagnostic format, not to mention estimates are simply not available for many parts of the world. The World Health Organization (WHO) formed the International Consortium of Psychiatric Epidemiology (ICPE; [68–70]) to address and coordinate cross-national analyses of large scale epidemiology studies around the world that use the WHO Composite International Diagnostic Interview (CIDI). The WHO-CIDI is a diagnostic instrument that can assess the onset and prevalence of substance use disorders and related behaviours, and has the ability to generate both International Classification of Diseases (ICD) and DSM diagnoses. The WHO World Mental Health Survey Initiative (WMH) was launched during 1990s to conduct a coordinated face-to-face community based survey effort in up to 17 countries (Demyttenaere et al. [71] and and Kessler et al. [72] for the WHO Mental Health Consortium; [73]). Table 22.4 presents 12-month and lifetime prevalence of substance use disorders as assessed 387

CHAPTER 22 Table 22.4

Twelve month and lifetime prevalence of substance use disorders using WHO-CIDIa.

Region

Country

Lifetime prevalence % (SE)

12-month prevalence % (95% CI)

Projected lifetime risk % (SE)

Americas

Colombia Mexico USA Belgium France Germany Italy Netherlands Spain Ukraine Israel Lebanon Nigeria South Africa Japan PR China Shanghai New Zealand

9.6 (0.6) 7.8 (0.5) 14.6 (0.6) 8.3 (0.9) 7.1 (0.5) 6.5 (0.6) 1.3 (0.2) 8.9 (0.9) 3.6 (0.4) 15.0 (1.3) 5.3 (0.3) 2.2 (0.8) 3.7 (0.4) 13.3 (0.9) 4.8 (0.5) 4.9 (0.7) – 12.4 (0.4)

2.8 (2.0–3.7) 2.5 (1.8–3.3) 3.8 (3.2–4.5) 1.2 (0.6–1.9) 0.7 (0.3–1.2) 1.1 (0.4–1.7) 0.1 (0.0–0.2) 3.0 (0.7–5.2) 0.3 (0.0–0.5) 6.4 (4.8–8.1) – 1.3 (0.0–2.8) 0.8 (0.3–1.2) – 1.7 (0.3–3.0) 2.6 (1.2–3.9) 0.5 (0.3–0.6) –

12.8 (1.0) 11.9 (1.0) 17.4 (0.6) 10.5 (1.1) 8.8 (0.6) 8.7 (0.9) 1.6 (0.3) 11.9 (1.0) 4.6 (0.5) 18.8 (1.7) 6.3 (0.4) – 6.4 (1.0) 17.5 (1.2) 6.2 (0.7) 6.1 (0.8) – 14.6 (0.5)

Europe

Middle East/Africa

Asia

Oceania a Adapted

from Demyttenaere et al. [71] and Kessler et al. [72].

by the WMH, as well as the projected lifetime risk of developing a substance use disorder by age 75. The Ukraine, United States, South Africa and New Zealand had the highest estimated prevalence of lifetime substance use disorders, with 15.0, 14.6, 13.3 and 12.4% reporting disorder among respondents (respectively). Similarly, the Ukraine, South Africa and United States reported the highest projected lifetime risk of disorder, suggesting nearly one-fifth of the population in these countries is estimated to eventually have a substance use disorder. Italy reported the lowest lifetime prevalence and projected lifetime risk; however, despite the coordinated WHO effort, differences in assessment in western Europe might have lead to artificially low estimates, thus highlighting the continued methodological challenges in cross-national comparisons of substance use disorders.

22.6 Comorbidities with psychiatric conditions Large-scale epidemiological surveys have also shown that drug use disorders – drug abuse or 388

drug dependence – are prevalent and characterised by substantial comorbidity with mental disorders. Findings from the Epidemiologic Catchment Area (ECA) survey [33], the National Comorbidity Survey (NCS, [55]), NLAES [6], ICPE [74] and the NESARC [7, 75–78] have each shown that mood, anxiety and personality disorders are strongly associated with drug use disorders. Moreover, epidemiologic studies of various types converge to suggest that the association between psychiatric and drug disorders is aetiologically meaningful. Epidemiological surveys of adults have consistently shown that anxiety, mood and antisocial personality disorders are more strongly associated with drug dependence than drug abuse [68, 79, 80]. Recent reports from the NESARC found, for example, that the odds ratios (OR) between lifetime psychiatric and drug use disorders are higher for drug dependence than drug abuse for any anxiety disorder (4.9 and 1.7, respectively), any mood disorder (7.1 and 2.3, respectively) and antisocial personality disorder (16.7 and 5.4, respectively [62, 63, 81]). Retrospective accounts of age-of-onset information from such surveys have informed the temporal ordering of some of these

EPIDEMIOLOGY OF ILLICIT DRUG USE DISORDERS

associations. Analyses from the ECA show that the risk of substance abuse in adulthood increases with each conduct problem endorsed prior to age 15 [82], thereby suggesting a dose-dependent relationship. Longitudinal epidemiological studies have been particularly informative by tracing the over-time development of psychopathology and drug involvement. Lewinsohn et al. [83] reported that adolescents classified as former or current daily smokers, compared to smokers who never smoked on a daily basis, were more likely to have a history of major depression (OR = 2.5) or conduct/oppositional defiant disorder (OR = 3.9). Breslau et al. [84] found that both current and past smoking were associated with onset of major depression in persons age 15 and older while only current smoking was associated with onset of panic disorder, agoraphobia and substance use disorders. Findings from the Pittsburgh Youth Study [85] indicate that higher levels of attention-deficit/hyperactivity disorder and conduct disorder predicted higher levels of marijuana use from ages 13 to 18. As seen in Table 22.5, findings from the Compton et al. [7] study document the importance of controlling for other psychiatric disorders when examining associations between drug use disorders and specific psychiatric disorders. Consistent with the results of previous epidemiological surveys [6, 32, 33, 54, 55], strong and significant associations were found between drug abuse and dependence and other Axis I and II disorders when sociodemographic characteristics were controlled. To understand this comorbidity, the unique relationships of other specific psychiatric disorders with drug use disorders were examined by controlling for all other disorders assessed in the NESARC. When controlling for other forms of comorbidity, associations between drug abuse and dependence and alcohol use disorders and nicotine dependence were reduced but remained strong. Consistent with twin and genetic studies [86–88], the decreased magnitude of these associations suggest that common causal pathways may underlie drug use disorders and other substance use disorders. Also consistent with this genetically informed research, associations remained strong, suggesting unique factors underlying the specific associations observed in this study. Taken together, these findings highlight the importance of

continued research on both common and specific factors underlying the comorbidity of drug abuse and dependence and other substance use disorders. With control for additional comorbidity [7], associations were diminished, but remained significant for drug abuse with major depressive and bipolar I disorders and antisocial personality disorder, and for drug dependence with mood, anxiety disorders (except social and specific phobias), avoidant, histrionic and antisocial personality disorders. These results indicate that, for these comorbid disorders, both common and unique factors may underlie these observed associations. However, the lack of associations between drug abuse and dependence and the remaining psychiatric disorders suggests that their apparent comorbidity is explained by overlap with other conditions. The clinical and societal impact of comorbidity is substantial. Comorbid disorders are more serious and chronic than single disorders [89], worsen treatment prognosis [90–93] and increase use of services and health care costs [80, 94]. Substance use can have a significant impact on the course of a comorbid mental disorder and the effect may vary depending on the type of drug misused, route of administration, severity of addiction and the presence of physical dependence. Similarly, mental disorders can impact the outcome of the addiction depending on the severity of the mental disorder, level of cognitive impairment and availability of internal resources to develop motivation to quit [95]. For instance, co-occurring depression or antisocial personality disorder (ASPD) worsens the course of treatment of individuals with drug use disorders [90, 91, 93]. The extensive comorbidity of psychiatric and drug use disorders reiterates an appeal for a comprehensive approach that identifies, evaluates and simultaneously treats both substance use and psychiatric disorders [33, 74, 96–98]. Patients with co-occurring disorders often exhibit more severe symptoms than those caused by either disorder alone, underscoring the need for integrated treatment. Careful diagnosis and monitoring will help ensure that symptoms related to drug abuse (e.g. intoxication, withdrawal) are not mistaken for a discrete mental disorder. Even in people whose comorbid disorders do not occur simultaneously, research shows that mental disorders can increase vulnerability to 389

390 9.0 (6.94–11.70) 2.7 (1.98–3.71) 9.7 (7.13–13.10) 5.8 (4.41–7.63) 3.5 (2.66–4.53) 2.2 (1.56–3.07) 5.1 (3.35–7.80) 2.4 (1.23–4.49) 4.0 (2.17–7.20) 2.7 (2.05–3.67) 3.9 (2.58–5.87) 5.6 (3.01–10.34) 3.1 (1.87–5.14) 2.6 (1.69–4.15) 2.3 (1.65–3.21) 4.5 (2.80–7.09) 4.1 (3.27–5.15) 3.4 (2.25–5.12) 7.3 (3.65–14.54) 2.3 (1.65–3.15) 3.5 (2.49–4.86) 3.4 (2.33–5.03) 4.5 (2.98–6.77) 6.4 (4.77–8.56)

Drug use disorder OR (CI)c 6.4 (4.75–8.65) 3.1 (2.18–4.50) 5.7 (3.95–8.27) 4.0 (2.86–5.69) 1.9 (1.34–2.70) 1.4 (0.88–2.32) 2.4 (1.38–4.21) 2.1 (1.02–4.32) 2.1 (0.85–5.25) 1.6 (1.15–2.25) 1.9 (1.02–3.62) 3.2 (1.20–8.33) 1.4 (0.62–3.32) 1.7 (0.94–3.00) 1.6 (1.06–2.47) 2.0 (0.98–4.00) 2.6 (1.94–3.49) 2.0 (1.05–3.69) 2.4 (0.89–6.67) 1.4 (0.87–2.17) 2.0 (1.28–3.00) 2.1 (1.26–3.56) 2.5 (1.45–4.21) 4.3 (2.84–6.50)

Drug abuse OR (CI) 15.0 (8.57–26.59) 1.6 (0.88–3.01) 18.7 (10.83–32.34) 11.0 (6.89–17.56) 8.5 (5.27–13.64) 3.8 (2.18–6.48) 10.3 (5.75–18.62) 2.6 (0.92–7.33) 6.9 (3.28–14.67) 6.0 (3.74–9.55) 7.8 (4.31–14.05) 9.2 (3.98–21.24) 6.4 (3.21–12.58) 4.5 (2.53–8.16) 3.8 (2.14–6.73) 9.5 (4.82–18.83) 9.6 (6.44–14.43) 6.0 (3.19–11.34) 14.9 (6.36–34.71) 4.6 (2.91–7.34) 6.7 (4.09–11.07) 5.8 (3.35–10.11) 8.4 (4.69–14.92) 9.7 (6.29–15.10)

Drug dependence OR (CI)

ORs adjusted for demographic characteristicsa

5.6 (4.28–7.42) 4.2 (3.03–5.85) 6.8 (4.86–9.63) 3.2 (2.38–4.38) 1.8 (1.33–2.41) 1.4 (0.97–1.96) 2.3 (1.49–3.67) 1.2 (0.58–2.63) 2.1 (1.15–3.84) 1.2 (0.88–1.73) 1.5 (0.91–2.39) 1.7 (0.80–3.57) 1.3 (0.75–2.28) 1.2 (0.71–1.93) 1.0 (0.68–1.41) 1.7 (0.97–2.92) 2.2 (1.71–2.91) 1.3 (0.85–2.05) 2.2 (1.02–4.80) 0.9 (0.57–1.33) 1.1 (0.66–1.68) 1.5 (0.88–2.44) 1.3 (0.79–2.20) 2.9 (2.08–4.12)

Drug use disorder OR (CI) 4.5 (3.25–6.25) 4.2 (2.87–6.13) 4.8 (3.11–7.31) 2.6 (1.76–3.79) 1.1 (0.73–1.67) 1.0 (0.63–1.69) 1.2 (0.61–2.24) 1.2 (0.50–2.68) 1.5 (0.62–3.76) 0.9 (0.62–1.34) 1.0 (0.49–2.10) 1.4 (0.51–4.03) 0.8 (0.32–2.13) 1.1 (0.58–2.04) 0.9 (0.58–1.46) 1.1 (0.51–2.28) 1.8 (1.26–2.48) 1.1 (0.56–2.30) 1.1 (0.37–3.20) 0.7 (0.40–1.23) 0.9 (0.48–1.50) 1.2 (0.66–2.32) 1.0 (0.58–1.86) 2.5 (1.57–3.99)

Drug abuse OR (CI)

b Odds

7.0 (3.89–12.48) 3.7 (1.79–7.58) 9.0 (4.66–17.16) 4.4 (2.63–7.42) 3.3 (1.92–5.56) 2.2 (1.20–4.10) 4.2 (2.14–8.35) 1.4 (0.40–4.59) 2.8 (1.16–6.67) 1.9 (1.07–3.24) 1.8 (0.85–3.81) 1.5 (0.44–4.93) 1.8 (0.85–3.94) 1.2 (0.58–2.48) 1.0 (0.53–2.00) 2.5 (1.02–5.88) 3.3 (2.00–5.33) 1.3 (0.63–2.60) 2.4 (0.75–7.77) 1.2 (0.69–2.10) 1.1 (0.59–2.22) 1.5 (0.74–3.21) 1.4 (0.63–3.03) 2.6 (1.45–4.53)

Drug dependence OR (CI)

ORs adjusted for demographic characteristics and other psychiatric disordersb

ratios adjusted for age, race-ethnicity, sex, education, income, marital status, urbanicity and geographic region. ratios adjusted for age, race-ethnicity, sex, education, income, marital status, urbanicity, geographic region and other psychiatric disorders. c CI = 99% confidence interval. Significant odds ratios are highlighted in boldface.

a Odds

Alcohol use disorder Alcohol abuse Alcohol dependence Nicotine dependence Any mood disorder Major depressive disorder Bipolar I Bipolar II Dysthymia Any anxiety disorder Any panic disorder Panic with agoraphobia Panic without agoraphobia Social phobia Specific phobia Generalised anxiety Any personality disorder Avoidant Dependent Obsessive-compulsive Paranoid Schizoid Histrionic Antisocial

Comorbid disorder

Table 22.5 Adjusted odds ratios (ORs) of 12-month DSM-IV drug use disorders and other psychiatric disorders controlling for demographic characteristics and comorbid psychiatric disorders in the NESARC study [7].

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subsequent drug abuse and that drug abuse constitutes a risk factor for subsequent mental disorders. Therefore, it is expected that the diagnosis and treatment of one disorder will likely reduce risk for the other, or at least improve its prognosis. Given that comorbidity between psychiatric and drug use disorders are truly the rule rather than the exception, insight into mechanisms of comorbidity and proactive broad-spectrum diagnosis and concurrent therapy has the potential of yielding beneficial help to many individuals.

22.7 Genetic epidemiology One major subdiscipline of epidemiology is genetic epidemiology, a field that seeks to identify genetic and environmental influences on disease. Indeed, of the various risk factors for drug use disorders, family history has been identified as the most potent and consistent. Results from family studies show that drug use disorders are prevalent in families [71, 99], and twin and adoption studies demonstrate that much of the familial clustering of drug use disorders can be explained by genetic factors [87, 100]. Several controlled family studies demonstrate that substance abuse or dependence in probands (i.e. the index case in genetically informative designs) is associated with substantial increased in risk for these disorders among first-degree adult relatives and among offspring [101], as well as on pre-morbid risk factors believed to be predictive of the development of substance abuse [102, 103]. Further, risk is conferred both generally across the various classes of illicit drugs and by particular drug classes [104, 105]. Importantly, genetic epidemiological studies of drug use disorders have yielded results that are compelling in terms of consistency, magnitude of relative risk and coherence of the message that drug use disorders have genetic and environmental underpinnings in need of further explication. For example, genetic factors appear to be more strongly associated with drug use disorders than drug use [87]. This finding has implications for prevention of onset of use of drugs compared to prevention of progression from use to addiction in that genetics may be important for the identification of persons at risk for drug use disorders, while the prevention of onset

of drug use is much more likely to be based on environmental manipulation. No matter the implications, missing from our studies is information on the specific genes and their distribution in populations of interest. Because we have not yet been successful in determining the specific genes involved in the transmission of drug use disorders, the usual epidemiological measures of prevalence and distribution cannot yet be calculated. What is apparent is that drug use disorders are genetically and phenotypically complex disorders that result from the interplay between underlying genetic susceptibility and environmental risk. Like many other relatively common human diseases, drug use disorders are now believed to arise from multiple genes exerting small effects, gene–gene interactions, gene–environment interactions and/or a host of environmental factors and risk-conferring behaviours [106]. Because the identification of gene–environment interactions is likely to prove key to understanding the aetiology of complex disorders [107, 108], advances in this important area will benefit from large, prospective, genetically informative studies drawn from community sources.

22.8 Future opportunities The future of epidemiology of illicit drug use disorders depends on the successful application of integrated approaches to studying complex human behaviours. Such a goal of studying multifactorial models is consistent with current trends in epidemiology [109] and builds on the rich history of drug use and drug use disorder epidemiology by incorporating perspectives from molecular genetics and neuroscience into individual and social epidemiology. By integrating these diverse transdisciplinary approaches, both prevention and treatment of drug use and drug use disorders may be enhanced [110]. Despite the multifactorial aetiology of drug use and drug use disorders conceptualised in several broad theories, drug use and drug use disorder research has focused largely on individual risk factors at the expense of understanding the interaction of broader and interrelated factors. When multiple contributive factors have been considered, the emphasis has commonly been on additive models of predispositional 391

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factors and these models have typically concentrated on factors from a single domain – that is the biological, the behavioural or the environmental. Comprehensive reviews have been written to identify empirically derived risk and protective factors for drug problems [111]. There is also evidence linking the number of risk factors with the magnitude of risk, whether additively [112], multiplicatively [113] or interactively [114]. In order to better understand the epidemiology of drug use and its consequences within and across populations, research must focus on the influence of social and cultural factors on the initiation and progression of drug use among population groups. Novel conceptualisation and measurement of social and cultural contexts within theoretically grounded research are suggested because increased understanding of how genetic, biological, social and contextual phenomena interact to influence behaviour will inform prevention and treatment for individuals at risk for drug use and drug use disorders [115]. For instance, parent drug use may influence child development through direct and indirect pathways. Direct pathways include genetic transmission of vulnerability to drug use disorders and environmental exposure to drug either in utero or in the home. Indirect pathways include child abuse and neglect or other stressful environments caused by the drug using lifestyle. Many of these factors emanate from or are reinforced by norms and behaviours of family members and other significant persons (including peers, authority figures, public figures, etc.), intervening processes such as collective socialisation, and peer-group influence, as well as social and institutional processes [111, 116–118]. Neighbourhood and community-level variables also may serve as risk or protective factors – that is residential instability, collective efficacy, social cohesion or other aspects of locally shared environments as contributors to drug abusing behaviours [60, 119]. In other words, the social environment is something of a sphere that encompasses the many factors interacting with individual characteristics [120, 121], and research targeted at understanding interactions of individual and social environmental influences with community-level factors require particular attention. For example, why certain population groups and communities have particularly 392

high rates of drug use and related disorders is a key question. Therefore, further studies of drug use and drug use disorders should examine the interaction of individual and social environmental factors on drug use, abuse and dependence, including both immediate and cumulative (life-course and transgenerational) effects. Drug use disorders are particularly clear examples of human disorders that pose great challenges because they are familial and heritable but do not follow Mendelian patterns of inheritance [122, 123]. And while the quest persists for the reliable detection of risk-conferring genes for drug use disorders, success in this endeavour and in the identification of gene–environment interactions will hinge in part on the systematic conceptualisation and categorisation of the environment and by linking developmental psychology, genetic epidemiology and neuroscience [122]. Social epidemiology should be added to the list of essential disciplines that can enrich this critical discussion. In many ways, the guiding principles of social epidemiology naturally complement the methods of genetic epidemiology, particularly family studies that extend across multiple generations. Although epidemiological studies have proven to be very valuable for describing drug use patterns across person, place and time, identifying factors associated with increased (or decreased) risk for drug use and drug use disorders and for testing specific hypotheses regarding putative causes, the specific processes through which such factors confer risk remain unclear. Thus, many fundamental questions remain unanswered. For example, although early drug use signals poor prognosis for many individuals, it remains uncertain why some drug users desist while others persist to greater drug involvement and drug addiction. Second, despite decades of research documenting comorbidity between psychiatric and drug use disorders, there is a pressing need for research into the specific mechanisms that underlie comorbidity. Third, despite overwhelming consistency of results showing that drug use disorders are familial and, at least in part, genetically influenced, little has been learned about how and under which conditions such liability manifests itself. To address these needs, large-scale epidemiological studies are uniquely capable of advancing research

EPIDEMIOLOGY OF ILLICIT DRUG USE DISORDERS

through the nesting and testing of hypothesised measures of causal mechanisms within ongoing epidemiological studies. At present, laboratory and clinical research is often conducted in isolation from epidemiologic research and epidemiological evidence is often not incorporated into laboratory and clinical research. Since much of what we know about liability to drug use and drug use disorders is based on clinical samples, a significant potential exists for selection bias with resulting reduction in generalisability of findings. Epidemiological studies offer unique, powerful and efficient opportunities for addressing this concern by embedding (nesting) hypothesised measures of causal mechanisms into existing studies and/substudies. In addition, epidemiologic evidence can be used to inform selection of participants for laboratory-based research. Study designs that efficiently combine the advantages of epidemiologic samples with testing of more intensive laboratory-based and biological measures are also cost-efficient. Clearly, one of the key challenges for epidemiology will be to harness selected measures from neurobiology that can be applied in general population studies. For example, given a focus on adolescence as the key period of risk for drug use and drug use disorders, measure of gonadarche, adrenarche and pubertal growth can be assessed [124]. In addition, through study in representative samples, the relationship of brain development during adolescence to cognitive and emotional development and to the onset of drug use and drug use disorders may be examined [124]. Such work will require conceptual integration of epidemiological studies with basic science studies of animal brain development where certain details can be examined that can not be examined in humans. In addition, measures that are already appropriate for epidemiological studies on select subsets of subjects include: neuroimaging, serum samples for metabolic studies [125] and biological specimens for genetic association studies. For example, as the technology for obtaining genetic material through mouthwashes and/or cheek swabs improves, applying such techniques at reasonable costs in broad samples is possible [126]. Further advances in the epidemiology of drug use and drug use disorders will also require the

development and application of innovative methods in statistical, epidemiological, sociological and genetic epidemiological study designs. Promising new techniques include ecological momentary assessment tools that capture information nearly at the time of its occurrence through the use of novel recording devices such as personal digital assistants (PDAs) or cell (mobile) phones [127]. Statistical innovations are needed to maximise use of longitudinal, prospective, multidisciplinary studies because they must account for the interactions among biological (including genetic), psychosocial and contextual factors. Statistical innovations, such as the recently developed growth mixture modelling [128], should address transitions in stages of drug use and trajectories of drug use, as well as drug use over the life course and the intergenerational transmission of drug use and its consequences. Of note, increases in the numbers of older persons and possible increases in drug use and drug use disorders in these populations may necessitate new work on elderly populations [65, 66]. Innovative analytic approaches will be required to identify homogeneous subgroups of drug users that emerge from the complex variety of indicators [129]. Moving into the study of interactions across domains of factors – gene/environment, individual susceptibility/social environment, neighbourhood environment as an effect modifier – provides great promise for the next generation of research on drug use and drug use disorders. In focusing on environmental influences, emphasis should be placed on augmenting existing population studies, both cross-sectional and longitudinal. For example, while national surveys can provide important information about trends in prevalence of drug use, they provide limited information about factors that lead to differences in drug use outcomes across communities [130]. Typical survey studies such as the NSDUH [2], MTF [1], the YRBS [3] and NESARC [75, 76] might be augmented with or compared to community-level research in order to examine relatively rare drug use (e.g. heroin), regional variations, pockets of drug use, emerging trends in drug use and certain high-risk groups not living in permanent households. 393

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22.9 Conclusions Epidemiology provides a foundation for much research on drug use and drug use disorders by demonstrating on whom, through what agents and where drugs exert their impacts. Through population-based studies, key clues are identified for detailed exploration in analytic epidemiological studies and in non-epidemiological studies. There are, however, limitations to epidemiological research. Large sample sizes can pose difficulties in terms of resources to obtain the needed intensive and detailed measures, particularly over extended periods of time. Also, the observational nature of much epidemiological research limits experimental control and manipulation of variables under study. Thus, epidemiological studies have the maximum impact when linked with basic science and clinical experimental studies in a thoughtful programme of investigation. It is clear that the future of illicit drug use disorder epidemiology holds great promise. First, because of strong environmental effects, drug use requires monitoring on a reasonably frequent basis with additional efforts to identify emerging trends/new drugs. But such monitoring is only one purpose of epidemiology. The second main activity is to test hypotheses and rule out or in certain plausible hypotheses. Once the epidemiology studies have been conducted, more detailed methods can then be applied. As the field of epidemiology moves into an integrative era [109], the epidemiology of drug use and drug use disorders can be at the forefront. The goal is to determine how social factors, exogenous agents and individual factors are linked across time to produce illness. Achieving this goal will require refinement of existing methods and development of new techniques for classifying the individual and the environment. The knowledge obtained from studies on these topics will improve the nation’s public health by promoting integrated approaches to understanding and addressing interactions between individuals and environments that contribute to the continuum of problems related to drug use. The goal is to marry elements of sampling methodologies, biological measures and qualitative analysis of social networks to better explain disease transmission dynamics. These approaches will allow us to 394

develop scientific knowledge with clear application to practice and public policy.

22.10 Disclaimer The views and opinions expressed in this chapter are those of the authors and should not be construed necessarily to represent the views of any of the sponsoring agencies or the US Government.

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The epidemiology of personality disorders: Findings, methods and concepts Michael J. Lyons,1,2 Beth A. Jerskey2 and Margo R. Genderson1 1 Department

of Psychology, Boston University, Boston, MA 02215, USA Harvard Institute of Psychiatric Epidemiology and Genetics, Department of Epidemiology, Harvard University, Boston, MA, USA 3 Alpert Medical School of Brown University 2

23.1 Introduction With the advent of the third edition of the Diagnostic and Statistical Manual (DSM-III) [1], the personality disorders (PDs) were assigned their own axis (axis II) in the diagnostic nomenclature. DSM-III was a major impetus for clinical and research interest in the PDs because it provided explicit diagnostic criteria for their diagnosis. It included 11 specific disorders, many of which, unlike Axis I disorders such as schizophrenia and depression, did not have long histories as subjects of clinical attention or systematic observation. In an important way, one may mark the advent of systematic studies of PD by the publication of DSM-III in 1980. Personality traits are described in the fourth edition of the DSM [2] as ‘enduring patterns of perceiving, relating to and thinking about the environment and oneself, and are exhibited in a wide range of important social and personal contexts.’ DSM-IV defines PDs as inflexible and maladaptive personality traits that cause either significant functional impairment or subjective distress. PDs are conceptualised as longterm characteristics that are likely to be evident by adolescence and continue through adulthood. Based

on the current conceptualisation of the PDs, key features for diagnosis include: early onset, stability and persistence, pervasiveness, interpersonal focus and impairment [3]. The diagnosis of a PD should not be made if the characteristics only occur during episodes of an axis I disorder. The DSM-IV suggests that PDs may become less obvious in middle and later life. In addition to specifying 10 individual PDs (passive-aggressive PD was dropped from the third edition), DSM-IV groups the PDs into three clusters. Cluster A includes paranoid, schizoid and schizotypal PDs; the basis for this cluster is the odd or eccentric characteristics common to individuals with each of these three disorders. Cluster B includes antisocial, borderline, histrionic and narcissistic PDs. This cluster is characterised by dramatic, emotional or erratic features. Cluster C includes avoidant, dependent and obsessive–compulsive PDs and has been designated the anxious or fearful cluster. Despite the advances that have been made with diagnosing and classifying PDs, many issues must be resolved with the development of the DSM-5, which is scheduled to be published in 2012. One of the

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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most critical topics is whether to preserve the current categorical system of classification or whether to move towards a more dimensional approach. The Personality and Personality Disorders Work Group was developed to review existing literature and conduct research in order to determine the best approach for understanding and diagnosing personality pathology in the DSM-5.

23.2 Substantive findings Compared to many Axis I disorders, there are less extensive data available on the prevalence of Axis II disorders. In general, the PDs, with the possible exception of antisocial personality disorder (APD) (and its related progenitors, moral insanity, psychopathy, sociopathy, etc.), have not been the object of empirical, let alone epidemiological, research for very long. In this section, a brief description of findings predating the publication of DSM-III will be provided. The largest part of this section will review findings bearing on the ‘true prevalence’ of PDs, that is rates of the disorders in representative community and non-clinical samples.

23.2.1 True prevalence studies 23.2.1.1 Pre-DSM-III In the Midtown Manhattan study in 1962, Srole and colleagues [4] reported a prevalence rate of 6% for sociopathy. In the Stirling County study using DSM-I diagnoses, Leighton [5] reported that 11% of males and 5% of females received a sociopathic diagnosis. In 1991, Merikangas and Weissman [6] reviewed the prevalence rates for PDs reported in pre-DSM-III studies that permitted the exclusion of alcoholism and drug abuse (which were classified as PDs in some older systems) from other PDs [7–10]. They concluded that, in spite of non-uniformity in diagnostic definition, the reported rates were quite similar. Approximately 6–9% of the samples were characterised as having a major personality disturbance. In these early data reviewed by Merikangas and Weissman, there is an indication that the overall sex ratio for PDs is about equal, with differences for

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specific disorders. Prevalence is fairly even across age groups with a slight increase in later life, higher rates in urban compared to rural populations, and higher rates in lower socio-economic groups compared to higher groups.

23.2.1.2 Post-DSM-III Since the publication of DSM-III in 1980, there have been several studies that provided data on the prevalence of having any PD [11–13]. Nestadt and his coworkers, in a series of reports [14–16] detailed the results of a follow-up assessment for PDs of the Epidemiologic Catchment Area (ECA) study. At the Baltimore site of the ECA, a total of 3481 individuals were interviewed with the Diagnostic Interview Schedule [17], the General Health Questionnaire [18] and the Mini-Mental State Exam [19]. Subsequent to the ECA, the Clinical Reappraisal was carried out and included all subjects who had been ‘screened positive’ for psychopathology and a random sample of original respondents. Board certified or boardeligible psychiatrists, blind to first stage information, interviewed the subjects with the Standardised Psychiatric Examination (SPE), a semi-structured interview. Nestadt and colleagues [20] reported a prevalence of 5.9% for a definite diagnosis of PD and a prevalence of 9.3% for their combined definite plus provisional diagnostic categories. Another follow-up of the Baltimore ECA sample was conducted between 1997 and 1999 [12]. Masters-level clinical psychologists interviewed 742 subjects using the International Personality Disorder Examination (IPDE) [21], and diagnoses were made according to DSM-IV and the International Classification of Diseases ICD-10. To gather additional information about each participant, examiners interviewed at least one informant by telephone. Researchers reported a 10% prevalence for a DSM-IV diagnosis of any PD (9% when they used a weighting scheme to adjust for sample selection) and a prevalence of 7% for any ICD-10 PD (5% weighted). In 2008, Johnson and colleagues [22] reported results of a longitudinal study with four assessments starting at mean age 14 and culminating at mean age 33. Results are reported for the final sample of 568 individuals. The first three assessments were adapted

THE EPIDEMIOLOGY OF PERSONALITY DISORDERS: FINDINGS, METHODS AND CONCEPTS

from the Personality Diagnostic Questionnaire [23] and the Structured Clinical Interview for DSM-III-R Personality Disorders (SCID-II) [24]. The fourth and final wave of data collection utilised the DSM-IV version of the SCID-II. In addition to the 10 PDs in the DSM-IV, the study included depressive PD and passive–aggressive PD. Results show that the point prevalence of any PD at age 33 was 12.7%, which includes the prevalence of the two non-DSMIV PDs, and this finding is consistent with results from other studies. An interesting feature of this study is its ability to provide cumulative prevalence rates based on the four assessments spanning 19 years. The cumulative prevalence of 26.6% includes individuals who had a DSM-IV PD diagnosis at any one of the four assessments, but may have remitted by the time of the final assessment. The authors attribute the difference between point and cumulative prevalence to temporal and contextual variability in symptom severity. Grant and colleagues [13] conducted the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Face-to-face interviews were conducted with over 43 000 individuals age 18 or older living in the community. DSM-IV PDs were assessed using the NIAAA Alcohol Use Disorder and Associated Disabilities Interview Schedule (the version of the interview utilised in this study did not include borderline, schizotypal and narcissistic PDs). They found a prevalence of 13.8% for having one or more of the seven PDs included in the study. Lenzenweger and colleagues [25] reported results from the National Comorbidity Survey Replication (NCS-R). The sample included 5692 subjects selected in a multistage clustered area probability design. Following screening, a probability subsample of 214 respondents were administered the IPDE by an experienced clinical interviewer over the telephone. They used a sophisticated multiple imputation approach to generate prevalence estimates and reported a prevalence of 9.1% for any DSM-IV PD. Zimmerman and Coryell [26] reported the rates of DSM-III PDs assessed with the Structured Interview for DSM-III Personality Disorders (SIDP) among a non-patient sample of 797 individuals. There are several features of the study that qualify somewhat the interpretation of their results. The sample that

they studied was a mixture of relatives of normal controls, relatives of schizophrenic probands, relatives of probands with psychotic depression, relatives of probands with non-psychotic depression and relatives of probands with other psychiatric disorders. Their sample is problematic because certain PDs may have a familial relationship to Axis I disorders. To the extent that a PD has a familial relationship to an Axis I disorder, the rate of the PD among first-degree relatives of probands with the Axis I disorder will be elevated and results may not generalise to the general population. Therefore, the prevalence of some PDs may be inflated in this sample. The prevalence of any DSM-III PD diagnosis using the SIDP, including mixed PD, was 17.9%. These investigators also administered the Personality Diagnostic Questionnaire (PDQ) [23] to their subjects [27]. Prevalence rates were fairly similar, but the PDQ produced higher rates of schizotypal, compulsive, dependent and borderline PDs. The SIDP yielded higher rates of antisocial and passive-aggressive PDs. More individuals were diagnosed with a PD by the SIDP, but the PDQ diagnosed multiple PDs more often. Reich and colleagues [28] conducted a random mailed survey of the adult population of Iowa City, Iowa. Data were collected using the PDQ [23]. The rate for receiving any DSM-III Axis II disorder was 11%. We administered the SIDP-IV, which yields DSMIV diagnoses, to a sample of 693 male twins who were members of the Vietnam Era Twin (VET) Registry. The sample was randomly selected from the VET Registry (details of construction of the Registry have been reported elsewhere [29, 30]), and the men were between the ages of 45 and 55 years. The sample is limited because it includes only males, and participants were screened for mental health at military induction during early adulthood. The prevalence of having any DSM-IV PDs among these men was 7.6%. Crawford and colleagues [31] studied approximately 800 youths over a period of 20 years in the Children in the Community Study. Longitudinal data on PDs were collected during the course of the study using the Children in the Community Self-Report Scales, and clinical interviewers administered a computer assisted version of the SCID-II to 644 subjects by telephone. The prevalence of any DSM-IV PD at

403

CHAPTER 23

age 33 according to the SCID-II was 15.7%; the Children in the Community scales indicated a prevalence of any PD at age 33 of 11.2 and 13.4% at age 22. There have been a number of studies of the true prevalence of PDs outside the United States. [32–34]. Casey and Tyrer [35] carried out a study of randomly selected community residents in the United Kingdom. They administered the Personality Assessment Schedule (PAS) [36] for the assessment of PDs, and 13% of subjects were diagnosed with a PD according to the ICD [37] criteria. PDs were diagnosed in 13% of their subjects. The PD with the highest prevalence was explosive personality, which probably corresponds most closely to the DSM-III diagnoses of APD or intermittent explosive disorder from Axis I. Suliman and colleagues [38] investigated the prevalence of DSM-IV PDs in a community sample in South Africa. They administered the Comprehensive International Diagnostic Interview (CIDI) and used a multiple imputation method to derive prevalence estimates. The prevalence of any DSM-IV PD was 6.8%. Maier and his co-workers [33] studied a sample of subjects in the mixed urban/rural Rhein-Main area of Germany. Subjects were recruited for a family study of affective disorders and schizophrenia. Control probands were selected randomly without regard to psychiatric status and were stratified by age, sex, residential area and educational status. The sample included the probands, their mates and all firstdegree relatives over age 20 who agreed to take part in face-to-face interviews. Subjects were administered the SCID-II [24], and the rate of receiving any PD diagnosis was 10.0%. Coid and colleagues [39] examined the prevalence of DSM-IV PDs in a community sample of 626 individuals (ages 16–74) in the United Kingdom. Subjects were administered the SCID-II by graduate psychologists, and the weighted prevalence of having any PD was 4.4%. The median prevalence of any PD from the 11 true prevalence studies conducted in the United States contained in Table 23.1 is 11.85%. The median prevalence for the six studies conducted outside the United States is 8.4%. It is not clear if the observed difference reflects methodological factors, actual differences in prevalence or simply random variation. Given the very broad range of reported prevalences, especially in the US studies, it would be inadvisable 404

to conclude that these results demonstrate significant substantive differences.

23.3 Course, prognosis and developmental issues PDs may be applied to children or adolescents in those unusual instances in which the maladaptive traits appear to be pervasive, persistent and unlikely to be limited to a particular developmental stage. To investigate persistence of PDs diagnoses in childhood, Bernstein and colleagues [40] performed a cross-sectional prevalence analysis across the age span from late childhood to early adulthood, as well as a longitudinal assessment of the stability of PD diagnoses. Subjects were a random sample of children from 976 families in two upstate counties in New York. An interview with the mother of the child was conducted, and follow-up assessments were conducted 8 and 10 years later (n = 724 and n = 733) when the original child subjects ranged from 11 to 21 years old. The findings suggested that there is a high proportion of adolescents not referred for treatment that meet DSM-III-R criteria for a PD diagnosis. Longitudinal data revealed that adolescents with an earlier diagnosis of a PD were at a considerably elevated risk for re-diagnosis two years later. However, these diagnoses had variable persistence over the 2-year follow-up, with less then half the subjects receiving a diagnosis at follow-up. Johnson and colleagues [41] reported three significant findings about the development and course of PDs in adolescents. First, they reported that significant declines (28%) in PD traits were observed between early adolescence and early adulthood. They also found that stability in these traits ranged from low to moderate over a 6-year interval that began in middle adolescence. Finally, they reported that adolescents with diagnoses of PDs tended to have elevated disordered traits in their early adult years. On the other end of the developmental continuum, Abrams and Horowitz [42] performed a meta-analysis of PDs in older adults. Their review of 11 articles published from 1980 to 1994 concluded that the overall prevalence for that age group of 50 years and older was 10%. However, the

THE EPIDEMIOLOGY OF PERSONALITY DISORDERS: FINDINGS, METHODS AND CONCEPTS Table 23.1 Community studies of prevalence of any personality disorder. References

Population

Instrument and Dx system

Prevalence % of any PD

Comments

Nestadt et al. [20]

810 subjects from the Clinical Reappraisal at Baltimore ECA site

DSM-III by SPE administered by psychiatrist

5.9%

(Definite)

9.3%

(Definite plus provisional)

Zimmerman and Coryell [26]

797 non-patient relatives of normal controls and probands with schizophrenia and depression 235 community residents, selected randomly

DSM-III by SIDP telephone 72.9% face-to-face 27.1%

17.9%

Sample limits generalisability

Personality Diagnostic Questionnaire, DSM-III

11%

Required criteria plus impairment/distress for diagnosis

Bernstein et al. [40]

733 adolescences and their mothers selected randomly

Ten diagnostic scales combined including structured interviews and self- report questionnaires

31.2% ate)

Lenzenweger et al. [11]

1646 college students

DSM-III-R by International Personality

6.7% (definite)

Disorder Examination (IPDE) DSM-IV by SIDP

11.0% (definite + probable) 7.6%

DSM-IV IPDE by clinical psychologists

9%

Also identified specific PDs to be over-represented in certain demographic subgroups.

NIAAA Alcohol Use Disorder and Associated Disabilities Interview Schedule- DSM-IV by experienced lay interviewers SCID-II for DSM-IV

13.79%

Did not assess borderline, narcissistic and schizotypal PDs; largest sample to date

15.7%

Results are also reported for age 22

Screening questions from DSM-IV IPDE. A sub-sample of 214 administered the IPDE by experienced clinician to determine clinical diagnoses.

11.9%

A method of multiple Imputation was implemented to estimate prevalence of PDs in the full sample.

Reich et al. [28]

(moder-

Not all criteria were matched by protocol items

17.2% (severe)

Lyons (2001)

Samuels et al. (2002) [12]

693 male twins from the Vietnam Era Twin Registry 742 community subjects ages 34–94

Grant et al. [13]

43 093 subjects from the National Epidemiologic Survey on Alcohol and Related Conditions

Crawford et al. [31]

644 subjects from the Children in the Community Study, mean age 33 5692 adults (ages 18+) from the National Comorbidity Survey Replication (NCS-R)

Lenzenweger et al. [25]

Results are estimated from a two-stage case identification procedure

Males only

405

CHAPTER 23 Table 23.1

(cont.)

References

Population

Instrument and Dx system

Prevalence % of any PD

Comments

Johnson et al. [22]

568 adults randomly sampled from community

SCID-II administered by mental health professionals

12.7% (includes depressive and passiveaggressive PD)

Tabled result is point prevalence at fourth assessment at mean age 33. Authors also report cumulative prevalence.

SCID-II administered by graduate psychologists Structured Interview for DSM-III-R Personality Disorders, face-to-face DSM-III-R by SCID

4.4% (after weighting)

Included a significant number of rural respondents

13.4%

Representative sample

10.0%

Did not use DSM-III-R exclusionary criteria

Personality Assessment Schedule, ICD

13%

Not DSM based

DSM-IV by CIDI

6.8%

Utilised multiple imputation

DSM-IV IPDE screening questions

6.1%

Utilised multiple imputation

Non-US True Prevalence Studies Coid 626 individuals (age 16 et al. [39] tp 74) living in the community in the UK Torgersen 2053 subjects ages et al. [32] 18–65 in Norway

Maier et al. (1990) [44]

Casey and Tyrer [35] Suliman et al. [38] Benjet et al. [34]

447 probands and relatives from randomly selected families in Germany 200 urban and rural UK residents, selected randomly 4315 community residents in South Africa 2362 urban adults in Mexico

prevalences ranged from 6 to 33% based upon the setting in which participants were recruited (e.g. senior citizen centre, psychiatric inpatients, psychiatric outpatients) and methods of assessment. The Collaborative Longitudinal Personality Disorders Study (CLPS) [43] has collected annual follow-up data on a sample of individuals with schizotypal, borderline, avoidant and obsessive–compulsive PDs. Fewer than half of PD patients meet or exceed full criteria for periods as short as 1 or 2 years. If ‘remission’ is defined as a minimum of 12 consecutive months with no more than two criteria within the first 2 years of follow-up, then more than half of patients with PD achieve remission. Among patients with borderline PD, 10% remit within 6 months of diagnosis. These data question the assumption that PDs are stable over extended periods of time. The CLPS investigators have suggested that PDs 406

may best be conceptualised as a combination of two elements – stable personality traits that may have normal variants and dysfunctional behaviours intended to adapt to, defend against, compensate for or cope with the stable pathological traits.

23.4 Treated prevalence Merikangas and Weissman [6] identified a number of factors that could lead to bias in treated samples: (i) differences in the availability of treatment; (ii) the role of cultural factors in help-seeking behaviour; (iii) differences in the severity of the disorder; (iv) the potential influence of other comorbid psychiatric disorders and (v) differences among the PDs in the likelihood of seeking treatment. However, the importance of PDs in clinical practice makes their prevalence in such settings valuable information in its own right.

THE EPIDEMIOLOGY OF PERSONALITY DISORDERS: FINDINGS, METHODS AND CONCEPTS

Dahl [45] reported results of the systematic assessment of DSM-III PDs in 231 consecutive admissions to a psychiatric inpatient unit in Norway. Approximately 45% of the sample received a PD diagnosis (40% of females and 49% of males). Forty-four per cent of those with a PD disorder received one diagnosis, 36% had two diagnoses, 15% had three diagnoses and 5% had four diagnoses. Schizotypal, histrionic, antisocial and borderline PDs were each present in approximately 20% of the sample. Avoidant PD was diagnosed in 9% of the sample and the remaining PDs were diagnosed much less frequently. In a series of 100 patients admitted with major psychiatric disorders (affective disorders, schizophrenia and other functional psychosis), Cutting and colleagues [46] found that 44% had an ‘abnormal personality’ based on informant interviews about the period preceding the acute episode. Oldham and Skodol [47] investigated the prevalence of PDs among the 129 268 patients treated in New York state mental health facilities in 1988. Using the system’s centralised database, they found that 10.8% received a PD diagnosis. The PD diagnosis was the primary diagnosis of 1.2% of patients, and only 0.2% of patients received more than one PD diagnosis. The most common diagnosis was borderline PD (17.2% prevalence rate). These authors concluded that the standard record keeping procedures underestimated the prevalence of PDs and that PDs were not being systematically assessed. Zimmerman and colleagues [48] examined the prevalence of DSM-IV PDs among 859 psychiatric outpatients. Using the SIDP, they observed a prevalence of 31.4% for any DSM-IV PD. Echeburua and colleagues [49] administered the IPDE and the Millon Clinical Multiaxial Inventory-II to samples of alcohol dependent outpatients, psychiatric patients with non-addictive disorders, and a control sample. The prevalence of any PD among the three groups was 44.3, 21.7 and 6.8%, respectively. As is the case for both Axis I and Axis II disorders, treated rates shed little light on the prevalence of disorders in untreated samples. However, because PDs have important implications for service provision, it is useful to consider their frequency in various clinical populations. Obviously, the nature of the setting

in which the disorders are studied and the method by which they are studied will influence the findings.

23.5 Prevalence of specific personality disorders In this section, each of the PDs will be presented briefly, starting with the essential features of the disorder according to the DSM-IV criteria. Available data about prevalence in clinical and non-clinical populations will be presented.

23.5.1 Paranoid personality disorder The essential feature of paranoid PD is a ‘pervasive distrust and suspiciousness of others such that their motives are interpreted as malevolent’ [2]. The eight non-clinical studies of paranoid PD found prevalences ranging from 0.4 to 5.1%, with a median prevalence of 1.8%. The rates in clinical samples reported by Widiger in 1991 [50] ranged from 1.0 to 36% with a median prevalence of 6.0%. The DSM-IV reports rates in the general population to be between 0.5 and 2.5%, in inpatient psychiatric settings 10–30%, and in outpatient mental health clinics 2.5–10% [2].

23.5.2 Schizoid personality disorder The essential feature of this disorder is a ‘pervasive pattern of detachment from social relationships and a restricted range of expression of emotions in interpersonal settings’ [2]. In general, schizoid PD has been a very infrequent diagnosis in clinical settings. For example, Zanarini and colleagues [51] found no cases in a clinical sample of 97 patients, and Koenigsberg and colleagues [52] reported no cases on the basis of chart review in a sample of 2462 patients. The prevalences in the 10 non-clinical samples reported in Table 23.2 range from 0.4 to 4.9%. The rates in clinical samples reported in Widiger [50] ranged from 0.0 to 8% with a median prevalence of 1.0%. Individuals with schizoid PD may be less likely to seek treatment as a function of their disorder. It may also be that the diagnosis of schizotypal PD is applied to a number of individuals that might have been characterised as schizoid before DSM-III or that the current criteria are inadequate [51]. 407

CHAPTER 23

Table 23.2

Prevalence of cluster A personality disorders.

References

Population

Instrument

Prevalence (%)

Comments

Lenzenweger et al. [25]

5692 adults (ages 18+) from the National Comorbidity Survey Replication (NCS-R)

Screening questions from DSM-IV IPDE.

6.2

A method of multiple imputation was implemented to estimate prevalence of PDs in the full sample.

DSM-III by SIDP telephone 72.9% face-to-face 27.1%

0.9

Sample limits generalisability

Personality Diagnostic Questionnaire, DSM-III

0.9

Required criteria plus impairment/distress for diagnosis

DSM-III-R by SCID

1.8

Did not use DSM-III-R exclusionary criteria

DSM-IV by SIDP

0.4

Males only

Structured interview for DSM-III-R personality disorders, face-to-face

4.41

Prevalence is considerably higher compared to previous studies; representative sample

A sub-sample of 214 subjects were administered the IPDE by experienced clinician to determine clinical diagnoses. Paranoid PD Zimmerman and Coryell [26]

Reich et al. [28]

Maier et al. (1990)

797 non-patient relatives of normal controls and probands with schizophrenia and depression 235 community residents, selected randomly

Torgersen et al. [32]

447 probands and relatives from randomly selected families in Germany 693 male twins from the Vietnam Era Twin Registry 2053 subjects ages 18–65 in Norway

Samuels et al. [12]

742 community subjects ages 34–94

DSM-IV IPDE by clinical psychologists

0.7

Cluster A PDs were more prevalent in men who had never married.

Grant et al. [13]

3093 subjects from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC)

2.4

Largest sample to date

Crawford et al. [31]

644 subjects from the Children in the Community Study, mean age 33 214 adults (ages 18+) from the National Comorbidity Survey Replication (NCS-R).

NIAAA Alcohol Use Disorder and Associated Disabilities Interview Schedule- DSM-IV by experienced lay interviewers SCID-II for DSM-IV

5.1

–

DSM-IV IPDE by experienced clinician

2.3

The IPDE is regarded as a conservative instrument.

Lenzenweger et al. [25]

408

THE EPIDEMIOLOGY OF PERSONALITY DISORDERS: FINDINGS, METHODS AND CONCEPTS

Table 23.2 (cont.) References

Population

Instrument

Prevalence (%)

Comments

797 non-patient relatives of normal controls and probands with schizophrenia and depression 200 urban & rural residents, selected randomly 235 community residents, selected randomly

DSM-III by SIDP telephone 72.9% face-to-face 27.1%

0.9

Sample limits generalisability

Personality Assessment Schedule, ICD

1.0

Not DSM based

Personality Diagnostic Questionnaire, DSM-III

0.9

DSM-III-R by SCID

0.4

DSM-IV by SIDP

0.9

Males only

Torgersen et al. [32]

447 probands and relatives from randomly selected families in Germany 693 male twins from the Vietnam Era Twin Registry 2053 subjects ages 18–65 in Norway

Required criteria plus impairment/ distress for diagnosis Did not use DSM-III-R exclusionary criteria

Structured Interview for DSM-III-R Personality Disorders, face-to-face

1.7

Samuels et al. [12]

742 community subjects ages 34–94

DSM-IV IPDE by clinical psychologists

0.9

Grant et al. [13]

43 093 subjects from the National Epidemiologic Survey on Alcohol and Related Conditions

3.31

Crawford et al. [31]

644 subjects from the Children in the Community Study, mean age 33 214 adults (ages 18+) from the National Comorbidity Survey Replication (NCS-R).

NIAAA Alcohol Use Disorder and Associated Disabilities Interview Schedule- DSM-IV by experienced lay interviewers SCID-II for DSM-IV

Prevalence is considerably higher compared to previous studies; representative sample Cluster A PDs were most prevalent in men who had never married. Largest sample to date

1.7

–

DSM-IV IPDE by experienced clinician

4.9

The IPDE is regarded as a conservative instrument.

DSM-III by SIDP; telephone 72.9%, face-to-face 27.1%

2.9

Sample limits generalisability

DSM-III by Schedule for Interviewing Borderlines; 70% interviews 30% family history

2.2

Adjustment for sensitivity of family history method

Schizoid PD Zimmerman and Coryell [26]

Casey and Tyrer [35] Reich et al. [28]

Maier et al. (1990) Lyons (2001)

Lenzenweger et al. [25]

Schizotypal PD Zimmerman and Coryell [26]

Baron et al. (1985) [137]

797 non-patient relatives of normal controls and probands with schizophrenia and depression 376 relatives of control subjects in a family study

409

CHAPTER 23 Table 23.2

(cont.)

References

Population

Instrument

Prevalence (%)

Comments

Reich et al. [28]

235 community residents, selected randomly

Personality Diagnostic Questionnaire, DSM-III

5.1

Maier et al. (1990)

DSM-III-R by SCID

0.7

DSM-IV by SIDP

0.3

males only

Torgersen et al. [32]

447 probands and relatives from randomly selected families in Germany 693 male twins from the Vietnam Era Twin Registry 2053 subjects ages 18–65 in Norway

Required criteria plus impairment/ distress for diagnosis Did not use DSM-III-R exclusionary criteria

0.6

representative sample

Samuels et al. [12]

742 community subjects ages 34–94

Structured Interview for DSM-III-R Personality Disorders, face-to-face DSM-IV IPDE by clinical psychologists

0.6

Crawford et al. [31]

644 subjects from the Children in the Community Study, mean age 33 214 adults (ages 18+) from the National Comorbidity Survey Replication (NCS-R). 43 092 (ages 18+) from the NESARC, Wave 2

Cluster A PDs were most prevalent in men who had never married. –

Lyons (2001)

Lenzenweger et al. [25]

Pulay et al. [53]

SCID-II for DSM-IV

1.1

DSM-IV IPDE by experienced clinician

3.3

The IPDE is regarded as a conservative instrument.

AUDADIS-IV by experienced lay Interviewers, face-to-face

3.9

Largest sample to date, prevalence was significantly greater among men than women

23.5.3 Schizotypal personality disorder The essential feature of schizotypal PD is a ‘pervasive pattern of social and interpersonal deficits marked by acute discomfort with, and reduced capacity for, close relationships as well as by cognitive or perceptual distortions and eccentricities of behaviour’ [2]. These characteristics must be present by early adulthood in various contexts, and the symptoms must not be severe enough to warrant a diagnosis of schizophrenia. The DSM-III diagnostic criteria for schizotypal PD were drawn from the definition for the diagnosis of borderline schizophrenia in the Danish Adoption study [54]. A number of studies have indicated a familial relationship between schizotypal PD and schizophrenia. However, a number of studies have failed to find an excess risk for schizophrenia among the relatives of schizotypal probands. 410

The prevalences in the non-clinical samples described in Table 23.2 range from 0.3 to 5.1% with a median value of about 1.1%. The rates in clinical samples reported in Widiger [50] ranged from 2.0 to 64% with a median prevalence of 17.5%.

23.6 Antisocial personality disorder The essential feature of APD is a ‘pervasive pattern of disregard for and violation of the rights of others’ [2]. The DSM-III and DSM-III-R criteria were heavily influenced by the work of Robins [55] and place an emphasis on antisocial and criminal behaviour. In prison populations the prevalence of DSM-III-R APD may be over 50%. Swanson and colleagues [56] found that 90.4% of their sample of individuals with APD had at least one other

THE EPIDEMIOLOGY OF PERSONALITY DISORDERS: FINDINGS, METHODS AND CONCEPTS

DSM-III lifetime psychiatric diagnosis. Moran [57] found several sociodemographic predictors of APD. Males outnumbered females by as much as 8 : 1 and younger age groups and people with limited education were at higher risk. Martin and colleagues [58] found that the mortality rates for patients with APD was almost four times greater than the comparison population. APD was the only PD included in the ECA study and the National Comorbidity Survey [59]. The prevalences in the 16 non-clinical samples described in Table 23.3 range from 0.2% (in two non-US samples) to over 6% in a sample from the United Kingdom. The median prevalence is 2.8%. The rates in clinical samples reported in Widiger [50] ranged from 0.0 to 37% with a median prevalence of 7%. The DSM-IV reports prevalence estimates in clinical settings ranging from 3 to 30% depending on the predominant characteristics of the populations that are sampled, with higher rates being associated with substance abuse treatment settings and prison and forensic settings [2].

23.6.1 Borderline personality disorder The essential feature of borderline personality disorder (BPD) is a ‘pervasive pattern of instability of interpersonal relationships, self-image, affects and control over impulses’ [2]. Swartz and colleagues [64] derived a diagnostic algorithm for diagnosing BPD from the Diagnostic Interview Schedule [17]. Using a cutoff of 11 items from their 24-item index, they classified 1.8% of their sample (from the Duke site of the ECA study) between ages 19 and 55 as having BPD. Merikangas and Weissman [67] estimated the prevalence of BPD to be between 1.7 and 2.0% based on community studies carried out before the diagnostic criteria for BPD were codified in DSM-III. In wave two of the NESARC, Grant and colleagues [65] found the rate of BPD to be 5.9% in a sample of 34 653 subjects. BPD is the most common PD seen in most psychiatric settings and is over-represented in clinical populations because of the tendency towards help seeking [68]. Widiger and Weissman [69] and Widiger and Trull [70] reviewed the epidemiology of BPD in clinical settings. They reported an average prevalence of 8.0% in studies of outpatients and 15% in studies of

inpatients. Among studies of patients with PDs, the average prevalence was 27% among outpatients and 51% among inpatients. These authors concluded that BPD is the most common PD diagnosis given in clinical samples, with prevalence rates of up to 70% found among inpatient samples [71]. The prevalences in the 11 non-clinical samples reported in Table 23.3 range from 0.4% to about 5.9% with a median value of 1.6%. The rates in clinical samples reported in Widiger [50] ranged from 11 to 70% with a median prevalence of 31%.

23.6.2 Histrionic personality disorder The essential feature of histrionic PD is a ‘pervasive pattern of excessive emotionality and attentionseeking’ [2]. The term ‘hysterical personality’ has been used in other classifications. There has been relatively little empirical work done on histrionic PD [72]. When structured diagnostic assessments have been utilised, no sex difference in histrionic PD has been observed. However, there is some suggestion that clinicians may more frequently apply the diagnosis to females [72]. Nestadt and his co-workers [15] examined histrionic PD in the Clinical Reappraisal of the ECA Baltimore site study. There were no differences in prevalence by sex (males – 2.2%; females – 2.1%), race or education. The prevalence declined with age in males but not in females. There was a higher rate of histrionic PD among separated and divorced subjects than among married subjects. There was also an increase in depressive disorder, suicide attempts and the occurrence of three or more unexplained medical symptoms in females associated with histrionic PD. In males, there was an increase in substance use disorders associated with histrionic PD. Subjects with histrionic PD were significantly more likely to seek medical and psychiatric treatment than subjects without. The prevalences in the 10 non-clinical samples reported in Table 23.3 range from 0 to 3.0% with a median value of 1.6%. The rates in clinical samples reported in Widiger [50] ranged from 2.0 to 45% with a median prevalence of 19%. Rates in inpatient and outpatient mental health settings have been found to be between 10 and 15% when a structured assessment has been conducted [73]. 411

CHAPTER 23 Table 23.3

Prevalence of cluster B personality disorder.

References

Population

Instrument

Prevalence (%)

Comments

Lenzenweger et al. [25]

5692 adults (ages 18+) from the National Comorbidity Survey Replication (NCS-R).

Screening questions from DSM-IV IPDE.

2.3

A method of multiple imputation was implemented to estimate prevalence of PDs in the full sample.

DIS

2.5

Very rigorous sampling

DIS

0.2

Very rigorous sampling and methodology

DSM-III by SIDP telephone 72.9% face-to-face 27.1%

3.3

Sample limits generalisability

(Included explosive personality) not DSM based –

A subsample of 214 subjects were administered the IPDE by experienced clinician to determine clinical diagnoses Antisocial PD Robins et al. [55]

Compton et al. (1991) [60] Zimmerman and Coryell [26]

Casey and Tyrer [35] Nestadt et al. [15] Reich et al. [28]

Oakley-Browne (1989) [61] Maier et al. (1990) [44] Oldham et al. (1995) [62] Swanson et al. [56] Kessler et al. [59] Lyons (2001)

Torgersen et al. [32]

412

9543 subjects strict probability sampling at three sites 11 004 community residents in Taiwan; strict probability sample 797 non-patient relatives of normal controls and probands with schizophrenia and depression 200 urban and rural residents, selected randomly 810 subjects from the Clinical Reappraisal at Baltimore ECA site 235 community residents, selected randomly

Personality Assessment Schedule, ICD

6.0

DSM-III by SPE administered by psychiatrist Personality Diagnostic Questionnaire, DSM-III

1.5

1498 New Zealand

DIS

3.1

447 probands and relatives from randomly selected families in Germany 200 applicants for inpatient care or psychotherapy 3258 (Edmonton, Canada)

DSM-III-R by SCID

0.2

Did not use DSM-III-R exclusionary criteria

SCID-II PDE

3.5

–

DIS

3.7

8098 strict probability sample

DSM-III-R by CIDI

3.5

Weighted prevalence rate National Comorbidity Survey

693 male twins from the Vietnam Era Twin Registry 2053 subjects ages 18–65 in Norway

DSM-IV by SIDP

0.9

Males only

Structured Interview for DSM-III-R Personality Disorders, face-to-face

0.7

Representative sample

0.4

Required criteria plus impairment/distress for diagnosis –

THE EPIDEMIOLOGY OF PERSONALITY DISORDERS: FINDINGS, METHODS AND CONCEPTS Table 23.3 (cont.) References

Population

Instrument

Prevalence (%)

Comments

Samuels et al. [12]

742 community subjects ages 34–94

DSM-IV IPDE by clinical psychologists

4.1

Grant et al. [13]

43 093 subjects from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC)

3.63

Crawford et al. [31]

644 subjects from the Children in the Community Study, mean age 33 5692 adults (ages 18+) from the National Comorbidity Survey Replication (NCS-R).

NIAAA Alcohol Use Disorder and Associated Disabilities Interview Schedule- DSM-IV by experienced lay interviewers Self-reported adaptation of questions asked in SCID-II structured interview for DSM-IV Screening questions from DSM-IV IPDE.

Cluster B PDs were most prevalent in young men without a high school degree Largest sample to date

1.2

–

1

A method of multiple imputation was implemented to estimate prevalence of PDs in the full sample.

DSM-III by SIDP telephone 72.9% face-to-face 27.1 %

1.6

Sample limits generalisability

DSM-III by Schedule for Interviewing Borderlines; 70% interviews 30% family history Personality Diagnostic Questionnaire, DSM-III

1.7

Unorthodox adjustment for sensitivity of family history method

0.4

DIS/Borderline Index, DSM-III DSM-III-R by SCID

1.8

Required criteria plus impairment/distress for diagnosis Included subjects between ages 19–55 Did not use DSM-III-R exclusionary criteria

DSM-IV by SIDP

1.2

Males only

Structured interview for DSM-III-R personality disorders, face-to-face

0.7

Prevalence is relatively lower compared to previous studies; representative sample

Lenzenweger et al. [25]

A subsample of 214 subjects were administered the IPDE by experienced clinician to determine clinical diagnoses. Borderline PD Zimmerman and Coryell [26]

Baron et al. (1985) [63]

797 non-patient relatives of normal controls and probands with schizophrenia and depression 376 relatives of control subjects in a family study

Reich et al. [28]

235 community residents, selected randomly

Swartz et al. [64]

1541 community residents from the Duke ECA site 447 probands and relatives from randomly selected families in Germany 693 male twins from the Vietnam Era Twin Registry 2053 subjects ages 18–65 in Norway

Maier et al. (1990) Lyons (2001)

Torgersen et al. [32]

1.1

413

CHAPTER 23 Table 23.3

(cont.)

References

Population

Instrument

Prevalence (%)

Comments

Samuels et al. [12]

742 community subjects ages 34–94

DSM-IV IPDE by clinical psychologists

0.5

Cluster B PDs were most prevalent in young men without a high school degree

Crawford et al. [31]

644 subjects from the Children in the Community Study, mean age 33 5692 adults (ages 18+) from the National Comorbidity Survey Replication (NCS-R).

SCID-II for DSM-IV

3.9

–

Screening questions from DSM-IV IPDE.

1.6

A method of multiple imputation was implemented to estimate prevalence of PDs in the full sample.

5.9

–

DSM-III by SIDP telephone 72.9% face-to-face 27.1%

3.0

Sample limits generalisability

DSM-III by SPE administered by psychiatrist Personality Diagnostic Questionnaire, DSM-III

2.2

–

2.1

Required criteria plus impairment/distress for diagnosis

DSM-III-R by SCID

1.3

Did not use DSM-III-R exclusionary criteria

DSM-IV by SIDP

0.0

Males only

Structured Interview for DSM-III-R Personality Disorders, face-to-face DSM-IV IPDE by clinical psychologists

2.0

Representative sample

0.2

–

Lenzenweger et al. [25]

Grant et al. [65]

34 653 subjects from Wave 2 of NESARC

A sub-sample of 214 subjects were administered the IPDE by experienced clinician to determine clinical diagnoses NIAAA Alcohol Use Disorder and Associated Disabilities Interview Schedule- DSM-IV by experienced lay interviewers

Histrionic PD Zimmerman and Coryell [26]

Nestadt et al. [15] Reich et al. [28]

Maier et al. (1990)

797 non-patient relatives of normal controls and probands with schizophrenia and depression 810 subjects from the Clinical Reappraisal at Baltimore ECA site 235 community residents, selected randomly

Torgersen et al. [32]

447 probands and relatives from randomly selected families in Germany 693 male twins from the Vietnam Era Twin Registry 2053 subjects ages 18–65 in Norway

Samuels et al. [12]

742 community subjects ages 34–94

Lyons (2001)

414

THE EPIDEMIOLOGY OF PERSONALITY DISORDERS: FINDINGS, METHODS AND CONCEPTS Table 23.3 (cont.) References

Population

Instrument

Prevalence (%)

Comments

Grant et al. [13]

43 093 subjects from the National Epidemiologic Survey on Alcohol and Related Conditions

1.85

Largest sample to date

Crawford et al. [31]

644 subjects from the Children in the Community Study, mean age 33

NIAAA Alcohol Use Disorder and Associated Disabilities Interview Schedule- DSM-IV by experienced lay interviewers SCID-II personality questionnaire

0.9

Lenzenweger et al. [25]

214 adults (ages 18+) from the National Comorbidity Survey Replication (NCS-R).

DSM-IV IPDE by experienced clinician

0.0

Data from the SCID-II clinical interview was not used due to a computer programming error The IPDE is regarded as a conservative instrument.

797 non-patient relatives of normal controls and probands with schizophrenia and depression 235 community residents, selected randomly

DSM-III by SIDP telephone 72.9% face-to-face 27.1%

0.0

Sample limits generalisability

Personality Diagnostic Questionnaire, DSM-III

0.4

DSM-III-R by SCID

0.0

DSM-IV by SIDP

0.9

Males only

Torgersen et al. [32]

447 probands and relatives from randomly selected families in Germany 693 male twins from the Vietnam Era Twin Registry 2053 subjects ages 18–65 in Norway

Required criteria plus impairment/distress for diagnosis Did not use DSM-III-R exclusionary criteria

0.8

Representative sample

Samuels et al. [12]

742 community subjects ages 34–94

Structured Interview for DSM-III-R Personality Disorders, face-to-face DSM-IV IPDE by clinical psychologists

0.0

Crawford et al. [31]

644 subjects from the Children in the Community Study, mean age 33 214 adults (ages 18+) from the National Comorbidity Survey Replication (NCS-R). 34 653 subjects from wave 2 of NESARC

Cluster B PDs were most prevalent in young men without a high school degree –

Narcissistic PD Zimmerman and Coryell [26]

Reich et al. [28]

Maier et al. (1990) Lyons (2001)

Lenzenweger et al. [25]

Stinson et al. [66]

SCID-II for DSM-IV

2.2

DSM-IV IPDE by experienced clinician

0.0

The IPDE is regarded as a conservative instrument.

NIAAA Alcohol Use Disorder and Associated Disabilities Interview Schedule- DSM-IV by experienced lay interviewers

6.2

–

415

CHAPTER 23

23.6.3 Narcissistic personality disorder The essential feature of this disorder is a ‘pervasive pattern of grandiosity (in fantasy or behaviour), need for admiration and lack of empathy’ [2]. There has been little empirical work done on narcissistic PD in general, and very little epidemiological work in particular. Although there is considerable clinical interest in the disorder, it has only recently been included in official nomenclatures. Narcissistic PD became part of the American nomenclature in 1980 with DSM-III and there is no counterpart to it in ICD-10. Gunderson, Ronningstam and Smith [74] reviewed several studies that reported the prevalence of DSMIII-R narcissistic PD in clinical populations [1, 2, 45, 51] and found prevalence rates ranging from 2.0 to 16%. The prevalences in the nine non-clinical samples reported in Table 23.3 range from 0.0 to 6.2% with a median value of about 0.4%. The rates in clinical samples reported in Widiger [50] ranged from 2.0 to 35% with a median prevalence of 6.0%.

23.6.4 Avoidant personality disorder The essential feature of avoidant PD is a ‘pervasive pattern of social inhibition, feelings of inadequacy and hypersensitivity to negative evaluation’ [71]. An important issue in the epidemiology of avoidant PD is its potential overlap with an axis I disorder, generalised social phobia. Turner and colleagues [75] studied Axis II co-morbidity in a sample of individuals with social phobias. Avoidant PD was present in 22.1% of the sample and an additional 52.9% of the sample had avoidant features that fell short of meeting the diagnostic threshold. Schneier and co-workers [76] studied a sample of 50 patients with social phobias and found that 70% of patients with social phobia met criteria for avoidant PD, and 89% of patients with generalised social phobia received a diagnosis of avoidant PD. Herbert and colleagues [77] found that 61% of patients in their series with generalised social phobia also met criteria for avoidant PD. Another study [78] found that 50% of their sample with generalised social phobia met criteria for avoidant PD. Researchers have suggested that generalised social phobia and avoidant PD may define a single psychopathological entity [76]. The prevalences in the nine non-clinical samples reported in Table 23.4 range from 0.0 to 6.4% with 416

a median value of about 2.3%. The rates in clinical samples reported in Widiger [50] ranged from 5.0 to 55% with a median prevalence of 16%.

23.6.5 Dependent personality disorder The essential feature of dependent PD is a ‘‘pervasive and excessive need to be taken care of, leading to submissive and clinging behaviours and fears of separation’’ [2]. Dependent PD was reviewed by Hirschfeld, Shea and Weise [79]. They explained that this disorder derives from psychoanalytic theory, social psychological theory and ethological theory. Dependent PD overlaps substantially with other PDs, having the greatest degree of overlap with BPD (over 50% in most studies), followed by avoidant, histrionic and schizotypal PDs [79]. Hirschfeld and associates also discussed the issues of sex differences and possible sex bias in the diagnosis of dependent PD. They noted that dependent PD was diagnosed more frequently in females when assessment was not carried out using standardised instruments. However, when standardised instruments were used, males and females did not differ in the frequency of the diagnosis. This suggests that clinicians, rather than the standardised diagnostic criteria, may be responsible for observed differences in male and female rates. Studies have also found that individuals with a depressive disorder may be more likely to display dependent personality traits [80]. According to the DSM-IV, dependent personality is among the most frequently reported of the PDs encountered in mental health clinics [73]. The prevalences of dependent PD in the nine nonclinical samples reported in Table 23.4 range from 0.1 to 5.1% with a median value of 0.8%. The rates in clinical samples reported in Widiger [50] ranged from 2.0 to 55% with a median prevalence of 20%.

23.6.6 Obsessive–compulsive personality disorder The essential feature of obsessive–compulsive PD is a ‘pervasive pattern of preoccupation with orderliness, perfectionism and mental and interpersonal control, at the expense of flexibility, openness and efficiency’ [73]. Obsessive–compulsive PD was examined by Nestadt and his coworkers (described above)

THE EPIDEMIOLOGY OF PERSONALITY DISORDERS: FINDINGS, METHODS AND CONCEPTS Table 23.4

Prevalence of cluster C personality disorders.

References

Population

Instrument

Prevalence Comments (%)

Lenzenweger et al. [25]

5692 adults (ages 18+) from the National Comorbidity Survey Replication (NCS-R).

Screening questions from DSM-IV IPDE.

6.8

A method of multiple Imputation was implemented to estimate prevalence of PDs in the full sample.

DSM-III by SIDP telephone 72.9% ace-to-face 27.1%

1.3

Sample limits generalisability

Personality Diagnostic Questionnaire, DSM-III

0.0

Required criteria plus impairment/ distress for diagnosis

DSM-III-R by SCID

1.1

Did not use DSM-III-R exclusionary criteria

DSM-IV by SIDP

2.3

Males only

Torgersen et al. [32]

447 probands and relatives from randomly selected families in Germany 693 male twins from the Vietnam Era Twin Registry 2053 subjects ages 18–65 in Norway

Structured Interview for DSM-III-R Personality Disorders, face-to-face

5.0

Prevalence is considerably higher compared to previous studies; representative sample

Samuels et al. [12]

742 community subjects ages 34–94

DSM-IV IPDE by clinical psychologists

1.8

Cluster C PDs were most prevalent in high school graduates who had never married

Grant et al. [13]

2.36 NIAAA Alcohol Use Disorder 43 093 subjects from the and Associated Disabilities National Epidemiologic Interview Schedule- DSM-IV Survey on Alcohol and Related by experienced lay interviewers Conditions SCID-II for DSM-IV 6.4 644 subjects from the Children in the Community Study, mean age 33 DSM-IV IPDE by experienced 5.2 214 adults (ages 18+) from the clinician National Comorbidity Survey Replication (NCS-R).

A subsample of 214 subjects were administered the IPDE by experienced clinician to determine clinical diagnoses. Avoidant PD Zimmerman and Coryell [26]

Reich et al. [28]

Maier et al. (1990) Lyons (2001)

Crawford et al. [31] Lenzenweger et al. [25]

797 non-patient relatives of normal controls and probands with schizophrenia and depression 235 community residents, selected randomly

Largest sample to date

–

The IPDE is regarded as a conservative instrument.

Dependent PD Zimmerman and Coryell [26]

Reich et al. [28]

Maier et al. (1990)

797 non-patient relatives of normal controls and probands with schizophrenia and depression 235 community residents, selected randomly

DSM-III by SIDP telephone 72.9% face-to-face 27.1%

1.8

Sample limits generalisability

Personality Diagnostic Questionnaire, DSM-III

5.1

Required criteria plus impairment/distress for diagnosis

447 probands and relatives from randomly selected families in Germany

DSM-III-R by SCID

1.5

Did not use DSM-III-R exclusionary criteria

417

CHAPTER 23 Table 23.4

(cont.)

References

Population

Instrument

Prevalence Comments (%)

Lyons (2001)

DSM-IV by SIDP

0.1

Males only

Torgersen et al. [32]

693 male twins from the Vietnam Era Twin Registry 2053 subjects ages 18–65 in Norway

1.5

Representative sample

Samuels et al. [12]

742 community subjects ages 34–94

Structured Interview for DSM-III-R Personality Disorders, face-to-face DSM-IV IPDE by clinical psychologists

0.1

Cluster C PDs were most prevalent in high school graduates who had never married

Grant et al. [13]

43093 subjects from the National Epidemiologic Survey on Alcohol and Related Conditions Crawford 644 subjects from the Children et al. [31] in the Community Study, mean age 33 Lenzenweger 214 adults (ages 18+) from the et al. [25] National Comorbidity Survey Replication (NCS-R). Obsessive–compulsive PD Zimmerman and 797 non-patient relatives of Coryell [26] normal controls and probands with schizophrenia and depression Nestadt et al. [15] 810 subjects from the Clinical Reappraisal at Baltimore ECA site Reich et al. [28] 235 community residents, selected randomly Maier et al. (1990) Lyons (2001) Torgersen et al. [32] Grant et al. [13]

Crawford et al. [31] Lenzenweger et al. [25]

418

447 probands and relatives from randomly selected families in Germany 693 male twins from the Vietnam Era Twin Registry 2053 subjects ages 18–65 in Norway

49 NIAAA Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV by experienced lay interviewers SCID-II for DSM-IV 0.8

Largest sample to date

DSM-IV IPDE by experienced clinician

0.6

The IPDE is regarded as a conservative instrument.

DSM-III by SIDP (telephone 72.9%; face-to-face 27.1%)

2.0

Sample limits generalisability

DSM-III by SPE administered by psychiatrist

1.7

Male rate five times female rate

Personality Diagnostic Questionnaire, DSM-III

6.4

Required criteria plus impairment/distress for diagnosis

DSM-III-R by SCID

2.2

Did not use DSM-III-R exclusionary criteria

DSM-IV by SIDP

2.

Males only

2.0 Structured Interview for DSM-III-R Personality Disorders, face-to-face NIAAA Alcohol Use Disorder 43 093 subjects from the 7.9 National Epidemiologic and Associated Disabilities Survey on Alcohol and Related Interview Schedule-DSM-IV by Conditions experienced lay interviewers 644 subjects from the Children SCID-II for DSM-IV 4.7 in the Community Study, mean age 33 DSM-IV IPDE by experienced 214 adults (ages 18+) from the 2.4 National Comorbidity Survey clinician Replication (NCS-R).

–

Representative sample

Most prevalent PD

–

The IPDE is regarded as a conservative instrument.

THE EPIDEMIOLOGY OF PERSONALITY DISORDERS: FINDINGS, METHODS AND CONCEPTS

in the Clinical Reappraisal of the ECA Baltimore site study. Males had a significantly higher prevalence (3.0%) than females (0.6%). White respondents had a significantly higher prevalence than black respondents. There was no association between age and risk of the disorder. The diagnosis of compulsive PD was associated with higher education, greater likelihood of being employed, and greater likelihood of being married compared to being widowed, separated, divorced or never married. Subjects with compulsive PD had a higher income than those without after correcting for age and sex. Nestadt and colleagues [16] found that compulsive traits were associated with greater risk of generalised anxiety disorder and simple phobia and lower risk of alcohol use disorder. Baer and colleagues [81] studied 96 patients with obsessive–compulsive disorder (OCD), which is an anxiety disorder recorded on axis I. Only 6% of the patients received a diagnosis of obsessive–compulsive PD; and of these six patients, five had onset of obsessive–compulsive symptoms before age 10. Pfohl and associates [82] found that among patients with OCD, 30% met criteria for obsessive–compulsive PD. (To put this finding in context, in the same study they found dependent PD in 46% of their OCD subjects and passive-aggressive PD in 49%.) The prevalences in the nine non-clinical samples reported in Table 23.4 range from 1.7 to 7.9% with a median value of 2.3%. The rates in clinical samples reported in Widiger [50] ranged from 1.0 to 20% with a median prevalence of 9%.

23.7 Conceptual issues A critical conceptual issue with implications for investigating the epidemiology of PDs is the question of whether PDs are best considered to be categories or whether they should be considered to represent extreme standings on universally occurring dimensions of personality.

23.8 Models of personality disorder The issue of the relative merits of categorical versus dimensional approaches cuts across most domains of

psychopathology. However, it is especially significant in the domain of PDs, in part, because of the long tradition of research in personality psychology based on dimensional models. Currently, clinicians using DSM-IV must decide whether a patient meets criteria for one or more PDs, each considered a separate and distinct category. If the true nature of PDs is dimensional, it would suggest that the most appropriate epidemiological approach might be to determine the mean and standard deviation of the population on the appropriate dimension. Consistent with such an approach, prevalence could be regarded as the proportion of the population that exceeds a threshold associated with impairment and/or distress. Costa and McCrae [83] suggested that PDs, at least as they are assessed by the scales created by a number of different investigators, represent an extreme standing or a configuration of extreme standings on the dimensions of normal personality. This might be termed the ‘defining model’, in which the disorder results from exceeding the threshold on some dimension (or dimensions); the dimension is causally related to the disorder and exceeding the threshold is a sufficient cause. A medical example of this model would be cases of essential hypertension without a demonstrable underlying pathophysiological cause for elevated blood pressure. That is, the factors that go into determining the blood pressure of the afflicted individuals are the same as those for the general population. Blood pressure, in general, is normally distributed in the population and some individuals, for the same multifarious genetic and environmental factors that determine the blood pressure of human beings in general, fall at the high end of the distribution. Because blood pressure at the high end of the distribution is associated with excess morbidity and mortality, it is justifiably considered a disorder – hypertension. The defining model of PD is similar to this example. Some individuals, due to the same genetic and environmental factors that influence everyone, are at the high or low end of a dimension or dimensions of personality and this standing defines (it is) their PD. The defining model, however, does not describe the only possible manner in which dimensions of normal personality may relate to PDs. There are at least two other models that are equally plausible. In 419

CHAPTER 23

the ‘descriptive (trivial) model’, the disorder can be described in terms of a dimension (or dimensions), but the relationship is not causal and is uninformative for understanding the disorder. A medical example of the descriptive model would be explaining Down’s syndrome in terms of the ‘universally occurring’ dimensions of height and IQ. That is, individuals with Down’s syndrome could be described as being at the low end of the continuum of height and at the low end of the continuum of IQ. However, this is an uninformative approach. A telling question would be, ‘Are people with Down’s syndrome short for the same reason that short people without Down’s syndrome are short?’ If the answer is no, then trying to understand Down’s syndrome through the mechanism that determines height for most people would not be informative. Individuals with Down’s syndrome differ qualitatively from the general population. If a PD is caused by some factor or factors that are independent of ‘normal’ personality, it still might be described in these terms, but such a Procrustean approach will be counterproductive for understanding the nature of the phenomenon. In the ‘predisposing model’, exceeding a threshold on the relevant dimension is a risk factor for the disorder and could be a necessary cause, but it is not a sufficient cause. Phenylketonuria (PKU) can serve as a medical example of this model. An individual who is homozygous for a defective gene that leads to the production of phenylalanine hydroxylase is vulnerable to the development of PKU. Such a genotype is a necessary but not sufficient cause. In order for the individual to manifest PKU, he or she must be exposed to dietary phenylalanine. If such an individual ingests phenylalanine, damage to the nervous system results. Without such exposure, there is no damage to the nervous system. For example, schizotypal PD could be due to an extreme position on a universal continuum of introversion – extroversion plus possessing a schizophrenia genotype. In this example, introversion is necessary but not sufficient to produce schizotypal PD. It seems likely that some aspects of PDs are related to deviation within normal dimensions of personality while other features are best considered categorical. It is unlikely that any single model will adequately describe all PDs. For example, Gunderson

420

and colleagues [84] suggested that the more severe PDs (e.g. schizotypal, paranoid and borderline) may be discrete clinical syndromes with discrete aetiological pathways, while less severe PDs (e.g. compulsive, avoidant and dependent) represent extremes of normally occurring traits. It is conceivable that certain types of personality pathology represent a ‘common final pathway’ for a number of different aetiological factors, while others might be uniquely related to a specific standing on universal dimensions of personality.

23.8.1 Issues and challenges for DSM-5 With the expected publication of DSM-5 in 2013, there has been renewed interest in comparing dimensional versus categorical approaches to diagnosis. It is unlikely that the diagnostic system will change because PDs can be described in dimensional terms – change is only likely to occur if it is demonstrated that PDs should be described dimensionally. Although the inclusion of a dimensional approach in an appendix of DSM-IV was proposed [73], a lack of consensus about the dimensions to include was a disadvantage of adopting a dimensional approach [85] and DSM-IV continues to use a categorical system. Since the publication of the DSM-IV, invaluable research has helped to increase knowledge regarding the utility of a dimensional system, which is informing the development of the DSM-5. Much research has demonstrated the advantages to adopting a dimensional model in the upcoming addition of the DSM. A dimensional approach would improve flexibility, improve reliability and validity for certain disorders, and save information lost in categorical classification. For example, a problem with reliability and stability of diagnoses exist due to symptom fluctuation around diagnostic thresholds, such that if a patient meets criteria for symptoms of a PD one day but one symptom remits the next day, that person may no longer meet criteria. Furthermore, high rates of ‘personality disorder not otherwise specified’ (PDNOS) might reflect gaps in the current categorical diagnostic system. Adopting a dimensional system may not only make theoretical sense, as numerous studies have demonstrated that PDs are at one end of a continuum of normal

THE EPIDEMIOLOGY OF PERSONALITY DISORDERS: FINDINGS, METHODS AND CONCEPTS

personality traits, but a dimensional approach may also be more clinically meaningful. On the other hand, there are numerous problems with adopting a dimensional system. Some argue that categories are advantageous for clinical utility because they convey clinical information in a succinct manner [86]. One study tested whether the trait-based Five Factor Model (FFM) of personality provides information, that is specific enough for clinicians to translate into features of PDs [87]. When clinicians were presented with case profiles using formats from DSM-IV symptoms or the FFM, clinicians rated the DSM-IV symptoms as more clinically useful. These results underscore challenges in adopting a dimensional model of PDs. First [86] also suggests that abandoning categories will be disruptive to administrative, clinical and research practices, complicating medical records and requiring significant retraining for clinicians. Trull and Durrett [88, 89] described the advantages and disadvantages of the major categorical and dimensional models. They discuss the analytic techniques of taxometric analysis, latent class analysis and multivariate genetic analysis that can be brought to bear to address the issue. They support a dimensional approach and conclude that there are four major relevant domains of personality: neuroticism/negative affectivity/emotional dysregulation; extraversion/positive emotionality; dissocial/ antagonistic behaviour and constraint/compulsivity/ conscientiousness. The authors also suggest how dimensional approaches might be integrated into the existing diagnostic system and identify issues that should be addressed if dimensional approaches are to gain widespread acceptance. Widiger and Samuel [90] provide a critique of the categorical model of mental disorders and review research on the relative merits of categorical versus dimensional approaches. They highlight excessive diagnostic co-occurrence and boundary disputes/overlap as significant flaws of the categorical classification system. They recommend that future editions of the DSM give more recognition to dimensional models and propose a dimensional classification system that they believe provides a useful model. Krueger and colleagues [91] also describe an approach for synthesising categorical

and dimensional approaches that could inform DSM-5. They propose: (i) having a set of core descriptive elements of personality for DSM-5, (ii) rating those elements for specific patients, (iii) combining those elements into PD prototypes and (iv) conceptualising PD as a construct separate from personality traits. A number of other novel and thoughtful critiques and alternative approaches have been suggested [43, 92–95]. The Personality and Personality Disorders Work Group was developed in 2007 to determine how PDs will be assessed and classified in the DSM-5. According to a report from the American Psychiatric Association [96], the Work Group is reworking the current classification system of PDs, which will consist of five parts, including ‘(i) an overall rating of personality (self and interpersonal) functioning ranging from normal to severely impaired; (ii) prototype descriptions of major personality (disorder) types; (iii) personality trait assessment, on which the prototypes are based, but which can also be used to describe major personality characteristics of patients who either do not have a PD or have a PD that does not conform to one of the prototypes; (iv) generic criteria for PD consisting of severe deficits in self-differentiation and integration and in the capacity for interpersonal relatedness and (v) measures of adaptive functioning’ [96]. Based on extensive reviews of the literature, clinically relevant personality traits, including emotional lability and impulsivity, have been identified and will be rated on a four-point scale, and adaptive traits, such as optimism and empathy, may also be included. This proposed system aims to address criticisms of adopting a dimensional system by creating a hybrid model, that is usable and appeals to varying levels of expertise and time constraints during assessments. Current research from the Work Group, including completing literature reviews, secondary analyses of existing datasets and field trials to evaluate real patients and test the clinical utility of this model, is ongoing.

23.8.2 Distinguishing Axis I from Axis II disorders DSM-III introduced the distinction between ‘clinical disorders’ on Axis I and ‘PDs’ on Axis II. DSM-IV

421

CHAPTER 23

states that this distinction is made to foster consideration of the presence of PDs that may be overlooked if attention is directed to more florid Axis I disorders. Although PDs are suggested to be more stable than Axis I disorders, questions have been raised regarding whether PDs are sufficiently different from other mental disorders to warrant their own axis. It has been suggested that the distinction between Axis I and Axis II is not supported by empirical evidence. The occurrence of certain spectrum relationships exist between PDs and axis I disorders, which are thought to represent phenotypic variations of the same underlying pathology. Such relationships have been suggested to exist between BPD and depression, schizotypal PD and schizophrenia, avoidant PD and social phobia, cluster B personality disorders and substance use, cluster B and C PDs and eating disorders, cluster C PDs and anxiety disorders and cluster A and schizophrenia [97]. Some research suggests that temperaments may be a unifying basis for personality and psychopathology. Clark [98] argues that basic temperamental factors are an underlying vulnerability for Axis I and Axis II disorders, explaining their high comorbidity. These factors comprise the ‘big three’ model, with two affective systems (positive affectivity and negative affectivity) and a third regulatory system (inhibition vs. disinhibition). Some researchers support a system in which Axis I and II disorders are combined into a more dimensional perspective based on underlying temperament dimensions that are differentiated through personality traits and environmental influences [98, 99]. For example, Krueger and colleagues [99] argue that disorders such as APD, substance use and personality traits of aggression and impulsivity should be classified together under the rubric of externalising disorders. One study found that diagnoses of conduct disorder, APD and drug and alcohol dependence form a single externalising factor, whereas mood and anxiety disorder diagnoses formed an internalising factor [100]. This study used data from the National Comorbidty Survey and results were replicated by other epidemiological surveys from around the world [101, 102]. A shared genetic risk for these externalising disorders has also been identified [103]. Such findings support a re-evaluation of the distinction between 422

Axis I and II disorders and support a dimensional conceptualisation of psychopathology.

23.9 Methodological issues 23.9.1 Diagnostic issues As in other areas of psychiatric research, the reliability and validity of diagnosis has been a central issue in research on PDs. As in other areas of psychopathology, there is no ‘gold standard’ that can be used to validate diagnoses, although the problem seems to be greater for PDs than for Axis I disorders. Skodol and colleagues [104] identified a number of issues that contribute to the difficulty in assessing PDs. In comparison to Axis I disorders, PDs are more likely to be ego syntonic. This means that to the individual with a PD, his or her symptomatology is not experienced as alien to his or her usual experience of self. The nature of PDs may make it more difficult for the individual to describe symptoms to an interviewer. The symptoms of a PD may be more recognisable and troublesome to individuals in the environment of the person with the disorder than to the person him or herself. Clinicians may be inclined to rely more on their own observations than on the patient’s reports when assessing the presence of PD. In assessment techniques typically used in research, the respondent is asked a series of questions about symptoms and the answer is generally taken to be veridical. There is usually some provision for the diagnostician to over-ride the subject’s self-report if there is contradictory information. Given that PDs are defined as long-standing stable characteristics, one time, cross sectional assessment may not be ideal. If the person has a concurrent Axis I disorder, the symptoms of that disorder may influence the report of Axis II symptoms. Zanarini and her colleagues [51] also identified a number of issues that contribute to the difficulty of making reliable and valid diagnoses of PDs. Many of the DSM diagnostic criteria are not clearly operationalised, necessitating clinical interpretation. Some symptoms, such as low self-esteem, have a very high base rate and may require significant clinical judgement as to whether they achieve clinical significance. Many diagnostic criteria (e.g. vanity or criminal behaviour) reflect traits that are generally held to

THE EPIDEMIOLOGY OF PERSONALITY DISORDERS: FINDINGS, METHODS AND CONCEPTS

be negative, and may be denied on the basis of social desirability. Reporting certain traits may require a level of insight, that is absent in some individuals.

23.9.2 Methods for collecting diagnostic information 23.9.2.1 Chart review/records It has been suggested that clinicians often do not systematically assess their patients for the presence of PDs [105]. When clinicians do assess PDs, they typically diagnose only one [47], while systematic evaluation suggests that an individual who meets criteria for one PD will usually meet criteria for at least one other PD [106]. Given the fact that an acute Axis I disorder is typically more florid than any accompanying PD, it is quite likely that in most clinical settings the sensitivity of diagnoses of PDs will be poorer than for Axis I disorders. Especially for diagnoses made at admission, the Axis II diagnosis is often deferred and may or may not be added to the record at a later time.

23.9.2.2 Informants Because the symptomatology of PD may be subtle, ego syntonic, socially undesirable to acknowledge, and not obvious to the subject because of lack of insight, it has been suggested that information should be sought from another person who is familiar with the individual being diagnosed. However, in most cases it is not clear how to combine and weight information from an informant relative to that provided by the subject or observed by the diagnostician [107].

23.9.2.3 Clinical interview Perry [108] suggested that structured diagnostic interviews may emphasise reliability to the point that validity is degraded. He proposed that the clinical interview, with its emphasis on history, important stories from the subject’s life and the discernment of longstanding patterns of behaviour in historical context, may be very useful for the identification of PDs. Perry advocated several steps to ensure the quality and comparability of information obtained from clinical interviews: (i) the use of guidelines

for the interview; (ii) a compendium of good case examples and (iii) good training procedures.

23.9.2.4 Semistructured interviews and self-report inventory Widiger and Samuel [90] described an approach to developing evidence-based assessment of the PDs. Their article focuses on integrated assessment strategies rather than specific PD instruments. They advocate a two-step procedure: (i) administration of a self-report inventory to alert oneself to the potential presence of particular maladaptive personality traits and (ii) administration of a semistructured interview to verify and document their presence [109].

23.9.2.5 Rating scales The Personality Assessment Form (PAF) [110] uses a prototypical rating approach to assess DSM PDs. The PAF includes a paragraph describing the diagnostic features of each PD. The clinician uses data from available clinical sources to rate the long-term personality functioning of the subject on a six-point scale for each PD. Shea and colleagues [111] reported inter-rater reliability assessed by kappa for the diagnosis of any PD and any Cluster C PD as 0.48 and 0.47, respectively.

23.9.2.6 Alternative approaches One of the major impediments to PD research may be reliance on self-reported symptoms, as described above (see ‘Informants’ section). Two alternatives to self-reported information have recently been described. The first approach, developed by Westen and his colleagues, uses information from a treating clinician. The Shedler-Westen Assessment Procedure-200 (SWAP-200) [112–114] is a 200-item Q-sort designed to assess personality and personality pathology. A Q-Sort is a set of statements printed on separate index cards, in this case, statements about personality and personality dysfunction. Using the SWAP-200, an experienced clinical observer sorts (rank-orders) the statements into categories (piles), from those that are not descriptive of the patient to those that are highly descriptive, with intermediate places of items that apply to varying degrees. Clinician-judges can sort the items based 423

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on the Clinical Diagnostic Interview [115] or on knowledge of the patient over the course of treatment. Oltmanns and colleagues [116] have proposed a technique in which peer assessments are utilised to identify the symptoms of PD. This procedure capitalises on naturally occurring social groupings by using members of a group to ‘nominate’ peers with characteristic features of PDs. Peers may be better able and more willing to identify socially undesirable traits. Weston et al.’s Q-sort procedure and Oltmann et al.’s peer nomination procedure may provide very valuable alternatives to reliance on self-reports of PD symptoms. However, logistical considerations in implementing these approaches in large, representative samples might limit their utility in epidemiological research.

23.9.3 Diagnostic agreement Although not directly addressing the issue of validity, it is certainly desirable that different instruments used to assess PDs yield similar, if not identical, results. Before the advent of DSM-III, the inter-rater reliability of PDs was poor. Based on a review in 1974, Spitzer and Fleiss [117] reported that the average pre-DSM-III kappa was 0.32 for clinical diagnosis. In the DSM-III field trials, a kappa for the presence of any PD of 0.61 was reported when both diagnosticians rated the same interview; when two separate interviews were conducted, kappa was 0.54 [118]. In 1992, Perry [108] reviewed results from eight studies that reported results from different structured interviews or self-report questionnaires on the same individuals. Perry concluded that the reliability of the individual instruments assessed by inter-rater reliability is in the fair to high range. As mentioned earlier, there is no clear-cut criterion for validity available. However, if these instruments measure valid constructs validly, then they should be in strong agreement and lead to similar diagnostic conclusions. Perry found that agreement across instruments was poor. One way that Perry summarised agreement between different instruments was to determine the median kappa for the individual disorders between the two instruments being considered. When he examined the highest kappa between disorders from each study, he found a median kappa of 0.54. For the lowest kappa between disorders from each study, he 424

found a median kappa of 0.0. Finally, he determined the median kappa from each study and computed a ‘median of medians’, which yielded a kappa value of 0.25, in the poor range. When Perry separated studies that compared two interviews from those that compared self-report questionnaires with interviews there were minor changes in the results. The median highest kappa from studies comparing two interviews was 0.61, the median lowest kappa was 0.09 and the ‘median of medians’ was 0.25. The results were somewhat poorer for studies comparing self-report questionnaires to interview. The median highest kappa was 0.50, the median lowest kappa was 0.01 and the ‘median of medians’ was 0.16. Perry suggested that demonstrating acceptable reliability for the individual instruments leaves unanswered questions about assessment validity. Perry concluded that the different instruments for assessing PDs, on average, agree with one another at a level, that is only slightly better than chance and often reach different conclusions.

23.9.4 Comorbidity and diagnostic overlap among personality disorders The literature consistently reports excessive cooccurrence among PDs [70]. In a non-patient sample of relatives of normal controls and probands with schizophrenia and depressive disorders, Zimmerman and Coryell [26] found that approximately one fourth of subjects with any PD met criteria for more than one. Fossati and colleagues [119] reported that the co-occurrence rate was greater then 50% for all DSM-IV PDs in a population of 431 consecutively admitted psychiatric patients with various Axis I diagnoses excluding psychotic disorders. Oldham and his coworkers (unpublished data described elsewhere [95]), in a study of 100 patients with suspected character pathology, reported an average of 3.4 PDs in patients with at least one PD. Zanarini and colleagues [51] reported 2.8 diagnoses per patient, Widiger and colleagues [106] reported 3.75 and Skodol and colleagues [120] reported 4.6. In the CLPS [43] the mean number of co-occurring PDs was 1.4. Coid and colleagues [39] found that the mean number of PD diagnoses among those who qualified for such a diagnosis was 1.92; of these, 53.5% had one disorder only, with 21.6%

THE EPIDEMIOLOGY OF PERSONALITY DISORDERS: FINDINGS, METHODS AND CONCEPTS

having two, 11.4% having three and 14.0% having between four and eight diagnoses. In the NESARC study, Grant and colleagues [121] found very high co-occurrence rates of PDs in their sample. The odds ratio (OR) relating avoidant PD to dependent PD was 118.6. The lowest OR between any two PDs was 5.8; the large majority of ORs was over 10.0. In addition to the high comorbidity among PDs, Axis II disorders are also highly comorbid with Axis I disorders. Widiger and Rogers [122] suggested that patterns of comorbidity between PDs and Axis I may best be understood in terms of the DSM-III clusters. The odd–eccentric cluster should be associated with psychotic disorders, the dramatic–impulsive cluster should be associated with affective disorders and the anxious–fearful cluster should be associated with anxiety disorders. Pfohl and colleagues [82] reviewed findings on the comorbidity of Axis I and Axis II disorders. They included studies that employed patient samples with major depression, OCD and panic disorder. The hospitalised depressed patients had an increased risk for dramatic/impulsive cluster (cluster B) PDs. The OCD and panic disorder samples were more likely to receive a diagnosis from the anxious/fearful cluster (cluster C). OCD subjects were also more likely to receive a diagnosis from the odd/eccentric cluster (cluster A) than either depressed or panic patients. In a meta-analysis of 25 studies published since the 1980s, Corruble and colleagues [123] found that 20–50% of inpatients and 50–85% of outpatients with current major depression have an associated PD. Another study [124] found that certain clinical characteristics of major depressive disorder and dysthymia disorder predicted PD co-occurrence. For major depression, the greater the number of prior episodes the more likely the association with BPD. Dependent PD, however, was more likely with fewer episodes. Earlier onset dysthymia also signified an increased likelihood of BPD, but no evidence was found that early onset of major depressive disorder increased the likelihood of any other PDs. The NESARC utilises the largest sample to date that examines the comorbidity between PDs and Axis I disorders (n = 43 093) [53, 65, 66, 125], Data from waves 1 and 2 of this study has examined the comorbidity between each of the 10 DSMIV PDs and 12-month prevalence DSM-IV mood

and anxiety disorders, such as major depression, dysthymia, mania hypomania, panic disorder with agoraphobia, social phobia, specific phobia and generalised anxiety disorder. Three PDs, borderline, narcissistic and schizotypal, were assessed in wave 2 of the NESARC, and additional Axis I disorders were examined in this wave, including substance use disorders. The authors of the NESARC studies found that the odds of having any specific PD are significantly higher for each psychiatric disorder examined. Results from wave 1 indicate that the prevalence of having at least one PD (excluding borderline, narcissistic and schizotypal) among respondents with any current mood disorder was 46.8%. Specifically, the prevalence of having any PD among respondents with major depression, dysthymia, mania and hypomania was 45.9, 54.8, 69.2 and 45.5%, respectively. The prevalence of having at least one PD among respondents with a current anxiety disorder was 41.8%, and for specific anxiety disorders, the prevalence among respondents with panic disorder with agoraphobia, panic without agoraphobia, social phobia, specific phobia and generalised anxiety disorder was 69.4, 44.1, 61.0, 38.3 and 60.65, respectively [125]. Obsessive–compulsive (21.6–38.3%), paranoid (16.1–36.1%) and avoidant (9.4–32.0%) PDs were the most prevalent among every mood and anxiety disorder examined, followed by schizoid (9.3–28.6%) and antisocial PDs (8.7–20.6%). Histrionic (7.2–17.4%) and dependent (0.8–13.5%) PDs were the least prevalent among respondents with mood and anxiety disorders [125]. For wave 2 of the NESARC, results show that the prevalences of narcissistic PD among respondents with mood, anxiety and substance use disorders were 17.4, 15.2 and 11.8%, respectively, and rates of NPD were greatest among specific disorders including bipolar I (31.1%), panic disorder with agoraphobia (23.9%) and drug dependence (34.9%). Among respondents with NPD, rates of 12-month mood, anxiety and substance use disorders were 40.6, 28.6 and 40.0%, respectively, with alcohol dependence (13.1%), bipolar I (14.1%) and PTSD (19.5%) most prevalent [66]. For BPD, the prevalence of BPD among respondents with mood, anxiety and substance use disorders were 29.4, 21.5 and 14.7%, respectively, and rates of BPD were greatest 425

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among individuals with 12-month bipolar I (50.1%), panic disorder with agoraphobia (51.0%) and drug dependence (45.8%). Among respondents with BPD, the rates of substance use, mood and anxiety disorders were 50.7, 50.9 and 59.6%, respectively, with specific disorders including alcohol dependence (18.0%), bipolar I (23.9%) and PTSD (31.6%) to be most prevalent [65]. Wave 2 also examined comorbidity of schizotypal PD with Axis I disorders and found that the prevalences of schizotypal PD among respondents with mood, anxiety and substance use disorders were 17.2, 13.7 and 8.2%, respectively, and rates of 12-month substance use, mood and anxiety disorders among respondents with lifetime schizotypal PD were 44.1, 44.4 and 56.6%, respectively [53]. For a detailed summary of prevalence estimates of the comorbidity between each Axis II disorder and Axis I disorders, refer to NESARC publications [53, 65, 66, 125]. When a PD is found to be highly comorbid with another disorder there are several possibilities to consider: (i) there may be some significant association between the two, such that one disorder is a risk factor for the other or both disorders share some underlying risk factor, vulnerability or pathophysiological process; (ii) the two diagnoses may describe only one disorder (e.g. avoidant PD and generalised social phobia might be the same disorder) or (iii) the diagnostic criteria for the disorders may include overlapping features that promote individuals with certain symptomatology to receive both diagnoses. The high comorbidity between PD and Axis I disorders have been demonstrated cross-culturally. One study of the World Health Organization World Mental Health (WMH) Surveys reported a high comorbidity between DSM-IV PD clusters and Axis I disorders in 13 countries [126]. The IPDE was used to assess PDs. Results also indicate that PDs are related to significant role impairments, after controlling for the effects of comorbid disorders. Other studies have consistently demonstrated that PDs adversely affect treatment compliance and outcome for Axis I disorders [127–129].

23.9.4.1 Personality disorders and phyical health Relatively little is known about the comorbidity between physical health problems and PD, although 426

recent research has begun to investigate this relationship. Researchers have demonstrated that individuals with PDs in general score lower on measures of health-related quality of life compared to control subjects [130] and are more likely to have a physical health condition [131]. More recent research highlights the relationship between physical illness and specific PDs, with the majority of the literature focusing on cardiovascular disease. For example, in a randomised sample of 8580 adults aged 16–74 years from Great Britain, Moran and colleagues [132] examined the prevalence of stroke and/or ischaemic heart disease among individuals with PDs. Avoidant, obsessive–compulsive and BPDs were significantly associated with stroke (OR = 4.0, 2.9 and 8.5, respectively) after controlling for potential confounders such as age, sex, social class (classified by occupational status), hypertension or diabetes, smoking history and alcoholism. Avoidant, paranoid, schizotypal, schizoid and BPDs were significantly associated with ischemic heart disease (OR = 2.2, 2.1, 3.6, 1.6, 7.2, respectively). In a dimensional analysis of PD traits, the authors reported a dose–response relationship in which the number of PD symptoms was associated with increased risk for heart disease. In a second study examining the association between cardiovascular disease and PDs, Pietrzak and colleagues [133] investigated the prevalence of coronary heart disease (CHD) in 10 573 individuals and assessed PDs using the NIAAA Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV (AUDADIS-IV). Researchers found that after adjusting for CHD risk factors, obsessive-compulsive, schizoid and avoidant PDs were significantly linked to CHD (OR = 1.37, 1.63, 1.80, respectively). Risk factors included those identified by Moran and colleagues in addition to race/ethnicity, education, marital status, morbid obesity and lifetime DSM-IV nicotine dependence, drug use disorder, mood disorder and anxiety disorder. The authors postulate that low social support, which is a robust feature of these three PDs, may account for their association with CHD, and previous research has found that low social support is a risk factor for CHD [134, 135]. Neither of these studies point to a relationship between APD and cardiovascular disease. However,

THE EPIDEMIOLOGY OF PERSONALITY DISORDERS: FINDINGS, METHODS AND CONCEPTS

in a sample of 43 093 adults from the NESARC, Goldstein and colleagues [136] found ASPD to be significantly associated with coronary heart disorder in addition to other physical concerns, such as total past-year medical conditions, gastrointestinal diseases, liver disease, arthritis and numbers of inpatient hospitalisations, inpatient days, emergency department visits and clinically significant injuries. The relationship between APD and alcohol and drug use disorders is particularly robust (OR = 4.8 and 11.8, respectively) [13], and the authors of this study controlled for such comorbid risk factors. Another study using data from NESARC examined the association between arthritis and PDs [137] and found that each of the seven PDs included in the analysis (avoidant, dependent, obsessive–compulsive, paranoid, schizoid, histrionic and antisocial) were significantly associated with arthritis, even after controlling for potential confounding variables. No other study has investigated this relationship, and more research is needed to understand the role of PDs in arthritis. Research also highlights a relationship between migraine headaches and PDs. For example, patients with BPD show an increased prevalence of severe headaches [138]. One study compared 50 patients with migraine headaches and BPD to 50 patients suffering from migraines with no history of BPD, matched for age, gender and headache frequency. Researchers reported significant differences between groups, with the BPD group demonstrating more pervasive headaches, greater disability due to headaches, higher self-reported depression, medication overuse, more unscheduled visits for headache treatment and greater treatment non-response. Another study evaluated whether personality traits influence the course of migraine treatment [139]. Since diagnostic categories were not used in this study, results cannot generalise to PDs. Examining personality dimensions, however, is often advantageous and is commonly used due to lower base rates of PDs and diagnostic complexities. Luconi and colleagues [139] administered the Multiphasic Personality Inventory-2 (MMPI-2) to 102 patients prior to treatment for chronic migraines and reassessed headache symptoms after 2 years. At follow-up, individuals who significantly improved on headache-related disability measures demonstrated

significantly lower scores on the MMPI-2 neurotic and schizophrenia scales compared to individuals who showed no improvement. Scores on all other factors included in the analysis, such as migraine severity and number of years from chronic migraine onset, did not differ between groups. Psychological factors, therefore, may play an important role in influencing the clinical course of migraine treatment. The mechanisms that account for the relationships between PDs and physical health concerns remain somewhat unclear. For example, PDs are highly comorbid with other disorders that directly lead to health problems, such as alcohol and drug use disorders [13]. Results from the NESARC indicate that approximately 29% of individuals with a current alcohol use disorder had at least one PD, and approximately 48% of individuals with a current drug use disorder had at least one PD [13]. While all of the studies described in this section adjusted for a wide range of comorbid risk factors, more research is needed to better understand the complexities and interactions among such mediators. Achieving a better understanding of the relationship between physical illness and PDs may lead to enhanced psychosocial and pharmacological interventions aimed at preventing or ameliorating physical illness.

23.10 Future directions In comparison to the major Axis I disorders, there is considerably less epidemiological information about the PDs, and a number of factors that have contributed to this state of affairs. Before DSM-III was published in 1980, there was less of a consensus about how to define the individual PDs (as well as disagreement about which disorders to include under the heading) than there was for the major Axis I disorders. The most ambitious studies of true prevalence, the ECA study and the NCS, included most of the important Axis I disorders, but only APD was included from Axis II. There is good evidence that PDs are strongly associated with use of mental health services, use of medical services [28], prognosis and responsiveness to treatment of Axis I disorders [140] and substantial impairment in functioning and subjective distress. There are important questions that remain unanswered 427

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about the true prevalence of most of these disorders, about the stability of these disorders, their natural course, as well as risk and protective factors for their development. Research on the aetiology of PDs and critical evaluations of the current categorical classification system has helped to guide the future of the DSM. While the DSM-III and -IV adopted a quantitative approach to diagnosis, the DSM-5 is likely to consider a more theoretical approach by incorporating dimensional models. Such models may better capture the nature of PDs, prove more reliable in assessing and diagnosing personality pathology, and provide more clinically meaningful information. However, there are many factors to consider before implementing such a major shift in the Axis II classification system, and therefore the field must proceed cautiously. The Personality and Personality Disorders Work Group has proposed a new system that utilises a dimensional model of personality functioning and traits in addition to preserving descriptions of disorder diagnoses as well as general personality prototypes. The development of this hyprid system is an exciting step, and much more additional research is needed and is currently being conducted before this system is adapted into the DSM-5.

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The epidemiology of depression and anxiety in children and adolescents Kathleen Ries Merikangas and Erin F. Nakamura Genetic Epidemiology Research Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA

24.1 Introduction In the past two decades, applications of epidemiological methods have flourished in the field of child mental health. There is now information on childhood psychopathology based on dimensional rating scales in numerous countries [1] and more than 50 international studies have published prevalence rates of mental disorders in children and adolescents using standardised diagnostic criteria [2, 3]. In addition to several new community surveys that provide information on the prevalence of Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) mood and anxiety disorders, several ongoing prospective studies are enhancing our understanding of mental disorders from childhood into adulthood and of childhood and adolescent risk factors for adult mental disorders. This chapter presents (i) a review of recent population-based studies of the prevalence of depression and anxiety disorders in children and adolescents based on DSM-IV criteria; (ii) a summary of the correlates and risk factors of mood and anxiety disorders in youth and (iii) an overview of service patterns and use for young people with mood and anxiety disorders from samples of the general population.

24.2 Magnitude of depression and anxiety in children and adolescents 24.2.1 Mood disorders Reviews of the magnitude of major depressive disorder (MDD) in community studies conducted prior to 2000 yield prevalence estimates ranging from 0.2 to 17%, depending upon the diagnostic criteria, method of assessment and characteristics of the study population [4]. Prevalence estimates of dysthymia among adolescents are generally lower than those of major depression. In contrast, prevalence estimates of sub-threshold depressive disorders and syndromes, including minor depression and depression not otherwise specified (NOS), are generally higher than those of major depression across all age groups [2, 5–7]. Rates of minor and subthreshold depression in youth have been estimated to be as high as 10% [3, 8]. Studies of mood and anxiety disorders in community samples of youth that have applied DSM-IV criteria are shown in Table 24.1. Several studies in the United Kingdom [8, 9], Taiwan [10], Mexico [11] and specific regions of the United States [2, 5] have applied DSM-IV criteria to estimate depression in

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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CHAPTER 24 Table 24.1

Community samples of children and adolescents using DSM-IV criteria.

Authors

Location

Angold et al. [5] Benjet et al. [11] Canino et al. [12] Costello et al. [2] Gau et al. [10]

North Carolina, USA Mexico City, Mexico Puerto Rico, USA North Carolina, USA Taipei, Taiwan

Green et al. [8] Meltzer et al. [9] Roberts et al. [13] Yang et al. [14]

United Kingdom United Kingdom Texas, USA Taipei, Taiwan

N

Age

Diagnostic Interviewa

Period

920 3005 1886 1015 1070 1051 1035 7977 10 438 4175 179

9–17 12–18 4–17 9–16 11 12 13 5–16 13–18 11–17 12–16

CAPA CIDI DISC CAPA KSADS KSADS KSADS DAWBA DAWBA DISC K-SADS

3 months 12 months 12 months 3 months 3 months 3 months 3 months Point Point 12 months Point

a

CAPA, Child and Adolescent Psychiatric Assessment; DISC, Diagnostic Interview Schedule for Children; K-SADS, Schedule for Affective Disorders & Schizophrenia for School-Aged Children; DAWBA, Developmental and Wellbeing Assessment. Table 24.2 Prevalence of DSM-IV depression in community samples of children and adolescents. Authors

Location

Angold et al. [5] Benjet et al. [11] Canino et al. [12] Costello et al. [2] Gau et al. [10] Green et al. [8] Meltzer et al. [9] Roberts et al. [13]

North Carolina, USA Mexico City, Mexico Puerto Rico, USA North Carolina, USA Taipei, Taiwan United Kingdom United Kingdom Houston, Texas, USA

children and adolescents. Table 24.2 presents the prevalence rates of depression derived from these studies. Rates of major depression range from 0.6% in Great Britain to 4.8% in Mexico, with a median of 2.2%. More similar rates emerged for dysthymic disorders, with a median of 0.5%. The median rate of any depressive disorder was 2.9%, with a range from 0.9 to 5.3%.

24.2.2 Anxiety disorders During the past decade, the results of international epidemiological surveys have revealed that anxiety disorders are the most prevalent class of mental disorders in adults throughout the world [15]. Similar to community studies of adults, anxiety disorders are also quite prevalent in the general population of children and adolescents. The median current (past year, or past 6-month) prevalence rate of all anxiety disorders in a recent review was 8% with 436

Major depression (%)

Dysthymia (%)

All depression (%)

1.0 4.8 3.6 0.4 4,4 0.6 – 1.7

0.3 0.5 0.6 0.3 0.6 – – 0.3

2.9 5.3 3.4 2.2 4.8 0.9 2.5 2.1

an extremely wide range of estimates (e.g. 2–24%). Table 24.3 presents the rates of anxiety disorders in recent community surveys of youth. Current or 12month rates of anxiety disorders range from 2.9% in North Carolina youth [16] to 29.8% in Mexico [11]. The variation in rates of anxiety is remarkable in light of the application of the same diagnostic criteria across studies. Specific Phobia and Social Anxiety Disorder are the most prevalent anxiety disorders in youth; whereas, panic disorder and obsessive–compulsive disorder (OCD) are both quite rare in children under 12. The results of prospective community-based research reveal differential peak periods of onset of specific subtypes of anxiety: separation anxiety and specific phobias in middle childhood; overanxious disorder in late childhood; social phobia in middle adolescence; panic disorder in late adolescence; and generalised anxiety disorder (GAD) and OCD in young adulthood [17].

THE EPIDEMIOLOGY OF DEPRESSION AND ANXIETY IN CHILDREN AND ADOLESCENTS Table 24.3 Prevalence rates of DSM-IV anxiety disorders in recent community surveys. Study

N

Age

Prevalence period

Panic

OCD

SAD

GAD

Agoraphobia

Social phobia

Specific phobia

Any anxiety

Angold et al. [5] Benjet et al. [11] Canino et al. [12] Costello et al. [2]

970 3005 1897 2002

Gau et al. [10]

1070

Green et al. [8] Roberts et al. [13]

7977 4175

9–17 12–19 4–17 9–12 13–16 11 12 13 5–16 11–17

3 months 12 months 12 months 3 months 3 months 3 months 3 months 3 months Point 12 months

0.3 1.6 0.7 0.1 0.3 0.2 0.2 0.0 0.2 0.7

0.2 – – 0.1 0.2 0.2 0.2 0.2 0.2 –

3.0 2.6 – 2.1 0.4 0.3 0.1 0.0 0.4 –

1.3 0.5 2.4 1.4 2.3 0.3 0.1 0.0 0.8 0.4

0.5 3.6 – 0.2 0.3 0.2 0.0 0.0 0.1 4.5

1.4 11.2 2.8 0.3 0.7 3.4 1.8 2.0 0.3 1.6

0.4 20.9 – 0.1 0.3 5.0 5.6 0.7 0.8 –

5.7 29.8 9.5 2.9 2.2 9.2 7.4 3.1 3.3 6.9

Comorbidity between anxiety disorders and other mental disorders is already apparent in childhood and adolescence. Anxiety disorders are associated with all other major disorders including mood disorders, disruptive behaviours, eating disorders and substance use disorders. The co-occurrence of anxiety disorders and mood disorders is so common that there is emerging evidence that suggests that anxiety disorders may be a part of the developmental sequence in which anxiety is expressed early in life followed by depression in adulthood [18]. There is a dramatic contrast between the relatively low prevalence rates derived from cross-sectional studies and the rates that emerge from prospective studies, which follow community samples of children

into early adulthood. Patton [19] recently showed that estimates of depression obtained from crosssectional data are dramatically lower than estimates from prospective research. Table 24.4 presents the rates of depression and anxiety from prospective studies of child and adolescent samples up to 30 years of follow-up. Rates of depression from these studies are strikingly high, with lifetime estimates ranging from 17.9% in New Zealand [20] to 43.3% in Oregon [21]. Rates of anxiety disorders range from 15.9% in Oregon to 35% in Munich, Germany [22]. In general, prospective studies have shown the onset of anxiety disorders as preceding that of mood disorders [23], with some exceptions such as GAD and major depression in the Dunedin study [24].

Table 24.4 Lifetime prevalence of major depression and anxiety from follow-up studies of community surveys of mental disorders in youth. Author (year)

Location

Wave

N

Age

Interview

Rates of major depression (%)

Rates of anxiety disorders (%)

Cohen et al. [25] and Chen et al. [26] Fergusson and Horwood [27] Kim-Cohen et al. [20] Lewinsohn et al. [28] Reinherz et al. [29] Wittchen et al. [30]

New York State, USA

6

593

27

DISC

22.3

20.7

Christchurch, New Zealand

4

1006

21

CIDI

21.6

18.2

Dunedin, New Zealand Oregon, USA

6

976

26

DIS

17.9

26.1

4

816

28

LIFE

43.3

15.9

Massachusetts, USA Munich, Germany

3 4

354 3021

18–26 14–24

DIS CIDI

23.2 19.5

– 35.0

DISC, Diagnostic Interview Schedule for Children; CIDI, Composite International Diagnostic Interview; DIS, Diagnostic Interview Schedule; LIFE, Longitudinal Interview for Epidemiology.

437

CHAPTER 24

24.3 Correlates and risk factors Risk factors for the development of mental disorders in children have been divided into those that characterise or affect the individual child, the child’sparents or family or both and the broader environment. Child characteristics include sex, age, ethnicity, physical health, cognitive and psychological functioning, preand perinatal exposures to illness, physical stress, substance use, infections and other environmental agents and stressful life events. Family and parent characteristics include parental education, age, social class, employment, psychiatric and medical history and family function and structure [27, 31–33], and broader contextual influences such as geographic region, cohort or neighbourhood can influence the health of children and their families [34].

24.3.1 Child characteristics 24.3.1.1 Age and sex 24.3.1.1.1 Mood disorders Retrospective studies of adults with depression suggest that first onset is most likely to occur between mid-adolescence and young adulthood [35–37]. In the only nationally representative general population sample to examine age of onset, approximately 25% of adults with major depression or dysthymia reported onset prior to young adulthood, and almost 50% by age 30 (see Figure 24.1). Longitudinal studies of treatment and community samples of children and adolescents suggest an earlier average age of onset between 11 and 14 years for major depression and dysthymia [38, 39]. Additionally, prospective studies that follow the same children over time reveal a dramatic increase in the prevalence of major depressive episodes after age 11 and again after age 15, and in young adulthood (ages 21–26) [20, 40, 41]. It has been well documented in national epidemiological studies that the prevalence of depression is nearly twice as high among adult females as rates among adult males [36]. In pre-adolescents, community studies report either no sex differences in depression [18, 43–45], or somewhat higher prevalence among preadolescent boys than girls [46–48]. During adolescence and young adulthood, however, 438

Age at Onset of Mood Disorders in U.S. General Population (N = 9282) Major Depresssion

25th %ile 50th %ile 75th %ile 99th %ile

Dysthymia 0

20

40 Age (in years)

60

80 Kessler et al, [42]

Fig 24.1 Age at onset of mood disorders in US general population from National Comorbidity Survey-Replication (NCS-R).

depression is more common among females than males [2, 25, 30, 39, 49–52]. The preponderance of depression in females emerges around the age of 13 [40, 53]. In a longitudinal follow-up of a large birth cohort, the change in the sex ratio was attributable to an increased incidence of depression among females after age 11 and between 15 and 18 [54]. Although depression increases among males and females during the middle adolescent years, incidence among females is far greater than among males [39, 54]. While sex differences in depression are evident for both major depression and dysthymia, findings on sex differences in minor depression [6, 49], recurrent brief depression [55] and depressive symptoms [56] are mixed across studies. There do not appear to be sex differences in the average age of onset of MDE [28]. 24.3.1.1.2 Anxiety disorders Retrospective reports of adults with anxiety disorders suggest that the first onset generally occurs in childhood or adolescence. Although there is substantial variation across studies, the results of prospective community-based research reveal differential peak periods for onset of specific subtypes of anxiety: separation anxiety and specific phobias in middle childhood (i.e. ages 7–9); overanxious disorder in late childhood (i.e. 10–13); social phobia in middle adolescence (i.e. 15–16) and panic disorder in late adolescence (i.e. 17–18) [25, 57–60]. Data from prospective studies reveal a sharp increase in rates of anxiety among girls beginning as

THE EPIDEMIOLOGY OF DEPRESSION AND ANXIETY IN CHILDREN AND ADOLESCENTS

Median Age at Onset of Anziety Disorders in U.S. General Population (N = 9282) Any Anxiety SAD OCD _ P SD GAD

25th Percentile

Social Phobia

50th Percentile

Specific Phobia

75th Percentile

Agoraphobia

99th Percentile

Panic Disorder 0

20

40

60

80

AGE (in years) Fig 24.2 Median age of onset of anxiety disorders in the National Comorbidity Survey Replication [42].

early as age 5 and increasing through adolescence. Although rates of anxiety among males also increase throughout childhood and adolescence, the trend is far more gradual than that of females, and begins to level off in late adolescence. Thus, by age 6, females have significantly greater rates of anxiety than males regardless of impairment [13, 61]. Despite the far more rapid increase in anxiety disorders with age in girls than in boys, there are no sex differences in the mean age at onset of anxiety disorders or in their duration [30]. Anxiety disorders, particularly the phobias, tend to persist across the life course. However, there are major differences among the anxiety subtypes in terms of specificity and chronicity. Whereas the phobic states tend to be fairly stable and non-progressive, GAD and panic disorder tend to be less specific and less stable over time [59, 62, 63]. Several follow-up studies of children and adolescents have shown that, in general, anxiety symptoms and disorders exhibit some stability, but with substantial switching across categories of anxiety disorders over time [64]. A recent 8-year follow-up study of a community sample of youth ages 9–18 at entry provides compelling evidence for the stability of specific and social phobias and yields some

interesting sex differences in the stability of anxiety over time [65].

24.3.1.2 Social class, race and ethnicity 24.3.1.2.1 Mood disorders While studies of adults suggest that depression is associated with lower social class [66], findings from samples of children and adolescents are less consistent. Whereas some studies report a lack of association between depressive disorders and social class [2, 47, 52], others report a significant association, especially for the most impoverished groups [67–70]. A large meta-analysis of 310 samples of children who completed the Children’s Depression Inventory (CDI) found no association between depressive symptoms and social class [71]. Because of differing measures, samples and reporting techniques, difficulties arise in attempting to compare depressive symptoms across studies of racial and ethnic groups. The small sample size of ethnic minority youth in most community studies of children and adolescents diminishes the statistical power to test differences in the prevalence of mood and anxiety disorders between specific ethnic subgroups. The few studies that have compared racial or ethnic groups yielded no differences in prevalence of 439

CHAPTER 24

depressive disorders between Caucasian and AfricanAmerican [5, 47] or Native American youth [72]. One study did find a lower rate of any depressive disorder in African-American adolescents when compared to white adolescents [5]. A more recent study found mood disorders without impairment were slightly lower in African-American compared to both Mexican American and European American youth, but when impairment was factored in, the rates for European American youth became significantly higher than African-American and Mexican American youth. In this study, European American youth were 2.7 times more likely to have a mood disorder with impairment than African-American youth [13]. Other recent studies have found that young African-Americans have lower rates of depression than either whites [5] or Latinos [73, 74]. Even though African-American and Latino youth seem to have lower rates of depressive disorder, there is some evidence that they may have more depressive symptoms. For example, two studies found AfricanAmerican males had higher rates of depressive symptoms than white males [75, 76], and other studies have shown an increase in the number of depressive symptoms among Hispanic youth compared to their white and African-American counterparts [77, 78], particularly among Mexican Americans [71, 73, 74, 77, 78]. 24.3.1.2.2 Anxiety disorders There are very few consistent differences in the distribution of anxiety disorders by ethnicity and social class [61]. Low socioeconomic status has been associated with anxiety in several studies [9]; one study found that anxiety disorders without impairment were significantly higher for AfricanAmerican and Mexican American youth than for European American youth. When impairment was factored in, Mexican Americans were highest, but the difference was not significant [13]. Several community studies have yielded greater rates of anxiety disorders, particularly phobic disorders, among African-Americans [79]. With respect to children, Compton et al. [60] found that white children were more likely to report symptoms of social phobia, whereas African-American children had more separation anxiety symptoms. 440

24.3.2 Other risk factors Most studies on risk factors for childhood mental disorders have focused on their general effects rather than their association with specific disorders [80]. However, there is emerging evidence that there may be links between some exposures and specific disorders [33, 80]. Childhood risk factors for mood and anxiety disorders have been investigated in several studies [33].

24.3.2.1 Early developmental factors Low birth weight and developmental delay have been associated with an increased risk of several mental disorders, particularly depression. A 50-year followup of the 1946 birth cohort in the United Kingdom revealed that after controlling for life events and social circumstances, birthweight and age of achievement of developmental milestones were associated with the subsequent development of mood and anxiety symptoms across the lifespan [81]. Costello et al. [82] recently showed that low birthweight was associated with depressive symptoms in girls, but only in combination with other adversities.

24.3.2.2 Physical conditions and illness Several studies have also suggested that there is an association between childhood medical conditions and subsequent development of anxiety and mood disorders. In a retrospective review of pre- and perinatal and early childhood risk factors for different forms of psychiatric disorders in adolescence and early adulthood, Allen et al. [83] found that anxiety disorders in adolescents were associated specifically with illness during the first year of life, particularly high fever [83]. He also found that adolescents and young adults with anxiety disorders were more likely to have suffered from infections during early childhood [84]. Several studies have shown that there is an association between anxiety disorders, particularly panic disorder [85], allergic conditions and asthma [86–88]. Immunological diseases and infections appear to be specifically associated with emotional disorders, since there is no increase in these conditions among those with behavioural disorders [89]. In the New York Follow-Up Study,

THE EPIDEMIOLOGY OF DEPRESSION AND ANXIETY IN CHILDREN AND ADOLESCENTS

Cohen et al. [90] found that immunological illnesses in adolescents were more strongly associated with depression than with anxiety disorders, and that the link was bidirectional [90]. Subsequent follow-up of the same sample showed that the associations between depression with pain and physical illness could be attributed to comorbid personality disorders [26]. Taken together, these findings suggest that future studies should examine the possible role of the immune system in anxiety and mood states. Gastrointestinal disorders have been associated with childhood anxiety [91]. Migraine has also been shown to be strongly associated with anxiety disorders in childhood and subsequently with depression in adolescence and adulthood [92, 93]. In a prospective longitudinal study, Waldie and Poulton [94] found that migraine was related to anxiety symptoms in childhood anxiety disorders in adolescence and young adulthood, and stress reactivity personality trait at the age of 18. The results of both family studies and prospective cohort studies suggest that there may be a subtype of migraine with shared liability for anxiety and depression [92]. Children with diabetes have been shown to have increased rates of depression, and recent evidence suggests that depression is associated with the subsequent development of diabetes [95]. A similar bidirectional association has been found in prospective research on depressive symptoms and diabetes in a cohort study in Finland [96]. The results of these studies all suggest that the roots of both mental and physical disorders may already be evident in childhood and adolescence, thereby highlighting the importance of prevention and intervention in these conditions.

24.3.2.3 Exposure to stressful life events Exposure to stressful life events is one of the most widely studied risk factors for depressive symptoms and disorder. Numerous studies of clinical and community samples of children and adolescents have reported a significant relationship between stressful life events and depression [47, 97] (e.g. [67, 98]). The major events associated with depression in both retrospective and prospective studies include early separation from a parent by death or divorce, serious illnesses and sexual and physical abuse [99], [66]. It

appears, however, that adverse life events comprise non-specific risk factors for depression. However, the prospective study of Lewinsohn et al. [100] found that daily hassles, major life events and low social support did not distinguish those with depression from those with other disorders. The role of life experiences has also been examined in the aetiology of anxiety states, particularly in phobias and panic disorder [101–104]. Conversely, while it is likely that life stress may exacerbate phobic and generalised anxiety states, Marks [105] concludes that phobic states resulting from exposure are far more rare than those that emerge with no apparent exposure. In contrast, post-traumatic stress disorder is defined as a sequela of a catastrophic life event. The major impediment to evaluation of the causal role of life events in anxiety (or depression), is the retrospective nature of most research addressing this issue. For example, Lteif and Mavissakalian [106] found that patients with panic disorder or agoraphobia exhibited an increased tendency to report life events in general; this suggests that studies that limit assessment of life events to those preceding onset of a disorder may be misleading because they fail to provide comparison for the time period of onset. Moreover, stressful life events may precipitate episodes of panic by interacting with other risk factors such as a family history of depression [107].

24.3.3 Parental and familial factors Family structure, particularly the lack of an intact home, has been shown to be specifically associated with the development of depression in childhood and adolescence. Children in single parent homes are more likely to develop depression than those in two parent homes [80], as are younger children in large sibships [29]. Results of a recent population survey of child mental health in Germany, the Befragung Seelisches Wohlbefinden und Verhalten (BELLA) study, found that multiple parental factors including family conflicts, households with single or step-parents, parental strain, parental psychiatric problems and both physical and mental symptoms in parents were associated with mental health problems in children [108]. However, this study did not 441

CHAPTER 24

investigate specific links between anxiety or mood disorders and familial risk factors. A parental history of mental disorders is one of the most consistent and potent risk factors for the development of mental disorders in children. There is substantial evidence for specificity of familial aggregation of the broad classes of mental disorders [109]. Follow-up studies of child cohorts have consistently shown a specific link between parental mental disorders and childhood mental disorders [24, 29]. Studies of the mechanisms that link parent and child psychopathology are beginning to emerge. For example, parental mental disorders, unemployment and poverty are related to exposures to stress in early childhood and to child neglect [8]. Likewise, parental depression is associated with marital discord and quality of the family environment, and is in turn related to depression in their children [33].

24.3.3.1 Combinations of risk factors Very few of these individual or familial factors operate alone in increasing the risk of mood and anxiety disorders. Inspection of multiple risk factors for childhood mental health problems in general revealed a direct association between the number of individual and familial risk factors and mental health problems in children and adolescents [108]. Different profiles of individual-level and familial-level risk factors have been found for comorbid and non-comorbid major depression and GAD [24]. Whereas maternal internalising symptoms, childhood socioeconomic status and poor childhood self-esteem conveyed risk for both GAD and major depression alone and together. Family history of depression and anxiety were specifically associated with these disorders in youth [24]. Parental history of mood or anxiety disorders or both has also been shown to moderate the impact of stressful life events on elevating the risk of depression in youth. Zimmermann et al. [110] showed that life stressors interacted with a parental history of depression to increase the risk of depression in adolescents and young adults. The results of a recent three-year follow-up of the large UK study of child health found that different risk factors predicted the onset and the persistence of mental and behaviour disorders in youth [111]. Aside from the well-established demographic 442

characteristics of youth, onset of emotional disorders was predicted by physical illness, changes in the number of parents in the home, the number of children at home and poor maternal mental health. Accumulation of stressful life events predicted the onset of mood and anxiety disorders across the 3-year follow-up period. Persistence of mental disorders in general was predicted by maternal mental health, low socioeconomic status and rented accommodations. This study also investigated strengths of the child, such as self esteem, that tend to protect against mental disorders [111]. Although the report did not describe the relationships of these risk factors, it is apparent that the social context of the child, particularly a lack of stability of the home environment, has substantial influence on both the onset and persistence of mental disorders. In summary, there is accumulating evidence from prospective research that both individual and contextual factors in childhood and adolescence elevate the risk of mood and anxiety disorders in children. Though there is some specificity in the risk of parental depression and anxiety on the development of these conditions in children, many other familial, parental and individual childhood risk factors elevate the risk of childhood psychopathology.

24.4 Service patterns and impact There has been tremendous progress in public awareness of depression and anxiety disorders in recent years and evidence suggests that those with severe disorders do indeed receive services [112]. Several studies of community samples in the United States have shown that more than half of those who suffer from major depression receive mental health services [113]. However, fewer among those who suffer from anxiety disorders receive mental health services [5, 12, 49, 57, 114–116]. Factors associated with service utilisation include ethnicity, high global impairment, comorbidity, prior history of depression, suicide attempts and impact of the child’s problem on the family [5, 12, 27, 30, 57, 117]; by contrast, the most important determinants of under-treatment of children with mood and anxiety disorders are low maternal education and a lack of health insurance [112]. This indicates that the low

THE EPIDEMIOLOGY OF DEPRESSION AND ANXIETY IN CHILDREN AND ADOLESCENTS

treatment rates for children with anxiety may be due in part to a lack of recognition of the impairment caused by anxiety disorders in children’s lives. Studies of children in developing countries show an alarming gap in services for children with mental disorders. For example, in the Mexico City study of adolescents, only 8% of those with a 12month anxiety disorder, and 12% of those with a 12-month mood disorder received mental health treatment [118]. Similar to US studies, service use in Mexican youth was strongly associated with parental education, suggesting one possible source of future intervention. Regarding the impact of childhood mood and anxiety disorders, several studies have shown that both mental and physical comorbidity are associated with poorer functional outcomes among children with anxiety disorders [119]. In contrast to adult mental disorders, the economic impact of childhood mental disorders has not been widely studied. Costs associated with childhood mental disorders include medical expenses, special education needs and burden to the criminal justice system and social services. Many studies that report on the cost of child mental disorders focus only on direct medical costs and do not consider the indirect costs to society. However, the results of prospective studies that demonstrate the continuity of mood and anxiety disorders from childhood to adulthood are beginning to raise awareness about child mental disorders and the need to institute preventive measures [120, 121].

24.5 Summary This chapter provides an update on the epidemiology of mood and anxiety disorders in children and adolescents. Rates of depressive disorders ranged from 0.6% in Great Britain to 5.3% in Mexico, with a median of 2.2%. Current or 12-month rates of anxiety disorders range from 2.2% in North Carolina to 29.8% in Mexico. Social anxiety disorder is the most prevalent anxiety disorder with lifetime prevalence as high as 11.2%. In contrast, panic disorder and OCD are both quite rare in children under 12. Mood and anxiety disorders are more common in females, with the sex difference in depression emerging in early adolescence. There is accumulating evidence

from prospective research that both individual and contextual factors in both childhood and adolescence elevate the risk of mood and anxiety disorders in children. Whereas there is some specificity in the risk of parental depression and anxiety on the development of these conditions in children, many other familial, parental and individual childhood risk factors elevate the risk of childhood psychopathology in general. The most consistent childhood correlates of anxiety and depression are early infections and illnesses, parental anxiety and/or depression, nonintact household and life stressors. Over the past decade, there has been a substantial increase in the awareness of mood and anxiety disorders; as well as greater proportions of youth with these conditions receiving mental health services. Treatment rates are far lower among children and adolescents with anxiety disorders than those with depression. The results of prospective studies demonstrate the continuity of mood and anxiety disorders from childhood to adulthood highlighting the need to institute preventive measures. A recent report by the Institute of Medicine [122] provides a comprehensive summary of the science of prevention and makes recommendations for preventive strategies to diminish the impact of mental health problems in youth.

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25

Epidemiology of attention deficit hyperactivity disorder Stephen V. Faraone Departments of Psychiatry and of Neuroscience and Physiology, SUNY Upstate Medical University, NY, USA

25.1 Introduction Attention deficit hyperactivity disorder (ADHD) is a disorder of inattention, impulsivity and hyperactivity. ADHD, in the diagnostic guise of minimal brain dysfunction and related terms, was the first psychiatric disorder to be diagnosed and treated in children, with studies of stimulant treatment dating back to 1937 and regulatory approval of stimulant treatment for children beginning in the 1960s. Despite this long history and a robust research literature, during the twentieth century divergent opinions about ADHD fuelled public controversy, clinical uncertainty and scientific debate. This chapter places controversies about ADHD in the context of this scientific literature, highlighting new developments and pointing to pathways of discovery that may lead to better treatments for patients suffering from this disorder. Unlike most other psychiatric disorders, there are important differences between the American diagnostic criteria for ADHD as defined by the Diagnostic and Statistical Manual of the American Psychiatric Association (fourth edition; DSM-IV) and the European diagnostic criteria for hyperkinetic disorder (HKD) as defined by the International Classification of Diseases, 10th edition (ICD-10). Both ADHD and HKD include children displaying developmentally inappropriate levels of inattention, hyperactivity, impulsivity that onset in childhood and cause impairment. But HKD criteria are more restrictive than DSM-IV criteria because, as Table 25.1 describes,

they require more symptoms and all these symptoms must be present in more than one setting (e.g. home and school). Systematic field trials show that the diagnosis of ADHD can be made with high reliability [1]. Unlike the ICD-10, DSM-IV allows for three symptom-based subtypes of ADHD: primarily inattentive, primarily hyperactive-impulsive and combined. Evidence for the validity and clinical utility of these subtypes is mixed (e.g. [2, 3]) and current work has not resolved the ongoing controversy about whether there exists a purely inattentive disorder that may be aetiologically different from either ADHD or HKD [4]. ADHD is associated with a wide range of impairments [5]. Children with ADHD show impairments in academic performance [6–8] and social skills (e.g. [9]). Their inattention means they cannot focus on a single task for any length of time, their impulsivity disrupts school work and their hyperactivity makes them fidget and talk excessively. They suffer from low self esteem and poor peer relations. The impact of ADHD on the family can be devastating. Parents of children with ADHD experience increased stress, self-blame, social isolation, depression and marital discord [10]. ADHD affects parenting efficacy – the extent to which a parent feels competent in the parenting role and capable of handling child problems. ADHD also affects parents’ work. In a survey of 154 caregivers, Noe et al. [11] found that 61% changed their work status following

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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CHAPTER 25 Difference between American and European concepts of ADHD/HKD.

Symptoms required

Pervasiveness

DSM-IV ADHD

ICD-10 HKD

Either or both of the following are required: At least six of nine inattentive symptoms At least six of nine hyperactive or impulsive symptoms Some impairment from symptoms is present in more than one settings

All of the following are required: At least six of eight inattentive symptoms At least one of four impulsive symptoms At least three of five hyperactive symptoms The criteria are met for more than one setting

the diagnosis of their child’s ADHD. Of these, 10% stopped work and 44% reduced their working hours. A further 14% changed the type of job they did because of their child’s ADHD and 21% altered their work schedule. Problems continue as ADHD children grow into their teenage years. Adolescents with ADHD have a high rate of delinquency [12] and the presence of ADHD exacerbates problems among teenagers with a diagnosis of conduct disorder. In a study of 42 incarcerated youths, Forehand et al. found that those with ADHD and conduct disorder were arrested at a younger age and had more total arrests than another group who had conduct disorder without ADHD [13]. ADHD is associated with initiating sexual activity earlier (1 year earlier on average) and engaging in riskier sexual activity (more partners, less use of contraception) [14, 15]. Teenage pregnancy and sexually transmitted diseases are also more common in adolescents with ADHD [14, 15]. Several studies found a high rate of driving incidents among adolescents and adults with ADHD. For example, Barkley et al. [16] compared the driving ability of 25 young adults with ADHD aged 17–30 years with a matched control group of 23 adults without ADHD. Those with ADHD were found to speed more often, were more likely to have their licenses suspended, were involved in more crashes and were more likely to have had crashes resulting in injury than those without the disorder. It appears that ADHD increases driving risks not through lack of driving knowledge, but by affecting motor control, hence driving performance. In addition to injuries caused by traffic accidents, ADHD greatly increases the risk of multiple injuries [17]. In that study, children with ADHD were more likely to sustain severe injuries than those without ADHD. 450

25.2 Prevalence of ADHD DSM-IV ADHD is more prevalent than ICD HKD, a finding which has sometimes been misinterpreted to mean that ADHD is more common in America compared with countries using ICD criteria. That controversy was resolved by a recent review of 50 epidemiological studies which found the prevalence of DSM defined ADHD to be similar around the world [18]. Figure 25.1 shows the results from studies using DSM-III, III-R or IV criteria stratified by whether the study was performed on an American or non-American population. As the figure shows, there is no systematic difference between the American and non-American studies. For the American studies, there is a trend for increased prevalence when comparing the DSM-III and DSM-IV criteria. This trend is evident for the non-American studies when considering the higher but not the lower end of the range. Perhaps the most notable feature of Figure 25.1 is the wide range of prevalence estimates. This

Prevalence of ADHD (%)

Table 25.1

25 20

19.8 16.1

15 10 5

12 9.1

11.2 5.8

12.8

10.9 3.9

0 US Other DSM-III

11.4

7.1

US Other DSM-III-R

2.4 US Other DSM-IV

Fig 25.1 Range of prevalence estimates for ADHD from American and non-American studies using different diagnostic criteria. Diagnostic and Statistical Manual of Mental Disorders, American Psychiatric Association. Based on Faraone et al. [18].

EPIDEMIOLOGY OF ATTENTION DEFICIT HYPERACTIVITY DISORDER

variability can be accounted for by several factors. Some researchers omitted the DSM requirement for symptoms to be present in at least two settings [19], while others omitted the requirement for functional impairment resulting from the symptoms [20, 21]. Diagnosing ADHD in the absence of these criteria over-estimates the prevalence of the disorder. Epidemiological studies show that failing to incorporate functional impairment into the diagnosis of ADHD overestimates its prevalence. In an American sample, Wolraich et al. [22] found the prevalence of ADHD to be 16.1% when based on symptoms alone and 6.8% when functional impairment was required. Similar results were reported from Australian [23] and German [24] studies. Other factors which affect the diagnosis of ADHD in different studies are: the informants used to assess symptoms (parents and/or teachers and/or subjects) and whether the diagnosis is based on scores on behaviour checklists, direct interviews or both. For example, Shekim et al. [25] reported the prevalence of ADHD, as determined from interviews with subjects, interviews with parents and the rate of agreement between the two methods. When the assessment was based on the child’s reports, the prevalence was 4%. When the diagnosis required positive reports from both parent and child, the prevalence was 2%. Further variations in the apparent prevalence rate arise from differences among populations. Community samples yield higher prevalence rates than school samples (mean prevalence: 10.3% for community samples vs. 6.9% for school samples) [26] and the prevalence of the disorder decreases with age and with female gender.

25.3 Pharmacoeconomics of ADHD Leibson et al. compared the use of medical care by 300 children and adolescents with ADHD with an equivalent group of 3810 non-ADHD individuals [27]. They found that ADHD increased the need for both outpatient visits (41% for ADHD children and adolescents compared with 33% for those without ADHD) and inpatient stays (26% versus 18%). The 9-year median medical costs for the children with ADHD far outweighed those without it ($4306 per patient for those with ADHD compared with

$1944 for the non-ADHD group). Chan et al. [28] concluded that children with ADHD incurred total mean healthcare costs ($1151; n = 165) comparable to children with asthma ($1091; p < 0.05; n = 322) but significantly greater than for children with neither diagnosis ($712; p < 0.001; n = 4952). These findings are in agreement with those of Guevara et al. [29] who showed that children with ADHD (n = 2992) incurred significantly greater per capita costs than those without ADHD (n = 11 968). Over the past decades, prescriptions of the stimulant medications used to treat ADHD have increased in the United States [30, 31], Israel [32], Canada [33] and Europe [34]. Although the use of stimulants has increased, these data should not be equated with over-treatment. For example, Zito et al.’s [30] study of preschoolers in the United States found that the prevalence of stimulant treatment tripled from 1991 to 1995, but in 1995 it was only 1%. In another US study, the rate of treatment for ADHD increased from 0.9% children in 1987 to 3.4% in 1997 [31]. In a German study, the treated prevalence of ADHD increased from 0.6 to 1.4% from 2000 to 2001 [34]. Similar results were reported from Israel [32], Canada [33] and Australia [35]. Although all these studies showed increases, in all studies, the treated-prevalence remained far below the population prevalence of ADHD. Community studies of this issue find some evidence for both inappropriate treatment and failure to treat. In Canada, Szatmari et al. [36] reported that 1% of children who did not meet criteria for ADHD had been treated with stimulants. This figure was 5% in an American study [37]. Evidence for failure to treat comes from community studies which show the prevalence of pharmacological treatment of ADHD youth to range from 5.3 to 72% [36–40].

25.4 Comorbid psychiatric disorders Over the past two decades, studies of both clinical and population samples have documented high rates of comorbidity between ADHD and other psychiatric disorders [41–43], as well as in clinical samples [44]. Conduct disorder, a precursor to antisocial personality disorder and drug use in adulthood, is 451

CHAPTER 25

seen in 30–50% of ADHD youth. Nearly all ADHD children with conduct disorder also have oppositional defiant disorder and an additional 10–30% of ADHD youth without conduct disorder will have oppositional defiant disorder. Mood disorders are also commonly observed among ADHD youth, with about 30% meeting criteria for a major depressive disorder and 5–15% meeting criteria for bipolar disorder. Although the diagnosis of bipolar disorder in youth has been a source of controversy, data from genetic, imaging and treatment studies suggest that the diagnosis can be validly made in youth and that the disorder frequently co-occurs with ADHD, especially when the onset of bipolar symptoms occurs before adolescence [45–49]. A comorbid association of approximately 25% between ADHD and anxiety disorders has been found in epidemiologic [42, 43] and clinical samples of children with anxiety disorders [50] and children with ADHD [44, 51, 52]. The prevalence of anxiety disorders in ADHD is typically larger in studies of ADHD adults than it is in studies of ADHD youth [53]. ADHD is also associated with substance use disorders (SUDs) [54, 55]. Prospective studies of ADHD children show that about one-third are at high risk to develop SUDs [56–58]. For example, in 5–8 year follow up studies, more alcohol use was shown among hyperactive and largely conduct disordered ADHD adolescents compared to non-ADHD controls [59]. The association between ADHD and SUDs has also been confirmed in longitudinal studies of SUDs [60, 61]. Biederman et al. [62] evaluated the developmental pathways of SUD in adults with ADHD from two perspectives. Consistent with prior reports, ADHD adults exhibited a twofold increased risk for SUDs. Among adult ADHD patients with alcohol use disorders, ADHD increased the risk for subsequent drug abuse or dependence. Also, compared with non-ADHD controls, ADHD patients were at increased risk for a chronic course of SUD. The effect of ADHD on these developmental pathways could not be accounted for by psychiatric comorbidity. Thus, in addition to

452

predicting SUD, ADHD also predicts developmental sequences among different types of SUD. Comorbidity raises questions as to whether psychiatric disorders are discrete and independent disease entities. Subgroups of patients with ADHD and comorbid disorders may represent more homogeneous subgroups within ADHD, which could be useful for research purposes. It is also possible that patients with ADHD and comorbid disorders may respond differentially to specific therapeutic approaches.

25.5 Demographic risk factors 25.5.1 Gender The male to female sex ratio for ADHD is greater in clinical compared with community studies, which indicates that females with ADHD are less likely to be referred for services. This differential in referral rates by gender is most likely due to the less disruptive nature of ADHD in females compared with males [63] and the greater population prevalence among males may be due to their greater exposure to environmental sources of aetiology such as head injury [64].

25.5.2 Age For many years, the idea that ADHD persisted into adulthood was met with skepticism. But, as reviewed by Faraone et al. [65], follow-up studies of ADHD confirm that the disorder persists into adulthood in many cases. There is, however, an age dependent decline in ADHD symptoms, but even when the symptoms of the disorder go below diagnostic threshold, they are frequently associated with clinically significant impairments. As a result, by mid-adulthood, the large majority of ADHD children no longer meet full threshold criteria for ADHD but about half will show continued impairing symptoms of the disorder consistent with the DSM-IV diagnosis of ADHD in partial remission [65]. Despite the partial remission of symptoms in adulthood, prospective

EPIDEMIOLOGY OF ATTENTION DEFICIT HYPERACTIVITY DISORDER

follow-up studies show that, when ADHD children reach adolescence and adulthood, they are at high risk for the impairments described above [15, 66].

25.5.3 Social class Low social class has been examined as a risk factor for many psychiatric disorders because it is a proxy for many adverse exposures such as poor nutrition, poor health care and chaotic living circumstances. It is, however, exceedingly difficult to determine if an apparent link between a disorder and low social class implicates low social class as a risk factor for the disorder or the disorder as a risk factor for low social class. This interpretative problem makes it impossible to interpret the many studies which show an association between ADHD and low social class (e.g. [67, 68]).

25.5.4 Ethnicity In a community setting, Bussing et al. [69] found no differences in the prevalence of ADHD symptoms between Caucasian and African-American children when symptom ratings were made by parents. By contrast, teachers systematically gave higher ADHD scores for African-American compared with Caucasian children. Dong Hun et al. [70] also found no differences in mean ADHD symptoms (as reported by either parents or patients) between African-American and Caucasian children. Epstein et al. [71] found that teachers rated AfricanAmerican children higher on ADHD symptoms compared with Caucasian children. Schneider and Eisenberg [72] studied a nationally representative sample of 9278 third-grade children. They did not assess these children for ADHD. Instead, they recorded if the children had been diagnosed as ADHD by a health professional. They found that African-American and Hispanic children were less likely to have the diagnosis compared with Caucasian children, even after controlling for other sociodemographic characteristics. This lower rate of diagnosis likely reflects sociocultural differences in referral and treatment for ADHD rather than ethnic

differences in the prevalence of the disorder. Support for this interpretation is found in the work of Bussing et al. [73]. They found that, compared with parents of Caucasian children, fewer African-American parents reported that they had ever heard of ADHD or that they knew some or a lot about it. Also notable in this regard, Samuel et al. [74] concluded that in African-American youth, ADHD may show a narrower pattern of psychiatric comorbidity and dysfunction than has been observed in Caucasian ADHD youth. This less complicated clinical picture could partly explain ethnic differences in referral rate and treatment.

25.6 Genetic risk factors 25.6.1 Family, twin and adoption studies Converging evidence from family, twin, adoption and molecular genetic studies shows there is a robust genetic component to the aetiology of ADHD, regardless of which diagnostic system is used [75]. Figure 25.2 presents heritability data from twin studies of ADHD or quantitative measures of ADHD symptoms from Australia, Sweden, the United Kingdom and many sites in the United States. These data estimate the heritability of ADHD to be 0.76 and show that genes play a substantial role in the aetiology of ADHD. Consistent with these twin studies, the adoptive relatives of ADHD children are less likely to have ADHD or associated disorders than are the biological relatives of these children [76, 77]. Another study found rates of ADHD to be greater among biological relatives of non-adopted ADHD children than adoptive relatives of adopted ADHD children [78]. The adoptive relatives had a risk for ADHD similar to the risk in relatives of control children.

25.6.2 Molecular genetic studies Three molecular genetic methods have been used to search for ADHD susceptibility genes:

453

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Boomsma 2003 Coolidge 2000 Edelbrock 1992 Gillis 1992 Gjone 1996 Goodman 1989 Hudziak 2000 Kuntsi 2001 Laarson 2004 Levy 1997 Martin 2002 Matheny 1980 Nadder 1998 Schmitz 1995 Sherman 1997 Silberg 1996 Stevenson 1992 Thapar 1995 Thapar 2000 Willcutt 2000 Willerman 1973 0

0.2

0.4

0.6

0.8

1

Heritability

Fig 25.2 Heritability of ADHD. For references, see Faraone et al. [79].

(i) Linkage studies screen the entire genome but can only implicate very large chromosomal regions; (ii) Candidate gene studies focus on specific genes implicated by ADHD’s putative pathophysiology and (iii) Genome-wide association studies (GWASs) screen the entire genome using the method of association. For details on these methods see Chapter 3.

25.6.2.1 Genetic linkage studies To date, there have been seven genome-wide linkage scans of ADHD. Table 25.2 summarises their main results. With the exception of 17p11, genomic regions implicated by these studies do not overlap. Of interest, in a Genome Scan Meta Analysis of these data, genome-wide significant linkage was identified on chromosome 16 between 64 and 83 Mb [80].

25.6.2.2 Candidate gene studies Many candidate gene studies have used the method of association to determine if biologically relevant genes influence ADHD. For an extensive review, see Mick and Faraone [91]. Table 25.3 summarises the most prominent findings. 454

25.6.2.2.1 The dopamine D4 receptor The D4 receptor is prevalent in frontal–subcortical networks implicated in the pathophysiology of ADHD by neuroimaging and neuropsychological studies [92]. Most studies examined a tandem repeat polymorphism in exon III of the dopamine D4 receptor (DRD4) because in vitro studies found that one variant (the seven-repeat allele) produces a blunted response to dopamine [93, 94]. Faraone et al. [79] meta-analysed studies of this variant and found a statistically significant association with ADHD in both case–control (odds ratio (OR) = 1.45 (95% confidence interval (CI) 1.27–1.65)) and family based OR = 1.16 (95% CI 1.03–1.31) studies. More recently, Li et al. [95] reported a significant pooled OR of 1.34 (1.23–1.45) in 33 studies. Studies published subsequent to these meta-analyses add support to the idea that the seven-repeat allele is a risk factor for ADHD. In the largest of these studies [96], although the association was not statistically significant (p < 0.09), the odds ratio (1.18) was very close to that observed in the meta-analyses. 25.6.2.2.2 The dopamine 5 receptor The most widely studied polymorphism for the dopamine D5 receptor (DRD5) has been a

EPIDEMIOLOGY OF ATTENTION DEFICIT HYPERACTIVITY DISORDER Table 25.2

Genome-wide linkage scans of ADHD.

Study

Sample

Region implicated

Fisher et al. [81–84]

270 sibling pairs from the USA

Bakker et al. [85]

164 sibling pairs from the Netherlands

16p13 17p11 12p13 5p13 6q12 15q15

Arcos-Burgos et al. [86]

Hebebrand et al. [87] Faraone et al. [88] Zhou et al. [89] Romanos et al. [90]

Colombian study of 14 three-generation pedigrees

German study of 155 sib-pairs American study of 217 sibling pairs European study of 1094 nuclear families German study of eight extended pedigrees

Table 25.3 Pooled odds ratios for potential ADHD susceptibility genes. Gene FB: DRD4 CC: DRD4 FB: DRD5 FB: SLC6A3 FB: SNAP25 CC: SLC6A4 FB: HTR1B

Number of studies

Odds ratio

95% confidence interval

17 13 14 14 5 3 2

1.16 1.45 1.24 1.13 1.19 1.31 1.44

1.03–1.31 1.27–1.65 1.12–1.38 1.03–1.24 1.03–1.38 1.09–1.59 1.14–1.83

CC, case control studies; FB, family-based studies; Based on Faraone et al. [79]; DRD4, dopamine-4 receptor gene; DRD5, dopamine-5 receptor gene; SLC6A3, dopamine transporter gene; SNAP25, gene for synaptosomal protein of 25 kilodaltons; SLC6A4, serotonin transporter gene; HTR1B, serotonin-1B receptor gene.

dinucleotide repeat that maps approximately 18.5 kb 5’ to the transcription start site [97]. Metaanalysis of 14 independent family-based studies found a significant association of the 148-bp allele with ADHD (OR = 1.2; 95% CI 1.1–1.4) [98]. That result was confirmed in an updated analysis by Li et al. [95] (OR = 1.3; 95% CI 1.2–1.5).

7p13 9q33 17p11 11q22 5q33.3 5p13 None 1p36 5q13.1 and 14q12

25.6.2.2.3 The dopamine transporter gene (DAT1, SLC6A3) There are several reasons that SLC6A3 has been considered a suitable candidate for ADHD. Stimulant medications, which are efficacious in treating ADHD, block the dopamine transporter as one mechanism of action for achieving their therapeutic effects [99]. And neuroimaging studies suggest that ADHD people have fewer or less active dopamine transporters than non-ADHD people (e.g. [100]). Cook et al. [101] first reported an association between ADHD and the 10-repeat allele of a tandem repeat polymorphism in the 3’ untranslated region (3’UTR) of SLC6A3. Faraone et al.’s [79] meta-analysis of family-based studies found a small but significant odds ratio (1.13, 95% CI 1.03–1.24). Li et al. [95] updated this work and found no evidence of association with ADHD and the 10-repeat allele in family-based studies (OR = 1.04, 95% CI = 0.99–1.14) and significant evidence of heterogeneity among studies. The most recent meta-analysis of this gene found a small but significant association with ADHD for family-based studies (OR = 1.17, 95% CI = 1.05–1.30) [102]. In contrast, meta-analysis of six case–control studies 455

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suggested this polymorphism is not associated with ADHD [102]. Thus, the lack of consistent association suggests that this marker itself is not directly involved in the aetiology of ADHD, but may be tagging a proximate functional polymorphism in partial linkage disequilibrium (LD) with the 10-repeat allele. In an attempt to further refine the SLC6A3 risk variant for ADHD, Brookes et al. [103] reported association with a haplotype comprising the 10-repeat allele in the 3’UTR and the 6-repeat allele of a 30-bp marker in intron 8. Of the four possible haplotype combinations of the two markers, only the 10−6 haplotype was associated with increased risk for ADHD in both populations. This finding was replicated by Brookes et al. [96] (OR = 1.19, p < 0.06) and Asherson et al. [104] in second set of 383 ADHD probands (OR = 1.27, p = 0.002). 25.6.2.2.4 The serotonin 1B receptor Of the family-based association studies of a silent single nucleotide polymorphism (SNP) 861G>C in the gene coding for the serotonin HTR1B receptor, only the multisite study by Hawi et al. [105] reached statistical significance suggesting over transmission of the G861C allele. Smoller et al. pooled data from several studies and identified statistically significant over transmission of this allele (OR = 1.35 (1.13–1.62), p = 0.009) that strongly suggested paternal (p = 0.00005) rather than maternal transmission (p = 0.2) [106]. Heiser et al. [107] identified no association with the G681C allele but examined only the combined ADHD subtype and did not assess for paternal transmission separately. In the study of Brookes et al., no marker in HTR1B was associated with ADHD [96]. 25.6.2.2.5 The serotonin transporter (HTT, SLC6A4) A 44-bp insertion/deletion polymorphism (HTTLPR) has been widely studied. According to the metaanalysis of Faraone et al. [79], the pooled odds ratio for the long (L) allele was 1.31 (95% CI 1.09–1.59). Subsequently, Curran et al. [108] found nominal evidence of over transmission of the L allele and strong evidence of association with a four SNP haplotype upstream that included the 5-HTTPR ins/del polymorphism. However, other studies have not 456

replicated this finding, perhaps due to the use of relatively small samples [91]. 25.6.2.2.6 Synaptasomal associated protein of 25 kD (SNAP25) Synaptasomal associated protein of 25 kD (SNAP25) is a gene on chromosome 20p12. The gene product is a presynaptic plasma membrane protein involved in the regulation of neurotransmitter release. Its relevance to ADHD was motivated by the coloboma mouse which has a hemizygous two centimorgan deletion of a segment on chromosome 2q including SNAP25. The coloboma mutation leads to spontaneous hyperactivity, delays in achieving complex neonatal motor abilities and deficits in Ca2+ -dependent dopamine release in dorsal striatum [109]. Four family-based studies of SNAP25 examined two SNPs (1069T>C and 1065T>G) (For details, see [91]). Faraone et al.’s [79] meta-analysis of these studies reported a significant association with ADHD and T1065G (OR = 1.19, 95%CI 1.03–1.38). Subsequently, Feng et al. [110] found a positive association in three different SNPs in families from Toronto, but not California. The Brookes et al. [96] analysis found nominally significant associations between other SNAP25 SNPs (rs363020 and rs362567). Kim et al. [111] examined the previously implicated SNPs and five additional SNPs (rs6077699, rs363006, rs362549, rs362987, rs362998) but found no evidence of association with ADHD in tests of individual markers or haplotypes. However, a combined transmission disequilibrium test (TDT) analysis of pooled data was modestly significant for rs3746544-T (p = 0.048) and rs6077690-T (p = 0.031).

25.6.2.3 Genome-wide association studies To date, there have been two GWAS of ADHD. One study examined 909 trio families (two parents and one ADHD child) using 438 784 tagging SNPs designed to be in high LD with all untyped SNPs in the genome. No finding achieved genome-wide significance (i.e. a p-value of 5 × 10−8 ) in the primary analysis of the ADHD diagnosis [112] but this analysis and several exploratory analyses implicated some novel genes that require further study [113–116]. Of interest, one of the exploratory analyses implicated the CDH13 gene, which was

EPIDEMIOLOGY OF ATTENTION DEFICIT HYPERACTIVITY DISORDER

also implicated in a second GWAS of 343 ADHD adults and 250 controls [117]. CDH13 was also implicated in the meta-analysis of ADHD linkage studies discussed above.

25.7 Environmental risk factors for ADHD 25.7.1 Diet Several researchers claimed to have cured ADHD by eliminating food additives from the diet. The Feingold diet for ADHD was popularised by the media and accepted by many parents of ill children as a testament to this claim. Systematic reviews, however, showed that the diet was not effective [118]. Schab et al. [119] used meta-analysis of 15 double-blind placebo-controlled trials to examine the hypothesis that artificial food colourings (AFCs) promoted hyperactivity in children already diagnosed with ADHD. The overall effect size for AFCs on hyperactivity was 0.28 (95% CI, 0.08–0.49). This was reduced to 0.21 (95% CI, 0.007–0.41) when the smallest and lowest quality trials were excluded. This finding suggests that AFCs exacerbate hyperactivity but cannot determine if they increase the likelihood of developing ADHD.

shown that prenatal PCB exposure leads to poorer concentration, less accurate performance and slower mean reaction times (for a review, see [122]). Rats prenatally exposed to PCBs show evidence of hyperactivity (e.g. [124, 125]). DasBanerjee et al. [126] examined the effects of prenatal exposure to PCBs on the expression of genes in rat brain. The expression levels of many genes relevant to ADHD were altered by exposure to PCBs. These included: GNAL, COMT, ADRBK1, NTRK2, HK1, SYT11, CSNK1A1, ARRB2, STX12, AQP6, SYT1, DDC and PGK1. Also of note, the epigenetic genes CREBBP, MECP2 and HDAC5 were significantly altered by PCBs. These genes are activated when environmental exposures modify the genome through effects such as methylation and histone acetylation.

25.7.3 Pregnancy and delivery complications The literature examining the association of ADHD with pregnancy and delivery tends to support the idea that these adverse events predispose children to ADHD (e.g. [127]). Specific complications implicated in ADHD include toxemia or eclampsia, poor maternal health, maternal age, fetal post-maturity, duration of labour, fetal distress, low birth weight and antepartum haemorrhage.

25.7.2 Exposure to toxins Lead contamination causes distractibility, hyperactivity, restlessness and lower intellectual functioning in children (see [120]). Exposure to mercury and manganese is also associated with the symptoms of ADHD. Small prenatal exposures resulting from maternal consumption of contaminated fish in New Zealand and the Faroe Islands adversely affected IQ, language development, visual-spatial skills, gross motor skills, memory and attention in offspring [121]. In small epidemiological studies, manganese hair levels have been associated with ADHD (for a review, see [122]). Polychlorinated biphenyls (PCBs) are a family of 209 compounds once produced for a variety of applications in the home environment [123]. Because PCBs are very stable, they bioaccumulate and have entered the food chain [124]. Several studies have

25.7.4 Fetal exposure to alcohol Children exposed to alcohol prenatally are at risk for hyperactive, disruptive, delinquent and impulsive behaviours. They also show impairments in overall intellectual performance, learning and memory, language, attention, reaction time and visuospatial abilities, executive functioning, fine and gross motor skills and adaptive and social skills (for a review, see [128]). Several mechanisms that might mediate the effects of prenatal alcohol exposure have been studied. For example, Ikonomidou et al. [129] showed that prenatal alcohol exposure led to neurodegeneration in the forebrain of rats. This finding is consistent with studies documenting ADHD-related reductions in a variety of brain structures, including frontal cortex [130]. 457

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25.7.5 Fetal exposure to maternal smoking Prenatal and postnatal effects of maternal smoking during pregnancy have been extensively studied. Fetal exposure to cigarette smoke leads to a variety of behavioural and cognitive problems in both children and adolescents (for a review, see [131]). Milberger et al. [132] reported a 2.7-fold increased risk for ADHD associated with maternal smoking in 140 cases and 120 controls and a 4.4-fold increased risk for ADHD in their 303 siblings. Weissman et al. [133] also reported a twofold increased risk for ADHD associated with prenatal exposure to tobacco use. Kotimaa et al. [134] reported a dose–response relationship between maternal smoking during pregnancy and degree of hyperactivity in children. Several mechanisms could explain the effects of maternal smoking on ADHD in their children. Cigarette smoke reduces uterine blood flow and deprives the fetus of nutrients and oxygen, which leads to episodes of fetal hypoxia–ischaemia and malnutrition [135]. Carbon monoxide and ingredients in tobacco tar can directly harm the fetal brain [136]. Nicotine directly stimulates nicotinic acetylcholine receptors. In rats, prenatal nicotine exposure produced persistent hypoactivity of nicotinic receptors in adolescence and adulthood [137]. Nicotine exposure induces abnormalities in cell proliferation and cell differentiation in rodents [138, 139], which lead to abnormalities in cell number and macromolecular content (e.g. [138, 139]). Prenatal nicotine exposure also leads to a dysregulation of neurotransmitter systems relevant to ADHD [140].

25.7.6 Psychosocial adversity Perhaps the best example of the delineation of psychosocial risk factors for childhood mental disorders comes from Rutter’s now classic studies of the Isle of Wight and the inner borough of London [141]. These studies examined the prevalence of mental disorders in children living in two very different geographical areas. This research revealed six risk factors within the family environment that correlated significantly with childhood mental disturbances: (i) severe marital discord; (ii) low social class; (iii) large family size; (iv) paternal criminality; (v) maternal mental disorder and (vi) foster placement. 458

This work found that it was the aggregate of adversity factors, rather than the presence of any single one, that impaired development. Other studies also find that as the number of adverse conditions accumulates, the risk of impaired outcome in the child increases proportionally [142]. Biederman et al. [68] found a positive association between Rutter’s index of adversity and ADHD, measures of ADHD-associated psychopathology, impaired cognition and psychosocial dysfunction. Other cross sectional and longitudinal studies have identified variables such as marital distress, family dysfunction and low social class as risk factors for psychopathology and dysfunction in children. For example, the Ontario Child Health Study showed that family dysfunction and low income predicted persistence and onset of one or more psychiatric disorders over a 4-year follow-up period [143]. Other work implicates low maternal education, low social class and single parenthood as important adversity factors for ADHD [5, 144, 145]. These studies suggest that the mothers of ADHD children have more negative communication patterns, more conflict with their child and a greater intensity of anger than do control mothers. Biederman et al. [146] showed that chronic conflict, decreased family cohesion and exposure to parental psychopathology, particularly maternal psychopathology, were more common in ADHD families compared with control families. The differences between ADHD and control children were not accounted for by social class or parental history of major psychopathology. Moreover, increased levels of family environment adversity predicted impaired psychosocial functioning. Measures indexing chronic family conflict had a more pernicious impact on the exposed child than those indexing exposure to parental psychopathology. Indeed, researchers have consistently found marital discord to predict disruptive behaviours in boys [147]. This research shows that it is the extent of discord and overt conflict, regardless of whether the parents are separated, that predicts the child’s risks for psychopathology and dysfunction [148]. Thus, dysfunctional family environments appear to be a non-specific risk factor for psychiatric disorders and psychological distress. Reid and Crisafulli [149] reported a meta-analysis of the impact

EPIDEMIOLOGY OF ATTENTION DEFICIT HYPERACTIVITY DISORDER

of marital discord on the psychological adjustment of children and found that parental conflict significantly predicted a variety of child behaviour problems. The Ontario Child Health Study provided a prospective example of the impact of parental conflict on children’s mental health: family dysfunction (and low income) predicted persistence and onset of one or more psychiatric disorders over a 4-year period [143]. Low maternal warmth and high maternal malaise and criticism have been previously associated with ADHD in children [12] and an epidemiological study examining family attributes in children who had undergone stressful experiences found that children’s perceptions of mothers, but not fathers, differentiated stress-resilient and stress-affected children [150]. An extensive literature documents maternal depression as a risk factor for psychological maladjustment and psychiatric disorder in children [151]. This is consistent with the known familial link between ADHD and depression [152]. Moreover, some have suggested that depressed mood may lead mothers to perceive their children as more deviant than warranted by the child’s behaviour. Richters [153], however, reviewed 22 studies of this issue and concluded that, due to methodological problems with research in the area, there was no empirical foundation for this claim. Other data revealed a link between maternal depression and child functioning that was independent of the mother’s perceptions. These data suggest that depressed mothers accurately perceive symptomatic behaviour but react to it in a negative manner that worsens the condition of the child. Mick et al. [154] found that the comorbidity between ADHD and depression was independent of maternal reporting or maternal depression. In a comprehensive review, Gelfand and Teti [151] found many studies to document the assertion that depressed mothers have attitudes of insensitivity, disengagement, disapproval and hostility towards their children. They also found maternal depression to be associated with undesirable parenting practices such as intrusiveness, unresponsiveness and inept discipline. In addition, their review supported the idea that depressed mothers had negative perceptions of their children.

Other work shows that ADHD in children predicts depression in mothers, but maternal depression provides no additional information for predicting ADHD in siblings of ADHD probands. This suggests that maternal depression is a heterogeneous disorder. It may be that some mothers have a disorder that is genetically linked to ADHD, whereas others may experience depression due to the stress of raising an ADHD child (and perhaps living with an ADHD or antisocial husband). Furthermore, given the literature discussed above, it is likely that maternal depression exacerbates family conflict and poor parenting, both of which could lead to non-genetic forms of ADHD. Notably, although many studies provide strong evidence for the importance of psychosocial adversity for ADHD, these factors tend to emerge as universal predictors of children’s adaptive functioning and emotional health, not predictors that are specific to ADHD. Thus, they can be conceptualised as nonspecific triggers of an underlying predisposition or as modifiers of the course of illness.

25.7.7 Television watching Cross-sectional studies have suggested that television viewing is associated with psychopathology in children. For example, a cross-sectional survey of students in grades 2 and 3 reported that higher levels of television viewing were associated with poor social and school achievement and with greater psychopathology in several areas as measured by the Child Behaviour Checklist: thought problems, attention problems, delinquent behaviour and aggressive behaviour [155]. But such studies are very difficult to interpret because they can establish association but not causality. To remedy this problem, Christakis [156] used a longitudinal study to assess the effects of early television exposure (at ages 1 and 3) on attention problems at age 7 in 1345 children. They evaluated attention problems with the hyperactivity subscale of the Behavioural Problems Index. Ten per cent of children had attention problems at age 7 and the number of hours of television viewed each day at both ages 1 and 3 was associated with age 7 attention problems. The odds ratios were 1.09 for both age predictions. An increase of one standard deviation 459

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in how many hours of television children watched at age 1 predicted a 28% increase in probability of having attention problems at age 7. Although intriguing, these results were difficult to interpret for several reasons. The authors did not include a standard measure of ADHD and some of the items constituting their attention problems measure were not ADHD symptoms (e.g. ‘easily confused’ and trouble with obsessions). Christakis et al. defined attention problems as being 1.2 standard deviations above the mean, which implicitly assumes the population prevalence is about 20%, which we know is not true [18]. Obel et al. [157] attempted to replicate these findings in a birth cohort of all children born from 1990 at the Aarhus University Hospital, Denmark. They evaluated whether the amount of time spent watching television at 3.5 years of age predicted attention problems at ages 10–11. They found no significant association between hours of watching television and behavioural problems. An 18 month longitudinal study compared 59 ADHD children with 106 non-ADHD children on laboratory measures of visual attention to television, cognitive engagement to televised stories, factual recall of televised stories, causal recall of televised stories and parental reports of a child’s weekly television viewing [158]. Among the non-ADHD children, baseline television viewing predicted poor visual attention and low cognitive engagement 18 months later. These patterns were not observed among ADHD children. This work suggest that, rather than causing ADHD, television viewing may lead to more subtle cognitive defects, the clinical significance of which needs to be established. Another study which attempted to replicate the Christakis et al. findings examined two samples of 2500 children, which were randomly selected from a longitudinal study [159]. Although the study did not include a standard measure of ADHD, it did have a measure that reflected ADHD symptoms. The authors tested the hypothesis that television viewing in the kindergarten years would predict ADHD symptoms during the first grade. In both samples, the authors found a small, non-significant association between television viewing and ADHD symptoms. They also reported that parental limits 460

on television watching did not predict subsequent levels of ADHD symptoms.

25.8 Summary and conclusions ADHD has emerged from the twentieth century with a large body of scientific literature that evaluates its validity, and clarifies clinical controversies. As indicated by the International Consensus Statement on ADHD, there is now good agreement among ADHD experts about the main features of the disorder [160]. ADHD is highly prevalent around the world, is associated with substantial life impairments and frequently persists into adulthood. Theories about ADHD’s genesis have evolved beyond simple one cause theories to the view that ADHD is a complex, multifactorial disorder caused by the confluence of many genes and environmental risk factors, each having a small effect on increasing vulnerability to the disorder through their additive and interactive effects. If an individual’s cumulative vulnerability exceeds a threshold, he or she will manifest the signs and symptoms of ADHD. According to this multifactorial model of ADHD, no single risk factor is necessary or sufficient for ADHD and risk factors are interchangeable (i.e. it doesn’t matter which factors one has, only the total number is important). This multifactorial view of ADHD is consistent with the observed heterogeneity in its pathophysiology and clinical expression. It is notable that many of the environmental risk factors for ADHD occur early in development, which is consistent with the idea that ADHD is a neurodevelopmental condition. Future work needs to determine if modifying environmental risk factors can lead to preventive interventions and there is a big gap in our knowledge about the mechanisms whereby genes and environment combine to produce ADHD.

25.9 Future directions Although great strides have been made in understanding the epidemiology of ADHD, much more work remains to be done. There are many facts about ADHD that have been replicated in numerous studies: it is more prevalent in males, it is associated

EPIDEMIOLOGY OF ATTENTION DEFICIT HYPERACTIVITY DISORDER

with other disorders, it is highly heritable and it is associated with brain dysfunction. Yet, despite this body of knowledge, we do not have explanatory mechanisms that can fully account for these data. In the decades to come, it is likely that neuroscience and genetic studies will shed much light on these mechanisms, which, in turn, should spur the development of new treatment approaches.

Acknowledgements This work was supported by NIMH grants R01MH066877, R01MH081803 and R13MH059126.

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[131] Linnet, K.M., Dalsgaard, S., Obel, C. et al. (2003) Maternal lifestyle factors in pregnancy risk of attention deficit hyperactivity disorder and associated behaviors: review of the current evidence. Am. J. Psychiatry, 160 (6), 1028–1040. [132] Milberger, S., Biederman, J., Faraone, S.V. and Jones, J. (1998) Further evidence of an association between maternal smoking during pregnancy and attention deficit hyperactivity disorder: findings from a highrisk sample of siblings. J. Clin. Child Psychol., 27 (3), 352–358. [133] Weissman, M.M., Warner, V., Wickramaratne, P.J. and Kandel, D.B. (1999) Maternal smoking during pregnancy and psychopathology in offspring followed to adulthood. J. Am. Acad. Child Adolesc. Psychiatry, 38 (7), 892–899. [134] Kotimaa, A.J., Moilanen, I., Taanila, A. et al. (2003) Maternal smoking and hyperactivity in 8-year-old children. J. Am. Acad. Child Adolesc. Psychiatry, 42 (7), 826–833. [135] Suzuki, K., Minei, L.J. and Johnson, E.E. (1980) Effect of nicotine upon uterine blood flow in the pregnant rhesus monkey. Am. J. Obstet. Gynecol., 136 (8), 1009–1013. [136] Ernst, M., Moolchan, E.T. and Robinson, M.L. (2001) Behavioral and neural consequences of prenatal exposure to nicotine. J. Am. Acad. Child Adolesc. Psychiatry, 40 (6), 630–641. [137] Abreu-Villaca, Y., Seidler, F.J., Tate, C.A. et al. (2004) Prenatal nicotine exposure alters the response to nicotine administration in adolescence: effects on cholinergic systems during exposure and withdrawal. Neuropsychopharmacology, 29 (5), 879–890. [138] Nasrat, H.A., Al-Hachim, G.M. and Mahmood, F.A. (1986) Perinatal effects of nicotine. Biol. Neonate, 49 (1), 8–14. [139] Slotkin, T.A. and Bartolome, J. (1986) Role of ornithine decarboxylase and the polyamines in nervous system development: a review. Brain Res. Bull., 17 (3), 307–320. [140] Seidler, F.J., Levin, E.D., Lappi, S.E. and Slotkin, T.A. (1992) Fetal nicotine exposure ablates the ability of postnatal nicotine challenge to release norepinephrine from rat brain regions. Brain Res. Dev. Brain Res., 69 (2), 288–291. [141] Rutter, M., Cox, A., Tupling, C. et al. (1975) Attainment and adjustment in two geographical areas: volume 1. The prevalence of psychiatric disorders. Br. J. Psychiatry, 126, 493–509. [142] Blanz, B., Schmidt, M.H. and Esser, G. (1991) Familial adversities and child psychiatric disorders. J. Child Psychol. Psychiatry, 32 (6), 939–950. [143] Offord, D.R., Boyle, M.H., Racine, Y.A. et al. (1992) Outcome, prognosis and risk in a longitudinal

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[153] Richters, J.E. (1992) Depressed mothers as informants about their children: A critical review of the evidence for distortion. Psychol. Bull., 112 (3), 485–499. [154] Mick, E., Santangelo, S., Wypij, D. and Biederman, J. (2000) Impact of maternal depression on ratings of comorbid depression in adolescents with attentiondeficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry, 39 (3), 314–319. [155] Ozmert, E., Toyran, M. and Yurdakok, K. (2002) Behavioral correlates of television viewing in primary school children evaluated by the child behavior checklist. Arch. Pediatr. Adolesc. Med., 156 (9), 910–914. [156] Christakis, D.A., Zimmerman, F.J., DiGiuseppe, D.L. and McCarty, C.A. (2004) Early television exposure and subsequent attentional problems in children. Pediatrics, 113 (4), 708–713. [157] Obel, C., Henriksen, T.B., Dalsgaard, S. et al. (2004) Does children’s watching of television cause attention problems? Retesting the hypothesis in a Danish cohort. Pediatrics, 114 (5), 1372–1373; author reply 3–4. [158] Acevedo-Polakovich, I.D., Lorch, E.P., Milich, R. and Ashby, R.D. (2006) Disentangling the relation between television viewing and cognitive processes in children with attention-deficit/hyperactivity disorder and comparison children. Arch. Pediatr. Adolesc. Med., 160 (4), 354–360. [159] Stevens, T. and Mulsow, M. (2006) There is no meaningful relationship between television exposure and symptoms of attention-deficit/hyperactivity disorder. Pediatrics, 117 (3), 665–672. [160] Barkley, R.A. (2002) International consensus statement on ADHD January 2002. Clin. Child Fam. Psychol. Rev., 5 (2), 89–111.

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26

The epidemiology of autism Gregory S. Liptak Center for Development, Behavior and Genetics, SUNY Upstate Medical University, Syracuse, NY, USA Department of Pediatrics, SUNY Upstate Medical University, Syracuse, NY, USA

26.1 Introduction

26.3 Definition and diagnosis

Autism has been described in the lay press as ‘the hidden epidemic.’ It is an ‘epidemic’ because of the dramatic increase in prevalence over the past 20 years – in the most recent surveys in the United States, the prevalence among school-aged children was greater than 1% [1, 2] – and ‘hidden’ because those who advocate for people with autism do not believe that it has received sufficient attention or funding. This chapter reviews our current knowledge of the epidemiology of autism.

The ASD includes three major categories: autistic disorder, Asperger’s syndrome and pervasive developmental disorder, not otherwise specified (PDDNOS). Leo Kanner first described children with autism in the 1940s [9] and categorised them as having impaired social relationships and a preoccupation with sameness. In the 1970s Michael Rutter added impaired language to these two criteria [10], developing the three core criteria used by most definitions since. While Kanner was describing children now labelled as having ‘autistic disorder’, Hans Asperger was describing children who had impaired social relationships and unusual behaviour, but were higher functioning. Although they often have difficult with the pragmatics of language, people with Asperger’s syndrome do not have the same degree of speech and language delay seen in other people on the spectrum. His work remained largely unread in this country until it was translated into English in the 1990s [11]. While autistic disorder and Asperger syndrome have specified criteria, PDD-NOS is a subthreshold diagnosis. Figure 26.3 describes these three conditions. Autism has been viewed both as a spectrum, with continuously varying traits (like social reciprocity) and levels of severity, as well as a heterogeneous collection of discrete entities with different aetiologies sharing a common presentation – that is a final common pathway. For example, disparate conditions like tuberous sclerosis, fragile X syndrome, congenital rubella, prenatal exposure to valproic

26.2 Background A popular conception exists that the United States and other countries are experiencing an epidemic of autism [3–6]. A glance at Figure 26.1, which graphs the number of people with autism spectrum disorder (ASD) enrolled in the California Department of Developmental Services delivery system between 1987 and 2007 [7] seems to support this notion. These increases have impacted health and education services across the country. Even Figure 26.2, which shows the cumulative prevalence of ASD corrected for population, age of child and birth cohort [8], shows a dramatic increase in autism for each subsequent cohort. However, closer scrutiny of the data raises major questions about the apparent ‘epidemic’. This chapter will address the epidemiology of autism and current views regarding its occurrence and aetiology.

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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Individuals with Autism Enrolled in California DDS 35 000

30 000

Number

25 000

20 000

15 000

10 000

5 000

0 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Year

Cumulative Incidence per 10 000 Births 0 10 20 30 40 50

Fig 26.1 Number of people with autism spectrum disorder enrolled in the California Department of Developmental Services (DDS) delivery system between 1987 and 2007.

2000 2001

1999

1998 1997

2002

1996 1995 1994 1993 1992 1991

2003

1990

1

2

3

4

5

6

7

8

9

DDS Record of Autism or Before this Age Fig 26.2 Cumulative incidence of individuals with autism spectrum disorder (ASD) enrolled in the California Department of Developmental Services (DDS) delivery system by age and by birth cohort.

470

acid, phenylketonuria (PKU) and Joubert syndrome (hypoplasia of the cerebellar vermis) all can present with signs and symptoms of autism. As Waterhouse stated, ‘it is unlikely that a single cause or pathophysiological mechanism will be described that applies to most individuals currently diagnosed with autism’ [12]. This chapter deals with ‘idiopathic’ autism, that is not linked to a specific known condition. The definition of autism has changed over time. For example, the first studies used Kanner’s criteria. By 1980 the American Psychiatric Association’s diagnostic manual (Diagnostic and Statistical Manual of Mental Disorders (DSM)-III) [13] required individuals to meet six of six criteria for an autism diagnosis. Neither PDD-NOS (added in 1987) nor Asperger’s syndrome (added in 1994) were included in that definition. The 1994 version (DSM-IV), which is currently in use [14], requires individuals to meet only 8 of 16 criteria and includes PDD-NOS as well as Asperger’s syndrome. Thus, the definition of ASD has changed to become more expansive. The

THE EPIDEMIOLOGY OF AUTISM Behaviour Asperger's Syndrome

Autistic Disorder PDD-NOS Social

Social Language

Language Behaviour

−

Social

Cognitive Level

Language Behaviour

+

−

Cognitive Level

Behaviour

+

−

Cognitive Level

+

Fig 26.3 Conceptualisation of autistic disorder, pervasive developmental disorder, not otherwise specified (PDD-NOS) and Asperger syndrome in terms of their position with cognitive level, social abilities, language and behaviour.

characteristics of individuals diagnosed with ASD has changed over time. For example, data from California show that in 1992 72% of individuals had mental retardation. By 2005 this had fallen to 36%; similarly the rate of epilepsy decreased from 16 to 7% [7]. This suggests that the current group of individuals labelled with ASD are higher functioning and less involved medically than they were 15–20 years ago [15]. In addition to the changing criteria for ASD, many have argued that diagnostic substitution is occurring, which increases the prevalence as well. For example, Shattuck [16] used administrative data from the US Department of Education from 1994 to 2003. The prevalence of ASD among children increased from 0.6 to 3.1 per 1000 by 2003. At the same time, the prevalence of mental retardation and learning disabilities decreased by 2.8 and 8.3 per 1000, respectively. Higher ASD prevalence was associated with corresponding declines in the prevalence of mental retardation and learning disabilities. In a Canadian study, diagnostic substitution accounted for onethird of the increase in autism prevalence over the study period [17]. Thus individuals who in the past would have been categorised as having mental retardation and other conditions are now being classified as having ASD. In addition to changes in the definition of ASD, changes in reporting have occurred. For example, schools are required to report data on students who receive special-education services, but autism was not added as a category until the 1991–1992 school

year. In 1990, the new Individuals with Disabilities Education Act (PL 101-476) added autism to its list of categories of children and youth served under the act. This led to growing awareness and increased reporting by school districts [18]. Thus, a significant amount of the increase in reported prevalence has been attributed to changes in diagnostic criteria, increased awareness and reportingand diagnostic substitution. Finally, in some communities having a diagnosis of an ASD allows a child to receive more services in school. Some have argued that this motivates physicians and psychologists to diagnose ASD in children who do not fulfil all the criteria of ASD in order to help them get services. This hypothesis has not been formally studied. The diagnosis of an ASD is made on the basis of a constellation of behavioural symptoms rather than on any biological markers. The Autism Diagnostic Observation Schedule (a standardised observation of communicative and social behaviour) [19] is considered by many to be the gold standard for diagnosing autism clinically [20, 21]. However, because it takes 45–60 minutes to administer, it has limitations for epidemiological prevalence or incidence studies in large samples. Therefore, many other varied methods have been used to ascertain cases of ASD. For example, the Centers for Disease Control (CDC) [22] has developed a multiple-source, population-based, active system using a records-based approach from both educational and clinical sources; they employ a common case definition (from the Diagnostic and Statistical Manual of Mental Disorders, 4th 471

CHAPTER 26

edition, Text Revision (DSM-IV-TR)) [23], standardised data abstraction and clinician review of records to define a case. In their most recent survey [1], they found an overall prevalence estimate among 8-year-old children of 1 per 110 population (confidence interval (CI)95 = 6.3–6.8). Other studies have used administrative datasets [24] or employed identification by clinicians or parents [25]. In a study comparing two different methods of ascertainment, Barbaresi et al. [26] evaluated children who had been diagnosed with ASD from a research study, which was based on a structured record review and determined whether or not they had had a clinical diagnosis of ASD. Only 47% of research-identified cases had received a clinical diagnosis of ASD. Mattila [27] found different rates of autism in the same sample when using different standards. These findings highlight the difficulty in validly identifying individuals with autism in epidemiologic studies. Children and youth with ASD frequently have associated conditions, including intellectual and developmental disability (mental retardation), anxiety (including obsessive compulsive disorder), epilepsy and attention deficit hyperactivity disorder (ADHD). Since children who have low cognitive skills typically have less well developed social and communication abilities, behavioural overlap occurs between children with mental retardation and children with ASD, making the diagnosis of ASD more challenging in this group [28]. Early diagnosis of ASD has several theoretical advantages. Earlier therapy, for example with applied behaviour analysis (ABA), has been advocated as being more effective than later therapy [29], (although the evidence for this argument is not optimal). In addition, earlier diagnosis can help families plan for subsequent pregnancies, since the rate of recurrence for ASD or the ‘broader autism phenotype’ (qualitatively similar but milder features of autism) is significantly increased (see below) once a family has an affected child. Finally, communities can help plan for resources, for example preschool and kindergarten programmes, if they know the current prevalence. Over time the average age of diagnosis of ASD has been decreasing. However, many have argued that ASD should be diagnosed earlier than it is now. In a 2004 survey of children the mean age of diagnosis was 3.1 years for children 472

with autistic disorder, 3.9 years for PDD-NOS and 7.2 years for Asperger’s syndrome [30]. Rural children received a diagnosis 0.4 years later than urban children. In this study, near-poor children received a diagnosis 0.9 years later than those with incomes above the poverty level. Because of the theoretical value of early diagnosis, the American Academy of Pediatrics has recommended that primary care physicians screen all children for ASD at 18 and 24 months of age [31]. Similar recommendations for universal screening in Great Britain have not been implemented [32]. Using specific criteria, the UK National Screening Committee has argued that the diagnosis of ASD is still uncertain; no valid screening test has been developed for use in a population setting, and insufficient evidence is available regarding the effectiveness of interventions.

26.4 Natural history The natural history of ASD has not been thoroughly studied. Although most infants with ASD demonstrate developmental disabilities consistently throughout childhood, a substantial group appear to develop normally until 12–24 months of age and then show regression in language and social skills [33]. Because this regression occurs at around the time of the measles, mumps and rubella (MMR) vaccine, some have attributed the regression to the vaccine. A study by Wakefield et al. [34] identifying antigens to the MMR components in the intestines of individuals with ASD heightened anxiety about this vaccine. The validity of Wakefield’s study has been questioned [35, 36]; however, many families still believe that vaccines lead to autism. For instance, in a recent survey, 24% of individuals surveyed said that because vaccines may cause autism it was safer not to have children vaccinated at all. Another 19% were not sure [37]. A small per cent of children who have autistic characteristics as toddlers seem to improve clinically by the time they enter school [38, 39]; thus it is important to have a comparison group whenever conducting an intervention study on these children. The development of children with ASD in middle childhood depends in part on the types of services

THE EPIDEMIOLOGY OF AUTISM

being received by the child. During adolescence, behaviour may deteriorate, with increasing aggression (felt by many to be the effects of changing hormones). Individuals with non-verbal intelligence quotients (IQs) below 70 usually live dependently as adults; few of them live alone, have close friends or permanent employment. Communication generally is impaired, reading is poor and restricted or repetitive behaviours or interests persist [40]. The death rate is somewhat higher for those who have ASD, especially with mental retardation. In California the overall standardised mortality rate (SMR) for people with ASD was 2.6 [41]; for those with moderate or worse mental retardation it was 3.1; the SMR was 22.6 for those with mild or no MR who had seizures [42]. Autism has no cure. Table 26.1 lists some behavioural and educational interventions that have been utilised in the treatment of individuals with ASD as well as their level of evidence [43]. A higher grade does not mean that the intervention is more effective. It simply means that higher quality evidence has been published regarding its efficacy or effectiveness. Right now, ABA has the most peer-reviewed evidence regarding its effectiveness and efficacy; however, even for ABA most of the studies are not optimal. In addition, in most studies, the ABA is applied for many hours per week – as high as 40 – which is very difficult to do in most real-life situations, especially because individuals trained to administer ABA are not that available in most communities; in addition, it is difficult to get payment for those services. Psychoactive medications like atypical neuroleptics (risperidone, aripiprazole, etc.) and selective serotonin reuptake inhibitors (e.g. fluoxetine and venlafaxine) are used to treat specific behavioural problems like repetitive behaviour, obsessive–compulsive symptoms, hyperactivity and aggression [44]. Because no effective allopathic treatment for autism is available, many families use complimentary and alternative treatments, which have very little evidence either refuting or supporting their efficacy [45].

26.5 Prevalence Figure 26.4 shows the reported prevalence of ASD by year of actual data collection and specific test used to identify cases in studies conducted since

1962. While prevalence has increased over time, it also has increased as newer criteria have been used to ascertain cases. The most widely cited pediatric prevalence rate for the United States is 1 in 152, which was determined in the CDC study [22]. In a Canadian study the prevalence of autistic disorder was 21.6 per 10 000; for PDD-NOS 32.8 per 10 000 and for Asperger’s syndrome 10.1 per 10 000 [46]. Williams et al. [47] analysed prevalence studies using meta-regression. They found a significant increase in prevalence over time. However, the variation by year was so closely linked to changes in diagnostic criteria that the two could not be examined separately. The age of the children screened was strongly associated with the prevalence estimates, with studies of younger children having higher rates. Prevalence rates also were higher in urban areas and studies done in certain countries like Japan. They concluded that 61% of the variation among prevalence studies was explained by a model that included specific diagnostic criteria, age of children screened and study location.

26.6 Risk factors Specific conditions like Fragile X syndrome and tuberous sclerosis increase the probability that the affected person will be diagnosed with autism. The remainder of this discussion will refer to ‘idiopathic’ autism. ASD is more common in males. For Asperger disorder the ratio is approximately 6 : 1, while for autistic disorder it is 4 : 1. The lower the IQ the lower the male : female ratio becomes [48]. In some studies the prevalence of autism is higher in whites than in blacks [49] and has raised the issue of racial disparity in diagnosis. However, a lower prevalence for blacks due to genetic differences is an alternate explanation. In other studies, this apparent racial disparity has not been found [50]. Latinos have been found in some studies to have lower rates as well, although this has not been confirmed in all studies. In one California study [51], Hispanic women had a corrected relative risk similar to that for whites for having a child with autism (RRc = 1.1). In another study from California [52], however, the prevalence of autism identified by the Department of Developmental Services was 7.5 per 10 000 for Hispanics while the rate for whites was 12.5 per 473

CHAPTER 26 Table 26.1

Some behavioural and educational interventions used in individuals with ASD.

Intervention

Description/rationale

Evidencea

Applied behaviour analysis (ABA)

Intensive one-on-one method of shaping behaviour using a system of rewards. Uses discrete trials – antecedent, behaviour (response) and consequence to the behaviour. Parents are taught as cotherapists Intensive, eclectic, developmental play-based approach, based on Piaget’s theory of how children explore their worlds to learn concepts. It enhances functional communication in the context of naturally occurring activities. It also uses behaviour analytic techniques to reduce aberrant behaviours One-on-one, child-directed play periods based on the premise that an exchange of emotional signals forms the basis for learning in childhood. Parents and teachers get down on the floor to enter the child’s world, helping turn repetitive acts into playful interactions. Focus is on social skills and language. Child is moved up a symbolic ladder from shared attention, engagement, simple and complex gestures and problem solving to ideas and abstract thinking. Key elements: (i) self-regulation and shared attention, (ii) engagement and relating, (iii) two-way intentional communication, (iv) purposeful complex problem solving communication, (v) creating and elaborating symbols (ideas) and (vi) building bridges between symbols (ideas) Special education programme. Development of instruction and supports based on each individual’s skills, interests and needs. Teachers create activities and environments that are organised to emphasise meaningfulness. (1) Focuses on structural teaching, especially independent work skills. (2) Emphasises strategies to enhance visual processing, including (i) the physical structure of the classroom, (ii) the use of visual activity schedule to help children anticipate future events, (iii) visual organisation of the work materials to teach the tasks and their sequences and (iv) a visual system to teach complicated skills such as language and imitation. (3) Involves the teaching of a communication system based on gesture, pictures, signs or printed words. (4) Teaches preacademic skills (colours, numbers, shapes, drawing, writing and assembly). (5) Encourages parents to work as co-therapists with their child in the home using the same techniques and materials

B

Denver method

DIR (Developmental, individual, difference, relationship – based)/ floortime/Greenspan method

Treatment and Education of Autistic and related Communication – handicapped CHildren (TEACCH)

C

D

C

Alternative communication Picture exchange communication system (PECS) Facilitated communication (FC)

474

PECS: Based on behavioural principles of stimulus, response and reward, to help facilitate functional communication. Children give the picture of a desired item to a ‘teacher’ who exchanges the card for the actual item requested, for example a banana FC: Children learn to communicate by typing on a keyboard or pointing at letters, images or other symbols to represent messages with the direct help of an aid. FC includes physical and emotional support to an individual who has difficulties with speech and with intentional pointing (i.e. unassisted typing). Most evidence suggests that the facilitator (though usually unintentionally) is responsible for the messages, and not the child

C

B

THE EPIDEMIOLOGY OF AUTISM Table 26.1 (cont.) Sensory integration therapy

Sensory integration is the neurological process that allows an individual to organise internal and environmental sensations in order to regulate and function efficiently in the environment. Sensory integration therapy focuses on improving the child’s abilities to take in and process sensory information, and to have more appropriate behavioural responses. Activities may include swinging, dancing to music, playing in boxes filled with beans, crawling through tunnels, building with clay or Play-Doh, hitting swinging balls, spinning on a chair and balancing

C

SS: Short, individualised narratives, written to help a student understand an activity and its associated behavioural expectations. Gives the child positive examples of what to do in a given situation, rather than what not to do SSPG: Uses cognitive and behavioural approaches. Includes didactic instruction, role-playing, feedback, social stories and visual supports

C

Social skills training Social stories (SS)

Social skills peer groups (SSPGs)

C

a Levels

of Evidence [43] A – based on high-quality randomised controlled trials (RCTs) or high-quality quantitative systematic review(s). B – based on non-randomised clinical trial, lower quality RCT or other type of study (like case-control or cohort). C – based on some evidence, for example case series. D – based on expert opinion. Prevalence of ASD by Year and by Test 10.00 9.00 8.00

Prevalence per 1000

7.00

Kanner [9] Rutter [10] DSM-III DSM-IIIR ICD-10 DSM-IV Other

6.00 5.00 4.00 3.00 2.00 1.00 0.00 1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

Year

Fig 26.4 Reported prevalence of ASD by year of actual data collection and specific test used to identify cases.

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10 000. Autism also is higher in children who were born prematurely, who had birth injury including hypoxic ischaemic encephalopathy [53], and who were born after prenatal maternal stress [54]. Earlier studies reported a higher prevalence of autism among children from higher socioeconomic status (SES). It is not clear whether this association reflects a link to aetiology or a link to case identification (ascertainment bias). In a case–control study from Atlanta, higher maternal education and higher median family income were significantly associated with autism without MR, but not associated with autism with MR [55]. In one study based on parent-identified ASD, poor children appeared to be diagnosed at a later age than children whose families were above the poverty level [56]. However, other studies, including one from Denmark where access to health care is universal and free, have found no differences based on SES [57].

26.7 Genetic factors ASD has a large genetic component. For instance, concordance in monozygotic twins is as high as 70% (and is close to 0 in dizygotic twins) [58]. The prevalence of autistic disorder among siblings of individuals with autistic disorder ranges from 2 to 14% [59]. This is 20 times higher than in the general population, but much lower than in single-gene conditions. When the ‘broader autism phenotype’ [60] is considered, about 20% of siblings who have ASD will have this phenotype [61]. Parents of children with ASD also are more likely to have this broader phenotype [62]. In addition, parents who have bipolar disorder or schizophrenia are more likely to have children on the spectrum, which some believe is related to copy number variations affecting the same genes [63]. The male dominance in ASD has not been explained by genetic studies and may be related to the effects of prenatal testosterone on the developing brain [64]. ASD is a heterogeneous group of disorders with comorbid conditions like mental retardation. This makes it difficult to determine whether any associations between genetic risk factors and ASD are specific to more homogeneous subgroups such as children with ASD and mental retardation. The 476

genetic mechanisms leading to changes in neurologic mechanisms have not been elucidated. More than 25 different loci that may be considered autism susceptibility candidate genes have been identified and many more implicated loci are under investigation [65]. However, all together they only account for 10% of individuals with ASD [66].

26.8 Public health impact ASDs are common, affecting more than 1% of school-aged boys. Children with ASD use more health and educational resources than those without ASD. They also lead to lost opportunities for their families, such as the mother who cannot return to work because she has to watch her child. Montes and Halterman estimated that the average loss of annual income associated with having a child with ASD was $6200 for an American family [67]. These researchers also found that 39% of the parents of children with ASD reported that child care problems had greatly affected their employment decisions; this effect was three times larger than the effect of poverty [68]. ¨ Assuming a prevalence of 5 per 10 000, Jarbrink and Knapp [69] estimated the annual societal cost of autism for the United Kingdom in 2001 to exceed £1 billion. The lifetime cost for a person with autism exceeded £2.4 million; the main costs were for living support and day activities. In a subsequent study in ¨ Sweden, Jarbrink [70] estimated that the additional annual societal cost due to autism was ¤50 000 per child. Parents of these children spent about 1000 hours per year additional time caring for and supporting them. Ganz estimated that the lifetime per capita incremental societal cost of autism in the United States was $3.2 million in 2003 [71]. One treatment, ABA, which has been studied the most, has been shown to be effective in improving cognition, and social skills while decreasing behavioural problems. However, it requires intensive one-to-one intervention with the child. In the original studies, Lovaas et al. [72] children received 40 hours of intervention per week. This utilises significant community resources. A recent intervention using the Denver model (an amalgam of ABA and other interventions) was estimated to cost $50,000 per year

THE EPIDEMIOLOGY OF AUTISM

per child [73]. Children in schools with ASD use resources including special education, therapy services (e.g. speech and occupational therapy), aides and psychologists. Some individuals with more severe ASD move to institutional settings, especially when they become adults. This increases costs to society as well. In one study of institutionalised people in Sweden more than 13% had autism [74]. Many adults with ASD are unemployed, which also has economic costs to society. Another public health impact of the increase in children diagnosed with autism has been a decrease in immunisation rates. Parents are choosing to delay vaccination of their children or follow alternate vaccine schedules. In the United Kingdom the rate of immunisation with MMR has decreased significantly; in 1998 56 cases of measles were reported; in 2007 1348 cases were reported [75]. Unlike undervaccinated children, unvaccinated children are more likely to be white, live in higher income households and have a married mother with a college education [76]. They choose not to have their children vaccinated, mostly because they fear that the vaccines will cause autism.

26.9 Associations and causal factors Prenatal conditions including maternal infection with rubella, and exposure to valproic acid, thalidomide and misoprostol have been linked to ASD in the offspring. Congenital disorders of the cerebellum and brainstem like Joubert’s syndrome and Moebius sequence also have been associated with ASD. Disorders of immunity in the affected individual have been suggested as aetiological factors for autism, which formed the basis of the studies on MMR vaccine. Several large epidemiological studies have shown no association between MMR vaccines and ASD. A Cochrane review of the MMR vaccine [77] concluded that exposure to MMR was unlikely to be associated with autism. The Institute of Medicine (IOM) issued a statement indicating that no reliable information linked MMR vaccine in the majority of individuals to ASD [78]. The possibility that other types of inflammations or autoimmunity are involved in ASD continues to be investigated.

Significant variation in the rates of autism among US states has suggested the possibility of an environmental trigger. Exposure to toxins, including mercury, have been suggested as causes of ASD. Several vaccines given to children contained methyl mercury (thimerosal) and were felt by some to contribute to the increase in ASD. In 1999 thimerosal was removed from most commonly given vaccines (except for killed influenza and RhoGam). Despite this, the prevalence of ASD has continued to rise [79]. Prenatal exposure to thimerosal-containing anti-D immune globulins (RhoGam) has been shown not to increase the risk of autism [80]. The IOM also stated that mercury was not a factor in the occurrence of ASD [78]. Several studies implicating exposure to pesticides have been published [81, 82]. One study has linked ASD with annual rainfall in three US states [83], although the likelihood of ecological fallacies in this study has been raised. For example, rates of medical specialists were higher in the areas of greater rainfall; these areas also were more likely to be urban, which is a factor previously associated with higher rates of autism [47].

26.10 Future directions Epidemiology provides data that are vital for the prevention and control of disease. It has a public health function – to monitor and reduce the burden of disease on the community; and it has a scientific function – to understand the causes of a condition. ASDs are a heterogeneous group of conditions that have shown a dramatic increase in occurrence. However, our knowledge of aetiologies and therapies regarding ASD is unsatisfactory. Epidemiology can help clarify our understanding and monitor the impact of ASD. However, the studies need to be scientifically rigorous. Well accepted criteria for a sound prevalence study include the following: specification of the target population, valid sampling, adequate sample size, adequate response rate, information on non-responders, valid and reliable disease definition, measurement with valid instruments and consistency of measurement across sites with reduction of observer bias [83]. Many of the current studies of ASD do not meet these criteria, especially with regard to valid and 477

CHAPTER 26

reliable disease definition, which have changed over time. ASDs represent a continuum of behaviourally defined conditions that are diagnosed through clinical observations. The complex nature of these behaviourally defined disorders, coupled with the current lack of genetic or biological markers for early and consistent identification, make epidemiologic investigation challenging. One future step that could dramatically improve these studies would be to find biological markers (biomarkers) like messenger RNA, genetic differences or patterns of enzymes and proteins (proteomics) that could help identify individuals who have ASD. Technological improvements in molecular genetics have made the collection of samples for DNA, including high-resolution DNA microarray, feasible in large field studies using cheek scrapings or small blood samples from finger sticks. They also have made testing for candidate genes economically feasible for large samples. Yet, because of the heterogeneity of ASD, it is likely that even when biological or genetic markers are found they will not be present in all individuals with ASD. Breaking down samples into subgroups with specific patterns (endophenotypes) such as people who have autistic disorder with mental retardation and a history of regression, or people with high-functioning Asperger’s syndrome and deficits in attention, motor control and perception (DAMP) [85] may allow more consistent identification and new discoveries into the aetiology of the conditions. Current work to develop multiple source surveillance networks [86], and large cohort or case–control studies [87] can help in the near future by yielding more reliable information. Work for the more distant future includes developing international studies using a variety of techniques, such as records-based approaches with structured services and school records, registry-based approaches and screening – even to the extent of going door-to-door – in less developed countries. Finally, it is critical to use the information obtained in a way that will convince parents that the benefits of vaccines outweigh the risks.

26.11 Summary The number of individuals with ASD has increased dramatically, which has increased the burden of care 478

for schools, preschool programmes and healthcare facilities. Epidemiological studies to monitor the changes in prevalence and provide insight into the causes and impact of ASD have been limited by methodological problems. Some of the increase in prevalence can be attributed to (i) intentionally broadened diagnostic criteria, (ii) greater public awareness and (iii) improved case finding. While these factors explain more than half the excess of cases, they do not completely explain the observed increases. It appears that ASDs are increasing with no sign yet of levelling off. Earlier low-powered studies to provide insights on aetiology have implicated the measles and MMR vaccines as well as thimerosal. Even though subsequent studies have not supported these earlier scientifically weak findings, the confidence of the public has been shaken and fewer individuals are being adequately immunised. Future studies that better define ASDs and examine subgroups should help monitor the impact of these conditions and provide insight into aetiologies. If information from these studies is used wisely, it could restore faith in vaccines as well as improve the lives of people with ASD.

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Mental illness, women, mothers and their children Kathryn M. Abel1 and Vera A. Morgan2 1 Centre

for Women’s Mental Health, University of Manchester, Manchester, UK Epidemiology Research Unit, UWA School of Psychiatry & Clinical Neurosciences, Perth, WA, Australia

2 Neuropsychiatric

27.1 Introduction This chapter explores ways in which epidemiology can further our understanding of the experience of mental illness for women by examining the complex relationships between maternal mental illness, maternal condition (the physical and psychological fitness of the mother during conception, pregnancy and the postpartum period) and risk of mental and other health outcomes in their children. We have attempted to use the best quality data as exemplars. In some instances, this has restricted us to studies examining only the more severe mental illness spectrum, such as schizophrenia, for which more high quality studies exist. This does not mean that arguments we put forward are only relevant to this relatively rare disease. However, it does reflect a bias in the literature in that many studies only consider schizophrenia as an offspring outcome following maternal exposures, or focus on mothers with schizophrenia. In so doing, the literature often fails to address the specificity of findings to schizophrenia or, for instance, whether mothers with schizophrenia represent greater risk to offspring than mothers with other mental disorder. We shall describe a wide range of complex processes that impinge on the mother and may subsequently affect her child, from early development

through to adulthood. In Section 27.4, we present a diagram which provides a schematic framework for understanding ways in which a variety of biological and social mechanisms might intersect with factors in children to inform their developmental pathways and influence risk of physical and mental health outcomes. We attempt to explore factors that have an impact beyond pregnancy, such as family environment and paternal factors and the detailed sections that follow will try to unpick some of the processes represented in the diagram. An important aim of this chapter is to understand better many of these processes and how they maybe linked. Untangling the links between maternal and child experience of mental illness should be a priority for future research in the interest of disease prevention, as well as to optimise quality of life for affected children by delivering appropriate interventions as early as possible. Therefore, we aim to draw out, from a range of epidemiological findings, the practical implications for clinicians working with patients. First, we consider incidence of mental disorder in women. Then we appraise fertility in women with mental illness before reviewing ways in which maternal condition and mental illness may affect pregnancy and offspring outcomes for women and their children.

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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27.2 The epidemiology of mental illness in women of reproductive age Assessing disease incidence provides a measure of how many new cases are expected to occur in a given population over a given period of observation. Gradients in the incidence of a disorder across time and place can provide powerful clues to help unravel aetiology. However, variations in incidence are also well described within or between various populations. This implies that aetiological factors are not uniformly distributed. Sex differences in incidence and prevalence, presentation and outcome of illnesses represent an important boundary between risk groups [1].

27.2.1 Schizophrenia In a recent meta analysis, McGrath et al. [2] identified a median ratio of male:female incidence

of schizophrenia of 1.40 (0.9, 2.4). This sex difference in rates of disease was significant (F1, 30 = 76.8, p < 0.001), as well as generally consistent between studies. Figure 27.1 shows the distribution of the rate ratio for females versus males. These ratios are matched, and derived from within-study female to male ratios. Values greater than one indicate a higher incidence of schizophrenia in males compared to females. The rate ratio curve is normally distributed. These incidence rate ratios tell of rates of disease within specified time frames. They do not reflect the interactions seen in some studies between disease age-of-onset and sex. Whilst the rate ratio curve for incidence is normally distributed, the curves for ageat-onset are not. Men show a modal incidence in their early 20s and perhaps a second peak around middle age. Women also show modal onset in their early 20s, but this is a broader, lower frequency mode and is followed by a more pronounced peak in middle age than men [4–6]. There is a switch from

100 90 80

Cumulative Percentage

70 60 50 40 30 20 10 0 0

1

2 Ratio Ratio

3

4

Fig 27.1 The cumulative percentage of the rate ratio of the incidence of schizophrenia in males versus females (McGrath et al. BMC Medicine 2004 2:13 [3]).

484

MENTAL ILLNESS, WOMEN, MOTHERS AND THEIR CHILDREN 175

GENDER Male Female

150

125

Count

100

75

50

25

0 10– 15– 20– 25– 30– 35– 40– 45– 50– 55– >60 14 19 24 29 34 39 44 49 54 59 Age at Onset

Fig 27.2 Age at onset incidence curves by sex from Drake et al. [19]. Data from a first episode sample (10–65 years) of broadly defined schizophrenia cases.

male predominance in incidence during the early 20s to a female predominance in incidence at older ages. Using admixture analysis of a relatively small case register sample of non-incident cases, Castle et al. [7] suggested that the early modal onset of non-affective psychosis lies at 21–22 years in both women and men, whereas there are secondary underlying curves with modes at ∼36 in men and ∼39 in women; and women alone have a third mode of onset in their early 60s. The higher incidence among older women did not offset the higher incidence among younger males, thus still resulting in a significantly higher rate among men when all ages were combined. For example the male:female risk ratio was 1.34 [7]. Sex and age-at-onset differences in the clinical presentation and course of non-affective psychosis are key biomarkers and may provide important clues to the aetiology of the disorder. Sex differences are broadly consistent in different countries and cultures [5–9]. During first episodes [4, 9, 10], levels of negative symptoms are generally higher in men, though this is not the case in Salokangas’ first

contact sample aged 15–44 (1997a), nor in the ABC cohort [6]. Women may have higher levels of depressive symptoms than men at onset and throughout disease progression [7, 11–13] and paranoid symptoms may be more severe in women than men with older onset (e.g. [6, 7]). But conclusions from this literature are unclear because of small sample sizes, cross-sectional sampling, lack of follow up or the bias inherent in non-incident samples (e.g. [7, 10–16]). More recently, one of the largest samples to date (n = 537) of first episode schizophrenia (i.e. two incident samples combined covering ages 10–65; [17, 18]) was examined for differences in presentation and course of disorder over a 12–18 month follow up period [19]. Admixture analysis suggested underlying distributions with modes in the early 20s and mid 40s for each sex. Males predominated under 43 years and females over 43 (Figure 27.2). Both sex and age-at-onset determined differences in incidence, presentation and early course of illness such that early onset cases were predominantly male, but in later onset cases females predominated. 485

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Early onset cases showed worse overall positive and negative symptoms scores (PANSS), negative and cognitive symptoms in men and worse depressionanxiety scores in women, consistent with previous studies [4, 12]. The association of older age-at-onset with fewer negative and cognitive symptoms at presentation was also consistent with other findings (e.g. [9, 20, 21]). Overall, men presented with more negative symptoms and women with more mood symptoms, independent of age at onset. Sex difference in incidence or prevalence of schizophrenia may depend on the stringency of the diagnostic criteria applied: the broader the criteria the less significant are the sex differences in incidence or prevalence (e.g. [7, 9]). Susser and Wanderling [22] further distinguished sex difference in incidence of schizophrenia from that of non-affective remitting psychosis (NARP). Using the 10 countries World Health Organization (WHO) cohort, they compared incidence between schizophrenia and NARP and looked at variation in sex difference by setting, that is developing versus developed country and rural versus urban settings. They reported that the annual incidence of NARP per 10 000 people was about double in women to that in men in the developing-country setting, 0.878 vs. 0.486, respectively, p = 0.07; in the industrialised-country setting, it was 0.104 vs. 0.040, respectively, p = 0.04. In the developing-country setting, the incidence was about 10-fold that in the industrialised-country setting for both sexes (men, 0.486 vs. 0.040, respectively, p < 0.001; women, 0.878 vs. 0.104, respectively, p < 0.001). Authors commented that these associations were sharply distinct from those found for schizophrenia in the WHO 10 countries study. Prevalence rates of a disorder index burden of disease and tell us about a range of factors that might differentially influence the course and outcome of illness. Case ascertainment methods and sample selection can also bias prevalence rates; availability of, and access to, appropriate treatments as well as response to treatment and the burden of diseasemaintaining rather than disease risk factors within the environment will also be reflected in prevalence rates. The most recent and comprehensive systematic review [23] concluded that, in contrast to their and others’ findings in incidence, there was no significant sex difference detected in prevalence for combined 486

point, period and lifetime estimates where median values ranged from 3.3 to 7.2 per 1000 persons. This was surprising given the clear sex difference in incidence and the consistently reported differences in course and prognosis of illness for women and men, both of which predict greater male prevalence. These prevalence findings require further investigation and replication if they are to influence change in service planning.

27.2.2 Depression Much depressive illness never presents to mainstream inpatient or outpatient mental health services, but remains within the primary care setting and much may not present at all, except in its most severe form. For this reason, large population registers that rely on hospital admission data are imperfect sources of incidence and prevalence data for depression compared to more severe disorders such as psychosis. Moreover, studies concerning sex differences in incidence and prevalence have also been criticised because women may be more likely to present with depression than men. National population surveys provide some of our best estimates of the prevalence of depressive disorders including sex differences. The National Comorbidity Survey (NCS) [24] was the first survey in the United States to administer a structured psychiatric interview to a nationally representative sample of the general population. The survey attended to some potential biases and provided basic epidemiological prevalence data on sex differences in Diagnostic and Statistical Manual of Mental Disorders (DSM)-III-R major depressive episodes (MDEs). Findings from the replication of this study, or NCS-R, published a decade later [25], were consistent with previous research, with women approximately 1.7 times as likely as men to report a lifetime history of MDE. Age-at-onset analysis showed that this sex difference begins in early adolescence and persists through to the mid-50s. Women also had a much higher rate of 12-month depression than men. However, women with a history of depression did not differ from men with a history of depression in either the probability of being chronically depressed in the past year or in the probability of having an acute recurrence in the

MENTAL ILLNESS, WOMEN, MOTHERS AND THEIR CHILDREN

past year. This suggests that the higher prevalence of 12-month depression among women may be largely due to women having a higher risk of first onset.

27.2.3 Suicide This section will consider suicide in women broadly. In Section 27.8.5 below we focus on suicide in the context of motherhood. It is well recognised that women commit less suicide than men overall and that young men are at greatest risk; this appears to hold in most cultures. However, suicide is a good example of how crude descriptive epidemiology can be misleading. In Asian countries (India and China) where over 60% of the world’s suicides occur, low male : female ratios for suicide are common [26]. Similarly, among US whites aged 40–64 years, the rate of completed suicide between 1999 and 2005 rose by 3.9% annually for women and by 2.7% annually for men, with increases of 2.3 and 6.3% for hanging/suffocation, 19.3 and 2.8% for poisoning and 1.9 and 1.5% for firearms for women and men, respectively. Rates did not increase for other age or racial groups. Being female and in middle age historically has been associated with lower risk of suicide, but the rate for white US women aged 40–49 years surpassed all other age groups [27] although it is unclear whether this trend is similar for women and men of middle age in other countries. Studies which assess suicide mortality by time period, method, age, race and sex appear more likely to uncover important increases in specific populations and may help to prioritise and guide prevention efforts. Findings from these approaches imply that risk factors are not uniformly distributed and allow us to question whether or not being female is always a protective factor. Studies suggest that some women are at very high risk of suicide (e.g. [28, 29]). Young immigrant Pakistani women were reported recently to show the fastest (growing) rate of suicide among any group in the United Kingdom [30, 31]. These excess rates of suicide are thought to reflect the relative isolation and displacement from family experienced by these women in the process of arranged marriages. Similarly, there is some indication that young married women in rural China might also be at particularly high risk of suicide [32]. Few studies have taken psychosocial factors associated with being

female or male into account in their explanation of suicide risk in different groups. Psychosocial factors may act independently of presence of mental illness. Violence against women is associated with high rates of mental illness, substance abuse, self harm and an increased risk of suicide [33]. But, for a woman, the experiences and risks associated with being married differ greatly across regions, countries, cultures and economies and attribution of causal pathways should not be oversimplified when describing suicide in different countries. At least in some settings, women who commit suicide may be more likely to have a known or recorded mental illness than men [34]; and some mental disorders such as eating disorder [35] or personality disorder [28] appear to place women at particular risk. Premenstrual exacerbation of depression is well recognised and may also relate to the consistently reported increased rates of admission and attempted suicide by women at this time [36–38]. Current information on suicide and gender may have important implications for clinicians and policy makers and raises the possibility that risk assessment instruments should be gender, as well as culture and age specific (e.g. [39]).

27.3 Fertility and fecundity in women with mental illness The fertility of people with mental illness, and in particular psychotic disorder, remains subject to much debate. Central to this is its relevance to rates of, and prevention of, obstetric complications (OCs) (see Section 27.4.2); its implications for family planning services and child-care support, and for the aetiology of these disorders. But there are few high quality population-based studies of fertility in mentally ill populations. Lower rates of marriage cannot explain the finding that people with mental illness (especially women) consistently have significantly fewer children than age-matched individuals in the general population. Our ability to explain reduced fertility is limited by a lack of information on key potential confounders related to illness. These include ‘contraceptive’ effects of medication, poor physical health, social isolation and a lack of opportunity to meet potential partners, poor maternal condition and 487

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the insidious effects of poor lifestyle on conception and successful pregnancy. Disentangling genetic and ‘environmental’ effects of illness on fertility therefore remains problematic. The two largest population studies only consider psychosis or schizophrenia and only Howard et al. [40] calculate a general fertility rate (GFR). They argue that fertility is most accurately measured using GFR where GFR = births during the year divided by women aged 15–44 at mid-year × 1000. This ratio represents the total yearly registered births to the population of women of ‘childbearing’ age. This is different from most studies examining fertility in mental illness which have used the presence or absence of offspring, or the number of children a woman has had, as an indicator of fertility. Howard et al. used a large UK general practice-based population in a case–control study with 6306 cases of psychotic disorder: 2104 (33.4%) had schizophrenia, 349 (5.6%) schizoaffective psychosis, 1078 (17.1%) paranoid psychosis, 1141 (18.1%) bipolar psychosis, 211 (3.4%) puerperal psychosis, 38 (0.6%) drug-related psychosis, 7 (0.1%) had an alcohol-related psychosis, 353 (5.6%) depressive psychosis and 1025 (16.3%) psychosis not otherwise specified (NOS). Contraceptive use was significantly less common in cases than controls (28.8% vs. 32.3%; odds ratio (OR) 0.84, 95% confidence interval (CI) 0.80, 0.89, χ2 (1) = 36.32, p < 0.001). Irreversible causes of non-conception (hysterectomy, bilateral oophorectomy, menopause, male infertility, sterilisation in male partners or subjects) were more common in controls: (8.7%) vs. (10.7%), OR 0.79 (95% CI 0.72, 0.86), χ2 (1) = 27.77, p < 0.001. Overall, women with psychotic disorders had a much lower GFR than controls, (relative risk (RR) 0.57, 95% CI 0.48, 0.67, p < 0.001), although fertility was only significantly reduced in women aged 25 and over. These findings suggest that physical causes of reduced fertility may be less important than psychosocial causes. Reduction in fertility was less marked in women with affective psychoses. There was no evidence that neuroleptic prescription influenced the fertility rate in women with non-affective psychoses. These data are consistent with reduced fertility rates in other smaller studies but have yet to be replicated in large population samples or in samples including other exposures 488

such as depression which may also be associated with reduced fertility rates in premenopausal women [41]. Hypotheses to account for the persistence of schizophrenia in the population, despite evidence of reduced fertility, include the proposal that schizophrenia carries some physiological advantage (for which there is no evidence (see [42]); that environmental factors in the aetiology of schizophrenia, such as prenatal infections or OCs, have increased over time compensating for the reduction in genetic factors caused by low fertility; that de novo mutations causing schizophrenia or other psychosis have increased and finally that lower fertility in patients is compensated by greater fertility in their relatives. Haukka et al. [42] examined whether increased sibling fertility could compensate for infertility in schizophrenia patients and did not find this to be the case. In a 10-year 1950s cohort of the whole Finnish population where 1.3% had a diagnosis of schizophrenia and 2.8% were their siblings, the mean number of offspring among female siblings of women with schizophrenia was only slightly, even though significantly, higher than among women in the general population (1.89 vs. 1.83), while the opposite was true for the male siblings (1.57 vs. 1.65 among men in the general population). The mean number of offspring among patients with schizophrenia was 0.83 for women and 0.44 for men. Thus, persistence of schizophrenia in the general population is not likely to be explained by this simple evolutionary mechanism and suggests that persistence of schizophrenia is related to multiple and non-specific genetic and other factors.

27.4 Maternal mental illness at the time of conception and during pregnancy 27.4.1 Ill mothers and fetal exposure to behavioural or modifiable risk factors Women with a mental illness who become pregnant represent a high risk group of mothers, not only because the mental illness may be more likely to recur or worsen following childbirth (see below), but also because they are more likely to be exposed, (and to expose their fetus), to a range of adverse

MENTAL ILLNESS, WOMEN, MOTHERS AND THEIR CHILDREN

circumstances. We may say that mothers with mental illness have relatively poor ‘maternal condition’. This means greater risk of poor antenatal care, poor nutrition, use of prescribed medications, illicit drugs and alcohol, greater likelihood of smoking and of continuing to smoke through pregnancy and less positive benefits available from material and emotional supports [43, 44]. Epidemiology has struggled to identify which of these many different risk factors are associated with

NEUROBIOLOGICAL (CELLULAR) PROCESSES

CONCEPTION - BIRTH

which of the many adverse outcomes that a mother with mental illness and her offspring are likely to suffer (see Figure 27.3). A good illustration of the difficulties encountered by researchers is the problem of understanding effects of psychotropic drug exposure on both maternal and fetal outcomes. The size and lipophilicity of these medications increases the likelihood that they will cross the placenta and adversely affect a developing fetus [45]. At the same time, while good data on their teratogenicity are

CHILDHOOD

ADOLESCENCE

ADULTHOOD

Proliferation Differentiation Migration Synaptogensis Pruning Myelination GENETIC PROCESSES

Susceptibility Genes Epigenetic Mechanisms Gene–environmental Interactions SEX CHROMOSOMAL AND HORMONAL PROCESSES ENVIRONMENTAL PROCESSES Obstetric Complications: hypoxic; infective/immunologic; growth restrictive Psychologic: stress, trauma, adversity Physiologic: head injury, toxic exposure Drug Abuse Pre-pregnancy maternal condition and maternal intrauterine environment 1st hit

Family rearing environment

External (social/physical) environment

Effect mediators

2nd hit

DISEASE ONSET AND TIMING

Severe Mental Illness Autism Spectrum Disorder Cognitive Deficits Intellectual Disability Birth Defects Minor Physical Anomalies Neurological Soft Signs

Fig 27.3 The potential timings and impact of neurobiological, genetic, chromosomal/hormonal, environmental and social processes on neuropsychiatric outcomes across the lifespan.

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limited, there is emerging evidence indicating that some of these medications may be harmful to the growing foetus. For example, certain anticonvulsants prescribed as mood stabilisers in bipolar disorder are associated with increased risk of congenital anomalies [46–48] and polypharmacy significantly increases risk of major congenital malformation [49, 50]. An association with developmental delay however is more controversial ([51]; reviewed in [48]. In contrast to earlier reports, recent investigations have also found an association between maternal use of selective serotonin reuptake inhibitors in pregnancy and infant birth defects [52, 53]. Discontinuation of psychotropic medication in ill women during pregnancy may in itself create risk and has been associated with worsening of symptoms, often leading to psychotic episodes in relatively asymptomatic women (reviewed in [43, 54–56]. Illness also creates more stress for a mother and may lead to poor nutrition and self-care, as well as other risky behaviours such as smoking, drug and alcohol use [43]. Thus, a balance must be reached between, on the one hand, the risk of harming the developing fetus and, on the other, the benefit of continuing psychotropic treatment to avoid deterioration or relapse of illness with attendant risks for mother and fetus [57]. In order to understand long-term effects of psychotropic medication exposure in pregnancy on maternal and infant outcomes, longitudinal population data are required. There are a limited number of population-based registers that are capable of identifying women in pregnancy and linking their records to pharmacy prescription data. While an important source of data, these registers are limited to the extent that it is not possible to assess medication compliance. There are few large, case-controlled studies and available reports show mixed findings (reviewed in [58, 59]). Ethical considerations preclude the participation of pregnant women in large-scale prospective, randomised controlled trials so much of the data are limited to case reports, small prospective studies and retrospective data collections. Finally, it is also possible that ill pregnant women experience increased anxiety and stress during pregnancy, which itself is associated with a range of OCs, such as shortened gestational length [60, 61]. Pregnancies of ill women are also more likely to be unplanned, unwanted or a result of coerced sexual 490

encounters [43, 62]. Ill mothers are far more likely to lose custody of their child after delivery, fear of which may exacerbate psychotic symptomatology during pregnancy and/or pregnancy-related anxiety [43, 44]. The experiences of pregnant women with severe mental illness and their sequelae require far more research; this includes an examination of differences across countries and cultures.

27.4.2 Pregnancy outcomes for mothers with mental illness Overall, mothers with mental illness appear more likely to experience OCs during pregnancy and delivery, with inadequate prenatal care and increased rates of prenatal hospitalisations, eclampsia and pre-eclampsia, pregnancy-associated hypertension and preterm delivery and growth restricted babies (e.g. [43, 63, 64]). Studies of hospital samples from at least three different countries suggest mothers with depression and/or anxiety are at greater risk of nausea and vomiting, prolonged sick leave before term, increased obstetric visits [65], pre-eclampsia [66], epidural analgesia, Caesarean and instrumental ´ vaginal delivery [67]. Banhidy et al. [68] compared infants born to mothers with and without panic disorders in a large Hungarian population-based cohort and reported elevated rates of anaemia and polyhydramnios in women with panic disorders. Recent population samples from Australia and Scandinavia examined OCs in severe maternal mental illness (schizophrenia, bipolar disorder, major depression) with varying results. In Western Australia, women with schizophrenia or major affective disorder had raised risk of placental abnormalities and antepartum haemorrhage [69]. Using a similar methodology, Bennedsen et al. [70, 71] reported no excess OCs for Danish women with schizophrenia. Offspring of these women also have significantly more perinatal difficulties than offspring of comparison groups, with decreased APGAR scores, lower birth weight and increased rates of neonatal medical treatments. Ellman et al. [72] explored whether these outcomes were related to a shared genetic liability in the mother to OC risk and schizophrenia by studying first-degree relatives of mothers with schizophrenia; they found no excess OCs in first

MENTAL ILLNESS, WOMEN, MOTHERS AND THEIR CHILDREN

degree relatives compared to controls with no family history of schizophrenia.

27.4.3 Physical health outcomes for the children of parents with mental illness A number of studies have examined offspring outcomes for mothers with severe mental illness and focused on very early outcomes that do not require long follow-up periods, in particular the risk of mortality and birth defects. Interest in mortality risk for the children of mothers with schizophrenia first appeared in the 1930s in the works of Essen-Moller and Kallmann. The majority of studies that have looked at the evidence since then have found an increased risk of infant mortality when women with mental illness severe enough to have required psychiatric admission give birth; these are summarised in Webb et al. [73]. The association between maternal schizophrenia and offspring mortality is supported by recent studies using large Danish [70, 71] and Swedish [74, 75] population registers; but not by Jablensky et al. [69] using the Western Australian registers. Nilsson et al. [75] also reported that the risk remains significantly elevated if the parent with schizophrenia is the father. In addition, many studies have found an increased risk of stillbirths in this group of women, with meta-analysis indicating a doubling of the risk [73]. A number of studies examined other maternal psychiatric disorders with mixed results. Similar findings have been reported for mothers with any psychosis [76]; the same is not true for mothers with affective psychoses [69, 77]. Using Danish registers, Webb et al. [78] found increased stillbirth and infant mortality risks for the children of either mothers or fathers who were admitted for any psychiatric illness; in the main, these risks remained elevated through to adulthood. In the Bennedsen and Nilsson studies, excess mortality was largely attributable to postneonatal death. Bennedsen et al. [70, 71] showed that the increased risk of postneonatal death was explained primarily by a higher rate of mortality from sudden infant death syndrome (SIDS) in the offspring. Similarly, Webb et al. [79, 80] report significantly raised rates of SIDS in Sweden for offspring of parents with any psychiatric inpatient admission. After the 1992 risk reduction campaign,

the relative risk in this group rose as rates in the general population declined; the authors attributed this to differential reduction in known risk factors, especially smoking, across case and comparison parents. By contrast, Nilsson et al. [75] did not find increased risk was due to SIDS, nor could it be explained by maternal behaviour during pregnancy. They postulated that postbirth parental social disadvantage and behaviours may be an important consideration. More work is needed to understand the reasons for these reported associations. These are likely to be multiple and complex, including influences in both the pre and postnatal period, and not limited to maternal factors. Findings for birth defects have been less consistent than those for mortality. Sobel reported an increased risk of birth defects in the children of mothers with schizophrenia [81]. More recently, Bennedsen et al. [70, 71] reported a small increase in risk of birth defects in children of mothers with schizophrenia in Denmark. Older studies have not found an association between birth defects and maternal nonorganic psychosis [82] or maternal postpartum psychosis [83]. Jablensky et al. [69] examined risk for children of mothers with schizophrenia, unipolar major depression and bipolar disorder and found no increased risk of birth defects overall in any group. However, they did report an increased risk of specific groups of defects, namely cardiovascular defects and other defects such as minor physical anomalies, only in the children of mothers with schizophrenia. Webb et al. [84] also observed an association between fatal birth defects and both maternal affective disorders, schizophrenia and drug and alcohol disorder. While increased rates of birth defects in the children of women with mental illness may suggest the involvement of genetic factors, other factors such as lifestyle and teratogenic effects of psychotropic medications during pregnancy are highly relevant as discussed above.

27.4.4 Neuropsychiatric outcomes for the children of parents with mental illness There is a dearth of literature on intellectual disability in children of women with severe mental illness, although more on cognitive impairments in this group. The literature focuses on the link between 491

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schizophrenia and intellectual disability and many studies now report increased rates of schizophrenia in individuals with intellectual disability [9, 85, 86]. Evidence also supports substantial familiality across these disorders including greater rates of intellectual disability in families of probands with schizophrenia [87–89], and vice versa [90]; as well as increased rates of multiply affected families in people with co-occurring intellectual disability and schizophrenia compared to those with schizophrenia only or intellectual disability only [91, 92]. Environmental risks factors, including OCs have been implicated in the aetiology of intellectual disability [93, 94], including autism [95], especially complications related to foetal distress [95, 96]. Since women with schizophrenia are more likely to experience OCs when they give birth, their children are at increased risk of intellectual disability from environmental risk factors, over and above a genetic liability. This is supported by parallel findings from autism research that suggest that prenatal environmental risk factors and parental psychopathology act independently on the risk for autism [97]. While older studies found increased rates of intellectual disability in children of mothers with mental illness [88, 89], there is only one known contemporary study to have undertaken a similar investigation. Morgan et al. [98] found children of mothers with schizophrenia, bipolar disorder and unipolar depression were up to three times more likely to have intellectual disability as comparison children of mothers with no psychiatric history; multivariate analysis indicated that genetic and environmental risks acted independently. There is a more extensive literature on developmental delays, generalised cognitive deficits and learning difficulties in children of mothers with severe mental illness. Following Fish’s publication of developmental delay and neurological deviations in high risk children of women with schizophrenia [99], many studies have reported delays in both neurological and motor development in this group. They have also reported generalised cognitive deficits and learning difficulties as well as poorer performance than controls on specific neurocognitive tasks. In addition to reports of findings from individual high risk studies, there have been a number of excellent reviews on these topics. See, for example [100–103]. At the same time, from a genetic perspective, the literature 492

on neurocognitive endophenotypes in schizophrenia points both to the primacy of cognitive impairment in schizophrenia [104] and its persistence in unaffected relatives of probands with schizophrenia [105]. The children of women with severe mental illness are at increased risk of other neuropsychiatric outcomes. Much research in this area has focused on the risk of schizophrenia. Evidence from twin, adoptive and family studies indicates that the disorder involves a significant genetic contribution. Children of women with schizophrenia have an 8–10-fold higher risk of developing the disorder compared to the general population [106], with the risk in a monozygotic cotwin increased to about 50-fold [107]. A recent Danish register study reported that the cumulative incidence by age 52 of schizophrenia in children where one parent had had a psychiatric inpatient or outpatient admission for schizophrenia was 7.0% [108]. If both parents had had schizophrenia admissions, the cumulative incidence of schizophrenia in children was 27.3%, rising to 39.2% for schizophrenia and related disorders (schizoptypal and delusional disorder) and 67.5% for any psychiatric diagnosis. However, the pattern of inheritance in schizophrenia is not a simple Mendelian one. It is likely that multiple genes of small effect contribute to the disorder, and that a range of environmental risk factors interact with this genetic susceptibility [109].

27.4.5 Parenting outcomes in mothers with serious mental illness Few epidemiological studies have assessed the parenting outcomes for mothers with serious mental illness. Kumar et al. [110] reporting on a small case-series of women admitted to a single UK mother and baby unit suggested that mothers with schizophrenia had the poorest outcome, 50% being separated from their babies at discharge. One follow-up study of women with postpartum psychosis reported that they are more likely to experience further psychiatric episodes than mothers with non-psychotic affective disorders, endangering the relationship between mother and child further [111]. In the largest reported sample, Salmon et al. [112] described clinical and parenting outcomes in 1081 joint mother–baby admissions (mean age 30 years), including 224 women with schizophrenia, 155 with bipolar disorder and 409

MENTAL ILLNESS, WOMEN, MOTHERS AND THEIR CHILDREN

with non-psychotic depression. Almost one-third had poor outcomes; these were associated with a diagnosis of schizophrenia, behavioural disturbance, low social class, psychiatric illness in the woman’s partner and the absence of a good relationship with a partner or others. More detailed analysis of an overlapping UK mother–baby sample of 1153 consecutive admissions (n = 239 schizophrenia; n = 693 affective disorder) [44] suggested that mothers with schizophrenia were characterised by staff as having more complex clinical and psychosocial problems, and were considerably more likely to have a range of poor parenting outcomes compared to mothers with affective disorder. Only half of the mothers with schizophrenia were discharged home to care for their infants without any level of formal supervision by the social services, in contrast to the great majority of mothers with affective disorders (91 and 80% for unipolar depression and bipolar disorder respectively). Successful parenting was related, in part, to stability within the family, and access to financial and social resources. Thus, mothers with schizophrenia who have better parenting outcomes may be protected by certain factors such as supportive marital and other relationships, and higher social class.

27.5 Gene–environment interactions and offspring outcomes How the expression of severe mental illness is mediated by life course experiences is critical to understanding the intimate nexus between genetic and environmental predictors of outcome for children of parents with mental illness. This includes not only prenatal exposures but also rearing environment and exposure to stressors and adversity. Animal models demonstrate that the experience of deprived rearing environments can permanently change gene expression [113] and chronically alter vital homeostatic systems [114]. There is evidence that high risk children of mothers with severe mental illness such as schizophrenia experience a significantly poorer child rearing environment than those of well mothers, with children of depressed mothers somewhere in between [115]. Weintraub also reported that families of children of a parent with schizophrenia functioned

significantly worse than those with well parents, but that they did not differ from families with a parent with affective psychosis [116]. In the Rochester high risk sample of children of women with schizophrenia, low socioeconomic status and maternal illness chronicity were more important predictors of early outcomes of children to age 4 years than maternal diagnosis per se [117]. When long-term outcomes are examined, high risk children who go on to develop severe mental illnesses themselves report poorer relationships with their parents than those who do not [118]. It is clear that far more translational research is needed to identify modifiable risk factors for high risk children, so that appropriate interventions can be developed and implemented for both mother and child across the developmental life course, commencing in the antenatal period.

27.6 Obstetric complications and risk of adult onset mental disorder in offspring Figure 27.3 shows the timings and potential impact on neuropsychiatric outcomes across the lifespan of complex genetic, neurobiological, chromosomal/hormonal, environmental and social processes from conception onwards. Some consideration of neurodevelopment is of particular importance to the study of risks and outcomes for the children of women with mental illness. In the neurodevelopmental model of disease risk, a disruption of the normal development of the central nervous system in utero or early infancy manifests itself in adulthood as a psychiatric disorder such as schizophrenia, but may also give rise to deficits in psychophysiological and neurological functioning in childhood and early adolescence [119, 120]. As a consequence, there has been considerable interest in the relationship between neuropsychiatric outcomes and exposure to OCs in utero or in the perinatal period. OCs thus represent potentially preventable causes of poor offspring outcomes which makes their possible role in disease processes important to unravel. However, the relative rarity of most OCs means untangling their individual effects is methodologically challenging especially when considering possible association with even rarer disorders such 493

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as schizophrenia. Utilising the efficiency of the case–control method [121], numerous studies have reported an excess risk of psychiatric disorders among individuals exposed to OCs in the prenatal or perinatal period compared to unexposed controls. Many of these studies used maternal recall of OCs which may represent a particular problem for the OC literature: a mother of an ill offspring or a proband with an illness may be more likely to recall adverse events than a mother of a well offspring or a well control. Although studies that use retrospective population based register data avoid recall bias, these studies suffer from other limitations. For example, OC data may be inconsistently recorded, defined and coded both within, and between, registers. Studies have yet to provide definitive information about which OCs increase risk of psychiatric illness, or whether OCs act specifically or non-specifically to increase risk; nor do they provide robust data on causal pathways (either the direction or mechanism of associated risk). These represent important obstacles to our understanding of how OCs may affect risk of neurodevelopmental or psychiatric disorder. Further studies are needed to fill the gaps in the research evidence.

27.6.1 Which obstetric complications are risky? Earlier studies used the term OCs to refer to a very large set of obstetric events, ranging from the rather enigmatic ‘delivered by an untrained person’ [122] to the more clearly defined notion of prematurity or premature labour (e.g. [123]). Even the latter variable can be unclear: some studies do not distinguish between delivery before 37 weeks and that before 33 weeks, despite rather different risk distributions associated with the two. Some studies attempted to get around the problem of rarity of exposure to OCs by grouping disparate OCs together and counting exposure as the total number. Later, scales were developed (see below) and scores of negative obstetric events within specified categories were calculated. These methods were appropriate for the early phase of investigation, because individual OCs could not be studied alone. One of the most popular groupings has been ‘hypoxic-ischaemic-related’ events (e.g. [124–126]). Like other categories (e.g. 494

maternal illness), this implicitly assumes common casual mechanisms for the various OCs within the group, despite a lack of evidence to justify this assumption. As we develop more understanding of the influences on fetal growth and development, it seems less likely that individual OCs share common mechanisms. For example, on closer inspection, events that cause chronic fetal hypoxia (e.g. maternal anaemia, pre-eclampsia, maternal smoking) may be quite different from those causing acute fetal hypoxia (e.g. apnoea at birth related to poor APGAR scores, strangulation by prolapse of cord, meconium inhalation, prematurity), and may have quite different sequelae in terms of brain development and risk of subsequent neurodevelopmental outcome. This problem will also affect studies which adjust for OCs as potential confounders of other associations using grouped measures of OCs.

27.6.2 Measurement of obstetric complications The validity of OCs as risk variables may also be compromised by the type and source of the data used which may include: • Retrospective data: relying on recall (by self or another person, e.g. mother). • Prospective data: interview/self-administered questionnaires or data recorded on administrative birth/midwives registers during pregnancy and birth. • Outcome data: on cerebral palsy, birth defects, intellectual disability and mortality registers. ¨ om ¨ The McNeil–Sjostr Scale [127] attempts to address some of the problems with OC measurement by operationalising the scoring of hundreds of OCs and their treatment. It is underpinned by aetiological considerations and is designed to take better account of the amount, timing and severity of OCs. The scale uses severity weights ‘to reflect the inferred probability of harm to offspring, with a focus on the offspring’s nervous system’ and considers timing of a complication: pregnancy, including trimester; labour and delivery and the neonatal period. When applied to a study sample alongside the Parnas Scale and the Lewis Scale, it was more sensitive at discriminating

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people with schizophrenia from controls on the basis of their obstetric histories [128].

27.6.3 Specificity of risk Some researchers observe that pooling OCs fails to provide sufficient additional information compared to analysis of single complications and may even obscure some differences [70, 71]. Even if we accept assumptions inherent in exposures such as ‘ischaemic–hypoxic events’, most studies only consider one outcome such as schizophrenia (e.g. [129, 130]) and those that report more than one psychiatric outcome have inconsistent findings (e.g. [131]). Pasamanick et al. [132] suggested that OCs cause a continuum of outcomes ranging from severe neurological abnormalities to subtle cognitive and behavioural disturbance. Low birth weight (LBW) is a common obstetric exposure which might be used to examine specificity of effect. Recent data from the largest population sample to date suggest that the association between birth weight and a range of psychiatric disorder is similar, and not confined to the smallest babies but crosses into the normal birthweight range [133]. This does not preclude the existence of a causal relationship between a risk factor and a specific outcome: fetal growth restriction (FGR) is associated with a large range of negative health consequences, but few people would contest that a specific causal relationship exists between FGR and adult hypertension [134]. Alternatively, OCs such as FGR or hypoxia at birth may increase vulnerability to a range of severe mental disorders

non-specifically, where expression of disorder also depends on the interactions between OCs and other gene or environmental risk factors later in life. Examining this kind of interaction in relation to the presence of maternal psychopathology may help further our understanding of the mechanisms of causal risk association.

27.6.4 Mechanisms of risk: Gene–environment or confounding by maternal psychopathology Some of the putative mechanisms underlying increased risk associated with OCs are outlined in Table 27.1. Several of these may be operating simultaneously. A gene–environment covariation model predicts that there would be an increase in the number of OCs in mothers who carry the genes for a psychiatric disorder, regardless of whether they express the mental illness [72]. Therefore, association studies reporting a positive association between OCs and offspring psychopathology may be highlighting unexpressed maternal genetic burden. However, at least two recent studies [72, 135] refute this notion and report no association in unaffected siblings of patients with schizophrenia who would also be expected to carry the same excess risk of OCs. Women with mental illness also have more ‘environmental’ health-risk exposures during pregnancy. They are less likely to have social supports or partners, or to attend or receive antenatal care, and more likely to use drugs and alcohol and to smoke compared to the general

Table 27.1 Some explanatory mechanisms for an increased risk of obstetric complications in mothers with severe mental illness. Mechanism

Description

Obstetric complications as an aetiological cofactor of genetic vulnerability

Some of the genes implicated in schizophrenia may also make a woman with severe mental illness more susceptible to obstetric complications. The effects may be interactive or additive A woman with severe mental illness may be exposed to other environmental risk factors for obstetric complications including poor antenatal care and nutrition, smoking, use of street drugs A child at genetic risk for severe mental illness may be more likely to interact adversely with its intrauterine environment, predisposing its mother to obstetric complications. Here the complications are a consequence of a fetal abnormality, not a cause of it

Obstetric complications as epiphenomena or covariates of mental illness in the mother Obstetric complications as a pleiotropic expression of a genetic liability to severe mental illness

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population of pregnant women (e.g. [136]). Many such exposures are independently associated with OCs. For example, maternal smoking significantly increases risk of LBW, fetal hypoxia, premature delivery, small-for-gestational-age infants and infant mortality [137–140].

27.6.5 Are the effects of obstetric complications confounded by antenatal maternal smoking? Although Nilsson et al. [74] suggested that at least part of the excess risk of OCs in mothers with severe mental illness was mediated by antenatal smoking (and, by implication, other maternal risk behaviours), studies examining this more directly do not. Heavy antenatal smokers are well known to be overrepresented in mothers with psychiatric disorder and these mothers are more likely to have offspring who develop psychiatric disorders. A simple way to manage this in cohort or case–control studies of OC exposure and offspring psychopathology is to add maternal smoking as a covariate. However, lack of maternal smoking data often precludes this as an option. A simple alternative is to exclude mothers with mental illness. Jablensky et al. examined risk of OCs in women who developed mental illness before birth of the child and women who did so after the birth. They found overall risk of OCs was significantly increased in births after illness onset. However, for some specific OCs, including LBW for gestational age, there was no difference in risk between the two groups. Although a less likely possibility, it could be that antenatal maternal smoking itself is a cause of excess psychiatric disorder in offspring but evidence to date suggests weak or non-causal associations [141].

27.7 Parental condition One of the hallmarks of mammalian behavioural development is its sensitivity to the social and physical environments provided by mothers during early development in the gestational and lactational periods. A mother’s social environment, habitat, physiology, diet and even her daily activities can have profound and lasting effects on offspring phenotypes [142]. Parental condition primarily, but not 496

exclusively, relates to the physical and psychological condition of the mother. The following sections consider aspects of parental condition and parental environment (primarily maternal) and the evidence linking them to psychopathological outcomes of offspring. The data emphasise schizophrenia; this probably represents a skew in available epidemiology rather true specificity of effects.

27.7.1 Parental age Parental age has been linked with a range of poor foetal and childhood outcomes. Perhaps the most widely known is the association between advancing maternal age (>35 years) and risk of Down’s syndrome. Most studies in neuropsychiatric disorder, and those reporting the strongest associations, have examined paternal rather than maternal age effects. Most report a significant association with increasing paternal age and schizophrenia [143–148]. Similar findings have been reported for autism, attention deficit hyperactivity disorder and bipolar disorder [149, 150]. Researchers have proposed that advancing parental age is associated with greater risk of genetic abnormalities: the aetiology of these associations has been suggested to involve new, autosomal dominant mutations in the male germ line [151]. This would help explain the continued prevalence of a disorder associated with reduced fertility (see above): gene mutations would constantly replenish the population with genetic abnormalities which contribute to the risk of schizophrenia. Indeed, Brown et al. [144] specified that new mutations secondary to advanced paternal age may operate to increase the risk of schizophrenia (linearly) by adversely affecting brain development. More recently, Saha et al. [152] reported associations with more common outcomes, namely, global and specific measures of child cognition. They reported that older paternal age (adjusted for maternal age) affects IQ between 8 months and 7 years of age incrementally resulting in a 6–14% reduction in scores. In other words, effects accrued as the child aged. A number of studies have also reported a linear relationship between paternal age and risk of neuropsychiatric outcomes from youth through to older age (e.g. [143, 144, 148, 153]). By contrast, in schizophrenia at least, maternal age is reported

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to exert an effect in younger [154] and in older mothers [155] such that shape of the relationship is quadratic rather than linear. In public health terms, deleterious effects of advanced parental age are increasingly relevant as women (and men) exercise greater control over their fertility; for women in the United Kingdom, the mean age for primiparity is now 29. Closer examination of the literature reporting parental age effects on risk of neuropsychiatric outcomes suggests the findings and their explanations may be less robust than the studies would have us believe, particularly, but not exclusively in relation to schizophrenia. In the last decade, four case–control studies [145–156] and three cohort studies [143, 144, 153] have reported positive associations between increasing paternal age and risk of schizophrenia; one meta-analysis [157] has reached more tentative conclusions. There are three key problems for all studies of parental age effects independent of the outcome. First, the stratum of ages and the width of the chosen strata (5 or 10 year bands) for the reference category varies between studies and can change the pattern of findings from non significant to significant and vice versa. In Wohl and Gorwood’s meta-analysis, choosing old paternal age as >35 years allowed for consistently significant excess risk of schizophrenia in offspring compared to offspring with a father either <35 or <20 at their birth. However, using 24–29-year-old fathers as the reference did not. Second, evidence about what the shape of any relationship may be is inconsistent. In part, this may depend on the number of cases available in the different age strata chosen by studies, while the number of cases available in turn may determine the width and age bands of strata chosen in different studies. For example, a linear pattern of excess risk of schizophrenia with increasing paternal age was reported by the same group in two separate publications [143, 144]. However, the Brown et al. [144] finding has wide confidence intervals for the excess risk ratios across the age strata. This is likely to be because the reference age category against which the rate ratios of risk are calculated is 15–24 years and has only eight cases within it. This makes for lack of stability in the rates and wide confidence intervals that cross unity for each stratum, despite excess risk (RR = 1.6) being reported,

even for fathers aged 25–34. Brown et al. [144] conclude that there is evidence for a linear positive association between increasing paternal age and risk of schizophrenia. They suggest that gene mutations may explain this finding. However, if a linear dose–response relationship exists between paternal age and schizophrenia risk, we would not expect gene mutations to produce this pattern because germ line mutations increase exponentially, not linearly, with human age [158]. The third problem with the evidence to date is perhaps the most crucial. Parents (fathers or mothers) who have their first child at older ages may confer risk of adult psychopathology for a range of reasons associated with their late primiparity. In other words, samples which use all births in either case–control or cohort designs and do not distinguish between parents with and without older age at first parenthood may be confounding their findings and showing effects not for paternal or maternal age per se, but for a particular group of ‘odd’ parents whose risk factors may be present at any age. To date, no study has been published showing whether separating offspring by birth order and by age of parents at first birth preserves or removes the so-called parental age effect. Finally, Saha et al. [152] found that the effects disappeared after adequately adjusting for social class. Speculations about mechanisms are therefore likely to be premature and also to be non specific and related to a range of psychiatric outcomes, as one recent Swedish population cohort study has indicated [150].

27.7.2 Maternal nutrition Two landmark studies have examined effects of antenatal maternal famine in an attempt to look at the association between maternal (fetal) nutrition and neurodevelopmental outcomes. They have used an ecological ‘naturalistic’ design to study populations exposed to famine over restricted periods of time: in the infamous Dutch Hunger Winter [159, 160] and the Chinese famines [161]. In the Dutch studies, infant mortality was more than double and fertility (reflected in birth rates 9 months later) was less than half that of the previous year. Health outcomes of a birth cohort conceived during the height of the famine were compared with those of unexposed persons born in the same cities but in gestation 497

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before or after the famine. This birth cohort experienced increased risk of congenital anomalies of the central nervous system including neural tube defects, increased risk of schizophrenia spectrum and personality disorders diagnosed in 18-year-old males at military induction, and increased risk of schizophrenia in adulthood (RR = 2.0; CI 1.2–3.4) [162]. Both studies describe excess risk of schizophrenia. The relative risk in the comparatively small sample from Holland was 2.2 for women and 1.9 for men; in the much larger Chinese sample, estimates were very similar. In both studies, the association was confined to offspring whose mothers conceived, or were in the first trimester, at the height of the famine. Despite the strength of these studies (especially the latter), it is important to keep in mind two sources of error in ecological studies. First, exposure definition cannot take individual experiences into account. In this example, the actual caloric and nutritional intake of individual mothers cannot be accounted for. Second, the timing of the exposure in pregnancy is imprecise which has implications both for the outcomes, and for understanding possible mechanisms. This is important because mechanisms are likely to be timedependent. A child’s date of birth, gestational age and the date of the exposure event are needed to time the exposure by trimester or pregnancy month. Gestational age was not available in the famine studies. For example, in the Dutch famine studies, date of birth was assumed to be month 10 of pregnancy. However, since women subjected to famine during pregnancy are more likely to deliver premature infants, the presumed timing of exposure may not be accurate, with the results open to measurement error. Third, estimates generally cannot be adjusted for key confounders such as family psychiatric history, pregnancy complications, socioeconomic factors or maternal smoking. Finally, specificity of the exposure to schizophrenia, as opposed to other psychiatric or neurocognitive outcomes, is relatively unexplored. Many studies, including studies of maternal nutrition, have insufficient data and/or inadequate designs to test hypotheses concerning plausible mechanisms. This problem is compounded because it is likely that famine, especially one which lasts for several years like the Chinese famine (1959–1961), directly or indirectly affects mothers, as well as fathers and the

498

new infants themselves. Finally, there is a particular paradox related to parental condition in women who manage to conceive at the height of famine. The drop in birth rate associated with the peak in famine is also the time when exposure is associated with greatest schizophrenia risk. This implies that women who are able to conceive are either not malnourished or are/were in much better condition than those in whom pregnancy fails. McClellan et al. [151] suggest de novo germline or somatic mutations may become more common in malnourished women associated with folate deficiency increasing neurodevelopmental risk; experiments to test this hypothesis in exposed Chinese populations are underway.

27.7.3 Antenatal maternal stress Over the past 15 years, a range of exposures linked to increased incidence of severe mental illness, particularly schizophrenia, have been conceived as evidence that in-utero exposure to ‘stress’ causes mental illness in offspring. Many of these have focussed on physical stressors rather than psychological stressors. Most have considered proxy measures of stress exposure using population level or ‘ecological exposures’ which cannot take account of individual experience. For example, in studies of the effect of maternal/fetal exposure to influenza, only 30–40% of the ‘exposed population’ developed influenza, but in the studies, the whole population is defined as exposed in a time period. Far fewer investigations exist examining risk of schizophrenia and prenatal exposure specifically to psychological stressors. Those that do exist rely mainly on population level exposures such as war (e.g. [163, 164]) and natural disaster (e.g. [165]). Imprecise exposure definition may have led to inaccurate estimate of risks in a number of studies which fail to find a significant association with schizophrenia [165, 166]. Selten et al. [166] meta-analysis of war studies suggests that studies should use more clearly defined measures of individual level stress. Despite the challenge of finding a truly representative proxy of individual maternal experience of stress, population epidemiology is the only currently viable approach. Irrefutable evidence that maternal psychological stress increases risk of mental disorder requires either a randomised

MENTAL ILLNESS, WOMEN, MOTHERS AND THEIR CHILDREN

controlled trial (which is impossible), or whole population, longitudinal biological measures of individual mothers’ stress responsivity from preconception to the end of pregnancy, valid measures of individual maternal coping and valid offspring outcomes for at least 30 years. These kinds of data do not currently exist. However, useful evidence has been provided by population epidemiology using psychological measures at individual level. Five studies assess maternal psychological stress using individual maternal data on bereavement [167, 168] or ‘unwantedness of pregnancy’ as the proxy variable [169–171]. The Huttunen and Niskanen paternal loss study did not have power to assess schizophrenia risk, but reported an excess of any psychiatric admission. The three ‘unwantedness’ studies reported raised rates of schizophrenia in offspring, but their conclusions are limited by small samples, imprecision of ‘unwantedness’ as a stressor and inability to time exposure or separate short term psychic stressors from longterm influences of disrupted environments. Khashan et al. [168] uniquely report a significant association between an individual mother’s exposure to a welldefined, clearly timed psychological stressor (death of her first degree relative in first trimester) and risk of adult schizophrenia in the offspring in an entire population: the level of risk was raised by nearly 80% (RR = 1.78 CI: 1.09–2.92) in the crude analysis and 70% (RR = 1.67 CI: 1.02–2.73) when adjusted for calendar year, country, season and place of birth (urbanicity), family psychiatric history, sex of offspring, maternal age, offspring age and missing partner. Despite providing the only high quality evidence following clearly timed maternal psychological stress in early pregnancy, this study was also limited in important ways. Although the Danish population is 5 million, the first trimester finding was based on just 16 schizophrenia cases. Only 1893/7331, that is 26% of all schizophrenia cases were used in the analysis because the necessary record linkage (between index mothers and their parents) in the Danish registration system was not available. New data (personal communication) from a Swedish sample with full follow up, in a larger population and more years of cohort will be over five times bigger and allow important subgroup analyses of sex and ageat-onset effects in severe mental illness which have

not been possible in smaller samples. In addition, the Danish study was unable to control for potentially important confounders such as social class, paternal age and maternal smoking during pregnancy, all of which will be available in future studies. Like the evidence for nutritional effects, the association between antenatal stress and other cognitive or psychiatric outcomes apart from schizophrenia is scant. Paternal loss in the first trimester is reported to increase risk of ‘all psychiatric disorders’ compared to loss of fathers in the first year of life [167]. A significant association between antenatal exposure to war at any time prenatally and boys’ behaviour at school has also been reported [172], although the control group was inadequate. The pilot cohort study by Malaspina et al. [173] (nine cases) reported highest risk of ‘all psychiatric disorder’ in offspring exposed in the second month of pregnancy: OR = 2.51 (CI 1.22–5.17) with highest risks in women: n = 5 (OR = 3.55 CI 1.31–9.65), compared to men: n = 4 (OR = 1.82 CI 0.63–5.24). Severe affective disorder is also associated with second trimester ‘stress’ exposures. Brown and colleagues examined the association between prenatal exposure to famine and major affective disorders and found an association only in men [174]. Khashan et al. [175] extend their stress and schizophrenia finding [168] to affective disorder following second trimester exposure to maternal bereavement; again confined to male offspring. A significant association between prenatal famine and antisocial personality disorder in men has also been reported [176]. The range of associations of severe antenatal maternal stress with other developmental and behavioural outcomes is important as it may indicate evidence of shared aetiology or shared pathways to disorder. Resurgence of interest in shared genetic factors between schizophrenia and bipolar disorder [177] questions whether the presence and nature of early environmental determinants play a crucial role in determining final phenotype, or whether risk factors and mediating cognitive and functional impairment are specific to schizophrenia. Examination of broader neuropsychiatric outcomes, for example cerebral palsy, epilepsy, congenital malformations, autism and mood disorders may enable us to examine this further.

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27.8 Motherhood and perinatal mental illness Childbirth has long been linked to risk of mental disorder in women. This link has been construed as evidence of constitutional female weakness [178]. Before we accept this notion and its implication for parenting and childcare needs within families, several important aspects of the association require closer examination. For example, we need to consider whether pregnancy and childbirth represent specific (and perhaps more biological) risk for the mother which can be avoided or minimised by adequate medical management. This may have implications for advice about future conception and childbearing for women and their partners. Alternatively, it may be that the psychosocial event of transition to parenthood and its attendant burden represent a non-specific life event that should be managed as any other life stressor. This might imply that fathers would also be at risk of greater onset of illness in association with new parenthood. The following synthesis will review how studies have attempted to answer some of these questions considering, first, more severe postpartum disorders such as postpartum psychosis and then more common disorders such as postpartum depression.

27.8.1 Definition of perinatal mental disorder There is a need to distinguish whether childbirth is a specific or non-specific risk factor for the onset of de novo maternal mental illness that may not necessarily have occurred, but for the event of childbirth, in women with no prior illness. Knowing likely rates of new onset or incident illness associated with childbirth has important ramifications for service planners and commissioners of specialist care for new mothers with mental illness and their infants [179] not least because these conditions are potentially lifethreatening both to the woman and her newborn child. But opinion remains unresolved about whether mental illness associated with childbirth is a nosologically distinct illness with diagnostic and time of onset specificity. The text revised edition of the DSMIV-TR uses the term ‘with postpartum onset’ for either major depression, a mixed or manic episodes 500

of bipolar disorder or other brief psychotic disorders and specifies onset within four weeks of birth. Conversely, the ICD-10 permits classification of mental disorders as ‘associated with the puerperium’ if they begin within 6 weeks of birth and cannot be classified elsewhere. Expert opinion [179] recommends 3 months as the time frame for defining postpartum onset for a variety of diagnoses, based on the early epidemiological studies of Kendell et al. [180].

27.8.2 Postpartum psychosis Kendell et al. [180] used a case–control design studying mothers admitted from a population of 54 087 women who gave birth. A key element in this study of postnatal admission was that the women were used as their own control group such that risk was compared to the admission rate among the same women in the 2-year period before birth (excluding gestation). The relative risk of admission for any functional psychosis was calculated by taking the ratio of the number of psychiatric admissions immediately after childbirth to the average admission rate in the same women in the two years before childbirth. The authors reported that the rate of admission to a psychiatric hospital for a new mother with a psychotic illness was highly increased (RR of admission with psychosis 12.7) during the first 90 days following delivery, and this increased to 21.7 if only the first 30 days post delivery were considered. The authors reported that risk of admission remained raised with a RR of 2.1 for all psychoses up to 2 years after childbirth. This was a small study with only 120 admissions of women in the first 90 days after delivery, 74 of which were first admissions and 49 of which were readmissions. It is possible that the method may have produced an inflated relative risk because rates of prepregnancy admission are low compared with the admission rate among women in the general population (RR = 0.08; 95% CI 0.07–0.09). Women with a prior psychiatric history may choose, or be advised, to avoid pregnancy and risk of psychotic illness, which would lower the readmission rate 10–24 months before childbirth, thereby lowering the baseline for the relative risk calculations and this might explain why the risk found by Kendell et al. [180] was so high. A decade later, a replication study undertaken in a population cohort in Denmark

MENTAL ILLNESS, WOMEN, MOTHERS AND THEIR CHILDREN

seems to confirm this possibility and demonstrates the importance of the denominator with which the comparison is made in generating relative risks. Thus, Terp and Mortensen [181] used all women in the general population as the control group; women could be parous or not. Their denominator was based on much higher rates of mental disorder in the control group thereby generating a much lower relative risk, almost 10-fold less than Kendell et al. [180]. Overall, both studies suggest a rate of severe mental illness postpartum of approximately 1 per 1000 live births, at least in western, predominantly Caucasian samples. The Danish study also was able to consider risk of first admission for mental illness in women following childbirth as compared to risk of postpartum readmission in women with prior illness. They were able to explore Kendell et al’s proposal that the increased risk among primiparous women could not be attributed to the avoidance of further pregnancies by mothers who had a puerperal psychosis after their first birth. Thus, they reported a RR of 3.21 (95% CI 2.96–3.49) for first admission for all functional psychosis and of 0.66 (95% CI 0.61–0.72) for being readmitted following relapse of an existing illness following childbirth. Several reasons were suggested for the relatively reduced readmission rate in the Danish study, First, the reduced fertility rate among women with a previous psychiatric admission might have meant that a relatively smaller proportion of these women at any given time were in their puerperal period, so the calculated rates of mental illness in relation to the number of deliveries in the general population of women would be high compared to the observed number of admissions in the same diagnostic categories. This would result in a reduced relative risk of admission. Second, if women with many recurring episodes of illness were especially infertile, this would lead to the same bias and again reduce the relative risk. Thus, choosing a comparison group to act as a control in studies of perinatal mental disorder is challenging. As premorbid psychological traits or other vulnerability factors for mental illness affect both the chance of developing perinatal mental illness de novo as well as the chance of an individual becoming a parent, they are also likely to affect the age at which an individual may become a parent. If parental age is also independently associated with development of

mental illness in the puerperium, then risk estimates should be adjusted for this. Rates reported in early prevalence studies may have also been inflated by including both postpartum psychosis and bipolar episodes, and because they do not distinguish between incident postpartum illness or recurrent illness. Harlow et al. [182] adjusted their estimates for maternal age at first birth, and were able to report cumulative incidences for postpartum psychosis and bipolar episodes separately as 0.07 and 0.03%, respectively. They also distinguished new onset illness by examining primiparous women alone. For first births they reported incidences of 0.04 and 0.01% for postpartum psychosis and bipolar episodes. For women with any psychiatric hospitalisation before delivery, the incidence was 9.24 and 4.48%, respectively and the risk increased significantly with the recency of prepregnancy hospitalisations, number of previous hospitalisations and length of most recent hospitalisation. They estimated that over 40% of women hospitalised during the prenatal period for a bipolar or a psychotic condition were hospitalised again during the postpartum period and that 90% of all postpartum psychotic and bipolar episodes occurred within the first 4 weeks after delivery.

27.8.3 Specificity and duration of risk: Maternal versus paternal postpartum illness At present, there are two main schools of thought as to why childbirth is a risk period for mental illness. One makes a link with the neurobiology of women who, it is suggested, are differentially sensitive to ‘the destabilising effects of hormonal withdrawal at birth’ [183]. The second suggests it is not childbearing, but the psychosocial (and physiological) disruption of childrearing that acts as a non-specific stressor. The second scenario has a number of important implications. First, the increased risk of postpartum mental disorders could extend well beyond the ICD/DSM time specifier of six weeks after initially becoming a parent. Second, risk might be expected to be greater with the first child, or greater as parity of the parent(s) increased. Finally, fathers as well as mothers may be at excess risk of postnatal mental illness. 501

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In the largest and most contemporary study of postnatal mental illness since Kendell et al. [184], Munk-Olsen et al. [180] were able to examine all of these factors. Their sample included 1171 women and 658 men with a first lifetime onset of psychiatric illness from their infant’s birth up to 1 year after becoming a parent. The prevalence of severe mental disorders through the first 3 months after childbirth was similar to Kendell et al’s at 1.03 per 1000 births for mothers, but they also reported rates for fathers: 0.37 per 1000 births. Recapitulating earlier studies, for primiparous women, the first weeks and months after the delivery were associated with greatest increased risk of first admission with any mental disorder and the period from 10 to 19 days postpartum was associated with the highest risk (RR = 7.31; 95% CI, 5.44–9.81) compared with women who had given birth 11–12 months prior. The increased risk was independent of the age of the mother and for women giving birth to their second live-born child, the increased risk was still present, but was reduced compared with primiparous women; after the birth of a third live child, no association between time since birth and mental disorders was found. This provided strong evidence that previously undiagnosed women have an increased risk for incident psychiatric treatment, particularly hospital admission and particularly in the first 3 months following childbirth and that this effect is independent of maternal age or parity. Munk-Olsen also reported that fathers had a notably different risk pattern from mothers: no temporal trends between childbirth and mental disorders were found. No period either during their partner’s pregnancy or during the first 0–12 months after firsttime parenthood was associated with increased risk of first-time hospital admission for a mental disorder in new fathers. On the contrary, results showed a slightly decreased risk of admission with any disorder during the first weeks after childbirth, although it was only statistically significant 10–19 days postpartum compared with men who became fathers 11–12 months prior (RR, 0.50; 95% CI, 0.26–0.95). These findings were not consistent with a paper that reported that 4% of fathers had depression at 8 weeks after childbirth [185]. However, since these fathers were not compared with a control group, the rates of depression may well have coincided 502

with those in an age-matched general population of non-fathers. In a further set of analyses, Munk-Olsen compared parents with non-parents. The non-parent comparison group was described as ‘dynamic’ inasmuch as it included non-parents, future parents and never-parents depending on the age group considered. Overall, women and men who did not become parents during the study period had a different risk pattern than parents: young non-parents, that is those younger than about 25 years, had a lower incidence of admission with a mental disorder, whereas older non-parents had a higher incidence of admission compared with parents of the same age. Individuals who became parents between ages 25 and 30 years (the age range of the majority of first-time parents in Denmark) had similar rates of psychiatric contacts as non-parents of the same age.

27.8.4 Postnatal depression Large national registers are relatively unreliable sources of data on more common mental disorders such as depression, as they tend to collect data on more severe disorders requiring inpatient admission. Even if outpatient contacts are recorded, postnatal depression is relatively poorly diagnosed in community samples and many cases may never reach secondary care. Nevertheless, Munk-Olsen [184] used the national registers and reported increased risk of admission with severe unipolar depressive disorder in new mothers which ranged from a RR (per 1000 person-years) of 0.44 (0.31–0.62) anytime during pregnancy to 2.79 (1.90–4.11) 0–30 days postpartum; 3.53 (2.47–5.05) 31–60 days postpartum and 2.08 (1.57–2.77) through to 5 months postpartum. By contrast, postpartum admission for schizophrenia-like disorders was increased only up to 30 days postpartum and up to 2 months postpartum for bipolar affective disorders. Estimating rates of illness from community samples is likely to be compromised by other factors, such as the use of scales (e.g. Edinburgh Postnatal Depression Scale [EDPS]) for ascertaining cases rather than identifying diagnoses through clinical assessment. Early data from UK and US samples suggest a prevalence rate in community samples ranging between 8 and 20% depending on diagnostic criteria,

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population sample and time period. Gaynes et al. [186] systematically reviewed this literature and concluded that studies were generally too small for reliable subgroup analyses and too localised to be representative of the population of women at risk. They excluded studies that assessed depression based on self-report screens alone, which may tend to overestimate prevalence, and separated estimates of major and minor depression. For major depression alone, they reported combined point prevalence estimates of 3.1–4.9% during pregnancy and 1.0–5.9% at during the first postpartum year. For major and minor depression, the point prevalence estimates were 8.5–11.0% during pregnancy and 6.5–12.9% during the first year postpartum. The authors commented that the confidence intervals were wide, suggesting that a fair amount of uncertainty remains in the combined estimates. Fewer estimates were available for the incidence of depression but they suggested that 14.5% of pregnant women have a new episode of major or minor depression at any time during pregnancy and 14.5% have a new episode during the first 3 months post-partum. This equates to one in seven new mothers developing a diagnosable depressive episode in relation to recent childbearing. When they included only major depression, incidence reduced to 7.5% during pregnancy, as might be expected, and 6.5% in the first 3 months postpartum. Interestingly, prevalence estimates for perinatal depression were not significantly different from the prevalence of depression among women of similar age who were not pregnant and had not recently given birth. Many studies do not have access to data on the range of risk factors for postpartum illness such as socioeconomic factors, domestic violence, prior illness, maternal age and maternal supports. Therefore, they have been unable to adjust estimates for key confounders of risk. More research is needed in order to identify useful targets for effective clinical intervention strategies.

27.8.5 Suicide and motherhood Compared to age matched men in the same population, suicide in childbearing women is a relatively rare phenomenon (e.g. [187–189]). This is not explained by higher rates of marriage in women.

Marriage reduces the risk of suicide in women only if they have children; otherwise married women appear more likely than single, childless women to commit suicide, especially in some cultures and ethnic groups (e.g. [26]; see above). By contrast, childless married men have two-thirds the risk of suicide of childless, age-matched single men, while married fathers have one-third the risk of childless, single men and onehalf that of married men without children [34, 188]. Nearly two decades ago, a population-based study using national data from England and Wales from 1973 to 1984, showed a much reduced suicide risk in the first postnatal year among mothers aged 15–44 years (Standardised Mortality Ratio – SMR 0.17) compared with the female general population [187]. A decade later, Dolhammer [190] compared having no children with being a mother in Austria and England and Wales. This study reported that childlessness in women was associated with significantly higher suicide risk compared with being a mother: mortality rate ratio (MRR) 1.15 and Austria: MRR 1.15. More recent studies in England and Taiwan [79, 80, 191, 192] are consistent with these earlier reports and indicate that motherhood reduces the risk of women from killing themselves. Webb et al. [79–80] have also found that the protection is greater in women who first became mothers aged 30 years and older compared with younger women who first became mothers at 20–29 years and in particular against unnatural compared to natural deaths. Linehan et al. [193] gathered information on why a mother experiencing suicidal ideation may be protected from completing the act. They suggested mothers had three types of ‘child-related concerns’ which prevented them from committing suicide: (i) that suicide would directly harm her children; (ii) that it would be wrong to leave her children in someone else’s care and (iii) a desire to see her children grow up. However, these protective effects may not extend to all new mothers. Population-based evidence from Sweden during 1990–1995 [194] and a more recent study from England and Wales [80] indicate that women who become mothers during their teen years are at increased risk of suicide and homicide. The UK study reported that teenage mothers from the 1970s were almost 30% more likely to die from any cause before reaching age 50 years than women without 503

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children, and were almost 60% more likely to die unnaturally. Teenage motherhood was linked with a greater than twofold increased risk of suicide at any time before age 50. Restricting the analyses to death below 35 years indicated a threefold higher risk of suicide across all three subsequent cohorts of teenage mothers: 1970s, 1980s and 1990s. Why motherhood fails to protect teenage girls is unknown. Studies using the 1958 British Birth Cohort suggest that these girls are more likely to develop psychiatric illness as adults [195]. Further data from the United Kingdom, the United States, Sweden, Australia and New Zealand indicate that girls with mental illness or socioeconomic difficulties are more likely to become teenage mothers [150, 196–198]. Whether or not girls who become mothers during teenage years are at high risk of poor outcomes because of their young maternal age or because of the social and family circumstances which predispose to teenage motherhood is unclear. More research is needed which can draw out the effects associated with young parenthood and deprivation, or change in social circumstances over time. Another useful strategy is to use a sibling control method. In this design, outcomes are compared between siblings; in other words, between siblings living within the same family who do not become teenage parents and those who do. This allows adjustment for so-called ‘family-wide influences’.

27.9 Designing studies examining the relationship between maternal mental illness and outcomes for their children Epidemiological approaches to the study of the children of mothers with mental illness range from small case–control studies to large cohort studies and data sources vary, from self-reported and/or informant data to data held on large administrative health and other population-based registers. In the most comprehensive of these approaches, the study design allows the researcher to explore the role of genes, environment and their interaction in outcomes for these children. Many case–control studies have looked at multiple risk factors for mental health outcomes in 504

child, adolescent and adult populations. A number of these have included an examination of the association between a mother’s mental illness and psychopathology in her offspring [199]. In general, the use of case–control studies to study the effects of parental mental illness on their children may be problematic to the extent that case–control studies are predicated on a single outcome. Moreover, while this is an efficient study design, these studies can suffer from a number of disadvantages that may introduce systematic biases into the study such as: small numbers, unrepresentative samples with unknown biases; inappropriate control groups; samples that are too heterogeneous and reliance on retrospective recall of events distal from the outcome of interest. In many cases, it may also be impossible to validate the parental psychiatric diagnosis. In addition, matching cases and controls on potential risk factors may camouflage important associations. Prospective, longitudinal cohort studies have been called the ‘archetype for all epidemiological studies’ [200]. They offer many advantages for epidemiological research, especially risk factor epidemiology. In particular, the prospective collection of data over time helps separate cause and effect. However, by their nature, they are costly, need large numbers to study rare outcomes, risk loss to follow-up over time and may require many years of observation to capture long-term outcomes where there is a lengthy interval between the exposure and the outcome. There are a number of well-characterised longitudinal cohorts worldwide whose data have been collected and analysed over many years, for example the Dunedin Multidisciplinary Health and Development Study [124, 125], the National Collaborative Perinatal Project [201], 1946 British Birth Cohort [202] and the 1966 North Finland Birth Cohort [203]. These datasets are extremely rich sources of information and studies do not predicate cohort selection on maternal mental health status which reduces the efficiency of using the cohort to examine familial as well as environmental contributions to the range of outcomes. Targeted cohorts examining outcomes for children of mothers with a mental illness have been established, mainly in the area of maternal severe mental illness, most commonly schizophrenia and, to a lesser extent, affective

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psychoses. These are prospective studies of so-called ‘strategic’ populations [204]. Strategic populations of ‘high risk’ children of mothers with severe mental illness, or high risk samples, offer researchers the opportunity to address parsimoniously key questions about risk factors and vulnerability. One of the earliest of the longitudinal investigations of developmental pathways in the children of mothers with severe mental illness was the New York Infant High Risk Study [99]. Fish laid the foundation for the neurodevelopmental hypothesis of schizophrenia suggesting that developmental retardation and neurological soft signs amongst the high risk children in her sample were early markers of an inherited neurointegrative defect which she named ‘pandysmaturation’. Subsequently, other prospectively-assessed cohorts were established including the Copenhagen High Risk Project in 1962 [205], the Israeli High Risk Study [206], the Swedish High-Risk Study [207] and the New York High Risk Project [208] and the Finnish Adoptive Study [209] of twins reared apart which provide a strong basis for disentangling independent contributions of environmental and genetic predictors mental disorders. See [100, 101] for an overview of the key high risk studies up to the end of the 1990s. While these are highly regarded studies, most are subject to important limitations including: reliance on small size selected samples; restriction to children of mothers with schizophrenia and absence of prospectively collected data on early environmental effects due to cohort formation in middle childhood or later. Furthermore, different designs have meant that a battery of different markers have been used in the followup of children, including neurological, biological, behavioural and cognitive markers, making comparisons across studies and replication difficult. In regard to strategic populations, we note a more recent use of the term ‘high risk’ to classify youth at predicted high risk of developing psychosis on the basis of their current behaviours and experience of psychotic-like symptoms rather than parental psychopathology. The latter studies need to be distinguished from those referred to as high-risk in this chapter. Retrospective cohort studies where data are collected prospectively, but cohort formation for research purposes occurs retrospectively, are often distinguished from the prospective cohort studies

described above. Retrospective cohort studies are most commonly found in jurisdictions with access to networks of longstanding, population-wide administrative registers including morbidity, mortality and psychiatric case registers. In these jurisdictions, it is possible to establish whole-ofpopulation electronic cohorts of children using prospectively collected data across linked registers. Some of the advantages offered by retrospective cohort studies are similar to those for prospective cohort studies including prospectively collected data on both exposures and outcomes, and the elimination of retrospective recall bias. In addition, whole-of-population registers offer efficient and cost-effective access to large samples for study, reducing the risk of Type II errors in the study of rare disorders. Where the register has long-standing (period) and broad (area) coverage, not only is the potential for unknown biases reduced, but risk factor identification is facilitated, especially where there is a long lag between risk exposure and illness onset. Where data collection has occurred over several generations, the identification of genealogies offers the researcher the potential to look at gene–environment interactions. The disadvantages relate to cleaning and operationalising fields originally compiled for administrative, rather than research purposes, and to potential risk factors that are missing altogether because the data were not collected. Many retrospective cohort studies rely on unselected birth cohorts that are not predicated on maternal mental illness status. As such, they are not ‘high risk’ cohorts. Nonetheless, they provide excellent opportunities to examine developmental pathways in children, some of whom have gone on to develop psychiatric disorders. Conscript cohorts are one type of retrospective cohort formation; these cohorts have proved very useful in longitudinal research, including research into cannabis use and risk of psychosis, as their data include prospective psychological assessments [210]. Alternatively, a number of studies have combined a prospective cohort approach with retrospective determination of parental or index child psychiatric status using either self-reports or data linkage to relevant registers [12, 13, 211, 212]. Of particular interest to the current topic are retrospective cohort studies that have used linkage across 505

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birth and psychiatric case registers to identify a strategic cohort of children born to women with severe mental illness. These studies offer a powerful, alternative approach to conventional high-risk studies. Children born to unaffected mothers form the comparison. Such an approach also makes it possible to parcel out the contribution of familial liability and environmental exposures at different developmental stages and to look at their relative contribution to the onset of neuropsychiatric outcomes. A number of such studies have come out of Scandinavian research centres. For example, Bennedsen and colleagues used linked Danish registers to follow-up OCs, congenital malformations and mortality focussing on the children of mothers with schizophrenia [70, 71]. Lichtenstein et al. [177] used the Swedish multigeneration register (9 009 202 individuals born 1932–2002 within 2 million families) linked to the hospital discharge register to examine genetic and environmental contributions to liability for bipolar disorder and schizophrenia. Jablensky and colleagues have established a longitudinal, multigenerational e-Cohort of high risk children of mothers with psychoses using whole-of-population data from Western Australia. This database covers 472 722 births from 1980 to 2001 to 249 116 mothers including children born to all women with psychosis on the psychiatric case register. Data on fathers and their recorded psychopathology are included. Of interest, this research group has been able to supplement register data with rich qualitative case note data for children with a history of a diagnosis of schizophrenia [213]. Another approach is to use unaffected full siblings of the index probands as a comparison group, as they experience similar social conditions during childhood and adolescence, have the same family psychiatric history and, on average, share 50% of their genetic material with affected siblings. Comparing associations between maternal exposures during pregnancy and later psychiatric illness in a cohort of siblings, and within sib-pairs, provides the opportunity to assess the importance of these shared factors in the associations. Such designs have successfully been applied in psychiatric epidemiology. For example, Agerbo et al. [214] examined suicides which tend to cluster both in families and in those with psychiatric disorder and socioeconomic disadvantage. They used a matched-sibling design to 506

compare these factors between suicide cases, their siblings and population-based controls to evaluate the familial and the individual element of risk. Designing studies to examine the relationship between maternal mental illness and outcomes for their children raises some specific considerations. One of these is the role of the father. Many studies have only been concerned with mental illness in the mother and its effects on her children because she provides the physiological prenatal environment for the developing fetus. Recent research acknowledges that fathers and paternal psychopathology may influence risk of adverse outcomes in numerous (non genetic or epigenetic) ways. For example, children living in families with mental illness share in the deprivations and adversities of both parents. Therefore, paternal/step-paternal psychopathology, like maternal psychopathology, can have important implications for the home environment: how parents care for, and interact with, children and the physical setting in which children grow up [215]. The focus in the literature on mothers is not surprising: mothers may be more accessible than fathers and biological paternity may be at variance with recorded paternity raising questions as to the validity of paternal data from a genetic perspective. Many studies must rely on the identification of fathers recorded on birth registration certificates. Biological fathers may be less likely to be named in the context of parental mental illness in either parent where relationships may be short term. If mothers and fathers with mental illness are more likely to find themselves in such relationships, then the bias will be differential and affect the accuracy of the determination of genetic liability for their children. A recent review of the literature indicates that the median rate of paternal discrepancy in cases not selected on the basis of disputed paternity is 3.7%, ranging from 0.8 to 30%, with lower socioeconomic status, younger age and higher levels of deprivation increasing the rate of paternal discrepancy [216]. A second consideration is access to family genealogies and multigenerational data. Understanding the aetiology of illness and risk transmission relies on good family data. Data on paternal as well as maternal psychopathology, as discussed above, is critical. Additional data on the extended family provides further capacity to look at intergenerational

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transmission of risk. Swedish researchers have used three generations of psychiatric case register data to assess genetic and environmental contributions to liability for bipolar disorder and schizophrenia [177]. Meanwhile, older children in the Western Australian e-Cohort of high risk children of women with psychoses have started to have their own children, making a third generation for future analysis within this whole-of-population cohort. Finally, the resultant longitudinal data, rich as they are, present a challenge for analysis. For example, the risks associated with an outcome may vary for different developmental pathways. Methods have been developed more recently for modelling developmental trajectories. Nagin’s semiparametric, group-based method is capable of identifying (not assuming) distinctive groups of trajectories within a study population, estimating the proportion of the population within each trajectory group and profiling the characteristics of group members [217]. The model has been successfully employed to study antisocial behaviour and delinquency [218] and depression [219]. The approach has a great deal to offer to well-designed longitudinal studies of the children of women with mental illness.

27.10 Conclusions A wealth of epidemiological data points to differences in the way that women and men experience mental illness. The reasons for these differences are only beginning to be appreciated. It is likely that some of these differences can be understood in the context of the psychological and social aspects particular to women’s lives while others may have biological underpinnings. Pregnancy, childbearing and motherhood all influence a woman’s risk of mental illness, as well as informing her care needs. At the same time, mental illness places a woman at risk of reduced fertility and poor pregnancy outcomes, and her children at increased risks of adverse outcomes. The links between a mother’s physical and mental health and that of her offspring need special consideration. Emerging data from quasiexperimental and sibling designs are helping epidemiology to explore and unravel the complexity of these associations. Moreover, epidemiologists are beginning to work

interactively, rather than along parallel streams, with researchers from other disciplines including geneticists, neuroscientists and molecular and animal biologists. This synergy is informing both study direction and design. Meanwhile, translational approaches are increasing the clinical and social usefulness of the research undertaken. Studies still need to be designed better to further our understanding of women’s mental health and its impact on their children by: collecting gender relevant data; systematically examining outcomes for women and men separately; undertaking longitudinal assessments of a range of physical and mental health conditions and focusing more broadly on a range of mental health outcomes. Where epidemiological designs fail to take account of the fact that women experience mental illness differently from men, their results will be of limited utility in identifying interventions, informing service development and changing the lives of vulnerable women and their families.

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processes in the genesis and epigenesis of schizophrenia. Dev. Psychopathol., 11, 467–485. Isohanni, M., Jones, P., Moilanen, K. et al. (2001) Early developmental milestones in adult schizophrenia and other psychoses. A 31-year follow-up of the Northern Finland 1966 birth cohort. Schizophr. Res., 52, 1–19. Morgan, V.A., Valuri, G., Croft, M. et al. (2010) Pathways of risk from conception to disease: The Western Australian Schizophrenia High Risk eCohort. Int. J. Epidemiol., doi:10.1093/ije/dyq167. Agerbo, E., Qin, P. and Mortensen, P.B. (2006) Psychiatric illness, socioeconomic status, and marital status in people committing suicide: a matched case-sibling control study. J. Epidemiol. Community Health, 60, 776–781. Ramchandani, P. and Psychogiou, L. (2009) Paternal psychiatric disorders and children’s psychosocial development. Lancet, 374, 646–653. Bellis, M.A., Hughes, K., Hughes, S. et al. (2005) Measuring paternal discrepancy and its public health consequences. J. Epidemiol. Community Health, 59 (9), 749–754. Nagin, D. (2005) Group-Based Modeling of Development, Harvard University Press, London. Broidy, L.M., Nagin, D.S., Tremblay, R.E. et al. (2003) Developmental trajectories of childhood disruptive behaviors and adolescent delinquency: a six-site, cross-national study. Dev. Psychol., 39 (2), 222–245. Dekker, M.C., Ferdinand, R., van Lang, N. et al. (2007) Developmental trajectories of depressive symptoms from early childhood to late adolescence: gender differences and adult outcome. J. Child Psychol. Psychiatry, 48 (7), 657–666.

Beautrais, A.L. (2006) Women and suicidal behavior. J. Crisis Interv. Suicide Prev., 27, 153–156. Cohen, L.S. (2007) Treatment of bipolar disorder during pregnancy. J. Clin. Psychiatry, 68 (Suppl. 9), 4–9. Dickerson, F.B., Brown, C.H., Kreyenbuhl, J. et al. (2004) Sexual and reproductive behaviors among persons with mental Illness. Psychiatr. Serv., 55 (11), 1299–1301. Howard, L.M. (2005) Fertility and pregnancy in women with psychotic disorders. Eur. J. Obstet. Gynecol. Reprod. Biol., 119, 3–10. Lewis, G., Drife, J., Botting, B. et al. (2001) Why Mothers die 1997–1999: The Fifth Report of the Confidential Enquiries into Maternal Deaths in the United Kingdom, Royal College of Obstetricians and Gynaecologists Press, London. MacCabe, J.H., Martinsson, L., Lichtenstein, P. et al. (2007) Adverse pregnancy outcomes in mothers with affective prognosis. Bipolar Disord., 9 (3), 305–309. McNeil, T.F., Kaij, L., Malmquist-Larsson, A. et al. (2005) Maternal care as a model for experience-dependent chromatin plasticity? Trends Neurosci., 28, 456–463. Raleigh, V.S. and Balarajan, R. (1992) Suicide and selfburning among Indians and West Indians in England and Wales. Br. J. Psychiatry, 129, 365–368. Selten, J.P. and Slaets, J.P. (1994) Evidence against maternal influenza as a risk factor for schizophrenia. Br. J. Psychiatry, 164, 674–676. Stein, Z.A. and Susser, M.W. (2000) Maternal starvation and birth defects, in Birth Defects: Risks and Consequences (ed. S. Kelly), Academic Press, New York, pp. 205–220. Susser, E., Neugebauer, R., Hoek, H.W. et al. (1996) Schizophrenia after prenatal famine. Further evidence. Arch. Gen. Psychiatry, 53, 25–31. Viguera, A.C., Nonacs, R., Cohen, L.S. et al. (2001) Stress, infection and preterm birth: a biobehavioural perspective. Paediatr. Perinat. Epidemiol., 15 (Suppl 2), 17–29.

Further reading Aleman, A., Kahn, R.S. and Selten, J.P. (2003) Sex differences in the risk of schizophrenia: evidence from meta-analysis. Arch. Gen. Psychiatry, 60 (6), 565–571.

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28

Epidemiology of suicide and attempted suicide Dianne Currier and Maria A. Oquendo Division of Molecular Imaging and Neuropathology, Department of Psychiatry, Columbia University, NY, USA

28.1 Introduction

28.2 Definitions

Suicide and non-fatal suicidal attempts are responsible for considerable mortality and morbidity world wide. The World Health Organization (WHO) estimates that 800 000–1 000 000 individuals died by suicide in 2000, making it the 13th leading cause of death that year [1]. In the United States in 2006, suicide was the cause of 33 000 lost lives [2]. The Centers for Disease Control estimate that in 2006 suicide resulted in 687 000 years of productive life lost [2]. The importance of addressing this preventable cause of death and injury was recognised in 1999 with the United States Surgeon General’s Call to Action to Prevent Suicide, which highlighted the scope of the problem and proposed a national prevention strategy be developed. Other nations have likewise have been developing suicide prevention plans and engaging in public health efforts to reduce mortality and morbidity related to suicidal behaviour [3]. Assessing the extent of the problem is complicated by social and cultural factors such as stigma and religious prohibitions that lead to underreporting, as well as underdeveloped public health surveillance systems in many parts of the world. Suicide and suicide attempts occur most often in the context of psychiatric disorders, and psychological autopsy studies show that 90% of suicides have a diagnosable disorder, most frequently mood disorders, albeit with regional and cultural variations. A range of other risk factors have also been identified in clinical, neurobiological, genetic and psychosocial domains.

Efforts to characterise the epidemiology of the suicide have been complicated by ill-defined terms within the field and disparate methods of data gathering. Professionals working in field realised the importance of developing a precise nomenclature for suicidal behaviour. In the United States, the impetus to define the terms related to suicide began in the 1988 with the formation of a workgroup at the Centers for Disease Control to develop consistent nomenclature to facilitate more accurate recording of cause of death by medical examiners [4]. This nomenclature has been further refined for the purposes of research and medical investigation, and in 2008 a set of consensus terms was adopted by the Food and Drug Administration (FDA) for the purpose of assessing suicide related adverse events in clinical drug trials in the United States. That schema is based on three basic constructs: (i) a suicidal act must be a self-directed act; (ii) it must be characterised by some intent to die, that is a desire to die at non-zero level, and some expectation that death will from the action and (iii) there must be some consideration of the lethality of the act, that is the potential for harm/death associated with the mechanism of injury, regardless of accurate expectation of dying from the act. Definitions based on these are detailed in Table 28.1. Suicide attempts also vary in intent, that is the degree of preparation and planning involved, and lethality, that is the degree of medical damage resulting from the attempt. Attempts with more highly lethal outcomes tend to involve more careful

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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CHAPTER 28 Table 28.1

Nomenclature of suicidal and non-suicidal self-injury behaviours [5].

Suicidal acts

Completed suicide

Suicide attempt

Preparatory acts towards imminent suicidal behaviour Suicidal ideation

Non-suicidal events

Self-injurious behaviour with no suicidal intent

Indeterminate or potentially suicidal events

Self-injurious behaviour, suicidal intent unknown

planning, including taking measures to avoid detection and the use of more lethal methods such as firearms. Surviving such an attempt is often the result of good fortune, and individuals who make more lethal attempts are demographically, biologically, and in terms of suicide intent, similar to those who die by suicide [6–9]. Lower lethality attempts are more often impulsive, occur in the context of a social crisis, and often have a strong element of appeal for help in that they are carried out in a manner that favours discovery and rescue. A finer grained characterisation of suicidal behaviour in surveillance data would clarify if these two groups represent distinct subtypes of suicide attempters, which might require different types of preventative interventions. There are a limited number of instruments developed for research purposes that aim to systematically record suicidal behaviour and assess characteristics such as intent and medical sequelae of non-fatal attempts, including Beck’s Medical Lethality Scale and Scale for Suicidal Intent [10], the Suicide Risk Assessment Scale [11], and newly developed Columbia Suicide Severity Rating Scale [5]. The latter has been recommended by the US FDA as a preferred instrument for assessing suicidal behaviour 518

A self-injurious behaviour that resulted in fatality and was associated with at least some intent to die as a result of the act. A potentially self-injurious behaviour, associated with at least some intent to die, as a result of the act. Evidence that the individual intended to kill him/herself, at least to some degree, can be explicit or inferred from behaviour or circumstance. A suicide attempt may or may not result in actual injury. The individual takes steps to injure him- or herself but is stopped by self or others from starting the self-injurious act before the potential for harm has begun Passive thoughts about wanting to be dead or active thoughts about killing oneself, not accompanied by preparatory behaviour. Self-injurious behaviour associated with no INTENT to die. Behaviour is intended purely for other reasons, either to relieve distress or to effect changes in others or the environment. Self-injurious behaviour where associated intent to die is unknown and cannot be inferred. The injury or potential for injury is clear, but why the individual is engaged in that behaviour is unclear.

in clinical trials, and has been widely adopted as a research tool both in the US and internationally. These instruments were not designed to collect population level morbidity and mortality data, however the Columbia Suicide Severity Rating Scale is based on comprehensive definitions of type and characteristics of suicidal acts (Table 28.1), that are suitable for surveillance purposes. In the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) diagnostic nosology suicidal behaviour is considered only in the context of a symptom of a major depressive episode or of borderline personality disorder. As we shall detail, suicidal behaviour can occur in the context of a variety of psychiatric disorders, prompting suggestions that suicidal behaviour be considered a separate DSM diagnostic category and perhaps documented on a sixth axis in the forthcoming DSM-5 [12].

28.3 Prevalence of suicide and attempted suicide It is believed that there is considerable underreporting of suicide, particularly in countries

EPIDEMIOLOGY OF SUICIDE AND ATTEMPTED SUICIDE

or subpopulations where there is strong stigma associated with suicide, or it is forbidden by religious strictures and/or criminalised [13, 14]. Moreover, even in countries with established reporting infrastructure such as the United States and the United Kingdom, there are still questions as to the completeness and accuracy of data [15, 16]. Opinions differ as to the magnitude of the underestimation, ranging from 10 to 50% [17]. Most of the missed cases of suicide are reported in the category of cause undetermined. The achievement of uniformly high-quality ascertainment practices would increase the number of suicides identified.

28.3.1 Global prevalence – suicide In 2000, WHO estimated that worldwide 800 000–1 000 000 people died from suicide, representing rate of between 14.5–16/100 000 per year [1, 16, 18], and making suicide the 13th leading cause of death worldwide [18]. Rates vary considerably across regions and nations with the highest reported rates in eastern Europe, for example Serbia 65.4/100 000 (2006), Lithuania, Latvia, Belarus ∼38/100 000 (2005), and the lowest in central and south America, Guatemala 4.1/100 000(2003), Paraguay 3.1/100 000 (2003) and Mexico 4.1/100 000 (2005) [19]. The WHO compiles available suicide mortality data from over 100 countries which is available online at http://www.who.int/mental_health/prevention/ suicide/country_reports/en/index.html. There is some variability in recency and in reliability of reported rates. Across the majority of countries reporting data, men almost always have higher rates of suicide than women – with male : female ratios in the range of 3 : 1–7.5 : 1, with some exceptions, for example rural China [19, 20]. Identifying secular trends in cross-national suicide rates is difficult due to the extreme variability in quantity and quality of data available from different countries. The WHO has collected suicide mortality data since 1950 and, despite inconsistencies in availability and reporting across countries, estimates that the global rate of suicide increased from 1950 to 2004, particularly for men [21]. The WHO estimates that there has been an increase in suicide deaths of about 60% from 1960 to 2005 [1], and projections

suggest that the number of self-inflicted deaths will increase by as much as 30% by 2030 [22].

28.3.2 Attempted suicide, suicidal ideation and planning As with suicide, there are difficulties in ascertaining accurate prevalence data for attempted suicide. General estimates suggest around 8–25 suicide attempts for each suicide death [23]. However, systematic collection of data on suicide attempts is very rare, and even data based on hospital admissions or emergency room treatment do not account for attempts of lower lethality where there is insufficient medical damage to bring an individual to clinical attention. Assessing prevalence of suicidal ideation and plans is perhaps even more difficult. Population-level studies provide most of the data on these. In a systematic review of published cross-national suicide attempt data, Nock et al. noted considerable variability between individual countries for attempts, plans and ideation. In adults, estimated lifetime prevalence was 0.4–5.1% for suicide attempts, 0.9–19.5% for suicide plans and 3.1–56.0% for suicidal ideation [24], and estimated 12-month prevalence was 0.1–3.8% for suicide attempts, 0.5–12.2% for a plan and 1.8–21.3% for suicidal ideation [24]. Studies of adolescents reported somewhat higher estimates than for adults for suicide attempt (1.5–12.1%), and in suicidal ideation (21–37%). It was not clear how much of the variability across studies was attributable to different definitions of behaviours or to differences in survey population selection methods. More consistent terminology and sampling is used in studies conducted by the WHO addressing suicidal behaviour. The World Mental Health survey of 17 countries also observed considerable variation between countries, and produced lifetime estimates towards the lower limit but within the above ranges: 2.7% for suicide attempt, 3.1% for a plan and 9.2% for suicidal ideation [25].

28.3.3 United States – suicide attempted suicide, suicidal ideation and planning In 2006, suicide was the 11th leading cause of death in the United States, resulting in approximately 33 300 deaths at a rate of 11.15 per 100 000 persons [2]. From 1990 to 2000 the overall 519

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US suicide rate declined from 12.4/100 000 to 10.7/100 000 [26], although in recent years it has risen again to 11.15/100 000. Suicide is the second leading cause of death in the 25–34-year age group, and third leading cause in 15–24-year-olds [2]. Tables 28.2 and 28.3 detail US suicide rates by age, gender and race. Males die by suicide more than four times as frequently as females, and older males have a disproportionally high suicide rate. White Americans and Native Americans/Alaskan Natives have similar rates of suicide, and both groups are more than twice as likely to die by suicide than black Americans. Black American women have the lowest suicide rate. Disproportionally high suicide rates are found amongst the elderly. In 1999 those over 65 comprised 13% of the US population but accounted for 20% of all suicide deaths [27]. Suicide rates increase with age in males and not females in the United States, and accordingly the male : female ratio for suicide increases whereby in the 65 and older age group males had more than a sevenfold higher rate of suicide than females (see Table 28.2). In 2006, for males over 65 the suicide rate was 28.5, and in males over 85 it reached 43.7, whereas for females over 65 it was much lower at 3.87/100 000 [2]. Consistent with the general decline in suicide rates in the United States since 1990, the suicide rate in those over 65 declined from 20.47% in 1990 to 14.2% in 2006 [2]. Table 28.2 2006 US crude suicide rates per 100 000 population by age group and gender [2].

All ages 15–24 years 25–34 years 35–44 years 45–54 years 55–64 years 65 years and over

Total

Males

Females

11.15 9.91 12.41 15.13 17.84 14.52 14.22

17.88 16.22 19.83 23.24 26.12 22.77 28.51

4.5 3.22 4.72 7.01 8.45 6.85 3.87

Table 28.3 2006, US adjusted suicide rates per 100 000 by gender and race group [2]. Total

Males Females

White 12.08 19.60 5.05 Black 5.07 9.4 1.39 Native American/Alaskan Native 11.63 18.25 5.07 Asian American/Pacific Islander 5.63 7.99 3.43

520

28.3.4 United States – attempted suicide, suicidal ideation and planning Official statistics are not gathered on suicide attempts and there is no national registry in the United States. Estimates of prevalence of suicide attempt come from national survey data, and using data from the National Comorbidity Survey, Kessler et al., estimated that there are approximately 500/100 000 suicide attempts per year, which is a ratio of approximately 45 : 1 non-fatal attempts to suicide fatality [28]. Meta-analysis of population studies comprising 500 000 adolescents found 9.7% (range 2–30%) reported a suicide attempt, with the rate in females twice that of males [29]. In 2007, in the Youth Risk Behaviour Survey, a national survey of high school students, 6.9% of respondents reported making a suicide attempt in the 12 months prior to the survey, and approximately 2% reported making an attempt that required medical attention [30]. Attempted suicide is less frequent later in life, with a ratio of approximately four attempts for every suicide [31] compared to reported ratios in adolescents of up to 87 : 1 [32]. Reasons for this disparity are suggested by findings in psychological autopsy studies that older suicides display a greater determination to die, giving fewer warnings and engage in greater planning than those in younger age groups [33–35]. Older individuals are also more likely to live alone thereby reducing chances of detection and rescue. Across age groups, women report attempting suicide approximately three times more often than men [36]. Systematic reviews estimate that in adults the lifetime prevalence of making a suicide plan is 3.9% and for suicidal ideation is 5.6–14.3% [24], and in adolescents, 3.1–8.8% for a plan and 19.8–24% for suicidal ideation [24]. From the 2007 Youth Risk Behaviour Survey, 12-month prevalence was 11% plan and 14.7% suicidal ideation, with higher representation of females in both categories [30].

28.4 Risk factors for suicide and attempted suicide 28.4.1 Mental illness Mental illness is one of the most well documented and important contributory factors to suicide. The

EPIDEMIOLOGY OF SUICIDE AND ATTEMPTED SUICIDE

presence of psychiatric disorders in suicide has been investigated in the main using the psychological autopsy method. Psychological autopsy determines a diagnosis in an individual who has died by suicide by ascertaining information on psychopathology from interviews with family members, attending physicians and others, in addition to reviewing hospital and other official records [37]. Diagnosis by psychological autopsy has proven to be reliable and valid [38]. Since the 1960s over 150 psychological autopsy studies have been conducted, a review of which reported that the proportion of suicides with a diagnosable psychiatric disorder was 91% (range 23–100%) in case-series reports, and 90% (range 86–97%) in case–control studies [37]. There are regional and cultural variations, for example in a Chinese psychological autopsy study 37% of suicides had no psychiatric disorder [39], however this high proportion was thought to be due, in large part, to a high number of impulsive attempts carried out in the context of acute interpersonal conflict using the highly lethal method of pesticide ingestion [39]. Such cases are the exception however, and psychiatric illness is a key causal contributor to suicidal behaviour. Different classes of psychiatric disorder carry differing risk for suicide and attempted suicide, with mood disorders being the most frequently made diagnosis in suicide deaths and, certain personality disorders having highest suicide attempt rates.

28.4.2 Mood disorders Mood disorders (major depressive disorder (MDD) and bipolar I and II disorder), are the most frequently diagnosed disorders in psychological autopsy studies. Reported rates range from 30 to 93% in case series studies, and 23–95% in case–control studies [40]. In meta-analyses pooling results of psychological autopsy studies, mood disorder diagnosis is found in 30–60% of suicides [40, 41, 42]. Estimates of the prevalence of suicide among individuals with mood disorders are derived from both clinical and community samples. Follow-up studies of hospitalised cohorts report the proportion of suicides in mood disorders as between 13.6 and 18%, while prospective studies of outpatient and community samples are rare, there are reports of between 2.7% to just slightly higher than general population levels

of depressed individuals dying by suicide (see [43]). The discrepancy between clinical and community rates likely reflects the practice of hospitalising suicidal patients, thus clinical samples represent high risk groups. A follow-up of hospitalised mood disorder patients for a period of over 30 years, observed a standardised mortality rate (SMR) of 18 for suicide, that is an 18-fold higher likelihood of dying by suicide in patients than in the general population [43]. There is some debate as to the relative levels of risk for suicide in bipolar disorder and major depressive disorder. In a review of 30 bipolar disorder studies, Goodwin and Jamison found that 18.8% of all deaths were by suicide which is more than double the widely cited rate of 7.7% for MDD [44]. However, other studies report higher risk for death by suicide in MDD than in bipolar disorder. Metaanalysis of follow-up studies with a minimum of 2 years follow-up calculated SMRs of approximately 20 for MDD and 15 for bipolar disorder [45]. More recently two large register-based follow-up studies of hospitalised mood disorder patients also report higher SMRs for MDD than bipolar patients: males 20 vs. 15, females 27 vs. 22 [43, 46] and males and females combined 26.7 vs. 12.3 [43, 46]. To date, there is less data on differences between bipolar I and II with respect to risk for suicide. One caseseries psychological autopsy of 100 suicides found similar proportions of MDD and bipolar II depression (53 and 46% respectively), but only 1% bipolar I [47]. However, others detected no difference in the SMR for suicide between bipolar I and II (11.53 and 14.15, respectively) [43]. Seventy per cent to 89% of bipolar suicides occur in a depressive phase, and risk is also elevated in mixed states (dysphoric mania) representing approximately 11–20% of suicides, but appears negligible in mania [48–51]. Individuals with mood disorders are also at risk for non-fatal suicide attempts, and indeed in the DSM-IV suicidal ideation and suicide attempt are assessed as symptoms of major depressive episode. The Epidemiologic Catchment Area study in the United States the rates of suicide attempts were 29.2% in bipolar and 15.9% in MDD. A community study of 1709 adolescents followed until age 30 documented the rate of suicide attempt in bipolar disorder at 44.4% compared to 22% in MDD [52]. Both case series and epidemiological studies 521

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report the highest suicide attempt rate in bipolar II (24–34%), the lowest in MDD (12–16%) with bipolar I intermediate (17–24%) [53].

28.4.3 Psychotic disorders Recent reviews of the published literature on suicide rates in schizophrenics estimated that 4–5.6% of schizophrenics die by suicide [54, 55], a lower figure than the commonly cited 10–13% [56, 57]. This still represents a higher risk than the general population, and meta-analysis of 38 reports comprising a total pooled sample of 30 000 patients calculated that individuals with schizophrenia had an 8.5-fold greater risk of dying by suicide compared to the general population [45]. Psychological autopsies report 14.1% of suicides have a diagnoses of schizophrenia (19.9% in clinical populations and 7.5% in the general population) [41]. Given the chronic and severe nature of schizophrenia, it is more likely to come to clinical attention and thus have a higher representation in clinical studies of suicidal patients [58]. Co-occurring depression in schizophrenia is a major risk factor for suicidal behaviour [59–62], as are comorbid substance use disorders [63]. With respect to non-fatal suicide attempt, approximately 20–50% of schizophrenics report a history of attempting suicide [64, 65]. There is little specific data available on schizoaffective or other psychotic disorders, which tend to be grouped together in psychological autopsy studies under umbrella terms such as ‘other psychosis’ to distinguish them from schizophrenia. Three psychological autopsy studies report that between 1 and 3% of suicides, representing between two and seven individuals, fell into this category [66–68]. Meta-analysis estimated higher rates, with psychotic disorders excluding schizophrenia being reported in 10.1% of suicides in clinical populations and 2.3% of suicides in the general population [41].

28.4.4 Anxiety disorders Community [69, 70] and clinical studies [71–76] report associations between anxiety disorders and suicide and attempted suicide. However, there are questions regarding the extent to which this relationship is mediated by comorbid psychiatric 522

disorder, particularly major depression. Analysis of pooled psychological autopsy results estimated a 2.5–2.7% prevalence of anxiety disorders (grouped with somatoform disorders) in individuals who died by suicide [41]; however that proportion increased to 6.8% when anxiety disorders were comorbid with other disorders. There are reports that severe agitation or anxiety are associated with increased risk of suicide in mood disorders [77], while others find anxiety is protective against suicide attempts in mood disorders [78]. In a community sample of adolescents, only 2% of respondents with anxiety disorder alone reported a past suicide attempt, compared to 22.4% of those with comorbid anxiety and MDD [79]. Posttraumatic stress disorder (PTSD) has been associated with suicide attempt, independently of comorbid major depression or substance use, in both community and clinical samples [80–84], and the national comorbidity study found that PTSD was the only anxiety disorder independently associated with suicide attempts and ideation [85]. In contrast, the association of panic disorder and suicidal behaviour appears largely mediated by psychiatric comorbidity, particularly major depression [71, 73–75, 86].

28.4.5 Alcohol and substance use disorders Meta-analysis of psychological autopsy studies report alcohol and substance use disorders in 17.6–25% of suicides [41, 42]. The lifetime risk of suicide with alcohol dependence in out- and inpatient populations is estimated at 2.2 and 4.4%, respectively [87]. Based on metaanalysis, SMRs for suicide in alcohol and substance use disorders are 5.9 and 14–20, respectively, with higher rates for polydrug use (15–44) [45]. Alcohol and substance use are frequently comorbid with other high-risk disorders, particularly mood and personality disorders [88] and comorbid alcohol and substance use disorders and Axis I disorders were present in 23–47% of suicides in psychological autopsy studies [40, 41]. Acute alcohol intoxication is also a risk factor and autopsy studies have detected the presence of alcohol in 20–50% of individuals who die by suicide [89].

28.4.6 Personality disorders In psychological autopsy studies, personality disorder diagnoses was made for 13–16% of suicides [41,

EPIDEMIOLOGY OF SUICIDE AND ATTEMPTED SUICIDE

42]. Personality disorders are frequently comorbid with other psychiatric disorders and suicide mortality rates for personality disorders do not always indicate if another disorder is present making it difficult to determine the suicide rate for personality disorder alone. One meta-analysis reported that in suicides in the general population 3.2% had personality disorder as the only diagnosis, but the rate rose to 13% and when personality disorder was comorbid with another diagnosis [40]. Suicidal behaviour in personality disorders is often in context of comorbid major depression and/or alcohol and substance use disorders [90–92]. Suicide attempt rates are strikingly elevated in certain personality disorders, particularly the impulsive/aggressive cluster B disorders such as borderline personality disorder, where suicide attempt rates of 84% have been reported [93].

28.4.7 Psychiatric comorbidity Psychiatric disorders are frequently comorbid, and particular combinations of disorders have been identified as increasing risk for suicide and suicide attempt. Psychological autopsy report more than 80% of suicides have more than one psychiatric diagnosis [94]. Comorbid mood disorder and substance use disorders were diagnosed in 19–57% of suicides in psychological autopsy studies [40, 41], and substance use disorders also increases the risk of suicide attempt in schizophrenia [95]. Suicide attempts are more frequent in mood disorder patients with comorbid personality disorders, particularly cluster B personality disorders [96, 97], alcohol and substance use disorders [98, 99] and comorbid PTSD [100].

28.4.8 Neuropsychiatric disorders and head injury Harris and Barraclough report increased risk of death by suicide in Huntington’s disease (threefold increase), epilepsy (fivefold increase), traumatic brain injuries (3.5-fold increase) and organic mental disorders/organic brain syndrome (2.5-fold increase), but no increase in suicide risk in dementia [45]. That meta-analysis did not examine comorbidity with major depression or other psychiatric disorders. In psychological autopsy studies, organic brain

disorders, as a primary diagnosis, were diagnosed in 4% of suicides [101, 102], and metaanalysis of psychological autopsy studies estimated 6.3% of suicides had organic mental disorders [41].

28.4.9 Genetic risk factors There is strong evidence supporting the familial transmission of suicide and attempted suicide [103–107]. Meta-analysis of 22 controlled family studies that included both suicide attempts and suicide calculated a approximately threefold overall increased risk of suicidal behaviour among close relatives of suicidal versus nonsuicidal individuals [108]. The transmission of suicidal behaviour in families is, in part, independent of the transmission of psychiatric disorders [103–106, 109–113]. Support for a genetic role in the transmission of suicidal behaviour in families is provided by twin and adoption studies. Twin studies have shown a higher concordance rate in monozygotic versus dizygotic twins for both suicide [114] and suicide attempts [115]. Pooled data from seven twin studies found the overall concordance for suicidal behaviour (suicide and/or attempt) was 23.5% in monozygotic twin pairs versus 0.13% in dizygotic twins [108]. Based on twin studies, 17–45% of the variance in suicidal behaviour is considered to be attributable to genetic factors [116, 117], while individual and shared environmental influences combined account for 35–75% of the variance [118]. A large Australian twin study found a genetic contribution to suicide attempts and even suicidal ideation [116]. Further support for a role for genetics in liability for suicidal behaviour is found in adoption studies that document higher rates of suicide in the biological parents of adoptees who die by suicide, compared to biological relatives of non-suicidal adoptees [119]. In general, the heritability of suicide is thought to be comparable to the heritability of other major psychiatric disorders such as bipolar disorder and schizophrenia [115]. Candidate genes for suicidal behaviour have been selected largely on the basis of established biological correlates of suicidal behaviour, and therefore have focused primarily on the serotonergic system and include genes related to the serotonin transporter, serotonin 1A (5-HT1A ), serotonin 1B (5-HT1B ) and serotonin 2A (5-HT2A ) receptors, monoamine 523

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oxidase A (an enzyme responsible for the degradation of serotonin) and tryptophan hydroxylase 1 and 2 (TPH1,TPH2), the rate-limiting biosynthetic enzyme for serotonin (see [120] for a review). These genetic studies have largely been association rather than linkage studies and have produced inconsistent results (see [108] for a review). Meta-analysis of 14 serotonin 5-HT2A receptor gene and 12 serotonin transporter gene association studies supported an association between the low expressing variant of the serotonin transporter promotor gene (5-HTTLPR) and suicide, but found no evidence of association for the 5-HT2A receptor 102T/C polymorphism [121]. Given the complexity and multicausal nature of suicide and suicide attempt, as well as the relative rarity of suicide, an endophenotype approach to investigating potential genetic contributions has been proposed [122], and is being adopted. These studies investigate associations between genes and behavioural, neurobiological, neuropsychiatric and other intermediate phenotypes related to suicidal behaviour (see [122] for a review). Notably, promising associations have been found for a functional monoamine oxidase gene promotor variant and violent and impulsive behaviour, particularly in men [123, 124]. Emerging techniques such as microarrays and single nucleotide polymorphism chips evaluate association of thousands of genes with disease and may further identify relevant genes. Other important developments in genetic studies are the examination of interaction effects between early life environment and genes and behavioural outcomes [125–127], and the investigation of epigenetic mechanisms such as methylation [122]. Genetic study of suicidal behaviour is evolving and developments in methodological and technological approaches will address some of the difficulties in replication that have hampered the field.

28.4.10 Biological risk factors Impairment in a number of neurobiological systems has been observed in individuals who die by suicide and/or make non-fatal suicide attempts. To date, the serotonergic system and the stress response systems of the hypothalamic–pituitary–adrenal (HPA) axis and noradrenergic system have been most studied. 524

Altered function in the serotonergic system has been consistently reported in suicides across psychiatric diagnoses indicating that these anomalies are related to the suicidal behaviour and not to any one psychiatric disorder (see [128] for a review). These alterations in serotonergic function associated with suicidal behaviour appear to be a trait and in prospective studies have predicted future suicidal acts (see [129] for a review). Metaanalysis of 5-hydroxyindoelacetic acid (5-HIAA) levels, the metabolite of serotonin found in the cerebrospinal fluid and a guide to serotonin activity in parts of the brain, found that individuals with below median cerebrospinal fluid 5-HIAA had a more than fourfold risk chance of dying by suicide, than those with above median levels [130]. Postmortem studies of the brains of those who died by suicide have localised abnormal serotonergic function to the ventromedial prefrontal cortex [131–133], a region of the brain involved in the executive function of behavioural and cognitive inhibition [134], and therefore decisionmaking. Lower serotonergic input into this brain region may result in impaired inhibition and contribute to a greater propensity to act on suicidal or aggressive feelings. Brain imaging studies have linked prefrontal cortical activity and serotonin release to suicide attempt [135] and impulsivity [136]. Abnormalities in the noradrenergic and the HPA stress response systems have also been associated with suicidal behaviour (for a review see [128]). There are fewer noradrenergic neurons in the rostral or upper locus coeruleus in the brainstem in depressed suicides, as well as indications of cortical noradrenergic overactivity such as lower alpha and high affinity beta1 -adrenergic receptor binding [137]. Suicidal individuals in diagnostically mixed populations exhibit HPA axis abnormalities, most commonly indicated by resistance to dexamethasone challenge [138–146]. Meta-analysis of prospective studies of HPA axis dysfunction and suicide reported a 4.65 increase in risk for those who showed blunted suppression of cortisol in response to dexamethasone [130]. Other biological systems of interest with respect to suicidal behaviour include fatty acids [147], cholesterol [148] and neurotrophic factors such as brain-derived neurotrophic factor [149].

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28.4.11 Clinical and psychosocial factors Studies in clinical samples have identified a number of factors that are consistently associated with suicide and suicide attempt, including aggressive/impulsive traits, hopelessness, a history of physical or sexual abuse during childhood and life stress.

28.4.12 Aggression and impulsivity Aggression levels are higher in suicide attempters than in non-attempters, and in those who die by suicides [150]. Elevated aggression has been linked to serotonergic dysfunction [151, 152] and may be, in part, heritable [153]. Thus the risk factors for aggression may overlap with suicidal behaviour as the two types of behaviours tend to aggregate in the same individuals and, as described above, share common abnormalities in both serotonin function and ventral prefrontal cortical function and structure, a neurotransmitter system and brain region involved in behavioural inhibition of aggressive and suicidal feelings. Suicide attempters are more impulsive than non-attempters [154] and disorders, such as borderline personality disorder, that are characterised by high levels of impulsivity, have high rates of suicide attempts. Impulsivity, however, is inversely correlated with lethality of suicide attempt [136, 155, 156]. That is, more impulsive suicide attempters tend to make less lethal attempts, perhaps due to lack of planning and not taking precautions to prevent discovery and rescue. Importantly, the impulsive nature of many suicide attempts does not mean they will not result in death, particularly when highly lethal means are readily available and commonly used, as is the case with pesticide ingestion in young women in rural China [39].

28.4.13 Hopelessness Prospective and retrospective studies have reported that hopelessness is associated with a range of suicidal behaviours including more severe suicidal ideation [157–159], suicide attempt [157, 159–163] and suicide [157, 159, 164, 165]. This association of suicidality and hopelessness has been documented across the lifespan including in children/adolescents [166–168] and the elderly [169],

as well as in the context of multiple psychiatric disorders including mood disorders [160, 162], schizophrenia [163, 170], personality disorders [171], substance abuse [172] and in community samples [157, 173].

28.4.14 Childhood abuse Childhood abuse, both physical and sexual, has been associated with suicide attempts in depressed patients [174], and in the community, where psychiatric illness is a partial but not total mediating factor [175]. Childhood abuse increases the likelihood of developing psychiatric illness [176], the main risk factor for suicidal behaviour. Animal studies have shown that adversity early in life has lasting neurobiological consequences, including in abnormalities in the serotonergic system [177] and HPA axis stress response [178], both which have been associated with suicide [130]. Childhood abuse also appears to increase the likelihood of developing impulsive/aggressive traits, which also elevate risk for suicidal behaviour [179].

28.4.15 Life events and psychosocial stressors In addition to the stress of an episode of psychiatric illness, other life events such as interpersonal and intimate relationship conflict, loss of employment, physical illness, financial and legal difficulties may all act as stressors precipitating suicidal behaviour in those with a predisposition to that behaviour. In psychological autopsy studies, interpersonal events and/or acute life stressors have been associated with suicide [180–185], and in case-control psychological autopsy studies prevalence of ‘adversity’ in the 3 months prior to suicide was reported in 29–93% of suicides compared to 5–88% of controls [40]. For adolescents and children psychosocial factors including parent-child conflict, adverse life events, personal relationships and family history of depression and substance abuse, appear to be equally important as the presence of a psychiatric illness [107]. Population-level studies find the unemployed, both males and females, at greater risk for suicide mortality than the employed [186]. However many of those studies did not consider the 525

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presence of a psychiatric disorder, which influences both the probability of employment difficulties and the risk for suicidal behaviour. Living alone or being socially isolated is a risk factor for suicide [187] particularly for older adults [63, 188].

28.5 Protective factors The preponderance of suicide research focuses on risk factors, however, there is an emerging interest in identifying protective factors which may then be targeted for enhancement in preventative interventions. The Reasons for Living Index assesses the beliefs and values held by individuals towards suicide, and higher scores on that scale distinguish suicide attempters from non-attempters [189, 190]. Religious beliefs, moral objections to suicide, survival and coping beliefs and family responsibilities also appear to have protective effects against suicidal behaviour [191–194].

28.6 Conclusions Suicide is one of the most serious outcomes of psychiatric disorder, and suicidal behaviour is an important public health issue. Much progress has been made in identifying risk factors and at-risk individuals, however there are still considerable gaps in knowledge relating to the extent of morbidity and mortality due to suicide and attempted suicide. More systematic and consistent reporting is needed to determine the true prevalence of suicide and attempted suicide, particularly in regions and cultures where there is strong stigma attached to suicide and mental illness in general. Improved surveillance data will reveal the magnitude of the problem, and thus permit the allocation of sufficient resources to develop prevention and treatment programs. Better information will also facilitate the identification of vulnerable groups within populations who can then be targeted for specific interventions.

Acknowledgements The authors would like to acknowledge Margaret Shirley MD for her assistance. 526

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Epidemiology and geriatric psychiatry Celia F. Hybels and Dan G. Blazer Department of Psychiatry and Behavioral Sciences, Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, NC, USA

29.1 Introduction

29.2 Issues of case identification

Adults 65 or older represent a growing segment of the population. In 2006, approximately 12% of the US population was age 65 and older. This age group numbered 37 million, an increase from 3 million at the beginning of the twentieth century. Those 85 and older grew from 100 000 in 1900 to 5.3 million in 2006. With the ageing of the ’baby boomers’, the number of adults 65 or older is expected to significantly increase, and by 2030 to number 71.5 million and represent nearly 20% of the US population. By 2050, the number of adults 85 or older is expected to number 21 million [1]. While the lifetime prevalence of any psychiatric disorder is lower in the current cohort of adults 60 years or older compared to younger age groups [2], as middle-aged adults move into the older age group, the number of older adults with a lifetime disorder is expected to increase, potentially creating a crisis in geriatric mental health [3]. For example, approximately 3.8 million adults 65 or older had a lifetime experience of a depressive disorder in 2005, yet by 2050 this number is projected to be 8.2 million [4]. In this chapter, we present data on the epidemiology of psychiatric disorders in older adults with a focus on two prevalent syndromes: depressive disorders and dementia. We discuss issues of case definition, the distribution of cases, aetiological and outcome studies, trends in suicide rates among older adults and use of mental health services by older adults.

Identifying and describing psychiatric disorders and clinically meaningful psychiatric syndromes is of critical importance in psychiatric epidemiology. This task is particularly challenging in elderly individuals, however, because of frequent comorbidity with medical illness and the presentation of symptoms as a consequence of medication use. Elders may fail to meet the specific criteria for a particular disorder, yet the syndrome can be significantly related to decreased functioning and quality of life. It may be difficult to separate psychiatric symptoms from symptoms related to aging itself or a disease process, such as age-related changes in sleep patterns or tiredness associated with congestive heart failure. The Diagnostic and Statistical Manual of Mental Disorders, 4th edition Text Revision (DSM-IVTR) [5], like its predecessors DSM-IV, DSM-III-R and DSM-III, defines cases by symptom presentation. This operationalisation has made possible the development of specific instruments such as the Diagnostic Interview Schedule (DIS) [6], the Structured Clinical Interview for DSM-IV (SCID) [7] and the Composite International Diagnostic Interview (CIDI) [8], which can be administered by clinicians and lay persons to capture the lifetime and current prevalence of disorders in both community and clinical populations. A widely accepted diagnostic system for Alzheimer’s disease (AD) is the National Institute of Neurologic, Communicative Disease and Stroke – Alzheimer’s Disease and Related Disorders

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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Association (NINDS-ADRDA) work group diagnoses [9]. In this system, clinical examination and the results of laboratory tests and imaging studies such as computerised topography or magnetic resonance imaging (MRI) scans are utilised. Numerous scales have also been used by epidemiologists in community and clinical studies as either screening instruments or for case identification without regard to specific DSM criteria. For example, the Mini-Mental State Examination (MMSE) [10] is often used to screen for cognitive impairment. The Center for Epidemiologic Studies – Depression (CES-D) Scale [11] and the Geriatric Depression Scale (GDS) [12] are widely used to screen for depressive symptoms. For each of these instruments, a somewhat arbitrary cutpoint or threshold has been established, above which symptomatology is considered clinically significant. The correspondence between these thresholds and DSM disorders is less established. While the DSM nomenclature has provided much assistance to psychiatric epidemiology in case identification, the criteria are less applicable for older adults. The potential consequences are not only misdiagnosis, but also underestimation of disorder prevalence in this population. Across all disorders, in addition to comorbidity with medical illness, there may be differences in symptom presentation or endorsement in older adults [13]. The nomenclature has been less successful in accounting for less severe but clinically significant symptom presentations such as subsyndromal depression and mild cognitive impairment (MCI). Both of these are particularly relevant for older adults. For example, among older adults, subsyndromal depression is the predominant form of depression. While the prevalence of major depression (MD) and dysthymia is lower in those 65 or older compared to other age groups, the prevalence of subsyndromal depression is higher among older compared to middle-aged adults [14]. Subsyndromal depression will be discussed later. Another limitation of the current diagnostic criteria for older adults is comorbidity of mental disorders. While there are many examples, we will discuss later the comorbidity of depression and cognitive impairment frequently encountered by clinicians treating neuropsychological disorders. Distinguishing between the two disorders may be 536

difficult and the two disorders may interact over time [15, 16]. In summary, the issue of case identification is complex in older individuals. The identification of geriatric syndromes that potentially lead to functional impairment, frailty and decreased quality of life, rather than focusing on specific psychiatric disorders, may be an important tool in determining who is a case [17].

29.3 The distribution of cases Several large-scale surveys conducted in the United States and other countries have provided information concerning the prevalence of psychiatric disorders in older adults. The landmark Epidemiologic Catchment Area (ECA) programme established by the National Institute of Mental Health to determine the prevalence of specific DSM-III disorders in community and institutional populations [18] was the first major study of its kind. Five community surveys were funded 1980–1984 and the DIS [6] was used to identify participants who met criteria for DSM disorders. A total of 18 571 persons 18+ years of age were interviewed, including a total of 5702 persons 65 or older. For all disorders, with the exception of severe cognitive impairment, the 1-month prevalence was lowest among those 65 or older compared to other age groups. The prevalence of all disorders with the exception of alcohol and drug abuse and dependence was higher in older women compared to older men. A total of 12.3% of those 65 or older reported one or more DIS disorders within the previous month. The most prevalent disorder in this age group was any anxiety disorder (5.5%), primarily due to the high prevalence of phobic disorder (4.8%). The prevalence of severe cognitive impairment was 4.9% [19]. The National Comorbidity Survey-Replication (NCS-R), a repeat of an earlier nationwide survey but including older adults for the first time, was conducted in the United States from 2001 to 2003. This study provides some of the most recent national estimates of the frequency and distribution of psychiatric disorders in a representative sample of 9282 adults. Psychiatric disorders were generated through the use of the CIDI [20]. A total of 26.1%

EPIDEMIOLOGY AND GERIATRIC PSYCHIATRY

of adults age 60 or older met lifetime criteria for one or more psychiatric disorders, 11.6% met criteria for two or more and 5.3% met criteria for three or more disorders. These lifetime estimates were lower than those for other age groups. MD was the most prevalent lifetime disorder in this age group (10.6%) followed by specific phobia (7.5%), social phobia (6.6%) and alcohol abuse (6.2%) [2]. In the National Epidemiologic Survey of Alcoholism and Related Conditions (NESARC), conducted in the United States during the same time period as the NCS-R, a total of 43 093 adults were interviewed and diagnoses were generated using the Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV (AUDADIS-IV) [21]. Twelve-month prevalence estimates among those 65 or older were lower than the lifetime estimates reported from the NCS-R. Specific phobia was the most common disorder (7.5%) in this age group, followed by MD (2.7%), social anxiety disorder (1.6%), alcohol abuse (1.2%), generalised anxiety disorder (1.0%) and panic disorder (0.8%). For all the disorders, the 12-month prevalence was lower in this age group compared to younger adults [22–27]. Based on these and similar studies, the current prevalence (1-month, 6-month or 12-month) of one or more disorders (excluding cognitive impairment) in non-institutionalised older adults ranges from 2.7 to 17.0% depending on the population studied and period assessed, and is higher in older females (11.7–20.7%) than males (9.9–11.8%) [28–30]. The current prevalence is much higher in institutionalised older adults. For example, in an epidemiological survey in Edmonton, the 6-month prevalence of any DSM disorder among institutionalised adults age 65 or older was 77.2% in females and 72.2% in males [28]. Prevalence estimates across all age groups for selected disorders reported from community-based epidemiological studies are presented in Table 29.1. Explanations why the prevalence of psychiatric disorders, with the exception of cognitive impairment or dementia, is lower in older adults generally fall into three categories: (i) methodological or sampling errors, (ii) cohort or period effects and (iii) older persons may be relatively protected against psychiatric disorders. Methodological arguments, with regard to depression, for example include the suggestion

that older adults are less likely to report depressive symptoms or to report depressive symptoms in somatic terms or in a way that makes it difficult to fit into the criteria for MD [31]. Using structural equation modelling, Christensen et al. found a direct effect of age on both anxiety and depression items, suggesting a differential endorsement by age for certain symptoms such as somatic items [32]. Newmann et al. found age differences in symptom reporting in a sample of females, with older women more likely to report feelings of worthlessness, feeling no interest in things, experiencing a loss of appetite and a general sense of hopelessness along with thoughts of dying and less likely to endorse symptoms of dysphoric mood and feelings of self-blame compared to younger females [33]. Older adults may be less likely to endorse sadness [34]. It is also plausible that although the symptoms are greater in number they are not distributed across the categories necessary to meet DSM criteria for MD. For example, vascular depression [35] may be more common in late-onset depression, and the clinical presentation may vary from depression seen in younger adults [13]. Selection bias may also contribute to the reduced prevalence of depression among older adults. It is possible elders with depression are less likely to participate in mental health studies or answer questions about their mood in the context of a general survey [36]. Individuals with a lifetime risk of depression may not survive to old age (selective survival). Elders with comorbid depression and physical illness may not be physically able to participate in a research study and objective information may be collected from a proxy respondent. Subjective information, such as depression symptoms, is typically not collected from proxy respondents in standard survey procedures. Each one of these possibilities would lead to an underestimate of the true prevalence of depression. Older adults may have a similar incidence of depression but the duration may be shorter which could result in a lower prevalence [31]. Finally, in studies such as the NCS-R and the NESARC, data are collected from community households and older adults in institutions are generally not included. Most epidemiologists agree the contribution of methodological and sampling bias to the low prevalence of depression in late life is quite small [36]. A 537

CHAPTER 29 Table 29.1 Prevalence of selected psychiatric disorders by age reported from community samples in the United States (selected studies). Authors

Study

Time period

Prevalence by age

Prevalence by sex among adults 65+

Major depressive disorder Kessler et al. [2]

NCS-R

Lifetime

Age 18–29 Age 30–44 Age 45–59 Age 60+ Age 18–29 Age 30–44 Age 45–64 Age 65+ Age 18–24 Age 25–44 Age 45–64 Age 65+

15.4% 19.8% 18.8% 10.6% 6.4% 5.5% 5.6% 2.7% 2.2% 3.0% 2.0% 0.7%

Hasin et al. [25]

NESARC

12-month

Regier et al. [19]

ECA

1-month

Kessler et al. [2]

NCS-R

Lifetime

Age 18–29 Age 30–44 Age 45–59 Age 60+ Age 18–29 Age 30–44 Age 45–64 Age 65+

13.3% 13.9% 14.1% 7.5% 8.0% 7.6% 7.4% 7.5%

Stinson et al. [27]

NESARC

12-month

ECA

1-month

Age 18–24 Age 25–44 Age 45–64 Age 65+

6.4% 6.9% 6.0% 4.8%

Age 18–29 Age 30–44 Age 45–59 Age 60+ Age 18–29 Age 30–44 Age 45–64 Age 65+

4.1% 6.8% 7.7% 3.6% 2.1% 2.5% 2.1% 1.0%

Age 18–29 Age 30–44 Age 45–69 Age 60+ Age 18–29 Age 30–44 Age 45–64 Age 65+

14.3% 16.3% 14.0% 6.2% 7.0% 6.0% 3.5% 1.2%

Females Males

0.9% 0.4%

Females Males

6.1% 2.9%

Specific phobia

Any Phobia Regier et al. [19]

Generalised anxiety disorder Kessler et al. [2]

NCS-R

Lifetime

Grant et al. [24]

NESARC

12-month

Alcohol abuse disorder Kessler et al. [2]

NCS-R

Lifetime

Hasin et al. [26]

NESARC

12-month

538

EPIDEMIOLOGY AND GERIATRIC PSYCHIATRY Table 29.1 (cont.) Authors

Study

Time period

Prevalence by age

Prevalence by sex among adults 65+

Alcohol abuse/dependence Regier et al. [19]

ECA

1-month

Age 18–24 Age 25–44 Age 45–64 Age 65+

4.1% 3.6% 2.1% 0.9%

Females Males

0.3% 1.8%

Schizophrenia Regier et al. [19]

ECA

1-month

Age 18–24 Age 25–44 Age 45–64 Age 65+

0.7% 0.9% 0.4% 0.1%

Females Males

0.1% 0.1%

NCS-R, National Comorbidity Survey Replication; NESARC, National Epidemiologic Survey of Alcohol and Related Conditions; ECA, Epidemiologic Catchment Area Survey.

more plausible explanation is a cohort effect. That is, those individuals born earlier in the twentieth century may be psychologically healthier than those born in later years. Elders are experiencing fewer disorders today because as a cohort they have always been healthy. A related explanation is that of a period effect. Some of the cohorts of older adults may have lived through the Great Depression and/or World War II and have experienced hard times. Today’s standard of living is likely to be by comparison more comfortable. Remembering poorer times may change the current perspective and result in less depressed mood. Similarly, younger adults are less likely to have seen economic depression and war, and any current difficulties may result in depressed mood. These effects are discussed later in the context of historical trends. While the ’baby boomers’ will bring a higher lifetime prevalence of psychiatric disorders, it is not yet known whether the estimates of current prevalence will be lower than in younger adults. The possibility of a true protective effect conferred by age cannot be dismissed, but discussion of this possibility is beyond the scope of the chapter.

29.3.1 Prevalence of depressive disorder The reported prevalence of MD in communitydwelling older adults varies depending on the time period assessed. The lifetime prevalence of MD in older adults is lower than that observed in younger adults, and is reported to be 8.2% among those 65 or older in the NESARC [25] and 10.6% among those

60 or older in the NCS-R [2]. Current estimates are considerably lower than lifetime estimates, and range from 0.7 to 3.1% [19, 28–30, 37]. The prevalence of MD in the Longitudinal Aging Study Amsterdam (LASA), a large community survey of adults 55–85, was 2.02%, and increased with age, from 1.3% among those 55–59 to 2.7% for those 80–85 years of age [38]. MD is more common in females than males [28, 29]. For example, in the Utah Cache County Study, the current prevalence of DSM-IV MD among those 65 or older was 4.4% in females and 2.7% in males [39]. Across these studies, estimates of current prevalence are lower than those observed in younger adults. For example, in the NESARC, the 12-month prevalence of MD was 2.7% among those 65 or older, compared to 5.6% among those 45–64, 5.5% among those 30–44 years and 6.4% among those 18–29 years [25]. The prevalence of clinically significant depressive symptoms reported from community studies can range from 8 to 16% [15]. In the Established Populations for Epidemiologic Studies of the Elderly (EPESE) studies [40], depressive symptoms were measured using the CES-D [11]. The prevalence of significant depressive symptoms in the North Carolina EPESE sample was 9.0% [41], while the prevalence in the New Haven EPESE sample was 15.1% [40]. The prevalence was higher in the Hispanic EPESE, 25.6%, with a prevalence of 17.3% in males and 31.9% in females [42]. These estimates consistently show the burden of depressive symptoms is quite high for older adults. 539

CHAPTER 29

In community surveys, the current prevalence of dysthymia ranges from 0.2 to 1.8% [19, 30]. Lifetime prevalence may be as high as 4.61% (3.00 in males and 5.82 in females) [43]. Approximately one-third of older adults with MD may have comorbid dysthymia at the index episode [44]. The prevalence of MD and depressive symptoms in primary care and institutional settings is higher than observed in community studies. In a review of the epidemiology of depression in primary care, Katon and Schulberg concluded the prevalence of MD increases linearly as studies move from the community to the primary care clinics to inpatient facilities [45]. In a sample of primary care patients 60 years of age or older, the prevalence of MD was 6.5% while the prevalence of minor depression was 5.2% [46]. In a study of HMO patients 65 or older, 14% scored above the traditional cutpoint on the CES-D [47]. The prevalence of MD in older adults hospitalised for medical illness ranges from 11.5 to 44.5% [48, 49]. In an early study, the prevalence of possible MD among 708 older nursing home and congregate housing residents was 12.4%, while another 30.5% reported less severe depressive symptoms. Possible MD was more prevalent among newly admitted residents, while long term residents were more likely to report minor depression [50]. In a more recent survey from the Netherlands, the prevalence of MD among 333 nursing home patients was 8.1%. The prevalence of minor depression in this sample was 14.1% and the prevalence of subclinical depression was 24% [51].

29.3.2 Subsyndromal depression Across all age groups, there is a growing body of evidence that there are cases of depression that do not meet DSM criteria but are clinically significant [52]. These cases are particularly common among older adults, suggesting that ‘major’ depression does not fully capture the spectrum of depression seen in late life [53]. In elders from the North Carolina ECA study, the overall prevalence of depressive symptoms was 27%. The prevalence of MD was 0.8%, mixed depression and anxiety syndrome was 1.2%, dysthymia 2%, symptomatic depression 4% and mild dysphoria 19% [54]. Those with symptomatic depression not meeting DSM criteria were more 540

likely than the dysphoric group to complain of poor health and to report benzodiazepine use in the previous year. One fourth reported their depression to be associated with the death of a loved one, to experience social isolation, to fear being in crowds and to feel useless. Subsyndromal depression is also prevalent in clinical populations. In a sample of primary care patients age 60 or older, the prevalence of subsyndromal depression was 9.9%, higher than the prevalence of MD (6.5%) and minor depression (5.2%) [46]. Older primary care patients with subsyndromal depression have as much medical burden as patients with major or minor depression and more functional impairment than nondepressed patients [55]. A significant proportion of patients with subsyndromal depression develop minor or MD or dysthymia [56]. In the LASA, the prevalence of minor depression, defined as cases above the CES-D cutpoint of 16 but not meeting DIS/DSM criteria for MD, was 12.9% [38]. The prevalence of minor depression increased with age, from 9.4% among those 55–59 to 16.7% among those 80–85 years of age. In the LASA, minor depression shared a similar risk factor profile to MD. In addition, lower level of education, urbanicity, chronic physical illness, cognitive impairment and smaller social network were associated with minor but not MD. In the Duke EPESE, depressive symptomatology below the customary cutpoint on the CES-D was prevalent in older adults (9.9%), and was associated with impairments in function, disability days, poorer self-rated health, the use of psychotropic medications, low perceived social support, female sex and being unmarried, like more symptomatic depression [57].

29.3.3 Incidence of depression in older adults The 1-year incidence of MD in the ECA surveys among those 65+ without a lifetime diagnosis at the first interview was 1.25 per 100 person-years at risk (0.90 for men and 1.48 for women) [58]. The incidence of first-onset depression may increase with age. In a study of 875 non-depressed older adults with a mean age of 85, the incidence of depression was 4.1%. The incidence of first-onset depression was 1.4% per person year (0.8% for males and

EPIDEMIOLOGY AND GERIATRIC PSYCHIATRY

1.5% for females). Characteristics at baseline associated with the incidence of depression included having a dementia, insufficient social network and having more than two depressive symptoms at baseline [59]. A study from Sweden reported the incidence of first-onset DSM depression was 17 per 1000 person-years for adults ages 70–79 and 44 per 1000 person-years for those ages 79–85 [60]. Incident and recurrence rates of depressive syndromes were recently reported from a sample of communitydwelling adults 56 or older from the Netherlands. The incidence rate for depressive syndromes was 7.0 per 1000 person-years and the recurrence rate was 27.5 per 1000 person-years. The incidence and recurrence rates more than doubled when episodes of depressive symptoms were also included. Most late life depression therefore occurs in persons with a history of depression. The recurrence rates did not differ between men and women [61].

29.3.4 Prevalence of dementia An important focus within epidemiology and geriatric psychiatry is the prevalence and incidence of dementia and cognitive impairment, disorders and syndromes with the highest prevalence among older adults. The recently conducted Aging, Demographics and Memory Study (ADAMS) sample included 856 individuals age 71 or older drawn from the Health and Retirement Study, and was the first nationally representative population-based study of dementia in the U.S. The prevalence of dementia in the ADAMS was 13.9%. Dementia prevalence increased with age, from 5.0% among those 71–79 years of age to 37.4% among those 90 or older [62]. In the Canadian Study of Health and Aging, the prevalence of dementia among adults 65 or older was 8.0%. The age-standardised rate ranged from 2.4% among those 65–74 to 34.5% among those 85 or older [63]. The prevalence of dementia has been shown to vary by the criteria used, which is problematic for epidemiologic research and geriatric psychiatry [64]. The prevalence of dementia using the International Classification of Diseases, 10th edition (ICD-10) appears to be consistently lower compared to the prevalence using DSM criteria [65]. For example, a recent study from Sweden reported the highest prevalence of dementia within the same sample was obtained using

DSM-IV criteria (9.6%), followed by 7.4% using ‘historical’ criteria, 6.3% using DSM-III-R, 3.1% using ICD-10 and 1.2% using ICD-9 criteria [66]. Within the umbrella term for dementia there are several subtypes, with AD being the most prevalent. However, AD and vascular dementia frequently overlap. In a community survey of 1085 persons age 65 or older conducted in Islington, 9.86% of the sample met screening criteria for dementia and over twothirds received a diagnosis. The prevalence by subtype among those with a diagnosis was: AD 31.3%, vascular dementia 21.9%, dementia with Lewy bodies 10.9% and frontal lobe dementias 7.8% [67]. The prevalence of AD in the ADAMS was 9.7% [62]. This prevalence was higher than that reported in the Canadian Study of Health and Aging (5.1%) [63], but comparable to the prevalence reported from the East Boston EPESE (10.3%) [68]. The prevalence estimates from the ADAMS showed an increase with age: 2.3% among those 71–79, 18.1% among those 80–89 and 29.7% among those 90 or older. Gender differences in AD were not significant. Vascular dementia in the ADAMS was less prevalent (2.4%) than AD and also increased with age: 1.0% among those 71–79, 4.1% among those 80–89 and 6.2% among those 90 or older [62]. In the Canadian Study of Health and Aging, both community and institutional samples were included. As expected, the prevalence of dementia in the institutional sample was much higher (569 per 1000 population) than the prevalence in the community sample (42 per 1000) [63]. In a review of the prevalence of dementia, Jorm et al. noted that studies that included institutionalised elders did not generally report higher rates than those covering only community residents. While a higher proportion of institutionalised older adults have dementia, institutionalised elders make up only a small proportion of older adults [69]. The prevalence of dementia and cognitive impairment from selected studies is shown in Table 29.2.

29.3.5 Mild cognitive impairment Recent research has focused on a syndrome referred to as MCI. This condition is not as severe as dementia, but individuals generally have more cognitive decline that would be expected by age but no 541

542 Mild cognitive impairment (MCI)

Clinic based samples without dementia

Cardiovascular Health Study (CHS) Cognition Study

Aging, Demographics and Memory Study (ADAMS)

Epidemiologic Catchment Area (ECA) Study

Lopez et al. [70]

Plassman et al. [71]

Regier et al. [19]

Community samples five sites

Severe cognitive impairment

Community Cognitive impairment representative sample without dementia

Dementia

Community Dementia representative sample

Outcome

Community and institutional samples

Aging, Demographics and Memory Study (ADAMS)

Plassman et al. [62]

Sample

CSHA Canadian Study of Health Working and Aging (CSHA) Group [63]

Study

Prevalence of dementia and cognitive impairment (selected studies).

Authors

Table 29.2

Age 65–74 Age 75–84 Age 85+

Age 71–79 Age 80–89 Age 90+

Age <75 Age 75–79 Age 80–84 Age 85+

2.9% 6.8% 15.8%

16.0% 29.2% 39.0%

18.8% 14.7% 22.6% 28.9%

2.4% 11.1% 34.5%

Age 65–74 Age 75–84 Age 85+

5.0% 24.2% 37.4%

Age 71–79 Age 80–89 Age 90+

Age Females

Females Males

Females Males

Males

Females

Males

Sex 15.74%

4.7% 5.1%

18.7% 19.0%

6.9%

8.6%

11.14%

White 14.1% African45.5% American

Race

EPIDEMIOLOGY AND GERIATRIC PSYCHIATRY

significant interference in daily activities. The condition may or may not proceed to dementia [72, 73]. Several subtypes of MCI have been noted including age-associated memory impairment, age-associated cognitive decline, amnestic MCI and cognitive impairment no dementia (CIND). As an example of the complexity of the syndrome, there are older adults with neuropathological changes that suggest a dementia process but with no evident memory problems [73]. In some studies, subtypes are grouped together whereas in other studies prevalence is estimated for an individual subtype or for a broader term of MCI that combines various subtypes [73, 74]. The prevalence of MCI therefore varies depending on the case definition used. The prevalence of MCI ranges from 3 to 19% among adults older than 65 years [72] In the ADAMS, the prevalence of MCI/CIND was 22.2% among those 71 or older. Subtypes included prodromal AD (8.2%) and cerebrovascular disease impairment (5.7%). Of the participants available for follow-up, 11.7% with CIND progressed to dementia annually whereas those with prodromal AD or stroke progressed at annual rates of 17 and 20% [71]. In the Canadian Health and Aging Study, the prevalence of CIND was 16.8% [75]. In the Cardiovascular Health Study (CHS) Cognition Study, the prevalence of MCI was 19% and increased with age from 19% among those younger than 75 years to 29% among those older than 85 years. At the Pittsburgh CHS site, the prevalence of MCI was 22%, of which 6% was MCI amnestic type and 16% MCI multiple cognitive deficits type [70]. The distribution of subtypes may be different in studies that include institutionalised adults.

29.3.6 Incidence of dementia and mild cognitive impairment Incidence rates of dementia scaled to age 80 recently reported from the CHS were 34.7 per 1000 person years for white women, 35.3 for white men, 58.8 for African-American women and 53.0 for African-American men. Sex differences were not significant within race, and racial differences were only of borderline significance when adjusted for age and education. In whites, the incidence at age 80 was 19.2 for AD compared to 14.6 for vascular

dementia. For African-Americans, the rates were 34.7 and 27.2 [76]. Comparable rates per 1000 person years were reported from an Italian study of brain ageing: 37.8 for dementia, 23.8 for AD and 11.0 for vascular dementia [77]. The incidence of dementia increases with age. Fiveyear incidence from the Framingham Study ranged from 7.0 per 1000 at ages 65–69 to 118.0 at ages 85–89. A similar increase in incidence with age for AD was noted – 3.5 at ages 65–69 and 72.8 at ages 85–89 [78]. The estimated annual incidence of AD reported from the East Boston EPESE among those 65–69 was 0.6%, compared to 1.0% for those 70–74, 2.0% for those 75–79, 3.3% for those 80–84 and 8.4% for those 85 or older [79]. Jorm and Jolley concluded the incidence of dementia rises exponentially up to the age of 90 [80]. Dementia incidence may continue to increase beyond age 85 but the rate of increase may slow [81]. One study from Sweden reported the observed incidence of cognitive impairment was lower after age 79 compared to ages 75–79. However, if the observed incidence rates were corrected for attrition due to death, incidence estimates were much higher and continue to increase after age 79 [82]. In examining the incidence of dementia, it is important to look at cases that develop among those with normal cognition as well as those that progress from predementia states. The Cache County investigators reported 46% of those with cognitive impairment converted to dementia over a 3-year period, compared to 3.3% among those without cognitive impairment [83]. In the CHS Cognition Study, the incidence of all dementias among those with MCI was 147 per 1000 person-years. Among those with MCI, 51% progressed to dementia while 18% returned to normal [84]. Higher conversion rates among those with multidomain MCI were also reported from Italy. Different subtypes of MCI may have different progression rates. In a Swedish study, two-thirds of those with multidomain MCI converted to AD compared to one-half of those with amnestic MCI [85]. The incidence of dementia in the Italian Longitudinal Study on Aging (per 1000 person years) was 7.63 in the total sample, 5.02 in participants with normal cognition at baseline, 21.37 among those with CIND and 13.59 among those with MCI at baseline. Among those with MCI, rates of incident 543

CHAPTER 29

dementia ranged from 8.74 in amnestic to 40.60 in multidomain subtype [86]. In the Monongahela Valley Independent Elders Survey (MoVIES) study, the prevalence of amnestic MCI was 2.9–4.0% over five assessments during a 10-year period. Among those with amnestic MCI at baseline, 27% developed dementia over the following 10 years. The risk increased over time, but a significant number of those with MCI remained stable or reverted to normal [87].

29.4 Aetiological studies In identifying causes of disease, epidemiology offers the opportunity to look for both correlates of psychiatric disorders and symptoms, as well as risk factors for symptom onset. In the sections following, we present examples from aetiological studies identifying risk factors for both depressive disorders and dementia in older adults. In addition, we present a summary of studies that identify factors that are associated with prevalence. Therefore, the term ‘risk factor’ is used here to include aetiological factors as well as correlates of depression and dementia.

29.4.1 Risk factors for depression Across studies, MD and depressive symptoms are more prevalent in females [19, 38, 39]. Studies have shown an increase in depressive symptoms with age [38, 41]. For example, in the LASA, the prevalence of MD was 1.3% among those 55–59 and 2.7% among those 80–85. Similar increases were reported for minor depression – 9.4% among those 50–59 and 16.7% among those 80–85 [38]. The increase with age may be related to other factors, for example more women in the older cohorts. In the Duke EPESE, depressive symptoms in the 65+ age group were associated with increasing age as well as being female, lower income, physical disability, cognitive impairment and having no close relatives. When these potential confounders were simultaneously controlled, however, the association between age and depressive symptoms reversed, with older adults having fewer symptoms than those younger [41]. The prevalence of depressive symptoms does not appear to differ by race [41], although 544

the prevalence may be higher in older MexicanAmericans [42]. Numerous psychosocial risk factors for late life depression have been documented, including the experience of stressful life events and ongoing difficulties, impaired social support, medical illness and declines in physical functioning [88, 89]. One study found severe events predicted the onset of a depressive episode while ongoing difficulties accounted for the most episodes. Milder events were found to trigger a recurrent episode but not a first episode [90]. Late life depressive symptoms have been linked to poorer life satisfaction and well-being as well as perception of poorer health [91]. Personality characteristics such as neuroticism may be associated with increased depressive symptoms [92] and to increase the risk of depression associated with disability [93]. In the LASA, MD was associated with being unmarried, having one or more functional limitations, perceived health as fair or poor, less instrumental support, perception of loneliness and external locus of control. Overall, MD appeared to be an exacerbation of a chronic mood disturbance with roots in vulnerability factors, while minor depression was more often a reaction to stressors [38]. Among Mexican-American elders, immigration status, level of acculturation and assimilation and recency of immigration were associated with higher levels of depressive symptomatology [42]. The LASA showed a differential relationship between depression and physical health by number of depressive symptoms. Minor depression was related to the presence of chronic disease, while MD was not [94]. Data from the World Health Surveys recently showed there were no differences in the relationship between age and depressive/anxiety disorders as a function of physical pain/comorbidity. The majority of older adults reported some physical comorbidity but most did not have a depressive/anxiety disorder, while adults with a disorder were more likely to report physical pain/comorbidity, particularly older adults. DSM depressive and anxiety disorders decreased with age in spite of increased physical comorbidity [95]. Among older adults, the relationship linking depressive symptoms to functional disability may be mediated by cognitive deficits. That is, some everyday tasks have a cognitive component

EPIDEMIOLOGY AND GERIATRIC PSYCHIATRY

which may be affected by depression [96]. For example, among older adults with MD, deficits in instrumental activities of daily living (ADL), which have a cognitive component, were associated with more severe depression [97]. Disability may lead to depression because of decreased capability. In a 2-year follow-up of 1457 community residents age 65 or older, increasing disability and decline in health preceded the emergence of depressive symptoms, while the number of medical conditions, social support, life events and demographic contributed little [98]. In a longitudinal Australian study, factors indicating increased dependency, that is changes in functional capacity, mental status and need for residential care, predicted depressive symptoms over an 8-year follow-up period in older adults [99]. Sleep disturbance has been shown to predict depression [89] and among older adults with a history of depression, to predict recurrence [100]. Risk factors for dysthymia in late life appear to be similar to those for MD [101]. For example, cerebrovascular disease appears to play a role in the aetiology of late onset dysthymia [102]. This is consistent with the vascular depression concept linking MRI-defined vascular lesions and late-onset MD [103]. Some biological variables have been explored to determine their relationship with depression. First, the genetic link is present across the life cycle. In twin studies from Sweden of older adults, for example genetic influences accounted for 16% of the variance in total CES-D scores and 19% of somatic symptoms [104]. Though serotonin activity, specifically 5-HT2A receptor binding, decreases dramatically in a variety of brain regions through mid-life, the decrease slows from midlife to late life. Sheline et al. found that 5-HT2A receptors in normal subjects decreased markedly from young adulthood to midlife (70% from the levels at age 20 through the fifth decade) and then levelled off as age advanced [105]. The activity of these receptors, however, may vary with age. With the advance of new tools of inquiry, especially MRI, investigators have proposed a vascular based depression among the elderly. In one study of 139 depressed older adults, 54% met neuroimaging criteria for subcortical ischaemic vascular depression. Age was most strongly associated with increased prevalence of these changes [106].

29.4.2 Risk factors associated with dementia In the ADAMS, age was the strongest risk factor for dementia and AD. More years of education were also associated with a lower risk. Although the prevalence of dementia was higher among females, the gender differences were not significant. African-Americans had a higher prevalence of dementia and AD, but these differences were not significant when gender and education were controlled [62]. Age was also a risk factor for CIND in the ADAMS, with a prevalence ranging from 16.0% among those 71–79 to 39.0% among those 90 or older [71]. In a study from Finland, MCI was more prevalent in older and less educated survey participants, but no gender differences were found [107], suggesting demographic risk factors may be similar for MCI and dementia. In the East Boston EPESE, fewer years of education was associated with a 3-year decline in broadly defined cognitive functioning independently of age, birthplace, language, occupation and income [108]. In the Nun Study, low linguistic ability in early life was a strong predictor of poor cognitive function and AD in late life [109]. Cross-sectional studies have shown cognitive impairment to be associated with, in addition to age, depressive symptoms [110], self-rated health [111], physical functioning as measured by ADLs [111] and medical comorbidity [112]. Longitudinal studies have shown depressive symptoms to predict cognitive decline [110, 113, 114]. In the MacArthur Studies of Successful Aging, depressive symptoms predicted cognitive decline and incident cognitive impairment over 7 years in previously high-functioning elders [115]. Some research has shown depressive symptoms to predict cognitive decline among elders with moderate cognitive impairment, but not among elders with intact cognitive functioning [116]. Depressive symptoms may affect different domains of cognitive function. In the LASA, for example depressive symptoms were associated with 3-year cognitive decline in speed of information processing but not decline in memory impairment or global mental deterioration [117]. Among older adults with MD, changes in white matter hyperintensity volume were associated with the development of dementia, particularly of the 545

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non-Alzheimer’s type [118]. In the CHS, depressive symptoms were associated with increased risk of MCI (10.0, 13.3 and 19.7% for those with no, low or moderate or high depressive symptoms respectively), and this association was independent of underlying vascular disease [119]. Depressive symptoms may not, however, affect the rate of progression from MCI to dementia [120]. Cardiovascular risk factors for dementia or AD include history of stroke [86], hypertension [121] and total cholesterol in midlife [122]. There may be a decreased risk of dementia and AD among users of statins [123], but the results are inconclusive [124]. Impairments in physical function and frailty may predict the risk of dementia or cognitive decline [86, 125], while physical activity may have a protective effect [126]. One study found older adults who developed a clinical diagnosis of dementia performed worse in instrumental ADL 10 years earlier, suggesting problems with these complex tasks may be an early marker of the disease [127]. Elevated levels of metals in the brain may be a risk [128]. Studies of the protective effects of oestrogen have yielded mixed results. In the Baltimore Longitudinal Study on Aging, women who were users of oestrogen had a reduced risk of AD [129], but the Women’s Health Initiative did not find an association [130]. Finally, an inverse relationship between AD and non-steroidal anti-inflammatory use has been reported [131, 132]. Both prevalent and incident cognitive dysfunction have been associated with a number of genetic risk factors, including Presinilin 1 and -2. Both are associated with a very high risk for AD (however both genes are rare in the population). Far more common is the susceptibility gene, the E4 allele of apoliprotein E (APOE) [133, 134]. The E4 allele can be viewed as a general risk factor for AD though the mechanisms have not been identified (the E2 allele has a corresponding protective effect). The E4 allele is a susceptibility in that not all (or even most) persons with the allele eventually develop overt dementia. Other biological risk factors are more difficult to clearly identify. However, much interest has recently been placed upon traumatic brain injury (TBI) [135], and these injuries are especially relevant to the experiences of soldiers deployed to Iraq and Afghanistan. We have witnessed an explosion of studies exploring 546

the neuropathology of AD over the past few years. A particular focus has been the production of βamyloid (a potential target of pharmacotherapy, including vaccine therapy).

29.4.3 Comorbidity of depression and dementia Depression and cognitive impairment are often comorbid in older adults [16]. Approximately 18–40% of AD patients treated in outpatient settings have mild to moderate depressive symptoms or MD as estimated in a number of studies, and depression is generally more severe in the less cognitively impaired [136–138]. Variability across studies depends in part how depression is assessed. Some instruments used to assess mood may not be valid in patients with dementia. Cognitive impairment is also observed in patients treated for MD. Alexopoulos et al. studied a group of severely depressed elders, some of whom experienced cognitive dysfunction in the midst of their depression and some who did not. All were treated and the depression remitted. In addition, after treatment for depression, the cognitive function in both groups was similar. Over 5 years of follow-up, they found that the elders who experienced cognitive dysfunction during their depressive episode were much more likely to develop dementia. This finding suggests either that a severe depression ‘unmasks’ a dementia or that the pathophysiological changes in depression and dementia are similar, that is these elders experienced one disease, not two [139]. Recent work has focused on the cognitive neuroscience of depression or brain changes associated with depression that may affect cognitive function [16]. Areas of the brain which have historically been associated with AD cannot be easily distinguished from areas associated with depression. In addition, many cases of AD are not ‘pure cases’ but associated with vascular lesions. Vascular lesions in turn are known to be associated with those areas of the brain and tracks of neurons associated with depression. In summary, what we at times may consider ‘comorbid’ depression and dementia may in fact derive from the same brain lesions and therefore result from a single neuropathological cause.

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29.5 Outcome studies Both depressive disorders and dementia have been shown to be associated with adverse outcomes in late life, particularly disability and increased mortality.

29.5.1 Outcomes associated with depressive disorders Depression has been linked to medical comorbidity, functional decline, cognitive decline and mortality [15]. The risk for adverse outcomes may be due, in part, to the chronicity of depression in late life. In the LASA, patients with depression at baseline were followed for six years. There were remissions in 23%, an unfavourable but fluctuating course in 44% and a severe chronic course in 32%. Those with subthreshold depression had the best outcome, followed by those with MD, dysthymic disorder and comorbid MD and dysthymia [140]. In a 25-year follow-up study of older depressed patients, only 12% of the patients available for contact at the end of the followup had recovered and remained continuously well, 84% experienced recurrences, 2% experienced an unremitting course and 2% died by suicide [141]. Persistence has been associated with chronic physical illness and/or worsening health, baseline functional limitations, external locus of control, female gender, more severe depression at initial contact and/or a history of depression, incident anxiety symptoms and advanced age [142–145]. The prognosis for older medical inpatients with depression is also poor. One study reported one year after hospitalisation, only 13% of those depressed had recovered, 14% partially recovered and 73% remained depressed with a protracted stable or fluctuating course. Among medical inpatients with minor depression, 28% recovered and 72% had a protracted course [146]. Overall, the recovery rate of older patients appears to be similar to that of younger adults, but depressed patients with first onset in late life may be at a higher risk of chronicity [147]. Outcomes of subthreshold depression has been less documented. In a sample of older patients selected from primary care, patients with subsyndromal or minor depression had an outcome more favourable than patients with MD and less favourable than the nondepressed. Greater medical burden, poor

subjective health status and poorer subjective social support predicted a poor outcome [148, 149]. One of the major strengths of longitudinal studies is the ability to establish temporality. While declines in physical functioning are associated with concurrent increases in depressive symptoms [150], depressive symptoms have also been shown to predict functional decline. Penninx et al. followed a cohort of 6247 older adults free from disability for 6 years and found those who were depressed at baseline had an increased risk of incident disability in both ADLs and mobility, controlling for baseline chronic conditions and sociodemographic factors. Less physical activity and fewer social contacts among those with depressive symptoms explained part of the increased risk [151]. Some studies have shown treating the depression in older patients may improve the physical functioning, particularly among those with limitations in instrumental ADLs [152, 153]. Finally, it is not just more symptomatic depression that puts older adults at risk for functional decline. In one study, the likelihood of becoming disabled increased with each additional symptom of depression. Also, as the number of depressive symptoms increased, the likelihood of recovering from a physical disability decreased [154]. As discussed earlier, decreased cognitive functioning is correlated with late life depression, and poor cognitive outcomes among patients with late life depression are not uncommon. In the Duke EPESE, depressive symptoms were associated with subsequent cognitive decline 3 years later, controlling for initial cognitive status [155]. In a 12-year study of 1265 older adults free of dementia, however, depressive symptoms were associated with lower cognitive scores at baseline but not subsequent cognitive decline. Cognitive decline was minimal among those who remained dementia free and marked among those who developed dementia. Depressive symptoms were not associated with rate of decline in either group [156]. Studies examining the risk of mortality associated with depression have also been inconsistent. In the Cardiovascular Health Survey of 5201 older adults, high levels of depressive symptoms were an independent predictor of 6-year mortality [157]. A study of older Mexican-Americans found high levels of depressive symptoms concurrent with chronic 547

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medical illness predicted mortality. For example, the odds of dying among diabetics with high levels of depressive symptoms were three times the odds of death among diabetics without high levels of depressive symptoms [158]. But other studies have not found an increased risk. For example, in the Duke ECA, neither MD nor depressive symptoms were associated with mortality among persons 60 or older followed for 2 years [159]. In the Duke EPESE, depressive symptoms were associated with mortality through multiple pathways such as chronic disease, functional impairment and social support [160]. Whether depression has an independent effect on mortality outside of chronic health conditions has also been studied in medical patients and nursing home residents. In a study of 573 medical inpatients age 70 or older, depressive symptoms were associated with three-year mortality after adjusting for potential confounders. The association was not explained by greater levels of comorbid illness, functional impairment and cognitive impairment in patients with more depressive symptoms [161]. In a study of institutionalised older adults, however, there was no increased risk of mortality when physical health, functional ability and cognitive status were simultaneously controlled, suggesting the effects of depression on mortality appear to be attributable to the correlation between depression and poorer health in this population [162]. In a study from Montreal, a sample of 500 medical inpatients was followed for 16–52 months, a group currently with MD and a group of non-depressed. A total of 23.2% of the enrolled patients had a history of depression. After adjusting for demographic factors, physical illness, cognitive impairment and prior service utilisation, a diagnosis of depression among those without a history of depression was not associated with an increased risk of mortality. Coincident MD and a history of depression were associated with a decreased risk of mortality [163], suggesting a protective effect. The inconsistent findings may be due in part to the severity of depression. For example, in the LASA data, MD was associated with 4-year mortality for both men and women, but minor depression was only associated with mortality for men [164]. In a study of 2558 Medicare recipients age 60 or older, 548

participants with mild to moderate depression did not have an increased risk of mortality compared to those without symptoms. However, those with more severe depressive syndromes had an increased mortality risk after demographics, health risk behaviours and chronic medical disorders were controlled [165].

29.5.2 Outcomes associated with dementia Witthaus et al. reported the burden of dementia in the general population has been shown to be similar to that of lung cancer or stroke. The authors used data from the Rotterdam Study and compared lost life years in a demented population to lost years in a healthy population. Fifty-five-year-old men were estimated to lose 1.2 life years due to morbidity and mortality due to dementia. Women lose an estimated 3.1 years to dementia [166]. In the last year of life, most demented patients require assistance with ADLs, have severe impairments in cognitive functioning and receive some hospital care [167]. Older patients with coexisting depression and dementia utilise more psychiatric and medical inpatient services compared to those with either depression or dementia alone [168]. Researchers from the Marshfield Clinic reported the adjusted hazard ratio (HR) of being admitted to a nursing home was 5.44 for patients with AD and 2.27 for patients with other dementias compared to controls [169]. Almost half of new nursing home admissions have a diagnosis of dementia [170]. Poorer cognitive functioning has been linked to decline in physical functioning in longitudinal studies. In the New Haven EPESE among persons who were free of limitations in physical functioning at baseline, individuals who scored more errors on a short cognitive screening measure were more likely to report incident ADL limitations 3 years later, independently of age, race, history of chronic health conditions, incident health conditions and type of residence [171]. This relationship between lower cognitive scores and functional decline has been observed even among older adults whose cognitive function at baseline was within the normal range [172]. In a Swedish study of 1745 elders 75 or older followed for 3 years, age and dementia were strongly associated with the development of functional dependence and decline. The

EPIDEMIOLOGY AND GERIATRIC PSYCHIATRY

population-attributable risk percentage of dementia in the development of functional dependence was 49% [173]. Older individuals with poor cognitive function have an increased risk of mortality [174]. In a US population-based study, the adjusted relative risk (RR) of death was 1.90 for patients with AD and 2.27 for patients with other dementias compared to controls [169]. The presence of cognitive impairment has been shown to increase the risk of mortality in parallel with the severity of the impairment [175]. In a study of 1855 community residents 65 or older in New York followed for 4 years, the survival probability was for 0.85 the cognitively unimpaired, 0.69 for the mildly impaired and 0.51 for the severely impaired [176]. Older adults with multiple-domain amnestic MCI had a higher risk of mortality than those with single-domain amnestic MCI [177]. The East Boston EPESE found persons with probable AD had a relative risk of death 1.44 times that of the non-diseased when followed for 4.9 years [178]. Survival probabilities may also vary by age, race and rate of decline. Bruce et al. examined 9-year mortality using data from the New Haven ECA. Poorer performance on cognitive assessments at baseline decreased the risk of survival. The effect was stronger for younger respondents than older respondents [179]. In the Dutch Longitudinal Study Among the Elderly, 211 adults age 65–84 were followed for 8 years. The rate of decline in cognitive function was associated with survival time in those 70 or older, with a larger decline having a shorter survival time. The association was not found in those 65–69 at baseline [180]. African-American and Latino-American patients with AD may have longer survival compared with white AD patients [181]. The rate of decline among patients with AD may be linearly related to mortality risk. Researchers reported a patient with AD declining minimally was 2.7 times less likely to die over approximately three years compared to a person declining rapidly [182]. Clinical studies have also found a relationship between cognitive decline and mortality. Among a sample of medically ill older adults, cognitive impairment predicted 1-year mortality independent of medical illness and disability [183]. In an 8-year follow-up of 606 nursing home dementia patients, comorbidity and severity of dementia were

independent predictors of mortality [184]. Another study reported after adjusting for comorbidities and potential confounders, hospitalised patients with cognitive impairment had an increased risk of death within the hospital, in the first year after hospitalisation, and cumulatively [185]. Research from the Life Events and Senile Dementia Study in England has suggested social factors may influence the outcome of dementia. Specifically, while the experience of life events did not appear to affect outcome, having social support was associated with increased survival time from dementia. Receiving delivered meals, higher dependency and poorer physical health were associated with poorer survival in patients with dementia [186]. Despite these increased risks for morbidity and mortality in the cognitively impaired, recent trends suggest a compression of cognitive morbidity. Using data from the Health and Retirement Study collected in 1993–1995 and 2002–2004, Langa et al. reported the prevalence of cognitive impairment was 12.2% among those 70 or older in 1993 compared to 8.7% in 2002. Cognitive impairment was associated with a higher risk of two-year mortality in both intervals. The risk of death for those with moderate/severe cognitive impairment was greater in 2002 compared with 1993 (age and sex adjusted HR 3.11 in 2002 vs. 2.53 in 1993). The authors concluded fewer older Americans reached the threshold for significant cognitive impairment in the second interval, but among those who did there was a more rapid decline to death [187].

29.6 Historical trends in the epidemiology of psychiatric disorders in late life One of the contributions of epidemiology to medicine is studying changes in the distributions of disease over time. For example, the incidence of a childhood disease such as measles was much higher prior to the development of a vaccine. In the case of psychiatric epidemiology, studying historical trends involves documenting the prevalence of disorders at different periods in history. The prevalence of a disorder may be affected by factors such as current events. For example, the prevalence of a disorder 549

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such as post-traumatic stress disorder (PTSD) may be higher in the years following a catastrophic event such as 9/11 in the United States or the Gulf Wars. A disorder such as depression may be less prevalent in times of economic prosperity and more prevalent in times of recession such as the global recession of 2008–2009. In addition, disorders may emerge in later life secondary to events earlier in life. For example, clinicians in the Veterans Administration are reporting a number of cases of PTSD with the onset of symptoms in later life secondary to experiences during the Vietnam era. The prevalence of the disorders may be affected by the nomenclature in place at the time the data are collected. For example, the prevalence of specific disorders in the ECA surveys were different from those obtained in the National Comorbidity Study less than 10 years later, and the differences were due in part to changes in the DSM criteria for the disorder [188].

29.6.1 Suicide in older adults Observing suicide rates over time can provide information about historical trends. Suicide in older adults has received much attention in psychiatric research as it has become apparent the rates of suicide in late life are high. In 2005 in the United States, the suicide rate across all age groups was 11.0 per 100 000 residents, while the rate among those 65 or older was 14.7, similar to 15.4 among those 45–64 years, but higher than 13.7 for those ages 25–44 and 10.0 for those 15–24 years. The higher rates in later life are driven almost exclusively by an increase over the life cycle in suicide among white non-Hispanic males. Suicide rates increased with age, with a rate of 12.6 among those 65–74 and 16.9 among those 75–84 and 85 or older. Among those 65 or older, white nonHispanic males had the highest rate, 33.2, compared to 14.1 among Hispanic males, 11.0 among Asian males, 10.2 among black males, 6.8 among Asian females, 4.3 among white non-Hispanic females, 2.0 among Hispanic females and 1.4 among black females. Although suicide rates are higher in this age group, the rates have declined over the past 50 years. In 1950, the rate among those 65 or older was 30.0 while in 1970, the rate was 20.8 and in 1990 the rate was 20.5 per 100 000 residents [189]. One reason for these varying rates may be the concept of a cohort 550

effect. Just as different cohorts may exhibit different rates of psychiatric illness, they may exhibit different rates of suicide at comparable ages because of generational effects. Older adults generally have less suicidal ideation, make fewer suicide attempts and give fewer warnings, but the attempts tend to be more successful. The attempts are more violent and carefully planned. These factors make suicide prevention in older adults a difficult task [190]. Completed suicides are associated with depressive illness, physical illness burden, functional limitations, disruption of social ties and stressors such as financial strain [190, 191].

29.7 Use of health care services Many older adults with psychiatric disorders do not receive treatment, and those who do are more likely to receive treatment from a primary care physician [192]. Across all age groups, less than half of individuals with a psychiatric disorder receive any medical treatment, and individuals less than 60 years of age are 1.5–2.0 times more likely to receive treatment than older adults. Among adults who do receive treatment, older adults are also the least likely of any age group to receive mental health speciality treatment [193]. Older adults with disorders are less likely than younger adults to perceive a need for mental health care and to receive a referral to mental health speciality services from primary care [194]. While the majority of persons with a lifetime disorder eventually receive some treatment, older adults are more likely to fail to make or delay in making a treatment contact after initial onset of the disorder [195].

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[183] Arfken, C.L., Lichtenberg, P.A. and Tancer, M.E. (1999) Cognitive impairment and depression predict mortality in medically ill older adults. J. Gerontol. A: Biol. Sci. Med. Sci., 54A, M152. [184] van Dijk, P.T.M., Dippel, D.W.J., van dee Meulen, J.H.P. and Habbema, J.D.F. (1996) Comorbidity and its effect on mortality in nursing home patients with dementia. J. Nerv. Ment. Dis., 184, 180. [185] Freedberg, D.E., Dave, J., Kurth, T. et al. (2008) Cognitive impairment over the age of 85: hospitalization and mortality. Arch. Gerontol. Geriatr., 46, 137. [186] Orrell, M., Butler, R. and Bebbington, P. (2000) Social factors and the outcome of dementia. Int. J. Geriatr. Psychiatry, 15, 515. [187] Langa, K.M., Larson, E.B., Karlawish, J.H. et al. (2008) Trends in the prevalence and mortality of cognitive impairment in the United States: is there evidence of a compression of cognitive morbidity? Alzheimers Dement., 4, 134. [188] Regier, D.A., Kaelber, C.T., Rae, D.S. et al. (1998) Limitations of diagnostic criteria and assessment instruments for mental disorders: Implications for research and policy. Arch. Gen. Psychiatry, 55, 109. [189] National Center for Health Statistics (2007) Health, United States 2007 With Chartbook on Trends in the Health of Americans, Hyattsville, MD. [190] Conwell, Y., Duberstein, P.R. and Caine, E.D. (2002) Risk factors for suicide in later life. Biol. Psychiatry, 52, 193. [191] Duberstein, P.R., Conwell, Y., Conner, K.R. et al. (2004) Suicide at 50 years of age and older: perceived physical illness, family discord, and financial strain. Psychol. Med., 34, 137. [192] Gallo, J.J. and Coyne, J.C. (2000) The challenge of depression in late life – Bridging science and service in primary care. J. Am. Med. Assoc., 284, 1570. [193] Wang, P.S., Berglund, P., Olfson, M. et al. (2005a) Failure and delay in initial treatment contact after first onset of mental disorders in the National Comorbidity Survey Replication. Arch. Gen. Psychiatry, 62, 603. [194] Klap, R., Unroe, K.T. and Unutzer, J. (2003) Caring for mental illness in the United States: a focus on older adults. Am. J. Geriatr. Psychiatry, 11, 517. [195] Wang, P.S., Lane, M., Olfson, M. et al. (2005b) Twelve-month use of mental health services in the United States. Arch. Gen. Psychiatry, 62, 629.

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Recent epidemiological studies of psychiatric disorders in Japan Masayoshi Kawai,1 Kenji J. Tsuchiya1 and Nori Takei1,2 1 Research

Centre for Child Mental Development, United Graduate School of Child Development, Osaka University, Kanazawa University and Hamamatsu University School of Medicine, 1-20 Handayama, Higashi-ku, Hamamatsu 431-2192, Japan 2 Division of Psychological Medicine, Department of Psychiatry, Institute of Psychiatry, King’s College, London, UK

30.1 Introduction This review focuses mainly on epidemiological studies that have been relatively recently conducted and reported by Japanese investigators. Among many reports, we describe studies that are related to incidence (prevalence), risk factors, illness course (i.e. factors determining the course) and treatment outcomes (i.e. clinical trials) for major psychiatric disorders, namely schizophrenia and mood disorders. In addition, since autistic disorder, one of pervasive developmental disorders, is now considered to be a serious mental problem beginning in infancy and influencing sufferers’ life through to adulthood, we additionally report some studies on this condition from Japan. The introduction of new diagnostic classification systems, such as International Classification of Diseases (ICD) and Diagnostic and Statistical Manual (DSM), in the 1980s has also influenced clinical practice as well as the research in Japan. We experienced ambiguity between diagnostic boundaries before such introduction. In this review, therefore, we put emphasis on studies that were performed in the 1980s and onwards. This is because Japanese psychiatrists had developed Japanese own unique diagnostic concepts and views in some areas of psychiatry [1] – although almost all of the concepts were introduced from

the European psychiatry, mainly German psychiatry in the early twentieth century – and therapeutic approaches [2], which may, in turn, have widened or narrowed the diagnostic criteria. Although diagnostic systems have yet to be completed and still in progress of elaboration, those studies that have been reported from Japan after international awareness of problems about diagnostic ambiguity was brought to light are more reliable in terms of the research quality and more comparable with the reports outside Japan than studies reported prior to that historical turning point. Some of the studies conducted in Japan have been publicised in international journals (i.e. written mainly in English) other than domestic journals (written in Japanese). However, most of the reports in the field of psychiatry from Japanese psychiatrists have appeared in the domestic journals. Therefore, in this review, we also consider those studies that have been reported only in Japanese but contain some scientific values in our judgement. In Japan, epidemiological studies of mental illnesses based upon community surveys have been reported since the 1940s [3]. Since then, on top of the incidence and prevalence research on serious mental problems, various epidemiological studies have been reported. In this chapter, we review epidemiological studies mainly on schizophrenia, mood disorders and

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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autistic disorder. We extend our survey into studies reported in Japanese and overview the history of epidemiology in Japan.

30.2 Schizophrenia 30.2.1 Prevalence and incidence The first systematic study of schizophrenia in Japan was carried out on Hachijo Island (a population of 8318) (Tokyo) in 1940 by Uchimura and colleagues. In this study, a preliminary search was performed for one week in 1939, with help of the police and the village officials, in order to identify ‘those who are currently mentally disorientated and those who were so in the past’. In 1940, eight psychiatrists visited the island and stayed for 10 days to interview the afflicted individuals. Based upon the direct interviews, the ‘disease-expectancy’ rate (morbidity risk: a rough estimate of lifetime risk) was calculated using Weinberg’s method of calculation. The prevalence rate was reported to be 0.38% and the morbidity risk (16–45 years of age) 0.91%. This study set the standard for later epidemiological studies of mental illnesses in Japan. After this publication, 15 regional studies had been conducted in Japan until 1987. Using these studies, Nakane et al. [4] computed the mean prevalence rate and morbid risk; the figure obtained was 0.55 and 1.09% (16–40 years of age), respectively (see review by Nakane et al. [4]). The rates slightly vary across different studies, which may reflect methodological differences (i.e. case identification procedures, diagnostic criteria and sample sizes involved). Many of the regions chosen for study were either rural or island communities with relatively small and generally immobile populations. These communities thus tended to have a higher rate of consanguineous marriages and the estimated prevalence of schizophrenia might be higher than that of the general population. Therefore, findings from these studies need to be interpreted with caution. Recently, Saha et al. [5] conducted a meta-analysis and estimated the prevalence and morbid risk of schizophrenia, using 188 studies (46 countries) reported between 1965 and 2002. They found the mean of the point prevalence to be 0.46% 560

and the lifetime morbid risk to be 0.72%. These results indicate that as expected, the prevalence and the morbid risk reported from Japan do not differ remarkably from the corresponding values in other countries. As with other countries, incidence rate studies in Japan have been less frequently reported than prevalence studies. A main study of incidence rates [6] was conducted in Nagasaki City, a local area in Kyu-shu Island of the south-west of Japan, as part of the WHO Collaborative Study on the Determinants of Outcome of Severe Mental Disorders [7]. In this study, the annual incidence rate per 10 000 persons aged 15–54 years for clinical diagnosis of schizophrenia was 2.0. In the World Health Organization (WHO) study, no remarkable differences in the incidence (range: 1.5–4.2 per 10 000 persons) were found across the centres (eight countries). In a more recent study using DSM-III-R diagnostic criteria and the direct interview methods, Hamada et al. [8] examined the incidence, prevalence and morbid risk of schizophrenia on a remote island (Tsushima in Nagasaki prefecture: with a population of 36 876 as of 1988). The study period was between 1988 and 1996. The incidence was 2.3 per 10 000 persons. The prevalence was 5.8 per 1000 persons, and the morbid risk was 0.95%; the latter value was derived from the direct method (calculated from the incidence data). These results indicate that introduction of new diagnostic systems did not convey a major impact on the risk estimates. In Japan, the focus of epidemiological research in Japan is rather limited to estimating the prevalence, incidence, and morbid risk of a particular disorder, and there is a paucity of hypothesis-driven approaches or identifying aetiological factors associated with the disorders. Lately, much attention has been paid to the effects of urban environments on the prevalence of schizophrenia and the high risk of schizophrenia among immigrants, particularly in the United Kingdom and Netherlands [9, 10]. Diversity exists in Japan as well. The differences in urban and rural environments have become larger with cities increasingly growing and living in cities becoming more complex, and Japan has, in the recent years, accommodated many immigrants due to a shortage of the labour force. These drastic changes, which may have an impact on mental health

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including schizophrenia, can be considered to be a target for research.

30.2.2 Seasonality of birth Tramer first described a winter–spring excess of schizophrenia births in 1929 [11]. Since then, a number of studies from many countries, including the southern hemisphere, have attempted to replicate the seasonal-of-birth effect in schizophrenia. Most of these studies have consistently found seasonality of birth in schizophrenia, predominantly in months from December to May, with its peak in January and February. The magnitude of the birth excess ranges from 5 to 15%. In contrast, some studies focused on deficit months of schizophrenia births; that is the months when individuals with the future development of schizophrenia are less likely to be born. The deficit period is in the summer and fall months, and the magnitude of the deficit births has been reported to be greater than that of the excess births [12–15]. However, in a recent study using Danish case registry data (n = 1.75 × 106 ), Mortensen et al. [16] showed that winter birth excess and summer birth deficit were almost identical. At present, the season-of birth phenomenon is considered to be a robust finding in the epidemiology of schizophrenia. In Japan, Shimura et al. [17] examined season of birth using 7960 schizophrenia patients in Tokyo and found a spring excess of schizophrenia births as in other reports in Western countries. Using data collected for examining the association between exposure to 1957/1958 influenza epidemics and the risk of the later development of schizophrenia, Kunugi et al. [18] examined the seasonality of schizophrenia births in Japanese subjects (n = 1024) born between 1955 and 1960, and found that the degree of a reduction of schizophrenia births in summer was more marked than that of excess births in winter. A similar finding was reported by Tatsumi et al. [19], who, using Japanese patients with schizophrenia (n = 2985), found a decrease in the summer births without an excess of winter birth. In a recent study of Japanese patients with schizophrenia (n = 3927), Tochigi et al. [20] compared the seasonally fluctuating distribution of schizophrenia births with the expected number of births that were calculated using data on national

births statistics. The study sample consisted of all inpatients at 10 psychiatric hospitals in and around Tokyo, who had been born in Japan – the place of birth was not taken into account in the study – between 1920 and 1974. The expected number of births for a month and season was calculated using data on births for the whole nation (the Population Statistics of the Ministry of Health and Welfare, Japan). No significant excess of winter births was observed, but a decrease in the summer births was found. However, no account was taken of birthplace in this study as mentioned above. The majority of the patients they examined may have been born in Tokyo and in surrounding areas. The monthly or seasonal fluctuation of birth-pattern may not necessarily be constant across the nation. Thus, for the sake of precision in calculation, in this sort of analysis, it would be optimal if births statistics for the relevant area were used as reference, and only those patients who had been born in the same restricted region were chosen. Recently, Mino and Oshima [21] have investigated the winter birth phenomenon in a large scale study of schizophrenia patients in Japan. Data were collected from governmental statistics, Patient Survey 1996 in Japan which had been conducted on a day in October. Hospitals were randomly selected from all of the mental hospitals in Japan. Both inpatients and outpatients were included in the study (n = 88 788). A significant winter to early spring birth excess was found in both male and female patients. The results of this study could be more reliable than the previous Japanese studies, because the representative and largest sample ever was employed. Such strengths are, however, compromised by use of clinical diagnosis in the study. Kitamura et al. [22] examined the relationship between seasonality birth of schizophrenia and the genetic risk in 55 patients with schizophrenia and 138 control patients with other psychiatric disorders. The risk of schizophrenia was significantly higher among relatives of the schizophrenia probands born in spring than among those of the controls born in the same season. Increased risk was also found in relatives of schizophrenia probands born in winter or spring (6.9%) compared with those of schizophrenia probands born in summer or autumn (0%). A contrasting result was, however, reported 561

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from a European large study. Suvisaari et al. [23] investigated seasonal variation of births in siblings of patients with schizophrenia who were born from 1950 to 1976. The number of unaffected siblings was 12 300 and the number of affected siblings was 1155. They did not find any association between probands’ month of birth and siblings’ odds of developing schizophrenia. Some, but not all, studies from Japan replicated the finding, reported worldwide, that there is an excess of births in winter to early spring in schizophrenia. In view of consistency of the finding and the fact that large scale studies and representative samples of patients with schizophrenia show such a phenomenon (e.g. [21]), failure in detection of the effect in some studies may be due to the methodological flaws, such as small sample sizes involved or calculation of expected numbers of births in a month or season. In contrast, researchers highlight a summer deficit in schizophrenia births rather than a winter or spring birth excess, as shown in some studies from Japan. However, considering recent climate changes and global warming and assuming that the seasonof-birth effect in schizophrenia is related to a cold climate, one may predict that such a phenomenon will be diminished or disappear in the future.

30.2.3 Obstetric complications Many previous epidemiological studies have shown an association between a history of obstetric complications (OCs), in particular perinatal complications, and the later development of schizophrenia [24, 25]. In Japan, we were the first to use the Mother and Child Health Handbooks (MCHHs) for the research into schizophrenia; the MCHH is a set of special notes provided to each pregnant woman in Japan to record medical events throughout pregnancy and delivery. Most Japanese mothers keep these notes even after their child has grown. The notes are invaluable because they are systematically recorded by the attending obstetrician, cover a wide range of pregnancy and delivery events and provide contemporaneously recorded data. Therefore, the data obtained from the MCHHs are totally free from recall bias. Utilising the MCHHs, we examined OCs in mothers of a Japanese population of patients with schizophrenia [26]. We found that 562

female but not male patients with schizophrenia had experienced significantly more perinatal complications than their siblings and healthy controls. In the subsequent study, we hypothesised that some of the events (i.e. perinatal complications) as a risk factor could be accounted for by preceding maternal factors and investigated the relationship between antenatal factors in mothers such as the frequency of antenatal care visits – as a measure of their care for baby and themselves – and mother’s body mass index (BMI) during the pregnancy, and histories of OCs in schizophrenia patients using data from MCHHs [27]. It was found that a low number of antenatal care visits and high maternal BMI at early pregnancy increase the risk of schizophrenia, and both of the antenatal factors (i.e. antenatal care visits and maternal BMI at early pregnancy) independently, but partly, contribute to an excess of occurrence of OCs in individuals with schizophrenia. Given the finding that there is an inverse relationship between the rate of having OCs and age of onset in adult populations of schizophrenia [28], we predicted that the rate of OCs would be relatively higher in the extreme form of the disorder: namely, childhood-onset schizophrenia. In a series of our studies, therefore, we studied occurrence of OCs in childhood-onset schizophrenia [29]. Thirty-three children, aged 8–13 years, meeting the DSM-III-R criteria for schizophrenia, were compared with control children matched for sex and age. Those children exposed to OCs were 3.5 times as likely to develop schizophrenia as those without OCs. The magnitude of the risk association was found to be higher in this extremely early onset population than that for adult-onset populations; the relative risk for the latter has been reported to be around 2.0 [30]. This association and the strength of the association were replicated in another independent set of patients with childhood-onset schizophrenia [31]. Several epidemiological studies have reported an association between hypoxia-related OCs and schizophrenia [32, 33]. One of the mechanisms underlying the association is postulated to be a hypoxic process in the brain of offspring around the time of birth. In this context, we have recently conducted animal studies. We examined any dopaminergic dysfunction, which has been established as one of the fundamental physiological

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changes related to schizophrenia, by inducing asphyxia in rat pups [34]. At 12 weeks following the anoxia-induced birth (12-week old in rats corresponding to adult in humans), we found an elevation in methamphetamine-induced (MAP) locomotor activity, which was associated with an increase of dopamine release in the nucleus accumbens. The result of this animal study lends support to the association between OCs, particularly hypoxia-related OCs and schizophrenia. It would be of interest to examine the interplay between OCs and other risk factors in relation to schizophrenia. We made an attempt to see the interaction between OCs and the season of birth using MCHHs of schizophrenia patients (n = 90) and controls (n = 183) [35]. The results showed that there was an approximately eightfold increased risk of schizophrenia associated with OCs in the winter-born males, but not in females. The head circumstance at birth (a proxy for the central nervous system (CNS) growth) in the winter-born patients with schizophrenia was shorter than that in the winter-born controls. This study suggests that both OCs and winter-birth may lead to brain damage and an impairment of the CNS development, predisposing individuals to the development of schizophrenia later in life. However, large studies are needed to confirm the finding.

30.2.4 Influenza infection European epidemiological studies have investigated the relationship between prenatal exposure to the 1957 influenza pandemic and the later development of schizophrenia [36, 37]. These reports support the hypothesis that exposure to influenza epidemics during the second trimester of gestation is associated with an increased risk of schizophrenia. Using data from Japan, we replicated these findings [38]. Kunugi et al. [38] studied patients at 18 psychiatric and general hospitals in the Tokyo metropolitan area. The number of individuals with schizophrenia who had been born 5 months after the peak prevalence of the 1957 influenza epidemic in Japan was compared with the mean number of individuals with schizophrenia who had been born in the corresponding months of the 4 years surrounding the year when the epidemic took place. There was an increase in

the development of schizophrenia for those born in the fifth month after the peak of the 1957 influenza epidemic. In another study conducted in Kochi prefecture in Shikoku-Island located in the western part of Japan, Izumoto et al. [39] examined the relationship between exposure to the 1957 influenza epidemic during the second trimester of gestation and the development of schizophrenia in later life. This study has also shown a significantly increased number of births in those exposed to the epidemic in the fifth month of gestation. Nevertheless, it is of note that this association has not always been reproducible; for instance, no association between exposure to influenza epidemics and the later development of schizophrenia was reported using data from Holland [40].

30.2.5 Methamphetamine (amphetamine) psychosis Among illicit drugs, MAP, a stimulant, has been most commonly abused in Japan for more than 60 years. MAP has been easily obtainable and, in fact, was able to be bought overt the counter from 1943 to 1950. MAP abusers develop a paranoid psychosis characterised by delusions (persecutory nature) and auditory and visual hallucinations. Sato [41] noted the similarities between schizophrenia and MAP psychosis. First, a paranoid-hallucinatory state in clear consciousness is common in both disorders. Second, reality testing is severely impaired in MAP psychosis, too. Third, antipsychotics are effective in the treatment of MAP psychosis as well. Fourth, repetitive MAP use raises susceptibility of reappearance of paranoid-hallucinatory states (designated as ‘sensitisation’). Similarly, it has been shown that a paranoid-hallucinatory state reappears or is exacerbated following administration of a small subpsychotomimetic dose of MAP in remitted or stable schizophrenia patients. Finally, symptom profiles for MAP psychosis are indistinguishable from those of acute or chronic schizophrenia. Accordingly, MAP psychosis has been considered to serve as an experimental model of schizophrenia. Especially, the behavioural sensitisation, which is a characteristic phenomenon showing that single or repeated exposure to MAP enhances the stimulating effect on locomotor activity in the 563

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rodents, could be a useful model for the relapse vulnerability in schizophrenia patients. In this regard, animal studies have been conducted to examine the pathophysiology of MAP psychosis with an attempt to elucidate the biological mechanism of schizophrenia. Ujike et al. [42] have demonstrated that the sigma antagonist BMY 14802 prevents the development of behavioural sensitisation induced by repeated administration of MAP in rats. This suggests that sigma receptors may play a crucial role in the mechanism(s) involved in the susceptibility of relapse in schizophrenia. Ito [43] reported that clonazepam, a gamma-aminobutyric acid (GABA)benzodiazepine agonist, prevented the acquisition of behavioural sensitisation induced by MAP in rats, whereas flumazenil, a GABA-benzodiazepine antagonist, did not have an effect on the acquisition of sensitisation. These results indicate that GABAbenzodiazepine receptors may be involved in relapse of psychotic episodes in patients with schizophrenia. It is apparent that we can learn more from in vivo human brains rather than animal brains. We carried out a positron emission tomography (PET) study using [(11)C]WIN 35 428, a dopamine transporter ligand, and found a decrease of dopamine transporter density in the orbitofrontal and dorsolateral prefrontal cortices in those with MAP psychosis [44]. In the subsequent study, we went on to investigate the serotonin transporter (SERT) density using PET with a radioligand of [(11)C](+)McN-5652 and demonstrated significantly lower SERT density in global brain regions in MAP abusers than in control subjects [45]. These findings suggest that abnormalities in both dopaminergic and serotonergic neurotransmitter systems are involved in the form of MAP psychosis and provide clues to the pathophysiology of schizophrenia. However, direct studies of patients with schizophrenia in this context are awaited. As debated, a question remains as to whether substance-induced psychosis such as MAP psychosis or cannabis psychosis is mediated by underlying vulnerability to schizophrenia. In other words, those individuals prone to the development of schizophrenia may tend to become addictive to substances and are consequently prompted to manifest the illness. In this regard, Chen et al. [46], in a Taiwanese study, investigated the family loading for schizophrenia in MAP users. They found that the first degree relatives 564

of MAP users with a diagnosis of MAP psychosis had a significantly higher morbid risk for schizophrenia (odds ratio, OR = 5.4) than those of MAP users with no history of MAP psychosis. This suggests that vulnerability to schizophrenia may underlie the development to MAP psychosis. Based on these findings, researchers explored whether patients with MAP psychosis would have alterations in the genes that have been reported to be candidate genes for schizophrenia. Kishimoto et al. [47] examined the relationship between Frizzled 3 gene – a transmembrane receptor for secreted Wnt glycoproteins involved in the Wnt signal transduction cascades – which was associated with susceptibility to schizophrenia [48], and MAP psychosis using 188 patients with MAP psychosis and 240 controls. They analysed six single nucleotide polymorphisms (SNPs) of the gene. Using haplotype analysis by the permutation method, they found that the Frizzled 3 gene is associated with MAP psychosis. The result indicates that genetic variants of the Frizzled 3 gene may affect susceptibility to both MAP psychosis and schizophrenia. Recently, we have investigated the relationship between a functional polymorphism in the serotonin transporter linked polymorphic region (5-HTTLPR) and MAP psychosis, using 166 patients with MAP psychosis and 197 controls [49]. We have found that the frequency on the short allele (S allele) in patients with MAP psychosis is higher than that of the controls. In the context, of interest is the ´ et al. [50], who showed that the findings by Saiz frequency of S allele is higher in patients with schizophrenia than in controls. This result, together with ours, points to evidence that the polymorphism in serotonin transporter-linked promoter region (5-HTTLPR) may play a role in predisposition to both MAP psychosis and schizophrenia. However, genetic composition in MAP psychosis is not always shared with schizophrenia. We investigated the relationship between the catecholO-methyl transferase (COMT) haplotypes and MAP psychosis, using 143 patients with MAP psychosis and 200 controls. We have displayed that the met allele frequency of the COMT is associated with MAP psychosis [51]. In contrast with our findings, Molero et al. [52] reported that val allele, but not met allele, was associated with the schizophrenia.

RECENT EPIDEMIOLOGICAL STUDIES OF PSYCHIATRIC DISORDERS IN JAPAN

30.2.6 Radiation Nakane and Ohta [53] investigated the prevalence of schizophrenia in the atomic bomb (A-bomb) survivors in Nagasaki. Using a clinical database of A-bomb survivors, they identified 1589 patients with schizophrenia among the A-bomb survivors (n = 26 678) in 1978. As a result, the authors estimated the prevalence of schizophrenia to be as high as 6%. However, there are certain limitations to the study. In the study, the researchers examined only Abomb survivors who stayed in Nagasaki City. Those individuals who had moved out the Nagasaki area were not taken into account. It is likely that relatively healthy individuals may have moved out of the region where they undergone terrible experiences after attack of the A-bomb, and that vulnerable people may rather have chosen to stay in the same region. Therefore, the risk could be overestimated. In a study of those individuals who survived the 1986 Chernobyl accident, Loganovsky et al. [54] investigated the relationship between exposure to irradiation and the risk of schizophrenia. Over a 12-year follow-up period after the accident, the researchers found that 42 of the survivors (n = 5329) developed schizophrenia. The annual incidence of schizophrenia was estimated to be 0.056%. This figure corresponded to a threefold increase when compared with that of the Ukrainian population. Since these two studies provide some evidence that exposure to radioactivity during life, especially adults in case of both studies, may be a risk factor for the development of schizophrenia, we investigated, in an animal study, whether irradiation experiment in rats would cause behavioural abnormalities that mimic the phenotype of schizophrenia [55]. Animals aged 8 weeks were exposed to a total dose of 15-Gy irradiation in the forebrain. We found that abnormal sensitivity in the dopaminergic system, expressed as hyperactivity, became manifest in irradiated rats after administration of a dopaminergic agonist, MAP. In these irradiated rats, we also found significantly reduced numbers of BrdU-positive cells in both the subventricular and subgranular zone, where neurogenesis is known to take place. These results indicate that the exposure to irradiation in adulthood leads to dopaminergic dysfunction, which is seen in the brains of patients with schizophrenia, and that impaired

neurogenesis caused by irradiation may be linked with the development of the phenotype of schizophrenia. There is ample evidence, from various epidemiological studies (e.g. [37]), suggesting that the risk period for forming a process of schizophrenia lies in the neurodevelopmental stage, more specifically a prenatal period. On the basis of this notion, Imamura et al. [56] examined the relationship between prenatal exposure to A-bomb radiation and the development of schizophrenia in adulthood. They investigated the lifetime prevalence of schizophrenia among individuals prenatally exposed to the 1945 Nagasaki A-bomb. Among 1867 prenatally exposed individuals, 18 subjects (0.96%) had developed schizophrenia later in life. The researchers found that the risk was significantly higher in those exposed in the second trimester of gestation (1.72%; 11 cases of schizophrenia) than those exposed in the third trimester (0.34%; 2 cases of schizophrenia). Although the results are intriguing, they have yet to be conclusive because the sample size involved in the study is too small and the number of those exposed to A-bomb in the third trimester who developed the disorder later in life is less than expected.

30.2.7 Factors affecting illness course and treatment Ogawa et al. [57] conducted a 21–27-year follow-up study of 130 patients with schizophrenia who had been consecutively discharged from a hospital between 1958 and 1962. The results showed that 31% were recovered, 46% improved and 23% unimproved in terms of symptomatology. In this study, factors affecting the illness course were not examined. To compare the outcome of schizophrenia from the perspective of cross-cultural aspects, Kurihara et al. [58] investigated the outcome of schizophrenia in a sample from Bali, as a representative of non-industrialised societies in Asia, and in a sample from Tokyo, representing industrialised societies in Asia. A total of 51 Balinese schizophrenia patients and 40 schizophrenia patients in Tokyo, who were first admitted to the hospitals, were evaluated with outcome measures including the need of medication and symptom severity assessed by the Positive and 565

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Negative Syndrome Scale (PANSS) at a 5-year follow-up, and readmission rates during the followup period. The cumulative length of stay in hospital during the follow-up period was significantly shorter for patients in Bali than for those in Tokyo. The percentage of Balinese patients who were free of medication at the follow-up was significantly higher compared with patients in Tokyo. However, no difference was found in the mean PANSS scores or readmission rates during the follow-up period between the two sites. The results in this study showing no differences in symptom profiles or admission rates for first-admission patients with schizophrenia between developing and developed countries are incompatible with the finding reported by Leff et al. [59] that patients in developing countries have a more benign course than patients in developed countries. However, the follow-up period may not be long enough to differentiate between the two sites. Recently, Yamazawa et al. [60], in a small study of 24 patients with first-episode schizophrenia, investigated what factors would predict outcome (assessed as global functioning) at a one-year follow-up. Factors examined included the duration of untreated psychosis, premorbid functioning and cognitive function. Cognitive performance was evaluated using the Letter Cancellation Test, Trail-Making Test, Digit Span and Verbal Fluency Test. The researchers found that short duration of untreated psychosis, good premorbid functioning and good cognitive performance (on all the tests examined) were related to good outcome. These findings have been consistently replicated across nations [61]. Studies investigating the efficacy and the safety of antipsychotic drugs are, needless to say, important in clinical epidemiology. In Japan, various types of antipsychotic agents, including new generation antipsychotics, are available, as in other countries. A new compound, called perospirone, has been manufactured originally by a Japanese pharmaceutical company and on market in Japan. On the basis of data from animal studies showing that perospirone exhibits high antagonistic activities for both 5-HT2 and D2 receptors [62], the agent is claimed to be an atypical antipsychotic. However, no scientific evidence is available in humans. Recently, 566

we investigated what pharmacological characteristic the agent of perospirone would have in the living human brain, using PET with radioligands of [11 C]N-methylspiperone (for 5-HT2 receptor occupancy) and [11 C]raclopride (for D2 dopamine receptor occupancy) [63]. The results have shown that perospirone acts at both the 5-HT2 and D2 receptors in humans. Furthermore, the occupancy of these two receptors increased in accordance with an increase in the plasma levels of perospirone, similar to the serotonin-dopamine antagonist (SDA) family of agents. Our PET study showing in vivo pharmacokinetic and pharmacodynamic properties for the compound, however, awaits clinical investigation. Multi-site or multi-nation studies of efficacy of pharmacological compounds are a powerful and clinically informative approach. Recently, large-scale studies of this kind investigating efficacy of new generation antipsychotics (i.e. SDAs) have been reported from Europe [64] and the United States [65]. Strangely, in Japan, no equivalent studies of scientifically high quality, with no influence from pharmaceutical companies and purely academic orientation, have thus far been conducted. As described above, there are some drugs that are only available in Japan and, furthermore, metabolic functions that are often influenced by genetic composition may differ according to ethnic origins. Therefore, multisite, large-scale studies are also needed in Japan to compare optimum doses of particular agents with those recommended in other countries and to establish efficacy of agents that are not available outside Japan.

30.3 Affective disorders A range of studies for affective disorders (i.e. major depressive disorders, bipolar disorder and other specific forms of affective disorders) have been conducted in Japan. Since the emergence of affective disorders, particularly major depressive disorder, is thought to result, in part, from cultural factors [66], of particular interest is investigation of the prevalence of affective disorders in Japan, as well as their culturally-determined manifestation of symptoms and syndrome that are unique to Japanese. We will focus on such studies.

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30.3.1 Prevalence studies of major depressive disorder Kitamura et al. [67] have reported a series of community-based studies, which was summarised in a book chapter. The studies were conducted in Kofu City, a local city with a population of approximately 300 000, and examined a randomly selected sample of 220 participants aged 20 years and over. Using the DSM-III-R classification and the interview method the authors originally devised, Kitamura and colleagues [67] found a lifetime prevalence of 14.0% for major depressive disorder. The corresponding estimate was 15.0% when the criterion according to the ICD-10 was applied. However, the drawbacks of this study are noticeable; the sample size was not large, the response rate was relatively low (43%), and the diagnostic interview method they employed was not an international standard. Kawakami et al. [68] conducted a population-based study in Gifu City, which is located in the central part of Japan with a population of about 450 000, as a part of the study initiated by the International Consortium of Psychiatric Epidemiology (ICPE: [69, 70]). They interviewed randomly selected 1029 individuals aged 20 years or over using Composite International Diagnostic Interview (WHO CIDI: [71, 72]). The lifetime prevalence of major depressive disorder based on DSM-III-R was 2.9%, and 3.1% for males and 2.8% for females. These estimates were distinctly lower than those reported from other countries, such as the United States and Brazil. The National Comorbidity Study from the United States showed the prevalence to be 12.7% [71], and a Brazilian study found it to be 12.6% [73]. In these two studies, the same diagnostic procedure was used. A disparity in the estimates between Japan and other countries is puzzling, but it is instructive to note that comparatively low prevalence estimates among Asian immigrants from East Asia to the United States have been reported [74], particularly among those from Japan [75]. In addition, a recent study from China [76] has shown that the lifetime prevalence of major depressive disorder among a combined population of two metropolitan cities, Shanghai and Beijing, was 3.5% [76], suggesting, together with the results of other studies, that the prevalence of depression may, in general, be relatively low among

East Asians. Nevertheless, the lower estimates reported by Kawakami and colleagues [68] may have stemmed from the lower response rate, as seen in their report, among participants aged 20–34 years, in addition to the fact that an overall response rate was at a moderate level (56.9%). The same group conducted a multicentre study in Japan [77], using CIDI and DSM-IV as a diagnostic criterion. With the use of 1664 individuals who enrolled in the study, they reported the 12-month prevalence of major depressive disorder to be 2.9% and the lifetime prevalence to be 6.5% (4.2% for males and 8.3% for females; the male–female ratio 1 : 2). Kessler et al. [70] summarised data from the ICPE, including data from Japan’s study by Kawakami and colleagues [77]. They concluded that there is a wide variation of the lifetime prevalence estimate for any mood disorders across nations; for instance, it was 7.6% in Japan, whereas it was 21.4% in US and 21.0% in France. The prevalence varied within Western countries as well; it was relatively low in Germany (9.9%) and Italy (9.9%) compared with that for the United States and France. The conspicuous difference in the prevalence of mood disorders, especially depressive disorder, across countries suggests that manifestation of mood disorders may be related to cultural and socioeconomic factors. However, any sources of bias need to be considered before a firm conclusion is drawn. Notably, a selection bias may have taken place and affected the results. The Japanese study showed one of the lowest response rates. Japanese tend to feel ashamed of having a mental breakdown and are reluctant to disclose that they have mental problems, as also referred to in a review by Chen et al. [74], who have indicated that a low prevalence of depression in Chinese immigrants in the United States is due to their tendency not to complain of psychological problems. This notion is compatible with the notoriously high suicide rate in Japan. It is acknowledged that such an attitude per se precludes people with major depressive disorder from seeking professionals’ care before choosing to commit suicide [78, 79]. Another concern is the sampling bias; the Japanese study was the sole one that did not adopt a cluster sampling method. Notwithstanding, albeit based upon the limited number of studies, the prevalence of major depressive disorder may be generally lower in Japan than 567

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in other developed countries. Focusing on particular age groups, however, conveys a different picture. High prevalence rates have been reported by some research groups who examined specifically young populations in Japan. Tomoda et al. [80] examined 116 university students of the first-grade, aged 18–23 years, to estimate the prevalence of the major depressive episode using the interview method the authors originally devised [67]. The results showed that the 12-month prevalence of the DSM-IV major depressive episode was 20.7%, and the Research Diagnostic Criteria (RDC) depressive episode was 23.3%. Denda et al. [81], using the Birleson Depression Self-rating Scale for Children [82], investigated the risk for depression among school-aged children and adolescents aged 6–15 years. The authors claimed that 14.9% of children and adolescents they studied had depression. This study should be interpreted with caution, because they relied merely on self-report and the scale employed was not devised originally for the purpose of discriminating individuals with childhood depression from non-depressed children. The scales such as Birleson [82] lay substantial weight on physical conditions (e.g. subtle physical weakness) among various depressive symptoms. Interestingly, somatic symptoms are, in general, more prevalent among East Asian individuals with depression than those with different ethnic background [83–85]. Nevertheless, a report from China also showed a higher prevalence of depression among cohorts of younger people (4.1% for those aged 18–34 years) compared with cohorts of older age (2.9% for those aged 35–49 years; [74]). Considering these, the estimates reported point towards the likelihood that young populations may have a higher rate of depression relative to adults in Japan. Further research with sound epidemiological approaches (e.g. using structured interview methods) is needed to confirm the claim that there is an increase in the rate of depression in young populations in Japan (as well as in other Asian countries) and, if established, to search for underlying causes.

30.3.2 Pre- and postnatal depression Okano et al. [86] examined consecutive 325 childbirths in two clinical facilities, and followed up 568

their mothers one month after the delivery using the Edinburgh Postnatal Depression Scale (EPDS: [87]). This study found that 10 of 325 women were diagnosed as having major depressive disorder postnatally, according to the RDC, showing the 1-month prevalence to be 3.1%. Although this figure is substantially lower than that summarised in a review by Kumar et al. [88], the subsequent study by the same authors showed the 1-month prevalence of postnatal major depressive disorder to be 8.5% (n = 47; [89]). In a separate study, Okano et al. [90] carried out a casenote study in 20 psychiatric hospitals and clinics in Mie Prefecture, covering a sample size of 93 739 live births over the period of 5 years. They identified 32 women admitted to hospital with RDC postnatal depression. The diagnosis was given if the disturbances took place in 3 months after childbirth. Consequently, the rate of postnatal depression that led to admission to hospital was estimated to be 0.10 per 1000 live births. Clearly, the investigators focused only on women admitted to hospital relatively soon after giving birth; women who may have sought care outside psychiatric services or treated at outpatient units were not included in the study. In a study, Kitamura et al. [91] approached 1159 pregnant women, consecutively referred to five university hospitals, and identified 756 as primiparous. Among those, 303 women (41%) agreed to participate in the study and were followed-up for 12 months after delivery. The result showed that the postnatal prevalence of major depressive disorder, based on DSM-III-R, during the period of 12 months after delivery was 5.0%. Since the response rate is apparently low in this study, it is likely that healthier individuals may have overrepresented the participants in the study. Therefore, the estimate could have been underestimated. Methodological rigorousness or weakness exerts the effect on the results in any investigation. Such methodological issues may account for variation existing in the literature on the prevalence of postnatal depression, ranging from 5 to 60% around the globe [92]. On the other hand, Kitamura et al. [93] conducted a survey of 122 consecutive pregnant women at an antenatal clinic, using the Schedule for Affective Disorders and Schizophrenia (SADS) as a structured interview tool. They found that prenatal prevalence

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of major depressive disorder was 16%. These findings suggest that in Japan, there is an overall increased rate of depression in the antenatal rather than postnatal period in women. This finding is of interest in that previous studies have exclusively highlighted frequent occurrence of depression in the postnatal period [94]. In studies from the United States [95], however, the risk of major depressive disorder, diagnosed with a structured interview (the National Institute on Alcohol Abuse and Alcoholism’s Alcohol Use Disorder and Associated Disabilities Interview Schedule – DSM-IV Version, AUDADIS-IV: [96]), has been shown to be 5.6% in pregnant women (i.e. women prior to childbirth), whereas 9.3% in postnatal women. The results from Japan, showing that the prevalence of postnatal depression is relatively low and, in contrast, the antenatal depression is comparatively high, may be attributed to methodological flaws such as problems with ascertainment procedures and small sample sizes utilised. Large-scale studies with thorough screening and identification procedures are required to clarify whether there is virtually a distinctive pattern of occurrence of depression among women who undergo pregnancy and delivery in Japan.

30.4 Autism and autism spectrum disorder Reports have indicated that the prevalence of autism and other pervasive developmental disorders, namely autistic (or autism) spectrum disorders (ASDs; [97]), has been increasing over the last three decades [98, 99]. While the first report on the prevalence of typical autism in the United Kingdom showed 4.5 per 10 000 young children (aged 8–10 years; [100]), the most recent figures from large-scale studies are 38.9 per 10 000 children in the United Kingdom (aged 9–10 years, [101]) and 41.0 per 10 000 young children in the United states (aged 3–5 years, [102]), suggesting a substantial increase in the prevalence over time [99, 102, 103]. Japanese researchers have contributed to the literature in this field in that early studies from Japan consistently reported higher estimates than did the studies conducted in countries outside Japan, during the same era.

30.4.1 Routine child-health check system in Japan In Japan, as a part of the general health services, all children aged 18 months and 3 years are obliged to receive routine health checkups at local Health Centres, which are free of charge, where various measures including anthropomorphic measures as well as general health, motor, cognitive and sensory development, nutrition and mother–child relationship are evaluated. Along with these two occasions of obligatory assessments, children of other ages, that is 1, 4, 6, 10, 14 months and 2, 4, 5 years are also invited to undergo health-checkups. After such a system commenced in 1965, Japanese researchers attempted to contact all infants in the defined catchment area at age of 18 months for a screening purpose, and then followed them up for establishing diagnostic assessment at several times. This health checkup system is a basis for epidemiological studies of autism conducted in Japan.

30.4.2 Prevalence studies: Why is the prevalence of autism higher in Japan? In an early study from Japan, Hoshino et al. [104] showed that the prevalence of typical autism was 2.3 per 10 000 persons aged 0–18 years, whereas it was reported to be 16.0 per 10 000 children aged 6–12 years in another study [105]. These studies adopted Kanner’s and Rutter’s criteria for diagnosing autism, respectively, instead of the modern DSM or ICD systems; at this stage, the estimate provided by Ishii and colleagues [105] was higher than that of any other studies conducted and reported from Europe. However, this difference could be ascribed to diagnostic methods used in the studies from Japan and Europe. Matsuishi et al. [106] reported the prevalence of infantile autism, defined according to the DSMIII, to be 15.5 among 10 000 children aged 4–12 years, born between 1971 and 1979, residing at the urban catchment area in Kurume City. They also showed that the prevalence was 24.8 per 10 000 male children and 5.7 per 10 000 female children, corresponding to the male : female ratio of 4 : 1. In another study, Tanoue et al. [107] conducted a populationbased study in the Southern part of Ibaraki Province 569

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where 95 394 children had been born between 1972 and 1978. The authors endeavoured to identify all potential data sources to make case ascertainment as complete as possible; that is they accessed obligatory health checkups at 1.5 and 3 years of age, records at the local Social Welfare Office and from teachers of schools and kindergartens, direct referral documents to hospital and medical records. All the screening-positive infants and children, as well as those showing a range of developmental delay or abnormality, were followed up to 7 years and invited to see child psychiatrists for examination of whether the infant had a diagnosis of infantile autism (DSM-III) at one or more times to facilitate diagnosis. The results showed the prevalence to be 13.8 per 10 000 children aged 7 years, with the male : female ratio of 4 : 1. In addition, Sugiyama et al. [108] conducted a population-based study in one of the residential areas in Nagoya City. Among 11 320 infants aged 18 months who showed up to the obligatory check up, 12% of children showed abnormality in developmental domains (‘designated as first screening-positive infants’), with the use of the Denver Scale by public health nurses, and with physical, neurological and developmental examinations by paediatricians. The authors conducted the second examination to all the first screening-positive infants and followed them up for every 3 months to 3 years of age to facilitate diagnosis. This study finally identified 16 children diagnosed as having DSM-III autism; as a result, the prevalence was estimated to be 13.0 per 10 000 children aged 3 years. This study design was one of the most refined among the studies that had been conducted in Japan as well as in other countries until then, partly because the screening was administered by specialists, although whether diagnosis made as early as 18 months is accurate has been addressed [109]. These prevalence estimates reported from Japan are 2–10-fold higher than those reported in contemporaneous studies, using DSM-III, conducted in the 1980s in other countries, such as a study from the United States showing 1.2 per 10 000 individuals aged 0–18 years [110] and a French study reporting 5.1 per 10 000 children aged 5–9 years [111]. In the next decade when the ICD-10 and DSM-IIIR diagnoses were introduced, Honda and colleagues 570

[112], using ICD-10 as a diagnostic criterion and ICD-10 Diagnostic Criteria for Research (DCR) as a structural diagnostic interview, conducted a population-based study in the Northern part of Yokohama City, a well-defined catchment area with a 5-year-old population of 8537, who were born in 1988 [112]. They also relied on systematic health checkup data for infants of 18 months old and, even if the initial screening was negative, the second screening was conducted through a health check-up at the age of 3 years. In addition to two waves of the screening stages, they also utilised potential data sources (i.e. Child Guidance Clinics, Medical Clinics, Kindergartens and Nursery Schools), to make case ascertainment as complete as possible. As a result, they found that the prevalence of childhood autism was 21.1 per 10 000 children at 5 years of age. The male-to-female ratio was approximately 2.5 : 1. This figure was highest among the studies reported until the year 2000, except for a small Swedish study (n = 826; [113]), and is even comparable to that of the most recent study in the United Kingdom (38.9 among 10 000 children aged 9–10 years; [101]). The study by Honda et al. [112] was, however, criticised on the ground that both the numerator and the denominator in the prevalence estimate might be incorrect [114]. Yokohama is the second largest city in Japan, where an inflow as well as outflow population cannot be negligible. Furthermore, the distribution of socioeconomic correlates, such as parental income, among the population residing in the catchment area may be largely different from that investigated in Western countries, and, since parental higher income has been shown to be associated with an increased risk for ASD in the offspring [115], taking account of composition of socioeconomic correlates in the population studied is important. These potential biases were challenged in the following study by the same group [116, 117] with a larger sample size (a cohort of 35 716 born between 1988 and 1991), showing that the cumulative incidence of infantile autism was 27.3 per 10 000 children aged 5 years, and the prevalence was 41.2 per 10 000 children aged 5 years. The former value of the cumulative incidence was derived from the number of children with autism born in the catchment area in the year of 1991 divided by all live births in the catchment area in the same year, as a denominator, and the

RECENT EPIDEMIOLOGICAL STUDIES OF PSYCHIATRIC DISORDERS IN JAPAN

latter value resulted from the number of children with autism born in 1991, regardless of birthplace, who lived in the catchment area at the age of 5 years divided by the number of 5-year-old children living in the catchment area. This study indicates that a possible overestimation of the prevalence may occur when the population in the catchment area is mobile, and that cumulative incidence is the better option for the estimation in studies conducted in metropolitans like Yokohama City. In this context, a recent study from Denmark estimated cumulative incidence using a national birth cohort, reporting 14.4 per 10 000 children aged 5 years born between 1998 and 1999 [118]. Nevertheless, the estimate reported by Honda and colleagues [116] is still higher than this estimate. One may pose a question as to why comparatively high prevalence of autism has been repeatedly reported from Japan. First, any changes in the diagnostic criteria may have affected the prevalence estimates equally across nations, but not specifically to Japan. In fact, diagnostic criteria adopted in studies from Japan as described above are not different from those in other countries. Second, all but one study from Japan [104] were conducted in urban areas. Prevalence studies of autism have consistently reported that the prevalence is higher in urban than rural areas [99, 104, 112], which is due partly to an influx of families including a child with autism into the urban areas, as directly indicated by the study of Honda et al. [116]. However, in the study by Sugiyama et al. [108], no children with autism have been found to be migrated into the catchment area. Since the area [108] studied was a newly growing residential town with an increase in size of population, an influx of relatively healthy families may have taken place. Therefore, the prevalence reported by Sugiyama et al. [108] might have been underestimated rather than overestimated. Third, diagnostic practice for ASD in Japan may have been different from that of other countries; namely, Japanese researchers may have been more aware of symptoms appearing in very young children than researchers in other countries. What enabled Japanese researchers to make early diagnosis in those studies includes nationwide obligatory health check-ups, efforts to make case ascertainment as complete as possible

(e.g. utilising multiple sources), and thorough (repetitive) prospective follow-ups to facilitate diagnosis. Interestingly, in a Danish study showing a time trend towards an increase in the prevalence estimates, this trend has been attributed partly to an decrease in age at first-ever diagnosis of autism [103]. This implicates that increased awareness of early autism symptoms may contribute to recently reported increased prevalence of autism in many countries. Nevertheless, it remains tenable that Japan is a country where the prevalence of autism (and ASD) is genuinely higher compared with other countries.

30.4.3 Factors accounting for increasing prevalence over time As suggested by Waterhouse [98], a part of the findings showing that the prevalence of autism (and ASD) has been increasing in the recent years could be attributed to growing awareness of this condition among the public as well as physicians. This view has been underpinned by the observation that more recent born children with autism tend to be diagnosed at earlier ages [103]. This may be the case in Japan, as Honda et al. [117] noted that cumulative incidence of childhood autism had risen in accordance with recent births in Japan. Mumps-measles-rubella (MMR) vaccination has become a worldwide concern since the thimerosalcontaining vaccine was believed to be a cause of ASD [98]. MMR vaccination was used between 1989 and 1993 in Japan. In a study, Honda et al. [117] examined whether the MMR vaccination given during that 5-year period would be associated with the risk of having a diagnosis of ASD among those children exposed to the vaccination. They found no evidence supporting that MMR vaccination is a cause of ASD, in line with negative reports from studies in Denmark [119, 120]. Despite these results, parents and the media remain concerned over MMR vaccination [121]. Fathering and mothering age is also of interest, since there is evidence indicating that advanced age in parents at the time of conception is linked with an increased risk of the CNS development in the offspring [122, 123]. We hypothesised, therefore, that advanced parental age would be associated 571

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with the risk of ASD. To test this, we focused on high-functioning ASD (full-scale IQ > 70; [124]). We chose the high-functioning ASD group because advanced paternal age at birth has been shown to be associated with lower IQ [122]. The results have shown that advanced paternal age, but not maternal age, is associated with an increased risk for high-functioning ASD among the offspring [124]. The finding is consistent with other studies from the United States, the United Kingdom and Europe [125–128]. The fact that fathering age has advanced in Japan as well as in Western countries in the last few decades, and that advance in paternal age at birth is associated with the risk for ASD in the offspring suggests that advanced fathering age may play a part in the increased prevalence of ASD that has been observed in Japan as well as in other countries. Epidemiological studies in Japan have consistently reported relatively higher prevalence of autism (and ASD) compared with counterpart studies in Western countries. If the high prevalence of the disorder in Japan were a genuine phenomenon, then one would speculate that factors predisposing individuals to autism (or ASD) were more prevailing in Japan than Western countries. At present, however, there is a paucity of studies exploring risk factors for the emergence of autism and ASD in Japan.

30.5 Summary In this chapter, we have given an overview of studies conducted and reported from researchers in Japan. The topic mainly covered the research in the field of major psychiatric disorders; that is schizophrenia, affective disorders and autistic disorders. We included studies on autism as well since public concern about it and awareness of life-long problems with this condition are growing. Relative to research activities on major psychiatric illnesses in adults (i.e. schizophrenia and affective disorders), there is a paucity of research into developmental problems in children, especially among Japanese investigators. However, through this review, it appears that Japanese researchers have contributed to the literature on the prevalence estimate of autism, which was consistently found to be higher in Japan than

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contemporaneous reports from Western countries. This was due not to diagnostic criteria used, but to repetitive, thorough assessments of each individual child at early ages using nationwide, obligatory check-ups that started in the mid-1960s in Japan. Notwithstanding, there have been few epidemiological studies that investigated risk factors for autism in Japan. Needless to say, to search for causes and, consequently, to exercise preventative measures, carrying out research with epidemiologically sound approaches is critical. The same is, however, applied to the research on any health-related diseases. Substance-induced psychosis (e.g. cannabis psychosis) has become a hot topic among researchers. MAP-induced psychosis is a major problem in Japan. MAP-induced psychosis and schizophrenia have many characteristics in common. Although animal models are beyond the scope of epidemiology, MAP-treated rodents may serve to elucidate dopaminergic dysfunction (and concurring other neural changes, i.e. impairments secondary to dopaminergic dysregulation) that are present in the brains of patients with schizophrenia. There are some compounds (antipsychotics) that are manufactured by Japanese pharmaceutical companies and only available in clinical practice in Japan. Large-scale studies of efficacy and tolerance on drugs, including Japanese original ones as well as other agents that are commonly used in the world may provide scientific benefits. At least, clinical trails of comparing antipsychotic medications (especially new generation antipsychotics) in Asian populations that are equivalent to those recently reported from Europe [64] and the United States [65] are demanded. With respect to treatment for mood disorders (more, specifically bipolar disorders), a study by Okuma et al. [129] from Japan was the first to demonstrate the prophylactic effect of carbamazepine for individuals with bipolar disorders. Since then, adjunctive use of carbamazepine has been confirmed to be effective for those patients, particularly with a rapidcycling feature in studies not only from Japan but also outside Japan [130, 131]. We anticipate that Japanese investigators will continue to contribute to better understanding of the pathophysiology of psychiatric illnesses and to improved care for the mentally ill.

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Epidemiology of migration and serious mental illness: The example of migrants to Europe Monica Charalambides, Craig Morgan and Robin M. Murray Institute of Psychiatry, King’s Institute, London, UK

31.1 Introduction The finding of elevated rates of schizophrenia and other psychoses in migrant and minority ethnic groups is well established. Concerns regarding the validity of these findings have now largely been addressed. This chapter will therefore review the evidence that there exists a genuine increase in the incidence of psychosis in migrant and minority ethnic populations, and will focus particularly on the black Caribbean population living in the United Kingdom. We will go on to also consider possible explanations and mechanisms. Although genetic predisposition and neurodevelopmental factors are of aetiological importance in psychosis in general, such factors alone cannot account for the increased incidence of psychosis in migrant groups. However, there is now growing evidence that social adversity in the form of early childhood trauma and later social disadvantage and discrimination can, in an already genetically vulnerable individual, increase risk of psychosis perhaps via cognitive biases which facilitate the formation and maintenance of symptoms. Such explanations are still speculative, but these approaches offer the possibility of unearthing the risk factors which drive up rates of schizophrenia and other psychoses in migrant and minority ethnic

populations. Further, by looking at cross-cultural comparisons and examining the factors which interact to bring about psychosis in such groups, our understanding of the aetiology of the disorder in general may be improved.

31.2 Defining the constructs The ways in which the terms migrant and ethnicity have been defined and measured in research have raised concerns. It is therefore imperative to initially highlight the complexity of these terms.

31.2.1 Migration Some authors have used definitions of migration that emphasise the processes of social change that occur in the transition from one culture to another, as well as acknowledging the heterogeneous reasons for migration [1]. Different types of migrants can be recognised as: individuals who temporarily move from one country to the next for short-term work; more permanent groups who travel for improved economic stability; refugees and asylum seekers fleeing in fear from countries entrenched in political conflict and violence. Migrants also separate into first and subsequent generations (i.e. the original migrants

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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and then their children, grandchildren, and so on, born in the new country), with different experiences and shifting social environments. This is a distinction which is at times glossed over but is nonetheless important given that second and subsequent generation migrants have been reported by some to have a higher risk of developing schizophrenia than first generation migrants (e.g. [2]). It is not only the types of migrants which have to be considered, but also the phases of the migratory process. Bhugra and Jones [3] outline three key processes involved in migration: premigration, transition and postmigration. This sees the individual facing at first the decision making process, then the actual movement from one culture to the next, and finally the challenges inherent in settling into new cultural surroundings. It is therefore important to view migration as consisting of a number of features, which can become blurred and oversimplified in epidemiological research.

31.2.2 Ethnicity: A multifaceted concept incorporating race and culture Ethnicity is an even more complex concept. It has been described as: . . . a mixture of cultural background and racial designation as experienced by a person, a family or a group of people – the significance of each (i.e. ‘culture’ and ‘race’) being variable, depending on the context (p. 17) [4]. Here, the entanglement of several distinct yet interconnecting terms – ethnicity, race and culture – becomes apparent. First, there is race. In western culture, people have long been placed into racial categories based predominantly on their physical characteristics. While Benedict [5] identified six physical features that are commonly used to distinguish racial groups including eye colour, hair colour and form, shape of nose and stature – it is skin colour that has most often been used as the fundamental characteristic dividing peoples into racial groups. Science has an unfortunate history of conducting research as if these distinguishing physical features marked and signified crucial differences between groups in terms of many 580

characteristics. Recent advances in genetics have established the spuriousness of the assumption that ‘races’ are genetically and biologically homogeneous. Nonetheless, the popular belief persists that people are separable into distinct groups on the basis of these characteristics. The use of racial categories has now largely disappeared from scientific research and been replaced by the use of ethnicity, a more acceptable and benign term. However, for the purpose of this chapter it is important to stress that researchers often categorise people into ‘ethnic’ categories in such a way that these are indistinguishable from racial categories – the crude dichotomy between black and white, for example [6]. As indicated above, racial designation as experienced by individuals is only one facet of ethnicity. Culture has been variously defined, but Fernando’s [7] summary of culture as characterised by behaviour and attitudes and as determined by upbringing and choice unifies many common expositions. What is common to all is the basic idea that culture provides a set of socially shared guidelines or rules that shape and constrain beliefs, attitudes and behaviour. This does not imply that culture is static or homogeneous. Beliefs and attitudes inevitably undergo modifications as individuals are exposed to, and experience, other cultural systems and ideas. As Helman [8] has argued, while ethnic, religious and other groups in modern societies will ‘each have their own inherited cultural perspective’ (p. 3), it is inevitable that in the process of migration and assimilation, or simply through exposure to other cultures within multicultural contexts, ‘many of these groups will undergo some process of acculturation whereby they incorporate some of the cultural attributes of the larger society (and of other groups)’ (p. 3). This is an important point, particularly in terms of distinguishing between culture and ethnicity. While cultural heritage may form a significant component of ethnic identity, it does not define it and those who perceive themselves as belonging to a particular ethnic group may well differ markedly in terms of the cultural reference points that inform their beliefs and actions. Aspects of race and culture may, therefore, engender a sense of common identity that determines ethnicity. For example, Fernando [7] suggests a sense of belonging may emerge from shared experiences in

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the host society – emergent ethnicity. This is arguably a key factor that has driven the emergence of a Caribbean identity among migrants from the culturally diverse Caribbean islands to the United Kingdom. Therefore, while race and culture form part of ethnicity, it is the common identity forged from these that determines ethnic identity. Further, ethnicity is potentially fluid and changeable over time and space, as exposure to other contexts and cultures allows for the reformulation of individual identity. It is evident then that the use of fixed ethnic categories in cross-sectional research is problematic in that key components of ethnicity – sense of belonging and changeability – are absent. This noted, in discussing the literature, we are bound by the categories initially used by the authors of the various papers. For example, in the United Kingdom, migrants from the Caribbean, and their children and grandchildren, are commonly categorised as black Caribbean – reflecting the combination of race, culture and nationality in the assigning of ethnicity.

31.3 High rates of psychosis in migrants: A genuine finding or methodological artefact? It is now well established that the incidence of diagnosed psychosis is raised in migrant and minority ethnic groups in comparison with native-born populations in many European countries, as well as in the United States and Australia. Attempts to examine this issue in a systematic manner began with Norwegian psychiatrist, Ødegaard [9] who compared Norwegian migrants to the United States with the native population in Norway. Higher rates of schizophrenia were found in those who migrated. This led to a debate of whether selective migration (i.e. those predisposed to develop schizophrenia being more likely to migrate) was responsible (an explanation favoured by Ødegaard), or whether the process of migration itself increased risk [10]. We shall return to this point at a later stage. Following this, a whole host of epidemiological studies have replicated the findings in different cultural settings. In a comprehensive meta-analysis of 18 studies spanning several countries (i.e.

Australia, Netherlands, Denmark, Sweden and the United Kingdom), Cantor-Graae and Selten [2] found a mean relative risk (RR) for schizophrenia of 2.9 for migrants (both first and second generation) compared with host populations. Second generation migrants were at even higher risk (RR 4.5), hinting at the operation of more complex environmental processes in illness genesis. Additionally, RR was greatest for migrants from developing countries (RR 3.3) and countries where the majority of the population were Black (RR 4.8). Such findings have been most frequently reported from the United Kingdom. In the late 1940s and 1950s there was a large influx of individuals from the Caribbean and the New Commonwealth to England predominantly to take advantage of postwar job opportunities. Early studies reported higher rates in these ethnic minorities (e.g. [11–14]), but were plagued with methodological shortcomings such as being restricted to hospital admissions and inadequate denominator data [15]. Harrison et al.’s [16] Nottingham study used more robust diagnostic criteria, more complete case ascertainment, standardised assessments, and attempted to address the problems with denominator data. They found a 12-fold increased incidence of schizophrenia in the black Caribbean community compared with the white British population. The 1991 UK census, the first to include questions on ethnicity, allowed the first truly comprehensive studies on migration and psychosis in the United Kingdom by providing reasonably accurate denominator data on ethnic minority populations (e.g. [17–19]). In one review, findings from these later and more robust studies yielded incidence rates between 2 and14 times higher in the black Caribbean population compared with the white population [20].

31.3.1 Methodological limitations Although these early studies appeared well founded, methodological inconsistencies mean that caution is needed when interpreting their findings. Some argued that findings of increased rates were a result of a lack of methodological rigour, and did not reflect legitimate vulnerability in ethnic minority groups. Others argued that migration as a category was oversimplified [1]. Thus, in some early studies 581

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the designation of migrant status is based on the birthplace of the participants, and in others on the birthplace of their parents, causing inconsistencies in how a migrant is defined [2]. Self-assigned ethnic categories versus observer-assigned ethnic categories also pose problems. Although self-assigned categories are preferable and allow the individual to place themselves in predefined groups, the categories may fail to truly represent the complexity of one’s ethnic identity underscored [4, 7, 8]. Further concerns have been expressed regarding the accuracy of estimates of the size of minority ethnic populations. This is of concern because ethnic minority groups are often under-enumerated in such statistics [21]; the consequence being that this could artificially increase rates relative to the host population. Most studies are based on cases treated by mental health services and this presents another problem. Those who do not present to secondary services (e.g. treated in primary care, in prison or untreated) are not included. Ethnic variations in pathways and access to care may, therefore, affect estimates of incidence. On the one hand, black Caribbean individuals are more likely to be compulsorily admitted to hospital than their White counterparts [22–25]. On the other, it has been found that treated cases are more likely to be of higher social class status, leading to a possible under-representation of ethnic minority groups [26]. Another difficulty arises when one reviews amalgamate findings from studies using various methods and data sets. Comparing both first contact and first admission samples, ethnicity as defined by birthplace and ethnic groups demarcated in censuses, first and second generation migrants, and individuals who ascribe themselves as either discrete or ‘mixed’ ethnicity, may lead to differences becoming distorted [1] – differences which may be crucial in identifying those most vulnerable to developing psychosis and the aetiological factors involved. A final point on context. McKenzie et al. [1] state that the measurement of context, which will vary in social and political terms dependent on the place and time of migration, is important to consider as such contexts may play a key role in the development of risk in some migrant communities and not others. 582

31.3.1.1 Misdiagnosis The issues surrounding methodological artefact extend to the argument concerning misdiagnosis. This argument takes two forms: (i) increased incidence rates are a product of misdiagnosis resulting from deep-rooted racism or cultural naivety exercised by Western psychiatry; (ii) black Caribbean people do not present a clinical picture typical of psychosis as seen in white Europeans, showing in particular a different type of course and outcome. First, some claim that what is observed as increased risk is fundamentally a function of White psychiatrists’ pathologising that which in reality is the expression of emotional distress experienced by ethnic minorities under immense socio-political strain. Here Western psychiatry is seen as bound to the biomedical model, incapable of acknowledging social, cultural and environmental influences on mental well-being [7]. The application of racist ideology through the practice of Western medicine, it is argued, explains findings of an apparent excess of schizophrenia in black and other migrant groups compared with White populations in both the United States and the United Kingdom (e.g. [14, 27–29]). In addition, evidence that symptoms such as hallucinations are more commonly reported by the black Caribbean community may reflect the fact that such experiences are more often considered ‘normal’ in non-Western cultures [30, 31] or at least not always indicative of mental disorder. Thus, what is perceived as psychotic under Western classificatory systems may not have such pathological connotations in other cultures. In an attempt to address the question of misdiagnosis, Hickling et al. [32] compared diagnoses of the same group of patients by British psychiatrists and a Jamaican psychiatrist. They found no difference in the percentage of black patients diagnosed with schizophrenia; a finding that does not support the suggestion that overdiagnosis is common in black patients. An alternative, less radical form of the misdiagnosis debate is that high rates of schizophrenia in the UK black Caribbean population are a consequence of atypical or brief reactive psychoses being wrongly classified as schizophrenic. There is some evidence that the clinical profile of patients in this population differ from that in white British. For example,

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findings from the three-centre aetiology and ethnicity in schizophrenia and other psychoses (ÆSOP) study of first onset psychosis point towards two important differences in the clinical profile of patients from different ethnic groups. The first is an increased prevalence of persecutory delusions in black Caribbean and black African patients. The second, the apparent absence of classic first-rank Schneiderian symptoms in black groups with schizophrenia compared with white groups with the same diagnosis. Intriguingly, these types of symptoms were more evident in black Caribbean and black African patients with a diagnosis of affective disorder [33, 34]. However, these variations are perhaps more likely to reflect the operation of different constellations of risk factors in each group. It is important to recall that there is evidence that all psychoses (not just schizophrenia) are elevated in black Caribbean patients in the United Kingdom. In addition, the available evidence is equivocal on outcomes in these groups – some suggest a more remitting course (which may be indicative of more reactive disorders) while others suggest similar patterns of course and outcome as in white groups [35–38]. Finally, it must also be noted that recent studies trying to redress methodological concerns and adopting a blind diagnostic procedure have still consistently shown higher rates of psychosis in the migrant population [20]. Therefore, it is unlikely that misdiagnosis can wholly explain the observed excess of schizophrenia and other psychoses in ethnic minority/migrant populations.

31.3.1.2 Modern studies The most extensive attempt to investigate elevated rates of psychosis in the black Caribbean population in England and to overcome the methodological limitations described above, has been the aforementioned ÆSOP study. Increased rates of all psychoses in all centres (London, Nottingham and Bristol) were found in the black Caribbean community across all ages and genders compared with the white British population. Rates of schizophrenia and manic psychosis were particularly elevated in the Black Caribbean population, with rate ratios of 9.1 (95% confidence interval (CI) 6.6–12.6) and 8.0 (95% CI 4.3–14.8) respectively [33].

But what of increased rates in other migrant groups? Interestingly, findings for black Caribbean groups in the ÆSOP study extended to African migrants (from sub-Saharan Africa), and to a lesser degree all other ethnic groups (non-British whites, Asian, mixed, other). As mentioned previously, Cantor-Graae and Selten [2] found an elevated risk in all migrants, but with evidence of variation by place of origin or generation. With regards to the UK Asian population, risk appears to be raised only to a moderate extent (e.g. [17, 19]). A recent study in East London modelled on ÆSOP found increased incidence rates in most migrant populations, but for those of Pakistani and Bangladeshi origin this was only apparent in the female participants [39]. Thus, research seems to indicate that although there is an excess of psychosis in all migrant groups, the excess varies with a hierarchical structure of risk affecting the Caribbean community the greatest, Africans intermediary, followed by other minority ethnic groups. Studies conducted in the Netherlands support findings in the United Kingdom of elevated rates of psychosis in migrants as well as variation among migrant groups. In one first-episode incidence study of psychosis undertaken in the Hague, Selten et al. [40] reported significantly increased rates in first generation migrants from Morocco (RR 4.5), Surinam (RR 3.2), Netherlands Antilles (RR 2.9) and other non-Western countries (RR 2.4), as well as elevated rates in Moroccan and Surinamese second generations (RR 8.0, RR 5.5 respectively). Rates for Turkish and Western migrants on the other hand were not markedly raised. Such findings have been supported by other studies in this population [41, 42]. What appears to be evident in several countries is a greater but variable risk for migrants to develop psychosis. Alongside evidence from incidence studies, there are parallel investigations looking at the prevalence of psychotic-like symptoms/experiences in the general population based on the view that psychosis lies on a continuum from normality through to clinical disorder. By drawing on data from community based controls included in the ÆSOP sample, Morgan et al. [43] used the Psychosis Screening Questionnaire which consisted of five subscales (hypomania, thought disorder, paranoia, 583

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strange experiences and hallucinations), to ascertain what percentage of the general population had psychotic-like symptoms. From 372 individuals, 19% reported one or more psychotic-like experiences with a marked increase in odds ratio (OR) for both black Caribbean (OR 2.08) and black African (OR 4.59) participants compared with white British (OR 1.0). This mirrors findings from larger studies [44, 45]. In addition, Laurens et al. [46] compared the prevalence of several antecedents of schizophrenia and psychotic-like experiences in children aged 9–12 years from different ethnic groups from a community-based sample in the United Kingdom. Black Caribbean children were more likely to experience at least one psychotic-like symptom and have a greater total score of such experiences compared to white British children. South Asian and Oriental children on the other hand, displayed a lowered risk of psychotic-like experiences than either their black Caribbean or white British counterparts. Overall, such findings indicate that the trend for elevated rates of psychosis in UK black Caribbean and black African individuals as well as variation in the frequency of psychotic symptoms between ethnic groups, extends into the general population. In summary, it appears that elevated rates of psychosis in ethnic minority migrants to the United Kingdom and indeed other countries, do not appear to represent methodological artefact nor can they be explained as a product of misdiagnosis by Western psychiatry. Instead, they form a genuine finding that warrants greater attention from the public health sector as a vulnerable group for which intervention, and indeed prevention, should be targeted. If a disorder is increased by up to five times in a particular population, this indicates a significant public health problem. Until now the main response has centred on reforming psychiatry so as to address supposed institutional racism under the premise that the problem lies with misdiagnosis and poor quality care [47, 48]. By acknowledging the legitimacy of increased risk in these groups and seeking to understand more fully the reasons for this, policies can strive to actually reduce the high rates displayed in ethnic minorities rather than challenging their existence.

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31.4 Possible explanations 31.4.1 Can selective migration explain high rates in migrants? What might account for such findings? An early explanation which is now widely discounted was based on the selection hypothesis. Supported by Ødegaard [9], this hypothesis suggested that findings of increased rates of schizophrenia in Norwegian migrants to the United States were due to the fact that those predisposed to mental illness had been more likely to migrate. Findings of increased rates of schizophrenia in Surinamese migrants to the Netherlands however go against this hypothesis, given that more than a third of the population of Suriname migrated there [49]. In addition, evidence of greater risk in second generation migrants cannot be explained by this mechanism [2]. Also a predisposition to migrate is not supported by the fact that some migrants are more likely to develop psychosis than others. For example, the heightened risk in black Caribbean migrants compared to the relatively lower risk found in South Asian populations in the United Kingdom cannot be explained by a general tendency for vulnerable individuals to migrate. Further, people migrate for a large number of reasons [15]. It is also important to bear in mind the premorbid cognitive dysfunction of some individuals with schizophrenia which would make them less likely able to succeed in the often challenging process of migration [2]. The demise of the selective migration hypothesis emphasised the need for epidemiological studies to investigate in more detail the complexities surrounding what precipitates the development of psychosis in migrant groups. In recent years there has been a growth in the understanding of the aetiology of psychosis in general. Psychosis is now seen as a multifactorial disorder with a strong genetic susceptibility resulting from multiple genes of small effect. This vulnerability is in turn impacted upon by subsequent environmental insults particularly occurring during, at, or around the time of pregnancy and birth. The latter has generated a view of psychosis as at least in part a neurodevelopmental disorder. There is also increasing

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evidence that the relevant environmental factors can be social in nature. Parallel to this is the development of research in cognitive psychology examining psychological mechanisms which may underpin the development of psychotic symptoms (e.g. faulty source monitoring, jumping to conclusions, attributional biases). Cognitive psychology may provide a key link in explaining how adverse social experiences can find expression in psychotic symptomatology. We will now consider concerning psychosis in the UK black Caribbean population in relation to this multifactorial framework.

inhabitants and native-born Dutch residents in The Hague, indicating that findings of raised rates in Surinamese immigrants to the Netherlands [40, 42] are likely due to the effect of mechanisms operating at an environmental level. However, Morgan and Fearon [34] point out that although incidence rates in the Caribbean are not as raised as in black Caribbean community in the United Kingdom, they are not quite as low as rates for the white British population. This highlights the need for more cross-cultural studies.

31.5.2 Neurodevelopmental insults

31.5 Biological considerations 31.5.1 Genetic influences Are those of black Caribbean origin at greater genetic risk of developing psychosis than the white population? This is a natural question given the role genes are known to play in the aetiology of psychosis in general. If this were so, what might be expected would be an increased likelihood that relatives of patients would suffer psychosis themselves. What is found however is more subtle. Similar levels of risk are found in the parents of black Caribbean and white British individuals with schizophrenia and in siblings living in the Caribbean; there is an increased risk only in those siblings of black Caribbean schizophrenia patients who are also living in the United Kingdom [50, 51]. A recent study showing increased rates in the siblings but not parents of patients with nonaffective psychosis of Moroccan origin compared to native-born patients in The Netherlands further supports the UK findings [52]. As aforementioned, this is indicative of environmental influences acting upon genetically vulnerable members of the second generation. Other compelling findings against a solely genetic explanation come from studies in Jamaica, Trinidad and Barbados in which incidence rates are more comparable to those of white British than black Caribbean individuals in the United Kingdom [53–55]. Studies in Suriname also reveal comparable rates of psychosis between Surinamese

Evidence of a greater exposure to adverse early neurodevelopmental insults in migrants is also weak. Pre- and perinatal complications are not as evident in black Caribbean patients with psychosis in London as in their white British counterparts [56]. Thus, whilst these have been flagged as risk factors for schizophrenia in general, they cannot explain high rates in the black Caribbean minority in England. Furthermore, in the ÆSOP study, there was neither greater effect for markers of neurodevelopmental vulnerability (e.g. minor physical anomalies, neurological soft signs) nor higher prevalence of such markers in the black Caribbean sample [57]. A lowered immunity to viral infections during pregnancy such as rubella in black Caribbean women migrating to the United Kingdom during the postwar era, and vitamin D deficiencies in dark-skinned migrants living in cold climates have also been proposed as a neurodevelopmental exposure that could increase the risk of developing schizophrenia in both first and second generation migrants (see reviews [2, 15]) – however, no evidence was found for these either. The evidence that such early risk factors are higher or have a greater impact in migrant populations is unconvincing. The only possible exception to this is that in her study of London schoolchildren, Laurens et al. [46] found not only increased rates of psychotic-like experiences in black Caribbean children in the general population, but that this group were more likely than white British children to present with speech and/or motor developmental delay or abnormality and social, emotional and

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behavioural problems. Interestingly, although ethnicity was associated with these antecedents, migrant status was not linked with increased risk.

31.6 Cannabis use Another possible candidate for explaining the high rates of psychosis in migrants is cannabis use. With evidence of the psychosis-inducing effects of cannabis over the last decade growing at a rapid pace, a convincing picture of this drug as a contributory risk factor in the development of the disorder is now evident. Arseneault et al.’s [58] review of five population-based studies conducted in the European Union and New Zealand concluded that cannabis use provides a twofold RR of developing schizophrenia with a causal relationship strongly supported. Cannabis use in the context of migration and psychosis however, is more debatable. Evidence has shown that there is no difference in cannabis use between black and white patients experiencing psychosis in the United Kingdom [59, 60], with findings instead of lower use amongst Caribbean migrants compared with the indigenous population in The Netherlands [41, 42]. Further, recent statistics from the British Crime Survey reveal that reported rates of cannabis use in the last year amongst 16–59-year-olds are lower in black/black British individuals (6.4%) compared with white British individuals (8.4%), with people of mixed ethnicity reporting the greatest use (13.1%) [61]. The category of black/black British is too crude however, and does not enable a separate enquiry into black Caribbean and black African populations. In addition, data collection via household surveys is often unreliable and the reality that some groups with high substance misuse are underrepresented by such methods means one must be wary when interpreting findings. Data from our own ongoing first episode studies (unpublished) suggest comparable rates of cannabis use between black Caribbean and white British patients, with lower rates in black African patients. This could contribute to the differences found between the two black ethnic groups in incidence rates of psychosis and why reports of cannabis use in crime statistics are relatively low when African and Caribbean groups are collapsed into 586

one category. Overall, evidence is unclear and at times contradictory.

31.7 Adverse social experiences Might social adversity play a crucial role in the increased risk of psychosis in migrant groups? There is an important conceptual distinction between arealevel and individual-level components which has been raised by several authors (e.g. [1, 62]). Processes that operate at an area/ecological level (characteristic of the environment) include urbanicity and ethnic density. Exposures which operate at an individual/family level on the other hand comprise of ethnic identity, childhood trauma and adult daily stress. Racial discrimination can operate both at the level of the individual and interpersonal, for example being victim of racist abuse, and an amalgamation of the two in translating research is problematic when analysis at a systems level is used to explain individual level effects and behaviour and vice versa. Despite the difficulties inherent in teasing the two apart, researchers must attempt to delineate which processes are involved and in which order to allow for effective preventative strategies to be formulated [1]. Studies examining such social factors independently and together highlight the need for multilevel analysis looking separately at the risk factors affecting different individuals within a population compared to risk factors which work in a wider context across populations.

31.7.1 Social processes at an area level Urbanicity has become of great interest in schizophrenia research. Factors operating in an urban environment which foster social adversity particularly for individuals migrating from other countries could help explain high rates of psychosis in these groups. Urban birth and upbringing are associated with an increased risk for schizophrenia in the European white population (e.g. [63–65]). What is more, Pedersen and Mortensen [65] found a positive association between the degree of urbanisation and the risk of schizophrenia, as well as an increase in risk coinciding with the length of exposure to an urban environment. Crucially, a reduction in risk occurred with the relocation to a less densely populated region. In one

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review, Krabbendam and van Os [66] concluded that urbanicity accounts for approximately 30% of schizophrenia. The ‘social drift’ hypothesis proposes that those in the process of developing or already in the midst of psychosis have a tendency to move into urbanised areas. However, this has not been supported by recent research, pointing more towards a social causation explanation. Are migrants more sensitive to this urban effect? Higher crime levels, stressful life events, social isolation, lower socioeconomic class and overcrowding, have all been found to be more widespread in cities [15]. It has been seen that black Caribbeans are more likely to encounter adverse situations such as social isolation, greater unemployment and more stressful life events which suggest urban factors expose this population to a greater extent and possibly contributing to the development of schizophrenia [15]. Other processes in urban settings which may impact migrant groups include rapid social and population changes which result in unstable norms in which to establish a firm identity [67]. However, the ÆSOP data from three British cities – London, Nottingham and Bristol – found elevated rates for ethnic minorities in all centres. Thus, the urban effect alone cannot explain the ethnicity/migrant effect as rates of psychosis appear elevated in migrant groups independent of place of residence [33]. Community level analyses seem to point tentatively towards isolation as a central risk indicator involved in the development of psychosis in ethnic minorities. Boydell et al. [68] conducted an ecological study looking at adults in contact with psychiatric services between 1988 and 1997 across 15 electoral wards in South London. They found a dose–response relationship where the lower the proportion of nonwhite ethnic minorities in one population, the greater the incidence rate of schizophrenia. Possible explanations for these results considered by the authors centred on consequences of a greater restriction in the availability of social networks thought to act as a protective buffer against discrimination and social disadvantage. Findings of an association between a lower proportion of ethnic minorities in the southeast London area with an increased risk of psychosis in the ÆSOP sample again highlighted the importance of ethnic density and social networks [69].

Other studies have addressed the role of discrimination. Veling et al. [70] used data from a first-contact incidence study in The Netherlands to examine whether perceived discrimination is a key reason behind differences in incidence rates amongst ethnic minorities. Findings showed that ethnic minorities with the highest degree of perceived discrimination at a group level had higher incidence rates of schizophrenia (Morocco) than other ethnic minority groups (Netherlands-Antilles, Suriname, ‘other non-Western countries’, Turkey and ‘other Western countries’). The incidence rates increased as perceived discrimination increased, indicating yet again a dose-response relationship. Veiling et al. [70] suggest experience of one’s group as discriminated and stigmatised against has a similar impact to one’s individual experience of discrimination on mental health, physical illness and academic achievement. More evidence to support such an argument is needed however.

31.7.2 Social processes at an individual level A link between mental illness and discrimination working at an individual level has also been proposed in a study in the United Kingdom. In the Fourth National Survey of Ethnic Minorities in the UK, an association was found between experiences of verbal abuse and racial attacks and an increased likelihood of suffering depression and psychosis [71]. However, this was a prevalence study, and so cannot tell us about a possible relationship between discrimination and onset of disorder. Gilvarry et al. [72] examined the frequency of life events and perceived discrimination in 147 patients with chronic psychosis. With the exception of one category (i.e. financial) there was no significant difference in the amount of life events reported between black ethnic and white British groups. However, black groups were more likely than white British and other ethnic groups to attribute their own life events and life events experienced by other group members to discrimination. This, combined with their findings of lower scores on ‘comfort’ (determined by how relaxed one feels within British culture), supports the notion that black ethnic minorities have a lack of social support when faced with adverse conditions. A related issue is that 587

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of ethnic identity. Tied in with findings of perceived discrimination and ethnic density, Veling et al. [73] examined how these adverse social experiences might bring about psychoses in non-Western migrants. The idea that a positive ethnic identity acts as a buffer against the impact of racial discrimination in the environment and is associated with mental illness in migrants has been previously proposed (e.g. [74, 75]). Veling et al. [73] found that non-Western migrants with first episode schizophrenia were more likely to have a negative ethnic identity (64%) compared with general hospital matched controls (35%). Psychotic patients also had more assimilated (national identity with no ethnic identity) and marginalised (neither national nor ethnic identity) identities compared to controls and less often had a separated identity (weak national identity). By not identifying with one’s own ethnic group it is thought that in specific social contexts, such as that of low ethnic density and resulting social isolation, ethnic minorities have weakened defences against stress produced by a discriminatory environment. From their case–control study, Mallet et al. [76] concluded that a triad of socio-environmental factors differentiated Black Caribbean patients with schizophrenia from white British and Asian groups: unemployment, living alone and long-term separation from parent(s) at a young age. Building on the idea of urban living and social support, they argued that migration into an urban environment in the United Kingdom has brought about changes in the family structure of the black Caribbean community. This, combined with the social adversity faced by migrants in the United Kingdom has weakened affective and familial ties. It is now well known that early separation from one or both parents affects the development of social relationships, having a knockon effect of isolation later on in life [76]. Results from the ÆSOP study indicate that social adversity over the life course is particularly important in explaining the high rates of psychosis in black Caribbeans [34]. Findings revealed that patients were two to three times more likely to experience separation from a parent before 16 years old compared to healthy controls, with black Caribbean controls more likely to experience separation (31%) than white British controls (18%). Additionally, the black Caribbean 588

sample had a greater prevalence of adult social adversity indicators mentioned above (e.g. social isolation, unemployment and limited social networks) which was related to the increased odds of psychosis in this population in a dose–response fashion [77]. The circumstances leading up to this separation are also significant, with family breakdown or the loss of a parent being particularly important in this sample and possibly a marker for previous family conflict or other adversity [34]. Conclusions drawn from this research are that having psychosis caused mainly by adverse social experiences over the life course results in a distinct phenomenology with a greater vulnerability to paranoid and persecutory ideation and a more affective component displayed in ethnic minorities and migrant populations [34]. What gives credence to this over and above the limits of current data comes from evidence that in psychosis in general there is a link between adult trauma and stress, and early experiences. As previously reported, early separation from a parent has been shown to have an association with rates of adult psychosis, but other experiences in childhood have also been linked with the disorder. In one review, childhood abuse (both physical and sexual in nature) was claimed to be present on average in 50% of men and women suffering with psychosis later on in life [78] – though this estimate is based on prevalence samples, such that this does not necessarily indicate a causal relationship. Contemporary studies based on the general population have enabled more meaningful ways of assessing the relationship between adversity at this stage of life and psychotic experiences. Janssen et al. [79] in their prospective study, used data of 4045 individuals from the Netherlands Mental Health Survey to show a sevenfold increase in the likelihood of developing psychosis following childhood abuse. It is important to note, however, that the number of participants with severe psychotic symptoms was relatively small compared to the total sample size. Other factors such as bullying and exposure to parental mental illness have also been raised as important, but evidence here is sparse and the latter is complicated by possible genetic influences [80]. In addition, more general indicators of childhood adversity have been considered in some studies. Socioeconomic disadvantage such as living in single parent households, rented accommodation

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and parental unemployment, have all displayed a relationship with increased risk of psychosis [81]. Others have shown that moving from one geographical area to another in adolescence, especially if this involves a change in school, brings about a heightened risk of developing schizophrenia [82]; this be a consequence of social isolation which has been linked with the formation of later psychosis [83]. Support for a link between social adversity during adulthood and psychosis has already been evidenced above by findings of greater social disadvantage (e.g. unemployment, social isolation, racial discrimination) in those experiencing psychosis. Such experiences occur more often in migrant populations in the United Kingdom. A review by Myin-Germeys and van Os [84] has highlighted the importance of adult life events as a central facet in developing stress in individuals with psychosis and exacerbating illness. Although there is much confusion regarding the period in which stressful life events can have an impact on risk of the disorder [83], it is the cumulative influence of these events, beginning in childhood, which appears to play a significant role in the development of psychosis. By using the Experience Sampling Method (ESM) (whereby participants record their daily life events, paying attention to their thoughts and feelings in context) a relationship between the amount of minor stresses encountered and intensity of psychotic experiences was found [85]. A link between childhood trauma and subsequent negative responses to stress has also been seen [86]. It seems likely that those who are exposed to early adversity are more prone to adverse experiences in later life, and this in combination with a general genetic vulnerability may contribute to the formation of psychotic symptoms. Mechanisms by which this could occur are explored below.

31.7.3 Criticisms of social hypotheses and research This said, we need to retain a considerable measure of caution. The evidence of greater exposure to different social adversities over the life course is derived from crude evidence using crude variables. Morgan et al. [87] underscore the importance of acknowledging that evidence at the moment points more towards social risk indicators rather than social risk factors.

Another important question is whether social factors generate a general vulnerability to illness which then interacts with other variables to bring about a specific disorder such as psychosis. In addition, enquiry into specific types of life events involved in psychosis has been limited [88, 89]. The specificity of childhood adversity in the pathogenesis of psychosis is also problematic considering such experiences have also been connected to other mental disorders such as depression and eating disorders [80]. What is more, the cross-sectional nature of most studies makes it difficult to establish the direction of causality. Additionally, Sharpley et al. [15] point out that such social factors seem to be associated with neurotic disorders rather than psychosis in the general population and that high rates of psychosis should also be expected in South Asian migrants who moved to the United Kingdom at a similar time and are likely to face similar discrimination. However, the fact that black Caribbeans seem to experience more social isolation with limited social networks may help explain more moderate rates in Asian groups given their tight-knit family and cultural structures [19, 29].

31.8 Mechanisms There are now plausible mechanisms that link experiences of social adversity with biological and psychological process that might explain why some (and not others) develop psychosis when exposed to adversity. These include gene–environment interactions and the impact of adversity on the developing brain in such a way as to predispose to psychosis [90, 91]. Of particular interest in relation to ethnic minorities, are new developments in cognitive psychology. Recent research in this field has approached the area of psychosis from the general premise that (i) psychotic experiences lie on a continuum and (ii) experiences/symptoms rather than distinct categories, are more useful into understanding psychosis and should form the basis of investigation (e.g. [92–95]). Emotions are also attributed a central role in this model. It is postulated, for example that a certain genotype inclines certain individuals to ascribe particular salience to aspects of the environment; when put under chronic stress, these individuals develop a cognitive style that interprets their experience in a 589

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negative way and results in psychosis. This could be occurring in the black Caribbean population. Garety et al. [93] state that an environmental stressor (e.g. adverse life event) brings about, in an already vulnerable individual, a disruption in cognitive processes combined with affective changes. The cognitive processes involved are said to include jumping to conclusions, the inability to understand the intentions of others and the formation of negative schemas about the self and the world. These disrupted processes lead to externalising appraisals of the experience which in turn causes anxiety, and (if exposure to adversity continues) the result is the formation of positive psychotic experiences, particularly delusions. Evidence has come from studies which, for example show that childhood adversity results in negative schemas which involve social humiliation and subordination; this serves to initiate symptoms such as paranoia [96]. The maintenance of this psychotic externalising appraisal system is postulated to be facilitated via confirmatory biases and disregarding disconfirming evidence by engaging in ‘safety behaviours’ which reduce threat but ultimately feed into the delusional system. Freeman et al. [95] conducted a cross-sectional study on 25 individuals experiencing persecutory delusions. They found all participants used at least one safety behaviour such as avoidance. Furthermore, emotional distress experienced before the onset of illness was found to be associated with the content of delusions, which in turn influenced subsequent levels of distress. Here we see how a cyclical pattern in the way cognition and emotion interact to generate and maintain symptoms. This single-symptom approach may prove useful when considering differences in the clinical profile of patients from different ethnic groups. An increased prevalence of persecutory delusions in black Caribbean and black African patients compared to other groups found in the ÆSOP study [33] suggests that more paranoia, for example may reflect the greater role of threat and hostility for this population. Considering these types of symptoms from a cognitive perspective may also be consistent with findings of increased perceived discrimination in migrants found in the Netherlands [70, 73]. Perhaps the sociopolitical struggles faced by many migrants and ethnic minorities in the form of racism and 590

chronic social adversity feeds into the formation of persecutory ideation in particular, and this is one reason why psychosis is seen so frequently in these populations. Based on the principles of the cognitive approach, Freeman et al. [97] adopted a virtual reality technique to assess the prevalence of paranoid thinking in the general population. They placed 200 members of the general public in a virtual reality underground train with neutral avatars (virtual characters) and found that over 40% of the sample experienced paranoid thoughts. Furthermore, those who reported paranoid thoughts in daily life were twice as likely to experience paranoid thoughts during the virtual task compared with those who reported no such thoughts in day-to-day life. Predictors of paranoia included high levels of anxiety, worry, negative ideas about self and others, cognitive inflexibility and perceptual anomalies – providing support for the threat-anticipation model whereby an individual who is in a stressful situation will interpret the event in a threatening way due to anxiety and past social adversity. It may be that chronic exposure to threat can increase paranoia so whole populations are shifted along the continuum towards psychosis [43]. Further social adversity may then move more people, such as those from minority ethnic groups, into full blown psychosis. Such considerations do, of course, remain speculative as yet.

31.9 Implications There are two main implications. The first centres on epidemiological research, and ways in which the precise social processes at play must be better understood. The second on public policy, and how a focus on migrant groups with the intention of interrupting problematic social development may prove crucial in reducing the incidence of psychosis in this population.

31.9.1 Research There remains controversy over the social factors suggested as involved in the aetiological pathway of psychosis. Much work is needed to clarify these both in general and in relation to ethnicity. We have already noted that what epidemiological

EPIDEMIOLOGY OF MIGRATION AND SERIOUS MENTAL ILLNESS: THE EXAMPLE OF MIGRANTS TO EUROPE

research has mostly identified are risk indicators (i.e. migration and ethnicity), rather than the direct risk factors which would yield the most valuable knowledge. So far evidence points towards adversity over the life course. However, detailed investigations into whether discrete and/or proximal life events or ongoing and/or distal problems are more crucial in increasing risk and which also delineates cause and effect more proficiently are required. The relative non-specificity of social factors and crudeness of dichotomies is also problematic. For future studies, cross-sectional research might be supplemented by more sophisticated methods which adopt momentary assessments such as ESM used by Myin-Germeys et al. [85] for daily life stresses. More detailed comparisons between first and subsequent generation migrants might also help ascertain which environmental features appear to preferentially influence the latter more distinctly, increasing rates ever higher.

31.9.2 Policy The stark reality of increased incidence rates of psychosis in black Caribbean and black African migrant groups in the United Kingdom raises the question of whether interventions might be targeted on those especially thought to be vulnerable. In order to initiate any policy change, clearly researchers must speak with one voice and convey a consistent argument with agreement over key explanations for high rates [98]. There is however a growing consensus that the cumulative effect of repeated and chronic exposure to adversity may provide the key to understanding and preventing cases of psychosis in these vulnerable groups. Thus, we have seen how childhood adversity, lack of social support, poor socioeconomic status, discrimination and low ethnic density are associated with psychosis in the black Caribbean population in United Kingdom. Preventative recommendations could be made which permeate the social structures that perpetuate disadvantage in the black Caribbean population in the United Kingdom [47]. In addition, such social problems burden the sufferers of psychosis throughout their illness. Thus, the social needs of individuals with psychosis must be given more significance in care plans implemented by mental health services [87]. It is a responsibility for politicians and

psychiatrists alike to address the needs of the migrant population who so clearly need our attention.

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Epidemiology of migration substance use disorder in Latin American populations and migration to the United States Mar´ıa Elena Medina-Mora, Guilherme Borges, Tania Real and Jorge Villatoro Ramon de la Fuente National Institute of Psychiatry, M´exico, DF

32.1 Introduction Reducing health disparities in both risk factors and onset of a disease as well as access to prevention and treatment is a major task for the health sciences including psychiatry. Ethnicity, part of which results from an intense flow of migration from developing countries, is an important determinant of the gap between the demand for services and access to them. Closing the gap implies increasing the availability of prevention programmes and services, as well as offering culturally sensitive interventions to match the perceived needs of the population and the services provided. Due to their recent increase in the US population, Latin American migrants require particular attention. Factors that need to be considered include the gap between their needs and the services available to them, the language barriers that affect parents and their offspring differently, the diversity of cultural backgrounds that alter the expression of disorders, the perceived need for services and the pathways to care and adherence to treatment. Substance abuse disorders are of particular interest due to the differences between foreign and native born Hispanics in both prevalence rates of

the disorders and access to treatment [1]. Better responses are possible, provided an improved understanding is acquired of what migrants bring to the United States and how they are affected by the process of migration and their contact with a different culture. This chapter reviews scientific evidence on the relation between substance abuse disorders and migration from Latin America. It also examines studies on drug use among Hispanics, born aboard and by generation in the US-born, as compared to the US general population, and the evidence on the relative risk attributed to migration. The chapter begins with a description of the sending countries and the situation of drug abuse in Latin America; it reviews the impact of migration to the United States on the migrant, and the effects of the communities of origin on both the families left behind, and the returning migrant. Inequalities in access to services and policies for reducing the gap are also discussed.

32.2 Definitions: What do we understand by migration? It has been estimated that there are over 200 million migrants around the world, accounting for 3% of

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the global population, between 20 and 30 million of whom are irregular migrants [2], a term that refers to a status of migrants that does not conform, for various reasons, to the norms of the country in which they reside and may refer variously to conditions of entry, stay and employment, including possession of appropriate documentation [2]. Migration has increased considerably in North America, from 15.9% in 1970, to 23.3% in 2000 [3]. Around 22 million persons born in Latin America have emigrated from their country of birth, around three quarters of whom reside in the United States. About half (49.7%) of the total immigrants in the United States come from Latin America and the Caribbean [3]. Recent estimates suggest that there are 9 million undocumented immigrants are in the United States, with Latinos comprising over 80% [4]. Migration is a dynamic, self-sustaining process; once established, social networks formed by successful immigrants serve as the basis for supporting the migratory flow. Migration from Latin America to the United States has not been a uniform process although economic reasons are the main trigger. Migration has been one of the most successful survival strategies; in Mexico, for example money sent by workers in the United States to their families constitutes the second largest source of foreign currency after oil [5, 6]. Latin America, as the region with the largest number of migrants of any developing region, received $57 billion in workers’ remittances from 2000 to 2005, three times the remittances received in 2000 [7]. It is estimated that in 9 of the 11 countries examined in the region, 50% or more persons residing in recipient households would be below the poverty line if this income were not taken into account [8]. Migration has also originated during changes in political regimes as in Cuba, through people fleeing political persecution as in Chile and Argentina, or natural disasters as in Honduras and displacement forced by guerrilla movements and drug production and dealing programmes as happened in Colombia, and more recently Mexico. The sociocultural backgrounds of the various waves of migration have also varied considerably, ranging from the poorest sectors of non-Spanish speaking indigenous populations to the wealthiest sectors of society. Evidence suggests that in the 596

1990s, Mexican migrants tended to include more rural indigenous people compared with earlier migration movements [9]. Migration by the highly educated, part of which originated during the process of obtaining a degree, is lower in numbers but has a major impact. It has been estimated, for example that 24% of Mexicans with a graduate degree live in the United States, 36% of whom have a PhD degree. This phenomenon affects both genders, for example 39% of Mexican females with a PhD live in the United States [10]. The vast majority of migrants in the United States are from Mexico. Migration from this country also has the longest history of resettlement, beginning in the 1900s, and increasing from 100 000 persons at the beginning of the last century to 10.2 million in 2004, 94% of which has occurred since the 1970s. According to the 2000 census, migrants of Mexican origin account for 58.5% of the Latino population in the United States, while migration from the Caribbean became noticeable in the 1950s, with migrants from this region accounting for 15.3%. Central Americans total 4.8% and migration from this region became salient in the 1970s while South Americans contribute the smallest proportion, estimated at 3.8%, as well as having the shortest history of migration, which became prominent in the 1980s (see Table 32.1) [3]. International migration is an intrinsic part of the development process. It is a response to difficult situations for people, mainly resulting from the dynamics of development and is a facilitator of social and economic change. It has been shown to have beneficial impacts on both sending and receiving countries through remittance and increasing links between migrants and their communities of origin through the growth of communication technology; migrants bring lifestyles and support systems that might protect them from some of the adverse effects that other low-income residents might experience in the receiving communities, including, as will be seen later in this chapter, less tolerance to illicit drug use. Migrants are also a particularly vulnerable group; the situations that triggered their move to another country are usually difficult, adaptation to a different culture can be complex and exposure to discrimination and xenophobia is not uncommon. In the process, migrants are exposed to greater availability of drugs and less social controls [12]; the

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families they have left behind are also vulnerable to greater risks derived from the introduction of foreign values that might include use of drugs, availability of resources to obtain the substances and family disintegration [12]. Returning migrants bring back new lifestyles that enrich their communities of origin but also have negative impacts especially when there are insufficient resources for preventing and treating new risks or disorders with which they return, such as drug use and related disorders [13]. In the following sections we review the drug situation in the countries of origin, rates of use among migrants in the United States, the families they have left behind and returning migrants.

32.3 Countries of origin: Social, political and other reasons that trigger migration Migrants bring to their new country sociocultural determinants of health, behaviours and practices of service use; this section briefly describes the contexts of the countries of origin. Latin American countries in which Spanish is spoken comprise 370 184 648 million people, 8% of the world’s population. With the exception of Brazil, which was conquered by Portugal, and some small countries that were colonised by other countries, such as Belize and Guyana, all these countries possess a common heritage linked to a Spanish colonial empire that lasted several centuries. As such, they share Spanish as a common language, although some indigenous languages continue to be spoken. The indigenous population has been estimated at between 33 and 40 million [14]. There is also a large mestizo population that resulted from the intermarriage of Spaniards and members of the various indigenous cultures that inhabited the region; recent migrations, notably from Italy, Germany and Portugal have broadened the cultural diversity of the region. Roman Catholicism is the prevailing religion, although a major shift to other Christian religions has been observed in recent decades. It is also a region with a significant demographic transition. Since the 1990s, there has been an overall increase in the elderly and adults of working age, fertility has declined and therefore, so have economic

Table 32.1 Demographic data of the Hispanic population in the United States. Per cent distribution of Hispanics Mexican 65.5% (28.3 million) Puerto Rican 8.6% (3.7 million) Cuban 3.7% (1.6 million) Central American 8.2% (3.5 million) South American 6.0% (2.6 million) Other Hispanic 8.0% (3.4 million) Median age Male 27.1 years Female 27.7 years Distribution of males by occupation and Hispanic origin Male Female Professional 12.9% 22.4% Sales and office 14.2% 32.6% Service 20.3% 30.7% Farming, fishing and 2.4% 0.8% foresty Construction and 28.7% 1.2% maintenance Production and 21.5% 12.3% transportation Households by type Family household 78.8% Married couple 53.1% Male householder 7.7% Female householder 18% Non family 21.2% Marital status Male Female Never married 39.8% 30.3% Married 50.6% 50.8% Separated, widow or 9.6% 18.9% divorced Educational attainment Less than 9th grade 24.4% 9th grade to 12th grade 16.3% (no diploma) High school graduate or 46.9% some college Bachelor’s degree or more 12.4% Age groups by type of Hispanic origin Under 18 65 and older Mexican 36.7% 4.3% Puerto Rican 33.6% 6.7% Cuban 23.3% 18.1% Central American 27.7% 3.6% South American 31.3% 68.7% Other Hispanic 68.4% 31.6% Current Population Survey, Annual Social and Economic Supplement 2006 [11].

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dependence rates, both reinforced by an increase in women’s participation in the labour force. Life expectancy also increased. The average is high, 73 years, yet there are significant country-tocountry differences in Latin America. For example, life expectancy in Bolivia is 65, whereas in Costa Rica it is 79 [7]. In 2005, life expectancy in Bolivia and Guatemala reached levels seen in the United States over 60 years ago. That same year, life expectancy in Brazil, Nicaragua and Peru was similar to the level attained in the United States in the 1950s. The difference between life expectancy in Latin America and the United States is decreasing. Whereas the gap was 10 years in the mid-1960s (57 in Latin America and 67 in the United States), in 2000–2005, it narrowed to 6 years (71 and 77, respectively) [15]. Infant mortality fell to less than half from 55 in 1990, to 25 per 1000 live births in 2006 [7]. According to Economic Commission for Latin America and the Caribbean (ECLAC) in 2007 [16] economic growth resulted in a 3.3% drop in the proportion of poverty from 2006 to 2007 and a 2% decrease in extreme poverty. This decrease was not enough to counteract the high proportion of poverty that has prevailed in the region, 36.5% of Latin America’s population (195 million people) are poor and 13.4% (71 million) extremely poor. Shortfalls in meeting the population’s basic needs, such as housing shortages with the resulting overcrowded dwellings and the absence of sewage pipes, vary between countries. The highest extreme poverty levels are found in Bolivia, Guatemala, Honduras, Nicaragua and Paraguay; while Chile, Costa Rica and Uruguay have the lowest rates of poverty within the region. The most impoverished countries are located in Central America excluding Costa Rica and Panama, and in the Andean region excluding Colombia. The unemployment rate has been rising in Latin America and the Caribbean in recent years, with informal employment increasing as a share of overall employment. Youth unemployment is also increasing, while female unemployment is much higher than male unemployment. Between 1995 and 2005, the unemployment rate in Latin America and the Caribbean remained steady at about 10%. Employment in the informal sector without access to social welfare and pension benefits accounts for a very high percentage of total employment, as does the 598

informal economy’s contribution to gross domestic product [17]. In 2005, 58.9% of the employed urban population in Latin America had health protection and/or pensions; whereas just 33.4% of informal workers were covered by some kind of health protection and/or pension plan [18]. According to ECLAC, in 2003–2004, the unemployment rate among youth ages 15–24 in Latin America and the Caribbean averaged 19.6% for males and 26.2% for females. These figures were much higher than in 1990 (11.5 and 13.9%, respectively) [18]. Unemployment and low quality employment is preventing the region from making productive use of the demographic bonus that resulted from the rapid growth of the working age population and the reduction of family dependency due to a low proportion of children and elderly to care for. Instead a large proportion of those in working age are migrating to other countries, mainly to the United States. Latin America and the Caribbean continue to be the region with the greatest inequality in income distribution in the world, except for sub-Saharan Africa [15]. The highest rates of income distribution inequality are observed in descending order in Guatemala, Brazil, Paraguay, Colombia, Chile, Panama and Mexico [8]. In the Americas as a whole, the ratio of the income of the wealthiest 20% to the poorest 20% is close to 20. The United States, Canada and Nicaragua have a ratio of under 10; conversely, Bolivia, Colombia and Paraguay, have a ratio of over 25 [19]. Gender inequality is also high. For example, in 2002, among 15–64-year-olds, 19% of women and 32% of men contributed to the social security system in 2002. These factors translate into pensions for women at age 65 that are equivalent, on average, to 77% of the pensions received by men [19]. According to Pan American Health Organization (PAHO) in 2007 [15], despite the advances in education, and health-associated social expenditure patterns in the region, with certain variations within countries, Latin America and the Caribbean (excluding sub-Saharan Africa) is still the most unequal region in terms of income distribution. The significant variations observed in public expenditure on health across countries, according to this report, is largely a reflection of the type of health system and the ability of different income strata to access

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available healthcare services. Public expenditure on social security is heavily concentrated in middle and high-income strata, reflecting the fact that historically these economies did not provide individuals with universal access to employment-based social security systems during their economically active years, which are restricted to workers in the formal labour market. In the region, the poor still account for 35–60% [15, 16] of the population, and it is estimated that in many countries (Brazil, Colombia, Guatemala, Haiti, Nicaragua, Paraguay and Peru) only 34% of the poorest people have access to health care services – compared with 94% of the most advantaged population sector [15]. Unequal opportunities are also reflected in health status. Different rates of infectious and chronic diseases disproportionately affect women and indigenous people [15, 20, 21].

32.4 Living conditions of migrants in the United States Hispanics in the United States, long-term residents from 20 Spanish-speaking countries across the Americas and Spain with more than half (64%) being of Mexican origin, 7.6% from Central America and 5.5% from South America, are currently the largest, fastest growing minority group in the United States. The US Census Bureau estimates that in 2006 there were 44.3 million Hispanics in the United States, which translates into 14.8% of the total population [22]; it is arguably the third largest Spanish-speaking country in the world after Mexico and Spain. This group accounted for half the population increase in the United States, with a growth rate of 24.3% as compared to 6.1% for the total population. By 2050 they are expected to number 102.6 million or 24.4% of the US population [23], meaning that Latinos will become the single largest ethnic group. Migration accounted for 52% of the change in the population of Hispanics between 2000 and 2006, 40% of whom are foreign-born. This group is younger on average (27 for males and 28 for females) than the total population in the United States (35 and 38 respectively) [24]. Their educational attainment is also lower, with 24% of males and 23% of females over 25 having completed less than ninth grade,

while for the total US population the proportions are 6.7 and 6.3% respectively. Likewise, while 31% of the male population in the United States are professionals, only 14% of Hispanics are, being more heavily represented in occupations such as construction and maintenance (27% vs. 18%) and services (20% vs. 13%) [22]. As a whole, 22% live below the poverty line as compared to 13% of the total population (Current Population Survey, annual social and economic supplement) [23]. Despite the disadvantaged situation in which Hispanics live in the United States, it is also true that there are significant differences between countries of origin. For example, whereas only 8.5% of Hispanics of Mexican origin have a bachelor’s degree, this is true of 10.2% of Central Americans, and 31% of South Americans, who have higher average rates than the total US population with rates of 28% (Current Population Survey, annual social and economic supplement) [24]. As a whole, Hispanics also constitute a group with significant health care disparities. According to National Healthcare Disparities Report (NHDR) reports (2005–2006), Hispanics had lower rates of use of most health care services, including routine care, emergency department visits, avoidable admissions and mental health care. According to the 2007 report, 60% of the quality of care measures and 67% of those that measure access to care had failed to improve for this group. Hispanics had poorer quality care than non-Hispanic whites for 23 of the 38 core report measures of quality. Similarly, for seven of the eight core report measures of access, Hispanics had poorer scores than non-Hispanic whites [25]. Around one third (29%) of Hispanics reported not being insured, double the rate for the total population of 14%. They had lower rates of having a usual source of care and higher rates of delaying necessary care than whites; treatment for mental disorders was lower and new cases of acquired immune deficiency syndrome (AIDS) were higher for this group [26]. Differences across the various subgroups of Hispanics can be obtained from the results of the California Health Interview Survey – 2005 where nearly 30% of the Hispanic population in the United States live [24]. In 2005, all Hispanic subgroups had a higher proportion of people uninsured all year than non-Hispanic Whites (5.8%) but the percentage for 599

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Mexicans was over three times higher (18.4%); over four times higher for Central Americans, (25.2%) and over twice as high for South Americans, (13.9%) as the proportion for non-Hispanic whites.

32.5 Alcohol and drug use in countries of origin and receiving communities To understand the impact of migration on substance abuse, it is important to consider what migrants bring with them when they come to the United States. In this section we review the alcohol problem and the changing situation of the drug scene in the countries of origin. World Health Organization estimates show that the burden of disease calculated from mortality rates and days lived without health due to substance abuse (tobacco, alcohol and illegal drugs), is highest for developed countries, followed by developing countries with low mortality rates; and that the lowest burden is found in the poorest countries with high mortality rates [27]. In the Americas, consistent with World Health Organization findings, the highest burden is observed in the most developed countries, the United States and Canada, followed by emerging economies with high life expectancies that include most Latin American countries except Bolivia, Ecuador, Guatemala, Nicaragua, Haiti and Peru that have high mortality rates and rank in the group with the lowest burden. Contrary to what has been observed in developed countries and countries with high mortality rates, in emerging economies in the Americas, the burden for alcohol is higher than that observed for tobacco [28]. At the same time, persons with substance dependence in developing countries have less access to treatment compared with addicts living in developed countries, meaning that people that have developed the disorder are more likely to live their lives with the condition [29]. Latin American countries share high rates of alcohol abuse, increasing rates of drug use disorders while homicide rates, are largely related to drug production and trafficking. Latin America is one of the most unsafe regions in the world; underlying this are 600

high unemployment rates and inequality, factors that trigger migration [30].

32.5.1 Alcohol Abuse Among developing countries, the highest per capita alcohol consumption occurs in Latin America; estimated to be 40% above the world standard with an average intake of 8.7 l per capita, while the global average is estimated at 6.2 l, with 2002 rates being as high as 10.5 in Argentina and as low as 3.6 l in Nicaragua [15, 30]. Per capita intake in Canada and the United States averages 9.3 l of alcohol [19] but, whereas in market-oriented economies, per capita intake has decreased, the opposite trend has been observed in Latin America and Caribbean (LA&C) countries that began with a lower per capita intake [31]. Alcohol-related problems are related to how available alcohol is consumed, with more problems being observed when alcohol is concentrated in fewer occasions of drinking and high quantities are consumed. Patterns of use and problems have been assessed according to a hazardous drinking score that ranges from 1 to 4, with four indicating the most hazardous pattern, based on survey data and/or expert judgements on six items, reflecting the predominance of intoxication on drinking occasions (four indicators), extent of drinking in public, frequency of drinking with meals (reverse scored) [29]. According to the Pan-American Health Organization [19], the average score for Latin American countries is estimated to be 3.1, compared with 2.0 in the United States and Canada. There are significant variations throughout the region. In a study conducted on a selection of Latin American countries, the highest scores for hazardous drinking (on a scale of 4) were estimated for Mexico and Costa Rica. Brazil scored 3 and Argentina 2 [32], showing that drinking patterns have wide variations with some countries typically drinking frequently and in low amounts as in Argentina, a wine-producing country with a large proportion of European origin population, and others favouring heavy episodic drinking such as Mexico where per capita consumption is lower (7.7 l), and teetotaller rates are high (35% for men and 64% for women), but occasions for drinking are often occasions for

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inebriation. Drinking does not occur with meals and injury rates derived from accidents and violence are high, therefore ranking highest for the level of risks [33]. This pattern of use is especially popular among youth, alcohol being the main substance of abuse among this group. Age of first use is early, with some children beginning to drink at the age of 10. In 2002, 69 000 deaths among youth between 15 and 29 years of age were attributed to alcohol [19]. As for injuries [34], reports percentages of alcohol use in the 6 hours prior to non-fatal injuries in 21.3% of all cases admitted to emergency rooms in Argentina, a proportion that increased to 25.3% when only violence-related injuries were considered; rates for Brazil were 12.8 and 25.4% respectively. In Mexico, the rates reported by the same author vary between 15 and 21% for all admissions for traumatic events in various parts of the country and 35 and 56% when only violent events were considered [35].

32.5.2 Drug use World Health Organization estimates indicate that the global burden of disease due to premature mortality and days lived without health [36], is higher in developed countries, followed by emerging economies, and lastly by developing countries with low life expectancy. The drug problem also varies depending on the drugs of preference. According to the 2008 World Drug Report [31], from the perspective of treatment demand, the main drug of abuse in South America, Central America and the Caribbean is cocaine, accounting for 65% of all admissions, followed by marihuana; while North America a region that includes Canada, the United States and M´exico, reports rates of 42% for cocaine and 23% for marihuana. Conversely, the main problem in Africa is marihuana, which accounts for 61% of treatment demand, although cocaine and heroin have been gaining ground with 10 and 8% respectively. In Europe the main drug problem is heroin, accounting for 72% of cases in treatment, followed by cannabis with 10%. In some Asian countries such as Japan, the main problem is stimulants.

32.5.2.1 Cocaine The way drugs affect countries and the variations within regions are linked to drug markets. According to the United Nations (UN), the Andean region produces almost all of the world’s cocaine (estimated at 1000 tons of cocaine annually). About half of total production is consumed in North America, despite the decline in demand (for example although in the United States, annual prevalence fell from 36% in 1998 to 19% in 2007) it remains the world’s largest cocaine consumer, with an estimated 7 million users [37]. Nowadays the largest proportion targeting the North American market is trans-shipped through the Mexico-Central America-United States Corridor; but also through the Caribbean, formerly the drug smugglers’ favourite route and one still used to transport large quantities of drugs. Cocaine aimed at satisfying the demand in Europe (estimated at 4 million), has increasingly been shipped through the South America–West Africa–Europe corridor. It has been estimated that approximately 35% of the cocaine produced and shipped from the coasts of Colombia, Venezuela, Brazil and Guyana, is smuggled trough this route. In this transit process, drugs remain in the countries, as a result of which prevalence rates are increasing. For example, cocaine use increased in Brazil from 0.4% of the population ages 12–65 in 2001 to 0.7% in 2005. According to the UN [37] due to the size of the country, it is the second largest cocaine market (some 870 000 persons) in the Americas after the United States. This same report [37] concludes that Argentina is the second largest cocaine market in South America (approximately 640 000 persons in 2006) although, in relative terms, it is the Latin American country with the highest rates. The results of a 2006 household survey conducted in that country show a high annual prevalence rate of cocaine use (2.6% of the population ages 12–65) significantly higher than the 1.9% rate reported in 1999 and similar to that reported in the United States (2.5% in 2006 among the population age 15–64 [38]. Rates of use for Uruguay are also high, with the annual prevalence among urban populations ages 12–65 conducted in the period from 2006 to 2007 reaching 1.68 %. In contrast, Chile has shown a 601

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downward trend, with the annual prevalence rate of cocaine use falling from 1.8% in 2000 to 1.7% in 2004, 1.5% in 2006 (about 170 000 persons), and 1.31% in 2007, among the general population ages 12–64. Nonetheless, prevalence rates in Chile are still among the highest in the region, lifetime use is 6.4%, only slightly lower than that reported for Argentina (8.25%) and higher than that reported in Uruguay (4.63%) [39]. Estimates for cocaine-producing countries and their neighbours show lower but increasing rates; annual cocaine use in Bolivia increased over the 2000–2005 period from 1.3 to 1.9% of the population ages 12–50. Usage rates in Peru are low, with only 0.31% of the urban population 12–65 years of age of that country reporting use in the year prior to the survey, with a lifetime prevalence of 1.55. Rates in the neighbouring country of Ecuador are lower, with 1.34 lifetime prevalence and 0.11 annual prevalence in the urban population within the same age range [37]. Although there are no data showing trends for Colombia, the National Survey of Psychiatric Epidemiology conducted in that country as part of the World Health Organization Mental Health Initiative, among the population 18 years of age and older, shows that in this country, similarly to what was documented for 17 countries around the world, including the United States and Mexico, in this region, there is a cohort effect in accumulated incidence (lifetime prevalence) with greater involvement by younger generations. According to this survey, the highest rate of cocaine use was reported in the United States (16% cumulative incidence), while a second group of four countries, New Zealand, Spain, Colombia and Mexico, showed prevalence rates ranging from 4.3 to 4%, double those reported by the Netherlands or Germany (1,9%), for example. The United States, followed by Colombia and Mexico, had the highest rates of all 17 countries that have published their surveys so far, with first use at the age of 15 or below [40]. Except for Costa Rica where lifetime cocaine use reached 3% in 2005 with no differences in relation to 2001 [41], there are no household surveys available for Central America. For other countries, data from school surveys of students ages 13–17 conducted in 2003 show low rates ranging from 0.7% 602

in El Salvador to 1.3% in Guatemala, lower than the ones reported in 2006 among a similar population in Argentina (2.45%) and Chile (2.4%), and more similar to rates reported by students in Bolivia (0.94%) and Peru (1.01%) [39]. In Mexico lifetime prevalence among the population ages 12–65, studied in household surveys increased from 1.44% in 2002 to 2.5% in 2008, a considerable increase if one uses the rate observed in 1988 of 0.33%. [42] As a reference, annual prevalence in 2008 was 0.4%, lower than the one reported in the southern cone and the United States, similar to that reported by Brazil in 2001, and slightly higher than that reported in the Andean region [37, 41].

32.5.2.2 Marihuana Cannabis is the most commonly used drug in the world and Latin America is no exception; according to the UN [37], the American hemisphere produces more than half the world’s cannabis herb. The highest prevalence rates are found in the United States, where almost half (46.1%) the population aged 12 years and older has tried this drug and 14.4% (35.6 million people) reported having used it in 2007 [38]. Chile is next in accumulated incidence with 27% of the urban population reporting having experimented, while the rate for Argentina is 16.7%, and Uruguay, 14%. Bolivia, Peru and Mexico have similar rates with 4% of their population reporting ever use of cannabis. Rates for use in the past year range from 7% in Chile and Argentina to 0.7% in Ecuador and Peru. School surveys conducted in selected countries in Central America show rates ranging from 7% in Panama to 2.2% in Nicaragua [41], lower than that reported for college students in the United States, which totalled 10, 24 and 32% among 8th, 10th and 12th grade students respectively in 2007 [43]. Except for the United States – where cannabis use was reported as stable after a reduction from 18, 33 and 35% among 8th, 10th and 12th grade students respectively observed in 1996 [43], in other countries where surveys are available, use is reported as increasing. In Brazil ever use increased from 6.9 to 8.8%, and annual use from 1 to 2.6% in the same period. Argentina showed an even stronger increase in the annual prevalence rate of cannabis use, rising from 1.9% of the population ages 16–64

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in 2004 to 6.9% of the population ages 12–64 in 2006 – reversing a previous downward trend, showing levels similar to those reported in West and Central Europe. In Uruguay, annual prevalence of cannabis use quadrupled among the population ages 15–65, from 1.3% in 2001 to 5.3% in 2007.In Mexico, ever use increased from 3.48% in 2002 to 4.19% in 2008, with rates for annual use being 0.6 and 1.03% respectively [37].

32.5.2.3 Heroin Afghanistan is the largest supplier of heroin in the world, accounting for 92% of global production. In the Americas, Colombia and Mexico meet most of the demand for the 1.3 million users in North America [37]. Heroin use in Mexico is low; in 2008 ever use rates were 0.14% considering rural and urban areas. We know from household surveys conducted in urban households of this country in different years that use was low and stable, 0.11% in 1988, 0.09% in 1998, 0.12% in 2002, which then increased to 0.2% in 2008 among the population between 12 and 65 years of age. Use has been traditionally been observed in northern states along the border with the United States. In the state of Chihuahua, for instance, the prevalence rate among the rural and urban population ages 12–65 reached 1.3% in 2008; more recently, use in other states has also been reported [42]. According to the UN, in South America, most reported opiate consumption is linked to the use of synthetic opiates diverted from licit sources. According to a national household survey in 2005, the largest opiate market in South America is Brazil, with around 600 000 opiate users accounting for 0.5% of the general population between 12 and 65 years of age. Annual prevalence for heroin use is low, less than 0.05% [44].

32.5.3 Methamphetamines Methamphetamine use is an emerging problem. UN reports suggest that methamphetamine production to meet domestic demand was initially most acute in the United States. Precursor controls pushed large-scale production southward into Mexico, which became one of the main producers. Ongoing, increasing

enforcement within this country with strict controls over legal importation of precursors to develop methamphetamines may push it further southward still. Canada has recently emerged as a global source of ecstasy-group drugs [44]. In Mexico, it was first documented in 1994 in statistics drawn from patients in treatment due to addictions, and has increased since. It was reported by 2.7% of patients in specialised treatment centres funded by the government in 1994 and increased to 15% of patients in 2006, slightly less than the proportion observed in 2005 (17.2%) [45]. It use is also higher in areas close to the border with the United States [45], mirroring the situation on the other side of the border [46, 47]. At centres run by Non-governmental Organizations in Mexico, the use of the form known as ‘crystal’ along Mexico’s northern border was first registered in 1994. That year, 6.4% of patients treated at centres located in this area of the country reported methamphetamines as the drug that caused them to seek treatment, a proportion that had risen to 29.3% by 2007 [45]. Statistics from this system also show a peak in the proportion of patients seeking treatment for the abuse of this substance in 2005 when the proportion reached 34.7%. Baja California is the state within that region with the highest rates; 58% of patients treated in 2006 reported that methamphetamines were their main drugs of abuse, a relative reduction from what was observed in 2005 when the proportion reached 65% of all patients. This same trend was observed in patients treated at the Centros de ´ Juvenil and in household surveys where Integracion year of first use is registered [48]. National household surveys also show a rise in the use of this substance; its cumulative incidence increased among the population ages 12–65 from 0.08% in 2002 to 0.5% in 2008 [42]. The above-mentioned household surveys conducted in Latin America, apart from Mexico; do not report rates of methamphetamine use although some data are available from student surveys. The aforementioned school survey conducted in several Central American cities reports annual prevalence of ‘ecstasy’ of 1.3% in Panama and rates varying between 0.2 and 0.4% for the other countries included [41]. In South America, student surveys 603

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show higher rates; 3% in Colombia, while similar prevalence to that reported in Panama was detected in Chile (1.6%) and Ecuador (1.1%), with low rates of 0.6% in Peru, 0.5% in Bolivia and 0.4% in Paraguay. In Mexico, annual use rates among students in various cities range from 0.1 to 10% [39, 49].

32.5.4 Inhalants Inhalant use is a relevant issue in Latin America, particularly among children and adolescents. Lifetime prevalence among the population ages 12 and older in Brazil are slightly lower than those in the United States (6.1 and 9.3% respectively) while rates in Peru, Paraguay and Mexico are significantly lower [39, 49]. As documented in national household surveys conducted in Latin American countries (including Central America, Panama, El Salvador, Nicaragua, Colombia and Chile), students ages 13–17 showed lower annual rates compared with their counterparts in North America; while Brazil registered the second highest annual prevalence in the Americas, lower only than the United States. The special case of Brazil can be explained by historical reasons. Since the 1930s, a mixture of perfume, ether and other substances called lanca perfumes (white perfume) was used to celebrate carnivals (communal parties or ceremonies) [41]. Considering all substances, use tends to be more prevalent in men than women and among youths (between 15 and 34 years old). Mean age of onset is approximately 18. In the region as a whole, the drug with highest rates of use is alcohol, marihuana being the main illicit drug [39, 50].

32.6 Dependence and treatment rates Few Latin-American studies have been conducted to evaluate evolution to drug dependence. According to a recent study that included the population ages 12–64 from the country’s 91 largest cities, alcohol abuse rates increased last year to 13%, with higher prevalence in men than women (20 and 4%, respectively) as well as in youths and adolescents (25 and 18%) [50]. 604

In Mexico, cocaine and heroin use has remained stable over the past 5 years. However, rates of last year use of marihuana increased significantly (from 3.7% in 1994, to 5% in 2002 and 7% in 2006), especially among youth, among whom a prevalence rate of 18.8% was registered in 2006. Prevalence of dependence is high, accounting for 23% of last year users. This increasing trend in cannabis use may be attributed to a significant decrease in drug risk perception [51]. According to the World Health Organization Regional Office for the Americas, lifetime prevalence of any substance disorder was 9.6% in Colombia, 7.8% in Mexico and 14.6% in the United States [52]. In Brazil, lifetime prevalence in the three largest cities (Brasilia, Porto Alegre and Sao Paulo) ranged from 7.6 to 14.9%, and has been estimated at 10% in Chile, 12.6% in Puerto Rico and 18.7% in Lima (Peru). Rates of lifetime abuse/dependence on illicit drugs were 1.9% in Sao Paulo (Brazil) [50]. In its recent study of drug consumption in a sample of six Latin American countries, the United Nations reported that the proportion of people with signs of marihuana dependence ranges from 20% in Chile to 36% in Peru and 51% in Ecuador. In the case of cocaine, over 40% of users presented signs of dependence in Argentina, Ecuador and Peru [39]. See Table 32.2. The treatment gap in mental health care in Latin America and the Caribbean is considerable, with almost three-quarters of individuals not receiving mental health care from either specialised or general health services [50].

32.6.1 Drugs and Human Immunodeficiency Virus (HIV)/AIDS Latin America, particularly the Caribbean Region, accounts for 60% of those infected with HIV in the Americas. Cases are concentrated in men, but an increase in infection in women and children has been widely documented [54]. Users of illegal drugs by injection constitute a particularly high risk group of HIV/AIDS infection, mainly in South America [54]; in 2003, 50% of HIV infection in Puerto Rico was related to intravenous drug use. The same year, Argentina showed a high prevalence of HIV infection among its intravenous

EPIDEMIOLOGY OF MIGRATION SUBSTANCE USE DISORDER Table 32.2 Prevalence of drug use per type of drugs in Latin America Household Surveys. Country, year

Sample

Age

United States, 2007a

National, urban/rural (n = 67 870)

12 and over

Ever use

Marihuana Cocaine Crack Methamph etamine Ecstasy Heroin Mexico, 2008b National, urban and rural (n = 52)

12–65 Marihuana Cocaine Crack Amphetaminic type stimulants Heroin

Colombia, 2003d

National urban sites (n = 4426)

Past year 2004

2007

40.2% 14.2% 3.3% 6.0%

40.6% 14.5% 3.5% 5.3%

4.6% 1.3% 2002 0.48% 0.23% 0.10% 0.08%

5% 1.5% 2008 4.2% 2.4%c 0.6% 0.5%

0.09%

18–65 Marihuana Cocaine

Chile, 2006e

National, urban in 13 regions. Cities with more than 30 000 inhabitants (n = 16 807)

National, urban. Cities with more than 80 000 inhabitants (n = 13 471)

National, urban. Cities with more than 200 000 inhabitants in 108 cities (n = 7939)

Marihuana Cocaine Base STA Heroin

12–65

12–65

2001

Marihuana Cocaine Crack Heroin Uruguay, 2006

Peru, 2002

National, urban. Cities with more than 10 000 inhabitants (n = 7000)

National, urban. Cities with more than 20 000 inhabitants (n = 4850)

12–65

10.6% 2.4% 0.5% 0.8%

10.1% 2.3% 0.6% 0.5%

0.8% 0.2% 2002 0.60% 0.35% 0.02% 0.04%

0.9% 0.1% 2008 1.03% 0.44%* 0.13% 0.11%

0.01% 0.04%

6.9% 2.3% 0.4% 0.1%

2002

2006

2001

2005

1.0% 0.4% 0.1% 0

2.6% 0.7% 0.1% 0

5% 7% 1.4% 1.2% 0.5% 0.6% 0.1% 0.1% 0 0 2006 NA

15.8% 7.9% 0.4% 2005

8.8% Marihuana 2.9% Cocaine 0.7% Crack 0.9% Heroin 2006 NA

Marihuana Cocaine

12.2% 4.0% 2002 NA

Marihuana Cocaine

5.8% 1.8%

12–64

2007

10.8% 4.0%

Marihuana Cocaine Heroin Brazil, 2005

Marihuana Cocaine Crack Amphetaminic type stimulants 0.14% Heroin 2003 NA

12–64

NA

Argentina, 2006f

Marihuana Cocaine Crack Methamph etamine Ecstasy Heroin

2004

a www.SAMHSA [53]. b CONADIC, National Institute on Psychiatry, National Institute on Public Health. National Survey on Addictions 2008, M´exico, 2009 [81]. c Includes crack. d Degenhardt et al. [40]. e CONACE. f http://www.cicad.oas.org/oid/esp/statisticscountryprofileindex.asp.

605

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drugs users (7.8%), the rate among pregnant women was 0.3% [54]. Risky behaviours are also related to injecting other substances, for example some Mexican migrants practice the therapeutic injection of vitamins and antibiotics, and a significant percentage reported sharing needles (3.5) [55]. Decades ago, migrants to the United States became another high risk group for contracting AIDS [56]. Some authors suggest that a confluence of sociodemographic factors explain this phenomenon, including limited education, cultural, linguistic and geographical barriers to health services, constant mobility, hazardous working conditions, low wages, chronic underemployment and substandard housing [57]. In short, migrants bring from their countries of origin high rates of alcohol abuse and hazardous patterns of drinking except for those from wineproducing countries, and increasing rates of substance use, with low service use rates. Risk of HIV is also important; recently, Magis et al. [58] concluded, based on their large documentary analysis of Mexican migrants that they had more sexual partners in the past years, and instead of showing increased condom use, they were also more likely to use injected medicines and drugs.

32.7 The process of migrating Exposure to social stress has been found to be a risk factor for psychiatric and substance problems. It has also been documented that the disadvantaged experience higher levels of such exposure [59]. Migrants share experiences of high stress in the process of both migrating and adapting to new communities; irregular migrants are particularly disadvantaged. This section briefly describes studies that have addressed the question of who migrates and the difficulties experienced by migrants during the process of changing their place of residence. It includes reports that document the circumstances before and during migration, the context where the so-called migration paradox occurs, with higher rates of disorders predicted by years spent in the United States and by generation in this country. It is largely based on studies conducted among Mexican migrants that come from poor areas, which represent the majority of Latino migrants in the United States. 606

Studies that compare rates of mental disorder including those derived from substance abuse among Hispanics in the United States and Latinos in the countries of origin, find that in their countries of origin Latinos have lower rates of disorders and alcohol and drug problems that increase with migration, the time spent in the United States and generation in this country [60]. How health is lost in the process of migration deserves more consideration; one possible explanation is the differences between those that decide to migrate compared with those that remain in their local communities. Persons attempting to cross the Mexico–United States border are at a high risk of dying either due to climate and geographical conditions, abuse by authorities on both sides of the border, being murdered by locals or criminal organisations, sexrelated crimes especially for females that migrate by themselves, and accidents. Mortality rates have been estimated at approximately 311 per year with an increase of 29% over that for 10 years ago [61]. Reasons for migrating might also play a role [62]. For example, found differences in rates of post-traumatic stress disorders according to the reasons that triggered migration; 52% of those who migrated as a result of war or political unrest, compared with 49% of Central Americans who migrated for other reasons and 25% of Mexican immigrants, reported symptoms of this disorder. Migrants also experience important stressors before leaving the country, often triggered by economic difficulties. According to studies conducted in sending communities, they know they will face particular dangers, including the possibility of losing their lives in the process of crossing the border, but this is ‘what they have to do’. Rural youth without plans for migration are considered ‘lazy’ and discriminated against [12]. Breslau in 2007 [63] compared English-speaking Mexican immigrants to the United States drawn from a subsample of the US National Comorbidity Survey Replication (NCSR) (see Table 32.3) [64], and a sample of urban Mexicans drawn from the Mexican National Comorbidity Survey (MNCS) [65]. The authors found that immigrants had higher rates of anxiety and mood disorders when compared to the Mexican Sample but that pre-existing anxiety disorders significantly predicted migration.

EPIDEMIOLOGY OF MIGRATION SUBSTANCE USE DISORDER Table 32.3 Lifetime prevalence for substance use disorders among immigrants and natives. Author

Sample

Life time prevalence and risk for substance use disorders among immigrants and Natives

Comparison of substance use disorders by age at immigration and duration of residence

Breslau [62]

Household residents 18 and older in the coterminous United States

Immigrants

Natives

Age substance use

6.1%

15%

0–12 0.5∗ 13–19 0.3∗ 20 + 0.2∗ Duration of residence Pre USA 0.1∗ 0.5 0.1∗ 6–10 0.2∗ 11+ 0.8

∗ Significant at p = 0.05. Breslau L (2007) Risk for psychiatry disorders among immigrants and their US-born descendants. The Journal of Nervous and Mental Disease. 195(3) 189–195 [63].

Also, consistent with previous findings, they found that immigration predicted the subsequent onset of anxiety and mood disorders and the persistence of anxiety disorders. Early age of migration was positively associated with the risk of presenting disorders, high risk of onset of disorders in the United States was more pronounced among those that arrived at a young age (from newborns to 12 years of age) than among those that migrated later. These studies suggest that migrants might bring problems to the new country, yet they reveal the increase in rates of disorders after migration. How migrants’ health is lost in the process of migrating is an issue that deserves more attention. Recent migrants have often acquired very high debts with the smuggling rings that have increased their wages, forcing migrants that had planned to return after earning money to build their houses, to acquire inputs to cultivate the land and so on, to stay longer in the United States both to pay their debts and because they are not sure whether they will be able to return if they visit their families [3], the lack of adequate communication with their communities

of origin makes it even more difficult for them to communicate with their families [12]. This has led to an increasing trend of males bringing their wives with them or sending them money later so that they can join them in the United States. Newly arrived migrant workers are often males that come to the United States unaccompanied by their families [66]; nonetheless, female migration, which is particularly exposed to the risk of sexual abuse, has also increased [61]. Migrants usually have a place to arrive through contacts with neighbours and friends that form a support network that determines the place where they migrate; it is not uncommon to find males from specific towns in Mexico living in determined areas in the United States [61] Migrants with adequate support systems have been found less likely to experience depression [9], yet loneliness and lack of social support due to having left the family behind, are not uncommon [4]. Regardless of their intentions, newly arrived migrant workers are often males that come to the 607

CHAPTER 32

United States unaccompanied by their families; although female migration is increasing [66]. Stress derived from living in a different culture, lack of social support and communication difficulties have also been proposed as reasons underlying the increased mental disorders and substance abuse found in workers that have spent longer in the United States. It has been documented that in many instances migrants do not learn the language although their offspring do and adapt more easily to the new culture, affecting parenting styles and increasing difficulties in communication [67]. Some authors have hypothesised that the increase in problems found among second and third generations of migrants in the United States, is related to this phenomenon, which is often attributed to the effect of ‘acculturative stressors’ such as discrimination, loss of connections with family and community networks and lower than expected social status in the host country [68]. In the past it was not uncommon for Mexican migrants to return annually for the local festivities [61]. New regulations that include tighter restrictions on the US southern borders have resulted in limitations on workers’ travelling back and forward periodically to visit their families, modifying the circular migration process to a more permanent migration. The Survey on Migration to the Northern Mexican Border conducted in Mexico in 2004, for example documents an increase in the time spent in the United States by temporary migrants from 5.5 months in the period from 1993 to 1997 to an average of 11.2 months in 2001–2004 and a drastic reduction in the proportion of persons without previous experience in the migration flow from 72 to 31% between 1993–1997 and 2001–2004 [69]. Despite the fact that international migrants are young, the new trend of migration that includes complete families and a reunification process with more females migrating some time after their spouses have been able to settle in the United States, has increased the average age of migrants. Between 1997 and 2000, for instance, the proportion of adults over 65 years of age that migrated to the United States increased by 32% [61]. Older migrants face particular challenges. They have more limitations than those migrating at younger ages on adapting to the new culture, they learn the new language less often and it is not uncommon 608

for this group to remain highly dependent on their families or ethnic communities [61]. The available evidence is scarce, with more studies being needed to draw conclusions. The few studies available suggest that underlying the observation of better health among Latinos living abroad and the increase in problems on arrival in the United States [60] are factors related to the decision that triggered migration and specific vulnerabilities among those that decide to migrate. Experiences linked to the process of leaving the country of origin and stress derived from difficulties in adapting to a new country are cited as possible explanations for the increase in disorders in migrants as compared to non-migrants. Resilience among migrants that succeeded in the process has also been proposed as factors that might explain the healthier status of recent migrants as compared to the US population. Few studies document the importance of the social context, only a handful analyse the link between stress and substance abuse and even fewer address the role of genetics in the manifestation of diseases, an area that deserves more attention [70].

32.8 Migration, substance use and access to services This section describes studies that report differences in prevalence associated with country of origin, length of stay in the United States and generation in that country. National household surveys conducted in the United States report lower rates of alcohol abuse among Hispanics (3.97%) than among whites (5.10%) and Native Americans (5.75%) but higher than those reported by blacks (3.29%) and Asians (2.13%). When only dependence is considered, the rates reported by Hispanics (3.95%) are higher than the national average (3.81%), lower only than those reported by Native Americans (6.35%) [71]. Binge drinking is common among Hispanics (23.4%), only slightly lower than that reported by whites (24.6%) [38]. As for illicit drugs, rates of use among Hispanics or Latinos report the second lowest prevalence rates, higher only than those reported by Asians [38]. Studies that report data by country of origin find higher prevalence rates

EPIDEMIOLOGY OF MIGRATION SUBSTANCE USE DISORDER Table 32.4 Illicit drug use for immigrants from selected countries compares with US born population age 18 or older 1999–2001.

Past month marijuana use Past year marijuana use Past month illicit drug use Past year illicit drug use

United States (%)

Mexico (%)

Puerto Rico (%)

Cuba (%)

El Salvador (%)

Jamaica (%)

Colombia (%)

5.1 8.8 6.6 11.5

0.9 2.3 2.2 5.5

2 4.7 7.7 13.6

0.2 0.8 0.5 2.6

1.4 1.8 2.7 4.6

5.5 6.4 5.6 6.8

0.9 2.9 2.1 4.9

SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 1999–2001 [73].

among Puerto Ricans that are just under three times higher than those reported by Cubans, double those reported by Mexicans and 2.5 times higher than those reported by Central and South Americans (see Table 32.4) [72]. Epidemiological surveys conducted in the United States since the early 1980s have consistently found lower lifetime risk for drug disorders among immigrants to the United States compared with US-born persons of the same national origin (see Table 32.5) [74]. This pattern has been particularly consistent in studies of Mexican-Americans; for instance, among Mexican-Americans in the National Epidemiological Survey of Alcohol and Related Conditions (NESARC), lifetime prevalence of illicit drug use disorders was 1.7% among those born in Mexico and 12% among those born in the US [75]. In a study conducted in the mid 1980s among Mexican migrants to the United States, Caetano and Medina Mora found that when compared to males in the United States, Mexican men drink less often but in higher quantities per occasion; they also found that the latter experience a rapid change in drinking patterns shortly after they arrive in the United States. Changes in drinking patterns among females were much slower and related more to acculturation than to time spent in the United States [76]. In a study in Fresno County, Vega and others [1] found that rates of anxiety, affective and substance abuse disorders among Mexican Americans (18.5%) were similar to those reported by the population in the United States (24.1%) and for those who were born in that country (20.3%). Rates among immigrants were lower, especially for those that had recently migrated (<13 years) (5.9, 7.6 and 9.7%), when compared to those reported by those that had spent over 13 years in the United States, (10.8, 17.1

Table 32.5 Prevalence of past year use of marijuana and illicit drugs 1991–2001. Marijuana Illicit drug (%) use (%) Foreign born Age group 18–25 26–49 50+ Foreign born in United States <5 years Male Female Age group 18–25 26–49 50+ Foreign born in United States <5 years Age group 18–25 26–49 50+ Born in United States Age group 18–25 26–49 50+

3.5

6

10 3.3 0.6 3.9

14.7 5.9 2 7.6

4.5 3.3

8.8 6.3

7 2.8

12.2 6.3

3.4

5.7

12 3.4 0.7 8.8

16.4 5.8 2.1 11.5

27.1 9.5 1

31.8 12.6 2.3

SAMHSA, Office of Applied Studies, National Survey on Drug Use and Health, 1999–2001 [73].

and 14.3%). Rates for Mexicans in Mexico were in the middle, totalling 9, 8.3 and 11.8% respectively. Worby and Organista [4] recently published a review of studies on alcohol use in newly arrived adult male Latino im/migrant labourers in the United States. They include articles published between 1986 and 2007 that present data for foreign-born Mexican and/or Central Americans separately or where the 609

CHAPTER 32

foreign-born from these countries were a significant majority (70%). These articles targeted occupations or areas with high concentrations of im/migrant workers, reported data on alcohol use and discussed the alcohol use of men separately from that of women. Of the approximately 200 articles identified, 30 met their criteria and were therefore included. The authors find evidence in the articles reviewed that supports previously published studies indicating that the most recently arrived im/migrants have lower rates of alcohol dependence and abuse when compared with US-born populations while that those that have recently arrived in the United States also have fewer alcohol problems on average. They also found evidence of variations among groups of im/migrant labourers with half reporting low or no alcohol intake, while a significant subgroup tends to drink heavily, whether sporadically or regularly. These authors compare findings from two regional and one national study that assessed alcohol dependence and find that despite differences in measures of alcohol dependence and/or abuse, the studies were consistent in finding that recent im/migrants from Mexico and Mexican im/migrants in general, had lower rates of both alcohol dependence and abuse when compared with their counterparts in the United States. When compared to Mexicans in Mexico, studies find lower rates of use and problems in the countries of origin. Length of stay in the United States also increases the likelihood of substance disorders; descendants of migrant workers have increased problems. The higher prevalence among males and the tendency for male Mexicans and Central Americans to drink greater amounts at a sitting but to drink less frequently than is the social norm among drinkers in the United States has also been documented. Heavy drinking in groups is a common way for males to socialise encouraged by a lack of alternative recreational options; drinking lightly on weekdays and heavily at weekends has also been documented. In conclusion, the authors report a range of 20–50% of nondrinkers and at the other extreme, 10–25% of men engaging in periodic high volume and/or frequent (6 to 7 days/week) consumption [4]. Ongoing

610

social assimilation in the United States might underlie these changes in drinking habits and problems, though the hypothesis of selection cannot be dismissed, nor can differences in genetic predispositions [70]. Religiosity, perceived social support, being married and having control over the decision to migrate have been associated with lower levels of psychological distress. These factors might protect the recent migrant, while further involvement over increased time in the United States has been associated with frustration over the limited opportunities for social mobility [4]. As for illegal drugs, in a review of studies, Wagner in 2006 [77] find a similar trend in higher drug use rates among Hispanics when compared with what has been reported in Latin American countries. Among Hispanics, reports document increasing rates of use among the younger generations, with older Hispanics reporting lower rates than non Hispanics within the same age range, although among the younger generations, rates among Hispanics are greater than among non Hispanic whites. The same trend is observed when data are analysed by gender, with significant differences being reported among older age cohorts with lower rates for Hispanic females, although prevalence is similar among native and foreign-born teenagers. Familism has been associated with protection against drug use, it has also been found that children have greater levels of acculturation than their parents and that acculturation has diminished familism, thus increasing the risk of drug abuse among this age group. On the other hand, reducing the gap in acculturation between parents and their offspring has shown to be beneficial, unless parents increase their use of drugs as result of their own acculturation process [77]. For mental health problems, Vega et al. [78] reported that only about one-fourth of Mexican immigrants had used a single service or a combination of services in a year, which was only 2/5 of that of Mexican-Americans born in the United States. Only 8.8% of persons with diagnosed mental disorders used mental health care providers.

EPIDEMIOLOGY OF MIGRATION SUBSTANCE USE DISORDER

32.9 Returning migrants and families left behind Patterns of migration have varying impacts on those who remain in the countries of origin where continued contact and communication with the migrant father has been associated with the wellbeing of children and wives. Several studies have documented the effects on those left behind, usually women who have to adopt traditional male roles in the community and with their children. These effects include increased stress and depression among women, behaviour problems in children and adolescents and substance abuse in returning migrants. The exposure to stress resulting from these situations increases the risk for mental and substance abuse disorders particularly when substance abuse is tolerated and drugs are available [12, 79].

32.10 Conclusions Alcohol and drug abuse and the associated social and health problems and costs are a major issue on the US and Latin American collaboration agenda. Current conditions mean that migration is likely to remain a social issue, as development problems, notably unemployment and inequality are common in many Latin American countries and job opportunities within the United States are available for potential migrants. The process affects migrants, the communities where they arrive and the communities from which they depart. Substance abuse rises with migration, risks are increased for those that remain in the United States and for those that return to Mexico. Families left behind also experience more substance abuse problems. Latin American countries share high alcohol abuse rates and increasing prevalence of substance dependence disorders; which will probably change the evidence for less use among recent migrants and increase the challenge for providing services. In addition, poor use of health services by migrants in the United States has been widely documented.

Migration and its negative consequences, as a complex, worldwide phenomenon, need different approaches and pathways for solution. Communication with the communities of origin has been recommended as a promising approach. Mexico and Central America are countries whose immigrant populations are geographically able to have more contact with their communities of origin, but South American migration (particularly from Argentina, Brazil, Chile and Colombia) requires a different kind of solution [80]. So, US and Latin-American drug and alcohol use issues ought to be addressed in a nation by nation way, on the basis of their migratory situation. There are no evidence-based generalisations to date. We have enough reliable data relating drug and alcohol consumption within countries, but theoretical associations between each region or national drug consumption and migration issues are far from available to address a rational public policy. Therefore, a new US–Latin American research agenda is needed based on a theoretical geopolitical background. Another group of interrelated factors must be considered. Migration phenomena and their healthrelated correlates are not the same as for those with higher educational attainment, for example. We might even hypothesise differences for gender, employment status, and return or stay expectations, psychiatry comorbidity and drug use patterns. This chapter does not include an analysis of migration and substance abuse among Latin American children and adolescents, who are the most vulnerable population and deserve increased research efforts and evidence-based interventions. Knowledge about relationships between immigration and mental health status advances by comparing immigrants pre- and postimmigration with residents of the immigrants’ home countries [63] might be the ideal methodology for further studies among children and adolescents populations.

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Early detection and intervention as approaches for preventing schizophrenia Ming T. Tsuang,1,2 William S. Stone,2,3 Margo Genderson4 and Michael Lyons2,4 1 Center for Behavioral Genomics, Department of Psychiatry; Institute of Genomic Medicine, University of California, San Diego, CA, USA 2 Harvard Institute of Psychiatric Epidemiology and Genetics, Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA 3 Massachusetts Mental Health Center Division of Public Sector Psychiatry, Beth Israel Deaconess Medical Center, Department of Psychiatry, Harvard Medical School, Boston, MA, USA 4 Boston University, Boston, MA, USA

33.1 Introduction There is no longer any doubt about the importance of genetic factors for influencing vulnerability for schizophrenia. Family studies have demonstrated that the biological relatives of individuals with schizophrenia are at increased risk for developing schizophrenia and related disorders, such as schizoaffective disorder, schizotypal personality disorder (SPD) and other non-affective psychoses [1]. Adoption and twin studies have allowed researchers to examine the extent to which this increased risk is due to genetic or common environmental factors. Adoption studies suggest a strong genetic influence, demonstrating that biologic but not adoptive relatives of schizophrenics are at an increased risk for the disorder [1]. Biologic children of non-schizophrenics do not have a greater risk for developing schizophrenia when raised by schizophrenic adoptive parents. Twin studies have found that monozygotic twins have a greater concordance rate of schizophrenia (about 50%) compared with dizygotic twins (about 10%), reflecting genetic influence and studies suggests that approximately 70% of the vulnerability to

schizophrenia is due to genetic factors [1]. Since the concordance rate of schizophrenia among identical twin pairs is not more than 50%, environmental factors must also influence risk of developing schizophrenia. Although family, twin and adoption studies show that the aetiology of schizophrenia is largely genetically driven, identifying susceptibility genes is challenging since many genes likely each contribute a small proportion of vulnerability [1]. In their review of the relationship between the neuropathology and genetics of schizophrenia, Harrison and Weinberger [2] identified seven putative susceptibility genes with positive findings in at least three published independent samples: catecholO-methyl transferase (COMT), neuregulin (NRG1), dysbindin (DTNBP1), disrupted-in-schizophrenia 1 (DISC1), regulator of G-protein signalling (RGS4), metabotropic glutamate receptor-3 (GRM3; mGluR3) and G72 (and D-amino acid oxidase). These genes may contribute to the risk of schizophrenia by influencing synaptic plasticity, because three of these genes affect plasticity and synaptogenesis (NRG1, dysbindin, DISC1), two affect receptors (GRM3, G72) and one affects signal transduction (RGS4).

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

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The glutamatergic system is most often implicated in association with these genes, though they are also strongly linked to the dopaminergic (namely COMT) and gamma-aminobutyric acid-ergic systems. An alteration of gene expression rather than of protein structure appears to be the rule. In a recent meta-analysis of five independent clinical datasets, Huffaker and colleagues [3] introduced a previously unreported susceptibility gene for schizophrenia, KCNH2 (a human ether-a-go-go (ERG)-family potassium channel). A newly discovered primateand brain-specific isoform (3.1) of the KCNH2 gene was also reported. This isoform modulates neuronal firing and has been found to be overly expressed in the hippocampus of individuals with schizophrenia, resulting in a nonadapting neuronal firing pattern. Researchers suggest that KCNH2 may be implicated in the aetiology and pathophysiology of psychosis. See the review by Williams et al. [4] for an update including recent findings from research utilising genome-wide association and copy number variation methodologies.

33.2 Modelling genetic and phenotypic heterogeneity The model in Figure 33.1 takes into account the complexity of the phenotypes that we wish to study and the determinants of those phenotypes. According to the model, individuals who are influenced by the relevant genetic and environmental risk factors demonstrate one of three outcomes: (i) they may be free of symptoms of the disorder; (ii) they may have

Genetic and Phenotypic Heterogeneity Model Phenocopies Clinical phenotype Spectrum disorder Symptom free What we now study

Envir

onme

nt

Genotype

What we want to study

Fig 33.1 Genetic and Phenotypic Heterogeneity Model.

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an attenuated version of the disorder that does not reach the full diagnostic criteria and are considered to have a ‘spectrum’ disorder or condition and (iii) they may show the clinical phenotype, which means they meet full Diagnostic and Statistical Manual of Mental Disorders (DSM) or International Classification of Diseases diagnostic criteria for the relevant disorder. Some individuals may meet the clinical diagnostic criteria, but do not have the same disorder from an aetiological perspective, and could thus be classified as ‘phenocopies’. The power of our genetic investigations will be reduced significantly if we classify the symptom free and spectrum disorder individuals in the figure as ‘unaffected’, and include phenocopies as ‘affected’. It will be enhanced, however, through approaches that utilise spectrum-related abnormalities, whether or not they are included in formal diagnostic criteria. Consistent with the growing list of genes that may contribute to the susceptibility for or expression of schizophrenia, many phenotypes have been proposed to reflect liability for the disorder. The importance of identifying alternate phenotypes or ‘endophenotypes’(e.g. social, psychophysiological or neuropsychological abnormalities), also reflects a growing awareness that multidimensional expressions of psychiatric disorders can advance the search for underlying aetiological and modulatory factors, and also for intervention strategies [5, 6]. Moreover, endophenotypes may be more specific and amenable to objective measurement than symptoms, presumably reflecting variation among smaller numbers of genes than do more distal clinical symptoms. The usual approach for identifying endophenotypes involves the demonstration of statistically significant differences between non-psychotic, adult relatives of individuals with schizophrenia and relevant control subjects [7, 8]. Much remains to be done, however, to determine which putative endophenotypes will be useful in distinguishing individuals who are more likely to express psychosis from those who are not, and which ones should be included formally in a syndrome of liability. In this regard, the statistical sensitivity and specificity of the proposed measures are as important as the demonstration of significant group differences [7, 8], as is the degree to which a predictor (e.g. inattention, social withdrawal, substance abuse) is specific for

EARLY DETECTION AND INTERVENTION AS APPROACHES FOR PREVENTING SCHIZOPHRENIA

schizophrenia [9]. However, no single factor predicts schizophrenia accurately (so far), even in high-risk groups [9]. This latter finding, at least in part, likely reflects a multifactorial, polygenic process in which multiple, sometimes heterogeneous, vulnerability factors of small or moderate effect add to the overall risk of developing psychosis. Therefore, multiple measures or combinations of measures often demonstrate greater predictive power than individual ones [10]. Measures of neuropsychological functioning have been particularly informative [11, 12]. Cognitive deficits are core components of schizophrenia and contribute to functional outcomes of the illness (e.g. [13–15]). Compared to control subjects, cognitive deficits occur in subjects with schizophrenia and related ‘spectrum’ conditions (e.g. schizoaffective disorder and SPD), and in clinical (e.g. prodromal) and genetic (e.g. the children of parents with schizophrenia) high risk subjects [16–18].

During the last 20 years, we and others have demonstrated widespread cognitive deficits in schizophrenia [7, 19]. In one study, we used linear combinations of neuropsychological tests to create scales assessing 10 neuropsychological functions: abstraction/executive, verbal ability, spatial ability, verbal memory, visual memory, learning, perceptual–motor speed, mental control/encoding, motor and auditory attention [20]. Based on previous neuropsychological studies of schizophrenia and our review of family studies, we predicted that relatives of individuals with schizophrenia would also exhibit deficits in abstraction/executive function, learning and memory and several components of attention (perceptual–motor, mental control/encoding and vigilance). Consistent with this hypothesis, a multivariate analysis of variance showed that the neuropsychological profiles of relatives were significantly more impaired than the corresponding profiles of controls (Figure 33.2).

NeuropsychologicProfiles: Schizophrenic Patients, Relatives, and Normal Controls 0.5 0.5 00

Z-Scores Z-Scores

–0.5 –0.5 –1 –1 –1.5 –1.5 –2 –2 –2.5 –2.5

Normal controls (n = 74) Relatives (n = 54) Schizophrenia patients (n = 68)

–3 –3 –3.5 –3.5 Abstract Abstract

Vigilance Vigilance

Percept -Motor

Mental Control

Verbal Memory

Visual Spatial

Verbal

Fig 33.2 Neuropsychological profiles in schizophrenia subjects, relatives and healthy control subjects.

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In particular, non-psychotic, adult relatives performed more poorly and variably than controls on three functions: abstraction/executive, verbal memory and auditory attention/vigilance. They had lower mean scores, but similar variability on verbal ability and mental control/encoding. They showed more variability, but not lower mean scores, on learning and motor abilities. The groups did not differ on visual–spatial ability, visual memory or perceptual–motor function. The neuropsychological deficits were unrelated to psychopathology in the relatives, level of education or parental social class. These results remained significant when the original sample was increased, with the additional finding of significantly impaired visual memory [21]. Our 4-year follow-up study showed that executive function, declarative memory and working memory deficits in adult relatives remained stable over time [22]. Working memory deficits and verbal declarative memory functions (e.g. story recall) were particularly robust indicators of impairment. Support for the importance of verbal learning and memory deficits, particularly impaired semantic clustering, was confirmed in an independent sample of relatives of patients with schizophrenia [23]. More recently, in the largest study of list-learning in relatives to date (including 305 subjects with DSM-IV schizophrenia, 449 of their adult, non-psychotic siblings, 232 of their non-psychotic parents and 509 community comparison subjects), we showed that siblings and parents of people with schizophrenia were poorer at discriminating words they had just learned from other words with semantic or phonemic similarities (Stone et al., submitted). Demonstrating cognitive deficits in relatives raises questions about the basis of observed individual differences. In particular, we were interested in which factors influence cognitive performance among relatives. We addressed this issue by comparing a sample of relatives in which one family member was affected with schizophrenia (‘simplex’ families), to a sample with two members affected (‘multiplex’ families). We assumed that the multiplex group has a higher genetic risk for schizophrenia than the simplex group [24]. Relatives from multiplex families performed significantly worse on verbal declarative memory compared to simplex relatives 620

and to controls, while relatives from simplex families performed significantly worse than controls on verbal and visual memory. Together, these results with schizophrenia patients and their non-psychotic relatives support the view that particular neuropsychological impairments (including verbal declarative memory impairments) are stable, core features of schizophrenia that occur relatively independently of psychosis. Neuropsychological impairments are related to other manifestations of schizophrenia. One of these involves abnormalities in brain structure, which we and other investigators have documented both in patients with schizophrenia and in their close biological relatives [25–28]. We assessed relationships between brain structure and neuropsychological performance in the simplex and multiplex samples described above, and in patients with schizophrenia [29]. Volumes of total cerebrum and the hippocampus were measured. Relatives from multiplex families and patients with schizophrenia demonstrated significantly smaller left hippocampi than controls. Moreover, verbal memory and left hippocampal volumes showed significant, positive correlations in individual subjects. Within families, hippocampal volumes did not differ between patients with schizophrenia and their non-psychotic relatives. The results support the hypothesis that the vulnerability to schizophrenia includes smaller left hippocampi and verbal memory deficits, and that the degree of genetic liability to schizophrenia influences both quantitative and qualitative deficits.

33.3 Defining a syndrome of liability using cognitive and clinical characteristics of relatives The notion that non-psychotic relatives of patients with schizophrenia share neuropsychological or other abnormalities with their more ill relatives raise the question of whether individuals with an elevated risk for schizophrenia (e.g. adolescent or young adult, first-degree biological relatives of people with schizophrenia) can manifest this liability overtly. One way to do so would be to meet DSM-IV diagnostic criteria for a related but milder condition,

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such as SPD. Although most individuals with SPD do not develop schizophrenia [7, 30], characterisations of their liability are important, partly to develop interventions that target clinical manifestations. Moreover, the study of non-psychotic, nonschizotypal adult relatives of schizophrenic patients who carry the risk, but do not develop schizophrenia, can also clarify the liability to develop the disorder. As the accuracy of prediction increases, the potential for early intervention increases. Over the last 100 years multiple sources have contributed to our current notions about the nature of liability to, and protection from, psychosis. Among the sources that have guided our conceptualisation and assessment is the well-established view that schizophrenia has a significant genetic component [31–33]. Based in part on the frequent observation that some non-psychotic relatives of people with schizophrenia showed eccentric or odd behaviours, and on Rado’s suggestion that ‘schizotypes’ resulted from a genetic liability to schizophrenia [34, 35], Paul Meehl proposed a model in which a ‘schizogene’ produced a ‘neural integrative defect’ that he referred to as ‘schizotaxia’ [36, 37]. He concluded that this deficit could not be observed directly through clinical function or other behaviour, though its underlying neurobiology would be defined eventually when technological advances permitted it. Instead, the neural defect produced a phenotype called ‘schizotypy’, which is a type of personality organisation that interacts with environmental variables like social learning and other, polygenic factors (e.g. a predisposition to high or low anxiety or to various features related to temperament). These latter polygenic factors differed from the major schizogene that causes schizotaxia in that they were more likely to include common genes of small or moderate effect. In relatively favourable environmental and genetic circumstances, the resulting phenotype would involve only minor clinical symptoms (e.g. ‘compensated schizotypy’), but less favourable genetic and/or environmental circumstances and would result in more severe, decompensated phenotypes, such as schizophrenia. For about 40 years, schizotaxia was used by researchers to conceptualise the premorbid, neurological liability for, and basis of, schizophrenia. Unlike schizotypy, however, which entered the psychiatric nomenclature in the (altered) form of

SPD, schizotaxia remained undefined operationally. The possibility that schizotaxia is both a definable and meaningful clinical syndrome emerged from the growing empirical literature showing that some first-degree, non-psychotic relatives of people with schizophrenia demonstrate abnormalities in cognition, psychophysiological function (e.g. eye tracking), brain structure and function, affect and social functioning, compared to healthy control subjects. In our recent work we have adopted and refined the concept ‘schizotaxia’ to integrate newer data. Although our conception is consistent with Meehl’s view of it as the underlying liability among people predisposed to schizophrenia, our view also differs in significant ways. For example, we proposed that the syndrome resulted from a combination of genes and adverse biological consequences of environmental events (e.g. pregnancy or delivery problems such as maternal diabetes or eclampsia) in most cases, rather than from a major gene. Based on evidence of abnormal function in several dimensions, as described above, Tsuang et al. [1] proposed a set of research diagnostic criteria for schizotaxia. The initial research criteria involved a subset of theoretically important symptoms/deficits that had been relatively well-studied at the time. They included a clinical variable (negative symptoms) and specific deficits in neuropsychological functioning. At least moderate levels of negative symptoms (defined as at least six items on the Schedule for the Assessment of Negative Symptoms rated 3 or higher) and neuropsychological deficits (defined as performance deficits in specific measures of attention, verbal declarative memory and/or executive function that are at least 2 standard deviations lower than expected in one of the three domains, and at least 1 standard deviation below expectation in a second domain) were required for a diagnosis of schizotaxia in never-psychotic, firstdegree, adult relatives of patients with schizophrenia. Additional inclusion and exclusion criteria (e.g. a history of a head injury or other neurological disorder known to impair cognitive function or increase negative symptoms, a current substance abuse diagnosis or very low levels of overall cognitive ability) were intended to minimise false positive diagnoses [38]. We assume the diagnostic criteria will be modified over time to encompass other likely endophenotypes and dimensions of function. We also assume that 621

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the ‘cut points’ or critical values of the current criteria will be modified as well, to produce the most empirically valid syndrome (or syndromes). One corollary of defining schizotaxia on the basis of these neuropsychological deficits is that a large proportion of ‘unaffected’ relatives of patients will demonstrate core features of the proposed syndrome. We estimated that 20–50% of first-degree relatives might demonstrate them, based largely on our studies of neuropsychological deficits this group [8, 20]. Because neither schizophrenia nor SPD occur at such high rates [7, 30, 39, 40], a significant clinical implication of the revised formulation is that schizotaxia does not lead to either SPD or to schizophrenia in most people. Instead, it may be a stable syndrome in most cases. This view is consistent with our finding that neuropsychological deficits in these areas were stable over a period of 4 years, as described above [22]. Although the proposed syndrome of schizotaxia coheres conceptually, its utility can only be established through empirical validation. Nevertheless, there are several reasons for cautious optimism. First, our initial pilot studies demonstrated that 8 out of 27 first-degree relatives met our criteria for schizotaxia [41], in agreement with earlier estimates of its likely rate of 20–50% [8, 20].

Second, six of the eight individuals who met criteria for schizotaxia agreed to enter a brief, openlabel drug trial with a low dose of risperidone (0.25–2.0 mg) We reasoned that if relatives shared genes and psychopathological features with their ill relatives, they might also respond to the same type of treatment (i.e. one of the newer antipsychotic medications) that benefits many individuals with schizophrenia. The findings were consistent with this prediction, as five out of six subjects improved on a demanding test of working memory and showed 25–50% reductions in negative symptoms (see Figures 33.3 and 33.4). Notably, the one subject who did not improve demonstrated an IQ score that was well below average and also below that of the other subjects, which may have implications for the limits of pharmacological treatment. More recently, Rybakowski et al. [42] used similar diagnostic criteria to identify schizotaxia in seven adult, never-psychotic, first- or second-degree biological relatives of patients with schizophrenia. Unlike our subjects, these subjects reported a decline in function over the previous year, and neuropsychological problems. None of them, however, met DSM-IV diagnostic criteria for a schizophrenia-related spectrum disorder. Subjects agreed to take low doses

SANS TOTAL SCORE 50 40 Case #1 Case #2 Case #3 Case #4 Case #5 Case #6

30 20 10 0 Recruitment

6 weeks

Fig 33.3 Changes in Assessment of Negative Symptoms (SANS) total scores in six relatives before and after treatment with risperidone for 6 weeks.

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A-CPT (% HITS) 100 90 80 70 60 50 40 30 20 10 0

Case Case Case Case Case Case

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#1 #2 #3 #4 #5 #6

6 weeks

Fig 33.4 Changes in performance on a working memory test (an auditory continuous performance test) in six relatives before and after 6 weeks of treatment with risperidone.

of risperidone for periods of 3–7 years. Similar to our results, all subjects demonstrated improvements in negative symptoms and neuropsychological functions, and also showed improvement in social and vocational function. These results represent the first published study by an independent group to replicate (and extend) portions of our findings. Third, a validation study performed with the same subjects showed that relatives who met criteria for schizotaxia performed worse on several independent measures of clinical function than did relatives who did not meet the diagnostic criteria [41]. Several measures were employed, including some that involved self-ratings (including the Social Adjustment Scale, Symptom Checklist-90-R and the Physical Anhedonia Scale) and one that was rated blindly by the investigators (DSM-IV Global Assessment of Functioning Scale). The groups did not differ significantly in age, education, paternal education, IQ or number of ill relatives. At the time these clinical data were collected, neither the raters nor the subjects knew their clinical status (i.e. schizotaxic or non-schizotaxic). This study was the first to show evidence of concurrent validation for the proposed syndrome. Fourth, Chen et al. [43] showed that deficits in cognition (i.e. sustained attention) were more associated

with negative and disorganised symptoms of schizotypy than they were with positive symptoms in nonpsychotic relatives of individuals with schizophrenia. This study, while not performed on subjects with schizotaxia per se, provides evidence that the major clinical components of schizotaxia coaggregate with each other in relatives. It is also consistent with our own findings that deficits in neuropsychological domains that are often impaired in schizotaxia coaggregate with each in relatives more than they do in control subjects [21]. Recently, we conducted an investigation of schizotaxia by recruiting relatives of patients with schizophrenia and assessing them at the Mental Health Institute, Second Xiangya Hospital of Central South University, Changsha (Hunan, China) [44]. Phase I of the study involved the identification of: (i) neuropsychological deficits in declarative memory, sustained concentration and executive function and/or (ii) negative symptoms, in adult, non-psychotic relatives. Additional measures of cognitive function were also administered, as were self-report measures of social function, and medical measures that emphasise cardiac, hepatic, lipid and glycaemic control. Relatives were classified as schizotaxic on the basis of diagnostic criteria in 623

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neuropsychological and negative symptom domains, and compared with relatives who do not meet criteria, and with healthy control subjects (n = 30). Initial analyses focused on data collected from the first 110 subjects to complete the protocol. Forty-five of these relatives met criteria for schizotaxia, and 65 did not. The major findings showed that: (i) nonschizotaxic relatives generally did not differ statistically from controls; (ii) schizotaxic relatives were impaired on a wide range of independent (i.e. non-criterion related) cognitive, clinical and social measures compared to both other groups and (iii) most physical measures, including glucose tolerance, did not differ between groups. Overall, these findings provide additional evidence that the combination of negative symptoms and neuropsychological deficits provides a useful way to identify schizophrenia-related liability. Nevertheless, the validation of schizotaxia in its current form, while promising, is at an early or interim stage, as noted above. Our conception of schizotaxia will expand to include other measures of cognitive and clinical function, and also other dimensions. Abnormalities in brain structure and function, for example are a likely direction, as is dysfunction in other physiological systems, such as glucose and other types of metabolic regulation [45, 46]. One of the most important avenues for future research will involve the attempt to define the genetic liability for schizotaxia/schizophrenia. The following discussion focuses on our current efforts in this domain.

33.4 Gene-based vs. genome-based research Gene-based research and genome-based research are two general approaches to discovering genes responsible for a disease. Gene-based research typically focuses on specific genes, providing a ‘window’ into a specific section of the total genome. Candidate-gene association studies are an example of gene-based research. Such studies have attempted to identify the underlying aetiology of schizophrenia by testing the association between candidate genes (one gene at a time) and disease-risk. However, findings from candidate-gene studies are inconsistent, and genes found to be associated with the disorder in one 624

study are often not replicated in others. Genomebased research examines the full genome, opening a complete window on all genes. Microarrays are a tool that can be used to measure the expression of hundreds or thousands of genes simultaneously and detect over-expressed or under-expressed gene subsets from a given biological sample [47]. Thus, relative to traditional candidate gene studies, microarray analysis is a less constrained strategy that could foster the discovery of novel risk genes that otherwise would not come under study. Gene expression refers to the production of messenger RNA (mRNA) and subsequently proteins based on genetic information encoded in DNA. The DNA forms a template for the transcribed mRNA, which is then translated into a protein. Gene expression is measured by the fluorescent intensity of probes, that indicate the amount of mRNA produced by specific genes [47]. Whether or not a protein is expressed can reflect both genetic and environmental influences. Microarray analysis, therefore, may be particularly useful for identifying risk factors for a complex disorder such as schizophrenia, which is thought to have a multifactorial polygenic aetiology in which many genes and environmental factors interact. Altered patterns of gene expression have been implicated in the development of various psychiatric disorders, and researchers have established differential gene expression profiles between individuals with and without various disorders [48]. Using microarray analysis to study neuropsychological disease is challenging due to the desirability to examine gene expression in the presumed pathological tissue; that is the central nervous system, which requires post-mortem human brain biopsy samples. Microarray techniques, however, have been applied to several neurological diseases, including human immunodeficiency virus-associated dementia, Alzheimer’s disease, multiple sclerosis and schizophrenia [49, 50]. Compared to other tissues, the gene and protein expression levels in the brain are very dynamic and require rapid turnover due to the nature of learning and adaptation [49]. Despite these limitations, a number of postmortem studies have been performed [51, 52]. One intriguing set of findings reported that key myelin-related genes were differentially expressed in postmortem schizophrenia brains as compared with control brains [53, 54].

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Another interesting finding demonstrated that risk-associated alleles of a recently discovered susceptibility gene for schizophrenia (KCNH2-3.1) predicted increased expression in postmortem hippocampus [3]. A considerable amount of research has been conducted regarding the value of using blood samples compared to brain tissue for microarray analysis to study psychiatric disorders. Using blood as a source material is advantageous since it allows for the collection of larger sample sizes, better standardisation of technical procedures and the ability to relatively non-invasively profile human subjects. Similar to the rapid turnover in gene and protein expression in the brain, circulating blood is a notably dynamic environment. Circulating blood involves the turnover of approximately one trillion blood cells daily, including 200 billion red blood cells (RBCs) and 70 billion neutrophilic leukocytes, and it may act as a ‘sentinel tissue’ that can reflect the state of health or disease within the body. In microarray analysis using blood samples, complementary DNA (cDNA) from lymphocytes is extracted from blood. Gladkevich and colleagues [55] have evaluated the use of cDNA as an acceptable alternative to central nervous system (CNS) DNA. The authors provide a compelling argument for the use of blood samples and explain that lymphocytic function has been shown to be altered in neuropsychological disorders [56, 57], that neuroactive proteins and processes are expressed in lymphocytes [58, 55], and that there are similar hormonal effects on nervous system processes and lymphocyte physiology [59]. Further, genes whose expression levels are influenced by genetic polymorphisms in their promoter regions or transcriptional regulatory sites might be expected to have similar levels of expression in all tissues depending more upon the presence or absence of such polymorphisms, rather than on the tissue in which they are being measured. Gladkevich et al. [55] and Muller and Achenheil [59] also examined the use of cDNA as an alternative to CNS DNA. The authors concluded that disturbances in the major CNS neurotransmitter systems (dopaminergic, noradrenergic and serotonergic) and in the hypothalamic–pituitary–adrenal-axis, which are both observed in psychiatric disorders, are concomitant with altered functioning and

metabolism of lymphocytes. The similarities in the receptor properties and transduction processes of lymphocytes and the CNS suggest that lymphocytes may serve as a tool in the investigation of CNS pathology in psychiatric disorders. While the application of microarray technology in live patients with psychiatric disorders using lymphocytes is relatively new, it has been successfully applied in several populations. Early confirmatory results by our group [60] demonstrated the ability to discriminate between patients with schizophrenia, bipolar disorder and healthy controls with 95–97% accuracy on the basis of the expression levels of as few as six genes. Of note, this work is preliminary and requires additional replications. In a study of Alzheimer patients, gene expression profiles of Alzheimer lymphocytes differed in magnitude from control lymphocytes. The authors also examined the effects of antidepressant medications on gene expression in this population and identified sequences that were differentially expressed before and after treatment [61, 62]. In another study of individuals exposed to trauma, peripheral blood cell-derived gene expression patterns differentiated individuals who would go on to develop posttraumatic stress disorder from those who showed no lasting ill effects of their traumatic exposure [63]. Vawter and colleagues [64] also applied microarray analysis to 1128 brain-focused genes using lymphocytic data from a high density multiplex schizophrenia pedigree. The authors identified two genes that were significantly downregulated and one that was significantly upregulated compared to family controls. They suggest that future research should screen a larger set of genes in additional family studies of schizophrenia. Examining gene expression in families raises a concern regarding whether or not the variation represents disease manifestations or family resemblance. While research has only begun to address this question, studies to date suggest that gene expression levels of certain genes are heritable. For example, Maas et al. [65] examined gene transcript levels in unaffected first-degree relatives of patients with autoimmune disease and concluded that variations in transcript levels are associated with family resemblance rather than manifestations of disease. Similarly, Aune et al. [66] found that lymphocytes 625

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from autoimmune disease probands and unaffected first degree relatives share a common gene expression profile. Additional research must be done to understand the degree to which familial resemblance in expression levels translates into heritability. Further, it remains to be determined if any disease-related genes also show heritable variation in affected and unaffected members of the same family. In order to validate these results, gene expression changes in the dorsolateral prefrontal cortex were examined using the same conservative analytic strategy employing the CORGON and MADCAP algorithms [67]. We identified 177 genes that were differentially expressed in postmortem dorsolateral prefrontal cortex tissue between 19 schizophrenia subjects and 27 non-psychiatric control subjects in the National Brain Databank maintained by the Harvard Brain Tissue Resource Center. Six genes were differentially expressed in both the peripheral blood and postmortem DLPFC from two entirely different samples of subjects. Such results are promising, as this constitutes a double replication because we obtained the same finding in two independent samples in which we assessed two different tissue compartments. These results suggest that peripheral blood cell gene expression patterns may overlap and reflect aspects of gene expression in the brain. We carried out a study that included schizophrenic and bipolar patients and controls to determine the sensitivity and specificity of mRNA expression profiles in peripheral blood samples [60]. Our results indicated that blood cell-derived mRNA profiles can differentiate schizophrenia and bipolar disorder patients and controls with excellent sensitivity and specificity. The data supported our hypothesis that this technique may allow for identification of differentially expressed genes that are involved in the underlying pathology and pathophysiology of schizophrenia. Examining peripheral blood cell gene expression patterns is strongly supported by our findings that such techniques accurately reflect aspects of gene expression in the brain. This work, therefore, demonstrates the vast potential utility of bloodbased mRNA profiling as an objective approach to candidate risk-gene identification, which may be particularly advantageous for the study of schizophrenia because brain tissue biopsies are not obtainable from human subjects. Although potential limitations of 626

blood-based mRNA profiling of schizophrenia have been noted, our use of state-of-the-art data analytic and bioinformatics approaches offers several innovations to overcome these restrictions. It is also important to note the possibility that medication effects account for the schizophrenia–control differences in gene expression. More details on the discussion surrounding these potential problems and confounding factors have been published elsewhere [68, 69].

33.5 Future directions For over 35 years, our research programme has focused on understanding the nature, extent and aetiology of schizophrenia. The first phase of this research involved an exploration of the nosology of schizophrenia, its familiality, its outcomes and its forms of expression (e.g. its subtypes). The second phase focused on the heterogeneity of the disorder, including the nature of gender differences, neuropsychological cognitive impairments, range of treatment responses and effects of adverse environmental events. The third phase expanded our horizon by focusing on brain-based abnormalities in schizophrenia, and on the nature of the liability to develop the disorder. We are now well into our fourth phase of our work on prevention, which focuses further on the nature of the liability to schizophrenia, and in so doing, on the clinical implications of our research. The fifth stage of our research involves future perspectives on research in schizophrenia, including areas such as stem cell research as a method of studying pathophysiology as well as the study of microRNA and other regulators. Furthermore, studies of environmental risk factors interacting with genotypes will become more important when genetic risk factors can be illuminated in the future. A new emerging field in epidemiology would include molecular epidemiology and pathophysiology. As we identify the nature of the liability to schizophrenia with more accuracy, we can focus our efforts in clinical intervention at earlier times. Increasing attention is now being directed towards the prodromal phase. A recent study from the North American Prodromal Longitudinal Study (NAPLS) reported, for instance, that 35% of 291

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subjects meeting Structured Interview for Prodromal Syndromes (SIPS) criteria converted to psychosis within 2.5 years [70]. In this study the patients were assessed by structural interviews for prodromes meeting the criteria of prodrome syndrome, which have shown both reliability [71] and validity [72]. The same group also showed in a naturalistic study that administration of atypical antipsychotic medication, but not antidepressant medication, during the prodromal period resulted in less severe positive, negative, disorganised and general psychiatric symptoms [73]. These findings show that individuals at high risk for psychosis can be identified with some accuracy, at least shortly before the onset of psychosis, and interventions at that point can attenuate the severity of their symptoms. These results encourage the view that such individuals can be identified at even earlier stages of illness. Similar studies elsewhere also confirmed these findings [74–76]. Promising findings from early intervention raises the possibility that at some time, the trajectory to schizophrenia may be altered in ways that mitigate or even prevent the expression of the illness. Our continued ability to make progress in this direction will depend upon multidisciplinary explorations and collaborations. One of the key components of these efforts will include a greater understanding of how genes interact with their environment to produce liability for, and from protection from, major mental illness. In the near future, progress towards the ultimate goal of prevention is likely to be characterised by progress in interim goals that include the following. (i) Identifying gene expression patterns of first degree relatives. By analysing the gene expression patterns of ‘normal’ relatives of schizophrenia patients, we can circumvent the problems associated with the effects of the medications and identify areas of convergence and divergence with their affected relatives. (ii) Applying bioinformatics methodologies. We need to incorporate the vast amounts of information being generated in the fields of bioinformatics and genomics, especially proteomics and the new fledgling field of metabolomics, to complement our genetic epidemiology studies. (iii) Focusing on early intervention. Increased genetic and epidemiologic information will allow us to move ever closer to our ultimate goal of preventing schizophrenia.

33.6 Clinical implications There is considerable evidence that the biological relatives of schizophrenic patients are at increased risk for schizophrenia and related disorders, but molecular genetic studies have been slow in identifying susceptibility genes that contribute a large proportion of vulnerability. If we are to make progress in identifying clinically relevant genetic influences, we will need to derive informative measures of the relevant clinical phenomena. We propose that the vulnerability to schizophrenia results from a combination of genes and adverse biological consequences of environmental events (e.g. pregnancy or delivery problems such as maternal diabetes or eclampsia) in most cases, rather than from a major gene. Because schizophrenia is at least in part an outcome of a multifactorial, polygenic process in which multiple vulnerability factors of small or moderate effect contribute to the overall risk of developing psychosis, multiple phenotypic measures or combinations of measures should provide more predictive power than individual ones. Neuropsychological functioning is among the most promising approaches for identifying clinically useful measures, but it is essential to explore multidimensional approaches that include (among others) brain function, psychophysiological function, social function, functional capacity and clinical function, as well. One of the most important avenues for future research will involve attempts to relate these measures to the responsible genes. Eventually, identification of endophenotypes or intermediate subclinical phenotypes and their responsible genes will produce treatment targets for early interventions in schizophrenia and schizophrenia-related conditions.

Acknowledgements This work is based on the chapter: Toward prevention of schizophrenia: early detection and intervention. In: Tsuang MT, Lyons MJ, Stone WS, eds. Recognition and Prevention of Major Mental and Substance Abuse Disorders (American Psychopathological Association Series), American Psychiatric Publishing, Inc., Arlington, VA, 2007, pp. 213–237. 627

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We would like to acknowledge and thank Stephen J Glatt PhD, Assistant Professor, Department of Psychiatry and Behavioral Sciences, Associate Director, Medical Genetics Research Center (MGRC) SUNY Upstate Medical University, for his critical reading and editing of the manuscript.

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[63] Segman, R.H., Shefi, N., Goltser-Dubner, T. et al. (2005) Peripheral blood mononuclear cell gene expression profiles identify emergent post-traumatic stress disorder among trauma survivors. Mol. Psychiatry, 10 (5), 500–513. [64] Vawter, M.P., Ferran, E., Galke, B. et al. (2004) Microarray screening of lymphocyte gene expression differences in a multiplex schizophrenia pedigree. Schizophr. Res., 67 (1), 41–52. [65] Maas, K., Chen, H., Shyr, Y. et al. (2005) Shared gene expression profiles in individuals with autoimmune disease and unaffected first-degree relatives of individuals with autoimmune disease. Hum. Mol. Genet., 14 (10), 1305–1314. [66] Aune, T.M., Parker, J.S., Maas, K. et al. (2004) Co-localization of differentially expressed genes and shared susceptibility loci in human autoimmunity. Genet. Epidemiol., 27 (2), 162–172. [67] Glatt, S.J., Everall, I.P., Kremen, W.S. et al. (2005) Comparative gene expression analysis of blood and brain provides concurrent validation of SELENBP1 up-regulation in schizophrenia. Proc. Natl. Acad. Sci. U.S.A., 102, 15533–15538. [68] Chana, G., Glatt, S.J., Everall, I.P. and Tsuang, M.T. (2008) Blood and gene expression in major psychiatric disorders: a search for biomarkers, in Biomarkers for Psychiatric Disorders (ed. C.W. Turck), Springer Science + Business Media, LLC, New York, pp. 1–21. [69] Chana, G., Kwok, J., Glatt, S.J., Everall, I.P. and Tsuang, M.T. (2009) Gene expression changes and potential impact of phenotypes in major psychiatric disorders, in The Handbook of Neuropsychiatric, Biomarkers, Endophenotypes and Genes, Molecular Genetic and Genomic Markers,Vol. IV (ed. M.S. Ritsner), Springer Science +Business Media, LLC, New York, pp. 77–91. [70] Cannon, T.D., Cadenhead, K., Cornblatt, B. et al. (2008) Prediction of psychosis in youth at high clinical risk: a multisite longitudinal study in North America. Arch. Gen. Psychiatry, 65, 28–37. [71] Miller, T.J., McGlashan, T.H., Rosen, J.L. et al. (2003) Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: predictive validity, interrater reliability, and training to reliability. Schizophr. Bull., 29, 703–715. [72] Woods, S.W., Addington, J., Cadenhead, K.S. et al. (2009) Validity of the prodromal risk syndrome for first psychosis: findings from the north american prodrome longitudinal study. Schizophr. Bull., 35 (5), 894–908. [73] Walker, E.F., Cornblatt, B., Addington, J. et al. (2009) The relation of antipsychotic and antidepressant medication with baseline symptoms and symptom progression: a naturalistic study of the North American

EARLY DETECTION AND INTERVENTION AS APPROACHES FOR PREVENTING SCHIZOPHRENIA Prodrome Longitudinal Sample. Schizophr. Res., 115, 50–57. [74] Yung, A.R., Phillips, L.J., Yuen, H.P. and McGorry, P.D. (2004) Risk factors for psychosis in an ultra high-risk group: psychopathology and clinical features. Schizophr. Res., 67, 131–142. [75] Yung, A.R., Stanford, C., Cosgrave, E. et al. (2006) Testing the Ultra High Risk (prodromal) criteria for

the prediction of psychosis in a clinical sample of young people. Schizophr. Res., 84, 57–66. [76] Riecher-Rossler, A., Gschwandtner, U., Aston, J. et al. (2007) The Basel early-detection-of- psychosis (FEPSY)-study – design and preliminary results. Acta Psychiatr. Scand., 115, 114–125.

631

Index

active gene–environment correlation (GEr) 57 adjacent category logit 27 adolescents see also depression and anxiety in children and adolescents major depression 297 adoptee-as-proband study design 36 adoption studies 36–7 adverse effects of medication 159–60 affected relative pair method 39–40 age attention deficit hyperactivity disorder (ADHD) 452–3 depression and anxiety in children and adolescents 438–9 dysthymia 303 eating disorders 352–3 major depression 296–7 panic disorder 314 schizophrenia 268–9 Aging, Demographics and Memory Study (ADAMS) 541 agoraphobia definition 316 rates 316 relationship with other anxiety disorders 317 risk factors 316–17 alcohol 361, 374–5 abuse in Latin American populations in USA 600–1 bipolar disorder (BPD) 333, 334 chronic disease 366–7 comorbidity with other psychiatric disorders 371–4

consumption 361–2 course of disorders 368–71 diagnostic classification of use disorders 367–8 fetal exposure and ADHD 457 genes, culture and health 175–6 injuries 365–6 mortality and morbidity 365 attributable deaths 369–70 prevalence of drinking 362–5 alcohol dependence syndrome (ADS) 367–8 alcohol use disorders (AUDs) 368 alpha coefficient 75, 76, 77 analysis of variance (ANOVA) 15 one-way 79–80 two-way 80–1 anorexia nervosa (AN) see eating disorders antisocial personality disorder (APD) 402, 410–11 anxiety disorders 311–13 see also depression and anxiety in children and adolescents aftermath of mass disasters 320–1 Hurricane Katrina 321 limitation of epidemiologic studies 322 September 11 terrorist attacks 321 tsunami in southern Thailand 321–2 agoraphobia definition 316 rates 316 relationship with other anxiety disorders 317 risk factors 316–17 epidemiological community surveys 312 future developments 323 generalised anxiety disorder (GAD) definition 318

Textbook in Psychiatric Epidemiology, Third Edition. Edited by Ming T. Tsuang, Mauricio Tohen and Peter B. Jones © 2011 John Wiley & Sons, Ltd. ISBN: 978-0-470-69467-1

633

INDEX

anxiety disorders (continued) rates 318–19 risk factors 319 obsessive–compulsive disorder (OCD) definition 319–20 rates 320 risk factors 320 panic disorder comorbid psychiatric disorder 315–16 definition 313 rates 313–14 risk factors 314–15 social phobia definition 317 rates 317–18 risk factors 318 ascertainment bias 246 Asperger’s syndrome 469, 470–1 association studies 41–4 attempted suicide see suicide and attempted suicide attention deficit hyperactivity disorder (ADHD) 333, 449–50, 460 comorbid psychiatric disorders 451–2 demographic risk factors age 452–3 ethnicity 453 gender 452 socioeconomic status 453 environmental risk factors diet 457 fetal exposure to alcohol 457 fetal exposure to maternal smoking 458 pregnancy and delivery problems 457 psychosocial adversity 458–9 television watching 459–60 toxin exposure 457 future directions 460–1 genetic risk factors candidate gene studies 454–6 dopamine D4 receptor 454 dopamine D5 receptor 454–5 dopamine transporter gene (DAT1, SLC6A3) 455–6 family, twin and adoption studies 453 gene linkage studies 454, 455 genome-wide association studies 456–7 molecular genetics 453–6 serotonin 1B receptor 456 634

serotonin transporter (HTT, SLC6A4) 456 synaptasomal associated protein of 25 kD (SNAP25) 456 pharmacoeconomics 451 prevalence 450–1 attrition definition 192–3 autism spectrum disorder (ASD) 469, 478 associations and causal factors 477 behavioural and educational interventions 474–5 definition and diagnosis 469–72 future directions 477–8 genetic factors 476 natural history 472–3 prevalence 473 prevalence 475 public health impact 476–7 risk factors 473–6 avoidant personality disorder 416 Beck Depression Inventory (BDI) 205–6, 103–4 Berkson’s Fallacy 89 bias 4–6 controlling 245 accounting for participants 247 blinding (masking) of treatment allocation 246 empirical evidence of bias 247–8 maximising follow-up 247 randomisation with allocation concealment 245–6 binge eating disorder (BED) see eating disorders binomial probability distribution 10–11 bipolar disorder (BPD) 329, 338 comorbidity patterns mental disorders 333–4 non-psychiatric medical disorders 334 epidemiology adults 329–31 youth 331–3 future directions 336–7 comorbidity with mental and physical disorders 338 diagnostic spectrum 337 research integration for adults and children 337–8 risk factors 334–5 family history/genetics 335–6 proband studies 336

INDEX

birth complications schizophrenia 276–7 blinding of treatment allocation 246 blood alcohol content (BAC) 366 borderline personality disorder (BPD) 411 bulimia nervosa (BN) see eating disorders buspirone 155 Calgary Depression Scale (CDS) 103–4 Camberwell Assessment of Need (CAN) inventory 149 carbamazepine 155 career of psychopathology 190 case only studies 63–4 case parent trio studies 64 case–control association study 42 case–control studies 5, 11, 56, 63 genetic and environmental bases of psychiatric disorders 89 causal gene–environment correlation (GEr) 57 causal inference 6–7 causation 2–3 alternative explanations 3–4 bias 4–6 confounding 4 reverse causation 6 sampling variation and chance 4 examples 174 alcohol 175–6 birthweight 176–7 neighbourhood and ethnic density 174–5 parental age 174 schizophrenia 176 violence and mental illness 177–8 historical overview 167–9 levels of causation 169 combining levels 172 contextual level 170–2 individual level 169–70 over (life)time 172–4 censoring definition 193 Center for Epidemiological Studies Depression Scale (CES-D) 104–6, 203 chance 4 children see depression and anxiety in children and adolescents Cholera in England 1848–1849 168

clinical antipsychotic trials of intervention effectiveness (CATIE) 257 Clinical Interview Schedule (CIS) 209–10 clozapine 155 cluster C types 417–18 clustered categorical data 27 cocaine 601–2 Cochran–Mantel–Haentzel test 16–17 cognitive impairment, mild 541–3 incidence 543–4 cognitive impairment no dementia (CIND) 543 Cohen’s kappa 82 coherence criterion 110 cohort studies 5, 56, 62 Collaborative Longitudinal Personality Disorders Study (CLPS) 406 comorbidity axes 9 definition 191–2 National Comorbidity Survey (NCS) findings 224 complementary log–log function 19 Composite International Diagnostic Interview (CIDI) 204, 208, 209, 222 concurrent validity 103 conditional logistic regression 25–6 conditional tables 16 confidence intervals 21 confounding 4 dealing with 244 gene–environment interplay 65–6 population stratification 66 conscript registers 122 consistency of association 110 Consolidated Standards of Reporting Trials (CONSORT) checklist 248, 250–1 construct validity 106 psychiatric diagnosis 106–7 content validity 100 assessment 100–2 contingency tables 2 × 2 analysis 11–13 odds ratio (OR) 13–15 2 × 2 table sets analysis 16–17 matched pair study design 17–18 R × C analysis 15–16 continuation odds 27 copy number variants (CNVs) 59, 65 Cornell Selectee Index (CSI) 203 635

INDEX

cost utility of the latest antipsychotic drugs in schizophrenia study (CUtLASS 1) 257–8 criterion validity 102–6 cross-fostering study design 36 crossing interactions 65 cross-product ratio 13 cross-sectional assessments 103 cumulative logit 27 dementia incidence 543–4 outcomes 548–9 prevalence 541 risk factors 545–6 dependent personality disorder 416 depression and anxiety in children and adolescents 435, 443 correlates and risk factors 438, 440 age and sex 438–9 combinations of risk factors 442 early developmental factors 440 parental and familial factors 441–2 physical conditions and illness 440–1 social class, race and ethnicity 439–40 stressful life events 441 magnitude anxiety disorders 436–7 mood disorders 435–6 service patterns and impacts 442–3 depression with atypical features 294 depression with psychotic features 294 depressive disorders 289–90, 304 as outcome of other conditions 191 dysthymia age 303 comorbidity 304 definition 302 gender 303 marital status 304 race/ethnicity 303 rates 302 risk factors 302–4 urban–rural residence 304 geriatric distribution 539–40 incidence 540–1 outcomes 547–8 risk factors 544–5 major depression 301–2 636

adolescence 297 age 296–7 definition 290 disablement 295 family history/genetics 300–1 gender 295–6 geriatric 297 incidence 293–4 marital status 299–300 measurement 304 prevalence 291–3 psychiatric comorbidity 300 race and ethnicity 297–8 risk factors 295–301 secular trends 301 socioeconomic status 298–9 subtypes 294–5 type of rates 290–1 urban–rural residence 299 risk relative and attributable 187 symptoms onset 186–7 prodrome 187 women of reproductive age 486–7 descriptive epidemiology 56 developmental abnormalities schizophrenia 278 Diagnostic Interview for Genetic Studies (DIGS) 34 Diagnostic Interview Schedule (DIS) 76, 100, 203–4, 208, 221 diet ADHD 457 diethylstilbestrol (DES) 173 differential effects 111 disability adjust life years (DALYs) 365 disasters see mass disasters and anxiety disorders discriminant validity 103, 107 dopamine D4 receptor (DRD4) ADHD 454 dopamine D5 receptor (DRD5) ADHD 454–5 dopamine transporter gene (DAT1, SLC6A3) AHDH 455–6 dose–response relationship 6 double-bind trials 246 Down’s syndrome 174 drug use disorders 381, 394

INDEX

comorbidities with psychiatric and medical conditions 388–91 consumption of drugs 381–4 definitions 384 future opportunities 391–3 genetic epidemiology 391 global rates 387–8 rates of abuse and dependence 384–7 dynamic modelling 178 dysthymia see depressive disorders eating disorder not otherwise specified (EDNOS) see eating disorders eating disorders 343 case definition 343–5 comorbidity 351–2 future directions 355–6 incidence studies 351 major prevalence studies 345–8 Australia and New Zealand 350 Europe 350 North America 000 summary 351 mortality 352 risk factors 241 age 352–3 genetic factors 354–5 race/ethnicity 354 sex 352 sociocultural factors 354 sociodemographic factors 352–4 socioeconomic status 352–3 urbanisation 352 effectiveness trials see practical trials efficacy of trials 251–3 endophenotypes 108–9 enthusiasm hypothesis 140 environmental risk factors 31 environmental exposure measurement of 59 Epidemiologic Catchment Area (ECA) 203, 221–2 epidemiology definition 1–2 epigenetic mechanisms 67 epistasis 33 equal-environments assumption 35–6 estimated expected count 14 ethnicity

attention deficit hyperactivity disorder (ADHD) 453 definition 580–1 depression and anxiety in children and adolescents 439–40 dysthymia 303 eating disorders 354 major depression 297–8 panic disorder 315 schizophrenia 269–71 Europe instruments for epidemiological research 208–10 instruments for psychiatric services and primary care 206–8 European first-episode schizophrenia trials (EUFEST) 258–9 European migrants 579 adverse social experiences 586 area level social processes 586–7 criticisms of social hypotheses and research 589 individual level social processes 587–9 biological considerations genetic influences 585 neurodevelopmental insults 585–6 cannabis use 586 defining constructs 579 ethnicity 580–1 migration 579–80 high psychosis rates 581 methodological limitations 581–2 misdiagnosis 582–3 modern studies 583–4 high psychosis rates, possible explanations selective migration 584–5 implications 590 policy 591 research 590–1 mechanisms 589–90 European Psychiatric Care Assessment Team (EPCAT) 143 European Service Mapping Schedule (ESMS) 143–5 evocative gene–environment correlation (GEr) 57 exact logistic regression 26–7 experimental epidemiology 243 classification of RCTs 251–4 clinical antipsychotic trials of intervention effectiveness (CATIE) 257 637

INDEX

experimental epidemiology (continued) cost utility of the latest antipsychotic drugs in schizophrenia study (CUtLASS 1) 257–8 effectiveness trials in schizophrenia 255 European first-episode schizophrenia trials (EUFEST) 258–9 non-randomised evidence 243–5 olanzapine 255–6 methodology 257 prioritising control 248–51 random error (noise) 248, 249 result reporting 248 size and cost of trials 259 systematic error (bias) 245 accounting for participants 247 blinding (masking) of treatment allocation 246 empirical evidence of bias 247–8 maximising follow-up 247 randomisation with allocation concealment 245–6 translation of experimental design to real world 245 extended phenotype 55 external validity 112 familial aggregation 56 familial disorders 32 assessment among relatives 33–4 caveats about study interpretation 34–5 family study vs. family history methods 34 proband selection 32–3 family studies 56, 64 family-based association tests (FBATs) 44 Farr, William 168 fetal exposure to alcohol ADHD 457 fetal exposure to maternal smoking ADHD 458 first incidence 187–8 Fisher’s exact test 14 Five Factor Model (FFM) of personality 421 fluoxetine 155 functioning definition 192 gender see also sex differences dysthymia 303 638

major depression 295–6 panic disorder 314 schizophrenia 268–9 gene location techniques 39 affected relative pair method 39–40 linkage analysis 39 lod score method 40–1 statistical methods 39 gene–environment interplay 53–4 epigenetic mechanisms 67 forms 55 gene–environment correlation (GEr) 55–8 gene–environment interaction (G×E) 58 measurement scale 61–2 models 60–1 study designs 62–5 validity 65–7 measurements environmental exposure 59 genetic variants 58–9 pathological phenotypes 60 gene–environment wide interaction studies (GEWISs) 59, 64–5 general fertility rate (GFR) 488 General Health Questionnaire (GHQ) 206–7 Generalisability Theory 76 generalisations 112 generalised anxiety disorder (GAD) definition 318 rates 318–19 risk factors 319 genetic and environmental bases of psychiatric disorders 87–9 combined influences 92–3 mediation 93 moderation 92–3 proxies 93–4 risk factors, independent 93 risk factors, overlapping 94 extensions multiple risk factors at multiple time points 95 multiple risk factors at two time points 95 one risk factor at two time points 94–5 further considerations 95–6 methodological barriers case–control studies 89 correlation vs. interaction 92

INDEX

odds ratio 90–2 statistical significance 89–90 genetic epidemiology 1, 31, 53–4 process 54–5 psychiatric genetics and psychiatric epidemiology 44–5 research 31–2 familial disorders 32–5 gene location 39–41 gene/environment relative contributions 35–7 risk-conferring variants 41–4 transmission mode 37–9 genetic exposure 56 genetic variants measurement of 58–9 genome-wide association studies (GWASs) 55 genomic control approaches 66 geriatric depression 297 geriatric psychiatry 534 aetiological studies 544 case distribution 536–9 depressive disorder 539–40 incidence of dementia 543–4 incidence of depression 540–1 mild cognitive impairment 541–3 prevalence of dementia 541 subsyndromal depression 540 case identification 535–6 comorbidity of depression and dementia 546 health care service use 550 historical trends in epidemiology 549–50 suicide 550 outcomes 547 dementia 548–9 depressive disorders 547–8 risk factors dementia 545–6 depression 544–5 goodness-of-fit 25 haplotypes 59 Health Opinion Survey (HOS) 203 health services research (HSR) 133–4 heroin 603 history effects 111 histrionic personality disorder 411 Hopkins Symptom Checklist (HSCL) 205

hyperkinetic disorder (HKD) 449 differentiation from ADHD 450 hypothesis testing 21 identical by descent (IBD) 64 immune function schizophrenia 279 incidence definition 187 infections schizophrenia 278–9 inference for a single proportion 10–11 influenza infection schizophrenia 563 intensive case management (ICM) 149 interaction between predictor variables 24–5 interclass correlation 78–9 internal consistency 75 internal validity 110 inverse normal function 19 IQ birthweight 176–7 item response theory 83 Japan, epidemiological studies 559–60, 572 affective disorders 566 pre- and postnatal depression 568–9 prevalence of depressive disorder 567–8 autism spectrum disorders 569 factors accounting for increasing prevalence 571–2 prevalence 569–71 routine health checkup system 569 schizophrenia factors affecting course and treatment 565–6 influenza infection 563 methamphetamine (MAP) psychosis 563–4 obstetric complications 562–3 prevalence and incidence 560–1 radiation 565 seasonality of birth 561–2 Jeffrey’s interval 11 Koch, Robert 168 Latin American populations in USA 595, 611 alcohol and drug use 600 alcohol abuse 600–1 639

INDEX

Latin American populations in USA (continued) cocaine 601–2 drug use 601 heroin 603 marihuana 602–3 methamphetamines (MAPs) 603–4 countries of origin 597–9 definition of migration 595–7 dependence and treatment rates 604 drugs and HIV/AIDS 601 emigration process 606–8 migrant living conditions 599–600 returning migrants 611 substance abuse and service access 608–10 liability 38–9, 54 lifetime comorbidity 191 likelihood ratio test (LRT) 14, 15–16, 21 linkage analysis 39, 54 linkage disequilibrium (LD) mapping 42, 54–5 lod score method 40–1 logistic function 19 logistic regression 18–20 conditional 25–6 confidence intervals 21 exact 26–7 goodness-of-fit 25 hypothesis testing 21 interactions 24–5 interpretation of coefficients 20–2 multiple logistic regression 22–3 polytomous 27–8 predictors with more than 2 levels 23–4 logit function 19 major depressive disorder (MDD) 289 see also depressive disorders validity of construct 99–100 major depressive episodes (MDEs) 289 marihuana 602–3 marital status dysthymia 304 major depression 299–300 schizophrenia 269 masking of treatment allocation 246 mass disasters and anxiety disorders 320–1 Hurricane Katrina 321 limitation of epidemiologic studies 322 640

September 11 terrorist attacks 321 tsunami in southern Thailand 321–2 matched pair study design 17–18 matching fallacy 33 McNemar’s test for matched pairs 17–18 measles, mumps and rubella (MMR) vaccine 472, 477 Japan 571–2 mediators 87–9 Medical Outcomes Study (MOS) 206 Mental Health Atlas (WHO) 142–3 mental health services research 133–4, 152 definition 134–5 framework 135–6 key concepts 137 appropriateness of care 138–9 efficacy, effectiveness and efficiency 137–8 equity 140–1 factors associated with access to health care 140 need 137 small area variations (SAV) 139–40 want, demand and supply 137 studies 141 administrative data 141–7 primary data collection 147–50 qualitative 150–2 Mental Status Schedule (MSS) 206 methamphetamine (MAP) psychosis schizophrenia 563–4 methamphetamines (MAPs) abuse in Latin American populations in USA 603–4 micro shared environments 59 minor physical abnormalities (MPAs) schizophrenia 277–8 moderators 87–9 modernisation schizophrenia 271 monozygotic (MZ) twins 33 gene/environment studies 35–6 mortality 192 mortality effects 112 multidimensional contingency table 16 multifactorial polygenic (MFP) model 38, 54 multigenerational pedigree studies 64 multinomial regression models 27–8 multiple logistic regression 22–3

INDEX

narcissistic personality disorder 416 National Comorbidity Survey (NCS) 221, 236 adolescent supplement (NCS-A) design and rationale 233–4 findings 234 baseline age at onset 224 background and design 221–2 comorbidity 224 findings 222–7 lifetime and prevalence of DSM disorders 223–4 primary and secondary disorders 225 primary prevention of secondary disorders 227 pure and comorbid lifetime disorders 225 societal costs of mental disorders 225–6 treatment 226–7 follow-up survey (NCS-2) design and rationale 227–8 findings 228–9 persistence of disorders and syndromes 228 primary and secondary disorders 228 progression of disorders 228–9 risk factors 228 replication (NCS-R) 204, 221 design and rationale 229–31 findings 231 prevalence trends 231 treatment trends 231, 232 natural history of psychopathology 183, 195 course remission 188–91 methodology 192 attrition 192–3 censoring 193 prevalence bias 193–4 recall 194–5 statistical techniques 195 onset 183–6 population measures 187–8 prodromes and precursors 186–7 outcome 191 comorbidity 191–2 functioning 192 mortality 192 Neuropsychiatric Screening Adjunct (NSA) 203 noise (random error) 248, 249 nominal variables 9

nomologic network 106 non-affective remitting psychosis (NARP) 486 non-causal gene–environment correlation (GEr) 57 non-randomised evidence, limitations of 243–5 Nordic Countries’ psychiatric epidemiology registers 117 cause of death 121 Denmark 122–3 epidemiological benefits 127 findings 126 Finland 123–4 hospital discharge 120 medication data 120–1 methodological and administrative challenges 126–7 Norway 124–5 other registers 121–2 psychiatric research 118–20 Sweden 125–6 North America instruments for epidemiological research 202–5 instruments for psychiatric services and primary care 205–6 not otherwise specified (NOS) diagnoses 107 null value 13 number needed to take (NNT) 90 obsessive–compulsive disorder (OCD) definition 319–20 rates 320 risk factors 320 obsessive–compulsive personality disorder 416–19 obstetric complications (OCs) 487, 490, 492 Japan schizophrenia 562–3 risk of adult onset mental disorder in offspring 493–4 influence of maternal smoking 496 measurement 494–5 mechanisms of risk 495–6 origin of risks 494 specificity of risk 495 odds ratio (OR) 13–15, 20–2 not clinically interpretable 90–2 olanzapine cost-effectiveness trial 255–6 methodology 257 oligogenetic models 54 641

INDEX

omega coefficient 75 oppositional defiant disorder (ODD) 333 ordinal variables 9 outcome definition 191 overmatching 33 panic disorder comorbid psychiatric disorder 315–16 definition 313 rates 313–14 risk factors age 314 gender 314 race/ethnicity 315 parallelism 61 paranoid personality disorder 407 parental age schizophrenia 276 parent-as-proband study design 36 partial tables 16 passive gene–environment correlation (GEr) 57 pathological phenotypes measurement of 60 Patient Health Questionnaire (PHQ-S) 206 patient practice variations hypothesis 140 Pearson chi-square test 14, 15–16 penetrance 38 performance bias 246 Personal Health Questionnaire (PHQ-G) 207 personality disorder not otherwise specified (PDNOS) 420 personality disorders (PDs) 401–2 conceptual issues 419 course, prognosis and developmental issues 404–6 future directions 427–8 methodological issues collecting diagnostic information 423–4 comorbidity and diagnostic overlap 424–7 diagnostic 422–3 diagnostic agreement 424 physical health 426–7 models 419–20 distinguishing Axis I from Axis II 421–2 issues and challenges for DSM-V 420–1 specific prevalence 407 antisocial personality disorder (APD) 410–11 avoidant personality disorder 416 642

borderline personality disorder (BPD) 411 cluster A types 408–10 cluster B types 412–5 cluster C types 417–18 dependent personality disorder 416 histrionic personality disorder 411 narcissistic personality disorder 416 obsessive–compulsive personality disorder 416–19 paranoid personality disorder 407 schizoid personality disorder 407 schizotypal personality disorder 410 substantive findings 402 true prevalence post-DSM-III 402–4, 405–6 true prevalence pre-DSM-III 402 treated prevalence 406–7 personality traits 401 pervasive developmental disorder, not otherwise specified (PDD-NOS) 469 pharmacoepidemiology 155–6, 162–3 data sources 157–8 governmental administrative 158 health maintenance organizations 158 large-scale surveys 158–9 practice-based networks 159 overview historical perspective 156–7 recent studies 159 adverse effects and unanticipated benefits 159–60 descriptive analyses 161 optimising use 161–2 pharmacoeconomic analyses 161 phenylketonuria (PKU) 420 polytomous logistic regression 27–8 population stratification confounding 66 population-attributable fraction (PAF) 122 Positive and Negative Syndrome Scale (PANSS) 566 positive predictive value 105 post-dictive validity 103 postnatal depression 502–3 post-traumatic stress disorder (PTSD) 320–1 Hurricane Katrina 321 September 11 terrorist attacks 321 tsunami in southern Thailand 321–2 poverty see socioeconomic status practical (pragmatic; effectiveness) trials 253 schizophrenia 255

INDEX

predictive validity 103 pregnancy and delivery problems ADHD 457 pregnancy mental illness in mother fetal exposure to behavioural or modifiable risk factors 488–90 neuropathic outcomes for children 491–2 parenting outcomes 492–3 physical outcomes for children 491 pregnancy outcomes 490–1 schizophrenia 276–7 prescription databases 121 Present State Examination (PSE) 207–8 prevalence bias 193–4 prevalence of DSM disorders National Comorbidity Survey (NCS) 223–4 Primary Care Evaluation of Mental Disorder (PRIME-MD) 206 proband selection 32–3 probit function 19 prodrome definition 186 product moment correlation 82–3 Psychiatric Epidemiologic Research Instrument (PERI) 203 psychiatric epidemiology 44–5 definition 2 future directions 7, 167, 178–9 psychiatric genetics 44–5 Psychiatric Status Schedule (PSS) 206 psychiatric syndrome 210 quality adjusted life year (QALY) 161 quasi-experimental studies 110 race depression and anxiety in children and adolescents 439–40 dysthymia 303 eating disorders 354 major depression 297–8 panic disorder 315 radiation schizophrenia 565 random error (noise) 248, 249 randomised controlled trials (RCTs) 1, 3, 243, 244 advantages and disadvantages 245

classification 251–4 clinical antipsychotic trials of intervention effectiveness (CATIE) 257 composition 4 cost utility of the latest antipsychotic drugs in schizophrenia study (CUtLASS 1) 257–8 effectiveness trials in schizophrenia 255 European first-episode schizophrenia trials (EUFEST) 258–9 olanzapine 255–6 methodology 257 prioritising control 248–51 random error (noise) 248, 249 result reporting 248 size and cost of trials 259 systematic error (bias) 245 accounting for participants 247 blinding (masking) of treatment allocation 246 empirical evidence of bias 247–8 maximising follow-up 247 randomisation with allocation concealment 245–6 translation of experimental design to real world 245 reactive gene–environment correlation (GEr) 57 recall bias 5–6 definition 194–5 receiver operating characteristic (ROC) analysis 104, 105 recurrence definition 189 regression to the mean 111 relapse definition 189 relative risk (RR) 13 reliability 73, 83 binary judgements 77–8 estimation 74–6 effect of population variance 76 statistical methods 78–9 Cohen’s kappa 82 Cronbach’s alpha 82 item response theory 83 one-way ANOVA 79–80 product moment correlation 82–3 two-way ANOVA 80–1 statistical remedies for low reliability 76–7 reliability coefficient 73–4 643

INDEX

remission 188–91 response bias 75 retrospective assessments 103 reverse causation 6 risk factors 2 individual level 169–70 risk-conferring variants of genes 41–4 risperidone 155 Rutter’s index of ADHD 458 sample size 66–7 sampling variation 4 Sanitarians 168 scales and schedules for psychopathology 199–202, 210–12 European instruments for epidemiological research 208–10 European instruments for psychiatric services and primary care 206–8 North American instruments for epidemiological research 202–5 North American instruments for psychiatric services and primary care 205–6 SCAN scale 189 Schedule for Affective Disorders and Schizophrenia (SADS) 100, 203, 206 schedules for psychopathology see scales and schedules for psychopathology schizoid personality disorder 407 schizophrenia 263, 280 biological risk factors childhood developmental abnormalities 278 genes 272–5 immune function 279 infections 278–9 minor physical abnormalities (MPAs) 277–8 parental age 276 pregnancy and birth complications 276–7 winter birth 275–6 course and outcome in developing and developed countries 176 demographic correlates age and sex 268–9 marital status 269 socioeconomic position 269 early detection and intervention 617–18 clinical implications 627 future directions 626–7 644

gene-based vs. genome-based research 624–6 modelling genetic and phenotypic heterogeneity 618–20 syndrome definition 620–2 effectiveness trials 255 incidence 265, 266 methods case identification 263–4 case-finding 264 natural history course 266–7 mortality 268 onset 265–6 outcome 267–8 prevalence 264–5 prevention 279–80 social risk factors ethnicity 269–71 modernisation 271 substance use 271–2 urban residence 271 women of reproductive age 484–6 schizotypal personality disorder 410 score test 14 segregation analyses 56 selection bias 5, 246 selection effects 112 selective gene–environment correlation (GEr) 57 serotonin 1B receptor ADHD 456 serotonin transporter (HTT, SLC6A4) ADHD 456 sex differences see also women, mothers and their children attention deficit hyperactivity disorder (ADHD) 452 depression and anxiety in children and adolescents 438–9 eating disorders 352 sharing pattern of alleles 64 sib-pair analyses 64 significance, statistical 89–90 simple sequence repeats (SSRs) 59 single nucleotide polymorphisms (SNPs) 59, 64–5 smoking fetal exposure to maternal smoking ADHD 458 risk of obstetric complications 496

INDEX

Snow, John 168 social phobia definition 317 rates 317–18 risk factors 318 social welfare registers 122 socioeconomic status (SES) 25 attention deficit hyperactivity disorder (ADHD) 453 depression and anxiety in children and adolescents 439–40 eating disorders 352–3 major depression 298–9 schizophrenia 269 Spearman–Brown formula 76–7 specificity 110 speed of remission 189 statistical methods 9–10, 29 advanced topics 25 clustered categorical data 27 conditional logistic regression 25–6 exact logistic regression 26–7 multinomial regression models 27–8 analysis of 2 × 2 contingency table sets 16–17 matched pair study design 17–18 analysis of 2 × 2 contingency tables 11–13 odds ratio (OR) 13–15 analysis of R × C contingency tables 15–16 inference for a single proportion 10–11 logistic regression 18–20 confidence intervals 21 goodness-of-fit 25 hypothesis testing 21 interactions 24–5 interpretation of coefficients 20–2 multiple logistic regression 22–3 predictors with more than 2 levels 23–4 statistical regression artefacts 111 statistical significance 89–90 stress maternal antenatal 498–9 Structured Clinical Interview for DSM-III-R (SCID) 222 substance use schizophrenia 271–2 subsyndromal depression 540 sufficient causal pies 58 sufficient-component cause framework 58

suicide and attempted suicide 517, 526 definitions 517–18 older adults 550 perinatal maternal mental illness 503–4 prevalence 518–19 attempted suicide, suicide ideation and planning 519 global prevalence 519 United States –attempted suicide, suicide ideation and planning 520 United States –suicide attempted, suicide ideation and planning 519–21 protective factors 526 risk factors aggression and impulsivity 525 alcohol and substance use disorders 522 anxiety disorders 522 biological risk factors 524 childhood abuse 525 clinical and psychosocial factors 525 genetic risk factors 523–4 hopelessness 525 life events and psychosocial stressors 525–6 mental illness 520–1 mood disorders 521–2 neuropsychiatric disorders and head injury 523 personality disorders 522 psychiatric comorbidity 523 psychotic disorders 522 women of reproductive age 487 symptoms of psychopathology course remission 188–91 onset 183–6 age at onset 224 population measures 187–8 prodromes and precursors 186–7 scales 199–202 European instruments for epidemiological research 208–10 European instruments for psychiatric services and primary care 206–8 North American instruments for epidemiological research 202–5 North American instruments for psychiatric services and primary care 205–6 synaptasomal associated protein of 25 kD (SNAP25) ADHD 456 645

INDEX

syndrome 210 definition 199 television watching ADHD 459–60 testing and instrumentation effects 111 test–retest design 74–5 time sequence 110 total incidence 187–8 toxin exposure ADHD 457 transitions in drug use 383 translational medicine 7 transmission disequilibrium test (TDT) 43–4 transmission mode of genetic disorders 37 mathematical modelling 37–8 model types 38–9 Tridimensional Personality Questionnaire (TPQ) 102 Twenty-Two Item Scale (22IS) 203 uncertainty hypothesis 139–40 urban living dysthymia 304 eating disorders 352 major depression 299 schizophrenia 271 validity 73, 99, 112–13 between variables 110–12 constructs 99–100 construct validity 106 construct validity, application to psychiatric diagnosis 106–7 content validity 100 content validity, assessment of 100–2 criterion validity 102–6 psychiatric disorders 107–10 valproate 155 Wald test 14, 21 wealth see socioeconomic status Wilson confidence interval 10–11 winter birth schizophrenia 275–6 women, mothers and their children 483, 507 epidemiology of mental illness 484

646

depression 486–7 schizophrenia 484–6 suicide 487 fertility and fecundity in mental illness 487–8 gene–environment interactions and offspring outcomes 493 mental illness at conception and during pregnancy fetal exposure to behavioural or modifiable risk factors 488–90 neuropathic outcomes for children 491–2 parenting outcomes 492–3 physical outcomes for children 491 pregnancy outcomes 490–1 obstetric complication risks 493–4 influence of maternal smoking 496 measurement 494–5 mechanisms of risk 495–6 origin of risks 494 specificity of risk 495 parental condition 496 age 496–7 antenatal maternal stress 498–9 nutrition 497–8 perinatal maternal mental illness 500 definition 500 postnatal depression 502–3 postpartum psychosis 500–1 specificity and duration of risk 501–2 suicide 503–4 study design 504–7 World Health Organization (WHO) Alcohol, Drug Abuse, and Mental Health Administration (WHO-ADAMAHA) 208 Mental Health Atlas 142–3 World Mental Health (WMH) Survey Initiative 147–8, 208–9 design and rationale 234–5 findings cross-national correlates 236 diagnostic criteria 234–5 disorder prevalence 234 relative impairments 234 treatment 234 Z statistic 23