Screening for Depression in Clinical Practice: An Evidence-Based Guide

Screening for Depression in Clinical Practice

This material is not intended to be, and should not be considered, a substitute for medical or other professional advice. Treatment for the conditions described in this material is highly dependent on the individual circumstances. While this material is designed to offer accurate information with respect to the subject matter covered and to be current as of the time it was written, research and knowledge about medical and health issues is constantly evolving, and dose schedules for medications are being revised continually, with new side effects recognized and accounted for regularly. Readers must therefore always check the product information and clinical procedures with the most up-to-date published product information and data sheets provided by the manufacturers and the most recent codes of conduct and safety regulation. Oxford University Press and the authors make no representations or warranties to readers, express or implied, as to the accuracy or completeness of this material, including without limitation that they make no representations or warranties as to the accuracy or efficacy of the drug dosages mentioned in the material. The authors and the publishers do not accept, and expressly disclaim, any responsibility for any liability, loss, or risk that may be claimed or incurred as a consequence of the use and/or application of any of the contents of this material.

SCREENING FOR DEPRESSION IN CLINICAL PRACTICE An Evidence-Based Guide

ALEX J. MITCHELL, MRCPsych Consultant and Honorary Senior Lecturer, Department of Liaison Psychiatry, Leicester General Hospital and University of Leicester, UK

JAMES C. COYNE, PhD Professor of Psychology, Department of Psychiatry, University of Pennsylvania Health System

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2010

1 Oxford University Press, Inc., publishes works that further Oxford University’s objective of excellence in research, scholarship, and education. Oxford New York Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi New Delhi Shanghai Taipei Toronto With offices in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam

Copyright  2010 by Oxford University Press, Inc. Published by Oxford University Press, Inc. 198 Madison Avenue, New York, New York 10016 www.oup.com Oxford is a registered trademark of Oxford University Press. 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, without the prior permission of Oxford University Press. Mitchell, Alex J. Screening for depression in clinical practice: an evidence-based guide / by Alex J. Mitchell, James C. Coyne. p. ; cm. Includes bibliographical references and index. ISBN 978-0-19-538019-4 1. Depression, Mental—Diagnosis. 2. Primary care (Medicine) I. Coyne, James C., 1947– II. Title. [DNLM: 1. Depressive Disorder—diagnosis. 2. Primary Health Care. WM 171 C881s 2009] RC537.M5625 2009 616.850 27075—dc22 2009007863 9

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Contents

List of Contributors, xi Preface, xv Wayne Katon

1. Is the Syndrome of Depression a Valid Concept?, 3 Alex J. Mitchell and Mark Zimmerman What is Meant by Depression?, 3 Value and Validity of the Syndrome Concept, 7 Diagnostic Checklists (including DSM and ICD), 10 Unstructured (Unassisted) Clinician Diagnosis, 15 Structured and Semi-Structured Assisted Diagnostic Interviews, 19 Conclusion, 22 References, 24

2. Overview of Depression Scales and Tools, 29 Alex J. Mitchell Background, 29 The Classic Severity Scales (1960–1980), 36 The New Severity Scales (1981–2008), 39 The Future of Screening Scales, 44 References, 51

3. Why Do Clinicians Have Difficulty Detecting Depression?, 57 Alex J. Mitchell Introduction to the Problem of Over- and Under-Detection, 57 Predictors of Detection, 62 v

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Patient and Clinician Influences on Detection, 66 Illness-Related Influences on Detection, 71 Conclusions, 74 References, 75

4. How Can Existing Mood Scales Be Improved? How to Test, Refine, and Improve Existing Scales, 83 Adam B. Smith Introduction, 83 The Rasch Model and Other Item Response Models, 86 Conclusion, 95 References, 96

5. How Do We Know When a Screening Test is Clinically Useful?, 99 Alex J. Mitchell How Do Clinicians Make a Diagnosis?, 99 Scientific Aspects of Diagnostic Accuracy, 103 Clinical Aspects of Diagnostic Accuracy, 105 Testing Screening via Implementation Studies, 109 Conclusions, 111 References, 111

6. Clinical Judgment and the Influence of Screening on Decision Making, 113 Howard N. Garb Introduction, 113 Research on Clinical Judgment, 114 The Limits of Screening, 119 References, 120

7. Implementing Screening as Part of Enhanced Care: Screening Alone is Not Enough, 123 Simon Gilbody and Dan Beck The Case for Screening, 123 Screening and Enhanced Care for Depression, 128 New and Additional Evidence Relating to Enhanced Care, 128 Is Screening a Necessary Intervention to Improve the Quality and Outcome of Care?, 129 To Screen or Not to Screen?, 136 References, 137

CONTENTS

8. Technological Approaches to Screening and Case Finding for Depression, 143 William H. Rogers, Debra Lerner, and David A. Adler Technological Methods of Screening for Depression, 144 Ten Issues When Developing Computerized Screening for Depression, 147 Examples of Implementation of Computerized Screening for Depression, 150 Discussion, 153 Conclusion, 154 References, 154

9. Screening for Depression in Primary Care: Can It Become More Efficient?, 161 Kathryn M. Magruder and Derik E. Yeager Introduction, 161 Epidemiology of Depression in Primary Care, 162 Is Screening for Depression in Primary Care Worthwhile?, 165 Which Screening Tool Should Be Used?, 169 Implementing Screening in Primary Care, 178 What Developments Are on the Horizon?, 183 Conclusions, 185 References, 185

10. Screening for Depression in Medical Settings: Are Specific Scales Useful?, 191 Gordon Parker and Matthew Hyett An Introductory Logic, 191 Depression in the Medically Ill, 192 ‘‘False-Positive’’ Depression Reflecting Confounding by Physical Symptoms Associated with Medical Illness, 193 Screening Measures Used to Assess Depression in the Medically Ill, 194 Discussion, 198 References, 199

11. Screening for Depression in Medical Settings: The Case Against Specific Scales, 203 Fariba Babaei and Alex J. Mitchell Overview of Depression in Physical Disease, 203 Defining Somatic Symptoms, 205

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Diagnostic Accuracy of Somatic Symptoms in Depression, 209 Evidence For and Against Somatic Symptoms when Diagnosing Comorbid Depression, 211 Implications for Screening, 217 References, 236

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Screening for Depression in Neurologic Disorders, 241 Andres M. Kanner Depression in Stroke, 242 Depression in Multiple Sclerosis, 246 Depression in Epilepsy, 249 Depression in Parkinson’s Disease, 255 Conclusions, 258 References, 258

13. Screening for Depression in Cancer Care, 265 Linda E. Carlson, Sheena K. Clifford, Shannon L. Groff, Olga Maciejewski, and Barry D. Bultz Prevalence of Depression in Cancer Care, 265 Screening Methods for Depression, 266 Screening for Depression in Oncology, 267 Implementing Screening Programs in Oncology Settings, 276 Special Issues in Screening Cancer Patients, 292 Summary, Integration, Future Directions, 293 Acknowledgments, 294 References, 295

14. Screening for Depression in Perinatal Settings, 299 Jodi Barton and Philip Boyce Introduction: Perinatal Screening in Context, 299 Why Screen, and What Are We Screening For?, 301 Screening Practices in Perinatal Settings, 303 Screening Guidelines and Recommendations, 304 Evidence-Based Comparison of Screening Methods, 305 Implementation in Practice: Does Screening Make any Real-World Difference?, 310 Service Delivery and Treatment Implications, 311 Summary and Key Recommendations, 313 References, 314

CONTENTS

15. Screening in Cardiovascular Care, 317 Brett D. Thombs and Roy C. Ziegelstein Depression in Cardiovascular Disease, 318 The Prevalence of Depression in Cardiovascular Disease, 319 Screening Instruments for Depression in Cardiovascular Care, 320 Recommendations for Evaluation and Treatment of Patients in Cardiovascular Care, 326 Conclusions, 328 References, 329

16. Screening in Diabetes Care: Detecting and Managing Depression in Diabetes, 335 Norbert Hermanns and Bernhard Kulzer Depression in Diabetes is a Major Health Problem, 337 Screening Tests, 340 Treatment Options, 343 Screening Program, 344 Conclusions for Clinical Practice, 345 References, 346

17. Commentary and Integration: Is it Time to Routinely Screen for Depression in Clinical Practice?, 349 James C. Coyne Integration: Deflating the Puffer Phenomenon and Making the Case Against Screening, 364 References, 366

Appendix, 371 Index, 385

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List of Contributors

David Adler, Professor of Psychiatry and Medicine, Tufts University School of Medicine, and Senior Psychiatrist, Department of Psychiatry and ICRHPS, Tufts Medical Center Fariba Babaei, Specialist Trainee in Psychiatry, Lincolnshire Partnership Trust, Grantham, UK Jodi Barton, Research Co-ordinator, Westmead Perinatal Psychiatry & Clinical Research Unit, Westmead Hospital Dan Beck, Research Fellow, Department of Health Sciences, University of York, UK Philip Boyce, Professor of Psychiatry, Department of Psychological Medicine, University of Sydney, Westmead Hospital Barry D. Bultz, Director, Department of Psychosocial Resources, Tom Baker Cancer Centre, and Head and Adjunct Professor, Division of Psychosocial Oncology, Department of Oncology, Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada Linda E. Carlson, Enbridge Research Chair in Psychosocial Oncology, Associate Professor, Division of Psychosocial Oncology, Department of Oncology, Faculty of Medicine, University of Calgary, and Clinical Psychologist, Tom Baker Cancer Centre, Calgary, Alberta, Canada Sheena K. Clifford, Department of Psychosocial Resources, Tom Baker Cancer Centre, Alberta Cancer Board/Alberta Health Services, Calgary, Alberta, Canada

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LIST OF CONTRIBUTORS

James C. Coyne, Director, Behavioral Oncology Program, Abramson Cancer Center, and Professor of Psychology, Department of Psychiatry, University of Pennsylvania School of Medicine Howard N. Garb, Lackland Air Force Base Simon Gilbody, Professor of Psychological Medicine and Health Services Research, Department of Health Sciences, University of York, UK Shannon L. Groff, Department of Psychosocial Resources, Tom Baker Cancer Centre, Alberta Cancer Board Norbert Hermanns, Head of the Research Institute of the Diabetes Academy Mergentheim Matthew Hyett, Research Assistant, Black Dog Institute, Sydney, Australia Andres M. Kanner, Department of Neurological Sciences, Rush Medical College, Rush Epilepsy Center, Rush University Medical Center, Chicago, IL Wayne Katon, Professor and Vice Chair of Psychiatry and Behavioral Sciences, Director of Division of Health Services and Epidemiology, University of Washington Medical School, Seattle, WA Bernhard Kulzer, Head of the Psychosocial Department of the Diabetes Centre Mergentheim Debra Lerner, Associate Professor of Medicine and Psychiatry, Tufts University School of Medicine (TUSM), and Senior Researcher, ICRHPS, Tufts Medical Center. Olga Maciejewski, Department of Psychosocial Resources, Tom Baker Cancer Centre, Alberta Cancer Board/Alberta Health Services, Calgary, Alberta, Canada Kathryn M. Magruder, Veterans Administration Medical Center, Charleston, SC, and Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC Alex J. Mitchell, Consultant in Liaison Psychiatry, Leicester General Hospital, Leicester, and Honorary Senior Lecturer in Liaison Psychiatry, Department of Cancer & Molecular Medicine, Leicester Royal Infirmary, UK Gordon Parker, Scientia Professor, School of Psychiatry, University of New South Wales, Sydney, Australia, Executive Director, Black Dog Institute

LIST OF CONTRIBUTORS

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William Rogers, Senior Statistician, Institute of Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center Adam B. Smith, Lecturer in Quantitative Methods, Centre for Health and Social Care, Leeds Institute of Health Sciences, University of Leeds, UK. Brett D. Thombs, Department of Psychiatry, McGill University and Jewish General Hospital, Montreal, Quebec Derik E. Yeager, Department of Biometry, Biostatistics, and Epidemiology, Medical University of South Carolina, Charleston, SC Roy C. Ziegelstein, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD Mark Zimmerman, Department of Psychiatry and Human Behavior, Brown University School of Medicine, Rhode Island Hospital, Providence, RI

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Preface

Researchers became interested in screening patients for depression in primary care in the early 1980s because of evidence of poor recognition of depression by primary care physicians and gaps in adequacy of treatment.1 Because of extensive epidemiologic research as well as the development of antidepressant medications that have fewer side effects and evidence-based brief therapies, recognition rates of depression by primary care physicians have improved over the past two decades, with recent studies suggesting that as many as 50% to 65% of patients are accurately diagnosed.2 Most studies also show that greater severity of depression and increased functional impairment are associated with higher rates of recognition.3 A study by Rost and colleagues that examined recognition rates over a 6-month period rather than for just one visit also found higher rates of accurate diagnosis by primary care physicians.4 This latter study is important because primary care physicians often make diagnoses over time as they work up patients over several visits. Studies have also shown that a much higher percentage of patients in primary care are exposed to antidepressant medications compared to two decades ago.5 However, there are many remaining gaps in the quality of care for depression in primary care: only 20% of patients receive the Health Employer Data and Information Set (HEDIS)-recommended three or more visits in the first 90 days after starting an antidepressant and only 40% to 50% remain on medication at 6 months.6 Over the past 20 years (from the tricyclic era to the selective serotonin reuptake inhibitor era), studies consistently report that only 40% of patients started on antidepressants for major depression recover (a greater than 50% decrease in symptoms) by 4 to 6 months.7 Less than 10% of patients with major depression in primary care receive evidence-based psychotherapy.5 There is clearly room for improvement of quality of care in patients with major depression from screening to improved detection, to healthcare models that provide enhanced exposure to evidence-based treatments. xv

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One of the unexpected findings of increased interest by primary care physicians in the detection and treatment of patients with depression is that approximately half of patients started on medication for depression actually meet DSM-IV criteria for minor depression.8 This is important because antidepressant-versus-placebo trials have generally shown high rates of placebo response in patients with minor depression and lack of active drug-versusplacebo differences.9 Screening for depression may actually increase the number of patients with minor depression who are potentially treated because many patients cluster around the DSM-IV diagnostic threshold and, depending on the stressful life events of the past few days, may or may not meet criteria for major depressive disorder. Patients with minor depression or adjustment reactions to stressful life events must be distinguished from those with a history of major depression who have significant residual symptoms necessitating active treatment. For patients who have mild major depression, brief counseling, watchful waiting, and rescreening them for depression 2 to 4 weeks later may allow better recognition of whether the patient needs treatment with medication or psychotherapy. If screening of depression is to be integrated into primary care, healthcare organizations are faced with the decision about which screening tool is optimal. Primary care organizations, the American Psychiatric Association, and many research foundations have recommended the use of the Patient Health Questionnaire (PHQ-9) as the optimal depression screening tool in primary care. The PHQ-9 has the advantage of being able to help measure the severity of depression (0 to 27 is the severity range of this tool) and, at a score of above 10, has high sensitivity and specificity compared to structured psychiatric interviews for the diagnosis of major depression.10 The U.S. Preventive Services Task Force recommended routine depression screening in primary care in systems that have been reorganized to provide effective treatment for depression.11 This reflects the fact that studies that tested depression screening alone showed mild to modest improvement in the quality of depression treatment provided, but generally no effect on depression outcomes.12 What do we know about methods to organize care to improve outcomes of depression? Although screening for depression alone has not been shown to improve outcomes, when screening is paired with an organized system of depression care, multiple studies have shown that depression outcomes can be improved.13 The chapter by Gilbody reviews the recent meta-analysis of an intervention called ‘‘collaborative care.’’ A total of 37 randomized trials that compared collaborative versus usual primary care found that collaborative care was associated with a twofold increase in adherence to antidepressant medication and improvements in depression that lasted 2 to 5 years. 13 The key elements of the most successful collaborative care interventions

PREFACE

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included two core components. The first component incorporates a depression care manager who improves patient education and, with telephone and/ or in-person frequent contacts, tracks depressive symptoms, side effects, and adherence to treatment.14 The care manager facilitates return appointments with the primary care doctor or, in some instances, a mental health specialty referral for patients with persistent symptoms, problematic side effects, or poor adherence.14 The second crucial component is supervision of the case manager by a psychiatrist who recommends changes in medication based on clinical response and side effects. Many recent collaborative care trials also have used psychologists’ skills to teach care managers motivational interviewing techniques and brief, evidence-based psychotherapies such as problem-solving therapy.15 In summary, this excellent book summarizes two decades of research on depression screening and quality-improvement efforts in primary care. We now have state-of-the-art depression screening tools, and research studies have shown that pairing depression screening with evidence-based models that enhance exposure to antidepressant medication and evidence-based psychotherapies can markedly improve depression outcomes for patients with major depression. Wayne Katon

References 1. Zung WW, Magill M, Moore JT, et al. Recognition and treatment of depression in a family medicine practice. J Clin Psychiatry. 1983;44:3–6. 2. Katon WJ, Simon G, Russo J, et al. Quality of depression care in a population-based sample of patients with diabetes and major depression. Med Care. 2004;42:1222–1229. 3. Coyne JC, Schwenk TL, Fechner-Bates S. Nondetection of depression by primary care physicians reconsidered. Gen Hosp Psychiatry. 1995;17:3–12. 4. Rost K, Zhang ML, et al. Persisently poor outcomes of undetected major depression in primary care. Gen Hosp Psychiatry. 1998;20(1):12–20. 5. Olfson M, Marcus SC, Druss B, et al. National trends in the outpatient treatment of depression. JAMA. 2002;287:203–209. 6. Druss BG, Miller CL, Rosenheck RA, et al. Mental health care quality under managed care in the United States: a view from the Health Employer Data and Information Set (HEDIS). Am J Psychiatry. 2002;159:860–862. 7. Simon GE. Evidence review: efficacy and effectiveness of antidepressant treatment in primary care. Gen Hosp Psychiatry. 2002;24:213–224. 8. Katon W, Von Korff M, Lin E, et al. Collaborative management to achieve treatment guidelines. Impact on depression in primary care. JAMA. 1995;273:1026–1031. 9. Barrett JE, Williams JW, Jr., Oxman TE, et al. Treatment of dysthymia and minor depression in primary care: a randomized trial in patients aged 18 to 59 years. J Fam Pract. 2001;50:405–412. 10. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16:606–613.

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11. Pignone MP, Gaynes BN, Rushton JL, et al. Screening for depression in adults: a summary of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med. 2002;136:765–776. 12. Katon W, Gonzales J. A review of randomized trials of psychiatric consultation-liaison studies in primary care. Psychosomatics. 1994;35:268–278. 13. Gilbody S, Bower P, Fletcher J, et al. Collaborative care for depression: a systematic review and cumulative meta-anlysis. Arch Intern Med. 2006;166:2314–2321. 14. Katon W, Unutzer J. Collaborative care models for depression: time to move from evidence to practice. Arch Intern Med. 2006;166:2304–2306. 15. Unu¨tzer J, Katon W, Callahan CM, et al. Collaborative care management of late-life depression in the primary care setting: a randomized controlled trial. JAMA. 2002;288:2836–2845.

Screening for Depression in Clinical Practice

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1 IS THE SYNDROME OF DEPRESSION A VALID CONCEPT? Alex J. Mitchell and Mark Zimmerman

1. 2. 3. 4. 5. 6.

What is Meant by Depression? Value and Validity of the Syndrome Concept Diagnostic Checklists (including DSM and ICD) Unstructured (Unassisted) Clinician Diagnosis Structured and Semi-Structured Assisted Diagnostic Interviews Conclusion

Context Depression is an everyday term, but if clinical management is to be empirically based, there needs to be a valid and reliable definition of the disorder that is distinct from normal sadness. The validity of the concept and all studies of screening for depression are hampered by the absence of a gold standard. Nevertheless, various thorough methods of assessment may help to improve the clinical utility of our concept of depression.

1.

What is Meant by Depression?

This book is built around the premise that major depressive disorder (MDD) exists in a way that is recognizable time and again by clinicians around the world. Considerable effort has been expended in developing and refining methods to measure depression. This chapter takes a step back and asks whether this effort is built upon a solid foundation. This begins with an important question: What is the purpose of making a meaningful diagnosis in any field of medicine? We suggest it is primarily to gain consensus and 3

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SCREENING FOR DEPRESSION IN CLINICAL PRACTICE

Textbox 1.1. Levels of Diagnostic Certainty in Psychiatry Highest Externally validated by ‘‘perfect’’ biological test High Consensus expert panel performing longitudinal evaluation using all possible data Medium to High Structured or semi-structured interview performed by a trained interviewer or clinician Low to High Severity questionnaires rated by the patient or clinician Low to Medium Unstructured, unassisted interview performed by an interested clinician Low Unstructured, unassisted interview performed by an inexperienced (or uninterested) clinician

knowledge that may help individuals and populations who have healthrelated ‘‘meetable unmet needs.’’ A medical diagnosis (spurious or not) has several other benefits (Textbox 1.1). It facilitates agreement with colleagues, it lends confidence to patients, it adds legitimacy to treatments, and it may allow the development of targeted interventions. Because many conditions can be successfully treated without knowing the true etiology or the precise diagnosis, the lack of gold standard should not be a cause of therapeutic nihilism. Consider neurologists attempting to treat a midlife inherited chorea in 1862. Meticulous clinical method could bring some success despite the absence of a name and a description for another 10 years and the absence of a known etiology for another 110 years. Although many early treatments were based largely on placebo effects or environmental manipulation, once a definitive cause is found and the pathophysiologic mechanism is revealed, the potential for treatment becomes vast, whereas once it was small. Yet there is an even more fundamental issue. Kraepelin believed the major psychiatric disorders were ‘‘natural disease entities’’ simply awaiting a discovery of a specific medical cause. After intensive effort the search for fundamental causes was resigned and nosology underwritten by internal cohesion of symptoms and signs.1 What if depression has no

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1 IS THE SYNDROME OF DEPRESSION A VALID CONCEPT?

single pathophysiologic explanation and is a complex manifestation of severe external stress?2 Would our concept be invalid and would existing treatment be rendered obsolete overnight? Similarly, if severe stress and mild depression were closely related, then attempting to find a test that separated them would be difficult to the point of impossibility (Fig. 1.1). After many decades of debate, it is not at all clear that depression is a discrete entity and justifies a categorical classification as opposed to a continuum merging with normal healthy but unhappy people.3 In the continuum argument, the distribution of symptoms of depression would theoretically approximate to a skewed normal or half-normal distribution with no point of rarity (Fig. 1.2).4 Cloninger stated that there is no empirical evidence for natural boundaries between major syndromes and that ‘‘no one has ever found a set of symptoms, signs, or tests that separate mental disorders fully into non-overlapping categories.’’5 Yet all current diagnostic systems that include MDD appear to assume there is a distinct syndrome (depressive disorder as distinct from depressive symptoms) and try to suggest an optimal method to identify it (Fig. 1.3). Even if this approach was correct and the current nosology of DSM-IV entirely perfect, there would be a significant danger of over relying on the concept of MDD to exclusion of other under researched forms. In other words, given Point of Partial Rarity Number of Individuals

Normal Stress

Depressed

True –ve

True +ve

False –ve

Score on Hypothetical Diagnostic Test

False +ve

Optimum Cut-off value

Figure 1.1. Hypothetical distribution of test scores in two related conditions. Two distinct conditions should be separated by a point of rarity on at least one fundamental measure (see also Fig. AP.4).

Distribution of HADS Scores in Cancer Outpatients (n=3071) 3000

2500

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S Se ix ve n Ei gh t N in e Te El n ev Tw en e Th lve Fo irte ur en te en Fi fte e Se Six n ve tee nt n ee n Ei gh te en

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0

16 14 12 10 8 6 4 2

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ro Tw o Fo ur Si x Ei gh t T Tw en Fo elve ur te Si en xt Ei een gh te Tw Tw en e en nt Tw ty- y en two Tw ty-f o Tw ent ur y en -s ty ix -E ig ht Th Th irt irty y Th -Tw irt y- o Th Fou irt r ySi x

0

Figure 1.2. Distribution of HADS scores in cancer outpatients (n ¼ 3,071). This continuous distribution of HADS scores in primary care and secondary (cancer care) illustrates a skewed normal distribution. Data from Thompson et al. Br J Psychiatry. 2001;179:317–323 and Sharpe et al. Br J Cancer. 2004;90:314–320. 6

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1 IS THE SYNDROME OF DEPRESSION A VALID CONCEPT?

Distribution of DSMIV Symptoms of Depression in Zurich Study 100 90 80 70 60 50 40 30 20 10 0

Zero

One

Two

Three

Four

Five

Six

Seven

Eight

Nine

Figure 1.3. Distribution of DSM-IV symptoms from Zurich study. The sample comprised 591 individuals originally selected in 1978 from the total population of 18- and 19-year-olds in Zurich, Switzerland, based on their scores of the Symptom Checklist-90 (SCL-90-R) (Derogatis, 1977). Two thirds of the sample was randomly selected from members of the total population who scored above the 85th percentile on the SCL-90-R, and one third was randomly selected from the remainder of the total population. Reprinted from Journal of Affective Disorders 62, Angst J, Merikangas KR, Multi-dimensional criteria for the diagnosis of depression, 7–15, Copyright (2001).

recent evidence, psychiatrists would be well advised to pay as much attention to minor (mild and syndromal) disorders as diabetologists are now paying to impaired glucose tolerance.6

2.

Value and Validity of the Syndrome Concept

The concept of a syndrome is fundamental to diagnostic classification and may be valuable even if imperfect.7 Without the concept of a syndrome, a disorder would be defined by a single symptom or simple symptom count. A syndrome is a special collection of symptoms that cluster in a peculiar way determined by the underlying pathophysiology, even if that mechanism is unknown. Careful identification of many psychiatric syndromes and their relationships has formed a detailed family of mental disorders not dissimilar to the Linnaean taxonomy proposed by Carl Linnaeus (1707–1778). In defining clinical syndromes, we rely on certain essential or core symptoms occurring commonly in those with the disorder but rarely in those without (Textbox 1.2). By the same token, we often ignore other symptoms that occur without much discrimination. Hence, some symptoms

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Textbox 1.2. Types of Validity Testing Content validity (Strength: Weak) The degree of measurement of all fields of interest Criterion validity (Strength: Strong) Agreement against a criterion that is external to the measuring instrument itself Construct validity (Strength: Moderate) Agreement with other measures consistent with theoretically derived hypotheses Procedural validity Strength: Weak) Agreement with an existing procedure

are more important diagnostically than others, but without large samples and rigorous examination, it isn’t obvious which ones these are. Further, life is rarely simple and rarely is any symptom both entirely unique to a psychiatric disorder and at the same time always manifest. If it were, then when this particular symptom was absent, we would know the disorder itself was impossible. We would therefore have a single question diagnostic test with perfect specificity (see Chapter 5). In MDD, DSM-IV suggests that the core features involve dysphoria (low mood) and anhedonia (loss of interest), and ICD-10 suggests that fatigue should also be an essential feature.8 In addition to these symptoms, aspects such as clinical significance, duration, disability, and distress have been added as a requirement in many diagnostic categories. We suggest it is no longer sufficient for an expert panel to mandate such features, no matter how logical it seems, because their predictive values will be uncertain until tested. In fact, all aspects of a definition (the symptoms, signs, associated features, and rules binding them together) should be amenable to clarification and empiric testing. If a syndrome is adopted too easily, the concept can become a pitfall, as Kendell and Jablensky explained: ‘‘Once a diagnostic concept such as syndrome has come into general use, it tends to become reified.’’9 In other words, its validity is assumed rather than tested. How, then, can a syndrome be tested and better tests developed? This is discussed in detail in Chapters 4 and 5, but in brief, accuracy is usually determined by validity and reliability. Reliability refers to the extent to which an observation yields the same results on repeated independent assessments. Essentially, this is a measure of consensus between assessors. Validity, derived from the Latin validus, meaning strong, refers to how well

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9

the instrument measures what it purports to measure (see Textbox 1.2). In essence this is a measure of truth—how much agreement is there with the actual disorder, assuming it could be defined by some criterion reference (or gold standard). In MDD there is no accepted gold standard,10 and therefore reliability and validity testing must be reduced to measures of agreement, where the critical question becomes: How good is the comparison? In medical specialties, aspects of the history such as nature of the chest pain have been subjected to diagnostic validity testing in a similar way to established investigations such as the electrocardiogram.11,12 In psychiatry (outside of organic brain disease), such objective tests are rarely if ever available. Many influences favor the adoption of a medical model in which an etiologic agent, a pathologic process, and symptoms and signs are assumed to be present even if unknown. This is often highly acceptable to patients, clinicians, and other interested parties (eg, the pharmaceutical industry), not least because stigma may be reduced and help-seeking and adherence encouraged. The flip side is that patient responsibility may be diminished and biologic treatments may be overprescribed. If the medical model of depression is correct, then eventually a definitive core disease process underlying depression will be found and a diagnostic test developed that (regardless of convenience) will enable current clinical diagnostic methods to be fully evaluated. If the medical model of depression is incorrect, then a definitive biologic test will never be developed, and we will continue to develop proxies of illness that may nevertheless correspond to important correlates of disorder and suffering, such as treatment response, course, and quality of life. The astute reader will probably conclude that measures of reliability and validity in psychiatry (and by implication diagnosis itself) are essentially all tests of agreement, albeit against different standards. Reliability is agreement with peers, and validity is agreement with an accepted method. As no group has yet found a robust biologic test for depression, most work has focused on attempts to improve the reliability of assessments conducted by researchers and clinicians. Often this involves refinement of the clinical interview using methods that assist the clinician. Semi-structured interviews provide questions that might best elicit symptoms but the clinician retains flexibility to deviate from this if necessary. Structured interviews provide questions that must be asked as described, purposely removing flexibility, with the useful benefit that clinical training is not a prerequisite and large population surveys using lay interviewers becomes possible. One level of assistance to clinicians that does not interfere with the clinical interview is provision of symptom checklists, together with the rules for their combination (Textbox 1.3). This essentially forms the basis of ICD-10 and DSM-IV.

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Textbox 1.3. Development of Diagnostic Checklists 1972 Feighner, Diagnostic Criteria (FDC): Primary Depression 1978 Spitzer, Research Diagnostic Criteria (RDC): Major Depressive Disorder 1980 Diagnostic and Statistical Manual III: Major Depressive Episode 1987 Diagnostic and Statistical Manual III-R: Major Depressive Episode 1990 ICD-10 International Classification of Diseases: Mild, Moderate, or Severe Depression 2000 Diagnostic and Statistical Manual IV: Major Depressive Episode 2012 ICD11 International Classification of Diseases Diagnostic and Statistical Manual V

3.

Diagnostic Checklists (including DSM and ICD)

Diagnostic checklists are a list of features, together with the rules for making a particular diagnosis. If the criteria are monothetic, then all the items must be present; if polythetic, then only a proportion are required. If features are necessary, then specific features must be present; if sufficient, then only certain criteria but no others are needed. Several checklists that generate one or more systems of psychiatric diagnosis have been proposed (Textbox 1.4).13–15 Checklists leave the clinician to conduct the clinical interview in any way he or she feels appropriate. Advanced systems may use diagnostic algorithms that prioritize certain items and use more complex rules, such as ‘‘if x, then y.’’ DSM and ICD-10 use diagnostic checklists but also include some suggestions for the interview itself. That said, a diagnostic interview defined only by DSM-IV/ICD-10 lacks clearly defined probe questions, requiring clinicians to formulate their own approach. Although this adds to the acceptability, equally it contributes to interrater variability.16 Some consider DSM and ICD distinct from other checklist methods because of the claim that DSM and ICD are operationalized—that is, each and every step is described and subject to unambiguous instructions as well as reliability or

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Textbox 1.4. Checklists for Aiding Psychiatric Diagnosis Lists of Integrated Criteria for the Evaluation of Taxonomy (LICET) LICET-D for depressive disorders assembles all criteria from 9 diagnostic systems. Operational Criteria Checklist (OPCRIT) OPCRIT generates diagnoses of 13 diagnostic systems and has been proposed to generate diagnoses direct from medical notes. ICD-10 Symptom Checklist Developed by Janca; takes about 15 minutes. International Diagnostic Checklists (IDCL) Two 30-item lists, one for ICD-10 and one for DSM-IV.

validity testing. This is probably not the case. Efforts to measure the reliability of DSM-IV have been published.17

ICD and DSM The World Health Organization (WHO) introduced mental disorders in the sixth revision of the International Classification of Diseases (ICD-6) in 1948.18 The American Psychiatric Association Committee on Nomenclature and Statistics published the first edition of the Diagnostic and Statistical Manual: Mental Disorders (DSM-I) in 1952 (see Textbox 1.3).19 Current diagnostic classification manuals (DSM-IV and ICD-10) deliberately do not contain mutually exclusive diagnostic categories; rather, they contain overlapping areas. Indeed, if carefully applied, each diagnostic system yields a different number of cases, as illustrated by Erkinjuntti and colleagues (1997) for dementia20 and Furukawa and associates21 for depression. Of note, agreement between diagnostic systems examined in the same sample is often modest (Table 1.1). It was in the eighth revision of ICD (ICD-8) in 1967 and in the third edition of DSM (DSM-III) in 1980 where a systematic effort to improve the diagnosis and classification of mental disorders was made. Until then, textbooks containing descriptions of individual conditions were the main source of information, but naturally this led to numerous disputes. DSM and ICD go beyond textbook descriptions by providing a checklist of useful criteria and, importantly, suggesting a diagnostic threshold determined by specific symptoms, which usually have to fulfill both frequency (symptom count) and duration criteria. The key difference between a severity questionnaire and an operational method is that certain criteria are required in the latter, whereas

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Table 1.1. Clinician Agreement (Kappa) Using Different Diagnostic Systems for Depression

DSM RDC ICD-10 FDC

DSR

DSM

RDC

ICD-10

0.95 0.71 0.71 0.59

0.71 0.7 0.6

0.74 0.77

0.63

Adapted from Philipp M, Delmo CD, Buller R, et al. Differentiation between major and minor depression. Psychopharmacology. 1992;106:S75–S78.

severity questionnaires usually rely on symptom counts alone, without weighting of symptoms (see Appendix Table 1). That said, questionnaires can be constructed to follow the DSM diagnostic algorithm.22 This is not surprising, because most mood questionnaires were proposed by experts based on clinical experience alone, whereas careful field testing is needed to rank important items (see Chapter 4). Given this, it is remarkable that severity questionnaires may perform quite well against structured interviews.

Validation of the DSM-IV/ICD-10 Criteria for Depression The criteria for major depression, minor depression, and dysthymia are shown in Table 1.2. Subsyndromal depression is not currently included in DSM-IV but can be considered present if there are at least two DSM-IV symptoms but the overall criteria for major or minor depression are not met.23 MDD is defined by depressed mood or loss of interest in nearly all activities for at least 2 weeks accompanied by at least three or four (for a total of 5) symptoms. The criteria for minor depression are identically but require two to four

Table 1.2. Diagnostic Categories for Depressive Disorders Diagnostic Category

DSM-IV Criteria

Symptom Duration

Major depression

5 depressive symptoms, including depressed mood or anhedonia, causing significant impairment in social, occupational, or other important areas of functioning 2–4 depressive symptoms, including depressed mood or anhedonia, causing significant impairment in social, occupational, or other important areas of functioning 3 or 4 dysthymic symptoms, including depressed mood, poor appetite or overeating, insomnia or hypersomnia, low energy, low self-esteem, poor concentration or indecisiveness, and hopelessness

2 weeks

Minor depression (research criteria diagnosis) Dysthymia

2 weeks 2 years

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13

symptoms and require exclusion of previous major depression in an attempt to avoid confusion over residual symptomatology. Dysthymia is characterized by fewer symptoms than major depression (three or four) and a chronic course lasting at least 2 years. In ICD-10 the core symptoms of depression include decreased energy or increased fatigability in addition to low mood and loss of interest. Further, only four symptoms are required for a mild episode, and six (five in early versions) symptoms qualify as moderate depressive episode. Thus, DSM-IV major depression is broadly analogous to the ICD-10 concept of moderate or severe depression. Both ICD and DSM suggest a minimum number of typical and associated symptoms and a minimum duration of symptoms of 2 weeks. In DSM-IV, but not in ICD-10, a third feature is added: that the disorder causes significant impairment in social, occupational, or other important areas of functioning. As a result, there is discordance in diagnosis based on ICD-10 versus DSM-IV.24–26 Over the past 10 years there have been accumulating challenges to the diagnostic criteria in DSM-IV, including but not limited to MDD. Philipp and colleagues (1992) were one of the first groups to show that the major depression concept may be too narrow.27 In a primary care study using DSM-III-R, MDD occurred in 17.4%, but the majority of depressed patients fell into the group of depression ‘‘not otherwise specified’’ (NOS). Adding the minor depression concept resulted in the reclassification of 38.3% of the NOS patients to minor depression. Data from the National Comorbidity Survey have shown that across the minor, major, and severe categories of depression (depending on the number of symptoms) there is a ‘‘monotonic’’ increase for a number of fundamental indices such as average number of episodes, impairment, comorbidity, and parental psychopathology,28 suggesting a continuum within depression rather than categorical groupings. Kendler and Gardner’s 1998 longitudinal analysis of the Virginia Twin Registry demonstrated that the presence of five or more symptoms of depression was not a more accurate definition of depression at 1-year follow-up than the presence of three or four symptoms.29 Additionally, there is little empirical support for the DSM-IV requirement for 2-week duration or, indeed, ‘‘clinically significant impairment.30,31 In the Rhode Island MIDAS project, Zimmerman and colleagues (2006)32 conducted an in-depth analysis of symptoms for MDD by having trained raters administer a semi-structured interview to 1,523 psychiatric outpatients. 54.4% of the sample had a current MDD. They analyzed a 17-item bank of possible symptoms of depression, including the standard 9 DSM items but separating the compound criteria that encompass more than one symptom (eg, increased sleep or insomnia), along with non-DSM diagnostic items such as hopelessness, helplessness, and unreactive mood. The authors found that some items

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Textbox 1.5. Inter-Rater Reliability Eliciting Individual Symptoms of Depression

Symptoms Suicidality Depressed mood Insomnia Anhedonia Decreased appetite Loss of energy Indecisiveness Thoughts of death Psychomotor agitation Feelings of worthlessness Increased weight Decreased concentration Excessive guilt Decreased weight Increased appetite Psychomotor retardation Hypersomnia

Kappa 0.94 0.92 0.91 0.90 0.89 0.88 0.88 0.86 0.83 0.80 0.79 0.78 0.76 0.69 0.63 0.63 0.54

were rated more reliably than others—for example, suicidal ideas, plan, or attempt (suicidality) achieved almost perfect agreement, whereas raters often disagreed about what constituted psychomotor retardation (Textbox 1.5). The authors found that the ranked order of diagnostic weight (by individual item) for DSM-IV membership on logistic regression was depressed mood > anhedonia > sleep disturbance > concentration/indecision > worthlessness/excessive guilt > loss of energy > appetite/weight disturbance > psychomotor change > death/suicidal thoughts. Some items seemed redundant in making a diagnosis. Zimmerman’s group also looked at a validity of so-called core criteria.33 Only 1.5% of the 1,800 patients reported five or more criteria in the absence of low mood or loss of interest or pleasure. Twenty-five of these 27 patients reported depressed mood at a subthreshold level, often in partial remission. Thus, only a small handful of cases would be false positives if no core criteria existed. In another paper in the series, they found that few patients who met the symptom criteria for MDD were ruled out of the diagnosis by the other components of the diagnostic algorithm, thereby explaining why selfadministered depression symptom questionnaires perform well as diagnostic

1 IS THE SYNDROME OF DEPRESSION A VALID CONCEPT?

15

proxies.34 Finally, they addressed the longstanding issue of applying some of the criteria in patients with comorbid medical illnesses because of symptom nonspecificity. Based on a series of psychometric analyses that were crossvalidated, they developed an alternative set of diagnostic criteria for MDD that did not include somatic symptoms but would nonetheless demonstrate a high level of concordance with the current DSM-IV definition.

4.

Unstructured (Unassisted) Clinician Diagnosis

Clinician-based assessment has been poorly investigated compared with assisted methods of diagnosis. In fields of medicine where a robust external validation such as postmortem is available, routine diagnostic accuracy has often proven to be remarkably poor.35,36 It should be no surprise, then, if in the absence of a gold standard, health professionals have considerable difficulty making accurate and reliable diagnoses (see Table 1.2).37,38 Regarding missed diagnoses, one study suggested that only 26% were complete mistakes; 25% were underestimates of severity and 38% misidentifications. Conversely, regarding false-positive diagnoses, 35% were overestimates of severity, 24% misdiagnoses, and 41% complete errors. To compound this problem, 90% of psychiatrists do not routinely use case identification and severity measurement for depression (and more than half never do so).39 Most clinicians rely on their own abilities based on training received earlier in their career. On the other hand, clinician-based assessment is purported to be a gold standard in psychiatry if the clinician is given adequate time and resources. This was best conceptualized by Spitzer, who proposed the LEAD standard.40 LEAD is an acronym that stands for the Longitudinal evaluation performed by Expert clinicians who utilize All available Data. The LEAD standard is an important way of obtaining the most likely diagnosis by requiring clinicians to use a collateral history, hospital records, psychological evaluations, and laboratory results. However, uncertainty about who is ‘‘expert’’ and which data are mandatory, as well as availability, limits both the actuarial and practical value of this standard.41 A related clinical standard is the best estimates procedure (BEP), which is simpler than the LEAD.42 In the BEP, all available information is evaluated by experienced clinicians who assign a consensus ‘‘best-estimate diagnosis.’’ As with the LEAD standard, the number of clinicians and source of information should always be stated.

Accuracy of Psychiatrists’ Routine Diagnoses The accuracy of psychiatrists’ diagnostic skills can be compared against BEP diagnoses and/or structured interviews. The value of BEP was investigated by

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Kosten and Rounsaville (1992),43 who interviewed 475 subjects using the Schedule for Affective Disorders and Schizophrenia-Lifetime (SADS-L). Two psychologists independently evaluated and diagnosed the same subjects, applying the BEP. Higher rates of diagnoses of major and minor depressive disorder, antisocial personality, alcoholism, and drug abuse were revealed when the BEP was applied than with routine interview alone and with a minimal rate of false positives. More recently, Taiminen and colleagues (2001)44 compared routine discharge diagnoses based on DSM-IV and BEP diagnoses in 116 first-admission patients with psychosis and severe affective disorder (Table 1.3). However, in this case the BEP included data from a Schedules for Clinical Assessment in Neuropsychiatry (SCAN) interview, enforcing an even higher gold standard. Diagnostic agreement was moderate (kappa 0.51), suggesting frequent errors in the routine diagnoses even when using DSM-IV criteria. Of note, clinicians tended to miss depressive symptoms in psychotic patients, to overdiagnose psychotic symptoms in depressive patients, and to overlook earlier hypomanic or depressive episodes in depressive patients. Spitzer and colleagues (1999)45 evaluated the unassisted accuracy of mental health professionals (1 psychologist and 3 mental health social workers) in comparison with 62 primary care physicians (PCPs) using the depression scale of the Patient Health Questionnaire (PHQ-9). Accuracy was calculated in 585 cases who had both assessments within a 48-hour period. PCPs recognized 61% of cases thought to have major depression by mental health professionals and excluded 98% of cases thought not to have major depression. Accuracy in the other direction was not reported. Recently Carballeira and colleagues from Switzerland (2007)46 studied 212 patients admitted to the internal medicine units of the University Hospitals of Geneva (Table 1.4). Each patient completed the PHQ-9 and underwent a blind DSM-IV diagnostic assessment by a psychiatrist. Compared to the PHQ-9, psychiatrists recognized 50% of cases with major depression but only 22% of those with Table 1.3. Diagnostic Accuracy of Primary Care Physicians Against CIDI

Depressed (Unassisted Diagnosis) Not Depressed (Unassisted Diagnosis) Total

Gold Standard Depressed (CIDI)

Gold Standard Not Depressed (CIDI)

70

76 (false positives) 459

104 (false negatives) Se 40.2%

PPV 48% NPV 81.5%

Sp 85.8%

Reprinted from General Hospital Psychiatry 21(2), Tiemens BG, VonKorff M, Lin EH, Diagnosis of depression by primary care physicians versus a structured diagnostic interview. Understanding discordance, 87–96, Copyright (1999).

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1 IS THE SYNDROME OF DEPRESSION A VALID CONCEPT?

Table 1.4. Diagnostic Accuracy of Psychiatrists vs. PHQ-9 (Patient-Rated) Psychiatrist

PHQ-9 PHQ-9 No PHQ-9 Mj Mn Mj Depressed Depressed Depressed

No PHQ-9 Mn Depressed

Depressed (Unassisted Diagnosis) Not Depressed (Unassisted Diagnosis) Total

12

5

12 (false negatives) Se 50%

18 (false negatives) Se 22%

26 (false positives) 162

30 (false positives) 159

Sp 86%

Sp 84%

PPV (Mj) 32% PPV (Mn) 14% NPV (Mj) 93% NPV (Mn) 90%

Mj, major (DSM-IV); Mn, minor (DSM-IV). Reproduced from Carballeira et al. Criterion validity of the French version of Patient Health Questionnaire (PHQ) in a hospital department of internal medicine. Psychology and Psychotherapy: Theory, Research and Practice (2007), 80, 69–77.

more milder forms. Rule-out accuracy was high but rule-in accuracy was poor, with a high rate of false positives. The authors also compared diagnoses of psychiatrists by internists in medicine, finding a kappa agreement of only 0.20. This study is valuable because patient-rated symptoms have particular importance.47 Several groups have explored the accuracy of routine diagnoses against the Structured Clinical Interview for DSM Disorders (SCID), although few have used other methods such as the Composite International Diagnostic Interview (CIDI).48 Helzer and colleagues (1985)49 examined the level of agreement between a lay-rated Diagnostic Interview Schedule (DIS) in the Epidemiologic Catchment Area project and routine clinical diagnoses made by psychiatrists. Overall agreement between the DIS and the psychiatrists ranged from 79% to 96%, but specificities were all 90% or better. Anthony and associates (1985)50 studied DSM-III diagnoses made by the DIS in comparison to a ‘‘standardized’’ DSM-III diagnosis by psychiatrists in the two-stage Baltimore Epidemiologic Catchment Area mental morbidity survey. There were considerable disagreements; the only category of modest agreement was alcohol use disorder. Steiner and colleagues (1995)51 studied the relationship between diagnoses generated by the SCID and unstructured psychiatric interviews. Diagnoses generated by researchers using the SCID and routinely by psychiatrists were compared for 100 patients. Overall agreement between the SCID diagnosis and the clinical diagnosis was low (kappa of 0.30). Shear and coworkers (2000)52 examined 164 nonpsychotic patients at two community treatment facilities using the SCID and compared results to diagnoses obtained from clinician

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records. The majority (59%) of patients met the SCID criteria for a primary depressive disorder. Diagnoses agreed in only a small minority of cases (kappa 0.24 overall and 0.33 for mood disorder). Overall, use of the SCID resulted in more diagnoses than the standard clinical procedures, particularly where comorbidity was present. Anxiety disorders, in particular, were much more likely to be overlooked by a clinical rater. One exception was ‘‘adjustment disorder,’’ which was more frequently diagnosed by a clinician than by the SCID rater. In an important but small-scale study, Miller and colleagues (2001)53 compared three methods of diagnosis for 56 psychiatric inpatients against the LEAD criterion standard. These were unassisted clinical assessment, SCID, and a structured Computer-Assisted Diagnostic Interview (CADI). Psychiatrists’ unassisted assessment had 54% agreement against LEAD (kappa 0.43), whereas SCID and CADI had agreements above 85% (kappa 0.81). Compared with similarly trained colleagues, there was an interrater agreement of only 45.5% (kappa 0.24) for unassisted clinicians, meaning independent clinicians disagreed most of the time.54 In one of the largest studies of diagnostic accuracy, Kashner and coworkers (2003)55 looked at 294 newly enrolled adult psychiatric patients based on clinical records. Within 2 weeks of their primary evaluation, patients were randomly assigned to receive a nurse-administered SCID with feedback to the attending psychiatrist or usual care. The kappa agreement between the SCID and chart diagnoses of MDD was 0.56 at baseline (unassisted), rising to 0.90 at the end of the study after feedback of results to clinicians. Against the SCID, clinicians underdiagnosed all psychiatric disorders (for example, missing over 60% of substance abuse disorders and anxiety disorders). However, unassisted clinicians also made several false-positive diagnoses, most commonly for schizophrenia, bipolar disorders, and MDD. Basco and associates (2003)56 interviewed 200 psychiatric outpatients and attempted to establish gold standard diagnoses based on SCID, all medical records, and a follow-up interview with a psychiatrist or a psychologist trained in diagnostic procedures (in effect, the LEAD procedure). The percentage of agreements with this gold standard was 53% for routine diagnoses, 68% for the SCID, and 79% for the SCID plus chart review. Concordance was better for depression. Looking at the subset of patients examined by a psychiatrist, 70% of those thought by psychiatrists to have MDD actually did on the SCID (43 of 61 participants), but half of the SCID cases of MDD were not previously recognized as such, typically assigned adjustment disorder or no clinical diagnosis, anxiety disorder, substance abuse, or bipolar disorder. The accuracy of unassisted clinical ability was examined for both rule-in and rule-out accuracy (Table 1.5). Psychiatrists were good at excluding depression but missed 50% of cases when attempting to rule in a diagnosis. In all groups, when discrepancies occurred, most were judged to

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1 IS THE SYNDROME OF DEPRESSION A VALID CONCEPT?

Table 1.5. Diagnostic Accuracy of Psychiatrists vs. SCID Plus

Depressed (Unassisted Diagnosis) Not Depressed (Unassisted Diagnosis) Total

SCID þ Standard Depressed

SCID þ Standard Depressed

17

7 (false positives)

PPV 76%

17 (false negatives)

155

NPV 89%

Se 50%

Sp 96%

SCID+ refers to SCID, plus all medical records and a follow-up interview with a trained psychiatrist or a psychologist; see text. Basco et al. Methods to improve diagnostic accuracy in a community mental health setting. Am J Psychiatry. 2000;157(10):1599–1605.

be of substantial clinical importance. Performance shows remarkable similarity to those of PCPs (see Table 1.4 for comparison). The kappa coefficients showed that administration of the SCID without the benefit of a medical record review improved accuracy beyond routine diagnosis alone, while adding information derived from the chart review resulted in an additional 25% improvement over and above the SCID alone. These findings are consistent with reports from other studies showing the advantage of diagnostic interviews over unstructured clinical interviews (see below).57,58 This is one study in which the importance of the competing diagnoses was investigated. Psychiatrists found separation of MDD versus obsessive-compulsive disorder and MDD versus dysthymia to be relatively straightforward but struggled with MDD versus adjustment disorder and MDD versus organic disorder, among others. Reasons for suboptimal accuracy are discussed in Chapter 3.

5.

Structured and Semi-Structured Assisted Diagnostic Interviews

Semi-structured diagnostic interviews were introduced in the 1970s as a method that would allow lay interviewers to obtain psychiatric diagnoses close to those a psychiatrist would obtain.59,60 Rogers suggested that one third of clinical variability was due to idiosyncratic questioning and two thirds to interpretation of the information gleaned.60 The premise is that standardization forces an assessor to cover all the areas of psychopathology and provides consistency in the way questions are asked. Three main components of the structured interview are (1) to use the standardized language of clinical method, (2) to sequence the order of inquiry, and (3) to quantify the responses.

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However, assisted interviews have several significant limitations. First, they are time-consuming: the average time to administer the SCID is approximately 1 hour and 44 minutes, compared to about 40 minutes for a standard interview (Textbox 1.6). Second, they have modest acceptability to patients and staff, who often find these interviews restrictive (for staff) and repetitive (for

Textbox 1.6. Summary of Assisted Interviews Partially Structured The PSE (Present State Examination)/ SCAN Type: Semi-structured interview Recommended Use by: Clinicians Generates: ICD-10 and DSM-IV criteria Duration: 45 minutes

SCID-I (Structured Clinical Interview for DSM-IIIR) Type: Semi-structured interview Recommended Use by: Trained interviewer and/or clinicians Generates: DSM-IV Duration: 1 hour and 44 minutes Schedule for Affective Disorders and Schizophrenia (SADS) Type: Semi-structured interview Recommended Use by: Trained interviewer and/or clinicians Generates: RDC Duration: 90 minutes Fully Structured CIDI (Composite International Diagnostic Interview) Type: Structured Recommended Use by: Trained interviewer (clinician optional) Generates: ICD-10 and DSM-III-R criteria Duration: 75 minutes

M.I.N.I (Mini-International Neuropsychiatric Interview) Type: Structured Recommended Use by: Trained interviewer (clinician optional) Generates: ICD-10 and DSM-IV criteria Duration: 20 minutes Diagnostic Interview Schedule (DIS), Type: Structured Recommended Use by: Trained interviewer (clinician optional) Generates: DSM-IV Duration: 120 minutes

1 IS THE SYNDROME OF DEPRESSION A VALID CONCEPT?

21

patients).61 Third, and perhaps unexpectedly, diagnostic interviews can produce far from uniform results even in the same population. For example, 12-month prevalence rates of major depression in the United States using two instruments were 4.2%62 and 10.1%.63 Further, no before-and-after study or randomized trial has shown how much these methods can improve routine care. These cautions call into question the value of these instruments for clinical care, at least until further data are available.64 The most common instruments are illustrated in Textbox 1.6. The SCID was developed alongside DSM-III-R.65 As with most instruments, raters must first be trained. Compared with the CIDI, the clinician makes more judgments as to whether each criterion is met and whether all criteria taken together validate the clinical diagnosis. Numerous studies have evaluated interrater reliability for major depression using the SCID. One of the largest, from Williams and colleagues (1992),66 evaluated the ability of psychiatrists (n = 14), psychologists (n = 6) and master’s degree students (n = 4) to diagnose depression. There was a modest kappa agreement of 0.64. There are also several studies comparing the SCID and CIDI. In a sample of 325 patients from the National Comorbidity Survey, the sensitivity of CIDI was 55% and specificity was 93.7% for lifetime major depression compared with the SCID (kappa 0.54).67 In the study by Basco and associates (2003) mentioned previously, the added value of SCID plus chart diagnoses suggests that the SCID can be improved using very experienced clinical raters—hence the need for a clinician-led assisted interview. Interestingly, feedback of SCID results to psychiatrists can lead to more positive outcomes.68 Philipp and colleagues (1986) proposed a refinement to the SCID called the Polydiagnostic Interview (PODI).69 The advantage of this approach is that the PODI generates diagnosis according to several completing diagnostic checklists, including DSM-III-R, ICD-10, Research Diagnostic Criteria (RDC), and Feighner Diagnostic Criteria. The Present State Examination (PSE) is a semi-structured interview designed for use only by clinicians. The current 10th edition can generate both ICD-10 and DSM-IV diagnoses. A computer program derived from PSE (CATEGO-5) has also been developed, as has a short version of PSE. SCAN is a semi-structured interview based on PSE and is also the product of a collaborative study between the World Health Organization (WHO) and the U.S. Alcohol, Drug Abuse, and Mental Health Administration (ADAMHA).70 Again, the PSE requires a thorough training course, making it expensive and time-consuming for many.

Fully Structured Assisted Interviews The DIS was developed by National Institute of Mental Health (NIMH) and was released in its first version in 1978. It was an adaption of the Renard Diagnostic Instruments designed to assess Feighner’s diagnostic criteria.

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DIS-4 focuses on DSM-IV and is similar to the CIDI. It has been validated, but one study found low sensitivity of the DIS versus the SCID.71 The CIDI was produced jointly by WHO and ADAMHA and is designed to enable a trained interviewer to arrive at a either an ICD-10 or a DSM diagnosis in about 75 minutes. The CIDI is an amalgamation of two pre-existing instruments, the DIS and the PSE. It contains 276 symptom questions, many of which are probes to evaluate symptom severity, as well as questions for assessing help-seeking and psychosocial impairments. A computerized version, CIDI 2.1, is available. The first field showed high interrater reliability but poor test–retest reliability for depressive disorders.72 Subsequent reliability studies (using slightly different versions of the CIDI) demonstrated a high interrater reliability.73,74 One validity study used a clinician-scored DSM-III-R symptom checklist as the gold standard.75 Compared with this gold standard checklist, the CIDI had a sensitivity of 85% and a specificity of 98% (kappa 0.84). A second study compared the CIDI against the SCIDassisted LEAD procedure.76 There was modest positive predictive value and a high negative predicted value (kappa 0.46). The Mini-International Neuropsychiatric Interview (M.I.N.I.) is an abbreviated structured psychiatric interview that takes only 15 to 20 minutes to administer.77 It uses decision-tree logic to elicit all the symptoms listed in the symptom criteria for DSM-IV and ICD-10 for 15 major Axis 1 diagnostic categories, for one Axis II disorder, and for suicidality. Several specific tools are available: M.I.N.I.-Screen, M.I.N.I.-Plus, and the M.I.N.I.-Kid. Validation of the M.I.N.I. in relation to the SCID Patient Version, the CIDI, and expert professional opinion has been conducted.77

6.

Conclusion

Some will find the conclusion that a diagnosis of mental disorders is not based on a robust gold standard surprising.78 Current evidence has repeatedly shown that unassisted psychiatric diagnoses are neither particularly reliable (when judged by repeat assessments) nor particularly valid (when judged by consensus methods or assisted interviews), especially when comorbidity is present.79 Miller and colleagues (2001)53 found that when unassisted, clinicians evaluated an average of only 53% of key criteria present in diagnostic algorithms (32% in the case of depression). Psychiatrists asked about low mood in 86% of cases but asked about loss of pleasure in only 8%.80 As awareness of these limitations increases, there will be an increased call for clinicians to use diagnostic aids as a routine in clinical practice. If this occurs with proper diagnostic scrutiny (comparing accuracy with and without assistance head to head), psychiatric diagnosis will slowly move from being a

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1 IS THE SYNDROME OF DEPRESSION A VALID CONCEPT?

nonscientific art based on the overall clinical impression to a science where the accuracy of each method—indeed each question—is known. As Kendell and Jablensky9 observed: ‘‘Psychiatry is in the position—that most of medicine was in 200 years ago—of still having to define most of its disorders by their syndromes. Because of the consequent need to distinguish one disorder from another by differences between syndromes, the validity of diagnostic concepts remains an important issue in psychiatry. In this situation, to search for boundaries between syndromes and to use zones of rarity as criteria of validity is, we contend, the best strategy available to us.’’ Here Kendell and Jablensky highlight a fundamental problem in the search for accuracy. That is the notion that many of our current diagnoses are labels of convenience not any more distinct from each other than short stature and normal height. Like many conditions based largely on phenotypes alone, normal height has a Gaussian (normal) distribution that overlaps with many diseases and disorders that cause growth retardation. The result may be two distributions with significant overlap and little point of rarity (see Fig. 1.1).

Kappa 160 140 120 1.00

100

0.80

80

0.60

60

0.40

40

0.20

20

0.00

Routine Diagnoses

Diagnoses Based on SCID

Diagnoses Based on SCID Plus Medical Records

Time Required (minutes)

Agreement With Gold Standatd on Specific Diagnoses (kappa)

Time required

0

Figure 1.4. Time required to produce accurate diagnoses. Time requirement and reliability of routine diagnoses, SCID-based diagnoses, and diagnoses based on the SCID plus medical records for 200 outpatients with severe mental illness. Reprinted from Basco RM, Bostic JQ, Davies D, Rush AJ, Witte B, Hendrickse W, Barnett V. Methods to improve diagnostic accuracy in a community mental health setting. Am J Psychiatry. 2000 Oct;157(10): 1599–605 with permission.

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DSM-III and ICD-8 were landmark publications that allowed us to scrutinize the mysterious process of psychiatric diagnosis. Each new release brings an incremental improvement. Although neither DSM nor ICD has been universally accepted (in one study, clinicians used DSM criteria in 23% of visits in which a psychosocial problem was recognized),81 they have had a beneficial influence.82 As these checklist-based diagnostic systems with rule-based criteria are field-tested, it becomes apparent that many of the suggested symptoms, combinations, and associated features are not particularly useful diagnostically. However, this could be seen as an advantage, as previously no attempt was made at all to change mainstream psychiatric diagnoses. Finding out what doesn’t work may be as valuable as finding out what does. Beyond the checklist approach lie assisted interviews, which have a good evidence base for reliability, validity, or both. What is missing are formal implementation trials where one group of clinicians are randomized to assisted interviews and one group to diagnosis as usual to discover if clinical outcome actually improves. Unfortunately, most of the assisted methods so far developed are too long for routine clinical use. Indeed, a rule of thumb in this field is that the more accurate the diagnostic method, the longer the time required—and, further, this effect may not be linear (Fig. 1.4). A key challenge for the future, therefore, is to develop reliable diagnostic methods of sufficient brevity that they become routinely accepted in busy clinical practice, including primary and secondary care.

References 1. Jablensky A. Categories, dimensions and prototypes: critical issues for psychiatric classification. Psychopathology. 2005;38:201–205. 2. van Praag HM. Can stress cause depression? Prog Neuropsychopharmacol Biol Psych. 2004;28(5):891–907. 3. Parker G. Classifying depression: should paradigms lost be regained? Am J Psychiatry. 2000;157:1195–1203. 4. Sneath PHA. Some thoughts on bacterial classification. J Gen Microbiol. 1957;17:184–200. 5. Cloninger CR. A new conceptual paradigm from genetics and psychobiology for the science of mental health. Aust N Z J Psychiatry. 1999;33:174–186. 6. Lyness JM, Kim JH, Tang W, et al. The clinical significance of subsyndromal depression in older primary care patients. Am J Geriatr Psychiatry. 2007;15:214–223. 7. Angst J, Merikangas KR. Multi-dimensional criteria for the diagnosis of depression. J Affect Disord. 2001;62:7–15. 8. The ICD-10 classification of mental and behavioral disorders: diagnostic criteria for research, 10th edition. Geneva: World Health Organization, 1993. 9. Kendell R, Jablensky A. Distinguishing between the validity and utility of psychiatric diagnoses. Am J Psychiatry. 2003;160:4–12.

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10. Aboraya A, Compton III W. Biological markers and external validators in psychiatry: progress report on the validity of psychiatric diagnoses. eCommunity Int J Mental Health Addiction. Nov. 7, 2004 [online]. 11. Tierney W, Fitzgerald J, McHenry R, et al. Physicians’ estimates of the probability of myocardial-infarction in emergency room patients with chest pain. Medical Decision Making. 1986;6(1):12–17. 12. Chun AA, McGee SR. Bedside diagnosis of coronary artery disease: A systematic review. Am J Med. 2004;117(5):334–343. 13. Pull CB, Pull MC, Pichot P. Integrated lists of taxonomic evaluation criteria: LICET-S and LICET-D. Acta Psychiatr Belg. 1984;84(4):297–309. 14. Mihalopoulos C, McGorry P, Roberts S, et al. The procedural validity of retrospective case note diagnosis. Aust N Z J Psychiatry. 2000;34(1):154–159. 15. Janca A, Hillerb W. ICD-10 checklists—A tool for clinicians’ use of the ICD-10 classification of mental and behavioral disorders. Comprehensive Psychiatry. 1996;37(3):180–187. 16. Hamilton JD. Do we underutilise actuarial judgement and decision analysis? EvidenceBased Mental Health. 2001;4:102–103. 17. Holzer III CE, Nguyen HT, Hirschfeld RMA. Reliability of the diagnosis in mood disorders. Psychiatric Clin North Am. 1996;19(1):73–84. 18. Manual of the international classification of diseases, injuries and causes of death, 6th ed. Geneva: World Health Organization, 1948. 19. Diagnostic and statistical manual of mental disorders. Washington, DC: American Psychiatric Publishing, 1952. 20. Erkinjuntti T, Ostbye T, Steenhuis R, et al. The effect of different diagnostic criteria on the prevalence of dementia. N Engl J Med. 1997;337(23):1667–1674. 21. Furukawa TA, Anraku K, Hiroe T, et al. A polydiagnostic study of depressive disorders according to DSM-IV and 23 classical diagnostic systems. Psychiatry Clin Neurosci. 1999;53(3):387. 22. Zimmerman M, Chelminski I, McGlinchey JB, et al. Diagnosing major depressive disorder VI: Performance of an objective test as a diagnostic criterion. J Nerv Ment Dis. 2006;194:565–569. 23. Diagnostic and statistical manual of mental disorders, 4th ed. Washington, DC: American Psychiatric Publishing, 1994. 24. Philipp M, Maier W, Delmo CD. The concept of major depression. I. Descriptive comparison of six competing operational definitions including ICD-10 and DSMIII-R. Eur Arch Psychiatry Clin Neurosci. 1991;240(4–5):258–265. 25. Andrews G, Slade T, Peters L, et al. Classification in psychiatry: ICD-10 versus DSM-IV. Br J Psychiatry. 1999;174(1):3–5. 26. Ravelli A, Bijl RV, Van Brink WD. Consequences of the use of different classification systems: A comparison of the DSM-III-R and the ICD10 for depression. Int J Methods Psychiatric Res. 1999;8(4):192–203. 27. Philipp M, Delmo CD, Buller R, et al. Differentiation between major and minor depression. Psychopharmacology. 1992;106:S75–S78. 28. Kessler RC, Zhao S, Blazer DG, et al. Prevalence, correlates, and course of minor depression and major depression in the National Comorbidity Survey. J Affect Disord. 1997;45:19–30. 29. Kendler KS, Gardner CO Jr. Boundaries of major depression: an evaluation of DSM-IV criteria. Am J Psychiatry. 1998;155:172–177. 30. Spitzer RL, Wakefield JC. DSM-IV diagnostic criterion for clinical significance: does it help solve the false positives problem? Am J Psychiatry. 1999;156:1856–1864.

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31. Beals J, Novins DK, Spicer P, et al., the AI-SUPERPFP Team. Challenges in operationalizing the DSM-IV clinical significance criterion. Arch Gen Psychiatry. 2004;61(12):1197–1207. 32. Zimmerman M, McGlinchey JB, Young D, et al. Diagnosing major depressive disorder, I. A psychometric evaluation of the DSM-IV symptom criteria. J Nerv Ment Dis. 2006;194:158–163. 33. Zimmerman M, McGlinchey JB, Young D, et al. Diagnosing major depressive disorder, IV. Relationship between number of symptoms and the diagnosis of disorder. J Nerv Ment Dis. 2006;194:450–453. 34. Zimmerman M, Chelminski I, McGlinchey JB, et al. Diagnosing major depressive disorder, VI. Performance of an objective test as a diagnostic criterion. J Nerv Ment Dis. 2006;194:565–569. 35. Lundberg GD. Low-tech autopsies in the era of high-tech medicine: continued value for quality assurance and patient safety. JAMA. 1998;2801:1273–1274. 36. Mayeux R, Saunders AM, Shea S, et al. Utility of the apolipoprotein E genotype in the diagnosis of Alzheimer’s disease. Alzheimer’s Disease Centers Consortium on Apolipoprotein E and Alzheimer’s Disease. N Engl J Med. 1998;338(8):506–511. 37. Matarazzo JD. The reliability of psychiatric and psychological diagnosis. Clin Psychol Rev. 1983;3:103–145. 38. Tiemens BG, VonKorff M, Lin EH. Diagnosis of depression by primary care physicians versus a structured diagnostic interview. Understanding discordance. Gen Hosp Psychiatry. 1999;21(2):87–96. 39. Gilbody SM, House AO, Sheldon TA. Psychiatrists in the UK do not use outcomes measures: National survey. Br J Psychiatry. 2002;80:101–103. 40. Spitzer RL. Psychiatric diagnosis: Are clinicians still necessary? Comprehensive Psychiatry. 1983;24:399–411. 41. Antony MM, Barlow DH. Structured and semistructured diagnostic interviews. In Barlow DH, ed. Handbook of assessment and treatment planning for psychological disorders. New York: Guilford, 2002:3–37. 42. Leckman JF, Sholomskas D, Thompson WD, et al. Best estimate of lifetime psychiatric diagnoses. Arch Gen Psychiatry. 1982;39:879–883. 43. Kosten TA, Rounsaville BJ. Sensitivity of psychiatric diagnosis based on the best estimate procedure. Am J Psychiatry. 1992;149:1225–1227. 44. Taiminen T, Ranta K, Karlsson H, et al. Comparison of clinical and best-estimate research DSM-IV diagnoses in a Finnish sample of first-admission psychosis and severe affective disorder. Nord J Psychiatry. 2001;55(2):107–111. 45. Spitzer RL, Kroenke K, Williams JBW, et al. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. JAMA. 1999;282:1737–1744. 46. Carballeira Y, Dumont P, Borgacci S, et al. Criterion validity of the French version of Patient Health Questionnaire (PHQ) in a hospital department of internal medicine. Psychol Psychotherapy Theory Res Pract. 2007;80:69–77. 47. Moller HJ. Rating depressed patients: observer- vs self-assessment. Eur Psychiatry. 2000;15(3):160–172. 48. Becker J, Kocalevent RD, Rose M, et al. Standardized diagnosing: Computer-assisted (CIDI) diagnoses compared to clinically-judged diagnoses in a psychosomatic setting. Psychotherapie Psychosomatik Medizinische Psychologie. 2006;56(1):5–14. 49. Helzer JE, Robins LN, McEvoy LT, et al. A comparison of clinical and diagnostic interview schedule diagnoses. Physician reexamination of lay-interviewed cases in the general population. Arch Gen Psychiatry. 1985;42:657–666.

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50. Anthony JC, Folstein M, Romanoski AJ, et al. Comparison of the Lay Diagnostic Interview Schedule and a standardized psychiatric diagnosis. Experience in eastern Baltimore. Arch Gen Psychiatry. 1985;42(7):667–675. 51. Steiner J, Tebes J, Sledge W, et al. A comparison of the structured clinical interview for DSM-III-R and clinical diagnoses. J Nerv Ment Dis. 1995;183(6):365–369. 52. Shear MK, Greeno C, Kang J, et al. Diagnosis of nonpsychotic patients in community clinics. Am J Psychiatry. 2000;157:581–587. 53. Miller PR. Dasher R, Collins R, et al. Inpatient diagnostic assessments: 1. Accuracy of structured versus unstructured interviews. Psychiatry Res. 2001;105:265–272. 54. Miller PR. Inpatient diagnostic assessments: 2. Interrater reliability and outcomes of structured vs. unstructured interviews. Psychiatry Res. 2001;105:265–271. 55. Kashner TM, Rush AJ, Suris A, et al. Impact of structured clinical interviews on physicians’ practices in community mental health settings. Psychiatr Serv. 2003;54:712–718. 56. Basco RM, Bostic JQ, Davies D, et al. Methods to improve diagnostic accuracy in a community mental health setting. Am J Psychiatry. 2000;157(10):1599–1605. 57. Riskind JH, Beck AT, Berchick RJ, et al. Reliability of DSM-III diagnoses for major depression and generalized anxiety disorder using the Structured Clinical Interview for DSM-III. Arch Gen Psychiatry. 1987;44:817–820. 58. Williams JBW, Gibbon M, First MB, et al. The Structured Clinical Interview for DSM-III-R (SCID), II: multisite test–retest reliability. Arch Gen Psychiatry. 1992;49:630–636. 59. Robins L. National Institute of Mental Health diagnostic interview schedule—its history, characteristics, and validity. Arch General Psychiatry. 1981;38:381. 60. Rogers R. Handbook of diagnostic and structured interviewing. New York: Guilford Publications, 2001. 61. Gibson C. Semi-structured and unstructured interviewing: a comparison of methodologies in research with patients following discharge from an acute psychiatric hospital. J Psychiatric Mental Health Nursing. 1998;5(6):469–477. 62. Robins LN. Psychiat Disorders A: 1991. 63. Kessler RC, McGonagle KA, Zhao S, et al. Lifetime and 12-month prevalence of DSMIII-R psychiatric disorders in the United States—results from the National Comorbidity Survey. Arch Gen Psychiatry. 1994;51:8. 64. Brugha TS, Bebbington PE, Jenkins R. A difference that matters: comparisons of structured and semi-structured psychiatric diagnostic interviews in the general population. Psychol Med. 1999;29:1013–1020. 65. Spitzer RL, Williams JB, Gibbon M, et al. The Structured Clinical Interview for DSM-III-R (SCID). I: History, rationale, and description. Arch Gen Psychiatry. 1992;49(8):624–629. 66. Williams JB, Gibbon M, First MB, et al. The Structured Clinical Interview for DSM-III-R (SCID), II: multisite test–retest reliability. Arch Gen Psychiatry. 1992;49:630–636. 67. Haro JM, Arbabzadeh-Bouchez S, Brugha TS, et al. 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 Psychiatric Res. 2006;15(4):167–180. 68. Kashner TM, Rush AJ, Suris A, et al. Impact of structural clinical interviews on physicians’ practices in community mental health settings. Psychiatric Services. 2003;54(5):712–718.

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69. Philipp M, Maier W. The polydiagnostic interview: a structured interview for polydiagnostic classification of psychiatric patients. Psychopathology. 1986;19:175–185. 70. Wing JK, Babor T, Brugha T, et al. SCAN. Schedules for Clinical Assessment in Neuropsychiatry. Arch Gen Psychiatry. 1990;47(6):589–593. 71. Murphy JM, Monson RR, Laird NM, et al. A comparison of diagnostic interviews for depression in the Stirling County Study Challenges for Psychiatric Epidemiology. Arch Gen Psychiatry. 2000;57:230–236. 72. Wittchen HU, Robins LN, Cottler LB, et al. Cross-cultural feasibility, reliability and sources of variance of the Composite International Diagnostic Interview (CIDI). The multicentre WHO/ADAMHA field trials. Br J Psychiatry. 1991;159:645–658. 73. Wittchen HU. Reliability and validity studies of the WHO-Composite International Diagnostic Interview (CIDI): a critical review. J Psychiatr Res. 1994;28:57–84. 74. Andrews G, Peters L. The psychometric properties of the Composite International Diagnostic Interview. Soc Psychiatry Psychiatr Epidemiol. 1998;33:80–88. 75. Janca A, Robins LN, Bucholz KK, et al. Comparison of Composite International Diagnostic Interview and clinical DSM-III-R criteria checklist diagnoses. Acta Psychiatr Scand. 1992;85:440–443. 76. Booth BM, Kirchner JE, Hamilton G, et al. Diagnosing depression in the medically ill: validity of a lay-administered structured diagnostic interview. J Psychiatric Res. 1998;32(6):353–360. 77. Sheehan DV, Lecrubier Y, Sheehan KH, et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998;59(Suppl 20):22–57. 78. Kendell RE. Clinical validity. Psychol Med. 1989;19:45–55. 79. Zimmerman M, Mattia JI. Psychiatric diagnosis in clinical practice: is comorbidity being missed? Comprehensive Psychiatry. 1999;40:182–191. 80. Miller PR. Inpatient diagnostic assessments: 3. Causes and effects of diagnostic imprecision. Psychiatry Res. 2002;111:191–197. 81. Gardner W, Kelleher KJ, Pajer KA, et al. Primary care clinicians’ use of standardized psychiatric diagnoses. Child Care Health Development. 2004;30(5):401–412. 82. Toshiyuki S, Makoto T. Is DSM widely accepted by Japanese clinicians? Psychiatry Clin Neurosci. 2001;55:437–450.

2 OVERVIEW OF DEPRESSION SCALES AND TOOLS Alex J. Mitchell

1. 2. 3. 4.

Background The Classic Severity Scales (1960–1980) The New Severity Scales (1981–2008) The Future of Screening Scales

Context There have been a large number of depression tools published for the purposes of detecting depression or rating its severity. Choosing between them is difficult without adequate information on their validity, reliability, and acceptability. Recently, ever-shorter-version mood measures have been released. Is a shorter scale a better scale? It is important to study each method against our best standard and ideally compare scales head to head to judge the optimal scale for each situation.

1.

Background

Clinicians and researchers have developed a bewildering number of tools for the assessment of depression. These are most often questionnaires designed to help elicit symptoms of depression for the purpose of screening, diagnosis, and monitoring progress (Textbox 2.1). Although we often use the terms screening, diagnosis, and case-finding interchangeably, in an epidemiologic sense screening refers to the attempted detection of disorder in those who had not sought testing or did not suspect they had a particular condition. Often a screening test is not usually intended to be diagnostic, in that those with suspicious findings may be referred for more definitive examination. The latter is perhaps better known as case-finding. This means a screening tool can favor negative predictive value 29

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Textbox 2.1. Definitions of Screening and Related Procedures Screening ‘‘The systematic application of a test or inquiry, to identify individuals at sufficient risk of a specific disorder to warrant further actions among those who have not sought medical help for that disorder’’ Case-Finding ‘‘The selected application of a test or inquiry, to identify those individuals with a suspected disorder and exclude those without a disorder, usually in a population who have sought medical help’’ Targeted (High-Risk) Case-Finding ‘‘The highly selected application of a test or inquiry, to identify individuals at high risk of a specific disorder by virtue of known risk factors’’ Severity Assessment ‘‘The application of a test or inquiry, to quantify the severity of a specific disorder’’ Adapted from Department of Health. Annual report of the National Screening Committee. London: DoH, 1997.

(NPV) over positive predictive value (PPV) (see Chapter 5). In both screening and case-finding the test may be applied ‘‘routinely’’ to all cases, or selectively to those thought to be at high risk. A screening test applied to many individuals should be as simple as possible to retain high uptake, and positive results must be paired with an acceptable next step.1 A case-finding measure may be more involved but should still consider acceptability. Adoption of a test in clinical practice probably depends more on acceptability than accuracy.2

Historical Aspects During the past five decades there has been a considerable effort to improve the methods used to detect and quantify depression (Textbox 2.2).3–6 Some scales, such as the Cronholm-Ottosson Depression Scale, have fallen into obscurity, while others, such as the Hamilton Depression Rating Scale and the Beck Depression Inventory, have each been cited over 10,000 times. Given that there are so many similar depression scales, it is not surprising that clinicians have trouble choosing between them. The American College of Psychology Consultants lists 213 psychologically oriented scales with variable validation and reliability data,7 simplified here to 50 depression scales (Textbox 2.3). Fortunately, this may be distilled further to ten key depression instruments, five created before 1980 and five more modern inventions (table 2.1, 2.2). The classic scales are the Hamilton Depression ˚ sberg Depression Rating Scale Rating Scale (HAM-D), the Montgomery-A

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(MADRS), the Beck Depression Inventory (BDI), the Zung Self-Rating Depression Scale (SDS), and the Centre for Epidemiologic Studies Depression Scale (CES-D). The five key scales developed since 1980 are the Hospital Anxiety Depression Scale (HADS), the Geriatric Depression Scale (GDS), the Edinburgh Postnatal Depression Scale (EPDS), the MOS 8-Item Depression Screener (Burnam Screen), and the Patient Health Questionnaire (PHQ-9). In addition, I have included the less-well-known Major Depression Inventory (MDI) as it has a special role, facilitating a diagnosis based on both DSM-IV and ICD-10 criteria. Tools examining more general psychopathology are purposely omitted from this chapter even if they include a rating of depression. This includes some seminal scales such as the General Health Questionnaire (GHQ) and the Hopkins Symptom Checklist (SCL) family (SCL-90, SCL-25, and SCL-8).8–10 To keep this chapter manageable I will also not discuss reliability and validity data in detail, but further information can be found in relevant chapters by setting. A comparison of these key scales is shown in Appendix 1.

Textbox 2.2. Development of Major Depression Scales 1952 1960 1961 1965 1968 1977 1977 1979 1980 1980 1982 1983 1986 1987 1987 1987 1988 1992 1994 1996 2001 2001

DSM-I published Hamilton Depression Scale (HAM-D) Beck Depression Inventory (BDI) Zung Self-Rating Depression Scale (SDS) DSM-II published Center for Epidemiologic Studies Depression Scale (CES-D) ICD-9 published ˚ sberg Depression Rating Scale (MADRAS) Montgomery-A DSM-III published The Bech–Rafaelsen Melancholia Scale (MES) Geriatric Depression Scale (GDS-30) Hospital Anxiety and Depression Scale (HADS) Abbreviated version of Geriatric Depression Scale (GDS-15) DSM-IIIR published Edinburgh Postnatal Depression Scale (EPDS) Inventory to Diagnose Depression (IDD) MOS-8 Burnam Screen ICD-10 published DSM-IV published Revision of BDI to BDI-II Patient Health Questionnaire (PHQ) Major Depression Inventory (MDI)

DSM (Diagnostic and Statistical Manual ICD – International Classification of Disease

of

Mental

Disorders);

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Textbox 2.3. Listing of Depression Scales Generic Scales Beck Depression InventoryTM-Second Edition (BDI-II)TM Brief Psychiatric Rating Scale (BPRS) Brief Symptom Inventory (BSI) Burns Depression Checklist (BDC) Carroll Depression Scales-Revised (CDS-R) Center for Epidemiological Studies Depression Scale (CES-D) Depression Anxiety Stress Scales (DASS) Depression Questionnaire (DQ) Depression 30 Scale (D-30) Diagnostic Interview Schedule (DIS-IV) Diagnostic Inventory for Depression (DID) Hamilton Depression Inventory (HDI) Hamilton Rating Scale for Depression (HRSD) Hopelessness Depression Symptom Questionnaire (HDSQ) Hospital Anxiety and Depression Scale (HADS) Inventory to Diagnose Depression (IDD) Inventory of Depressive Symptomatology (IDS) IPAT Depression Scale Manual for the Diagnosis of Major Depression (MDMD) Minnesota Multiphasic Personality Inventory 2 (MMPI-2) Depression Scale ˚ sberg Depression Rating Scale Montgomery–A (MADRS) MOS 8-Item Depression Screener Multiple Affect Adjective Checklist-Revised (MAACL-R) Multiscore Depression Inventory for Adolescents and Adults (MDI) Newcastle Scales Positive and Negative Affect Scales (PANAS) Primary Care Evaluation of Mental Disorders (PRIME-MD) Profile of Mood States (POMS) Raskin Three-Area Severity of Depression Scale Revised Hamilton-Rating Scale for Depression (RHRSD): Reynolds Depression Screening Inventory (RDSI) Rimon’s Brief Depression Scale (RBDS) State Trait-Depression Adjective Check List (ST-DACL) Symptom Checklist-90-Revised (SCL-90-R) Zung Self-Rating Depression Scale (Zung SDS)

Special Population Scales Aphasic Depression Rating Scale (ADRS) Calgary Depression Scale for Schizophrenia (CDSS) Children’s Depression Inventory (CDI) The Children’s Depression Index (CDI) Children’s Depression Rating Scale-Revised (CDRS-R) Cornell Scale for Depression in Dementia (Cornell Scale) Depression and Anxiety in Youth Scale (DAYS) Depression Intensity Scale Circles (DISCs) Depression Rating Scale (DRS) Geriatric Depression Scale (GDS) Kiddie-Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL) Medical-Based Emotional Distress Scale (MEDS) Multiscore Depression Inventory for Children (MDI-C) Postpartum Depression Interview Schedule (PDIS) Psychopathology Inventory for Mentally Retarded Adults (PIMRA) Reynolds Adolescent Depression Scale (RADS) Reynolds Child Depression Scale (RCDS) Signs of Depression Scale (SDSS) Stroke Aphasic Depression Questionnaire (SADQ) Visual Analog Mood Scales (VAMS) Youth Depression Adjective Checklist (Y-DACL)

Adapted from Nezu AM, Ronan GF, Meadows EA, eds. Practitioner’s guide to empirically-based measures of depression. Springer, 2007.

2 OVERVIEW OF DEPRESSION SCALES AND TOOLS

33

The Limitations of Severity Scales Most mood scales have only an approximate relationship to the criteria of ICD and DSM (see Textbox 2.2). None adhere strictly to these algorithmic criteria (including duration and function), and as such they do not produce operational diagnoses. Several early scales were developed to measure severity (see Sensitivity to Change below) during treatment.11 Yet the value of a scale does not necessarily correspond to its original or intended use—for example, the EPDS may not be the optimal choice in perinatal settings and yet may be valuable elsewhere. The evaluation and refinement of existing scales is discussed in Chapter 4. It remains a significant limitation that only a small number of well-powered studies have compared the value of multiple scales head to head.12,13 From these comparative studies, most suggest that severity scales provide somewhat distinct estimates of depression diagnosis and severity (this has been confirmed by Rasch analysis).14–16 For example, although all measure low mood, not all measure anhedonia, somatic symptoms, anxiety, suicidal ideation, and well-being. Depression scales are predominantly symptom counts over a narrowly defined period. They do not tend to measure chronicity or effect on daily function. Thus, they should not be considered a precise measure of burden of depression. Neither do they measure met or unmet needs or the desire for help. One fundamental issue is that it is not clear which of many possible symptoms of depression are most important for diagnosis (see Chapter 1). For example, some symptoms appear more likely to be associated with greater severity and pervasiveness of depression.17 If some symptoms are more important than others, should the scale weight items differently? This has been tried, but without good validation and at a cost of significant scale complexity. A second unresolved issue is whether depression differs significantly by setting and by comorbid disease. If one presupposes that there is one syndrome of depression manifest in all situations (eg, primary care, specialist care) and all medical conditions, then the role of any scale is simply to best identify and quantify these core symptoms. Although the ‘‘one size fits all’’ approach sounds unlikely, it is essentially the approach taken by DSM-IV and ICD-10. These do not attempt to define a syndrome of, say, ‘‘post-stroke depression’’ as opposed to uncomplicated depression in primary care. A number of very specific depression scales have been proposed to elicit special types of mood disorders. Examples are listed in Textbox 2.3 and include the Depression Scale in Schizophrenia (DEPS) scale,18 the Cornell Scale for the Assessment of Depression in Dementia (CSDD),19 the post-stroke depression scale,20 the Stroke Aphasic Depression Questionnaire (SADQ),21 and the Aphasic Depression Rating Scale.22 The scientific basis for and against having special scales for medical settings is discussed in Chapters 10 and 11. This usually

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revolves around the issue of whether to keep or omit somatic items (see Appendix 2). A final limitation is the temptation to overrely on scales to improve quality of care. Numerous studies have explored this issues, which is discussed by Gilbody in Chapter 7.

Patient-Rated Versus Clinician-Rated Scales In the case of a mental illness where there is no foolproof gold standard, it is by no means clear whether patient-rated or clinician-rated measures are more useful.23 A list of such scales is shown in box 4. Neither patient (self)-rated Textbox 2.4. Major Clinician vs. Self-Report Scales Clinician-Rated Protocols Hamilton Rating Scale for Depression Inventory of Depressive Symptomatology (IDS-C) Manual for the Diagnosis of Major Depression Montgomery–Asberg Depression Rating Scale Newcastle Scales Raskin Three-Area Scale Rimon’s Brief Depression Scale Self-Report Inventories Beck Depression Inventory-Second Edition Carroll Depression Scales-Revised Center for Epidemiological Studies Depression Scale Diagnostic Inventory for Depression Hamilton Depression Inventory Hopelessness Depression Symptom Questionnaire Inventory to Diagnose Depression Inventory of Depressive Symptomatology (IDS-SR) IPAT Depression Scale Minnesota Multiphasic Personality Inventory 2 Depression Scale MOS 8-Item Depression Screener Multiscore Depression Inventory for Adolescents and Adults Positive and Negative Affect Scales Revised Hamilton Rating Scale for Depression: Self-Report Reynolds Depression Screening Inventory State Trait-Depression Adjective Check Lists Zung Depression Self-Rating Depression Scale Adapted from Nezu AM, Ronan GF, Meadows EA, eds. Practitioner’s guide to empiricallybased measures of depression. Springer, 2007.

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35

scales nor clinician-rated scales are inherently more sensitive to change nor more accurate.24,25 A self-rated scale has certain benefits over interviewerrated scales and clinical interviews in large population studies. A self-rated scale takes less time and does not require trained personnel. The administration and scoring process is probably more standardized for self-rated scales.26 Clinician-rated scales can directly augment a clinical interview. If training is a requirement, then the skills of the clinician may also improve. The major advantage of interviewer-rated scales is that the experience of the interviewer comes into play. Faravelli and coworkers (1986)27 compared the distributions of three doctor-rated scales and three self-rated scales in a series of 100 depressed patients and noted that doctor-rated scales tend to be asymmetric toward the left, while self-rated scales tend to be asymmetric toward the right. This may result from the tendency of patients to judge their own condition as more severe than average, while doctors tend to rate severity as less than average. On the other hand, patients can underreport symptoms in some situations.28 Our advice is to choose the type of scale most suited to the purpose at hand.

Sensitivity to Change In psychiatry the concept of sensitivity to change of mood was first used in psychometric research during the 1970s.29,30 Yet sensitivity to change is a phrase that has been variably defined in the literature and is poorly understood. Most consider sensitivity to change to be the ability of a severity scale to detect small changes in outcomes over time with repeated assessment. A more accurate description of sensitivity to change is the proportion of those who actually changed according to a gold standard (eg, responders) that were correctly identified by the instrument under study (Fig. 2.1). One should also consider specificity to change as a useful concept. This is the proportion of those who actually did not change (eg, nonresponders) who are correctly identified as such by the instrument. That said, no group has yet documented specificity to change. The HAM-D has been the main comparator in most sensitivity to change papers.31 The HAM-D, MADRAS, BDI, and HADS have all been compared head to head, but results do not demonstrate any consistent superiority of one scale over another. Vermeersch and associates (2004)32 describe five factors that may influence the sensitivity of a scale: inclusion of irrelevant items, categorical items, items not conducive to detect change, items assessing traits, and items susceptible to floor and ceiling effects. Fundamentally, scales with many items are more likely to be sensitive to subtle changes.

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Gold Standard Change

Gold Standard No Change

Instrument Change

A/A + B PPV A

B

Instrument No Change

Total

D/C + D NPV C

D

A/A + C Se to Change

D/B + D Sp to Change

Figure 2.1. Accuracy of change in 2  2 format.

2.

The Classic Severity Scales (1960–1980)

Hamilton Rating Scale for Depression (HAM-D)33 In 1953 Max Hamilton moved to Leeds, where he developed one of the bestknown scales in psychiatry.34 The original HAM-D was developed to quantify severity after an interview had established a diagnosis of depression. Despite its age the HAM-D remains the most commonly used scale in treatment studies, helped by the fact that it is in the public domain.34 Indeed, it may have been a victim of its own success, as independent groups have produced as many as 20 conflicting variations.35 The HAM-D is rather unusual in that it is designed to be administered by a trained clinician on the basis of the clinical condition at the time of the interview. It requires a rather long semi-structured interview, taking 15 to 20 minutes. As such, it is probably not a good choice for screening in busy clinical settings. It was developed before DSM criteria were established for depression and differs significantly from the DSM approach, assessing four of the nine DSM-IV criteria. It may favor somatic presentations, as eight items are related to six somatic symptoms: insomnia, psychomotor retardation, loss of appetite, loss of energy, loss of weight, and loss of libido. There have been other criticisms, such as lack of a single unifying structure; differential item weighing, and limited interrater reliability (although this can be improved).36,37 In the past 5 years several shortened versions of the

2 OVERVIEW OF DEPRESSION SCALES AND TOOLS

37

HAM-D have appeared, including a seven-item version and a six-item version.38–40 Using Rasch analysis, Bech and coworkers (1981)41,42 confirmed that six items associated with unidimensionality could be combined. These were depressed mood, guilt, work/interests, psychomotor retardation, anxiety psychic, and general somatic symptoms. Several versions provide standardized explicit scoring conventions and/or structured interview guidance.43

˚ sberg Depression Rating Scale (MADRS)44 Montgomery-A ˚ sberg45 published this 10-item scale in 1979 following Montgomery and A earlier development of the Comprehensive Psychopathological Rating Scale (CPRS).46 Ratings of patients on the 65-item CPRS were used to identify the 17 most common symptoms in depression, which were fieldtested in four antidepressant trials and hence refined to 10 items suggested to show the largest changes with treatment. However, it is a mistake to assume the MADRS is necessarily most sensitive to change (see above); indeed, a meta-analysis showed that the HAM-D has superior sensitivity to change.47 Like the HAM-D, this is a clinician-rated scale designed for a trained interviewer, although a self-rating form was later developed. It covers the clinical condition at the time of the interview and does not specify a timeframe during which the patient should be rated. The 10-item checklist actually consists of 1 observational item and 9 question items that require about 15 minutes of additional interview time. The items covered are apparent sadness, reported sadness, inner tension, reduced sleep, reduced appetite, concentration difficulties, lassitude, inability to feel, pessimistic thoughts, and suicidal thoughts. These items also cover all the DSM-IV criteria for major depression, with the exception of psychomotor retardation or agitation.

Beck Depression Inventory (BDI)48 The original version of this scale was developed by Aaron Beck and colleagues at the University of Pennsylvania and first published in 1961.49 It can be administered by a trained professional or self-administered and covers an explicit 2 weeks before the evaluation (1 week in the original version). The 21-item version requires 5 to 10 minutes. Each item is scored on a consistent scale of 0 to 3, with options presented in a multiple-choice format. A reading age of about 10 years is required for a person who is self-administering the test. In the original publication no timeframe is mentioned, but in the BDI-IA

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revision, this was changed to 1 week and in the BDI-II the time frame was extended to 2 weeks to more closely follow the DSM criteria for MDD. Version II (1996) also replaced body image change, weight loss, somatic preoccupation, and work difficulty with agitation, worthlessness, concentration difficulty, and loss of energy. The scale is considered to emphasize psychological items. In fact, there are eight ‘‘cognitive items’’ (pessimism, past failures, guilty feelings, punishment feelings, self-dislike, self-criticalness, suicidal thoughts or wishes, and worthlessness) and nine ‘‘somatic items’’ (crying, agitation, indecisiveness, loss of energy, change in sleep patterns, change in appetite, concentration difficulties, tiredness and/or fatigue, and loss of interest in sex). Other items are sadness, loss of pleasure, loss of interest, and irritability. The cognitive and somatic items, when considered as subscales, are typically moderately correlated.50 Recently Beck and associates developed the Beck Depression Inventory Fast Screen (BDI-FS) to address possible somatic contamination.51 It contains 7 of the original 21 BDI-II items to assess cognitive and affective aspects of depression, conforming with DSM-IV diagnostic criteria. It was developed to permit more rapid detection of depression in primary care and hospital settings. Original validation data was derived two samples, a group of 500 patients from four psychiatric outpatient facilities and a group of 120 college students. Rasch analysis of BDI has been reported.52 The BDI was administered to 660 adult patients with unipolar depression and examined using factor analysis. BDI was internally consistent but yet distinct in severity rating from the MADRS.53

The Zung Self-Rating Depression Scale (SDS)54 The Zung SDS is a 20-item scale in its original form that takes about 5 to 8 minutes to administer.55 It is the prototypical self-report depression scale. Of the 20 items, half are worded positively (‘‘I feel hopeful about the future’’) and half negatively (‘‘I feel downhearted and blue’’). Each item is consistently rated with a 4-point Likert scale (a little of the time ¼ 1; some of the time ¼ 2; a good part of the time ¼ 3; or most of the time ¼ 4). A meta-analysis summarized validity studies up to 1986.56 A large factor analysis in over 1,000 cancer patients showed a four-factor solution: a cognitive symptom factor, a depressed mood factor, and two somatic factors (eating-related and non–eating-related), accounting for 20%, 13%, 7%, and 8% of the variance on the Zung, respectively.57 Rasch analysis of the Zung SDS has been performed.58 Several short forms have been developed, including a 12-item,59 an 11-item,60 and a 10-item version.61

2 OVERVIEW OF DEPRESSION SCALES AND TOOLS

39

The Centre for Epidemiologic Studies Depression Scale62 This 20-item scale was originally developed as a screening instrument for community-based studies from existing scales such as the BDI and Zung SDS.63 It was designed at the U.S. National Institute of Mental Health (NIMH) with government rather than university funding. It bridged both epidemiologic and clinical needs and was first used in an epidemiologic study of Kansas City64 and became the most used depression scale in the 1990s. It includes items concerning low mood and loss of interest but not suicidal ideation. Original psychometric properties were based on three community samples and two psychiatric patient samples consisting of about 5,000 healthy individuals but only 70 adult psychiatric patients. Four of the 20 items are positively worded and reverse scored (negatively keyed). CES-D is designed for self-completion, telephone administration, or webbased administration. The approach is mostly psychological, with some somatic items. The CES-D has four separate factors: low mood, somatic symptoms, positive affect, and interpersonal relations. A revised version has been published, the CESD-R, which is more in line with DSM. There are a variety of short forms, most notably several 10-item versions and a 5-item version.65 Recently Rasch-modeled short forms have been reported in a general population.66 A second model has been applied to the depressed population.67

3.

The New Severity Scales (1981–2008)

Hospital Anxiety Depression Scale (HADS)68 The HADS can be considered the first in a new generation of scales that were shorter, easier to score, and no less accurate than the first generation. It is a relatively brief self-administered rating scale of symptoms and functioning. Anxiety and depression are assessed as separate components, each with seven items that are rated from 0 (no problem) to 3. A cut-off of 7v8 in each subscale is usually recommended, although others have been used.69 Although the scores for the two components have often been added together to give a composite anxiety–depression score (or emotional distress), this is not recommended by the authors. It is a fairly simple scale that does not include somatic and cognitive signs of depression. Limitations are that seven of nine DSM criteria are not covered in the HADS and the reverse rating of some items, together with the random sorting of depression and anxiety questions, can cause confusion. It excludes reduced appetite, weight loss, sleeping disturbances, fatigue, and concentration difficulties and also excludes guilt, worthlessness, and suicidality. Notably, it does not include a

40

SCREENING FOR DEPRESSION IN CLINICAL PRACTICE

3000

2500

2000

1500

1000

500

ev en Tw el v Th e irt ee n Fo ur te en Fi fte en Si xt Se een ve nt ee n Ei gh te en

El

e

n Te

t

in N

n

gh ei

x

ve

Si

Se

ur

e

ve Fi

Fo

o

re Th

ne

Tw

O

Ze

ro

0

Figure 2.2. Distribution of HADS-D scores in 18,414 primary care attendees. Adapted from Thompson C, Ostler K, Peveler RC, et al. Dimensional perspective on the recognition of depressive symptoms in primary care: The Hampshire Depression Project 3. Br J Psychiatry. 2001;179:317–323.

question on low mood per se. These choices may or may not be advantageous in general hospital and primary care settings (see Chapters 10 and 11 for discussion). Despite these limitations, the HADS has found an important place and has been used in impressive studies involving thousands of patients (Fig. 2.2).70–72 Good data are also available on values in nonclinical populations.73

Geriatric Depression Scale (GDS)74 In its original form the GDS consists of a simple list of 30 questions, all of which require a ‘‘yes’’ or ‘‘no’’ answer.75 However, a 15-item version is very commonly used. Ten of the items on the GDS-30 and five of the items on the GDS-15 are negatively keyed (ie, a ‘‘no’’ response is an endorsement of a depressive symptom). The GDS is a self-report instrument, and a telephone version has demonstrated good agreement with the self-report questionnaire. The GDS focuses on the psychological symptoms of depression, particularly changes in mood and thoughts. Few somatic items are included on the GDS—specifically, sleep, appetite, gastrointestinal symptoms, autonomic symptoms, and sexual symptoms

2 OVERVIEW OF DEPRESSION SCALES AND TOOLS

41

are not assessed. GDS-30 covers five of the DSM-IV criteria using differing terminology (lowered mood, loss of interest, loss of energy, impaired concentration, and restlessness), and GDS-15 covers three (lowered mood, loss of interest, and loss of energy). Questions about suicidal ideation were intentionally not included, and the scoring of items makes the GDS a poor choice for rating the burden or severity of depression. Rasch analysis of GDS has been reported.76 In one study of 526 people over 65 in home care, the optimal cutoff on the GDS-15 was 5, which yielded a sensitivity of 71.8% and a specificity of 78.2%.77 A systematic review of the GDS found 42 studies with a mean sensitivity of 0.753 and specificity of 0.770 for the GDS-30 and a sensitivity of 0.805 and a specificity of 0.750 for the GDS-15.71 GDS versions showed significantly better validity indices than the ‘‘Yale-1-question’’ screen but were similar to the CES-D. Briefer 10-item, 5-item, and 4-item versions and even a 1-item version have been developed, but their value is currently uncertain.

The Edinburgh Postnatal Depression Scale (EPDS)78 Cox and colleagues developed this scale after noting that some women endorse somatic items on existing scales because of the physiologic changes of childbearing and because of normal postnatal sleep disturbance.79,80 The authors used clinical intuition to identify possible items from questionnaires such as the SAD and HAD scales and the BDI. Thirty items were initially tested, and 13 items that were thought likely to detect mothers with clinical depression were tested on a sample of 60 postnatal women against the Clinical Interview Schedule. After factor analysis this was shortened to the final 10-item scale. Interestingly, the EPDS contains no specific item about mother–baby interaction or about irritability, which allowed its use to be expanded beyond perinatal settings. Its appeal is enhanced by its simple Likert scoring—0 for no presence of the symptom through 3 for marked presence/change in usual state. It incorporates anxiety but not suicidality. Studies suggest that the EPDS includes three factors expressing euthymic mood, anxiety, and depression. Anxiety (items 3, 4, 5, 6, and 7), depression (items 8, 9, and 10), and anhedonia (items 1 and 2) are the main components of the questionnaire, accounting for 63% of the variance.81 A short five-item version of the EPDS was developed after stepwise multiple regression analysis was used to find the combination of items that explains the maximum proportion of the variance of the full-scale sum score in 2,730 women. The selected EPDS items were thereafter correlated with the Hopkins Symptom Check List (HSCL-25)82 for external validation. The five items were ‘‘I have felt sad or

42

SCREENING FOR DEPRESSION IN CLINICAL PRACTICE

miserable,’’ ‘‘I have been anxious or worried for no good reason,’’ ‘‘I have been so unhappy that I have had difficulty sleeping,’’ ‘‘I have blamed myself unnecessarily when things went wrong,’’ and ‘‘I have looked forward with enjoyment to things.’’ Rasch analysis of the EPDS suggested that a revised eight-item version (EPDS-8) might provide a more psychometrically robust scale.83 Recent mandated screening programs in Australia and the United States have recommended routine administration of the EPDS, although National Institute for Health and Clinical Excellence (NICE) guidance in the United Kingdom does not.

MOS 8-Item Depression Screener (Burnam Screen)84 This short tool was developed for use in the National Study of Medical Care Outcomes (MOS).85 It was essentially an adaptation of the CES-D, although two items related to duration of symptoms (required for DSM diagnosis/ caseness) were drawn from the DIS. The tool has only eight items, although #7 and #8 are rather unwieldy single questions: 1. I felt depressed, 2. My sleep was restless, 3. I enjoyed life, 4. I had crying spells, 5. I felt sad, 6. I felt that people disliked me, 7. In the past year, have you had 2 weeks or more that you felt sad, blue, depressed, or lost pleasure in things that you usually cared about or enjoyed?, 8. Have you had 2 years or more in your life when you felt depressed or sad most days, even if you felt okay sometimes? (If yes:) Have you felt depressed or sad much of the time in the past year? Validation data were provided by two samples: 3,132 adults in the Los Angeles sample of the Epidemiological Catchment Area (ECA) study, and 525 adults from the Psychiatric Screening Questionnaire for Primary Care Patients (PSP) study. However, a limitation is that a complex scoring algorithm has been suggested. Additionally, in comparison with the NIMH’s Structured Clinical Interview for DSM-IV, the screen had low positive predictive value (Tuunainen et al., 2001).86

The Patient Health Questionnaire (PHQ)87 The PHQ is the self-administered version of the Primary Care Evaluation of Mental Disorders (PRIME-MD) instrument, which was designed to diagnose specific disorders in primary care settings using DSM criteria.88 The whole PRIME-MD has two components: a 1-page patient questionnaire (PQ) and a 12-page clinician evaluation guide (CEG). The PQ, which is completed by the patient before seeing the primary care physician (PCP), consists of 26 yes/no questions inquiring about symptoms that were present during the past month.

2 OVERVIEW OF DEPRESSION SCALES AND TOOLS

43

The focus is on a depressive episode (the SCID focuses on depressive disorder). The depression module comprises nine questions (PHQ-9). The first two questions (known as the PHQ-2), which refer to the ‘‘cardinal’’ symptoms of anhedonia and depressed mood, can be administered separately as a screening tool. This scale rates the proportion of time from ‘‘0’’ (not at all) to ‘‘3’’ (nearly every day). Rated linearly, a cutoff of 10 is suggested to represent mild depression. However, individual items can be combined according to a DSM-IV algorithm to generate a diagnosis of major or minor depression. The DSM-IV exclusion criteria for a depressive disorder are not included in the PHQ-9; therefore, the PHQ9 diagnosis closely approximates but is not identical to a DSM-IV diagnosis. Validation of the PHQ-9 took place in 6,000 patients in eight primary care clinics and seven obstetricsgynecology clinics.89 The short version of the PHQ is almost as well known as the long version. The PHQ-2 is a two-item screen which uses the first two items from the PHQ that inquire about the frequency of depressed mood (question 2) and loss of interest (question 1) over the past 2 weeks, scoring each as 0 (‘‘not at all’’) to 3 (‘‘nearly every day’’). A score of three points or more on this version of the PHQ-2 is sometimes recommended.81 However, an even simpler version calls for simple ‘‘yes’’ or ‘‘no’’ responses, with a ‘‘yes’’ response to either question constituting a positive screen. The questions are as follows: Over the past month, have you often had little interest or pleasure in doing things? (Yes/ No) Over the past month, have you often been bothered by feeling down, depressed, or hopeless? (Yes/ No). A two-stage screening with the PHQ-2 and then the PHQ-9 has been investigated and is probably more efficient than either test alone. However, when given by pen and paper, the time taken to check if there is a positive PHQ-2 may limit the efficiency saving.

Major Depression Inventory (MDI)90 This self-rated questionnaire aims to help make a diagnosis of major depression, according to either the DSM-IV criteria or the ICD-10 criteria.91 It covers the previous 2 weeks and requires 5 to 10 minutes. An answer of ‘‘more than half of the time’’ to at least 5 of the 10 questions is indicative of major depression. It has 10 questions, although items 8 and 10 each have two subitems, a and b—therefore, it can be considered 12 items. Ratings are consistent from 0 (at no time) to 5 (all of the time), giving a total score from 0 to 50. A score of 4 or more on an item (ie, most of the time) qualifies for the algorithm of ICD-10 or DSM-IV. The ICD-10 algorithm requires a score of 4

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SCREENING FOR DEPRESSION IN CLINICAL PRACTICE

or 5 on two of the three top items and on at least four of the remaining items. The DSM-IV algorithm requires a score of 4 or 5 on five of the nine items (item 4 being excluded), but at least one of these five items must be either depressed mood or loss of interest. Few validation studies or translations of the MDI exist.92 A comparative study of the SDS and MDI in 89 patients with Parkinson’s disease suggested that the MDI is superior to the SDS.93 The largest study compared the MDI in 1,093 persons also interviewed by psychiatrists using SCAN. The specificity of the MDI was 0.22, the sensitivity 0.67, and kappa 0.25 when major depression according to SCAN was considered as the index of validity, and with all depressive disorders the specificity was 0.44, the sensitivity 0.51, and kappa 0.33. More highly educated persons and those with reported disability were less likely to be false negatives.94

4.

The Future of Screening Scales

The ideal scale is one that is very brief, highly acceptable, and very accurate when tested against an accepted reference standard. It may also be an advantage if it obeys current conventional diagnostic rules from ICD or DSM and is freely available but long enough to gauge severity and measure change. It is unclear whether one scale can fulfill all these purposes, but there is a trend to develop ever-shorter scales that attempt to retain high accuracy. All scales must consider the tension between acceptability and accuracy.

Improving Acceptability Following on from the originals, ever-shorter versions of every major scale have been released, usually comprising 10 items or less (Textbox 2.5). A good example is the 8-item Even Briefer Assessment Scale for Depression (EBAS DEP) derived from the 21-item Brief Assessment Scale.95 Of course, eight items might not be short enough for many settings, and in the extreme case single-item methods (applied by pen and paper, verbally, or in visual analog form) have been evaluated. The first ‘‘ultra-short’’ scales began to appear in the 1970s with early visual analog methods of rating mood.96 Just how good are these short and ultra-short scales?97 Whooley and colleagues (1997)98 compared CES-D (20- and 10-item versions), BDI (20- and 13-item versions), Symptom-Driven Diagnostic System for Primary Care (SDDS-PC), and MOS-8 against the Quick Diagnostic Interview Schedule for major depression. Using summary statistics

Table 2.1. Conventional Cutoff Scores for Different Severities of Depression Scale

Abbreviation No Depression (asymptomatic and subsyndromal)

Mild Moderate Severe Depression

Hamilton Depression Scale Beck Depression Inventory Beck Depression Inventory II Geriatric Depression Scale (original) Zung Self-Rating Depression Scale Hospital Anxiety and Depression Scale Montgomery˚ sberg A Depression Rating Scale Center for Epidemiologic Studies Depression Scale Edinburgh Postnatal Depression Scale Patient Health Questionnaire Patient Health Questionnaire (remapped to DSM-IV) Major Depression Inventory

HAM-D

0 to 7

8 to 13

14 to 18

19 to 63

BDI

0 to 9

10 to 16

17 to 29

30 to 63

BDI-II

0 to 13

14 to 19

20 to 28

29 to 63

GDS-30

0 to 9

10 to 19

20 to 30

20 to 30

SDS

0 to 49

50 to 59

60 to 69

70 to 80

HADS-D

0 to 7

8 to 10

11 to 14

15 to 21

MADRS

0 to 6

7 to 19

20 to 34

35 to 60

CESD

0 to 15

16 to 20

21 to 26

27 to 60

EPDS

0 to 9

9 to 12

13 to 30

13 to 30

PHQ-9

0 to 5

6 to 9

10 to 19

20 to 27

PHQ-9

0 to 9

10 to 16

17 to 22

23 to 27

MDI

0 to 13

14 to 19

20 to 26

27 to 50

45

Table 2.2. Summary of Scale Properties Year Scale

Abbreviation Original Items

1960

HAM-D

21

63

BDI

21

63

1961

Hamilton Depression Scale Beck Depression Inventory

Max Rater Score

Copyright

Clinician Public domain Patient Harcourt Assessment

Duration Time Frame 15 min 10 min

1965

Zung Self-Rating Depression Scale

SDS

20

80

Patient

Public domain

5–8 min

1977

Center for Epidemiologic Studies Depression Scale Montgomery˚ sberg Depression A Rating Scale

CESD

20

60

Patient

Public domain

4–5 min

MADRS

10

60

Observer Copyright

1979

10 min

Past week Past few days (BDI) Last 2 weeks (in BDI II) Past several days Past week

Current

Cites Per Year

Suicidality Included?

Somatic Bias (most to least)

237

Yes

#1

225

Yes

#6

84

Yes

#5

256

No

#7

107

Yes

#4

Table 2.2. (Continued) Year Scale

Abbreviation Original Items

1982

GDS-30

30

30

Patient

HADS

14

42

Patient

GDS-15

15

15

Patient

EPDS

10

30

Patient

MOS-8

8

20

Patient

RAND 2–5 min Corporation

PHQ

9

27

Patient

MDI

10

60

Patient

Public domain Elsevier

1983

1986 1987 1988

2001 2001

Geriatric Depression Scale (original) Hospital Anxiety and Depression Scale Geriatric Depression Scale (modified) Edinburgh Postnatal Depression Scale MOS-8 Burnam Screen Patient Health Questionnaire Major Depression Inventory

Max Rater Score

Copyright

Duration Time Frame

Public domain NFERNelson

10 min

Public domain Copyright

5 min

5 min

Past week Past week

Cites Per Year

Suicidality Included?

Somatic Bias (most to least)

94

No

#10

195

No

#6

31

No

#8

50

No

#11

12

No

#9

2–4 min

Past week Past week 2 weeks and 2 years 2 weeks

53

Yes

#2

3–5 min

2 weeks

7

Yes

#3

1–2 min

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SCREENING FOR DEPRESSION IN CLINICAL PRACTICE

(Table 2.3), the optimal tests appear to be MOS-8 > CES-D20 > CESD10 > BDI-20 > BDI-13 >SDDS-PC, with the least accurate method being the PHQ-2. However, even the PHQ-2 was good at excluding nondepressed cases with a high negative predictive value. However, this finding does not allow for test efficiency—that is, correcting for the length of the scale. Such weighting requires an economic evaluation, and such studies are in progress. This finding has since been extended, showing that even single-item mood scales can be valuable, albeit as a form of rule out (reassurance) for those who answer negatively.

Textbox 2.5. Short Versions of Rating Scales (10 items or less) Ten Items EPDS-10 (original) SDS-10 CES-D 10 DEPS-10 MADRS-10 (original) Nine Items PHQ9 HDI-Short Form Eight Items MOS-8 EPDS-8 PHQ-8 EBAS-Dep Seven Items HADS-Depression HADS-Anxiety HAM-D-7 BDI-7 DADS-7 EPDS-7 (depression items) Six Items EPDS-6 HAM-D-6 CES-D-6

Five Items EPDS-5 WHO-5 GDS-5 Emotion Thermometers Four Items GDS-4 Three Items PHQ2 + help question EPDS-3 (anxiety items) Two Items PHQ2 Whooley / NICE 2 Questions BDI-2 EPDS-2 One Item PHQ Q1 PHQ Q2 GDS-1 Distress Thermometer

Short methods improve acceptability, but there may be other techniques to improve uptake. A tool can be administered in the waiting room or by

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2 OVERVIEW OF DEPRESSION SCALES AND TOOLS

Table 2.3. Accuracy of Various Depression Scales Head to Head Questionnaire PHQ2 SDDS-PC MOS-8 CESD20 CESD10 BDI21 BDI13

Sensitivity Specificity 0.96 0.96 0.93 0.93 0.90 0.89 0.92

0.57 0.51 0.72 0.69 0.72 0.64 0.61

PPV

NPV PSI

0.33 0.30 0.42 0.40 0.41 0.35 0.34

0.98 0.98 0.98 0.98 0.97 0.96 0.97

0.31 0.28 0.40 0.38 0.38 0.31 0.31

Youden 0.53 0.47 0.65 0.62 0.62 0.53 0.53

FC

AUC

63.99 59.14 75.75 73.32 75.19 68.47 66.42

0.82 0.86 0.89 0.89 0.87 0.87 0.86

PSI, predictive summary index; PPV, positive predictive value; NPV, negative predictive value; FC, fraction correct; AUC, area under the curve. Data from Whooley MA, Avins AL, Miranda J, et al. Case-finding instruments for depression. J Gen Intern Med. 1997;12(7):439.

mail. Increasingly, questionnaires are becoming computerized and can be given using a Palm Pilot or Tablet or over the Internet (this is discussed further in Chapter 8). The format of a questionnaire can be influential. For example, a single-item visual analog item takes no more time than a verbal item but can quantify a symptom. The seven-item version of the emotion thermometers tested in cancer and cardiovascular settings is shown in the Appendix Figure 5.

Improving Accuracy Algorithmm Approaches In clinical practice, prevalence is typically low (between 10% and 30%), and therefore a high negative predictive value is relatively easy to achieve but a high positive predictive value is difficult. For example, if one applied a screening test with 80% sensitivity and specificity to a sample of 1,000 individuals with a 20% rate of depression, the positive predictive value would be 0.50 and the negative predictive value 0.94 (overall accuracy ¼ 0.80 by fraction correct) (see Appendix Table Single 3). Given that only 50% of those with a positive result would actually have depression, what would happen if you applied a second test to those who scored positive but relied on the results from the first screen for those who scored negative? This is illustrated in Appendix Figure 3. From Appendix Table MultiStep 3 providing the second instrument’s sensitivity and specificity of 80% held for the filtered population, the positive predictive value rises to 0.67 at a cost of a small fall in the negative predictive value to 0.85 (overall accuracy ¼ 0.83). In short, applying a second step to those who screen positive in step 1 favors specificity at a cost of sensitivity but with a gain in overall accuracy. This example of the application of two tests with 80% sensitivity and specificity might

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SCREENING FOR DEPRESSION IN CLINICAL PRACTICE

be unrealistic in clinical practice. Often different test performances are achievable in each step. A difficult question to answer is: What would be best, to choose instruments with high sensitivity or high specificity applied in step 1 or step 2? The answer from Table AP.4 is that it is best to apply the most accurate instrument first, where clinically possible (although often in screening the reverse occurs). If both instruments have the same combined value but different sensitivity and specificity values, the optimal yield can be calculated. The rule of thumb for a two-step approach for a low-prevalence setting is to avoid putting two instruments that favor sensitivity together, particularly if one has high sensitivity in the second step, and this may produce low overall yields. Practical application of two-step approaches have been recently described.99,100 Weighting Specific Items In the future there will be re-examination of the weighting of specific symptoms of depression in relation to depression in each setting. The current concept of depression is that there are certain essential core symptoms that define the disorder and others that contribute to severity.101–104 This may or may not hold true. A scientific understanding of optimal depression items has appeared only in the past 3 years. Zimmerman and colleagues have re-examined the traditional symptoms of depression to discover if all the conventional symptoms listed in DSM-IV or ICD-10 contribute to a diagnosis of depression. The difficulty with this method that there is no accepted gold standard (see Chapter 1). One way around this problem is to simply examine how many fulfill full DSM-IV (or ICD-10) criteria if only certain symptoms are counted. Zimmerman and colleagues proposed combining two core and three psychological symptoms—namely depressed mood, lack of interest, worthlessness, poor concentration, and thoughts of death. Against full DSM-IV, this abbreviated checklist had a sensitivity was 93.7%, specificity 94.8%, positive predictive value 95.5%, and negative predictive value 91.6%. Andrews and associates (2007)105 replicated this finding from data from the 10,641 respondents to the Australian National Survey of Mental Health and Well-Being using the 12-month version of the Composite International Diagnostic Interview. In this study sensitivity was 92.9%, specificity 99%, positive predictive value 94%, and negative predictive value 99.7%. Another method is to start with short versions and only add in items that prove useful. Brody and colleagues (1998)106 found that adding four follow-up questions on sleep disturbance, appetite, anhedonia, and self-esteem to the two-question PRIME-MD markedly improved the specificity while maintaining the sensitivity. Future developments will also take into account aspects of depression not measured by symptom counts alone—for example, tools that measure duration, impact, function, and desire for professional help.

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References 1. Wittkampf KA, van Zwieten M, Smits FT, et al. Patients’ view on screening for depression in general practice. Fam Pract. 2008;25:438–444. 2. Jepson R, Clegg A, Forbes C, et al. The determinants of screening uptake and interventions for increasing uptake: a systematic review. Health Technol Assess. 2000;4:14. 3. Grinker RR Sr, Miller J, Sabshin M, et al. The phenomena of depressions. New York: Hoeber, 1961. 4. Nezu AM, Ronan GF, Meadows EA, et al. Practitioners’ guide to empirically based measures of depression. Kluwer Academic/Plenum Publishers 2000. 5. Williams JW, Pignone M, Ramirez G, et al. Identifying depression in primary care: a literature synthesis of case-finding instruments. Gen Hosp Psychiatry. 2002;24(4): 225–237. 6. Gilbody S, Sheldon T, House A. Screening and case-finding instruments for depression: a meta-analysis. Can Med Assoc J. 2008;178:997–1003. 7. http://www.mentaltests.com/cms/mentaltests_list. 8. Parloff MB, Kelman HC, Frank JD. Comfort, effectiveness, and self-awareness as criteria of improvement in psychotherapy. Am J Psychiatry. 1954;111:343–351. 9. Derogatis LR, Lipman RS, Covi L. SCL-90: An outpatient psychiatric rating scale, preliminary report. Psychopharmacol Bull. 1973;9:13–28. 10. Fink P, Ornbol E, Hansen MS, et al. Detecting mental disorders in general hospitals by the SCL-8 scale. J Psychosom Res. 2004;56(3):371–375. 11. Demyttenaere K, De Fruyt J. Getting what you ask for: On the selectivity of depression rating scales. Psychotherapy Psychosomatics. 2003;72(2):61–70. 12. Ruhe HG, Dekker JJ, Peen J, et al. Clinical use of the Hamilton Depression Rating Scale: is increased efficiency possible? A post hoc comparison of Hamilton Depression Rating Scale, Maier and Bech subscales, Clinical Global Impression, and Symptom Checklist-90 scores. Comprehensive Psychiatry. 2005;46(6):417–427. 13. Leentjens AF, Lousberg R, Verhey FRJ. The psychometric properties of the Hospital Anxiety and Depression Scale in patients with Parkinson’s disease. Acta Neuropsychiatr. 2001;13:83–85. 14. Richter P, Werner J, Heerlein A, et al. On the validity of the Beck Depression Inventory. A review. Psychopathology. 1998;31(3):160–168. 15. Shafer AB. Meta-analysis of the factor structures of four depression questionnaires: Beck, CES-D, Hamilton, and Zung. J Clin Psychol. 2005;62(1):123–146. 16. Uher R, Farmer A, Maier W, et al. Measuring depression: comparison and integration of three scales in the GENDEP study. Psychol Med. 2008;38(2):289–300. 17. Faravelli C, Servi P, Arends JA, et al. Number of symptoms, quantification, and qualification of depression. Comprehensive Psychiatry. 1996;37(5):307–315. 18. Huttunen J, Taiminen T, Ka¨hko¨nen J, et al. Depression Scale (DEPS) in schizophrenia. Acta Psychiatr Scand. 1999;99(3):220–222. 19. Alexopoulos GS, Abrams RC, Young RC, et al. Cornell Scale for Depression in Dementia. Biol Psychiatry. 1988;23(3):271–284. 20. Gainotti G, Azzoni A, Razzano C, et al. The Post-Stroke Depression Rating Scale: a test specifically devised toinvestigate affective disorders of stroke patients. J Clin Exp Neuropsychol. 1997;19(3):340–356.

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21. Leeds L, Meara RJ, Hobson JP. The utility of the Stroke Aphasia Depression Questionnaire (SADQ) in a stroke rehabilitation unit. Clin Rehab. 2004;18(2):228–231. 22. Benaim C, Cailly B, Perennou D, et al. Validation of the Aphasic Depression Rating Scale. Stroke. 2004;35:1692. 23. Clements KM, Murphy JM, Eisen SV, et al. Comparison of self-report and clinicianrated measures of psychiatric symptoms and functioning in predicting 1-year hospital readmission. Administration And Policy In Mental Health And Mental Health Services Research. 2006;33(5):568–577. 24. Moller HJ. Rating depressed patients: observer- vs self-assessment. Eur Psychiatry. 2000;15(3):160–172. 25. Rush AJ, Carmody TJ, Ibrahim HM, et al. Comparison of self-report and clinician ratings on two inventories of depressive symptomatology. Psychiatr Serv. 2006;57(6):829–837. 26. Biggs JT, Wylie LT, Ziegler VE. Validity of the Zung Self-Rating Depression Scale. Br J Psychiatry. 1978;132:381–385. 27. Faravelli C, Albanesi G, Poli E. Assessment of depression: a comparison of rating scales. J Affect Disord. 1986;11:245–253. 28. Hunt M, Auriemma J, Cashaw ACA. Self-report bias and underreporting of depression on the BDI-II. J Personality Assess. 2003;80(1):26–30. 29. Vaughan M, Krawiecka M. Sensitivity to change in symptoms of new scales for rating chronic psychotic patients. Int Pharmacopsychiatry. 1979;14(3):121–126. 30. Maier W, Philipp M, Demuth W, et al. Reliability, validity, transferability and sensitivity to change of 3 rival observer rating-scales for the severity of depression (HAM-D, MADRS, BRMS). Int J Neurosci. 1986;31(1–4):288. 31. Bagby RM, Ryder AG, Schuller DR, et al. The Hamilton Depression Rating Scale; has the gold standard become a lead weight? Am J Psychiatry. 2004;161:2163–2177. 32. Vermeersch DA, Whipple JL, Lambert MJ, et al. Outcome questionnaire: Is it sensitive to changes in counselling center clients? J Counsel Psychol. 2004;51(1):38–49. 33. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56–62. 34. http://healthnet.umassmed.edu/mhealth/HamD.pdf. 35. Zitman FG, Mennen MF, Griez E, et al. The different versions of the Hamilton Depression Rating Scale. Psychopharmacology. 1990;9:28–34. 36. Bagby RM, Ryder AG, Schuller DR, et al. The Hamilton Depression Rating Scale: has the gold standard become a lead weight? Am J Psychiatry. 2004;161:2163–2177. 37. Williams JB. A structured interview guide for the Hamilton Depression Rating Scale. Arch Gen Psychiatry. 1988;45:742–747. 38. Khullar A, McIntyre RS. An approach to managing depression. Defining and measuring outcomes. Can Fam Physician. 2004;50:1374–1380. 39. McIntyre RS, Konarski JZ, Mancini DA, et al. Measuring the severity of depression and remission in primary care: validation of the HAMD-7 scale. Can Med Assoc J. 2005;173:1327–1334. 40. Bobes J, Bulbena A, Luque A, et al. The sufficiency of the HAM-D6 as an outcome instrument in the acute therapy of antidepressants in the outpatient setting. Int J Psychiatry Clin Practice. 2007;11(2):146–150. 41. Bech P, Gram LF, Dein E, et al. Quantitative rating of depressive states. Acta Psychiatr Scand. 1975;51:161–170.

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42. Bech P, Allerup P, Gram LF, et al. The Hamilton Depression Scale: evaluation of objectivity using logistic models. Acta Psychiatr Scand. 1981;63:290–299. 43. Kalali A, Williams JBW, Kobak KA, et al. The new GRID HAM-D: pilot testing and international field trials. Int J Neuropsychopharmacol. 2002;5:S147–S148. ˚ sberg M. A new depression scale designed to be sensitive to 44. Montgomery SA, A change. Br J Psychiatry. 1979;134:382–389. 45. http://www.neurotransmitter.net/depressionscales.html. 46. Asberg M, Montgomery SA, Perris C, et al. A comprehensive psychopathological rating scale. Acta Psychiatr Scand Suppl. 1978;271:5–27. 47. Carroll BJ, Wilson WH. HAM-D and MADRS as depression change measures. In: New Clinical Drug Evaluation Unit (NCDEU) Program Abstracts, 40th Annual Meeting, 2000. Rockville, MD: National Institute of Mental Health, poster number 9. 48. Beck AT, Ward CH, Mock J, et al. An inventory for measuring depression. Arch Gen Psychiatry. 1961;4:561–571. 49. http://harcourtassessment.com/haiweb/cultures/en-us/productdetail.htm?pid=015– 8018–370. 50. Storch EA, Roberti JW, Roth DA. Factor structure, concurrent validity, and internal consistency of the Beck Depression Inventory-Second Edition in a sample of college students. Depression Anxiety. 2001;19(3):187–189. 51. Beck AT, Steer RA, Brown GK. BDI-II fast screen for medical patients manual. London: The Psychological Corporation, 2000. 52. Bouman TK, Kok AR. Homogeneity of Beck’s Depression Inventory (BDI): Applying Rasch analysis in conceptual exploration. Acta Psychiatr Scand. 1987;76(5):568–573. 53. Uher R, Farmer A, Maier W, et al. Measuring depression: comparison and integration of three scales in the GENDEP study. Psychol Med. 2008;38:289–300. 54. Zung WW. A self-rating depression scale. Arch Gen Psychiatry. 1965;12:63–70. 55. http://healthnet.umassmed.edu/mhealth/ZungSelfRatedDepressionScale.pdf. 56. Lambert MJ, Hatch DR, Kingston MD, et al. Zung, Beck, and Hamilton Rating Scales as measures of treatment outcome: a meta-analytic comparison. J Consulting Clin Psychol. 1986;54(1):54–59. 57. Passik SD, Lundberg JC, Rosenfeld B, et al. Factor analysis of the Zung Self-Rating Depression Scale in a large ambulatory oncology sample. Psychosomatics. 2000;41:121–127. 58. Hong S, Min SY. Mixed Rasch modeling of the Self-Rating Depression Scale incorporating latent class and Rasch rating scale models. Educational and Psychological Measurement. 2007;67(2):280–299. 59. Hulstijn EM, Deelman BG, de Graaf A, et al. The Zung-12: a questionnaire for depression in the elderly. Tijdschr Gerontol Geriatr (Netherlands). 1992;23:85–93. 60. Dugan W, McDonald MV, Passik SD, et al. Use of the Zung Self-Rating Depression Scale in cancer patients: feasibility as a screening tool. Psychooncology. 1998;7(6):483–493. 61. Tucker MA, Ogle SJ, Davison JG, et al. Validation of a brief screening test for depression in the elderly. Age Ageing. 1987;16(3):139–144. 62. Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1:385–401. 63. http://www.mdlogix.com/cesdr.htm.

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64. Markush RE, Favero RV. Epidemiologic assessment of stressful life events, depressed mood, and psychophysiological symptoms: A preliminary report. In Dohrenwend BS, Dohrenwend BP, eds. Stressful life events: their nature and effects. New York: Wiley, 1974:171–190. 65. Furukawa T, Anraku K, Hiroe T, et al. Screening for depression among first-visit psychiatric patients: Comparison of different scoring methods for the Center for Epidemiologic Studies Depression Scale using receiver operating characteristic analyses. Psychiatry Clin Neurosci. 1997;51:71–78. 66. Cole JC, Rabin AS, Smith TL, et al. Development and validation of a Rasch-derived CES-D short form. Psychol Assess. 2004;16(4):360–372. 67. Chan KS, Orlando M, Ghosh-Dastidar B, et al. The interview mode effect on the Center for Epidemiological Studies Depression (CES-D) scale: an item response theory analysis. Med Care. 2004;42:281–289. 68. Zigmond AS, Snaith RP. The Hospital Anxiety and Depression Scale. Acta Psychiatr Scand. 1983;67:361–370. 69. Bjellard I, Dahl AA, Tangen Haug T, et al. The validity of the Hospital Anxiety and Depression Scale. An updated literature review. J Psychosom Res. 2002; 52:69–77. 70. Sharpe M, Strong V, Allen K, et al. Major depression in outpatients attending a regional cancer centre: screening and unmet treatment needs. Br J Cancer. 2004;90:314–320. 71. Martin CR, Thompson DR, Barth J. Factor structure of the Hospital Anxiety and Depression Scale in coronary heart disease patients in three countries. J Eval Clin Pract. 2008;14(2):281–287. 72. Thompson C, Ostler K, Peveler RC, et al. Dimensional perspective on the recognition of depressive symptoms in primary care: The Hampshire Depression Project 3. Br J Psychiatry. 2001;179:317–323. 73. Crawford JR, Henry JD, Crombie C, et al. Normative data for the HADS from a large non-clinical sample. Br J Clin Psychol. 2001;40:429–434. 74. Yesavage JA, Brink TL, Rose TL, et al. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res. 1983;17:37–49. 75. www.stanford.edu/~yesavage/GDS.html. 76. Tang WK, Wong E, Chiu HFK. The Geriatric Depression Scale should be shortened: results of Rasch analysis. Int J Geriatr Psychiatry. 2005;20(8):783–789. 77. Marc LG, Raue PJ, Bruce ML. Screening performance of the 15-item Geriatric Depression Scale in a diverse elderly home care population. Am J Geriatr Psychiatry. 2008;16(11):914–921. 78. Cox JL, Holden JM, Sagovsky R. Detection of postnatal depression: development of the 10-item Edinburgh Postnatal Depression Scale. Br J Psychiatry. 1987;150:782–786. 79. Wancata J, Alexandrowicz R, Marquart B, et al. The criterion validity of the Geriatric Depression Scale: a systematic review. Acta Psychiatr Scand. 2006;114(6):398–410. 80. www.aap.org/practicingsafety/Toolkit_Resources/Module2/EPDS.pdf. 81. Cox J, Holden J. Perinatal mental health—A guide to the EPDS. RCPsych Publications, 2003. 82. Chabrol H, Teissedre F. Relation between the Edinburgh Postnatal Depression Scale scores at 2–3 days and 4–6 weeks postpartum. J Reprod Infant Psychol. 2004;22:33–39.

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83. Hesbacher PT, Rickels K, Morris RJ, et al. Psychiatric illness in family practice. J Clin Psychiatry. 1980;41:6–10. 84. Burnam MA, Wells KB, Leake B, et al. Development of a brief screening instrument for detecting depressive disorders. Med Care. 1988;26:775–789. 85. Pallant JF, Miller RL, Tennant A. Evaluation of the Edinburgh Post Natal Depression Scale using Rasch analysis. BMC Psychiatry. 2006;6:28. 86. www.patient.co.uk/showdoc/40025272/. 87. Spitzer RL, Williams JB, Kroenke K, et al. Utility of a new procedure for diagnosing mental disorders in primary care. The PRIME-MD 1000 study. JAMA. 1994;272:1749–1756. 88. Tuunainena A, Langer RD, Klauber MR, Kripke DF. Short version of the CES-D Burnam screen for depression in reference to the structured psychiatric Interview. Psychiatry Research 2001; 103: 261–270. 89. Kroenke K Spitzer RL, Williams JBW. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–613. 90. Kroenke K, Spitzer RL, Williams JB. The Patient Health Questionnaire-2: validity of a two-item depression screener. Med Care. 2003;41:1284–1292. 91. http://www.gp-training.net/protocol/psychiatry/who/mdi.doc. 92. Fountoulakis KN, Iacovides A, Kleanthous S, et al. Reliability, validity and psychometric properties of the Greek translation of the Major Depression Inventory. BMC Psychiatry 2003;3:2. 93. Bech P, Wermuth L. Applicability and validity of the MDI in patients with Parkinson’s Disease. Nord J Psychiatry. 1998;52:305–309. 94. Forsell Y. The Major Depression Inventory versus schedules for clinical assessment in neuropsychiatry in a population sample. Soc Psychiatry Psychiatric Epi. 2005;40(3):209–213. 95. Weyerer S, Killmann U, Ames D, et al. The Even Briefer Assessment Scale for Depression (EBAS DEP): its suitability for the elderly in geriatric care in English- and German-speaking countries. Int J Geriatr Psychiatry. 1999;14(6): 473–480. 96. Folstein M. Reliability, validity, and clinical application of visual analog mood scale. Psychol Med. 1973;3:479. 97. Blank K, Gruman C, Robison JT. Case-finding for depression in elderly people: balancing ease of administration. J Gerontol A Biol Sci Med Sci. 2004;59:M378–M384. 98. Whooley MA, Avins AL, Miranda J, et al. Case-finding instruments for depression. J Gen Intern Med. 1997;12(7):439. 99. Thombs BD, Ziegelstein RC, Whooley MA. Optimizing detection of major depression among patients with coronary artery disease using the Patient Health Questionnaire: Data from the Heart and Soul Study. J Gen Intern Med. 23(12): 2014–2017. 100. Bech P, Rasmussen N, Olsen R, et al. The sensitivity and specificity of the MDI using the Present State Examination as the index of diagnostic validity. J Affect Disord. 2001;66:159–164. 101. Mitchell AJ, Baker-Glenn EA, Park B, et al. Can the distress thermometer be improved by additional mood domains? Part II: What is the Optimal Combination of Thermometers? Psychooncology. 2009 [e-pub March 18]. 102. Evans KR, Sills T, DeBrota DJ, et al. An item response analysis of the Hamilton Depression Rating Scale using shared data from two pharmaceutical companies. J Psychiat Res. 2004;38:275–284.

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103. Maier W, Philipp M. Improving the assessment of severity of depressive states: a reduction of the Hamilton Depression Scale. Pharmacopsychiatry. 1985;18: 114–115. 104. Gibbons RD, Clark D, Kupfer DJ. Exactly what does the Hamilton Depression Rating Scale measure? J Psychiat Res. 1993;27:259–273. 105. Andrews G, Slade T, Sunderland M, et al. Issues for DSM-V: Simplifying DSM-IV to enhance utility: the case of major depressive disorder. Am J Psychiatry. 2007;164: 1784–1785. 106. Brody DS, Hahn SR, Spitzer RL et al. Identifying Patients With Depression in the Primary Care Setting:A More Efficient Method. Arch Intern Med. 1998;158:2469– 2475.

3 WHY DO CLINICIANS HAVE DIFFICULTY DETECTING DEPRESSION? Alex J. Mitchell

1. 2. 3. 4. 5.

Introduction to the Problem of Over- and Under-Detection Predictors of Detection Patient and Clinician Influences on Detection Illness-Related Influences on Detection Conclusions

Context Hundreds of studies reveal than most cases of depression remain undetected and untreated. Yet there is growing concern that efforts to increase detection of depression entail unacceptable numbers of persons who are not depressed nonetheless being given a diagnosis and receiving medication. What factors underlie false-positive and false-negative errors? How might clinicians and services address these detection errors?

1.

Introduction to the Problem of Over- and Under-Detection

Only about half of primary care practitioners (PCPs) feel confident in diagnosing depression or assessing suicide risk.1–6 Yet the issue of underdetection is by no means confined to PCPs7–13 or to depression.14,15 Convincing data show that clinicians in all medical specialties have difficulty recognizing mental disorders. This includes depression, anxiety, and delirium and dementia.16,17 Less discussed in the literature but increasingly recognized as important is the issue of overdetection. In this chapter I will review the predictors of diagnostic errors (false positives and false negatives) with 57

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reference to depression in primary care. I will focus on two essential barriers to correct identification: communication and illness complexity. To meaningfully discuss errors in recognition, it is important to first establish baseline rates of depression. Prevalence exerts a powerful influence upon detection accuracy, not least because clinicians usually have a higher index of suspicion for high-risk patients. The World Health Organization (WHO) study on Psychological Problems in General Health Care (PPGHC), conducted across 14 countries, found that 26% of individuals visiting their PCP had at least one psychiatric disorder as defined by ICD-10 criteria.18 Fourteen percent had major depression. Almost identical rates were reported from the European Study of the Epidemiology of Mental Disorders (ESEMeD).19,20 If one examines depression in older people, the point prevalence of major depression is lower in rural than urban primary care practices (8.3% versus 14.8%).21 Further, if one combines a 14% rate of major depression with 10% who have minor depression, then the combined rate approaches 25%.22

How Many Cases of Depression Are Detected in Routine Care? Approximately 100 studies concerning the unassisted recognition rate of depression in primary care have been published, but only a third have used a robust semi-structured interview as a gold standard.23 Of these at least 10 have had samples of more than 1,000 and 17 studies examined both the ability of clinicians to rule in and rule out a diagnosis (see table 3.1). From these studies PCPs’ pooled sensitivity is 48% and specificity 70%. At a prevalence of 16%, the positive predictive value (PPV) is 21.4% and the negative predictive value (NPV) is 87.4%. In a low-risk sample where the prevalence is 10%, the PPV becomes 14% and NPV 92%. This is best illustrated in a Bayesian plot of conditional probabilities (Fig. 3.1). Looked at descriptively at a prevalence of 16%, an average PCP would correctly identify 8 out of 16 depressed cases, missing 8 true positives. He or she would correctly reassure 57 out of 84 non-cases but falsely diagnose 27 people as depressed (Fig. 3.2). Thus, the number of correctly identified people per 100 screened would be 64 (the number needed to screen would be 3.5 to correctly identify one true case or non-case). Out of every five cases thought to be depressed, only one would be a true case (PPV = 21.4%). Out of every 10 cases thought to be well, approximately 9 would be correctly reassured (NPV = 87.4%). In a low-risk sample (such as a rural practice) where the prevalence is 10%, an average PCP would correctly identify 5 out of 10 cases, missing 5 true positives, and would correctly reassure 60 out of 90 non-cases, falsely diagnosing 30 people as depressed. In a high-risk sample (such as patients with

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known physical disease), at a prevalence of 25%, Bayesian analysis suggests that an average PCP would correctly identify 12 out of 25 cases, missing 13 true positives, and would correctly reassure 50 out of 75 non-cases, falsely diagnosing 25 people as depressed.

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Clinicians do less well with minor depression as well as mild depression—a problem that is shared by those using screening tools as well.24 Underrecognition converts into undertreatment, as recognized patients are more likely to be offered mental health interventions.25 Data from ESEMeD shows that only 15.1% of those with an identified mood disorder and 23.2% with an anxiety disorder received either drug or psychological treatment.26 Maginn and colleagues (2004)27 found that PCPs recorded active management of a psychological problem in 37% of patients whom they rated as cases. Of these, 24% were prescribed psychoactive drug treatment, 5% were referred to psychiatric or psychological services, and 3% were offered both drug and psychological treatments. Surprisingly, only 5% were offered a follow-up appointment with their PCP. Wittchen and colleagues28 found somewhat more favorable rates of conversion to treatment in a large study of 20,421 primary care patients in Germany. After correctly identifying depression (according to the ICD-10 definition), doctors prescribed drug treatments in 60.8%, prescribed non-drug treatments in 24.9%, and referred the patient to a mental health specialist in 10%. The take-home message is that the typical proportion of recognized patients offered treatment from the large ESEMeD, PPGHC, and INSERM studies is approximately 20%.

Textbox 3.1. Case History: An Example of a Difficult Case? A previously well 58-year-old man comes to see his GP for the first time soon after discharge from hospital with a dominant hemisphere stroke from which he has difficulty walking and word finding. His main complaints are physical, notably discomfort on walking, fatigue, loss of appetite, and insomnia. His GP is not sure if he is depressed but asks about low mood and low of interest. Mood is indeed low since the stroke and motivation is poor, but interest, weight, and concentration are preserved. There is no hopelessness, guilt, or suicidal thoughts.

Understanding Detection Errors To go beyond raw rates of detection accuracy, detailed studies examining the types of diagnostic error are needed. Tiemens and colleagues (1999)12 found that that only 26% of missed cases (false negatives) were complete omissions, while 25% were underestimates of severity (eg, diagnosing subthreshold instead of mild) and 38% were misidentifications. Conversely, of false-positive diagnoses, 35% were overestimates of severity, 24% were misdiagnoses, and 41% were complete errors. Diagnostic errors are illustrated in Figure 3,

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using data from Wittchen and colleagues (2002).16 It can be seen that when deliberating both true cases and true non-cases, there is about a 25% rate of uncertainty, which is an area for improvement. It also helps explain the considerable variance between recognition studies, as these possible cases are sometimes included in those detected and sometimes in those missed. In the MAGPIE study, Bushnell and associates (2004)29 found that 38% of depression cases were not recognized. Reasons for this were not categorizing the patient’s psychological issues as clinically significant (23.4%), recognizing clinical significance but not ascribing a particular diagnosis (7.1%), or the PCP making an explicit diagnosis of something other than depression (7.7%). What, then, distinguishes one clinician from another? Rogers (2001)30 suggested several types of common clinical error when attempting to make a psychiatric diagnosis: idiosyncratic language in clinical questioning, idiosyncratic coverage in clinical questioning, idiosyncratic sequence of clinical questioning, idiosyncratic recording of responses and idiosyncratic rating of severity. (a) 60.0

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Figure 3.3a. and 3.3b. Severity estimates by general practitioners of nondepressed and depressed patients. Adapted from Wittchen HU, Kessler RC, Beesdo K, et al. Generalized anxiety and depression in primary care: prevalence, recognition, and management. J Clin Psychiatry. 2002;63(suppl 8):24–34.

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Predictors of Detection

There have been some impressive studies examining what factors influence correct detection, although few concerning the influences upon willingness to look for symptoms of depression. Borowsky and colleagues (2000)31 conducted an impressive study involving 19,309 patients from 349 PCPs in Boston, Chicago, and Los Angeles. All underwent the MOS eight-item Burnam screen for depression, and 1,610 underwent a Diagnostic Interview Schedule (DIS) for DSM-III. Of the patients, 661 were depressed, although only 70 had current major depression. Physicians were less likely to detect depression in African Americans, men, and those younger than 35 years and more likely to detect depression when comorbid hypertension or diabetes was present. Hickie and colleagues (2001)32 looked at a large sample of 46,515 patients attending 386 PCPs; 56% of cases were not recognized. This is probably the most comprehensive study of predictors of recognition available. Patients were more likely to be assessed psychologically if they were middleaged, female, Australian-born, unemployed, single, or presenting with mainly psychological symptoms or for psychological reasons. Doctor characteristics

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associated with willingness to assess were being over 35 years old, having an interest in mental health, having had previous mental health training, being in part-time practice, seeing fewer than 100 patients per week, and working in regional centers. Thompson and colleagues (2001)33 examined recognition among 156 PCPs in the United Kingdom, involving 18,414 individuals. The prevalence of depression was 20% based on a 7v8 cutoff on the HADS depression subscale. The mean recognition sensitivity was 36% and recognition specificity was 91.5% (Fig. 3.4). Women and unemployed people were more likely to be detected, while the elderly and retired were more likely to be missed. However, these relationships were confounded by severity of depression or anxiety: increased anxiety improved recognition of depression. Wittchen and colleagues (2002)16 conducted a large study of PCP recognition in Germany. This impressive nationwide study recruited a total of 20,421 patients, attending 633 PCPs. Taking the doctors’ decision of definite or probable depression, 75% of all DSM and 59% of all ICD-10 diagnoses were 0.3

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Figure 3.4. Burden and detection of depression by Hampshire (U.K.) general practitioners. 36% of depression (blue) was detected and 64% was missed (red). 72.6% of all omissions occurred at a HADS-D score of between 8 and 10. Adapted from Thompson C, Ostler K, Peveler RC, et al. Dimensional perspective on the recognition of depressive symptoms in primary care: The Hampshire Depression Project 3. Br J Psychiatry. 2001;179:317–323.

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recognized by the treating physician, albeit with an 11.7% false-positive rate. Multiple logistic regression revealed that recognition was associated with prior treatment episodes, increasing number of depression symptoms, patient’s higher age, practice experience of greater 5 years, and the presence of psychomotor retardation. In the MAGPIE study from New Zealand, 63.7% of patients with a CIDI-diagnosed disorder were recognized as having psychological problems, although only 40% were recognized as having a clinically significant psychological problem and only 33.8% were given an explicit diagnosis.28 In those seen five or more times during the previous year, these recognition figures increased to 80.2% compared with 28.8% among patients not seen in the previous year. Maginn and associates (2004)26 examined PCP recognition of distress in South London. Overall, PCPs identified 65% of cases, but Black African patients were less likely to be detected or treated than Black Caribbean and White English patients. Willingness to talk to the doctor about psychological problems was the main predictor of detection. Ethnicity did not independently predict detection, but Black African individuals were less likely to talk to their PCP about psychological problems. Worryingly, half as many Black African individuals with detected distress were offered treatment compared with English cases (41% versus 22%). Pfaff and Almeida (2004)34 found that 39.9% of patients (87/218) were correctly classified as depressed by their PCP. Older patients were more likely to be incorrectly classified as ‘‘not depressed’’ by their PCP when they were born outside of Australia or New Zealand, did not smoke or use sleeping tablets, acknowledged milder levels of depression, and presented with primarily somatic complaints. Aragones and colleagues (2004)35 screened 209 Zung-positive patients and 97 negative patients with the SCID. Detection was associated with educational level, severity of the depression, level of impairment, and the complaint of explicit psychological symptoms. Antidepressant treatment was associated with marital status, severity of and impairment from the depression, frequency of visits to the family physician, and the patient’s complaint of psychological symptoms. Aragones and colleagues went on to study of predictors of falsepositive diagnoses (2006)36 and found that PCPs had a nearly 50% rate of falsepositive diagnosis. Factors associated independently with overdiagnosis were higher levels of symptoms SDS score, lower Global Assessment of Functioning, a previous history of depression, and the absence of generalized anxiety. Nuyen and colleagues (2005)37 found that among 191 depressed primary-care patients diagnosed using the CIDI, 28.8% were recognized and recorded by PCPs over the same period. Patients without chronic somatic comorbidity, with a lower educational level, with less severe depression, and with fewer PCP contacts were all significantly more likely not to be diagnosed as depressed. Verhaak and coworkers (2006)38 conducted a survey of primary care contacts of patients with a DSM-IV diagnosis of affective disorder,

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anxiety disorder, or alcohol abuse. Forty percent visited their PCP but received only a somatic diagnosis and 50% were given a psychological or social diagnosis at least once during 1 year. The chances of a psychological PCP diagnosis increased with the number of PCP contacts. Patients who were given a psychological or social diagnosis by their PCP had a higher GHQ score, lower mental functioning scores on the SF-36, and far more visits to their PCP than those not diagnosed as psychologically ill. Finally, patients given a diagnosis tended to express slightly more confidence in their PCP. McCall and colleagues (2007)39 looked at predictors of recognition of distress in Austrian primary care practice. Twenty-eight PCPs completed a clinical audit on 868 of their patients who completed the GHQ-28. PCPs correctly identified 43% of GHQ-positive cases as having distress. For individual PCPs the rate of correct recognition varied considerably, from 4% to 100%. Correct recognition was associated with years of experience as a PCP, older age of patient, and greater severity of distress. Clearly, there is a wide variation in the ability of GPs to diagnose mental health problems, due in part to differences in knowledge, skills, and attitudes (Textbox 3.2).40,41 Most clinicians have difficulty recalling the current criteria for major depression.42 Further, only one third claim for make diagnoses based on validated criteria.43 Self-confident, outgoing physicians with high academic ability appear to make more accurate diagnoses44—yet this same formula would apply to psychiatrists’ ability to detect physical illness. One apparently simple solution is to increase the length of the consultation. There is reasonably good evidence that short appointments impair detection in difficult cases.45 However, paradoxically, lengthening the consultation may not improve recognition.46 Verhaak and colleagues (2007)47 found that in general, healthcare system characteristics do affect PCPs’ performance in psychosocial care. PCPs’ workload was not related to their awareness of psychological problems and hardly related to their communication, except for the finding that a PCP with a subjective experience of a lack of time is less patient-centered (Textbox 3.3).48 Textbox 3.2. Possible Barriers to Recognition (Diagnostic Barriers) Patient Related Younger patient Male gender Reluctance to seek help Reluctance to disclose symptoms Disclosure of only somatic symptoms Low awareness of emotional symptoms Fear of stigma/label of mental illness

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Textbox 3.2. (Continued) Clinician Related Low clinician confidence and skills Low therapeutic alliance Low consultation time Single appointment only Low index of suspicion Rare inquiry about depressive symptoms Caution re: stigma of mental illness

Textbox 3.3. Basic Patient-Centered Interviewing Method Step 1. Welcoming Welcome the patient Introduce self and identify specific role Ensure patient comfort and privacy Step 2. Set agenda Indicate time available and objective Summarize what is already known and others involved Indicate own needs Clarify what patient wants to discuss Step 3. Non-focused interviewing Open-ended beginning question: ‘‘How have things been recently?’’ Attentive (active) listening (with prompts): ‘‘That sounds difficult’’ Observe nonverbal cues Step 4. Focused interviewing Obtain description of main problem and secondary problems Clarify the development and context of the problems Ask about emotional and functional impact of the problems Step 5. Transition to agreed action Give brief summary and check accuracy

3.

Patient and Clinician Influences on Detection

Do Patients Volunteer Symptoms of Depression? It should be no surprise that recognition of distress and depression is linked with the number of symptoms reported during a consultation.49 Recognition is

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facilitated when patients report psychological symptoms of anxiety or depression early in the consultation.50 Patients who normalize or minimize their symptoms are less likely to be identified.51 It has been reported that detection rates may be 100% in those who spontaneously complain of emotional problems.52 However, patients do not usually complain of ‘‘depression,’’ and patients’ views about their depressive symptoms are significantly different from conventional medical views.53,54 Many groups have noted that patients with depression often present with physical symptoms rather than psychological complaints, and the depression is less likely to be recognized as a consequence.56–62 Perhaps 60% to 70% of patients with depression and anxiety have predominantly somatic presentations.63,64 Such patients tend to be older and have less severe depression but not necessarily more comorbid physical illness. Many authors have shown that patients are often reluctant to discuss emotional issues with health professionals.65–67 Patients have their own readiness to disclose.68 Indeed, willingness to discuss emotional issues may be one of the strongest predictors of detection.69 Some ethnic groups (whites and Hispanics) appear more likely to communicate with a clinician about depression than others (African Americans).70 However, most patients will discuss psychological symptoms if asked.71,72 Reassuringly, Davenport and associates (1987)73 found that there is some association between severity of distress and spontaneous verbal cues, but this is by no means a perfect correlation, and those clues are easily overlooked. O’Conner and colleagues (2001)74 examined 1,021 older patients in Melbourne, Australia. Symptom disclosure was associated with higher depressive scores, previous contact with a psychiatrist, and female gender; even so, 48% of persons with ICD-10 moderate or severe depressive episode had not reported any current complaints to their doctor at the time of the interview. In the MAGPIE study 30% of all primary care patients of all patients (and 37% of patients with current psychological symptoms) did not disclose their psychological problems spontaneously; younger patients, those consulting more frequently, and those with greater psychiatric disability were more likely to report non-disclosure.75 However, in this study, reported nondisclosure did not influence detection rates. Verhaak and colleagues76 collected comprehensive data on detection rates from consultations across 10 European countries and found low rates of spontaneous emotional complaints. What, then, are the reasons for not discussing emotional difficulties? The most frequently given reason in the MAGPIE study was the belief that the PCP is not the ‘‘right’’ person to talk to (33.8%) or that mental health problems should not be discussed at all (27.6%). In a survey of primary care attendees who were high scorers on the GHQ, more than 75% had not mentioned any emotional problems during a consultation.77 Thirty-six percent felt they were able to cope without emotional help, but 45% gave reasons including

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psychological embarrassment and hesitation to trouble the doctor, and a further 19% were deterred by the doctors’ interview behaviors (see below). Thirtynine percent felt there was little the doctor could do to help with their emotional problems. In a study by Del Piccolo and associates (1998),78 about two thirds of patients with stressful life events and social problems had mentioned them to their PCP. A positive attitude about confiding and emotional distress were the best predictors of confiding. In women, past confiding and a longstanding relationship with the PCP were also important. Pollock79 summarized the difficulty, stating that medical consultations are difficult encounters for most patients, who often strive to protect their privacy and personal integrity by ‘‘maintaining face,’’ but this in turn may impede the diagnostic process.

Do Clinicians Ask About Depression? Communication behaviors of clinicians have been much discussed. Individual clinicians differ in their communicative style, with some more patient-centered and others less so, but most adjust their style according to the situation, such as illness severity.79–81 In a large study recording responses of PCPs to standardized patients, biomedical inquiry/explanations, nonspecific acknowledgment, and reassurance were common, whereas empathy, expressions of uncertainty, and exploration of psychosocial factors and emotions were uncommon.82 Yet in consultations about psychosocial issues, doctors show more emotional behavior, ask more questions, and give less information than in other consultations.83,84 Feldman and colleagues (2007)85 found that history taking about depression was directly associated with the likelihood of a chart diagnosis of depression and the provision of minimally acceptable initial depression care. When PCP decisions for late-life depression were monitored, a recorded treatment decision occurred in about 5% of visits, a deferred or monitor-only decision occurred in about a third of visits, and no decision was made in about half of visits.86 Saltini and coworkers (2004)87 found that although occupational, financial, and housing problems and life events of loss were the most important predictors of the GHQ12 case definition, PCPs gave significantly more importance to psychiatric treatment, psychopharmacological drug, use and chronic illness. A number of authors have commented on suboptimal communication strategies from clinicians.88 Inadequate interview and diagnostic skills influence detection.89,90 For example, clinicians appear to miss most cues and concerns and adopt behaviors that discourage disclosure.91,92 More sophisticated analysis with video recording of consultations is revealing. In one of the best examples, Deveugele and colleagues (2004)93 analyzed 2,095 consultations from 168 PCPs using the Roter Interactional Analysis System. Clinicians differed markedly in their psychosocial and emotional communication. Some

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studies attempt to go further and uncover an explicit link with detection. In a seminal study from Marks and associates (1979),94 a research psychiatrist made detailed observations on 2,098 interviews carried out by 55 PCPs. The authors found that PCPs who had a better conceptual understanding of mental illness produced a more accurate diagnosis of the patient’s condition. They also noted that PCPs with an interest in psychological medicine, those with higher levels of empathy, and those who asked about social and family problems more accurately diagnosed psychiatric illness. Badger and colleagues (1994)95 found two communication behaviors that predicted successful recognition of depression: the proportion of the interview devoted to emotional issues and the use of broad, open-ended psychosocial questions. Carney and coworkers (1999)96 found that PCPs who recognized depression asked twice as many questions about feelings and affect compared with those who did not. In a series of interviews, Rost and colleagues (2000)97 found that physicians and patients discussed depression in 47.9% of untreated patients. Chronic physical comorbidity decreased the odds that physicians and untreated patients discussed depression as a possible diagnosis. Interestingly, PCPs who have a preference for psychotherapy rather than antidepressant treatment also appear more accurate in diagnosing depression.98 There are a number of important barriers to detection, including clinician attitude (Textbox 3.4). Saltini and associates (2004)99 found that although occupational, financial, and housing problems and life events of loss were the most important predictors of the GHQ-12 case definition, PCPs gave significantly more importance to psychiatric treatment, psychopharmacological drug use, and chronic illness. Travado and colleagues6 found that low

Textbox 3.4. Top 10 GP Perceived Barriers to Dealing with Depression 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Lack of access to mental health specialists (51.4%) Lack of time (50.6%) Poor reimbursement for depression treatment (50.4%) Distracted by other presenting problems (39.4%) Patient reluctant to be referred to a specialist (37.3%) Workload prevents adequate attention to depression (32.3%) Patient/family reluctance to accept diagnosis of depression (21.7%) Patient inability/unwillingness to discuss depressive symptoms (16.2%) Lack of accessible assessment tools for depression (15.9%) Patient reluctant to begin antidepressant medications (8.6%)

Adapted from Richards JC, Ryan P, McCabe MP, et al. Barriers to the effective management of depression in general practice. Aust N Z J Psychiatry. 2004;38:795–803.

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psychosocial orientation and burnout symptoms were associated with lower confidence in communication skills and higher expectations of a negative outcome after physician–patient communication. In a study of 50 PCPs and 473 patients in Portland, Oregon, routine office visits were audiotaped and analyzed for communication behaviors and emotional tone using the Roter Interactional Analysis System.100 Physicians with more positive attitudes to psychosocial aspects of patient care had more psychosocial discussions in visits. A large-scale practice audit in Australia found that PCPs with a declared interest in mental health and those who had obtained mental health training were more likely to see more patients with depression and more likely to provide appropriate mental health assessment and treatments. In some studies insufficient undergraduate and postgraduate training is influential,101 as well as insufficient time devoted to adequate diagnostic assessment, and a lack of acquisition of new knowledge relevant to provision of treatments. Three recent observation studies have examined physician habits in relation to late-life depression. In a study based in nine primary care clinics involving 1,023 individuals, Fischer and colleagues (2003)102 found that physicians were only 6% as likely to ask older depressed patients about suicide risk and about one-fifth as likely to ask if they felt depressed compared with younger depressed patients. Tai-Seale and colleagues (2005)103 observed 389 elderly patients and 33 physicians using video of their clinical interactions. Physicians assessed depression in only 14% of the visits and used validated tools only three times. Depression assessment was more likely in visits that covered multiple topics, contrary to the ‘‘crowding-out’’ hypothesis. Tai-Seale et al (2007)104 observed 35 PCPs interviewing 366 of their elderly patients. Discussion of mental health topics occurred in only 22% of visits despite a high prevalence of depression. A typical mental health discussion lasted approximately 2 minutes.104 Adelman and colleagues (2008)105 audiotaped 482 follow-up visits at three sites. Depression was discussed in 7.3% of medical visits. Physicians raised the topic of depression in 41% of visits, patients raised the topic in 48% of visits, and accompanying persons raised it in 10% of visits. The topic of depression was raised almost exclusively in the first 2.5 years of the patient– physician relationship. Physicians with some geriatric training were more likely to discuss depression. However, it is important to remember that patient and clinician communication are reciprocally related. Patient perceptions of how the PCP related to him or her in the consultation correlates with reduction in symptom severity 3 months later.106 Goldberg and colleagues (1993)107 found that patient cues were influenced by the PCP’s behavior, increasing with patient-centered behaviors such as empathic statements or directive questioning about psychological issues, and decreasing with medical questions and other doctor-led

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behaviors. Similarly, others found that the patient’s willingness to disclose information is related to physician facilitation, and patient emotional expression is associated with a warm and empathetic attitude of the physician.108 Physicians may signal to patients, wittingly or unwittingly, how emotional problems will be addressed, influencing how patients perceive their interactions with physicians regarding emotional problems. Del Piccolo and coworkers (2000)109 also found that the proportion of cues given by patients was related also to the PCP’s verbal behavior, increasing with closed psychosocial questions and decreasing with the use of active interview techniques. In fact, patients with detected distress gave more cues, often with psychological content, whereas patients with undetected distress gave mainly cues related to their lifestyle and life episodes. Recently, an international study by Verhaak and colleagues (2007)76 found that eye contact and empathy and asking questions about psychological or social topics were associated with more awareness of patients’ psychological problems. One other important predictor of diagnostic sensitivity (recognition) includes the amount of contact with the patient.110,111 In the MAGPIE study from New Zealand, 80.2% of cases seen five or more times during the previous year were correctly identified, compared with 28.8% of those patients not seen in the previous year. For example, over time, only 30% remain undetected at 1 year and 14% at the end of 3 years.112,113 Using patient self-report regarding the adequacy of diagnosis/treatment, Jackson and colleagues114 found that the cumulative recognition rate was a modest 56% for major depression and 20% for minor depression, even after 5 years.

4.

Illness-Related Influences on Detection

There is some evidence that clinicians find mental illness difficult to deal with and awkward to diagnose. For example, PCPs in the United States appear reluctant to code patients as depressed.115 Somatic complaints thought to have a psychological basis are also perceived as difficult.116,117 In a study of 500 primary care visits, 15% were perceived as difficult by clinicians, and these were more likely to involve a mental disorder, more than five somatic symptoms, more severe symptoms, poorer functional status, more unmet expectations, less satisfaction with care, and higher use of health services.118 Interestingly, clinicians with poorer psychosocial attitudes perceived three times as many encounters as being difficult. In the same study, the authors showed that a 2-hour physician workshop followed by information provided before each visit improved physician-perceived difficulty of the encounter.119

Table 3.1. Large-Scale International Studies on Mood Disorders Recognition and Treatment Study

Setting

Sample

Instrument

Prevalence of Mood Disorders

Recognition in Primary Care

% Offered Antidepressants

Institut National de la Sante´ et de la Recherche Me´dicale (INSERM) study

Paris, France, 1996–97

2,419 patients (aged 18–70 years) 238 were found to be depressed and were followed up for 6 months.

MINI

Major depression (14.0%), minor depression (3.1%), and dysthymia (2.1%)

Major depression (21%)

European Study of the Epidemiology of Mental Disorders (ESEMeD)

Community study in Belgium, France, Germany, Germany, Italy, the Netherlands, and Spain

21,425 non-institutionalized adults  18 years old (including those 65 years and older)

WMH-CIDI

World Health Organization study on Psychological Problems in General Health Care (PPGHC)

14 countries worldwide

26,422 consecutive patients (aged 15–65 years)

General Health Questionnaire (GHQ-12)

Lifetime prevalence rates of 13.4% for major depression and 4.4% for dysthymia were reported. Mental disorders (24%) Major depression (13.7%) Minor depression (3.6%) Dysthymia (3.6 %)

Major depression (26%) Any mental disorder (58%) Not examined

Major depression (15%) Any mental disorder (54%)

Major depression (15%)

Major depression (21.2%)

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1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

Te n El ev en Tw el Th ve irt e Fo en ur te en Fi fte e Si n xt Se e e ve n nt e Ei e n gh te e N in n et ee Tw n Tw en en ty ty -o ne

in e N

ei gh t

0

Figure 3.5. Detection sensitivity (%) by severity of depression according to the HADS scale. Adapted from Thompson, C., Ostler, K., Peveler, R. C., et al (2001) Dimensional perspective on the recognition of depressive symptoms in primary care. The Hampshire Depression Project 3. British Journal of Psychiatry, 179, 317–323.

Most depressions in primary care are mild to moderate in severity (90% have a score of 8 to 13 on the HADS), and the detection of mild disorders is a challenge because symptoms do not differ greatly from those of healthy but stressed individuals.120,121 Thompson and colleagues (2001)32 examined the relationship between severity of depression on the HADS-D and proportion of cases detected (Fig. 3.5). Generally, higher severity of depression is associated with greater recognition, but because of the great burden of mild depression, 50% of all correct recognition occurs at a HADS-D score of between 8 and 10. Further, many cases feature physical or mental comorbidities such as anxiety. Comorbidity may decrease recognition.122 In primary care only about 10% of all depressions do not feature comorbidity (5% of those with major depression). About 50% have physical comorbidity and an overlapping 70% to 80% psychiatric comorbidity (of which 40% to 50% is anxiety). Patients with anxiety or chronic mixed anxiety and depression were less likely to be offered active treatment than those considered to have depression.123 One hypothesis is that somatic complaints, particularly in late-life depression, might cause the clinician to focus on physical rather than mental symptoms. Many clinicians have been taught to take an exclusive approach and ignore such complaints, but accumulating evidence suggests this is probably incorrect and that somatic symptoms should be ‘‘counted’’ toward depression even when another physical

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illness like stroke or Parkinson’s disease is present. This is discussed further in Chapters 10 and 11. However, this ‘‘crowding-out’’ hypothesis has been refuted. For example, Ani and coworkers (2008)124 found that comorbidity had no effect of recognition accuracy. Pfaff and Almedia (2005)125 found that predictors of detection included concomitant polypharmacy (imply higher comorbidity) as well as higher CESD scores, presenting with psychological complaints, and higher risk of suicide. O’Conner and associates (2001)126 found that comorbid pain positively influenced detection of late-life depression. Similarly, Borowsky and associates (2000)30 found superior detection of depression if comorbid diabetes or hypertension were present. Other factors were previous psychiatric consultation, number of years as a patient, severity of depression, and disclosure of depression to the physician. Indeed, the co-occurrence of MDD and anxiety might actually facilitate recognition of depression127 or psychiatric caseness.128–130 When faced with ambiguity and diagnostic difficulties, some evidence suggests that only a minority of clinicians choose to explore the issues in more detail.131

5.

Conclusions

Depression is often a complex comorbid presentation associated with frequent primary care attendance.132 Recognition of depression in primary care and hospital settings is poor, yet in part it is worth remembering that depression is a relatively uncommon reason for presentation in primary care, with at least six out of seven unselected cases not having depression. In primary care, time and resources are limited, and hence psychological or even structured self-help programs are often not available. The most plausible factor explaining undertreatment is underrecognition. Antidepressants are typically the treatment of choice for clinicians but not for patients, and hence managing depression can be seen as difficult.133 Against this background, only about a half of true cases are diagnosed and perhaps a quarter treated. Conversely, about 70% of noncases are correctly reassured. Two major factors appear to influence detection: how the person with depression describes his or her symptoms and how the clinician interviews the patient. The nature of the therapeutic relationship is important. Even in the face of a high frequency of contact, a therapeutic relationship that is noted by the clinician (or patient) to be unhelpful is likely to decrease the recognition rate. Discussion of emotional distress in primary care is also linked with high patient satisfaction.134 Additional factors such as the skill of the clinician and the use of tools may also play a role (see Chapter 7). There are certainly many potential barriers to successful diagnosis and treatment.135 Mental health skills training has been

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effective in improving recognition and management of somatizing and depressed patients by PCPs, but it remains uncertain whether this translates into improved clinical outcomes.136–138 Interventions are likely to be most successful where problems are most serious. For example, Shapiro and colleagues (1987)139 conducted a randomized clinical trial involving 1,242 patients attending inner-city PCPs by giving feedback of GHQ scores. Results showed marked increases in detection but only among the elderly, African Americans, and men. Clinicians should have a high index of suspicion in frequent attendees, those with serious or chronic illness, and those who have persistent but unexplained pain. High vigilance is warranted in patients with those somatic symptoms, in men, and in younger patients.140,141 Ultimately, it is useful to reflect on patients’ opinions on the importance of primary care for depression.142 The top four most important needs are the clinician’s interpersonal skills, ability to recognize depression, the effectiveness of treatment, and problems associated with treatment.

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47. Verhaak PFM, Van Den Brink-Muinen A, Bensing JM, et al. Demand and supply for psychological help in general practice in different European countries—Access to primary mental health care in six European countries. Eur J Public Health. 2004;14(2):134–140. 48. Zantinge EM, Verhaak PFM, de Bakker DH, et al. The workload of general practitioners does not affect their awareness of patients’ psychological problems. Patient Education Counseling. 2007;67(1–2):93–99. 49. Kruse J, Schmitz N, Woller W, et al. Why does the general practitioner overlook psychological disorders in his patient? Determinates of physicians’ identification with psychological disorders. Psychotherapie Psychosomatik Medizinische Psychologie. 2004;54(2):45–51. 50. Tylee A, Freeling P, Kerry S, et al. How does the content of consultations affect the recognition by general practitioners of major depression in women? Br J Gen Pract. 1995;45:575–578. 51. Kessler D, Lloyd K, Lewis G, et al. Cross sectional study of symptom attribution and recognition of depression and anxiety in primary care. BMJ. 1999;318:436–439. 52. Weich S, Lewis G, Mann AH, et al. The somatic presentation of psychiatric morbidity in general practice. Br J Gen Pract. 1995;45:143–147. 53. Yeung A, Chang D, Gresham RL, et al. Illness beliefs of depressed Chinese American patients in primary care. J Nerv Mental Dis. 2004;192(4):324–327. 54. Cornford CS, Hill A, Reilly J. How patients with depressive symptoms view their condition: a qualitative study. Fam Pract. 2007;24(4): 358–364. 55. Bridges KW, Goldberg DP. Somatic presentation of DSM-III psychiatric disorders in primary care. J Psychosom Res. 1985;29:563–569. 56. Susman JL, Crabtree BF, Essink G. Depression in rural family practice: easy to recognize, difficult to diagnose. Arch Fam Med. 1995;4:427–431. 57. Sartorius N, Ustun TB, Lecrubier Y, et al. Depression comorbid with anxiety: results from the WHO study on psychological disorders in primary health care. Br J Psychiatry. 1996;168(Suppl. 30):38–43. 58. Freeling P, Rao BM, Paykel ES, et al. Unrecognised depression in general practice. BMJ. 1985;290:1880–1883. 59. Tylee AT, Freeling P, Kerry S. Why do general practitioners recognize major depression in one woman patient yet miss it in another? Br J Gen Pract. 1993;43:327–330. 60. Tylee A, Freeling P, Kerry S, et al. How does the content of consultations affect the recognition by general practitioners of major depression in women? Br J Gen Pract. 1995;45:575–578. 61. Coulehan JL, Schulberg HC, Block MR, et al. Medical comorbidity of major depressive disorder in a primary medical practice. Arch Intern Med. 1990;150:2363–2367. 62. Freeling P, Rao BM, Paykel ES, et al. Unrecognized depression in general practice. BMJ. 1985;290:1880–1883. 63. Keeley RD, Smith JL, Nutting PA, et al. Does a depression intervention result in improved outcomes for patients presenting with physical symptoms? J Gen Intern Med. 2004;19:615–623. 64. Vuorilehto M, Melartin T, Isometsa E. Depressive disorders in primary care: recurrent, chronic, and co-morbid. Psychol Med. 2005;35(5):673–682. 65. Priest RG, Vize C, Roberts A, et al. Lay people’s attitudes to treatment of depression: Results of opinion poll for defeat depression campaign just before its launch. BMJ. 1996;313:858–859.

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66. Prior L, Wood F, Lewis G, et al. Stigma revisited, disclosure of emotional problems in primary care consultations in Wales. Social Sci Med. 2003;56(10):2191–2200. 67. Cape J, McCullough Y. Patients’ reasons for not presenting emotional problems in general practice consultations. Br J Gen Pract. 1999;49(448):875–879. 68. Leaf PJ, Livingston MM, Tischler GL, et al. Contact with health professionals for the treatment of psychiatric and emotional problems. Med Care. 1985;23:1322–1337. 69. Maginn S, Boardman AP, Craig TKJ, et al. The detection of psychological problems by general practitioners—Influence of ethnicity and other demographic variables. Social Psychiatry Psychiatric Epidemiol. 2004;39(6):464–471. 70. Probst JC, Laditka SB, Moore CG, et al. Race and ethnicity differences in reporting of depressive symptoms. Administration And Policy In Mental Health And Mental Health Services Research. 2007;34(6):519–529. 71. Williams JWJ, Mulrow CD, Kroenke K, et al. Case-finding for depression in primary care: a randomized trial. Am J Med. 1999;106:36–43. 72. Simon GE, Von Korff M, Picinelli M, et al. An international study of the relation between somatic symptoms and depression. N Engl J Med. 1999;341:1329–1335. 73. Davenport S, Goldberg D, Millar T. How psychiatric disorders are missed during medical consultations. Lancet, 1987;330(8556):439–441. 74. O’Connor DW, Rosewarne R, Bruce A. Depression in primary care. 1:Elderly patients’ disclosure of depressive symptoms to their doctors. Int Psychogeriatr. 2001;13(3):359–365. 75. Bushnell J, McLeod D, Dowell A, et al. Do patients want to disclose psychological problems to GPs? Fam Pract. 2005;22(6): 631–637. 76. Verhaak PFM, Bensing JM, Van der Brink-Mulinen A. GP mental health care in 10 European countries: patients’ demands and GPs’ responses. Eur J Psychiatry. 2007;21(1):7–16. 77. Cape J, McCulloch Y. Patients’ reasons for not presenting emotional problems in general practice consultations. Br J Gen Pract. 1999;49(448): 875–879. 78. Del Piccolo L, Saltini A, Zimmermann C. Which patients talk about stressful life events and social problems to the general practitioner? Psychol Med. 1998;28(6):1289–1299. 79. Pollock K. Maintaining face in the presentation of depression: constraining the therapeutic potential of the consultation. Health (London). 2007;11(2): 163–180. 80. Zandbelt LC, Smets EMA, Oort FJ, et al. Determinants of physicians’ patientcentred behaviour in the medical specialist encounter. Social Sci Med. 2006;63(4):899–910. 81. Del Piccolo L, Mazzi M, Saltini A, et al. Inter- and intra-individual variations in physicians’ verbal behaviour during primary care consultations. Social Sci Med. 2002;55(10):1871–1885. 82. Epstein RM, Hadee T, Carroll J, et al. ‘‘Could this be something serious?’’— Reassurance, uncertainty, and empathy in response to patients’ expressions of worry. J Gen Intern Med. 2007;22(12): 1731–1739. 83. Deveugele M, Derese A, De Bacquer D, et al. Is the communicative behavior of GPs during the consultation related to the diagnosis? A cross-sectional study in six European countries. Patient Education Counseling. 2004;54(3):283–289. 84. Deveugele M, Derese A, De Maeseneer J. Is GP-patient communication related to their perceptions of illness severity, coping and social support? Social Sci Med. 2002;55(7):1245–1253.

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85. Feldman MD, Franks P, Epstein RM, et al. Do patient requests for antidepressants enhance or hinder physicians’ evaluation of depression? A randomized controlled trial. Med Care. 2006;44(12):1107–1113. 86. Watts SC, Bhutani GE, Stout IH, et al. Mental health in older adult recipients of primary care services: is depression the key issues? Identification, treatment and the general practitioner. Int J Geriatr Psychiatry. 2002;17:427–437. 87. Saltini A, Mazzi MA, Del Piccolo L, et al. Decisional strategies for the attribution of emotional distress in primary care. Psychol Med. 2004;34(4):729–739. 88. Maguire P. Improving the recognition of concerns and affective disorders in cancer patients. Recent Advances in Clinical Psychiatry. 1992;7:15–30. 89. Goldberg DP, Jenkins L, Millar T, et al. The ability of trainee general practitioners to identify psychological distress among their patients. Psychol Med. 1993;23:185–193. 90. Tobin M, Hickie I, Urbanc A. Increasing general practitioner skills with patients with serious mental illness. Aust Health Rev. 1997;20:55–67. 91. Zimmermann C, Del Piccolo L, Finset A. Cues and concerns by patients in medical consultations: A literature review. Psychol Bull. 2007;133(3):438–463. 92. Deveugele M, Derese A, De Maeseneer J. Is GP-patient communication related to their perceptions of illness severity, coping and social support? Social Sci Med. 2002;55(7):1245–1253. 93. Deveugele M, Derese A, De Bacquer D, et al. Is the communicative behavior of GPs during the consultation related to the diagnosis? A cross-sectional study in six European countries. Patient Education and Counseling. 2004;54(3):283–289. 94. Marks JN, Goldberg DP, Hillier VF. Determinants of the ability of general practitioners to detect psychiatric illness. Psychol Med. 1979;9(2):337–353. 95. Badger LLW, deGruy FV, Hartman MA, et al. Psychosocial interest, medical interviews, and the recognition of depression. Arch Fam Med. 1994;3:899–907. 96. Carney PA, Eliassen MS, Wolford GL, et al. How physician communication influences recognition of depression in primary care. J Fam Pract. 1999;48(12):958–964. 97. Rost K, Nutting P, Smith J, et al. The role of competing demands in the treatment provided primary care patients with major depression. Arch Fam Med. 2000;9:150–154. 98. Dowrick C, Gask L, Perry R, et al. Do general practitioners’ attitudes towards depression predict their clinical behaviour? Psychol Med. 2000;30:413–419. 99. Saltini A, Mazzi MA, Del Piccolo L, et al. Decisional strategies for the attribution of emotional distress in primary care. Psychol Med. 2004;34(4):729–739. 100. Levinson W, Roter D. Physicians psychosocial beliefs correlate with their patient communication-skills. J Gen Intern Med. 1995;10(7):375–379. 101. A report of the Joint Consultative Committee. Primary care psychiatry—the last frontier. Canberra: Royal Australian College of General Practitioners and Royal Australian and New Zealand College of Psychiatrists, 1997. 102. Fischer LR, Wei F, Solberg LI, e tal. Treatment of elderly and other adult patients for depression in primary care. J Am Geriatr Soc. 2003;51(11):1554–1562. 103. Tai-Seale M, Bramson R, Drukker D, et al. Understanding primary care physicians’ propensity to assess elderly patients for depression using interaction and survey data. Med Care. 2005;43(12):1217–1224. 104. Tai-Seale M, McGuire T, Colenda C, et al. Two-minute mental health care for elderly patients: Inside primary care visits. J Am Geriatr Soc. 2007;55(12):1903–1911.

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105. Adelman RD, Greene MG, Friedmann E, et al. Discussion of depression in follow-up medical visits with older patients. J Am Geriatr Soc. 2008;56(1):16–22. 106. Cape J. Patient-rated therapeutic relationship and outcome in general practitioner treatment of psychological problems. Br J Clin Psychol. 2000;39(4):383–395. 107. Goldberg D, Jenkins L, Millar T, et al. The ability of trainee general practitioners to identify psychological distress among their patients. Psychol Med. 1993;23:185–193. 108. Ishikawa H, Takayama T, Yamazaki Y, et al. The interaction between physician and patient communication behaviors in Japanese cancer consultations and the influence of personal and consultation characteristics. Patient Education Counseling. 2002;46(4):277–285. 109. Del Piccolo L, Saltini A, Zimmermann C, et al. Differences in verbal behaviours of patients with and without emotional distress during primary care consultations. Psychol Med. 2000;30(3):629–643. 110. Nuyen J, Volkers AC, Verhaak PFM, et al. Accuracy of diagnosing depression in primary care: the impact of chronic somatic and psychiatric co-morbidity. Psychol Med. 2005;35:1185–1195. 111. Verhaak PFM, Schellevis FG, Nuijen J, et al. Patients with a psychiatric disorder in general practice: determinants of general practitioners’ psychological diagnosis. Gen Hosp Psychiatry. 2006;28:125–132. 112. Rost K, Zhang M, Fortney J, et al. Persistently poor outcomes of undetected major depression in primary care. Gen Hosp Psychiatry. 1998;20:12–20. 113. Kessler D, Bennewith O, Lewis G, et al. Detection of depression and anxiety in primary care: follow-up study. BMJ. 2002;325:1016–1017. 114. Jackson JL, Passamonti M, Kroenke K. Outcome and impact of mental disorders in primary care at 5 years. Psychosom Med. 2007;69(3):270–276. 115. Rost K, Smith R, Matthews DB, et al. The deliberate misdiagnosis of major depression in primary care. Arch Fam Med. 1994;3(4):333–337. 116. Hahn SR. Physical symptoms and physician-experienced difficulty in the physicianpatient relationship. Ann Intern Med. 2001;134(9):897–904. 117. Carson AJ, Stone J, Warlow C, et al. Patients whom neurologists find difficult to help. J Neurol Neurosurg Psychiatry. 2004;75(12):1776–1778. 118. Jackson JL, Kroenke K. Difficult patient encounters in the ambulatory clinic: clinical predictors and outcomes. Arch Intern Med. 1999;159:1069–1075. 119. Jackson JL, Kroenke K, Chamberlin J. Effects of physician awareness of symptomrelated expectations and mental disorders—A controlled trial. Arch Fam Med. 1999;8(2):135–142. 120. Olfson M, Gilbert T, Weissman M, et al. Recognition of emotional distress in physically healthy primary care patients who perceive poor physical health. Gen Hosp Psychiatry. 1995;17:173–180. 121. Perez Stable E, Miranda J, Munoz RF. Depression in medical outpatients: underrecognition and misdiagnosis. Arch Intern Med. 1990;150:1083–1088. 122. Schwenk TL, Coyne JC, Fechner-Bates S. Differences between detected and undetected patients in primary care and depressed psychiatric patients. Gen Hosp Psychiatry. 1996;18:407–415. 123. Hyde J, Evans J, Sharp D, et al. Deciding who gets treatment for depression and anxiety: a study of consecutive GP attenders. Br J Gen Pract. 2005;55(520):846–853. 124. Ani C, Bazargan M, Hindman D, et al. Depression symptomatology and diagnosis: discordance between patients and physicians in primary care settings. BMC Family Practice 2008;9:1.

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125. Pfaff JJ, Almeida OP. A cross-sectional analysis of factors that influence the detection of depression in older primary care patients. Australian N Z J Psychiatry. 2005;39(4):262–265. 126. O’Conner DW, Rosewarne R, Bruce A. Depression in primary care 2: General practioners’ recognition of major depression in elderly patients. Int Psychogeratrics. 2001;13(3):367–374. 127. Coyne JC, Schwenk TL, Fechner-Bates S. Nondetection of depression by primary care physicians reconsidered. Gen Hosp Psychiatry. 1995;17:3–12. 128. Ormel J, Van den Brink W, Koeter MW, et al. Recognition, management and outcome of psychological disorders in primary care: a naturalistic follow-up study. Psychol Med. 1990;20:909–923. 129. Pini S, Berardi D, Rucci P, et al. Identification of psychiatric distress by primary care physicians. Gen Hosp Psychiatry. 1997;19:411–418. 130. Pini S, Perkonnig A, Tansella M, et al. Prevalence and 12-month outcome of threshold and sub-threshold mental disorders in primary care. J Affective Disorders. 1999;56:37–48. 131. Seaburn DB, Morse D, McDaniel SH, et al. Physician responses to ambiguous patient symptoms. J Gen Intern Med. 2005;20(6):525–530. 132. Menchetti M, Cevenini N, De Ronchi D, et al. Depression and frequent attendance in elderly primary care patients. Gen Hosp Psychiatry. 2006;28(2):119–124. 133. van Schaik DJF, Klijn AFJ, van Hout HPJ, et al. Patients’ preferences in the treatment of depressive disorder in primary care. Gen Hosp Psychiatry. 2004;26(3):184–189. 134. Gross R, Brammli-Greenberg S, Tabenkin H, et al. Primary care physicians’ discussion of emotional distress and patient satisfaction. Int J Psychiatry Med. 2007;37(3):331–345. 135. Simon GE. Evidence review: efficacy and effectiveness of antidepressant treatment in primary care. Gen Hosp Psychiatry. 2002;24:213–224. 136. Gask L, McGrath G, Goldberg D, et al. Improving the psychiatric skills of established general practitioners: evaluation of group teaching. Med Educ. 1987;21:362–368. 137. Gask L, Usherwood T, Thompson H, et al. Evaluation of a training package in the assessment and management of depression in primary care. Med Educ. 1998;32:190–198. 138. Kaaya S, Goldberg D, Gask L. Management of somatic presentations of psychiatric illness in general medical settings: evaluation of a new training course for general practitioners. Med Educ. 1992;26:138–144. 139. Shapiro S, German PS, Skinner EA, et al. An experiment to change detection and management of mental morbidity in primary care. Med Care. 1987;25:327–339. 140. Gallo JJ, Rabins PV. Depression without sadness: alternative presentations of depression in late life. Am Fam Physician. 1999;60:820–826. 141. Gallo JJ, Rabins PV, Anthony JC. Sadness in older persons: 13-year follow-up of a community sample in Baltimore, Maryland. Psychol Med. 1999;29:341–350. 142. Cooper LA, Brown C, Vu HT, et al. Primary care patients’ opinions regarding the importance of various aspects of care for depression. Gen Hosp Psychiatry. 2000;22(3):163–173.

4 HOW CAN EXISTING MOOD SCALES BE IMPROVED? HOW TO TEST, REFINE, AND IMPROVE EXISTING SCALES Adam B. Smith

1. Introduction 2. The Rasch Model and Other Item Response Models 3. Conclusion

Context Many scales and tools have been developed by expert opinion. Several methods are available by which tools can be field tested in order to more accurately gauge their diagnostic potential. Promising new methods including item banks and computer-adaptive tests are under development to maximize the efficiency of screening tools for depression.

1.

Introduction

Various methods are available to diagnose psychiatric disorders (see Chapter 2), but in the absence of a formal semi-structured psychiatric assessment, which remains impractical, the most commonly used method for assessing and screening levels of emotional distress remains by self-completed questionnaire.1 There have been many hundreds of validation attempts, comparing the severity questions against clinical judgment, semi-structured interviews, DSM and ICD criteria, and of course each other. Almost universally in primary care, community, and specialist settings, their accuracy is imperfect and further refinement is required. When tested according to their ability to enhance the detection and quality of care for depression, the efficacy of these instruments remains modest.2 A recent review from Gilbody and colleagues3 83

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found that screening and case-finding instruments were associated with a modest increase in the recognition of depression by clinicians (relative risk [RR] 1.27, 95% confidence interval [CI] 1.02 to 1.59) and only a borderline significant effect on the overall management of depression (RR 1.30, 95% CI 0.97 to 1.76). Seven studies provided data on the impact of screening on depression outcomes, but there was no evidence of an effect (standardized mean difference –0.02, 95% CI –0.25 to 0.20). No doubt some of the problem lies with the organizational elements that may (or may not) accompany screening and some lies with clinicians’ willingness to treat a probable case. However, some blame also lies with the instruments themselves, as most were developed by expert opinion rather than by a scientific process.

Tool Development The quantitative methods that enable evaluation of the diagnostic accuracy of severity scales are discussed in Chapter 5. However, the evaluation of scales should be viewed in a wider context of tool development (Table 4.1). In the preclinical phase a tool is developed, often in the case of depression borrowing from existing scales and usually by consensus rather than by scientific testing. In phases I and II preliminary testing occurs, ideally in the clinically representative sample with several competing comparison groups. These diagnostic validity studies do not prove that the tool is useful, rather that it is potentially

Table 4.1. Stages in the Evaluation of the Screening Tool Stage

Purpose

Description

Preclinical

Tool development

Phase I screen

Early diagnostic validity testing in a selected sample and refinement of tool

Phase II screen

Diagnostic validity in a representative sample

Here the aim is to develop a screening method that is likely to help in the detection of the underlying disorder, either in a specific setting or in all settings. Issues of acceptability of the tool to both patients and staff must be considered for implementation to be successful. The aim is to evaluate the early design of the screening method against a known (ideally accurate) standard known as the criterion reference. In early testing the tool may be refined, selecting the most useful aspects and deleting redundant aspects to make the tool as efficient (brief) as possible while retaining its value. The aim is to assess the refined tool against a criterion (gold standard) in a real-world sample where the comparator subjects may represent several competing conditions that may otherwise cause difficulty regarding differential diagnosis.

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Table 4.1. (Continued) Stage

Purpose

Description

Phase III screen

Screening randomized controlled trial; clinicians using vs. not using a screening tool

Phase IV screen

Screening implementation studies using real-world outcomes

This is an important step in which the tool is evaluated clinically in one group with access to the new method compared to a second group (ideally selected in a randomized fashion) who make assessments without the tool. This is akin to randomized controlled trials for drugs, and the outcome of interest is the number of additional cases correctly diagnosed or ruled out compared with assessment as usual. In this last step the screening tool/method is introduced clinically but monitored to discover the effect on important patient outcomes such as new identifications, new cases treated, and new cases entering remission. In short, the question here is how much the tool influences patient outcomes and how well the tool is accepted by clinicians (uptake).

After Mitchell AJ Psycho-Oncol 17: S141, 2008.

accurate. Given a sufficient sample, a tool may be refined by field testing. This is the basis of the remainder of this chapter. Ultimately the value of a tool must be proven in the clinical environment by comparison against either an established tool or clinical skills alone. The acceptability and availability of the tool will ultimately influence its uptake as much as its efficacy. Given that there are a large number of imperfect but widely used instruments, it follows many could be refined by adding or removing items or changing the weighting of scoring or possibly the diagnostic algorithm. There have been recent attempts to improve efficacy of screening instruments using modern psychometrics, most notably using Rasch models. These models are part of a family of measurement models developed for educational psychology and increasingly employed in test development and refinement in medicine. Very frequently it is found that conventional instruments may be shortened in length without significantly decreasing screening efficacy. Occasionally this shortening is dramatic, reducing an instrument by half or by a quarter. Yet it should be acknowledged that the ability of these adapted instruments to identify levels of a key outcome variable, such as ‘‘distress warranting intervention,’’ remains less than perfect. Combining items drawn from a number of emotional distress instruments into an item bank may improve screening efficacy while at the same time minimizing the number of questions patients are required to answer and consequently reducing patient burden. Item banks such as these and computer-adaptive tests, which tailor the

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questions presented to patients’ responses, have already been successfully developed for assessing emotional distress in a psychiatric population.4,5 This chapter describes the Rasch model and its application to mental health research in more detail.

2.

The Rasch Model and Other Item Response Models

In classical test theory, item difficulty (eg, the probability of subjects responding ‘‘yes’’ or ‘‘no’’ to items or selecting a category from a number of response options) is calculated from the number of responses or proportion of responses in the sample.6 The major drawback of this approach is that estimation of item difficulty is sample dependent: the ‘‘endorsability’’ of any given item will be larger if drawn from a more able population (eg, a healthier population) than if drawn from a less able population. A similar approach can also be applied to estimating ‘‘person ability’’ (eg, quality of life, physical health). Any given estimate of an individual’s ability on a latent (ie, not directly observable) trait will be dependent on the range of difficulties of the items presented. Rasch models7 overcome this problem of sample dependency by estimating person ability and item difficulty independently.8 The raw data are the sufficient statistics for estimating these parameters—that is, the models use only the raw scores from individuals for estimating item difficulties and the response sets across items for estimating person ability estimates.8 To achieve the separation of item and person parameter estimations, the Rasch models rely on two assumptions: unidimensionality and local dependence. Rasch models assume that a uniform latent trait or construct underlies the data being investigated (eg, mathematical knowledge, physical health). This assumption is then tested using fit statistics and/or principal components analysis of residuals. Local independence is related to unidimensionality and refers to the assumption that the single latent trait (ie, the unidimensionality) accounts for all the variance in the data—that is, the association between the variables in a dataset should disappear once the Rasch model has been controlled for.9 It is possible to have unidimensionality but not local dependence; however, if local independence is proven, then there must also be unidimensionality in the data set. If the assumptions have been met, then the (log) probability of a person responding to an item can be expressed as the difference between the individual’s ability and the item difficulty. Unlike in classical test design, the person ability and item difficulty parameters are estimated jointly to produce estimates (referred to as ‘‘logits’’ or log-odds), which are independent of both the items and sample employed.

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Assessing the Rasch Model A fundamental criterion underlying these models is unidimensionality—that is, a single latent trait should explain the variance in the data. In the absence of unidimensionality, constituent parts of an instrument cannot be summed to create a summary index. Unidimensionality can be assessed through principal components analysis, where the first factor extracted corresponds to the Rasch ‘‘factor,’’ or latent trait.10 Any additional factors extracted can be investigated to confirm whether these form true factors or random noise. In addition to this, unidimensionality can be assessed using fit statistics. Both item fit and person fit to the Rasch model can be evaluated. Fit statistics have an expected value of 1.0 and can range from 0 to infinity. Deviations in excess of the expected value can be interpreted as noise or lack of fit between the items and the model, whereas values significantly lower than the expected value can be interpreted as item redundancy or overlap. Identifying misfitting items allows those items adding noise to the analysis to be removed from a scale. The suggested limits for fit statistics are between 0.7 and 1.3, with those items with fit statistics greater than 1.3 being identified as misfitting.11,12 A similar analysis may also be applied to the response categories and thresholds (ie, the point at which response to categories is equally probable between categories). Within the Rasch model the average level of the latent trait (‘‘ability’’) should increase monotonically across categories. Disordering of categories, where the average level of the latent trait does not increase in this manner, may interfere with measurement precision. Therefore, disordered response categories may be collapsed or items removed to improve fit to the Rasch model.9 Finally, an additional requirement for Rasch models is item invariance— that is, item parameter estimates should be independent of the sample used. Item invariance or differential item functioning (DIF) may be evaluated using defined subgroups (eg, gender, diagnosis). When items fit the model, an interval scale is produced where differences between adjacent scores on a scale are equally spaced. This has important implications for measurement, since this allows meaningful comparisons to be made of changes in scores of equal intervals along the latent trait.13 Recent work has suggested that changes of around 0.5 logits may suggest a clinically meaningful difference.14

Features of the Rasch Model The Rasch model is more accurately referred to as belonging to a family of models. Rasch’s original dichotomous model7 has been extended to

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incorporate polytomous data—that is, from questionnaires incorporating multiple (more than two) response options. Popular models within health research are the Rating Scale15 and the Partial Credit Model.16 In the Rasch model the estimates of person ability (or person measure) and item location or difficulty are located along the same continuum (eg, Depression). For instance, Figure 4.1 shows a ‘‘person-item map’’ from an item bank developed for assessing emotional distress in cancer patients.17 The left side of the map represents the distribution of person measures along the continuum and the right side describes the location of the items. As discussed above, the Rasch model describes a probabilistic relationship between a person’s measure and the item location. For instance, from Figure 4.1, the Rasch model allows us to state that a patient with a level of distress around –1 logits will be more likely to endorse items at a corresponding level, such as General Health Questionnaire (GHQ)-1 item (‘‘concentration’’) and MHI-1 (‘‘nervousness’’), as well as items below this point, but would be less likely to endorse items further along the latent trait, such as Patient Health Questionnaire (PHQ)-9 (‘‘suicidal ideation’’). This analysis can be extended to the thresholds between each response category (Fig. 4.2). An additional important feature of Rasch models is that the models can equate different questionnaires completed by different subgroups of patients, assuming that a common subset of items exists that all patients have completed. This process then enables a range of items measuring the same latent trait to be collated to form an item bank. The development of an item bank may help improve static questionnaires by including fewer and more relevant questions, which could cover a broader and more representative spectrum of the latent trait (for assessment) or may be more focused on discrete areas of clinical interest, such as clinical thresholds (for screening). It also paves the way for the development of computer-adaptive testing,18 creating programs that tailor questions to individual patients based on their previous responses, allowing an accurate assessment of the patient (eg, level of psychological distress) with fewer questions. Taken together, Rasch models offer a number of advantages, including improving existing measures, reducing the number of items in questionnaires, and allowing the development of item banks and computer-adaptive tests.

Application of the Rasch Model to Mental Health Measures In traditional test theory, questionnaires are often designed and validated using techniques such as factor analysis. In addition to the sample dependence of these approaches as described above, rating scales produce ordinal data that do not meet the assumptions behind factor analyses, potentially leading to

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< more > | + | . | + | . | . + | . . |T . + .# | . # T | S bdi6 . ## + . #### | M bdi1 . ###### S | bdi2 . ######### + bdi4 | S d2 . ########## | . ############ phq1 . ############ M + a5 . ######### | T d1 | . ######## a1 . ######### + bdi11 . ###### S | | . ##### bdi12 . + | . #### . T| . ### + | . | . + | | + | ghq8 | + ghq12 | ghq3 | + ghq1 | | + | ghq7 | . ### + |

.2 .2 .2 .2 .2 .2 .2 .2 .2 .2

bdi6 bdi1

.3 .3

bdi2 phq9

.3 .3

d2 ef4 a5 bdi11 a1 bdi12 ewb1 d4 ewb6 ghq7

.3 .3 .3 .3 .3 .3 .3 .3 .3 .3

.2

.2 .2 .2 .2

.2

Figure 4.2. Rasch-Thurstone Thresholds for Item Bank.

bdi6 bdi1 ghq4 bdi2 ghq3 phq9 d2 ef4 d6 a5 a2 a1 bdi12 d4 ewb1 phq4 ewb6

.4 .4 .4 .4 .4 .4 .4 .4 .4 .4 .4 .4 .4 .4 .4 .4 .4

ewb4 ewb1 mhi4 ewb6

.5 .5 .5 .5

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misinterpretation of results.19 Furthermore, these ordinal scales are often summed to produce total scores that are assumed to meet the criteria of interval scales; frequently these assumptions are not tested.13 A number of studies have recently described the application of Rasch models to mental health instruments to overcome the shortcomings of traditional test theory and design.

Unidimensionality, Item Reduction, and Differential Item Functioning The Rasch model has been applied to a number of mental health instruments, including the Beck Depression Inventory (BDI),20 the Zung Self-Rating Depression Scale,21 the Geriatric Depression Scale (GDS),22 and the Symptom Checklist (SCL-90 and SCL-90R) (see table 4.2).23 The application of the model to four of the most commonly used mental health instruments, namely the Center for Epidemiologic Studies Depression Scale (CES-D),24 the Hospital Anxiety and Depression Scale (HADS),25 the Hamilton Depression Scale (HAM-D),26 and the Edinburgh Postnatal Depression Scale (EPDS),27 is discussed in this section. These four instruments have been well validated using traditional test theory involving reliability and validity studies and factor analyses, yet despite this Table 4.2. Examples of Rasch-Refined Mood Scales Stage

Original Length

Rasch-Derived Length

Unidimensionality Shown

Reference

CES-D

20 items

13 items

Yes

HADS

14 items

11 items

Yes

EPDS

10 items

8 items

Yes

Hamilton

17 items

6 items

No

Beck

21 items

Not changed

No

Zung SDS

20 items

Not changed

Yes

GDS

15 items

11 items

Yes

SCL90

92 items

63 items

SCL25

25 items

8 items

Yes (for nonpsychotic items) Yes

Covic et al. (2007)29 Smith et al. (2006)31 Pallant et al. (2006)32 Licht et al. (2005)35 Bouman & Kok (1987)20 Hong & Min (2007)21 Tang et al. (2005)22 Olsen et al. (2004)23 Fink et al. (1995)47

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there has been little previous evidence to support the assumption that these questionnaires are unidimensional. Stansbury and colleagues28 applied the Rasch model to the full CES-D completed by a large community sample of elderly participants. Four of the positively worded items were identified as misfitting and removed. The remaining 16 items formed a unidimensional structure that was verified using confirmatory factor analysis. Additionally, the removal of the misfitting items also reduced the floor effects that had been observed in this sample. Covic and colleagues29 demonstrated, using a sample of patients with rheumatoid arthritis, that three additional items (appetite, restlessness, sadness) misfitted the Rasch model. The resulting 13-item CES-D demonstrated good internal validity. In contrast to these two studies, Pickard and colleagues30 found no misfit for the CES-D in primary care patients, although misfit was reported for three items that were not positively worded in stroke patients. Additionally, four items from this instrument demonstrated differential item functioning when comparing the two patient samples. Rasch studies of the HADS with cancer patients31 and patients attending an outpatient musculoskeletal rehabilitation program32 showed that the full instrument is broadly unidimensional, although the individual subscales contained items that misfitted. Similarly, an analysis of the Edinburgh Postnatal Depression Scale has recommended that the original 10-item form be reduced to eight items to produce a unidimensional instrument.33 In addition to identifying misfit, Rasch models have also been used to develop short forms of these standard instruments. For instance, a 10-item version of the CES-D has been validated using both Rasch and traditional test methods,34 as well as the 6-item version of the HAM-D.35 Licht and colleagues35 compared the unidimensionality of the Bech-Rafaelsen Melancholia Scale (MES) and the 17-item HAM-D in 1,629 patients with a major depressive episode using Mokken and Rasch analysis. Unidimensionality of the HAM-D-17 could not be confirmed; however, the HAM-D-6 and the MES did fulfill criteria for unidimensionality. There have also been recent attempts to apply Rasch models to the standardized psychiatric interview schedule for major depression.36 A modified SCID interview was used on a large sample of twins from the Virginia Twin Registry (n = 2,163). Participants were asked to report whether they had experienced any of the 14 disaggregated DSM-III-R criteria for major depression. The Rasch model was used to derive liability thresholds (the point at which there is a 50% probability of a given diagnostic category being endorsed) for the 10 symptom criteria for major depression. The results demonstrated an uneven spacing between liability thresholds where ‘‘depressed mood’’ was easiest to endorse (–1.8 logits) and ‘‘suicidal ideation’’ at the other end of the latent trait (2.5 logits) was hardest to endorse, suggesting a tentative link between the

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latent trait as measured by the Rasch model and that derived from a formal psychiatric interview. Other more general distress and psychopathology tools have also been tested using Rasch models. For example, the 90-item SCL and the 25-item SCL-25 have been improved.23 Clinical Testing and Clinical Impact Ultimately any tool (original or adapted) should be field tested, even if the refinement is minor. In a robust test of a newly developed tool (let’s use the hypothetical example of CES-D-Revised), the new scale would be compared alongside the original scale, and unassisted clinical diagnosis against a robust gold standard such as the SCID for DSM-IV major depression. Any additional detection beyond the unassisted clinician would suggest that the scale is clinically useful; any additional detection beyond that achieved by the original scale would suggest that the new scale is an improvement. If the new version is shorter, both accuracy and efficiency may be enhanced, and hence acceptability increased. If the new version is longer, accuracy may be improved at the expense of efficiency, and then a clinical judgment is required to explore which is most useful. Sadly, very few well-designed validation studies exist. A few studies have employed Rasch models to assess the impact of misfit and the subsequent removal of misfitting on the diagnostic accuracy of mental health measures. Smith and colleagues31 applied the Rasch model to both the full 14-item HADS25 as well as the 7-item anxiety and depression subscales. In addition to completing the HADS, a subset of cancer patients had also received a psychiatric assessment in the form of either the Present State Examination (PSE)37 or the Schedules for Assessment in Clinical Neuropsychiatry (SCAN World Health Organisation).38 Three items from the full HADS were identified as misfitting the Rasch model, in addition to one misfitting item from the subscales. Removal of the items had little or no impact on the specificity and sensitivity of the scales (including the area under the curve [AUC]). Similarly, Tang and colleagues22 identified four items from the GDS that did not fit the Rasch model. The GDS data were derived from a community sample of patients with pneumoconiosis who had also received a structured psychiatric interview with the aim of diagnosing depressive disorders. Once again, the results demonstrated that removing the misfitting items did not affect the AUC or sensitivity and specificity. Item Banking and Computer-Adaptive Testing The ability of the Rasch model to derive item locations for different instruments and to allow evaluations of whether these items form a unidimensional construct creates the opportunity to generate item banks. Various methods

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exist for item banking39; however, a frequently employed approach is common item equating,10 where patients complete a core set of questionnaires. Additional items or instruments may be added by anchoring the locations for the core set of items. Typically in this scenario patients will have completed the core set of items along with further items. The benefit of item banking is that patients do not have to complete all the questionnaires, which therefore reduces not only patient burden but also the costs of developing the item bank. After item banks are developed, two further steps can be taken: (1) the development of multiple fixed short forms derived from the item bank (see Ware and associates40 for an example of the development of a short form of the headache impact scale) and (2) the development of computer-adaptive tests. Computer-adaptive tests (eg, Wainer18) tailor the items presented to the patient on the basis of his or her previous responses. They generally present an initial item aimed at the average level of the latent trait in the target population (eg, average level of depression); subsequent questions presented are either easier or harder to endorse. At each step the patient’s level of latent trait (eg, depression) is estimated until a predetermined number of questions has been presented or the standard error of the estimate falls below a given predetermined level. Computer-adaptive test systems provide a greater level of precision in estimating the latent trait and may be designed to allow a broad assessment of, for instance, depression, or specifically designed to present more questions around diagnostic categories. Another benefit of these systems is that fewer questions need to be completed by the patient (for the same or greater level of accuracy). The development of item banks and computer-adaptive tests has been progressing apace in fields such as physical health,41 although in mental health this area is still in its infancy. However, recently an item bank has been developed for assessing psychological distress in cancer patients.17 A large sample of cancer patients completed the HADS25 in addition to a variety of other instruments, including the GHQ-12,42, BDI,43 PHQ-9,44 and Spielberger State-Trait Anxiety Inventory (STAI).45 Common item equating using the HADS as the anchor was used to create the item bank. The initial 83 items were reduced to a unidimensional item bank with good internal reliability (Cronbach’s alpha = 0.84) consisting of 63 items once misfitting items had been removed. An analysis of the item-person map (see Fig. 4.1) demonstrated good face validity: questions concerning suicidal ideation were hardest to endorse, whereas questions concerning fatigue and energy were easiest to endorse. Further analysis of the item-person map revealed that items tended to be targeted at moderate to high levels of distress, indicating a floor effect for low levels of distress, potentially requiring additional items.

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Computer-adaptive tests have already been developed for use with psychiatric populations to identify emotional distress.4,5 Fliege and associates4 have developed a system for measuring depression (‘‘D-CAT’’) in a psychosomatic patient sample. Patients completed 11 mental health questionnaires that were subsequently rated as indicative of depressive symptomatology by expert reviewers. A total of 320 items from the original questionnaires produced an item bank of 64 items. A simulation study using patients’ actual responses to the questions demonstrated that levels of depression could be estimated reliably from six items. Scores generated from the D-CAT system fell within 2 standard deviations of the sample mean and correlated well with the overall item bank and two standard mental health measures (BDI and CES-D). Finally, recently Gibbons and colleagues46 developed a computer-adaptive test derived from the 626-item Mood and Anxiety Spectrum Scales (MASS). This system was designed to identify anxiety and mood disorders in patients attending outpatient clinics. The study demonstrated that the number of items presented to patients could be reduced to 24 to 30 items without a loss of information, representing a significant reduction in both administration time and patient burden.

3.

Conclusion

Despite the intuitive appeal and ease of use of brief self-report instruments to screen for depressive disorders, there remains a great deal of variability in the efficacy of a number of commonly employed instruments. Many instruments have been comprehensively validated by traditional test methods, but issues still remain about unidimensionality, floor and ceiling effects, and instrument performance across different groups of patients. Rasch models7 have the potential to address and overcome these issues, generating instruments that are independent across samples and providing the basis for item banks and computer-adaptive tests. Although item banking is a relatively new area of development in health measures, the U.S. National Institutes of Health has recently provided major funding for the Patient-Reported Outcomes Measurement Information System (PROMIS) initiative, with one of the goals to produce computer-adaptive tests for the clinical research community (http://nihroadmap.nih.gov/clinicalresearch/ promis.asp). The next step in the development of the item bank will be to develop computer-adaptive testing systems. An important corollary to this will be to continue to map the item bank, in particular levels of emotional distress, to both psychiatric diagnoses of clinical anxiety and major depression, as well as clinical guidelines. This will not only provide a potentially more sensitive instrument for

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assessing and screening for distress, but will also assist in tailoring the management of distress and associated interventions to individual patients.

References 1. Wright AF. Should general practitioners be testing for depression? Br J Gen Pract. 1994;44(380):132–135. 2. Gilbody S, House AO, Sheldon TA. Screening and case finding instruments for depression. Cochrane Database of Systematic Reviews. 2005, Issue 4. 3. Gilbody S, Sheldon T, House A. Screening and case-finding instruments for depression: a meta-analysis. CMAJ. 2008;178:997–1003. 4. Fliege H, Becker J, Walter OB, et al. Development of a computer-adaptive test for depression (D-CAT). Qual Life Res. 2005;14:2277–2291. 5. Walter OB, Becker J, Bjorner JB, et al. Development and evaluation of a computer adaptive test for ‘Anxiety’ (Anxiety-CAT). Qual Life Res. 2007;16:S143–S155. 6. Suen HK. Principles of test theories. Hillsdale, NJ: Lawrence Erlbaum Associates, 1990. 7. Rasch G. Probabilistic models for some intelligence and attainment tests. Chicago: The University of Chicago Press, 1960/1980. 8. Wright BD, Masters G. Rating scale analysis. Chicago: MESA Press, 1982. 9. Bond TG, Fox CM. Applying the Rasch model: fundamental measurement in the human sciences. Mahwah, NJ: Lawrence Erlbaum Associates, 2001. 10. Linacre JM. A user’s guide to WINSTEPS/MINISTEPS Rasch-model computer programs. 2007. 11. Lai JS, Cella D, Chang CH, et al. Item banking to improve, shorten and computerize self-reported fatigue: an illustration of steps to create a core item bank from the FACIT-Fatigue Scale. Qual Life Res. 2003;12(5):485–501. 12. Wright BD, Linacre JM, Gustafson J-E, et al. Reasonable mean-square fit values. Rasch Measurement Transactions. 1994;8:370. 13. Stucki G, Daltroy L, Katz JN, et al. Interpretation of change scores in ordinal clinical scales and health status measures: the whole may not equal the sum of the parts. J Clin Epidemiol. 1996;49:711–717. 14. Lai JS, Eton DT. Clinically meaningful gaps. Rasch Measurement Transactions. 2002;15:850. 15. Andrich D. A rating formulation for ordered response categories. Psychometrika. 1978;43:561–573. 16. Masters GN. A Rasch model for partial credit scoring. Psychometrika. 1982;47:149–174. 17. Smith AB, Rush R, Velikova G, et al. The initial development of an item bank to assess and screen for psychological distress in cancer patients. Psychooncology. 2007;16:724–732. 18. Wainer H. Computerized adaptive testing: a primer. Hillsdale, NJ: Lawrence Erlbaum Associates, 1990. 19. Schumacker RE, Linacre JM. Factor analysis and Rasch. Rasch Measurement Transactions. 1996;9:470. 20. Bouman TK, Kok AR. Homogeneity of Beck’s Depression Inventory (BDI): applying Rasch analysis in conceptual exploration. Acta Psychiatr Scand. 1987;76(5):568–573.

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21. Hong S, Min SY. Mixed Rasch modeling of the Self-Rating Depression Scale incorporating latent class and Rasch rating scale models. Educ Psych Measure. 2007;67(2):280–299. 22. Tang WK, Wong E, Chiu HF, et al. The Geriatric Depression Scale should be shortened: results of Rasch analysis. Int J Geriatr Psychiatry. 2005;20:783–789. 23. Olsen LR, Mortensen EL, Bech P. The SCL-90 and SCL-90R versions validated by item response models in a Danish community sample. Acta Psychiatr Scand. 2004;110(3):225–229. 24. Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. Applied Psych Measure. 1977;384–401. 25. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand. 1983;67:361–370. 26. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56–62. 27. Cox JL, Holden JM, Sagovsky R. Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale. Br J Psychiatry. 1987;150:782–786. 28. Stansbury JP, Ried LD, Velozo CA. Unidimensionality and bandwidth in the Center for Epidemiologic Studies Depression (CES-D) Scale. J Pers Assess. 2006;86:10–22. 29. Covic T, Pallant JF, Conaghan PG, et al. A longitudinal evaluation of the Center for Epidemiologic Studies-Depression scale (CES-D) in a rheumatoid arthritis population using Rasch analysis. Health Qual Life Outcomes. 2007;5:41. 30. Pickard AS, Dalal MR, Bushnell DM. A comparison of depressive symptoms in stroke and primary care: applying Rasch models to evaluate the Center for Epidemiologic Studies-Depression scale. Value Health. 2006;9:59–64. 31. Smith AB, Wright EP, Rush R, et al. Rasch analysis of the dimensional structure of the Hospital Anxiety and Depression Scale. Psychooncology. 2006;15:817–827. 32. Pallant JF, Tennant A. An introduction to the Rasch measurement model: an example using the Hospital Anxiety and Depression Scale (HADS). Br J Clin Psychol. 2007;46:1–18. 33. Pallant JF, Miller RL, Tennant A. Evaluation of the Edinburgh Postnatal Depression Scale using Rasch analysis. BMC Psychiatry. 2006;6:28. 34. Cole JC, Rabin AS, Smith TL, et al. Development and validation of a Rasch-derived CES-D short form. Psychol Assess. 2004;16:360–372. 35. Licht RW, Qvitzau S, Allerup P, et al. Validation of the Bech-Rafaelsen Melancholia Scale and the Hamilton Depression Scale in patients with major depression; is the total score a valid measure of illness severity? Acta Psychiatr Scand. 2005;111:144–149. 36. Aggen SH, Neale MC, Kendler KS. DSM criteria for major depression: evaluating symptom patterns using latent-trait item response models. Psychol Med. 2005;35:475–487. 37. Wing J Cooper JE, Sartorius N. The description of psychiatric symptoms: an introduction manual for the PSE and CATEGO System. Cambridge: Cambridge University Press, 1974. 38. World Health Organization. Mental health: new understanding, new hope. Geneva, Switzerland: WHO, 1993. 39. Wolfe EW. Equating and item banking with the Rasch model. J Applied Measure. 2000;1(4):409–434. 40. Ware JE Jr, Kosinski M, Bjorner JB, et al. Applications of computerized adaptive testing (CAT) to the assessment of headache impact. Qual Life Res. 2003;12(8):935–952.

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41. Rose M, Bjorner JB, Becker J, et al. Evaluation of a preliminary physical function item bank supported the expected advantages of the Patient-Reported Outcomes Measurement Information System (PROMIS). J Clin Epidemiol. 2008;61:17–33. 42. Goldberg DP, Hillier VF. A scaled version of the General Health Questionnaire. Psychol Med. 1979;9:139–145. 43. Beck AT, Ward CH, Mendelson M, et al. An inventory for measuring depression. Arch Gen Psychiatry. 1961;4:561–571. 44. Kroenke KJ, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. Gen Intern Med. 2001;16:606–613. 45. Spielberger CD. Manual for the State-Trait Anxiety Inventory (STAI). Palo Alto, CA: Consulting Psychologists Press, 1983. 46. Gibbons RD, Weiss DJ, Kupfer DJ, et al. Using computerized adaptive testing to reduce the burden of mental health assessment. Psychiatr Serv. 2008;59(4):361–368.

5 HOW DO WE KNOW WHEN A SCREENING TEST IS CLINICALLY USEFUL? Alex J. Mitchell

1. 2. 3. 4. 5.

How Do Clinicians Make a Diagnosis? Scientific Aspects of Diagnostic Accuracy Clinical Aspects of Diagnostic Accuracy Testing Screening via Implementation Studies Conclusions

Context There is no shortage of suggested methods to screen for depression, including clinical interviews. Assuming these are applied to a group containing patients with depression and patients without depression, how do we decide which are the optimal methods? In addition, how can tests be compared and how can tests be combined? This chapter discusses the methods used to compared scales and tools.

1.

How Do Clinicians Make a Diagnosis?

The terms diagnosis and screening both refer to the application of an agreed method to confirm those with a condition and to exclude those without the condition (for discussion see Chapter 2). When attempting to separate depressed versus non-depressed individuals there is always an overlap of symptoms (or biological markers) (see Chapter 1, Fig. 1); therefore, a perfect test based on current tests is unobtainable. Testing may be focused on those at high risk of the condition (such as screening for depression after myocardial infarction) or applied to a wider population (screening for depression in all 99

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primary care patients). The former is a high-prevalence setting, which favors the ability to confirm a condition, whereas the latter is a low-prevalence setting, which favors the ability to refute a condition. It is often forgotten that the clinical process of making a diagnosis is a form of screening itself. Here the tool is the clinician’s clinical skill and the sample is all patients seen by the clinician. If a clinician is attuned to the concept of depression, has a high index of suspicion, and asks the right questions, then it is likely he or she will have high personal diagnostic accuracy. If the clinician is unconfident, inexperienced, and untrained, it is less likely that he or she will be able to make a correct diagnosis (see Table 5.1 and Chapter 3). Some literature suggests that the added value of screening tools for depression is apparent only in the latter situation. A diagnostic test for depression is designed to help the clinician elicit and weigh symptoms and signs to make a diagnosis. How, then, is this achieved, and how does a screening test work in scientific terms?

Case Example Consider the case illustrated in Textbox 5.1. A man who suffered a stroke 2 months previously now complains of five troubling symptoms. Assuming these symptoms are elicited correctly, is he clinically depressed? Could the somatic symptoms be features of stroke and not depression (see Chapters 10 and 11)? Five symptoms may immediately sound sufficient for a diagnosis, but not all symptoms qualify under DSM-IV or ICD-10. For example, loss of drive is not a qualifying feature and therefore, under these guidelines, must be ignored. This leaves four qualifying symptoms and only one core symptom, which is insufficient for a DSM-IV-based diagnosis of major depression. However, using ICD-10, he does have two core features and two associated features listed, but only at a level designated as a mild depressive episode. Thus, clinicians who use a strict operational checklist approach may or may not diagnose depression in this case. In fact, research suggests that fewer than one in five psychiatrists would take this strict operational approach, and fewer still use validated questionnaires such as the Patient Health

Table 5.1. Levels of Diagnostic Confidence

Use a checklist or screening tool Do not use a checklist or screening tool

Prior Experience & Training

No Prior Experience & Training

i. Trained, Assisted

ii. Untrained, Assisted

iii. Trained, Unassisted

iv. Untrained, Unassisted

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Textbox 5.1. Case History: Post-Stroke Depression? A previously well 58-year-old man who suffered a dominant hemisphere stroke 2 months previously is referred to an outpatient psychiatry clinic. He reports that he has had five symptoms—low mood, loss of drive, low energy, poor appetite, and insomnia—for the past 3 weeks. He has no other symptoms on detailed questioning. Core Symptoms

ICD-10

DSM-IV

Persistent sadness or low mood Loss of interests or pleasure Fatigue or low energy Disturbed sleep Poor concentration or indecisiveness Low self-confidence Poor or increased appetite Suicidal thoughts or acts Agitation or slowing of movements Guilt or self-blame Significant change in weight

Yes (core) Yes (core) Yes (core) Yes Yes Yes Yes Yes Yes Yes No

Yes (core) Yes (core) Yes Yes Yes No No Yes Yes Yes Yes

Questionnaire (PHQ)-9. Most trained psychiatrists rely on their own clinical skills. Similarly, in primary care, in a survey of 2,500 Australian primary care practitioners (PCPs), Krupinski and Tiller (2001)1 found that 28% asked about at least five of the nine standard DSM-IV symptoms. The two symptoms that were most frequently asked about were sleep disturbance (cited by 86.8%) and loss of appetite (cited by 55.6%). Only 0.2% of this sample said they would make a diagnosis using a rating scale.

Toward Evidence-Based Diagnosis Is ICD or DSM right to place more weight on some symptoms than others? If so, there must be evidence that specific symptoms have more diagnostic importance than others. This means that these methods have been subject to comparative diagnostic validity testing. Most clinicians (psychiatrists and non-psychiatrists alike) use their own clinical acumen to make a diagnosis without using any specific tool, but they may have personal experience of the diagnostic importance of specific symptoms. Even those using DSM-IV still have to use clinical judgment because there are no recommended structured

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questions in DSM.2 Conventional clinical method replies on experience and pattern recognition, whereas actuarial judgment uses decision theory informed by empirically established tests.3 In both cases, reaching a diagnosis means narrowing down a long list of possibilities in light of accumulating clinical evidence. However, in the former case it is difficult to check for inaccuracy, whereas in the latter case there is an attempt to diagnose on the basis of calculated probabilities. The standard model for this task is Bayes’ theorem, which calculates post-test probability in relation to the baseline probability (Fig. 5.1). The baseline (pre-test) probability of the condition is the local prevalence of the disease, and the post-test probability is the probability of disease given new information such as a positive test result.4 Before assuming that assisted methods (eg, screening) are helpful, it is worth checking on the evidence base for unassisted detection (see Chapter 3).

Textbox 5.2. Definitions of Measures of Diagnostic Accuracy Sensitivity (Se) A measure of accuracy defined the proportion of patients with disease in whom the test result is positive: a/(a + c) Specificity (Sp) A measure of accuracy defined as the proportion of patients without disease in whom the test result is negative: d/(b + d) Positive Predictive Value A measure of rule-in accuracy defined as the proportion of true positives in those with a positive screening result: a/(a + b) Negative Predictive Value A measure of rule-out accuracy defined as the proportion of true negatives in those with a negative screening result: c/(c + d) Youden’s J A composite of overall accuracy using sensitivity and specificity that is unaffected by prevalence: sensitivity + specificity – 1 Predictive Summary Index A composite of overall accuracy using all positive and negative screens that reflects the prevalence: PPV + NPV – 1 Kappa An index that compares the agreement against that which might be expected by chance. Kappa can be thought of as the chance-corrected proportional agreement: (Observed agreement – Chance agreement)/(1 – Chance agreement)

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Decision Trees Test Positive Condition Pre valence

Sensitivity Test Negative 1-sensitivity

Screen

Test Positive No condition

1-specificity

1-Pre valence

Test Negative specificity

Condition Pre valence Don’t Screen No condition 1-Pre valence

Treated condition Pre valence X Sens Untreated condition Pre valence X T-Sens False positive 1-Prev X 1-Spec Healthy child 1-Prev X Spec Untreated condition Pre valence

Healthy child 1-Pre valence

Figure 5.1. Decision Theory.

2.

Scientific Aspects of Diagnostic Accuracy

Attempts to distinguish patients with a condition from those without on the basis of a test or clinical method are most simply represented by a 2  2 table that generates sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) (Textbox 5.2).5 It is critical to understand the difference between looking vertically down cells and looking horizontally across (Figure 5.2). Vertically, the denominator is the number of cases with or without the condition, a number that is unknown to the clinician but is known in a research setting with a gold standard. Horizontally, the dominator is the number of positive or negative screens, a number that is known to clinicians and hence the reason why PPV and NPV reveal proportions of interest in the real world. There is a complex relationship between these variables. In real life the performance of a test varies with the baseline prevalence of the condition. Put simply, it is simple to spot cases when nothing but cases exist (prevalence = 100%); conversely, it is hard when the prevalence is low.6 Rule-in and rule-out accuracy are essentially independent variables, although a test may perform well in both directions. Rule-in accuracy is best measured by the PPV, but a high specificity also implies there are few false positives, and hence any positive results will suggest a true case.7 Rule-out accuracy is best measured by the NPV where the denominator is all who test negative, but again if the

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Gold Standard Gold Standard Disorder No Disorder Test +ve

A

B

A/A + B PPV

C

D

D/C + D NPV

A/A + C Se

D/B + D Sp

Test –ve

Total

Figure 5.2. Generic 2  2 Table.

sensitivity is high, there will be few false-negative results, and hence any negative implies a true non-case. Optimal accuracy is often achieved by choosing one test for rule in (casefinding) and another for rule out, but not uncommonly only a single test can be applied and it must perform as well as possible in both directions. In this situation summary accuracy statistics are useful. The simplest are Youden’s J and the predictive summary index, which are essentially averages of sensitivity + specificity and PPV + NPV, respectively.8 The fraction correct (ratio of true cases and non-cases/all cases and non-cases) is also useful, as it can easily be used to compare different methods. All such methods work well when the optimal cutoff is known or in binary (yes/no) tests. However, where performance varies by cutoff threshold, sensitivity versus specificity for each cutoff generates a receiver-operator curve, and the area under the curve gives a measure of the overall performance. Where multiple tests need to be compared, each with different optimal sensitivity and specificity values, results can be combined in a summary receiver operator characteristic curve (sROC).9 Additionally when the relative importance of false positives or false negatives is significant, then a cutoff may be chosen that favors rule-in or rule-out accuracy.

Likelihood Ratios Likelihood ratios can be clinically useful because they do not vary with prevalence and because they can be calculated for several levels of test result. A positive likelihood ratio is the odds that a positive test result came from a patient with the disorder (sensitivity/[1 – specificity]). The negative likelihood ratio represents the odds that a negative result came from a patient with the disorder ([1 – sensitivity]/specificity). A normogram (Fig. 5.3) has been developed for use with likelihood ratios to determine the post-test probability of disease if the pre-test probability and the likelihood ratio for the specific test are known. A likelihood ratio greater than 1 produces a post-test probability that is higher than the pre-test probability.

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0.1

99

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98

0.5 1 2

95 2000 1000 500

90

200 100 50

80

10

20 10 5

60 50 40

20

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0.5

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0.1

Pre-Test Probability (%)

105

Likelihood Ratio

Post-Test Probability (%)

Figure 5.3. Likelihood Ratio Normogram.

3.

Clinical Aspects of Diagnostic Accuracy

The best way to understand the clinical applicability of a screening test is to consider the example listed in Textbox 5.1. The patient complains of five symptoms and has data from a single Hospital Anxiety and Depression ScaleDepression (HADS-D) rating. Are these symptoms likely to be symptoms of depression or do they occur in people with stroke who are not depressed? The diagnostic impact of each piece of information can be evaluated scientifically, provided its rate of occurrence is known in both groups (Textbox 5.3 lists these rates). The occurrence rate in the depressed sample is in fact the sensitivity of each specific item. Thus, the symptom with optimal sensitivity

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is ‘‘persistent low mood.’’ Specificity is derived from the non-occurrence in the non-depressed subject, and in this case the optimal specificity is a HADS score of 9 of above, closely followed by poor appetite. However, does this mean these are the best ‘‘tests’’ for this condition?

Textbox 5.3. Post-Stroke Depression: Symptom Counts A previously well 58-year-old man who suffered a dominant hemisphere stroke 2 months previously is referred to an outpatient psychiatry clinic. He reports that he has had five symptoms—low mood, loss of drive, low energy, poor appetite, and insomnia—for the last 3 weeks. His score on the HADS depression scale is 9 out of 21. Out of the last 100 patients seen in this clinic, 54 were depressed. Patient’s Symptoms

% of Depressed Stroke Patients from Previous Studies

Persistent low mood Loss of drive Low energy Disturbed sleep Poor appetite HADS score 9 or above

93% 88% 87% 83% 45% 60%

% of Nondepressed Stroke Patients from Previous Studies 18% 30% 32% 32% 11% 9%

Pre-Test–Post-Test Change As previously noted, raw sensitivity and specificity figures are of only moderate use by themselves. More useful are the PPV and NPV, which can be calculated from the above data. The data from Textbox 5.3 are reproduced in detail in Table 5.2. From this study of 1,000 people following stroke, we see the complexity of deciding upon the optimal test. Persistent low mood is the symptom with highest sensitivity and NPV. Thus, if low mood is not present, there is a 98% chance of identifying a healthy subject on this symptom alone. This alone improves upon the pre-test probability of 0.80 by 0.18 (pre–post gain) (Fig. 5.4). Similarly, if all five symptoms listed are present, there is an 88% chance of major depression, a large pre–post gain. This is different from calculating the value of any one of the five symptoms, which compares ‘‘or’’ rather than ‘‘and’’ combination.

Table 5.2. Summary of Diagnostic Accuracy Results from a Hypothetical Study of Post-Stroke Depression Patient’s Symptoms Single Symptoms Persistent low mood Loss of drive Low energy Disturbed sleep Poor appetite Composite Measures All five symptoms PHQ2 (Q1 or Q2 positive) HADS: score 9 or above Algorithm: PHQ2 then HADS (if positive)

Depressed after Stroke

TP

Sensitivity NonDepressed after Stroke

TN

Specificity

200 200 200 200 200

186 176 174 166 90

0.93 0.88 0.87 0.83 0.45

200 200

56 160

200 200

PPV NPV Youden PSI

FC

UI+ UI

800 800 800 800 800

656 560 544 544 712

0.82 0.70 0.68 0.68 0.89

0.56 0.42 0.40 0.39 0.51

0.98 0.96 0.95 0.94 0.87

0.75 0.58 0.55 0.51 0.34

0.54 0.38 0.36 0.33 0.37

0.84 0.74 0.72 0.71 0.80

0.52 0.37 0.35 0.33 0.23

0.28 0.80

800 800

792 560

0.99 0.70

0.88 0.40

0.85 0.93

0.27 0.50

0.72 0.85 0.25 0.84 0.33 0.72 0.32 0.65

130

0.60

800

728

0.91

0.64

0.91

0.51

0.56 0.86 0.39 0.83

96

0.48

800

778

0.97

0.81

0.88

0.45

0.70 0.87 0.39 0.86

Sample size = 1,000; prevalence = 0.20 TP, true positives; TN, true negatives; PSI, predictive summary index; FC, fraction correction; UI, utility Index.

0.80 0.67 0.65 0.64 0.77

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1

Post-test probability

0.8

0.6

Max gain 0.4

0.2

0 0

0.2

0.4

0.6

0.8

1

Prevalence of prior probability

Figure 5.4. Conditional probabilities graph of pre-test post-test gain from a hypothetical diagnostic test.

Surely, then, the five-symptom method is the best method to identify post-stroke depression? In the real world, the situation is more complex than it first appears because all five symptoms are positive in only 28% of true cases.

Clinical Utility of a Discriminating Test Even when a test has a high PPV or NPV, a correction is needed for occurrence of that test in each respective population. Thus, in this example, if a combination of five symptoms occurs, then it is 88% likely that major depression is present; however, this combination is actually uncommon (28%) in clinical practice. For the clinician, any test with a high PPV will be devalued if it occurs rarely in true cases. Clinically relevant rule-in accuracy (also known as the positive utility index) is a product of the PPV and sensitivity. Thus, the positive utility index for all five symptoms is 0.88  0.28 = 0.32. A similar calculation applies for ruling out a diagnosis. For example, the symptom ‘‘loss of drive’’ has a high NPV but is negative in only 70% of non-depressed stroke patients. Thus, its corrected rule-out value can be calculated by the negative utility index, 0.96  0.70 = 0.67. Utility index scores can be converted into qualitative grades as follows: excellent  0.81, good  0.64, satisfactory  0.49, and poor < 0.49.

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In this example, the most useful population-based rule-in test is low mood, although it is only a ‘‘satisfactory’’ test. The most useful rule-out test is the algorithm approach, which can be graded as an ‘‘excellent’’ rule-out test. Algorithm approaches are worth examining in a bit more detail.

Algorithm Approaches In this example, three questionnaire approaches are shown. The PHQ-2 achieves modest sensitivity and specificity and identifies 77% of all true cases. The HADS-D has excellent specificity and NPV and thus could be used as a rule-out test. Indeed, it could be combined with a high cutoff (eg, 15v16) as a good rule-in test, leaving a cohort scoring 9 to 15 as diagnostically uncertain and requiring a second-stage test. The HADS can also be combined with another questionnaire, in this case the PHQ-2 (see Appendix Fig. 2). This is a basic algorithm approach where a second test is applied only in those positive in the first step. This two-step strategy has the effect of reducing the false positives, improving the PPV and specificity but at the expense of sensitivity and NPV. In low-prevalence conditions, the overall gain in accuracy may be worth the effort of the extra step. Thus, the two-step strategy improves on the 0.40 PPV from the PHQ-2 alone to 0.81 but reduces the NPV from 0.93 to 0.88. However, there is an overall gain in accuracy from 65% to 86% correctly identified. Clinicians may use their own clinical method as an algorithm—for example, offering a follow-up interview to those who are suspected of having a disorder on initial examination. The algorithm often offers a potential economic and efficiency advantage over a conventional approach. Here the majority of patients receive a simple, inexpensive screening test and a minority receive a more lengthy case-finding test. However, the algorithm approach is efficient only where the prevalence of a condition is very low (or very high, in which case the second step is applied to those who screen negative to reduce the false negatives). As the prevalence approaches 0.50, the yield of two-steps converges on the yield from onesteps. The gain is also at its greatest when the accuracy of the single-step approach is least (see Appendix Tables 3 and 4 for more details). A practical example of an algorithm approach to the detection of depression can be found here.10

4.

Testing Screening via Implementation Studies

Even a test of high predictive value and high utility index cannot be assumed to be beneficial. Guidelines from the U.K. National Screening Committee are

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Textbox 5.4. U.K. National Screening Committee Guidelines The condition should:  Be an important health issue  Have a well-understood history, with a detectable risk factor or disease marker  Have cost-effective primary preventions implemented The screening tool should:  Be a valid tool with known cutoff  Be acceptable to the public  Have agreed diagnostic procedures The treatment should:  Be effective, with evidence of benefits of early intervention  Have adequate resources  Have appropriate policies as to who should be treated The screening program should:  Show evidence that benefits of screening outweigh risks  Be acceptable to public and professionals  Be cost-effective (and have ongoing evaluation)  Have quality-assurance strategies in place Adapted from UK National Screening Committee Criteria for appraising the viability, effectiveness and appropriateness of a screening programme. Available at: http://www.nsc.nhs.uk/pdfs/criteria.pdf

helpful here (Textbox 5.4). Ultimately, the case for a screening test has to be proven in an implementation study. This has two important parts: the feasibility of the tool in a clinical setting and the added value of the tool beyond what could be achieved without it.

Feasibility of Depression Screening Feasibility asks whether a tool is practical both in application and scoring to gain acceptance by healthcare professionals and patients. This has been rarely studied in relation to depression severity scales. Bermejo and associates (2005)11 looked at attitudes to the PHQ-9 in general practice in Germany. This study enrolled 1,034 patients from 17 PCPs; both patients and healthcare professionals were asked about acceptability. Patients found the instrument highly acceptable, but 62.5% of the PCPs thought it was too long and 37.5% thought it was too time-consuming, even though it typically took 1 to 2 minutes. Half of the PCPs rated the PHQ as an impediment to daily practice and 75% thought it was impractical, compared with

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only 25% of patients. One proxy for feasibility is willingness of clinicians to use the test: any screening roll-out will be compromised if front-line staff find the tool too difficult to administer or score.

Added Value Demonstrating the possible benefit of a screening tool is akin to demonstrating benefit from a new medicine. Ideally, a randomized controlled trial using representative clinicians and patients takes place. The design should be a randomized trial where one group (arm 1) use their clinical skills uninfluenced by the study taking place (Hawthorn effect) and the other group (arm 2) use their clinical skills plus the screening tool or method. The advantage of this design is that the results reveal the unassisted detection rate (arm 1) as well as added value beyond usual care (the difference between arm 2 and arm 1). Possible stages of tool development are discussed in Chapter 4. Ideally, implementation should not stop with demonstration of superior detection; rather, it should attempt to demonstrate further patient benefits, such as better quality of care and greater resolution of depression. This is discussed further in Chapter 7.

5.

Conclusions

Although depression is one of the world’s most prevalent disorders and antidepressants are the most commonly prescribed class of drug, the science of diagnosing depression has been hampered by the paucity of simple studies documenting the rate of symptoms and signs in depressed and non-depressed subjects. Once these data become available, calculating the diagnostic value of specific symptoms (both individually and in combination) becomes straightforward. Better data exist for depression severity scales and other assisted methods. Beyond this, further implementation studies are required in which the true benefit of all proposed diagnostic methods to patients are compared with conventional unassisted approaches.

References 1. Krupinski J, Tiller J. The identification and treatment of depression by general practitioners. Aust N Z J Psychiatry. 2001;35:827–832. 2. Steiner JL, Tebes JK, Sledge WH, et al. A comparison of the structured clinical interview for DSM-III-R and clinical diagnoses. J Nerv Ment Dis. 1995;183:365–369. 3. Steadman HJ, Silver E, Monahan J, et al. A classification tree approach to the development of actuarial violence risk assessment tools. Law and Human Behavior. 2000;24:83–100.

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4. Elstein AS, Schwarz A. Clinical problem solving and diagnostic decision making: selective review of the cognitive literature. BMJ. 2002;324:729–732. 5. Yerushalmy J. Statistical problems in assessing methods of medical diagnosis, with special reference to X-ray techniques. Pub Health Rep. 1947;62:1432–1449. 6. Whiting P, Rutjes AWS, Dinnes J, et al. Development and validation of methods for assessing the quality of diagnostic accuracy studies. Health Technology Assessment. 2004;8(25):1–234. 7. Sackett DL, RB Haynes. The architecture of diagnostic research. BMJ. 2002;324:539–541. 8. Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3:32–35. 9. Macaskill P. Empirical Bayes estimates generated in a hierarchical summary ROC analysis agreed closely with those of a full Bayesian analysis. J Clin Epidemiol. 2004;57:925–932. 10. Thombs BD, Ziegelstein RC, Whooley MA. Optimizing detection of major depression among patients with coronary artery disease using the Patient Health Questionnaire: Data from the Heart and Soul Study. J Gen Intern Med. 2008;23(12):2014–2017. 11. Bermejo I, Niebling W, Mathias B, et al. Patients’ and physicians’ evaluation of the PHQ-D for depression screening. Primary Care & Community Psychiatry. 2005;10(4):125–131.

6 CLINICAL JUDGMENT AND THE INFLUENCE OF SCREENING ON DECISION MAKING Howard N. Garb

1. Introduction 2. Research on Clinical Judgment 3. The Limits of Screening

Context How do clinicians arrive at diagnostic decisions? In most cases the decision is not made following formal criteria, but by intuition. In addition, routine interviews are often narrow and the feedback gleaned from patients is inadequate. Yet it is not clear if screening helps or hinders clinical judgment. It might be that only clinicians who have low confidence and interviewing and diagnostic skills are open to the use of and actually helped by diagnostic tools.

1. Introduction To provide a theoretical framework for understanding why it is difficult for physicians to detect depression in primary care settings, a broad array of research in the mental health fields can be described. For example, more than 1,000 studies have been conducted on clinical judgment in the area of mental health practice,1,2 and the results from these studies can be used to illuminate the challenges physicians face in judging whether a patient is clinically

*

The views expressed in this article are those of the author and are not the official policy of the Department of Defense or the United States Air Force.

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depressed and can benefit from treatment. In this chapter, results on clinical judgment will be described. A second topic will also be briefly discussed. Results from research on clinical judgment would seem to indicate that screening should be of value. Yet, as noted in Chapter 7, stand-alone screening programs have added little or nothing to outcomes. Reasons for this unexpected result will be explored.

2. Research on Clinical Judgment Three topics will be discussed: (1) narrowness of interviews, (2) nature of patient feedback, and (3) the cognitive processes of clinicians.

Narrowness of Interviews Depression goes undetected because in many cases physicians do not ask patients if they have symptoms of a depressive mood disorder.3 To place this in context, it can be noted that mental health professionals also often do not ask patients about important symptoms and behaviors. Failure to inquire about depression in primary care settings can be viewed in the broader context of failure to inquire about important symptoms and events in mental health settings. Research on clinical judgment has demonstrated that lack of comprehensiveness is often a problem for interviews made in clinical practice. For example, in one study,4 mental health professionals saw patients in routine clinical practice, and afterwards research investigators conducted semi-structured interviews with the patients. Remarkably, the mental health professionals had evaluated only about 50% of the symptoms that were recorded using the semi-structured interviews. Similarly, a number of studies have found that mental health professionals often do not ask about important events when formulating a case history. For example, in a study by Malone and associates (1995),5 clinicians at a psychiatric hospital failed to document a history of suicidal behavior for 12 of 50 patients who had a history of suicidal behavior. This is important because past suicidal behavior is one of the best predictors of suicide. In another study,6 26 of 69 psychiatric inpatients reported on a research questionnaire that they were victims of severe physical abuse by family members or partners during the past year. The abuse had been documented in medical charts for only nine of the patients. To give one more example, in another study a computer interview was used to collect a psychiatric history.7 Important history information was obtained using the computer interview that had not been obtained by mental health professionals in the course of their routine work. This was especially

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true for obtaining information about criminal history (26% of patients), amnesic blackouts after drinking heavily (23%), repeatedly being fired from jobs (17%), recent drug abuse (10%), and debts (10%). Another type of error that occurs when evaluations of psychopathology are not comprehensive is called diagnostic overshadowing. Diagnostic overshadowing is said to occur when clinicians make one or two diagnoses but overlook other disorders.8,9 For example, when diagnoses are made by mental health professionals, mental disorders tend to be missed among clients with mental retardation,10,11 alcohol and drug abuse is often underdiagnosed among clients presenting with psychiatric problems,12 and diagnoses of personality disorder are often missed among clients with an Axis I disorder (eg, among clients with obsessive-compulsive disorder).13 If mental health professionals fail to ask about important emotional and behavioral problems and overlook mental disorders, it is not surprising that physicians who are not trained in psychiatry do the same. Since patients in primary care settings almost always present with physical complaints, we should not be surprised when diagnostic overshadowing occurs and physicians do not explore other possible problems.

Nature of Patient Feedback Another reason why physicians may have difficulty detecting depression in primary care settings is because they are unlikely to receive accurate feedback. If a patient with clinically significant depression presents with a medical problem and the physician misses the diagnosis, it is unlikely that the physician will later learn that the diagnosis of depression was missed. One of the most surprising findings on clinical judgment is that it can be very difficult to learn from clinical experience. Training is often positively related to validity, but experience is not.14,15 Thus, once physicians and mental health professionals complete residency or graduate-school levels of training, the amount of experience they gain is weakly related, or even negatively related, to the accuracy of judgments and treatment outcomes. In a review of the literature on the relationship between clinical experience and quality of healthcare,16 physicians who had been in practice longer were found to be at risk for providing lower-quality care. A decreasing level of performance (or treatment) was associated with increasing years in practice for all outcomes assessed in 32 of 62 studies. In the other studies, decreasing level of performance was associated with increasing experience for some outcomes but not for others (13 of 62 studies), no association was observed for 13 of 62 studies, mixed results were obtained for 3 of 62 studies, and an increasing level of performance with increasing years in practice for all outcomes was obtained in 1 of 62 studies.

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Similarly, in routine clinical practice in the mental health fields, professionals with extensive clinical experience are typically no more accurate than other clinicians. For example, in one study,17 different participants (eg, marital therapists, undergraduates) viewed videotaped conversations of 10 married couples and predicted which couples were likely to divorce in the future. Attitudes about marriage, but not amount of clinical experience, were related to the validity of predictions.

The Cognitive Processes of Clinicians It is likely that depression often goes undetected in primary care settings not only because interviews are narrow and feedback is inadequate, but also because the cognitive processes of clinicians are fallible. The primacy effect, confirmatory hypothesis testing, cognitive heuristics, and causal reasoning are described in this section. One can wonder if one reason physicians miss diagnosing depression is because they make judgments too quickly. The tendency to make judgments quickly, sometimes after collecting relatively few data, is called the primacy effect. It is characteristic of social judgments made in everyday situations as well as of clinical judgments made in mental health settings.1,18 For example, Gauron and Dickinson reported that psychiatrists who observed a videotaped interview routinely formed diagnostic impressions in 30 to 60 seconds.19 Similarly, Kendell found that psychiatrists are often ready to make a diagnosis for a patient within a few minutes.20 One can wonder if physicians in primary care settings also tend to reach conclusions surprisingly quickly, and if this is a reason for their missing diagnoses of depression. Another reason depression may go undetected is because physicians may rely on confirmatory hypothesis testing. Confirmatory hypothesis testing refers to a tendency to seek, use, and remember information that is likely to confirm, but not refute, a hypothesis. Research on clinical judgment indicates that mental health professionals tend to seek and remember information that will support a hypothesis and this leads them to not consider alternative hypotheses. For example, in an especially well-designed study,21 psychology graduate students watched a videotape of an initial psychotherapy session. They listed questions they would like to ask the client portrayed in the videotape, and they described their reasons for wanting to ask the client these questions. An independent panel of psychologists coded each question as being likely to elicit information that could confirm or disconfirm their hypothesis. The style of hypothesis testing was confirmatory 64% of the time, neutral 21% of the time, and disconfirmatory 15% of the time. These results, along with results from other studies, provide insight into why clinicians do not routinely consider alternative hypotheses.

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Cognitive heuristics are simple rules that describe how judgments are made. Made famous by Daniel Kahneman and Amos Tversky, cognitive heuristics describe cognitive processes that allow us to efficiently process vast amounts of information.22 However, these same cognitive processes also cause us to sometimes make characteristic types of mistakes. Cognitive heuristics include the affect, representativeness, and availability heuristics. The affect heuristic refers to the fact that people often make judgments and decisions based, in part, on their feelings. ‘‘Snap judgments’’ and judgments based on ‘‘gut instinct’’ or intuition are often described by the affect heuristic. Kahneman believes that the formulation of the affect heuristic is ‘‘probably the most important development in the study of judgment heuristics in the past few decades.’’23, p. 703 But how does the affect heuristic relate to the detection of depressive disorders in primary care settings? For whatever reasons, in many cases, physicians’ reliance on affect and intuition does not allow them to detect depression in these settings. The representativeness heuristic is said to be descriptive of a clinician’s cognitive processes when a judgment is made by deciding if a patient is representative of a category.24 For example, when a screening instrument indicates that a patient may be depressed and physicians must decide if treatment for depression is required, the physicians may compare the patient to (a) patients they have worked with who have been clinically depressed, (b) their concept of the ‘‘typical’’ person with clinically significant depression, or (c) a theoretical standard that serves to define clinically significant depression. The representativeness heuristic is often descriptive of how judgments are made in everyday life,25 and it is even descriptive of how many mental health professionals make diagnoses.26 Since the representativeness heuristic is often descriptive of how people make judgments, it is likely to also be descriptive of physicians in primary care settings. If they are not comparing patients to appropriate exemplars, stereotypes, or prototypes, then this may explain why they are having difficulty with this task. The third heuristic, the availability heuristic, is descriptive of memory when clinicians are influenced by the ease with which events or different patients can be remembered. For example, the ease with which information is remembered can be related to its recency or its vividness. The point to be understood here is that memory is fallible. We are unable to remember all of the patients we have seen. By being selective for memory, cognitive efficiency is enormously enhanced, but learning from experience becomes difficult. One more feature of the cognitive processes of clinicians will be described. A major finding on clinical judgment in recent years is that causal reasoning underlies the manner in which mental health professionals make many different types of judgments, including treatment decisions, predictions, and diagnoses.27,28

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With regard to treatment decisions, Witteman and Koele addressed the following questions: ‘‘What explains which treatment is proposed to a (depressed) patient? Is it the patient characteristics, such as her or his specific symptoms, social context, and seriousness of the disorder, or is it the theoretical background of the proposing psychotherapist?’’29, p. 100 For a group of 56 therapists, treatment plans were highly variable, and Witteman and Koele concluded, ‘‘The best explanations of the treatment proposals seemed to be the therapist’s theory-inspired interpretations of the patient complaints.’’29, p. 100 Causal reasoning also underlies how mental health professionals make predictions. In one study, clinicians predicted whether patients would become violent in the next 6 months.30 Ratings were made by mental health professionals working in a psychiatric emergency room. Mulvey and Lidz observed: Clinicians did not appear to be making simple ‘‘yes’’ or ‘‘no’’ judgments of dangerousness. Rather, they seemed to be making contextualized judgments regarding future violence. Instead of stating whether they thought someone was highly likely or unlikely to be involved in violence, the clinicians instead gave what we called ‘‘conditional judgments’’ regarding future violence. . . . In other words, they saw the violence as dependent upon certain conditions in the person’s life.’’30, p. S108

Thus, clinicians will frequently make predictions by formulating case conceptualizations. Finally, when clinicians make diagnoses, they are influenced not only by diagnostic criteria but also by their implicit causal theories.27,31 Clinicians weigh diagnostic criteria more heavily when the criteria describe symptoms and behaviors that are part of a clinician’s implicit causal model for a disorder.27 When using DSM, clinicians are supposed to weigh each criterion equally. Similarly, mental health professionals’ implicit theories influenced their memories of their clients’ mental status. Causally central symptoms were recalled more often than causally peripheral symptoms and isolated symptoms. In addition, false memories of a patient having symptoms the patient did not really have were most likely to occur for symptoms that were causally central to clinicians’ theories of different disorders. The finding that causal reasoning underlies different types of clinical judgments is important for helping us understand the actions of physicians in primary care settings. To understand the etiology and course of a patient’s physical complaint, physicians should understand the effect of depression. In other words, for some patients, vague physical complaints and complaints of fatigue and aches and pains are highly correlated with depression and anxiety. To the extent that this is recognized by physicians, they will become more

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adept at detecting depression. Thus, to some degree, to bring about change in primary care settings, we must be concerned with the implicit causal theories of physicians.

3. The Limits of Screening The use of screening questionnaires can help physicians overcome some problems but not others. Screening questionnaires can compensate for interviews that are not comprehensive, and they can help physicians overcome some cognitive processes that are counterproductive, such as diagnostic overshadowing and confirmatory hypothesis testing. In particular, screening questionnaires will prompt physicians to consider alternative hypotheses—that is, results from a screening questionnaire can lead a physician to consider whether a patient is depressed. Otherwise, the physician may not even consider the hypothesis that a particular patient has a mood disorder. Given everything we know about clinical judgment, it is somewhat surprising that the use of screening questionnaires has not been related to improved clinical outcomes. A number of reasons can be given for why this is the case. Two reasons will be described here. First, some patients overreport symptoms while other patients underreport them. This can occur if a patient misunderstands an item or if the patient wants to create an impression of being healthy or of being impaired. To the extent that symptoms are overreported or underreported on screening instruments, we should not expect better clinical outcomes. Second, even with the use of screening questionnaires, physicians must still rely on clinical judgment. Thus, if a patient tests positive for depression on a screening instrument, physicians must rely on their clinical judgment to determine whether the patient’s responses should be viewed as indicating a need for treatment or as a false positive. If someone is clinically depressed, physicians will need to determine if he or she may have a bipolar disorder (and should not be treated with an antidepressant). They must also determine if the patient is at serious risk for suicide. If physicians are not making the right judgments when a patient tests positive (eg, making a referral to a mental health professional, providing treatment for depression, making a differential diagnosis of bipolar disorder), then the use of screening questionnaires will not lead to improved clinical outcomes. This is a challenging task for physicians, in part because they will not receive feedback on the validity of their judgments or the utility of their decision making and in part because they are unlikely to have specialized training in mental health diagnosis and treatment. It is also a challenging task because when patients complete questionnaires inquiring about mental health

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symptoms, false positives are common, usually because patients (and everyone else) will sometimes interpret items in an idiosyncratic manner.32 In conclusion, we are faced with a dilemma. Clinical judgment is fallible, and the use of screening questionnaires has not been related to improved clinical outcomes. However, the use of screening tools should help to improve clinical judgment, and, much of the time, an optimal strategy will be to conduct screening and then rely on clinical judgment. Although a large body of research describes errors and mistakes in clinical judgment, it can still be of considerable value, if only to review responses on a screening questionnaire with a patient so as to better understand how the patient interpreted the items. In addition, it may be that use of screening assists in the diagnosis of underconfident clinicians but could be unhelpful in those skilled in making the diagnosis in question.

References 1. Garb HN. Studying the clinician: judgment research and psychological assessment. Washington, DC: American Psychological Association, 1998. 2. Garb HN. Clinical judgment and decision making. Ann Rev Clin Psychol. 2005;1:67–89. 3. Nichols GA, Brown JB. Following depression in primary care—Do family practice physicians ask about depression at different rates than internal medicine physicians? Arch Fam Med. 2000;9:478–482. 4. Miller PR, Dasher R, Collins R, et al. Inpatient diagnostic assessments: 1. Accuracy of structured vs. unstructured interviews. Psychiatry Res. 2001;105:255–264. 5. Malone KM, Szanto K, Corbitt EM, et al. Clinical assessment versus research methods in the assessment of suicidal behavior. Am J Psychiatry. 1995;152:1601–1607. 6. Cascardi M, Mueser KT, DeGiralomo J, et al. Physical aggression against psychiatric inpatients by family members and partners. Psychiatr Serv. 1996;47:531–533. 7. Carr AC, Ghosh A, Ancill RJ. Can a computer take a psychiatric history? Psychol Med. 1983;13:151–158. 8. Jopp DA, Keys CB. Diagnostic overshadowing reviewed and reconsidered. Am J Ment Retard. 2001;106:416–433. 9. Reiss S, Szyszko J. Diagnostic overshadowing and professional experience with mentally retarded persons. Am J Mental Defic. 1983;87:396–402. 10. Mason J, Scior K. Diagnostic overshadowing amongst clinicians working with people with intellectual disabilities in the UK. J Appl Res Int Dis. 2004;17:85–90. 11. Spengler PM, Strohmer DC, Prout HT. Testing the robustness of the overshadowing bias. Am J Mental Retard. 1990;95:204–214. 12. Drake RE, Osher FC, Noordsy DL, et al. Diagnosis of alcohol use disorders in schizophrenia. Schizophr Bull. 1990;16:57–67. 13. Tenney NH, Schotte CKW, Denys DAJP, et al. Assessment of DSM-IV personality disorders in obsessive-compulsive disorder: Comparison of clinical diagnosis, self-report questionnaire, and semi-structured interview. J Personal Disord. 2003;17:550–561.

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14. Garb HN. Clinical judgment, clinical training, and professional experience. Psychol Bull. 1989;105:387–396. 15. Garb HN, Schramke CJ. Judgment research and neuropsychological assessment: a narrative review and meta-analyses. Psychol Bull. 1996;120:140–153. 16. Choudhry NK, Fletcher RH, Soumerai SB. Systematic review: The relationship between clinical experience and quality of health care. Ann Intern Med. 2005;142:260–273. 17. Ebling R, Levenson RW. Who are the marital experts? J Marriage Fam. 2003;65:130–142. 18. Ambady N, Rosenthal R. Thin slices of expressive behavior as predictors of interpersonal consequences: A meta-analysis. Psychol Bull. 1992;111:256–274. 19. Gauron EF, Dickinson JK. Diagnostic decision making in psychiatry. Arch Gen Psychiatry. 1966;14:225–232. 20. Kendell RE. Psychiatric diagnoses: A study of how they are made. Br J Psychiatry. 1973;122:437–445. 21. Haverkamp BE. Confirmatory bias in hypothesis testing for client-identified and counselor self-generated hypotheses. J Couns Psychol. 1993;40:303–315. 22. Tversky A, Kahneman D. Judgments under uncertainty: heuristics and biases. Science. 1974;185:1124–1131. 23. Kahneman D. A perspective on judgment and choice: Mapping bounded rationality. Am Psychol. 2003;58:697–720. 24. Kahneman D, Slovic P, Tversky A, eds. Judgment under uncertainty: Heuristics and biases. New York: Cambridge University Press, 1982. 25. Gilovich T, Griffin D, Kahneman, D, eds. Heuristics and biases. New York: Cambridge University Press, 2002. 26. Garb HN. The representativeness and past-behavior heuristics in clinical judgment. Prof Psychol Res Pr. 1996;27:272–277. 27. Kim NS, Ahn W. Clinical psychologists’ theory-based representations of mental disorders predict their diagnostic reasoning and memory. J Exp Psychol Gen. 2002;131:451–476. 28. Wakefield JC, Kirk SA, Pottick KJ, et al. Disorder attribution and clinical judgment in the assessment of adolescent antisocial behavior. Soc Work Res. 199;23:227–238. 29. Witteman C, Koele P. Explaining treatment decisions. Psychother Res. 1999;9:100–114. 30. Mulvey, EP, Lidz CW. Clinical prediction of violence as a conditional judgment. Soc Psychiatry Psychiatr Epidemiol. 1998;33:S107–S113. 31. Pottick KJ, Kirk SA, Hsieh DK, et al. Judging mental disorder in youths: Effects of client, clinician, and contextual differences. J Consult Clin Psychol. 2007;75:1–8. 32. Nease DE, Klinkman MS, Aikens JE. Depression case findings in primary care: A method for the mandates. Int J Psychiatry Med. 2006;36:141–151.

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7 IMPLEMENTING SCREENING AS PART OF ENHANCED CARE: SCREENING ALONE IS NOT ENOUGH Simon Gilbody and Dan Beck

1. 2. 3. 4.

The Case for Screening Screening and Enhanced Care for Depression New and Additional Evidence Relating to Enhanced Care Is Screening a Necessary Intervention to Improve the Quality and Outcome of Care? 5. To Screen or Not to Screen?

Context There are conflicting conclusions and policy recommendations relating to the effects of screening on the outcome of depression, but what does the latest evidence suggest? Based on the best available information to date, it emerges that screening alone is not a sufficient intervention to improve the quality and outcomes of care for depression. What is less clear is whether screening is a necessary condition for enhanced and improved quality of care and, given additional components, to what extent screening programs can potentially improve quality of routine care.

1.

The Case for Screening

Depression is the most common mental health problem and is associated with decrements in functioning and quality of life comparable to other chronic physical diseases.1 The prevalence, chronicity, and burden of suffering are such that the World Bank has predicted that depression will become the second leading cause of global disability by 2020.2 The economic consequences of depression are also profound, with the healthcare costs, welfare costs, and 123

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SCREENING FOR DEPRESSION IN CLINICAL PRACTICE

losses to productivity amounting to £9 billion ($20 billion) in the United Kingdom3 and $53 billion in the United States.4 Depression is most commonly encountered in primary care and in hospital settings, yet it often goes unrecognized by healthcare professionals.5–7 This has led to calls to implement screening programs to aid in the detection and management of this problem.8,9 The rationale and evidence base to support screening for depression is the focus of the present book and is discussed extensively in other chapters (see Chapters 2, 4, and 9). In the United States, screening has shifted from being an intervention that was not initially supported in national policy recommendations10 to being one that is regarded as being of proven effectiveness.11 An evolution in thinking has occurred that places screening at the center of mental health policy and practice, and is based upon the general assumption that screening will logically lead to improvements in the quality and outcome of care. Some have termed this the screening– detection–treatment–improvement paradigm.12,13 Recently screening for common mental health problems in the United States has become the cornerstone of the president’s agenda to improve the mental health of the U.S. population.14

Arguments For and Against Screening Screening has a long and honorable tradition in helping to improve the health and well-being of populations and individuals.15 However, screening is a ‘‘special case’’ in the armory of healthcare interventions, since testing and treatment may be offered to those who do not necessarily know they have a condition or do not specifically ask for help for that problem.16 Screening programs have also been implemented in the past without due consideration of their effectiveness, their ethical and clinical implications, and their impact on finite healthcare resources.17 Consequently, clear criteria have evolved that must be satisfied before screening programs are adopted (see Chapter 5).18 In the case of depression, screening is just one of a range of possible interventions that might be offered to improve care for depression at a population level,19 and the implementation of screening programs should be supported by sound clinical and economic evidence.20 The relative merits of screening for depression more generally have been reviewed by Gilbody and colleagues 20 and by Palmer and Coyne.13 Gilbody and colleagues used a set of analytic principles laid down by the World Health Organization18 and adopted by the U.K. National Screening Committee.21 In their analysis, they agued that the relative merits of screening programs are sometimes overstated, and that convincing evidence that screening substantially influences the outcomes of depression is difficult to find. The principal concerns that have been highlighted are that

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screening for depression uncovers a substantial body of undetected psychological need that is not currently well met within existing healthcare systems. Much of this represents short-term and self-limiting distress, the natural history of which is not readily influenced by active intervention.22 In addition, the common belief that unrecognized depression is as responsive to the evidencesupported interventions (antidepressants and brief psychotherapy) currently used for already recognized depression is not necessarily true: unrecognized depression may be more difficult to treat because it tends to be mild or atypical. Most importantly of all, they highlighted the relative lack of evidence in the form of randomized controlled trials to show that the introduction of screening programs for depression makes any substantial difference to the outcomes of depression itself.23 There is also a dearth of economic data to inform this population-level policy intervention. It is this area of supportive epidemiologic and economic evidence that has produced the greatest amount of debate and controversy, which we will review in more detail within this chapter. Two strategies have been scrutinized and variously rejected10,24,25 or advocated.11,26,27 The first is the use of screening as a ‘‘stand-alone’’ quality improvement strategy. The second is the use of screening within a more general enhancement of the care for depression in non-specialist settings. Let us examine each of these strategies in turn to establish whether screening is a sufficient or necessary condition in improving the quality and outcome of care for depression.

Is Screening a Sufficient Intervention to Improve the Quality and Outcome of Care? The effectiveness of screening for depression was first addressed with reference to the research literature the 1990s. The first evidence synthesis was conducted by the U.S. Agency for Health Care Policy and Research, which looked at the evidence to support various aspects of the management of depression in primary care settings, including screening.28 This review examined the totality of research and came down firmly against screening. On the basis of a review of the literature published in May 1993, the U.S. Preventive Services Task Force (USPSTF) concluded that there was ‘‘sufficient evidence to exclude screening for depression in the primary care setting’’ (a ‘‘grade D’’ recommendation). This research highlighted that screening instruments did not generally improve the detection rate or management of depression. The evidence they reviewed was primarily related to the use of screening programs as a ‘‘stand-alone’’ measure. A similar conclusion was found in a 2001 evidence review24 also published under the auspices of the Cochrane Collaboration (first in 200523 and updated

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SCREENING FOR DEPRESSION IN CLINICAL PRACTICE

again in 200829,30). The most recent version of this review of ‘‘stand-alone’’ depression screening, which now includes 16 primary trials of the effectiveness of screening strategies (5,000 + patients), concluded, ‘‘There is substantial evidence that routinely administered case finding/screening questionnaires for depression have minimal impact on the detection, management or outcome of depression by clinicians.’’ The most important finding from the Cochrane reviews29,30 has been the consistent demonstration that screening had minimal impact on the actual outcomes of depression when screened populations were followed up over time. This review concurs with the first USPSTF review,11 and an overall summary diagram of the lack of effect of simple screening strategies based on the Cochrane review is shown in Figure 7.1. A review conducted at around the same time as the first Cochrane review, to provide updated guidance to the USPSTF,11 examined a similar body of research and found a similar lack of effect in relation to the impact of standalone screening strategies. However, this review was altogether more positive about screening (Textbox 7.1). The reasons for this shift in recommendation by the USPSTF deserve examination in some detail, and relate to the additional consideration of screening alongside ‘‘additional enhancements of care.’’

Study

Depression outcomes (SMD) (95% CI)

Bergus 2005

-0.29 (-1.40, 0.82)

Callaghan 1994

-0.05 (-0.97, 0.86)

Johnstone 1976

-0.77 (-1.54, 0.00)

Lewis GHQ 1996

0.10 (-0.09, 0.29)

Lewis PRQ 1996

-0.06 (-0.25, 0.13)

Whooley 2000

-0.16 (-0.72, 0.39)

Williams 1999

-0.22 (-0.81, 0.37)

Overall

-0.03 (-0.16, 0.10)

–1.5

–1

–.5

0

.5

1

1.5

Depression outcomes (SMD) Favors screening

Favors control

Figure 7.1. Summary of random effects meta-analysis of the effect of simple screening/ case-finding instruments on the outcome of depression at follow-up (adapted from references 23, 29, and 30).

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Textbox 7.1. Current Policy Recommendations on Screening for Depression U.K. National Institute of Clinical Excellence31 ‘‘Screening should be undertaken in primary care and general hospital settings for depression in high-risk groups—for example, those with a past history of depression, significant physical illnesses causing disability, or other mental health problems such as dementia.’’ Review of reviews to inform practice and policy in Australia and New Zealand32 ‘‘Brief self-report instruments have acceptable psychometric properties and are practical for use in general practice settings. Screening increases the recognition and diagnosis of depression and, when integrated with a commitment to provide coordinated and prompt follow up of diagnosis and treatment, clinical outcomes are improved. Although controversial, the evidence is now in favour of the appropriate use of screening tools in primary care.’’ U.S. Preventive Services Task Force11 ‘‘The USPSTF found good evidence that screening improves the accurate identification of depressed patients in primary care settings and that treatment of depressed adults identified in primary care settings decreases clinical morbidity. Trials that have directly evaluated the effect of screening on clinical outcomes have shown mixed results. Small benefits have been observed in studies that simply feed back screening results to clinicians. Larger benefits have been observed in studies in which the communication of screening results is coordinated with effective follow-up and treatment. The USPSTF concluded the benefits of screening are likely to outweigh any potential harms.’’ Strength of recommendation: B (‘‘there is at least fair evidence that the intervention improves important health outcomes and that the benefits outweigh the harms’’) Canadian Task Force on Preventive Health Care27 ‘‘The CTFPHC concludes that there is fair evidence to recommend screening adults for depression in primary care settings since screening improves health outcomes when linked to effective follow-up and treatment.’’ Strength of recommendation: B (‘‘there is fair evidence to recommend the clinical preventive action’’) ‘‘The CTFPHC concludes that there is insufficient evidence to recommend for or against screening adults for depression in primary care settings where effective follow-up and treatment are not available.’’ Strength of recommendation: I (‘‘insufficient evidence [in quantity and/or quality] to make a recommendation, however other factors may influence decision-making’’)

128

2.

SCREENING FOR DEPRESSION IN CLINICAL PRACTICE

Screening and Enhanced Care for Depression

The major shift between recommendations produced in 1996 and 2003 turns upon the change in the scope of the evidence review and the inclusion criteria that were set.25 In contrast to earlier reviews, the USPSTF in their updated report reviewed both stand-alone screening programs and those embedded within enhancements of care. An example of such an enhanced care study was that conducted by Wells and colleagues (the Partners in Care study),33 which provided practice-level enhancements in the quality of care for depression, including structured psychotherapy or medication management, clinician education and consultation/liaison, treatment guidelines, and structured followup. Recruitment to this trial was by screening and, as such, was considered by the USPSTF as evidence to support the effectiveness of screening in practice. This study showed strongly positive results on the outcomes of depression and was included a summary meta-analysis (accounting for 33% of the overall weight of evidence). On the basis of this evidence, the USPSTF concluded, ‘‘benefits have been observed in studies in which the communication of screening results is coordinated with effective follow-up and treatment.’’ A subsequent 2005 review published by the Canadian Task Force on Preventive Health Care (CTFPHC)27 made a nearly identical recommendation, highlighting the ineffectiveness of stand-alone screening and the effectiveness of screening plus enhanced care. A similar recommendation was made in the United Kingdom in guidance offered by the U.K. National Institute of Clinical Excellence (NICE) (see Textbox 7.1).31

3.

New and Additional Evidence Relating to Enhanced Care

The specific recommendations made by the USPSTF, CTFPHC and NICE relating to screening plus enhanced care fit into a much wider body of research relating to organizational enhancements to the process of care for depression.34 The enhancement of primary care for depression is an active area of research, and a substantial body of research evidence now exists to show that this is an effective intervention.35 The most recent review of this topic has included pooled data from over 30 randomized trials, based on over 12,000 patients with depression, and has shown that enhanced care is effective in the short and medium term.35 The finding that enhanced or collaborative care is effective is now a consistent one that has been supported in several independently conducted meta-analyses (see Bower and Gilbody36 for an overview of reviews in this area). In the aforementioned Partners in Care study, the benefits of an enhanced care intervention have persisted at up to 5 years.37 However, while the effectiveness of enhanced care is now beyond reasonable doubt, the USPSTF review included only 438–41 of the 36 trials of enhanced care that

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were summarized in the largest or most comprehensive review to date. From these four studies, the U.S. and Canadian reports drew quite specific conclusions about the effectiveness of screening (the topic of their review) rather than about the effectiveness of enhanced and collaborative care in general.25 Many studies of enhanced care do not use screening as an entry criterion or component of quality improvement, but these were not reviewed by the USPSTF. This is not just of academic interest, since it is clear that many healthcare systems have taken the positive endorsement of screening within enhancements of care as an endorsement of screening per se. In the United Kingdom, for example, financial inducements have been introduced to encourage primary care physicians to screen for depression, without any requirement that further enhancements in the quality of care are introduced.20 Clearly, the specific question about the relative contribution of screening to the effectiveness of quality improvement strategies is important from a policy and practice perspective. To what extent is screening the critical component in determining the quality of depression care?

4.

Is Screening a Necessary Intervention to Improve the Quality and Outcome of Care?

What remains unclear from the preceding discussion and the work of the USPSTF is whether screening is a necessary component or condition for effective enhanced care, and whether enhancements of care without screening are in themselves ineffective. Recent research has emerged to answer this question, which was not effectively addressed by the USPSTF11 and a subsequent review by the CTFPHC.27 The overall effectiveness of enhanced care for depression has most recently been reviewed by Gilbody and colleagues,35 who found that collaborative care strategies were effective far beyond conventional levels of significance in improving depression outcomes in the short and medium term. This dataset provides a more comprehensive body of research within which to begin to examine whether screening is a necessary ingredient of effective enhanced care for depression. Among enhanced care studies as a whole, the authors found a moderate pooled standardized effect size of 0.25 for enhanced care compared to usual care (95% confidence interval 0.18 to 0.32). They also found that there was significant between-study variation in the magnitude of effect size (that is, heterogeneity). When conducting a meta-analysis, the most rigorous approach to heterogeneity is to seek to explain or explore the causes of this heterogeneity.42 This technique can provide useful insights into mechanisms of effect and variations in treatment response according to the population under study or the intervention under evaluation. This information is often of interest to clinicians and policymakers charged with implementing or interpreting

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SCREENING FOR DEPRESSION IN CLINICAL PRACTICE

research evidence. One technique that can be used is regression modeling, whereby the relationship between study-level design variables and a dependent variable (study effect size) is examined (this is termed meta-regression42,43). This technique was applied to the dataset of enhanced care for depression by Bower and colleagues to identify some of the ‘‘active ingredients’’ in enhanced care for depression.44 Among 34 studies, there was substantial variation in the content and intensity of collaborative care. Some studies, such as the previously discussed Partners in Care study,39 provided relatively intensive packages of enhanced care, including face-to-face clinician education, computerized decision support, individualized treatment algorithms, the active support of a nurse case manager. and regular consultation/liaison with a specialist mental health clinician (psychologist or psychiatrist). This study39 accounted for 30% to 47% of the weighted information in the meta-analyses produced on behalf of the USPSTF.11 In contrast, less intensive packages of care were also included in the collaborative review by Gilbody and colleagues and involved simple telephone follow-up by practice nurses.45 Bower and colleagues44 used metaregression to examine the relative contributions of various aspects of the content of enhanced care interventions in improving depression outcome within the dataset of collaborative care studies. They specified and were able to find sufficient study-level information on eight aspects of care and study design, including the method of recruitment—whether by screening or by clinician referral of already recognized depression. Stratification according to this variable showed that the majority of studies used screening, but that 12 collaborative care studies did not.45–55A stratified meta-analysis according to this variable is shown in Figure 7.2, and the methods of patient recruitment (by screening or by other means) are detailed in Table 7.1. From this stratified analysis, it is evident that the majority of studies were positive, and that screening studies showed the most strongly positive effect size (Standardized Mean Differencescreening = 0.30, 95% confidence interval 0.21 to 0.38), while non-screening studies were still significantly positive, but the magnitude of effect was less pronounced (Standardized Mean Differenceno-screening = 0.15, 95% confidence interval 0.03 to 0.26). When the difference between these two effect sizes was tested using logistic metaregression,56 this trend was positive but nonsignificant (difference in standardized mean differences = 0.15, 95% confidence interval –0.03 to 0.29, p = 0.09). Of particular interest from the point of view of the present chapter was the fact that several additional study-level variables were also related to the magnitude of effect size in collaborative care, and that three of these predictive covariates were either strongly significant (p < 0.05) or more significant than screening (p < 0.1). These were better antidepressant concordance, having a trained case manager, and regular and planned supervision of case managers.

Standardized Depression Outcomes (95% CI)

Study referred by clinician Wilkinson 1993 Mann 1998 Peveler 1999

–0.29 (–0.79, 0.22) –0.08 (–0.29, 0.13) 0.21 (–0.11, 0.54)

Akerblad 2003 Brook 2003

0.26 ( 0.07, 0.45) 0.00 (–0.34, 0.34) 0.19 (–0.12, 0.49) 0.49 ( 0.13, 0.86)

Katon 1995 Katon 1996 Finley 2000 Hunkeler 2000 Datto 2003

–0.30 (–0.83, 0.24) 0.28 ( 0.03, 0.53) 0.42 (–0.14, 0.98)

Dietrich 2004 Cappocia 2004

0.16 (–0.08, 0.39) 0.17 (–0.38, 0.72) 0.15 ( 0.03, 0.26)

Subtotal identified by screening Blanchard 1995 Araya 2003

0.43 (–0.01, 0.87) 1.13 ( 0.79, 1.47)

Bosmans 2006 Callahan 1994 Katon 1999

0.07 (–0.28, 0.42) 0.05 (–0.48, 0.58) 0.31 ( 0.01, 0.61) –0.14 (–0.53, 0.25) 0.22 (–0.02, 0.46)

Coleman 1999 Wells-medication 2000 Simon 2000 Katzelnick 2000 Wells-therapy 2000

0.30 ( 0.07, 0.52) 0.43 ( 0.22, 0.63) 0.22 (–0.01, 0.45)

Unutzer 2001 Katon 2001

0.40 ( 0.31, 0.50) 0.11 (–0.09, 0.32)

Rost 2001b Rost 2001a Oslin 2003

0.29 (–0.05, 0.62) 0.20 (–0.10, 0.50) 0.61 ( 0.08, 1.13)

Swindle 2003 Rickles 2004

0.18 (–0.30, 0.66) 0.25 (–0.37, 0.87)

Adler 2004 Bruce 2004

0.19 (–0.01, 0.39) 0.30 ( 0.07, 0.52)

Simon 2004b Katon 2004 Jarjoura 2004

0.33 ( 0.05, 0.62) 0.24 (–0.03, 0.51) 0.41 ( 0.00, 0.82)

Simon 2004a Wang 2007

0.18 (–0.11, 0.46) 0.82 (–0.06, 1.70)

Subtotal

0.30 ( 0.21, 0.38)

Overall

0.25 ( 0.18, 0.32)

–1.5

–1

–.5

0

.5

1

1.5

Standardized Depression Outcomes

Figure 7.2. Enhanced care for depression: a random effects meta-analysis of 36 studies, comparing depression outcomes at 6 months in studies that use screening to recruit patients, versus those where clinicians recruit patients with recognized depression. (Re-analysis of data from Bower P, Gilbody SM, Richards D, et al. Collaborative care for depression: making sense of complex interventions through systematic review and meta-regression. Br J Psychiatry. 2006;189:484–493.)

Table 7.1. Study Details and Method of Patient Recruitment from Studies of Collaborative or Enhanced Care for Depression Study Name

References

Setting

Adler 2004

62

US

Akerblad 2003 Araya 2003

46

Sweden

63

Chile

Blanchard 1995

64

UK

Brook 2003

47

Bruce 2004

Callahan 1994 Capoccia 2004 Coleman 1999

Sample Size 533

1,031 240

Patient Population

Recruitment Method

Adults with major depression or dysthymia (DSM-IV)

Screening of primary care attenders using the Primary Care Screener for Affective Disorders (PC-SAD) Physician referral, no screening

Adults with major depression and an indication for antidepressants Women with major depression

96

Elderly with depression warranting clinical intervention

Netherlands

147

65

US

598

Adults with depressive complaints, prescribed new antidepressant Elderly with major depression, dysthymia, and minor depression

66

US

175

48

US

74

67

US

169

Elderly with newly diagnosed depression Adults with depression, prescribed a new antidepressant Depressed frail elderly

Screening of primary care attenders using GHQ-12 (score 5 or more on two occasions) Elderly nursing home residents screening positive with diagnostic depression scale (DPDS) Physician referral, no screening Elderly patients screening positive using the CES-D (score > 20) or responding positively to previous history of depression Elderly patients screening positive using the CES-D (score > 20) Physician referral of new episode of depression, no screening Frail older adults who screened positive for a predictive index of hospitalization. Use of CES-D as a screening instrument integrated into chronic care clinics.

Table 7.1. (Continued) Study Name

References

Setting

Sample Size

Patient Population

Recruitment Method Physician referral of patients with depression, no screening Physician referral of patients with depression, no screening as method of recruitment, but had to score SCL-20 > 0.5 at enrollment Physician referral of patients already prescribed antidepressants

Datto 2003

49

US

61

Adults with depressive symptoms

Dietrich 2004

68

US

405

Adults with major depression and dysthymia (DSM-IV), starting/ changing treatment

Finley 1999

51

US

125

Hunkeler 2000

52

US

302

Jarjoura 2004 Katon 1995

69

US

121

53

US

217

Katon 1996

53

US

153

Katon 1999

70

US

228

Katon 2001

71

US

386

Katon 2004

72

US

329

Adults with current major depression, prescribed a new antidepressant Adults with major depression or dysthymia, prescribed a new antidepressant Adults with major depression not currently in treatment Adults with depression, prescribed a new antidepressant Adults with depression, prescribed a new antidepressant Adults at high risk of persistent depression, recurrent depression, or dysthymia Adults, prescribed a new antidepressant, at high risk of relapse Adults with diabetes with depressive symptoms

Physician referral of patients with a new diagnosis of depression, and prescribed antidepressant Screening for inclusion using the PRIME-MD Physician referral of patients with definite or probable depression Physician referral of patients with definite or probable depression Telephone screening using the SCID

Telephone screening using the SCID

Telephone screening using the PHQ-9 (score >=10) (Continued )

Table 7.1. (Continued) Study Name

References

Setting

Sample Size

Patient Population

Recruitment Method Two-stage telephone screening procedure with the SCID and Hamilton Depression Rating Scale Primary care physician referral; patients currently with a diagnosis and in receipt of care for depression Primary care screening with CES-D (score > 15) Physician referral; patients with a new diagnosis of depression commencing antidepressant medication Patients with a newly initiated prescription of antidepressant medication Two-stage screening procedure using WHO-CIDI administered by practice nurses Two-stage screening procedure using WHO-CIDI administered by practice nurses Patients identified from computerized records with a new diagnosis of depression and commencing antidepressant medication Patients identified from computerized records with a new diagnosis of depression and commencing antidepressant medication. No screening.

Katzelnick 2000

38

US

407

Adults, high utilizers of services, with depressive symptoms

Mann 1998

54

UK

419

Adults with depression

Oslin 2003

73

US

97

Peveler 1999

45

UK

160

Rickles 2005

74

US

63

Rost 2001

41

US

243

Rost 2002b

41

US

189

Simon 2000

75

US

392

Simon 2004a

76

US

402

Adults with depression or dysthymia, at-risk drinking Diagnosis of depression, prescribed a new antidepressant Prescribed a new antidepressant

Adults with major depression, prescribed a new antidepressant, recently treated Adults with major depression, prescribed a new antidepressant, beginning new episode Adults with depression, prescribed a new antidepressant

Adults with depression, prescribed a new antidepressant

Table 7.1. (Continued) Study Name

References

Setting

Sample Size

Patient Population

Recruitment Method Patients identified from computerized records with a new diagnosis of depression and commencing antidepressant medication. No screening. Primary care patients screening positive with the PRIME-MD

Simon 2004b

76

US

393

Adults with depression, prescribed a new antidepressant

Swindle 2003

77

US

268

Unutzer 2001

78

US

1801

Adults with major depression, Dysthymia, or partially remitted major depression Elderly with major depression, dysthymia, or both

Wells 2000a

39

US

867

Wells 2000b

39

US

932

Whooley 2000

40

US

331

Wilkinson 1993

55

UK

61

Adults with major depression or dysthymia Adults with major depression or dysthymia Elderly with depressive symptoms

Adults with depression, prescribed a new antidepressant

Patients screened face to face or by phone from primary care lists or attendance using CIDI Consecutive primary care attenders screened using the CIDI Consecutive primary care attenders screened using the CIDI Consecutive elderly primary care attenders screened using the GDS (score >=6) Physician referral of patients with already diagnosed depression

Adapted from Gilbody SM, House AO, Sheldon TA. Screening and case-finding instruments for depression: a Cochrane systematic review and exploration of heterogeneity. CMAJ. 2008;178:1023–1024; and Gilbody S, Bower P, Fletcher J, et al. Collaborative care for depression: a cumulative meta-analysis and review of longer-term outcomes. Arch Intern Med. 2006;166:2314–2321.

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The review by Bower and colleagues44 provides a richer and more complete dataset than the USPSTF review within which to examine the relative contribution of screening to the effectiveness of enhanced care. However, there are several limitations to their approach. The most important limitation is the fact that, despite using randomized studies, the exploratory comparison within a meta-regression is an observational one and is therefore susceptible to confounding (alternative explanations for observed effects and relationships).56 In this case, the use of screening could be confounded by other design-level variables (such as increased intensity of care). Bower and colleagues44 sought to address this limitation by conducting a multivariate analysis of these data to adjust for other potentially confounding covariates. They found in their multivariate analysis that several of the positive associations found in univariate meta-regression (such as this highlighted above) ceased to be significant in multivariate analysis. The only study-level variable that remained after adjusting for other potentially confounding variables was the mental health background of the case manager (p = 0.03). Screening, in contrast, became less significant (p = 0.19) when other variables were accounted for. The most likely conclusion that can be drawn from this analysis is that the effect of screening is weak and is potentially confounded by other study-level variables. Screening as a recruitment strategy is not therefore likely to be an independently significant predictor of the effectiveness of enhanced care strategies. One might go further and suggest that good-quality collaborative care is likely to be effective, whether or not screening is used.

5.

To Screen or Not to Screen?

Despite the apparently differing conclusions and policy recommendations relating to screening for depression, an evidence-based consensus seems to emerge that screening when given alone is an ineffective strategy. This conclusion should not be surprising, since the quality of care for depression is often poor57,58 and the addition of screening is likely only to identify an unmet need without offering anything positive to improve the management and outcome of this condition. It has been discussed elsewhere that screening identifies a qualitatively different population of people with depression from those who are already identified and managed in primary care (what Goldberg calls ‘‘conspicuous psychiatric morbidity’’59). The people identified by screening programs tend to have less severe psychopathology, a better outcome, and a general reluctance to take antidepressants or to benefit from medical or psychosocial interventions (see Palmer and Coyne13 for review). Low expectations and poor outcome of screening strategies have led to a more fundamental rethinking of the organization of delivery of care for

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depression.58 A direct result of the failure of the screening–detection–treatment–improvement paradigm12 has been the emergence of organizational enhancements of care, such as collaborative care.60,61 The conclusion that should be drawn from the re-analysis of existing studies of collaborative care in the present chapter is that this strategy is generally effective, but the assumption that screening is a key element of effective enhancement might not be true. This is not a small and insignificant epidemiologic issue of causal inference and confounding, but one that is of importance to practitioners and policymakers. The concerns relating to the relative importance of screening in quality enhancement are important for two main reasons. Firstly, policymakers have readily picked up on the positive endorsement of screening from bodies such as the USPSTF and NICE without reading the small print. Quality enhancement strategies have sometimes begun and ended with screening, without the implementation of wider enhancements of care. Screening is a quick and easy policy to implement, measure, and reward. The experience in the United Kingdom is that screening and case-finding is financially rewarded without any explicit requirement that the process of care be improved any further.20 Secondly, for those who do choose to follow the evidence and implement collaborative care, there are many decisions that need to be made in the design of effective care systems. The use of screening as a point of entry to enhanced care raises a number of ethical and logistical issues.13 Screening usually identifies an unmet need and creates an increased demand for care. If this demand is not met, screening itself might do more harm than good. Services will have to be planned accordingly to meet this need (and expectation of care) from within finite healthcare resources. Ultimately, the most thorough way in which the effectiveness of screening as a necessary or active component of enhanced care could be established would be through the conduct of a randomized controlled trial of enhanced care with screening, versus identical enhanced care without screening. To date (and to our knowledge) there are no such trials, and it is debatable whether any such trial will ever be conducted. In the interim, it is clear that screening is not a sufficient intervention to improve the quality and outcomes of care for depression. What is less clear is whether screening is a necessary condition for enhanced and improved quality of care for this important condition.

References 1. Wells KB, Stewart A, Hays RD, et al. The functioning and well-being of depressed patients. Results from the Medical Outcomes Study. JAMA. 1989;262(7):914–919. 2. Murray CJ, Lopez AD. The global burden of disease: a comprehensive assessment of mortality and disability from disease, injuries and risk factors in 1990. Boston: Harvard School of Public Health on behalf of the World Bank, 1996.

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3. Thomas C, Morris S. Cost of depression among adults in England in 2000. Br J Psychiatry. 2003;183:514–519. 4. Greenberg PE, Kessler RC, Birnbaum HG, et al. The economic burden of depression in the United States: how did it change between 1990 and 2000? J Clin Psychiatry. 2003;64:1465–1475. 5. Cepoiu M, McCusker J, Cole MG, et al. Recognition of depression by non-psychiatric physicians—a systematic literature review and meta-analysis. J Gen Intern Med. 2008;23:25–36. 6. Simon G, Von Korff M. Recognition and management of depression in primary care. Arch Fam Med. 1995;4:99–105. 7. Katon W, Ciechanowski P. Impact of major depression on chronic medical illness. J Psychosom Res. 2002;53:859–863. 8. Wright A. Should general practitioners be testing for depression? Br J Gen Pract. 1994;44:132–135. 9. Sharp LK, Lipsky MS. Screening for depression across the lifespan: a review of measures for use in primary care settings. Am Fam Physician. 2002;66:1001–1008. 10. U/S/ Preventive Services Task Force. Guide to clinical preventive services, 2nd ed. Alexandria, VA: International Medical Publishing, 1996. 11. Pignone MP, Gaynes BN, Rushton JL, et al. Screening for depression in adults: a summary of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med. 2002;136:765–776. 12. Klinkman MS, Coyne JC, Gallo S, et al. False positives, false negatives and the validity of the diagnosis of major depression in primary care. Arch Family Med. 1998;7:451–461. 13. Palmer SC, Coyne JC. Screening for depression in medical care: pitfalls, alternatives, and revised priorities. J Psychosom Res. 2003:54(4):279–287. 14. New Freedom Commission on Mental Health. Achieving the promise: transforming mental health care in America—final report. Rockville, MD: DHHS Pub. No. SMA03–3832, 2003. 15. Cochrane AL, Holland WW. Validation of screening procedures. Br Med Bull. 1971;27:3–8. 16. Mant D, Fowler G. Mass screening: theory and ethics. BMJ. 1990;300:916–918. 17. Stewart-Brown S, Farmer A. Screening could seriously damage your health. BMJ. 1997;314:533–534. 18. Wilson JM, Junger CT. Principles and practice of screening for disease: World Health Organization Public Health Paper 34. Geneva: World Health Organization, 1968. 19. Gilbody S, Whitty P, Grimshaw JG, et al. Improving the recognition and management of depression in primary care. Effective Health Care Bulletin, University of York. 2002;7(Number 5). 20. Gilbody S, Sheldon T, Wessely S. Should we screen for depression? BMJ. 2006;332(7548):1027–1030. 21. National Screening Committee. The UK National Screening Committee’s Criteria for appraising the viability, effectiveness and appropriateness of a screening programme (available at http://www.nsc.nhs.uk/pdfs/criteria.pdf). London: HMSO, 2003. 22. Oxman TE, Sengupta A. Treatment of minor depression. Am J Geriatr Psychiatry. 2002;10:256–264. 23. Gilbody SM, House AO, Sheldon TA. Screening and case finding for depression. The Cochrane Library (Issue 4). Chichester: Wiley Publishing, 2005.

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24. Gilbody SM, House AO, Sheldon TA. Routinely administered questionnaires for depression and anxiety: a systematic review. BMJ. 2001;322:406–409. 25. Coyne JC, Palmer SC, Sullivan PA. Screening for depression in adults. Ann Intern Med. 2003;138(9):767–768. 26. AHCPR Depression Guideline Panel. Depression in primary care: detection, diagnosis, and treatment. Technical report. Number 5. Rockville, MD: US Department of Health and Human Services, Public Health Service, 2000. 27. MacMillan HL, Patterson CJS, Wathen CN, and The Canadian Task Force on Preventive Health Care. Screening for depression in primary care: recommendation statement from the Canadian Task Force on Preventive Health Care. CMAJ. 2005;172(1):33–35. 28. Agency for Health Care Policy Research. Depression in primary care. Washington DC: US Department of Health and Human Services, 1993. 29. Gilbody SM, House AO, Sheldon TA. Screening and case-finding instruments for depression: a Cochrane systematic review and exploration of heterogeneity. CMAJ. 2008;178:1023–1024. 30. Beck D, Gilbody SM. Screening and case finding for depression. The Cochrane Library (Issue 4). Chichester: Wiley Publishing, 2008. 31. National Institute for Clinical Excellence. Depression: core interventions in the management of depression in primary and secondary care. London: HMSO, 2004. 32. Hickie IB, Davenport TA, Ricci CS. Screening for depression in general practice and related medical settings. Med J Austr. 2002;177(7 Suppl):S111–S116. 33. Wells KB. The design of Partners in Care: evaluating the cost effectiveness of improving care for depression in primary care. Social Psychiatry Psychiatr Epidemiol. 1999;34:20–29. 34. Gilbody S, Whitty P, Grimshaw J, et al. Educational and organizational interventions to improve the management of depression in primary care: a systematic review. JAMA. 2003;289:3145–3151. 35. Gilbody S, Bower P, Fletcher J, et al. Collaborative care for depression: a cumulative meta-analysis and review of longer-term outcomes. Arch Intern Med. 2006;166:2314– 2321. 36. Bower P, Gilbody S. Managing common mental health disorders in primary care: conceptual models and evidence base. BMJ. 2005;330:839–842. 37. Wells K, Sherbourne C, Schoenbaum M, et al. Five-year impact of quality improvement for depression: results of a group-level randomized controlled trial. Arch Gen Psychiatry. 2004;61:378–386. 38. Katzelnick DJ, Simon GE, Pearson SD, et al. Randomized trial of a depression management program in high utilizers of medical care. Arch Fam Med. 2000;9:345– 351. 39. Wells KA, Sherbourne C, Schoenbaum M, et al. Impact of disseminating quality improvement programs for depression in managed primary care: a randomized controlled trial. JAMA. 2000;283:212–220. 40. Whooley MA, Stone B, Soghikian K. Randomized trial of case-finding for depression in elderly primary care patients. J Gen Intern Med. 2000;15:293–300. 41. Rost K, Nutting PA, Smith J, et al. Improving depression outcomes in community primary care practice: a randomised trial of the QuEST intervention. J Gen Intern Med. 2001;16:143–149. 42. Thompson S. Why sources of heterogeneity in meta-analysis should be investigated. In: Chalmers I, Altman DG, eds. Systematic reviews. London: BMJ, 1995.

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43. Thompson SG, Higgins JP. How should meta-regression analyses be undertaken and interpreted? Stat Med. 2002;21:1559–1573. 44. Bower P, Gilbody SM, Richards D, et al. Collaborative care for depression: making sense of complex interventions through systematic review and meta-regression. British Journal of Psychiatry 2006;189:484–493. 45. Peveler R, George C, Kinmonth AL, et al. Effect of antidepressant drug counselling and information leaflets on adherence to drug treatment in primary care: randomised controlled trial. BMJ. 1999;319:612–615. 46. Akerblad AC, Bengtsson F, Ekselius L, et al. Effects of an educational compliance enhancement programme and therapeutic drug monitoring on treatment adherence in depressed patients managed by general practitioners. Int Clin Psychopharmacol. 2003;18:347–354. 47. Brook O, van Hout H, Nieuwenhuyse H, et al. Impact of coaching by community pharmacists on drug attitude of depressive primary care patients and acceptability to patients; a randomized controlled trial. Eur Neuropsychopharmacol. 2003;13:1–9. 48. Capoccia K, Boudreau D, Blough D, et al. Randomized trial of pharmacist interventions to improve depression care and outcomes in primary care. Am J Health System Pharmacy. 2004;61:364–372. 49. Datto CJ, Thompson R, Horowitz D, et al. The pilot study of a telephone disease management program for depression. Gen Hosp Psychiatry. 2003;25:169–177. 50. Dietrich AJ, Oxman TE, Williams JW Jr, et al. Going to scale: re-engineering systems for primary care treatment of depression. Ann Fam Med. 2004;2(4):301–304. 51. Finley P, Rens H, Gess S, et al. Case management of depression by clinical pharmacists in a primary care setting. Formulary. 1999;34:864–870. 52. Hunkeler EM, Meresman JF, Hargreaves WA, et al. Efficacy of nurse telehealth care and peer support in augmenting treatment of depression in primary care. Arch Fam Med. 2000;9:700–708. 53. Katon W, Robinson P, Von Korff M, et al. A multifaceted intervention to improve treatment of depression in primary care. Arch Gen Psychiatry. 1996;53(10):924–932. 54. Mann A, Blizard R, Murray J. An evaluation of practice nurses working with general practitioners to treat people with depression. Br J Gen Pract. 1998;48:875–879. 55. Wilkinson G, Allen P, Marshall E. The role of the practice nurse in the management of depression in general practice: treatment adherence to antidepressant medication. Psychol Med. 1993;23:229–237. 56. Higgins JPT, Thompson SG. Controlling the risk of spurious findings from metaregression. Statistics in Medicine. 2004;23:1663–1682. 57. Katon W, von Korff M, Lin E, et al. Adequacy and duration of antidepressant treatment in primary care. Med Care. 1992;30:67–76. 58. Katon W, Von Korff M, Lin E, et al. Population-based care of depression: effective disease management strategies to decrease prevalence. Gen Hosp Psychiatry. 1997;19:169–178. 59. Goldberg D. The detection of psychiatric illness by questionnaire. Oxford: Oxford University Press, 1972. 60. Simon G. Collaborative care for depression. BMJ. 2006;332:249–250. 61. Unutzer J, Schoenbaum M, Druss BG, et al. Transforming mental health care at the interface with general medicine: report for the President’s Commission. Psychiatr Serv. 2006;57:37–47.

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62. Adler DA, Bungay KM, Wilson IB, et al. The impact of a pharmacist intervention on 6-month outcomes in depressed primary care patients. Gen Hosp Psychiatry. 2004;26(3):199–209. 63. Araya R, Rojas G, Fritsch R, et al. Treating depression in primary care in low-income women in Santiago, Chile: a randomised controlled trial. Lancet. 2003;361:995–1000. 64. Blanchard MR, Waterreus A, Mann AH. The effect of primary care nurse intervention upon older people screened as depressed. Int J Geriatr Psychiatry. 1995;10:289–298. 65. Bruce M, Ten Have T, Reynolds C, et al. Reducing suicidal ideation and depressive symptoms in depressed older primary care patients. JAMA. 2004;291(9):1081–1091. 66. Callahan C, Hendrie H, Dittus R, et al. Improving treatment of late life depression in primary care: a randomized clinical trial. J Am Geriatr Soc. 1994;42:839–846. 67. Coleman EA, Grothaus LC, Sandhu N, et al. Chronic care clinics: a randomized controlled trial of a new model of primary care for frail older adults. J Am Geriatr Soc. 1999;47:775–783. 68. Dietrich AJ, Oxman TE, Williams JW, et al. Re-engineering systems for the treatment of depression in primary care: cluster randomised controlled trial. BMJ. 2004;329:602–609. 69. Jarjoura D, Polen A, Baum E, et al. Effectiveness of screening and treatment for depression in ambulatory indigent patients. J Gen Intern Med. 2004;19(1):78–84. 70. Katon W, Von Korff M, Lin E, et al. Stepped collaborative care for primary care patients with persistent symptoms of depression: a randomized trial. Arch Gen Psychiatry. 1999;56:1109–1115. 71. Katon W, Rutter C, Ludman EJ, et al. A randomized trial of relapse prevention of depression in primary care. Arch Gen Psychiatry. 2001;58:241–247. 72. Katon WJ, Von Korff M, Lin EHB, et al. The Pathways Study: a randomized trial of collaborative care in patients with diabetes and depression. Arch Gen Psychiatry. 2004;61:1042–1049. 73. Oslin D, Sayers S, Ross J, et al. Disease management for depression and at risk drinking via telephone in an older population of veterans. Psychosom Med. 2003;65:931–937. 74. Rickles N, Svarstad BL, Statz-Paynter JL, et al. Pharmacist telemonitoring of antidepressant use: effects on pharmacist–patient collaboration. J Am Pharm Assoc. 2005;45:344–353. 75. Simon G, Von Korff M, Rutter C, et al. Randomised trial of monitoring, feedback and management of care by telephone to improve treatment of depression in primary care. BMJ. 2000;320:550–554. 76. Simon GE, Ludman EJ, Tutty S, et al. Telephone psychotherapy and telephone care management for primary care patients starting antidepressant treatment: a randomized controlled trial. JAMA. 2004;292(8):935–942. 77. Swindle R, Rao J, Helmy A, et al. Integrating clinical nurse specialists into the treatment of primary care patients with depression. Int J Psychiatry Med. 2003;33(1):17–37. 78. Unutzer J, Katon W, Williams J, et al. Improving primary care for depression in late life: the design of a multicenter randomized trial. Med Care. 2001;39(8):785–799.

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8 TECHNOLOGICAL APPROACHES TO SCREENING AND CASE FINDING FOR DEPRESSION William H. Rogers, Debra Lerner, and David A. Adler

1. 2. 3. 4. 5.

Technological Methods of Screening for Depression Ten Issues When Developing Computerized Screening for Depression Examples of Implementation of Computerized Screening for Depression Discussion Conclusion

Context What are the strengths and weaknesses of computer-based and other automated methods of detecting depression? Two promising technologies make use of the Internet and speech recognition. Whatever technology is used, each method needs to be assessed rigorously using the same high standards that have been applied to pencil-and-paper tests. We are in the midst of a technological revolution that inevitably will transform psychiatric clinical practice. A consensus for routine depression screening is building,1,2 and at the same time methods by which it could be accomplished are emerging. The hope is that the right technology can provide an easy, inexpensive, valid, and reliable public health approach to depression screening. Computerized assessment is well accepted in diverse fields, and the use of Internet-based survey technology has grown exponentially.3–7 Issues regarding the strengths and limitations of computerized assessments are addressed regularly in the literature.3–11 For example, such assessments have been shown to improve data quality while at the same time reducing cost as well as the time to score, analyze, and report results. Increasingly, as depressive disorders have 143

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been recognized as highly prevalent with significant morbidity, multiple screeners using an array of technological advances have been developed2,12–33 (Table 8.1 lists selected studies).34–49 This chapter will review the technologies that are currently available for automated depression screening and will discuss them in terms of criteria that should dictate their adoption.

1.

Technological Methods of Screening for Depression

The growing list of technologies can be classified on several dimensions. Perhaps the most important of these is adaptive vs. non-adaptive. In an adaptive technology pioneered by the Educational Testing Service,50 a computer, using a preprogrammed algorithm, decides which question to ask next given the responses so far.3,9,48,49,51–55 Paper-and-pencil is the classical non-adaptive technology— everyone gets the same paper with the same questions in the same order. Technological modality is a second dimension. Currently available technologies include the phone, the Internet, and hand-held electronic devices.5 The phone can be split into several groups, including agent: computer-assisted telephone interview (CATI), speech recognition, and touch-tone. Phone can also be classified as inbound (the patient initiates the call to a toll-free number) or outbound (the system initiates the call). Hand-held devices could include tablets such as personal digital assistants, game consoles, modern cell phones, or ‘‘electronic paper.’’ Internet-based screeners (eg, Patient Health Questionnaire-9 [PHQ-9], Zung Self-Rating Depression Scale),13,20 can be implemented through standard web browsers, at public kiosks, or through connected hand-held devices. In this chapter, all of these methods are classified together under the term ‘‘Internet’’ because they follow a common approach of visually presenting the screener or monitoring instrument and taking responses by interaction with that visual image. There is always a computer involved in presenting the data and recording the responses. One can even envision the day when more futuristic technologies such as eye-tracking equipment, brain scans, blood tests, or electrical system monitors for depression will be available. Two basic premises underlie our discussion: 1. There is no fail-proof methodology. There is no single technology that guarantees success, but some technologies have inherent failures. 2. Implementation and circumstances matter. A technology that performs well in one setting (eg, Internet screening at home) may be unacceptable in another (automated screening on a desktop computer in a physician’s waiting room). In the current marketplace, there are no full-service automated systems that are embedded in an electronic medical record.

Table 8.1. Technological Methods of Depression Screening: Summary of Studies Technological Method

Author/ Publication

Mental Health-Based Studies Computer voice Gonzales (2007), recognition: Hisp J Behav Sci VIDAS

Sample/Setting

Accuracy of Computerized Method

Comment

English- and Spanish-speaking patients, n = 217, visual

CES-D 20, alpha = 0.87–0.91 computer/written; CES-D vs. BDI-2: r = 0.74–0.86; ROC CES-D (cut point of 16) vs. CIDI-SF: Se: 0.88–1.0; Sp: 0.42–0.20; PPV: 0.61–0.28; NPV: 0.77–1.0 PRIME-MD IVR/Desktop and SCIDIV for MDD Kappa 0.49/0.27; Se: 0.77/0.77; Sp: 0.75/0.50; PPV: 0.87/ 0.77; NPV: 0.77/0.69; similar prevalence rates CES-D 20 and MDD (18-item DIS Mood questions) Screener K=0.82/ 0.89 for current and lifetime MDD for computer vs. interview K = 0.81/0.75 computer vs. interview of MDD vs. PRIME-MD Se: 0. 89/0.91 current/ lifetime MDD; Sp: 0.93/0.91 current/ lifetime MDD

Computerized CES-D speech recognition vs. written acceptable in both English and Spanish speakers; visual somewhat better than aural

Zung (SDS)-20 found acceptable by subjects

No direct comparison with other forms of screening

ISP-D for MDD vs. MINI (N=55): Kappa 0.80; Se: 0.82; Sp: 0.73; PPV: 0.67; NPV: 0.86

Internet-based Self-assessment Program for Depression (ISP-D) is reliable and valid online tool for assessing depression with excellent retest reliability

Computer vs. IVR telephone

Kobak (1997), Psychiatr Serv

CMHC, n = 51

Computer voice recognition

Munoz (1999), J Consult Clin Psychol

Women’s health clinic, n = 104 English- and Spanish-speaking women

Population-Based Studies IVR using Baer (1995), telephone JAMA keypad Computer Lin (2007), BMC touchscreen Psychiatry

Midwest Univ. and NE high-tech firm; n = 1,812; 1,597/1,812 Zung completers Taiwanese volunteers, n = 579

IVR vs. Desktop of PRIME-MD and compared to phone SCID-IV, Ham D-17 and chart Dx, both acceptable phone SCID and chart Dx, both acceptable Voice recognition of CES-D and MDD screener to clinician interview of both plus PRIME-MD yielded comparable results

(Continued )

Table 8.1. (Continued) Technological Method

Author/ Publication

Sample/Setting

Accuracy of Computerized Method

Comment

Computer

Patton (1999), Soc Psychiatry Psychiatr Epidemiol

Australian HS students; n = 2,032 65 of 1,729 completers with MDD

Computerized CIS-R to live CIDI 2–9 weeks late CISR/CIDI Se: 0.97; Sp: 0.18; PPV: 0.49; NPV: 0..91

Students favorable to computer

Australian amb. oncology center; n = 450, median age 61

BDI-2, Cancer Needs Questionnaire; EORTC QLQ-C30

No direct comparison with other forms of screening. Acceptable to patients

Medical-Based Studies Computer Allenby (2002), touchscreen Eur J Cancer Care Computer touchscreen

Bliven (2001), Quality of Life Research

Cardiac OPD, n = 55

SF-36, 8 subscales/Seattle Angina Quest. SF-36 computer/written r = 0.54–0.76; SF-MH mean scale computer/written: 66.19/65.77; r = 0.54

Compared computer to written, 82% preferred computer

Computer touchscreen

Cull (2001), Br J Cancer

Outpatient chemotherapy patients, n = 172

Two (HADS and MHI-5) screeners 2– 4 weeks apart compared to an inperson interview using Present State Exam (PSE) within a week

Computer touchscreen

Kurt (2004), Computer Methods in Biomedicine Sharpe (2004), Br J Cancer

Pts. >65, PCP office; n = 240; 68/ 240 participated

MHI-5>10, Hospital Anxiety and Depression Scale (HADS) >8, computer vs. PSE diagnosis of MDD: Se: 0.85; Sp: 0.71; PPV: 0.47; NPV: 0.26 CESD-20 (or 35) and GDS (Geriatric Depression Scale) computer/written: BL reliability: 0.74/0.72 computer/ written: F/Up reliability: 0.61/0.83 Comparison of Hospital Anxiety and Depression Scale (HADS) with DSMIV SCID clinician telephone interview

Computer touchscreen

Cancer center, n = 5,613; 891/ 3,938 HADS completers, score >14; 196/570 interviewed had MDD

Patients favorable to computer

No direct comparison with other forms of screening

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2.

147

Ten Issues When Developing Computerized Screening for Depression

With this in mind, we now consider the issues that arise regarding the use of automated screeners in general and depression-monitoring instruments specifically.

Quality Control and Accuracy The first question posited in any discussion of automation is its accuracy. Technology-based methods are more consistently applied, which implies more comparable and interpretable data.3,6,17,20,47,56–66 No human bias is introduced. Clinician interviews and agent-administered phone CATI depend on a human being. A clinician or an agent speaks and listens differently every time. Paper-and-pencil screeners, as well as automated electronic surveys, eliminate this source of variation. If this advantage is pursued, agreement with known standards can be improved beyond what is possible with a clinician or agent. While the technology already exists, ensuring accuracy rests on the craftsmanship of the instrument (eg, inaccurate or poorly designed programming will result in poor-quality data).

Error Control Evidence to date is that different data collection methods do not change the probability that the answer is recorded as intended.7 In paper-and-pencil screeners, respondents can make stray marks that scanners cannot easily interpret. These can be reduced to acceptable levels by providing clear instructions with examples on how to make marks. In speech recognition systems, respondents can speak responses outside of the answer set, but asking questions in a way that prompts a response in range and challenging responses that do not seem to be within range can reduce this.36 For both of these systems, human post-response review of questionable responses is desirable. For example, scanners can detect stray marks and voice recognizers can identify problematic voice input. With these measures, very low error rates (eg, over 99.5% correct) are possible. Without these measures, the error rates are low but errors do occur (eg, over 98% correct). Numerous data companies report error control checks within these ranges and better. Nominal error rates for touch-tone and for the Internet and related technologies such as kiosks and hand-held devices are low because these systems enforce a single answer. However, this does not mean that such devices are free of error. The error rates on the Internet are low if the respondent can see all the responses and no default choices are premarked. Several studies have found

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that Internet surveys and mail are equivalent.67 If the respondent has to click a mouse to see all the responses, then the results will be biased. For touch-tone interactive voice recognition (IVR), elderly respondents and those whose touch-tone buttons are in the receiver are likely to have high error rates, but no further identification of errors is possible without a very laborious review of each response—a practice suitable for banking but not for screening questionnaires. Touch-tone also invites cognitive errors because the verbal responses must be converted to numerical form before they can be entered. Most studies have concluded that touch-tone is not equivalent to mail.67

Honesty Research has shown repeatedly that respondents even with depression are more honest with computers or mail than they are with live interviewers, translating into better acccuracy.59,60,64,68–70

Physical Clues Conversely, human interpreters, and especially clinician interviewers, are best at dealing with clues such as crying, gaps in speech, or slurred, sped-up, or retarded speech that might have important implications in the screening process.4 Voice recognition systems could also be trained to find these, but this has not happened yet to our knowledge, and it would never be as good as trained clinicians meeting with depressed individuals.

Performance Case-specific performance data are key to successful use of an automated system, given the potential time savings.7,20 Physicians can use the results most efficiently if patient-specific reports of positive predictive value (PPV) and negative predictive value (NPV) are included. In one of the few studies addressing depression, Kobak and colleagues,20 using the PHQ-9, reported a PPV of 0.87 and a sensitivity for touch-tone and IVR of 0.84 to 0.88. The cost of untreated depression is high, particularly among employed patients,71–74 so automated screening will normally be cost-effective compared with the haphazard approach characteristic of population screening. If the screener cannot find cases (poor sensitivity or low NPV), then other case-finding tools may need to be used anyway.

Workload Considerations A highly effective automated system that is used to screen all individuals routinely has the potential to generate many possible or probable cases very

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quickly. For example, as found in studies by Sharpe30 and Cull40 and their colleagues, if every attendee at a regional cancer center is assessed, it is possible that 20% might be flagged as high scorers on a depression scale. Even with a second filter such as request for help, a large number of people may need to be seen. The potential benefit of a high yield of true cases might come at the expense of a large number (in absolute terms) of false positives, each of whom has higher expectations on the basis of the first-stage alert and needs to be have follow-up. Alternatively, fear of workload may defeat the screening process itself. When the PPV is too much below 70%, physicians may choose to ignore screening results on the grounds that following up 30% or more who are false positives is too much work.7,75 Although PPV and sensitivity are affected by response errors, they are more influenced by the screening instrument itself. The balance between them is implementation-specific. In general, demanding criteria for diagnosing depression will result in good PPV but poor sensitivity.27

Acceptability A system is useful only if subjects are willing to use it; acceptability is a necessity for implementation of any automated screening system. Most of the evidence to date suggests that patients accept automated screening as a general idea compared with visits to mental health specialists.3,6,20,24,30,40 A number of national studies have had excellent response rates with no particular item nonresponse on depression screening questions.38,47,76–78 With respect to the technologies, the survey response literature has some lessons to teach. The technological challenge to the respondent of touch-tone IVR is higher than speech recognition; touch-tone response rates are lower. The Internet (and associated device-related technologies) is generally regarded as usable, but not every home has a computer, and in many businesses personal computer use is restricted or frowned upon.4 In addition, many people have privacy worries about the Internet, and in some businesses these are justified.79 Some degree of computer skill and literacy is necessary.38 The impact of age cohort, gender, and cultural issues requires further study. This suggests that alternatives to the Internet will remain useful. Combination approaches involving Internet, phone, and either outbound calling or mail achieve the best coverage.67

Prices As a general rule, prices are highly implementation-dependent, and a bid is necessary to know what the price will be. However, some general principles apply. Paper-and-pencil surveys depend on a combination of mailing costs and

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processing costs.80,81 Very efficient high-end scanners are available, but they must still be fed. Even a ‘‘free’’ screener that is entered by fax machine in a doctor’s office costs more than $3 when the cost of handing out the survey, collecting the response, and feeding it into a fax machine is counted. If mail is involved, back-end duties can be handled by clerks, but this cost reduction is more than offset by the price of mailing.6,67,76 The traditional methods of screening such as paper surveys and scanning are only suited to large-scale data-collection systems with central mail processing facilities and are difficult to manage in smaller settings. For Internet screeners and voice recognition or touch-tone, the marginal cost of the screener ranges from nothing to a dollar, but there are fixed costs associated with developing and fielding the system purposes.82–84 Such costs are typically between $10,000 and $25,000.12

Availability All of the methods except for scanned paper-and-pencil surveys can be processed immediately, with real-time feedback to respondents about what to do. Patients often have time to consider the possibilities at times of the day when physicians are not available (eg, the middle of the night). Results are immediately available without transcription error.47

Embedding in a System To be useful, a screening system needs to be embedded in a healthcare system that can deal with the information.3,7,20,85,86 Unless the results are available and retrievable, they are useless. This very important issue is mostly beyond the scope of this paper. Technology has some impact. A mailed and scanned questionnaire cannot be acted on in a timely way. All of the electronic methods can be followed up with questions about context (Did someone important to you die recently? Are you thinking of taking your life soon?). In principle, the results can be transmitted to electronic medical records (EMR) or physician e-mail, if the setting allows for one. Contextual data such as medications could also be drawn from an EMR. In the current environment, embedding screeners is still a custom operation—EMR is not at this point sold with a depression screener or monitor website included.

3.

Examples of Implementation of Computerized Screening for Depression

Whether a system is actually acceptable in practice depends on both the technology and the context. All of the technologies have been shown to be

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acceptable in some context (see Table 8.1 for selected studies discussed below). For example, in our prior work, most patients in primary care offices were willing to fill out a two-page depression screener that was immediately scanned.26 We are now using web-based touchscreen methodology to screen

Figure 8.1.a Work and Health Initiative depression pre-screener.

Figure 8.1.b Work and Health Initiative depression pre-screener.

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Figure 8.1.c Sample electronic WHI Patient Depression Report.

employed individuals for depression in workplace settings (Fig. 8.1). The study by Baer and associates13 using IVR with telephone keypad response was one of the first to demonstrate the use and acceptability of fully automated technology for confidential mass depression screening. Two recent studies—Gonzalez and associates,36 using computer voice recognition, and Lin and coworkers,35 using computer touchscreen—found good psychometric properties for wellaccepted depression screeners compared to standardized diagnostic in-person interviews. Kobak and associates,19,20,47,61 in a series of studies, demonstrated the acceptability and equivalence of all forms of depression screening (clinician interview by telephone, phone IVR, and computer touchscreen). Kurt and colleagues22 found similar results for a computer-assisted assessment of depression in geriatric primary care patients. Even in a minority population, Munoz and associates24 met no resistance to depression screening with computerized voice-recognition technology. In non-mental health outpatient settings Allenby and colleagues12 in oncology and Bliven and associates80 in cardiology found high degrees of acceptability for computer-assisted technology in screening for psychosocial distress. Sharpe and colleagues30 applied touchscreen technology and found no resistance to screening for depression and anxiety in a regional ambulatory cancer center. Cull and colleagues40 used touchscreen technology to administer the Mental Health Index and Hospital Anxiety and Depression Scale to develop a depression screening algorithm with adequate psychometric properties among outpatient cancer patients.

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153

Discussion

Automated methods for both general health and depression-specific screening are here to stay. They produce more accurate answers, are more suited to evidence-based medicine, and are less expensive than paper-and-pencil person-dependent methods or mail. Electronic methods are also superior to paper and pencil because they produce timely answers and can also explore some of the follow-up issues, such as more detail about suicidal ideation or how the patient fits into the care process. While mental health clinicians’ faceto-face observations of patients can identify verbal and nonverbal depressive cues and lead to more immediate response, most individuals with depression are not seen in the mental health specialty sector. However, gaps in both evidence and barriers remain to effective widespread use. Once a screening context is established, then some methods that are acceptable in principle become unacceptable in practice. For example, most patients would feel uncomfortable conducting a phone interview while sitting in a crowded waiting room, or taking an Internet-based screener on a home computer known to be infected with a virus. On the other hand the same patients might feel comfortable taking a phone interview at home or completing an Internet-based screener on a computer in a private room off the waiting area at the doctor’s office. A number of groups have studied the issues of implementation in a number of settings focusing on acceptability and accuracy (see Table 8.1). In general, these pilot projects find that depressed patients are able to accurately complete both computer (desktop and web) and telephone screener methodologies and find them acceptable alternatives to both paper-and-pencil and clinician interviews. Just as with conventional methods, there is no one-size-fits-all answer: multiple modalities are needed to meet varied patient and provider needs. Solution modality by itself (eg, Internet, phone, or tablet) is not the answer—much of the value lies in the craft with which it is executed. Good-quality solutions are available in all three modalities, but so are poor solutions. Choice is dependent upon purpose. If technology such as computer-adaptive testing is to be applied to population screening, a multi-tiered approach can improve the accuracy. For example, a general mental health prescreening can efficiently reduce the number of individuals who might then be followed with a diagnosis-specific pre-screener, reserving full screening for at-risk populations and for following patients known to have a depressive disorder. With respect to acceptability, the evidence to date suggests that automated depression screening via web, computer, telephone, or soon tablet does not incur reluctance by those screened. With respect to follow-up, however, the story may differ. In most health risk-appraisal systems, patients and providers can ignore a positive depression screener. On the other hand, a positive

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screener can lead to overreaction. Work needs to be done on the back end of a positive screener to identify cases that are appropriate for follow-up. Careful thought needs to be given to how results will be handled with providers, what follow-up would be cost-effective, and who will need to deliver follow-up services. Nonetheless, without an electronic system, there is no mechanism to help the system address these issues. The marketplace will continue to define and redefine solutions that are available and affordable. We have raised a set of questions that should be asked of such systems and put them into two categories: concerns that are frequently raised but usually do not turn out to be important issues (eg, accuracy and acceptability) and concerns that have often led to existing systems working less well than they could and that need to be addressed in every implementation (eg, privacy, follow-up, and the interface of automated results to the physician–patient relationship).

5.

Conclusion

Thirty years of research has led to the conclusion that the benefits of automated methods outweigh their limitations in general,3,6,7 for mental and specifically for depression health issues,3,20,58,61,62,64,68,87 13,15,16,20,24,35,36,47,88,89 screening. In the absence of information about a particular implementation and the setting it is in, one cannot say that it is automatically worthwhile or unacceptable. However, one can say that pencil-andpaper screeners will be effective only under a limited set of conditions that avoid the costs and delays commonly associated with mail. The two most promising technologies seem to be the Internet (using web browsers and/or hand-held devices) and speech recognition. Whatever technology is used, there needs to be a good fit between the technology and the system within which it is deployed.86 Acceptability depends on context; accuracy depends on craft. The system needs to connect the patient to a physician and support that physician with the correct information.

References 1. Agency for Health Care Policy and Research. Depression in primary care: detection and diagnosis. Rockville, MD, 1993. 2. U.S. Preventive Services Task Force. Guide to clinical preventive services, 2nd ed. Baltimore: Williams & Wilkins, 1996. 3. Berger M. Computer-assisted clinical assessment. Child Adolesc Mental Health. 2006;11(2):64–75. 4. Butcher JN, Perry J, Hahn J. Computers in clinical assessment: historical developments, present status, and future challenges. J Clin Psychol. 2004;60(3):331–345.

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5. Dillman DA. Mail and Internet surveys: the tailored design method, 2nd ed. Hoboken, NJ: John Wiley & Sons, 2007:352–412. 6. Epstein J, Klinkenberg WD. From Eliza to Internet: a brief history of computerized assessment. Computers in Human Behavior. 2001;17:295–314. 7. Garb HN. Computer-administered interviews and rating scales. Psychol Assess. 2007;19(1):4–13. 8. Buchanan T, Smith JL. Using the Internet for psychological research: personality testing on the World Wide Web. Br J Psychol. 1999;90(Pt 1):125–144. 9. Revicki DA, Cella DF. Health status assessment for the twenty-first century: item response theory, item banking and computer adaptive testing. Qual Life Res. 1997;6(6):595–600. 10. Truell AD, Bartlett JE, Alexander MW. Response rate, speed, and completeness: a comparison of Internet-based and mail surveys. Behav Res Methods Instrum Comput. 2002;34(1):46–49. 11. Schleyer TK, Forrest JL. Methods for the design and administration of web-based surveys. J Am Med Inform Assoc. 2000;7(4):416–425. 12. Allenby A, Matthews J, Beresford J, et al. The application of computer touch-screen technology in screening for psychosocial distress in an ambulatory oncology setting. Eur J Cancer Care (Engl). 2002;11(4):245–253. 13. Baer L, Jacobs DG, Cukor P, et al. Automated telephone screening survey for depression. JAMA. 1995;273(24):1943–1944. 14. Beck AT, Steer RA, Garbin MG. Psychometric properties of the Beck Depression Inventory: twenty-five years of evaluation. Clin Psychol Rev. 1988;8:77–100. 15. Gonzalez GM, Spiteri CB, Knowlton JP. An exploratory study using computerized speech recognition for screening depressive symptoms. Computers in Human Behavior. 1995;11(1):85–93. 16. Carr AC, Ancill RJ, Ghosh A, et al. Direct assessment of depression by microcomputer. A feasibility study. Acta Psychiatr Scand. 1981;64(5):415–422. 17. Carr AC, Ghosh A, Ancill RJ. Can a computer take a psychiatric history? Psychol Med. 1983;13(1):151–158. 18. Klinkman MS, Coyne JC, Gallo S, et al. Case finding instruments to be used to improve physician detection of depression in primary care. Arch Fam Med. 1997;6:567–573. 19. Kobak KA, Reynolds WM, Rosenfeld R, et al. Development and validation of a computer-administered version of the Hamilton Depression Rating Scale. Psychol Assess. 1990;2:56–63. 20. Kobak KA, Taylor LVH, Dottl SL, et al. Computerized screening for psychiatric disorders in an outpatient community mental health clinic. Psychiatr Serv. 1997;48(8):1048–1057. 21. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–613. 22. Kurt R, Bogner HR, Straton JB, et al. Computer-assisted assessment of depression and function in older primary care patients. Comput Methods Programs Biomed. 2004;73(2):165–171. 23. Mulrow CD, Williams JW Jr, Gerety MB, et al. Case-finding instruments for depression in primary care settings. Ann Intern Med. 1995;122(12):913–921. 24. Munoz RF, McQuaid JR, Gonzalez GM, et al. Depression screening in a women’s clinic: using automated Spanish- and English-language voice recognition. J Consult Clin Psychol. 1999;67(4):502–510.

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25. Patton GC, Coffey C, Posterino M, et al. A computerised screening instrument for adolescent depression: population-based validation and application to a two-phase case-control study. Soc Psychiatry Psychiatr Epidemiol. 1999;34(3):166–172. 26. Rogers WH, Wilson IB, Bungay KM, et al. Assessing the performance of a new depression screener for primary care (PC-SAD(c)). J Clin Epidemiol. 2002;55(2):164–175. 27. Rogers WH, Adler DA, Bungay KM, et al. Depression screening instruments make good severity measures in a cross-sectional analysis. J Clin Epidemiol. 2005;58:370–377. 28. Schade CP, Jones ER Jr, Wittlin BJ. A ten-year review of the validity and clinical utility of depression screening. Psych Serv. 1998;49(1):55–61. 29. Schwenk TL. Screening for depression in primary care: a disease in search of a test. J Gen Intern Med. 1996;11:437–439. 30. Sharpe M, Strong V, Allen K, et al. Major depression in outpatients attending a regional cancer centre: screening and unmet treatment needs. Br J Cancer. 2004;90(2):314–320. 31. Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary Care Evaluation of Mental Disorders. Patient Health Questionnaire. JAMA. 1999;282(18):1737–1744. 32. Valenstein M, Vijan S, Zeber JE, et al. The cost-utility of screening for depression in primary care. Ann Intern Med. 2001;134(5):345–360. 33. Whooley MA, Avins AL, Miranda J, et al. Case-finding instruments for depression. Two questions are as good as many. J Gen Intern Med. 1997;12(7):439–445. 34. Kim H, Bracha Y, Tipnis A. Automated depression screening in disadvantaged pregnant women in an urban obstetric clinic. Arch Womens Ment Health. 2007;10(4):163–169. 35. Lin CC, Bai YM, Liu CY, et al. Web-based tools can be used reliably to detect patients with major depressive disorder and subsyndromal depressive symptoms. BMC Psychiatry. 2007;7:12. 36. Gonzalez GM, Carter C, Blanes E. Bilingual computerized speech recognition screening for depression symptoms: comparing aural and visual methods. Hispanic Journal of Behavioral Sciences. 2007;29(2):156–180. 37. Fann J, Berry DL, Wolpin SE, et al. Feasibility of depression screening using the PHQ-9 administered on a touchscreen computer. Psychooncology. 2006;15(1):S18–S18. 38. Ekman A, Dickman PW, Klint A, et al. Feasibility of using web-based questionnaires in large population-based epidemiological studies. Eur J Epidemiol. 2006;21(2):103–111. 39. Hyler SE, Gangure DP, Batchelder ST. Can telepsychiatry replace in-person psychiatric assessments? A review and meta-analysis of comparison studies. CNS Spectr. 2005;10(5):403–413. 40. Cull A, Gould A, House A, et al. Validating automated screening for psychological distress by means of computer touchscreens for use in routine oncology practice. Br J Cancer. 2001;85(12):1842–1849. 41. Houston TK, Cooper LA, Vu HT, et al. Screening the public for depression through the internet. Psychiatr Serv. 2001;52(3):362–367. 42. Leon AC, Kelsey JE, Pleil A, et al. An evaluation of a computer-assisted telephone interview for screening for mental disorders among primary care patients. J Nerv Ment Dis. 1999;187(5):308–311. 43. Brodey BB, Rosen CS, Brodey IS, et al. Reliability and acceptability of automated telephone surveys among Spanish- and English-speaking mental health services recipients. Ment Health Serv Res. 2005;7(3):181–184.

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44. Mitchell AM, Mittelstaedt ME, Schott-Baer D. Postpartum depression: the reliability of telephone screening. MCN Am J Matern Child Nurs. 2006;31(6):382–387. 45. Ogles BM, France CR, Lunnen KM, et al. Computerized depression screening and awareness. Community Ment Health J. 1998;34(1):27–38. 46. Fliege H, Becker J, Walter OB, et al. Development of a computer-adaptive test for depression (D-CAT). Qual Life Res. 2005;14(10):2277–2291. 47. Kobak KA, Mundt JC, Greist JH, et al. Computer assessment of depression: automating the Hamilton Depression Rating Scale. Drug Inf J. 2000;34:145–156. 48. Gibbons RD, Weiss DJ, Kupfer DJ, et al. Using computerized adaptive testing to reduce the burden of mental health assessment. Psychiatr Serv. 2008;59(4):361–368. 49. Gardner W, Shear K, Kelleher KJ, et al. Computerized adaptive measurement of depression: a simulation study. BMC Psychiatry. 2004;4:13. 50. Educational Testing Services. Educational testing services. [Web document], 2000. Accessed 7-30-2007. 51. Green B, Bock R, Humphreys L, et al. Technical guidelines for assessing computerized adaptive tests. J Educ Measure. 1984;21:347–360. 52. Sands WA, Waters BK, McBride JR. Computerized adaptive testing: from inquiry to operation. Washington, DC: APA Books, 1997. 53. Wainer H, Dorans NL. Computerized adaptive testing: a primer. Hillsdale, NJ: Erlbaum Associates, 2000. 54. Ware JE Jr, Bjorner JB, Kosinski M. Practical implications of item response theory and computerized adaptive testing: a brief summary of ongoing studies of widely used headache impact scales. Med Care. 2000;38(9 Suppl):II73–II82. 55. Weiss DJ. Adaptive testing by computer. J Consult Clin Psychol. 1985;53(6):774–789. 56. Baer L, Brown-Beasley MW, Sorce J, et al. Computer-assisted telephone administration of a structured interview for obsessive-compulsive disorder. Am J Psychiatry. 1993;150(11):1737–1738. 57. Buchanan T. Online assessment: desirable or dangerous. Professional Psychology: Research and Practice. 2002;33:148–154. 58. Carr AC, Ghosh A. Accuracy of behavioural assessment by computer. Br J Psychiatry. 1983;142:66–70. 59. Erdman HP, Klein MH, Greist JH. Direct patient computer interviewing. J Consult Clin Psychol. 1985;53(6):760–773. 60. Erdman HP, Greist JH, Gustafson DH, et al. Suicide risk prediction by computer interview: a prospective study. J Clin Psychiatry. 1987;48(12):464–467. 61. Kobak KA, Greist JH, Jefferson JW, et al. Computer-administered clinical rating scales. A review. Psychopharmacology (Berl). 1996;127:291–301. 62. Peters L, Andrews G. Procedural validity of the computerized version of the Composite International Diagnostic Interview (CIDI-Auto) in the anxiety disorders. Psychol Med. 1995;25(6):1269–1280. 63. Robins L, Helzer J, Cottler L, et al. NIMH Diagnostic Interview Schedule, Version III Revised (DIS-III-R). St. Louis, MO: Washington University, 1989. 64. Rosenfeld R, Dar R, Anderson D, et al. A computer-administered version of the YaleBrown Obsessive-Compulsive Scale. Psychol Assess. 1992;4:329–332. 65. Shaffer D, Fisher P, Lucas CP, et al. NIMH Diagnostic Interview Schedule for Children Version IV (NIMH DISC-IV): description, differences from previous versions, and reliability of some common diagnoses. J Am Acad Child Adolesc Psychiatry. 2000;39(1):28–38.

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66. Wilson FR, Genco KT, Yager GG. Assessing the equivalence of paper-and-pencil versus computerized tests: Demonstration of a promising technology. Computers in Human Behavior. 1985;1:265–275. 67. Rodriguez HP, von GT, Rogers WH, et al. Evaluating patients’ experiences with individual physicians: a randomized trial of mail, internet, and interactive voice response telephone administration of surveys. Med Care. 2006;44(2):167–174. 68. Davis LJ Jr, Hoffmann NG, Morse RM, et al. Substance Use Disorder Diagnostic Schedule (SUDDS): the equivalence and validity of a computer-administered and an interviewer-administered format. Alcohol Clin Exp Res. 1992;16(2):250–254. 69. Millstein S. Acceptability and reliability of sensitive information collected via computer interview. Educational and Psychological Measurement. 1987;47:523–533. 70. Rosenman SJ, Levings CT, Korten AE. Clinical utility and patient acceptance of the computerized composite international diagnostic interview. Psychiatr Serv. 1997;48(6):815–820. 71. Adler DA, McLaughlin TJ, Rogers WH, et al. Job performance deficits due to depression. Am J Psychiatry. 2006;163(9):1569–1576. 72. Greenberg PE, Kessler RC, Birnbaum HG, et al. The economic burden of depression in the United States: how did it change between 1990 and 2000? J Clin Psychiatry. 2003;64(12):1465–1475. 73. Kessler RC, Berglund P, Demler O, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA. 2003;289(23):3095–3105. 74. Wang PS, Patrick A, Avorn J, et al. The costs and benefits of enhanced depression care to employers. Arch Gen Psychiatry. 2006;63(12):1345–1353. 75. Grove WM, Zald DH, Lebow BS, et al. Clinical versus mechanical prediction: a metaanalysis. Psychol Assess. 2000;12(1):19–30. 76. Selim AJ, Berlowitz DR, Fincke G, et al. The health status of elderly veteran enrollees in the Veterans Health Administration. J Am Geriatr Soc. 2004;52(8):1271–1276. 77. Tarlov AR, Ware JE Jr, Greenfield S, et al. The Medical Outcomes Study. An application of methods for monitoring the results of medical care. JAMA. 1989;262(7):925–930. 78. Wells KB, Burnam MA, Camp P. Severity of depression in prepaid and fee-forservice general medical and mental health specialty practices. Med Care. 1995;33(4):350–364. 79. Kilbourne AM, McGinnis GF, Belnap BH, et al. The role of clinical information technology in depression care management. Adm Policy Ment Health.2006;33(1):59–69. 80. Bliven BD, Kaufman SE, Spertus JA. Electronic collection of health-related quality of life data: validity, time benefits, and patient preference. Qual Life Res. 2001;10(1):15–22. 81. Radosevich DM, Werni TL. A practical guide for implementing, analyzing, and reporting outcomes measurements. Health Outcomes Institute, 1998. 82. Rind DM, Kohane IS, Szolovits P, et al. Maintaining the confidentiality of medical records shared over the Internet and the World Wide Web. Ann Intern Med. 1997;127(2):138–141. 83. Soetikno R, Young HS, Keefe EB. Role of emerging technology in the era of cost containment. Am J Gastroenterol. 1997;92:1038–1040. 84. Subramanian AK, McAfee AT, Getzinger JP. Use of the World Wide Web for multisite data collection. Acad Emerg Med. 1997;4(8):811–817.

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9 SCREENING FOR DEPRESSION IN PRIMARY CARE: CAN IT BECOME MORE EFFICIENT? Kathryn M. Magruder and Derik E. Yeager

1. 2. 3. 4. 5. 6. 7.

Introduction Epidemiology of Depression in Primary Care Is Screening for Depression in Primary Care Worthwhile? Which Screening Tool Should Be Used? Implementing Screening in Primary Care What Developments Are on the Horizon? Conclusions

Context Screening for depression has been so widely advocated that the burden of proof has shifted to skeptics who argue against it. Yet only recently has sufficient evidence accrued to judge dispassionately the advantages and disadvantages of screening. Here we discuss the evidence for specific tools and specific strategies in improving the outcome of depression screening in primary care.

1. Introduction In 1978, the Institute of Medicine defined primary care as ‘‘care that is accessible, comprehensive, coordinated, continuous, and accountable.’’1 While the definition has evolved over time,2 these fundamental characteristics are still valid today. Included in the primary care mission is to serve as the first line for detection and either treatment or referral of common mental disorders, including depression. The inclusion of first-line mental health services as a component of primary care distinguishes primary care (including outpatient 161

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clinics in managed care organizations, community hospitals, Veterans Administration hospitals, teaching institutions, and other medical centers) from care in more specialized clinical settings. The comprehensiveness of primary care and the obligation of its providers for first-line care make it a logical and appropriate venue for mental health screening.3 Complicating the issue, however, are the time constraints on primary care providers. Although the amount of time spent per patient visit is about 20 minutes in the United States,4 the recommended services that should be provided in that short period of time are daunting. It is therefore imperative that these recommended services—in particular preventive health services— be provided in the most efficient manner possible. Services that cannot be provided efficiently and fit within the busy, fast-paced world of primary care are at risk of being omitted. This is especially true for preventive mental health services. Screening for depression is such a service; therefore, it is critical that primary care providers make use of the best and most efficient depression screening approaches possible. In this chapter, we will address issues related to screening for depression in the primary care context. We will start by briefly reviewing the epidemiology of depression as related to primary care. Next, we will provide a critical examination of the applicability to depression screening of the World Health Organization’s criteria. Then we will review published screening tools and their attributes for use in primary care settings. Last, we will provide a discussion of future directions, including additional ways that screening for depression in primary care can be made more efficient and thus more effective and more widely implemented.

2. Epidemiology of Depression in Primary Care Population Prevalence of Depression The National Comorbidity Survey Replication (NCS-R), conducted on adults over 18 years old, found a 12-month prevalence of 9.5% for any DSM-IV mood disorder, with 6.7% for major depression and 1.5% for dysthymia.5 From this survey, 19.5% of major depression cases in the community are classified as mild, with 50.1% and 30.4% classified as moderate and serious, respectively.5 Thus, about 80% of those with major depressive disorder have symptoms that are moderate to serious, and it is likely that those who seek health services are in the higher spectra of disorder. In a European epidemiologic study of mental disorders involving six countries, major depression was the single most common disorder assessed, with a 12-month prevalence of 3.9%.6 Wittchen and Jacobi7 conducted a meta-analysis of 27 studies with data on the prevalence of

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mental disorders in European countries. The 12-month prevalence of major depression ranged between 3.1% and 10.1%, with a median prevalence of 6.9%. Clearly, depression may be the most prevalent of mental disorders and constitutes a worldwide problem affecting approximately 5% to 10% of adults in a given year.

Primary Care Prevalence of Depression An early compendium of studies showed that pre-DSM-III-R depression prevalence in primary care ranged from 4.8% to 8.6%.8 More recently, one of the most comprehensive assessments of mental disorders in primary care was conducted by the World Health Organization and involved 15 cities in 14 countries.9 Using the Composite International Diagnostic Interview (CIDI) as the diagnostic assessment tool for DSM-III-R and ICD-10 conditions, this study showed that the prevalence of current psychiatric disorders is 24% but varies substantially by country.9 In particular, prevalence estimates for major depression ranged from 2.6% in Nagasaki, Japan, to an exceptionally high 29.5% in Santiago de Chile (over 12% greater than the next highest—16.9% in Manchester, England). The total prevalence of ICD-10 major depression was 10.4%. Although it is acknowledged that there is considerable variability within a city or country based on the characteristics of a primary care clinic (eg, inner-city clinics that serve disadvantaged patients may have higher depression prevalence), and thus the findings of this study do not generalize as national primary care prevalences, this important international study has helped to solidify the importance of depression in primary care settings throughout the world. A number of studies have found significant prevalence and morbidity of subthreshold disorders. For example, in a study of 619 primary care patients, Backenstrass and associates10 found a prevalence of 4.6% for major depression, 6.2% for minor depression, and 9.1% for nonspecific depression symptoms. Levels of disability followed a similar pattern, with highest levels for major depression and lowest levels for nonspecific depression symptoms.10 Thus, these ‘‘sub-major’’ forms of depression are not without associated morbidity.

Primary Care is the ‘‘De Facto’’ Mental Health Treatment System Primary care has been termed the de facto mental health treatment system since as many people with mental disorders receive treatment in general medical settings as in mental health specialty settings.11,12 From Epidemiologic

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Catchment Area (ECA) data, it has been estimated that only 45% of those with unipolar major depression used any health service in the 12 months prior; 27.8% sought care in the specialty mental health sector, while 25.3% sought care in the general medical sector.11 Paralleling ECA findings, NCS-R data have shown that 51.6% of those who met the criteria for major depression received some health services for depression in the past 12 months, with 27.2% in the general medical sector.13 This paper also examined symptom severity with respect to treatment and found that only 12.8% of those in treatment in the general medical sector were classified as mild cases—all others were moderate and above. It has been estimated that 50% to 80% of depression management occurs in primary care. Harman and colleagues14 found that for older adults 64% of depression visits occurred in primary care, representing only 3% of all elder primary care visits, contrasted with 26% of depression visits occurring in psychiatric care, representing 58% of all psychiatric elder visits. Thus, the index of suspicion is likely to be low in primary care settings where the prevalence is also low. An analysis of National Ambulatory Medical Care Survey data showed that for the average primary care doctor, 10.33 visits per week were considered antidepressant medication visits, compared with 11.04 such visits for the average psychiatrist.15 While antidepressant medication visits are slightly higher for psychiatrists than for primary care physicians, it is likely that primary care physicians initiate more antidepressant prescriptions but fewer monitoring visits, while psychiatrists have fewer antidepressant-initiating visits but more monitoring visits.

Unassisted Recognition of Depression in Primary Care Ironically, while general medical settings are a primary venue for treating mental disorders, a very large percentage of such disorders go unrecognized by primary care providers and therefore untreated. Some reports suggest that fewer than 50% of those with depression are so diagnosed in primary care settings.16–18 The WHO primary care study found that overall, 54.2% of those who met criteria for depression (ICD F32/33) were recognized as having a psychological illness by their treating physician. This ranged from a low of 19.3% in Nagasaki to a high of 74.0% in Santiago de Chile.19 Thus, studies show that depression is relatively common in primary care settings, but many with depression go unrecognized. It is no wonder that a number of screening tools have been developed to assist providers in recognizing and diagnosing depression. Yet there are other issues to consider before initiating screening programs.

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3. Is Screening for Depression in Primary Care Worthwhile? Screening is an important aspect of prevention and early intervention for many diseases and conditions, and this includes depression. WHO describes 10 criteria for initiating a screening program. Below, we discuss each criterion along with issues that should be considered for clinically effective depression screening. Because our focus is on primary care, we consider these criteria in that context.

The Condition Should Be an Important Health Problem With a depression prevalence of approximately 5% to 10% worldwide and 5% to 20% in primary care settings, depression is considered an important health problem. In addition to personal suffering, those with depression have significantly worse functioning. Based on the landmark publication on worldwide disability,20 Ustun and associates21 have updated earlier data and estimate that depression was the fourth leading cause of global disease burden in the year 2000. The burden of depression on the healthcare system is equally significant. The average medical costs (6-month period) for primary care patients in the United States diagnosed with depression or anxiety were approximately twice the average costs for patients with subthreshold depression or anxiety or no disorder ($2,390 vs. $1,248),22 resulting in national annual medical costs of approximately $26 billion (1990 dollars).23 For the most part, this burden is on primary care in terms of recognition and treatment,24 including antidepressant prescribing.25,26 On another level, the societal burden of depression is great, and patients need not receive a clinical diagnosis of depression to experience impaired functioning,27 missed workdays (at an annual national cost of $17 billion),23 and disability days,28 with impairment equal to or greater than that found with other chronic conditions such as diabetes, arthritis, gastrointestinal disturbances, lung disturbances, bronchitis, emphysema, and back problems.29 Thus, there is no doubt that at all levels depression is an important health and public health problem.

There Should Be a Treatment for the Condition A number of effective treatments exist for depression, including cognitivebehavioral therapy and medications. In fact, the robust research basis for these treatments has prompted a proliferation of treatment guidelines that provide practical approaches for implementing these evidence-based practices for primary care providers (see, for example, the Agency for Healthcare Research and Quality website with depression guidelines).30

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Facilities for Diagnosis and Treatment Should Be Available Although this tends to be setting-specific, more and more primary care practitioners are recognizing their roles as first-line responders for depression diagnosis and treatment. Additionally, many primary care practices incorporate mental health care specialists in their practice (eg, psychiatric nurse specialist), are aligned with mental health specialists (ie, have a ready referral source), or are part of larger healthcare organizations that incorporate mental health services (eg, HMOs, U.S. Veterans Health Administration). Thus, when there is a positive screen and a diagnosis of depression is made, treatment is typically available within the practice or within a referral network.

There Should Be a Latent Stage of the Disease Although the diagnosis of depression depends on the presence of symptoms, the disorder can be considered to have a latent stage in the following sense. Depression is often not detected clinically, patients do not spontaneously report symptoms to providers, and patients themselves may not be aware that their symptoms constitute depression. From NCS-R data, it has been estimated that there is a delay of approximately 8 years between the onset of depression and first receipt of professional help.31 Additionally, longstanding depression is associated with disability as well as psychiatric and medical comorbidities, which early detection and intervention may prevent.

There Should Be a Test or Examination for the Condition As is detailed in the next section, a number of adequate depression screening tools exist, including standard screeners (eg, the Zung Self-Rating Depression Scale [SDS]),32 short screens (eg, Medical Outcomes Study Depression Screen [MOSD]),33 and some ultra-brief screens (eg, Patient Health Questionnaire [PHQ]-2).34 In addition, there are diagnostic interviews suitable for use in primary care, such as the depression module of the Mini International Neuropsychiatric Interview (M.I.N.I.),35 the Primary Care Evaluation of Mental Disorders (PRIME-MD),36 and the Symptom-Driven Diagnostic System for Primary Care (SDDS-PC).37

The Test Should Be Acceptable to the Population Screens for depression are generally acceptable to both participants and the staff who administer them.38,39 Diagnostic tools are lengthier and may be more difficult for some patients; however, they are considered acceptable in terms of risk and time. Certainly, relative to other recommended primary care screenings (eg, colonoscopy), screening for depression is noninvasive, brief, and well

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Burden

Screening Tasks

Patient, PC Staff

Screen

PC Staff

Score Review results – | +

PCP Patient, PC Staff, PCP

2nd Stage Screen – +

Diagnostic Work-up – + Psych Education

Patient, PC Staff, PCP Watchful Wait

Referral

Treatment

Figure 9.1. Screening burden by task.

tolerated by patients, and results are relatively easy to interpret. In contrast to some screenings such as colonoscopy and mammography, which require only a referral from the primary care provider, depression screening typically requires more clinician (nurse or physician) time to administer, interpret, and assess, and (if positive) to treat or refer. Thus, the screening burden to clinicians is significantly greater than to patients, and may well influence acceptability in clinical practice (Fig. 9.1).

The Natural History of the Disease Should Be Adequately Understood Depression is known as a disorder with exacerbations and remissions. Persistent depression is a risk factor for disability,40 both medical and psychiatric comorbidities,5 and suicide.41 There is evidence that early recognition and effective treatment of depression can alter the trajectory by reducing disability and premature mortality,42 promoting remission, and preventing relapse.43 There is also evidence suggesting that early recognition and effective treatment of depression can improve patient outcomes such as social functioning, productivity,44 and absenteeism.45 ‘‘Sub-major’’ depression is often considered to be an integral part of the natural course of major depression and is sometimes referred to as the prodromal phase.46 Research has demonstrated that both subthreshold and subsyndromal depression are associated with increased functional disability47 and have a negative impact on quality of life.48 Data from a randomized trial of older adults (PROSPECT) show that patients initially presenting with

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sub-major depression were five times more likely to have major depression after 1 year.47 Thus, identification of these patients may help broaden the focus of depression treatment to include a more preventive approach,49 allowing patients to benefit from improved functional and quality-of-life outcomes and receive more aggressive assessment and symptom monitoring to hasten recognition of major depressive disorder. Patients presenting with sub-major depression may, in fact, benefit from treatment. Seligman and colleagues50 followed ‘‘at-risk’’ university students and found that those randomized to receive weekly cognitive-behavioral therapy workshop meetings had significantly fewer depressive symptoms after 8 weeks.

There Should Be an Agreed Policy on Whom to Treat This may vary from site to site, with some advocating treatment for minor depression and adjustment disorders with depressed mood. All clinical practice guidelines advocate treating patients who meet the criteria for a diagnosis of major depression. Several groups have shown that patients whose depression is not recognized have milder forms of the disorder with less disability.51 To some extent, treating those with ‘‘sub-major’’ depression may be a resource issue. Some have advocated low-cost, low-intensity, nontraditional treatments (eg, bibliotherapy, web-based self-help) where therapeutic intensity and cost are aligned with symptom severity.52 While there may be benefit to treating these sub-major conditions, those policy decisions should not compromise system capacity to provide treatment for other important conditions.

The Total Cost of Finding a Case Should Be Economically Balanced in Relation to Medical Expenditure as a Whole Given the relatively short and inexpensive screening instruments, the availability of structured diagnostic assessments for depression that can be administered inhouse for diagnostic follow-up, and the relatively moderate cost of treatment, contrasted with the medical and psychiatric comorbid problems that are apt to develop from lack of treatment, economics favor screening for depression. In a cost-utility study, Valenstein and coworkers53 concluded that one-time screening for depression is cost-effective, and more frequent screening is likely to become more cost-effective with improvements in treatments.

Case-Finding Should Be a Continuous Process Several studies have shown that depression can occur throughout the lifespan.5,54 Furthermore, it may have been present but not detected until many years later. Thus, it makes sense to have in place a system that will screen periodically throughout the lifespan.

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4. Which Screening Tool Should Be Used? Primary care providers have a great deal to consider when selecting a screening instrument, and there are many tools from which to choose, each with its own set of attributes. Time is of obvious importance in primary care, and typically the provider time to administer a screening tool and score it (rather than patient time) is a key consideration. In the quest for brevity, screening tools have evolved from standard screeners to short screeners to ultra-brief screeners. Below, we consider a number of published screening tools organized by administration time. In addition to time, we also consider scope of use, administration/scoring, and performance.

Standard Screeners In a recent article, Mitchell and Coyne55 defined a ‘‘standard’’ screening tool as one that contains 15 or more items and takes, on average, more than 5 minutes to complete. In addition to the term standard, many of these screeners can also be defined as traditional, as many, including the Zung SDS,32 Beck Depression Inventory (BDI),56 and Center for Epidemiologic Studies Depression Scale (CES-D),57 have been in use since the early 1960s. Also, they have been translated into dozens of languages and have been used in virtually every health setting, including primary care and specialty clinics, and for research. Table 9.4 provides details about the administration, scoring, and psychometric performance of five ‘‘standard’’ depression screeners: the BDI,56 CES-D,57 Geriatric Depression Scale (GDS),58 Inventory for Depression (ID),59 and the Zung SDS.32 The BDI,56 CES-D,57 and GDS58 are available in multiple, typically shorter, versions. Some of these screeners offer situational advantages over the others; for example, scoring results for the BDI and the Zung SDS provide an estimate of symptom severity. The GDS was designed specifically for use with geriatric patients. One must take these characteristics (and others, such as self-administration and time frame of symptoms) into account when selecting a screening tool. In general, all five of these screeners are well suited for use in primary care settings; they are easy to administer, they are easy to score, and they offer decent accuracy. Despite this, standard-length screeners may seem cumbersome to some busy primary care providers who prefer shorter alternatives.

Short Screeners Short screeners, defined as consisting of 5 to 14 items and taking between 2 and 5 minutes to complete,55 include the Hospital Anxiety and Depression Scale

Table 9.4. Standard Depression Screening Instruments Commonly Used in Primary Care Scope of Use BDI

60–64

Depression only* Severity of symptoms today

Administration 7, 13, or 21 items* 2–5 min to complete Literacy: Easy Scoring: Simple Can be self-administered

CES-D60–64

Depression only Frequency of symptoms in the past week

10 or 20 items 2–5 min to complete Literacy: Easy Scoring: Simple

Scoring Score range: 0–63 Usual cut point:10–19 (mild), 20–29 (moderate), 30 (severe)

Score range: 0–60 Usual cut point: 16

Can be self-administered GDS60,62

Depression only Endorsement of symptoms (y/n) in the past week

15 or 30 items 2–5 min to complete Literacy: Easy Scoring: Simple

Score range: 0–30 Usual cut point: 11

Performance

Reference 63

64

Sensitivity: 97% ; 89% (81–95) Specificity: 99%63; 64% (59–68)64 Efficiency: 0.9963 False positive: 0.0163 False negative: 0.0063 LRþ: 4.2 (1.2;13.6)61; 2.564 LR: 0.17 (0.1;0.3)61; 0.1764 PPV: 84.0%63; 29.6% (10.7;57.6)62 AUC (95% CI): 0.87 (0.82–0.91)64 Sensitivity: 81%63; 93% (85–97)64 Specificity: 72%63; 69% (65–74)64 Efficiency: 0.7263 False positive: 0.2763 False negative: 0.0163 LRþ: 3.3 (2.5; 4.4)61; 3.064 LR: 0.24 (0.2; 0.3)61; 0.1064 PPV: 13.0%63; 24.8% (20; 30.6)62 AUC (95% CI): 0.89 (0.85–0.92)64 LRþ: 3.3 (2.4; 4.7)62 LR: 0.16 (0.1; 0.3)62 PPV: 24.8% (19.4; 32)62

Original citation: Beck AT, Ward CH, Mock J, et al. An inventory for measuring depression. Arch Gen Psychiatry. 1961;4:561–571 www.psychcorpcenter.com/ content/bdi-ll.htm Original citation: Radloff L. The CES-D scale: A self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1:385–401. www.mhhe.com/hper/health/ personalhealth/labs/stress/ activ2-2.html Original citation: Yesavage JA, Brink TL, Rose TL, et al. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res. 1982–83;17(1):37–49. www.stanford.edu/ ~yesavage/GDS.html

Table 9.4. (Continued) Scope of Use

Administration

Scoring

ID60,61

Depression only Recently

15 items 2–5 min to complete Literacy: Easy

Score range: 0–15 Usual cut point: 10

SDS60–63

Depression only Frequency of symptoms recently

20 items 2–5 min to complete Literacy: Easy Scoring: Simple

Score range: 25–100 Usual cut point: 50–59 (mild), 60–69 (moderate), 70 (severe)

Can be self-administered

Performance

Sensitivity: 100%63 Specificity: 71%63 Efficiency: 72%63 False positive: 0.2863 False negative: 0.0063 LRþ: 3.3 (1.3; 8.1)62 LR: 0.35 (0.2; 0.8)62 PPV: 15.0%63; 24.8% (11.5; 44.8)62

Reference Original citation: Popoff, L. M. A simple method for diagnosis of depression by the family physician. Clinical Medicine. 1969 March: 24–29. Original citation: Zung, WW (1965) A self-rating depression scale. Arch Gen Psychiatry 12, 63–70.

fpinfo.medicine.uiowa.edu/ calculat.htm AUC, area under the curve; CI, confidence interval; LR, likelihood ratio; PPV, positive predictive value. Adapted from General Hospital Psychiatry, 24/4, Williams JW, Pignone M, Ramirez G, Perez Stellato C, Identifying depression in primary care: a literature synthesis of case-finding instruments, 225–237, Copyright (2002), with permission from Elsevier.

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(HADS),65 MOS-D,33 and PHQ34 (Table 9.5). Many authors consider the diagnostic performance of these intermediate-length screeners to range from modest to good.55,64,66 Despite the advantage of both diagnostic performance and brevity, a national U.K. survey demonstrated that they continue to be underused in primary and secondary care settings.67 This lack of use may have led to the development of even shorter screeners.

Ultra-short/Ultra-brief Screeners What is the minimum number of items required to effectively screen for depression? With the quest to reduce screening time, several new screening instruments with four or fewer questions have been published. Mitchell and Coyne have defined ultra-short/ultra-brief screeners as consisting of four or fewer items and taking less than 2 minutes to complete (Table 9.6).55 Whooley and colleagues64 reported data supporting a two-item screener, and the U.S. Veterans Administration has adopted a four-item screener to satisfy a 1998 universal depression screening mandate. A meta-analysis on 22 studies that assessed the accuracy of ultra-short screeners for depression in primary care found that diagnostic rule-in accuracy increases with the number of items, with two- and three-item screeners offering the greatest accuracy (80%) and oneitem screeners providing very poor accuracy (30%).55 No four-item screeners met inclusion criteria for this analysis. The authors concluded that while twoand three-item screeners can help providers identify 8 out of 10 depression cases, it is most often at the expense of a high false-positive rate. They therefore argue for a two-stage screening approach when an ultra-brief screener is employed.

Two-Stage Approaches Another approach that may offer advantages in some situations or practices is the use of a two-stage process. Screening followed by a standardized diagnostic assessment has often been used in research projects for efficient identification of potential subjects who meet criteria for major depression. The approach enables investigators to avoid conducting diagnostic assessments on all subjects, yet has the advantage of having screening information available on all subjects, with diagnostic data on those above a certain screening threshold. While in theory any screener could be combined with any acceptable diagnostic assessment, two instruments that ‘‘package’’ both screening and diagnosis, the SDDS-PC and PRIME-MD, were developed in the late 1990s specifically for use in primary care settings.36, 37, 68, 69 These instruments were intended for both clinical and research purposes. They were both designed to

Table 9.5. Short Depression Screening Instruments Commonly Used in Primary Care Scope of Use

Administration

Scoring

Performance

Reference

HADS

Anxiety and depression Severity of symptoms in the past week

14 items £2 min to complete Literacy: Difficult Scoring: Simple

Score range: 0–21 Usual cut point: 11

LRþ: 7.0 (2.9; 11.2)62 LR: 0.3 (0.3; 0.4)62 PPV: 41.3% (22.6; 52.8)62

Original citation: Zigmond AS, Snaith RP. The Hospital Anxiety and Depression Scale. Acta Psychiatr Scand 1983;67:361–370. www.clinical-supervision.com/ hads.htm

MOS-D60,61,64

Depression only Frequency of symptoms in the past week

8 items <2 min to complete Literacy: Average

Score range: 0–1 (logistic regression) Usual cut point: 0.06

Sensitivity: 93% (86–97)64 Specificity: 72% (68–76)64 LRþ: 3.364 LR: 0.1064 AUC (95% CI): 0.89 (0.85–0.91)64

Original citation: Burnam MA, Wells KB, Leake B, et al. Development of a brief screening instrument for detecting depressive disorders. Medical Care. 1988;26,775–789.

Depression only Frequency of symptoms in the past 2 weeks

9 items <2 min to complete Literacy: Average Scoring: Simple

Diagnosis: Score range: 0–9 Usual cut point: 5 symptoms Severity: Score range: 0–27 Usual cut point: 0–4 (none), 5–9 (mild), 10–14 (moderate), 15–19 (major), 20 (severe)

LRþ: 12.2 (8.4; 18)62 LR: 0.28 (0.2; 0.5)62 PPV: 55% (45.7; 64.3)62

Original citation: Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med. 2001;16:606–613. www.depression primarycare.org/ap1.html

60,62

PHQ60,62

Can be self-administered

Can be self-administered

LR, likelihood ratio; PPV, positive predictive value. Adapted from General Hospital Psychiatry, 24/4, Williams JW, Pignone M, Ramirez G, Perez Stellato C, Identifying depression in primary care: a literature synthesis of case-finding instruments, 225–237, Copyright (2002), with permission from Elsevier.

Table 9.6. Ultra-Short Depression Screening Instruments Commonly Used in Primary Care Scope of Use PRIME-MD (PHQ-2)60–63

SDDSPC60–62,64

Administration

Multiple components with depression category Presence of symptoms in the past month

2 items 1–2 min to complete Literacy: Average Scoring: Complex

Multiple components with depression category Presence of symptoms in the past month

5 items 1–2 min to complete Literacy: Easy Scoring: Complex

Scoring

Performance 60

Reference 63

Score range: 0–2 Usual cut point: 160

Sensitivity: 96% Specificity: 57% Efficiency: 0.5963 FP: 0.4163 FN: 0.0063 LRþ: 2.7 (2.0; 3.7)62 LR: 0.14 (0.1; 0.3) 62 PPV: 21.3% (16.7–27)62

Score range: 0–560 Usual cut point: 260

Sensitivity: 96%64 Specificity: 51%64 LRþ: 3.5 (2.4; 5.1)62 LR: 0.2 (0.1; 0.4)62 PPV: 25.9% (19.4; 33.8)62 AUC (95% CI): 0.86 (0.82–0.89)64

Can be self-administered

Original citation: Spitzer RL, Williams JB, Kroenke K, et al. Utility of a new procedure for diagnosing mental disorders in primary care. The PRIME-MD 1000 study. JAMA. 1994;272:1749–1756. Kroenke K, Spitzer RL, Williams JBW. The Patient Health Questionnaire-2: Validity of a two-item depression screener. Medical Care. 2003;41:1284–1292. Original citation: Broadhead WE, Leon AC, et al. Development and validation of the SDDS-PC screen for multiple mental disorders in primary care. Arch Fam Med. 1995;4:211–219.

AUC, area under the curve; CI, confidence interval; FN, false negative; FP, false positive; LR, likelihood ratio; PPV, positive predictive value. Adapted from General Hospital Psychiatry, 24/4, Williams JW, Pignone M, Ramirez G, Perez Stellato C, Identifying depression in primary care: a literature synthesis of case-finding instruments, 225–237, Copyright (2002), with permission from Elsevier.

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take minimum clinician time and still provide multiple psychiatric diagnoses in primary care. Both instruments have a quick screen (sometimes referred to as stem questions) for multiple psychiatric disorders, followed by specific disorder modules when so indicated by the quick screen. Both instruments include major depression. Time burden is placed mainly on patients for the quick screen and clinicians for the disorder modules (but only for the subset of patients with a high likelihood of disorder). For practices interested in only a single disorder, the screen questions and module for that disorder can be selected for use. Notably, the developers of the PRIME-MD developed the PHQ (with slightly improved sensitivity and specificity for major depression) because the PRIME-MD was still considered too long to be clinically useful.36

Screening for General Emotional Distress One fundamental issue is whether screening should be aimed at identifying distress rather than depression alone. There are several popular tools that screen for nonspecific psychiatric distress, including the General Health Questionnaire (GHQ),70 the Hopkins Symptom Checklist (HSCL),71 the World Health Organization Well-Being Scale (WHO-5),72 and the Emotional State Questionnaire-2 (EST-Q2)73 (Table 9.7). A prospective cohort study found that the WHO-5, a well-being screener, performed better in a primary care setting than the GHQ-12, PHQ-9, or an unaided physician diagnosis when compared to the CIDI as the gold standard for detection of depression.74 Despite the broadness of this approach, brevity can be achieved by taking advantage of shared symptomatology and diagnostic comorbidity. Thus, the specificity of the screener for a disorder may not matter so much, and it will be up to the provider to sort out, for example, major depression from posttraumatic stress or other anxiety disorders. Because first-line primary care treatments for many disorders are similar (eg, pharmacotherapy with selective serotonin reuptake inhibitors), this approach could work reasonably well in primary care.

Screening for Multiple Disorders For many providers, it may be worthwhile to implement a screener that covers many disorders, including only one or two items for each disorder. MeansChristensen and coworkers75 tested such an approach with the Anxiety and Depression Detector (ADD) and found that screening for panic disorder, posttraumatic stress disorder, social phobia, generalized anxiety disorder, and major depression simultaneously offered advantages in time efficiency while maintaining screener performance. The SDDS-PC and PRIME-MD

Table 9.7. General Psychiatric Screening Instruments Commonly Used in Primary Care Scope of Use

Administration

WHO-5

Measures degree of well-being

5 items

GHQ60,61,63

General psychiatric distress Frequency of symptoms in the past week

12, 28, or 30 items 2–10 min to complete Literacy: Easy Can be self-administered

Scoring

Score range: 0–28 Usual cut point: 4

Performance

Reference

Sensitivity: 94% Specificity: 65% False negative: 0.06 PPV: 0.37 NPV : 0.98 LRþ: 2.69 LR: 0.09

Original citations: Bech P, Gudex C, Johansen KS. The WHO(Ten) Well-Being Index: validation in diabetes. Psychother Psychosom. 1996;65:183–190. Bech P, Olsen LR, Kjoller M, et al. Measuring well-being rather than the absence of distress symptoms: a comparison of the SF-36 Mental Health subscale and the WHO-Five Well-Being Scale. Int J Methods Psychiatr Res. 2003;12:85–91.

Sensitivity: 76%63 Specificity: 74%63 Efficiency: 0.7463 False positive: 0.2563 False negative: 0.0163 PPV: 13.0%63

Original citation: Goldberg DP. The detection of psychiatric illness by questionnaire. London, Oxford University Press, 1972.

Table 9.7. (Continued) Scope of Use

Administration

Scoring

60,61

HSCL

General distress Frequency of symptoms in the past week

13 or 25 items 2–5 min to complete Literacy: Average

Score range: 25–100 Usual cut point: 43

EST-Q276

Detection of symptoms characteristic of depressive and anxiety disorders during the past four weeks

28 items Depression subscale: 8 items Time to complete: unknown Literacy: unknown

Score Range: 0–112 Depression subscale: Score Range: 0–32 Usual Cutpoint: >11

Performance

Reference Original citation: Derogatis LR, Lipman RS, Rickels K, Uhlenhuth EH, Covi L. The Hopkins Symptom Checklist (HSCL): a selfreport symptom inventory Behav Sci. 1974 Jan; 19(1):1–15.

Sensitivity: 81%76 Specificity: 81%76 False Positive: 0.1976 False Negative: 0.1976 PPV: 0.4476 NPV : 0.9676 LRþ: 4.376 LR-: 0.2376

Aluoja A, Shlik J, Vasar V, Luuk K, Leinsalu M. Development and psychometric properties of the Emotional State Questionnaire, a selfreport questionnaire for depression and anxiety. Nord J Psychiatry 1999; 53: 443–449.

LR , likelihood ratio; NPV, negative predictive value; PPV; positive predictive value. Adapted from General Hospital Psychiatry, 24/4, Williams JW, Pignone M, Ramirez G, Perez Stellato C, Identifying depression in primary care: a literature synthesis of case-finding instruments, 225–237, Copyright (2002), with permission from Elsevier.

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(mentioned above) cover multiple disorders (including major depression) that are prevalent and often undetected in primary care settings. They also cover suicidality, an important consideration regardless of diagnosis.

Severity Rating Depression screening instruments are important beyond case-finding. Additional uses for certain instruments include monitoring symptom levels (eg, frequency, severity) for ‘‘at-risk’’ patients or evaluating treatment response/effectiveness. The types of screening instruments that would be most valuable in these situations are those that provide severity levels (eg, Zung SDS). The practice of ‘‘watchful waiting’’ (see Fig. 9.1) involves following patients who present with symptomatology that may be subthreshold or otherwise not sufficient for a clinical diagnosis of depression, yet suggestive of an increased risk of developing depression in the future. In this scenario, depression screeners can be administered repeatedly over time to monitor symptom levels and determine symptom changes and patterns (in much the same way that prostate-specific antigen levels are monitored over time). Patients who have been clinically diagnosed with depression and are receiving treatment can be routinely administered screeners both to assess treatment effectiveness and to determine if additional interventions are required (the U.S. Preventive Services Task Force recommends the PHQ-9 for this purpose).

5. Implementing Screening in Primary Care Implementation of a screening strategy must be undertaken with both the screening instrument performance characteristics and clinical context in mind. In addition to considering overall staffing patterns and underlying nonpsychiatric case mix, a key contextual issue is the estimated underlying prevalence of depression in the clinic population. This, along with screening instrument performance characteristics of sensitivity and specificity, allow one to estimate resource use for various implementation strategies. Such exercises can aid in determining the most parsimonious approach under various scenarios. Table 9.1a/b illustrates a one-stage screening approach using an instrument with sensitivity and specificity both 80%. We present the results of using this instrument under different prevalence scenarios: 5% and 10% (see Appendix Tables 3 and 4 for additional scenarios). Assuming 5% prevalence, if 1,000 patients were screened for major depression, 230 would screen as positive, but only 40 would actually have major depression (positive predictive value

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Table 9.1a/b. Sample Performance Yields for Single-Stage Screening in Primary Care Setting 9.1a Prevalence: Low (5% or 50 MDD cases) Gold Standard MDD +

MDD 

Total

Screen +

40 True Positive

190 False Positive

230 Screen Positive

Screen 

10 False Negative

760 True Negative

770 Screen Negative

50 MDD Positive

950 MDD Negative

1000 Total Sample

PPV: 40/230 = 17.4%. For every 100 subjects who screen positive, only approximately 17 would be depressed. Excess diagnostic burden: 190/1000 = 19%. Diagnostic assessment would be performed on 190 patients who were not depressed.

9.1b Prevalence: Average (10% or 100 MDD cases) Gold Standard MDD +

MDD 

Total

Screen +

80 True Positive

180 False Positive

260 Screen Positive

Screen 

20 False Negative

720 True Negative

740 Screen Negative

100 MDD Positive

900 MDD Negative

1000 Total Sample

PPV: 80/260 = 30.8%. For every 100 subjects who screen positive, only approximately 31 would be depressed. Excess diagnostic burden: 180/1000 = 18%. Diagnostic assessment would be performed on 180 patients who were not depressed.

n = 1,000; Screener sensitivity 80%, specificity 80%

[PPV] 17.4%). That means that 190 false-positive patients would undergo diagnostic assessment for major depression—an excess diagnostic burden of 19% (190/1,000). From this chart, it can be seen that as prevalence increases, PPV also increases and excess diagnostic burden declines. Table 9.2a/b illustrates a two-stage approach using an initial screener with sensitivity of 95% and specificity of 60% and a follow-up screener of

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sensitivity 80% and specificity 80%. Assuming prevalence of 5%, of the 1,000 patients screened in the first stage, 428 will be positive, of whom 48 are true positive (PPV 11.2%). In stage two, these 428 are screened again, yielding 38 true positives of 114 screen positives for a more favorable PPV of 33.3%. The cumulative yield from steps 1 and 2 combined would be 38 true positives (76% sensitivity) and 874 true negatives (92%), with a PPV of 33% and a negative predictive value (NPV) of 99%. Table 9.2a/b. Sample Performance Yields for Two-Stage Screening in Primary Care Setting 9.2a Depression Prevalence: Low (5% or 50 MDD cases) Stage I Gold Standard MDD +

MDD 

Total

Screen +

48 True Positive

380 False Positive

428 Screen Positive

Screen 

2 False Negative

570 True Negative

572 Screen Negative

50 MDD Positive

950 MDD Negative

1000 Total Sample

PPV: 48/428 = 11.2%. For every 100 subjects who screen positive, approximately 11 would be depressed.

Stage II Gold Standard MDD +

MDD 

Total

Screen +

38 True Positive

76 False Positive

114 Screen Positive

Screen 

10 False Negative

304 True Negative

314 Screen Negative

48 MDD Positive

380 MDD Negative

428 Total Sample

9.2b Depression Prevalence: Average (10% or 100 MDD cases) Stage I

PPV: 38/114 = 33.3%. For every 100 subjects who screen positive, approximately 33 would be depressed. Overall excess diagnostic burden: 76/1,000 = 7.6%. Diagnostic assessment would be performed on 76 patients who were not depressed.

Table 9.2a/b. (Continued) Gold Standard MDD +

MDD 

Total

Screen +

95 True Positive

360 False Positive

455 Screen Positive

Screen 

5 False Negative

540 True Negative

545 Screen Negative

100 MDD Positive

900 MDD Negative

1000 Total Sample

PPV: 95/455 = 20.9%. For every 100 screen positives, approximately 21 would be depressed

Stage II Gold Standard MDD +

MDD 

Total

Screen +

76 True Positive

72 False Positive

148 Screen Positive

Screen 

19 False Negative

288 True Negative

307 Screen Negative

95 MDD Positive

360 MDD Negative

455 Total Sample

PPV: 76/148 = 51.4%. For every 100 screen positives, approximately 51 would be depressed Overall excess diagnostic burden: 72/ 1,000 = 7.2%. Diagnostic assessment would be performed on 72 patients who were not depressed.

n = 1,000; stage I screener sensitivity 95%, specificity 60%; stage II screener sensitivity 80%, specificity: 80%

Table 9.3 assigns time costs to the various screening tasks, as well as diagnostic assessment for screen-positive patients. In this table, we estimate patient, staff, and clinician time under the various screening scenarios (prevalence of 5%, 10%, 20%; one- and two-stage screening approaches). We assume the same sensitivity and specificity of the screening instruments as in Table 9.2. In a sample of 1,000 patients where the prevalence of depression is 5%, we estimate the burden in patient time for a single-stage screener to be 6,600 minutes. This is based on an estimate of 2 minutes per patient for the initial screen, with 20 additional minutes for each screenpositive patient. We estimate 2,000 minutes of non-physician staff time (based on 2 minutes per patient) and 4,600 minutes of clinician time (based on 20 minutes per screen-positive patient). In the single-screener 181

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Table 9.3. Screening and Diagnosis Time Burden for Patients, Staff, and Providers Time Burden (min)

MDD Prevalence 5%

10%

20%

A. Single-Stage Screening Approach Sensitivity 80%, specificity 80% Screening (patient) Scoring (staff) Screening yield

2,000 (1000*2) 2,000 (1000*2) 23.0% (230/1000)

2,000 (1000*2) 2,000 (1000*2) 26.0% (260/1000)

2,000 (1000*2) 2,000 (1000*2) 32.0% (320/1000)

Diagnostic interview Patient Provider

4,600 (230*20) 4,600 (230*20)

5,200 (260*20) 5,200 (260*20)

6,400 (320*20) 6,400 (320*20)

Positive predictive value

17.4% (40/230)

30.8% (80/260)

50% (160/320)

Total time Patient Staff Provider

6,600 min 2,000 min 4,600 min

7,200 min 2,000 min 5,200 min

8,400 min 2,000 min 6,400 min

B1. Two-Stage Screening Approach: Stage I Sensitivity 95%, specificity 60% Screening (patient) 1000 (1000*1) Scoring (staff) 1000 (1000*1) Screening yield 42.8% (428/1000)

1000 (1000*1) 1000 (1000*1) 45.5% (455/1000)

1000 (1000*1) 1000 (1000*1) 51.0% (510/1000)

B2. Two-Stage Screening Approach: Stage II Sensitivity 80%, specificity 80% Screening (patient) 856 (428*2) Scoring (staff) 856 (428*2) Screening yield 26.6% (114/428)

910 (455*2) 910 (455*2) 32.5% (148/455)

1,020 (510*2) 1,020 (510*2) 42.4% (216 /510)

Diagnostic interview Patient Provider

2,280 (114*20) 2,280 (114*20)

2,960 (148*20) 2,960 (148*20)

4,320 (216*20) 4,320 (216*20)

Positive predictive value

33.3% (38/114)

51.4% (76/148)

70.4% (152/216)

Total time Patient Staff Provider

4,136 min 1,856 min 2,280 min

4,870 min 1,910 min 2,960 min

6,340 min 2,020 min 4,320 min

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example, patient burden and provider burden increase with increasing prevalence. We provide similar time estimates for the two-stage screening approach. Here, patient burden decreases (because the initial screener is half the time of the more comprehensive second-stage screener), staff burden decreases slightly for prevalences of 5% and 10% but increases slightly for 20% prevalence, and provider time decreases significantly (because there are fewer false positives to evaluate). In the above examples, we have emphasized tangible costs and have not estimated costs of non-detection (false negative) or costs of treatment.

6. What Developments Are on the Horizon? Opinions concerning the appropriateness of screening for depression in primary care have shifted over the past two decades. As more effective treatments have become available to primary care providers, and as providers have become more knowledgeable about the importance of recognizing and treating depression, there has been a shift towards advocating routine screening in primary care settings. Many patients, however, are still unwilling to accept a diagnosis of depression or treatment for depression. Clinicians need to explain screening and diagnostic results in a way that is non-stigmatizing. Providers must offer educational information and motivate patients to accept treatment. Building depression treatment capabilities may increase patient acceptance of both the diagnosis and treatment, as treatment in primary care is seen as less stigmatizing, more timely, and more integrated into overall healthcare. Over the past two decades, remarkable progress has been made in screening for depression in primary care. This can be seen in the change in U.S. Preventive Services Task Force guidance,30 which recommends screening adults ‘‘in clinical practices with systems in place to assure accurate diagnosis, effective treatment, and follow-up.’’ It can also be seen in the myriad of guidelines for detecting and treating depression in clinical practice, and the tools that have been developed to assist in this. Clear advances have come in reducing the burden of screening tools, so that some instruments with excellent performance characteristics are as short as two questions. With increasing acceptance of depression as a treatable illness to both patients and providers, parallel gains need to be made in terms of implementation of screening and early detection practices. Further efficiencies in the screening benefit–cost ratio will need to be made by improving treatment outcomes or by reducing screening time. Psychometricians will be hard-pressed to develop briefer screening tools

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than the current group of ultra-brief instruments described in Table 9.1, but new methods might be able to focus on those who most need help. For example, New Zealand researchers have found that following the two PRIME-MD screening items with the question, ‘‘Is this something with which you would like help?’’ improves specificity from 78% to 89% (sensitivity remained the same at 96%) (CIDI was the gold standard).77 This would theoretically improve efficiencies by selecting patients who are likely to accept treatment for depression. Another possibility is to reduce clinician and staff time by modifying the screening modality. For example, waiting rooms could contain carrels with computers where patients could update their histories and answer screening questions. Notable results could be flagged and printed for clinicians to address with the patient in the exam room. Similarly, patients could undertake similar updates and self-screens on their home computers, again with results going automatically and confidentially to providers. Automated computer reminders for clinicians to perform depression (and other) screens could also improve efficiency, as would making use of trained nurses to administer the screens and flag positive results for the provider (see Chapter 8 for further discussion). Depending on the practice, a two-stage screening process is another possibility, using extremely brief first-level screens followed by more intense second-level diagnostic assessments when indicated. Such second-level assessments could even include self-administered instruments that are considered diagnostic in nature, such as the PRIME-MD or the SDDS. Considering depression screening in the context of other psychiatric illness may broaden our notion of screening effectiveness. For example, depressive symptoms frequently co-occur with generalized anxiety, post-traumatic stress, and substance use disorders. False-positive screening results for depression may be less worrisome in that such patients may be positive for any of these three. Thus, a positive screen—though false positive for depression—may in essence correctly identify patients in need of mental health treatment, even if not for depression. Timing is yet another way to improve efficiency. Screening less often (eg, every 2 to 5 years instead of every year) would minimize the cost of the screening itself but at the expense of a lower detection rate. Approaches could be developed that take into account patient profiles to target screening to those at highest risk. Similarly, prior screening results (eg, subthreshold scores, positive screens for other mental health conditions, or answers to highly predictive questions) could be used to generate a screening frequency algorithm. In an age with electronic medical records and computer-generated clinical reminders, the ability to develop and implement such frequency algorithms based on individual profiles may not be as far away as it once seemed.

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7. Conclusions Screening for depression in primary care has changed radically in the past 20 years. With improvements in depression treatment, reduced stigmatization, better acceptance of depression as a treatable illness, and more efficient screening tools, primary care providers have embraced the notion that they are responsible for recognizing and treating this condition. Fortunately, providers have many excellent screening tools from which to choose. For additional efficiencies to be realized, advances in technology (eg, computerized screening and scoring), along with improved treatment outcomes, will need to take place to change the benefit–cost ratio for depression screening even more favorably.

References 1. Institute of Medicine (IOM). A manpower policy for primary health care. Washington, DC: National Academy of Sciences, 1978. 2. Starfield B. Primary care: concept, evaluation, and policy. New York: Oxford University Press, 1992. 3. Culpepper L. The active management of depression. J Fam Pract. 2002;51:769–776. 4. Mechanic D, McAlpine DD, Rosenthal M. Are patients’ office visits with physicians getting shorter? N Engl J Med. 2001;344(3):198–204. 5. Kessler RC, Chiu WT, Demler O, et al. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):617–627. 6. Alonso J, Angermeyer MC, Bernert S, et al. 12-Month comorbidity patterns and associated factors in Europe: results from the European Study of the Epidemiology of Mental Disorders (ESEMeD) project. Acta Psychiatr Scand. 2004;109(s420):28–37. 7. Wittchen H-U, Jacobi F. Size and burden of mental disorders in Europe—a critical review and appraisal of 27 studies. Eur Neuropsychopharmacol. 2005;15(4):357–376. 8. Depression Guideline Panel. Depression in primary care, vol 1. Detection and diagnosis. Clinical Practice Guideline, No. 5. Rockville, MD: DHHS Pub Hlth Serv. AHCPR Publication No. 93–0550, 1993. 9. Goldberg D, Lecrubier Y. Chapter 4.1. Form and Frequency of Mental Disorders across Centres. In Mental illness in general health care: an international study. Chichester: John Wiley and Sons, 1995. 10. Backenstrass M, Frank A, Joest K, et al. A comparative study of nonspecific depressive symptoms and minor depression regarding functional impairment and associated characteristics in primary care. Compr Psychiatry. 2006;47(1):35–41. 11. Regier D, Goldberg I, Taube C. The de facto US mental and addictive disorders service system. Epidemiologic Catchment Area prospective 1-year prevalence rates of disorders and services. Arch Gen Psychiatry. 1993;50:85–94. 12. Regier D, Narrow W, Rae D, et al. The de facto US mental health services system: a public health perspective. Arch Gen Psychiatry. 1978;35(6):685–693. 13. Kessler RC, Berglund P, Demler O, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA. 2003;289(23):3095–3105.

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14. Harman JS, Veazie PJ, Lyness JM. Primary care physician office visits for depression by older Americans. J Gen Intern Med. 2006;21(9):926–930. 15. Pincus HA, Tanielian TL, Marcus SC, et al. Prescribing trends in psychotropic medications: primary care, psychiatry, and other medical specialties. JAMA. 1998;279(7):526–531. 16. Bridges K, Goldberg D. Somatic presentation of DSM III psychiatric disorders in primary care. J Psychosom Res. 1985;29:563–569. 17. Magruder-Habib K, Zung W, Feussner J. Improving physicians’ recognition and treatment of depression in general medical care: results of randomized clinical trial. Med Care. 1990;28(3):239–250. 18. Wilson D, Widmer R, Cadoret R, et al. Somatic symptoms: a major feature of depression in a family practice. J Affective Disorders. 1983;5:299–307. 19. Ustun T, Von Korff M. Chapter 4.3. Primary mental health services: access and provision of care. In Mental illness in general health care: an international study. Chichester: John Wiley and Sons, 1995. 20. Murray C, Lopez A, eds. The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries and risk factors in 1990 and projected to 2020. Cambridge, MA: Harvard University Press on behalf of the World Health Organization and the World Bank, 1996. 21. Ustun TB, Ayuso-Mateos JL, Chatterji S, et al. Global burden of depressive disorders in the year 2000. Br J Psychiatry. 2004;184:386–392. 22. Simon G, Ormel J, VonKorff M, et al. Health care costs associated with depressive and anxiety disorders in primary care. Am J Psychiatry. 1995;152(3):352–357. 23. Greenberg P, Stiglin L, Finkelstein S, et al. Depression: a neglected major illness. J Clin Psychiatry. 1993;54(11):419–424. 24. Manderscheid RW, Rae DS, Narrow WE, et al. Congruence of service utilization estimates from the Epidemiologic Catchment Area Project and other sources. Arch Gen Psychiatry. 1993;50(2):108–114. 25. Beardsley RS, Gardocki GJ, Larson DB, et al. Prescribing of psychotropic medication by primary care physicians and psychiatrists. Arch Gen Psychiatry. 1988;45(12):1117–1119. 26. Simon GE, VonKorff M, Wagner EH, et al. Patterns of antidepressant use in community practice. Gen Hosp Psychiatry. 1993;15(6):399–408. 27. Wells KB, Stewart A, Hays RD, et al. The functioning and well-being of depressed patients. Results from the Medical Outcomes Study. JAMA. 1989;262(7):914–919. 28. Broadhead WE, Blazer DG, George LK, et al. Depression, disability days, and days lost from work in a prospective epidemiologic survey. JAMA. 1990;264(19):2524–2528. 29. Wells KB, Golding J, Burnam MA. Psychiatric disorder and limitations in physical functioning in a general population. Am J Psychiatry. 1988;145:712–717. 30. Guide to clinical preventive services. AHRQ Publication No. 06–0588, Agency for Healthcare Research and Quality, Rockville, MD. Available at: http://www.ahrq.gov/ clinic/pocketgd.htm. 2006. 31. Wang PS, Berglund P, Olfson M, et al. Failure and delay in initial treatment contact after first onset of mental disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):603–613. 32. Zung, WW (1965) A self-rating depression scale. Arch Gen Psychiatry 12, 63–70. 33. Burnam MA, Wells KB, Leake B, & Landsverk J (1988). Development of a brief screening instrument for detecting depressive disorders. Medical Care, 26, 775–789. 34. Kroenke K, Spitzer RL, Williams JBW. The Patient Health Questionnaire-2: Validity of a two-item depression screener. Medical Care. 2003;41:1284–1292.

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35. Lecrubier Y, Sheehan DV, Weiller E, Amorim P, Bonora I, Sheehan K Harnett, Janavs J and Dunbar GC (1997) The Mini International Neuropsychiatric Interview (MINI). A short diagnostic structrued interview: reliability and validity according to the CIDI. Eur Psychiat 12, 224–231. 36. Spitzer RL, Williams JB, Kroenke K, et al. Utility of a new procedure for diagnosing mental disorders in primary care. The PRIME-MD 1000 study. JAMA. 1994;272:1749–1756. 37. Broadhead WE, Leon AC, et al. Development and validation of the SDDS-PC screen for multiple mental disorders in primary care. Arch Fam Med. 1995;4:211–219. 38. Bermejo I, Niebling W, Berger M, et al. Patients’ and physicians’ evaluation of the PHQ-D for depression screening. Primary Care and Community Psychiatry. 2005;10(4):125–131. 39. Loerch B, Szegedi A, Kohnen R, et al. The primary care evaluation of mental disorders (PRIME-MD), German version: a comparison with the CIDI. J Psychiatr Res. 2000;34(3):211–220. 40. Ormel J, Von Korff M, Oldehinkel A, et al. Onset of disability in depressed and nondepressed primary care patients. Psychol Med. 1999;29:847–853. 41. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. Vol. 4th ed., text rev. Washington, DC, 2000. 42. Sherman L. Depression and medical illness. Audio Digest Psychiatry. 2004;33(16):1–6. 43. Halfin A. Depression: the benefits of early and appropriate treatment. Am J Manag Care. 2007;13:S92–S97. 44. Coulehan J, Schulberg H, Block M, et al. Treating depressed primary care patients improves their physical, mental, and social functioning. Arch Intern Med. 1997;157:1113–1120. 45. Rost K, Smith J, Dickinson M. The effect of improving primary care depression management on employee absenteeism and productivity. A randomized trial. Med Care. 2004;42:1202–1210. 46. Eaton WW, Badawi M, Melton B. Prodromes and precursors: epidemiologic data for primary prevention of disorders with slow onset. Am J Psychiatry. 1995;152(7):967–972. 47. Lyness JM, Heo M, Datto CJ, et al. Outcomes of minor and subsyndromal depression among elderly patients in primary care settings. Ann Intern Med. 2006;144(7):496–504. 48. Wells KB, Burnam MA, Rogers W, et al. The course of depression in adult outpatients. Arch Gen Psychiatry. 1992;49:788–794. 49. Cuijpers P, Smit F. Subthreshold depression as a risk indicator for major depressive disorder: a systematic review of prospective studies. Acta Psychiatr Scand. 2004;109(5):325–331. 50. Seligman MEP, Schulman P, DeRubeis RJ, et al. The prevention of depression and anxiety. Prevention & Treatment. 1999;2(1). 51. Simon G, Goldberg D, Tiemens B, et al. Outcomes of recognized and unrecognized depression in an international primary care study. Gen Hosp Psychiatry. 1999;21(2):97–105. 52. Magruder KM, Calderone GE. Public health consequences of different thresholds for the diagnosis of mental disorders. Compr Psychiatry. 2000;41(2, Supplement 1):14–18. 53. Valenstein M, Vijan S, Zeber JE, et al. The cost-utility of screening for depression in primary care. Ann Intern Med. 2001;134(5):345–360.

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¨ stling S, Skoog I. The incidence of first-onset depression in a population 54. Pa´lsson S, O followed from the age of 70 to 85. Psychol Med. 2001;31:1159–1168. 55. Mitchell AJ, Coyne JC. Do ultra-short screening instruments accurately detect depression in primary care? A pooled analysis and meta-analysis of 22 studies. Br J Gen Practice. 2007;57:144–151. 56. Beck AT, Ward CH, Mock J, Erbaugh J. An inventory for measuring depression. Archives of General Psychiatry. 196;4:561–571. 57. Radloff, L. The CES-D scale: A self-report depression scale for research in the general population. Appl Psychol Meas 1:385–401, 1977. 58. Yesavage JA, Brink TL, Rose TL, Lum O, Huang V, Adey M, Leirer VO. Development and validation of a geriatric depression screening scale: a preliminary report. J Psychiatr Res. 1982–83;17(1):37–49. 59. Popoff LM. A simple method for diagnosis of depression by the family physician. Clinical Medicine. 1969 March:24–29. 60. Williams JW, Pignone M, Ramirez G, Perez Stellato C. Identifying depression in primary care: a literature synthesis of case-finding instruments. General Hospital Psychiatry 2002;24(4):225–237. 61. Mulrow CD, Williams JW, Gerety MB, Ramirez G, Montiel OM, Kerber C. CaseFinding Instruments for Depression in Primary Care Settings. Ann Intern Med 1995;122(12):913–921. 62. Nease DE, Jr., Malouin JM. Depression screening: a practical strategy. (Applied evidence: research findings that are changing clinical practice). Journal of Family Practice 2003;52(2):118(8). 63. McAlpine DD, Wilson AR. Screening for depression in primary care: what do we still need to know? Depression & Anxiety (1091–4269) 20041;19(3):137–145. 64. Whooley M, Avins A, Miranda J, et al. Case-finding instruments for depression: two questions are as good as many. J Gen Intern Med. 1997;12(7):439–445. 65. Zigmond AS, Snaith RP. The hospital anxiety and depression scale, Acta Psychiatr Scand 1983;67:361–70. 66. Schade CP, Jones ER Jr, Wittlin BJ. A ten-year review of the validity and clinical utility of depression screening. Psychiatr Serv. 1998;49(1):55–61. 67. Gilbody S, Whitty P, Grimshaw J, et al. Improving the recognition and management of depression in primary care. Effective Health Care Bull. 2002;7(5). 68. Weissman MM, Broadhead WE, Olfson M, et al. A diagnostic aid for detecting (DSM-IV) mental disorders in primary care. Gen Hosp Psychiatry. 1998;20(1):1–11. 69. Weissman M, Olfson M, Leon AC, et al. Brief diagnostic interviews (SDDS-PC) for multiple mental disorders in primary care: a pilot study. Arch Fam Med. 1995;4(3):220–227. 70. Goldberg DP. The detection of psychiatric illness by questionnaire. London, Oxford University Press, 1972. 71. Derogatis LR, Lipman RS, Rickels K, Uhlenhuth EH, Covi L. The Hopkins Symptom Checklist (HSCL): a self-report symptom inventory Behav Sci. 1974 Jan;19(1):1–15. 72. Bech P, Olsen LR, Kjoller M, Rasmussen NK: Measuring well-being rather than the absence of distress symptoms: a comparison of the SF-36 Mental Health subscale and the WHO-Five Well-Being Scale. Int J Methods Psychiatr Res 12:85–91, 2003. 73. Aluoja A, Shlik J, Vasar V, Luuk K, Leinsalu M. Development and psychometric properties of the Emotional State Questionnaire, a self-report questionnaire for depression and anxiety. Nord J Psychiatry 1999;53:443–449.

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74. Henkel V, Mergl R, Kohnen R, et al. Identifying depression in primary care: a comparison of different methods in a prospective cohort study. BMJ. 2003;326(7382):200–201. 75. Means-Christensen AJ, Sherbourne CD, Roy-Byrne PP, Craske MG, and Stein MB. Using five questions to screen for five common mental disorders in primary care: diagnostic accuracy of the Anxiety and Depression Detector. General Hospital Psychiatry 2006; 28(2): 108–111. ¨ o¨pik P, Aluoja A, Kalda R, et al. Screening for depression in primary care. Fam Pract. 76. O 2006;23(6):693–698. 77. Arroll B, Goodyear-Smith F, Kerse N, et al. Effect of the addition of a ‘‘help’’ question to two screening questions on specificity for diagnosis of depression in general practice: diagnostic validity study. BMJ. 2005;331:884–887.

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10 SCREENING FOR DEPRESSION IN MEDICAL SETTINGS: ARE SPECIFIC SCALES USEFUL? Gordon Parker and Matthew Hyett

1. An Introductory Logic 2. Depression in the Medically Ill 3. ‘‘False-Positive’’ Depression Reflecting Confounding by Physical Symptoms Associated with Medical Illness 4. Screening Measures Used to Assess Depression in the Medically Ill 5. Discussion

Context There are two broad strategies for screening and quantifying depression in medical settings. The first approach is replying upon measures developed in psychiatric samples, and the second approach is to concede that symptoms are substantially different and to develop customized scales. Here we discuss the merits of several specific scales for measuring depression in physical settings and make the case for scales tailored to specific populations. A subsequent chapter (Babaei and Mitchell) will present a contrasting position.

1. An Introductory Logic There are two broad strategies for screening and quantifying depression in medical settings. The first approach involves using measures developed in psychiatric samples and assuming that their relevance holds. The second approach is to concede that there are intrinsic limitations to extrapolating those ‘‘general’’ measures to medically ill populations. In the former case the hypothesis is that symptoms of depression are essentially the same when 191

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depression occurs with and without physical illness. In the latter case the hypothesis is the symptoms are substantially different. Pursuing the latter, there are two key concerns. Firstly, such an approach assumes some constancy to the nature of depression across differing psychiatric and medical settings. Depression, however, is difficult enough to define in psychiatric patient samples. Even ignoring the debate as to whether depression is viewed as comprising a set of subtypes or is best modeled along a continuum, quantifying clinical depression remains problematic, as detailed elsewhere in this book. Over the past few decades, clinical depression has most commonly been viewed as synonymous with major depression, but, as numerous studies have shown, comparable symptomatic distress and disability associated with major depression and minor depression—and even with subsyndromal depression1,2—begs an obvious question: Can imposing a cutoff score on a dimensional measure of depression accurately distinguish true cases and true non-cases in a psychiatric sample? Further, assuming that a cutoff is derived with an acceptable classification rate, can we extrapolate decision rules derived from psychiatric samples to screen and quantify depression caseness in the medically ill? As measures that have been widely used for decades (such as the Zung and the Beck Depression Inventory) generate widely differing cutoff scores across psychiatric, general practice, and medical settings, there would appear to be quantitatively and possibly qualitative differences to the nature of depression in medical contexts, making general measure extrapolation problematic. The second issue of concern is a methodologic one. Many measures used to assess depression in psychiatric samples weight features such as fatigue, anergia, anhedonia, and loss of interest, as well as appetite and sleep changes. However, it is quite possible for nondepressed patients with a medical illness to rate positively on such items purely as a consequence of their physical problem or of the drugs being used to treat the medical condition, or even of being hospitalized. Such confounding clearly risks false-positive scores, which then will inflate case identification and severity estimates. This issue also requires some consideration.

2. Depression in the Medically Ill The 12-month prevalence and odds of major depression are high in individuals with chronic medical conditions, and major depression is associated with significant increases in utilization, lost productivity, and functional disability.3 Those with a medical illness may have a co-occurring depressive illness (melancholic or nonmelancholic) that is similar in all regards to those depressive conditions observed in a psychiatric context. However, many with a medical illness will more have a grief-like reaction to the

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medical illness per se. Here, instead of experiencing the primary defining feature of depression—a loss in self-esteem or self-worth—as might be expected for an individual with clinical depression, they may more be grieving the loss of their previous healthy role and have no impairment of self-esteem. In addition, medical illness itself can cause psychological features approaching the phenomenology of depression. Cassell4 has emphasized (i) disconnection from the usual world, (ii) a loss of the sense of indestructibility or omnipotence, (iii) a loss of competence and completeness of reason, and (iv) a loss of control of the sufferer’s world. He notes that, as illness deepens, medically ill people become more and more withdrawn from their usual world, their previous interests, friends, and families, reflecting that, ‘‘We exist to the extent that we are connected.’’ When medically ill patients experience such feelings, they will frequently develop irritability, anxiety, fear, and even depression. The disconnection can occur rapidly after events such as a myocardial infarction or severe trauma, or be gradual following the development of a chronic disease or long-term illness.4 The loss of the sense of omnipotence is commonly handled by denial and/or disavowal as the individual seeks to preserve his or her intactness. The loss of control—where the patient perceives himself or herself as helpless—can be one of the most distressing of human experiences. According to Cassell,4 such features are illness. While they sometimes approximate to depressive phenomenology, they can be distinguished by careful clinical inquiry—but not always by simple screening measures. In essence, there is a distinction between the experiential components of illness and depression. Thus, in screening for depression in the medically ill, there is a need to ensure that items are not confounded by questions that risk falsepositive responses emerging from those with a nondepressive illness.

3. ‘‘False-Positive’’ Depression Reflecting Confounding by Physical Symptoms Associated with Medical Illness As noted earlier, individuals with many medical conditions might be expected to report features such as loss of interest, anergia, and sleep and appetite disturbances, which, if secondary to the medical illness and not a reflection of depression, will tend to inflate depression estimates. A number of options have been proposed to redress such confounding influences. Several authors5 have argued for an inclusive approach. Here, every relevant depressive symptom is counted even if secondary to the illness or its management, with or without subsequent adjustment to threshold scores to calibrate caseness estimates. A contrasting exclusive approach6 ignores features common to those with medical illness. A third substitutive approach7 involves substituting psychological symptoms (eg, tearfulness and social withdrawal) or vegetative symptoms (eg, weight loss,

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appetite and sleep disturbance, fatigue, and concentration difficulties). Fourthly, both DSM-III-R and DSM-IV decision rules allow an etiologic approach, whereby symptoms are counted only if they are judged as not being caused by a general medical condition. The last approach requires the rater to make interpretative (and thus subjective) judgments. Common sense would suggest that there would be advantages to having measures of depression in medical settings that assess items defining depression per se and that are unlikely to be confounded by aspects of the medical illness or of its treatment. Such an approach therefore rejects the use of general depression measures, and argues for consideration only of measures that have been designed to preempt confounding influences. We now review measures that have been widely advanced and/or specifically designed for measuring depression in medical settings.

4. Screening Measures Used to Assess Depression in the Medically Ill The Hospital Anxiety Depression Scale This seven-item subscale (HADS8) is one of the most commonly used research measures of depression in the medically ill. As the authors judged anhedonia to be a central feature of depression and a predictor of antidepressant drug response, five of its seven items assess anhedonia (eg, ‘‘I still enjoy the things I used to enjoy’’; ‘‘I feel cheerful’’; ‘‘I have lost interest in my appearance’’; ‘‘I look forward with enjoyment to things’’; and ‘‘I can enjoy a good book or program’’), suggesting some redundancy. For this dimensional measure, the authors suggested that a cutoff score of 11 or more indicates a definite case of depression, while noncases score less than 8 and doubtful cases score in the 8 to 10 range. While this scale is widely used, Hermann9 noted that there is ‘‘still no comprehensive documentation of its psychometric properties,’’ while its actual validity has been challenged in both medically unwell and psychiatric patients.10 The positive predictive value (PPV) of the HADS in the latter study showed poor discrimination, with only 17% of medically ill patients accurately diagnosed at a cutoff of 8, rising to just 25% at a cutoff of 11. Moreover, a recent review of the validity of the HADS11 identified differing optimal cutoffs across differing primary care populations, suggesting that its case-finding ability is dependent on sample characteristics. For instance, its use in general practice settings revealed areas under the curve in the range of 0.84 to 0.96, though its translation to more specific medical settings (eg, stroke clinics) reveals uncharacteristically low case-finding cutoffs (ie, 4). Thus, the validity of the HADS as a measure of depression in divergent medical settings lacks support.

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The Beck Depression Inventory for Primary Care (BDI-PC) This seven-item measure12 was developed for primary care (and therefore medical settings) by removing somatic items from the well-established Beck Depression Inventory.13 Sadness and loss of pleasure or anhedonia were included on an a priori basis, as at least one of these symptoms is necessary for a DSM-IV diagnosis of major depression. Suicidal ideation was also chosen on an a priori basis, being judged as an important clinical indicator of suicidal risk. The remaining four items—pessimism, past failure, self-dislike, and selfcriticalness—were derived empirically from data obtained from a study of 500 psychiatric patients. A cutoff score of 4 or more is used to define depression caseness, with sensitivity and specificity being quantified at 82% to 99% across medical inpatient and outpatient samples.14–16 In a head-to-head comparison of the BDI-PC and HADS depression measures, the former was shown to be superior in distinguishing depressed nondepressed patients referred to a consultation-liaison service.12

The Depression in the Medically Ill (DMI) Scales These scales were developed by our research team17 with the objective of developing a valid measure of depression in the medically ill by focusing on cognitive symptoms. In comparison to Beck’s strategy of stripping somatic items from an accepted measure of depression, we adopted a ‘‘bottom-up’’ approach of specifically studying those with medical illness to generate possible salient constructs. In essence, we selected 81 items assessing the impact of a medical illness on the individual4 as well as ones capturing cognitive aspects of depression (eg, anhedonia, self-reproach, nonreactive mood). Items were scored by subjects on a three-point scale (‘‘not true at all,’’ ‘‘true to some degree,’’ ‘‘very true’’) for the previous 2 to 3 days. The initial study population comprised inpatients and outpatients of a large Sydney teaching hospital being treated for a primary medical condition. A research psychiatrist subsequently (i) made a dimensional estimate of any depression and (ii) judged whether there was any current depression of clinical significance (ie, major depression or an adjustment disorder with depressed mood). A number of the subjects also completed the HADS and BDI-PC measures so that the comparative properties of the measures could be examined. We refined the initial 81-item measure by removing items affirmed by both depressed and nondepressed subjects. We also deleted items that, while weighted to depression, had a low prevalence (eg, suicidal ideation), resulting in a final set of 16 items. Of interest, the measure did not appear limited to

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assessing depressed mood, including a brooding item and two items having anxiety connotations (fearfulness and insecurity). The internal consistency of the derived DMI-16 was high (alpha = 0.95), and total score measures correlated highly with the BDI-PC (0.80) and HADS (0.72) measures. When correlated against depression severity as estimated by the psychiatrist, the DMI-16 returned a high coefficient (0.74), slightly in excess of the BDI-PC (0.68) and superior to the HADS (0.54). A receiver operating characteristic curve (ROC) analysis derived a cutoff score of 18 or more with both high sensitivity (100%) and specificity (96%). Of the 29 subjects who received a psychiatrist-rated judgment of a clinically significant depression, the DMI-16 cutoff discriminated highly (kappa = 0.91) and was superior to the BDI-PC (0.68) and the HADS (0.57). In a second study18 involving a larger sample of hospitalized medically ill patients, we derived a briefer version (the DMI-10) and further examined its properties (along with the DMI-16) in comparison to the BDI-PC and HADS measures. While anhedonia is included in the DMI-16, as it was affirmed by a significant number of nondepressed medically ill subjects, it was excluded from the DMI-10 measure. Analysis against clinically judged caseness established similar overall classification rates for the DMI-10 and DMI-18 measures, comparable to that derived for the BDI-PC but superior to the HADS measure. In this study, the formally recommended HADS cutoff of 8 or more for a probable case was also the optimal cutoff suggested by our ROC analysis using clinical judgment as a criterion. The recommended HADS cutoff of 11 or more for a definite case, however, showed low sensitivity. Our ROC analysis of the BDI-PC established a cutoff score of 5 or more, close to its recommended cutoff score of 4 or more. In a third development study report,19 the capacity of the DMI-10 to screen for a depression in a general practice setting—where it might be assumed that the majority of the subjects would have a primary medical illness—was again supported. The DMI-10 measure is shown in Table 10.1.

Parsimonious Screening Chochinov and colleagues20 compared four screening measures in a sample of inpatients with advanced cancer who were receiving palliative care. A single item (‘‘Are you depressed?’’) was reported to have perfect sensitivity and specificity, with the authors concluding that this question provides a ‘‘reliable and remarkably accurate screen.’’ However, as responses to the outcome measure (Research Diagnostic Criteria status) and the single predictor question could both have been derived by subjective response bias (ie, affirming or denying depression), this study risks a tautologic bias. Subsequent metaanalysis showed more modest results.21 The need for economical accurate

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Table 10.1. Ten-Item Depression in the Medically Ill Screening Measure DMI-10 Depression Self-Report Questionnaire Please consider the following questions and rate how true each one is in relation to how you have been feeling lately (ie. in the last 2 to 3 days) compared to how you usually or normally feel. Please tick ([) the most relevant option

Not True

Slightly

Moderately

Very True

1. Are you stewing over things? 2. Do you feel more vulnerable than usual? 3. Are you being self-critical and hard on yourself? 4. Are you feeling guilty about things in your life? 5. Do you find that nothing seems to be able to cheer you up? 6. Do you feel as if you have lost your core and essence? 7. Are you feeling depressed? 8. Do you feel less worthwhile? 9. Do you feel hopeless or helpless? 10. Do you feel more distant from other people? Adapted from www.blackdoginstitute.org.au/docs/DMI-10.pdf

measures encouraged development of a four-item screener (the Brief Case Finder for Depression [BCD]22), assessing whether depressed mood or ‘‘restless and disturbed nights’’ were present, together with items assessing inability to overcome difficulties and/or dissatisfaction with life. This measure also tends to be overly inclusive because its broad questioning generates many false positives; however, the sensitivity of the measure and negative predictive power appear adequate for ruling out those who are not depressed.23

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The Primary Care Evaluation of Mental Disorders (PRIME-MD) The PRIME-MD24 assesses four domains of mental disorders commonly observed in general population settings: mood, anxiety, somatoform, and alcohol disorders. The two-tier assessment structure of the PRIME-MD allows patients who score positively on the patient questionnaire (PQ) to then receive a physician-administered structured interview (Clinician Evaluation Guide [CEG]) involving modularized DSM-III-R criteria. A patient who scores positively on one of two depressive symptoms (demoralization and/or anhedonia) on the PQ is subsequently assessed for more specific criteria. Due to the length of administration time, consequent selfreport measures (PRIME-MD Patient Health Questionnaire [PHQ]) have been designed,25 including the PHQ-9 measure of depressive status. Standard DSM-IV major depressive disorder criteria apply, and recognition of symptomatology is comparable—if not slightly more sensitive—than the original PRIME-MD.25 In the initial PHQ primary care study,25 measured sensitivity and specificity were 73% and 98% respectively for the self-report version, compared with 57% and 94% for the original clinician-administered version. More specifically, at a cutoff score of 10 or more, the PHQ-9 derived sensitivity estimates of 88%, likewise for specificity, to meet diagnostic criteria for major depression.26 However, diagnostic concordance of the PHQ-9, while higher than both the HADS and the WHO Wellbeing Index (WBI-5),27 is still relatively low in comparison with DSM-IV criteria (kappa ¼ 0.56). The comparative validity of the PHQ-9 with physicians’ diagnoses (sensitivities of 98% and 40% respectively) is, however, superior.28 Thus, the PHQ-9 is suggested to be somewhat more accurate29 than HADS and physicians’ diagnoses, though comparable to more general measures of well-being in primary care populations.

5. Discussion The capacity for medical illness and/or hospitalization to distort the assessment of depression in the medically ill argues against use of any general depression measure, and we have therefore not reviewed studies using such measures other than the PRIME-MD. The last does risk confounding by medical illness nuances but has the advantage of delivering DSM case status decisions, although the risk is that the intrinsic limitations to such diagnoses in medically ill groups may fail to be recognized. We therefore take as a given that any valid depression measure excludes items that can be confounded by illness or

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hospitalization, and have focused on relevant measures. Two—the HADS and the BDI-PC—have adopted the exclusive approach by effectively removing potentially confounding items from established depression measures. In developing the DMI measures, we adopted a differing ‘‘bottom-up’’ approach of examining the properties of items capturing the world of medically ill patients (both depressed and nondepressed). While the HADS measure has long been in use, it has been criticized for the lack of studies examining its psychometric properties and even for its validity. Its focus on anhedonia respects that construct’s utility in psychiatric subjects but, as we established its high rate of affirmation of anhedonia in nondepressed medically ill subjects,18 that construct may not be as central to depression as imagined. Our quantifying18 low sensitivity for the HADS in diagnosing definite depression is of concern if the aim of the screening measure is to prioritize detection of those with probable or definite depression. In our initial DMI study,17 we established that the DMI-16 had high internal consistency and was distinctly superior to the HADS and somewhat superior to the BDI-PC when compared against a psychiatrist’s independent clinical judgment of depression severity and case status. These findings were essentially confirmed in our second study,18 where we again compared the three relevant measures. Any measure of depression in the medically ill needs to be acceptable, brief, and minimally intrusive. The last issue is worthy of consideration. We deleted a provisional item assessing suicidal ideation as it proved intrusive to a number of our medically ill subjects. However, we demonstrated that its omission (in the final DMI measures) was not of concern, as all those admitting to suicidal ideation scored above the cutoff on the DMI-16. Our studies of the three principal candidate screening measures (HADS, BDI-PC and the DMI) suggest that the BDI-PC and DMI measures are roughly comparable—but superior to the HADS—in terms of their capacity to separate depressed and nondepressed individuals in medical settings. We would recommend both the use of the BDI-PC and DMI-10.

References 1. Kessler R. Prevalence, correlates, and course of minor depression and major depression in the National Comorbidity Survey. J Affect Disord. 1997;45:14–30. 2. Cuijpers P, Smit F. Subthreshold depression as a risk indicator for major depressive disorder: a systematic review of prospective studies. Acta Psychiatr Scand. 2004;109(5):325–331. 3. Egede LE. Major depression in individuals with chronic medical disorders: prevalence, correlates and association with health resource utilization, lost productivity and functional disability. Gen Hosp Psychiatry. 2007;29(5):409–416.

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4. Cassell EJ. Reactions to physical illness and hospitalisation. In Usdin G, Lewis JM, eds. Psychiatry in general nedical practice. New York: McGraw Hill, 1979. 5. Cohen-Cole SA, Brown FN, McDaniel JS. Diagnostic assessment of depression in the medically ill. In Stoudermire A, Fogel B, eds. Psychiatric care of the medical patient. New York: Oxford University Press, 1993:53–70. 6. Plumb MM, Holland J. Comparative studies of psychological function in patients with advanced cancer-I. Self-reported depressive symptoms. Psychosom Med. 1977;39(4):264–276. 7. Endicott J. Measurement of depression in patients with cancer. Cancer. 1984;53(10 Suppl):2243–2249. 8. Zigmond AS, Snaith RP. The Hospital Anxiety and Depression Scale. Acta Psychiatr Scand. 1983;67(6):361–370. 9. Hermann C. International experiences with the Hospital Anxiety and Depression Scale: a review of validation data and clinical results. J Psychosom Res. 1997;42(1):17–41. 10. Silverstone PH. Poor efficacy of the Hospital Anxiety and Depression Scale in the diagnosis of major depressive disorder in both medical and psychiatric patients. J Psychosom Res. 1994;38(5):441–450. 11. Bjelland I, Dahl AA, Haug TT, et al. The validity of the Hospital Anxiety and Depression Scale: An updated literature review. J Psychosom Res. 2002;52(2):69–77. 12. Beck AT, Guth D, Steer RA, et al. Screening for major depression disorders in medical inpatients with the Beck Depression Inventory for Primary Care. Behav Res Ther. 1997;35(8):785–791. 13. Beck AT, Beck RW. Screening depressed patients in family practice—rapid technique. Postgrad Med. 1972;52(6):81–85. 14. Beck AT, Steer RA, Ball R, et al. Use of Beck anxiety and depression inventories for primary care with medical outpatients. Assessment. 1997;4(Suppl 3):211–219. 15. Steer RA, Cavalieri DO, Leonard DM, et al. Use of the Beck Depression Inventory for Primary Care to screen for major depressive disorders. Gen Hosp Psychiatry. 1999;21(2):106–111. 16. Winter LB, Steer RA, Jones-Hicks L, et al. Screening for major depression disorders in adolescent medical outpatients with the Beck Depression Inventory for Primary Care. J Adolesc Health. 1999;24(6):389–394. 17. Parker G, Hilton T, Hadzi-Pavlovic D, et al. Screening for depression in the medically ill: the suggested utility of a cognitive-based approach. Aust N Z J Psychiatry. 2001;35(4):474–480. 18. Parker G, Hilton T, Bains J, et al. Cognitive-based measures screening for depression in the medically ill: the DMI-10 and the DMI-18. Acta Psychiatr Scand. 2002;105(6):419–426. 19. Parker G, Hilton T, Hadzi-Pavlovic D, et al. Clinical and personality correlates of a new measure of depression: a general practice study. Aust N Z J Psychiatry. 2003;37(1): 104–109. 20. Chochinov HM, Wilson KG, Enns M, et al. ‘‘Are you depressed?’’ Screening for depression in the terminally ill. Am J Psychiatry. 1997;154(5):674–676. 21. Mitchell AJ. Are one or two simple questions sufficient to detect depression in cancer and palliative care? A Bayesian meta-analysis. Br J Cancer. 2008;98(12): 1934–1943. 22. Clarke DM, McKenzie DP, Marshall, RJ, et al. The construction of a brief case-finding instrument for depression in the physically ill. Integr Psychiatry. 1994;10:117–123.

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23. Jefford M, Mileshkin L, Richards K, et al. Rapid screening for depression—validation of the Brief Case-Finder for Depression (BCD) in medical oncology and palliative care patients. Br J Cancer. 2004;91(5):900–906. 24. Spitzer RL, Williams JBW, Kroenke K, et al. Utility of a new procedure for diagnosing mental disorders in primary care: The PRIME-MD 1000 study. JAMA. 1994;272(22):1749–1756. 25. Spitzer RL, Kroenke K, Williams JBW. Validation and utility of a self-report version of PRIME-MD: The PHQ primary care study. JAMA. 1999;282(18): 1737–1744. 26. Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–613. 27. World Health Organization (WHO). Wellbeing measures in primary health care: The DepCare Project. WHO, Regional Office for Europe, Copenhagen: 1998. 28. Lo¨we B, Spitzer RL, Gra¨fe K et al. Comparative validity of three screening questionnaires for DSM-IV depressive disorders and physicians’ diagnoses. J Affect Disord. 2004;78(2):131–140. 29. Wittkampf KA, Naeije L, Schene AH, et al. Diagnostic accuracy of the mood module of the Patient Health Questionnaire: a systematic review. Gen Hosp Psychiatry. 2007;29(5):388–395.

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11 SCREENING FOR DEPRESSION IN MEDICAL SETTINGS: THE CASE AGAINST SPECIFIC SCALES Fariba Babaei and Alex J. Mitchell

1. 2. 3. 4.

Overview of Depression in Physical Disease Defining Somatic Symptoms Diagnostic Accuracy of Somatic Symptoms in Depression Evidence For and Against Somatic Symptoms when Diagnosing Comorbid Depression 5. Implications for Screening

Context The prevailing view for detecting mood disorders in the presence of physical disease is to exclude somatic symptoms that might contaminate a diagnosis (See Parker and Hyatt, Chapter 10 for a presentation of this point of view). This chapter will examine whether this approach is beneficial, with a view to deciding whether new depression scales for each physical disorder (each excluding somatic symptoms) are required.

1. Overview of Depression in Physical Disease There is a bidirectional relationship between depression and physical illness. New evidence suggests that among depressed individuals presenting in primary care, most have at least one comorbid psychiatric condition and at least one physical condition.1,2 At least 75% of elderly depressed patients in primary care also have a known physical illness, and in 30–50% this is of high severity.3–6 In one study only 10% of elderly depressed patients in primary care had pure depression with no comorbidity.7 Thus, comorbid depression should be considered ‘‘normal’’ in primary care. Some evidence suggests that 203

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those with comorbidity are less likely to have depression treatment initiated by their primary care practitioner.8 They are also less likely to recover from depression.9 Specific conditions such as speech disorders, arthritis, and dermatologic problems have been linked with worse outcomes of depression.10,11 The exact relationship of depression and comorbidities is complex. In one of the largest studies, Egede (2007)12 examined data from 30,801 adults captured in the 1999 Household National Health Interview Survey. The community prevalence of major depression was 4.7% in those without chronic medical illness but 7.7%, 9.8%, and 12% in those with one, two, or three or more chronic disorders, respectively (Fig. 11.1). Major depression was associated with significant increases in utilization, lost productivity, and functional disability. Patients with chronic medical illness and comorbid depression (and anxiety) also have significantly higher numbers of medical symptoms, even controlling for severity of disease.13 Around one in four people in the general population have functional disability, but in those with depression and medical comorbidity, at least three out of four have functional limitations.14 18 16 14 12 10 8 6 4 2

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A population survey in New Zealand found that a quarter of people with chronic physical conditions suffered from a comorbid mental disorder, compared with 15% of the population without chronic conditions.15 Further, those with a mental disorder had higher rates of chronic pain, cardiovascular disease, high blood pressure, and respiratory conditions as well as the risk factors smoking, overweight/obesity, and hazardous alcohol use. In a primary care survey of 6,641 patients with multiple physical disorders, Nuyen and colleagues (2006)16 used morbidity data recorded by Dutch general practitioners to examine both psychiatric and physical comorbidities. The top three conditions linked with lifetime depression were schizophrenia, anxiety disorders, and substance abuse. The top three medical disorders were Parkinson’s disease, male genital problems, and stroke. Physical disease is also strongly linked with suicide. Juurlink and associates (2004)17 examined 1,354 provincial coroners’ records of Ontario residents 66 years or older who committed suicide between 1992 and 2000. Their prescription records during the preceding 6 months were compared with those of living matched controls (1:4) to determine the presence or absence of 17 illnesses potentially associated with suicide. Conditions associated with suicide are shown in Figure 11.2. Compared with patients with no identified illness, for example, patients with three illnesses had about a threefold increase in the estimated relative risk of suicide, and patients with five illnesses had about a fivefold increase in risk.

2. Defining Somatic Symptoms Defining and Eliciting ‘‘Somatic Symptoms’’ What exactly is meant by ‘‘somatic symptoms’’? At face value the answer seems obvious: somatic symptoms are physical complaints relating to bodily sensations. These would include aches, low energy, fatigability, muscle weakness, leaden paralysis, and gastrointestinal symptoms (low appetite and weight loss). Pain, sexual dysfunction, and sleep disturbance are certainly core somatic symptoms, but are these strictly bodily sensations? For example, pain may be defined as ‘‘an unpleasant sensory and emotional experience associated with actual or potential tissue damage or is described in terms of such damage.’’18 Thus pain (and sleep) may represent physical and psychological aspects. Even more difficult to classify but still conventionally regarded as somatic are concentration problems, agitation/retardation, and changes in arousal. This short list is not exhaustive. Less common somatic symptoms of depression might include shortness of breath, dry mouth, constipation or diarrhea, urinary frequency or hesitancy, menstrual disturbances, dizziness, changes in libido, palpitations, increased sweating, flushing, blurred vision, tremor, pins and needles, restless legs, and rash. Indeed, any bodily sensation

and the risk of suicide in the elderly. Arch Intern Med. 2004;164:1179–1184. ar

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might be included, although some symptoms may be due to medication rather than the underlying depression. One study examined how reliably clinicians elicit somatic compared to nonsomatic symptoms. In the Rhode Island MIDAS project, Zimmerman and colleagues (2006)19 conducted an in-depth analysis of symptoms for major depressive disorder by trained raters administering a semi-structured interview to 1,523 psychiatric outpatients. They analyzed a 17-item bank of possible symptoms of depression, including the standard 9 DSM items but separating the compound criteria that encompass more than one symptom (eg, increased sleep OR insomnia), along with non-DSM diagnostic items such as hopelessness, helplessness, and unreactive mood. The authors found that some items were rated more reliably than others—for example, suicidal ideas/plan/ attempt (suicidality) achieved almost perfect agreement, whereas raters often disagreed about what constituted psychomotor retardation (Textbox 11.1). There was no overall pattern indicating that somatic symptoms were rated more or less reliably than nonsomatic symptoms.

Textbox 11.1. Inter-Rater Reliability Eliciting Individual Symptoms of Depression Symptoms

Kappa

Suicidality Depressed mood Insomnia Anhedonia Decreased appetite Loss of energy Indecisiveness Thoughts of death Psychomotor agitation Feelings of worthlessness Increased weight Decreased concentration Excessive guilt Decreased weight Increased appetite Psychomotor retardation Hypersomnia

0.94 0.92 0.91 0.90 0.89 0.88 0.88 0.86 0.83 0.80 0.79 0.78 0.76 0.69 0.63 0.63 0.54

Bold text indicates somatic symptoms.

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Somatic Symptoms in Current Diagnostic Systems and Scales Somatic items are included in both ICD-10 and DSM-IV. In fact, ICD-10 includes fatigue as a core feature. Neither ICD-10 nor DSM-IV gives clear guidance about how to judge these specific symptoms in the case of depression and physical disease (Table 11.1). As many as 70% of patients with depression (and to a lesser extent anxiety) present with somatic symptoms as their first complaint. Emotional symptoms are less likely to be mentioned if they are not specifically asked about by the interviewer.20 That said, physical complaints are seldom attributed to psychological causes, and the focus for clinical examination is usually physical disorders with somatic symptoms.21 Thus, somatic symptoms may indicate major depression or an underlying physical disorder. Particular difficulty arises in the case of major depression occurring in the context of a comorbid physical disorder. In this situation it is unclear how to judge the significance of somatic symptoms.22,23 In an attempt to improve upon the discriminatory value of the Beck Depression Inventory (BDI) and the Zung Self-Rating Depression Scale, questionnaires such as the Hospital Anxiety and Depression Scale (HADS) and the General Health Questionnaire (GHQ-12) omit most somatic symptoms of depression in favor of cognitive aspects.24 Most commonly fatigue and appetite and weight changes are omitted.25 In this approach somatic symptoms are assumed to contaminate a diagnosis of comorbid depression. The concern is that somatic symptoms may lead to an overdiagnosis of depression because of the lack of Table 11.1. Somatic Symptoms of Depression in ICD and DSM Somatic or NonSomatic

Core Symptom

ICD-10

DSM-IV

Nonsomatic

Persistent sadness or low mood

Nonsomatic

Loss of interests or pleasure

Somatic

Fatigue or low energy

Yes (core) Yes (core) Yes

Somatic Somatic

Disturbed sleep Poor concentration or indecisiveness Low self-confidence Poor or increased appetite Suicidal thoughts or acts Agitation or slowing of movements Guilt or self-blame Significant change in weight

Yes (core) Yes (core) Yes (core) Yes Yes

Yes Yes

Yes Yes Yes Yes Yes No

No No Yes Yes Yes Yes

Nonsomatic Somatic Nonsomatic Somatic Nonsomatic Somatic

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discrimination regarding the cause of the symptoms.26 One way to investigate this is to compare the ability of somatic items to distinguish between healthy controls and those with major depression. A second method is to compare the ability of somatic items to distinguish between those with uncomplicated major depression and those with comorbid major depression and physical illness. A third method is to compare those with comorbid depression and those with physical illness alone. We consider each of these in turn below.

3. Diagnostic Accuracy of Somatic Symptoms in Depression Given the almost endless list of possible somatic symptoms, it is important to first establish which, if any, are of diagnostic significance in primary depression and then in the diagnosis of comorbid depression and physical illness. For example, Chochinov and associates (1994)27 compared results from semistructured diagnostic interviews in 130 patients receiving palliative care. Diagnoses according to the Research Diagnostic Criteria (RDC) were compared with diagnoses made according to Endicott’s revised criteria (which replace the somatic symptoms change in weight or in appetite, sleep disturbance, loss of energy, and reduced concentration with the nonsomatic alternatives depressed appearance, social withdrawal, brooding, self-pity or pessimism, and lack of reactivity). The authors found that including somatic symptoms in the diagnostic criteria increased the rates of diagnosis, but only when these symptoms are used in conjunction with a low-threshold approach. Similarly, Dugan and coworkers (1998)28 analyzed the Zung Self-Rating Depression Scale both with and without somatic items and reported 5% more false positives when measuring depression in cancer with somatic items. However, to confirm or refute this effect, a diagnostic validity study is needed in which somatic symptoms are added or removed from the model to examine the effect on accuracy of ruling in or ruling out the condition according to the gold standard. Once this information is gathered, then a decision can be made whether to include or exclude the somatic symptoms. A slightly more sophisticated approach uses somatic symptoms only if they are caused by depression (Textbox 11.2). In reality this etiologic approach is challenging, because causation of specific symptoms is usually impossible to establish except in the crudest terms. One reason for uncertainty is that the rate of somatic complaints is not clear in each subgroup. For example, although somatic symptoms are certainly common in depressed patients, they also appear to be common in the general population: more than 75% of respondents in one community study reported at least one somatic complaint during the previous 30 days.29 The most common symptoms were tiredness (50%), headache (42%), and lower back pain (35%).

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Textbox 11.2. Approaches to Somatic Symptoms of Depression Inclusive The inclusive approach uses all of the symptoms of depression, regardless of whether they may or may not be secondary to a physical illness. This approach is used in the Schedule for Affective Disorders and Schizophrenia (SADS) and the Research Diagnostic Criteria. Etiologic The etiologic approach attempts to assess the origin of each symptom and counts a symptom of depression only if it is clearly not the result of the physical illness. This is proposed by the Structured Clinical Interview for DSM and Diagnostic Interview Schedule (DIS), as well as the DSM-III-R/IV. Substitutive The substitutive approach assumes somatic symptoms are a contaminant and replaces these with additional cognitive symptoms. However, it is not clear what specific symptoms should be substituted. Exclusive The exclusive approach eliminates somatic symptoms but without substitution. There is concern that this might lower sensitivity, with an increased likelihood of missed cases (false negatives). Adapted from Trask PC. Assessment of depression in cancer patients. J Natl Cancer Inst Monogr. 2004;32:80–92.

However, only about one third of patients with somatic symptoms seek medical help. From the reverse perspective, mood disorders are a common finding in those with somatic symptoms, accounting for approximately 30% of patients presenting with physical complaints.30 In the Epidemiological Catchment Area Study (ECA), the presence of physical symptoms was associated with at least a twofold increase in anxiety or depressive disorders.31,32 In the HUNT-II study, which surveyed all inhabitants from the Nord-Trøndelag County of Norway, women had a mean of 3.8 somatic symptoms and men 2.9 symptoms.33 There was a linear association between the number of somatic symptoms and the total HADS score. Gerber and associates (1992)34 showed that sleep disturbance, fatigue, more than three complaints, nonspecific musculoskeletal complaints, back pain, shortness of breath, amplified complaints, and vaguely stated complaints distinguished between depressed and nondepressed patients in a general medical primary care practice. Better evidence was recently reported in the Rhode Island MIDAS project. Zimmerman and colleagues (2006)35 found that the ranked order of diagnostic weight (by individual item) for DSM-IV

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membership on logistic regression was depressed mood > anhedonia > sleep disturbance > concentration/indecision > worthlessness/excessive guilt > loss of energy > appetite/weight disturbance > psychomotor change > death/suicidal thoughts. In the 8.9% who fulfilled the minimum DSM-IV criteria for major depressive disorder (five features only), increased weight, decreased weight, and indecisiveness rarely influenced diagnostic classification and in fact were influential in diagnosis in the whole sample in about 1% of cases. More detailed analysis of the MIDAS project was recently reported by Mitchell and colleagues (2008).36 We found that somatic symptoms had value in ruling in and ruling out primary depression (Fig. 11.3). When ruling in depression (case-finding), the most successful single symptoms were psychomotor retardation, diminished interest/pleasure, indecisiveness, depressed mood, and worthlessness. When ruling out depression (reassurance), the most successful symptoms were depressed mood, diminished drive, loss of energy, diminished interest/pleasure, and diminished concentration. Therefore, it may be concluded that psychomotor retardation, loss of energy, and diminished concentration do indeed help clinicians diagnose uncomplicated depression. What is the evidence that somatic symptoms assist in a diagnosis of comorbid depression?

4. Evidence For and Against Somatic Symptoms when Diagnosing Comorbid Depression Evidence from Comparative Studies of Primary Depression versus Secondary Depression Lipsey and colleagues (1986)37 studied 43 post-stroke depressed patients against 43 patients with functional major depression to compare their depressive symptoms. They concluded that the depressive syndrome profiles in the two patient groups were similar, and only two symptoms were significantly different: slowness was more common and lack of interest/concentration was less common in post-stroke patients. Simon and associates (2005)38 examined the validity of the DSM-IV depression criteria in 235 individuals with medical comorbidities, including diabetes, ischemic heart disease, or chronic obstructive lung disease, versus 204 depressed subjects without those conditions. At the midpoint of the depression severity scale, patients with medical comorbidity had a 54% probability of reporting fatigue compared to 45% in those without comorbidity. All four somatic symptoms showed robust improvement with treatment, and this improvement did not differ significantly between patients with and without medical comorbidity. They could find only limited evidence that fatigue, changes in weight or appetite, psychomotor agitation/ retardation, and sleep disturbance are less valid indicators of depression in

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Figure 11.3. Added value in diagnosing primary depression. Adapted from Mitchell AJ, McGlinchey JB, Young D, et al. Accuracy of specific symptoms in the diagnosis of major depressive disorder in psychiatric out-patients: data from the MIDAS project. Psychol Med. Nov. 12, 2008:1–10.

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patients with chronic medical illness. Pickard and associates (2006)39 used Rasch methods to compare symptoms of depression in 32 subjects with poststroke depression versus 366 depressed primary-care patients. They found that four items demonstrated statistically significant differential item functioning: ‘‘my sleep was restless,’’ ‘‘I felt that people disliked me,’’ ‘‘I did not feel like eating,’’ and ‘‘I had crying spells.’’ Each of these items identified with statistically significant Differential Item Functioning (DIF) demonstrated a logit difference of approximately 0.5 or more across the two groups. Overall, however, the authors found few differences between groups. Van Wilgen and associates (2006)40 analyzed the influence of somatic symptoms on the Center for Epidemiologic Studies Depression Scale (CES-D) in 509 patients with oropharyngeal, gynecologic, colorectal, and breast cancer after treatment versus a control group of 223 depressed patients without cancer. They concluded that the incidences of somatic morbidity within cancer types differ, but somatic items do not interfere with the outcome of depression as measured with the CES-D. Interestingly, some cancer groups showed both less somatic morbidity (colorectal cancer) while others showed more (oral/oropharyngeal, breast) than the comparison group. In the analyses of the CES-D with and without the somatic domain, the prevalence of depression symptoms with the somatic domain is lower for the cancer groups. Ehrt and colleagues (2007)41 compared the individual depressive symptoms of 145 depressed patients with Parkinson’s disease and 100 depressed patients without Parkinson’s disease by comparing item scores on the Montgomery˚ sberg Depression Rating Scale. Depressed patients with Parkinson’s disease A showed significant less reported sadness, less anhedonia, fewer feelings of guilt, and slightly less loss of energy but more concentration problems than depressed control subjects. Thus, some but not all somatic symptoms were increased in comorbid groups. The results of this study support the hypothesis that depression profile in Parkinson’s disease differs to a certain extent from that in non-Parkinson’s disease patients with major depression. Yates and colleagues for STAR*D (2007)42 analyzed the effect of specific somatic symptoms in separating primary depression from depression with comorbid physical disease. Clearly, if somatic symptoms were overrepresented in the comorbid group, then the classic view that somatic symptoms may contaminate a diagnosis of depression in physical disease would be supported. Two somatic symptoms occurred in 80% or more of those with noncomplicated depression and four occurred in 80% or more of those with comorbid depression. The two most common were impaired concentration (91%) and fatigue (87%). Although somatic symptoms were common in patients with both depression and physical ill health, somatic symptoms were also common in patients without comorbidity. In

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particular, impaired concentration and fatigue occurred in approximately 90% of both groups. Other studies have examined this issue in relation to comorbid depression versus healthy controls.

Evidence from Comparative Studies of Comorbid Depression versus Healthy Controls Aikens and associates (1999)43 evaluated the depressive symptoms in 105 multiple sclerosis patients and compared the results with 80 healthy controls as well as three other comparison groups: diabetes (n = 71), chronic pain (n = 80), and psychiatric patients with depressive disorder (n = 37). They evaluated the appropriateness of omitting somatic items from the original BDI when assessing depressive symptoms in multiple sclerosis patients. They suggested that somatic items appear to function quite normally for this group, with psychometric indices comparable to those observed in psychiatric and nonpsychiatric samples, and recommended against dropping items from the original BDI for routine depression assessment in multiple sclerosis samples. Guo and colleagues (2006)44 looked at a small sample of 33 cancer patients, 13 patients with major depression without cancer, and 12 normal comparison subjects. The authors examined which HAM-D items would optimize the diagnosis of depression among cancer patients. Their final model contained six HAM-D items, combining somatic and nonsomatic items (late insomnia, agitation, psychic anxiety, diurnal mood variation, depressed mood, and genital symptoms). At a cutoff of 6 the sensitivity was 81.3% and specificity 87.5%. However, in this study, certain somatic items, including middle insomnia, retardation, somatic symptoms (gastrointestinal and general), and loss of weight, were not discriminatory. Holzapfel and associates (2008)45 examined depressed patients with (n = 113) and without (n = 137) chronic heart failure in relation to individual DSM-IV depressive symptoms, as measured with the Patient Health Questionnaire (PHQ)-9. Among the patients meeting the criteria for major depressive disorder, patients with heart failure reported significantly lower levels of depressed mood (p = 0.006) and worthlessness/guilt (p = 0.019) than patients without. No significant differences were found for sleep disturbance, loss of energy, change in appetite, poor concentration, psychomotor agitation/ retardation, and suicidal thoughts (Fig. 11.4).

Evidence from Comparative Studies of Comorbid Depression versus Physical Illness Alone Symptom profiles of depressed and nondepressed patients with cancer were examined by Chen and Chang (2004),46 who recruited 121 hospitalized

215

11 THE CASE AGAINST SPECIFIC SCALES Symptom severity: CHF > Non-CHF

Symptom severity: CHF > Non-CHF

Loss of interest

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Worthlessness/feelings of guilt

Weak concentration

Psychomotor agitation/retardation

Suicidal thoughts

–1.0

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+0.5

+1.0

Figure 11.4. Differences in severity of individual depression symptoms in patients with major depressive disorder with and without chronic heart failure. Data from Holzapfel N, Mu¨ller-Tasch T, Wild B. et al. Depression profile in patients with and without chronic heart failure. J Affect Disord. 2008;1:53–62.

patients with breast, esophageal, and head and neck cancer. Using a HADS-D cutoff score of 11, 30 patients were classified as depressed and 91 as nondepressed. Depressed patients showed a significantly higher occurrence rate than nondepressed patients on insomnia (83% versus 62%), pain (83% versus 55%), anorexia (63% versus 42%), fatigue (67% versus 32%), and wound/ pressure sore (30% versus 13%). A significant chi-squared statistic with Yates correction (w2 = 10.74, p = 0.001) indicated an association between multiple symptoms and depression in this sample. Patients simultaneously experiencing multiple symptoms (insomnia, pain, anorexia, and fatigue) had a significantly higher risk of being depressed. Both groups showed similar rankings of symptom occurrence rates.

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Evidence from Noncomparative Studies (eg, Rasch Analysis) Stein and coworkers (1996)47 found that somatic items of depression were less sensitive than nonsomatic items in the diagnosis of post-stroke depression. In this study 189 persons with unilateral ischemic or embolic cerebrovascular accident were interviewed by a psychologist, 4 weeks or more after stroke, using the BDI and the HAM-D. Findings suggested that the most discriminating individual symptoms of post-stroke depression were nonsomatic. Somatic items from both scales were significantly less specific when diagnosing post-stroke depression than were the nonsomatic items. Somatic symptoms were neither specific to post-stroke depression nor added incremental validity over nonsomatic symptoms for diagnosing post-stroke depression. Kathol and colleagues (1990)48 investigated the relation of scores on the HAM-D and BDI to the presence or absence of criteria-based diagnoses of depression in cancer. The diagnoses of major depression in 152 cancer patients differed as much as 13% depending on the diagnostic system used. The BDI and the HAM-D were useful tools for screening patients with depressive symptoms but frequently misclassified those who had no major depression according to one or more of the criteria-based diagnostic systems. Kalichman and colleagues (2000)49 worked on overlapping somatic symptoms of depression and HIV disease in 357 people living with HIV/AIDS. They directly compared the diagnostic use of the BDI and the CES-D in this single sample. Results of a factor analysis entering the six depression factor scores from the BDI and CES-D showed that HIV symptoms were most strongly associated with the somatic depression symptom factors of the BDI and CES-D. In other words, the findings suggested that depression scales that include somatic symptoms will inflate depression scores in people living with HIV infection, and available methods for distinguishing overlapping symptoms should be employed when assessing people living with HIV infection. Leentjens and coworkers (2001)50 assessed the sensitivity of individual depressive symptoms and their relative contribution to the diagnosis of depressive disorder using the Structured Clinical Interview for DSM Disorders (SCID) in 149 patients with Parkinson’s disease. Applying the HAM-D and ˚ sberg Depression Rating Scale, they showed that only two the Montgomery-A somatic symptoms, early morning awakening and reduced appetite, had good discriminative properties. Therefore, they concluded that the core symptoms were most important in distinguishing depressed and nondepressed Parkinson’s disease patients. Akechi and associates (2003)51 used data from 220 cancer patients with major depression to examine the intercorrelations among the DSM-IV somatic and nonsomatic symptom criteria as well as whether the presence of an individual somatic symptom could discriminate the severity of major

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depression. Appetite changes and a diminished ability to think but not sleep disturbance and fatigue were significantly associated with nonsomatic symptoms. These associations were consistent after adjusting for physical functioning and pain. Only patients with appetite changes showed a higher severity of depression. De Coster and colleagues (2005)52 studied 206 patients with first-ever stroke with the SCID for DSM-IV and the HAM-D. In a discriminant analysis HAM-D item scores correctly classified 88.3% of patients as depressed or nondepressed. Depressed mood discriminated best between depressed and nondepressed stroke patients, but many psychological symptoms, such as hypochondriasis, lack of insight, and feelings of guilt, were not very sensitive. In contrast, somatic symptoms, such as reduced appetite, psychomotor retardation, and fatigue, had high discriminative properties.

5. Implications for Screening Somatic symptoms have a role in the diagnosis of uncomplicated depression, but their role in comorbid depression has been subject to considerable confusion. Two early studies suggested that including somatic symptoms in scales might result in an overdiagnosis of comorbid depression and cancer (low specificity and low positive predictive value). Since that time, our search revealed six studies comparing primary depression and secondary depression, three studies comparing comorbid depression and healthy controls, but only one study comparing comorbid depression versus physical illness alone. From the first group, somatic symptoms were certainly common in patients with comorbid depression, but they were also common in those with uncomplicated depression and less common in patients in physical illness alone and least uncommon in healthy controls. Taking the example of cancer, individuals with cancer undergoing active treatment clearly have numerous somatic symptoms. Indeed, compared with healthy controls, individuals with cancer have a higher level of all somatic symptoms rated by items 14 to 21 on the BDI, with the exception of loss of libido.53 However, such differences are easy to overestimate. Individuals with comorbid and uncomplicated depressions have an even higher rate of somatic symptoms. Overall, somatic symptoms did not emerge as insignificant in primary or secondary depressions. Indeed, of the possible list of symptoms potentially discriminating depressed patients with and without comorbid physical illness, several nonsomatic items such as guilt appear to be better discriminators than somatic symptoms (see Fig. 11.4). Thus, the formulation of custom secondary depression scales by indiscriminately omitting somatic items does not appear to be justified. That said, it is possible that certain medical disorders might be atypical and feature somatic symptoms that have special significance. For example, van Wilgen and colleagues

Table 11.2. Systematic Review of Comparative Studies Examining Value of Somatic Symptoms in Comorbid Depression Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

1997

Suh T, Gallo JJ. Symptom profiles of depression among general medical service users compared with speciality mental health service users. Psychol Med. 1997;27(5):1051–1063.

ECA (Epidemiologic Catchment Area) program: series of epidemiologic surveys conducted by collaborators (1980–1984) at 5 sites in US. ECA data include both community and institutional populations interviewed in person. Measurement strategy: used standardized and generally pre-coded questions as part of highly structured interview administered by an agency lay interviewer with DIS (Diagnostic Interview Schedule) training. Logistic regression models were used to implement item response theory in the framework of the symptom criteria of major depression in DSM-III

4,931 and 363 household respondents from 3 ECA sites (Baltimore, Durham, and Los Angeles) who used general medical sector or speciality mental health respectively within 6 months of interview

(1) Except for gender, there were significant differences between the two groups according to the sociodemographic factors (p < 0.001). (2) Speciality mental health service users were more likely to report all the depression symptoms. (3) General medical users were less likely to report dysphasia (OR = 0.49; 95% CI = 0.33– 0.72) and worthless/sinful/ guilty (OR = 0.55; 95% CI = 0.35–0.86) after holding constant the level of depression but were more likely to report fatigue (OR = 1.82; 95% CI = 1.17–1.83).

Uncertain

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

2001

Leentjens AFG, Marinus J, Van Hilten JJ, et al. The contribution of somatic symptoms to the diagnosis of depressive disorder in Parkinson’s disease. J Neuropsychiatry Clin Neurosci. 2003;15:74– 77.

DSM-IV diagnosis of depressive disorder was considered the gold standard. All patients completed the Hamilton Rating Scale for Depression (HAM-D) and 111 patients completed the MADRS, which were highly significant and used as symptom checklists. The contribution of the individual items of these scales to the diagnosis of ‘‘depressive disorder’’ was calculated by discriminant analysis. Then, a correlation coefficient with this discriminant function was obtained for each of the individual items on these scales to reflect the relative strength of association of each symptom with the discriminant function. Wilks’ lambda was

169 patients with primary PD, as defined by the United Kingdom Parkinson’s Disease Society Brain Bank (UKPDS-BB), were referred from the neurologic outpatient department for a protocolized mental status examination. 20 (11.8%) were excluded because of dementia.

Using the HAM-D, suicidality was the best discriminator between depressed and nondepressed patients, followed, in descending order, by feelings of guilt, psychic anxiety, reduced appetite, depressed mood, and reduction of work and interest. Most somatic items had low discriminative properties, but reduced appetite and earlymorning wakening (or late insomnia) had relatively high discriminative properties. On the MADRS, the two ‘‘core’’ symptoms of depression, depressed mood and anhedonia, had the highest correlation coefficients. Somatic items as well as the item ‘‘concentration difficulties’’had low correlation coefficients. However, reduced appetite was a relatively important indicator of depression. Following a post hoc analysis,

No

(Continued )

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

calculated as a test of the discriminant function. Physical disability and cognitive status were rated according to the Hoehn and Yahr staging system (I–V) and Mini-Mental State Examination, respectively. 1990

Kathol RG, Mutgi A, Williams J, et al. Diagnosis of major depression in cancer patients according to four sets of criteria. Am J Psychiatry. 1990;147:1021–1024.

DSM-III, RDC (Research Diagnostic Criteria), all symptoms were recorded regardless of etiology. DSM-III-R, only symptoms that had no definite relationship with physical condition. Endicott criteria: to identify depression retrospectively, t-test and w2 square test were used to assess differences in parametric and nonparametric scores, respectively.

In an investigation of the treatment of depression in patients with terminal solid tumors, 152 of 808 patients (age 16–88, 59% female) reported symptoms of depression during clinical evaluation or screening with the Hamilton scale and/or Beck inventory. All of them had potentially fatal solid tumors at different stages.

it was discovered that after excluding the somatic items of the HAMD (items 4, 5, 6, 8, 11– 14, and 16), 86.6% of patients were correctly classified as depressed or nondepressed. After excluding the somatic items of the MADRS (items 4– 7), 88.3% of the patients were classified correctly. One third of patients had major depression according to one or more of diagnostic systems. BDI total score of <11 predicted 7% major depression according to DSM-III, RDC, and the Endicott criteria but not DSM-III-R. At BDI and Hamilton scale scores of 11–25 and 15–19 respectively, the percentage of major depression dropped substantially. The correlation between psychological items (1–14) and somatic symptoms

No

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

(15–21) was 0.55. Somatic symptoms were less discriminating. Hamilton scale was also comparable to BDI, and in fact it allowed a greater number to be assessed with the positive predictive value. 2006

Pickard AS, Dalal MR, Bushnell DB. Comparison of depressive symptoms in stroke and primary care: Applying Rasch models to evaluate the Center for Epidemiologic StudiesDepression Scale. Value in Health. 2006;9(1):59–64.

Center for Epidemiologic Studies-Depression scale (CES-D) (a 20-item scale) as a measure of depression Depression = CES-D score 16 or higher. After informed consent, participants completed a screening questionnaire that included the CES-D.

Two data sources were analyzed: (1) 32 depressed patients who were 3 months poststroke, from two large teaching hospitals in Edmonton, Canada, age 18 or more, and (2) 366 depressed primary-care patients for which data on USA-based primary-care patients with depression were obtained from the Longitudinal Investigation of Depression Outcomes (LIDO) study, age 18–75.

Misfitting items—that is, MNSQ higher than 1.40—in poststroke depression included ‘‘my sleep was restless,’’ ‘‘I had crying spells,’’ ‘‘people were unfriendly,’’ and ‘‘I felt just as good as other people.’’ No items misfit the scale in the primary care-based depression group. Four items demonstrated statistically significant DIF: ‘‘my sleep was restless,’’ ‘‘I felt that people disliked me,’’ ‘‘I did not feel like eating,’’

No

(Continued )

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

2004

Chen ML, Chang H-K. Physical symptom profiles of depressed and nondepressed patients with cancer. Palliative Med. 2004;18(8):712– 718.

Depression was measured using the Hospital Anxiety and Depression Scale. Occurrence of symptoms was evaluated with the Patient Disease Symptom/ Sign Assessment Scale.

121 hospitalized patients with breast, esophageal, and head and neck cancer

and ‘‘I had crying spells.’’ Each of these items identified with statistically significant DIF demonstrated a logit difference of approximately 0.5 or more across the two groups. Using the HADS-D cutoff score of 11, 30 patients (25%) were classified as depressed and 91 (75%) as nondepressed. The Mann/Whitney test indicated that depressed patients had a significantly higher number of symptoms (p = 0.001). Depressed patients showed a significantly higher occurrence rate (p < 0.05) than that of nondepressed patients on the following symptoms: insomnia (83% vs. 62%), pain (83% vs. 55%), anorexia (63% vs. 42%), fatigue (67% vs. 32%), and wound/ pressure sore (30% vs. 13%).

No

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

2000

Kalichman SC, Rompa D, Cage M. Distinguishing between overlapping somatic symptoms of depression and HIV disease in people living with HIV-AIDS. J Nerv Ment Dis. 2000;188(10):662–670.

Beck Depression Inventory (BDI). The BDI consists of 21 items that reflect cognitive, affective, behavioral, and somatic symptoms of depression over the previous 7 days. Center for Epidemiological Studies Depression Scale (CESD) is a 20-item scale that assesses symptoms of depression over the previous 7 days. Anxiety: To assess anxiety in the current study, we used the 20-item Trait-Anxiety scale from the State-Trait Anxiety Inventory (Spielberger et al., 1983). Future Pessimism: We developed a six-item scale

Participants were 242 (68%) men, 110 (31%) women, and 5 (1%) transgender persons living with HIV-AIDS. The majority of the sample was African-American (76%), with 19% white participants, 2% Hispanic, and the remaining 3% of other ethnic backgrounds. They were recruited from AIDS service organizations, healthcare providers, social service agencies, community residences for people living with HIV-AIDS, and infectious disease clinics.

Factor scores were computed for BDI Self-Defeating Thoughts, BDI Affective Symptoms, and BDI Somatic Symptoms. We found that the strongest degree of association with HIV symptoms occurred for BDI items involving ability to work (r = 0.42), sleep (r = 0.37), fatigue (r = 0.41), appetite (r = 0.34), and worry about health (r = 0.31). The BDI items with the strongest associations with HIV symptoms were therefore those items reflecting somatic complaints. For the CES-D, HIV symptoms were significantly correlated with depression items indicating fatigue (r = 0.43), sleep

Uncertain

(Continued )

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

to assess future pessimism. To assess symptoms of obsessive-compulsiveness, we used six items from the Obsessive-Compulsive scale of the Schedule for Nonadaptive Personality (SNAP; Clark, 1993).

1986

Lipsey JR, Spencer WC, Rabins PV, et al. Phenomenological comparison of poststroke depression and functional depression. Am J Psychiatry, 1986;143:527–529.

Structured clinical interviews. Only patients fulfilling DSM-III criteria for major depression were included in this study. The major instrument used during examination of all patients was modified PSE

(r = 0.40), appetite (r = 0.30), not being able to shake the blues (r = 0.34), feeling bothered (r = 0.33), feeling depressed (r = 0.31), and lack of concentration (r = 0.32). CESD items that did not reflect somatic complaints were also closely associated with HIV symptoms. Results of a factor analysis entering the six depression factor scores from the BDI and CES-D showed that HIV symptoms were most strongly associated with the somatic depression symptom factors of the BDI and CESD. The 43 poststroke patients were 23 acutely ill inpatients,14 patients admitted for rehabilitation following acute stroke, and 6 referred to outpatient clinic for poststroke depression. The 43

Poststroke patients had similar Hamilton depression scores to those of functional depression. PSE syndrome profiles were remarkably similar between groups. Of the 17 depression symptoms, only 2 were significantly

No

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

2008

Holzapfel N, Mu¨llerTasch T, Wild B, et al. Depression profile in patients with and without chronic heart failure. J Affect Disorders. 2008;1:53–62.

(Present State Examination) with 59 items specifically related to anxiety and depression. Depressed patients with chronic heart failure (CHF; n = 113) and without CHF (n = 137) were compared with respect to severity of individual DSM-IV depressive symptoms, as measured with the PHQ-9. Of all patients, only those who met DSM-IV diagnostic criteria for major depressive disorder or other depressive disorders and were able to complete the study questionnaire were included in the study. Statistical method: ANCOVAs with

functionally depressed patients were from inpatient admissions for major depression to the hospital. Of a total of 921 patients from a CHF and a psychosomatic outpatient clinic of the Medical Hospital at the University of Heidelberg, 137 met DSM-IV diagnostic criteria for major depressive disorder and 113 for other depressive disorders. Depressed patients with CHF (n = 113) and without CHF (n = 137).

different: slowness was more frequent and lack of interest and concentration was less frequent in poststroke patients. The 677 patients from the CHF outpatient clinic ranged in age from 16 to 90 years. 42 patients (6.2%) met the diagnostic criteria for major depressive disorder, and 71 patients (10.5%) met the diagnostic criteria for other depressive disorders according to the PHQ-9 diagnostic algorithm. 244 patients had evidence of CHF in their medical history and record. The age range was 16 to 79 years. 248 patients from the psychosomatic outpatient clinic participated in this study. 95 patients (38.9%) met the diagnostic criteria for

No

(Continued )

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

major depressive disorder, and 42 patients (17.2%) met the diagnostic criteria for other depressive disorders according to the PHQ-9 diagnostic algorithm.

sociodemographic characteristics as covariates were performed separately for patients with major depressive disorder and other depressive disorders. 2006

Ehrt U, Brønnick K, Leentjens AFG, et al. Depressive symptom profile in Parkinson’s disease: a comparison with depression in elderly patients without Parkinson’s disease. Int J Geriatr Psychiatry. 2006;21(3):252–258.

We compared the individual depressive symptoms of 145 nondemented depressed patients with Parkinson’s disease (PD) and 100 depressed patients without PD by comparing item scores on the ˚ sberg Montgomery-A Depression Rating Scale by way of MANCOVA. Dementia was diagnosed according to DSM-IIIR

PD patients included in this study came from two different cross-sectional studies: a community study in Norway and an outpatient study in the Netherlands. PD patients from both populations were included in the study if they did not suffer from dementia and had at least mild depressive symptoms, which was operationalized as a

Patients with PD had less reported sadness, slightly less loss of energy, more concentration problems, fewer feelings of guilt, and lower anhedonia.

Uncertain

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

1994

Chochinov HM, Wilson KG, Enns M, et al. Prevalence of depression in the terminally ill: effects of diagnostic criteria and symptom threshold judgments. Am J Psychiatry. 1994;151:537–540.

(Stavanger) or DSM-IV (Maastricht) after a clinical examination that included an interview with a caregiver in addition to cognitive testing.

MADRS sum score  7 (Snaith et al., 1986). The control group consisted of 100 consecutive patients referred to the old age psychiatry outpatient clinic at Stavanger University Hospital, Norway, suffering from at least mild depressive symptoms with a MADRS score  7. In both populations, cognition was assessed with the MiniMental State Examination (MMSE).

Semi-structured diagnostic interviews were conducted with 130 patients receiving palliative care. Diagnoses according to the RDC (Research Diagnostic Criteria) were compared with diagnoses according to Endicott’s revised criteria (which involve replacing somatic

130 inpatients from two hospital-based palliative care services with solid tumors were interviewed. Mean age 71.5(SD = 11.0).

A low-threshold (less stringent) diagnostic approach greatly increased the overall prevalence of major and minor depressive episodes with both the RDC and the Endicott criteria. With high thresholds, the RDC and the Endicott criteria were

Uncertain

(Continued )

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

equivalent, whereas with low thresholds the Endicott substitutions identified fewer cases of major (but not minor) depression.

symptoms [change in weight or in appetite, sleep disturbance, loss of energy, and reduced concentration] with nonsomatic alternatives [depressed appearance, social withdrawal, brooding, self-pity or pessimism, and lack of reactivity]) when either a low-severity or a highseverity threshold for classifying RDC criterion A symptoms was used. 2006

Vilalta-Franch J, GarreOlmo J, Lo´pez-Pousa S, et al. Comparison of different clinical diagnostic criteria for depression in Alzheimer’s disease. Am J Geriatr Psychiatry. 2006;14(7):589–597.

This was a cross-sectional, observational study of 491 patients with probable Alzheimer’s disease. Depression was diagnosed using five classification systems (ICD-10, DSM– IV, Cambridge Examination for Mental Disorder of the Elderly

The patients who completed the baseline visit of the EDAC study from 1998–2003 where CAMDEX and NPI were used

The prevalence of depression was 4.9% (95% CI: 3.2–7.1) according to ICD-10 criteria; 9.8% (95% CI: 7.3–12.6) according to CAMDEX; 13.4% (95% CI: 10.6–16.6) according to DSM–IV; 27.4% (95% CI: 23.6–31.5) according to PDC-dAD criteria; and 43.7% (95% CI: 39.4–48.2)

Uncertain

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

when using the screening questions from the NPI depression subscale. The level of agreement between the classification systems was low to moderate (<0.52). The characteristics associated with the most diagnostic disagreement were loss of confidence or self-esteem and irritability.

[CAMDEX], Provisional Diagnostic Criteria for depression in AD [PDCdAD], Neuropsychiatric Inventory [NPI]). Cognitive function was assessed by MMSE and CAMCOG.

1996

Stein PN, Sliwinski MJ, Gordon WA. Discriminative properties of somatic and nonsomatic symptoms for post-stroke depression. Clin Neuropsychologist. 1996;10:141–148.

Mood evaluation comprised the Beck Depression Inventory (BDI) and the Hamilton Rating Scale for Depression (HRSD).

Average age = 67. Patients were from three hospitals in New York City. At least 4 weeks after a unilateral cerebrovascular accident (CVA) of ischemic and/or embolic origin without past history of psychiatric illness, neurologic disease, and substance abuse.

Somatic items from both scales were significantly less specific when diagnosing poststroke depression than were the nonsomatic (intrapsychic) items. Somatic symptoms were neither specific to poststroke depression nor added incremental validity over nonsomatic symptoms for diagnosing poststroke depression.

Yes

(Continued )

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

2005

Simon GE, Von Korff M. Medical comorbidity and validity of DSM-IV depression criteria. Psychol Med. 2006;36:27–36.

Telephone assessments at baseline, 2 months, and 6 months included the Structured Clinical Interview for DSM-IV and other measures of depression severity and functional status. Item Response Theory analyses compared patterns of depressive symptoms across groups and specifically evaluated somatic symptoms (fatigue, change in weight or appetite, psychomotor agitation/ retardation, and sleep disturbance) as indicators of depression.

At staff-model clinics of Group Health Cooperative (GHC), a prepaid health plan serving 450,000 members in western Washington state. Computerized records were used to identify all adult health-plan members filling new (no more than last 150 days) prescriptions for antidepressants from primary care physicians, those with visit diagnoses of depression, to exclude patients with diagnoses of bipolar disorder or psychotic disorder and identify patients with specific comorbid medical conditions.

Overall item response analysis indicated differential item functioning between groups (w2 = 33.7, df = 18, p = 0.017). Two of eight item-level comparisons were statistically significant; one in the predicted direction (patients with comorbidity reported more fatigue at low levels of depression: w2 =17.9, df = 1, p < 0.001) and one in the opposite direction from predicted (patients with comorbidity reported less psychomotor agitation/ retardation at low levels of depression : w2 = 8.0, df = 1, p = 0.005). Observed differences were modest: at the midpoint of the depression severity scale, patients with medical comorbidity had a 54% probability of reporting fatigue compared to 45% in those without comorbidity.

No

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

2005

de Coster E, Leentjens AFG, Lodder J, et al. The sensitivity of somatic symptoms in post-stroke depression: a discriminant analytic approach. Int J Geriatr Psychiatry. 2005;20:358–362.

Structured Clinical Interview for DSM-IV within 1 month after the stroke to confirm or reject the diagnosis of major depressive disorder. Severity of depression was measured with the Hamilton Depression Rating Scale (HAM-D). At the follow-up interviews at 3, 6, 9, and 12 months, depression was diagnosed using a two-step procedure. First, three psychiatric rating scales for depression (BDI, HADS, SCL-90). Then patients who exceeded the previously defined cutoff value on at least one of these scales were called in and reinterviewed using the SCID and HAM-D.

From 1/9/1997 to 1/9/ 1999, all eligible patients with an acute first-ever clinical presentation of cerebral infarction who were seen in the Accident and Emergency Department and the Department of Neurology of Maastricht University Hospital were entered in a prospective stroke registry. The only inclusion criterion was an ischemic stroke.

Wilks’ lambda, as a test of discriminant function, was highly significant (p < 0.001). In total 88.3% of the patients were correctly classified as depressed or nondepressed. In this discriminant model, as expected, depressed mood was the best discriminator between depressed and nondepressed patients, followed by reduced appetite, thoughts of suicide, psychomotor retardation, psychic anxiety, and fatigue.

No

(Continued )

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

2003

Akechi T, Nakano T, Akizuki N, et al. Somatic symptoms for diagnosing major depression in cancer patients. Psychosomatics. 2003;44:244–248.

A computerized database was used to identify patients with major depression. The database included demographic factors, medical factors such as performance status and pain, and psychiatric diagnoses based on a structured clinical interview based on the DSM-IV criteria.

220 of a total of 1,721 cancer patients referred to the Psychiatry Divisions at National Cancer Center Hospital and National Cancer Center Hospital East in Japan between 1996 and 1999 were reviewed.

The results of the logistic regression analyses demonstrated that weight loss or appetite change and a diminished ability to think or concentrate were positively associated with a diminished interest or pleasure after adjusting for possible physical confounders. Patients with weight loss or appetite change showed a significantly higher severity of major depression than those without this symptom (p < 0.003), while patients with the other three somatic symptoms did not (sleep disturbance, fatigue, diminished ability to think).

Uncertain

2006

Guo Y, Musselman D, Manatunga A. The diagnosis of major depression in patients with cancer:

SCID-32 and the dimensional 21-item HAM-D were administered to all in study

Study subjects with cancer were recruited from outpatients and inpatients at Emory University.

The HAM-D items on weight loss was not different in two groups. In the cancer patients, 6 of the 21 HAM-D items

No

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

a comparative approach. Psychosomatics. 2006;47:376–384.

2006

van Wilgen CP, Dijkstra PU, Stewart RE, et al. Measuring somatic symptoms with the CES-D to assess depression in cancer patients after treatment: comparison among patients with oral/ oropharyngeal, gynecological, colorectal, and breast cancer.

participants by a single rater who was either a master’slevel nurse or a fourth-year psychiatry resident. Final psychiatric diagnoses were provided by consensus of the research team, comprising the aforementioned individuals and two board-certified psychiatrists. One-way analysis of variance (ANOVA) was used to compare the continuous variables. The CES-D, which contains 20 items divided in four domains, was administered to patients at least 1 year after the first cancer treatment (and to a control group). Patients with tumor recurrences were excluded. The CES-D in cancer patients has a good internal

Healthy comparison subjects were recruited from Emory and the surrounding community by advertisements or word of mouth.

were significantly associated with an increased probability of major depression: depressed mood (p < 0.004), late insomnia (p < 0.022), agitation (p < 0.008), psychic anxiety (p < 0.001), genital symptoms (p < 0.017), and diurnal variation (p < 0.046).

The data of comparison subjects and patients with cancer were obtained from the hospital or health center database. The comparison group was matched for gender and age with the cancer group and lived in the same area as the patients with cancer.

With ANOVA, the cancer group scored significantly higher than the control group on the domain of Somatic Retarded Activity and Depressed Affect. The four cancer groups and the comparison group differed significantly on Total score, Somatic Retarded Activity, and Depressed Affect scores.

No

(Continued )

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

1999

Psychosomatics. 2006;47:465–470.

consistency (a = 0.89) and the test–retest reliability was 0.51 (p < 0.001).

Aikens JE, Reinecke MA, Pliskin NH, et al. Assessing depressive symptoms in multiple sclerosis: is it necessary to omit items from the original Beck Depression Inventory? J Behav Med. 1999;22(2):127–142.

Poser criteria (Poser et al., 1983) for definite or probable multiple sclerosis (MS). Mean duration since MS diagnosis was 11.0 years with moderate disability due to MS. Beck Depression Inventory was used to diagnose depression and also

The correlations between the domains of Somatic Retarded Activity and Depressed Affect were significant for the control group (0.54; p < 0.01) and the cancer group (0.66; p < 0.01). The cancer groups, except the oral/oropharyngeal patients, and comparison group showed significantly higher incidences of depression symptoms without the Somatic items as compared with the CES-D with Somatic items. MS sample was recruited from the Neurology Department at the University of Chicago. Healthy control (HC) subjects comprised 49 students from the University of Chicago and 39 subjects from the community.

Relative scores for the eight somatic BDI items were analyzed by multivariate analysis of variance with demographic variables and BDI total as covariates. The only significant difference was MS > HC (item 15). On raw scores, MS patients exceeded HCs on items 15

No

Table 11.2. (Continued) Year

Reference

Method

Setting

Results (Description)

Supports Unique Scales? (Yes, no, uncertain)

computed with Mohr et al.’s proposed 18-item BDI modification (BDI18), as well as the cognitive/affective (items 1  13) and somatic (items 14 – 21) BDI subscales suggested by Cavanaugh et al. (1983). MS severity (MS sample only) was assessed with the widely used Expanded Disability Status Scale (EDSS; Kurtzke, 1983), administered by a neurologist.

and 21 (sexual disinterest), but this was attributable to the low HC item endorsement. There were no other differences on somatic items or item – total correlations.

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(2006)40 found that some specific cancers showed less somatic morbidity, while others featured more. Similarly, Ehrt and associates (2007)41 found that depressed patients with Parkinson’s disease had less loss of energy but more concentration problems than depressed control subjects. Ultimately, any new scales should be tested head to head with existing tools (see Chapter 4 for further discussion). To date, attempts to produce custom scales based on exclusion of somatic items have not proven their superiority in well-designed implementation studies showing superiority of new over old. We therefore conclude that based on the evidence to date, including somatic symptoms does not lead to many false-positive diagnoses when attempting to diagnose depression in the context of physical disease (Table 11.2). Indeed, the systematic exclusion of somatic symptoms might cause an under-recognition of major depression. Given the limited evidence in specific areas, we suggest that further studies are required in relation to minor depression and subsyndromal forms.

References 1. Niles BL, Mori DL, Lambert JF, et al. Depression in primary care: Comorbid disorders and related problems. J Clin Psychol Med Settings. 2005;12(1):71–77. 2. Dwight-Johnson M, Sherbourne CD, Liao D, et al. Treatment preferences among depressed primary care patients. J Gen Intern Med. 2000;15(8):527–534. 3. Berardi D, Menchetti M, De Ronchi D, et al. Late-life depression in primary care: A nationwide Italian epidemiological survey. J Am Geriatr Soc. 2002;50(1):77–83. 4. Wells KB, Rogers W, Burnam A, et al. How the medical comorbidity of depressed patients differs across health care settings: results from the Medical Outcomes Study. Am J Psychiatry. 1991;148:1688–1696. 5. Yates WR, Mitchell J, Rush AJ, et al. Clinical features of depressed outpatients with and without co-occurring general medical conditions in STAR*D. Gen Hosp Psychiatry. 2004;26(6):421–429. 6. Aragones E, Pinol JL, Labad A. Depression and physical comorbidity in primary care. J Psychosom Res. 2007;63(2):107–111. 7. Vuorilehto M, Melartin T, Isometsa E. Depressive disorders in primary care: recurrent, chronic, and co-morbid. Psychol Med. 2005;35(5):673–682. 8. Nuyen J, Spreeuwenberg PM, Van Dijk L, et al. The influence of specific chronic somatic conditions on the care for co-morbid depression in general practice. Psychol Med. 2008;38(2):265–277. 9. Cole MG, Bellavance F. Depression in elderly medical inpatients: a meta-analysis of outcomes. Can Med Assoc J. 1997;157:1055–1060. 10. Oslin DW, Datton CJ, Kallan MJ, et al. Association between medical comorbidity and treatment outcomes in late-life depression. J Am Geriatr Soc. 2002;50:823–828. 11. Bogner HR, Cary MS, Bruce ML, et al. The role of medical comorbidity in outcome of major depression in primary care—The PROSPECT study. Am J Geriatr Psychiatry. 2005;13(10):861–868.

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12. Egede LE. Major depression in individuals with chronic medical disorders: prevalence, correlates and association with health resource utilization, lost productivity and functional disability. Gen Hosp Psychiatry. 2007;29(5):409–416. 13. Katon W, Lin EHB, Kroenke K. The association of depression and anxiety with medical symptom burden in patients with chronic medical illness. Gen Hosp Psychiatry. 2007;29(2):147–155. 14. Egede LE. Diabetes, major depression, and functional disability among US adults. Diabetes Care. 2004;27(2):421–428. 15. Scott KM, Browne MAO, McGee MA, et al. Mental-physical comorbidity in Te Rau Hinengaro: The New Zealand Mental Health Survey. Aust N Z J Psychiatry. 2006;40(10):882–888. 16. Nuyen J, Schellevis FG, Satariano WA, et al. Comorbidity was associated with neurologic and psychiatric diseases: A general practice-based controlled study. J Clin Epidemiol. 2006;59(12):1274–1284. 17. Juurlink DN, Herrmann N, Szalai JP. Medical illness and the risk of suicide in the elderly. Arch Intern Med. 2004;164:1179–1184. 18. International Association for the Study of Pain, Subcommittee of Taxonomy. Pain terms: a current list with definitions and notes on usage. Part II. Pain. 1979;6:249–252. 19. Zimmerman M. McGlinchey JB, Young D, et al. Diagnosing major depressive disorder, I. A psychometric evaluation of the DSM-IV symptom criteria. J Nerv Ment Dis. 2006;194:158–163. 20. Simon GE, VonKorff M, Piccinelli M, et al. An international study of the relation between somatic symptoms and depression. N Engl J Med. 1999;341:1329–1335. 21. Marple RL, Kroenke K, Lucey CR, et al. Concerns and expectations on patients presenting with physical complaints: frequency, physician perceptions and actions, and two-week outcome. Arch Intern Med. 1997;157:1482–1488. 22. Cavanaugh SV. Depression in the medically ill: critical issues in diagnostic assessment. Psychosomatics. 1995;36:48–59. 23. Koenig HG, George LK, Peterson BL, et al. Depression in medically ill hospitalized older adults: prevalence, characteristics, and course of symptoms according to six diagnostic schemes. Am J Psychiatry. 1997;154:1376–1383. 24. Cavenaugh S, Clark D, Gibbons R. Diagnosing depression in the hospitalized medically ill. Psychosomatics. 1983;24:809–815. 25. Plumb M, Holland J. Comparative studies of psychological function in patients with advanced cancer: 2. Interviewer-rated current and past psychological symptoms. Psychosom Med. 1981;43:243–254. 26. Kathol RG, Mutgi A, Williams J, et al. Diagnosis of major depression in cancer patients according to four sets of criteria. Am J Psychiatry. 1990;147:1021–1024. 27. Chochinov HM, Wilson KG, Enns M, et al. Prevalence of depression in the terminally ill: effects of diagnostic criteria and symptom threshold judgments. Am J Psychiatry. 1994;151:537–540. 28. Dugan W, McDonald MV, Passik SD, et al. Use of the Zung Self-Rating Depression Scale in cancer patients: feasibility as a screening tool. Psychooncology. 1998;7:483–493. 29. Eriksen HR, Svendsrød R, Ursin G, et al. Prevalence of subjective health complaints in the Nordic European countries in 1993. Eur J Public Health. 1998;8:294–298.

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30. Kroenke K, Jackson J, Chamberlin J. Depressive and anxiety disorders in patients presenting with physical complaints: clinical predictors and outcome. Am J Med. 1997;103:339–347. 31. Kroenke K, Price R. Symptoms in the community. Prevalence, classification and psychiatric comorbidity. Arch Intern Med. 1993;153:2474–2480. 32. Simon GE, Vonkorff M. Somatization and psychiatric disorder in the NIMH Epidemiologic Catchment area study. Am J Psychiatry. 1991;148(11):1494–1500. 33. Haug TT, Mykletun A, Dahl AA. The association between anxiety, depression, and somatic symptoms in a large population: The HUNT-II Study. Psychosom Med. 2004; 66:845–851. 34. Gerber PD, Barrett JE, Barrett JA, et al. The relationship of presenting physical complaints to depressive symptoms in primary care patients. J Gen Intern Med. 1992;7:170–173. 35. Zimmerman M. McGlinchey JB, Young D, et al. Diagnosing major depressive disorder, III. Can some symptoms be eliminated from the diagnostic criteria? J Nerv Ment Dis. 2006;194:313–317. 36. Mitchell AJ, McGlinchey JB, Young D, et al. Accuracy of specific symptoms in the diagnosis of major depressive disorder in psychiatric out-patients: data from the MIDAS project. Psychol Med. Nov. 12, 2008:1–10. 37. Lipsey JR, Spencer WC, Rabins PV, et al. Phenomenological comparison of poststroke depression and functional depression. Am J Psychiatry. 1986;143:527–529. 38. Simon G, Von Korff M. Medical co-morbidity and validity of DSM-IV depression criteria. Psychol Med. 2006;36:27–36. 39. Pickard AS, Dalal MR, Bushnell DB. Comparison of depressive symptoms in stroke and primary care: applying Rasch models to evaluate the Center for Epidemiologic Studies-Depression Scale. Value in Health. 2006;9(1):59–64. 40. van Wilgen CP, Dijkstra PU, Stewart RE, et al. Measuring somatic symptoms with the CES-D to assess depression in cancer patients after treatment: comparison among patients with oral/oropharyngeal, gynecological, colorectal, and breast cancer. Psychosomatics. 2006;47:465–470. 41. Ehrt U, Brønnick K, Leentjens AFG, et al. Depressive symptom profile in Parkinson’s disease: a comparison with depression in elderly patients without Parkinson’s disease. Int J Geriatr Psychiatry. 2006;21(3):252–258. 42. Yates WR, Mitchell J, Rush AJ, et al. Clinical features of depression in outpatients with and without co-occurring general medical conditions in STAR*D. J Clin Psychiatry. 2007;9:7–15. 43. Aikens JE, Reinecke MA, Pliskin NH, et al. Assessing depressive symptoms in multiple sclerosis: is it necessary to omit items from the original Beck Depression Inventory? J Behav Med. 1999;22(2):127–142. 44. Guo Y, Musselman D, Manatunga A. The diagnosis of major depression in patients with cancer: a comparative approach. Psychosomatics. 2006;47:376–384. 45. Holzapfel N, Mu¨ller-Tasch T, Wild B, et al. Depression profile in patients with and without chronic heart failure. J Affect Disorders. 2008;1:53–62. 46. Chen ML, Chang H-K. Physical symptom profiles of depressed and nondepressed patients with cancer. Palliative Med. 2004;18(8):712–718. 47. Stein PN, Sliwinski MJ, Gordon WA. Discriminative properties of somatic and nonsomatic symptoms for post-stroke depression. Clin Neuropsychologist. 1996;10:141–148.

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48. Kathol RG, Mutgi A, Williams J, et al. Diagnosis of major depression in cancer patients according to four sets of criteria. Am J Psychiatry. 1990;147:1021–1024. 49. Kalichman SC, Rompa D, Cage M. Distinguishing between overlapping somatic symptoms of depression and HIV disease in people living with HIV-AIDS. J Nerv Ment Dis. 2000;188(10):662–670. 50. Leentjens AFG, Marinus J, Van Hilten JJ, et al. The contribution of somatic symptoms to the diagnosis of depressive disorder in Parkinson’s disease. A discriminant analytic approach. J Neuropsychiatry Clin Neurosci. 2003;15:74–77. 51. Akechi T, Nakano T, Akizuki N, et al. Somatic symptoms for diagnosing major depression in cancer patients. Psychosomatics. 2003;44:244–248. 52. de Coster E, Leentjens AFG, Lodder J, et al. The sensitivity of somatic symptoms in post-stroke depression: a discriminant analytic approach. Int J Geriatr Psychiatry. 2005;20:358–362. 53. Wedding U, Koch A, Rohrig B, et al. Requestioning depression in patients with cancer: Contribution of somatic and affective symptoms to Beck’s Depression Inventory. Ann Oncol. 2007;18(11):1875–1881.

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12 SCREENING FOR DEPRESSION IN NEUROLOGIC DISORDERS Andres M. Kanner

1. 2. 3. 4. 5.

Depression in Stroke Depression in Multiple Sclerosis Depression in Epilepsy Depression in Parkinson’s Disease Conclusions

Context Depression appears to be particularly common in several neurologic disorders, including epilepsy, stroke, dementias, Parkinson’s disease, Huntington’s disease, and multiple sclerosis. There is some evidence that the ‘‘depression’’ associated with each neurologic disorder is distinct in symptoms and course. This suggests it may be useful to have depression scales validated for each neurologic disorder, yet most instruments appear to yield comparable acceptable sensitivities and specificities. However, head-to-head comparisons of scales and implementation studies are needed to resolve this issue. Depressive disorders are a common psychiatric comorbidity of neurologic disorders, including epilepsy, stroke, dementias, Parkinson’s disease (PD), essential tremor, Huntington’s disease, migraines and multiple sclerosis (MS), to name the principal ones.1 It is typically assumed that depressive disorders are a complication of these neurologic disorders. However, data published in the past 15 years have suggested a bidirectional relation between depression and stroke,2–4 epilepsy,5–7 dementia,8–10 and PD.11,12 In other words, not only are patients with these neurologic conditions at greater risk of developing depression, but patients with depression are at greater risk of developing one of these disorders. 241

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Early identification of comorbid depressive disorders is of the essence given their negative impact on quality of life and the course and response to treatment of most of these neurologic disorders. Unfortunately, depression often goes unrecognized and hence untreated. Clearly the use of screening instruments by neurologists may help remedy this problem. Several caveats need to be considered, however. First, the clinical presentation of comorbid depressive disorders may differ in several ways from that of primary depression, such as in cases of depression in epilepsy.13 Second, several somatic and cognitive symptoms are common in primary depression and most neurologic disorders (ie, fatigue, poor concentration, and slow thinking). Thus, a higher score may be a reflection of such symptoms and not of a depressive episode per se. Third, most of the available screening instruments for depression were developed for primary mood disorders and hence may yield false-positive or -negative findings. Fourth, the presence of cognitive deficits related to the underlying neurologic disorder may limit the patient’s ability to complete on his or her own self-report screening instruments in a reliable manner. In such cases, the use of examiner-administered instruments may yield more accurate data. The aim of this chapter is to provide a practical review of the prevalence and clinical manifestations of depression in four major neurologic disorders— stroke, epilepsy, PD, and MS—as well as of its impact on the course of these diseases and quality of life. In addition, this chapter provides a review of the literature of the screening instruments frequently used to identify depression in these neurologic disorders.

1. Depression in Stroke Epidemiologic Considerations The prevalence rates of post-stroke depression (PSD) have ranged from 30% to 50% in several cross-sectional studies.14–17 In a review of the literature, Robinson14 calculated the pooled prevalence of all types of PSD in various populations to be 31.8% (range 30% to 44%) from four community-based studies. Prevalence rates ranged from 25% to 47% from studies carried out in acute hospitals and from 35% to 72% in studies done in rehabilitation hospitals. As stated above, a bidirectional relation has been identified between depressive disorders and stroke. Five studies have investigated the impact of depression on the risk of stroke in large cohorts ranging from 1,703 to 6,675 subjects.2–4,18,19 Four found that depression increased the risk of developing stroke, after controlling for other risk factors associated with this neurologic condition.2,3,18,19 For example, Larson and colleagues3 followed 1,703 subjects for 13 years; patients with a depressive disorder or depressive symptoms had a 2.67 (confidence interval [CI] 1.08–6.63) relative risk of developing a

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243

stroke, after controlling for vascular risk factors (ie, hypertension, diabetes, hyperlipidemia, heart disease, and use of tobacco). May and colleagues2 followed 2,201 men aged 45 to 59 years for 14 years. Patients with significant symptoms of depression had a 3.36 (CI 1.29–8.71) relative risk of developing a fatal stroke. The increased risk of stroke in patients with depression may be mediated through a direct impact on coagulation and central nervous system vascular parameters, and indirectly by increasing risks of cardiovascular disease, hypertension, cardiac arrhythmias, and diabetes.14

Clinical Manifestations PSD can present as major depressive episodes and minor depression. Various investigators have proposed the existence of another type of PSD, referred to as vascular depression. This is a late-