Diabetes atlas 3rd edtion

DIABETES ATLAS THIRD EDITION

The mission of the International Diabetes Federation is to promote diabetes care, prevention and a cure worldwide.

DIABETES ATLAS COMMITTEE Jean-Claude Mbanya (co-chair) Delice Gan (co-chair) Bjørnar Allgot Karel Bakker Jonathan Betz Brown Ambady Ramachandran Gojka Roglic Jonathan Shaw Martin Silink Linda Siminerio Gyula Soltèsz Rhys Williams Paul Zimmet Editor and project manager: Delice Gan Project coordinator: Olivier Jacqmain Diabetes Atlas, third edition, and other IDF publications are available from: International Diabetes Federation Executive Office 19 Avenue Emile de Mot B-1000 Brussels Belgium Tel +32 2 538 5511 Fax +32 2 538 5114 [email protected] www.idf.org Online version of Diabetes Atlas: www.eatlas.idf.org © International Diabetes Federation, 2006 No part of this publication may be reproduced or transmitted in any form or by any means without the prior written permission of the International Diabetes Federation. First published, 2000 Second edition, 2003 Third edition, 2006 Permission has been obtained from the United Nations, Population Division to use data from the World Population Prospects: The 2004 Revision. http://www.bms.com/ ISBN 2-930229-45-4

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ACKNOWLEDGEMENTS

CONTRIBUTIONS

The International Diabetes Federation (IDF) would like to express its

CHAPTER 1

thanks to its partners for their generous support in making the



1.1 Richard Sicree, Jonathan Shaw, Paul Zimmet

Diabetes Atlas, third edition, possible:



1.2 Richard Sicree, Jonathan Shaw, Paul Zimmet



1.3 Robyn Tapp, Richard Sicree, Jonathan Shaw, Paul Zimmet

World Diabetes Foundation Sanofi-Aventis Groupe

CHAPTER 2

Merck and Co, Inc



2.1 Gyula Soltész, Chris Patterson, Gisela Dahlquist

Roche Diagnostics



2.2 Ravinder Singh, Jonathan Shaw, Paul Zimmet

Novartis Bristol Myers-Squibb

CHAPTER 3

Astra Zeneca



A publication such as this would not have been possible without

CHAPTER 4

the commitment and contribution of many people around the



Boyd Metzger

Gojka Roglic

world. IDF would like to thank and express its deep appreciation of the contributions of the following authors:

CHAPTER 5



Jonathan Betz Brown, Dorte Vistisen, Richard Sicree,



Jonathan Shaw, Gregory Nichols, Ping Zhang

CHAPTER 6

6.1 Richard Sicree, Jonathan Shaw, Paul Zimmet



6.2 David Beran, John Yudkin

CHAPTER 7



Ravinder Singh, Jonathan Shaw, Paul Zimmet

CHAPTER 8



Paul Zimmet, KGMM Alberti, Jonathan Shaw

CHAPTER 9

Rhys Williams, Jonathan Shaw

CHAPTER 10

Martin Silink

IN TOUCH WITH Sylvia Brunoldi, Marguerite de Clerk, Fatema Jawad, Ambady Ramachandran Special thanks to Shirley Murray for coordinating the work at the International Diabetes Institute, and to Morten Agergaard for the design of this publication. Special thanks also to the IDF Regional Chairs for assistance: Morsi Arab, Gordon Bunyan, Susana Feria de Campanella, Debbie Jones, Kaushik Ramaiya, Wim Wientjens, Mahen Wijesuriya

_

ACKNOWLEDGEMENTS IDF also gratefully acknowledges the help of the following for their contribution to the publication: IDF member associations Zulfiqarali G Abbas, Carlos A Aguilar-Salinas, Nizar Albache, Mansour M Al-Nozha, Maha Al-Till, Karolina Antonov, Fereidoun Azizi, Michael Barry, Lee-Ming Chuang, Aynina Cisse, Kirsten Coppell, Max de Courten, Wendy Davis, John Day, Anne FagotCampagna, Juan Jose Gagliardino, Geoff Gill, Dan Hackam, Markolf Hanefeld, Hans Hauner, Günther Heller, Lex Herreburgh, Julia Hippisley-Cox, Akhtar Hussain, Tazeen Jafar, Jak Jervell, Harry Keen, Hilary King, Ross Lawrenson, Warren Lee, Theodoros G Loizou, Berit Lundman, Rachid Malek, Jaana Martakainen, Arne Melander, V Mohan, Errol Morrison, Henrietta Mulnier, Bruce Neal, Annemette Nielsen, Cynthia Perez, Catherine Regniers, Antti Reunanen, Shaukat M Sadikot, Helmut Schröder, Ulrich Schwabe, Laidon Shapo, Claudia P Sánchez-Castillo, Mohammed Tazi, Lesley Tilson, Tom Walley, Peter Watkins, Kumudu Wijewardene

CONTENTS Foreword Introduction Executive Summary What is Diabetes?



1 3 5 7

PART 1 THE GLOBAL BURDEN OF DIABETES Diabetes and Impaired Glucose Tolerance 10 CHAPTER 1 1.1 Prevalence and Projections 15 1.2 Known and Newly Diagnosed Diabetes 105 1.3 Complications of Diabetes 111

Diabetes in the Young: a Global Perspective

CHAPTER 2 2.1 Global Trends in Childhood Type 1 Diabetes 153

2.2 Type 2 Diabetes in the Young

193



CHAPTER 3 Gestational Diabetes Mellitus



CHAPTER 4



CHAPTER 5 The Economic Impacts of Diabetes

211

Diabetes Mortality

219 237

PART 2 THE CHALLENGES Access to Insulin, Medication and Diabetes Supplies CHAPTER 6 6.1 Diabetes Medication Use: International Prescription Patterns 6.2 Managing Insulin-Requiring Diabetes in Sub-Saharan Africa

267 271 288



CHAPTER 7 Mental Health, Antipsychotic Drugs and Hyperglycaemia



CHAPTER 8



PART 3 PREVENTION AND ACTION Prevention and action CHAPTER 9 Prevention and Diabetes: Possibilities for Success and Consequences of Inaction



CHAPTER 10 From Vision to Action 329





299

The Metabolic Syndrome

307

APPENDICES Appendix 1.1 Methodology for Chapter 1.1 Appendix 1.2 Methodology for Chapter 1.3 Appendix 2 Methodology for Chapter 2.1 Appendix 3 Methodology for Chapter 5 Glossary Acronyms References World Diabetes Foundation





313 317

335 339 341 343 350 352 354 376

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FOREWORD

D

iabetes is no longer an epidemic that can be ignored. Each new edition of the Diabetes Atlas, strengthened by the latest prevalence studies, confirms the fact that diabetes is increasing – and increasing rapidly – in every part of the world. At the same time, there is now good evidence showing that type 2 diabetes can be prevented in many cases, and that there are cost-effective measures for preventing diabetic complications. The third edition of the Diabetes Atlas firmly confounds what many have believed for so long. Once thought of as a disease of affluent countries, type 2 diabetes is now a growing burden on developing economies. More than 80% of the 246 million people with diabetes live in low- and middle-income countries, where health resources are needed to combat both contagious and chronic diseases. Once thought of as a disease of the elderly, people in younger age groups now form the bulk of those with diabetes. Some 46% of adults with diabetes are in the 40–59 age group. Once thought of ‘as a touch of sugar’, studies show that diabetes at any age, if not properly managed, will lead to serious outcomes, and, in some cases, death. It is estimated that 3.8 million men and women will die from diabetes in 2007, more than 6% of total world mortality.

these issues and challenges. It is now up to us to take up the challenge and find cost-effective ways to tackle one of the largest health problems we now face. We must unite – governments, organizations, individuals – to prevent diabetes, to improve diabetes care for the millions affected and to, ultimately, find a cure. I would like to express my appreciation and thanks to the many colleagues around the world whose invaluable contribution has made this edition of the Diabetes Atlas possible. I would also like thank our sponsoring partners who have given their support in so many ways. The Diabetes Atlas will be a significant resource for all of us who are working to improve the lives of millions touched by diabetes.



Pierre Lefèbvre IDF President 2003 - 2006

If left unchecked, the number of people with diabetes will reach 380 million in less than 20 years. This is more than the current adult population of the African Region or of the North American Region. This will mean that 1 out of 14 adults worldwide will have diabetes in the year 2025. The loss of earnings and lives because of diabetes will be hard to bear. This edition of the Diabetes Atlas brings us face to face with

FOREWORD



INTRODUCTION

C

hronic diseases account for a large proportion of the global burden of disease and are the major cause of death in almost all countries. It is estimated that diabetes, cardiovascular disease, cancer and other chronic, noncommunicable diseases caused 35 million deaths in 2005. Total deaths from infectious diseases, maternal and perinatal conditions and nutritional deficiencies are projected to decline by 3% by 2015. At the same time, deaths due to chronic diseases are projected to increase by 17%. According to the recent report from the World Health Organization, Preventing Chronic Disease: a vital investment, each year at least 4.9 million people die as a result of tobacco use, 2.6 million people die as a result of being overweight or obese, 4.4 million die as a result of raised total cholesterol levels and 7.1 million die as a result of raised blood pressure. The same report stresses that common and modifiable risk factors that underlie the major chronic diseases are widespread, and that the global response to address them is inadequate. The inadequate response is in large part due to misconceptions regarding the major chronic diseases and their shared risk factors. Chronic diseases do not affect only rich people. In all but the least-developed countries, poor people are more likely to develop chronic diseases and more likely to die from them. Chronic diseases are increasingly affecting younger people. Childhood obesity is a growing problem even in poor countries and the incidence of type 2 diabetes in children and adolescents is increasing. Although we have been extremely vigilant towards the spread of infectious diseases, such as SARS and avian influenza in recent years, we have failed to keep in check the ‘silent killers’ like diabetes, cardiovascular disease and cancer. These are widely perceived to be the result of voluntary choice of an unhealthy lifestyle. However, the choice of lifestyle is very limited among the young and the poor, and government action is needed to improve every person’s access to a healthy life. INTRODUCTION

The world has also been slow to recognize that there are interventions that have been proven cost-effective in chronic disease prevention and control. Many of these interventions are feasible and inexpensive, even in the poorest countries. The World Health Organization is developing a framework for assisting diverse countries in developing, implementing and evaluating national policies and programmes for the prevention and control of chronic diseases. It supports an integrated, comprehensive, stepwise and multisectoral approach focused on the common risk factors. An integrated approach to public health prevention and control of chronic disease is necessary to achieve the global goal of reducing death rates by 2% per year over the next 10 years; achievement of this goal will prevent 36 million premature deaths by 2015. Diabetes presents major challenges to patients, health systems and national economies. The World Health Organization together with the International Diabetes Federation is working to raise awareness of diabetes worldwide along with improving the quality of care. This latest edition of the Diabetes Atlas is a welcome update of the trends in the global burden of diabetes and its related economic implications. It also spotlights recentlydocumented dimensions of the disease — such as the increase in diabetes in children and the often underestimated mortality attributable to diabetes. I am confident that the Diabetes Atlas will continue to be a valuable resource for advocates, policy-makers, researchers and healthcare providers.

Robert Beaglehole Director Department of Chronic Diseases and Health Promotion Noncommunicable Diseases and Mental Health Cluster World Health Organization Geneva



EXECUTIVE SUMMARY Global projections for the number of people with diabetes (20-79 age group), 2007-2025 (millions)

53.2 64.1 +21%

28.3 40.5 +43%

67.0 99.4 +48% 24.5 44.5 +81%

10.4 18.7 +80%

16.2 32.7 +102%

Africa Eastern Mediterranean and Middle East Europe North America South and Central America South-East Asia Western Pacific

T

he third edition of the Diabetes Atlas confirms beyond all doubt that the diabetes epidemic is real and that its magnitude is larger than previous projections had anticipated. It reveals that the major burden of diabetes falls on the developing world where it threatens not only to subvert the gains of economic development but also the gains brought about by international humanitarian programmes addressing the UN Millennium Development Goals. The third edition of the Diabetes Atlas has been structured on four key messages: • • • •

Diabetes is common and getting commoner Diabetes is a life-threatening condition A full and healthy life is possible with diabetes In many cases, type 2 diabetes may be prevented

The book is thus divided into three parts: the global burden, the challenges, and prevention and action. Part 1 of the Diabetes Atlas looks at the global burden of diabetes. It describes the diabetes pandemic and shows the evidence that is hard to ignore. It points out the consequences of inaction by revealing the mortality caused by diabetes as well as the mounting health expenditures in countries EXECUTIVE SUMMARY

46.5 80.3 +73%

World 2007: 2025: Increase:

246 380 +55%

around the world. There is also a closer examination of type 1 and type 2 diabetes in the young, as well as gestational diabetes mellitus. Data are provided for 215 countries and territories for the years 2007 and 2025. Estimates show that there will be some 246 million people with diabetes in 2007. This figure already outstrips an estimate made in 1994 in which it was predicted that there would be 239 million people with diabetes in the year 2010. New studies have allowed us to revise the estimate for 2025 from the second edition of the Atlas. If action is not taken to put preventive measures in place, some 380 million people are expected to have diabetes in 2025. This would be more than the current adult population of the African region. Two sets of prevalence estimates have been provided in this edition: national or regional prevalence, and comparative prevalence. The national or regional prevalence rate is ideal for assessing the burden of diabetes for each country or region, while the comparative prevalence is ideal for making comparisons between countries or regions. The data underline once again that the burden of diabetes is greatest in the developing world, where some 80% of 

those with diabetes live. The world is expected to spend at least USD232 billion in 2007 to treat and prevent diabetes and its complications. However, estimates in the Diabetes Atlas show that more than 80% of expenditures for medical care for diabetes are made in the world’s economically richest countries, not in the low- and middle-income countries. In the world’s poorest countries, not enough is spent to provide even the least expensive lifesaving diabetes drugs. For the first time, country by country estimates of mortality attributable to diabetes are presented in the Diabetes Atlas. It is estimated that there will be 3.8 million deaths attributable to diabetes in 2007, similar in magnitude to those reported for HIV/AIDS in the year 2002. The number of deaths attributable to diabetes calculated here is three to four times greater than those given in the conventional international statistical reports largely based on diabetes given as an underlying cause on death certificates. Although these mortality estimates may not be accurate, given the assumptions on which the calculations are based, they do provide a more realistic estimate of diabetes-attributable mortality than currently exist.

public health strategies to improve nutrition, prevent overweight and obesity, increase physical activity and reduce smoking, as these strategies will prevent not only diabetes but many of the chronic diseases. Part three also describes IDF’s lead to put diabetes on the global agenda through its Unite for Diabetes campaign and World Diabetes Day. It reinforces the message that the epidemic of diabetes is one of the most serious challenges facing the modern world. Diabetes is largely a hidden, silent epidemic causing much hardship, but it has not as yet received serious consideration from the world community. Prevention of diabetes is essential, as millions, especially in low- and middle-income countries, develop the disease each year. To do nothing is not an option and is morally indefensible.

Part two focuses on some of the major challenges facing people with diabetes. It examines the access to insulin, medication and diabetes supplies, as well the association between mental health, antipsychotic drugs and hyperglycaemia. The issue of the metabolic syndrome is also addressed here. Access to insulin, medication and diabetes supplies remains a major challenge for millions of people with diabetes around the world. Part two provides a description of the pattern of use of diabetes therapies, both pharmacological and dietary, in as many countries as data are available. Describing the use of hypoglycaemic treatments is valuable in gaining an understanding of how therapies are actually used in practice, as against the advice given in various published guidelines. This edition of the Atlas also takes a closer look at the access to diabetes care in three sub-Saharan countries as examples of where solutions for better access could be found for people with diabetes in the poorer countries. Part three is a call for action. It provides the evidence that much of type 2 diabetes can be prevented, and underlines the need to implement cost-effective strategies to reduce or prevent diabetic complications. Governments are encouraged to take stock of the evidence and to implement 

EXECUTIVE SUMMARY

DIABETES ATLAS THIRD EDITION

WHAT IS DIABETES ?

D

iabetes is recognized as a group of heterogeneous disorders with the common elements of hyperglycaemia and glucose intolerance, due to insulin deficiency, impaired effectiveness of insulin action, or both1. Diabetes mellitus is classified on the basis of aetiology and clinical presentation of the disorder into four types:

need injections of insulin every day in order to control the levels of glucose in their blood. Without insulin, people with type 1 diabetes will die.

• • • •

• • • • • • • •

type 1 diabetes type 2 diabetes gestational diabetes mellitus (GDM) other specific types

Type 1 diabetes Type 1 diabetes is sometimes called insulin-dependent, immune-mediated or juvenile-onset diabetes. It is caused by an auto-immune reaction, where the body’s defence system attacks the insulin-producing cells. The beta cells of the pancreas therefore produce little or no insulin, the hormone that allows glucose to enter body cells. The reason why this occurs is not fully understood. The disease can affect people of any age, but usually occurs in children or young adults. People with this form of diabetes WHAT IS DIABETES ?

The onset of type 1 diabetes is often sudden and dramatic and can include symptoms such as: abnormal thirst and a dry mouth frequent urination extreme tiredness/lack of energy constant hunger sudden weight loss slow-healing wounds recurrent infections blurred vision

Studies now show that while incidence is on the increase among children, it is not increasing among young adults. This indicates a shift to a younger age at onset. The causes of the changes over time are unknown and although migration might slowly change the genetic background within a population, the rapid changes in incidence rate reported to occur within comparatively short time spans are more likely to be due to changes in environmental risk factors. These 

environmental risk factors may initiate autoimmunity or accelerate and precipitate an already ongoing beta cell destruction2. These risk factors include: Early events

Potential risk factors which may initiate the autoimmune process include early fetal events e.g. blood group incompatibility; maternal viral infections during pregnancy; and early exposure to cow’s milk components and other nutritional factors. Population-based case-control studies have identified some protective factors, including a long duration of breast feeding3, early vitamin D supplementation4, pre-school day care (as a proxy measure of infections)5 and atopic diseases6. Lifestyle

Since type 1 diabetes in childhood is associated with estimates of general wealth such as GDP, it has been suggested that lifestyle habits related to welfare might be responsible for the changes in trend. Wealth is a well-known determinant of birth weight and childhood growth. Weight and growth

Different estimates of child growth such as high birth weight, an increased height, weight, weight for height and body mass index (BMI) have repeatedly been shown to be risk factors for childhood onset diabetes7-11. Rapid growth is associated with high growth hormone levels and an increased number of fat cells both leading to insulin resistance and thereby an overloading of the beta cell. Although autoimmune mechanisms are responsible for the beta cell destruction leading to type 1 diabetes, overload factors may accelerate this process.

Type 2 diabetes Type 2 diabetes is characterized by insulin resistance and relative insulin deficiency, either of which may be present at the time that diabetes becomes clinically manifest. The specific reasons for the development of these abnormalities are not yet known. The diagnosis of type 2 diabetes usually occurs after the age of 40 years but could occur earlier, especially in populations with a high diabetes prevalence. Type 2 diabetes can remain undetected, i.e. asymptomatic, for many years and the diagnosis is often made from associated complications or incidentally through an abnormal blood or urine glucose test. 

Type 2 diabetes is often, but not always, associated with obesity, which itself can cause insulin resistance and lead to elevated blood sugar levels. It is strongly familial, but major susceptibility genes have not yet been identified. There are several possible factors in the development of type 2 diabetes. These include: • • • • •

ethnicity obesity, diet and inactivity insulin resistance family history intrauterine environment

In contrast to type 1 diabetes, persons with type 2 diabetes are not dependent on exogenous insulin and are not ketosisprone, but may require insulin for control of hyperglycaemia if this is not achieved with diet alone or with oral hypoglycaemic agents. Type 2 diabetes constitutes about 85 to 95% of all diabetes in developed countries, and accounts for an even higher percentage in developing countries.

Gestational diabetes Gestational diabetes mellitus (GDM) is a carbohydrate intolerance of varying degrees of severity which starts or is first recognized during pregnancy. The definition applies regardless of whether insulin is used for treatment or if the condition persists after pregnancy. It does not exclude the possibility that unrecognized glucose intolerance may have antedated the pregnancy. Increased maternal glucose (blood sugar) levels are associated with an increased rate of complications in the baby, including large size at birth, birth trauma, hypoglycaemia (low blood sugar), and jaundice. Maintaining control of blood sugar levels significantly reduces the risk to the baby. Women who have had GDM have an increased risk of developing type 2 diabetes in later years. GDM is also associated with increased risk of obesity and abnormal glucose metabolism during childhood and adult life in the offspring.

Impaired glucose tolerance Impaired glucose tolerance (IGT) is an asymptomatic condition defined by elevated (though not diabetic) levels of blood DIABETES ATLAS THIRD EDITION

Insulin production and action

Raises Blood Sugar

High Blood Sugar Promotes insulin release

Glucagon Liver

Glycogen

Glycose

Stimulates breakdown of glycogen Stimulates formation of glycogen

Pancreas

Insulin Stimulates glucose uptake from blood Tissue Cells (muscle, brain, fat, etc)

Lowers Blood Sugar

Promotes glucagon release

Low Blood Sugar

Insulin is a hormone produced by the pancreas that is necessary for cells to be able to use blood sugar. In response to high levels of glucose in the blood, the insulinproducing cells in the pancreas secrete the hormone insulin. Type I diabetes occurs when these cells are destroyed by the body’s own immune system. People with type 2 diabetes produce insulin but cannot use it effectively.

glucose two hours after a 75g oral glucose challenge. Along with impaired fasting glucose (IFG), it is now recognized as being a stage in the transition from normality to diabetes.

survival. Insulin may also be used by people with type 2 diabetes. In type 2 diabetes, the body needs more insulin than it can produce.

Thus, individuals with IGT are at high risk of progressing to type 2 diabetes, although such progression is not inevitable, and probably over 30% of individuals with IGT will return to normal glucose tolerance over a period of several years. Not surprisingly, IGT shares many characteristics with type 2 diabetes, being associated with obesity, advancing age, insulin resistance and an insulin secretory defect.

Since the landmark discovery of insulin by Frederick Banting and Charles Best in 1921, huge steps forward have been made in research and development in creating genetically engineered human insulin. Until recently insulin was derived from a limited resource of the pancreas of cattle and pigs.

Insulin Insulin is the internal secretion of the pancreas formed by groups of beta cells in the islets of Langerhans in this organ. It is the hormone needed to enable glucose to enter the cells and provide energy. Insulin is also important in keeping blood glucose levels within the acceptable limits. Insulin is injected into the body by people with type 1 diabetes in whom the cells that produce insulin have been destroyed. This is the most common form of diabetes in children and young adults, and they depend on insulin for WHAT IS DIABETES ?



PART 1 THE GLOBAL BURDEN It is now recognized that it is the developing countries that presently face the greatest burden of diabetes. However, many governments and public health planners still remain largely unaware of the current magnitude, or, more importantly, the future potential for increases in diabetes and its serious complications in their own countries.

D

iabetes is now one of the most common noncommunicable diseases globally. It is the fourth or fifth leading cause of death in most developed countries and there is substantial evidence that it is epidemic in many developing and newly industrialized nations. Complications from diabetes, such as coronary artery and peripheral vascular disease, stroke, diabetic neuropathy, amputations, renal failure and blindness are resulting in increasing disability, reduced life expectancy and enormous health costs for virtually every society. Diabetes is certain to be one of the most challenging health problems in the 21st century. The number of studies describing the epidemiology of diabetes over the last 20 years has been extraordinary. It is now recognized that it is the developing countries that presently face the greatest burden of diabetes. However, many governments and public health planners still remain largely unaware of the current magnitude, or, more importantly, the future potential for increases in diabetes and its serious complications in their own countries. In addition to diabetes, the condition of impaired glucose tolerance (IGT ) also constitutes a major public health 10

PART 1

problem, both because of its association with diabetes incidence and its own association with an increased risk of the development of cardiovascular disease. Part 1 of the Diabetes Atlas looks at the global burden of diabetes. It describes the diabetes pandemic and shows the evidence that is hard to ignore. It points out the consequences of inaction by revealing the mortality caused by diabetes as well as the mounting health expenditures in countries around the world. Chapter 1 presents estimates of the prevalence of diabetes mellitus and IGT for 215 countries and territories for the years 2007 and 2025, which should provide some concept of the current and likely future burden. This chapter also provides a review of studies which allow an estimate of the proportion of cases of diabetes that are undiagnosed. Data are also provided on the prevalence of many of the complications of diabetes, which illustrate the seriousness of the disease. Chapter 2 looks at the global trends in childhood type 1 diabetes and provides estimates for type 1 diabetes in children and adolescents. This chapter also reviews the available epidemiological data on type 2 diabetes in the DIABETES ATLAS THIRD EDITION



young from around the world. By focusing on such data it is hoped that deficiencies in our knowledge of the disease will be highlighted, and that strategies to deal with it will be developed. Chapter 3 highlights the rising trend of gestational diabetes and the increased risk of developing type 2 diabetes in later years in both the mother and the offspring. Chapter 4 provides estimates of mortality attributable to diabetes for 193 countries for the year 2007, which underlines the need for preventive measures for diabetes and its complications worldwide. Chapter 5 examines the economic impact of diabetes and estimates national health expenditures to treat and prevent diabetes and its complications in 193 countries and territories for the years 2007 and 2025. The results show that more than 80% of expenditures for medical care for diabetes are made in the world’s economically richest countries, not in the lowand middle-income countries where 80% of persons with diabetes will soon live. In the world’s poorest countries, not enough is spent to provide even the least expensive lifesaving diabetes drugs. THE GLOBAL BURDEN

PART 1

11

CHAPTER 1 DIABETES AND IMPAIRED GLUCOSE TOLERANCE

The number of people with diabetes in Tanzania is expected to increase by 50% within the next 20 years. 14

CHAPTER 1

DIABETES ATLAS THIRD EDITION

1.0 DIABETES AND IMPAIRED GLUCOSE TOLERANCE With the forces of globalization and industrialization proceeding at an increasing rate, the prevalence of diabetes is predicted to increase dramatically over the next few decades. The resulting burden of complications and premature mortality will continue to present itself as a major and growing public health problem for most countries.

D

iabetes is recognised as a group of heterogeneous disorders with the common elements of hyperglycaemia and glucose intolerance, due to insulin deficiency, impaired effectiveness of insulin action, or both. Diabetes mellitus is classified on the basis of aetiology and clinical presentation of the disorder into four types: type 1 diabetes, type 2 diabetes, gestational diabetes, and other specific types. Impaired glucose tolerance (IGT ) is an asymptomatic condition defined by elevated (though not diabetic) levels of blood glucose two hours after a 75g oral glucose challenge. Along with impaired fasting glucose, it is now recognized as being a stage in the transition from normality to diabetes. Thus, individuals with IGT are at high risk of progressing to type 2 diabetes, although such progression is not inevitable, and probably over 30% of individuals with IGT will return to normal glucose tolerance over a period of several years.

should be used when interpreting data and their limitations will be discussed further throughout the text. Comparison of country, regional, and even global rates from one report to the next can be misleading and should be performed with extreme caution. Large changes in the prevalence or numbers of people with diabetes from one edition of the Diabetes Atlas to another are usually due to the use of a more recent study rather than a genuine change in the profile of diabetes within that country. Thus, the inclusion of recent, and more reliable research brings us closer to the actual rates of diabetes, but also brings with it dangers in comparing global reports and estimates over time. These limitations need to always be considered, and the reader must realize that the key purpose of reports such as these is to stimulate action in the form of preventive and management programmes, as well as further research.

The data presented in this chapter should be cautiously interpreted as general indicators of diabetes frequency, and the estimates will need to be revised as new and better epidemiological information becomes available. When reporting data in this form, various assumptions need to be made that give rise to a number of limitations. Caution DIABETES AND IMPAIRED GLUCOSE TOLERANCE

CHAPTER 1

15

1.1 PREVALENCE AND PROJECTIONS This report should act as a stimulus for intervention. Perhaps the most essential aspect of research is the action taken as a result of findings. Diabetes requires culturally appropriate intervention in order to reduce the enormous personal suffering and economic burden that grows with this epidemic.

Introduction

D

iabetes mellitus and lesser forms of glucose intolerance, particularly impaired glucose tolerance (IGT), can now be found in almost every population in the world and epidemiological evidence suggests that, without effective prevention and control programmes, diabetes will likely continue to increase globally1.

Figure 1.1 highlights the large range of type 2 diabetes prevalences even within the same or similar ethnic groups, when living under different conditions. Clearly, many of the differences between these rates reflect underlying behavioural, environmental and social risk factors, such as diet, level of obesity and physical activity.

Type 1 diabetes usually accounts for only a minority of the total burden of diabetes in a population; it is the predominant form of the disease in younger age groups in most developed countries. Type 1 diabetes is increasing in incidence in both developing and developed countries, and there is an indication of a shift towards type 1 diabetes developing in children at earlier ages (see Chapter 2).

Within ethnic groups, high rates of type 2 diabetes are usually found in migrant or urbanized populations that may have experienced a greater degree of lifestyle change. The lowest rates are generally found in rural communities where people have lifestyles incorporating high levels of physical activity.

Type 2 diabetes constitutes about 85 to 95% of all diabetes in developed countries1, and accounts for an even higher percentage in developing countries. Type 2 diabetes is now a common and serious global health problem, which, for most countries, has evolved in association with rapid cultural and social changes, ageing populations, increasing urbanization, dietary changes, reduced physical activity 16

and other unhealthy lifestyle and behavioural patterns 1.

CHAPTER 1

The incidence and prevalence of type 2 diabetes is also reported to be increasing in children. Studies from America and Japan have demonstrated an increasing incidence2,3, while other ethnic groups with high adult diabetes prevalence such as the Pima Indians4 are also reporting increasing adolescent prevalences (see Chapter 2). The importance of this problem and the need for further research are emphasized by the authors of this chapter. DIABETES ATLAS THIRD EDITION



Figure 1.1 Differences in the prevalence of type 2 diabetes among selected ethnic groups, 2007

Rural Bangladesh

Asian Indian

Arab

Singapore Indian Rural Tunisia United Arab Emirates

Hispanic

Chinese

African

Oceania

Rural Colombia Urban Mexican China Singapore Chinese Rural Tanzania African Jamaican Rural Fiji Nauru Prevalence (%)

0

5

10

2007 2025

In addition to estimating the prevalence of diabetes for the years 2007 and 2025, data on case numbers and national prevalence of impaired glucose tolerance (IGT) are presented for both years. The decision to include data on IGT was based on two major factors associated with its presence: it greatly increases the risk of developing diabetes5, and it is associated with the development of cardiovascular disease6,7.

Classification criteria and reporting standards Standardization of methods and reporting in diabetes epidemiology promotes comparison between studies and may permit the pooling of results from different studies8,9. Standardized criteria for detecting and reporting glucose intolerance have evolved greatly since the 1960s10. In the late 1970s both the US National Diabetes Data Group (NDDG) and the World Health Organization (WHO) produced new criteria on which to diagnose diabetes mellitus. In 1985, WHO modified their criteria to be more consistent with NDDG values. More recently, the American Diabetes Association (ADA) 11 and WHO 12 have produced new recommendations for the diagnosis of diabetes. The major PREVALENCE AND PROJECTIONS

15

20

25

30

35

40

45

Prevalence rates are age standardized to Segi’s World Population for ages 30-64 year

change recommended is the lowering of the diagnostic value of the fasting plasma glucose concentration to 7.0 mmol/l. For glucose tested in whole blood, the new recommended threshold is 6.1 mmol/l12. In many population studies, individuals have been categorized as having diabetes mellitus based on blood glucose values measured after an overnight fast and/or two hours after a 75g oral glucose load. Whilst WHO still recommends the oral glucose tolerance test (OGTT) as being the single best choice, they also state that “if it is not possible to perform the OGTT (e.g. for logistical or economic reasons), the fasting plasma glucose alone may be used for epidemiological purposes” 12. It is important to realize that different screening and diagnostic criteria may have been used for different studies in this report. The impact that the recent diagnostic cut-off level changes have on prevalence estimates seems to vary from country to country13. In this section, the criteria used will be reported when they are known.

Global estimates of diabetes The global burden of diabetes has been estimated several CHAPTER 1

17

Estimates for 2025

Why two prevalence estimates?

The estimates for 2025 of this edition are slightly different to those published most recently in 2003. There are two main reasons for this. Most importantly 30 new studies, applied to 70 countries, have been used. New studies were only used when it was felt that they improved the assessment of prevalence.

Prevalences have been calculated for each country and region in two ways:

Secondly, the 2004 edition of the United Nations Population Prospects, rather than the 2000 edition for the population of each country, was used. This has only very marginally changed the estimate of world adult population for 2025, but for individual countries the changes are occasionally important. For example, the population estimate (age 20–79 years) for Bangladesh has been reduced by nine million, while that for Ethiopia has increased by seven million.

National or regional prevalence The national or regional prevalence indicates the percentage of each country’s or region’s population that has diabetes. It is ideal for assessing the burden of diabetes for each country or region. However, because the prevalence of diabetes increases with age, it cannot be used for comparing prevalences between countries or regions which have different age structures. For example, the national prevalence of diabetes is higher in Japan (7.2%) than in Samoa (6.5%), but we cannot tell if this is just because Japan has an older population or because Japanese are more prone to develop diabetes than are Samoans.

times14-17. In 1994, the International Diabetes Federation (IDF) Directory14 included type 1 and type 2 diabetes estimates supplied by member nations. Using these data IDF estimated that over 100 million people worldwide had diabetes. Also in 1994, McCarty et al15 used data from population-based epidemiological studies and estimated that the global burden of diabetes was 110 million in 1994 and that it would likely more than double to 239 million by 2010.

obtaining age-specific prevalences for those countries with adequate data are given.

WHO16 also produced a report using epidemiological information and estimated the global burden at 135 million in 1995, with the number reaching 299 million by the year 2025. In 1997, Amos et al17 estimated the global burden of diabetes to be 124 million people, and projected that this would increase to 221 million people by the year 2010. Despite using different methodologies, and at times showing large differences in country-specific estimates, these reports have arrived at remarkably similar global figures of diabetes.

2. Employing the methodology indicated in Appendix 1.1 to create smoothed curves for prevalence (with respect to age).

Methodology The principal details of the methodology are provided in Appendix 1.1, where details of the rationale and process of 18

1. National or regional prevalence 2. Comparative prevalence

CHAPTER 1

The principal aspects of the determination of prevalence were: 1. Identification of studies through a detailed literature search, and contact with IDF member organizations.

3. Applying the prevalence rates to the population distribution of that country, and where no data for those countries were available, to those other countries of similar ethnicity and economic circumstances, for which no local data were available. 4. Assuming an urban/rural prevalence ratio of 2:1 for diabetes (but not IGT ), except in those countries classified by WHO16 as market economies, or former socialist economies. The urban proportion of the population was derived from UN estimates18. DIABETES ATLAS THIRD EDITION



At a glance

Comparative prevalence The comparative prevalence has been calculated by assuming that every country and region has the same age profile (the age profile of the world population has been used). This removes the differences of age between countries and regions, and makes this figure ideal for making comparisons, for example, the comparative prevalence shows that Samoans (7.5%) are in fact more prone to have diabetes than are Japanese (4.9%). The comparative prevalence should not be used for assessing the proportion of people within a country or region who have diabetes.

5. The data for diabetes rates include both type 1 and type 2 diabetes, with a separate chapter providing estimates on type 1 diabetes in children and adolescents (see Chapter 2). 6. The prevalence of diabetes throughout the Diabetes Atlas includes both undiagnosed and previously diagnosed diabetes. This section contains prevalence estimates of diabetes and IGT for the years 2007 and 2025, and although the Tables contain data listed to one decimal point, it should not be inferred that this indicates the degree of precision, but rather to facilitate calculations and the appearance of the Tables. In general, no predictions of diabetes or IGT numbers should be taken as having reliability of more than one significant figure. The consequence of applying current age and gender specific prevalence rates to estimate prevalences and number of cases for the year 2025 is that only changes in the age and urban/rural distribution of the population will affect the estimates. Since it is likely that the age specific prevalence rates (the prevalence at any given age) will rise due to increasing obesity, the figures are probably underestimates. PREVALENCE AND PROJECTIONS

Total world population (billions) Adult population (age 20-79, billions)

2007 2025 6.6 4.1

7.9 5.2

WORLD DIABETES AND IGT (20-79 age group) Diabetes Comparative prevalence (%) 6.0 Number of people with diabetes (millions) 246

7.3 380

IGT Comparative prevalence (%) Number of people with IGT (millions)

8.0 418

7.5 308

Results The main aim of this section is to estimate the prevalence of diabetes mellitus and IGT for each country for the years 2007 and 2025. Data are provided for 215 countries and territories, which have been allocated mostly on a geographical basis into one of the seven IDF regions: Africa (AFR), Eastern Mediterranean and Middle East (EMME), Europe (EUR), North America (NA), South and Central America (SACA), South-East Asia (SEA), and the Western Pacific (WP). The prevalence of diabetes and IGT has been calculated in two ways: 1. National prevalence: the age and sex structure of each specific country has been used to provide an accurate estimate of the percentage of adults affected within each country. 2. Comparative prevalence: the age and sex structure of the world population has been used to provide a prevalence estimate for each country that can readily be compared to other countries. CHAPTER 1

19

MAP 1.1 Prevalence estimates of diabetes, 2007

>20% 14% 10% 8% 6% 4% <4%

-

20% 14% 10% 8% 6%

MAP 1.2 Prevalence estimates of diabetes, 2025

>20% 14% 10% 8% 6% 4% <4%

20

CHAPTER 1

-

20% 14% 10% 8% 6%

DIABETES ATLAS THIRD EDITION



MAP 1.3 Prevalence estimates of impaired glucose tolerance, 2007

>20% 14% 10% 8% 6% 4% <4%

-

20% 14% 10% 8% 6%

MAP 1.4 Prevalence estimates of impaired glucose tolerance, 2025

>20% 14% 10% 8% 6% 4% <4%

-

20% 14% 10% 8% 6%

PREVALENCE AND PROJECTIONS

CHAPTER 1

21

Figure 1.2

Figure 1.3

World population (20-79 age group) by region, 2007 and 2025

Prevalence of diabetes* (20- 79 age group) by region, 2007 and 2025

Millions

12

2,000

Prevalence (%)

10

1,600 8

1,200 6

800 4

400

2

0

0

AFR

EMME

EUR

NA

SACA

SEA

WP

2007 2025

The data presented are for all diabetes combined, i.e. type 1 and 2 diabetes, and for IGT. Only adults aged from 20 to 79 years of age are considered because the majority of all people who have diabetes and IGT are adults. Type 1 and type 2 diabetes in children and adolescents are addressed separately in Chapter 2. It should be noted that column numbers in the Tables may not always exactly be the sum of the components because of rounding effects.

Demography The total populations of the regions and the population aged from 20-79 years are shown in Figure 1.2. It is clear that the Western Pacific Region, which includes China, and the South-East Asian Region, which has India as a member, have the greatest numbers of people.

Diabetes Prevalence It is estimated that approximately 246 million people, or 5.9%, in the age group 20-79 will have diabetes worldwide in 2007. Some 80% of these live in the developing countries. The worldwide estimate is expected to increase to some 380 22

CHAPTER 1

AFR

EMME

2007

EUR

NA

SACA

SEA

WP

*Comparative prevalence

2025

million, or 7.1% of the adult population, by 2025 (see Table 1.1). The largest increases will take place in the regions dominated by developing economies. The Western Pacific Region with 67 million and the European Region with 53 million will have the highest number of people with diabetes in 2007. However the comparative prevalence rate (adjusted to the world population) of 4.4% for the Western Pacific Region is significantly lower than 9.2% for the Eastern Mediterranean and Middle East Region, and 8.4% in the North American Region (see Figure 1.3). By 2025 the diabetes prevalence of the South and Central America Region is expected to be nearly as high (9.3%) as that of the North American Region (9.7%). The Eastern Mediterranean and Middle East Region will continue to have the highest rate of prevalence with 10.4% of its adult population affected by diabetes. The age structure of the population has a large effect on the relative prevalences. The European and North American Regions have considerably older populations, so that without reference to an age-standardized population, the European DIABETES ATLAS THIRD EDITION



Figure 1.4

Figure 1.5

Number of people with diabetes (20-79 age group) by region, 2007 and 2025

Number of people with diabetes in age groups by region, 2007

100

Millions

Millions 40

90

35

80 30 70 25

60 50

20

40

15

30 10 20 5

10 0

0

AFR

EMME

EUR

NA

SACA

SEA

WP

2007

AFR 20 - 39

EMME 40 - 59

EUR

NA

SACA

SEA

WP

60 - 79

2025

Region has the second highest prevalence, for both years (see Tables 1.17 and 1.18). When adjusting to the same population structure, the European Region has the third highest prevalence for 2007, and the fourth for 2025 (see Table 1.1).

there are expected to be two million more women than men with diabetes (124 million women vs 122 million men), with this difference expected to be about four million by 2025 (192 million vs 188 million).

It is the Western Pacific Region, however, which will have the highest number of people with diabetes, with some 100 million, representing an almost 50% increase from 2007 (see Figure 1.4).

Urban/rural distribution In 2007 the expected number of people with diabetes in urban areas will be 86 million, compared to 66 million in rural areas in countries not considered to be established market economies or former socialist economies. By 2025 it is expected that this discrepancy will increase to 179 million urban and 81 million rural persons with diabetes.

Age distribution The 40-59 year age group currently has the greatest number of persons with diabetes with some 113 million, of which more than 70% live in developing countries (see Figure 1.5). By 2025, because of the ageing of the world’s population, there will be 166 million with diabetes aged 40-59, over 80% of which will be in newly developed or developing countries. There will be almost as many in the 60-79 age group, approximately 164 million (see Figure 1.6). Gender distribution The estimates for both 2007 and 2025 showed little gender difference in the number of persons with diabetes. For 2007 PREVALENCE AND PROJECTIONS

Impaired Glucose Tolerance Prevalence It is estimated that approximately 308 million, or 7.5% in the age group 20 – 79, will have IGT in 2007, of which more than 80% live in developing countries. By 2025 the number of people with IGT is projected to increase to 418 million, or 8.1%, in the adult population (see Table 1.2). The Western Pacific Region is expected to have the greatest number of people with IGT in 2007 with some 112 million, CHAPTER 1

23

Figure 1.6

Figure 1.7

Number of people with diabetes by age group, 2007 and 2025

Prevalence of impaired glucose tolerance* (20-79 age group) by region, 2007 and 2025

200

Millions

12

175

Prevalence (%)

10

150

8 125

6

100

75

4

50

2 25

0

0

Age group

20 - 39

40 - 59

60 - 79

AFR

EMME

EUR

NA

SACA

SEA

WP



*Comparative prevalence

2007

2007 2025

2025

Figure 1.8

Figure 1.9

Number of people with impaired glucose tolerance (20-79 age group) by region, 2007 and 2025

Prevalence of diabetes and impaired glucose tolerance* (20-79 age group) by region, 2007 and 2025

Millions

Prevalence (%)

160

25

140 20 120

100

15

80 10

60

40 5 20

0

0

AFR

24

EMME

EUR

NA

SACA

SEA

WP

AFR

EMME

EUR

SACA

SEA

WP



2007

Diabetes

2007

2025

IGT

2025

CHAPTER 1

NA

*Comparative prevalence

DIABETES ATLAS THIRD EDITION



Figure 1.10

Figure 1.11

Number of people with diabetes and impaired glucose tolerance (2079 age group) by region, 2007 and 2025

Number of people with impaired glucose tolerance by age group, 2007 and 2025

Millions

175

300

Millions

150

250

125 200 100 150 75 100 50

50

25

0

0

AFR

EMME

EUR

Diabetes

2007

IGT

2025

NA

SACA

SEA

WP

although the European Region has the highest prevalence rate with 9.1% of the adult population affected by IGT (see Figure 1.7). By 2025, absolute numbers of persons with IGT are generally likely to increase by 30-70% in many regions, with the greatest increases in Africa, and the Eastern Mediterranean and Middle East (see Figure 1.8). The prevalence of IGT is generally similar to that of diabetes, but somewhat higher for the African and Western Pacific Regions, but slightly lower than of diabetes in the North American Region (see Figure 1.9). Figure 1.10 highlights the large increases in absolute numbers of both diabetes and IGT over the 18-year period. Age distribution As with diabetes, the 40-59 year age group is expected to have the greatest number of persons with IGT for 2007 with some 122 million, and this will remain true in 2025 with 164 million as shown in Figure 1.11. It is also of note that one-third of all those who will have IGT for 2007 are in the 20-39 year age group.

Age group

20-39

40-59

60-79

2007 2025

Regional overview Africa Diabetes exerts a considerable toll on the health resources of the developing countries of sub-Saharan Africa. The chronicity of the disease and diabetic complications places an immense burden on people with diabetes and their families. The landscape of sub-Saharan Africa is dominated by the twin disasters of poverty and HIV infection. While HIV infection and consequent AIDS so dominate the health needs for sub-Saharan Africa, there is only a small proportion of the population reaching ages at which type 2 diabetes becomes a major health concern. In 2007 only 9.9% of the population will be 50 years of age or older, and this is expected to increase to only 10.5% by 2025. Thus the effects of HIV and malnutrition combine to greatly reduce the size of groups most at risk for type 2 diabetes. Diabetes and IGT prevalence It is estimated that there will be 10.4 million people with diabetes, or 3.1% of the adult population, in the African

PREVALENCE AND PROJECTIONS

CHAPTER 1

25

AFRICA At a glance Total population (millions) Adult population (millions) (20-79 years)

2007 2025 747 1,088 336 537

Diabetes and IGT (20-79 age group) Diabetes Regional prevalence (%) 3.1 Comparative prevalence (%) 3.6 Number of people with diabetes (millions) 10.4

3.5 4.5 18.7

IGT Regional prevalence (%) 7.2 Comparative prevalence (%) 8.2 Number of people with IGT (millions) 24.2

7.5 9.2 40.3

Region in 2007 (see Table 1.7). There are marked discrepancies between the rates of diabetes prevalence among different communities in sub-Saharan Africa. The highest prevalences are among the ethnic Indian population of Tanzania19 and South Africa20. The studies from Tanzania21,22 (urban:rural ratio of 5:1) and Cameroon23 (ratio of 2:1) both confirm the marked urban/rural discrepancy in diabetes prevalence, with the consequent likely increases in numbers with diabetes as more people move to urban areas. The availability of prevalence data for sub-Saharan Africa is very limited, and nearly all the data here were derived from studies from South Africa 24-27, Tanzania 21,22, Ghana 28, Cameroon23,29 and Sudan30. This meant that data from these studies were applied to populations living up to several thousand kilometres from where the study was undertaken. In the three years since the last edition of the Diabetes Atlas (2003), only two further unpublished studies27,29 have been made available for this report. Whereas the previously used Cameroon data23 indicated a much lower prevalence of urban diabetes than the Ghana data, the new data from Cameroon29 indicate a very similar diabetes prevalence to that for Ghana, but much lower IGT 26

CHAPTER 1

prevalence. Notwithstanding that Cameroon and Ghana are about 1,000 kilometres apart, and classified by the United Nations (UN)31 as being in different parts of Africa (central and western, respectively), it was decided to use the average of the results of the two studies to apply to the other African countries in the region. That the data should need to be extrapolated to such distant and probably dissimilar countries and populations indicates the great need for further epidemiological investigation in the region. Such a need can also be linked with the high proportion of diabetes that has not been previously detected, but found only at the time of surveying. Undiagnosed diabetes accounted for 80% of those with the condition in Cameroon29, 70% in Ghana28 and over 80% of the 2002 Tanzania survey22 (See Chapter 1.2). The impact of type 2 diabetes is bound to continue if nothing is done to curb the rising prevalence of impaired glucose tolerance, which now varies between 0.9% and 16.5% (see Table 1.9).

Eastern Mediterranean and Middle East Studies performed in six countries of the Eastern DIABETES ATLAS THIRD EDITION



Eastern Mediterranean and Middle East At a glance Total population (millions) Adult population (millions) (20-79 years)

2007 2025 592 814 318 492

Diabetes and IGT (20-79 age group) Diabetes Regional prevalence (%) 7.7 Comparative prevalence (%) 9.2 Number of people with diabetes (millions) 24.5

9.0 10.4 44.5

IGT Regional prevalence (%) 7.0 Comparative prevalence (%) 8.1 Number of people with IGT (millions) 22.4

7.8 8.8 38.6

Mediterranean and Middle East Region – Bahrain32, Egypt33,34, Kuwait35, Oman36, Saudi Arabia37-40 and United Arab Emirates41 – have shown their current diabetes prevalence to be among the world’s 10 highest, and a similar situation applies for the IGT prevalences of some of these countries (see Tables 1.3 and 1.5). The ageing of populations, together with socioeconomic changes and westernization, has resulted in the dramatic increase in the diabetes prevalence. Over the past three decades major social and economic changes have occurred in the majority of these nations. These include progressive urbanization, decreasing infant mortality and increasing life expectancy. Rapid economic development, especially among the more wealthy oilproducing countries, has been associated with tremendous changes in lifestyle towards the westernized pattern reflected by changes in nutrition, less physical activity, tendency to increased obesity and more smoking42-46. Diabetes and IGT prevalence The explosion of diabetes in the EMME Region is mainly due to type 2 diabetes. As with many other countries with high diabetes prevalence, the onset of type 2 diabetes tends to occur at a relatively young age. An estimated 24.5 million PREVALENCE AND PROJECTIONS

people, or 7.7% of the adult population, will have diabetes in 2007 (see Table 1.12), with the number of those with diabetes expected to nearly double by 2025. Similarly the number of persons with IGT is expected to also rise markedly by 2025, increasing the likelihood of further increases in the prevalence of diabetes as the century proceeds. The comparative prevalences for 2007, when applied to a world standard population distribution rather than the young population distribution common in the region, are as high as 20% in the United Arab Emirates41, 15% in Bahrain and Qatar32, but even in much less affluent Pakistan the prevalence is 9.6%47-49. In contrast to Africa, there is a large number of studies reporting diabetes prevalence, so that of the 22 countries of the Region, 18 have data available, from which national prevalence estimates could be derived (see Table 1.16). Since the second edition of the Diabetes Atlas, new data have been included for Algeria50, Iran51, Morocco52, Occupied Palestinian Territory53,54, Saudi Arabia38-40, Syrian Arab Republic55 and Yemen56. These new studies have led to an increase in estimated prevalence for all of these populations, except Yemen, for which data from Oman had previously been used36. CHAPTER 1

27

EUROPE At a glance Total population (millions) Adult population (millions) (20-79 years)

2007 2025 883 891 634 653

Diabetes and IGT (20-79 age group) Diabetes Regional prevalence (%) 8.4 Comparative prevalence (%) 6.6 Number of people with diabetes (millions) 53.2

9.8 7.8 64.1

IGT Regional prevalence (%) 10.3 Comparative prevalence (%) 9.1 Number of people with IGT (millions) 65.3

10.9 9.6 71.2

Europe There exists a great diversity of populations and affluence among the 53 countries and territories in the European Region, with gross domestic product (GDP) varying from over USD60,000 per capita for Luxembourg to less than USD2,000 for several of the former socialist republics57. Diabetes and IGT prevalence The number of people with diabetes in this vast region is expected to reach 53.2 million, or 8.4% of the adult population in 2007. National prevalence rates for diabetes show a wide variation from 2.0% in Iceland to 11.8% in Germany (see Table 1.17). Abnormal glucose tolerance in this region shows little association with affluence, and there was no evidence that any difference in urban/rural prevalence existed except in Turkey58, and the Central Asian Republics of Kazakhstan, Kyrgyzstan, Tajikistan and Turkmenistan (for which data were extrapolated from neighbouring Uzbekistan59). The lack of data from several of the former socialist republics required that data for many countries be extrapolated from two studies from Poland - urban Krakow60, and the urban and rural areas near Lublin61. These data suggested high levels of 28

CHAPTER 1

diabetes currently, and such high levels of IGT that the diabetes prevalence will almost certainly increase by 2025 to levels above those indicated in Table 1.18, as these took no account of the higher incidence of diabetes among those with IGT. Surprisingly there is a paucity of good data from many of the more affluent western countries of the region. Much of the data for Europe is based on surveys establishing the prevalence of ‘known diabetes’. This applied to reports from France62, Germany63-65, Israel66, Italy67, Netherlands68 and Norway69. The prevalence rates of these reports were doubled to estimated total diabetes, based on other European data70-73. In comparison with the second edition of the Diabetes Atlas, national data from several countries – Albania, Cyprus, Denmark, France and Norway – have been used, which has reduced the need to extrapolate from other countries. Nonetheless, there remains a marked lack of data for eastern Europe, so that survey results from Poland were used for 12 other countries. To a large degree the high prevalence of abnormal glucose tolerance is a consequence of the relatively old population of the European Region, such that currently a third of the DIABETES ATLAS THIRD EDITION



North America At a glance Total population (millions) Adult population (millions) (20-79 years)

2007 2025 462 536 306 376

Diabetes and IGT (20-79 age group) Diabetes Regional prevalence (%) 9.2 Comparative prevalence (%) 8.4 Number of people with diabetes (millions) 28.3

10.8 9.7 40.5

IGT Regional prevalence (%) 6.4 Comparative prevalence (%) 5.8 Number of people with IGT (millions) 19.6

7.3 6.7 27.5

population is over 50 years of age, and is expected to increase to over 40% by 2025. Thus the number of persons with diabetes and IGT will increase, although the total regional population will have decreased. This will place an increasing financial burden on the declining working-age population to provide resources for the health consequences of rising diabetes prevalence in the older population. The region has the resources to be at the forefront of efforts to amend lifestyle factors contributing to the prevalence of diabetes.

North America The North American Region has the highest prevalence of diabetes among the IDF regions with 9.2%, or 28.3 million persons with diabetes in the adult population, for 2007 (see Table 1.22). The region is expected to continue to have the highest prevalence in 2025 when 10.8% of adults are anticipated to have diabetes. Although the region comprises 24 countries and territories, 68% of the adult population reside in the United States of America (USA), with a further 21% living in Mexico and 8% in Canada, so that only 3% of the region’s adult population reside in the other 21 smaller nations. PREVALENCE AND PROJECTIONS

Diabetes and IGT prevalence The high prevalence of abnormal glucose tolerance for Canada and the USA are very much a consequence of their older age distribution, such that 30% of their population are over 50 years of age, and expected to be 36% by 202531. This is in contrast to 16% of those over 50 years of age increasing to 26% for Mexico, and 20% increasing to 28% for the Caribbean. The data published in Tables 1.24 and 1.25 indicate the expected number of persons with impaired fasting glucose (IFG) for Canada and the USA, based on the data from the National Health and Nutrition Examination Survey (NHANES) 1999-200074, which based assessment on the fasting glucose. NHANES III data75 suggested that IGT prevalence was about 50% higher than for IFG, on which basis about 18 million Americans are likely to affected by IGT in 2007 (9% prevalence), and nearly 25 million in 2025. As all the Caribbean islands other than Barbados, Guadeloupe and Martinique had their estimates extrapolated from Jamaican data76, the differences in prevalence are a consequence only of different age distributions for the islands. There were new studies of diabetes prevalence used for CHAPTER 1

29

South and Central America At a glance Total population (millions) Adult population (millions) (20-79 years) Diabetes and IGT (20-79 age group)

2007 2025 451 548 272 364

Diabetes Regional prevalence (%) 6.0 Comparative prevalence (%) 6.3 Number of people with diabetes (millions) 16.2

9.0 9.3 32.7

IGT Regional prevalence (%) 7.3 Comparative prevalence (%) 7.5 Number of people with IGT (millions) 19.8

7.6 7.9 27.6

USA77 and Mexico78,79, which hardly affected the USA estimate, but somewhat increased that for Mexico.

South and Central America The South and Central American Region encompasses 22 countries and territories, most of which are still developing economically. It is estimated that 16.2 million persons, or 6.0% of the adult population, will have diabetes in 2007 (see Table 1.27). In the next 18 years, the number of people with diabetes is expected to rise dramatically to 32.7 million. Diabetes and IGT prevalence Considerable extrapolation was required in this region as 15 countries did not have any epidemiological data from which prevalence estimates could be derived. New studies in Mexico78,79 were used to extrapolate prevalence estimates for several countries in the region. New studies were also used for Argentina80, Brazil81 and Chile82, all of which were of specific regional populations. South America and Central America have similar age distribution profiles. Currently about 17% of the population is older than 50 years, with this figure likely to increase to 30

CHAPTER 1

25% by 2025. Thus the region has a markedly younger age distribution than most of North America. The likelihood is that diabetes will become a more major health priority for the region given the decreasing difference in age distribution between this region and North America, and with the continuing momentum for urbanization.

South-East Asia The South-East Asian Region comprises only seven countries. The adult population of India accounts for 85% of that of the region. Mauritius has the highest per capita GDP at USD13,300, while the other countries all have per capita GDPs of less than USD5,000, although India with a current annual growth of 7.1% is experiencing economic development at a faster pace than almost anywhere in the world except its neighbour, China57. Diabetes and IGT prevalence There will be an estimated 46.5 million people, or 6.0% of the adult population, with diabetes in the region in 2007 (see Table 1.32). Economic progress is inevitably associated with increasing urbanization, and it appears that features of urban life tend to increase the prevalence of diabetes among persons of Indian ethnic background to a greater extent than for other populations83. DIABETES ATLAS THIRD EDITION



South-East Asia At a glance Total population (millions) Adult population (millions) (20-79 years)

2007 2025 1,336 1,656 770 1,083

Diabetes and IGT (20-79 age group) Diabetes Regional prevalence (%) 6.0 Comparative prevalence (%) 6.5 Number of people with diabetes (millions) 46.5

7.4 8.0 80.3

IGT Regional prevalence (%) 5.9 Comparative prevalence (%) 6.0 Number of people with IGT (millions) 45.2

6.5 6.7 70.5

The second edition of the Diabetes Atlas used data from a single report84, based on a population-based survey from the six largest Indian cities, and extrapolated these results nationwide, applying a 4:1 urban:rural ratio from these findings for diabetes prevalence (the majority of the Indian population is classified as rural). For this report, two additional reports of population data collected on a nationwide basis85,86 were used, which suggest that diabetes prevalence in smaller urban centres (100,000 – 1,000,000 inhabitants) tends to be about half of the larger cities, but still twice that of rural areas (less than 100,000 persons). This has led to a 30% reduction in expected urban diabetes cases, but no change to rural diabetes estimates. The anticipated increase in regional diabetes prevalence from 6.0% to 7.4% in 2025 is very much a consequence of the increasing life expectancy in India, where the proportion of the population over 50 years is expected to increase from 16% to 22% between 2007 and 202531, and the urban proportion from 31% to 43%87 (see Table 1.33). Evidence suggests that in more affluent parts of the country, the rural prevalence is higher than less affluent rural areas88, indicating that increasing economic growth will increase diabetes prevalence in India even more than these possibly conservative estimates have indicated. PREVALENCE AND PROJECTIONS

With regard to IGT, the same nationwide study indicated the same pattern as for diabetes, suggesting large cities to have twice the prevalence of smaller cities, for which the prevalence is twice those of rural areas. As India is a predominantly rural country, this has led to a marked reduction in the overall IGT numbers projected for 2007 and 2025 (see Tables 1.34 and 1.35), such that both are about half those previously projected in the second edition of the Diabetes Atlas. Mauritius, the second smallest country of the region, highlights the extent to which persons of Indian ethnicity appear predisposed to diabetes, when exposed to more affluent economic circumstances. This island has the world’s eighth highest diabetes prevalence (of countries with representative prevalence data); currently 11% and expected to be 13% by 2025, and a similarly high IGT prevalence of 16%, likely to rise to nearly 18%. Additional data have also become available for Nepal89,90, Bangladesh 91 and Sri Lanka 92. These additional studies have not markedly affected the Bangladeshi or Nepali estimates, but have markedly increased those for Sri Lanka. CHAPTER 1

31

Western Pacific At a glance Total population (millions) Adult population (millions) (20-79 years)

2007 2025 2,168 2,397 1,469 1,732

Diabetes and IGT (20-79 age group) Diabetes Regional prevalence (%) 4.6 Comparative prevalence (%) 4.4 Number of people with diabetes (millions) 67.0

5.7 5.1 99.4

IGT Regional prevalence (%) 7.6 8.2 Comparative prevalence 7.5 7.8 Number of people with IGT (millions) 111.9 142.7

Western Pacific The world’s most populous region contains 39 disparate countries and territories with populations ranging from 1.3 billion for China to less than 5,000 in the smallest Pacific island nations of Niue and Tokelau. Similarly the economic profile varies from per capita GDPs of over USD30,000 for Australia, Hong Kong, Japan and Singapore to less than USD3,000 in one-third of the other countries. The less economically advanced countries struggle with the double burden of managing infectious diseases and the diabetes epidemic with limited resources. Many also face the lack of government awareness of the seriousness of the diabetes threat to their populations. Diabetes and IGT prevalence Not surprisingly there is a great diversity in the prevalence of diabetes, with the world’s highest found in the Micronesian population of Nauru (30.7% of the adult population). Several new studies have been included since the second edition of the Diabetes Atlas, incorporating data from urban and rural areas of Cambodia93, Philippines94 and Thailand95, and 32

CHAPTER 1

new surveys from South Korea96, Viet Nam97, Singapore98 and China99. The age specific prevalence estimates of these studies were also applied to several other countries of the region. The use of these studies led to higher prevalence estimates for all of the countries in which they were performed, except Singapore. For Cambodia, this was a doubling in national prevalence, for the Philippines a tripling, and for Thailand more than tripling; all of these countries had prevalence estimates previously based on a 1996 report from Thailand100. For Viet Nam, the estimates based on the Ho Chi Minh City survey of 2001 are twice those from the survey a decade previously from Hanoi101. For South Korea, the new survey96 showed a 25% higher prevalence than of that used previously102. The new Singapore data led to slightly lower overall prevalence than the 1998 data103. The new Chinese data have the largest impact on overall prevalence and case number estimates, as 60% of the region’s population live there. The national prevalence estimate is 60% higher than that derived from the previously used survey104. Whereas that survey from 1994 was OGTT-based, with classification based on 1985 WHO criteria105, the current report based diagnosis (of diabetes, or IFG) on fasting glucose DIABETES ATLAS THIRD EDITION



Figure 1.12 Top 10: Prevalence of diabetes* (20-79 age group) in 2007 (with 2025 prevalence)

COUNTRY Nauru United Arab Emirates Saudi Arabia Bahrain Kuwait Oman Tonga Mauritius Egypt Mexico

Prevalence (%)

5

0

10

15

2007

20

25

30

35

40

*Comparative prevalence

2025

levels106 as well as self-report. The current report is also more nationally representative, albeit with fewer participants. In addition to the fasting criterion indicating a higher diabetes prevalence than previously estimated, the Chinese IFG prevalence of 6.9% is double that of the previous IGT estimate. There was no evident urban/rural IFG gradient, but diabetes prevalence was about 50% higher in urban areas. Only 25% of diabetes had been previously diagnosed. The diabetes epidemic has the greatest potential to explode in China, simply because of its population size. Although the current national prevalence there of 4.3% is among the region’s lowest, the high prevalence among Chinese populations in the more urbanized and affluent cities of Hong Kong and Singapore indicate what may develop as China rapidly urbanizes and expands economically. The data indicated for 2025 in Table 1.38 are likely to represent an underestimate of China’s diabetes problem if it continues to develop economically faster than almost any other country in the world.

Discussion In order to make national, regional and global predictions for PREVALENCE AND PROJECTIONS

the prevalence of diabetes, a number of assumptions needed to be made, and therefore the results are subject to a number of limitations. In addition to those highlighted in the Methodology section in Appendix 1.1, some of these are that: • The studies included in this section often used differing screening techniques. The majority of studies used an OGTT to screen for diabetes. However, some studies used a fasting blood glucose (FBG), some a two-hour blood glucose (2BG), some a random blood glucose (RBG), and some based their data on self-report (SR). It is difficult to control for this unless, for example, only those studies that used an OGTT were included. This would also have the effect of excluding findings from countries lacking OGTT data, which would result in data for those countries being extrapolated from another country. The other consequence of incorporating studies that had no OGTT data is that impaired fasting glucose (IFG) rather than IGT represented the non-diabetic, but abnormal, glucose metabolism. • There were inconsistencies in the diagnostic criteria adopted, resulting from the updating of the diagnostic CHAPTER 1

33

Figure 1.13 Top 10: Prevalence of impaired glucose tolerance* (20-79 age group) in 2007 (with 2025 prevalence)

COUNTRY Nauru United Arab Emirates Bahrain Kuwait Singapore Kiribati Mauritius Poland Tonga Denmark

Prevalence (%)

0

5

10

15

2007

20

25

30

35

40

*Comparative prevalence

2025

criteria in 199711. The use of a lower fasting diagnostic criterion for diabetes will tend to result in a higher prevalence of diabetes and lower prevalence of IGT. The diagnostic criteria used for each country are indicated in the Tables on data sources. • Studies from several countries (Canada, France, Germany, Israel, Italy, Netherlands, New Zealand, Norway) only provided data on self-reported diabetes. To account for undiagnosed diabetes, the prevalence of diabetes for Canada was multiplied by a factor of 1.5, in accordance with findings from the USA75, and for the other countries doubled, based on data from a number of countries70-73,107. • If a country lacked data, it was assumed that their age and sex-specific prevalence rates of diabetes mellitus were the same as those rates in another socio-economically, ethnically and geographically similar country. • Some of the studies were performed more than a decade ago, and thus may not reflect current prevalence rates. The prevalences and numbers of persons predicted based on such studies are likely to be conservative estimates. 34

CHAPTER 1

With the forces of globalization and industrialization proceeding at an increasing rate, the prevalence of diabetes is predicted to increase dramatically over the next few decades. The resulting burden of complications and premature mortality will continue to present itself as a major and growing public health problem for most countries. It is hoped that this report will assist in monitoring the trends of diabetes prevalence over time, by adopting the same methodology for future reports. A report such as this should also be an indicator of a country’s and region’s ‘database’ of research. It should stimulate research in those countries lacking data, as well as encourage further and improved research in those countries where available data may not be representative of national rates. Finally, this report should act as a stimulus for intervention. Perhaps the most essential aspect of research is the action taken as a result of findings. Diabetes requires culturally appropriate intervention in order to reduce the enormous personal suffering and economic burden that grows with this epidemic.

DIABETES ATLAS THIRD EDITION



Table 1.1 Regional estimates for diabetes (20-79 age group), 2007 and 2025

2007

2025

Population (20-79)



2007/2025

No. of No. of Increase in the people with Diabetes Population people with Diabetes no. of people Diabetes prevalence* (20-79) diabetes prevalence* with diabetes

Region AFR EMME EUR NA SACA SEA WP

millions 336 318 634 306 272 770 1,469

millions 10.4 24.5 53.2 28.3 16.2 46.5 67.0

% 3.6 9.2 6.6 8.4 6.3 6.5 4.4

millions 537 492 653 376 364 1,083 1,732

millions 18.7 44.5 64.1 40.5 32.7 80.3 99.4

% 4.5 10.4 7.8 9.7 9.3 8.0 5.1

% 80.1 81.4 20.6 43.4 101.7 72.6 48.4

Total

4,107

246.1

5.9

5,237

380.3

7.1

54.5

*comparative prevalence



Table 1.2 Regional estimates for IGT (20-79 age group), 2007 and 2025

2007

2025



2007/2025



No. of No. of Increase in the Population people with IGT Population people with IGT no. of people (20-79) IGT prevalence* (20-79) IGT prevalence* with IGT Region AFR EMME EUR NA SACA SEA WP

millions 336 318 634 306 272 770 1,469

millions 24.2 22.4 65.3 19.6 19.8 45.2 111.9

% 8.2 8.1 9.1 5.8 7.5 6.0 7.5

millions 537 492 653 376 364 1,083 1,732

millions 40.3 38.6 71.2 27.5 27.6 70.5 142.7

% 9.2 8.8 9.6 6.7 7.9 6.7 7.8

% 66.9 72.4 9.0 40.3 39.4 56.1 27.5

Total

4,107

308.3

7.5

5,237

418.4

8.1

35.7

*comparative prevalence

PREVALENCE AND PROJECTIONS



CHAPTER 1

35

Table 1.3 Top 10: Prevalence of diabetes* (20-79 age group), 2007 and 2025 2007 2025 Country Prevalence (%) Country Prevalence (%) 1 Nauru 2 United Arab Emirates 3 Saudi Arabia 4 Bahrain 5 Kuwait 6 Oman 7 Tonga 8 Mauritius 9 Egypt 10 Mexico

30.7 19.5 16.7 15.2 14.4 13.1 12.9 11.1 11.0 10.6

1 Nauru 2 United Arab Emirates 3 Saudi Arabia 4 Bahrain 5 Kuwait 6 Tonga 7 Oman 8 Mauritius 9 Egypt 10 Mexico

32.3 21.9 18.4 17.0 16.4 15.2 14.7 13.4 13.4 12.4

*comparative prevalence Includes only countries where surveys with glucose testing were undertaken for that country



Table 1.4 Top 10: Number of people with diabetes (20-79 age group), 2007 and 2025 2007 2025 Country Persons (millions) Country Persons (millions) 1 India 2 China 3 United States of America 4 Russian Federation 5 Germany 6 Japan 7 Pakistan 8 Brazil 9 Mexico 10 Egypt

36

CHAPTER 1

40.9 39.8 19.2 9.6 7.4 7.0 6.9 6.9 6.1 4.4

1 India 2 China 3 United States of America 4 Brazil 5 Pakistan 6 Mexico 7 Russian Federation 8 Germany 9 Egypt 10 Bangladesh

69.9 59.3 25.4 17.6 11.5 10.8 10.3 8.1 7.6 7.4

DIABETES ATLAS THIRD EDITION



Table 1.5 Top 10: Prevalence of impaired glucose tolerance* (20-79 age group), 2007 and 2025 2007 2025 Country Prevalence (%) Country Prevalence (%) 1 Nauru 2 United Arab Emirates 3 Bahrain 4 Kuwait 5 Singapore 6 Kiribati 7 Mauritius 8 Poland 9 Tonga 10 Denmark

20.4 18.7 18.7 18.7 18.7 17.3 16.3 15.2 13.0 12.4

1 Nauru 2 Bahrain 3 United Arab Emirates 4 Kuwait 5 Singapore 6 Kiribati 7 Mauritius 8 Poland 9 Ghana 10 Tonga

21.2 19.9 19.9 19.9 19.7 18.1 17.0 16.3 15.0 13.8

*comparative prevalence Includes only countries where surveys with glucose testing were undertaken for that country

Table 1.6 Top 10: Number of people with impaired glucose tolerance (20-79 age group), 2007 and 2025 2007 2025 Country Persons (millions) Country Persons (millions) 1 China 2 India 3 Russian Federation 4 Indonesia 5 Japan 6 United States of America 7 Brazil 8 Bangladesh 9 Pakistan 10 Ukraine

PREVALENCE AND PROJECTIONS

64.3 35.9 17.8 14.1 12.9 12.4 8.4 6.8 6.4 6.0

1 China 2 India 3 Indonesia 4 Russian Federation 5 United States of America 6 Japan 7 Brazil 8 Pakistan 9 Bangladesh 10 Mexico



79.1 56.2 20.6 17.8 16.5 12.7 11.5 11.0 10.6 7.7

CHAPTER 1

37

Table 1.7 Prevalence estimates of diabetes mellitus (DM), 2007 - African Region



DM prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* R

Angola 7,159 2.7 3.3 Benin 4,047 3.8 4.4 Botswana 872 4.2 5.2 Burkina Faso 5,823 3.0 3.7 Burundi 3,536 1.3 1.7 Cameroon 8,027 4.3 3.7 Cape Verde 260 4.2 5.1 Central African Republic 1,877 3.8 4.4 Chad 4,275 2.7 3.6 Comoros 401 2.6 3.2 Congo 1,754 4.2 5.0 Congo, Democratic Republic of 25,387 2.4 3.0 Côte d’Ivoire 8,790 4.0 4.6 Djibouti 393 4.1 5.2 Equatorial Guinea 232 4.2 4.7 Eritrea 2,086 1.8 2.3 Ethiopia 36,121 1.9 2.3 Gabon 695 4.3 4.9 Gambia 793 3.7 4.1 Ghana 11,531 3.5 4.2 Guinea 4,469 3.7 4.1 Guinea-Bissau 697 3.3 3.8 Kenya 16,266 2.6 3.3 Lesotho 854 4.0 3.8 Liberia 1,435 3.8 4.6 Madagascar 8,896 2.6 3.0 Malawi 5,578 1.7 2.1 Mali 5,822 3.3 4.1 Mauritania 1,510 3.6 4.6 Mozambique 9,178 3.1 3.7 Namibia 971 4.0 4.2 Niger 6,004 3.1 3.7 Nigeria 61,213 3.8 4.5 a Réunion 510 12.7 13.5 Rwanda 4,197 1.1 1.5 b Sao Tome and Principe 81 3.7 4.6 Senegal 5,654 3.8 4.6 a,b Seychelles 51 12.6 12.6 Sierra Leone 2,696 3.8 4.3 Somalia 3,982 2.4 2.8 South Africa 27,085 4.5 4.4 Swaziland 463 3.8 4.0 Tanzania, United Republic of 18,316 2.4 2.9 Togo 2,948 3.5 4.1 Uganda 11,788 1.6 2.0 Western Sahara 212 5.0 5.6 Zambia 5,068 3.0 3.8 Zimbabwe 6,207 3.6 4.0 AFR Total 336,211 3.1 3.6

a. Réunion and the Seychelles were deemed as having the same ethnicity distribution as Mauritius b. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of world population from 2005 to 2007 * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

38

CHAPTER 1

DIABETES ATLAS THIRD EDITION



Number of people with DM (000’s) in the 20-79 age group Rural Urban Male Female

20-39

40-59

60-79 TotaL

37.5 153.4 101.4 89.5 67.2 85.3 38.3 190.8 56.2 95.9 81.7 70.4 57.4 62.6 32.0 152.0 5.4 31.5 13.0 23.8 5.5 18.6 12.8 36.9 113.1 61.1 92.6 81.7 73.8 60.7 39.8 174.3 26.3 20.8 24.4 22.6 16.6 18.9 11.6 47.1 107.2 239.0 178.6 167.6 154.7 114.9 76.7 346.2 2.1 8.9 5.2 5.8 4.3 4.2 2.5 11.0 28.1 43.7 36.7 35.1 25.1 27.0 19.7 71.8 68.2 47.3 44.7 70.7 14.2 62.4 38.8 115.4 2.1 8.3 5.7 4.8 3.7 4.6 2.1 10.5 15.3 58.7 38.8 35.1 29.1 27.6 17.3 74.0 146.0 473.2 331.4 287.7 218.0 262.3 138.8 619.1 117.2 231.3 198.1 150.4 124.5 137.6 86.3 348.5 1.3 14.6 6.4 9.6 2.0 9.0 5.0 15.9 2.9 6.8 5.2 4.6 3.3 3.9 2.6 9.7 13.4 24.0 19.2 18.2 14.3 15.0 8.2 37.4 253.3 428.4 371.0 310.7 226.4 287.5 167.8 681.7 7.6 22.3 16.1 13.8 10.4 12.0 7.5 29.9 14.0 15.7 15.9 13.7 9.8 12.4 7.4 29.6 166.4 233.6 225.9 174.1 107.9 181.2 110.8 400.0 77.3 88.3 91.3 74.3 57.0 65.3 43.3 165.6 13.4 9.5 12.1 10.9 8.5 8.9 5.5 22.9 84.2 333.2 227.7 189.7 154.1 179.9 83.4 417.4 20.2 13.7 12.3 21.6 6.5 15.3 12.1 33.9 17.5 36.4 28.7 25.2 21.7 21.6 10.6 53.9 53.1 174.2 123.6 103.6 74.4 102.2 50.6 227.3 40.7 56.1 52.4 44.5 32.4 38.0 23.0 96.9 95.7 96.0 97.9 93.8 79.5 67.9 44.3 191.7 12.4 41.9 20.8 33.5 6.8 29.2 18.3 54.3 45.1 244.0 147.2 141.9 94.4 126.9 67.9 289.1 18.9 20.2 16.1 23.0 8.2 19.4 11.4 39.1 114.2 69.2 99.9 83.5 74.8 74.7 33.8 183.4 819.6 1,526.8 1,263.3 1,083.0 875.9 926.5 543.9 2,346.3 9.9 54.8 32.3 32.4 10.8 32.5 21.4 64.6 31.6 15.6 25.1 22.2 17.4 17.4 12.3 47.2 1.0 2.0 1.5 1.5 1.2 1.0 0.7 3.0 71.4 144.7 110.1 106.0 83.5 83.0 49.6 216.1 1.5 4.9 3.1 3.2 1.1 3.2 2.1 6.4 43.9 58.8 54.4 48.3 34.6 42.8 25.3 102.8 23.8 69.9 50.4 43.2 32.5 42.2 18.9 93.6 502.4 710.4 481.6 731.2 213.4 631.1 368.4 1,212.9 9.2 8.2 6.8 10.6 3.6 8.2 5.7 17.4 111.3 334.7 239.9 206.1 148.6 188.1 109.3 446.0 48.5 55.0 54.4 49.1 39.0 40.6 24.0 103.5 81.7 101.5 100.3 82.9 71.1 65.5 46.6 183.2 0.2 10.4 5.0 5.6 1.0 6.6 3.0 10.6 23.4 130.2 83.5 70.1 55.4 61.9 36.3 153.6 122.0 99.4 94.4 127.0 52.6 100.5 68.3 221.4 3,678 6,728 5,348 5,058 3,428 4,408 2,566 10,406





PREVALENCE AND PROJECTIONS



CHAPTER 1

39

Table 1.8 Prevalence estimates of diabetes mellitus (DM), 2025 - African Region



DM prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* R

Angola 12,108 3.4 4.4 Benin 7,206 4.2 5.3 Botswana 887 3.8 6.3 Burkina Faso 10,528 3.3 4.4 Burundi 5,902 1.9 2.5 Cameroon 12,033 4.7 4.3 Cape Verde 438 5.0 5.9 Central African Republic 2,655 3.9 5.2 Chad 7,206 2.7 3.1 Comoros 693 3.6 4.3 Congo 3,159 4.3 5.8 Congo. Democratic Republic of 43,720 3.1 4.1 Côte d’Ivoire 13,691 4.2 5.3 Djibouti 621 4.3 4.3 Equatorial Guinea 329 3.9 5.1 Eritrea 3,689 2.5 3.3 Ethiopia 59,447 2.6 3.3 Gabon 1,021 4.7 5.6 Gambia 1,276 4.2 4.9 Ghana 17,951 4.1 5.0 Guinea 7,415 4.0 4.9 Guinea-Bissau 1,207 3.5 4.6 Kenya 27,659 3.5 4.5 Lesotho 869 3.2 4.8 Liberia 2,435 4.0 5.4 Madagascar 15,282 3.3 4.1 Malawi 9,001 2.2 3.1 Mali 10,657 3.6 4.9 Mauritania 2,541 4.0 4.0 Mozambique 13,680 3.7 4.9 Namibia 1,394 3.4 5.3 Niger 11,296 3.4 4.5 Nigeria 97,842 4.2 5.3 a Réunion 659 16.4 15.7 Rwanda 6,824 1.5 2.0 b Sao Tome and Principe 131 4.4 5.4 Senegal 9,532 4.3 5.4 a.b Seychelles 58 14.9 14.9 Sierra Leone 4,136 4.1 5.1 Somalia 6,797 3.2 3.9 South Africa 29,250 4.4 5.3 Swaziland 485 2.6 5.1 Tanzania. United Republic of 28,548 3.2 4.1 Togo 5,108 3.9 4.9 Uganda 23.,910 2.1 2.9 Western Sahara 426 5.4 4.5 Zambia 7,726 3.5 4.9 Zimbabwe 7,689 3.0 5.0 AFR Total 537,116 3.5 4.5

a. Réunion and the Seychelles were deemed as having the same ethnicity distribution as Mauritius b. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of world population from 2005 to 2025 * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

40

CHAPTER 1

DIABETES ATLAS THIRD EDITION



Number of people with DM (000’s) in the 20-79 age group Rural Urban Male Female

20-39

40-59

60-79 TotaL

56.7 350.9 217.7 189.9 149.3 182.4 75.9 407.6 79.2 225.4 167.3 137.3 110.0 122.9 71.6 304.6 1.8 32.0 13.1 20.7 5.8 9.5 18.5 33.8 173.0 176.2 189.7 159.6 144.7 140.1 64.5 349.2 45.8 65.8 59.8 51.8 41.8 46.2 23.7 111.6 123.1 438.8 296.2 265.7 250.8 193.9 117.2 561.9 2.8 18.9 11.4 10.4 7.0 9.6 5.2 21.7 29.7 74.9 55.2 49.4 43.1 35.2 26.3 104.6 89.2 106.9 77.0 119.1 27.6 107.0 61.6 196.1 3.6 21.4 13.6 11.3 7.7 12.4 4.9 25.0 20.3 115.5 72.6 63.2 56.4 51.8 27.6 135.8 221.6 1,131.1 732.0 620.7 502.3 602.1 248.3 1,352.8 139.9 441.3 322.8 258.5 220.5 219.7 141.1 581.3 1.7 25.2 11.0 16.0 3.2 15.1 8.7 27.0 3.9 9.0 6.9 5.9 5.1 4.2 3.5 12.8 23.4 69.5 49.2 43.8 33.5 44.2 15.2 92.9 397.1 1,121.5 829.6 689.1 538.1 653.1 327.5 1,518.7 8.2 39.4 26.1 21.5 16.9 17.4 13.4 47.6 18.4 35.3 29.0 24.7 17.1 22.0 14.5 53.6 218.3 511.7 417.9 312.1 181.7 339.6 208.7 730.0 100.4 195.7 165.3 130.8 107.0 115.1 74.1 296.1 18.7 23.2 22.2 19.6 16.9 16.0 9.0 41.8 132.8 841.1 543.2 430.6 340.7 449.8 183.4 973.8 10.4 17.4 8.9 19.0 4.4 8.4 15.0 27.8 22.6 74.3 52.4 44.5 40.9 36.6 19.5 96.9 83.4 425.7 277.5 231.6 167.6 230.6 110.9 509.1 59.1 137.4 108.2 88.2 80.0 73.7 42.8 196.5 139.6 243.9 203.2 180.3 159.7 153.2 70.6 383.5 16.2 85.8 40.7 61.3 11.5 58.4 32.0 101.9 56.3 444.2 265.0 235.5 185.3 206.6 108.6 500.5 12.3 35.4 17.9 29.7 7.8 18.8 21.1 47.7 181.4 200.7 212.2 169.9 154.9 151.9 75.2 382.1 987.6 3,077.7 2,240.1 1,825.2 1,579.8 1,566.1 919.3 4,065.3 11.9 96.1 50.3 57.7 13.2 49.0 45.7 108.0 57.1 48.6 55.6 50.1 40.6 43.5 21.5 105.7 1.4 4.4 3.1 2.7 1.9 2.6 1.2 5.8 96.5 314.3 216.2 194.6 148.3 171.3 91.2 410.9 1.4 7.3 4.2 4.5 1.2 4.1 3.4 8.7 51.8 116.5 89.4 78.9 60.6 67.5 40.1 168.2 39.0 176.3 116.1 99.2 69.1 102.9 43.3 215.3 302.2 976.7 460.7 818.2 141.7 485.4 651.8 1,278.9 3.9 8.8 4.5 8.2 2.8 3.1 6.8 12.7 155.1 754.5 501.3 408.3 323.0 394.2 192.3 909.6 68.9 132.4 107.2 94.0 73.9 79.6 47.7 201.2 158.2 343.9 274.1 228.0 203.1 218.4 80.6 502.1 0.2 22.8 10.1 12.8 2.3.0 12.7 8.0 23.0 29.9 237.0 149.9 117.0 111.4 106.1 49.4 266.9 72.6 158.4 89.7 141.4 41.6 89.0 100.5 231.1 4,529 14,211 9,887 8,852 6,454 7,743 4,543 18,740





PREVALENCE AND PROJECTIONS



CHAPTER 1

41

Table 1.9

Prevalence estimates of impaired glucose tolerance (IGT), 2007 - African Region

IGT prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* M

Angola 7,159 7.4 8.5 Benin 4,,047 6.7 7.6 Botswana 872 7.3 9.6 Burkina Faso 5,,823 6.6 7.6 Burundi 3,536 7.4 8.5 Cameroon 8,027 0.9 1.1 Cape Verde 260 6.7 7.6 Central African Republic 1,877 7.0 7.6 Chad 4,275 2.2 2.8 Comoros 401 7.4 8.5 Congo 1,754 6.7 7.6 Congo, Democratic Republic of 25,387 7.4 8.5 Côte d’Ivoire 8,790 6.8 7.6 Djibouti 393 2.3 2.8 Equatorial Guinea 232 7.0 7.6 Eritrea 2,086 6.0 8.5 Ethiopia 36,121 7.6 8.5 Gabon 695 6.9 7.6 Gambia 793 7.0 7.6 Ghana 11,531 12.7 14.0 Guinea 4,469 7.0 7.6 Guinea-Bissau 697 6.8 7.6 Kenya 16,266 7.2 8.5 Lesotho 854 8.6 9.6 Liberia 1,435 6.5 7.6 Madagascar 8,896 7.6 8.5 Malawi 5,578 7.6 8.5 Mali 5,822 6.6 7.6 Mauritania 1,510 2.3 2.8 Mozambique 9,178 7.7 8.5 Namibia 971 7.7 9.6 Niger 6,004 6.5 7.6 Nigeria 61,213 6.7 7.6 Réuniona 510 16.5 16.3 Rwanda 4,197 7.3 8.5 Sao Tome and Principeb 81 6.6 7.6 Senegal 5,654 6.7 7.6 Seychellesa.b 51 16.2 16.3 Sierra Leone 2,696 7.0 7.6 Somalia 3,982 7.5 8.5 South Africa 27,085 8.0 9.6 Swaziland 463 7.8 9.6 Tanzania, United Republic of 18,316 7.6 8.5 Togo 2,948 6.7 7.6 Uganda 11,788 7.2 8.5 Western Sahara 212 2.5 2.8 Zambia 5,068 7.4 8.5 Zimbabwe 6,207 7.6 9.6 AFR Total 336,211 7.2 8.2

a. Réunion and the Seychelles were deemed as having the same ethnicity distribution as Mauritius b. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of world population from 2005 to 2007 * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

42

CHAPTER 1

DIABETES ATLAS THIRD EDITION



Number of people with IGT (000’s) in the 20-79 age group Male Female

20-39

40-59

60-79 Total

224.7 304.6 246.3 183.5 99.5 529.3 128.7 141.8 126.1 91.8 52.7 270.5 41.6 22.1 18.0 17.1 28.6 63.7 181.4 200.1 195.1 108.5 77.9 381.5 106.5 153.4 121.4 89.6 49.0 259.9 31.7 43.5 28.2 28.3 18.6 75.1 7.4 10.1 8.2 5.6 3.8 17.6 59.0 71.8 56.6 40.8 33.3 130.7 34.2 61.2 24.5 43.7 27.2 95.4 12.8 16.7 13.9 10.1 5.5 29.5 54.7 62.6 56.3 36.0 25.1 117.4 809.3 1,076.5 876.4 621.7 387.7 1,885.8 300.3 296.9 269.1 193.9 134.3 597.2 3.3 5.6 2.3 4.3 2.3 8.9 7.6 8.7 6.8 5.4 4.1 16.3 62.2 62.2 62.2 62.2 62.2 124.4 1,183.1 1,547.7 1,208.8 946.8 575.1 2,730.7 22.8 25.5 20.8 16.1 11.3 48.3 26.1 29.5 23.0 19.6 12.9 55.5 712.9 754.4 664.4 475.5 327.5 1,467.3 151.5 159.4 133.2 102.4 75.2 310.8 22.0 25.3 21.7 15.3 10.4 47.4 520.0 657.0 573 384.2 219.9 1,177.0 45.9 27.9 16.4 14.6 42.8 73.8 44.5 49.3 46.3 30.9 16.7 93.8 294.1 382.9 294.7 240.9 141.3 677.0 182.5 238.7 188.8 137.0 95.4 421.2 175.4 208.2 192.1 111.2 80.2 383.6 12.4 22.3 8.8 15.9 9.9 34.6 288.3 418.8 307.2 238.7 161.2 707.1 50.2 24.3 19.5 19.6 35.4 74.5 190.0 199.9 194.4 130.3 65.2 389.9 1,969.0 2,152.3 1,903.7 1,343.4 874.1 4,121.3 32.7 51.8 28.9 38.7 16.8 84.4 125.7 178.8 148.5 99.6 56.3 304.5 2.5 2.9 2.7 1.5 1.2 5.4 171.5 207.5 178.5 120.8 79.7 379.0 3.3 4.9 3.0 3.5 1.8 8.2 87.2 100.5 78.7 66.0 43.0 187.7 127.3 170.4 135.9 106.9 54.9 297.8 1,426.6 726.8 501.2 635.0 1,017.2 2,153.4 23.5 12.4 9.6 8.0 18.3 35.9 599.1 785.1 614.8 457.9 311.5 1,384.2 92.5 106.3 92.0 64.4 42.3 198.7 372.9 477.7 430.8 246.8 173.0 850.6 2.2 3.0 1.1 2.9 1.3 5.2 165.6 207.8 177.6 111.3 84.5 373.4 316.1 153.0 136.0 104.1 229.0 469.1 11,505 12,650 10,467 7,852 5,897 24,154





PREVALENCE AND PROJECTIONS



CHAPTER 1

43

Table 1.10

Prevalence estimates of impaired glucose tolerance (IGT), 2025 - African Region

IGT prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* M Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Congo, Democratic Republic of Côte d’Ivoire Djibouti Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mozambique Namibia Niger Nigeria Réuniona Rwanda Sao Tome and Principeb Senegal Seychellesa.b Sierra Leone Somalia South Africa Swaziland Tanzania, United Republic of Togo Uganda Western Sahara Zambia Zimbabwe

AFR Total

12,108 7,206 887 10,528 5,902 12,033 438 2,655 7,206 693 3,159 43,720 13,691 621 329 3,689 59,447 1,021 1,276 17,951 7,415 1,207 27,659 869 2,435 15,282 9,001 10,657 2,541 13,680 1,394 11,296 97,842 659 6,824 131 9,532 58 4,136 6,797 29,250 485 28,548 5,108 23,910 426 7,726 7,689

7.4 7.5 8.5 7.1 7.5 2.2 7.8 7.3 2.1 7.8 7.1 7.3 7.4 2.4 7.3 7.4 7.6 7.7 7.9 13.2 7.5 7.2 7.5 8.4 7.0 7.7 7.2 7.0 2.4 7.5 8.0 7.1 7.3 17.4 7.5 7.6 7.5 17.0 7.5 7.7 9.7 7.5 7.5 7.5 7.1 2.7 7.1 7.6

9.2 9.1 11.3 9.1 9.2 3.2 9.1 9.1 3.1 9.2 9.1 9.2 9.1 3.1 9.1 9.2 9.2 9.1 9.1 15.0 9.1 9.1 9.2 11.3 9.1 9.2 9.2 9.1 3.1 9.2 11.3 9.1 9.1 17.0 9.2 9.1 9.1 17.0 9.1 9.2 11.3 11.3 9.2 9.1 9.2 3.1 8.5 9.2

537,116

7.5

9.2

a. Réunion and the Seychelles were deemed as having the same ethnicity distribution as Mauritius b. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of world population from 2005 to 2025

* All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

44

CHAPTER 1

DIABETES ATLAS THIRD EDITION



Number of people with IGT (000’s) in the 20-79 age group Male Female

20-39

40-59

60-79 Total

383.3 278.9 49.7 383.6 188.1 179.9 16.7 95.8 55.8 23.4 113.2 1,392.5 530.7 5.5 12.2 113.9 1,972.9 40.4 50.7 1,155.1 293.7 43.4 926.6 43.8 87.7 516.1 289.1 376.8 22.1 440.6 74.8 422.2 3,721.0 45.1 212.0 5.0 353.4 4.1 154.9 224.9 1,838.1 23.0 955.6 191.1 743.5 4.6 250.6 397.4

511.2 261.4 25.6 361.2 252.3 89.7 17.6 96.8 95.9 30.4 110.4 1,803.1 485.6 9.2 11.8 157.7 2,516.9 38.3 50.0 1,207.3 265.7 43.0 1,147.0 28.9 83.4 660.9 356.4 372.0 38.1 582.5 36.2 379.4 3,434.7 69.5 297.0 5.0 361.9 5.9 155.9 296.5 1,000.3 13.1 1,197.6 190.7 953.0 6.8 297.2 188.8

422.1 219.5 21.2 344.4 204.3 43.6 12.3 88.0 43.4 21.8 103.7 1,531.4 431.4 3.6 10.8 125.5 2,026.0 31.2 36.3 975.0 227.8 39.0 935.1 19.3 80.6 503.3 329.0 347.8 14.1 480.4 30.7 365.6 3,113.0 32.9 238.1 3.7 288.4 3.1 125.7 219.7 561.2 12.6 975.4 157.0 864.4 2.3 288.6 175.1

302.1 197.9 11.1 267.7 146.9 148.5 13.9 57.8 69.0 20.9 76.2 1,068.3 347.8 7.1 7.0 101.1 1,497.6 26.1 37.8 816.5 196.6 29.7 725.9 10.9 57.7 409.3 180.0 268.3 29.8 311.5 23.6 286.1 2,486.8 47.0 176.3 4.2 271.9 4.1 113.2 194.0 593.6 4.3 703.5 136.9 568.9 5.7 153.9 119.6

170.3 122.9 42.9 132.6 89.1 77.4 8.1 46.8 39.4 11.2 43.6 595.9 237.1 4.0 6.3 45.1 966.2 21.4 26.6 570.9 135.0 17.7 412.6 42.6 32.8 264.4 136.4 132.7 16.2 231.2 56.7 149.8 1,555.9 34.6 94.6 2.0 155.0 2.7 71.8 107.7 1,683.6 19.3 474.4 87.9 263.2 3.5 105.3 291.5

894.5 540.3 75.2 744.8 440.4 269.6 34.3 192.6 151.7 53.8 223.5 3,195.6 1,016.3 14.6 24.0 271.6 4,489.8 78.6 100.7 2,362.5 559.4 86.4 2,073.6 72.8 171.1 1,177.0 645.5 748.7 60.1 1,023.2 111.0 801.6 7,155.7 114.6 508.9 10.0 715.3 9.9 310.7 521.4 2,838.5 36.2 2,153.3 381.8 1,696.5 11.5 547.8 586.2

19,704

20,600

17,129

13,335

9,839

40,303







PREVALENCE AND PROJECTIONS



CHAPTER 1

45

Table 1.11 Data sources: prevalence estimates of diabetes mellitus (DM) and impaired glucose tolerance (IGT)

Country/territory

Data used S

Angolaa Beninb Botswanac,d Burkina Fasob Burundia Cameroon Cape Verdeb Central African Republicb Chad Comorosa Congob Congo, Democratic Republic of a Côte d’Ivoireb Djibouti Equatorial Guineab Eritreaa Ethiopiaa Gabonb Gambiab Ghana Guineab Guinea-Bissaub Kenyaa Lesothoc,d Liberiab Madagascara Malawia Malib Mauritania Mozambiquea Namibiac,d Nigerb Nigeriab Réunion Rwandaa Sao Tome and Principeb Senegalb Seychelles Sierra Leoneb Somaliaa South Africac,d Swazilandc,d Tanzania, United Republic of a Togob Ugandaa Western Sahara Zambiaa Zimbabwec,d

Tanzania (McLarty et al, 1989 and Aspray et al, 2002)21,22 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 South Africa (Omar et al, 1993; Levitt et al 1993; Erasmus et al, 2001; Motala, 2006)24-27 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Tanzania (McLarty et al, 1989 and Aspray et al, 2002)21,22 Cameroon (Mbanya, 2006)29 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Sudan (Elbagir et al, 1996)30 Tanzania (McLarty et al, 1989 and Aspray et al, 2002)21,22 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Tanzania (McLarty et al, 1989 and Aspray et al, 2002)21,22 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Sudan (Elbagir et al, 1996)30 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Tanzania (McLarty et al, 1989 and Aspray et al, 2002)21,22 Tanzania (McLarty et al, 1989 and Aspray et al, 2002)21,22 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Ghana (Amoah et al, 2002)28 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Tanzania (McLarty et al, 1989 and Aspray et al, 2002)21,22 South Africa (Omar et al, 1993; Levitt et al 1993; Erasmus et al, 2001 and Motala, 2006)24-27 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Tanzania (McLarty et al, 1989 and Aspray et al, 2002)21,22 Tanzania (McLarty et al, 1989 and Aspray et al, 2002)21,22 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Sudan (Elbagir et al, 1996)30 Tanzania (McLarty et al, 1989 and Aspray et al, 2002)21,22 South Africa (Omar et al, 1993; Levitt et al 1993; Erasmus et al, 2001 and Motala, 2006)24-27 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Mauritius (Dowse et al, 1990)109 Tanzania (McLarty et al, 1989 and Aspray et al, 2002)21,22 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Mauritius (Dowse et al, 1990)109 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Tanzania (McLarty et al, 1989 and Aspray et al, 2002)21,22 South Africa (Omar et al, 1993; Levitt et al 1993; Erasmus et al, 2001; Motala, 2006)24-27 South Africa (Omar et al, 1993; Levitt et al 1993; Erasmus et al, 2001 and Motala, 2006)24-27 Tanzania (McLarty et al, 1989 and Aspray et al, 2002)21,22 Cameroon (Mbanya, 2006)29 and Ghana (Amoah et al, 2002)28 Tanzania (McLarty et al, 1989 and Aspray et al, 2002)21,22 Sudan (Elbagir et al, 1996)30 Tanzania (McLarty et al, 1989 and Aspray et al, 2002)21,22 South Africa (Omar et al, 1993; Levitt et al 1993; Erasmus et al, 2001 and Motala, 2006)24-27

a. The prevalence was calculated after the combination of the data of the two studies, notwithstanding the different criteria. IGT figures were calculated from the McLarty data, as the Aspray study only used FBG criteria b. The prevalence was calculated as the average of the two studies as their sample sizes differed considerably c. The prevalence was calculated after the combination of the data of the four studies d. IGT figures were based only on the study of Omar et al26

46

CHAPTER 1

DIABETES ATLAS THIRD EDITION

- African Region

d Screening method OGTT/FBG OGTT OGTT OGTT OGTT/FBG OGTT OGTT OGTT 2hBG OGTT/FBG OGTT OGTT/FBG OGTT 2hBG OGTT OGTT/FBG OGTT/FBG OGTT OGTT OGTT OGTT OGTT OGTT/FBG OGTT OGTT OGTT/FBG OGTT/FBG OGTT 2hBG OGTT/FBG OGTT OGTT OGTT OGTT OGTT/FBG OGTT OGTT OGTT OGTT OGTT/FBG OGTT OGTT OGTT/FBG OGTT OGTT/FBG 2hBG OGTT/FBG OGTT

Diagnostic criteria Sample size Age (yrs) WHO - 1985, 1999 WHO - 1999 WHO - 1985, 1999 WHO - 1999 WHO - 1985, 1999 WHO - 1999 WHO - 1999 WHO - 1999 WHO - 1985 WHO - 1985, 1999 WHO - 1999 WHO - 1985, 1999 WHO - 1999 WHO - 1985 WHO - 1999 WHO - 1985, 1999 WHO - 1985, 1999 WHO - 1999 WHO - 1999 WHO - 1999 WHO - 1999 WHO - 1999 WHO - 1985, 1999 WHO - 1985, 1999 WHO - 1999 WHO - 1985, 1999 WHO - 1985, 1999 WHO - 1999 WHO - 1985 WHO - 1985, 1999 WHO - 1985, 1999 WHO - 1999 WHO - 1999 WHO - 1985 WHO - 1985, 1999 WHO - 1999 WHO - 1999 WHO - 1985 WHO - 1999 WHO - 1985, 1999 WHO - 1985, 1999 WHO - 1985, 1999 WHO - 1985, 1999 WHO - 1999 WHO - 1985, 1999 WHO - 1985 WHO - 1985, 1999 WHO - 1985, 1999

7,781 14,110 3,780 14,110 7,781 9,377 14,110 14,110 1,284 7,781 14,110 7,781 14,110 1,284 14,110 7,781 7,781 14,110 14,110 4,733 14,110 14,110 7,781 3,780 14,110 7,781 7,781 14,110 1,284 7,781 3,780 14,110 14,110 4,929 7,781 14,110 14,110 4,929 14,110 7,781 3,780 3,780 7,781 14,110 7,781 1,284 7,781 3,780

15+ 15+ 15+ 15+ 15+ 15+ 15+ 15+ 25-84 15+ 15+ 15+ 15+ 25-84 15+ 15+ 15+ 15+ 15+ 25+ 15+ 15+ 15+ 15+ 15+ 15+ 15+ 15+ 25-84 15+ 15+ 15+ 15+ 25-74 15+ 15+ 15+ 25-74 15+ 15+ 15+ 15+ 15+ 15+ 15+ 25-84 15+ 15+





PREVALENCE AND PROJECTIONS



CHAPTER 1

47

Table 1.12

Prevalence estimates of diabetes mellitus (DM), 2007 - Eastern Mediterranean and Middle East Region



DM prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* R

Afghanistan 13,825 8.0 9.7 Algeria 20,346 7.3 8.4 Armenia 2,056 8.7 7.7 Bahrain 491 14.3 15.2 Egypt 43,071 10.1 11.0 Iran, Islamic Republic of 42,984 6.0 7.8 Iraq 14,699 7.5 10.0 Jordan 3,169 7.3 9.8 Kuwait 1,953 10.1 14.4 Lebanon 2,262 7.4 7.7 Libyan Arab Jamahiriya 3,659 3.7 4.4 Morocco 19,125 7.1 8.1 Occupied Palestinian Territory 1,719 6.4 8.4 Oman 1,482 10.7 13.1 Pakistan 83,527 8.3 9.6 Qatar 622 12.7 15.2 Saudi Arabia 13,730 13.5 16.7 Sudan 19,056 3.2 4.0 Syrian Arab Republic 10,473 8.1 10.6 Tunisia 6,639 4.8 5.2 United Arab Emirates 3,377 13.0 19.5 Yemen 9,456 2.5 2.9 EMME Total 317,720 7.7 9.2 * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

48

CHAPTER 1

DIABETES ATLAS THIRD EDITION





Number of people with DM (000’s) in the 20-79 age group Rural Urban Male Female

20-39

40-59

60-79 Total

858.2 254.2 606.6 505.8 288.0 581.3 243.2 1,112.4 553.9 921.2 730.1 744.9 480.6 650.1 344.4 1,475.1 40.1 139.5 66.0 113.7 13.1 89.8 76.8 179.6 2.4 68.1 43.9 26.6 14.4 46.6 9.5 70.5 1,455.3 2,901.8 1,920.8 2,436.2 1,064.1 2,144.4 1,148.6 4,357.1 560.5 2,005.0 1,091.6 1,473.9 328.9 1,330.5 906.2 2,565.5 131.5 974.9 547.6 558.8 186.9 622.5 297.1 1,106.5 30.9 201.6 121.5 111.0 43.1 122.7 66.8 232.6 2.1 195.9 127.7 70.2 62.6 102.5 32.9 197.9 7.7 159.3 79.7 87.3 7.4 72.9 86.7 167.0 7.9 127.6 56.7 78.9 45.6 49.7 40.3 135.5 355.2 1,005.0 647.7 712.5 327.5 706.1 326.6 1,360.2 13.0 96.2 46.2 63.0 9.8 66.1 33.3 109.2 9.4 149.7 99.0 60.1 57.1 75.9 26.1 159.1 4,279.6 2,649.9 3,700.6 3,229.0 1,631.6 3,774.7 1,523.2 6,929.5 2.8 76.4 60.5 18.6 21.4 51.5 6.3 79.2 140.3 1,714.6 1,016.4 838.5 520.9 986.2 347.8 1,854.9 255.9 351.6 244.5 363.0 71.6 334.3 201.6 607.5 233.5 616.6 422.4 427.7 290.9 311.3 247.9 850.1 57.4 259.8 135.0 182.2 72.7 158.6 86.0 317.2 29.9 409.6 320.9 118.6 181.7 228.9 28.9 439.5 94.3 137.8 115.4 116.7 95.7 100.7 35.7 232.1 9,122 15,416 12,201 12,337 5,815 12,607 6,116 24,538



PREVALENCE AND PROJECTIONS



CHAPTER 1

49

Table 1.13

Prevalence estimates of diabetes mellitus (DM), 2025 - Eastern Mediterranean and Middle East Region

DM prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* R

Afghanistan 25,221 7.9 10.6 Algeria 28,553 8.9 9.2 Armenia 2,155 10.3 9.0 Bahrain 715 17.0 17.0 Egypt 62,958 12.2 13.4 Iran, Islamic Republic of 60,541 8.4 9.4 Iraq 25,477 9.0 11.6 Jordan 5,128 9.8 11.5 Kuwait 2,893 15.4 16.4 Lebanon 2,947 9.1 9.3 Libyan Arab Jamahiriya 5,216 4.5 5.0 Morocco 26,372 9.1 9.5 Occupied Palestinian Territorya 3,365 6.7 9.8 Oman 2,379 13.3 14.7 Pakistan 135,664 8.5 9.9 Qatar 808 17.1 16.9 Saudi Arabia 23,007 15.7 18.4 Sudan 29,494 3.8 5.0 Syrian Arab Republic 17,367 10.2 12.7 Tunisia 8,633 6.2 6.1 United Arab Emirates 5,039 19.5 21.9 Yemen 18,267 2.8 3.4 EMME Total 492,202 9.0 10.4

a. Occupied Palestinian Territory assigned urban/rural distribution of Jordan * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

50

CHAPTER 1

DIABETES ATLAS THIRD EDITION



Number of people with DM (000’s) in the 20-79 age group Rural Urban Male Female

20-39

40-59

60-79 Total

1,290.6 691.6 1,059.9 922.2 534.8 1,042.5 404.9 1,982.1 716.1 1,812.5 1,261.3 1,267.2 555.5 1,234.8 738.2 2,528.5 37.2 184.8 77.4 144.7 17.5 82.9 121.7 222.1 2.8 118.8 69.4 52.2 16.6 65.4 39.6 121.6 1,842.1 5,807.5 3,295.4 4,354.2 1,619.1 3,583.7 2,446.8 7,649.6 804.8 4,310.1 2,164.8 2,950.1 513.7 2,566.0 2,035.2 5,114.9 199.4 2,082.4 1,128.3 1,153.4 313.8 1,307.1 660.8 2,281.7 48.5 451.9 260.7 239.7 58.2 302.7 139.5 500.4 3.7 441.3 278.9 166.1 58.9 247.9 138.2 445.0 9.0 258.3 125.1 142.2 9.0 115.6 142.7 267.3 10.5 225.7 94.8 141.4 51.0 101.7 83.5 236.2 441.9 1,953.6 1,156.6 1,238.9 412.1 1,253.7 729.7 2,395.6 13.0 213.4 99.5 126.8 18.4 133.4 74.6 226.4 10.4 305.7 185.8 130.3 77.3 164.5 74.3 316.1 5,433.1 6,104.6 5,612.4 5,925.3 2,712.2 6,242.3 2,583.2 11,537.6 3.6 134.5 93.5 44.6 18.7 95.0 24.5 138.2 171.5 3,438.6 1,876.2 1,733.9 786.9 1,958.9 864.3 3,610.0 329.8 799.6 464.9 664.4 117.2 623.8 388.4 1,129.4 352.5 1,426.8 882.7 896.6 438 735.3 605.9 1,779.3 65.1 469,9 224.4 310.6 82.3 272..5 180.2 535.0 50.7 930.8 673.6 308.0 194.1 625.7 161.8 981.6 145.7 360.0 251.4 254.3 208.4 211.6 85.8 505.7 11,982 32,522 21,337 23,167 8,814 22,967 12,724 44,504



PREVALENCE AND PROJECTIONS



CHAPTER 1

51

Table 1.14 Prevalence estimates of impaired glucose tolerance (IGT), 2007

IGT prevalence (%) Country/territory Population (20-79) (000’s) National Comparative* M

Afghanistan 13,825 7.5 8.7 Algeria 20,346 5.7 6.4 Armenia 2,056 7.4 6.7 Bahrain 491 17.0 18.7 Egypt 43,071 4.6 5.1 Iran, Islamic Republic of 4,984 9.6 11.3 Iraq 14,699 7.2 8.6 Jordan 3,169 7.2 8.6 Kuwait 1,953 15.5 18.7 Lebanon 2,262 3.9 4.1 Libyan Arab Jamahiriya 3,659 5.6 6.4 Morocco 19,125 6.0 6.4 Occupied Palestinian Territory 1,719 7.2 8.6 Oman 1,482 9.1 10.8 Pakistan 83,527 7.7 8.7 Qatar 622 15.1 18.7 Saudi Arabia 13,730 11.0 11.7 Sudan 19,056 2.3 2.8 Syrian Arab Republic 10,473 9.2 12.8 Tunisia 6,639 6.1 6.4 United Arab Emirates 3,377 14.0 18.7 Yemen 9,456 2.8 4.0 EMME Total 317,720 7.0 8.1 *All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

52

CHAPTER 1

DIABETES ATLAS THIRD EDITION

- Eastern Mediterranean and Middle East Region

Number of people with IGT (000’s) in the 20-79 age group Male Female

20-39

40-59

60-79 Total

344.2 687.0 407.0 403.2 221.0 1,031.2 387.5 780.8 454.9 516.8 196.5 1,168.3 46.5 105.9 30.4 70.6 51.4 152.4 42.8 40.6 32.6 40.5 10.4 83.5 988.2 988.2 988.2 988.2 988.2 1,976.5 1,762.8 2,351.9 1,322.7 1,918.6 873.4 4,114.7 527.3 529.7 352.8 513.9 190.3 1,057.0 119.5 107.7 80.3 103.5 43.3 227.2 165.8 136.0 156.2 118.0 27.6 301.8 37.1 51.7 11.7 39.4 37.8 88.8 73.3 132.7 82.4 87.7 35.9 206.0 363.4 781.0 414.0 525.3 205.0 1,144.4 61.5 61.6 40.5 59.8 22.8 123.1 66.3 68.1 60.9 57.6 15.9 134.4 2,186.5 4,235.9 2,340.1 2,621.2 1,461.1 6,422.4 61.8 32.2 45.0 42.5 6.5 94.0 874.6 638.1 782.9 567.7 162.1 1,512.70 164.2 278.2 108.6 208.8 125.0 442.4 423.7 541.7 225.4 412.0 328.0 965.4 132.5 271.7 138.1 187.3 78.8 404.2 314.9 156.4 279.0 170.0 22.3 471.2 131.8 136.4 53.2 133.5 81.5 268.2 9,276 13,114 8,407 9,786 5,185 22,390



PREVALENCE AND PROJECTIONS



CHAPTER 1

53

Table 1.15 Prevalence estimates of impaired glucose tolerance (IGT), 2025

IGT prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* M

Afghanistan 25,221 7.4 9.4 Algeria 28,553 6.7 6.8 Armenia 2,155 8.1 7.3 Bahrain 715 19.3 19.9 Egypt 62,958 4.9 5.5 Iran, Islamic Republic of 60,541 11.8 12.2 Iraq 25,477 7.9 9.3 Jordan 5,128 8.4 9.3 Kuwait 2,893 18.6 19.9 Lebanon 2,947 4.6 4.7 Libyan Arab Jamahiriya 5,216 6.6 6.8 Morocco 26,372 6.7 6.8 Occupied Palestinian Territory 3,365 7.4 9.3 Oman 2,379 10.7 11.4 Pakistan 135,664 8.1 9.4 Qatar 808 17.9 19.9 Saudi Arabia 23,007 11.5 12.1 Sudan 29,494 2.5 3.1 Syrian Arab Republic 17,367 11.5 14.8 Tunisia 8,633 7.0 6.8 United Arab Emirates 5,039 17.0 19.9 Yemen 18,267 3.0 4.6 EMME Total 492,202 7.8 8.8 *All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

54

CHAPTER 1

DIABETES ATLAS THIRD EDITION

- Eastern Mediterranean and Middle East Region

Number of people with IGT (000’s) in the 20-79 age group Male Female

20-39

40-59

60-79 Total

614.7 1,247.7 757.4 726.8 378.2 1,862.4 641.9 1,278.5 522.2 974.3 423.9 1,920.4 53.0 122.1 32.7 64.2 78.3 175.1 69.8 68.5 40.3 55.7 42.3 138.3 1,552.9 1,552.9 1,552.9 1,552.9 1,552.9 3,105.8 3,117.5 4,018.0 1,696.8 3,575.9 1,862.9 7,135.5 1,000.1 1,008.4 567.7 1,029.4 411.4 2,008.5 224.1 207.3 105.6 238.1 87.7 431.4 287.3 252.0 155.3 268.7 115.4 539.3 57.6 78.3 13.6 60.3 62.0 135.9 116.8 227.4 92.6 179.6 72.0 344.1 568.2 1,198.1 477.2 863.9 425.2 1,766.3 127.3 123.4 77.6 120.7 52.4 250.7 118.4 135.3 84.0 125.1 44.6 253.7 3,867.4 7,144.0 3,749.7 4,446.0 2,815.7 11,011.4 85.2 59.2 40.9 78.0 25.6 144.4 1,443.1 1,199.8 1,162.4 1,078.4 402.2 2,642.9 276.1 457.1 160.2 352.8 220.1 733.2 902.1 1,103.8 332.0 916.9 757.0 2,005.9 198.9 404.9 146.4 299.3 158.2 603.8 529.3 328.3 294.9 446.7 116.0 857.6 265.3 277.8 110.5 253.4 179.3 543.1 16,117 22,493 12,172 17,707 10,283 38,610



PREVALENCE AND PROJECTIONS



CHAPTER 1

55

Table 1.16 Data sources: prevalence estimates of diabetes mellitus (DM) and impaired glucose tolerance (IGT)

Country/territory

Data used S

Afghanistana Algeria Armenia Bahrainb Egyptc Iran, Islamic Republic of Iraq Jordan Kuwaitb Lebanon Libyan Arab Jamahiriya Moroccod Occupied Palestinian Territorye Omanf Pakistana Qatarb Saudi Arabiaa,b Sudan Syrian Arab Republic Tunisiac,e United Arab Emirates Yemen

Pakistan (Shera et al, 1995, 1999a, 1999b)47-49 Algeria (Malek et al, 2001)50 Turkey (Satman et al, 2002)58 Bahrain (Al-Mahroos et al, 1998)32 Egypt (Herman et al, 1995 and Arab, 1997)33,43 Iran (Azizi et al, 2003)51 Jordan (Ajlouni et al, 1998)110 Jordan (Ajlouni et al, 1998)110 Kuwait (Abdella et al, 1998)35 Lebanon (Salti et al, 1997)111 Libyan Arab Jamahiriya (Kadiki et al, 1999)112 Morocco (Tazi et al, 2003)52 Occupied Palestinian Territory (Abdul-Rahim et al, 2001 and Husseini, 2000)53,54 Oman (Al-Lawati et al, 2002)36 Pakistan (Shera et al, 1995, 1999a, 1999b)47-49 Bahrain (Al-Mahroos et al, 1998)32 Saudi Arabia (El-Hazmi et al, 1998; Al-Nozha et al, 2004 and Al-Nuaim 1997)38,39,113 Sudan (Elbagir et al, 1996)30 Syrian Arab Republic (Albache, 2006)55 Tunisia (Papoz et al, 1988 and Ghannem et al, 1997)114,115 UAE (Malik et al, 2005)41 Yemen (Al-Habori, 2004)56

a. The prevalence was obtained by combining the data from the three studies b. Because of the absence of data for IGT in the study used for diabetes, IGT figures were calculated from UAE data c. The prevalences were calculated as the average of the two cited studies as their sample sizes differed considerably d. Because of the absence of data for IGT in the studies used for diabetes, IGT figures were calculated from Libyan data e. Because of the absence of data for IGT in the studies used for diabetes, IGT figures were calculated from Jordanian data f. Because of the absence of data for IGT in the studies used for diabetes, IGT figures were calculated from other Oman data (Asfour et al, 1995)116

56

CHAPTER 1

DIABETES ATLAS THIRD EDITION

- Eastern Mediterranean and Middle East Region



d Screening method OGTT FBG/ SR 2hBG OGTT OGTT/Post prandial GT OGTT OGTT OGTT OGTT OGTT Registration FBG/ SR OGTT OGTT OGTT OGTT OGTT 2hBG OGTT FBG/ SR OGTT OGTT

Diagnostic criteria Sample size Age (yrs) WHO - 1985 ADA - 1997 WHO - 1999 WHO - 1985 WHO - 1985 WHO - 1999 WHO - 1985 WHO - 1985 WHO - 1985 WHO - 1985 N/A WHO - 1980 WHO - 1985 WHO - 1999 WHO - 1985 WHO - 1985 WHO - 1985, ADA 1997 WHO - 1985 WHO - 1999 WHO - 1980 WHO - 1999 WHO - 1999

3,409 1,457 24,788 2,128 5,251 10,368 2,776 2,776 3,003 2,518 15,912 6,570 992 5,731 3,409 2,128 47,573 1,284 1,700 6,570 6,612 498

25+ 30-64 20+ 40-69 20+ 20+ 25-79 25-79 20+ 30+ 20+ 20+ 30-65 20-79 25+ 40-69 14+ 25-84 20+ 20+ 19+ 20-69





PREVALENCE AND PROJECTIONS



CHAPTER 1

57

Table 1.17

Prevalence estimates of diabetes mellitus (DM), 2007 - European Region



DM prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* R Albania Andorraa Austria Azerbaijan Belarus Belgium Bosnia and Herzegovina Bulgaria Channel Islands Croatia Cyprus Czech Republic Denmark Estonia Finland France Georgia Germany Greece Hungary Iceland Ireland Israel Italy Kazakhstan Kyrgyzstan Latvia Liechtensteina Lithuania Luxembourg Macedonia, the Former Yugoslav Republic of Malta Moldova Monacoa Netherlands Norway Poland Portugal Romania Russian Federation San Marinoa Serbia and Montenegrob Slovakia Slovenia Spain Sweden Switzerland Tajikistan Turkey Turkmenistan Ukraine United Kingdom Uzbekistan EUR Total

1,995 52 6,129 5,474 7,241 7,600 2,972 5,880 112 3,445 604 7,792 3,889 983 3,837 43,116 3,130 62,580 8,554 7,543 204 3,017 4,302 44,006 9,846 3,106 1,707 25 2,481 344 1,465 296 3,026 24 11,883 3,242 28,686 7,922 16,212 106,481 22 7,625 4,014 1,518 33,181 6,456 5,336 3,280 46,513 2,857 34,309 42,771 15,293 634,373

4.8 7.8 11.1 6.9 9.2 7.9 9.0 10.1 3.9 9.5 10.3 9.7 7.5 9.9 8.4 8.4 9.1 11.8 8.6 9.8 2.0 5.6 7.8 8.7 5.6 4.3 10.0 10.7 9.7 6.9 8.2 9.7 8.3 8.1 7.3 4.7 9.1 8.2 9.4 9.0 7.8 8.9 8.8 9.8 7.5 7.2 11.2 3.5 7.1 4.0 9.8 4.0 4.0 8.4

4.5 5.7 7.9 7.3 7.6 5.2 7.0 7.6 2.9 7.1 8.9 7.6 5.5 7.6 5.9 5.9 7.4 7.9 5.9 7.6 1.6 5.1 6.9 5.8 5.6 5.1 7.6 7.9 7.6 5.2 7.1 6.7 7.6 5.9 5.2 3.6 7.6 5.7 7.6 7.6 5.8 7.1 7.6 7.6 5.7 5.2 7.9 4.9 7.8 5.2 7.6 2.9 5.1 6.6

a. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of developed world population from 2005 to 2007 b. Estimates made prior to the establishment of Serbia and Montenegro as independent countries * All comparisons between countries should be done using the

58

CHAPTER 1

DIABETES ATLAS THIRD EDITION





Number of people with DM (000’s) in the 20-79 age group Rural Urban Male Female

123.4 257.0 129.4 466.3 83.7 202.4 140.7 410.0 59.6 72.6 123.4 257.0 497.7 2,789.1 45.8 69.6 256.9 348.7 • •



46.2 2.0 327.7 152.0 306.5 296.7 110.8 239.1 2.2 135.2 41.4 379.6 153.3 43.4 173.6 1,695.0 110.8 3,563.3 345.8 352.2 2.3 87.0 176.2 1,967.3 295.8 76.1 76.0 1.2 109.5 11.8 51.8 11.9 122.1 0.9 442.5 88.7 1,295.2 317 759.0 4,392.6 0.9 287.8 175.6 75.3 1,249.7 214.2 283.9 69.5 1,386.1 67.0 1,498.7 839.4 360.7 25,270

49.6 2.1 354.7 228.4 362.7 302.1 155.7 356.7 2.2 193.6 20.7 377.2 137.3 53.8 148.1 1,921.6 175.4 3,815.9 390.9 389.4 1.8 82.6 160.9 1,882.8 254.9 56.1 94.3 1.4 130.4 11.8 68.5 16.8 127.9 1.0 429.5 63.7 1,312.6 331.3 765.5 5,239.0 0.8 387.3 177.7 73.6 1,248.1 253.3 313.2 46.1 1,900.8 48.4 1,848.5 869.9 244.9 27,884

20-39

40-59

19.1 0.1 32.3 45.1 61.3 9.9 20.3 39.5 0.2 21.7 6.6 69.8 20.0 8.2 21.6 128.2 21.1 305.9 38.9 67.8 0.2 33.2 13.9 123.8 35.7 9.7 14.1 0.1 21.1 0.5 10.9 0.3 28.0 0.1 16.6 14.2 253.8 15.4 155.9 910.5 0.1 52.7 37.5 13.0 73.6 36.8 27.1 9.2 431.3 10.1 279.0 85.3 51.5 3,703

36.8 1.4 215.6 195.2 259.1 166.5 108.2 246.8 1.7 134.5 29.0 282.6 115.1 33.7 105.2 1,231.5 125.2 2,189.0 191.4 269.5 1.3 65.9 119.5 1,197.6 303.3 75.3 57.3 0.9 83.0 7.2 53.7 9.5 106.6 0.7 270.2 52.6 1,071.7 193.6 539.6 3,937.5 0.6 288.2 144.6 56.8 757.5 137.3 198.9 66.1 1,672.6 67.4 1,197.0 598.0 351.7 19,621

60-79 Total 40.0 2.6 434.5 140.1 348.7 422.4 138.0 309.4 2.4 172.5 26.6 404.4 155.5 55.4 195.0 2,256.9 139.8 4,884.3 506.5 404.3 2.6 70.5 203.7 2,528.8 211.7 47.2 98.9 1.6 135.8 15.9 55.8 18.9 115.3 1.2 585.1 85.6 1,282.2 439.3 829.0 4,783.7 1.0 334.2 171.2 79.1 1,666.7 293.3 371.1 40.3 1,183.0 37.9 1,871.3 1,026.1 202.4 29,830

95.8 4.1 682.3 380.4 669.2 598.8 266.5 595.7 4.3 328.7 62.1 756.8 290.6 97.3 321.7 3,616.6 286.2 7,379.2 736.7 741.5 4.1 169.7 337.1 3,850.2 550.7 132.2 170.3 2.7 239.9 23.6 120.3 28.6 250.0 1.9 872.0 152.4 2,607.7 648.3 1,524.5 9,631.6 1.7 675.1 353.3 148.9 2,497.8 467.5 597.1 115.6 3,286.9 115.4 3,347.3 1,709.4 605.6 53,154

comparative prevalence, which is adjusted to the world population.

PREVALENCE AND PROJECTIONS



CHAPTER 1

59

Table 1.18

Prevalence estimates of diabetes mellitus (DM), 2025 - European Region



DM prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* R Albania Andorraa Austria Azerbaijan Belarus Belgium Bosnia and Herzegovina Bulgaria Channel Islands Croatia Cyprus Czech Republic Denmark Estonia Finland France Georgia Germany Greece Hungary Iceland Ireland Israel Italy Kazakhstan Kyrgyzstan Latvia Liechtensteina Lithuania Luxembourg Macedonia, the Former Yugoslav Republic of Malta Moldova Monacoa Netherlands Norway Poland Portugal Romania Russian Federation San Marinoa Serbia and Montenegrob Slovakia Slovenia Spain Sweden Switzerland Tajikistan Turkey Turkmenistan Ukraine United Kingdom Uzbekistan EUR Total

2,400 62 6,293 6,800 6,698 7,787 2,879 5,155 121 3,247 745 7,560 4,107 922 3,949 45,455 2,972 60,962 8,587 7,255 242 3,700 5,783 42,240 10,519 4,192 1,545 29 2,368 428 1,542 319 3,074 26 12,724 3,651 28,716 8,219 15,451 97,182 28 7,625 4,132 1,467 33,222 6,847 5,465 5,296 61,913 4,066 28,853 46,068 22,507 653,394

7.5 9.3 13.2 8.9 10.5 9.7 12.0 11.5 4.6 10.6 11.3 11.6 11.4 10.8 10.0 10.4 10.6 13.3 9.7 11.2 2.5 6.4 8.5 10.4 7.0 5.6 11.0 12.5 10.7 8.2 9.5 11.6 9.9 9.5 9.6 5.4 11.0 9.8 10.7 10.6 5.5 9.7 10.8 11.8 9.7 8.1 13.3 4.4 8.9 5.5 10.9 4.6 5.4 9.8

6.9 7.1 9.4 8.6 8.7 6.6 9.5 8.9 3.5 8.1 10.1 8.7 8.5 8.7 6.9 7.3 8.7 9.4 7.0 8.7 1.9 5.9 7.6 6.9 6.7 6.2 8.7 9.4 8.7 6.6 8.1 8.2 8.7 7.3 6.6 4.1 8.7 7.1 8.7 8.7 7.1 8.1 8.7 8.7 7.1 6.0 9.4 6.0 9.1 6.3 8.7 3.5 6.3 7.7

a. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of developed world population from 2005 to 2025. b. Estimates made prior to the establishment of Serbia and Montenegro as independent countries. * All comparisons between countries should be done using

60

CHAPTER 1

DIABETES ATLAS THIRD EDITION





Number of people with DM (000’s) in the 20-79 age group Rural Urban Male Female

144.8 465.5 97.3 498.9 68.4 249.3 141.6 596.5 80.8 154.2 95.3 139.5 539.8 4,981.5 68.7 157.3 393.5 822.8 • •



93.6 2.9 418.5 234.2 322.4 396.9 138.8 240.1 3.0 145.7 55.4 436.7 174.0 45.7 218.1 2,397.1 120.0 3,995.7 402.2 391.0 3.4 135.0 242.3 2,302.6 383.7 131.9 79.5 1.7 119.5 18.5 64.3 16.3 148.8 1.1 641.4 115.8 1,555.2 446.9 820.2 4,556.9 0.7 323.2 221.2 87.8 1,775.7 248.6 347.2 133.9 2,325.2 126.7 1,387.6 1,099.0 700.5 30,794

87.6 3.0 416.9 376.1 386.7 359.8 207.5 356.1 2.6 198.3 29.3 441.6 297.6 54.4 178.4 2,369.0 197.7 4,149.2 430.5 421.3 2.7 102.1 252.3 2,122.9 354.3 103.2 91.0 1.9 136 16.7 82.9 21.0 157.6 1.3 580.8 83.4 1,615.4 361.8 838.2 5,769.2 0.8 419.4 227.1 85.5 1,466.0 305.8 382.5 101.0 3,196.0 99.3 1,783.1 1,061.5 515.8 33,302

20-39

40-59

9.7 2.1 27.8 58.4 57.1 8.8 18.3 30.9 2.2 20.6 10.3 53.9 30.9 9.5 21.4 117.0 21.0 276.6 31.5 54.5 0.2 30.0 26.2 85.2 46.3 15.9 14.4 0.1 21.8 0.6 12.2 2.3 29.6 0.1 14.4 14.5 220.9 13.4 121.7 788.1 0.1 51.0 33.5 11.8 44.8 37.7 25.6 20.8 481.3 16.9 228.6 80.1 92.4 3,445

56.9 1.6 234.9 264.9 237.8 158.3 141.4 237.3 1.6 121.9 34.8 291.2 1.2 32.8 88.5 1,188.7 118.3 2,191.0 231.2 277.1 1.5 87.6 133.3 1,299.1 339.6 115.3 55.7 1.0 84.0 8.9 59.4 8.5 106.6 0.7 267.2 54.5 1,021.9 222.7 622.3 3,360.2 0.5 287.7 154.1 55.3 947.3 138.0 186.3 117.8 2,734.1 117.5 1,009.6 622.1 625.3 20,868

60-79 Total 116.6 4.1 572.7 287.0 416.1 589.6 186.5 328.0 3.8 203.4 41.5 535.3 328.6 59.9 286.5 3,460.4 178.4 5,677.3 572.0 482.7 4.4 121.5 337.2 3,043.2 352.2 103.9 102.4 2.5 151.6 25.8 77.5 28.5 172.2 1.7 940.6 130.3 1,929.0 574.6 916.4 6,179.8 0.9 405.9 262.6 108.1 2,251.6 378.6 517.8 96.2 2,305.8 91.6 1,934.5 1,458.4 498.6 39,837

181.2 5.8 835.5 610.3 709.1 756.7 346.3 596.2 5.6 344.0 84.7 878.4 471.6 100.2 396.5 4,766.1 317.7 8,144.9 832.7 812.3 6.1 237.1 494.6 4,425.5 738.0 235.0 170.5 3.7 255.5 35.3 147.2 37.3 306.4 2.5 1,222.1 199.2 3,170.5 808.8 1,658.5 10,326.2 1.5 742.6 448.3 173.3 3,241.7 554.4 729.7 234.8 5,521.2 226.0 3,170.7 2,160.6 1,216.3 64,096

the comparative prevalence, which is adjusted to the world population.

PREVALENCE AND PROJECTIONS



CHAPTER 1

61

Table 1.19

Prevalence estimates of impaired glucose tolerance (IGT), 2007 - European Region



IGT prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* M Albania Andorraa Austria Azerbaijan Belarus Belgium Bosnia and Herzegovina Bulgaria Channel Islands Croatia Cyprus Czech Republic Denmark Estonia Finland France Georgia Germany Greece Hungary Iceland Ireland Israel Italy Kazakhstan Kyrgyzstan Latvia Liechtensteina Lithuania Luxembourg Macedonia, the Former Yugoslav Republic of Malta Moldova Monacoa Netherlands Norway Poland Portugal Romania Russian Federation San Marinoa Serbia and Montenegrob Slovakia Slovenia Spain Sweden Switzerland Tajikistan Turkey Turkmenistan Ukraine United Kingdom Uzbekistan EUR Total

1,995 52 6,129 5,474 7,241 7,600 2,972 5,880 112 3,445 604 7,792 3,889 983 3,837 43,116 3,130 62,580 8,554 7,543 204 3,017 4,302 44,006 9,846 3,106 1,707 25 2,481 344 1,465 296 3,026 24 11,883 3,242 28,686 7,922 16,212 106,481 22 7,625 4,014 1,518 33,181 6,456 5,336 3,280 46,513 2,857 34,309 42,771 15,293 634,373

2.5 9.9 6.0 5.9 16.9 6.4 6.8 7.7 5.1 7.1 6.7 17.3 14.8 17.5 6.9 7.5 7.1 6.4 7.3 17.4 5.5 1.9 5.4 5.9 7.1 6.4 17.6 5.7 17.3 5.8 6.4 7.8 16.0 7.4 6.1 8.5 16.4 10.1 16.9 16.8 5.5 6.8 16.4 17.3 9.8 9.0 6.1 5.9 6.1 6.1 17.4 5.1 6.1 10.3

2.4 8.9 4.0 6.0 15.2 4.8 5.7 6.3 4.7 5.8 5.9 15.2 12.4 15.2 4.2 6.6 6.1 4.0 5.9 15.2 4.8 1.6 5.1 4.7 7.0 7.0 15.2 4.0 15.2 4.8 5.8 6.0 15.2 6.6 4,8 7.2 15.2 8.9 15.2 15.2 4.7 5.8 15.2 15.2 8.9 7.2 4.0 7.0 6.5 7.0 15.2 4.7 7.0 9.1

a. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of developed world population from 2005 to 2007. b. Estimates made prior to the establishment of Serbia and Montenegro as independent countries * All comparisons between countries should be done using the

62

CHAPTER 1

DIABETES ATLAS THIRD EDITION





Number of people with IGT (000’s) in the 20-79 age group Male Female



16.2 2.1 176.2 113.1 483.8 242.8 78.3 166.5 3.5 94.6 18.6 581.2 263.8 66.9 111.5 2,245.5 78.5 1,951.8 286.8 541.8 6.0 28.4 143.3 1,081.4 270.5 80.6 116.9 0.7 169.7 10.3 37.1 11.1 197.5 1.2 368.4 90.3 2,029.9 324.0 1,169.8 6,981.7 0.5 203.7 279.0 115.0 1,328.0 188.4 152.3 79.2 1,019.4 69.7 2,310.6 1,363.8 383.0 28,135

34.6 3.1 191.9 210.9 740.5 245.6 123.5 286.1 2.1 151.2 21.7 762.9 313.5 105.0 154.5 996.0 144.5 2,083.1 335.4 770.6 5.3 28.5 91.1 1,532.5 431.0 119.2 182.7 0.8 259.3 9.8 56.9 11.8 285.6 0.5 352.2 185.8 2,675.6 475.3 1,574.8 10,858.4 0.7 311.3 380.8 148.2 1,922.3 390.0 171.0 113.3 1,818.7 104.2 3,658.8 808.9 544.3 37,186

20-39

40-59

13.0 3.3 0.6 82.0 282.7 52.8 33.1 70.6 3.4 36.2 9.0 308.1 106.2 39.2 2.5 603.2 38.4 5.6 97.8 297.9 3.6 3.6 49.2 434.4 155.8 55.6 65.6 0.0 96.7 2.6 18.3 3.4 132.4 0.3 86.7 53.6 1,091.8 203.4 686.9 4,206.6 2.2 90.6 170.4 58.6 917.9 101.4 0.5 63.0 812.1 53.6 1,290.6 571.3 287.5 13,856

17.8 1.9 87.1 152.0 507.3 150.9 81.9 180.1 2.6 100.4 16.1 528.4 197.1 65.1 44.2 1,552.8 94.4 877.6 216.8 507.8 3.9 22.6 94.4 844.7 296.6 82.8 111.7 0.4 163.9 6.7 40.4 11.0 207.6 0.8 245.1 87.5 1,952.2 273.2 1,025.8 7,634.9 0.4 214.6 275.8 107.8 1,098.5 167.3 82.8 78.3 1,252.4 73.7 2,332.6 919.8 386.7 25,479

60-79 Total 22.0 2.0 280.4 90.0 436.3 284.8 86.8 201.9 1.6 109.2 17.2 509.6 276.0 69.6 221.4 1,087.5 90.3 3,151.7 307.7 508.8 5.8 32.7 92.8 1,336.7 249.1 61.4 124.3 1.1 170.5 10.7 35.3 10.6 145.1 0.6 390.7 135.0 1,581.8 324.7 1,034.0 6,000.6 0.5 211.8 215.6 98.8 1,236.0 309.7 240.0 51.2 773.7 46.7 2,348.3 683.5 253.1 25,967

50.8 5.2 368.1 324.0 1,224.3 488.5 201.7 452.6 5.6 245.8 40.3 1,344.1 577.4 171.8 266.1 3,241.4 223.0 4,034.9 622.3 1,312.5 11.3 56.9 234.4 2,613.9 701.5 199.8 299.7 1.4 429.0 20.0 94.0 23.0 483.1 1.8 720.6 276.1 4,705.5 799.4 2,744.6 17,840.0 1.2 515.0 659.9 263.2 3,250.4 578.4 323.3 192.5 2,838.2 173.9 5,969.4 2,172.7 927.3 65,322

comparative prevalence, which is adjusted to the world population .

PREVALENCE AND PROJECTIONS



CHAPTER 1

63

Table 1.20

Prevalence estimates of impaired glucose tolerance (IGT), 2025 - European Region



IGT prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* M Albania Andorraa Austria Azerbaijan Belarus Belgium Bosnia and Herzegovina Bulgaria Channel Islands Croatia Cyprus Czech Republic Denmark Estonia Finland France Georgia Germany Greece Hungary Iceland Ireland Israel Italy Kazakhstan Kyrgyzstan Latvia Liechtensteina Lithuania Luxembourg Macedonia, the Former Yugoslav Republic of Malta Moldova Monacoa Netherlands Norway Poland Portugal Romania Russian Federation San Marinoa Serbia and Montenegrob Slovakia Slovenia Spain Sweden Switzerland Tajikistan Turkey Turkmenistan Ukraine United Kingdom Uzbekistan EUR Total

2,400 62 6,293 6,800 6,698 7,787 2,879 5,155 121 3,247 745 7,560 4,107 922 3,949 45,455 2,972 60,962 8,587 7,255 242 3,700 5,783 42,240 10,519 4,192 1,545 29 2,368 428 1,542 319 3,074 26 12,724 3,651 28,716 8,219 15,451 97,182 28 7,625 4,132 1,467 33,222 6,847 5,465 5,296 61,913 4,066 28,853 46,068 22,507 653,394

2.8 10.6 7.6 7.1 18.2 7.3 7.7 8.6 2.4 8.1 7.4 19.0 16.2 18.4 9.2 7.9 8.0 7.7 8.0 18.7 6.6 2.4 5.9 6.6 8.0 7.2 19.0 10.1 18.3 6.4 7.4 8.6 17.6 7.8 7.2 9.6 17.3 10.8 18.3 18.3 6.1 7.5 18.4 19.1 10.7 9.8 7.6 6.3 7.1 7.0 18.6 5.2 6.9 10.9

2.7 9.4 5.0 6.8 16.3 5.5 6.5 7.1 4.7 6.5 6.5 16.3 13.5 16.3 5.4 7.1 6.9 5.0 6.5 16.3 5.5 2.0 5.7 5.1 7.8 7.8 16.3 6.9 16.3 5.5 6.5 6.9 16.3 7.1 5.5 7.9 16.3 9.4 16.3 16.3 5.1 6.5 16.3 16.3 9.4 7.9 5.0 7.8 7.3 7.8 16.3 4.9 7.8 9.6

a. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of developed world population from 2005 to 2025 b. Estimates made prior to the establishment of Serbia and Montenegro as independent countries * All comparisons between countries should be done using the

64

CHAPTER 1

DIABETES ATLAS THIRD EDITION





Number of people with IGT (000’s) in the 20-79 age group Male Female



20.6 2.8 239.9 165.3 488.4 279.4 87.3 164.3 3.5 117.2 25.0 630.9 303.1 68.6 162.5 2,451.2 83.1 2,319.9 316.3 575.4 8.2 44.9 214.9 1,190.4 325.9 122.9 125.3 1.4 178.9 13.8 53.1 13.5 227.6 1.4 452.7 114.3 2,291.5 381.7 1,220.3 6,894.7 0.7 266.7 328.1 125.9 1,571.9 216.4 200.9 134.8 1,615.8 114.3 2,070.7 1,504.8 644.6 31,178

47.4 3.8 238.3 314.2 733.5 288.2 135.2 277.4 2.3 145.3 29.8 807.1 361.2 101.3 201.3 1,127.0 155.4 2,396.0 366.8 778.1 7.8 45.4 129.1 1,587.2 520.0 179.2 168.8 1.6 255.6 13.5 60.8 13.8 314.3 0.6 465.6 235.2 2,675.6 503.2 1,604.9 10,864.6 1.0 308.6 430.9 154.8 1,984.7 451.7 215.8 201.4 2.807.1 170.9 3,306.2 896.2 911.5 39,997

20-39

40-59

14.4 3.4 0.5 97.6 2.2 48.8 28.5 52.5 1.4 29.1 10.1 221.3 107.7 33.1 2.5 567.4 35.2 4.9 73.7 226.8 3.7 3.4 59.6 317.5 156.4 69.8 52.9 0.2 84.4 2.9 16.0 3.5 116.6 0.3 84.0 56.2 1,091.8 160.9 503.7 3,329.5 2.3 79.3 134.7 43.4 596.7 102.4 0.4 103.6 910.9 69.7 960.1 592.7 391.5 11,892

19.7 2.2 106.9 198.6 4.4 142.1 86.3 173.4 2.5 87.3 19.5 554.4 190.5 63.5 36.4 1,469.1 87.8 1,004.3 257.0 527.6 4.3 34.3 124.3 857.6 315.7 116.3 107.2 0.8 160.4 8.6 42.7 10.4 210.2 0.9 235.7 90.0 1,952.2 311.4 1,181.2 6,614.7 0.5 207.1 297.5 104.6 1,368.2 166.8 85.0 126.7 2,005.9 116.3 1,974.3 914.5 624.2 25,864

60-79 Total 35.9 3.0 370.8 183.4 527.9 376.7 107.6 215.8 1.9 146.0 27.1 664.4 368.0 75.3 326.9 1,543.7 115.5 3,706.7 352.5 601.3 9.9 54.5 162.0 1,604.5 373.9 116.0 136.0 1.9 191.7 15.8 55.2 15.5 217.1 0.8 600.5 203.3 1,581.8 414.6 1,142.4 7,817.2 0.9 291.0 328.7 134.7 1,593.6 398.9 331.1 105.8 1,506.1 99.1 2,444.4 895.8 540.5 33,136

68.0 6.6 478.2 479.5 1,221.9 567.6 222.5 441.6 2.9 262.5 54.8 1,438.0 664.3 169.9 363.8 3,578.2 238.5 4,715.9 683.1 1,353.6 15.9 90.3 344.0 2,777.6 846.0 302.1 294.2 2.9 434.5 27.4 113.9 27.3 541.9 2.0 918.3 349.5 4,967.1 884.9 2,825.2 17,759.3 1.7 575.3 759 280.7 3,556.5 668.1 416.6 336.2 4,422.9 285.2 5,376.8 2,401.0 1,556.2 71,175

comparative prevalence, which is adjusted to the world population

PREVALENCE AND PROJECTIONS



CHAPTER 1

65

Table 1.21 Data sources: prevalence estimates of diabetes mellitus (DM) and impaired glucose tolerance (IGT)



66

Country/territory

Data used S

Albania Andorra Austriaa,b Azerbaijan Belarusb Belgium Bosnia and Herzegovina Bulgaria Channel Islandsb Croatiac Cyprus Czech Republicb Denmark Estoniab Finlandb Franceb Georgia Germanya,b Greecec Hungaryb Icelandd Irelandb Israele Italyf Kazakhstanb Kyrgyzstanb Latviab Liechtenstein Lithuaniab Luxembourg Macedonia, the Former Yugoslav Republic of c Malta Moldovab Monacob Netherlands Norway Polandb Portugal Romaniab Russian Federationb San Marinob Serbia and Montenegroc Slovakiab Sloveniab Spain Sweden Switzerlanda,b Tajikistanb Turkey

Albania (Shapo et al, 2003)117 Spain (Castell et al, 1999)118 Germany (Rathmann et al, 2003; Thefeld et al, 1999 and Meisinger et al, 2004 )64,65,71 Germany (Hauner et al, 2003)63 Turkey (Satman et al, 2002)58 Poland (Szurkowska et al and Lopatynski et al, 2001)60,61 The Netherlands (Mooy et al, 1995)72 Turkey (Satman et al, 2002)58 Turkey (Satman et al, 2002)58 United Kingdom (Unwin et al, 1997 and Yudkin et al, 1993)119,120 Turkey (Satman et al, 2002)58 Cyprus (Loizou et al, 2006)121 Poland (Szurkowska et al and Lopatynski et al, 2001)60,61 Denmark (Glumer et al, 2003)122 Poland (Szurkowska et al and Lopatynski et al, 2001)60,61 Finland (Tuomilehto et al, 1986 and Yliharsila et al, 2005)123,124 France (Gourdy et al, 2001 and Ricordeau et al, 2000)62,70 Turkey (Satman et al, 2002)58 Germany (Rathmann et al, 2003; Thefeld et al, 1999 and Meisinger et al, 2004 )64,65,71 Germany (Hauner et al, 2003)63 Greece (Panagiotakos et al, 2005)125 Poland (Szurkowska et al and Lopatynski et al, 2001)60,61 Iceland (Vilbergsson et al, 1997)126 Ireland (Smith et al, 2003)127 Israel (Bar-On et al, 1992 and Stern et al, 1999)128,129 Israel (Chodick et al, 2003)66 Italy (Cricelli et al, 2003)67 Uzbekistan (King et al, 1998 and 2002)130,131 Uzbekistan (King et al, 1998 and 2002)130,131 Poland (Szurkowska et al and Lopatynski et al, 2001)60,61 Germany (Rathmann et al, 2003; Thefeld et al, 1999 and Meisinger et al, 2004 )64,65,71 Germany (Hauner et al, 2003)63 Poland (Szurkowska et al and Lopatynski et al, 2001)60,61 The Netherlands (Mooy et al, 1995)72 Turkey (Satman et al, 2002)58 Malta (Schranz, 1989)132 Poland (Szurkowska et al and Lopatynski et al, 2001)60,61 France (Gourdy et al, 2001 and Ricordeau et al, 2000)62,70 The Netherlands (Ubink-Veltmaat et al, 2003)68 Norway (Stene et al, 2004)69 Poland (Szurkowska et al and Lopatynski et al, 2001)60,61 Spain (Castell et al, 1999)118 Poland (Szurkowska et al and Lopatynski et al, 2001)60,61 Poland (Szurkowska et al and Lopatynski et al, 2001)60,61 Italy (Cricelli et al, 2003)67 Turkey (Satman et al, 2002)58 Poland (Szurkowska et al and Lopatynski et al, 2001)60,61 Poland (Szurkowska et al and Lopatynski et al, 2001)60,61 Spain (Castell et al, 1999)118 Sweden (Eliasson et al, 2002)133 Germany (Rathmann et al, 2003; Thefeld et al, 1999 and Meisinger et al, 2004 )64,65,71 Germany (Hauner et al, 2003)63 Uzbekistan (King et al, 1998 and 2002)130,131 Turkey (Satman et al, 2002)58

CHAPTER 1

DIABETES ATLAS THIRD EDITION

- European Region



d Screening method



OGTT OGTT SR/OGTT SR 2hBG OGTT OGTT 2hBG 2hBG OGTT 2hBG OGTT OGTT OGTT OGTT OGTT SR and FBG 2hBG SR/OGTT SR FBG OGTT OGTT (50-100g) OGTT OGTT SR SR 2hBG 2hBG OGTT SR/OGTT SR OGTT OGTT 2hBG OGTT OGTT SR and FBG SR SR OGTT OGTT OGTT OGTT SR 2hBG OGTT OGTT OGTT OGTT SR/OGTT SR 2hBG 2hBG

PREVALENCE AND PROJECTIONS

Diagnostic criteria Sample size Age (yrs) WHO - 1985 WHO - 1985 WHO - 1999 Known diabetes WHO - 1999 WHO - 1985 WHO - 1985 WHO - 1999 WHO - 1999 WHO - 1985 WHO - 1999 WHO - 1999 WHO - 1985 WHO - 1999 WHO - 1985 WHO - 1985, 1999 Known diabetes, ADA 1997 WHO - 1999 WHO - 1999 Known diabetes ADA - 1997 WHO - 1985 WHO - 1985 WHO - 1999 WHO – 1980,1985 Known diabetes Known diabetes WHO - 1994, 1999 WHO - 1994, 1999 WHO - 1985 WHO - 1999 Known diabetes WHO - 1985 WHO - 1985 WHO - 1999 WHO - 1985 WHO - 1985 Known diabetes, ADA 1997 Known diabetes Known diabetes WHO - 1985 WHO - 1985 WHO - 1985 WHO - 1985 Known diabetes WHO - 1999 WHO - 1985 WHO - 1985 WHO - 1985 WHO - 1999 WHO - 1999 Known diabetes WHO - 1994, 1999 WHO - 1999



1,120 3,839 12,732 300,000 24,788 6,842 2,540 24,788 24,788 2,529 24,788 1,200 6,842 6,784 6,842 2,775 3,508 24,788 12,732 300,000 3,032 6,842 18,887 3,821 6,918 1,600,000 432,747 2,865 2,865 6,842 12,732 300,000 6,842 2,540 24,788 1,422 6,842 3,508 155,574 combination 6,842 3,839 6,842 6,842 432,747 24,788 6,842 6,842 3,839 6,952 12,732 300,000 2,865 24,788

25+ 30-79 18-79 20+ 20+ 35+ 50-74 20+ 20+ 25-75 20+ 20-79 35+ 30-60 35+ 45-84 35-64 20+ 18-79 20 20+ 35+ 30-79 40+ 25-64 25+ 15+ 35+ 35+ 35+ 18-79 20+ 35+ 50-74 20+ 35+ 35+ 35-64 50-74 30+ 35+ 30-79 35+ 35+ 15+ 20+ 35+ 35+ 30-79 25-74 18-79 20+ 35+ 20+

CHAPTER 1

67

Table 1.21 Data sources: prevalence estimates of diabetes mellitus (DM) and impaired glucose tolerance (IGT)

Country/territory

Data used S

Turkmenistanb Ukraineb United Kingdomb Uzbekistanb

Uzbekistan (King et al, 1998 and 2002)130,131 Poland (Szurkowska et al and Lopatynski et al, 2001)60,61 United Kingdom (Unwin et al, 1997 and Yudkin et al, 1993)119,120 Uzbekistan (King et al, 1998 and 2002)130,131

a. IGT prevalences were derived from the data of Rathmann et al b. The prevalences were obtained by combining the data from the two (or more) studies c. Because of the absence of data for IGT in the study used for diabetes, IGT figures were calculated from Cyprus data d. Because of the absence of data for IGT in the study used for diabetes, IGT figures were calculated from Netherlands data (Mooy et al) e. IGT prevalence for Israel was derived only from the data in Bar-On et al f. IGT prevalence for Italy were derived from other reports: Garancini et al, 1995134, Verillo et al135

68

CHAPTER 1

DIABETES ATLAS THIRD EDITION

- European Region

d Screening method 2hBG OGTT OGTT 2hBG

Diagnostic criteria Sample size Age (yrs) WHO - 1994, 1999 WHO - 1985 WHO - 1985 WHO - 1994, 1999

2,865 6,842 2,529 2,865

35+ 35+ 25-75 35+





PREVALENCE AND PROJECTIONS



CHAPTER 1

69

Table 1.22

Prevalence estimates of diabetes mellitus (DM), 2007 - North American Region



DM prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* R

Anguillaa 8 5.7 5.7 Antigua and Barbudaa 43 7.0 7.0 Arubaa 45 10.1 10.1 Bahamas 207 9.8 10.2 Barbados 194 8.4 7.7 Belize 148 7.6 9.6 Bermudaa 41 10.1 10.1 British Virgin Islandsa 15 10.1 10.1 Canada 23,879 9.3 7.4 Cayman Islandsa 29 10.1 10.1 Dominicaa 42 11.2 11.2 Grenadaa 56 9.1 9.1 Guadeloupe 299 10.2 8.8 Guyana 463 8.1 9.2 Haiti 4,441 7.1 9.0 Jamaica 1,536 9.9 10.3 Martinique 272 10.6 8.6 Mexico 64,939 9.4 10.6 Saint Kitts and Nevisa 24 8.8 8.8 Saint Lucia 98 8.4 9.0 Saint Vincent and the Grenadinesa 72 8.2 8.1 Trinidad and Tobago 901 11.4 11.5 United States of America 208,667 9.2 7.8 US Virgin Islands 74 12.4 9.6 NA Total 306,493 9.2 8.4

a. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of the world population from 2005 to 2007 * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

70

CHAPTER 1

DIABETES ATLAS THIRD EDITION





Number of people with DM (000’s) in the 20-79 age group Rural Urban Male Female

20-39

40-59

60-79 Total

0.4 0.1 0.2 0.3 0.1 0.2 0.2 0.5 1.3 1.7 1.3 1.7 0.3 1.5 1.2 3.0 2.0 2.5 0.5 2.2 1.8 4.5 8.5 11.7 2.4 10.2 7.6 20.2 5.0 11.3 6.7 9.6 1.6 8.6 6.1 16.3 4.0 7.2 4.4 6.8 2.2 6.2 2.7 11.2 1.8 2.3 0.5 2.0 1.6 4.1 0.6 0.8 0.2 0.7 0.6 1.5 1,207.2 1,008.4 154.9 953.0 1,107.8 2,215.6 1.3 1.6 0.3 1.4 1.2 2.9 0.8 4.0 1.8 3.0 0.7 2.6 1.5 4.8 2.1 2.9 1.9 3.2 0.7 2.8 1.6 5.1 0.0 30.5 12.9 17.6 3.0 15.7 11.8 30.5 15.5 22.0 13.0 24.6 6.7 21.0 9.9 37.6 140.7 173.4 109.0 205.0 59.5 163.7 90.9 314.1 39.6 112.0 56.0 95.7 22.7 82.3 46.6 151.7 0.6 28.1 11.7 17.0 2.3 14.4 12.0 28.7 854.9 5,260.7 2,413.4 3,702.3 1,123.0 2,762.3 2,230.4 6,115.7 1.0 1.1 0.8 1.3 0.3 1.2 0.7 2.1 3.6 4.7 3.1 5.2 1.3 4.6 2.4 8.3 1.5 4.4 2.6 3.3 0.7 2.9 2.3 5.9 13.9 88.6 37.9 64.6 13.4 58.6 30.6 102.5 10,389.5 8,767.5 1,945.0 8,375.6 8,836.4 19,157.0 3.2 6.0 3.2 6.0 0.7 4.8 3.7 9.2 1,088 5,759 14,291 13,962 3,343 12,498 12,411 28,253



PREVALENCE AND PROJECTIONS



CHAPTER 1

71

Table 1.23 Prevalence estimates of diabetes mellitus (DM), 2025 - North American Region

DM prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* R

Anguillaa 12 7.1 7.1 Antigua and Barbudaa 51 8.8 8.8 Arubaa 52 11.6 11.6 Bahamas 274 12.0 11.8 Barbados 212 11.9 9.5 Belize 231 9.7 11.4 Bermudaa 49 11.6 11.6 British Virgin Islandsa 22 10.1 10.1 Canada 28,132 11.1 8.6 Cayman Islandsa 49 11.6 11.6 Dominicaa 43 13.0 13.0 Grenadaa 61 11.1 11.1 Guadeloupe 350 12.2 10.0 Guyana 499 11.8 11.2 Haiti 6,281 8.6 11.0 Jamaica 1,823 11.8 12.2 Martinique 294 12.8 9.9 Mexico 88,723 12.2 12.4 Saint Kitts and Nevisa 28 10.6 10.6 Saint Lucia 124 10.7 10.9 Saint Vincent and the Grenadinesa 85 10.0 10.0 Trinidad and Tobago 962 14.7 13.2 United States of America 247,747 10.3 8.8 US Virgin Islands 73 13.2 10.4 NA Total 376,180 10.8 9.7

a. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of the world population from 2005 to 2025 * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

72

CHAPTER 1

DIABETES ATLAS THIRD EDITION

Number of people with DM (000’s) in the 20-79 age group Rural Urban Male Female

20-39

40-59

60-79 Total

0.6 0.3 0.4 0.5 0.1 0.4 0.4 0.9 1.4 3.1 2.0 2.5 0.4 2.0 2.1 4.5 2.6 3.4 0.5 2.7 2.7 6.0 13.5 19.3 2.7 14.8 15.3 32.8 5.6 19.7 10.7 14.6 1.4 10.3 13.6 25.3 6.2 16.3 8.5 13.9 3.5 12.5 6.5 22.4 2.5 3.2 0.5 2.6 2.6 5.7 0.3 2.0 1.0 1.3 0.2 1.0 1.0 2.3 1,714.6 1,404.4 172.6 971.1 1,977.3 3,119.0 2.5 3.2 0.5 2.6 2.6 5.7 0.7 5.0 2.1 3.5 0.6 2.9 2.1 5.6 2.1 4.8 2.5 4.3 0.8 3.5 2.5 6.8 17.7 25.1 2.7 18.5 21.6 42.8 17.4 41.3 20.4 38.3 5.7 32.4 20.6 58.7 174.3 364.9 201.1 338.1 99.6 291.3 148.2 539.2 40.6 173.7 75.9 138.4 26.8 106.6 80.9 214.3 0.6 37.1 15.2 22.4 2.1 15.0 20.6 37.7 1,159.3 9,651.6 4,206.5 6,604.5 1,272.5 4,910.5 4,627.9 10,810.9 1.1 1.8 1.1 1.8 0.3 1.5 1.1 2.9 4.4 8.9 4.9 8.4 1.6 7.0 4.6 13.3 1.4 7.1 3.8 4.7 0.8 4.0 3.7 8.5 14.0 127.0 49.9 91.1 13.6 69.0 58.4 141.0 13,625.2 11,786.7 2,222.7 8,330.1 14,859.2 25,411.9 2.5 7.2 2.9 6.8 0.9 3.5 5.3 9.7 1,432 10,472 19,987 20,540 3,833 14,816 21,881 40,528



PREVALENCE AND PROJECTIONS



CHAPTER 1

73

Table 1.24

Prevalence estimates of impaired glucose tolerance (IGT), 2007 - North American Region



IGT prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* M

Anguillaa 8 11.4 11.4 Antigua and Barbudaa 43 11.4 11.4 Arubaa 45 11.4 11.4 Bahamas 207 11.3 11.4 Barbados 194 12.0 11.4 Belize 148 9.9 11.4 Bermudaa 41 11.4 11.4 British Virgin Islandsa 15 11.4 11.4 Canadab 23,879 6.1 5.0 Cayman Islandsa 29 11.4 11.4 Dominicaa 42 11.4 11.4 Grenadaa 56 11.4 11.4 Guadeloupe 299 12.7 11.4 Guyana 463 10.4 11.4 Haiti 4,441 9.8 11.4 Jamaica 1,536 11.3 11.4 Martinique 272 13.2 11.4 Mexico 64,939 7.4 8.0 Saint Kitts and Nevisa 24 11.4 11.4 Saint Lucia 98 11.0 11.4 Saint Vincent and the Grenadinesa 72 10.5 11.4 Trinidad and Tobago 901 11.3 11.4 United States of America b 208,667 5.9 5.0 US Virgin Islands 74 13.8 11.4 NA Total 306,493 6.4 5.8

a. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of world population from 2005 to 2007 b. Prevalence figures are for IFG (not IGT) as only fasting specimens were measured for the majority of NHANES III participants * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

74

CHAPTER 1

DIABETES ATLAS THIRD EDITION





Number of people with IGT (000’s) in the 20-79 age group Male Female

20-39

40-59

60-79 Total

0.4 0.6 0.3 0.4 0.3 1.0 2.0 2.9 1.3 2.1 1.6 4.9 2.1 3.0 1.4 2.1 1.6 5.1 8.9 14.4 6.6 10.0 6.8 23.3 9.2 14.2 5.4 10.9 7.1 23.4 5.7 8.9 5.4 5.7 3.5 14.6 1.9 2.8 1.2 2.0 1.5 4.7 0.7 1.0 0.4 0.7 0.5 1.7 915.2 548.6 158.9 638.2 666.7 1,463.8 1.3 2.0 0.9 1.4 1.0 3.3 2.0 2.9 1.3 2.0 1.5 4.9 2.6 3.8 1.7 2.7 2.0 6.4 15.4 22.6 8.2 16.8 13.1 38.0 17.1 31.1 16.3 19.8 12.1 48.2 154.2 281.3 162.6 158.0 114.8 435.5 69.1 104.6 48.7 69.9 55.0 173.7 14.4 21.5 6.6 15.7 13.6 35.9 1,871.1 2,909.7 1,491.3 1,788.7 1,500.7 4,780.7 1.1 1.6 0.7 1.2 0.9 2.8 4.2 6.6 3.2 4.4 3.2 10.8 3.0 4.6 2.5 2.8 2.3 7.6 40.5 61.5 26.9 44.4 30.8 102.1 7,621.2 4,754.3 1,445.8 5,413.2 5,517.4 12,375.4 4.2 6.0 1.6 4.3 4.3 10.2 10,768 8,810 3,399 8,217 7,962 19,578





PREVALENCE AND PROJECTIONS



CHAPTER 1

75

Table 1.25

Prevalence estimates of impaired glucose tolerance (IGT), 2025 - North American Region



IGT prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* M

Anguillaa 12 12.5 12.5 Antigua and Barbudaa 51 12.5 12.5 Arubaa 52 12.5 12.5 Bahamas 274 12.8 12.5 Barbados 212 14.6 12.5 Belize 231 11.0 12.5 Bermudaa 49 12.5 12.5 British Virgin Islandsa 22 12.5 12.5 Canadab 28,132 7.2 5.7 Cayman Islandsa 49 12.5 12.5 Dominicaa 43 12.5 12.5 Grenadaa 61 12.5 12.5 Guadeloupe 350 14.5 12.5 Guyana 499 12.6 12.5 Haiti 6,281 10.4 12.5 Jamaica 1,823 12.2 12.5 Martinique 294 15.1 12.5 Mexico 88,723 8.6 8.8 Saint Kitts and Nevisa 28 12.5 12.5 Saint Lucia 124 12.2 12.5 Saint Vincent and the Grenadinesa 85 12.5 12.5 Trinidad and Tobago 962 13.6 12.5 United States of America b 247,747 6.7 5.7 US Virgin Islands 73 14.6 12.5 NA Total 376,180 7.3 6.7

a. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of world population from 2005 to 2025 b. Prevalence figures are for IFG (not IGT) as only fasting specimens were measured for the majority of NHANES III participants * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

76

CHAPTER 1

DIABETES ATLAS THIRD EDITION





Number of people with IGT (000’s) in the 20-79 age group Male Female

20-39

40-59

60-79 Total

0.7 0.9 0.3 0.6 0.6 1.6 2.7 3.6 1.4 2.5 2.4 6.4 2.8 3.7 1.4 2.6 2.5 6.5 14.2 20.9 7.3 14.1 13.7 35.1 13.8 17.2 4.3 11.8 15.0 31.1 10.2 15.3 7.4 10.6 7.4 25.5 2.6 3.5 1.3 2.4 2.4 6.1 1.2 1.6 0.6 1.1 1.1 2.8 1,262.9 751.1 173.7 648.2 1,192.2 2,014 2.6 3.5 1.3 2.5 2.4 6.2 2.3 3.1 1.2 2.2 2.1 5.4 3.3 4.4 1.7 3.1 2.9 7.7 21.7 29.0 7.8 19.0 23.9 50.7 24.8 38.0 12.4 27.9 22.5 62.7 245.7 404.6 219.9 259.4 171.0 650.3 91.7 130.9 52.9 83.9 85.8 222.7 19.0 25.3 5.8 15.6 22.9 44.3 3,201.4 4,456.9 1,606.4 3,044.9 3,007.0 7,658.3 1.5 2.0 0.8 1.4 1.3 3.5 6.3 8.9 3.5 6.2 5.4 15.2 4.5 6.1 2.3 4.5 3.8 10.6 55.5 75.0 22.7 50.6 57.2 130.5 10,177.0 6,314.7 1,681.6 5,439.2 9,371.9 16,491.7 4.4 6.3 1.8 2.9 6.0 10.7 15,173 12,327 3,820 9,657 14,023 27,499





PREVALENCE AND PROJECTIONS



CHAPTER 1

77

Table 1.26 Data sources: prevalence estimates of diabetes mellitus (DM) and impaired glucose tolerance (IGT)

Country/territory

Data used S

Anguillaa Antigua and Barbudaa Arubaa Bahamas Barbadosa Belize Bermudaa British Virgin Islandsa Canadab Cayman Islandsa Dominica Grenada Guadeloupea Guyana Haiti Jamaica Martiniquea Mexicoc Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadinesa Trinidad and Tobago United States of America US Virgin Islands

Barbados (Hennis et al, 2002)136 Barbados (Hennis et al, 2002)136 Barbados (Hennis et al, 2002)136 Barbados (Hennis et al, 2002)136 Barbados (Hennis et al, 2002)136 Jamaica (Wilks et al, 1999)76 Barbados (Hennis et al, 2002)136 Barbados (Hennis et al, 2002)136 Canada (Hux et al, 2003)137 Barbados (Hennis et al, 2002)136 Jamaica (Wilks et al, 1999)76 Jamaica (Wilks et al, 1999)76 Guadeloupe (Costagliola et al, 1991)138 Jamaica (Wilks et al, 1999)76 Jamaica (Wilks et al, 1999)76 Jamaica (Wilks et al, 1999)76 Guadeloupe (Costagliola et al, 1991)138 Mexico (Aguilar-Salinas et al, 2003 and Sanchez-Castillo et al, 2005)78,79 Jamaica (Wilks et al, 1999)76 Jamaica (Wilks et al, 1999)76 Barbados (Hennis et al, 2002)136 Jamaica (Wilks et al, 1999)76 USA (Cowie et al, 2006)77 Jamaica (Wilks et al, 1999)76

a. Because of the absence of data for IGT in the study used for diabetes, IGT figures were calculated from Jamaican data b. Because of the absence of data for IGT in the study used for diabetes, IGT figures were calculated from USA data c. Because of the absence of data for IGT in the studies used for diabetes, IGT figures were calculated from Brazilian data81,139,140 N/A Not available

78

CHAPTER 1

DIABETES ATLAS THIRD EDITION

- North American Region



d Screening method SR or HBA1c > 10% SR or HBA1c > 10% SR or HBA1c > 10% SR or HBA1c > 10% SR or HBA1c > 10% OGTT SR or HBA1c > 10% SR or HBA1c > 10% Registry SR or HBA1c > 10% OGTT OGTT SR or FPG > 8.0 OGTT OGTT OGTT SR or FPG > 8.0 OGTT/FBG OGTT OGTT SR or HBA1c > 10% OGTT FBG OGTT

Diagnostic criteria Sample size Age (yrs) Known diabetes Known diabetes Known diabetes Known diabetes Known diabetes WHO - 1980 Known diabetes Known diabetes Known diabetes Known diabetes WHO - 1980 WHO - 1980 WHO - 1980 WHO - 1980 WHO - 1980 WHO - 1980 WHO - 1980 ADA - 1997 WHO - 1980 WHO - 1980 Known diabetes WHO - 1980 ADA - 1997 WHO - 1980

4,104 4,104 4,104 4,104 4,104 1,303 4,104 4,104 N/A 4,104 1,303 1,303 1,036 1,303 1,303 1,303 1,036 84,054 1,303 1,303 4,104 1,303 4,761 1,303

40-79 40-79 40-79 40-79 40-79 25-74 40-79 40-79 20+ 40-79 25-74 25-74 18+ 25-74 25-74 25-74 18+ 20+ 25-74 25-74 40-79 25-74 20+ 25-74









PREVALENCE AND PROJECTIONS



CHAPTER 1

79

Table 1.27

Prevalence estimates of diabetes mellitus (DM), 2007 - South and Central American Region

DM prevalence (%) Country/territory Population (20-79) (000’s) National Comparative* R

Argentina 24,952 6.0 5.6 Bolivia 4,910 5.1 5.8 Brazil 119,519 5.8 6.2 Chile 10,883 5.9 5.6 Colombia 27,860 4.6 5.0 Costa Rica 2,746 8.5 9.3 Cuba 8,117 10.4 9.3 Dominican Republic 5,219 7.3 8.7 Ecuador 7,792 5.2 5.7 El Salvador 3,980 7.7 9.0 French Guiana 111 11.5 11.8 Guatemala 6,034 7.2 8.6 Honduras 3,764 7.1 9.1 Netherlands Antilles 126 13.7 11.3 Nicaragua 2,870 7.6 10.1 Panama 2,002 9.0 9.7 Paraguay 3,344 4.0 4.8 Peru 16,642 5.4 6.0 Puerto Rico 2,704 12.8 10.7 Suriname 274 9.7 10.2 Uruguay 2,280 6.4 5.6 Venezuela 16,297 4.9 5.4 SACA Total 272,427 6.0 6.3

* All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

80

CHAPTER 1

DIABETES ATLAS THIRD EDITION



Number of people with DM (000’s) in the 20-79 age group Rural Urban Male Female

20-39

40-59

60-79 Total

143.1 1,344.7 695.9 791.9 121.7 736.1 629.9 1,487.8 46.4 201.7 113.9 134.2 36.3 126.3 85.6 248.1 620.4 6,292.9 3,101.0 3,812.3 919.5 3,245.7 2,748.1 6,913.3 73.9 571.6 310.4 335.1 80.4 349.1 216.0 645.5 166.6 1,111.2 571.5 706.3 122.7 729.7 425.3 1,277.8 68.6 165.2 98.4 135.3 37.5 114.0 82.3 233.8 95.2 749.5 309.9 534.8 100.8 447.3 296.6 844.7 72.8 310.7 147.4 236.1 66.3 219.5 97.7 383.5 84.3 320.6 191.1 213.7 55.1 209.1 140.6 404.8 106.4 201.3 119.5 188.3 59.6 130.2 117.9 307.7 1.4 11.3 5.6 7.2 2.5 7.6 2.7 12.8 176.1 259.7 171.3 264.5 82.6 183.6 169.6 435.8 89.1 178.6 110.7 157.1 56.2 119.1 92.5 267.8 2.8 14.4 6.3 10.9 2.0 10.0 5.2 17.2 42.8 174.0 87.6 129.2 48.5 99.3 69.0 216.8 45.1 135.4 75.8 104.7 29.8 82.3 68.4 180.4 33.9 98.8 59.5 73.2 24.1 69.1 39.5 132.7 130.2 763.8 425.0 469.0 126.8 464.0 303.2 894.0 45.0 300.3 129.2 216.1 35.3 140.6 169.4 345.3 7.7 19.0 10.6 16.0 5.3 14.3 7.1 26.7 11.5 134.9 67.5 78.9 10.7 69.0 66.7 146.4 48.1 746.9 371.6 423.3 74.2 440.6 280.1 795.0 2,111 14,106 7,180 9,038 2,098 8,007 6,113 16,218



PREVALENCE AND PROJECTIONS



CHAPTER 1

81

Table 1.28

Prevalence estimates of diabetes mellitus (DM), 2025 - South and Central American Region

DM prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* R

Argentina 31,093 6.4 6.4 Bolivia 7,490 5.7 7.0 Brazil 154,392 11.4 11.5 Chile 13,639 6.9 6.4 Colombia 38,483 5.9 6.0 Costa Rica 3,866 11.3 11.4 Cuba 8,467 12.9 10.6 Dominican Republic 7,156 9.3 10.2 Ecuador 10,946 6.4 6.9 El Salvador 5,766 9.7 11.0 French Guiana 180 12.6 13.3 Guatemala 10,227 7.9 10.6 Honduras 6,151 8.7 11.1 Netherlands Antilles 146 14.3 12.4 Nicaragua 4,686 9.4 12.1 Panama 2,810 11.4 11.7 Paraguay 5,389 4.8 5.7 Peru 23,552 6.6 7.1 Puerto Rico 3,031 14.5 12.6 Suriname 323 12.2 11.9 Uruguay 2,627 6.8 6.4 Venezuela 23,232 6.0 6.3 SACA Total 363,651 9.0 9.3

* All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

82

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DIABETES ATLAS THIRD EDITION



Number of people with DM (000’s) in the 20-79 age group Rural Urban Male Female

20-39

40-59

60-79 Total

143.3 1,852.4 944.0 1,051.8 149.2 975.6 871.0 1,995.7 55.7 375.0 199.2 231.4 56.6 217.0 157.0 430.6 1,111.2 16,516.3 9,588.6 8,038.8 2,166.8 7,837.6 7,623.0 17,627.5 82.0 863.3 457.4 487.9 93.1 434.1 418.1 945.3 215.2 2,036.1 1,014.6 1,236.7 153.0 1,117.4 980.8 2,251.3 91.9 345.7 183.6 254.0 51.7 180.3 205.5 437.6 90.0 1,006.2 393.9 702.3 78.6 537.9 479.7 1,096.2 89.5 576.2 244.8 421.0 86.9 360.9 218.0 665.8 103.0 599.1 326.6 375.5 74.7 341.5 285.9 702.1 142.8 416.7 217.2 342.3 77.6 264.6 217.2 559.4 1.8 20.9 9.3 13.3 4.0 11.1 7.6 22.6 239.2 572.2 303.1 508.3 154.3 351.7 305.4 811.4 127.7 409.1 223.1 313.7 94.3 243.0 199.5 536.8 2.5 18.3 8.2 12.6 3.9 6.1 10.8 20.8 63.2 378.5 178.7 263.0 78.1 206.9 156.6 441.7 57.8 262.3 132.3 187.9 37.5 138.4 144.3 320.1 46.3 210.6 110.2 146.7 40.4 119.9 96.6 256.9 165.1 1,378.9 724.3 819.6 169.8 778.5 595.7 1,544.0 42.1 396.3 162.6 275.8 38.4 160.1 239.9 438.4 8.1 31.3 15.7 23.7 5.7 20.1 13.6 39.4 10.8 168.2 84.8 94.2 11.6 84.3 83.0 179.0 61.5 1,326.1 648.1 739.6 101.3 680.3 606.0 1,387.6 2,951 29,759 16,170 16,540 3,728 15,067 13,915 32,710



PREVALENCE AND PROJECTIONS



CHAPTER 1

83

Table 1.29

Prevalence estimates of impaired glucose tolerance (IGT), 2007 - South and Central American Region

IGT prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* M

Argentina 24,952 9.9 9.6 Bolivia 4,910 7.2 8.0 Brazil 119,519 7.0 7.3 Chile 10,883 10.1 9.6 Colombia 27,860 4.3 4.4 Costa Rica 2,746 6.9 7.3 Cuba 8,117 12.5 11.4 Dominican Republic 5,219 10.2 11.4 Ecuador 7,792 7.5 8.0 El Salvador 3,980 6.7 7.3 French Guiana 111 7.4 7.6 Guatemala 6,034 6.6 7.3 Honduras 3,764 6.3 7.3 Netherlands Antilles 126 8.6 7.6 Nicaragua 2,870 6.2 7.3 Panama 2,002 7.0 7.3 Paraguay 3,344 8.6 9.6 Peru 16,642 7.3 8.0 Puerto Rico 2,704 8.1 7.3 Suriname 274 7.5 7.6 Uruguay 2,280 10.4 9.6 Venezuela 16,297 4.8 5.0 SACA Total 272,427 7.3 7.5

* All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

84

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DIABETES ATLAS THIRD EDITION



Number of people with IGT (000’s) in the 20-79 age group Male Female

20-39

40-59

60-79 Total

837.3 1,627.9 528.3 1,244.7 692.2 2,465.2 141.2 214.6 110.9 130.6 114.2 355.7 3,427.4 4,932.2 2,582.8 3,527.5 2,249.3 8,359.5 380.7 714.3 230.1 606.3 258.7 1,095.0 464.2 723.1 446.5 479.6 261.1 1,187.3 81.9 106.7 59.0 81.5 48.1 188.7 432.5 584.2 221.0 423.1 372.5 1,016.7 208.6 324.7 180.3 222.5 130.5 533.3 242.2 341.4 168.5 219.6 195.5 583.6 108.8 156.8 97.2 96.9 71.5 265.6 2.9 5.4 2.9 4.0 1.4 8.3 162.3 234.2 146.8 142.0 107.8 396.5 103.1 134.5 93.7 88.2 55.6 237.6 3.2 7.6 2.4 5.6 2.8 10.8 75.3 101.4 73.4 66.0 37.2 176.7 60.3 79.3 44.1 56.8 38.7 139.6 103.2 182.9 81.8 154.9 49.5 286.1 503.9 717.2 365.1 462.6 393.4 1,221.1 86.1 134.1 47.6 86.4 86.1 220.1 6.8 13.6 7.3 8.9 4.3 20.5 78.9 157.1 45.7 117.1 73.3 236.0 315.8 465.0 296.4 303.8 180.6 780.8 7,827 11,958 5,832 8,529 5,424 19,785



PREVALENCE AND PROJECTIONS



CHAPTER 1

85

Table 1.30

Prevalence estimates of impaired glucose tolerance (IGT), 2025 - South and Central American Region

IGT prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* M

Argentina 31,093 9.9 10.2 Bolivia 7,490 7.4 8.7 Brazil 154,392 7.4 7.7 Chile 13,639 10.6 10.2 Colombia 38,483 4.4 4.6 Costa Rica 3,866 7.9 7.7 Cuba 8,467 14.5 12.3 Dominican Republic 7,156 11.4 12.3 Ecuador 10,946 8.2 8.7 El Salvador 5,766 6.9 7.7 French Guiana 180 7.8 7.9 Guatemala 10,227 6.0 7.7 Honduras 6,151 6.7 7.7 Netherlands Antilles 146 7.8 7.9 Nicaragua 4,686 6.6 7.7 Panama 2,810 7.7 7.7 Paraguay 5,389 9.1 10.2 Peru 23,552 8.1 8.7 Puerto Rico 3,031 7.8 7.7 Suriname 323 7.9 7.9 Uruguay 2,627 10.2 10.2 Venezuela 23,232 5.1 5.2 SACA Total 363,651 7.6 7.9

* All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

86

CHAPTER 1

DIABETES ATLAS THIRD EDITION



Number of people with IGT (000’s) in the 20-79 age group Male Female

20-39

40-59

60-79 Total

1,112.5 1,979.3 633.1 1,627.8 830.9 3,091.8 234.9 319.8 161.8 207.9 184.9 554.6 4,934.4 6,525.3 2,798.6 4,727.0 3,934.0 11,459.6 517.2 923.3 267.5 718.8 454.1 1,440.4 680.3 1,019.2 536.2 659.3 503.9 1,699.5 130.7 173.0 74.0 120.5 109.2 303.6 568.1 659.4 181.0 487.7 558.8 1,227.5 339.7 474.0 219.3 338.7 255.7 813.7 400.7 500.8 210.8 331.8 358.8 901.4 170.8 224.8 117.3 174.0 104.4 395.6 4.9 9.1 4.7 5.6 3.8 14.0 269.7 341.8 237.7 217.6 156.2 611.5 180.1 234.4 143.0 166.9 104.5 414.5 4.2 7.3 3.0 4.1 4.4 11.4 133.6 174.5 106.8 127.6 73.7 308.1 92.8 122.4 53.4 88.4 73.4 215.2 177.3 313.0 130.0 250.4 110.0 490.3 836.8 1,070.8 460.1 740.1 707.5 1,907.7 102.6 134.6 49.4 90.7 97.1 237.3 8.7 16.7 7.4 11.2 6.8 25.4 96.7 172.4 50.0 140.1 79.0 269.1 471.6 708.1 374.0 446.9 358.8 1,179.7 11,468 16,104 6,819 11,683 9,070 27,572



PREVALENCE AND PROJECTIONS



CHAPTER 1

87

Table 1.31 Data sources: prevalence estimates of diabetes mellitus (DM) and impaired glucose tolerance (IGT)

Country/territory

Data used S

Argentinaa,b Bolivia Brazil Chile Colombia Costa Ricac Cuba Dominican Republic Ecuador El Salvadorc French Guiana Guatemalac Hondurasc Netherlands Antillesb Nicaraguac Panamac Paraguay Peru Puerto Rico Suriname Uruguay Venezuela

Argentina (de Sereday et al, 2004)80 Bolivia (Barceló et al, 2001)141 Brazil (Oliveira et al, 1996; Malerbi et al, 1992 and Torquato et al, 2003)81,139,140 Chile (Baechler et al, 2002)82 Colombia (Aschner et al, 1993)142 Mexico (Aguilar-Salinas et al, 2003 and Sanchez-Castillo et al, 2005)78,79 Jamaica (Wilks et al, 1999)76 Jamaica (Wilks et al, 1999)76 Bolivia (Barceló et al, 2001)141 Mexico (Aguilar-Salinas et al, 2003 and Sanchez-Castillo et al, 2005)78,79 Suriname (Schaad et al, 1985)143 Mexico (Aguilar-Salinas et al, 2003 and Sanchez-Castillo et al, 2005)78,79 Mexico (Aguilar-Salinas et al, 2003 and Sanchez-Castillo et al, 2005)78,79 Suriname (Schaad et al, 1985)143 Mexico (Aguilar-Salinas et al, 2003 and Sanchez-Castillo et al, 2005)78,79 Mexico (Aguilar-Salinas et al, 2003 and Sanchez-Castillo et al, 2005)78,79 Paraguay (Jimenez et al, 1998)144 Bolivia (Barceló et al, 2001)141 Jamaica (Wilks et al, 1999)76 Suriname (Schaad et al, 1985)143 Argentina (de Sereday et al, 2004)80 Brazil (Oliveira et al, 1996; Malerbi et al, 1992 and Torquato et al, 2003)81,139,140

a. Persons with previously diagnosed diabetes were excluded from the study, and obtained prevalence doubled b. Because of the absence of data for IGT in the Argentinian and Barbados studies, the following countries had IGT prevalence determined from the study indicated below: Argentina: Paraguay (Jimenez et al, 1998)144 Netherland Antilles: Jamaica (Wilks et al, 1999)76 c. Diabetes prevalence was derived by combining the data of the two studies indicated; IGT prevalence was calculated from Brazilian data

88

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DIABETES ATLAS THIRD EDITION

- South and Central American Region



d Screening method OGTT 2hBG OGTT OGTT 2hBG OGTT/FBG OGTT OGTT 2hBG OGTT/FBG OGTT OGTT/FBG OGTT/FBG OGTT OGTT/FBG OGTT/FBG OGTT 2hBG OGTT OGTT OGTT OGTT

Diagnostic criteria Sample size Age (yrs) WHO - 1999 WHO - 1985 WHO - 1985 WHO - 1999 WHO - 1985 ADA - 1997 WHO - 1980 WHO - 1980 WHO - 1985 ADA - 1997 WHO - 1980 ADA - 1997 ADA - 1997 WHO - 1980 ADA - 1997 ADA - 1997 WHO - 1985 WHO - 1985 WHO - 1980 WHO - 1980 WHO - 1999 WHO - 1985

2,397 2,948 25,371 1,315 670 84,054 1,303 1,303 2,948 84,054 1,218 84,054 84,054 1,218 84,054 84,054 1,606 2,948 1,303 1,218 2,397 25,371

20-69 25+ 30-69 20+ 30-79 20+ 25-74 25-74 25+ 20+ 30+ 20+ 20+ 30+ 20+ 20+ 20-74 25+ 25-74 30+ 20-69 30-69





PREVALENCE AND PROJECTIONS



CHAPTER 1

89

Table 1.32 Prevalence estimates of diabetes mellitus (DM), 2007 – South-East Asian Region

DM prevalence (%) Country/territory Population (20-79) (000’s) National Comparative* R

Bangladesh 80,196 4.8 5.3 Bhutan 1,145 4.7 5.4 India 659,570 6.2 6.7 Maldives 167 6.2 7.1 Mauritius 847 11.3 11.1 Nepal 14,288 3.5 4.2 Sri Lanka 14,136 8.4 8.4 SEA Total 770,350 6.0 6.5 * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

Table 1.33 Prevalence estimates of diabetes mellitus (DM), 2025 – South-East Asian Region

DM prevalence (%) Country/territory Population (20-79) (000’s) National Comparative* R

Bangladesh 120,909 6.1 6.6 Bhutan 1,858 3.6 4.5 India 918,761 7.6 8.2 Maldives 294 9.8 11.2 Mauritius 1,019 14.6 13.4 Nepal 22,915 4.4 5.8 Sri Lanka 16,746 10.7 10.2 SEA Total 1,082,501 7.4 8.0 * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

Table 1.34

Prevalence estimates of impaired glucose tolerance (IGT), 2007 - South-East Asian Region

IGT prevalence (%) Country/territory Population (20-79) (000’s) National Comparative* M Bangladesh Bhutan India Maldives Mauritius Nepal Sri Lanka SEA Total

80,196 8.5 1,145 3.0 659,570 5.4 167 12.2 847 16.5 14,288 3.8 14,136 12.1 770,350 5.9

8.9 3.2 5.6 12.4 16.3 4.1 12.1 6.0

* All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

90

CHAPTER 1

DIABETES ATLAS THIRD EDITION

Number of people with DM (000’s) in the 20-79 age group Rural Urban Male Female

20-39

40-59

60-79 Total

1,935.0 1,913.1 1,844.2 2,003.9 1,487.3 1,644.6 716.2 3,848.1 45.2 9.2 29.2 25.2 12.9 22.4 19.0 54.3 20,871.3 19,979.5 22,189.6 18,661.2 8,418.0 19,645.4 12,787.5 40,850.8 3.8 6.6 5.2 5.2 4.5 4.1 1.7 10.4 38.3 57.6 46.9 49.0 15.1 53.4 27.3 95.9 313.6 183.5 186.6 310.5 77.9 233.7 185.5 497.1 645.9 540.7 519.1 667.5 325.0 519.3 342.3 1,186.6 23,853 22,690 24,821 21,722 10,341 22,123 14,079 46,543

Number of people with DM (000’s) in the 20-79 age group Rural Urban Male Female

20-39

40-59

60-79 Total

2,614.4 4,804.8 3,440.5 3,978.7 2,354.9 3,436.9 1,627.4 38.7 28.5 35.0 32.2 14.3 32.8 20.2 26,755.7 43,125.9 37,726.1 32,155.5 11,713.0 33,162.1 25,006.5 11.5 17.4 14.5 14.4 11.5 12.0 5.4 43.5 105.1 70.7 77.9 16.3 68.8 63.6 474.1 535.3 369.5 640.0 160.6 454.0 394.8 729.0 1,057.2 774.0 1,012.2 361.3 774.7 650.3 30,667 49,674 42,430 37,911 14,632 37,941 27,768

7,419.2 67.2 69,881.6 28.9 148.6 1,009.4 1,786.2 80,341



Number of people with IGT (000’s) in the 20-79 age group Male Female

3,443.1 17.1 18,286.5 11.3 55.5 191.7 920.9 22,926



PREVALENCE AND PROJECTIONS

20-39

40-59

60-79 Total

3,376.2 3,455.2 2,404.9 959.1 6,819.3 17.2 14.0 9.9 10.4 34.3 17,619.6 15,852.7 12,959.3 7,094.0 35,906.1 9.0 12.2 5.1 2.9 20.3 84.2 47.5 66.2 26.0 139.7 350.6 246.2 153.2 143.0 542.3 786.6 783.8 541.2 382.6 1,707.5 22,244 20,412 16,140 8,618 45,169



CHAPTER 1

91

Table 1.35

Prevalence estimates of impaired glucose tolerance (IGT), 2025 - South-East Asian Region

IGT prevalence (%) Country/territory Population (20-79) (000’s) National Comparative* M

Bangladesh 120,909 8.8 9.2 Bhutan 1,858 3.2 3.6 India 918,761 6.1 6.3 Maldives 294 12.9 13.7 Mauritius 1,019 17.7 17.0 Nepal 22,915 4.8 5.5 Sri Lanka 16,746 13.6 13.4 SEA Total 1,082,501 6.5 6.7 * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

Table 1.36 Data sources: prevalence estimates of diabetes mellitus (DM) and impaired glucose tolerance (IGT)



92

Country/territory

Data used S

Bangladesh Bhutan India Maldives Mauritius Nepal Sri Lanka

Bangladesh (Hussain et al, 2005)91 Bangladesh (Sayeed et al, 2003)145 India (Sadikot et al, 2004)85 India (Shah et al, 2006)86 India (Ramachandran et al, 2001)84 India (Sadikot et al, 2004)85 India (Shah et al, 2006)86 Sri Lanka (Fernando et al, 1994)146 Mauritius (Dowse et al, 1990)109 Nepal (Singh et al, 2003)90 Nepal (Karki et al, 2000)89 Sri Lanka (Wijewardene et al, 2005)92

CHAPTER 1

DIABETES ATLAS THIRD EDITION



Number of people with IGT (000’s) in the 20-79 age group Male Female

20-39

40-59

60-79 Total

5,287.7 5,359.6 4,611.2 4,063.6 1,972.5 29.5 29.4 23.0 19.4 16.4 28,515.3 27,712.9 21,773.2 21,158.9 13,296.1 20.8 17.2 20.9 10.8 6.3 72.9 107.2 46.9 77.2 56.0 394.7 705.6 463.9 320.8 315.7 1,206.3 1,065.8 765.8 764.1 742.2 35,527 34,998 27,705 26,415 16,405

10,647.3 58.9 56,228.2 38.0 180.1 1,100.4 2,272.1 70,525

- South-East Asian Region



d Screening method



OGTT FBG OGTT SR OGTT OGTT SR OGTT OGTT FBG OGTT FBG

PREVALENCE AND PROJECTIONS

Diagnostic criteria Sample size Age (yrs) WHO - 1999 ADA - 1997 WHO - 1999 Known Diabetes WHO - 1999 WHO - 1999 Known Diabetes WHO - 1985 WHO - 1985 WHO - 1999 WHO - 1985 ADA - 1997



6,312 4,923 18,363 39,429 11,216 18,363 39,429 633 5,080 1,841 1,840 6,047

20+ 20+ 25+ 15-64 20+ 25+ 15-64 30-64 25-74 20+ 30+ 30-65

CHAPTER 1

93

Table 1.37 Prevalence estimates of diabetes mellitus (DM), 2007 - Western Pacific Region

DM prevalence (%) Country/territory Population (20-79) (000’s) National Comparative* R Australia Brunei Darussalam Cambodia China China, Hong Kong China, Macau Cook Islandsa Fiji French Polynesia Guam Indonesia Japan Kiribatia Korea, Democratic People’s Republic of Korea, Republic of Lao People’s Democratic Republic Malaysia Marshall Islandsa Micronesia, Federated States ofa Mongolia Myanmar Naurua New Caledoniab New Zealandc Niuea Palaua Papua New Guinea Philippines Samoa Singapore Solomon Islands Taiwana Thailand Timor-Leste Tokelaua Tongaa Tuvalua Vanuatu Viet Nam WP Total

14,504 242 7,599 929,432 5,560 349 13 510 165 107 142,635 97,326 67 15,114 35,704 2,991 15,390 38 55 1,598 31,394 8 154 2,790 1 13 3,043 47,038 90 3,245 246 14,340 44,194 501 1 55 7 109 51,972

6.4 9.3 4.3 4.3 9.5 8.5 5.5 8.5 13.1 6.7 2.0 7.2 6.4 5.3 8.6 2.5 9.9 8.8 5.2 1.6 2.8 30.7 3.2 7.7 4.5 8.9 1.9 6.5 6.5 11.9 2.0 5.7 7.2 1.3 8.5 11.9 13.4 2.2 2.5

5.0 12.2 5.0 4.1 8.2 8.2 5.5 9.2 13.5 6.5 2.3 4.9 6.4 5.2 7.8 3.1 10.7 8.8 5.9 1.9 3.2 30.7 4.4 6.4 4.5 8.9 2.9 7.6 7.5 10.1 3.0 7.4 6.9 1.7 8.5 12.9 13.4 3.0 2.9

1,468,598

4.6

4.4

a. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of world population from 2005 to 2007, except Taiwan (developed world population) b. For New Caledonia, the Melanesian population was ascribed as having the national urban/rural population distribution, whereas the French population was deemed as having the diabetes prevalence of Metropolitan France, and assigned to the urban component, and each assigned 50% of the total population c. New Zealand data only self-reported * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

94

CHAPTER 1

DIABETES ATLAS THIRD EDITION

Number of people with DM (000’s) in the 20-79 age group Rural Urban Male Female

20-39

40-59

60-79 Total

3.3 178.7 20,705.1 0.2 20.2 5.7 2.3 1,102.8 1.4 173.1 42.8 394.1 0.4 1.2 5.2 467.9 0.0 1.0 0.0 0.1 27.6 1,053.6 4.0 2.1 2,213.8 5.6 0.1 2.1 0.3 1.1 852.3

19.3 147.4 19,104.5 0.6 23.4 15.9 4.9 1,785.0 2.8 633.5 30.7 1,136.6 3.0 1.6 20.1 404.8 2.6 3.9 0.0 1.0 31.6 2,001.5 1.9 2.9 948.6 0.9 0.0 4.4 0.7 1.4 442.3

515.8 10.0 141.6 20,531.5 238.7 14.3 0.3 20.2 9.8 3.7 1,353.7 3,599.1 2.1 446.0 1,647.8 31.3 625.8 1.7 1.4 11.3 369.9 1.3 0.8 106.3 0.0 0.6 25.1 920.6 2.7 201.6 2.1 324.5 1,436.0 3.3 0.0 2.9 0.4 1.0 557.6

410.1 12.6 184.6 19,278.2 287.3 15.6 0.4 23.3 11.8 3.5 1,534.1 3,379.3 2.1 360.6 1,426.0 42.3 904.8 1.7 1.5 14.0 502.8 1.3 4.1 109.8 0.0 0.6 34.1 2,134.4 3.1 183.2 3.0 487.8 1,726.4 3.3 0.0 3.6 0.5 1.4 737.1

40.0 3.1 65.9 6,421.0 41.6 2.6 0.2 7.0 4.2 1.4 261.4 376.9 0.9 145.2 427.3 13.3 149.7 0.6 0.7 5.3 141.4 0.5 1.0 20.0 0.0 0.2 5.7 553.2 0.6 21.6 0.5 95.8 497.7 0.6 0.0 1.3 0.2 0.2 217.5

346.4 14.1 191.7 21,531.4 236.6 15.9 0.4 26.8 12.6 4.0 1,408.3 2,497.1 2.2 388.6 1,463.0 34.1 875.8 1.8 1.6 15.1 412.5 1.5 2.2 95.7 0.0 0.6 31.6 1,627.6 3.1 184.4 2.5 355.3 1,577.1 3.5 0.0 3.4 0.5 1.2 613.7

539.5 5.4 68.6 11,857.3 247.8 11.3 0.2 9.7 4.8 1.9 1,218.1 4,104.3 1.2 272.8 1,183.4 26.1 505.1 1.0 0.5 4.9 318.8 0.6 1.7 100.3 0.0 0.3 21.9 874.2 2.2 178.8 2.0 361.3 1,087.6 2.5 0.0 1.8 0.3 1.0 463.4

925.9 22.6 326.2 39,809.6 526.0 29.9 0.7 43.6 21.6 7.2 2,887.8 6,978.4 4.2 806.6 3,073.8 73.5 1,530.6 3.4 2.8 25.3 872.7 2.6 4.9 216.1 0.1 1.1 59.2 3,055.1 5.9 384.8 5.0 812.3 3,162.4 6.6 0.1 6.5 1.0 2.4 1,294.6

27,268

26,778

33,163

33,830

9,526

33,984

23,483

66,993





PREVALENCE AND PROJECTIONS



CHAPTER 1

95

Table 1.38 Prevalence estimates of diabetes mellitus (DM), 2025 - Western Pacific Region

DM prevalence (%) Country/territory Population (20-79) (000’s) National Comparative* R

Australia 17,547 7.7 6.0 Brunei Darussalam 366 12.7 14.5 Cambodia 11,743 5.2 6.1 China 1,067,160 5.6 4.8 China, Hong Kong 6,604 13.0 9.6 China, Macau 425 12.8 9.6 Cook Islandsa 17 6.3 6.4 Fiji 626 10.2 10.5 French Polynesia 220 16.0 15.6 Guam 145 8.2 7.9 Indonesia 183,541 2.8 2.9 Japan 90,209 7.9 5.7 Kiribatia 106 7.0 6.9 Korea, Democratic People’s Republic of 17,307 6.3 5.8 Korea, Republic of 38,604 10.8 8.8 Lao People’s Democratic Republic 4,927 2.9 4.0 Malaysia 22,293 12.3 13.1 Marshall Islandsa 61 10.3 10.1 Micronesia, Federated States of a 64 8.2 7.3 Mongolia 2,252 2.2 2.2 Myanmar 41,444 3.8 4.1 Naurua 12 33.0 32.3 New Caledoniab 216 3.9 4.3 New Zealandc 3,244 8.8 7.3 Niuea 1 5.3 5.3 Palaua 18 10.3 10.1 Papua New Guinea 4,901 2.8 4.1 Philippines 70,161 7.9 9.3 Samoa 114 8.1 9.1 Singapore 4,054 17.1 11.9 Solomon Islands 415 3.0 4.4 Taiwana 17,140 6.6 8.4 Thailand 52,258 8.9 8.0 Timor-Leste 882 1.4 2.1 Tokelaua 1 9.4 9.3 Tongaa 63 14.4 15.2 Tuvalua 10 15.8 15.7 Vanuatu 172 3.2 4.3 Viet Nam 72,238 3.5 3.7 WP Total 1,731,564 5.7 5.1

a. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of world population from 2005 to 2025 , except Taiwan (developed world population) b. For New Caledonia, the Melanesian population was ascribed as having the national urban/rural population distribution, whereas the French population was deemed as having the diabetes prevalence of Metropolitan France, and assigned to the urban component, and each assigned 50% of the total population c. New Zealand data only self-reported; total diabetes calculated as twice the self-reported prevalence * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

96

CHAPTER 1

DIABETES ATLAS THIRD EDITION

Number of people with DM (000’s) in the 20-79 age group Rural Urban Male Female

20-39

40-59

60-79 Total

744.2 601.8 44.6 390.5 911.0 1,346.0 4.8 41.8 19.8 26.9 4.2 25.2 17.3 46.6 239.2 368.9 281.0 327.0 132.1 313.9 162.1 608.1 22,105.6 37,164.0 29,474.2 29,795.5 5,854.7 29,076.2 24,338.7 59,269.7 347.7 511.7 39.0 264.9 555.6 859.4 22.9 31.3 3.0 15.7 35.5 54.3 0.2 0.9 0.4 0.7 0.2 0.5 0.4 1.1 21.6 42.5 30.0 34.1 7.6 36.5 20.1 64.2 6.8 28.3 15.4 19.7 5.5 18.7 11.0 35.1 2.6 9.2 6.0 5.8 1.8 5.6 4.5 11.9 1,352.7 3,776.3 2,457.0 2,672.0 309.6 2,499.6 2,319.9 5,129.0 3,674.7 3,496.7 265.2 2,512.2 4,393.9 7,171.4 2.5 4.9 3.7 3.7 1.2 3.7 2.5 7.4 169.3 912.4 598.8 483.0 145.7 567.2 368.8 1,081.8 2,119.8 2,043.5 336.4 1,715.4 2,111.5 4,163.3 62.3 80.7 61.5 81.5 24.3 65.7 53.0 143.0 473.3 2,269.6 1,127.6 1,615.2 204.2 1,345.3 1,193.3 2,742.9 0.5 5.8 3.2 3.1 0.9 3.1 2.2 6.3 1.5 3.8 2.6 2.6 0.9 2.6 1.8 5.3 7.2 41.7 21.5 27.4 7.2 29.4 12.3 48.9 619.2 947.3 636.4 930.1 180.7 713.8 671.9 1,566.4 0.0 4.1 2.1 2.0 0.7 2.2 1.1 4.1 1.2 7.2 1.2 7.2 1.2 3.5 3.7 8.4 138.9 146.7 22.7 96.6 166.4 285.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 1.7 0.9 0.9 0.3 0.9 0.6 1.8 48.0 90.0 53.6 84.4 12.8 71.0 54.1 137.9 1,565.9 4,006.8 1,620.5 3,952.2 780.7 2,855.0 1,937.0 5,572.7 5.1 4.2 4.4 4.8 0.6 4.7 3.9 9.2 348.8 343.6 22.2 183.2 487.0 692.4 3.6 8.9 4.8 7.7 1.3 6.6 4.6 12.5 433.8 690.0 104.8 449.9 569.1 1,123.9 2,630.4 2,029.8 2,114.4 2,545.8 507.4 2,013.4 2,139.4 4,660.2 9.8 2.9 6.3 6.4 1.4 6.0 5.3 12.7 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 2.1 7.0 4.1 5.0 1.4 5.1 2.6 9.1 0.3 1.3 0.7 0.9 0.2 0.9 0.5 1.6 1.7 3.7 2.1 3.3 0.5 2.6 2.3 5.5 1,333.8 1,166.8 1,046.5 1,454.2 290.5 1,132.0 1,078.2 2,500.7 30,672 53,033 47,432 51,969 9,318 46,439 43,643 99,401





PREVALENCE AND PROJECTIONS



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97

Table 1.39

Prevalence estimates of impaired glucose tolerance (IGT), 2007 - Western Pacific Region

IGT prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* M Australia Brunei Darussalam Cambodia China China, Hong Kong China, Macau Cook Islandsa Fiji French Polynesia Guam Indonesia Japan Kiribatia Korea, Democratic People’s Republic of Korea, Republic of Lao People’s Democratic Republic Malaysia Marshall Islandsa Micronesia, Federated States ofa Mongolia Myanmar Naurua New Caledonia New Zealand Niuea Palaua Papua New Guinea Philippines Samoa Singapore Solomon Islands Taiwana Thailand Timor-Leste Tokelaua Tongaa Tuvalua Vanuatu Viet Nam WP Total

14,504 242 7,599 929,432 5,560 349 13 510 165 107 142,635 97,326 67 15,114 35,704 2,991 15,390 38 55 1,598 31,394 8 154 2,790 1 13 3,043 47,038 90 3,245 246 14,340 44,194 501 1 55 7 109 51,972

9.6 19.6 9.4 6.9 12.2 11.7 10.1 10.1 12.9 17.7 9.9 13.2 17.3 8.5 9.0 2.2 18.9 17.3 21.0 9.2 2.3 20.4 4.9 9.6 6.9 17.3 7.7 9.4 6.0 17.2 7.7 3.8 4.3 9.1 13.0 12.0 13.0 8.0 2.3

8.2 17.6 10.4 6.9 10.9 10.9 10.1 10.7 13.0 17.3 10.6 10.9 17.3 8.2 8.2 2.5 14.9 17.3 17.3 10.3 2.5 20.4 4.8 8.2 6.9 17.3 9.3 10.7 6.5 18.7 9.4 4.6 4.2 10.6 13.0 13.0 13.0 9.4 2.5

1,468,598

7.6

7.5

a. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of world population from 2005 to 2007, except Taiwan (developed world population) * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

98

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Number of people with IGT (000’s) in the 20-79 age group Male Female

20-39

40-59

60-79 Total

601.5 25.0 306.8 40,001.1 307.5 19.2 0.6 21.6 10.0 8.8 7,594.0 5,706.6 5.2 651.9 1,644.5 24.9 1,460.3 3.0 5.2 53.6 279.9 0.8 3.0 115.2 0.0 1.0 93.7 1,264.0 2.3 279.3 7.4 249.0 1,025.3 25.2 0.1 2.9 0.4 3.4 459.4

785.2 22.4 407.3 24,322.5 368.9 21.7 0.7 29.9 11.3 10.2 6,550.5 7,185.0 6.3 631.7 1,579.2 39.8 1,454.6 3.6 6.3 92.7 444.9 0.9 4.5 152.2 0.0 1.2 142.0 3,146.3 3.1 279.0 11.4 302.7 870.3 20.6 0.1 3.7 0.5 5.3 715.8

215.0 22.2 304.5 24,566.4 133.4 8.4 0.3 18.2 5.6 5.9 4,583.1 2,276.2 3.8 266.0 556.1 21.9 1,120.8 2.2 3.8 66.3 223.0 0.7 1.7 40.4 0.0 0.7 90.7 1,135.7 1.5 121.8 7.7 135.5 552.8 16.1 0.0 1.8 0.2 3.3 370.1

595.3 21.4 268.5 25,358.4 350.5 23.6 0.6 23.1 12.1 9.4 5,490.3 4,837.4 5.3 577.0 1,554.1 30.4 1,425.6 3.0 5.3 52.9 352.6 0.7 3.7 116.4 0.0 1.0 104.9 2,134.6 2.6 329.3 7.6 228.0 977.2 18.5 0.1 3.4 0.5 3.6 578.6

576.4 3.7 141.1 14,398.8 192.5 9.0 0.5 10.1 3.6 3.7 4,071.1 5,777.9 2.4 440.6 1,113.5 12.5 368.5 1.4 2.4 27.2 149.1 0.4 2.0 110.6 0.0 0.5 40.0 1,140.0 1.3 107.3 3.5 188.2 365.6 11.2 0.0 1.4 0.2 1.8 226.5

1,386.7 47.4 714.1 64,323.6 676.4 41.0 1.3 51.4 21.3 19.0 14,144.4 12,891.6 11.5 1,283.6 3,223.7 64.7 2,914.9 6.6 11.5 146.3 724.7 1.7 7.5 267.4 0.1 2.2 235.6 4,410.3 5.4 558.3 18.8 551.7 1,895.6 45.8 0.1 6.6 0.9 8.8 1,175.1

62,263

49,634

36,884

45,507

29,507

111,898





PREVALENCE AND PROJECTIONS



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99

Table 1.40

Prevalence estimates of impaired glucose tolerance (IGT), 2025 - Western Pacific Region

IGT prevalence (%) N Country/territory Population (20-79) (000’s) National Comparative* M Australia Brunei Darussalam Cambodia China China, Hong Kong China, Macau Cook Islandsa Fiji French Polynesia Guam Indonesia Japan Kiribatia Korea, Democratic People’s Republic of Korea, Republic of Lao People’s Democratic Republic Malaysia Marshall Islandsa Micronesia, Federated States ofa Mongolia Myanmar Naurua New Caledonia New Zealand Niuea Palaua Papua New Guinea Philippines Samoa Singapore Solomon Islands Taiwana Thailand Timor-Leste Tokelaua Tongaa Tuvalua Vanuatu Viet Nam WP Total

17,547 366 11,743 1,067,160 6,604 425 17 626 220 145 183,541 90,209 106 17,307 38,604 4,927 22,293 61 64 2,252 41,444 12 216 3,244 1 18 4,901 70,161 114 4,054 415 17,140 52,258 882 1 63 10 172 72,238

10.6 20.5 10.4 7.4 14.1 14.0 11.2 11.2 14.1 18.1 11.2 14.1 18.1 9.4 11.0 2.3 19.9 18.1 29.9 10.2 2.6 21.2 5.3 10.6 7.4 18.1 8.6 10.8 6.4 17.6 8.7 4.2 4.6 9.5 13.8 13.3 13.8 9.0 2.6

9.1 18.7 11.6 6.9 11.9 11.9 11.2 11.6 13.8 18.1 11.6 11.7 18.1 9.1 9.1 2.7 16.3 18.1 18.1 11.0 2.7 21.2 5.2 9.1 7.4 18.1 10.4 12.1 7.1 19.7 10.5 5.1 4.4 11.6 13.8 13.8 13.8 10.5 2.7

1,731,564

8.2

7.8

a. Population number as described in the CIA World Factbook 2005108, with growth and age distribution adjustment to that of world population from 2005 to 2025, except Taiwan (developed world population) * All comparisons between countries should be done using the comparative prevalence, which is adjusted to the world population

100

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Number of people with IGT (000’s) in the 20-79 age group Male Female

20-39

40-59

60-79 Total

825.5 38.0 533.3 45,482.7 406.9 26.5 0.9 28.8 14.5 12.3 11,572.2 5,691.4 8.9 821.1 2,177.5 43.8 2,237.5 5.1 8.9 94.2 409.4 1.3 5.3 150.7 0.1 1.5 161.3 2,168.7 3.3 370.9 13.9 332.0 1,291.8 45.9 0.1 4.0 0.7 5.8 736.9

1,042.8 37.1 688.3 33,576.0 526.3 32.9 1.1 41.2 16.6 14.0 9,024.9 7,013.1 10.4 803.2 2,062.3 67.8 2,204.0 6.0 10.4 136.2 677.0 1.4 6.2 193.7 0.1 1.7 260.6 5,413.7 4.0 344.2 22.3 395.8 1,107.0 38.0 0.1 4.4 0.8 9.7 1,165.5

234.9 28.4 493.3 19,175.3 119.0 8.9 0.4 19.9 6.7 7.3 4,787.6 1,628.4 5.5 256.8 436.3 35.6 1,486.7 3.2 5.5 75.2 253.6 0.9 2.1 44.5 0.0 0.9 146.0 1,560.4 1.7 130.1 12.6 145.9 545.4 30.1 0.0 1.9 0.3 5.2 444.2

663.2 35.0 430.3 31,709.5 377.6 22.3 0.8 30.3 16.6 11.2 8,643.7 4,885.1 8.6 797.5 1,798.7 52.8 2,097.0 5.0 8.6 95.8 548.8 1.0 5.5 116.2 0.0 1.4 186.0 3,611.8 3.3 300.3 16.3 284.5 1,164.9 30.8 0.1 4.7 0.8 6.5 953.4

970.1 11.7 297.9 28,173.8 436.6 28.2 0.8 19.8 7.8 7.7 7,165.8 6,191.0 5.1 570.1 2,004.8 23.2 857.8 2.9 5.1 59.3 284.1 0.7 4.0 183.7 0.0 0.9 89.8 2,410.1 2.4 284.7 7.3 297.4 688.5 23.0 0.0 1.9 0.4 3.8 504.8

1,868.2 75.1 1,221.5 79,058.6 933.1 59.4 1.9 70.0 31.1 26.3 20,597.1 12,704.5 19.3 1,624.3 4,239.8 111.6 4,441.5 11.1 19.3 230.4 1,086.4 2.6 11.5 344.3 0.1 3.2 421.8 7,582.3 7.3 715.1 36.2 727.8 2,398.8 83.9 0.1 8.4 1.4 15.4 1,902.4

75,733

66,960

32,141

58,926

51,627

142,693





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101

Table 1.41 Data sources: prevalence estimates of diabetes mellitus (DM) and impaired glucose tolerance (IGT)

Country/territory

Data used S

Australia Brunei Darussalam Cambodiaa China China, Hong Kongb China, Macau Cook Islands Fiji French Polynesia Guam Indonesia Japanb Kiribati Korea, Democratic People’s Republic of Korea, Republic of c Lao People’s Democratic Republic Malaysia Marshall Islands Micronesia, Federated States of Mongolia Myanmar Nauru New Caledonia New Zealandd Niue Palau Papua New Guinea Philippines Samoa Singapore Solomon Islands Taiwanb Thailanda Timor-Lested Tokelau Tonga Tuvalu Vanuatu Viet Nam

Australia (Dunstan et al, 2002)107 Singapore (Ministry of Health Survey, 1998)103 Cambodia (King et al, 2005)93 China (Gu et al, 2003)99 Hong Kong (Janus et al, 2000 and Cockram et al, 1993)147,148 Hong Kong (Janus et al, 2000 and Cockram et al, 1993)147,148 Rarotonga (King et al, 1986)149 Fiji (Zimmet et al, 1983)150 Tonga (Colaguiri et al, 2002)151 Kiribati (King et al, 1984)152 Indonesia (Waspadji et al, 1983)153 Japan (Ohmura et al, 1993 and Sekikawa et al, 2000)154,155 Kiribati (King et al, 1984)152 Republic of Korea (Park et al, 1995)102 Republic of Korea (Kim et al, 2006)96 Viet Nam (Duc Son et al, 2004)97 Singapore (Ministry of Health Survey, 1998)103 Kiribati (King et al, 1984)152 Kiribati (King et al, 1984)152 Mongolia (Suvd et al, 2002)156 Viet Nam (Duc Son et al, 2004)97 Nauru (Zimmet et al, 1984)157 New Caledonia (Zimmet et al, 1982)158 New Zealand (Ministry of Health, 2002)159 Niue (King et al, 1986)149 Kiribati (King et al, 1984)152 Fiji Melanesians (Zimmet et al, 1983)150 Philippines (Baltazar et al, 2004)94 Samoa (Collins et al, 1994)160 Singapore (Ministry of Health Survey, 2004)98 Fiji Melanesians (Zimmet et al, 1983)150 Taiwan (Chou et al, 1992, 1994)161,162 Thailand (Aekplakorn et al, 2003)95 Indonesia (Waspadji et al, 1983)153 Tonga (Colaguiri et al, 2002)151 Tonga (Colaguiri et al, 2002)151 Tonga (Colaguiri et al, 2002)151 Fiji Melanesians (Zimmet et al, 1983)150 Viet Nam (Duc Son et al, 2004)97

a. Because of the absence of data for IGT in the study used for diabetes, IGT figures were calculated from unpublished Indonesian data163 (862 participants) b. The prevalences for the studies based on the Hong Kong, Japanese and Taiwanese studies were obtained by combining the data from the two studies respectively c. IGT figures were calculated using data from Park et al, 1995102 d. Because of the absence of data for IGT in the study used for diabetes, IGT figures were calculated from Australian data

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- Western Pacific Region



d Screening method OGTT OGTT OGTT FBG OGTT OGTT OGTT OGTT OGTT OGTT OGTT OGTT OGTT OGTT FBG FBG OGTT OGTT OGTT OGTT FBG OGTT OGTT SR OGTT OGTT OGTT OGTT OGTT OGTT OGTT OGTT FBG OGTT OGTT OGTT OGTT OGTT FBG

Diagnostic criteria Sample size Age (yrs) WHO - 1999 WHO - 1985 WHO - 1999 ADA - 1997 WHO - 1985 WHO - 1985 WHO - 1985 WHO - 1980 WHO - 1999 WHO - 1980 WHO - 1980 WHO - 1985 WHO - 1980 WHO - 1985 ADA - 1997 WHO - 1999 WHO - 1985 WHO - 1980 WHO - 1980 WHO - 1999 WHO - 1999 WHO - 1980 WHO - 1980 Known Diabetes WHO - 1985 WHO - 1980 WHO - 1980 WHO - 1999 WHO - 1985 WHO – 1999 WHO - 1980 WHO - 1985 ADA - 1997 WHO - 1980 WHO - 1999 WHO - 1999 WHO - 1999 WHO - 1980 WHO - 1999

11,247 3,568 2,246 15,838 4,413 4,413 1,127 2,638 1,024 2,938 2,704 5,211 2,938 2,520 5,844 2,932 3,568 2,938 2,938 2,996 2,932 1,583 707 7,862 1,149 2,938 1,340 7,044 1,776 4,168 1,340 4,287 5,350 2,704 1,024 1,024 1,024 1,340 2,932

25+ 18-69 25+ 35-74 20-79 20-79 20+ 20+ 15+ 20+ 15+ 40+ 20+ 30+ 20+ 15+ 18-69 20+ 20+ 35+ 15+ 20+ 20+ 25+ 20+ 20+ 20+ 20-65 25-74 18-69 20+ 30-79 35+ 15+ 15+ 15+ 15+ 20+ 15+





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103



1.2 Known and Newly Diagnosed Diabetes Studies have shown that a substantial proportion of all people found to have diabetes had not been previously diagnosed.

Introduction

I

t has been a consistent finding of population-based diabetes studies that a substantial proportion of all people found to have diabetes had not been previously diagnosed. Thus, diabetes surveys identify people with previously diagnosed, or known, diabetes (KDM), as well as those with newly diagnosed diabetes, whose diabetes is only found through blood tests undertaken in the survey. The uncovering of new cases of diabetes when mass blood testing is undertaken is primarily because of the lack of symptoms associated with the early years of type 2 diabetes, meaning that those with diabetes may be unaware of their condition and therefore not seek medical attention for it. However, it should also be noted that since the clinical diagnosis of diabetes requires diagnostic blood glucose levels on two separate days, a proportion of those labelled as having undiagnosed diabetes in research studies may not in fact have diabetes if re-tested. In any survey, the percentage of all people with diabetes, whose diabetes has been previously diagnosed, is often taken as a measure of how well the standard clinical services are managing to screen for and identify people with diabetes. A high KNOWN AND NEWLY DIAGNOSED DIABETES

percentage indicates successful screening, while a low number reflects an inability of medical services to screen for diabetes, and is often seen in developing countries where resources are limited.

Studies Tables 1.42-1.48 show the numbers with KDM as a percentage of all those with diabetes in over 80 studies. In general, the lowest percentages were seen in studies from developing countries, and the highest from developed countries. In Tanzania, rural India, Nepal, Tonga and China only 20-25% of all people with diabetes had been previously diagnosed. Overall, across all the surveys, approximately 50% of all people with diabetes were undiagnosed. It should be noted that in some studies that report a high percentage of previously diagnosed cases, there may have been some bias in study design, which resulted in this finding. For example, the study from Ireland127 only undertook blood glucose testing on those individuals with symptoms or risk factors for diabetes. This is likely to have underestimated the numbers of individuals with newly diagnosed diabetes, as some of these individuals may not have had risk factors or symptoms. CHAPTER 1

105

Table 1.42 Proportion of known diabetes (KDM) in studies - African Region Country/territory Author Journal Total KDM diabetes (n) (n)

KDM proportion of total diabetes (%)

Cameroon Mbanya, 200629 Unpublished 489 101 Ghana Amoah et al, 200228 Diabetes Research and Clinical Practice 300 91 South Africa Levitt et al, 199325 Diabetes Care 46 24 Omar et al, 199326 South African Medical Journal 20 12 Motala, 200627 Unpublished 488 101 Tanzania, United Republic of McLarty et al, 198921 Lancet 53 7 Mean Median

21 30 52 60 21 13 33 26

Table 1.43 Proportion of known diabetes (KDM) in studies - Eastern Mediterranean and Middle East Region Country/territory Author Journal Total KDM KDM proportion of diabetes total diabetes (n) (n) (%) Algeria Malek et al, 200150 Diabetes and Metabolism 120 54 45 Bahrain al-Mahroos et al,199832 Diabetes Care 604 393 65 Egypt Herman et al, 199533 Diabetic Medicine N/A N/A 57 Iran, Islamic Republic of Azizi et al, 2003164 Eastern Mediterranean Health Journal 21,637 12,024 56 Jordan Ajlouni et al, 1998110 Journal of Internal Medicine 379 N/A 67a Kuwait Abdella et al, 199835 Diabetes Research and Clinical Practice 443 N/A 50a Lebanon Salti et al, 1997111 Eastern Mediterranean Health Journal 331 259 78 Occupied Palestinian Territory Abdul-Rahim et al, 200153 Eastern Mediterranean Health Journal 59 46 78 Husseini et al, 2003165 Medical Science Monitor 49 35 71 Oman Al-Lawati et al, 200236 Diabetic Medicine 677 N/A 33a Pakistan Shera et al, 199548 Diabetic Medicine 131 72 55 Shera et al, 199947 Diabetes Research and Clinical Practice 127 69 54 Shera et al, 199949 Journal of the Pakistan Medical Association 115 42 37 Saudi Arabia Al-Nuiam, 199739 Diabetic Medicine 41 21 51 Al-Nozha, 200438 Saudi Medical Journal 4,004 2,888 72 Sudan Elbagir,199630 Diabetes Care 44 16 36 Tunisia Papoz et al, 1988114 International Journal of Epidemiology 168 97 58 United Arab Emirates Malik et al, 200541 Diabetes Research and Clinical Practice 505 299 59 Mean 57 Median 57

a. These figures were quoted in the original papers as simple fractions (e.g. 1/2, 2/3), or provided only separate prevalences of new and known diabetes, so that the ratio could be deduced N/A not available

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Table 1.44 Proportion of known diabetes (KDM) in studies – European Region Country/territory Author Journal Total KDM diabetes (n) (n)

KDM proportion of total diabetes (%)

Albania Shapo et al, 2004117 Diabetic Medicine 70 38 54 Cyprus Loizou et al, 2006121 Diabetes Care 123 84 68 Denmark Glumer et al, 2003122 Diabetes Care 404 139 34 Finland Ylihärsilä et al, 2005124 Diabetic Medicine 188 83 44 France Gourdy et al, 200170 Diabetes and Metabolism 230 121 53 Lecomte et al, 2002166 Diabetes and Metabolism 1,675 993 59 Germany Rathmann et al, 200371 Diabetologia 253 128 51 Greece Panagiotakos et al, 2005125 Diabetic Medicine 210 154 73 Iceland Vilbergsson et al, 1997126 Diabetic Medicine 467 282 60 Ireland Smith et al, 2003127 Diabetic Medicine 353 270 76 Israel Stern et al, 1988167 Diabetes 192 113 59 Stern et al, 1999129 Acta Diabetologica 345 310 90 Bar-On et al, 1992128 Nutrition, Metabolism and Cardiovascular Diseases 100 N/A 67a Italy Garancini et al, 1995134 Diabetologia 476 213 45 Netherlands Mooy et al, 199572 Diabetes Care 184 78 42 Poland Lopatynski et al, 200161 Polskie Archiwum Medycyny Wewnetrznej 586 204 35 Szurkowska et al, 200160 Polskie Archiwum Medycyny Wewnetrznej 321 161 50 Spain Botas et al, 2003168 Diabetic Medicine 120 47 39 Castell et al, 1999118 Diabetes Research and Clinical Practice 258 167 65 Sweden Eliasson et al, 2002133 Diabetic Medicine 214 N/A 50a 169 Turkey Kelestimur et al, 1999 Acta Diabetologica 99 58 59 Satman et al, 200258 Diabetes Care 1,792 578 32 United Kingdom Forrest et al, 1986170 Diabetic Medicine N/A N/A 45a Uzbekistan King et al, 1998130 Diabetic Medicine 162 49 30 King et al, 2002131 Diabetes Research and Clinical Practice 61 26 43 Mean 53 Median 51

Table 1.45 Proportion of known diabetes (KDM) in studies – North American Region Country/territory Author Journal Total KDM diabetes (n) (n)

KDM proportion of total diabetes (%)

Guadeloupe Costagliola et al, 199138 Diabetes Research and Clinical Practice 81 66 81 Jamaica Ragoobirsingh et al, 1995171 Diabetes Care 378 196 52 Mexico Aguilar-Salinas et al, 200378 Diabetes Care 3,597 2,878 80 United States of America CDC, 200374 Morbidity and Mortality Weekly Report N/A 288 71a Mean 71 Median 76

a. These figures were quoted in the original papers as simple fractions (e.g. 1/2, 2/3), or provided only separate prevalences of new and known diabetes, so that the ratio could be deduced N/A not available

KNOWN AND NEWLY DIAGNOSED DIABETES



CHAPTER 1

107

Table 1.46 Proportion of known diabetes (KDM) in studies – South and Central American Region Country/territory Author Journal Total KDM diabetes (n) (n)

KDM proportion of total diabetes (%)

Bolivia Barceló et al, 2001141 Revista Panamericana de Salud Pública 185 132 Brazil Malerbi et al, 1992140 Diabetes Care 1,660 896 Chile Baechler et al, 200282 Revista Medica de Chile 115 63 Colombia Aschner, 1993142 Diabetes Care 34 22 Paraguay Jimenez et al, 1998144 Diabetic Medicine 99 44 Mean Median

71 54 55 65 45 58 55

Table 1.47 Proportion of known diabetes (KDM) in studies – South-East Asian Region Country/ Author Journal Total KDM territory diabetes (n) (n)

KDM proportion of total diabetes (%)

Bangladesh Abu Sayeed et al, 1997172 Diabetes Care 123 35 28 India Ramachandran et al (large cities), 200184 Diabetologia 1,684 1,175 70 Sadikot et al (urban), 200485 Diabetes Research and Clinical Practice 624 199 32 Sadikot et al (rural), 200485 Diabetes Research and Clinical Practice 193 37 19 Mauritius Dowse et al, 1990109 Diabetes 633 269 42 Soderberg et al, 2005173 Diabetic Medicine 1,317 671 51 Nepal Karki et al, 200089 Southeast Asian Journal of Tropical 116 30 25 Medicine and Public Health Mean 38 Median 32

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Table 1.48 Proportion of known diabetes (KDM) in studies – Western Pacific Region Country/ Author Journal Total KDM territory diabetes (n) (n)

KDM proportion of total diabetes (%)

Australia Dunstan et al, 2002107 Diabetes Care 943 475 50 Cambodia King et al, 200593 Lancet 185 66 36 China Gu et al, 200399 Diabetologia N/A N/A 24 China, Hong Kong Cockram et al, 1993148 Diabetes Research and Clinical Practice 41 16 38 Janus, 2000147 Diabetic Medicine 269 77 29 Japan Sekikawa et al, 1993174 Diabetes Care 109 52 48 Korea, Republic of Kim et al, 200696 Diabetes Care N/A N/A 57a Mongolia Suvd et al, 2002156 Diabetic Medicine 72 46 64 Nauru Zimmet et al, 1984157 Diabetes Research 374 221 59 Philippines Baltazar, 200494 Diabetes Research and Clinical Practice 362 N/A 67a Samoa Collins et al, 1994160 Diabetes Care 203 101 50 Singapore Ministry of Health, Singapore, 1999103 Government Report N/A N/A 38 Taiwan Chou et al, 1992161 Diabetes Care 143 77 54 Chou et al, 1994162 Diabetes Care 209 63 30 Thailand Aekplakorn et al, 200395 Diabetes Care 607 N/A 50a Tonga Colagiuri et al, 2002151 Diabetes Care 106 N/A 20a 97 Viet Nam Duc Son et al, 2004 Diabetic Medicine 194 118 61 Mean 46 Median 50

a. These figures were quoted in the original papers as simple fractions (e.g. 1/2, 2/3), or provided only separate prevalences of new and known diabetes, so that the ratio could be deduced N/A not available

KNOWN AND NEWLY DIAGNOSED DIABETES



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109



1.3 Complications of Diabetes Diabetic complications account for much of the social and financial burden of diabetes. Diabetes is ranked among the leading causes of blindness, renal failure and lower limb amputation in many countries, while some 50% of people with diabetes die of cardiovascular disease.

Introduction

O

ver the last 30 years, type 2 diabetes has changed from being seen as a relatively mild ailment associated with ageing and the elderly (‘just a touch of sugar’) to one of the major contemporary causes of premature mortality and morbidity in most countries. In virtually every developed society, diabetes is ranked among the leading causes of blindness, renal failure and lower limb amputation. Through its effects on cardiovascular disease (50% of people with diabetes die of cardiovascular disease), it is also now one of the leading causes of death. The changing perceptions of diabetes relate partly to a better appreciation of its devastating complications, but mainly to the rapid rise in its prevalence that has occurred in the last few decades. The main relevance of diabetic complications in a public health perspective is the relationship to human suffering and disability, and the huge socio-economic costs through premature morbidity and mortality175. Chronic elevation of blood glucose, even when no symptoms are present to alert the individual to the presence of diabetes, will eventually lead to tissue damage, with consequent, and COMPLICATIONS OF DIABETES

often serious, disease. Whilst evidence of tissue damage can be found in many organ systems, it is the kidneys, eyes, peripheral nerves and vascular tree, which manifest the most significant, and sometimes fatal, diabetic complications. Indeed, diabetic complications are those aspects of the disease that are most feared (such as blindness and amputation), and account for much of the social and financial burden of diabetes. The mechanism by which diabetes leads to these complications is complex, and not yet fully understood, but involves the direct toxic effects of high glucose levels, along with the impact of elevated blood pressure, abnormal lipid levels and both functional and structural abnormalities of small blood vessels. In an attempt to better describe and understand the burden of diabetic complications, this section presents data on the rates of coronary heart disease (CHD), stroke, diabetic retinopathy, diabetic nephropathy, diabetic peripheral neuropathy and lower extremity amputations. The results of each study are presented against the country in which it was conducted, although given the design and small size of some of the studies, the results should not necessarily be seen as being representative of that country. CHAPTER 1

111

THE MAJOR DIABETIC COMPLICATIONS

Figure 1.14 Heart attacks in people with and without diabetes over a period of seven years

Eyes (retinopathy)

Brain and cerebral circulation (cerebrovascular disease)

Incidence (%) 50 45 40

Heart and coronary circulation (coronary heart disease)

35 30

Kidney (nephropathy)

25 20 15 10 Peripheral nervous system (neuropathy)

Lower limbs (peripheral vascular disease)

Major diabetic complications Over the last two or three decades, there has been an increasing awareness of the magnitude of the problem presented by diabetic complications. The major complications are: • cardiovascular disease (CVD); • nephropathy; • neuropathy; • amputation; and • retinopathy.

Cardiovascular disease Cardiovascular disease is the major cause of death in diabetes, accounting for some 50% of all diabetes fatalities, and much disability. The kinds of CVD that accompany diabetes include angina, myocardial infarction (heart attack), stroke, peripheral artery disease, and congestive heart failure (CHF). Angina is the pain that arises when the blood supply to the heart muscle itself is temporarily insufficient. This is usually due to narrowing of the arteries feeding the heart muscle. When one of these arteries becomes fully blocked, a myocardial infarction occurs, which kills heart muscle and is CHAPTER 1

0 People without diabetes

Diabetic foot (ulceration and amputation)

112

5

People with diabetes

No prior heart attack Prior heart attack

Adapted from Haffner et al, 1998176

often fatal. People with diabetes without previous heart attacks have been shown to have as high a risk of heart attacks as have non-diabetic persons with previous heart attacks (see Figure 1.14)176. Strokes occur when areas of the brain die from arterial blockage or arterial breakage and bleeding. Strokes are also sometimes fatal but they also often cause paralysis and loss of speech, and other problems. Peripheral artery disease results from blockages in arteries that feed the legs; it causes pain while walking and can lead to major surgery and the need for amputation. Heart failure results when the heart cannot pump strongly and fluid backs up in the legs, lungs and other tissues. Smoking, high cholesterol, high blood pressure, stress from social inequality and oppressive work environments, poor diet, high blood sugar and abdominal fat all increase the likelihood of cardiovascular disease events in persons living with diabetes and impaired glucose tolerance. Several interventions, some of which are relatively inexpensive, can dramatically reduce the risk of CVD, including stopping smoking, general blood pressure control, low-dose daily aspirin, ACE-inhibitor pills, and statin drugs, which improve DIABETES ATLAS THIRD EDITION



Figure 1.15 Numbers of people with diabetes entering the Australian dialysis register 1980-2004178

People with diabetes (n) 600

500

400

300

200

100

0 80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

00

01

02

03

04

Year of entry

cholesterol in the blood. More information on diabetes and CVD can be found online at www.cvd.idf.org.

Nephropathy Diabetes is an increasingly important cause of renal failure (see Figure 1.15), and indeed has now become the single most common cause of end stage renal disease, i.e. that which requires either dialysis or kidney transplantation, in the USA177, and in other countries. A more detailed discussion of diabetic nephropathy can be found in the publication Diabetes and Kidney Disease: Time to Act, International Diabetes Federation, 2003.

cause tingling, burning, and stabbing pain, extreme sensitivity to touch, aching and numbness. Both pain and loss of feeling can occur at the same time. Loss of feeling (loss of protective sensation) is a particular risk because it can allow foot injuries to escape notice and treatment, leading to major infections and amputation. Good blood sugar and blood pressure control can help prevent peripheral neuropathy; smoking and heavy drinking make it much more likely. Regular inspection of the feet, careful nail trimming, avoidance of ill-fitting footwear, and wearing shoes or sandals rather than going barefoot can all prevent foot injury, ulceration and amputation.

Neuropathy

Amputation

When blood sugar and blood pressure are not controlled, diabetes can harm nerves throughout the body. Problems with digestion and urination, impotence, and many other functions can result, but the most commonly affected area is the feet and legs. Nerve damage in these areas is called peripheral neuropathy, and leads to loss of feeling in the feet and toes.

Through effects on peripheral nerves and arteries, diabetes can lead to foot ulceration, infection and the need for amputation. People with diabetes carry a risk of amputation that may be more than 25 times greater than that seen in those without diabetes179. A recent publication, Diabetes and Foot Care: Time to Act, International Diabetes Federation, 2005, takes a closer look at the diabetic foot and ways in which complications can be prevented.

Peripheral neuropathy is frequently asymptomatic, but can COMPLICATIONS OF DIABETES

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113

Retinopathy Diabetes can harm sight and cause blindness in several ways. The most common cause of blindness in diabetes is macular oedema, caused by fluid build-up behind the retina of the eye. A more common complication is background and proliferative retinopathy, which can cause blindness as a result of repeated haemorrhages at the back of the eye. Diabetes also increases the risk of cataracts and glaucoma. However, the risk of blindness in diabetes can be greatly reduced by strict control of blood sugar and blood pressure; and regular eye exams can detect macular oedema and proliferative retinopathy so that laser treatments can be used to control these conditions before they cause visual loss.

114

2. Studies with ≥100 participants were included; where more than one study was available for a country, preference was given to larger and population-based studies, those published after 1989, and those with the fewest restrictions. 3. Prevalences are reported for coronary heart disease, stroke, nephropathy, neuropathy, retinopathy, and incidence and prevalence for lower extremity amputations. 4. Where possible, the age ranges of the populations are reported. Where the age range of the population was not available, the mean or median age is reported.

Methods

5. Diagnostic criteria for each complication are recorded, as variation in definitions can affect the prevalences reported.

Details of the methods used in this report on diabetic complications are found in Appendix 1.2. The main principles in collating available prevalence data were:

6. For some countries, results from more than one study are presented. This is usually because they cover different aspects of the diabetic population.

1. Studies were identified through a detailed literature search, as well as contact with IDF member organizations.

There are some important differences between this section and Chapter 1.1 on diabetes prevalence and IGT. The total

CHAPTER 1

DIABETES ATLAS THIRD EDITION



numbers of individuals within a country who may have complications are not estimated, nor is a national prevalence. Furthermore, data have not been projected from one country onto other countries. This is for two reasons: firstly, such calculations require knowledge of the age and sex structure of both the original study population, and of the target (national diabetic) population. In most cases, neither of these is known. Secondly, many studies are clinic-based, and so their generalizability is limited.

Results The results are provided in Tables 1.49 - 1.58. A brief summary of results for each complication is presented below. The interquartile range was used to describe the middle fifty percent of prevalences for each complication.

Cardiovascular disease The prevalence of coronary heart disease in those with diabetes (both type 1 and type 2) ranged from 1.0% to 25.2% in clinic-based populations and from 1.8% to 43.4% in population-based studies (see Table 1.49). The prevalence of stroke in those with type 1 and type 2 diabetes ranged from 1.0% to 11.3% in clinic-based populations and from 2.8% to 12.5% in population-based studies. For CHD, the middle fifty percent of the prevalences were between 4.0% and 13.5%. For stroke, the middle fifty percent of the prevalences were between 4.0% and 6.2%.

Nephropathy The prevalence of microalbuminuria in those with type 1 diabetes ranged from 4.3% to 37.6% in clinic-based populations and from 12.3% to 27.2% in population-based studies (see Table 1.51). Among those with type 2 diabetes, the prevalence of microalbuminuria ranged from 2.5% to 57.0% in clinic-based populations and from 19.4% to 42.1% in population-based studies. The prevalence of overt nephropathy in those with type 1 diabetes ranged from 0.7% to 27.0% in clinic-based populations, and from 0.3% to 24.0% in population-based studies. Among those with type 2 diabetes, the prevalence of overt nephropathy ranged from 5.4% to 20.0% in clinic-based populations, and from 9.2% to 32.9% in population-based studies. For microalbuminuria, the middle fifty percent of the COMPLICATIONS OF DIABETES

prevalences were between 17.9% and 23.8% for type 1 diabetes, and between 19.4% and 42.0% for type 2 diabetes. For overt nephropathy, the middle fifty percent of the prevalences in type 1 diabetes were between 6.1% and 13.8%, and between 10.1% and 18.2% for type 2 diabetes.

Neuropathy The prevalence of neuropathy in those with type 1 diabetes ranged from 3.0% to 65.8% in clinic-based populations and from 12.8% to 54.0% in population-based studies (see Table 1.53). Among those with type 2 diabetes, the prevalence of neuropathy ranged from 7.6% to 68.0% in clinic-based populations, and from 13.1% to 45.0% in population-based studies. The middle fifty percent of the neuropathy prevalences for type 1 diabetes were between 21.2% and 29.3%, and between 19.7% and 37.5% for type 2 diabetes.

Amputations The prevalence of lower extremity amputations ranged from 0.2% to 4.8%, and the annual incidence ranged from 46.1 to 936 per 100,000 diabetic population (see Table 1.55). The middle fifty percent of the amputation prevalences were between 0.9% and 2.4%, and between 181 per 100,000 diabetic population and 463 per 100,000 diabetic population for the incidence of amputations.

Retinopathy The prevalence of retinopathy in those with type 1 diabetes ranged from 10.8% to 60.0 % in clinic-based populations and from 14.5% to 79.0% in population-based studies (see Table 1.57). Among those with type 2 diabetes, the prevalence of retinopathy ranged from 10.6% to 65.4% in clinic-based populations and from 10.1% to 55.0% in population-based studies. The middle fifty percent of the retinopathy prevalences for type 1 diabetes were between 34.1% and 53.0%, and between 23.7% and 36.2% for type 2 diabetes.

Discussion The aims of this section were to describe the burden of diabetic complications, and to be able to examine the differences existing between countries and between ethnic CHAPTER 1

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IN TOUCH WITH: RAMÓN AGUIRRE People with diabetes are at an increased risk of developing a number of complications associated with the disease. Diabetes is among the leading causes of blindness, renal failure and lower limb amputation. Many complications have the potential to reduce the quality of life of people with diabetes and their families. However, diabetic complications may be prevented or delayed by good diabetes management by the person with diabetes and the healthcare team.

Ramón Aguirre, 52, was born in Argentina, and took his first job in a bakery when he was eight years old, after his mother’s death. Eight years ago, while working as a pastry cook, he started feeling ill and went to the doctor. He was diagnosed with a discus hernia, tuberculosis and type 2 diabetes. He started treatment but was soon without a job or income, and could not afford to continue with the insulin and medication his doctor had prescribed. It was not long before Ramón developed complications. His feet problems brought him to a podologist who recommended an urgent consultation with a doctor specialized in diabetes. Ramón rejected the idea because he did not want to lose a day’s work which he now had, but after some months he started having symptoms of blindness: he could not see his hands, so needed for his work.

groups. In order to do this with confidence, it is necessary that there is a degree of uniformity in the methodology of the different studies. This, however, was not the case. For each of the complications, a wide range of results was found, but understanding the underlying causes of this diversity is difficult. For example, the low prevalence of neuropathy seen in Mauritius180 could be due to a low inherent risk for neuropathy in that population, the availability of high quality diabetes care, the relative youth of a population in a developing country, the methods used in the study for defining neuropathy or the population-based study design. In the absence of similar studies, it is almost impossible to determine which of these explanations may be correct. The problem of accurately describing the burden and making comparisons is made particularly difficult by the relative lack of population-based studies. Studies based on secondary care tend to over-represent those with advanced disease, as those requiring more intensive treatment are generally referred on for specialist care. Furthermore, prevalences in clinic populations will depend on local referral patterns, which are likely to vary widely around the world.

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PART 1

Neither the diagnostic tools nor diagnostic thresholds used to define a complication were equivalent across studies. For example, neuropathy was defined as the absence of ankle reflexes in one study181, and as the presence of symptoms and clinical signs (using validated scales) in another182. The inconsistent use of diagnostic tools to classify a complication has been shown to dramatically affect the prevalence reported. The Diabetes Control and Complications Trial (DCCT) compared the prevalence of neuropathy using 11 different criteria and found that within the one population, the prevalence of neuropathy varied from 0.3%, using sensory examination, reflexes and symptoms, through to 21.8% using nerve conduction tests183. Another example of the variability brought about by changing methodology is highlighted by a study on amputation. The study compared the incidence of lower extremity amputations in one population, when primary or all amputations were included184. The incidence of lower extremity amputations varied from 276 per 100,000 person years (primary amputation) to 388 per 100,000 person years (all amputations). Further differences in amputation incidences are likely to be due to the (rather imprecise) method of estimating the total diabetic population from DIABETES ATLAS THIRD EDITION



With the help of Estela, his wife, Ramón went to see the recommended diabetologist, Dra María Teresa Enrico. Ramón’s retinopathy had developed to an advanced stage and the retina in one eye had loosened. He was given an appointment for surgery in a year’s time at the Ophthalmology Hospital. However, with his condition, Ramón could not wait. But without money he could not pay for the intervention elsewhere. Dr Enrico put him in contact with the ‘Fundación de Cirugía Ocular Dr. Zambrano’ and with the diabetes association, Liga Argentina de Protección al Diabético (LAPDI). These associations helped Ramón obtain the surgery he so needed and paid half of the hospital costs. Once his sight was regained, he would pay the rest.

new house that the Government of Buenos Aires plans to give the community where he has lived for 10 years. Ramón reminds people with diabetes “not to lose time, because diabetes is controllable and everything depends on oneself”.

Today, with the help of his family, Ramon has bought the necessary machines to start again as a pastry cook. He works from home making bread and pastries that his wife and his mother-in-law sell every day. He keeps his diabetes under control with the free medication from the hospital. His dream is to open a bakery and move with Estela to a

which those with amputations were drawn. One large study, EURODIAB, examined the prevalence of retinopathy, neuropathy and nephropathy in type 1 diabetes across several European countries185,186. This study used standard methodology, making it possible to gain some insight into whether observed differences in prevalence estimates are due to methodological problems or represent actual differences. The study found the prevalence of complications did vary between countries. The prevalence of retinopathy ranged from 21% in Germany to 60% in Portugal. However, much less variation was seen for neuropathy and microalbuminuria, for which the prevalences clustered fairly tightly around 25% and 23%, respectively. Any conclusions about the burden of disease attributable to diabetic complications must be very guarded, and comparisons between different parts of the world should be extremely cautious. Nevertheless, some tentative comments can be made: • The prevalence of retinopathy is probably around 30% in type 2 diabetes.

COMPLICATIONS OF DIABETES

• Of the seven population-based studies giving figures for neuropathy in type 2 diabetes, two of the three highest prevalences were from the USA. • Populations from Europe had high rates of heart disease and stroke, while migrant Indian populations (Mauritius and Fiji) also had high rates of heart disease. • No discernable patterns relating to geographic distribution or study design were apparent for nephropathy or amputations. In summary, the interpretation of these studies of diabetic complications is severely hampered by the lack of populationbased studies, and the wide variability in study design. Nevertheless, the data from EURODIAB would indicate that at least for some complications in type 1 diabetes, genuine differences exist between countries. What is absolutely clear from this review is that there are large parts of the world for which there are no useful data, and that there is a great need for population-based studies, using standardized protocols so that meaningful estimates of the prevalence of diabetic complications can be made.

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Table 1.49 Prevalence of cardiovascular disease Region Country/territory

Data used U

AFR South Africa Rotchford et al, 2002187 EMME Egypt Arab et al, 2002188 Pakistan Hashim et al, 1999189 Sudan Elmahdi et al, 1991190 EUR Austria Muhlhauser et al, 1992191 Belgium Van Acker et al, 2001192 Denmark Gall et al, 1991193 Estonia Vides et al, 2001194 Finland Isomaa et al, 2001195 Hu, 2003a,196 France Delcourt et al, 1998197 Le Floch et al, 2000198 Germany Liebl et al, 2002199 Italy DAI Study Group, 2004200 Netherlands Verhoeven et al, 1991201 Reenders et al, 1993202 de Visser et al, 2002203 Spijkerman et al, 2004204 Serbia and Montenegro Vlajinac et al, 1992205 Miljus, 2002206 Slovakia Slovakian Diabetes Society, 2002a,207 Spain Esmatjes et al, 1996208 Diamante, 1997209 Arteagoitia et al, 2003210 Sweden Lundman et al, 1998211 Wandell, 2004212 United Kingdom Morgan et al, 2000213 NA United States of America Maser et al, 1991214 Qureshi et al, 1998215 Alexander et al, 2000216 Barzilay et al, 2001217 Alexander et al, 2003218 Malik et al, 2005219 SEA Bangladesh Sayeed et al, 1998220 Chuang et al, 2002b,221 India Ramachandran et al, 1999b,222 Ramachandran et al, 2000223 Mauritius Collins et al, 1993224 Sri Lanka Fernando et al, 1993225 Chuang et al, 2002b,221 WP China Chi et al, 2001226 Chuang et al, 2002b,221 Fiji (Asian Indian) Tuomilehto et al, 1988227 Indonesia Chuang et al, 2002b,221 Japan Kuzuya et al, 1994228 Korea, Republic of Lee et al, 1995229 Chuang et al, 2002b,221 Malaysia Chuang et al, 2002b,221 Nauru Collins et al, 1993224 New Zealand (European) Simmons et al, 1996230 New Zealand (Maori) Simmons et al, 1996230 New Zealand (Pacific Islanders) Simmons et al, 1996230 Philippines Chuang et al, 2002b,221

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DIABETES ATLAS THIRD EDITION

Coronary heart disease (%) Stroke (%) d UnDM Type 1 DM Type 2 DM Total DM UnDM Type 1 DM Type 2 DM Total DM

• • • • • • • • • • • 5.1 • • • • 6.2 30.8 • • 26.4 • • • • • 13.4 • • • • • 8.4 • 16.3 13.3 • • 10.6 • • • • • 40.0 • • 9.0 • • • 13.3 • 13.4 • • 35.5 • • • • • • • • 11.0 • 0.5 • • • 12.4 • 4.7 12.3 • • 12.1 • • • • 3.4 • • • • • • • • • • • • • • • • 18.6 • • • • • • • 11.4 • 0.5 • • • • • • 12.0 • • • • • • • • • • • • • • • • • • • • 7.8 • • • • • • • • • • • • • • • • • • • • •



COMPLICATIONS OF DIABETES

• • • • 7.5 15.0 • • • • 19.8 • • • 6.2 • • • 4.4 • 11.3 • • • 6.2 21.0 • • • • • • • • • 7.7 • • • 2.8 • • • 5.2 • 8.1 • • • 7.6 • • • • • 13.5 • 4.3 4.1 4.1 • • • 6.7 • 9.9 • • • • • • • • • • • • 5.0 • 20.9 • • • 9.1 • • • • • • • • • • 3.3 • • • • 6.0 • • • 5.6 • • • 5.0 • • • 0.5 • • • • • 9.8 • 11.5 • 3.1 12.3 11.3 • • 5.4 • 25.2 • • • 9.6 • • • • • 10.7 • • • 5.0 (incl UnDM) (incl UnDM) 9.8 • • • • 43.4 • • • 12.5 17.6 • • • • 26.7 • • • • • • • • • 2.0 • • • 1.0 • • • • • • • • • • Male 22.0 • • • • Female 33.7 • • • 3.6 • 6.0 • • • 2.0 8.7 • • • 3.4 1.0 • • • 5.0 31.3 • • • • 5.0 • • • 4.0 2.1 • • • 5.7 • • • 8.4 • 3.0 • • • 6.0 12.0 • • • 6.0 15.8 • • • • 11.0 • • • • 11.0 • • • • 6.0 • • • • 3.0 • • • 6.0



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119

Table 1.49 Prevalence of cardiovascular disease

Region Country/territory Data used U

WP Singapore Thai et al, 1990231 Chuang et al, 2002b,221 Taiwan Chuang et al, 2002b,221 Fuh, 2002a,232 Tseng et al, 2005233 Thailand Thai Multicenter Group, 1994234 Tatsanavivat et al, 1998235 Tandhanand et al, 2001236 Viet Nam Chuang et al, 2002b,221

DM diabetes mellitus Total DM previously diagnosed diabetes (both type 1 and type 2) UnDM undiagnosed diabetes

a. Unpublished data b. Extra details supplied by authors

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DIABETES ATLAS THIRD EDITION

Coronary heart disease (%) Stroke (%) d UnDM Type 1 DM Type 2 DM Total DM UnDM Type 1 DM Type 2 DM Total DM

• • • 12.8 • • • • (incl UnDM) • • • 5.0 • • • 3.0 • • • 4.0 • • • 6.0 • • 18.1 • • • 4.7 • • • • • • 7.5 • • • 10.5 • • • 3.7 • • • • 1.8 • • • • • • • 3.0 • • • 3.0 • • • 1.0 • • • 3.0







COMPLICATIONS OF DIABETES



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121

Table 1.50 Data sources: prevalence of cardiovascular disease Region Country/territory

Data used Study type Sample size A

AFR South Africa Rotchford et al, 2002187 Clinic (secondary care) 253 EMME Egypt Arab et al, 2002188 Clinic (primary care) 2,000 Pakistan Hashim et al, 1999189 Clinic (primary care) 805 Sudan Elmahdi et al, 1991190 Clinic (secondary care) 413 EUR Austria Muhlhauser et al, 1992191 Clinic (primary care) 375 Belgium Van Acker et al, 2001192 Clinic (secondary care) 1,653 Denmark Gall et al, 1991193 Clinic (secondary care) 549 Estonia Vides et al, 2001194 Register 181 Finland Isomaa et al, 2001195 Clinic (primary care) 1,697 Hu, 2003a,196 Population based 172 France Delcourt et al, 1998197 Clinic (secondary care) 427 Le Floch et al, 2000198 Clinic (primary care) 7,391 Germany Liebl et al, 2002199 Clinic (primary and secondary care) 2,701 Italy DAI Study Group, 2004200 Clinic (secondary care) 19,468 Netherlands Verhoeven et al, 1991201 Clinic (primary care) 137 Reenders et al, 1993202 Clinic (primary care) 387 de Visser et al, 2002203 Population based 281 Spijkerman et al, 2004204 Population based 255 Serbia and Montenegro Vlajinac et al, 1992205 Population based 152 Miljus, 2002206 N/A N/A Slovakia Slovakian Diabetes Society, 2002a,207 Clinic (secondary care) N/A Spain Esmatjes et al, 1996208 Clinic (primary and secondary care) 1,157 Diamante, 1997209 Clinic (secondary care) 1,822 Arteagoitia et al, 2003210 Clinic (primary care) 2,920 Sweden Lundman et al, 1998211 Clinic (primary and secondary care) 4,027 Wandell, 2004212 Clinic (primary care) 389 United Kingdom Morgan et al, 2000213 Clinic (primary and secondary care) 10,287 NA United States of America Maser et al, 1991214 Clinic (secondary care) 657 Qureshi et al, 1998215 Population based 1,532 Alexander et al, 2000216 Population based N/A Barzilay et al, 2001217 Population based 479 Alexander et al, 2003218 Population based 600 Malik et al, 2005219 Population based 465 SEA Bangladesh Sayeed et al, 1998220 Clinic (secondary care) 693 Chuang et al, 2002b,221 Clinic (secondary care) 1,607 India Ramachandran et al, 1999b,222 Clinic (secondary care) 3,010 Ramachandran et al, 2000223 Clinic (secondary care) 617 Mauritius Collins et al, 1993224 Population based 259 Sri Lanka Fernando et al, 1993225 Clinic (secondary care) 500 Chuang et al, 2002b,221 Clinic (secondary care) 1,213 WP China Chi et al, 2001226 Clinic (secondary care) 447 Chuang et al, 2002b,221 Clinic (secondary care) 2,430 Fiji (Asian Indian) Tuomilehto et al, 1988227 Population based 151 Indonesia Chuang et al, 2002b,221 Clinic (secondary care) 2,093 Japan Kuzuya et al, 1994228 Clinic (secondary care) 2,120 Korea, Republic of Lee et al, 1995229 Clinic (secondary care) 631 Chuang et al, 2002b,221 Clinic (secondary care) 952 Malaysia Chuang et al, 2002b,221 Clinic (secondary care) 1,045 Nauru Collins et al, 1993224 Population based 215 New Zealand (European) Simmons et al, 1996230 Population based 176 New Zealand (Maori) Simmons et al, 1996230 Population based 286 New Zealand (Pacific Islanders) Simmons et al, 1996230 Population based 495 Philippines Chuang et al, 2002b,221 Clinic (secondary care) 2,657 Singapore Thai et al, 1990231 Population based 117

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DIABETES ATLAS THIRD EDITION

Age (yrs)

Duration DM (yrs)

21-81 4 ± N/A N/A N/A 31->70 N/A 20+ <5->15 median=67 median=6 21-69 range 5-27 <76 range 0-20 15-82 11 ± 10 35-70 N/A 25-64 N/A 35-74 11 ± 7 mean=63 11 ± 0.1 mean=67 N/A 66 (9) 9.5 (7.5) mean=68 8 ± 7 mean=68 8 ± 6 22-96 8 ± 7 62.9 (7.0) 0 35-54 N/A N/A N/A N/A N/A 45-70 9 ± 7 >18 14 ± 9 68 (11) 8.1 (5.4) ≥18 10 ± 7 54.6 (6.9) 6.5 (5.3) mean=61 N/A mean=28 19 ± 8 40-74 N/A ≥20 N/A ≥65 N/A 55 N/A 52.8 (>18) N/A 30-60 0-2 10-91 8 ± 6 mean=52 8 ± 6 10-50 median=4 35-74 N/A mean=52 8 ± N/A 14-91 8 ± 7 35-54 range <7-14 7-92 8 ± 6 N/A N/A 22-89 8 ± 6 <24->75 11 ± N/A 30-75 8 ± 7 15-92 11 ± 7 15-87 11 ± 7 35-80 N/A median=61 N/A median=50 N/A median=52 N/A 7-93 9 ± 7 18+ N/A



COMPLICATIONS OF DIABETES

Diagnostic tool - CHD#

Diagnostic tool - stroke+

N/A Self-report / medical record review Self-report (angina) • Medical record review / self-report (MI, angina) Medical record review (includes TIA) Self-report confirmed by ECG (CHD) Self-report (stroke, TIA) Self-report (MI) Self-report (stroke) Medical record review (CHD) Medical record review (CBVD) ECG (Minnesota codes) N/A ECG / self-report / medical record review (CHD) Self-report / medical record review (CBVD) Self-report / medical record review (MI) Self-report / medical record review (stroke) Self-report Self-report Self-report / ECG (MI) N/A Self-report / medical record review (CHD) Self-report / medical record review (CBVD) Medical record review (MI, angina, Medical record review (stroke) congestive heart failure, coronary bypass surgery) Hospital admission or ECG/ CABG or PTCA (MI, angina) • ECG (Minnesota codes) N/A Medical record review (MI, angina) Medical record review (stroke) Medical record review (MI, PTCA, coronary artery bypass) Medical record review (stroke, TIA) Prior MI • ECG (Minnesota codes), questionnaire (MI, angina) N/A N/A N/A N/A N/A Self-report (CHD) Self-report (stroke) Self-report (CHD) Self-report (stroke) Self report (MI or angina) Medical record review (stroke) Medical record review (MI, angina) Medical record review (stroke) Medical history (MI, angina) Medical record review (stroke, TIA) Hospital codes and primary care audit (CHD) Hospital codes and primary care audit (CBVD) ECG / medical record review (MI, angina) N/A Self-report (MI) Self-report (stroke) Self-report, Rose questionnaire (MI, angina) N/A Medical record review (MI, angina, Medical record review (stroke, TIA) congestive heart failure, coronary bypass surgery) Self report (MI, angina) • Self report (MI, stroke or CCF ) • ECG and self-report (MI, angina) N/A Medical record review (CHD) Medical record review (CBVD) ECG (Minnesota codes) / medical record review (MI) N/A ECG (Minnesota codes) / medical record review (MI) N/A ECG (Minnesota codes) N/A ECG (Minnesota codes) Questionnaire Medical record review (CHD) Medical record review (CBVD) ECG (Minnesota codes) / self-report (MI, angina) Self-report (stroke) Medical record review (CHD) Medical record review (CBVD) ECG (Minnesota codes) N/A Medical record review (CHD) Medical record review (CBVD) Doctor questionnaire (CHD) Doctor questionnaire (CBVD) ECG (Minnesota codes) / self-report (MI, angina) Self-report (stroke) Medical record review (CHD) Medical record review (CBVD) Medical record review (CHD) Medical record review (CBVD) ECG (Minnesota codes) N/A Self-report (MI) N/A Self-report (MI) N/A Self-report (MI) N/A Medical record review (CHD) Medical record review (CBVD) ECG (Minnesota codes) / self-report (MI, angina) N/A



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123

Table 1.50 Data sources: prevalence of cardiovascular disease Region Country/territory

Data used Study type Sample size A

Singapore Taiwan Thailand Viet Nam

Chuang et al, 2002b,221 Chuang et al, 2002b,221 Fuh, 2002a,232 Tseng et al, 2005233 Thai Multicenter Group, 1994234 Tatsanavivat et al, 1998235 Tandhanand et al, 2001236 Chuang et al, 2002b,221

Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (primary and secondary care) Clinic (secondary care) Population based Clinic (secondary care) Clinic (secondary care)

1,674 2,420 4,535 12,531 1,747 278 2,379 1,169

CABG coronary artery bypass graft CBVD cerebrovascular disease CCF

congestive cardiac failure

CHD

coronary heart disease

DM

diabetes mellitus

ECG

electrocardiogram

MI

myocardial infarction

N/A

not available

PTCA percutaneous transluminal coronary angioplasty TIA

transient ischaemic attack

Diagnostic tool:

124

# +

The type of CHD (e.g MI or MI and angina) reported is stated. If this was not reported in the study, the term CHD is used. The type of CBVD (e.g stroke or stroke and TIA) reported is stated. If this was not reported in the study, the term CBVD is used.

a.

Unpublished data

b.

Extra details supplied by authors

CHAPTER 1

DIABETES ATLAS THIRD EDITION

Age (yrs)

Duration DM (yrs)

Diagnostic tool - CHD#

Diagnostic tool - stroke+

4-91 15-92 N/A 64.1 (10.8) 24-88 ≥30 mean=59 3-89

10 ± 8 10 ± 7 N/A 10.4 (6.9) 8 ± 7 N/A 10 ± 7 6 ± 5

Medical record review (CHD) Medical record review (CHD) N/A • ECG ECG (Minnesota codes) Medical record review (CHD) Medical record review (CHD)

Medical record review (CBVD) Medical record review (stroke) N/A Self report (interview) Self-report (CBVD) / examination N/A Medical record review (CBVD) Medical record review (CBVD)





COMPLICATIONS OF DIABETES



CHAPTER 1

125

Table 1.51 Prevalence of diabetic nephropathy





PREVALENCE OF DIABETIC NEPHROPATHY (%) - OVERT PREVALENCE Region Country/territory Data used UnDM Type 1 DM Type 2 DM Total DM U AFR Cameroon Ethiopia Nigeria Senegal South Africa Zambia EMME Egypt Iran, Islamic Republic of Saudi Arabia Sudan EUR Austria Belgium Croatia Czech Republic Denmark Finland France Germany Greece Hungary Ireland Israel Italy Luxembourg Netherlands Norway Poland Portugal Romania Slovakia Spain Sweden Ukraine United Kingdom NA Mexico United States of America

126

CHAPTER 1

Sobngwi et al, 1999238 • • Rahlenbeck et al, 1997239 • • Erasmus et al, 1992240 • • Cisse et al, 2003241 • • Kalk et al, 1997242 • • Levitt et al, 1997243 • • Rotchford et al, 2002187 • • Rolfe, 1988245 • • Herman et al, 1998246 6.8 • Manaviat et al, 2004247 • • Alzaid et al, 1994248 • • Elmahdi et al, 1991190 • • Muhlhauser et al, 1992191 • • EuroDiab, 1994b,185 • 3.6 Bouten et al, 1996249 • • EuroDiab, 1994b,185 • 13.0 Van Acker et al, 2001192 • • EuroDiab, 1994b,185 • 15.9 Perusicova et al, 1993c,250 • 13.8 Czech Health Statistics, 2002d,251 • • Mortensen et al, 1990252 • 0.7 Gall et al, 1991193 • • EuroDiab, 1994b,185 • 15.1 EuroDiab, 1994b,185 • 11.2 Delcourt et al, 1998197 • • EuroDiab, 1994b,e,185 • 7.5 Bennett et al, 2001253 • • EuroDiab, 1994b,185 • 6.5 EuroDiab, 1994b,e,185 • 6.1 EuroDiab, 1994b,185 • 12.5 Norymberg et al, 1991254 • • EuroDiab, 1994b,e,185 • 6.9 Bruno et al, 2003255 • • Bo et al, 2005256 • • EuroDiab, 1994b,185 • 3.9 Verhoeven et al, 1991201 • • Reenders et al, 1993202 • • EuroDiab, 1994b,185 • 7.0 Spijkerman et al, 2003257 • • Joner et al, 1992258 • 0.3 EuroDiab, 1994b,185 • 8.6 Bennett et al, 2001253 • • EuroDiab, 1994b,185 • 15.8 EuroDiab, 1994b,185 • 17.3 Slovakian Diabetes Society, 2002d,207 • • Esmatjes et al, 1996208 • • Diamante, 1997209 • 5.0 Arteagoitia et al, 2003210 • • Lundman et al, 1998211 • 11.8 Wandell, 2004212 • • Kravchenko et al, 1996259 • • Higgs et al, 1992260 • 24.0 EuroDiab, 1994b,185 • 5.7 Harvey et al, 2001b,261 • (cumulative prevalence) 9.6 Cueto-Manzano et al, 2005262 • • Orchard et al, 1990263 • 26.4

• • • • • • • • • 14.5 12.8 9.2 • • • • • • • • • 14.0 • • 6.1 • • • • • 7.0 • • 18.2 • 16.0 13.0 • • • • • • • • 5.4 • 23.0 12.1 10.9 • • • • 10.1 •

• 17.1 • • 14.5 5.3 13.4 23.8 6.7 • • • 15.0 • • • • • • • • • • • • • 7.9 • • • • • • • • • • • • • • 9.7 • • 7.6 • • • 12.1 • • • • • • •

DIABETES ATLAS THIRD EDITION

OVERT PREVALENCE OF DIABETIC NEPHROPATHY (%) - MICROALBUMINURIA M UnDM Type 1 DM Type 2 DM Total DM





COMPLICATIONS OF DIABETES

• • • • • • • 19.6 • • • • • • • • 14,1 • • • • • • • • • • 23.4 • 14.0 • 25.2 • 37.6a • 29.0 • • • • • 4.3 • • • 23.7 • 21.6 • • • 21.6 • • • 23.8 • 19.8 • 17.9 • • • 19.3 • • • • • 23.5 • • • • • 23.4 17,2 • • 12.3 • 19.8 • • • 24.8 • 26.4 • • • • • 14.1 • • • • • • • • • 17.0 • 21.7 • (cumulative prevalence) 27.2 • • • 21.6



• • 57.0 16.7 • • • • • 25.9 36.0 • • • • • 35.6a • • • • 27.0 • • 21.8 • • • • • • • 38.9 12.6 • 42.0 44.0 • 19.4 • • • • • • 23.1 • • • • • • • • 18.7 •

53.1 34.1 • • 31.0 36.7a 32.8 • 14.3 • • • 32.0 • • • 35.4a • • 7.3a • • • • • • 14.5 • • • • • • • • • • • • • • 18.8 • • • • • • • • 9.6 • • • • •

CHAPTER 1

127

Table 1.51 Prevalence of diabetic nephropathy

PREVALENCE OF DIABETIC NEPHROPATHY (%) - OVERT PREVALENCE Region Country/territory Data used UnDM Type 1 DM Type 2 DM Total DM U United States of America SACA Brazil SEA India Mauritius Sri Lanka WP Australia China China, Hong Kong Indonesia Japan Korea, Republic of Malaysia Nauru New Zealand (European) New Zealand (Pacific Islanders) Philippines Samoa Singapore Taiwan Thailand Viet Nam

Garg et al, 2002264 Foss et al, 1989265 Ramachandran et al, 1999b,222 Ramachandran et al, 2000223 Mohan et al, 2000266 Dowse et al, 1998267 Weerasuriya et al, 1998268 Tapp et al, 2004269 Chi et al, 2001226 Chan et al, 1993270 Ko et al, 1999271 Tam et al, 2004272 Diabcare Asia, 2003d,273 Kuzuya et al, 1994228 Kawano et al, 2001181 Lee et al, 1995229 Shriwas et al, 1996274 Collins et al, 1989275 Simmons et al, 1994276 Simmons et al, 1994276 Lantion-Ang, 2000277 Collins et al, 1995278 Thai et al, 1990231 Fuh, 2002d,232 Thai Multicenter Group, 1994234 Diabcare Asia, 2003d,273



• • • • • • • 2.4 • • • • • • • • • 24.4 • • • • • • • •

• • • • • (persistant 5.5%) 19.7 7.1 • • 10.7 • • • • • 6.1 • • • 20.0 • • • • • • 27.0 19.5 • • • 14.0 • • • 32.9 • • • • • • • • • • • • • 18.7 • •

6.1 11.3 • • • 3.8 • 4.3 57.1 • • • 3.3 20.1 27.3 • 28.2 • 5.4 13.0 1.0 (incl UnDM) 5.0 (incl UnDM) 18.3 19.0 • 6.1



DM diabetes mellitus Total DM previously diagnosed diabetes (both type 1 and type 2) UnDM undiagnosed diabetes

a. Includes both micro and macroalbuminuria b. Extra details supplied by authors c. Abstract only d. Unpublished data e. More than one centre used to derive prevalence figure

128

CHAPTER 1

DIABETES ATLAS THIRD EDITION

OVERT PREVALENCE OF DIABETIC NEPHROPATHY (%) - MICROALBUMINURIA M UnDM Type 1 DM Type 2 DM Total DM



• • • • • • 29.0a 15.4 • • • • • • • • • 38.9 • • • • • • • •

• • • • • • • • • • • • • • • • • • • • • • • • • •

• • • • 2.5 • • 26.5 • 27.0 24.8a 13.4 • • • 20.0 • 42.1 • • • • • • • •

28.1 • • • • 10.7 • 21.0 • • 22.7a • 4.6 • • • • • 22.1 33.3 48.7 (incl UnDM) 19.9 • • • 14.2









COMPLICATIONS OF DIABETES



CHAPTER 1

129

Table 1.52 Data sources : Prevalence of diabetic nephropathy Region Country/territory

Data used Study type Sample size A

AFR Cameroon Sobngwi et al, 1999238 Clinic (secondary care) 64 Ethiopia Rahlenbeck et al, 1997239 Clinic (secondary care) 170 Nigeria Erasmus et al, 1992240 Clinic (secondary care) 113 Senegal Cisse et al, 2003241 Clinic (secondary care) 587 South Africa Kalk et al, 1997242 Clinic (secondary care) 448 Levitt et al, 1997243 Clinic (primary care) 243 Rotchford et al, 2002187 Clinic (secondary care) 253 Zambia Rolfe, 1988245 Population based 600 EMME Egypt Herman et al, 1998246 Population based 283 Iran, Islamic Republic of Manaviat et al, 2004247 Clinic (secondary care) 330 Saudi Arabia Alzaid et al, 1994248 Clinic (secondary care) 211 Sudan Elmahdi et al, 1991190 Clinic (secondary care) 413 EUR Austria Muhlhauser et al, 1992191 Clinic (primary care) 375 EuroDiab, 1994b,185 Clinic (secondary care) 111 Belgium EuroDiab, 1994b,185 Clinic (secondary care) 123 Bouten et al, 1996249 Clinic (secondary care) 271 Van Acker et al, 2001192 Clinic (secondary care) 1,653 Croatia EuroDiab, 1994b,185 Clinic (secondary care) 138 Czech Republic Perusicova et al, 1993c,250 Register 1,443 Czech Health Statistics, 2002d,251 Population based N/A Denmark Mortensen et al, 1990252 Clinic (secondary care) 957 Gall et al, 1991193 Clinic (secondary care) 549 Finland EuroDiab, 1994b,185 Clinic (secondary care) 139 France EuroDiab, 1994b,185 Clinic (secondary care) 116 Delcourt et al, 1998197 Clinic (secondary care) 427 Germany EuroDiab, 1994b,e,185 Clinic (secondary care) 241 Bennett et al, 2001253 Clinic (secondary care) 214 Greece EuroDiab, 1994b,185 Clinic (secondary care) 231 Hungary EuroDiab, 1994b,e,185 Clinic (secondary care) 131 Ireland EuroDiab, 1994b,185 Clinic (secondary care) 112 Israel Norymberg et al, 1991254 Clinic (secondary care) 1,019 Italy EuroDiab, 1994b,e,185 Clinic (secondary care) 944 Bruno et al, 2003255 Clinic (primary and secondary care) 1,253 Bo et al, 2005256 Clinic (secondary care) 3,892 Luxembourg EuroDiab, 1994b,185 Clinic (secondary care) 102 Netherlands Verhoeven et al, 1991201 Clinic (primary care) 137 Reenders et al, 1993202 Clinic (primary care) 376 EuroDiab, 1994b,185 Clinic (secondary care) 128 Spijkerman et al, 2003257 Clinic (primary care) and population based 255 Norway Joner et al, 1992258 Population based 351 Poland EuroDiab, 1994b,185 Clinic (secondary care) 116 Bennett et al, 2001253 Clinic (secondary care) 186 Portugal EuroDiab, 1994b,185 Clinic (secondary care) 137 Romania EuroDiab, 1994b,185 Clinic (secondary care) 110 Slovakia Slovakian Diabetes Society, 2002d,207 Clinic (secondary care) N/A Spain Esmatjes et al, 1996208 Clinic (primary and secondary care) 1,157 Diamante, 1997209 Clinic (secondary care) 1,822 Arteagoitia et al, 2003210 Clinic (primary care) 2,920 Sweden Lundman et al, 1998211 Clinic (secondary care) 4,027 Wandell, 2004212 Clinic (primary care) 389 Ukraine Kravchenko et al, 1996259 Clinic (secondary care) 4,123 United Kingdom Higgs et al, 1992260 Population based 358 EuroDiab, 1994b,185 Clinic (secondary care) 175 Harvey et al, 2001b,261 Population based 903



130

CHAPTER 1

DIABETES ATLAS THIRD EDITION

Age (yrs)

Duration DM (yrs) Overt diagnostic criteria Microalbuminuria diagnostic criteria

51.9 (1.9) 5 ± 0.7 • mean=42 6 ± 5 Albuminuria >300mg/la mean=51 5 ± 1 N/A 41.3 (17.2) 5.9 (5.6) • mean=54 6 ± 6 A/CR >19.9 mg/mmol 20-85 8 ± 8 N/A 21-81 4 ± N/A A/CR ≥20 mg/mmol mean=49 9 ± 6 Grade 2 proteinuria 20+ N/A A/CR ≥300 mg/g 54.9 (10.2) 10.2 (6.6) A/CR >300 mg/g 56 10 ± 5 Dipstick proteinuria 20+ <5->15 Proteinuria >0.5g/24h or blood urea of >40mg%a median=67 median=6 Albuminuria >200 mg/l 15-60 16 ± 10 AER ≥200 µg/min 15-60 15 ± 9 AER ≥200 µg/min mean=37 15 ± 10 N/A 21-69 range 5-27 N/A 15-60 14 ± 9 AER ≥200 µg/min >18 N/A N/A N/A N/A N/A <20 5 ± 3 AER>150µg/mina <76 range 0-20 AER ≥300 mg/24hr 15-60 15 ± 10 AER ≥200 µg/min 15-60 16 ± 10 AER ≥200 µg/min 35-74 11 ± 7 AER >300 mg/24hr or proteinuria >0.5g/24h 15-60 15 ± 10 AER ≥200 µg/min N/A N/A A/CR ≥300 mg/g 15-60 13 ± 8 AER ≥200 µg/min 15-60 15 ± 9 AER ≥200 µg/min 15-60 15 ± 9 AER ≥200 µg/min 31+ 12 ± 9 Proteinuria >30mg/dla 15-60 14 ± 9 AER ≥200 µg/min 67.5 (10.2) • AER > 200 µg/min 69.7 (10.2) 11.7 ± 8.6 AER ≥ 200 µg/min 15-60 14 ± 9 AER >200 µg/min mean=68 8 ± 7 Proteinuria >0.5 g/24h mean=68 8 ± 6 AER >200 mg/la 15-60 16 ± 10 AER ≥200 µg/min 62.9 (7.0) Not relevant • 8-30 10 ± 3 AER >200 µg/mina 15-60 15 ± 10 AER ≥200 µg/min N/A N/A A/CR ≥300 mg/g 15-60 15 ± 10 AER ≥200 µg/min 15-60 13 ± 8 AER ≥200 µg/min N/A N/A N/A 45-70 9 ± 7 AER >200 µg/mina >18 14 ± 9 AER >200 µg/mina 68 (11) 8.1 (5.4) N/A 18+ 10 ± 7 Proteinuria ≥300mg/ga 54.6 (6.9) 6.5 (5.3) AER >200 µg/min 14-75 N/A N/A 6-92 11 ± N/A Albustix reading ≥0.3 g/l 15-60 17 ± 10 AER ≥200 µg/min 3-80 N/A AER >200 µg/mina

Albuminuria > 30 mg/24hr Albuminuria 30-299 mg/la Albuminuria 20-199 µg/mina Albuminuria > 30mg/l A/CR 3.0-19.9 mg/mmol A/CR >3.4 mg/mmola Men A/CR 2.6-19.9 mg/mmol, women A/CR 3.6-19.9 mg/mmol N/A A/CR 100-299 mg/g A/CR 30-300 mg/g AER 30-300 mg/24hr N/A Albuminuria 21-200 mg/l AER 20-199 µg/min AER 20-199 µg/min AER 20-200 µg/mina Albuminuria ≥ 30mg/dl AER 20-199 µg/min N/A N/A AER >20-150µg/mina AER 31-299 mg/24hr AER 20-199 µg/min AER 20-199 µg/min AER 31-300mg/24hr AER 20-199 µg/min A/CR 30-299 mg/g AER 20-199 µg/min AER 20-199 µg/min AER 20-199 µg/min N/A AER 20-199 µg/min AER 20-200 µg/min AER 20-199 µg/min AER 20-199 µg/min Albuminuria 20-200 µg/min AER 20-200 mg/la AER 20-199 µg/min A/CR ≥ 2.0 mg/mmol AER 16 -200 µg/mina AER 20-199 µg/min A/CR 30-299 mg/g AER 20-199 µg/min AER 20-199 µg/min N/A AER 20-200 µg/mina AER 20-200 µg/mina AER ≥20 mg/l or albuminuria >30 mg/24 h N/A AER 20-200 µg/mina Albuminuria ≥20 mg/la A/CR ≥2.5 mg/mmol AER 20-199 µg/min AER 20-200 µg/mina



COMPLICATIONS OF DIABETES



CHAPTER 1

131

Table 1.52 Data sources: Prevalence of diabetic nephropathy Region Country/territory

Data used Study type Sample size A

NA Mexico Cueto-Manzano et al, 2005262 Clinic (primary care) 756 United States of America Orchard et al, 1990263 Clinic (secondary care) 592 Garg et al, 2002264 Population based 1,192 SACA Brazil Foss et al, 1989265 Clinic (secondary care) 546 SEA India Ramachandran et al, 1999b,222 Clinic (secondary care) 3,010 Mohan et al, 2000266 Clinic (secondary care) 1,848 Ramachandran et al, 2000223 Clinic (secondary care) 617 Mauritius Dowse et al, 1998267 Population based 746 Sri Lanka Weerasuriya et al, 1998268 Clinic (primary care) 597 WP Australia Tapp et al, 2004269 Population based 879 China Chi et al, 2001226 Clinic (secondary care) 447 China, Hong Kong Chan et al, 1993270 Clinic (secondary care) 397 Ko et al, 1999271 Clinic (secondary care) 150 Tam et al, 2004272 Clinic (primary care) 1,161 Indonesia Diabcare Asia, 2003d,273 Clinic (primary care) 717 Japan Kuzuya et al, 1994228 Clinic (secondary care) 2,120 Kawano et al, 2001181 Clinic 6,472 Korea, Republic of Lee et al, 1995229 Clinic (secondary care) 631 Malaysia Shriwas et al, 1996274 Clinic (secondary care) 131 Nauru Collins et al, 1989275 Population based 318 New Zealand (European) Simmons et al, 1994276 Clinic (primary and secondary care) 297 New Zealand (Pacific Islanders) Simmons et al, 1994276 Clinic (primary and secondary care) 123 Philippines Lantion-Ang, 2000277 Clinic (primary care) 359 Samoa Collins et al, 1995278 Population based 141 Singapore Thai et al, 1990231 Population based 117 Taiwan Fuh, 2002d,232 Clinic (secondary care) 4,535 Thailand Thai Multicenter Group, 1994234 Clinic (secondary care) 2,060 Viet Nam Diabcare Asia, 2003d,273 Clinic (primary care) 521

A/CR AER DM N/A

albumin/creatinine ratio albumin excretion rate diabetes mellitus not available

a. Diagnosis of nephropathy required two or more urine samples b. Extra details supplied by authors c. Abstract only d. Unpublished data e. More than one centre used to derive prevalence figure

132

CHAPTER 1

DIABETES ATLAS THIRD EDITION

Age (yrs)

Duration DM (yrs) Overt diagnostic criteria Microalbuminuria diagnostic criteria

59 (11) N/A AER >300 mg/daya AER 30-30 mg/daya 18-30+ 16 ± N/A AER >200 µg/mina AER 20-200µg/mina 20-80+ N/A A/CR >37.8mg/mmol A/CR 3.0-37.8mg/mmol 25-84 8 ± 7 Proteinuria >200 mg/24hra N/A mean=52 8 ± 6 Proteinuria ≥500 mg/24hra N/A mean=52 7 ± 6 Proteinuria ≥500 mg/24hra Proteinuria 150-499 mg/24hra 10-50 median=4 Proteinuria ≥500 mg/24hra N/A 25+ 6 ± N/A Albuminuria ≥300 mg/ml Albuminuria 30-299 mg/ml 25-65 0 ± 0 Albuminuria >50 mg/l N/A 25+ median=5 A/CR ≥25 mg/mmol Men A/CR 2.5-25 mg/mmol, women 3.5-25 mg/mmol 35-54 N/A Semi-quantitative test N/A mean=57 range 0-30 A/CR >40.3 mg/mmola A/CR 5.4-40.3 mg/mmola <40 5 ± 0 N/A A/CR ≥3.5 mg/mmol and AER ≥20 µg/mina 18-81, mean=58 (21) 5.7 N/A A/CR ≥ 30 mg/g 25-85 7 ± 6 Albuminuria >300 mg/l Albuminuria 20-300mg/l A/CR >25 mg/mol Men A/CR 2.5-25mg/mol, women A/CR 3.5-25 mg/mol <24->75 11 ± N/A Doctor report Doctor report mean=61 10 ± 10 Doctor report Doctor report 30-75 8 ± 7 AER >200 µg/mina AER 20-200 µg/mina 0-80+ range <5->20 Dipstick proteinuria or creatinine > 97µmol/l N/A 25+ range 0->15 Albuminuria ≥300 µg/ml Albuminuria 30 -299 µg/ml 18-79 range 0-47 AER ≥300mg/24hr AER 30-299 mg/24hr 18-79 range 0-32 AER ≥300mg/24hr AER 30-299 mg/24hr 7-93 9 ± 7 Albuminuria >300 mg/l Albuminuria 20-300 mg/l 25-74 4 ± N/A Albuminuria ≥300 µg/ml Albuminuria 30-299 µg/ml 18+ N/A Albuminuria ≥30 mg/dl or creatinine ≥1.5 mg/dl N/A N/A N/A A/CR >300 mg/g or blood urea N/A >26 mg/dl or serum creatinine >1.3 mg/dl 24-88 8 ± 7 Dipstick proteinuria 2+ N/A 1-85 7 ± 5 Albuminuria >300 mg/l Albuminuria 20-300mg/l A/CR >25 mg/mol Men A/CR 2.5-25mg/mol, women A/CR 3.5-25 mg/mol







COMPLICATIONS OF DIABETES



CHAPTER 1

133

Table 1.53 Prevalence of diabetic neuropathy



Prevalence of diabetic neuropathy (%)

134

Region Country/territory

Data used UnDM Type 1 DM Type 2 DM Total DM

AFR South Africa Tanzania, United Republic of Zambia EMME Egypt Saudi Arabia Sudan EUR Austria Belgium Croatia Czech Republic Finland France Germany Greece Hungary Ireland Israel Italy Luxembourg Netherlands Poland Portugal Romania Spain Sweden Turkey Ukraine United Kingdom NA United States of America SACA Brazil

Levitt et al, 1997243 Wikblad et al, 1997280 Rolfe, 1988245 Herman et al, 1998246 Arab et al, 2002188 Nielsen, 1998281 Akbar et al, 2000282 Elmahdi et al, 1991190 Muhlhauser et al, 1992191 EuroDiab, 1996a,283 Kastenbauer et al, 2004284 EuroDiab, 1996a,283 Van Acker et al, 2001192 EuroDiab, 1996a,283 Perusicova et al, 1993b,250 Partanen et al, 1995285 EuroDiab, 1996a,283 EuroDiab, 1996a,283 Delcourt et al, 1998197 Detournay et al, 2000286 EuroDiab, 1996a,c,283 EuroDiab, 1996a,c,283 Manes et al, 2002287 EuroDiab, 1996a,283 EuroDiab, 1996a,283 Norymberg et al, 1991254 Veglio et al, 1993288 EuroDiab, 1996a,c,283 Fedele et al, 1997289 EuroDiab, 1996a,283 Verhoeven et al, 1991201 Reenders et al, 1993202 EuroDiab, 1996a,283 Spijkerman et al, 2003257 EuroDiab, 1996a,283 EuroDiab, 1996a,283 EuroDiab, 1996a,283 Esmatjes et al, 1996208 Cabezas-Cerrato, 1998290 Arteagoitia et al, 2003210 Lundman et al, 1998211 Bolukbasi, 1998b,291 Bürö et al, 2004292 Kravchenko et al, 1996259 Walters et al, 1992293 Young et al, 1993294 Kumar et al, 1994295 EuroDiab, 1996a,283 Abbott et al, 2005296 Orchard et al, 1990263 Franklin et al, 1990297 Dyck et al, 1993182 Lavery et al, 2003298 Gregg et al, 2004299 Foss et al, 1989a,b,265

CHAPTER 1

• • • 13.6 • • • • • • • • • • • 8.3 • • • • • • • • • • • • • • • • • 48.1 • • • • • • • • • • • • • • • • • • • • •

• • • • • • • • • 23.3 16.0 29.3 25.7 57.6 32.8 • 26.1 21.2 • • 20.1 25.5 • 29.0 24.1 • 28.5 26.0 • 21.5 • • 23.9 • 25.6 36.9 65.8 • 12.9 • 22.8 • • • 12.8 22.7 • 22.7 • 32.4 • 54.0 • • •

• • • • • 19.7 • 31.5 • • 37.5 • 38.3 • • • • • 28.8 8.8 • • • • • 23.4 • • • • 18.0 68.0 • • • • • 20.0 24.1 19.0 27.9 • 60.0 • 17.2 32.1 41.6 • • • 27.8 45.0 • • 50.9

27.6 28.1 31.2 21.9 55.0 • 56.0 • 26.0 • • • 33.7 • • • • • • • • • 33.5 • • • • • 32.3 • • • • • • • • • 22.7 • 27.3 26.9 • 27.9 16.8 28.5 • • 21.2 • • 47.6 41.4 28.5 •

DIABETES ATLAS THIRD EDITION



Prevalence of diabetic neuropathy (%) Region Country/territory

Data used UnDM Type 1 DM Type 2 DM Total DM

SEA Bangladesh India Mauritius Sri Lanka WP Australia China China, Hong Kong Indonesia Japan Korea, Republic of Malaysia Philippines Singapore Taiwan Thailand Viet Nam

Chuang et al, 2002a,221 Ramachandran et al, 1999222 Ramachandran et al, 2000223 Shaw et al, 1998180 Fernando, 1996300 Weerasuriya et al, 1998268 Tapp et al, 2004269 Chuang et al, 2002a,221 Ko et al, 1999271 Chuang et al, 2002a,221 Kawano et al, 2001181 Chuang et al, 2002a,221 Chuang et al, 2002a,221 Chuang et al, 2002a,221 Thai et al, 1990231 Chuang et al, 2002a,221 Wang et al, 2000301 Fuh, 2002d,232 Hsu et al, 2005302 Tandhanand et al, 2001236 Chuang et al, 2002a,221

• • • 3.6 • 10.0 7.1 • • • • • • • • • • • • • •

• • 3.0 • • • • • • • • • • • • • • • • • •

• 15.0 27.5 • • • • 12.7 30.6 • • • 13.1 • • 31.0 7.6 7.3 • 55.0 • 41.4 • 33.0 • 61.0 • 42.0 • (incl UnDM)15.9 • 12.0 • 32.4 24.4 • 32.4 • • 27.0 • 44.0

DM Total DM UnDM

diabetes mellitus previously diagnosed diabetes (both type 1 and type 2) undiagnosed diabetes

a. Extra details supplied by authors b. Abstract only c. More than one centre used to derive prevalence figure d. Unpublished data

COMPLICATIONS OF DIABETES



CHAPTER 1

135

Table 1.54 Data sources: Prevalence of diabetic neuropathy Region Country/territory

Data used Study type Sample size A

AFR South Africa Tanzania, United Republic of Zambia EMME Egypt Saudi Arabia Sudan EUR Austria Belgium Croatia Czech Republic Finland France Germany Greece Hungary Ireland Israel Italy Luxembourg Netherlands Poland Portugal Romania Spain Sweden Turkey Ukraine United Kingdom NA United States of America SACA Brazil

Levitt et al, 1997243 Wikblad et al, 1997280 Rolfe, 1988245 Herman et al, 1998246 Arab et al, 2002188 Nielsen, 1998281 Akbar et al, 2000282 Elmahdi et al, 1991190 Mühlhauser et al, 1992191 EuroDiab, 1996a,283 Kastenbauer et al, 2004284 EuroDiab, 1996a,283 Van Acker et al, 2001192 EuroDiab, 1996a,283 Perusicova et al, 1993b,250 Partanen et al, 1995285 EuroDiab, 1996a,283 EuroDiab, 1996a,283 Delcourt et al, 1998197 Detournay et al, 2000286 EuroDiab, 1996a,c,283 EuroDiab, 1996a,c,283 Manes et al, 2002287 EuroDiab, 1996a,283 EuroDiab, 1996a,283 Norymberg et al, 1991254 Veglio et al, 1993288 EuroDiab, 1996a,c,283 Fedele et al, 1997289 EuroDiab, 1996a,283 Verhoeven et al, 1991201 Reenders et al, 1993202 EuroDiab, 1996a,283 Spijkerman et al, 2003257 EuroDiab, 1996a,283 EuroDiab, 1996a,283 EuroDiab, 1996a,283 Esmatjes et al, 1996208 Cabezas-Cerrato, 1998290 Arteagoitia et al, 2003210 Lundman et al, 1998211 Bolukbasi, 1998b,291 Börü et al, 2004292 Kravchenko et al, 1996259 Walters et al, 1992293 Young et al, 1993294 Kumar et al, 1994295 EuroDiab, 1996a,283 Abbott et al, 2005296 Orchard et al, 1990263 Franklin et al, 1990297 Dyck et al, 1993182 Lavery et al, 2003298 Gregg et al, 2004299 Foss et al, 1989a,b,265

Clinic (primary care) Clinic (secondary care) Clinic (secondary care) Population based Clinic (primary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (primary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Register Clinic Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (primary and secondary care) Clinic (secondary care) Clinic (secondary care) Population based Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Population based Clinic (primary care) Clinic (secondary care) Clinic (primary care) and population based Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (primary and secondary care) Population based Clinic (primary care) Clinic (primary and secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Population based Clinic (secondary care) Clinic (primary care) Clinic (secondary care) Population based Clinic (secondary care) Population based Population based Clinic (primary and secondary care) Population based (NHANES) Clinic (secondary care)

243 153 600 509 2,000 375 237 413 395 116 350 116 1,653 132 1,443 132 138 104 427 4,119 229 216 821 138 116 1,019 379 894 8,757 107 137 387 134 255 117 130 114 1,157 2,644 2,920 4,027 297 866 4,123 1,077 6,487 811 181 15,692 588 279 359 1,666 419 546



136

CHAPTER 1

DIABETES ATLAS THIRD EDITION

Age (yrs)

Duration DM (yrs)

20-85 mean=44 ≥35 ≥20 20-70 median=50 mean=54 20+ mean=67 15-60 57.1 (11.9) 15-60 21-69 15-60 >18 45-65 15-60 15-60 35-74 mean=66 15-60 15-60 18-70 15-60 15-60 ≥31 15-59 15-60 18-70 15-60 mean=68 mean=68 15-60 62.9 (7.0) 15-60 15-60 15-60 45-70 15-74 68 (11) ≥18 N/A 57.2 (10.3) 14-75 30-80+ 18-90 34-96 15-60 61.4 (13.8) mean=24 20-74 57 69.1 (11.1) 40+ 25-84

Diagnostic tool

8 ± 8 Clinical score 5 ± 6 Clinical score (NDS, NSS), quantitative sensory testing 7 ± 6 Clinical score N/A Quantitative sensory testing N/A Clinical score median=8 Clinical score 11 ± 7 Clinical score (DNI) N/A Clinical score N/A Quantitative sensory testing 16 ± 10 Clinical score, quantitative sensory testing, autonomic function tests 11.8 (9.7) NSS (Michigan Neuropathy Screening Instrument) 15 ± 9 Clinical score, quantitative sensory testing, autonomic function tests range 5-27 Quantitative sensory testing 14 ± 9 Clinical score, quantitative sensory testing, autonomic function tests N/A N/A 0 ± 0 Clinical score, electrophysiology 15 ± 10 Clinical score, quantitative sensory testing, autonomic function tests 16 ± 10 Clinical score, quantitative sensory testing, autonomic function tests 11 ± 7 Clinical score, quantitative sensory testing 9 ± N/A Medical record review 15 ± 10 Clinical score, quantitative sensory testing, autonomic function tests 13 ± 8 Clinical score, quantitative sensory testing, autonomic function tests 8 ± 7 Clinical score (NDS, NSS), quantitative sensory testing 15 ± 9 Clinical score, quantitative sensory testing, autonomic function tests 15 ± 9 Clinical score, quantitative sensory testing, autonomic function tests 12 ± 9 Clinical score N/A Clinical score, autonomic function tests 14 ± 9 Clinical score, quantitative sensory testing, autonomic function tests 12 ± 9 Clinical score (DNI) 14 ± 9 Clinical score, quantitative sensory testing, autonomic function tests 8 ± 7 Clinical score 8 ± 6 Clinical score, autonomic function tests 16 ± 10 Clinical score, quantitative sensory testing, autonomic function tests 0 Quantitative sensory testing (monofilament) 15 ± 10 Clinical score, quantitative sensory testing, autonomic function tests 15 ± 10 Clinical score, quantitative sensory testing, autonomic function tests 13 ± 8 Clinical score, quantitative sensory testing, autonomic function tests 9 ± 7 Clinical score 10 ± 0 Clinical score (NDS, NSS) 8.1 (5.4) Clinical score, quantitative sensory testing 10 ± 7 Clinical score, quantitative sensory testing N/A Clinical score, electrophysiology 8.5 (7.1) NDS, NSS N/A Clinical score N/A Clinical score, quantitative sensory testing range 0-62 Clinical score (NDS, NSS) 7 ± N/A Clinical score (NDS), quantitative sensory testing 17 ± 10 Clinical score, quantitative sensory testing, autonomic function tests mean=5.0 Clinical score (NDS) 16 ± N/A Clinical score N/A Clinical score N/A Clinical score (NSS, NDS, NSP), quantitative sensory testing, electrophysiology, autonomic function test 11.2 (9.5) Quantitative sensory testing (monofilament, VPT) N/A Quantitative sensory testing (monofilament) 8 ± 7 Clinical score, quantitative sensory testing



COMPLICATIONS OF DIABETES



CHAPTER 1

137

Table 1.54 Data sources: Prevalence of diabetic neuropathy Region Country/territory

Data used Study type Sample size A

SEA Bangladesh India Mauritius Sri Lanka WP Australia China China, Hong Kong Indonesia Japan Korea, Republic of Malaysia Philippines Singapore Taiwan Thailand Viet Nam

Chuang et al, 2002a,221 Ramachandran et al, 1999222 Ramachandran et al, 2000223 Shaw et al, 1998180 Fernando, 1996300 Weerasuriya et al, 1998268 Tapp et al, 2004269 Chuang et al, 2002a,221 Ko et al, 1999271 Chuang et al, 2002a,221 Kawano et al, 2001181 Chuang et al, 2002a,221 Chuang et al, 2002a,221 Chuang et al, 2002a,221 Thai et al, 1990231 Chuang et al, 2002a,221 Wang et al, 2000301 Fuh, 2002d,232 Hsu et al, 2005302 Tandhanand et al, 2001236 Chuang et al, 2002a,221

Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Population based Clinic (secondary care) Clinic (primary care) Population based Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Population based Clinic (secondary care) Population based Clinic (secondary care) Population based Clinic (secondary care) Clinic (secondary care)

1,606 3,010 617 847 500 597 821 2,344 150 2,084 6,472 948 1,045 2,635 117 1,625 219 4,535 587 2,314 1,179

DM DNI N/A NDS NSP NSS VPT

diabetes mellitus diabetic neuropathy index not available neuropathy disability score neuropathy symptom profile neuropathy symptom score vibration perception threshold

a. b. c. d.

138

Extra details supplied by authors Abstract only More than one centre used to derive prevalence figure Unpublished data

CHAPTER 1

DIABETES ATLAS THIRD EDITION

Age (yrs)

Duration DM (yrs)

Diagnostic tool

10-91 mean=52 10-50 ≥25 30-60 25-65 ≥25 7-92 <40 22-89 mean=61 15-92 15-87 7-93 18+ 4-91 35-85 N/A N/A mean=59 3-89

8 ± 6 8 ± 6 median=4 N/A 5 ± 6 0 ± 0 median=5 8 ± 6 5 ± 0 8 ± 6 10 ± 10 11 ± 7 11 ± 7 9 ± 7 N/A 10 ± 8 N/A N/A N/A 10 ± 7 6 ± 5

Medical record review Clinical score, quantitative sensory testing Clinical score Quantitative sensory testing Clinical score (NDS, NSS), quantitative sensory testing Clinical score (NDS, NSS), quantitative sensory testin Clinical score (NSS, NDS), quantitative sensory testing Medical record review Clinical score, quantitative sensory testing Medical record review Clinical score Medical record review Medical record review Medical record review Clinical score Medical record review Clinical score Quantitative sensory testing Clinical score (NSS), electrophysiology Medical record review Medical record review











COMPLICATIONS OF DIABETES



CHAPTER 1

139

Table 1.55 Prevalence and incidence of lower limb amputations Incidence per 100,000 Region Country/territory Data used Prevalence (%) diabetic population AFR South Africa Levitt et al, 1997243 1.4 • EMME Egypt Arab, 2002188 3.0 • Saudi Arabia Nielsen, 1998281 1.3 • (below ankle only) EUR Austria Muhlhauser et al, 1992191 3.5 • Belgium Van Acker et al, 2001192 4.2 • Czech Republic Czech Health Statistics, 2002a,251 0.9 • Denmark Ebskov et al, 1996303 • 156b 304 Holstein et al, 2000 • 430c Finland Siitonen et al, 1993305 • 480b Germany Trautner et al, 2001306 • 463b 307 Heller et al, 2004 • • Netherlands van Houtum et al, 1996308 • 251 (age adjusted)c van Houtum et al, 2004309 • 363b 310 Norway Witso et al, 2001 • 440b d,311 Poland Nazim, 2001 • 165c Slovakia Slovakian Diabetes Society, 2002a,207 1.3 • Spain Calle-Pascual et al, 1997312 • 46b Almaraz et al, 2000d,313 • 136c 210 Arteagoitia et al, 2003 1.4 • Sweden Larsson et al, 1995314 • 410b Lundman et al, 1998211 1.8 • United Kingdom Deerochanawong et al, 1992315 • 570c Morris et al, 1998316 • 367b Abbott et al, 2002317 1.3 • Rayman et al, 2004318 • 285c Rayman et al, 2004318 • 162c 319 NA Barbados Hennis et al, 2004 • 936c Hennis et al, 2004319 • 379c Canada Lawee et al, 1992320 • 400c Trinidad and Tobago Gulliford et al, 2002321 4.0 • United States of America Humphrey et al, 1994184 • 271b Lavery et al, 2003298 3.5 590c MMWR, 2003322 • 340b Gregg et al, 2004299 2.4 • SACA Brazil Spichler et al, 2001323 • 181c SEA India Ramachandran et al, 1999222 0.7 (type 2 DM) • Mauritius Shaw et al, 1998180 0.2 (incl UnDM) • Sri Lanka Fernando, 1996300 4.8 (type 2 DM) • WP China Chi et al, 2001226 0.7 • Japan Kuzuya et al, 1994228 0.6 • Nauru Humphrey et al, 1996324 • 810b 301 Taiwan Wang et al, 2000 1.4 (incl UnDM) • Thailand Tandhanand et al, 2001236 1.0 • Thai Multicenter Group, 1994234 1.3 • DM Total DM UnDM

diabetes mellitus previously diagnosed diabetes (both type 1 and type 2) undiagnosed diabetes

a. Unpublished data b. First amputation c. All amputations or not stated d. Abstract only

140

CHAPTER 1

DIABETES ATLAS THIRD EDITION



COMPLICATIONS OF DIABETES



CHAPTER 1

141

Table 1.56

Data sources: prevalence and incidence of lower limb amputations Region Country/territory

Data used Study type S

AFR South Africa Levitt et al, 1997243 Clinic (primary care) EMME Egypt Arab, 2002188 Clinic (primary care) Saudi Arabia Nielsen, 1998281 Clinic (secondary care) EUR Austria Muhlhauser et al, 1992191 Clinic (primary care) Belgium Van Acker et al, 2001192 Clinic (secondary care) Czech Republic Czech Health Statistics, 2002a,251 Population based Denmark Ebskov et al, 1996303 Register Holstein et al, 2000304 Clinical records Finland Siitonen et al, 1993305 Operating theatre records Germany Trautner et al, 2001306 Operating theatre records Heller et al, 2004307 Hospital discharge codes Netherlands van Houtum et al, 1996308 Hospital discharge records van Houtum et al, 2004309 Hospital discharge records Norway Witso et al, 2001310 Hospital records Poland Nazim, 2001b,311 Hospital records and limb fitting centre Slovakia Slovakian Diabetes Society, 2002a,207 Clinic (secondary care) Spain Calle-Pascual et al, 1997312 Operating theatre records, hospital discharge records, medical records Almaraz et al, 2000b,313 Hospital records Arteagoitia et al, 2003210 Clinic (primary care) Sweden Larsson et al, 1995314 Amputation register Lundman et al, 1998211 Clinic (primary and secondary care) United Kingdom Deerochanawong et al, 1992315 Operation records and hospital discharge records Morris et al, 1998316 Hospital discharge records and database of rehabilitation service Abbott et al, 2002317 Clinic (primary and secondary care) Rayman et al, 2004318 Clinic (secondary care) Rayman et al, 2004318 Clinic (secondary care) NA Barbados Hennis et al, 2004319 Operating theatre records Hennis et al, 2004319 Operating theatre records Canada Lawee et al, 1992320 Hospital discharge records Trinidad and Tobago Gulliford et al, 2002321 Clinic (secondary care) United States of America Humphrey et al, 1994184 Hospital discharge codes Lavery et al, 2003298 Clinic (primary and secondary care) MMWR, 2003322 Hospital discharge records Gregg et al, 2004299 Clinic (secondary care) SACA Brazil Spichler et al, 2001323 Register SEA India Ramachandran et al, 1999222 Clinic (secondary care) Mauritius Shaw et al, 1998180 Population based Sri Lanka Fernando, 1996300 Clinic (secondary care) WP China Chi et al, 2001226 Clinic (secondary care) Japan Kuzuya et al, 1994228 Clinic (secondary care) Nauru Humphrey et al, 1996324 Operating theatre records, welfare office and health survey data Taiwan Wang et al, 2000301 Population based Thailand Thai Multicenter Group, 1994234 Clinic (secondary care) Tandhanand et al, 2001236 Clinic (secondary care)

a. Unpublished data b. Abstract only

142

CHAPTER 1

DIABETES ATLAS THIRD EDITION





Sample size Age (yrs) No. of amputees Lowest level of amputation

243 20-85 3 N/A 2,000 20-70 N/A N/A 375 N/A 5 N/A 375 N/A 13 below ankle 1,653 21-69 69 toe • N/A N/A N/A • 25-97 2,848 ray • N/A 463 ankle • all 254 toe • all 39 toe • all 29,000 toe • all 1,575 toe N/A 71.5 (11.9) 1,673 toe • all 74 toe • N/A 139 toe • N/A N/A N/A • all 48 toe • N/A 316 N/A 2,920 68 (11) 41 N/A • all 21 toe 4,027 >18 73 toe • N/A 93 toe 7,079 all 52 toe 9,710 mean=62 122 toe N/A N/A 79 toe N/A N/A 79 above ankle • 70.9 (12.6) 167 toe • 70.9 (12.6) 167 tibia • all 926 toe 2,106 all 84 toe 2,015 N/A 57 toe 1,666 69.1 (11.1) N/A toe N/A N/A 307 toe 3,515 ≥40 N/A toe • N/A N/A N/A 3,010 mean=52 21 N/A 847 mean=54 2 • 500 30-60 24 N/A 447 35-54 3 toe 2,115 <24-75+ 13 N/A 375 25+ 46 toe 219 35-85 3 N/A 2,060 24-88 27 toe 2,378 N/A N/A N/A







COMPLICATIONS OF DIABETES



CHAPTER 1

143

Table 1.57

Prevalence of diabetic retinopathy Proliferative Total retinopathy (%) retinopathy (%) Region Country/territory Data used UnDM Type 1 DM Type 2 DM Total DM Total DM AFR Cameroon Ethiopia Nigeria South Africa Zambia Zimbabwe EMME Egypt Iran, Islamic Republic of Jordan Oman Sudan EUR Austria Belgium Croatia Czech Republic Denmark Finland France Germany Greece Hungary Ireland Israel Italy Luxembourg Netherlands Norway Poland Portugal Russian Federation Slovakia Spain Sweden

Moukouri Dit Nyolo et al, 1995325 • Sobngwi et al, 1999238 • Seyoum et al, 2001326 • Erasmus et al, 1989327 • Kalk et al, 1997242 • Levitt et al, 1997243 • Rotchford et al, 2002187 • Rolfe, 1988245 • Bartels et al, 1999329 • Herman et al, 1998246 15.7 Arab et al, 2002188 • Manaviat et al, 2004247 • Al-Till et al, 2005330 • el Haddad et al, 1998331 • Khandekar et al, 2003332 • Elmahdi et al, 1991190 • Muhlhauser et al, 1992191 • EuroDiab, 1994185 • EuroDiab, 1994185 • Van Acker et al, 2001192 • EuroDiab, 1994185 • Perusicova et al, 1993a,250 • Czech Health Statistics, 2002b,251 • Hove et al, 2004333 • Falck et al, 1993334 • EuroDiab, 1994185 • EuroDiab, 1994185 • Delcourt et al, 1998197 • Detournay et al, 2000286 • EuroDiab, 1994c,185 • Hesse et al, 2001a,335 • EuroDiab, 1994c,185 • EuroDiab, 1994185 • EuroDiab, 1994185 • Norymberg et al, 1991254 • Segato et al, 1991336 • EuroDiab, 1994c,185 • Giuffre et al, 2004337 • Bo et al, 2005256 • EuroDiab, 1994185 • Verhoeven et al, 1991201 • Reenders et al, 1993202 • EuroDiab, 1994185 • Spijkerman et al, 2003257 7.6 Joner et al, 1992258 • Hapnes et al, 1996338 • Luzniak et al, 1997 a,339 • Pinto-Figueiredo et al, 1992340 • EuroDiab, 1994185 • Betts et al, 1999341 • Slovakian Diabetes Society, 2002b,207 • Fernandez•Vigo et al, 1993342 • Esmatjes et al, 1996208 • Arteagoitia et al, 2003210 • Falkenberg et al, 1994343 •

• • 34.1 • • • • • • • • • 43.9 • 19.8 • • 23.0 47.0 43.6 59.0 42.2 • • 10.8 54.0 35.0 • • 21.0 • 46.7 51.0 53.0 • 46.2 40.8 • • 30.0 • • 47.0 • 32.8 34.4 • 41.6 60.0 12.0 • • • • •

• • 41.0 • • • • • • • • 39.3 65.4 • 15.0 17.4 • • • 35.0 • • • 31.2 • • • 33.5 10.6 • • • • • 28.0 24.6 • • 29.3 • 35.0 13.5 • 6.3 • 10.1 31.4 • • • • • 29.0 20.0 26.5

37.3 37.5 37.8 15.1 39.3 55.4 40.3 34.0 35.9 41.5 32.1 • 64.1 42.4 14.4 • 23.3 • • 38.5 • • 11.3 • • • • • • • 16.1 • • • • 26.2 • 34.1 • • • • • • • 13.8 • • • • 17.4 44.7 • • •

• • 1.7 0,0 • 4.3 5.6 4.0 • • 9.5 5.4 9.3 12.8 2.7 • • • • • • • 2.4 2.9 • • • 1.4 • • • • • • • 1.8 • 4.5 • • 4.0 • • • 0.0 2.4 • 7.3 • 1.1 • 5.8 • 9.2 3.4

DM diabetes mellitus Total DM previously diagnosed diabetes (both type 1 and type 2) UnDM undiagnosed diabetes a. Abstract only b. Unpublished data

144

CHAPTER 1

DIABETES ATLAS THIRD EDITION









Proliferative Total retinopathy (%) retinopathy (%) Region Country/territory Data used UnDM Type 1 DM Type 2 DM Total DM Total DM EUR Sweden Turkey Ukraine United Kingdom NA Barbados Mexico United States of America USA (Mexican Americans) USA (Non-Hispanic Blacks) USA (Non-Hispanic Whites) SACA Brazil Brazil (Caucasians) SEA Bangladesh India Mauritius Sri Lanka WP Australia China China, Hong Kong Fiji Indonesia Japan Korea, Republic of Malaysia New Zealand Philippines Samoa Singapore Taiwan Thailand Viet Nam

Henricsson et al, 1996344 Kernell et al, 1997345 Larsson et al, 1999346 Wandell, 2004212 Bürö et al, 2004292 Kravchenko et al, 1996259 Higgs et al, 1992260 Sparrow et al, 1993347 EuroDiab, 1994185 Broadbent et al, 1999348 Leske et al, 1999349 Gonzalez Villalpando et al, 1994350 Cueto-Manzano et al, 2005262 Klein et al, 1992351 Dyck et al, 1993182 Kramer et al, 2003352 Harris et al, 1998353 Harris et al, 1998353 Harris et al, 1998353 Foss et al, 1989a,265 Santos et al, 2005354 Chuang et al, 2002d,221 Rema et al, 1996355 Ramachandran et al, 1999222 Dandona et al, 1999356 Ramachandran et al, 2000223 Dowse et al, 1998267 Fernando et al, 1993357 Weerasuriya et al, 1998268 Fairchild et al, 1994358 McKay et al, 2000359 Tapp et al, 2003360 Hu et al, 1991361 Chi et al, 2001226 Chuang et al, 2002d,221 Wang et al, 1998362 Ko et al, 1999271 Brooks et al, 1999363 Chuang et al, 2002d,221 Kuzuya et al, 1994228 Kawano et al, 2001181 Lee et al, 1995229 Chuang et al, 2002d,221 Shriwas et al, 1996274 Chuang et al, 2002d,221 Florkowski et al, 2001364 Chuang et al, 2002d,221 Collins et al, 1995278 Lau et al, 1995365 Chuang et al, 2002d,221 Chen et al, 1992366 Fuh, 2002232 Tandhanand et al, 2001236 Thai Multicenter Group, 1994234 Chuang et al, 2002c,221

• • • • • • • • • • • • • 10.2 • • • • • • • • • • • • 14.8 • 15.2 • • 6.2 31.0 • • 21.9 • • • • • • • • • • • 15.4 • • • • • • •

64.0 14.5 75.1 • • • • • 51.0 36.7 • • • 68.4 79.0 • • • • • • • • • • 13.4 • • • 42.0 • • • • • • • • • 56.0 • • • • • 37.4 • • • • • • • • •

36.0 • • 25.2 27.8 • • 52.0 • 36.2 • 49.5 23.1 • 55.0 16.7 33.4 26.5 18.2 29.1 47.1 • 34.1 23.7 • • 44.3 31.3 • • • 21.9 • • • • 11.4 52.6 • 35.9 • 35.2 • 47.3 • • • 43.2 • • 35.0 25.1 • 32.1 •

43.6 • • • • 22.4 42.6 • • 33.6 28.5 • • 36.8 (incl UnDM) 62.1 • • • • • • 11.0 • • 23.5 • 30.2 (incl UnDM) • • • 29.1 24.5 • 47.4 28.0 • 14.0 • 17.0 38.3 34.5 • 33.0 48.6 37.0 • 18.0 • 21.8 12.0 • • 21.0 • 13.0

6.7 2.3 21.8 • • • 2.7 4.0 • 1.1 0.9 5.7 • 1,8 8.9 • 5.6 1.8 0.9 • • • 3.4 3.7 0.8 1.9 1.3 5.9 • 0.0 4.2 2.1 2.8 • • • • • • 10.3 • 8.2 • 3.6 • • • 4.5 0.6 • 2.2 • • 6.6 •

c. More than one centre used to derive prevalence figure

COMPLICATIONS OF DIABETES

d. Extra details supplied by authors

CHAPTER 1

145

Table 1.58

Data sources: prevalence of diabetic retinopathy

146

Region Country/territory

Data used Study type S

AFR Cameroon Ethiopia Nigeria South Africa Zambia Zimbabwe EMME Egypt Iran, Islamic Republic of Jordan Oman Sudan EUR Austria Belgium Croatia Czech Republic Denmark Finland France Germany Greece Hungary Ireland Israel Italy Luxembourg Netherlands Norway Poland Portugal Russian Federation Slovakia Spain Sweden

Moukouri Dit Nyolo et al, 1995325 Sobngwi et al, 1999238 Seyoum et al, 2001326 Erasmus et al, 1989327 Kalk et al, 1997242 Levitt et al, 1997243 Rotchford et al, 2002187 Rolfe, 1988245 Bartels et al, 1999329 Herman et al, 1998246 Arab et al, 2002188 Manaviat et al, 2004247 Al-Till et al, 2005330 el Haddad et al, 1998331 Khandekar et al, 2003332 Elmahdi et al, 1991190 Muhlhauser et al, 1992191 EuroDiab, 1994185 EuroDiab, 1994185 Van Acker et al, 2001192 EuroDiab, 1994185 Perusicova et al, 1993a,250 Czech Health Statistics, 2002b,251 Hove et al, 2004333 Falck et al, 1993334 EuroDiab, 1994185 EuroDiab, 1994185 Delcourt et al, 1998197 Detournay et al, 2000286 EuroDiab, 1994c,185 Hesse et al, 2001a,335 EuroDiab, 1994c,185 EuroDiab, 1994185 EuroDiab, 1994185 Norymberg et al, 1991254 Segato et al, 1991336 EuroDiab, 1994c,185 Giuffre et al, 2004337 Bo et al, 2005256 EuroDiab, 1994185 Verhoeven et al, 1991201 Reenders et al, 1993202 EuroDiab, 1994185 Spijkerman et al, 2003257 Joner et al, 1992258 Hapnes et al, 1996338 Luzniak et al, 1997 a,339 Pinto-Figueiredo et al, 1992340 EuroDiab, 1994185 Betts et al, 1999341 Slovakian Diabetes Society, 2002b,207 Fernandez-Vigo et al, 1993342 Esmatjes et al, 1996208 Arteagoitia et al, 2003210 Falkenberg et al, 1994343 Henricsson et al, 1996344

CHAPTER 1

Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (primary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Population based Clinic (primary care) Clinic (secondary care) Clinic (primary and secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (primary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Register Population based Population based Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (primary and secondary care) Clinic (secondary care) Population based Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Population based Clinic (secondary care) Population based Clinic (secondary care) Clinic (secondary care) Population based Clinic (primary care) Clinic (secondary care) Clinic (primary care) and population based Population based Population based Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (primary and secondary care) Clinic (primary and secondary care) Clinic (primary care) Population based Population based

DIABETES ATLAS THIRD EDITION





Sample size Age (yrs)



284 64 302 377 507 243 251 600 117 376 2,000 585 986 500 2,249 413 375 122 123 1,653 140 1,443 N/A 378 194 141 127 427 4,119 229 2,801 244 140 124 1,019 1,291 989 132 3,892 116 137 360 136 255 371 210 1,334 1,302 138 266 N/A 1,179 1,157 2,920 117 2,232

COMPLICATIONS OF DIABETES

10-79 51.9 14-85 11-60+ mean=54 20-85 21-81 mean=49 N/A 20+ 20-70 54.9 (10.2) 55.3 (12.5) mean=39 40+ 20+ median=67 15-60 15-60 21-69 15-60 >18 N/A 65 (12) 4-17 15-60 15-60 35-74 mean=66 15-60 mean=66 15-60 15-60 15-60 31-71+ mean=60 15-60 40+ 69.7 (10.2) 15-60 mean=68 mean=68 15-60 62.9 (7.0) 8-30 mean=66 N/A <9-79 15-60 <16 N/A 8-93 45-70 68 (11) <70 <75

Duration DM (yrs)

Diagnostic tool

range 0-20 5 ± 0.7 9 ± 5 range 0-22 7 ± 7 8 ± 8 4 ± N/A 9 ± 6 N/A N/A N/A 10.2 (6.6) 11.9 (6.3) 9 ± 4 median 6-10 <5->15 median=6 16 ± 10 15 ± 9 range 5-27 14 ± 9 N/A N/A 9 (8) 5 ± 3 15 ± 10 16 ± 10 11 ± 7 9 ± N/A 15 ± 10 10 ± 8 13 ± 8 15 ± 9 15 ± 9 12 ± 9 range <5->20 14 ± 9 N/A 11.7 ± 8.6 14 ± 9 8 ± 7 8 ± 6 16 ± 10 0 10 ± 3 9 ± 8 N/A 10 ± 10 15 ± 10 3 ± N/A N/A range <5->15 9 ± 7 8.1 (5.4) 8 ± 5 8 ± 8

Clinical fundoscopy Clinical fundoscopy Clinical fundoscopy Clinical fundoscopy Fundus photography Clinical fundoscopy Clinical fundoscopy Clinical fundoscopy Clinical fundoscopy Fundus photography Clinical fundoscopy Clinical fundoscopy Clinical fundoscopy Clinical fundoscopy Clinical fundoscopy Clinical fundoscopy Clinical fundoscopy or fundus photography Fundus photography Fundus photography Medical record review Fundus photography N/A N/A Fundus photography Fundus photography Fundus photography Fundus photography Fundus photography Questionnaire Fundus photography Medical record review Fundus photography Fundus photography Fundus photography Clinical fundoscopy Clinical fundoscopy Fundus photography Clinical fundoscopy and fundus photography Clinical fundoscopy and fundus photography Fundus photography Clinical fundoscopy and fundus photography Clinical fundoscopy Fundus photography Fundus photography Fundus photography Clinical fundoscopy and fundus photography N/A Clinical fundoscopy and fundus photography Fundus photography Clinical fundoscopy N/A Clinical fundoscopy and fundus photography Clinical fundoscopy Clinical fundoscopy Fundus photography Fundus photography



CHAPTER 1

147

Table 1.58

Data sources: prevalence of diabetic retinopathy Region Country/territory

Data used Study type S

Sweden Turkey Ukraine United Kingdom NA Barbados Mexico United States of America USA (Mexican Americans) USA (Non-Hispanic Blacks) USA (Non-Hispanic Whites) SACA Brazil Brazil (Caucasians) SEA Bangladesh India Mauritius Sri Lanka WP Australia China China, Hong Kong Fiji Indonesia Japan Korea, Republic of Malaysia New Zealand Philippines Samoa Singapore Taiwan Thailand Viet Nam

Kernell et al, 1997345 Larsson et al, 1999346 Wandell, 2004212 Bürö et al, 2004292 Kravchenko et al, 1996259 Higgs et al, 1992260 Sparrow et al, 1993347 EuroDiab, 1994185 Broadbent et al, 1999348 Leske et al, 1999349 Gonzalez Villalpando et al, 1994350 Cueto-Manzano et al, 2005262 Klein et al, 1992351 Dyck et al, 1993182 Kramer et al, 2003352 Harris et al, 1998353 Harris et al, 1998353 Harris et al, 1998353 Foss et al, 1989a,265 Santos et al, 2005354 Chuang et al, 2002d,221 Rema et al, 1996355 Ramachandran et al, 1999222 Dandona et al, 1999356 Ramachandran et al, 2000223 Dowse et al, 1998267 Fernando et al, 1993357 Weerasuriya et al, 1998268 Fairchild et al, 1994358 McKay et al, 2000359 Tapp et al, 2003360 Hu et al, 1991361 Chi et al, 2001226 Chuang et al, 2002d,221 Wang et al, 1998362 Ko et al, 1999271 Brooks et al, 1999363 Chuang et al, 2002d,221 Kuzuya et al, 1994228 Kawano et al, 2001181 Lee et al, 1995229 Chuang et al, 2002d,221 Shriwas et al, 1996274 Chuang et al, 2002d,221 Florkowski et al, 2001364 Chuang et al, 2002d,221 Collins et al, 1995278 Lau et al, 1995365 Chuang et al, 2002d,221 Chen et al, 1992366 Fuh, 2002232 Thai Multicenter Group, 1994234 Tandhanand et al, 2001236 Chuang et al, 2002c,221

DM diabetes mellitus

148

CHAPTER 1

N/A not available

a. Abstract only

b. Unpublished data

Population based Population based Clinic (primary care) Clinic (secondary care) Clinic (secondary care) Population based Population based Clinic (secondary care) Clinic (primary care) Population based Population based Clinic (primary care) Population based Population based Population based Population based Population based Population based Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Population based Clinic (secondary care) Population based Clinic (secondary care) Clinic (primary care) Clinic (secondary care) Population based Population based Clinic (primary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Population based Clinic (secondary care) Clinic (secondary care) Clinic Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Population based Clinic (secondary care) Population based Clinic (primary care) Clinic (secondary care) Population based Clinic (secondary care) Clinic (secondary care) Clinic (secondary care) Clinic (secondary care)

c. More than one centre used to derive prevalence figure

DIABETES ATLAS THIRD EDITION





Sample size Age (yrs)



557 285 389 866 4,123 291 101 172 357 636 210 756 435 380 1,127 308 261 345 546 210 1,608 6,792 3,010 119 617 746 1,003 597 255 234 703 423 447 2,228 465 150 403 2,062 2,120 6,472 631 934 140 1,045 286 2,398 166 13,296 1,578 527 4,535 2,060 2,034 1,113

mean=15 15-50 54.6 (6.9) 57.2 (10.3) 14-75 6-92 28-91 15-60 13-92 40-84 35-65 59 (11) 43-84 mean=57 61 (21) 40+ 40+ 40+ 25-84 58.7 (12) 10-91 mean=55 mean=52 31-86 10-50 ≥25 mean=52 25-65 median=15 40+ ≥25 35-74 35-54 7-92 mean=54 <40 mean=56 22-89 <24->75 mean=61 30-75 15-92 0-80+ 15-87 mean=30 7-93 25-74 <30->70 4-91 40+ N/A 24-88 mean=59 3-89

Duration DM (yrs)

Diagnostic tool

5 ± N/A 17 ± 11 6.5 (5.3) 8.5 (7.1) N/A 11 ± N/A 7 ± 6 17 ± 10 N/A median=5 8 ± 7 N/A range 0-20+ range 0-64 N/A range 0-15+ range 0-15+ range 0-15+ 8 ± 7 10.5 (9.7) 8 ± 6 9 ± 6 8 ± 6 range 0-20+ median=4 6 ± N/A 7 ± 4 0 ± 0 2 ± 18 9.1 ± N/A median=5 0 ± 0 range <7-14 8 ± 6 N/A 5 ± 0 8 ± N/A 8 ± 6 11 ± N/A 10 ± 10 8 ± 7 11 ± 7 range <5->20 11 ± 7 10 ± 6 9 ± 7 4 ± N/A N/A 10 ± 8 <4->10 N/A 8 ± 7 10 ± 7 6 ± 5

Fundus photography Clinical fundoscopy and fundus photography Fundus photography Clinical fundoscopy Clinical fundoscopy Fundus photography Clinical fundoscopy and fundus photography Fundus photography Clinical fundoscopy and fundus photography Clinical fundoscopy and fundus photography Fundus photography Clinical fundoscopy Fundus photography Fundus photography Fundus photography Fundus photography Fundus photography Fundus photography Clinical fundoscopy Clinical fundoscopy Medical record review Clinical fundoscopy Clinical fundoscopy Clinical fundoscopy Clinical fundoscopy Fundus photography Clinical fundoscopy Clinical fundoscopy Fundus photography Fundus photography Fundus photography Clinical fundoscopy Clinical fundoscopy Medical record review Clinical fundoscopy Clinical fundoscopy Clinical fundoscopy Medical record review Doctor report Doctor report Clinical fundoscopy Medical record review Clinical fundoscopy Medical record review Clinical fundoscopy Medical record review Fundus photography Fundus photography Medical record review Clinical fundoscopy N/A Clinical fundoscopy Medical record review Medical record review

d. Extra details supplied by authors

COMPLICATIONS OF DIABETES



CHAPTER 1

149

CHAPTER 2 DIABETES IN THE YOUNG

Some 70,000 children aged 14 years and under develop type 1 diabetes annually.



2.0 DIABETES IN THE YOUNG Type 1 diabetes is the predominant form of the disease in children and adolescents in most developed countries, where there is still a significant number of diabetes-related deaths among children. Further, mortality in undiagnosed diabetes is probably a large but hidden problem in the global perspective. At the same time, type 2 diabetes in the young is an emerging problem with potentially serious outcomes.

D

iabetes is rapidly increasing in children and adolescents in many countries and a shift to younger age at onset is indicated. The increase in incidence in type 1 diabetes has been shown in countries having both high and low prevalence. There is, however, an indication of a steeper increase in some of the low prevalence countries and an association between the risk increase and gross national product (GNP) estimates. Thus part of the increasing trend may be due to potentially preventable lifestyle factors. Although type 1 diabetes usually accounts for only a minority of the total burden of diabetes in a population, it is the predominant form of the disease in younger age groups in most developed countries. Apart from the rise in the incidence, factors contributing to a continued upward trend in the global prevalence include better diagnosis of type 1 diabetes, improving availability of insulin and access to treatment, and increases in overall population growth. Further, there are also indications of a decrease in deaths from both unrecognized diabetic ketoacidosis in children and from late complications in young adults in some developed countries which could lead to an additional increase in the prevalence of type 1 diabetes. At the same time, there is a growing awareness DIABETES IN THE YOUNG

that type 2 diabetes in the young is an emerging problem with potentially serious outcomes, at least in some ethnic groups. Yet our understanding of the worldwide burden of this disease among the young is somewhat fractured, with few studies mainly not population based but rather reporting on specific communities or ethnic groups. This chapter looks at the global trends in childhood type 1 diabetes and reviews the available epidemiological data on type 2 diabetes in the young from around the world. By focusing on such data it is hoped that deficiencies in our knowledge of the disease will be highlighted, and that strategies to deal with it will be developed.

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153

2.1 GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES The incidence of type 1 diabetes in the young is increasing in many countries in the world, and there is indication that the incidence is rising more steeply in some of the low prevalence countries. The rapidity of the changes and the almost universally increasing trends in younger age groups are unlikely to be due to changes in the genetic background of the disease.

Introduction

T

he incidence of childhood onset diabetes is increasing in many countries in the world. There are clear indications of geographic differences in trends but the overall annual increase is estimated at around 3%1-3. Some 70,000 children worldwide are expected to develop type 1 diabetes annually. There is some indication that incidence is increasing more steeply in some of the low prevalence countries such as those in Central and Eastern Europe. Moreover, several European studies have suggested that, in relative terms, increases are greatest in young children4-6 .

In addition, unsatisfactory metabolic control in children can result in stunted growth and exposure to severe hypoglycaemia may affect neurodevelopment so that in children who develop type 1 diabetes very early in age, structural brain abnormalities and impaired cognitive function7,8 may occur making the everyday treatment specifically important. Therefore type 1 diabetes places a particularly heavy burden on the individual, the family and the health services.

The predominant cause of hyperglycaemia in type 1 diabetes is an autoimmune destruction of the beta cells leading to absolute dependence on insulin treatment and a high rate of complications typically occurring at relatively young ages (see section on ‘What is Diabetes?’).

Children are more sensitive to a lack of insulin than adults and are at higher risk of a rapid and dramatic development of diabetic ketoacidosis. It has also been shown that in developed countries there is still significant excess mortality from ketoacidosis9-12 among children with type 1 diabetes, and mortality in undiagnosed diabetes is probably a large but hidden problem in the global perspective.

Although the cumulative incidence of diabetic nephropathy (kidney disease) has fallen over the last decades in dedicated centres, this trend is by no means universal. Recent observations have shown, however, that those who survive 154

microvascular complications still face the prospect of accelerated atherosclerosis.

CHAPTER 2

Increasing incidence among the young Analyses of cumulative incidence rates into the fourth decade DIABETES ATLAS THIRD EDITION



Figure 2.1 Incidence rates of type 1 diabetes with onset in the age range 0-29 years in 1996-1997 for three European countries Incidence rate per 100,000 60

50

40

30

20

10

0 Age groups 0 - 4

5-9

10 -14

Italy - Sardinia Sweden Spain - Catalonia

15 -19

Female

20 -24

24 -29

Male

Source: Green et al, 20013 and Kyvik et al, 200420

of life13,14 suggest that incidence is not increasing among young adults indicating rather a shift to a younger age at onset. The causes of these changes with time are unknown but the rapidity of the changes and the almost universally increasing trends in younger age groups are unlikely to be due to changes in the genetic background of the disease. Historically studies have tended to record incidence data of type 1 diabetes only up to the age of 15 years although recently studies reporting results up to the age of 30 or 35 years have become more common15-20. From these more recent studies it would seem that the incidence in older age groups is lower than that seen in the 0-14 year age range confirming the incidence peak occurring around puberty. An increased male to female sex ratio is also seen in these studies (see Figure 2.1 and Table 2.1). It is important to remember that the distinction between type 1 and type 2 diabetes becomes more difficult in these older age groups since people with type 2 diabetes may receive insulin therapy. Moreover, type 1 diabetes in an adult may masquerade as type 2 diabetes at presentation with a slow deterioration in metabolic control, and subsequent progression to insulin dependency. This form is called latent GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES

autoimmune diabetes mellitus in adults (LADA)21, and in the new WHO classification, LADA falls within type 1 autoimmune diabetes but in a slowly progressive form. The continued mapping of global trends in incidence of type 1 diabetes in all age groups is thus important, and in conjunction with other scientific research may provide a logical basis for intervention studies and future primary prevention strategies which must be the ultimate goal. Two international collaborative projects, the Diabetes Mondiale study (DiaMond), and the Europe and Diabetes study (EURODIAB), began in the 1980s and have been instrumental in monitoring trends in incidence through the establishment of population-based registries using standardized definitions, data collection forms and methods for validation.

Methods Systematic searches of bibliographic databases were performed as explained in Appendix 2 to identify studies that provided incidence or prevalence rates of type 1 diabetes in children. Criteria were then applied to select the most suitable study in a given country or, if necessary, results from a number of studies were pooled. CHAPTER 2

155

Type 1 diabetes among children AT A GLANCE

Type 1 diabetes (0 – 14 years)

2007

Total child population (billion) 1.8 Number of children with type 1 diabetes 440,000 Type 1 diabetes prevalence (%) 0.02 Annual increase of incidence (%) 3.0 Estimated number of newly-diagnosed cases per year 70,000

For countries that had no incidence or prevalence rates available the choice of country to use for extrapolation was based on proximity, the state of economic development measured by the gross domestic product (GDP) per capita and the ethnic composition as assessed from the Central Intelligence Agency (CIA) World Factbook 200222. The choice was also influenced by the quality rating of the studies in the various countries. The majority of studies found by the literature search provided incidence rates rather than prevalence rates, and the method used to translate incidence rates to prevalence rates is described in Appendix 2. The quality of estimates was assessed using the following simple rating system: A Studies from the country in question that were based on registers that were population based with validated ascertainment levels of 90% or more. B 156

Other studies from the country in question, provided population denominators were given to enable rates to be calculated (so excluding case-series studies which provided no population denominator).

CHAPTER 2

X

Extrapolation using rates from a different country, the identity of the chosen country being indicated.

Results Tables 2.2 – 2.15 contain information on population size in the 0-14 age group together with incidence and estimated numbers of prevalent cases in 2007, organized by IDF region. In those countries for which rates were found in the literature search the following information is given: • geographical coverage; • calendar period; • number of cases; • estimated completeness of ascertainment; and • a classification of the source as either A or B using the criteria described under ‘Methods’. Countries for which no rates were found in the literature search were assigned the classification X, as described under ‘Methods’.

Incidence and prevalence It is estimated that on an annual basis some 70,000 children aged 14 years and under develop type 1 diabetes worldwide. DIABETES ATLAS THIRD EDITION



Figure 2.2

Figure 2.3

Estimated number of prevalent cases of type 1 diabetes in children by region

Top 10 countries: incidence rate for type 1 diabetes in children (0-14 years)

120

Finland

Thousands

Sweden Norway

100

United Kingdom 80

Canada Australia

60

Denmark Germany

40

New Zealand Puerto Rico

20

0 AFR

EMME

EUR

NA

SACA

SEA

WP

Incidence rate per 100,000 per year

0

5

10

15

20

25

30

35

40 45

Only countries where studies have been carried out in that country have been included

Of the estimated total of approximately 440,000 prevalent cases of type 1 diabetes in childhood, more than a quarter come from the South-East Asian (SEA) Region, and more than a fifth from the European (EUR) Region, where reliable, up-to-date estimates of incidence were available for the majority of countries (see Figure 2.2). Only some 5% of children with type 1 diabetes come from the Western Pacific (WP) Region, despite it having the largest childhood population. Figure 2.3 shows the top 10 countries in incidence rates for type 1 diabetes in children.

Regions Africa

The need for extrapolation of rates of childhood type 1 diabetes was particularly evident in the sub-Saharan African (AFR) Region. Published rates were found for only three of the countries in this region, and some of the studies were of poor quality and based on small numbers. Consequently imperfect estimates of rates from Nigeria, Zambia and Tanzania have had to be used for widespread extrapolations because of the dearth of published studies. Mortality among children with diabetes is likely to be high in parts of this region, but as numbers of cases in these countries were GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES

mainly derived directly from prevalence rates rather than indirectly from incidence rates the effects of mortality are incorporated in the estimates in Table 2.2. Tropical and malnutrition diabetes may account for a proportion of cases in this region, but reliable data are lacking. For these reasons the validity of the estimates of numbers of children with type 1 diabetes in many parts of this region are questionable and must therefore be treated with considerable caution. Eastern Mediterranean and Middle East

In contrast to the situation in sub-Saharan Africa, reliable data are available for childhood type 1 diabetes rates in a number of the African countries bordering the Mediterranean Sea. About half of the countries in the Eastern Mediterranean and Middle East (EMME) Region as a whole have published incidence rates. By far the largest contribution to the total number of estimated childhood type 1 cases for this region comes from Egypt whose estimate accounts for almost a quarter of the region’s total (see Table 2.4). In Egypt the incidence of type 1 diabetes is reported as 8 per 100,000 population per year for those aged 14 years and under, while in Pakistan it is less than 1 per 100,000 population. CHAPTER 2

157

Europe

Compared with other regions, the European (EUR) Region has by far the most complete and reliable data on the rates of childhood type 1 diabetes with a large proportion of countries having registries that are either nationwide or cover several different parts of the country (see Table 2.7). Where extrapolation for the incidence rate was necessary it was usually for countries with small populations, and therefore any error associated with the extrapolation will have little impact on the estimate of the region’s total. The countries making the largest contribution to the total rates for childhood type 1 diabetes were United Kingdom, Germany and Russia reflecting to some degree the large childhood populations in these countries (see Table 2.6). It is worth noting that the estimates for Russia were based on a study from Karelia in the north-west of the country which may not be representative of such a large country although very similar rates have also been reported in the northern region of Novosibirsk23. North America

Although no published rates were available for childhood type 1 diabetes in many of the smaller Caribbean islands in the North American (NA) Region, it was usually possible to extrapolate rates from an island in close proximity, although such rates were often based on very small numbers of cases. The USA estimate, which accounts for more than threequarters of the region’s total, and to a lesser extent the estimate for Canada predominate (see Table 2.8). South and Central America

Although the incidence of childhood type 1 diabetes in the South and Central American (SACA) Region is generally low, there are some sharp contrasts between the rates in neighbouring countries (see Table 2.10). In this region a strong inverse ecological correlation has been reported24 between a country’s incidence rate and the proportion of its population that is Amerindian (indigenous). This has influenced the selection of countries to use for extrapolation, but the choice can still make a considerable difference to the resulting estimate. Such estimates must therefore be interpreted with caution. The Brazilian estimate accounts for more than half of the region’s total. South-East Asia

Only two countries in the South-East Asian (SEA) Region have published rates for type 1 diabetes in childhood and therefore extrapolation of rates was necessary (see Table 158

CHAPTER 2

2.13). The rate from China, although outside the region, was used for some extrapolations, but the rate for India was more frequently used and it therefore plays a pivotal role in the estimates for this region. Two sources of rates for India were available, both from urban Madras and therefore probably not representative of the country as a whole. The first was a small prevalence study25 giving an equivalent incidence rate which was less than half that of the second, larger study26, the rate from the latter study having needed correction for under-ascertainment. Given that even the lower of these two rates far exceeds the rates reported from other countries in the area and that the incidence in urban Madras is likely to be higher than that for India as a whole, the decision was made to use the lower of these two rates even though it was based on the smaller study. The large childhood population in India and the widespread use of the Indian data for extrapolation in this region means that this decision has important consequences not only for the total in the region but also for the worldwide estimate, both of which would be considerably larger had the higher estimate of incidence been used. Notwithstanding the use of the lower rate, the South-East Asian Region contributes more than any other to the worldwide childhood type 1 diabetes total (see Table 2.12). Diabetes-associated mortality and tropical or malnutrition diabetes are also likely to play important roles in this region, but unfortunately there is inadequate information to address these issues. These points reinforce the need for much more detailed data on childhood diabetes in this region. Western Pacific

With the exception of Australia and New Zealand, the rates of childhood type 1 diabetes in the Western Pacific (WP) Region appear uniformly low (see Table 2.14). Few of the Pacific islands had published data and the rate for Papua New Guinea had to be extrapolated far into the Pacific Ocean, although any error induced in the region’s total by this extrapolation is likely to be small because of the generally low rates and small populations involved. The rate for Thailand was used extensively for extrapolation in the Indochina peninsula. Despite its very low incidence, China accounts for almost half of the region’s total. However, the Western Pacific Region makes the smallest contribution of all to the world total of type 1 diabetes even though it has the largest childhood population. DIABETES ATLAS THIRD EDITION



Map 2.1 Incidence rates of type 1 diabetes in children - 0-14 years (cases per 100,000 population per year)

>20 16 12 8 4 <4 No data

-

20 16 12 8

Mortality in childhood Small numbers of deaths either before or at the time of diagnosis continue to occur in many countries, but ascertainment of such deaths may be incomplete so comparison between countries is difficult. A recent study of mortality among children diagnosed with type 1 diabetes from 10 European centres27 showed that there were 78 deaths during follow up, approximately twice as many as would have been expected from the national age/sex specific mortality rates in the countries (see Table 2.16). However the standardized mortality ratio (SMR), defined as the ratio of observed deaths to expected deaths, varied from under 1 to 4.7 in the various countries. Over a third of the deaths could be directly attributed to diabetes, and these were mainly from metabolic disturbances, with very few deaths in this age range from cardiovascular or renal complications. These results are in good accordance with several larger studies from single European centres.

Discussion Methodology The global distribution of childhood type 1 diabetes clearly GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES

indicates large area to area variations. This variability may partly be due to different distributions of risk genes for the disease as well as different distributions of environmental exposures, but part of the apparent variability both between countries and regions may also be due to methodological problems: • The available incidence data sometimes covers only one small part of a large country. For example, in India incidence data were extrapolated from studies performed in Madras and data from Russia were extrapolated from a small dataset from Karelia. Obviously there may be considerable variability within such large countries in both the distribution of risk genes and environmental exposures such as climate and lifestyle-related factors28. • The need for extrapolation was evident in the African continent, particularly in sub-Saharan Africa. Here rates from undesirably small datasets have had to be used in extrapolations because of the lack of published studies. • Another problem was the need to make extrapolations involving isolated island populations such as in Polynesia where both genetic predisposition and lifestyle habits may CHAPTER 2

159

IN TOUCH WITH: NZAMBA MULOPO AND IKULE BOLIA

These are the stories of two young lives touched by type 1 diabetes in Kinshasa, Democratic Republic of Congo. The difference between them was not a problem of access to insulin and treatment: the real difference was that Nzamba has a loving mother, very poor but caring, and Ikule was rejected, and in fact died from lack of love.

Nzamba Mulopo, 11, was diagnosed with type 1 diabetes when he was six years old. That year he was admitted to hospital after having fallen into a coma from ketoacidosis. When he was admitted he weighed only 14 kg and was 105 cm tall. A very lively and happy boy, Nzamba does the best he can to manage his diabetes. The health centre not far from his home has done everything to help mother and child.

be very different. The danger inherent in such extrapolations is clear from recent publications of island populations that have very different rates compared with their mainland neighbours: Crete has a lower rate than mainland Greece29, Newfoundland has a higher rate than other parts of Canada30 and Sardinia has a higher rate than peninsular Italy31.

for mortality was not necessary. In such countries the relationship between incidence rate and prevalence rate is difficult to predict, and consequently incidence rates are not available from sub-Saharan Africa other than Tanzania (see Table 2.2).

• For some extrapolations a choice had to be made between countries whose reported incidence rates were very different, possibly on occasions because they were based on small datasets.

In addition to the geographical variation in the incidence of childhood type 1 diabetes there are also well-documented secular trends over time, which may also differ from country to country and from region to region within a country. Such time trends have not explicitly been incorporated in these estimates since reliable data are available for only a very small number of countries, but these trends are of considerable importance for healthcare planning. Only a few studies looked at time trends for the age group over 15 years and the trend so far is not clearly an increase and cumulative rates from 0 to 3413-15,19 have not shown an increase so far, indicating rather a shift to younger ages.

• Another methodological problem is the lack of data on mortality rates among children with diabetes in most populations. In less developed countries, in which mortality could have a significant impact, the disease rates were often based on small numbers of cases or on extrapolation so that the application of an adjustment to incidence data to allow for mortality was not justified. In sub-Saharan Africa, where mortality among children with diabetes are reported to be high32,33, numbers of cases were mainly derived from Nigerian and Zambian prevalence rates rather than indirectly from incidence rates so that adjustment 160

CHAPTER 2

Time trends

Potential risk factors The causes of the changes over time are unknown and although migration might slowly change the genetic background within DIABETES ATLAS THIRD EDITION



A special programme provides Nzumba with insulin and other supplies totally free of charge. He is now able to inject insulin three times a day without help from his mother. However, it is difficult for Nzamba to have regular meals according to the schedules of insulin injections. His mother is sometimes too busy fending for the whole family’s survival to be able to provide regular meals. Another major concern is his blood glucose control, which is not very good, but it is not from lack of trying. Nzamba does urine testing, but for blood glucose he has to go either to the health centre or to a clinic for children. The health centre programme does not have the resources to provide patients with personal blood glucose meters or strips for frequent testing. Ikule Bolia was diagnosed with type 1 diabetes when he presented with the classical signs of diabetes — frequent urination accompanied by excessive thirst — at the age of 14 years. He was 149 cm tall and weighed only 30 kg. As

a population, the rapid changes in incidence rate reported to occur within comparatively short time spans are more likely to be due to changes in environmental risk factors. These environmental risk factors may initiate autoimmunity or accelerate and precipitate an already ongoing beta cell destruction28, and are discussed in more detail in the section on ‘What is Diabetes?’. Briefly, these risk factors include:

Ikule’s father had died a few years before, he lived in his uncle’s house, while his mother lived in her village. Ikule was a very quiet and sad boy. He was rejected by the family and accused to be a sorcerer, an easy way to get rid of a person. He was allowed to live in a small corner of the house, and was not cared for. Food was very irregular although his uncle was well fed and could afford the care. Ikule was a regular customer of a health centre, which, in a certain way, was his home. Insulin was given free as well as the treatment and tests for blood glucose. However, it was not possible to inject enough insulin due to the irregular food supply. Many other patients helped him with food or money. His blood glucose level remained between 250 and 400 mg/dl most of the time. Ketoacidosis occurred from time to time. Between 1997 and 2003 when he died his weight stayed at 30 kg. In 2002 he was diagnosed with pulmonary tuberculosis. Together with diabetes this was to cause his death. He did not die of lack of insulin but lack of care and love.

an increased height, weight, weight for height and body mass index (BMI) have repeatedly been shown to be risk factors for childhood onset diabetes 39-43 . Although autoimmune mechanisms are responsible for the beta cell destruction leading to type 1 diabetes, overload factors may accelerate this process44-46.

Early events

Potential risk factors, such as early fetal events34, viral infections during pregnancy35,36 , and early exposure to cow’s milk components and other nutritional factors37 may initiate the autoimmune process. Lifestyle

Since type 1 diabetes in childhood is associated with estimates of general wealth such as GDP 38 it has been suggested that lifestyle habits related to welfare might be responsible for the changes in trend. Wealth is a wellknown determinant of birth weight and childhood growth. Weight and growth

Different estimates of child growth such as high birth weight, GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES

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161

Table 2.1 Incidence rates of type 1 diabetes with onset in the age range 0-29 years in 1996-1997 for eight European countries

BELGIUM Antwerp

ITALY Sardinia

LITHUANIA Whole nation

ROMANIA Bucharest

Age 0-4 M Cases 6 30 13 3 Population 56,217 76,738 221,212 90,118 Rate 10.7 39.1 5.9 3.3 F Cases 5 15 14 2 Population 53,700 71,147 210,441 85,070 Rate 9.3 21.1 6.7 2.4 Age 5-9 M Cases 4 40 13 8 Population 56,331 89,523 289,484 162,968 Rate 7.1 44.7 4.5 4.9 F Cases 4 36 29 9 Population 54,343 84,418 276,717 154,996 Rate 7.4 42.6 10.5 5.8 Age 10-14 M Cases 8 54 37 10 Population 55,132 100,176 289,460 164,148 Rate 14.5 53.9 12.8 6.1 F Cases 9 40 36 11 Population 52,491 94,320 278,996 157,520 Rate 17.1 42.4 12.9 7.0 Age 15-19 M Cases 2 27 22 10 Population 54,912 132,228 266,393 198,462 Rate 3.6 20.4 8.3 5.0 F Cases 6 15 22 10 Population 52,860 126,304 257,848 189,204 Rate 11.4 11.9 8.5 5.3 Age 20-24 M Cases 6 23 20 10 Population 55,357 147,940 270,050 174,890 Rate 10.8 15.6 7.3 5.7 F Cases 5 13 12 8 Population 54,646 142,850 568,420 183,266 Rate 9.2 9.1 4.5 4.4 Age 25-29 M Cases 9 21 39 25 Population 67,298 142,134 289,658 198,906 Rate 13.4 14.7 13.5 12.6 F Cases 5 5 17 14 Population 65,205 140,826 274,912 217,588 Rate 7.7 3.6 6.2 6.4 Source: Green et al, 20013 and Kyvik et al, 200420

162

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DIABETES ATLAS THIRD EDITION

SPAIN Catalonia

UNITED KINGDOM Leicestershire

UNITED KINGDOM West Yorkshire

SWEDEN Whole nation

SLOVAKIA Whole nation

31 5 32 130 34 274,557 64,190 242,127 547,816 336,840 11.3 7.8 13.2 23.7 10.1 25 8 31 135 16 263,120 60,930 230,864 522,206 321,105 9.5 13.1 13.4 25.9 5.0 35 12 40 199 52 286,845 65,360 259,279 623,091 400,974 12.2 18.4 15.4 31.9 13.0 44 15 55 204 44 274,515 61,300 248,317 589,826 383,414 16.0 24.5 22.1 34.6 11.5 58 15 64 232 59 339,875 62,090 246,602 530,995 445,100 17.1 24.2 26.0 43.7 13.3 45 11 53 179 33 326,575 58,060 233,500 501,943 426,343 13.8 18.9 22.7 35.7 7.7 60 7 17 82 31 469,466 58,200 138,793 517,570 477,822 12.8 12.0 12.3 15.8 6.5 37 3 13 46 28 451,474 56,766 129,020 429,652 464,422 8.2 5.3 10.1 9.3 6.0 76 6 16 79 27 518,716 65,512 145,760 583,936 459,071 14.7 9.2 11.0 13.5 5.9 42 9 9 41 14 502,088 66,112 139,750 561,884 430,154 8.8 13.6 6.4 7.3 3.3 64 16 34 77 19 489,730 66,170 166,989 628,080 381,718 13.0 24.2 20.4 12.3 5.0 37 10 13 40 7 477,504 68,650 159,238 601,842 362,530 7.8 14.6 8.2 6.7 1.9



GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES



CHAPTER 2

163

Table 2.2





Estimates of type 1 diabetes in children - African Region COUNTRY/TERRITORY



POPULATION SIZEa (000’s) 0-14 yrs

INCIDENCE RATESb (cases per 100,000 population per year) 0-4 yrs 5-9 yrs 10-14 yrs Total

PREVALENT CASES (000’s)

Angola 7,787 0.4 Benin 3,921 0.7 Botswana 648 0.0 Burkina Faso 6,578 1.2 Burundi 3,608 0.2 Cameroon 6,818 1.2 Cape Verde 204 0.0 Central African Republic 1,768 0.3 Chad 4,878 0.9 Comoros 349 0.0 Congo 2,009 0.1 Congo, Democratic Republic of 29,025 1.4 Côte d’Ivoire 7,727 1.4 Djibouti 334 0.2 Equatorial Guinea 235 0.0 Eritrea 2,095 0.1 Ethiopia 35,674 2.0 Gabon 558 0.1 Gambia 632 0.1 Ghana 8,773 1.6 Guinea 4,259 0.8 Guinea-Bissau 803 0.1 Kenya 15,410 0.9 Lesotho 676 0.0 Liberia 1,634 0.3 Madagascar 8,540 0.7 Malawi 6,354 0.3 Mali 6,891 1.2 Mauritania 1,395 0.3 Mozambique 8,959 0.5 Namibia 828 0.0 Niger 7,309 1.3 Nigeria 60,024 10.8 Réunion 216 0.0 Rwanda 4,040 0.2 Sao Tome and Principe 64 0.0 Senegal 5,109 0.9 Seychellesc 21 0.0 Sierra Leone 2,490 0.4 Somalia 3,883 0.2 South Africad 15,359 5.0 Swaziland 408 0.0 Tanzania, United Republic of 16,654 0.1 0.5 2.2 0.9 0.5 Togo 2,779 0.5 Uganda 15,639 0.9 Western Sahara 123 0.1 Zambia 5,477 0.3 Zimbabwe 5,130 0.3 AFR Total 324,099 • • • • 38.8

a. UN population projections for 2007 - medium variant 2004 b. Likely high mortality rate and shortage of good-quality incidence studies makes it problematic to derive incidence from prevalence in these countries c. Population estimates extracted from CIA World Factbook 2005 d. Adjusted to take account of the higher rates in those of European origin

164

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DIABETES ATLAS THIRD EDITION



GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES



CHAPTER 2

165

Table 2.3 Data sources: estimates of type 1 diabetes in children - African Region







COUNTRY/TERRITORY

DATA USED

Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Congo, Democratic Republic of Côte d’Ivoire Djibouti Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mozambique Namibia Niger Nigeria Réunion Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Tanzania, United Republic of Togo Uganda Western Sahara Zambia Zimbabwe

Zambia (Rolfe et al, 1989)47

PERIOD

Nigeria (Afoke et al, 1992)48 Zambia (Rolfe et al, 1989)47 Nigeria (Afoke et al, 1992)48 Tanzania (Swai et al, 1993)49 Nigeria (Afoke et al, 1992)48 Nigeria (Afoke et al, 1992)48 Nigeria (Afoke et al, 1992)48 Nigeria (Afoke et al, 1992)48 Tanzania (Swai et al, 1993)49 Zambia (Rolfe et al, 1989)47 Zambia (Rolfe et al, 1989)47 Nigeria (Afoke et al, 1992)48 Sudan (Elamin et al, 1997)50 Nigeria (Afoke et al, 1992)48 Tanzania (Swai et al, 1993)49 Tanzania (Swai et al, 1993)49 Nigeria (Afoke et al, 1992)48 Nigeria (Afoke et al, 1992)48 Nigeria (Afoke et al, 1992)48 Nigeria (Afoke et al, 1992)48 Nigeria (Afoke et al, 1992)48 Tanzania (Swai et al, 1993)49 Zambia (Rolfe et al, 1989)47 Nigeria (Afoke et al, 1992)48 Mauritius (Karvonen et al, 2000)51 Zambia (Rolfe et al, 1989)47 Nigeria (Afoke et al, 1992)48 Nigeria (Afoke et al, 1992)48 Tanzania (Swai et al, 1993)49 Zambia (Rolfe et al, 1989)47 Nigeria (Afoke et al, 1992)48 Nigeria (Afoke et al, 1992)48 1990 Mauritius (Karvonen et al, 2000)51 Tanzania (Swai et al, 1993)49 Nigeria (Afoke et al, 1992)48 Nigeria (Afoke et al, 1992)48 Mauritius (Karvonen et al, 2000)51 Nigeria (Afoke et al, 1992)48 Tanzania (Swai et al, 1993)49 Zambia (Rolfe et al, 1989)47 & UK (EURODIAB, 2006)52 Zambia (Rolfe et al, 1989)47 Tanzania (Swai et al, 1993)49 1982-1991 Nigeria (Afoke et al, 1992)48 Tanzania (Swai et al, 1993)49 Algeria (DIAMOND, 2006)53 Zambia (Rolfe et al, 1989)47 pre-1989 Zambia (Rolfe et al, 1989)47

A Studies from the country in question that were based on population-based registers with validated ascertainment levels of 90% or more. B Other studies from the country in question, provided population denominators were given to enable rates to be calculated (excludes case-series studies which provided no population denominator). X Extrapolation using rates from a different country. N/A Not available

166

CHAPTER 2

DIABETES ATLAS THIRD EDITION

GEOGRAPHY

NO. OF CASES

COMPLETENESS

CLASSIFICATION

Anambra 14 N/A Dar es Salaam 36 100% Copperbelt 37 90%

X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X B X X X X X X X X X A X X X B X





GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES



CHAPTER 2

167

Table 2.4 Estimates of type 1 diabetes in children - Eastern Mediterranean and Middle East Region COUNTRY/TERRITORY



POPULATION SIZEa (000’s) 0-14 yrs

INCIDENCE RATES (cases per 100,000 population per year) 0-4 yrs 5-9 yrs 10-14 yrs Total

PREVALENT CASES (000’s)

Afghanistan 14,923 1.2 1.1 Algeria 9,584 3.9 9.0 13.1 8.6 4.8 Armenia 576 8.1 0.3 Bahrain 196 2.5 0.0 Egypt 25,402 8.0 12.6 Iran, Islamic Republic of 19,085 2.3 3.6 5.2 3.7 4.5 Iraq 12,187 3.7 2.8 Jordan 2,168 1.3 3.2 5.5 3.2 0.4 Kuwait 685 12.3 26.3 28.4 22.3 0.9 Lebanon 1,014 3.2 0.2 Libyan Arab Jamahiriya 1,807 2.6 7.3 17.1 9.0 0.7 Morocco 9,852 8.6 5.3 Occupied Palestinian Territory 1,779 3.2 0.4 Oman 895 1.3 2.6 4.0 2.5 0.1 Pakistan 61,196 0.3 0.4 0.8 0.5 1.6 Qatar 186 11.4 0.1 Saudi Arabia 9,352 5.7 8.5 24.2 12.3 5.9 Sudan 14,608 10.1 9.1 Syrian Arab Republic 7,205 3.2 1.4 Tunisia 2,539 4.2 5.9 11.8 7.3 1.1 United Arab Emirates 1,020 2.5 0.2 Yemen 10,194 2.5 1.6 EMME Total 206,454 • • • • 55.1

a. UN population projections for 2007 - medium variant 2004

168

CHAPTER 2

DIABETES ATLAS THIRD EDITION



GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES



CHAPTER 2

169

Table 2.5 Data sources: estimates of type 1 diabetes in children - Eastern Mediterranean and Middle East Region









COUNTRY/TERRITORY

DATA USED

Afghanistan Algeria Armenia Bahrain Egypt Iran, Islamic Republic of Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Morocco Occupied Palestinian Territory Oman Pakistan Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen

Uzbekistan (Rakhimova et al, 2002)54

PERIOD

Algeria (DIAMOND, 2006)53 1990-1999 Ukraine (Timchenko et al, 1996)55 Oman (Soliman et al, 1996)56 Egypt (Arab, 1992)57 pre-1992 Iran (Pishdad, 2005)58 1991-1996 Iran (Pishdad, 2005)58 Jordan (Ajlouni et al, 1999)59 1992-1996 Kuwait (DIAMOND, 2006)53 1992-1999 Jordan (Ajlouni et al, 1999)59 Libya (Kadiki et al, 2002)60 1991-2000 Algeria (DIAMOND, 2006)53 Jordan (Ajlouni et al, 1999)59 Oman (Soliman et al, 1996)56 1993-1994 Pakistan (DIAMOND, 2006)53 1990-1999 Qatar (Al-Zyoud et al, 1997)61 1992-1996 Saudi Arabia (Kulaylat et al, 2000)62 1986-1997 Sudan (Elamin et al, 1997)50 1991-1995 Jordan (Ajlouni et al, 1999)59 Tunisia (DIAMOND, 2006)53 1990-1999 Oman (Soliman et al, 1996)56 Oman (Soliman et al, 1996)56

A

Studies from the country in question that were based on population-based registers with validated ascertainment levels of 90% or more.

B

Other studies from the country in question, provided population denominators were given to enable rates to be calculated (excludes case-series studies which provided no population denominator).

X Extrapolation using rates from a different country. N/A not available

170

CHAPTER 2

DIABETES ATLAS THIRD EDITION

GEOGRAPHY

NO. OF CASES

COMPLETENESS

CLASSIFICATION

Oran 223 N/A Alexandria, Damahour N/A N/A Fars 298 100% Whole country 275 96% Whole country 531 79-96% Benghazi 276 100% Whole country 31 96% Karachi 104 51% Whole country 80 N/A Eastern Province 46 100% Khartoum 534 97% Beja, Gafsa, Kairoan, Monastir 297 N/A

X B X X B A X A B X A X X A B B A A X B X X





GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES



CHAPTER 2

171

Table 2.6



Estimates of type 1 diabetes in children - European Region COUNTRY/TERRITORY



POPULATION SIZEa INCIDENCE RATES (000’s) (cases per 100,000 population per year) 0-14 yrs 0-4 yrs 5-9 yrs 10-14 yrs Total



PREVALENT CASES (000’s)

Albania 816 • • • 3.9 0.2 Andorrab 10 • • • 12.9 0.0 Austria 1,225 11.1 14.6 14.4 13.3 1.2 Azerbaijan 2,043 • • • 1.2 0.2 Belarus 1,390 • • • 5.6 0.5 Belgium 1,722 10.9 18.3 16.8 15.3 1.9 Bosnia and Herzegovina 622 0.6 4.7 5.2 3.5 0.1 Bulgaria 1,013 5.9 9.6 12.7 9.4 0.6 Channel Islands 24 • • • 22.5 0.0 Croatia 685 2.6 8.2 8.5 6.4 0.3 Cyprus 161 • • • 14.8 0.1 Czech Republic 1,431 13.2 18.2 18.7 16.7 1.7 Denmark 1,014 • • • 19.4 1.2 Estonia 194 14.5 19.4 11.9 14.9 0.2 Finland 887 • • • 41.4 2.3 France 11,022 4.6 8.8 11.6 8.3 5.5 Georgia 784 • • • 4.6 0.2 Germany 11,487 13.3 19.3 21.4 18.0 14.6 Greece 1,574 12.9 7.3 9.6 10.4 1.3 Hungary 1,526 7.9 12.1 13.9 11.3 1.2 Iceland 64 • • • 14.7 0.1 Ireland 858 10.9 21.3 16.9 16.3 0.9 Israel 1,922 4.3 10.5 13.2 9.3 1.0 Italy 8,089 6.7 9.8 9.0 8.4 4.9 Kazakhstan 3,273 • • • 1.2 0.2 Kyrgyzstan 1,630 • • • 1.2 0.1 Latvia 317 5.0 8.2 9.2 7.5 0.2 Liechtensteinb 6 • • • 9.2 0.0 Lithuania 528 4.3 8.1 10.9 7.8 0.3 Luxembourg 89 10.0 16.0 16.7 14.2 0.1 Macedonia, the Former Yugoslav Republic of 380 1.4 5.7 4.8 3.9 0.1 Malta 68 11.1 16.4 18.9 15.6 0.1 Moldova 713 • • • 4.7 0.2 Monacob 5 • • • 8.3 0.0 Netherlands 2,938 12.9 19.3 24.2 18.6 3.6 Norway 894 17.1 30.6 36.0 27.9 1.6 Poland 5,974 8.1 14.4 16.6 13.0 5.3 Portugal 1,673 13.1 11.2 15.4 13.2 1.6 Romania 3,223 • • • 4.7 0.9 Russian Federation 21,293 • • • 7.4 9.8 San Marinob 5 • • • 8.4 0.0 Serbia and Montenegroc 1,869 • • • 10.7 1.2 Slovakia 853 10.8 13.4 16.5 13.5 0.8 Slovenia 267 6.7 11.7 13.8 10.7 0.2 Spain 6,342 6.7 14.1 18.0 12.9 4.7 Sweden 1,536 22.8 34.6 37.8 31.7 3.4 Switzerland 1,157 6.5 8.4 12.0 9.2 0.7 Tajikistan 2,507 • • • 1.2 0.2 Turkey 21,480 • • • 3.2 4.3 Turkmenistan 1,494 • • • 1.2 0.1 Ukraine 6,388 • • • 8.1 3.2 United Kingdom 10,491 15.4 23.0 29.3 22.5 15.7 Uzbekistan 8,642 • • • 1.2 0.6 EUR Total 156,599 • • • • 99.7 a. UN population projections for 2007 - medium variant 2004 b. establishment of Serbia and Montenegro as independent countries

172

CHAPTER 2

Population estimates extracted from CIA World Factbook 2005

c. Estimates made prior to

DIABETES ATLAS THIRD EDITION



GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES



CHAPTER 2

173

Table 2.7 Data sources: estimates of type 1 diabetes in children - European Region



COUNTRY/TERRITORY

DATA USED

Albania Andorra Austria Azerbaijan Belarus Belgium Bosnia and Herzegovina Bulgaria Channel Islands Croatia Cyprus Czech Republic Denmark Estonia Finland France Georgia Germany Greece Hungary Iceland Ireland Israel Italy Kazakhstan Kyrgyzstan Latvia Lithuania Liechtenstein Luxembourg Macedonia, the Former Yugoslav Republic of Malta Moldova Monaco Netherlands Norway Poland Portugal Romania Russian Federation San Marino Serbia and Montenegro Slovakia Slovenia Spain Sweden Switzerland Tajikistan Turkey Turkmenistan Ukraine United Kingdom Uzbekistan

Macedonia (Green et al, 2001)3

PERIOD

Spain (EURODIAB, 2006)52 Austria (EURODIAB, 2006)52 1999-2003 Uzbekistan (Rakhimova et al, 2002)54 Belarus (Zalutskaya et al, 2004)63 1997-2002 Belgium (EURODIAB, 2006)52 1999-2003 Bosnia and Herzegovina (Bratina et al, 2001)64 1990-1998 Bulgaria (DIAMOND, 2006)52 1990-1999 United Kingdom (EURODIAB, 2006)52 Croatia (Green et al, 2001)3 1994-1998 Cyprus (Bacopoulou et al, 2005)65 2000-2004 Czech Republic (EURODIAB, 2006)52 1999-2003 Denmark (Svensson et al, 2002)66 1996-2000 Estonia (Tillman et al, 2004)67 1999-2003 Finland (Kondrashova et al, 2005)68 1990-1999 France (EURODIAB ACE, 2000)2 1989-1994 Georgia (Amirkhanashvili et al, 2000)69 1998-1999 Germany (EURODIAB, 2006)52 1999-2003 Greece (EURODIAB, 2006)52 1995-1999 Hungary (EURODIAB, 2006)52 1999-2003 Iceland (EURODIAB, 2006)52 1994-1998 Ireland (Roche et al, 2002)70 1997 Israel (Israel IDDM Registry Study Group, 2002)71 1998 Italy (Carle et al, 2004)31 1990-1999 Uzbekistan (Rakhimova et al, 2002)54 Uzbekistan (Rakhimova et al, 2002)54 Latvia (Green et al, 2001)3 1994-1998 Lithuania (EURODIAB, 2006)52 1999-2003 Switzerland (Schoenle et al, 2001)78 Luxembourg (EURODIAB, 2006)52 1997-2001 Macedonia (Green et al, 2001)3 1994-1998 Malta (Schranz, 1998)72 1990-1996 Romania (Serban et al, 2005)73 France (EURODIAB ACE, 2000)2 Netherlands (van Wouwe et al, 2002)74 1996-1999 Norway (Joner et al, 2005)75 1999-2003 Poland (EURODIAB, 2006)52 1999-2003 Portugal (Green et al, 2001)3 1994-1998 Romania (Serban et al, 2005)73 1995-2004 Russia (Kondrashova et al, 2005)68 1990-1999 Italy (Carle et al, 2004)31 Serbia and Montenegro (Mira et al, 2004)76 1993-2002 Slovakia (EURODIAB, 2006)52 1999-2002 Slovenia (EURODIAB, 2006)52 1999-2003 Spain (EURODIAB, 2006)52 1999-2003 Sweden (Pundziute-Lycka et al, 2004)77 1992-2000 Switzerland (Schoenle et al, 2001)78 1991-1999 Uzbekistan (Rakhimova et al, 2002)54 Jordan (Ajlouni et al, 1999)59 Uzbekistan (Rakhimova et al, 2002)54 Ukraine (Timchenko et al, 1996)55 1985-1992 United Kingdom (EURODIAB, 2006)52 1999-2003 Uzbekistan (Rakhimova et al, 2002)54 2000

A Studies from the country in question that were based on population-based registers with validated ascertainment levels of 90% or more. B Other studies from the country in question, provided population denominators were given to enable rates to be calculated (excludes case-series studies which provided no population denominator). X Extrapolation using rates from a different country. N/A not available

174

CHAPTER 2

DIABETES ATLAS THIRD EDITION







GEOGRAPHY

NO. OF CASES

COMPLETENESS

Whole country 911 97% Gomel, Minsk approx 375 100% Antwerp 124 54% Tuzla 43 100% Varma, West Bulgaria 924 99-100% Zagreb 69 100% Greek population approx 110 N/A Whole country 1,419 99% Whole country 839 99% Whole country 181 100% Whole country approx 3,800 100% Four regions 837 99% Whole country 115 N/A Dusseldorf, Baden-Württemberg, Westphalia 4,570 95-100% Attica 279 100% 18 counties 737 79% Whole country 47 100% Whole country 140 91% Whole country approx 150 100% Eight peninsular centres 2,515 96-99% Whole country 196 100% Whole country 358 100% Whole country 57 100% Whole country 96 98% Whole country 90 N/A Whole country 1,264 N/A Whole country 1,260 100% Gliwice 548 N/A Algarve, Madeira 74 85-100% Whole country N/A N/A Karelia 133 100% Montenegro 166 N/A Whole country 581 100% Whole country 177 100% Catalonia 571 99% Whole country approx 4,000 96% Whole country 941 91-92% Whole country N/A N/A Leeds, Oxford, N Ireland 1,847 99% Whole country N/A N/A

CLASSIFICATION x X A X A B A A X A B A A A A A B A A B A A A A X X A A X A A B X X B A B A/B B A X B A A A A B X X X B A B





GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES



CHAPTER 2

175

Table 2.8 Estimates of type 1 diabetes in children - North American Region COUNTRY/TERRITORY

POPULATION SIZEa INCIDENCE RATES (000’s) (cases per 100,000 population per year) 0-14 yrs 0-4 yrs 5-9 yrs Total 10-14 yrs

PREVALENT CASES (000’s)

Anguillab 3 3.5 0.0 Antigua and Barbudab 19 3.5 0.0 Arubab 14 0.1 0.0 Bahamas 91 10.1 0.1 Barbados 50 2.0 0.0 Belize 100 1.5 0.0 Bermudab 12 2.3 0.0 British Virgin Islandsb 5 3.5 0.0 Canada 5,557 14.7 24.0 26.3 21.7 8.4 Cayman Islandsb 9 2.3 0.0 Dominicab 18 5.7 0.0 Grenadab 30 2.0 0.0 Guadeloupe 110 5.7 0.0 Guyana 216 0.1 0.0 Haiti 3,235 16.8 3.4 Jamaica 808 2.3 0.1 Martinique 83 2.0 0.0 Mexico 32,621 0.5 2.0 1.1 1.5 2.7 Saint Kitts and Nevisb 11 3.5 0.0 Saint Lucia 45 2.0 0.0 Saint Vincent and the Grenadines 34 2.0 0.0 Trinidad and Tobago 272 2.0 0.0 United States of America 62,136 9.5 16.9 22.0 16.1 62.6 US Virgin Islands 26 12.8 0.0 NA Total 105,453 • • • • 77.3

a. UN population projections for 2007 - medium variant 2004 b. Population estimates extracted from CIA World Factbook 2005

176

CHAPTER 2

DIABETES ATLAS THIRD EDITION



GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES



CHAPTER 2

177

Table 2.9 Data sources: estimates of type 1 diabetes in children - North American Region









COUNTRY/TERRITORY

DATA USED

Anguilla Antigua and Barbuda Aruba Bahamas Barbados Belize Bermuda British Virgin Islands Canada Cayman Islands Dominica Grenada Guadeloupe Guyana Haiti Jamaica Martinique Mexico Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Trinidad and Tobago United States of America US Virgin Islands

Antigua and Barbuda (Tull et al, 1997)a, 79

PERIOD

Antigua and Barbuda (Tull et al, 1997)a, 79 1989-1993 Venezuela (Karvonen et al, 2000)51 Bahamas (Peter et al, 2005)80 2001-2002 Barbados (Karvonen et al, 2000)51 1990-1993 Mexico (Karvonen et al, 2000)51 Cuba (DIAMOND, 2006)53 Antigua and Barbuda (Tull et al, 1997) a, 79 Canada (DIAMOND, 2006)53 1990-1999 Cuba (DIAMOND, 2006)53 Dominica (Karvonen et al, 2000)51 1990-1993 Barbados (Karvonen et al, 2000)51 Dominica (Karvonen et al, 2000)51 Venezuela (Karvonen et al, 2000)51 Puerto Rico (DIAMOND, 2006)53 Cuba (DIAMOND, 2006)53 Barbados (Karvonen et al, 2000)51 Mexico (Karvonen et al, 2000)51 1990-1993 Antigua and Barbuda (Tull et al, 1997) a, 79 Barbados (Karvonen et al, 2000)51 Barbados (Karvonen et al, 2000)51 Barbados (Karvonen et al, 2000)51 USA (DIAMOND, 2006)53 1990-1999 US Virgin Islands (DIAMOND, 2006)53 1990-1996

A

Studies from the country in question that were based on population-based registers with validated ascertainment levels of 90% or more.

B

Other studies from the country in question, provided population denominators were given to enable rates to be calculated (excludes case-series studies which provided no population denominator).

X

Extrapolation using rates from a different country

a. Relates to 0 -19 years age range N/A Not available

178

CHAPTER 2

DIABETES ATLAS THIRD EDITION

COMPLETENESS

CLASSIFICATION

Antigua 4 100% Whole country 9 N/A Whole country 5 N/A Edmonton, Calgary, Prince Edward Island 636 75-100% Whole country 5 N/A Veracruz 9 100% Allegheny, Chicago, Jefferson 1,185 51-100% Whole country 22 N/A

X A X B B X X X A/B X B X X X X X X B X X X X A/B B



GEOGRAPHY

GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES

NO. OF CASES



CHAPTER 2

179

Table 2.10 Estimates of type 1 diabetes in children - South and Central American Region COUNTRY/TERRITORY



Argentina Bolivia Brazil Chile Colombia Costa Rica Cuba Dominican Republic Ecuador El Salvador French Guiana Guatemala Honduras Netherlands Antilles Nicaragua Panama Paraguay Peru Puerto Rico Suriname Uruguay Venezuela

POPULATION SIZEa (000’s) 0-14 yrs 10,211 3,545 52,451 3,947 14,144 1,215 2,080 2,925 4,295 2,363 65 5,643 2,863 40 2,156 997 2,372 8,999 865 134 840 8,413

INCIDENCE RATES (cases per 100,000 population per year) 0-4 yrs 5-9 yrs 10-14 yrs Total 3.3 4.9 0.9 1.1 0.5 0.6 0.3 1.0 0.1

9.1 8.4 1.4 2.7 0.5 0.9 0.5 9.2 0.2

7.9 9.8 1.6 3.2 0.5 1.3 0.8 14.6 0.1

6.8 0.5 7.7 5.9 1.3 1.3 2.3 0.5 1.3 1.5 0.1 1.5 1.5 0.1 1.5 1.3 0.9 0.5 16.8 0.1 8.3 0.1

PREVALENT CASES (000’s) 4.4 0.1 25.4 1.4 1.2 0.1 0.3 0.1 0.3 0. 0. 0.5 0.3 0.0 0.2 0.1 0.1 0.3 0.9 0.0 0.4 0.1

SACA Total 130,563 • • • • 36.5 a.

180

UN population projections for 2007 - medium variant 2004

CHAPTER 2

DIABETES ATLAS THIRD EDITION



GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES



CHAPTER 2

181

Table 2.11 Data sources: estimates of type 1 diabetes in children - South and Central American Region

COUNTRY/TERRITORY

DATA USED

Argentina Bolivia Brazil Chile Colombia Costa Rica Cuba Dominican Republic Ecuador El Salvador French Guiana Guatemala Honduras Netherlands Antilles Nicaragua Panama Paraguay Peru Puerto Rico Suriname Uruguay Venezuela

Argentina (DIAMOND, 2006)53

PERIOD

1990-1999 Peru (DIAMOND, 2006)53 Brazil (DIAMOND, 2006)53 1990-1999 Chile (Carrasco et al, 2006)81 1999-2003 Colombia (DIAMOND, 2006)53 1990-1999 Colombia (DIAMOND, 2006)53 Cuba (DIAMOND, 2006)53 1990-1999 Dominican Republic (DIAMOND, 2006)53 1995-1999 Colombia (DIAMOND, 2006)53 Mexico (Karvonen et al, 2000)51 Venezuela (Karvonen et al, 2000)51 Mexico (Karvonen et al, 2000)51 Mexico (Karvonen et al, 2000)51 Venezuela (Karvonen et al, 2000)51 Mexico (Karvonen et al, 2000)51 Colombia (DIAMOND, 2006)53 Paraguay (DIAMOND, 2006)53 1990-1999 Peru (DIAMOND, 2006)53 1990-1994 Puerto Rico (DIAMOND, 2006)53 1990-1999 Venezuela (Karvonen et al, 2000)51 Uruguay (Karvonen et al, 2000)51 1992 Venezuela (Karvonen et al, 2000)51 1992

A

Studies from the country in question that were based on population-based registers with validated ascertainment levels of 90% or more.

B

Other studies from the country in question, provided population denominators were given to enable rates to be calculated (excludes case-series studies which provided no population denominator).

X

Extrapolation using rates from a different country.

N/A Not available

182

CHAPTER 2

DIABETES ATLAS THIRD EDITION

GEOGRAPHY

NO. OF CASES

COMPLETENESS

CLASSIFICATION

Avellaneda, Cordoba, Corrientes, Tierra del Fuego 141 88-100% Sao Paulo, Passo Fundo 47 70-100% Santiago approx 440 100% Cali, Santafé de Bogotá 76 N/A, 97% Whole country 572 25-100% Whole country 34 39-67% Whole country 168 N/A Lima 53 35-100% Whole country 1,625 90-97% Montevideo 26 97% Caracas 43 N/A

A/B X A/B A A/B X B B X X X X X X X X B B A X A B





GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES



CHAPTER 2

183

Table 2.12 Estimates of type 1 diabetes in children - South-East Asian Region COUNTRY/TERRITORY



POPULATION SIZEa (000’s) 0-14 yrs



INCIDENCE RATES (cases per 100,000 population per year) 0-4 yrs 5-9 yrs 10-14 yrs Total

PREVALENT CASES (000’s)

Bangladesh 50,790 4.2 13.2 Bhutan 844 0.6 0.0 India 354,299 4.2 92.3 Maldives 137 4.2 0.0 Mauritius 302 0.8 0.9 2.4 1.4 0.0 Nepal 10,720 0.6 0.4 Sri Lanka 4,926 4.2 1.3 SEA Total 422,018 • • • • 107.3

a. UN population projections for 2007 - medium variant 2004

Table 2.13 Data sources: estimates of type 1 diabetes in children - South-East Asian Region

COUNTRY/TERRITORY

DATA USED

Bangladesh Bhutan India Maldives Mauritius Nepal Sri Lanka

India (Ramachandran et al, 1992)82 China (DIAMOND, 2006)53 India (Ramachandran et al, 1992)82 1991 India (Ramachandran et al, 1992)82 Mauritius (Karvonen et al, 2000)51 1990-1994 China (DIAMOND, 2006)53 India (Ramachandran et al, 1992)82

PERIOD

A

Studies from the country in question that were based on population-based registers with validated ascertainment levels of 90% or more.

B

Other studies from the country in question, provided population denominators were given to enable rates to be calculated (excludes case-series studies which provided no population denominator).

X

Extrapolation using rates from a different country.

N/A not available

184

CHAPTER 2

DIABETES ATLAS THIRD EDITION

COMPLETENESS

CLASSIFICATION

Madras 30 N/A Whole country 21 35-100%

GEOGRAPHY

NO. OF CASES

X X B X B X X







GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES



CHAPTER 2

185

Table 2.14 Estimates of type 1 diabetes in children - Western Pacific Region COUNTRY/TERRITORY



POPULATION SIZEa (000’s) 0-14 yrs







INCIDENCE RATES (cases per 100,000 population per year) 0-4 yrs 5-9 yrs 10-14 yrs Total

PREVALENT CASES (000’s)

Australia 3,914 13.7 20.1 28.7 20.9 5.2 Brunei Darussalam 112 0.3 0.0 Cambodia 5,270 0.3 0.1 China 272,242 0.3 0.6 0.9 0.6 9.3 China, Hong Kong 999 2.0 0.1 China, Macao 67 2.0 0.0 Cook Islandsb 8 0.1 0.0 Fiji 266 0.1 0.0 French Polynesia 71 0.1 0.0 Guam 52 0.1 0.0 Indonesia 63,136 0.3 1.2 Japan 17,819 1.2 1.4 2.7 1.7 1.9 Kiribatib 39 0.1 0.0 Korea, Democratic People’s Republic of 5,450 1.1 0.4 Korea, Republic of 8,426 0.6 0.9 2.0 1.1 0.6 Lao People’s Democratic Republic 2,482 0.3 0.0 Malaysia 8,226 0.3 0.2 Marshall Islandsb 23 0.1 0.0 Micronesia, Federated States of 44 0.1 0.0 Mongolia 792 0.6 0.0 Myanmar 14,418 0.3 0.3 Naurub 5 0.1 0.0 New Caledonia 67 0.1 0.0 New Zealand 850 11.5 19.4 23.3 18.0 1.0 Niueb 1 0.1 0.0 Palaub 5 0.1 0.0 Papua New Guinea 2,395 0.1 0.0 Philippines 29,254 0.6 1.1 Samoa 75 0.1 0.0 Singapore 799 2.4 1.6 3.3 2.5 0.1 Solomon Islands 201 0.1 0.0 Taiwanb 4,506 2.0 0.6 Thailand 15,118 0.3 0.3 Timor-Leste 441 0.3 0.0 Tokelaub 1 0.1 0.0 Tonga 36 0.1 0.0 Tuvalub 4 0.1 0.0 Vanuatu 86 0.1 0.0 Viet Nam 24,258 0.3 0.5 WP Total 481,957 • • • • 22.8

a. UN population projections for 2007 - medium variant 2004 b. Population estimates extracted from CIA World Factbook 2005

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GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES



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187

Table 2.15 Data sources: estimates of type 1 diabetes in children - Western Pacific Region





COUNTRY/TERRITORY

DATA USED

Australia Brunei Darussalam Cambodia China China, Hong Kong China, Macao Cook Islands Fiji French Polynesia Guam Indonesia Japan Kiribati Korea, Democratic People’s Republic of Korea, Republic of Lao People’s Democratic Republic Malaysia Marshall Islands Micronesia, Federated States of Mongolia Myanmar Nauru New Caledonia New Zealand Niue Palau Papua New Guinea Philippines Samoa Singapore Solomon Islands Taiwan Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam

Australia (Taplin et al, 2005)83

A

PERIOD

1997-2002 Thailand (Tuchinda et al, 2002)84 Thailand (Tuchinda et al, 2002)84 China (DIAMOND, 2006)53 1990-1996 Hong Kong (Huen et al, 2000)85 1992-1996 Hong Kong (Huen et al, 2000)85 Papua New Guinea (Ogle et al, 2001)86 Papua New Guinea (Ogle et al, 2001)86 Papua New Guinea (Ogle et al, 2001)86 Papua New Guinea (Ogle et al, 2001)86 Thailand (Tuchinda et al, 2002)84 Japan (Karvonen et al, 2000)51 1990-1993 Papua New Guinea (Ogle et al, 2001)86 Republic of Korea (Karvonen et al, 2000)51 Republic of Korea (Karvonen et al, 2000)51 1990-1991 Thailand (Tuchinda et al, 2002)84 Thailand (Tuchinda et al, 2002)84 Papua New Guinea (Ogle et al, 2001)86 Papua New Guinea (Ogle et al, 2001)86 China (DIAMOND, 2006)53 Thailand (Tuchinda et al, 2002)84 Papua New Guinea (Ogle et al, 2001)86 Papua New Guinea (Ogle et al, 2001)86 New Zealand (Campbell-Stokes et al, 2005)87 1999-2000 Papua New Guinea (Ogle et al, 2001)86 Papua New Guinea (Ogle et al, 2001)86 Papua New Guinea (Ogle et al, 2001)86 1996-2000 China (DIAMOND, 2006)53 Papua New Guinea (Ogle et al, 2001)86 Singapore (Lee et al, 1998)a, 88 1992-1994 Papua New Guinea (Ogle et al, 2001)86 Hong Kong (Huen et al, 2000)85 Thailand (Tuchinda et al, 2002)84 1991-1995 Thailand (Tuchinda et al, 2002)84 Papua New Guinea (Ogle et al, 2001)86 Papua New Guinea (Ogle et al, 2001)86 Papua New Guinea (Ogle et al, 2001)86 Papua New Guinea (Ogle et al, 2001)86 Thailand (Tuchinda et al, 2002)84

Studies from the country in question that were based on population-based registers with validated ascertainment levels of 90% or more.

B

Other studies from the country in question, provided population denominators were given to enable rates to be calculated (excludes case-series studies which



provided no population denominator)

X Extrapolation using rates from a different country. a. Only up to age 12 years N/A Not available

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DIABETES ATLAS THIRD EDITION

COMPLETENESS

CLASSIFICATION

New South Wales approx 1,500 99% 22 regions 500 69-100% Whole country 120 N/A Chiba, Hokkaido, Okinawa 167 77-100% Seoul 61 N/A Whole country 298 95% Whole country 8 N/A Whole country 40 92% North, north-east, south and central regions 191 N/A

A X X A/B B X X X X X X A/B X X B X X X X X X X X A X X B X X A X X B X X X X X X



GEOGRAPHY

GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES

NO. OF CASES



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189

Table 2.16 Mortality among children with type 1 diabetes in 10 European centres Centre

Cases Registration Person Observed Expected period years deaths (O) deaths (E)

SMR =O/E (95%CI)

Lithuania 1,006 1989-2003 7,568 15 5.2 2.9 (1.6,4.7) Bulgaria (Eastern) 443 1989-1999 4,069 10 2.1 4.7 (2.3,8.7) Hungary 1,968 1989-2002 13,432 6 4.6 1.3 (0.5,2.9) Austria 1,989 1989-2002 14,744 6 4.9 1.2 (0.5,2.6) Spain (Catalonia) 1,806 1989-2002 13,316 3 4.7 0.6 (0.1,1.9) Germany (Düsseldorf ) 764 1989-2001 3,778 2 0.9 2.2 (0.3,8.0) Iceland 151 1989-2004 1,160 0 0.5 0.0 ( - , - ) Denmark 2,287 1989-2002 13,104 12 3.3 3.6 (1.9,6.3) United Kingdom (N Ireland) 1,311 1989-2002 9,622 10 3.2 3.1 (1.5,5.7) Sweden 7,094 1989-2002 45,158 14 9.7 1.4 (0.8,2.4) 18,819 125,951 78 39.2

CI

confidence interval

SMR

standardized mortality ratio

Source: Patterson et al, 200527

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GLOBAL TRENDS IN CHILDHOOD TYPE 1 DIABETES



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191



CHAPTER 2.2 Type 2 Diabetes IN THE YOUNG Type 2 diabetes in children and adolescents is on the increase, and affects children in both developed and developing nations. Many of these children risk developing diabetic complications at an early age, which would place a significant burden on the individual, national health budgets as well as society as a whole.

Introduction It is well recognized that the global burden of type 2 diabetes is both significant and rising, with most of the increase registered in the last two decades. From 2007 to 2025 the worldwide prevalence of diabetes in adults is expected to increase from 5.9% to 7.1% of the adult population, or from 246 million to 380 million people (see Chapter 1). The largest proportional and absolute increases will occur in developing countries. In India and China, the number of adults with diabetes is expected to increase by 50-70% between 2007 and 2025, to reach 70 million in India and 59 million in China by 2025. In 1990 it was estimated that 0.2% of the total global diabetic population of 118 million was under 15 years of age89. The prevalence of type 2 diabetes increases with age and affects some 17% of all 65-74 year olds in the USA, and a similar proportion in Australia90-92. Amongst the young, type 2 diabetes is thought to account for 2-3% of all types of diabetes. This however, may be an underestimate, as depending on the study, 8-45% of recently diagnosed diabetes in the young in the USA is due to type 2 diabetes93. TYPE 2 DIABETES IN THE TOUNG

Compared to adults there is little information on type 2 diabetes incidence and prevalence in the young with many surveys being clinic based or case series with a paucity of population-based surveys, particularly outside North America91, Japan94 and Taiwan95. Similarly, information on the natural history and aetiology of type 2 diabetes in the paediatric age range is also sparse. Other deficiencies include a lack of uniformity in case definition, data collection and follow-up, with the diagnosis often made retrospectively96. There are, however, ever increasing reports of type 2 diabetes in children worldwide, with some as young as eight years of age being affected97. These are mostly in ethnic groups known to be at high risk of type 2 diabetes. There are now also reports of type 2 diabetes occurring amongst Europid (White Caucasoid) teenagers98. In Japan, the prevalence of type 2 diabetes amongst junior high school children has doubled from 7.3 per 100,000 in 1976-80 to 13.9 per 100,000 in 1991-95, with type 2 diabetes now outnumbering type 1 diabetes in that country94. Despite the paucity of information, it is now becoming recognized that type 2 diabetes in children is becoming a global public health issue with potentially serious health outcomes99. CHAPTER 2

193

In response to this the American Diabetes Association (ADA) has issued a consensus statement on the screening, diagnosis and treatment of children with type 2 diabetes93. As with adults, it is expected that youth with type 2 diabetes will also develop diabetes-related micro- and macrovascular complications. Studies on youth with diabetic complications have important implications in that they highlight the risk of complications occurring at a relatively young age and that these complications can occur relatively soon after diagnosis. This will place a significant burden on health budgets as well as society as a whole, as many of these people would be entering their peak working and earning capacity. Early detection and intervention is therefore essential to reduce the risk of future complications.

The impact of misclassification There may be underestimation in type 2 diabetes rates due to a misclassification of the type of diabetes at initial presentation. The presence of diabetic ketoacidosis (DKA) is classically a manifestation of type 1 diabetes. However, a number of reports have shown that DKA may occur at initial presentation in people who are eventually found to have 194

CHAPTER 2

type 2 diabetes. That is, they have elevated C-peptide and an absence of islet cell or anti-GAD antibodies91. This type of presentation has also been termed Flatbush100,101, or atypical diabetes mellitus (ADM)102. Unlike type 1 diabetes, most children with type 2 diabetes are asymptomatic. However, approximately a third present with ketonuria, an excess of ketones in the urine103. One study found DKA occurred in 4.2% of all patients attending their paediatric clinic, all of whom were of Canadian aboriginal descent104. A case series examining African Americans adolescents found that up to 42% presented with ketonuria and 25% with DKA105. Similarly another report has shown that some 30% of Hispanic youth with type 2 diabetes can present with ketosis106. Why type 2 diabetes can present with ketosis and in particular why this presentation is more likely to occur in African Americans or Hispanics is not clear107.

Factors in the development of type 2 diabetes There are several possible factors in the development of type 2 diabetes. Major factors include: DIABETES ATLAS THIRD EDITION



Figure 2.4 Annual incidence of type 2 diabetes and prevalence of obesity among Japanese school children

8.0

Incidence of type 2 diabetes per 100,000 population per year

7.0 6.0 5.0 Obesity (%) 4.0

8.0

3.0

7.0

2.0

6.0

1.0

5.0 1975

1980

1985

1990

1995

Type 2 diabetes Obesity

• Ethnicity • Obesity, diet and activity • Insulin resistance • Family history • Intrauterine environment Ethnicity

Ethnicity is an important factor in type 2 diabetes development in both adults and children with higher rates being reported in Asians, Hispanics, indigenous peoples (USA, Canada, Australia) and African Americans, with some of the highest rates in the world being observed amongst Pima Indians108,109. For instance from the period 1967-76 to 1987-96 the prevalence of type 2 diabetes in Pimas increased four to six-fold, reaching a prevalence of 22.3 per 1,000 for 10-14 year olds and 50.9 per 1,000 for 15-19 year olds by 1992-96103. Obesity, diet and activity

On a global basis the rise in type 2 diabetes rates seems to mirror the growth in urbanization and economic development, and may be due to mal-adaptation to a rapidly changing environment110,111. Closely associated with this is the increase in overweight and obesity. TYPE 2 DIABETES IN THE TOUNG

Source: Kitagawa et al, 199894

Obesity has been linked to changing patterns in diet and physical activity levels112,113. Allied to this are studies from Japan which have demonstrated a parallel rise in type 2 diabetes incidence in children and levels of obesity from 1975 to 199594 (see Figure 2.4). Of note is that over this time period there have also been significant increases in fat and animal protein intake among Japanese youth, now mirroring the kind of westernized diets consumed by JapaneseAmericans114. Dietary changes are not only confined to the home environment. A survey of Californian public schools found that 85% sold fast food, which in turn accounted for 70% of all food sales115. Of concern is that almost 70% of school districts allowed advertising on campus, with 24% allowing advertising in exchange for cash or equipment. The prevalence of obesity among Japanese children has increased from 5% to 8% from 1976 to 1992 and is similar to data reported from the United States116 . In the USA, the National Longitudinal Survey of Youth, which is a prospective cohort study conducted from 1986 to 1998, showed that over this time period the overweight prevalence increased annually by 3.2% in non-Hispanic whites, 5.8% in African CHAPTER 2

195

Americans and 4.3% in Hispanics. Thus by 1998, 21.5% of African Americans, 21.8% of Hispanics and 12.3% of nonHispanic whites were overweight117. A more recent study of nearly 5,000 children in the USA has shown that during 19992000, 15% of 6-19 year olds were overweight, compared to 11% in 1994-98. The biggest rises were recorded in African American and Mexican American adolescents118. This study also showed that the prevalence of being overweight (BMI ≥25) reached a staggering 65% in US adults. Increasing obesity is also a problem in Australia, with a recent study examining children aged 7-15 years reporting that the prevalence of obesity has increased two to four-fold from 1985 to 1997119. The problem of obesity also extends to developing nations, particularly in the more affluent urban areas. In India, a recent study found that the age adjusted prevalence of being overweight among 13-18 year olds was around 18%. Prevalence rates increased with age and decreasing physical activity and with higher socioeconomic status120 . Other factors also thought to be important amongst Indian Asians are low birth weight and insulin resistance111. Obesity is also being increasingly observed in indigenous populations, such as the Objiwa-Cree community in Canada, where a study found that 48-51% of children aged 4-19 years have a weight more than the 90th percentile121. Changes in traditional lifestyles among indigenous communities such as a reduction in hunting and gathering as well as the adoption of a more sedentary life with a westernized diet are thought to contribute to rising obesity levels122. Currently some 85% of children with type 2 diabetes are either overweight or obese at diagnosis93. Inactivity is one of the major contributors to being overweight. In the developed world, use of computers and increasing time spent in front of the television are some of the factors impacting on activity113,118. A recent longitudinal study showed a marked decline in physical activity in adolescent girls with 56% of black and 31% of white girls aged 16-17 years having no habitual leisure-time physical activity123. Pregnancy, cigarette smoking, higher body mass index (BMI) and lower parental education at baseline were all associated with a subsequent decline in physical activity. Another study highlighting racial differences in physical activity levels found that white students in the US have generally higher physical activity levels than other ethnic groups, with boys usually more active than girls, whatever the race124. 196

CHAPTER 2

A lifestyle predisposing to obesity and type 2 diabetes seems to characterize families with adolescents who have type 2 diabetes. Specifically they have shown that members of such families tend to be overweight, inactive and have a tendency to high fat intake and even binge eating125. Overall in the USA, only 50% of young people aged 12-21 years are regularly involved in physical activity, with some 25% admitting to no physical activity at all. Even in schools there is a decline in physical education, with participation rates down from 41.6% in 1991 to 24.5% in 1995126. Insulin resistance

The onset of type 2 diabetes is frequently reported around puberty and is thought to coincide with a physiological rise in insulin resistance (IR) associated with puberty, where insulin sensitivity may be reduced by as much as 30%96 . Healthy young adolescents compensate for the peri-pubertal rise in IR by increasing insulin secretion as they have normal pancreatic beta cell function. This is not the case with adolescents with type 2 diabetes, where both insulin action and eventually beta cell function are impaired93. There appear to be ethnic differences in IR, with African American children being more hyperinsulinemic (having high levels of insulin in the blood) and insulin resistant than Europids127. Similarly, the Bogalusa Heart Study has shown that compared to Europids, African Americans, especially girls, had higher insulin levels and insulin:glucose ratios128. Both African American and Hispanic children have been shown to have greater insulin resistance than Europid children129. In another study, the hyperinsulinaemia seen in African-American children has been shown to be due to a combination of lower insulin clearance (the rate at which insulin is removed from the circulation) and higher insulin secretion130. Insulin resistance may be lowered by simple means such as increasing activity levels. This has been demonstrated in obese children and more recently in non-diabetic, normal weight children131, where the more active children had lower fasting insulin and greater insulin sensitivity. Acanthosis nigricans

Acanthosis nigricans (AN) is thought to be a physical marker of IR and is reported to occur in up to 60-90% of young people with type 2 diabetes108. This seems to be especially true for African Americans and some Native Americans, but so far not demonstrated in other populations such as in Japan114. However, DIABETES ATLAS THIRD EDITION



despite its ubiquitous occurrence in some populations with type 2 diabetes, it should not exclusively be used as a reliable marker of hyperinsulinemia and insulin resistance. A study has shown that only 35% of obese children with hyperinsulinemia had AN, whether African American or white132. Similarly a recent survey of obese Hispanic children (BMI ≥95th percentile) found that there was no association between AN and markers of insulin resistance. In contrast, AN was positively associated with BMI but negatively with birth weight133. Polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is associated with menstrual irregularities, hyperandrogenism and IR134. It is also said to affect up to 5-10% of females in their reproductive years135 and is thought to predispose to glucose intolerance, with studies showing up to 30-40% being affected by IGT and up to 7-10% with type 2 diabetes134,136,137. It may explain the female preponderance in type 2 diabetes rates amongst adolescents96,134. Family history

Many studies show a strong family history among affected youth with 45-80% having at least one parent with diabetes and 74-100% having a first or second degree relative with type 2 diabetes93,138 . Children with diabetes are also more likely to have a family history of cardiovascular disease (CVD), with one study showing that up to 28% have a positive family history of CVD139. The Bogalusa Heart Study140 has shown that children of individuals with type 2 diabetes were more likely to be obese and have higher blood pressures, fasting insulin, glucose and triglycerides. In a study among Pima Indians, it was shown that the cumulative incidence of type 2 diabetes was highest in offspring if both parents had diabetes141. Intrauterine environment

Apart from genes, the intrauterine environment may be important as there is evidence of higher rates of type 2 diabetes in offspring of mothers who develop gestational diabetes (GDM)142. A prospective study143 found that the prevalence of IGT in the children of mothers with a diabetic pregnancy increased with time from 1.2% at less than five years of age to 19.3% at 10-16 years of age. This was compared to 2.5% in control subjects. In addition, higher levels of amniotic fluid insulin TYPE 2 DIABETES IN THE TOUNG

(AFI) measured at 33-38 weeks gestation was a strong predictor of later IGT143. AFI is also thought to correlate with later childhood obesity144, which in turn may lead to later type 2 diabetes development. Birth weight is strongly influenced by the intrauterine environment, particularly in diabetic pregnancies, which can be associated with high birth weight145. Conversely there is also evidence that low birth weight can result in later adult type 2 diabetes development146. This is most likely due to poor maternal nutrition leading to impaired islet cell development145,147, but may also occur in a number of other conditions such as pregnancies complicated by hypertension/ pre-eclampsia, which is not an uncommon condition complicating up to 3-5% of all pregnancies148. There is a hypothesis that insulin resistance is a product of fetal programming and that gestational metabolic perturbations affect fetal size, leading to low birth weight and hence later development of IR149-152. Two recent studies challenge this. First, a study examining 300 five-year old British children found that girls were more insulin resistant than boys, and insulin resistance was not related to birth weight153 . The second is a study from Belgium154, which examined twins aged 18-35 years and found that among twin pairs discordant for birth weight, there was little evidence that the lighter twin had abnormal glucose-insulin metabolism in adult life. Low pre-pregnancy maternal BMI and older maternal age at delivery were independently associated with IR in the offspring. These findings suggest that maternal factors may be more important than feto-placental factors in determining glucose-insulin metabolism in the offspring.

Methods A Medline search was conducted using Ovid of papers written in the English language from 1965-2006. Key words used were: Diabetes, diabetics, non-insulin dependent diabetes, type II diabetes, impaired glucose tolerance, insulin resistance, child, childhood, young, adolescence, overweight, obesity and polycystic ovary syndrome. The keywords related to diabetes were combined with those related to children and then further combined with terms related to obesity and then finally with polycystic ovary syndrome. CHAPTER 2

197

All available studies with relevant data have been included and have been grouped by study type (population based, case series and clinic based). These have further been divided into the IDF regions of Africa (AFR), Europe (EUR), North America (NA), Eastern Mediterranean and Middle East (EMME), South and Central America (SACA), South-East Asia (SEA) and Western Pacific (WP).

Eastern Mediterranean and Middle East

Studies which include young people 20 years of age and under have been selected. However, data are presented for studies which have given higher age ranges but which include subjects less than 20 years of age within those ranges. In these cases it has not been possible to separate those under 20 from the information given.

A large study of some 25,000 individuals aged 2-77 years old was carried out in Saudi Arabia173. The figures for those less than 29 years of age have been selected. In the under14 year age group, IGT prevalence was double that of type 2 diabetes (0.25% vs 0.12%). The opposite was true for the 14-29 year age group, where type 2 diabetes outnumbered IGT almost 3:1 (0.79% for type 2 diabetes vs 0.21% for IGT).

Results

Europe

Results are reported as presented in the original papers, unlike in the adult diabetes and childhood type 1 diabetes sections (Chapters 1.1 and 2.1), in which figures have been calculated for the national population. In some of the studies used, the prevalence of type 2 diabetes in the general population (child and adolescent) has been determined from a representative populationbased sample 155 . However, many studies have simply reported a series of cases (sometimes supplemented by a calculation of the prevalence in the general population, using estimated figures for the size of the population from which the cases were drawn)156, or examined only a specific sub-population, such as from a diabetes registry157-160 or an obesity clinic161-165.

Population-based studies In general, it is very difficult to compare the studies due to wide differences in study design. Population-based studies were found from all regions except South and Central America (see Table 2.17). The size of the studies were very variable, ranging from less than 100166 to over eight million participants167. In the case of Africa168-171, studies found are few and in most examples conducted some 20 years ago. Africa

Five studies were found and included two from west Africa168,170 and three from east Africa169,171,172. All but one, which looked only at Indians172, showed a zero or low prevalence of diabetes. Apart from the study examining Indians, all the others were conducted in the 1980s. Therefore 198

taking into account the current information on type 2 diabetes rates around the world, the results for Africa are probably an underestimate and may not represent the contemporary situation.

CHAPTER 2

Two recent studies have provided the first population-based data for European countries. A study of Turkish adolescents174 found no cases of diabetes, although 2% had impaired fasting glucose (IFG), and a very large study of 17-year old Israeli military conscripts175 reported type 2 diabetes in 0.036% of males and in 0.01% of females. North America

Compared to other regions, data from North America on type 2 diabetes and IGT prevalence are relatively extensive and recent. The ethnicity of the study subjects is diverse with many including African Americans, Mexican Americans and non-Hispanic white Americans in the same study. In the USA, national data from 1988-94176, and data from a single school district177 collected approximately 10 years later showed diabetes prevalences of 0.13% and 0.4% respectively. A study from Texas in 1981 examining 15-24 year-old Mexican Americans found no type 2 diabetes in males and only a low prevalence of 0.4% in females178. By 2002, a study surveying Mexican American fourth graders found not only an overall type 2 diabetes prevalence of 0.3%, but also cases of IGT (0.14%) as well as IFG (0.14%)179. In contrast, another study reported relatively high rates of type 2 diabetes (1.5%) and IFG (10.8%) in a population of Europids and African Americans180. A study which examined Pima Indians since 1967 demonstrated rising rates of glucose intolerance over time, as well as a female preponderance181. From 1967-76 to 198796 the prevalence of type 2 diabetes markedly increased from 2.4% in males and 2.7% in females to 3.8% in males and 5.3% in females. A female preponderance of type 2 diabetes DIABETES ATLAS THIRD EDITION



of almost 4:1 among Navajo adolescents was also found in another study182. A study of American Indian and Alaskan Native adolescents reported that the type 2 diabetes prevalence increased by 68% from 1990 to 1998 among those aged 15-19 years (0.32% to 0.54%)109. In addition although the prevalence of type 2 diabetes was higher among females, the relative increase over this time period was greater among males (0.23% to 0.41% for males vs 0.42% to 0.68% for females). Even though the overall prevalence among under-15 year olds remained the same at 0.12%, there was regional variation, and Alaska recorded the biggest rise of 114% (0.04% to 0.09%). From Canada, a study among Cree-Objiway children found greater rates for IFG (2.6%) compared to type 2 diabetes (1.1%), but no female preponderance183. South-East Asia

Studies in the South-East Asian Region were identified from India184 and Bangladesh185,186 . The study from Chennai in south India184 was conducted in the early 1990s and found a zero prevalence rate for type 2 diabetes. This may no longer be the case, given the recent worldwide rise in type 2 diabetes in children, and a case series report in 2003 from the same city, noting 18 cases of childhood type 2 diabetes187. Western Pacific

Two very large studies involving the mass screening of schoolchildren in Japan94,167 and Taiwan95 were identified in the Western Pacific area. The largest study reported is from Japan167, with over eight million youth being studied between 1974 and 2002. Over this time, type 2 diabetes incidence increased from 1.73 per 100,000 per year in the period 197480 to 2.76 per 100,000 per year in the period 1981-2002. A cohort of indigenous Australian children aged 7-18 years was surveyed in 1989 and again in 1994. Over the five years, the prevalence of type 2 diabetes almost doubled to 1.3%, while that for IGT increased almost seven-fold to 8.1%188. By 18 years of age, 18% of the population were overweight or obese. In addition one-third of the children had elevated cholesterol levels, with almost half reporting alcohol use and smoking. In contrast to the Australian study, the Tongan study 166 examining 15-19 year olds found no glucose intolerance in that population. TYPE 2 DIABETES IN THE TOUNG

Clinic/ register-based studies Clinic and register-based studies make up the largest group of studies conducted on youth IGT and type 2 diabetes (see Tables 2.18 and 2.19). They reveal type 2 diabetes occurring in children as young as under the age of five years189. In addition, they have demonstrated a female preponderance158,159,190,191, strong family history158,192,193, obesity97,155,158,192,193, and AN97,155,193. These studies have also shown an increase in incidence rates, with one study157 finding that type 2 diabetes incidence rates rose by 9% per year from 1985 to 1994 in the USA, reaching 3.8 per 100,000 per year by 1990-94, while another American study193 found a 10-fold increase in type 2 diabetes incidence rates from 0.7 per 100,000 per year in 1982 to 7.2 per 100,000 per year in 1994. Yet another American study194 reported that in 1994, 9.4% of new cases of diabetes were due to type 2 diabetes, rising to 20% by 1998. Similarly, a study in Thailand155 reported a rise in the proportion with type 2 diabetes referred to a diabetic clinic from 5% during 1986-95 to 17.9% during 1996-99. A study from a clinic in Hungary195 from 1989-2001 also reported rising incidence rates over time with 57% of all type 2 diabetes and 77% of all IGT being diagnosed in the last six years of the 13-year study. While, there has been much evidence of type 2 diabetes becoming a major problem, and perhaps the dominant form of diabetes among youth in the USA and Asia, a series of studies from Europe indicate that it remains a rarity in these populations. Well-designed studies from Germany, Austria, France and the UK196-198 all show type 2 diabetes accounting for only 1-2% of all cases of diabetes. Incidence rates rise with age, with one study189 demonstrating that 15-19 year olds with rates of 5.9 per 100,000 per year have three times the rate of 10-14 year olds with rates of 1.8 per 100,000 per year. A number of studies have pre-selected subjects for obesity, AN or PCOS. One study162 in 1965 found an IGT prevalence of 23% in subjects pre-selected for obesity. More recently, another study164 selected children and adolescents whose weight was more than the 95th percentile for age and sex attending an obesity clinic and found similar rates of IGT: 25% in 4-10 year olds and 21% in 11-18 year olds. A study165 from Italy also reported an IGT prevalence of 12.6%, CHAPTER 2

199

but other studies of obese youth from the US163, Germany199, and another from Italy200 have reported lower IGT prevalences of 4.1%, 7.5% and 4.5% respectively. Another study from Singapore also found lower rates of IGT (4.3%) in young obese children161. The cause for the discrepancy in results is uncertain, but it has been suggested that for at least one of the high prevalence studies164, it may be due to the study population being enriched with children with extreme obesity, with PCOS, and having a high proportion of children from highrisk ethnic groups (non-Hispanic Black or Hispanic). Studies pre-selecting for PCOS have demonstrated significant rates of glucose intolerance. One study135 reported an IGT prevalence of 26.9% while another201 found a rate of 13%. However, both studies report lower prevalences of type 2 diabetes of 3.7% and 0% respectively. One of the few studies pre-selecting for the presence of AN202 reported an IGT prevalence of 24% in subjects with AN. The authors of the study suggest that children with AN may benefit from diabetes screening and early intervention. However, AN is not a universal phenomenon in children with type 2 diabetes, and has so far not been noted to any degree among the large studies surveying Japanese youth94.

Diabetic complications As with adults it is expected that youth with type 2 diabetes will also develop diabetes- related micro- and macrovascular complications. This was reported recently in a study from Canada, where subjects who developed type 2 diabetes as children were then surveyed as young adults, aged between 18 and 33 years. Of the 51 adults, 9% had died, 6% were on dialysis, while one had a toe amputation and one was blind203.

duration of type 2 diabetes, nephropathy was present in all age groups (incidence/1,000 person years: 13/1,000 youth, 8/1,000 young adults and 7/1,000 older). However, retinopathy only appeared among those with youth onset diabetes after 5-10 years duration (incidence/1,000 person years: 10/1,000 youth, 29/1,000 young adults and 35/1,000 older).

Discussion Compared to adults there is a paucity of information on both the epidemiology and natural history of type 2 diabetes in the young. This needs to be urgently addressed given the potential threat of an explosion in childhood type 2 diabetes. There are only a few large scale population-based studies focusing on youth with type 2 diabetes. Most of the information available comes from case series or clinic-based studies. On a global basis, the majority of data come from developed countries, particularly North America and Japan, with a distinct lack of information from many regions in the world, particularly from Africa and South America. Notably, there is also a lack of standardization in study methods, with many surveys only examining small numbers of people, as well as using different diagnostic methods and criteria. In addition some studies have only looked at very high risk subjects, such as those with PCOS135, presence of AN202, or obesity164. This can make comparisons between studies difficult.

Conclusion Despite apparent deficiencies in research, some valid conclusions can still be made regarding type 2 diabetes in the young: 1. It is a global phenomenon, which is on the increase.

Another follow-up study from Japan compared those with type 1 and type 2 diabetes diagnosed at under 30 years of age for development of nephropathy204. After 30 years of diabetes, 44% of those with type 2 and 20.2% of those with type 1 had nephropathy. Yet another study205 looked at incidence of retinopathy and nephropathy among Pima Indians diagnosed with type 2 diabetes at under 20 years of age (youth), 20-39 years (young adults) and 40-59 years of age (older). At less than five years 200

CHAPTER 2

2. Children are being affected in both developed and developing nations. 3. Reports are appearing that show its existence in populations hitherto thought not to be at risk, such as British Europids. 4. The risk of type 2 diabetes is clearly linked to an increasing prevalence of obesity. This in turn is associated with DIABETES ATLAS THIRD EDITION



changing dietary and lifestyle patterns. In particular an increase in fatty foods as well as a reduction in activity levels both at home and in the school. The change in lifestyle is a worldwide phenomenon, occurring in both developed and emerging nations, where it is most prevalent in urban areas. In these nations as well as among indigenous communities residing in developed nations, there seems to be a gradual abandonment of traditional ways of living in favour of a ‘westernized’ lifestyle. 5. A number of studies have noted an association between type 2 diabetes and PCOS. Not all studies look for this condition and it is possible that it may partly explain the female preponderance in youth onset type 2 diabetes. Future work may need to address the issue of PCOS, especially since it is amenable to treatment. 6. Studies have shown that youth with type 2 diabetes will also develop diabetes-related micro- and macrovascular complications, as with adults. These studies have important implications in that they highlight the risk of complications occurring at a relatively early age, which will place a significant burden on health budgets as well as society as a whole. TYPE 2 DIABETES IN THE TOUNG

The increasing prevalence of type 2 diabetes in the young may be blunted by encouraging increasing physical activity, and changing dietary habits. Interventional programmes should be considered to address the underlying cause, with an emphasis on diet, weight, exercise and lifestyle issues. It is recognized that in an ideal world it would be possible to implement the proposed recommendations. In reality this may be difficult given the poor economic condition that many have to endure and the already tight health budgets governments have to deal with. However, many regions of the world are progressing economically and hence becoming more urbanized. A consequence of urbanization is the parallel emergence of cardiovascular disease and diabetes, which hitherto, was mainly a problem of the developed world. Therefore, governments will be forced to deal with the problem of type 2 diabetes in children. As such, it would be better to address the problem as a public health issue under the heading of primary care and prevention, rather than dealing with the consequences of an entrenched condition and its complications in a young population.

CHAPTER 2

201

Table 2.17 Type 2 diabetes and impaired glucose tolerance in the young - population-based studies

REGION COUNTRY/TERRITORY AUTHOR YEAR OF STUDY ETHNICITY

AFR Mali Fisch et al, 1987170 1984-1985 Mixed tribal Tanzania, United Republic of McLarty et al, 1989171 1988 African Ahren et al, 1984169 1982-1983 African Ramaiya et al, 1991172 • Indian Togo Teuscher et al, 1987168 1987 Mixed tribal EMME Saudi Arabia el-Hazmi et al, 2000173 1998 Arab EUR Israel Bar Dayan et al, 2005175 • Mixed Turkey Uckun-Kitapci et al, 2004174 • Mixed NA Canada Dean et al, 1998183 1996-1997 Cree-Ojibway Delisle et al, 1993206 1989 Algonquin Harris et al, 1997207 1996 Cree-Ojibway United States of America Harrell et al, 2002180 2001-2002 Caucasian 69% African American 24% Hale et al, 2002179 2002 Mostly Mexican American Hanis et al, 1996178 1981-2002 Mexican American Chavez et al, 2002208 2002 N/A Freedman et al, 1997182 1991-1992 Navajo Dabelea et al, 1998181 1987-1996 Pimas Acton et al, 2002109 1990-1998 American Indian and Alaskan native Kim et al, 1999209 1999 Navajo Dolan et al, 2005177 2002 Non-Hispanic white 50% African American 45% Fagot-Campagna et al, 2001176 1988-1994 Mixed Lee et al, 2004210 • Cherokee SEA Bangladesh Abutt Sayeed et al, 1995186 1995 South Asian rural Sayeed et al, 1997185 1997 South Asian urban India Bai et al, 1995184 1994 South Asian WP Australia Braun et al, 1996188 1989 Indigenous Daniel et al, 1999211 1987-1995 Indigenous Japan Urakami et al, 2005167 1974-2002 Japanese Taiwan Wei et al, 200395 1993-1999 South-East Asian Tonga Colagiuri et al, 2002166 1998-2000 Polynesian

DM FBG FCG FPG IFG IGT N/A OGT RBG

202

type 2 diabetes fasting blood glucose fasting capillary glucose fasting plasma glucose Impaired fasting glucose impaired glucose tolerance not available Toral glucose tolerance test random blood glucose

CHAPTER 2

DIABETES ATLAS THIRD EDITION

AGE (YRS) DIAGNOSTIC METHOD SOURCE OF CASES

TYPE 2 CASES (NO.)

PREVALENCE (%)

DM INCIDENCE per 100,000 per year

15-24 FCG 2,558 0.39 • • 15-24 OGTT 1,178 0.40 6.70 • ≤19 OGTT 1,327 0.15 • • 15-24 OGTT 156 Male 0.00 Male 2.30 • Female 0.00 Female 4.20 <20 OGTT 864 0.00 • • 2-29 OGTT 9,917 <14 years: 0.12 <14 years: 0.25 (<14) 14-29 years: 0.79 14-29 years: 0.21 • 17 FBG or RBG or OGTT 76,732 Male 0.036 • • <14 Female 0.01 • FBG 1,647 0.00 IFG 1.96 4-19 FBG 717 1.10 2.60 • 15-20 OGTT 106 • 1.90 • 10-19 OGTT 244 3.00 10.00 • 10-15 FPG 668 1.50 10.80 • 4th grade OGTT 1,417 0.30 0.14 • 15-24 OGTT 729 Male 0.00 • • Female 0.40 15-19 RBG 778 0.13 • • 12-19 OGTT 160 Male 3.00 Male 3.00 • Female 13.00 Female 13.00 5-19 OGTT 3,098 5-9 years: Male 0.00 • • Female 0.00 10-14 years: Male 1.50 Female 2.90 15-19 years: Male 3.80 Female 5.30 ≤19 Chart review <15 years: 0.12 • • 15-19 years: 0.54 Male: 0.41 Female: 0.68 13-20 OGTT 234 0.42 3.40 • 9-20 OGTT if risk factor positive 2,501 0.40 0.30 • 12-19 FBG 2,867 0.13 IFG 1.76 5-19 FBG 989 1.00 IFG 0.70 • 15-29 OGTT 371 0.50 5.70 • 15-19 OGTT 271 0.06 0.04 • 5-19 OGTT 3,515 0.00 • • 7-18 OGTT 74 1.30 8.10 • 15-24 OGTT • • • 1,070 6-15 Annual urinalysis. 8,812,356 • • 1974-1980 : 1.73 If glycosuria x2, then OGTT 1981-2002 : 2.76 6-18 Urinalysis.If glycosuria, 3x106 Male 0.009 • • then OGTT Female 0.015 • • 15-19 OGTT 59 0.00 • •



TYPE 2 DIABETES IN THE TOUNG



CHAPTER 2

203

Table 2.18 Type 2 diabetes in the young - case series

















REGION COUNTRY/TERRITORY AUTHOR YEAR OF STUDY ETHNICITY AGE (YRS)

EMME Libyan Arab Republic Kadiki et al, 1996189 1981-1990 Arab ≤19 United Arab Emirates Punnose et al, 2002212 1990-1998 Arab ≤18 EUR United Kingdom Ehtisham et al, 2001159 1999-2000 Mixed <18 Ehtisham et al, 2004213 2000 Mixed <16 NA Canada Harris et al, 1996158 1978-1994 Cree-Ojibway <16 Dean, 1998214 1996 First Nation 5-14 United States of America Jones, 1998215 1993-1998 Mexican American 67% 5-17 Lipton et al, 2002216 1985-1994 African American ≤17 Latino Pihoker et al, 199897 1995-1998 African American 8-21 Caucasian Hispanic Macaluso et al, 2002194 1994-1998 Hispanic 5-19 African American SEA India Ramachandran et al, 2002120 2002-2003 Indian Asian 9-15 WP Australia McMahon et al, 2004190 1990-2002 Mixed <17 Sinha et al, 2000138 1999-2000 Indigenous 6-16 New Zealand Campbell-Stokes et al, 200587 1999-2000 Mixed <15

* Prevalence and incidence rates are calculated using an estimate of the total at-risk population. DM

Type 2 diabetes

N/A

Not available

OGTT Oral glucose tolerance test

204

CHAPTER 2

DIABETES ATLAS THIRD EDITION



DIAGNOSTIC METHOD SOURCE OF CASES

TYPE 2 CASES (NO.)

PREVALENCE* (%)

DM INCIDENCE* per 100,000 per year

Chart review Diabetes register and hospital clinic 0-4 years: 0 N/A 5-9 years: 0.10 5-9 years: 1 10-14 years: 1.80 10-14 years: 11 15-19 years: 5.90 15-19 years: 30 Chart review Hospital clinic Male 1 N/A • Female 4 Chart review Paediatric clinics 10 0.004 DM 1.52 Questionnaire of paediatric centres Paediatric diabetes centres in UK 25 0.0002 • Chart review Diabetes register Male 1 Male 0.07 N/A Female 14 Female 0.42 Total 0.25 Chart review Diabetes register 15 0.77 N/A OGTT or Sustacal Medical centre and clinics 18 N/A N/A challenge test Chart review Diabetes register N/A N/A DM 3.80 Chart review Diabetes clinic 37 N/A N/A 12 1 Chart review Diabetes clinic 92 14 N/A Chart review Diabetes clinic 18 N/A • Chart review Paediatric centre Male 15 N/A N/A Female 28 Chart review / OGTT Diabetes clinic 20 N/A N/A Chart review Diabetes register 12 • 0.72



TYPE 2 DIABETES IN THE TOUNG



CHAPTER 2

205

Table 2.19 Type 2 diabetes and impaired glucose tolerance in the young - clinic-based studies













REGION COUNTRY/TERRITORY AUTHOR YEAR OF STUDY ETHNICITY AGE (YRS)

EUR Austria Rami et al, 2003217 1999-2001 Mixed European <15 France Ortega-Rodriguez et al, 2001197 1993-1998 Mixed <17 Germany Wiegand et al, 2004199 2000-2002 Mixed European 7-18 Wabitsch et al, 2004218 • Caucasian 9-20 Germany and Austria Schober et al, 2005196 1991-2004 Mixed <20 Hungary Korner, 2002195 1989-2001 Caucasian ≤19 Italy Invitti et al, 2003200 1994-2001 Caucasian 6-18 Ciampalini et al, 2002165 N/A Caucasian 3-19 United Kingdom Feltbower et al, 2003198 2000 Mixed <20 NA Canada Zdravkovic et al, 2004219 1994-2002 Mixed <18 United States of America Paulsen et al, 1968162 1965 Mixed 4-16 Legro et al, 1999201 1983-1991 Mixed 14-20 Pinhas-Hamiel et al, 1996193 1984-1994 African American 68% ≤19 White 32% Neufeld et al, 1998106 1990-1994 Mexican American <17 Uwaifo et al, 2002163 1996-2002 Caucasian 6-11 African American Sinha et al, 2002164 1999-2001 Caucasian 58% 4-18 Hispanic 19% African American 23% Brickman et al, 2002202 1999-2002 Mixed Mean 11.9; +/- 2.9 Palmert et al, 2002135 2001 Mixed 13-19 Oeltmann et al, 2003220 1999 Mixed <19 SACA Puerto Rico Perez-Perdomo et al, 2005221 1995-2003 Mixed <20 WP China, Hong Kong Huen et al, 200085 1984-1996 Chinese <15 New Zealand McGrath et al, 1999160 1978-1998 Maori DM onset before 30 Hotu et al, 2004222 1996-2002 Mixed 14-20 Singapore Lee et al, 1999161 1999 Malay 42% ≤15 Indian 28% Chinese 33% Thailand Likitmaskul et al, 2003155 1996-1999 South-East Asian <15

* prevalence of type 2 diabetes within the specific population (e.g. within an obesity clinic or within the total diabetic population) ** incidence (or prevalence if stated) of type 2 diabetes within the general population AN DM OGTT BMI IGT N/A PCOS

206

acanthosis nigricans type 2 diabetes oral glucose tolerance test body mass index (kg/m2) impaired glucose tolerance not available polycystic ovary syndrome

CHAPTER 2

DIABETES ATLAS THIRD EDITION

POPULATION DIAGNOSTIC METHOD SAMPLE SIZE

PREVALENCE* (%) DM IGT

DM INCIDENCE per 100,000 per year **

Diabetes register Chart review 529 1.50 • 0.25 Diabetes clinic Chart review 382 2.00 • • Obese OGTT if risk factor positive 491 1.20 7.50 N/A Obese OGTT 520 1.50 2.10 • Diabetes register Chart review 25,706 0.90 • • Diabetic clinic Chart review 524 10.70 • N/A Obese OGTT 710 0.10 4.50 N/A Obese OGTT 191 0.50 12.60 N/A Diabetes clinics Chart review 280 1.80 • Prevalence 1/105 Diabetes clinic Chart review 1,020 4.30 • • Obese OGTT 66 0.00 23.00 N/A PCOS - all female OGTT 16 0.00 13.00 N/A N/A N/A N/A • • 7.20 Diabetic clinic Chart review 55 31.00 • N/A Overweight BMI at <95th percentile OGTT Overweight 121 0.00 4.10 N/A Not overweight 104 0.00 0 Obesity clinic OGTT 4-10 years: 55 0.00 25.00 N/A 11-18 years: 112 3.60 21.00 Presence of AN OGTT 33 3.00 24.00 N/A PCOS - all female OGTT 27 3.70 26.90 N/A Diabetes register Chart review 181 19.10 • N/A Diabetes clinics Chart review 2,800 3.30 • Prevalence 13.5/105 Diabetes register Chart review 255 7.10 • 0.10 Diabetes register Chart review 51 55.00 • N/A Diabetes clinic Chart review 1996: 110 1.80 • N/A 2002: 163 11.00 Obese children in a paediatric clinic OGTT 23 17.00 4.30 N/A Diabetes clinic Chart review 39 17.90 • N/A







TYPE 2 DIABETES IN THE TOUNG



CHAPTER 2

207

CHAPTER 3 GESTATIONAL DIABETES MELLITUS

A disciplined life, exercise, a healthy diet and close monitoring of blood glucose is the secret for a successful outcome of pregnancy with GDM.



3.0 Gestational Diabetes Mellitus Women with previous gestational diabetes mellitus are an appropriate population in which to direct efforts at diabetes prevention because of the increased risk of developing type 2 diabetes in later years. GDM is also associated with increased risk of obesity and abnormal glucose metabolism during childhood and adult life in the offspring.

Introduction

G

estational diabetes mellitus (GDM) is common and, like obesity and type 2 diabetes that are related conditions, is increasing in frequency throughout the world. The risk of developing diabetes after GDM is very high. Recently completed research studies as well as ongoing studies promise important advances in the diagnosis and treatment of GDM. In addition, the risk of progressing to diabetes after GDM can be greatly reduced or delayed by moderate, sustained improvement in lifestyle or with medication in women who have impaired glucose tolerance (IGT). Women with previous GDM are an appropriate population in which to direct efforts at diabetes prevention because of the increased risk of developing type 2 diabetes in later years. Results reported recently from a randomized clinical trial (RCT) confirm that treating ‘mild GDM’ decreases the risk of adverse perinatal outcome1. Studies currently in progress hold much hope of providing the data from which ‘outcome based’ diagnostic criteria and appropriate strategies for the detection of GDM can be developed. The most widely accepted definition of gestational diabetes GESTATIONAL DIABETES MELLITUS

mellitus is “carbohydrate intolerance of varying degrees of severity with onset or first recognition during pregnancy”2,3. The definition applies regardless of treatment method or whether the condition persists after pregnancy. It does not exclude the possibility that unrecognized glucose intolerance may have antedated the pregnancy. Though the definition is simple, there is much controversy concerning the diagnostic methods and criteria as well as the appropriate therapy.

Consequences of GDM During the interval of more than four decades that GDM has been viewed as a distinct entity, many approaches to detection and diagnosis have been proposed. The criteria for GDM were originally set based on the power to detect women at risk for development of diabetes mellitus outside of pregnancy in later years4. Some of the criteria that have been proposed subsequently are based on the distribution of glucose values from oral glucose tolerance tests performed in other population samples of women during the last half of gestation. The high risk of progression to diabetes mellitus following GDM has been confirmed extensively among many different racial/ethnic groups. CHAPTER 3

211

Figure 3.1

Figure 3.2

Diabetes begets diabetes

Increasing incidence of GDM in Northern California, USA, 1991 – 2000

PGDM

12

MATERNAL FUELS

Incidence (%) 11

11 10

GDM

9.8

9.7

9 8.3

Impaired Adult Islet Function

Altered Fetal Islet Function Child Obesity

PUBERTAL IGT

IGT impaired glucose tolerance PGDM pre-gestational diabetes mellitus

8

8 7

7.2

7.2

6

6.9

6.9 6.4 5.8

5.1

5.1

4

4.1 3.9

4.1

1991

7.4

6.2

5.4

5

3

8.3

8.1

5.1

5.7 5.7

4.7

1993

1995

1997

2000

All ethnic groups ‡

Values presented = mean ± standard error

White (Non Hispanic) † African American †

‡ Adjusted for age and race-ethnicity † Adjusted for age

Hispanic †

Adapted from Ferrara et al, 20048

Asian †

In recent years, several important studies have demonstrated that the development of type 2 diabetes can be prevented or delayed by intervention with lifestyle changes or medications. Importantly, women with GDM were included among those recruited as subjects for the Diabetes Prevention Program5 in the USA. Women with previous GDM represented the target population that was used for the Troglitazone in the Prevention of Diabetes (TRIPOD) intervention trial6, also in the USA. It is also recognized that intrauterine exposure to the altered metabolic environment of diabetes or GDM is associated with increased risk of obesity and abnormal glucose metabolism during childhood and adult life in the offspring, thus, contributing to the progressive increase of obesity, GDM and type 2 diabetes in the population (see Figure 3.1). However, to date, the extent to which GDM adds to the risk of diabetes has been estimated in only one population, the Pima Indians of Arizona. Interest in the detection of GDM for the purpose of identifying pregnancies in which there may be increased risk of adverse perinatal outcome, for the most part, developed after the diagnostic criteria had been established. Consequently, one 212

CHAPTER 3

major source of controversy about GDM derives from uncertainty about what level of maternal hyperglycaemia is associated with a significant risk of adverse pregnancy outcome. There is general agreement that overt diabetes, diagnosed prior to pregnancy, clearly increases the risk of adverse pregnancy outcome. What is yet to be established is the level of glucose intolerance short of diabetes that is associated with significantly increased risk. The currently ongoing multicentre, international Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study is designed to address this issue7. It is anticipated that the collection of clinical data in the HAPO study will be completed in 2006 and the analysis of outcomes will begin early in 2007. In the meantime it is recommended that clinicians and investigators who currently have programmes in place for the diagnosis and treatment of GDM continue to use their present paradigms or adopt the recommendations that were made by the participants in the Fourth International Workshop Conference on Gestational Diabetes Mellitus2 and recently endorsed by the Fifth International Workshop Conference on GDM3. DIABETES ATLAS THIRD EDITION



Table 3.1 Treatment of GDM reduces adverse outcomes Outcome

Routine Care (N = 510)

Serious complications Neonatal nursery care Induction of labour Caesarean delivery EPDS Score >12

Intervention (N = 490)

Relative Risk (95% CI)

1% 71% 39% 31% 8%

0.33 (0.14-0.75) 1.13 (1.03-1.23) 1.36 (1.15-1.62) 0.97 (0.81-1.16) 0.46 (0.29-0.73)

4% 61% 29% 32% 17%

p 0.01 0.01 < .001 ns 0.001

Adjusted Treatment Effect (95% CI) Birth weight (BW) LGA Macrosomia (BW > 4 kg) SGA

3,482 ± 660 22% 21% 7%

CI

Confidence Interval

EPDS Score

Edinburgh Postnatal Depression Scale (3 months postpartum)

LGA

Large for gestational age;

SGA

Small for gestational age

ns

not significant

3,335 ± 551 13% 10% 7%

-145 (-219 to -70) 0.62 (0.47-0.81) 0.47 (0.34-0.64) 0.88 (0.56-1.39)

< .001 < .001 < .001 ns

Adapted from Crowther et al, 20051

Prevalence The reported prevalence of GDM has varied widely among different populations around the world. Much of this variability results from the differences in diagnostic criteria and methods for detection that are used in different centres. Recent data from Australia and the USA confirm earlier findings that the incidence of GDM varies among ethnic and racial groups. It is important to note that within each ethnic group, GDM prevalence has increased over time. For any given maternal age, the frequency of GDM has exhibited a similar increase. Large population-based studies in the United States that have controlled for maternal age and ethnicity have found a substantial increase in the overall incidence of GDM over the past decade, particularly in populations in rapid transition. While GDM is probably increasing globally, well designed, population-based studies to confirm this assumption are not presently available outside of the USA. Data from a report drawn from the data of the Kaiser Permanente Medical Care Program in Northern California 8 illustrate the points mentioned above very well. In this study, it was possible to compare trends in yearly incidence of GDM among Caucasian, GESTATIONAL DIABETES MELLITUS

African American, Hispanic and Asian populations, as shown in Figure 3.2. The increase in GDM has paralleled the increase in obesity within the population in the reproductive age. The increase in overweight and obesity among adolescents and young adults is of major concern. Further investigation is required to establish if this weight increase is either causal for or a concomitant of GDM.

Benefits of treating GDM The lack of convincing evidence for benefit from treating ‘mild GDM’ has also been a source of controversy concerning the value of efforts to identify women with GDM. However, the results of a randomized clinical trial conducted in Australia and reported recently help to clarify this issue1. Some of the key findings from this report are summarized in Table 3.1. The RCT demonstrated that treating GDM (diagnosed when the fasting plasma glucose was 4.8 + 0.7 mmol/L and the two-hour value after a 75 gm oral glucose load was from 7.811.0 mmol/L) significantly reduced the likelihood of serious. CHAPTER 3

213

IN TOUCH WITH: SAMINA FAISAL

Every woman desires a normal and healthy child and gestational diabetes mellitus should not come in the way of achieving this goal. A disciplined life, exercise, a healthy diet and close monitoring of blood glucose is the secret for a successful outcome of pregnancy with GDM. Educating the woman on diabetes is the most important factor.

Samina Faisal delivered her first child, a normal baby girl, nine years after her marriage to a fellow banker in Pakistan. It was an uneventful pregnancy after two sad experiences of an abortion and a tubal pregnancy. Samina had enjoyed her food throughout her pregnancy, especially sweets, which resulted in an increase of 27 kgs in weight. After delivery, she found it difficult to shed those extra kilos. Four years later she conceived again and, on screening in her sixth month, was diagnosed as having gestational diabetes mellitus (GDM). In these six months she had had a weight gain of some 11 kgs. “I felt so very hungry and I love sweets,” Samina recalled. She was advised an appropriate

neonatal morbidity (complications) compared to outcomes in those receiving routine prenatal care. Birth weight and the frequency of delivering babies that were large for gestational age (LGA) or macrosomic (> 4.0 kg birth weight) were also less in the treatment/intervention group. Treatment included individualized medical nutrition therapy, daily self-monitoring of blood glucose and insulin when needed (20% of cases). Other RCT studies of similar design are in progress.

New approaches to treatment As pointed out above, there is uncertainty about “the level of hyperglycaemia, short of overt diabetes that conveys increased perinatal risk”7. However, there is general consensus that in women with a diagnosis of GDM that have clearly elevated fasting and/or one or two hours after meal blood glucose concentrations at diagnosis, lowering the maternal blood glucose levels to near normal concentrations may reduce the risk of excessive fetal growth to approximate the risk in the general population2,3 and a major objective of treating GDM is to reduce adverse perinatal events, primarily those associated with excess weight or adiposity of the newborn (Caesarean delivery, birth trauma, neonatal morbidities). 214

CHAPTER 3

Medical nutrition therapy remains the initial and primary modality for treatment of GDM. When optimal glycaemia is not achieved or maintained, additional treatment with biosynthetic human insulin has been used globally for many years. Following the demonstration that the sulfonylurea, glyburide (glibenclamide), crosses the placenta to a very limited extent9, a randomized clinical trial was carried out comparing glycaemic control and outcomes in women with GDM treated with human insulin or glyburide (glibenclamide)10. Comparable results were found in the two groups. Additional reports with smaller numbers of subjects have been published subsequently that in general confirm the results of the original study. The participants of the Fifth International Workshop Conference on GDM cautioned that the findings with glyburide (glibenclamide) could not be extrapolated to other sulfonylurea agents or other classes of oral medication. They further emphasized the need for continued close surveillance of maternal glycaemia during all forms of treatment of GDM to assure that treatment goals are met and sustained3.

DIABETES ATLAS THIRD EDITION



diet along with insulin injections to control her blood sugar. Samina eventually underwent a Caesarian section as the baby was under stress, and delivered another baby girl. After delivery, Samina no longer had diabetes but her blood glucose levels did not revert back to normal; she had impaired glucose tolerance (IGT). She was then careful with her diet and in a period of two years lost about 7 kgs. Samina became pregnant with her third child and this time, she was monitored from the very beginning. She continued with her professional responsibilities along with the work at home with her two girls. The blood glucose remained in the normal range with a an appropriate diet; insulin was not required. She maintained regular meal timings and had a daily walk. Throughout the pregnancy she was extra careful, and the baby grew beautifully as was seen on the ultrasonography pictures. In this pregnancy, Samina had a weight gain of the normal 10 kgs. She felt well and truly enjoyed her baby within her.

An elective Caesarian section was performed on completing 37 weeks, delivering another baby girl weighing 3 kgs. Samina and her husband Faisal are proud parents of three healthy and beautiful girls. She is nursing the little one and has to be very attentive to the middle one who had demanded more attention after the birth of her sister. “It is a very difficult task”, said Samina, “but I manage alright.” Samina has understood gestational diabetes very well. “My case can be taken as an example for other women,” she offered. With a family history of diabetes, it is mandatory to acquire a healthy lifestyle to prevent the onset of the metabolic disorder. When a woman marries she should take extra care to avoid obesity as that is a major contributor to the risk of GDM. If GDM develops, a very strict diet regime, insulin if advised, exercise and close monitoring of blood glucose are the tools to eventually hold a normal smiling baby in one’s arms.

Prevention As indicated above, risk of developing diabetes following GDM in women with IGT can be reduced greatly by moderate, sustained changes in lifestyle, or by taking medication to improve insulin sensitivity. There have not been randomized clinical trials to determine if GDM can be prevented in women at ‘high risk’ for its development during pregnancy (obese, strong family history of type 2 diabetes, member of an ethnic group with a high prevalence of type 2 diabetes). However, epidemiological data suggest that sustaining a healthy lifestyle would also reduce the risk of GDM in `high risk´ individuals.

GESTATIONAL DIABETES MELLITUS

CHAPTER 3

215

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CHAPTER 4 DIABETES MORTALITY

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In India, an estimated 15.5% of all deaths in adult women are attributable to diabetes.

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Routine health statistics based upon death certification seriously underestimate mortality from diabetes1. This is because only a minority of persons with diabetes die from a cause uniquely related to the condition, such as diabetic ketoacidosis or hypoglycaemia. About 50% of persons with diabetes die of cardiovascular disease, and 10-20% die of renal failure2. A study of death certificate coding practices in nine European countries demonstrated that much of the reported variation in diabetes mortality was due to variations in certification practices and in the national coding procedures for assigning the ‘underlying cause of death’3. Most of the variations concerned the coding of co-morbidity involving diabetes and ischaemic heart disease or stroke. Analysing all the conditions mentioned on the certificate was shown to overcome some of these problems4. However, multiplecause encoding is not likely to become generally available, even in industrialized countries, because of the cost involved. Another problem is that only some 30% of deaths worldwide are medically certified, and alternative sources of information have to be identified to obtain information on causation. DIABETES MORTALITY

Chapter4Fin.indd Sek1:219

DisMod II, a software programme, was used to estimate the number of deaths attributable to diabetes in the year 2007 in persons 20-79 years old. The programme was developed for the Global Burden of Disease 2000 study5, and is based on a set of differential equations that describe age-specific incidence, remission, case fatality (or relative risk on total mortality), ‘all other causes’ mortality, prevalence and diseasespecific mortality. DisMod II implements exact mathematical solutions of these equations. The input data in this study were: • Number of persons by 10-year age and sex groups for each country for the year 2007 (UN population projections). • Expected number of all deaths in each country, by 10-year age stratum and gender (by applying the age and sex-specific mortality rate for the year 2001 to the population of the year 2007). • Estimates of diabetes prevalence by 10-year age and sex groups for each country for the year 2007 (see Chapter 1). • Remission (equal to zero) and age and sex-specific relative risks of mortality. CHAPTER 4

219

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FIGURE 4.1

FIGURE 4.2

Number of deaths attributable to diabetes (20-79 age group) by region, 2007

Deaths attributable to diabetes as percentage of all deaths (20-79 age group) by region, 2007

Number of deaths

Percentage of all deaths (%) 14

600,000

12

500,000

10 400,000 8 300,000 6 200,000 4 100,000

2

0

0 AFR

EMME

EUR

NA

SACA

SEA

WP

AFR

EMME

EUR

NA

SACA

SEA

WP

Males Females

• Remission of diabetes (equal to zero). • Relative risk of dying for persons with diabetes compared to those without diabetes, from population-based followup studies (see Table 4.1). The published studies were from USA6 and Taiwan7. The unpublished relative risks of death by age and sex were obtained by personal communication from investigators of the DECODE and DECODA studies8,9. DisMod was used to calculate the number of excess deaths that could be attributed to diabetes in each region, i.e. the number of deaths among those with diabetes over and above those expected according to underlying mortality rates. Given that the diabetes-specific variables in the computer model are the prevalence, remission and relative risk of death, DisMod II smoothes out the age-specific relative risks of death available from the different studies (see Table 4.1) and calculates what proportion of all deaths is attributable to diabetes using a simple formula for population-attributable fraction10.

Global excess mortality attributable to diabetes in adults 20220

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79 years old in the year 2007 is estimated at 3.8 million deaths (1.8 million men and 2 million women). Since most deaths attributable to diabetes occur in persons 20-79 years old, these 3.8 million deaths account for more than 6% of total world mortality. The total number of deaths attributable to diabetes in adults 20-79 years old, as well as the percentage of deaths attributable to diabetes in this particular age group are shown in Table 4.2. Tables 4.3 - 4.16 show the number of deaths attributable to diabetes in the year 2007 for 193 countries. The number of deaths attributable to diabetes in each IDF region is shown in Figure 4.1, and the percentage of all deaths that are due to diabetes is shown in Figure 4.2. Figure 4.3 shows the percentage of all deaths that are due to diabetes in the top 10 countries with the highest diabetes prevalence in 2007. The percentage of excess deaths was lowest in the poorest African countries, and in Mongolia, Chile, Paraguay, Iceland, and highest in North America, the Eastern Mediterranean and Middle East, Mauritius and in the small Western Pacific island countries. However, even in poor African countries diabetes accounts for about 5% of all mortality in the productive age group of 30-60 year olds. Over two-thirds of DIABETES ATLAS THIRD EDITION

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FIGURE 4.3 Deaths attributable to diabetes as percentage of all deaths in the top 10 countries for diabetes prevalence (20-79 age group), 2007

Nauru United Arab Emirates Saudi Arabia Bahrain Kuwait Oman Tonga Mauritius Egypt Mexico

0

10

20

30

40

50

Percentage of all deaths (%) Males Females

deaths attributable to diabetes occur in developing countries. In countries with a high prevalence of diabetes in younger age groups (in South-East Asia, Eastern Mediterranean, North America and Western Pacific islands), the percentage of excess deaths peaked at 50-59 years of age. In the rest of the world, where the prevalence is higher in older age groups the percentage of excess deaths due to diabetes was highest in persons 60-69 years old. In almost all countries the proportion of deaths due to diabetes was higher in females than in males.

Although many studies of mortality in persons with diabetes have demonstrated the deceptive character of routinely collected mortality statistics on diabetes, they are, nevertheless, widely used in national and international health reports. Such underestimation has potentially undesirable consequences, since mortality rates are often used as a basis for priority setting and resource allocation. The number of deaths attributable to diabetes calculated in DIABETES MORTALITY

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this study is three to four times greater than those given in the conventional international statistical reports largely based on diabetes given as an underlying cause on death certificates11. The number of excess deaths attributable to diabetes is similar in magnitude to those reported for HIV/ AIDS in the year 200211. The higher proportion of excess deaths in females compared to males is explained by their lower background mortality levels, and the larger increase in the absolute risks of dying in women compared with men if diabetic, in almost all age groups. Although diabetes is often perceived as a disease of affluent countries, the proportion of all deaths that are attributable to diabetes in developing countries is not negligible. This issue of considerable preventable mortality due to diabetes has been recognized for type 1 diabetes12, but the vast majority of persons have type 2 diabetes. A potential source of error in this study is that relative risks of dying in persons with diabetes, compared to those without diabetes, were obtained from studies conducted in a relatively small number of countries, most of them developed. Because little data from developing countries has been published in a format suitable for this study, unpublished CHAPTER 4

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MAP 4.1 Deaths attributable to diabetes as percentage of all deaths in males (20-79 age group), 2007

>20% 14% - 20% 10% - 14% 8% - 10% 6% - 8% 4% - 6% <4% no data

data from the DECODE and DECODA studies have been used (Jaakko Tuomilehto, personal communication). This is unlikely to have overestimated the burden of diabetes mortality in developing countries, as available studies from Mauritius and Brazil show that the risk of death is about three times higher in persons with known diabetes, compared to individuals with normal blood glucose, or the general population13,14. This is consistent with the sparse information available from low-income countries indicating a poor prognosis for persons with diabetes15. The latest validated available country-specific mortality rates were for the year 2001 and it is possible that the overall number of deaths in each country has not been accurately estimated because these mortality rates, rather than those for the year 2007, were applied to the estimated population size in the year 2007. It is unlikely, however, that the countryspecific adult mortality rates have changed substantially since 2001. The age and sex-specific prevalence of diabetes by country used in this study was estimated from population-based surveys. However, population-based studies of diabetes 222

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prevalence have not been conducted for more than onethird of the countries of the world, so the diabetes prevalence for many countries was obtained by extrapolation, and error is possible. The prevalence estimates include both diagnosed and undiagnosed diabetes. The relative risks of dying were derived from cohort studies of patients with diabetes. The proportion of undiagnosed diabetes varies between populations, but is rarely lower than 30%, and is often higher than 50%, even in developed countries. The relative risk of dying may not be the same for diagnosed and undiagnosed diabetes, but published data from the DECODE study show that mortality in people with undiagnosed diabetes is as high as in people with previously diagnosed diabetes8. Moreover, people with a lesser degree of hyperglycaemia, impaired glucose tolerance (IGT), have a 40% increased mortality, regardless whether they progress to diabetes or not16. One of the reasons for non-diagnosis of diabetes could be lack of symptoms, which could reflect a milder metabolic disturbance and possibly a better prognosis. If so, the calculated 3.8 million deaths could be an overestimate of the true number. The results of the DECODE study indicate that DIABETES ATLAS THIRD EDITION

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MAP 4.2 Deaths attributable to diabetes as percentage of all deaths in females (20-79 age group), 2007

>20% 14% - 20% 10% - 14% 8% - 10% 6% - 8% 4% - 6% <4% no data

risk of death is not significantly different between previously and newly diagnosed persons with diabetes, when unrecognized diabetes is defined by the two-hour post-load glucose value rather than by fasting glucose8. Although these mortality estimates are unlikely to be accurate, given the assumptions on which the calculations are based, they do provide a more realistic estimate of diabetes-attributable mortality than currently exist. The number of deaths related to hyperglycaemia would likely be even higher if mortality attributable to IGT were taken into account17.

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TABLE 4.1 Age and sex-specific relative risks of death used to estimate the proportion of all deaths attributable to diabetes DECODE STUDY Age group 20-29 30-39 40-49 50-59 60-69 70-79

a. b. c. d. e.

a

Males

Females

3.66 3.38 1.85 1.63 1.60 1.39

6.05 5.41 3.14 2.64 2.04 1.79

b

DECODA STUDY (Indians in Mauritius and Fiji) Males Females 3.40 3.50 2.60 2.30 1.60 1.50

5.12 4.98 3.65 3.29 2.51 2.42

c

DECODA STUDY (All) Males Females 3.70 3.30 1.95 1.65 1.62 1.40

5.95 5.61 3.41 2.73 2.08 1.78

TAIWAN STUDY

d

NHANES

e

Males

Females

Males

Females

5.42 5.26 4.24 3.02 2.22 1.46

4.68 4.64 4.25 3.44 2.58 1.61

3.08 4.60 2.80 2.00 1.65 1.40

3.20 3.10 2.80 2.60 2.10 1.60

Used for Europe, Australia and New Zealand Used for South Asia Used for Africa and Eastern Mediterranean Used for Western Pacific (except Australia and New Zealand) Used for North and South America and the Caribbean

TABLE 4.2 Regional estimates of death attributable to diabetes in males and females (20-79 age group), 2007 REGION AFR EMME EUR NA SACA SEA WP Total

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MALE

FEMALE

TOTAL

DM AS % OF ALL DEATHS

133,055 115,933 329,423 122,505 90,461 430,109 544,719

204,322 181,531 391,873 119,129 98,192 587,100 432,918

337,377 297,464 721,296 241,634 188,653 1,017,209 977,637

5.4 11.5 11.1 11.8 9.4 12.1 8.6

1,766,205

2,015,065

3,781,270

9.6

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TABLE 4.3 Number of deaths attributable to diabetes in males (20-79 age group), 2007 - African Region

COUNTRY Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Congo, Democratic Republic of Côte d’Ivoire Djibouti Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Tanzania, United Republic of Togo Uganda Zambia Zimbabwe

AFR Total - males

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20-29

30-39

540 171 31 333 216 723 5 153 2 7 95 1,272 660 0 8 53 928 20 19 206 173 25 887 67 130 154 316 302 1 452 31 323 2,865 263 2 149 1 256 207 785 589 810 140 494 527 564

1,131 346 138 791 330 1,145 8 333 67 12 228 2,787 1,506 6 15 126 2,150 41 38 670 341 54 2,365 192 187 315 653 579 19 1,160 123 612 5,305 331 3 245 3 556 481 3,610 2,051 2,053 289 999 1,619 1,868

15,958

37,880

AGE GROUPS (YRS) 40-49 50-59

TOTAL

DM AS % OF ALL DEATHS

222 155 35 284 51 540 12 119 104 9 85 770 531 13 15 39 976 52 35 527 241 27 560 59 71 331 164 411 37 441 47 181 2,960 104 3 172 8 146 130 1,198 982 553 130 346 231 329

3,406 1,418 421 2,635 901 3,930 40 1,104 905 64 734 8,935 5,209 108 86 376 7,822 279 226 3,522 1,664 222 6,332 613 698 1,873 1,907 2,457 343 4,086 398 2,240 22,825 1,041 12 1,235 37 1,875 1,503 12,666 7,969 6,262 1,079 3,016 3,830 4,754

3.6 5.1 3.4 4.3 1.8 5.7 5.4 5.1 2.7 3.4 5.8 3.3 5.3 4.3 5.2 2.3 2.6 5.4 4.7 4.7 4.7 4.1 3.9 4.1 5.3 3.5 2.6 4.6 3.6 4.3 4.1 4.4 5.2 1.8 5.0 5.2 11.8 5.0 3.4 4.2 4.2 3.3 4..8 2..5 4.6 4.0

14,434

133,055

•

60-69

70-79

449 244 66 333 90 416 3 148 268 12 91 1,235 759 32 15 41 1,112 52 42 664 274 36 725 93 101 345 221 325 101 642 56 365 3,626 95 1 212 9 271 213 2,194 1,338 840 156 336 390 551

402 260 49 467 65 594 7 162 285 15 119 1,292 864 38 21 63 1,351 78 57 884 390 44 632 78 122 441 228 487 124 696 57 345 4,563 114 2 282 12 275 204 1,976 1,471 878 190 396 344 433

23,339 19,586

21,857

662 243 102 427 148 512 5 187 177 10 116 1,577 889 18 12 54 1,304 36 35 571 246 37 1,164 123 88 287 326 352 62 696 84 415 3,508 136 1 175 5 370 269 2,903 1,536 1,129 174 445 718 1,008

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TABLE 4.4 Number of deaths attributable to diabetes in females (20-79 age group), 2007 - African Region

COUNTRY Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Republic Chad Comoros Congo Congo. Democratic Republic of Côte d’Ivoire Djibouti Equatorial Guinea Eritrea Ethiopia Gabon Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa Swaziland Tanzania. United Republic of Togo Uganda Zambia Zimbabwe

AFR Total - females

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20-29

30-39

943 444 151 1,171 294 2,022 5 527 99 8 297 2,190 2,055 7 21 114 1,920 48 33 579 358 67 2,347 211 316 270 672 807 34 1,024 103 828 7,360 281 3 323 0 639 338 2,561 96 1,922 403 855 1,565 1,899

1,380 485 426 1,277 452 1,984 8 537 477 15 361 3,664 2,188 52 23 208 3,286 68 47 925 433 82 3,552 428 235 459 945 827 111 1,794 277 833 7,683 403 5 379 2 706 628 8,480 182 3,059 455 1,362 2,397 3,771

38,212

57,348

AGE GROUPS (YRS) 40-49 50-59

TOTAL

DM AS % OF ALL DEATHS

404 362 125 530 145 722 32 247 431 16 174 1,546 787 59 29 92 1,575 102 58 1,006 423 61 726 164 136 450 243 801 195 646 113 344 5,551 148 8 291 15 282 172 3,507 56 1,107 255 464 321 730

4,708 2,196 1,233 4,297 1,421 6,776 74 1,938 2,354 88 1,232 12,995 7,354 341 131 709 12,067 410 278 4,718 2,200 364 9,731 1,436 1,022 2,550 2,963 3,917 885 6,049 902 3,341 34,530 1,278 28 1,927 39 2,651 1,960 28,216 587 10,300 1,742 4,215 5,933 10,237

6.2 8.5 9.1 7.5 3.2 10.2 8.9 8.7 7.6 5.8 9.8 5.6 9.1 11.9 8.6 4.4 4.3 8.9 7.7 7.4 7.8 7.4 6.3 8.8 8.9 5.5 4.0 7.7 10.0 7.0 9.5 7.5 8.6 2.7 8.3 8.6 22.7 8.4 5.5 10.0 8.7 5.5 8.1 3.8 7.3 8.5

25,651

204,322

•

60-69

70-79

679 310 160 408 171 634 7 198 455 17 121 1,865 737 79 20 101 1,777 71 47 735 319 52 976 210 118 494 364 458 190 867 129 466 4,677 142 3 339 8 336 281 4,319 83 1,400 201 511 487 1,173

507 324 104 433 129 641 13 199 446 18 136 1,725 722 75 24 99 1,643 75 54 862 398 55 652 158 129 472 295 604 210 760 102 412 5,066 133 6 338 10 316 212 3,436 58 1,234 212 452 362 746

30,858 27,194

25,058

794 272 267 476 230 773 8 229 445 15 143 2,005 865 69 15 95 1,866 46 39 612 268 47 1,477 265 88 405 445 420 145 958 179 458 4,194 173 3 258 3 372 330 5,912 112 1,578 215 570 800 1,917

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TABLE 4.5 Number of deaths attributable to diabetes in males (20-79 age group), 2007 - Eastern Mediterranean and Middle East Region

COUNTRY

AGE GROUPS (YRS) 40-49 50-59

20-29

30-39

Afghanistan Algeria Armenia Bahrain Egypt Iran. Islamic Republic of Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Morocco Oman Pakistan Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen

1,111 398 5 2 811 203 125 17 14 4 36 171 29 1,225 8 91 15 307 48 29 188

3,312 548 22 14 1,858 630 591 87 18 26 46 376 52 3,525 46 270 243 373 98 121 297

2,842 553 80 55 2,629 1,284 886 128 52 62 41 401 103 5,504 79 867 591 312 137 358 133

EMME Total - males

4,839

12,554

17,096

TOTAL

DM AS % OF ALL DEATHS

1,030 1,539 554 29 2,807 3,291 1,067 214 173 405 182 1,015 114 5,243 11 1,106 471 1,238 255 266 144

13,662 5,327 1,263 230 17,013 12,276 5,974 999 628 1,008 576 3,944 604 34,360 315 5,869 3,966 3,686 1,070 1,967 1,196

9.5 8.0 7.6 13.3 8.4 7.5 9.4 9.4 14.6 9.1 4.0 7.5 11.3 8.7 14.7 10.9 3.0 11.0 4.0 17.3 3.0

21,153

115,933

•

60-69

70-79

2,738 899 205 74 4,201 3,106 1,368 188 114 154 85 790 154 8,801 107 1,720 1,176 474 224 540 178

2,630 1,391 397 55 4,707 3,761 1,937 364 257 357 186 1,191 153 10,062 64 1,815 1,470 981 308 654 256

27,296

32,996

TABLE 4.6 Number of deaths attributable to diabetes in females (20-79 age group), 2007 - Eastern Mediterranean and Middle East Region

COUNTRY

AGE GROUPS (YRS) 40-49 50-59

20-29

30-39

Afghanistan Algeria Armenia Bahrain Egypt Iran, Islamic Republic of Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Morocco Oman Pakistan Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen

1,741 404 5 1 767 191 157 14 7 1 51 111 23 2,869 2 93 319 227 57 18 299

3,345 843 31 9 1,742 775 766 86 13 15 83 451 48 6,066 17 276 1,162 390 152 58 450

2,926 1,072 138 41 3,520 1,796 1,244 161 39 93 90 681 74 7,681 37 644 1,361 464 269 180 391

EMME Total - females

7,357

16,778

22,902

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TOTAL

DM AS % OF ALL DEATHS

1,990 2,732 1,137 71 14,872 5,557 1,909 272 151 642 286 1,836 156 12,042 11 1,121 1,966 1,822 785 133 292

16,530 8,480 2,261 254 41,136 17,100 8,362 1,117 466 1,496 861 5,694 572 53,118 130 4,539 9,222 4,799 2,234 791 2,368

15.9 15.1 16.6 25.0 21.6 14.9 17.4 17.6 25.8 16.8 10.9 14.0 21.7 15.9 26.9 18.8 8.9 20.4 11.6 31.6 6.0

49,783

181,531

•

60-69

70-79

3,462 1,582 356 51 8,402 4,207 1,959 249 98 278 151 1,265 119 11,678 40 1,029 2,089 756 450 213 504

3,066 1,847 595 81 11,834 4,575 2,326 333 158 467 199 1,350 152 12,782 23 1,375 2,325 1,140 521 189 432

38,938

45,771

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TABLE 4.7 Number of deaths attributable to diabetes in males (20-79 age group), 2007 - European Region

COUNTRY

AGE GROUPS (YRS) 40-49 50-59

20-29

30-39

Albania 13 Andorra 0 Austria 5 Azerbaijan 27 Belarus 149 Belgium 2 Bosnia and Herzegovina 10 Bulgaria 11 Croatia 7 Cyprus 5 Czech Republic 41 Denmark 1 Estonia 13 Finland 10 France 54 Georgia 11 Germany 45 Greece 17 Hungary 42 Iceland 0 Ireland 10 Israel 11 Italy 22 Kazakhstan 74 Kyrgyzstan 11 Latvia 18 Liechtenstein 0 Lithuania 35 Luxembourg 0 Macedonia, the Former Yugoslav Republic of 9 Malta 0 Moldova 40 Monaco 0 Netherlands 2 Norway 6 Poland 224 Portugal 7 Romania 116 Russian Federation 3,586 San Marino 0 Serbia and Montenegroa 16 Slovakia 29 Slovenia 9 Spain 18 Sweden 7 Switzerland 4 Tajikistan 15 Turkey 125 Turkmenistan 16 Ukraine 597 United Kingdom 2 Uzbekistan 52

25 0 27 79 388 17 37 66 29 8 113 14 39 29 158 57 283 53 211 0 23 37 175 370 74 55 0 94 1 23 0 111 0 17 16 608 55 458 7,055 0 65 79 25 173 23 21 69 590 84 1,787 35 276

33 1 90 193 569 64 70 169 115 18 191 57 59 58 470 122 1,017 79 382 0 30 53 396 763 158 69 0 142 3 33 2 178 0 77 23 1,104 120 642 9,909 0 168 146 44 358 45 60 139 1,417 179 2,667 178 600

14,034

23,462

EUR Total - males

5,524

TOTAL

DM AS % OF ALL DEATHS

240 9 1,394 968 1,615 1,817 576 1,357 934 89 1,587 472 227 883 7,593 752 17,602 1,907 1,779 9 386 947 9,250 937 243 434 5 543 65 248 59 600 4 2,302 391 5,612 1,869 4,002 26,936 3 1,681 1,701 310 6,294 813 1,044 197 4,306 213 9,238 3,973 994

587 18 2,828 2,415 5,279 3,117 1,394 3,323 2,091 244 4,010 1,317 674 1,676 15,116 1,808 34,761 3,153 5,157 16 789 1,780 17,277 5,101 1,165 1,085 9 1,526 122 629 112 1,880 7 4,202 726 14,937 3,505 9,802 94,087 6 3,886 3,054 753 11,565 1,563 2,025 914 15,323 1,128 28,354 8,517 4,609

4.4 8.3 10.8 7.0 8.6 8.5 7.3 7.6 7.4 10.8 9.0 7.0 8.8 9.0 8.1 7.3 11.1 8.5 8..9 2.8 6.4 13.1 8.6 6.6 6.2 8.9 10..7 8.8 8.3 7.1 9.2 8.7 8.0 8.6 5.6 8.8 8.3 8.9 8.6 8.5 7.4 12.9 9.0 8.4 6.3 1.8 5.4 7.4 6.1 8.8 4.2 6.2

127,412

329,423

•

60-69

70-79

204 6 1,006 699 1,475 919 502 1,147 749 86 1,418 562 215 500 4,786 609 12,737 829 1,749 4 253 515 5,843 1,576 369 337 3 471 41 216 40 568 2 1,411 218 4,455 1,104 2,975 26,818 2 1,320 693 250 3,585 526 686 304 5,520 346 9,151 3,386 1,565

54,241 104,750

73 2 306 449 1,082 298 199 572 258 39 661 210 121 195 2,055 256 3,077 267 995 2 87 217 1,591 1,382 310 172 1 241 12 100 11 383 1 393 72 2,933 352 1,609 19,783 1 635 406 114 1,137 149 210 189 3,365 290 4,915 943 1,122

a. Estimates made prior to the establishment of Serbia and Montenegro as independent countries

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TABLE 4.8 Number of deaths attributable to diabetes in females (20-79 age group), 2007 - European Region

COUNTRY

AGE GROUPS (YRS) 40-49 50-59

20-29

30-39

Albania Andorra Austria Azerbaijan Belarus Belgium Bosnia and Herzegovina Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Georgia Germany Greece Hungary Iceland Ireland Israel Italy Kazakhstan Kyrgyzstan Latvia Liechtenstein Lithuania Luxembourg Macedonia, the Former Yugoslav Republic of Malta Moldova Monaco Netherlands Norway Poland Portugal Romania Russian Federation San Marino Serbia and Montenegroa Slovakia Slovenia Spain Sweden Switzerland Tajikistan Turkey Turkmenistan Ukraine United Kingdom Uzbekistan

24 0 7 24 15 1 6 11 4 1 5 6 1 7 34 9 69 6 5 0 10 1 9 4 1 2 0 3 0 3 0 6 0 1 3 24 1 18 321 0 12 3 1 3 9 7 2 176 1 67 34 6

23 0 30 104 74 11 34 74 24 3 30 24 8 18 132 46 308 26 58 0 33 11 94 63 15 10 0 19 0 15 0 29 0 17 11 130 15 144 1,410 0 77 17 6 48 24 26 23 848 18 394 141 72

35 0 115 308 272 69 96 207 118 9 119 63 30 48 513 149 1,174 66 231 0 41 35 379 437 87 34 0 75 3 50 2 121 0 112 21 610 80 415 4,777 0 238 78 29 225 53 77 111 2,167 90 1,313 377 387

EUR Total - females

962

4,740

16,048

TOTAL

DM AS % OF ALL DEATHS

508 11 1,926 2,579 4,063 2,181 1,024 2,945 1,692 122 2,867 899 466 910 8,899 1,754 22,584 3,110 3,645 13 378 1,013 11,301 1,939 427 580 6 1,055 84 532 97 1,425 5 2,828 402 10,098 2,494 8,013 58,655 4 3,665 1,490 506 7,574 1,574 1,287 245 10,454 358 21,047 6,696 1,585

805 17 3,107 4,873 6,986 3,259 1,998 5,351 2,750 194 4,814 1,683 813 1,420 15,052 3,264 36,595 4,123 6,514 20 718 1,624 17,390 6,234 1,169 1,003 10 1,776 128 1,020 165 2,807 8 4,656 642 17,201 3,777 13,354 107,103 6 6,562 2,516 834 11,022 2,373 2,244 880 26,769 1,115 37,374 10,992 4,763

10.6 15.0 19.6 15.8 16.4 14.9 15.5 16.5 15.6 12.9 16.8 12.3 16.6 13.5 15.2 16.1 19.7 17.5 16.6 4.7 9.4 16.6 14.5 9.9 8.6 16.5 19.4 16.4 14.8 15.7 20.2 16.2 15.2 14.6 8.1 16.6 15.1 16.6 16.2 14.4 15.7 16.7 16.7 15.1 14.8 19,.3 7.6 16.9 8.7 16.5 7.7 8.5

220,013

391,873

•

60-69

70-79

60 2 318 730 921 316 276 744 286 19 593 205 105 151 2,155 375 3,283 235 902 2 94 183 1,547 1,594 286 134 1 197 13 142 17 454 1 522 68 2,524 334 1,587 16,215 1 924 312 105 872 202 274 239 4,969 305 4,435 1,150 1,191

154 4 711 1,129 1,641 680 563 1,369 626 40 1,200 486 204 285 3,319 931 9,178 679 1,672 4 162 382 4,060 2,198 353 242 2 427 28 278 49 774 2 1,176 138 3,815 853 3,177 25,724 1 1,644 616 188 2,300 511 572 260 8,155 343 10,117 2,594 1,523

52,570

97,539

a. Estimates made prior to the establishment of Serbia and Montenegro as independent countries

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TABLE 4.9 Number of deaths attributable to diabetes in males (20-79 age group), 2007 - North American Region

COUNTRY 20-29

30-39

AGE GROUPS (YRS) 40-49 50-59

60-69

TOTAL

DM AS % OF ALL DEATHS

70-79

Antigua and Barbuda Bahamas Barbados Belize Canada Dominica Grenada Guyana Haiti Jamaica Mexico Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Trinidad and Tobago United States of America

0 1 1 1 29 0 0 3 133 4 545 0 0 0 4 1,078

1 14 6 5 224 2 3 31 1,499 40 3,138 1 3 4 46 2,332

2 21 14 9 687 5 6 53 1,231 100 3,733 2 8 9 97 15,578

4 23 19 13 1,650 5 9 60 788 144 4,823 3 10 9 145 16,747

5 23 16 11 3,002 4 7 42 514 152 5,777 3 8 10 119 26,976

5 15 11 5 3,987 2 3 18 184 85 5,066 1 4 7 38 21,250

17 96 66 43 9,579 18 29 206 4,350 525 23,082 9 34 40 449 83,962

8.2 10.3 7.8 8.2 11.4 10.3 8.3 8.5 8.4 8.0 11.2 7.8 8.1 10.9 9.5 10.0

NA Total - males

1,800

7,348

21,555

24,454

36,669

30,679

122,505

•

TABLE 4.10 Number of deaths attributable to diabetes in females (20-79 age group), 2007 - North American Region

COUNTRY Antigua and Barbuda Bahamas Barbados Belize Canada Dominica Grenada Guyana Haiti Jamaica Mexico Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Trinidad and Tobago United States of America

NA Total - females

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AGE GROUPS (YRS) 40-49 50-59

20-29

30-39

60-69

0 1 0 1 13 0 0 4 167 5 224 0 0 0 5 610

0 6 2 3 67 1 2 19 586 29 927 0 2 2 21 1,099

2 18 9 8 377 3 7 52 865 112 2,712 2 10 9 80 5,618

5 27 20 14 1,276 6 15 90 1,245 198 6,860 4 15 13 190 10,343

7 36 26 17 2,241 7 16 99 1,251 273 9,884 5 20 15 259 28,312

1,031

2,766

9,884

20,320

42,469

TOTAL

DM AS % OF ALL DEATHS

8 29 30 17 3,164 7 13 88 840 344 9,123 4 15 12 180 28,786

21 117 87 58 7,137 25 54 352 4,955 960 29,731 16 62 51 736 74,768

13.7 17.4 14..9 17.1 13.2 20.1 17.6 16.9 14.3 18.4 20.2 17.1 17.6 17.0 21.2 12.4

42,660

119,129

•

70-79

DIABETES ATLAS THIRD EDITION

27/10/06 4:19:12

TABLE 4.11 Number of deaths attributable to diabetes in males (20-79 age group), 2007 - South and Central American Region

COUNTRY Argentina Bolivia Brazil Chile Colombia Costa Rica Cuba Dominican Republic Ecuador El Salvador Guatemala Honduras Nicaragua Panama Paraguay Peru Suriname Uruguay Venezuela SACA Total - males

AGE GROUPS (YRS) 40-49 50-59

20-29

30-39

35 27 862 23 120 16 11 36 46 67 101 55 34 16 9 79 4 2 140

347 183 4,397 194 844 64 216 344 299 332 384 263 153 81 63 618 21 26 472

998 306 7,595 431 1,302 124 515 518 415 287 402 264 185 96 134 889 40 59 672

1,683

9,301

15,230

TOTAL

DM AS % OF ALL DEATHS

2,062 316 9,786 615 1,307 236 316 169 448 327 664 381 195 175 51 1,069 21 188 958

8,669 1,784 44,367 2,731 7,167 823 2,431 2,012 2,284 1,629 2,566 1,641 988 646 589 5,096 158 654 4,225

7.1 7.1 7.8 7.0 5.7 9.9 7.8 7.7 6.7 9.2 8.8 9.4 10.4 10.1 5.1 7.2 9.7 6.9 7.5

19,284

90,461

•

60-69

70-79

2,230 456 10,171 652 1,625 166 658 520 544 294 470 302 202 113 192 1,151 39 149 881

2,998 497 11,555 816 1,969 217 715 425 533 323 546 375 219 164 141 1,290 34 231 1,103

20,814

24,149

TABLE 4.12 Number of deaths attributable to diabetes in females (20-79 age group), 2007 - South and Central American Region

COUNTRY

AGE GROUPS (YRS) 40-49 50-59

20-29

30-39

Argentina Bolivia Brazil Chile Colombia Costa Rica Cuba Dominican Republic Ecuador El Salvador Guatemala Honduras Nicaragua Panama Paraguay Peru Suriname Uruguay Venezuela

14 18 219 7 20 9 11 38 15 23 52 33 21 7 9 38 2 1 29

106 88 1,005 42 162 23 98 146 74 88 142 94 64 30 25 182 8 6 109

549 257 4,170 220 760 85 410 354 258 192 330 188 167 63 75 653 27 34 412

1,712 593 10,799 558 2,078 201 878 597 628 475 753 359 313 142 179 1,427 46 115 979

2,477 766 16,063 797 2,779 317 1,480 762 758 636 1,088 529 401 229 270 1,819 58 174 1,563

SACA Total - females

566

2,492

9,205

22,832

32,967

DIABETES MORTALITY

Chapter4Fin.indd Sek1:231

60-69

TOTAL

DM AS % OF ALL DEATHS

2,285 577 15,534 671 1,473 363 1,235 549 623 661 1,317 642 372 211 356 1,555 48 162 1,499

7,143 2,301 47,790 2,296 7,272 996 4,112 2,445 2,356 2,074 3,682 1,846 1,337 682 914 5,674 189 492 4,591

9.7 10.3 12.6 9.6 9.7 18.1 18.2 15.8 10.4 17.1 16.1 1.3 18.2 18.2 10.3 10.8 16.9 9.8 12.6

30,131

98,192

•

70-79

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TABLE 4.13 Number of deaths attributable to diabetes in males (20-79 age group), 2007 - South-East Asian Region

COUNTRY 20-29 Bangladesh Bhutan India Maldives Mauritius Nepal Sri Lanka SEA Total - males

30-39

AGE GROUPS (YRS) 40-49 50-59

60-69

TOTAL

DM AS % OF ALL DEATHS

70-79

1,819 16 10,533 3 5 24 654

3,286 41 32,458 5 29 85 882

4,467 62 58,432 11 101 234 1,267

6,492 123 110,831 23 176 2,040 1,988

3,905 119 88,131 17 115 1,191 1,286

3,603 151 94,043 7 84 296 1,078

23,573 511 394,427 65 510 3,869 7,154

6.2 7.9 9.7 7.7 11.8 5.1 8.8

13,054

36,786

64,574

121,672

94,762

99,262

430,109

•

TABLE 4.14 Number of deaths attributable to diabetes in females (20-79 age group), 2007 - South-East Asian Region

COUNTRY 20-29 Bangladesh Bhutan India Maldives Mauritius Nepal Sri Lanka SEA Total - females

30-39

AGE GROUPS (YRS) 40-49 50-59

3,702 29 18,254 3 4 413 210

6,283 38 24,566 8 14 752 430

7,408 65 49,497 8 50 1,037 830

22,615

32,090

58,895

9,849 138 106,537 27 140 1,934 1,594

60-69

TOTAL

DM AS % OF ALL DEATHS

70-79

9,791 194 144,264 38 169 2,711 2,432

12,697 269 171,386 49 256 4,235 4,791

49,729 732 514,505 133 633 11,082 10,287

13.6 13.4 15.5 16.9 23.5 13.6 22.1

120,219 159,598

193,683

587,100

•

TABLE 4.15 Number of deaths attributable to diabetes in males (20-79 age group), 2007 - Western Pacific Region

COUNTRY 20-29 Australia Brunei Darussalam Cambodia China Cook Islands Fiji Indonesia Japan Kiribati Korea, Democratic People’s Republic of Korea, Republic of Lao People’s Democratic Republic Malaysia Marshall Islands Micronesia, Federated States of Mongolia Myanmar Nauru

232

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Chapter4Fin.indd Sek1:232

7 0 303 6,343 0 3 142 127 1 169 271 43 53 1 2 9 655 1

30-39 47 5 870 20,648 0 19 1,297 813 4 834 1,557 118 368 4 6 40 1,932 4

AGE GROUPS (YRS) 40-49 50-59 101 25 1,246 51,194 1 80 6,029 2,991 9 2,053 5,324 225 1,559 11 13 103 2,527 9

403 57 1,279 97,676 1 131 12,090 9,933 15 2,701 7,198 279 3,488 16 30 79 3,341 11

60-69 1,224 54 820 99,086 1 107 13,268 16,865 14 3,766 8,346 265 4,284 13 28 78 3,186 8

TOTAL

DM AS % OF ALL DEATHS

3,648 163 4,731 323,544 4 363 37,274 42,068 51 10,595 26,562 1,056 11,846 52 95 323 13,081 37

7.9 24.5 10.1 8.0 7.9 17.5 5.1 10.6 12.2 12.3 15.7 5.8 17.9 17.0 28.8 4.1 5.9 40.6

70-79 1,867 21 213 48,598 1 23 4,449 11,340 6 1,072 3,866 126 2,094 5 15 13 1,440 3

DIABETES ATLAS THIRD EDITION

27/10/06 4:19:13

COUNTRY 20-29 New Zealand Niue Palau Papua New Guinea Philippines Samoa Singapore Solomon Islands Thailand Timor-Leste Tonga Tuvalu Vanuatu Viet Nam WP Total - males

30-39

AGE GROUPS (YRS) 40-49 50-59

9 0 0 3 441 0 7 0 1,249 0 0 0 0 319

20 0 1 18 2,178 2 46 1 4,425 3 1 1 1 822

33 0 2 103 5,460 10 228 4 6,330 16 2 2 3 2,024

10,159

36,087

87,719

TOTAL

DM AS % OF ALL DEATHS

338 0 2 71 1,544 8 507 6 3,698 8 1 1 4 2,199

748 1 12 766 23,016 56 2,068 51 30,994 91 10 10 29 11,372

7.4 11.1 16.3 5.4 10.1 13.8 20.9 6.1 13.1 4.0 19.4 19.6 5.9 5.3

83,538

544,719

•

60-69

70-79

248 0 4 285 6,027 19 746 23 7,489 33 3 3 13 3,089

157,820 169,395

100 0 4 285 7,366 17 535 16 7,803 31 3 3 9 2,920

TABLE 4.16 Number of deaths attributable to diabetes in females (20-79 age group), 2007 - Western Pacific Region

COUNTRY 20-29

30-39

AGE GROUPS (YRS) 40-49 50-59

Australia Brunei Darussalam Cambodia China Cook Islands Fiji Indonesia Japan Kiribati Korea, Democratic People’s Republic of Korea, Republic Lao People’s Democratic Republic Malaysia Marshall Islands Micronesia, Federated States of Mongolia Myanmar Nauru New Zealand Niue Palau Papua New Guinea Philippines Samoa Singapore Solomon Islands Thailand Timor-Leste Tonga Tuvalu Vanuatu Viet Nam

10 1 143 1,033 0 7 317 32 1 98 126 31 43 1 2 3 257 0 5 0 0 11 467 0 2 1 804 1 0 0 0 103

47 5 256 5,021 0 12 807 285 2 247 78 52 174 2 3 10 477 1 20 0 0 26 1,080 1 13 1 1,070 1 0 0 1 251

130 11 346 10,633 0 23 1,515 772 2 345 356 69 466 3 4 20 612 3 60 0 1 47 1,699 3 49 2 1,262 4 1 1 1 494

WP Total - females

3,500

9,945

18,936

DIABETES MORTALITY

Chapter4Fin.indd Sek1:233

433 37 970 54,225 1 73 6,683 4,251 8 1,074 1,779 250 2,170 10 16 50 2,604 6 161 0 2 205 5,471 11 284 10 4,413 16 2 2 6 2,004

60-69

TOTAL

DM AS % OF ALL DEATHS

70-79

783 28 791 62,388 1 65 10,116 7,480 9 1,855 3,494 312 2,970 9 17 61 3,182 5 264 0 2 210 6,600 15 463 14 5,256 21 2 2 8 2,652

1,807 42 753 113,488 2 60 18,223 19,930 15 2,253 9,773 495 4,876 13 26 46 5,503 4 401 0 4 247 8,814 24 1,028 15 8,984 28 3 3 9 7,368

3,210 124 3,259 246,787 4 240 37,659 32,750 37 5,872 15,606 1,209 10,699 38 69 190 12,636 19 911 0 9 745 24,130 54 1,840 44 21,789 72 9 9 26 12,873

11.4 27.7 7.6 8.3 10.7 15.1 5.7 15.8 10.6 8.4 16.8 6.9 22.9 14.6 25.8 3.6 7.5 32.6 13.3 10.3 15.0 6.4 18.0 16.3 27.6 7.4 13.2 3.9 19.7 19.8 7.2 8.1

87,230 109,074

204,234

432,918

•

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233

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CHAPTER 5 THE ECONOMIC IMPACTS OF DIABETES

In 2007, the mean health expenditure for a person with diabetes in Fiji is estimated to be at least USD142.



5.0 THE ECONOMIC IMPACTS OF DIABETES Diabetes costs hundreds of billions of dollars to treat each year. World treatment costs are growing more quickly than world population. However, the larger costs of diabetes arise from disability and loss of life caused by its preventable complications, including heart, kidney, eye and foot disease.

Introduction

G

lobal health expenditures to treat and prevent diabetes and its complications will total at least USD232.0 billion in 2007. By 2025, this number will exceed USD302.5 billion. Expressed in international dollars (ID), which correct for differences in purchasing power, at least ID286.1 billion of goods and services will be consumed by diabetes in 2007, and at least ID381.1 billion in 2025. More than 80% of expenditures for medical care for diabetes are made in the world’s economically richest countries, not in the low- and middle-income countries where 80% of persons with diabetes will soon live. In the world’s poorest countries, not enough is spent to provide even the least expensive lifesaving diabetes drugs. In poor and middle-income countries, medical care purchases primarily go towards preventing the immediate lifethreatening diabetic complication, high blood sugar. Little is spent to prevent cardiovascular disease, the predominant cause of death from diabetes, and little is available to treat complications when they appear.

THE ECONOMIC IMPACTS OF DIABETES

Higher-income countries spend large sums to treat diabetic complications. In these countries, even expensive interventions to prevent these complications can be costeffective. However, a long list of diabetes interventions is cost-effective around the globe, in both developing and developed locales. Some of these treatments can save further medical expenditures. The world suffers huge losses in the form of foregone economic growth as a result of diabetes. Lost economic growth may be a relatively greater problem in poorer countries. Between 2005 and 2015, the World Health Organization (WHO) predicts net losses in national income from diabetes and cardiovascular disease of ID557.7 billion in China, ID303.2 billion in the Russian Federation, ID336.6 billion in India, ID49.2 billion in Brazil and ID2.5 billion in the United Republic of Tanzania (2005 ID). These losses arise from the premature death and disability that untreated diabetes causes. Perhaps 25 million years of life are lost annually to mortality caused by diabetes. Reduced quality of life may reach a similar magnitude among the living.

CHAPTER 5

237

Health expenditures for diabetes New estimates for 2007 and 2025 In 2003, the second edition of the Diabetes Atlas presented the first worldwide Tables of country-specific estimates of expenditures for health and medical care caused by diabetes. These estimates were generated using a formula that combines estimates of total national health spending, the prevalence of diabetes by country, and the ratio of diabetic to non-diabetic medical care expenses (‘R’)1. For this edition of the Atlas, a more complex version of this formula was used. Instead of relying on one value for R and one value for countrywide per capita health expenditures, the expanded formula used different values for each of 42 different subgroups, according to age and sex within each country. Expenditure estimates nearly doubled using the more precise formula. Appendix 3 details and critiques the formula and the data.

capita health spending into estimates of spending caused by diabetes. Based on current evidence, which is very limited, we think that R rarely falls below 2.0 in any country and rarely exceeds 3.0. In the industrialized countries of North America, Europe and the Western Pacific, R has probably been falling and a value closer to 2.0 should be assumed3. In other countries, it is impossible as yet to recommend a ratio (see Appendix 3 for details). Annual total health expenditures for diabetes in 2007

The annual global health expenditure for diabetes in 2007 is estimated to fall between USD232.0 billion (assuming R=2) and USD421.7 billion (assuming R=3). One country, the United States of America (USA), will spend more than half of this amount (USD119.4 billion assuming R=2, or 52%). The European Region (EUR, Table 5.5) will spend about half of the US amount (USD64.0 billion, R=2) and the Western Pacific Region (WP, Table 5.9), which includes Australia, China, Japan and Korea, will spend slightly less than half the European total (USD28.8 billion, 12.5% of global spending).

How to read the Tables of health expenditures

Table 5.1 provides a summary of the global health expenditure in the years 2007 and 2025. Table 5.2 summarizes the new estimates by region, age and sex. Tables 5.3 - 5.9 display total estimated national health expenditures caused by diabetes for 193 countries in the years 2007 and 2025. For 2007, these Tables also show the estimated per person expenditure for diabetes for each person with diabetes. These are not total expenditures per person with diabetes; they are only the expenditures caused by diabetes. They include expenditures for public health programmes as well as payments for medical care (see Appendix 3).

238

The remaining 9.2% of global spending will be divided among South-East Asia, the Eastern Mediterranean and Middle East, South and Central America, Africa and the remainder of North America. India, the country with the largest population of persons living with diabetes, will spend an estimated USD2.0 billion (R=2). The 47 countries in the African Region (AFR, Table 5.3) will spend, in total, USD0.7 billion for diabetes (R=2, 0.3% of the global total). Figure 5.1 graphs expected 2007 expenditures for diabetes by IDF region, assuming R=2.

Estimates are shown in both United States dollars (USD) and international dollars (ID). US dollars are best used to compare currency prices or expenditures for diabetes care. International dollars are corrected for differences in prices among countries (see explanatory note). Estimates in ID are best used to compare the amount of diabetes care that countries produce. Amounts in both USD and ID are expressed as of their value in 2002, the most recent year for which national health expenditure estimates have been published2.

Three-quarters of global expenditures for the care of diabetes in 2007 will be used for persons who are between 50 and 80 years of age. This is because the prevalence of diabetes is much higher in older age groups, and because persons who have lived with diabetes for many years have higher rates of complications, which are expensive to treat. Also, the countries that spend the most per capita for diabetes have older populations. In countries and regions with younger populations, such as Africa, the Eastern Mediterranean and Middle East, and South-East Asia, younger persons consume larger shares of spending.

Estimates are shown at two values of the diabetes expenditure ratio, R. R is the ratio of medical care expenditures for persons with diabetes to age- and sex-matched persons without diabetes. R is the key parameter in the conversion of per

More money is expected to be spent on diabetes care for women than for men (about 9% more, USD121.0 billion vs USD111.0 billion assuming R=2, Table 5.2). In these calculations, per capita diabetes-care expenditures are higher

CHAPTER 5

DIABETES ATLAS THIRD EDITION



Figure 5.1 Health expenditures for diabetes in 2007 (USD) by region, assuming R=2

 

INTERNATIONAL DOLLAR (ID)

-ILLIONS53$

The international dollar (ID) is a hypothetical unit of currency that has the same purchasing power in every country. Conversions from local currencies to international dollars are calculated using tables of purchasing power parities (PPP), which are taken from studies of prices for the same basket of goods and services in different countries.

 

 

 

 

 

 



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%--%

%52

.!

3!#!

3%!

70

for women than for men, especially at younger ages, mirroring the US data that was used to calculate age- and sex-specific values for R. Expenditures per person

Countries vary widely in the resources they spend on diabetes. In 2007 (R=2), the IDF formula predicts that the US will spend an average of USD6,231 for diabetes for each person who has the condition, Norway will spend USD4,714 and Switzerland will spend USD4,430. However, spending at this level is uncommon. Globally, the mean of each country’s average 2007 expenditure will be USD505 (ID725) at R=2. In each IDF region, some countries are predicted to spend very little for the prevention and treatment of diabetes in 2007: USD6 in Burundi, USD18 in Iraq, USD10 in Tajikistan, USD78 in Guyana, USD48 in Haiti, US19 in Bangladesh, and almost nothing in the Democratic People’s Republic of Korea. Most of these amounts could not cover the annual wholesale price of a generic oral agent capable of preventing acute, life-threatening hyperglycaemia. Fortunately, poorer countries provide more medical care than these estimates in USD imply, because the US dollar THE ECONOMIC IMPACTS OF DIABETES

buys more in these countries. Estimates adjusted for purchasing power are shown in international dollars (ID). For the countries just listed, ID estimates are ID30 in Burundi, ID72 in Iraq, ID81 in Tajikistan, ID334 in Guyana, ID137 in Haiti, ID94 in Bangladesh, and ID80 in the Democratic People’s Republic of Korea. Figure 5.2 compares the number of persons living with diabetes to annual ID health expenditures for diabetes (R=2), in the 25 countries with the largest populations of persons with diabetes. This figure shows that the need for medical care is not the primary determinant of spending for medical care. The great majority of spending in 2007 will occur in the world’s largest industrialized countries. Less than 20% of global spending will occur in low- and middle-income countries, where the large majority of persons with diabetes live. By 2025, if the resources devoted to diabetes in low- and middle-income countries are not increased, this disparity will widen. Projections to 2025

Tables 5.3 - 5.9 also show projected 2025 aggregate health expenditures for diabetes in 2002 dollars. The 2025 CHAPTER 5

239

Figure 5.2 Annual health expenditure for diabetes (ID) vs persons with diabetes in the 25 countries with the largest numbers of persons with diabetes in 2007 *OEJB $IJOB 6OJUFE
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estimates differ from the 2007 estimates only as a result of predicted changes in population size, age, sex and degree of urbanization4. Age- and sex-specific diabetes prevalences are assumed to remain the same — for example, no specific increase in diabetes prevalence due to increased obesity (apart from the increase that will be reflected by increased urbanization) is factored in, despite the near certainty that this will occur. In addition, health expenditures per capita remain in 2002 dollars and take the same values they had when estimated by WHO for 20022. Given these assumptions, the annual global direct expenditure for diabetes for 2025 is projected to fall between USD302.5 billion (assuming R=2) and USD558.6 billion (assuming R=3). The international dollar projection falls between ID381.1 billion (assuming R=2) and ID701.2 billion (assuming R=3) (see Table 5.1). These figures indicate that health expenditures for diabetes will grow by 30% to 35% between 2007 and 2025, somewhat more than assumed global population growth among persons 20-80 years of age over the same period, which is 27.6%. Expenditures are estimated to grow more quickly than population because populations are becoming older in the countries where diabetes will be most prevalent, such as 240

CHAPTER 5

China, and in the countries where medical care spending is highest, such as the United States and Europe. Comparisons to other estimates

Despite the many difficulties that accompany international comparisons of economic studies5, the estimates presented here are largely confirmed by independent estimates obtained from industrialized countries where direct studies of diabetes expenditures have been conducted. Because these are the countries in which the great bulk of medical care spending for diabetes occurs, these studies also suggest that, at R=2, the estimates presented here of global health expenditures for diabetes are roughly accurate. For example, a recently published study of the expenditure burden of diabetes in Germany in 2001 (CoDiM) reported net per capita expenditures of EUR2,507, quite similar to the estimate here of USD2,713 in 2002 dollars (R=2)3. CoDiM also observed an overall R for direct medical care of 2.0. Earlier, the CODE-2 Study estimated annual per capita type 2 diabetes expenditures at EUR2,834 in Western Europe in 1999. CODE-2 estimated expenditures that ranged from EUR1,305 +/-2,197 in Spain to EUR3,576 +/-920 in Germany6. For Spain, IDF’s formula-based per capita estimate for 2007 DIABETES ATLAS THIRD EDITION



(in 2002 USD) is very similar: USD1,276, assuming R=2. (In 1999 and 2002, the exchange rate between euros and US dollars was approximately 1.0.)

estimate for India (USD47 per person a year) is somewhat lower than a report from Madras, a large city in southern India11.

In Australia, the DiabCo$t Study7 estimated a direct medical care expenditure per person with diabetes (including expenditures for treating both diabetes and other health conditions) of AUD4,260 (in 2001 AUD). This equals approximately USD2,179 at mid-2001 exchange rates, similar to IDF’s formula-based estimate for 2007 of USD2,369 in 2002 dollars. However, the IDF estimate omits expenditures not caused by diabetes, so these estimates do not confirm each other.

What do medical care expenditures for diabetes buy?

The American Diabetes Association’s most recent estimate expenditures for medical care for diabetes over all age groups in the USA is USD91.8 billion in 2002 (USD 5,642 per person)8. The IDF formula-based estimate for the USA in 2007 is somewhat higher, USD119.4 billion (USD6,537 per person) at R=2. However, the IDF per capita estimate includes more diabetes-caused medical care than the ADA’s study was able to capture, and includes more health expenditures than those used for medical care. Some of these additional expenditures, such as payments for medical research, are substantial in the US. For a more precise comparison, the ADA’s aggregate estimate also should be adjusted to account for undiagnosed diabetes and for the increase in US diabetes prevalence since 2002. Published studies of expenditures for diabetes are nearly all from developed countries. Therefore, confirmation of estimates for developing countries remains uncertain. The IDF per capita estimate appears similar to a recently published estimate for the publicly supported medical care systems in Mexico, after removing indirect costs and adjusting for inflation9. In China, a 2002 study of patients of endocrinologists from 11 different provincial capitals, including Beijing and Shanghai, estimated that patients with type 2 diabetes without diabetic complications consumed USD450 per year in direct medical expenditures, while patients with both microvascular and macrovascular complications consumed USD4,66510. IDF’s countrywide estimate for China based on formula is much lower, USD89 and ID351 per person, but it encompasses nearly a billion rural Chinese who have no health insurance, and tens of millions of urban residents who cannot afford treatment by the endocrine specialists who contributed the Chinese data. Similarly, IDF’s countrywide THE ECONOMIC IMPACTS OF DIABETES

In industrialized countries, about a quarter of the medical expenditures for diabetes is spent for the control of elevated blood sugar. Another quarter goes to treat longterm complications (largely cardiovascular disease), and half is consumed by the additional general medical care that accompanies diabetes and diabetic complications, including intensified efforts to prevent cardiovascular and microvascular complications12. In these countries, persons with major complications of diabetes incur much higher medical care expenditures than persons without major complications 13-16 . For example, published estimates of expenditures for diabetic foot ulceration not requiring amputation range from USD993 to USD30,724 (1998 USD)17. Expenditures for ulceration requiring amputation are even higher, USD16,488 to USD60,215 (1998 USD) 17. Total expenditures of diabetic foot ulceration and amputation totalled USD10.9 billion in 200117,18 . Because of amounts like these, in the USA, acute hospitalization consumes 44% of diabetes-attributable expenditures, followed by outpatient care (22%), drugs and supplies (19%), and nursing home care (15%)8 . Similar proportions are reported from other high-income countries such as Finland19. In middle-income countries, a higher proportion of expenditures — half — goes for blood sugar control, which is essential for the prevention of acute life-threatening hyperglycaemia. The remainder is split between general medical care and chronic complications20. In Latin America and the Caribbean, anti-hyperglycaemic drugs alone are believed to account for about half of all spending20 . In Bangladesh, even among patients with diabetes sampled from a tertiary diabetes care hospital, 52% of annual medical care expenditures were consumed by drugs21. These data confirm anecdotal reports from IDF member associations indicating that most persons in these countries do not receive a great deal of medical care once complications appear, and many may not survive acute hyperglycaemic crises to develop longer term sequellae. Finally, it is significant to note that diabetes increases medical care expenditures even before it is diagnosed. Researchers in CHAPTER 5

241

the USA found that persons destined to have diabetes incurred an extra USD1,205 per year (1993 USD) during the eight years preceding diagnosis22. In 2004 dollars, this adds up to USD14,896 over all eight pre-diagnosis years. Increased utilization before diagnosis has also been reported from the United Kingdom23.

Out-of-pocket expenditures The expenditures for medical care described above impose different demands on people with diabetes and their families, depending on their economic status and the social insurance policies of the countries in which they live. Although expenditures for the medical care of diabetes are much higher in industrialized countries, nearly all these countries have organized systems of medical care insurance and/or governmental provision of medical services. This allows families to survive financially when diabetes strikes. The exception is the United States, which lacks a comprehensive national medical care service or insurance system24. In 2025, however, 80% of all cases of diabetes will be in low- and middle-income countries25,26 . In Latin America, families pay 40-60% of expenditures for medical care from 242

CHAPTER 5

their own pockets20. In the poorest countries, people with diabetes and their families bear almost the whole cost of whatever medical care they can afford. In India, for example, the poorest persons with diabetes spend an average of 25% of their total income on private care 11. In low- and middle-income countries, illness, injury and death is one of the main causes, possibly the main cause, of household impoverishment25,26 .

Other direct economic effects of diabetes The direct economic impact of diabetes includes nonmonetary as well as monetary effects. Non-monetary effects include changes in quality of life (or disability) and length of life. Disability, pain, and lost years of life are as important as financial loses.

Reduced years of life Diabetes dramatically reduces life expectancy when glucose, blood pressure and lipids are not aggressively controlled. Chapter 4 describes global mortality attributable to diabetes, providing estimates for every country. Diabetes is expected DIABETES ATLAS THIRD EDITION



Figure 5.3 Projected foregone national income due to heart disease, stroke and diabetes in selected countries, 2005-2015

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Source: WHO, 200526

to cause 3.8 million deaths worldwide in 2007, about 6% of total world mortality, about the same as HIV/AIDS27. From an economic point of view, the cost associated with these deaths is conceptualized as the years of life lost because death came sooner by diabetes rather than later by something else. For 2002, the WHO Burden of Disease project estimated that 8.59 million years of life were lost because of diabetes28. This is undoubtedly an underestimate because it is based on mortality rates obtained from death certificates, which under-report diabetes as a cause of death27,29. The true estimate is probably at least three times higher — 25 million years of life lost annually. Underestimation of diabetes mortality is less of a concern when diabetes is combined with cardiovascular disease, because CVD accounts for half of diabetes-cased death worldwide (see Chapter 4). WHO estimates that, if the value of a year of life is set country by country at 100 times GDP per capita, then diabetes, heart disease and stroke cost about ID250 billion in China, ID225 billion in the Russian Federation, and ID210 billion in India in 200526. In poor countries, many children die because access to lifesaving insulin is not subsidized by governments (who instead THE ECONOMIC IMPACTS OF DIABETES

sometimes tax it heavily), or because insulin is not available at any price, or is of very low quality (see Chapter 6). In these locations, untreated type 1 diabetes can be particularly costly in terms of life years lost, because it attacks children and younger adults who would otherwise live for many additional decades.

Disability and reduced quality of life Quality of life effects may be as deleterious as premature mortality to persons living with diabetes. Studies indicate that persons in perfect health would willingly give up 2.763.73 months of life to avoid a year of living with diabetes30,31. Interestingly, however, persons who actually have diabetes without complications rate their quality of life only slightly below similarly aged persons in the general population32. But quality of life decreases when complications appear32. Persons with diabetes say they would sacrifice about two months of life per year to avoid a year of diabetes-caused blindness, about one month per year to avoid kidney dialysis, one to 3.3 months per year to avoid amputation, three weeks per year to avoid painful diabetic neuropathy, one to two months per year to avoid a stroke, and three weeks per year to avoid a history of heart attack30,31. Disability and reduced quality CHAPTER 5

243

IN TOUCH WITH: KM Haridas The story of Mr KM Haridas highlights the importance of early diagnosis and aggressive treatment of people with diabetes to prevent complications. Through self-management, people with diabetes can substantially reduce their costs of diabetes care. Substantial savings can be achieved by motivating people with diabetes to realize the importance of selfmanagement of diabetes through well–conducted education sessions. Mr KM Haridas, 50, from Kerala, India, has lived with type 2 diabetes for almost half his life. He was diagnosed at 27 years old, and retired early from government service two years ago because of his diabetes-related illnesses. He bears the total cost of his diabetes care with about 70% of his pension being spent on medical expenses. In 2003, Mr Haridas entered hospital and was diagnosed with hypertension, peripheral vascular disease and an ulcer in his right foot. Apart from ensuring better diabetic control, he had to be investigated and treated for his complications. He was examined by a team of superspecialists in the disciplines

of life may be a proportionately larger problem in low-income countries33. WHO assumes that diabetes-caused blindness reduces the value of a year of life by more than half34. Using estimates like these, the World Health Report 2004 calculated that the equivalent of 7.6 million years of life were lost to diabetes-caused disability in 200228 . This estimate omits the effects of complications such as heart attack, stroke and other cardiovascular complications, though.

Impact of diabetes on national economies Diabetes affects all persons living in society, not just those who live with diabetes. Many studies have tried to measure this larger societal impact by adding a category of effects called indirect costs. The ADA estimated that the US economy lost USD39.8 billion or USD3,290 per person with diabetes in 2002, as a result of lost earnings due to lost work days, restricted activity days, mortality and permanent disability caused by diabetes8. Indirect costs of diabetes in Germany have been estimated at EUR1,328 per person for the year 20013. One way to create more comprehensive national estimates 244

CHAPTER 5

of diabetology, vascular surgery and plastic surgery. As a result, Mr Haridas had to undergo angiography followed by peripheral bypass surgery in both lower limbs in a multisuperspeciality hospital. During his working years as a cargo officer in the United Arab Emirates, Mr Haridas neglected his health which subsequently led to the development of his various complications. He ate irregularly, and also did not exercise nor take his medication regularly. He incurred high expenditures due to repeated hospitalization and therapeutic procedures including repeated laser treatment for retinopathy. He spent about 20% of his salary on medical care. In addition to the direct costs of diabetes medical care, Mr Haridas has incurred indirect costs due to frequent travel from Kerala to Chennai for treatment. His wife and two children have been subjected to intangible costs such as stress and anxiety caused by frequent hospitalization of their husband and father.

is to calculate the impact of diabetes on economic growth. No studies have yet done this for diabetes, alone, but in 2005 WHO used econometric models to estimate that diabetes, heart disease, and stroke together would cost ID557.7 billion in lost national income in China between 2005 and 2015, ID303.2 billion in the Russian Federation, ID236.6 billion in India, ID49.2 billion in Brazil and ID2.5 billion even in a very poor country, United Republic of Tanzania (see Figure 5.3)26. These are very large losses. WHO limited its calculations to the effects of premature death. Accounting for disability might increase these estimates.

Economic value of diabetes prevention and treatment Cost-effectiveness in developed countries Elevated HbA1c15 and other risk factors21 are associated with higher medical care expenditures. Studies indicate that the prevention and treatment of diabetes can be highly costeffective. Often, in fact, treatment is cost-saving, because medical care expenditures are avoided when therapy stops complications35,36. DIABETES ATLAS THIRD EDITION



Computer simulation models have predicted that blood pressure, blood sugar and lipid control actually save money in industrialized countries, because they prevent open heart surgery, renal dialysis, and the need for many other expensive interventions37-39. Foot care for persons with diabetes who are at high risk of ulcers, and preconception care for women with diabetes also are predicted to save money40. The Centers for Disease Control and Prevention (CDC) Diabetes CostEffectiveness Group41 recently calculated that intensified hypertension control would save USD1,957 per person with type 2 diabetes (1997 USD).

very low price of aspirin, its use is probably cost-effective or cost-saving for everyone with diabetes45.

Other interventions that appear cost-effective in developed countries according to economic studies include lifestyle improvement and possibly metformin to prevent type 2 diabetes, smoking cessation programmes, annual eye examinations to detect retinal disease, ACE-inhibitor use in all persons with diabetes, and influenza vaccines in elderly persons living with type 2 diabetes40. The cost-effectiveness of low-dose aspirin to prevent cardiovascular disease in all persons with diabetes has not been formally assessed. However, aspirin is cost-effective in persons with pre-existing cardiovascular disease42 and risk-beneficial in others when, as in diabetes, cardiovascular risk is elevated43,44. Given the

Cost-effectiveness in developing countries

THE ECONOMIC IMPACTS OF DIABETES

The cost-effectiveness of screening for type 2 diabetes is less clear. The CDC group calculated that, in persons without risk factors for diabetes, screening for diabetes, at least in the USA, may not be cost-effective46. A European modelling study recently concluded that the cost-effectiveness of diabetes screening depends on the presumed benefit of treatment47. A simulation study in Taiwan concluded that mass screening for type 2 diabetes would be cost-effective there48.

Developing countries cannot justify paying for all the treatments that high-income countries use. Still, many lowcost treatments should be cost-effective or cost-saving everywhere, as the IDF Task Force on Diabetes Health Economics argued it its publication, Cost-effective approaches to diabetes care and prevention49. In 2006, the World Bank systematically assessed the costeffectiveness and feasibility of diabetes interventions in developing countries40. Their table of findings by region is CHAPTER 5

245

MAP 5.1 Mean health expenditure per person with diabetes (USD), R=2, 2007

5,000 - 7,000 3,000 - 5,000 1,000 - 3,000 500 - 1,000 100 - 500 50 - 100 <50 no data

reproduced here as Table 5.10. The World Bank classified cost-effective interventions into three levels of implementation priority, based on cost per quality-adjusted life year (QALY, its measure of cost-effectiveness), technical feasibility, and cultural feasibility. Level 1 interventions were predicted to be cost-saving and highly feasible in all regions. Three interventions achieved level 1: glycaemic control when HbA1c is higher than 9.0%, hypertension control when blood pressure exceeds 160/95 mmHg, and foot care for persons at high risk of ulcers. (As explained above, low-dose aspirin also might be added to this list.) Level 2 interventions (N=6) are usually highly costeffective but face some barriers to implementation. Level 3 (N=5) priorities all cost more than USD1,500 per QALY saved and were judged challenging to implement. Although the World Bank assessment was not based on actual effectiveness trials in developing countries — none have been conducted — their work leaves little doubt that many diabetes interventions could and should be pursued aggressively throughout the world. A recent high-level modelling analysis suggests that large gains for the entire economy, as well as for individuals with diabetes, would 246

CHAPTER 5

result from recommendations like the World Bank’s, whether carried out in Bangladesh or Denmark50. Researchers are now debating and testing the use of inexpensive and convenient ‘polypills’ that combine in a single, once-a-day treatment, several compounds that already have been judged cost-effective or risk-effective51. This approach could increase the feasibility of diabetes treatment in developing countries, and make it even more cost-saving. Finally, it is worth noting that although ‘Level 2’ activities like smoking cessation, increased physical activity, and more prudent eating face administrative costs and implementation barriers when done as social programmes, individuals in developing countries who decide on their own to stop smoking or increase their walking can do so very cheaply.

Efficiency and equity Analyses of cost-effectiveness usually assess the impact of new programmes and policies under the assumption that they are implemented as designed, with little waste or corruption. Implicitly, analysts also assume that access will DIABETES ATLAS THIRD EDITION



be based on the ability to benefit, with no discrimination by class, race, or tribe. These assumptions do not always hold in practice. Indeed, it has been estimated that if only 50% of persons with diabetes are diagnosed, and 50% of these receive care, and 50% of these achieve treatment targets, and if treatment prevents complications 50% of the time, only 6% of persons with diabetes will have a successful outcome50. Increasing the efficiency of diabetes care can yield large improvements. If the reach of intervention were increased to 75% in the calculation just described, the percentage of persons with successful outcomes would be five times larger.

towards agriculture, transportation, housing and land drive patterns of eating and exercise. Perhaps even more importantly, countries with less egalitarian distributions of income, wealth and power also have higher rates of insulin resistance, diabetes and cardiovascular disease. Much research now indicates that this relationship is causal, and that it operates in high- as well as low-income countries, independently of average levels of resources. Feelings of shame, vulnerability, powerlessness and uncertainty trigger many of the same physiologic cascades that transmit the ill-effects of genes, obesity and lack of exercise53,54.

Many existing policies are ill-designed to operate efficiently and equitably. In the Northwest Frontier Province of Pakistan, for instance, the government pays for insulin only when citizens are hospitalized. This causes multiple costly hospitalizations — patients become desperately sick when they lack insulin — and leads to severe complications and premature loss of life. In Pakistan, it would probably be cost-effective for government to buy insulin for all persons with diabetes52.

Conclusion

Finally, it is important to recognize that, just as diabetes harms economies, economies create diabetes. Policies THE ECONOMIC IMPACTS OF DIABETES

Diabetes is one of the world’s most important causes of expenditure, mortality, disability and lost economic growth. A long list of simple, cheap treatments can help prevent these losses and many of these treatments will actually save hard money in countries, rich and poor. In fact, the returns to better diabetes prevention and treatment are relatively higher in the world’s low- and middle-income countries, where most persons with diabetes live but few are treated cost-effectively. CHAPTER 5

247

Table 5.1 Global health expenditure for diabetes, 2007 and 2025



















Health expenditure for diabetes in 2007 (‘000) US Dollars (USD) International Dollars (ID) per Region R=2 R=3 R=2 R=3

AFR 710,180 1,270,784 2,137,027 3,824,368 EMME 3,195,799 5,349,671 7,511,656 12,787,425 EUR 63,987,133 119,090,727 82,158,649 152,427,546 NA 128,691,947 230,344,260 132,163,097 236,460,803 SACA 4,503,248 8,049,214 12,555,309 22,511,523 SEA 2,067,942 3,664,551 6,811,580 12,070,481 WP 28,811,441 53,886,502 42,729,935 78,832,195 Global Total 231,967,689 421,655,708 286,067,252 518,914,340 R is the ratio of all medical care expenditures for persons with diabetes to all medical care expenditures for age- and sex-matched persons who do not have diabetes.

Totals for expenditure per person are means of the mean per person expenditure per region.

Table 5.2 Health expenditure for diabetes (USD*) by sex, age and region, and the diabetes cost ratio, R, in 2007









Health expenditure for diabetes by age group (‘000 USD), both sexes, R=2 Age AFR EMME EUR 20-29 69,345 211,977 734,634 30-39 111,330 550,193 2,168,418 40-49 153,265 760,089 6,411,682 50-59 185,757 808,187 14,162,315 60-69 131,860 580,897 20,913,114 70-79 58,623 284,455 19,596,970 Total 710,180 3,195,799 63,987,133 Health expenditure for diabetes by age group (‘000 USD), both sexes, R=3 Age AFR EMME EUR 20-29 120,104 359,146 1,280,595 30-39 190,308 891,092 3,711,817 40-49 256,095 1,178,921 10,661,843 50-59 317,170 1,285,887 23,764,537 60-69 242,290 981,781 36,105,083 70-79 144,817 652,844 43,566,853 Total 1,270,784 5,349,671 119,090,727 Health expenditure for diabetes by age group (‘000 USD), women, R=2 Age 20-29 30-39 40-49 50-59 60-69 70-79 Total

AFR 39,107 62,104 88,068 110,352 76,742 39,108 415,481

EMME 104,375 238,297 344,070 411,226 309,432 169,440 1,576,840

EUR 433,356 1,157,740 3,263,932 7,443,472 10,648,774 12,106,195 35,053,468

*2002 USD

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CHAPTER 5

DIABETES ATLAS THIRD EDITION

Mean health expenditure Health expenditure for diabetes in 2025 (‘000) per person with diabetes in 2007 USD ID US Dollars (USD) International Dollars (ID) R=2 R=2 R=2 R=3 R=2 R=3

71 180 922,542 1,672,591 2,754,377 5,001,165 334 514 6,191,714 10,366,100 13,982,139 23,825,035 1,288 1,561 76,258,302 143,136,315 97,909,474 183,064,381 973 1,188 174,485,018 319,487,464 179,903,410 329,140,328 265 625 7,196,796 13,007,973 19,969,324 36,192,479 73 233 3,239,120 5,790,771 10,676,508 19,087,682 439 684 34,187,702 65,105,388 55,857,411 104,880,442 492 712 302,481,194 558,566,601 381,052,643 701,191,512









NA SACA SEA WP TotaL 6,316,745 136,380 146,331 455,382 8,070,795 6,584,148 389,745 254,668 1,648,413 11,706,915 25,512,851 826,549 413,759 3,682,128 37,760,322 27,822,654 1,308,579 562,643 7,630,543 52,480,678 39,418,186 1,193,073 467,321 9,098,972 71,803,424 23,037,364 648,921 223,219 6,296,003 50,145,555 128,691,947 4,503,248 2,067,942 28,811,441 231,967,689 NA SACA SEA WP TotaL 10,926,602 235,486 250,920 771,530 13,944,382 11,368,769 663,942 425,067 2,751,351 20,002,346 40,394,405 1,365,764 673,733 6,042,496 60,573,256 46,915,708 2,173,607 940,799 12,903,671 88,301,379 67,352,629 2,091,877 832,434 16,295,128 123,901,221 53,386,146 1,518,539 541,598 15,122,327 114,933,125 230,344,260 8,049,214 3,664,551 53,886,502 421,655,708



NA SACA SEA 4,000,473 70,838 77,309 4,169,990 211,009 115,885 11,197,125 465,993 205,001 12,741,220 780,290 294,938 19,835,457 692,085 233,753 13,148,723 426,909 116,392 65,092,988 2,647,124 1,043,277

THE ECONOMIC IMPACTS OF DIABETES

WP 182,480 639,358 1,668,860 3,907,324 4,717,882 4,006,894 15,122,798

Total 4,907,938 6,594,382 17,233,049 25,688,821 36,514,125 30,013,661 120,951,975

CHAPTER 5

249

Table 5.2 Health expenditure for diabetes (USD*) by sex, age and region, and the diabetes cost ratio, R, in 2007

Health expenditure for diabetes by age group (‘000 USD), women, R=3 Age AFR EMME EUR 20-29 30-39 40-49 50-59 60-69 70-79 Total

72,190 113,229 156,240 185,744 138,093 91,353 756,850

190,750 420,943 580,379 647,677 512,471 364,834 2,717,054

802,720 2,124,221 5,846,219 12,391,504 18,149,124 25,453,931 64,767,719

Health expenditure for diabetes by age group (‘000 USD), men, R=2 Age 20-29 30-39 40-49 50-59 60-69 70-79 Total

AFR

EMME

EUR

30,239 49,226 65,198 75,405 55,119 19,514 294,700

107,602 311,897 416,019 396,961 271,465 115,015 1,618,959

301,277 1,010,678 3,147,750 6,718,843 10,264,340 7,490,775 28,933,664

Health expenditure for diabetes by age group (‘000 USD), men, R=3 Age 20-29 30-39 40-49 50-59 60-69 70-79 Total

AFR

EMME

EUR

47,914 77,079 99,855 131,426 104,196 53,464 513,934

168,396 470,149 598,541 638,210 469,310 288,010 2,632,617

477,875 1,587,596 4,815,624 11,373,033 17,955,958 18,112,922 54,323,008

*2002 USD

250

CHAPTER 5

DIABETES ATLAS THIRD EDITION

NA SACA SEA WP Total 7,278,930 7,574,501 19,581,867 21,560,216 33,627,319 28,957,460 118,580,293

131,330 384,363 819,162 1,276,651 1,184,468 939,432 4,735,406

142,068 210,532 360,647 488,510 411,516 268,221 1,881,495

338,171 1,176,768 2,989,348 6,564,294 8,318,100 9,071,831 28,458,512

8,956,159 12,004,558 30,333,863 43,114,596 62,341,090 65,147,062 221,897,328

NA SACA SEA 2,316,272 2,414,158 14,315,725 15,081,434 19,582,729 9,888,641 63,598,959

65,542 178,736 360,555 528,289 500,989 222,012 1,856,124

69,023 138,784 208,757 267,705 233,569 106,828 1,024,665

WP

Total

272,902 1,009,055 2,013,268 3,723,219 4,381,090 2,289,108 13,688,643

3,162,857 5,112,533 20,527,273 26,791,857 35,289,300 20,131,894 111,015,714

NA SACA SEA 3,647,672 3,794,269 20,812,538 25,355,492 33,725,310 24,428,687 111,763,967

104,155 279,578 546,601 896,956 907,409 579,107 3,313,808

108,851 214,535 313,086 452,289 420,918 273,377 1,783,056

WP

Total

433,359 1,574,582 3,053,148 6,339,377 7,977,028 6,050,496 25,427,990

4,988,223 7,997,788 30,239,393 45,186,783 61,560,130 49,786,063 199,758,380





THE ECONOMIC IMPACTS OF DIABETES

CHAPTER 5

251

Table 5.3 Health expenditure for diabetes, 2007 and 2025 - African Region

















Health expenditure for diabetes in 2007 (‘000) US Dollars (USD) International Dollars (ID) per COUNTRY/TERRITORY R=2 R=3 R=2 R=3

Angola 13,313 23,887 32,232 57,831 Benin 5,433 9,659 11,953 21,251 Botswana 10,758 19,199 24,347 43,449 Burkina Faso 3,501 6,308 12,095 21,791 Burundi 265 489 1,416 2,609 Cameroon 4,825 9,232 10,585 20,250 Cape Verde 1,207 2,164 3,376 6,054 Central African Republic 1,338 2,416 6,082 10,980 Chad 2,972 5,415 9,979 18,179 Comoros 185 331 498 893 Congo 2,343 4,156 3,254 5,772 Congo, Democratic Republic of 4,646 8,367 17,421 31,376 Côte d’Ivoire 26,427 47,155 64,267 114,674 Djibouti 1,496 2,651 2,161 3,830 Equatorial Guinea 1,366 2,465 2,288 4,128 Eritrea 537 971 2,418 4,372 Ethiopia 6,160 11,254 25,873 47,268 Gabon 7,453 13,392 11,624 20,887 Gambia 879 1,583 4,054 7,300 Ghana 11,437 20,600 49,113 88,460 Guinea 3,900 7,215 18,614 34,436 Guinea-Bissau 353 638 1,492 2,695 Kenya 14,201 25,442 52,319 93,733 Lesotho 1,383 2,530 6,581 12,044 Liberia 376 666 1,034 1,832 Madagascar 2,013 3,646 7,246 13,126 Malawi 2,485 4,585 8,519 15,722 Mali 4,029 7,245 11,079 19,922 Mauritania 1,340 2,404 5,170 9,272 Mozambique 5,586 10,040 25,391 45,638 Namibia 6,650 11,903 22,233 39,796 Niger 2,422 4,307 9,341 16,612 Nigeria 78,250 139,892 177,093 316,597 Réunion • • • • Rwanda 954 1,767 4,164 7,713 Sao Tome and Principe 189 342 567 1,027 Senegal 10,223 18,294 23,474 42,008 Seychelles 3,440 5,730 4,508 7,509 Sierra Leone 1,072 1,925 4,826 8,662 Somalia 1,061 1,928 2,299 4,178 South Africa 385,824 688,013 1,290,449 2,301,169 Swaziland 1,945 3,512 9,104 16,442 Tanzania, United Republic of 10,492 19,004 25,018 45,317 Togo 6,501 11,685 29,436 52,908 Uganda 6,357 11,682 27,194 49,973 Western Sahara • • • • Zambia 5,621 10,068 14,333 25,674 Zimbabwe 46,972 84,626 60,506 109,009 AFR Total 710,180 1,270,784 2,137,027 3,824,368

Population data are from the UN 2004 revision (medium variant) of the World Population Prospects4 (for non-UN members, the CIA World Factbook55 was used). Expenditure data are in 2002 USD and derived from the WHO World Health Report2.

Totals for expenditure per person are means of the mean per person expenditure per country.

252

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DIABETES ATLAS THIRD EDITION







Mean health expenditure Health expenditure for diabetes in 2025 (‘000) per person with diabetes in 2007 USD ID US Dollars (USD) International Dollars (ID) R=2 R=2 R=2 R=3 R=2 R=3

70 169 22,565 40,464 54,632 97,966 36 79 9,987 17,865 21,972 39,304 292 660 9,647 17,951 21,833 40,626 20 69 6,336 11,292 21,887 39,010 6 30 457 844 2,439 4,501 56 122 7,206 13,742 15,806 30,143 110 308 2,278 4,025 6,372 11,257 19 85 1,794 3,221 8,153 14,639 26 86 4,607 8,369 15,465 28,097 18 48 349 628 943 1,697 32 44 4,093 7,214 5,685 10,020 8 28 7,868 14,113 29,506 52,923 76 184 40,931 73,117 99,537 177,806 94 136 2,487 4,422 3,592 6,387 140 235 1,790 3,237 2,997 5,421 14 65 979 1,763 4,405 7,935 9 38 10,136 18,547 42,570 77,896 249 389 11,099 20,024 17,312 31,233 30 137 1,450 2,627 6,685 12,114 29 123 19,091 34,470 81,978 148,018 39 188 6,298 11,660 30,057 55,651 15 65 586 1,052 2,472 4,440 34 125 26,033 46,537 95,912 171,451 41 194 1,261 2,322 6,003 11,055 7 19 627 1,111 1,724 3,055 9 32 3,520 6,397 12,672 23,028 26 88 3,717 6,829 12,745 23,415 21 58 7,288 12,940 20,043 35,585 25 95 2,369 4,226 9,139 16,300 19 88 7,997 14,336 36,350 65,165 170 569 8,988 16,256 30,052 54,351 13 51 4,573 8,155 17,640 31,456 33 75 124,149 221,620 280,970 501,561 • • • • • • 20 88 1,632 3,013 7,124 13,149 63 190 339 601 1,018 1,803 47 109 17,991 32,072 41,313 73,647 539 707 4,417 7,413 5,789 9,715 10 47 1,604 2,876 7,219 12,943 11 25 1,897 3,465 4,111 7,507 318 1,064 424,815 778,004 1,420,861 2,602,157 112 523 1,623 2,990 7,599 13,997 24 56 16,456 29,770 39,242 70,990 63 284 11,498 20,676 52,060 93,615 35 148 12,717 23,052 54,401 98,611 • • • • • • 37 93 8,219 14,619 20,958 37,279 212 273 56,776 102,663 73,135 132,244 71 180 922,542 1,672,591 2,754,377 5,001,165



THE ECONOMIC IMPACTS OF DIABETES

CHAPTER 5

253

Table 5.4 Health expenditure for diabetes, 2007 and 2025 - Eastern Mediterranean and Middle East Region









Health expenditure for diabetes in 2007 (‘000) US Dollars (USD) International Dollars (ID) per COUNTRY/TERRITORY R=2 R=3 R=2 R=3 Afghanistan Algeria Armenia Bahrain Egypt Iran, Islamic Republic of Iraq Jordan Kuwait Lebanon Libyan Arab Jamahiriya Morocco Occupied Palestinian Territory Oman Pakistan Qatar Saudi Arabia Sudan Syrian Arab Republic Tunisia United Arab Emirates Yemen EMME Total

25,602 170,268 10,757 48,137 383,482 459,796 19,839 65,273 158,005 142,952 28,388 114,432 • 63,387 143,224 99,205 649,377 20,544 83,677 62,805 436,507 10,145

43,089 294,101 19,420 75,454 645,613 808,621 33,268 110,408 253,092 254,081 52,428 198,084 • 103,150 242,140 152,287 1,087,018 37,280 143,634 113,083 665,232 18,190

62,175 402,451 55,459 73,742 1,247,940 1,909,921 79,356 165,357 159,449 175,418 52,084 386,987 • 97,656 683,070 94,854 1,005,122 62,713 157,255 206,857 408,205 25,583

104,644 695,148 100,121 115,589 2,100,976 3,358,888 133,070 279,700 255,405 311,786 96,191 669,884 • 158,918 1,154,820 145,609 1,682,515 113,802 269,932 372,455 622,100 45,871

3,195,799

5,349,671

7,511,656

12,787,425

Population data are from the UN 2004 revision (medium variant) of the World Population Prospects4 (for non-UN members, the CIA World Factbook55 was used). Expenditure data are in 2002 USD and derived from the WHO World Health Report2. Totals for expenditure per person are means of the mean per person expenditure per country.

Table 5.5 Health expenditure for diabetes, 2007 and 2025 - European Region

















Health expenditure for diabetes in 2007 (‘000) US Dollars (USD) International Dollars (ID) per COUNTRY/TERRITORY R=2 R=3 R=2 R=3 Albania Andorra Austria Azerbaijan Belarus Belgium Bosnia and Herzegovina Bulgaria Channel Islands Croatia Cyprus Czech Republic Denmark Estonia Finland France

254

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13,864 6,593 1,418,579 14,765 72,337 1,407,445 43,883 95,986 • 138,180 62,589 421,616 932,157 28,930 662,712 9,512,269

26,196 12,344 2,568,559 26,508 131,171 2,694,142 80,461 174,494 • 255,128 110,041 762,676 1,736,742 52,711 1,250,674 17,860,020

44,541 9,102 1,599,414 65,622 453,466 1,639,520 108,695 330,324 • 235,917 62,660 935,252 849,298 66,439 695,275 11,084,143

84,160 17,042 2,895,988 117,814 822,284 3,138,382 199,296 600,502 • 435,584 110,166 1,691,808 1,582,365 121,055 1,312,127 20,811,335

DIABETES ATLAS THIRD EDITION





























Mean health expenditure Health expenditure for diabetes in 2025 (‘000) per person with diabetes in 2007 USD ID US Dollars (USD) International Dollars (ID) R=2 R=2 R=2 R=3 R=2 R=3 23 115 60 683 88 179 18 281 798 856 209 84 • 398 21 1,253 575 34 98 194 993 44

56 273 309 1,047 286 744 72 711 806 1,050 384 285 • 614 99 1,198 891 103 185 637 929 110

45,913 291,948 13,310 88,649 623,930 877,240 40,018 137,225 371,078 227,350 49,878 192,031 • 127,742 251,975 180,143 1,343,594 34,769 167,560 102,549 1,004,551 20,263

77,162 505,816 23,809 143,660 1,058,541 1,540,378 67,047 230,271 603,195 398,805 93,407 333,924 • 211,434 427,194 280,008 2,262,964 63,282 288,003 185,177 1,535,606 36,417

111,502 690,059 68,618 135,802 2,030,415 3,643,919 160,073 347,637 374,470 278,985 91,512 649,413 • 196,806 1,201,725 172,243 2,079,650 106,136 314,898 337,760 939,418 51,098

187,393 1,195,564 122,751 220,075 3,444,744 6,398,491 268,189 583,353 608,708 489,379 171,375 1,129,269 • 325,746 2,037,385 267,730 3,502,675 193,177 541,247 609,910 1,436,041 91,834

334

514

6,191,714

10,366,100

13,982,139

23,825,035

















Mean health expenditure Health expenditure for diabetes in 2025 (‘000) per person with diabetes in 2007 USD ID US Dollars (USD) International Dollars (ID) R=2 R=2 R=2 R=3 R=2 R=3



145 1,606 2,079 39 108 2,350 165 161 • 420 1,008 557 3,207 297 2,060 2,630

THE ECONOMIC IMPACTS OF DIABETES

465 2,217 2,344 173 678 2,738 408 554 • 718 1,009 1,236 2,922 683 2,161 3,065

18,778 9,340 1,735,041 23,632 77,479 1,724,365 48,223 94,217 • 145,022 86,196 487,655 1,077,735 29,790 808,920 11,890,384

36,017 17,689 3,161,339 41,800 139,834 3,316,871 88,955 172,391 • 269,671 153,838 907,877 2,058,791 54,368 1,576,982 22,681,853

60,328 12,895 1,956,217 105,032 485,702 2,008,697 119,444 324,237 • 247,598 86,294 1,081,743 981,936 68,416 848,668 13,855,234

115,715 24,421 3,564,334 185,778 876,595 3,863,794 220,334 593,264 • 460,414 154,012 2,013,902 1,875,788 124,860 1,654,469 26,429,961

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Table 5.5 Health expenditure for diabetes, 2007 and 2025 - European Region

















Health expenditure for diabetes in 2007 (‘000) US Dollars (USD) International Dollars (ID) per Country/territory R=2 R=3 R=2 R=3

Georgia 8,563 15,604 42,130 76,774 Germany 20,019,621 36,449,053 21,434,919 39,025,839 Greece 937,089 1,787,088 1,418,932 2,705,992 Hungary 412,936 747,059 897,471 1,623,649 Iceland 16,090 33,521 15,461 32,210 Ireland 481,278 905,264 505,181 950,226 Israel 662,298 1,212,229 836,727 1,531,492 Italy 6,976,071 13,212,424 8,699,004 16,475,596 Kazakhstan 44,462 79,107 207,224 368,696 Kyrgyzstan 2,909 5,265 24,315 43,997 Latvia 38,560 70,197 90,608 164,946 Liechtenstein • • • • Lithuania 67,188 122,449 153,056 278,939 Luxembourg 80,048 151,858 83,168 157,776 Macedonia, the Former Yugoslav Republic of 19,320 35,016 53,131 96,294 Malta 31,887 57,376 32,153 57,856 Moldova 8,753 15,636 48,950 87,444 Monaco 6,954 12,964 8,100 15,098 Netherlands 2,313,400 4,360,145 2,581,183 4,864,844 Norway 718,277 1,410,691 607,143 1,192,424 Poland 942,223 1,705,287 2,043,037 3,697,603 Portugal 784,478 1,483,221 1,222,693 2,311,760 Romania 227,560 416,263 833,795 1,525,213 Russian Federation 1,688,443 3,039,210 6,022,115 10,839,848 San Marino 4,554 8,513 5,693 10,642 Serbia and Montenegroa 96,209 176,383 244,531 448,307 Slovakia 113,277 203,628 309,054 555,560 Slovenia 150,765 274,814 252,965 461,103 Spain 3,187,683 6,044,157 4,385,738 8,315,787 Sweden 1,278,965 2,443,814 1,290,783 2,466,396 Switzerland 2,645,206 4,779,729 2,160,554 3,903,993 Tajikistan 1,198 2,157 9,381 16,900 Turkey 844,059 1,485,155 2,061,074 3,626,541 Turkmenistan 15,452 27,578 35,597 63,534 Ukraine 152,316 276,314 799,660 1,450,650 United Kingdom 4,156,075 8,261,948 4,420,050 8,786,710 Uzbekistan 21,059 37,740 143,405 256,994 EUR Total 63,987,133 119,090,727 82,158,649 152,427,546

Population data are from the UN 2004 revision (medium variant) of the World Population Prospects4 (for non-UN members, the CIA World Factbook55 was used). Expenditure data are in 2002 USD and derived from the WHO World Health Report2. Totals for expenditure per person are means of the mean per person expenditure per country. a. Estimates made prior to the establishment of Serbia and Montenegro as separate countries.



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Mean health expenditure Health expenditure for diabetes in 2025 (‘000) per person with diabetes in 2007 USD ID US Dollars (USD) International Dollars (ID) R=2 R=2 R=2 R=3 R=2 R=3

30 147 9,364 16,992 46,069 83,602 2,713 2,905 22,141,589 40,382,885 23,706,901 43,237,776 1,272 1,926 1,062,772 2,011,273 1,609,239 3,045,451 557 1,210 449,019 826,327 975,892 1,795,929 3,947 3,793 24,183 51,482 23,237 49,469 2,837 2,978 700,488 1,337,591 735,279 1,404,026 1,965 2,482 1,028,116 1,907,663 1,298,890 2,410,082 1,812 2,259 8,029,085 15,257,991 10,012,089 19,026,373 81 376 58,196 104,208 271,237 485,684 22 184 5,000 8,995 41,783 75,176 226 532 38,663 70,362 90,849 165,332 • • • • • • 280 638 72,161 130,777 164,383 297,911 3,385 3,517 115,514 218,318 120,015 226,826 161 442 23,813 43,556 65,485 119,779 1,113 1,123 41,207 76,602 41,551 77,243 35 196 10,770 19,450 60,233 108,778 3,616 4,212 8,882 16,752 10,344 19,510 2,653 2,960 3,130,062 6,012,829 3,492,375 6,708,831 4,714 3,985 941,213 1,904,070 795,585 1,609,466 361 783 1,153,034 2,127,252 2,500,143 4,612,557 1,210 1,886 972,470 1,839,374 1,515,699 2,866,863 149 547 247,834 453,262 908,080 1,660,781 175 625 1,822,801 3,305,982 6,501,323 11,791,336 2,690 3,363 6,788 12,849 8,486 16,062 143 362 106,173 196,000 269,858 498,166 321 875 144,733 265,376 394,874 724,025 1,013 1,699 176,029 326,275 295,354 547,448 1,276 1,756 4,121,387 7,724,658 5,670,365 10,627,886 2,736 2,761 1,508,291 2,943,833 1,522,229 2,971,036 4,430 3,619 3,227,608 5,921,278 2,636,250 4,836,388 10 81 2,345 4,184 18,373 32,777 257 627 1,409,251 2,482,645 3,441,194 6,062,273 134 308 29,418 52,318 67,774 120,529 46 239 145,073 264,130 761,634 1,386,684 2,431 2,586 4,997,164 10,067,385 5,314,561 10,706,820 35 237 41,031 73,113 279,401 497,866 1,288 1,561 76,258,302 143,136,315 97,909,474 183,064,381





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257

Table 5.6 Health expenditure for diabetes, 2007 and 2025 - North American Region















Health expenditure for diabetes in 2007 (‘000) US Dollars (USD) International Dollars (ID) per COUNTRY/TERRITORY R=2 R=3 R=2 R=3 Anguilla Antigua and Barbuda Aruba Bahamas Barbados Belize Bermuda British Virgin Islands Canada Cayman Islands Dominica Grenada Guadeloupe Guyana Haiti Jamaica Martinique Mexico Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Trinidad and Tobago United States of America US Virgin Islands NA Total

• 2,155 • 32,458 13,621 3,181 • • 5,719,191 • 1,307 2,519 • 2,930 15,073 38,209 • 3,461,699 1,374 2,869 1,533 35,377 119,358,449 •

• 3,838 • 55,319 23,936 5,388 • • 10,341,843 • 2,185 4,307 • 4,967 25,656 64,941 • 5,925,755 2,353 4,903 2,645 58,701 213,817,522 •

• 2,417 • 30,932 20,726 5,423 • • 7,544,081 • 1,976 4,110 • 12,549 43,141 49,672 • 5,023,574 1,962 3,834 2,897 57,354 119,358,449 •

• 4,304 • 52,717 36,422 9,185 • • 13,641,738 • 3,304 7,028 • 21,273 73,430 84,423 • 8,599,381 3,361 6,551 4,996 95,167 213,817,522 •

128,691,947

230,344,260

132,163,097

236,460,803

Population data are from the UN 2004 revision (medium variant) of the World Population Prospects4 (for non-UN members, the CIA World Factbook55 was used).

Expenditure data are in 2002 USD and derived from the WHO World Health Report2.

Totals for expenditure per person are means of the mean per person expenditure per country.

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Mean health expenditure Health expenditure for diabetes in 2025 (‘000) per person with diabetes in 2007 USD ID US Dollars (USD) International Dollars (ID) R=2 R=2 R=2 R=3 R=2 R=3 • 718 • 1,605 835 285 • • 2,581 • 274 496 • 78 48 252 • 566 644 346 260 345 6,231 •

• 805 • 1,530 1,271 485 • • 3,405 • 414 809 • 334 137 328 • 821 920 463 492 559 6,231 •

• 2,953 • 53,482 20,086 6,080 • • 8,091,559 • 1,498 3,148 • 4,284 23,430 52,014 • 6,014,551 1,771 4,332 2,514 47,849 160,155,467 •

• 5,305 • 91,999 36,029 10,271 • • 14,985,105 • 2,521 5,423 • 7,290 39,834 88,069 • 10,332,820 3,057 7,400 4,271 80,752 293,787,319 •

• 3,312 • 50,967 30,564 10,363 • • 10,673,430 • 2,265 5,136 • 18,348 67,060 67,618 • 8,728,240 2,530 5,788 4,748 77,573 160,155,467 •

• 5,948 • 87,673 54,824 17,508 • • 19,766,581 • 3,812 8,848 • 31,223 114,009 114,490 • 14,994,857 4,366 9,888 8,067 130,916 293,787,319 •

973

1,188

174,485,018

319,487,464

179,903,410

329,140,328









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259

Table 5.7 Health expenditure for diabetes, 2007 and 2025 - South and Central American Region











Health expenditure for diabetes in 2007 (‘000) US Dollars (USD) International Dollars (ID) per COUNTRY/TERRITORY R=2 R=3 R=2 R=3 Argentina Bolivia Brazil Chile Colombia Costa Rica Cuba Dominican Republic Ecuador El Salvador French Guiana Guatemala Honduras Netherlands Antilles Nicaragua Panama Paraguay Peru Puerto Rico Suriname Uruguay Venezuela SACA Total

490,972 26,377 2,158,335 227,704 311,767 134,155 202,104 91,126 58,140 85,634 • 70,608 27,957 • 22,042 93,402 19,376 128,818 • 7,618 67,562 279,552

894,039 47,085 3,879,880 408,792 558,855 232,183 345,698 154,756 104,210 149,950 • 124,377 48,646 • 37,703 161,689 34,674 228,975 • 12,938 124,323 500,441

1,972,140 74,943 6,401,665 594,251 1,106,669 260,254 242,114 174,560 125,863 178,966 • 151,086 72,687 • 75,676 151,548 81,049 313,042 • 14,889 150,657 413,251

3,591,181 133,782 11,507,799 1,066,847 1,983,750 450,422 414,136 296,449 225,597 313,378 • 266,140 126,481 • 129,447 262,347 145,037 556,435 • 25,285 277,229 739,782

4,503,248

8,049,214

12,555,309

22,511,523

Population data are from the UN 2004 revision (medium variant) of the World Population Prospects4 (for non-UN members, the CIA World Factbook55 was used),

Expenditure data are in 2002 USD and derived from the WHO World Health Report2.

Totals for expenditure per person are means of the mean per person expenditure per country.

Table 5.8 Health expenditure for diabetes, 2007 and 2025 - South-East Asian Region















Health expenditure for diabetes in 2007 (‘000) US Dollars (USD) International Dollars (ID) per COUNTRY/TERRITORY R=2 R=3 R=2 R=3

Bangladesh 73,321 129,815 359,941 637,275 Bhutan 1,165 2,120 7,377 13,429 India 1,916,123 3,397,767 6,131,592 10,872,854 Maldives 2,187 3,803 5,594 9,731 Mauritius 11,173 19,037 31,345 53,405 Nepal 11,166 20,373 59,549 108,659 Sri Lanka 52,808 91,634 216,181 375,128 SEA Total 2,067,942 3,664,551 6,811,580 12,070,481

Population data are from the UN 2004 revision (medium variant) of the World Population Prospects4 (for non-UN members, the CIA World Factbook55 was used).

Expenditure data are in 2002 USD and derived from the WHO World Health Report2. Totals for expenditure per person are means of the mean per person expenditure per country.

260

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Mean health expenditure Health expenditure for diabetes in 2025 (‘000) per person with diabetes in 2007 USD ID US Dollars (USD) International Dollars (ID) R=2 R=2 R=2 R=3 R=2 R=3 330 106 312 353 244 574 239 238 144 278 • 162 104 • 102 518 146 144 • 286 462 297

1,326 302 926 921 866 1,113 287 455 311 582 • 347 271 • 349 840 611 350 • 559 1,029 439

657,474 43,647 3,508,468 337,970 542,514 238,977 260,304 153,916 96,434 145,687 • 121,042 52,423 • 42,955 158,540 35,882 216,464 • 10,820 82,702 490,578

1,201,380 78,173 6,391,837 618,411 988,113 418,747 450,766 262,837 174,623 253,849 • 212,562 91,158 • 73,497 276,466 65,040 387,454 • 18,430 151,607 893,023

2,640,944 124,013 10,406,185 882,020 1,925,744 463,603 311,836 294,839 208,764 304,469 • 259,005 136,301 • 147,477 257,236 150,093 526,030 • 21,145 184,417 725,203

4,825,712 222,111 18,958,314 1,613,903 3,507,472 812,348 540,004 503,486 378,030 530,515 • 454,838 237,010 • 252,338 448,575 272,059 941,556 • 36,018 338,071 1,320,121

265

625

7,196,796

13,007,973

19,969,324

36,192,479



































Mean health expenditure Health expenditure for diabetes in 2025 (‘000) per person with diabetes in 2007 USD ID US Dollars (USD) International Dollars (ID) R=2 R=2 R=2 R=3 R=2 R=3

19 94 122,269 218,683 600,227 1,073,534 21 136 1,941 3,522 12,290 22,304 47 150 3,003,613 5,371,754 9,611,562 17,189,613 211 540 4,121 7,167 10,542 18,336 146 409 15,958 27,797 44,767 77,980 22 120 19,114 34,973 101,941 186,520 45 182 72,105 126,875 295,179 519,395 73 233 3,239,120 5,790,771 10,676,508 19,087,682







THE ECONOMIC IMPACTS OF DIABETES

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Table 5.9 Health expenditure for diabetes, 2007 and 2025 - Western Pacific Region















Health expenditure for diabetes in 2007 (‘000) US Dollars (USD) International Dollars (ID) per COUNTRY/TERRITORY R=2 R=3 R=2 R=3 Australia Brunei Darussalam Cambodia China China, Hong Kong China, Macau Cook Islands Fiji French Polynesia Guam Indonesia Japan Kiribati Korea, Democratic People’s Republic of Korea, Republic of Lao People’s Democratic Republic Malaysia Marshall Islands Micronesia, Federated States of Mongolia Myanmara Nauru New Caledonia New Zealand Niue Palau Papua New Guinea Philippines Samoa Singapore Solomon Islands Taiwan Thailand Timor-Leste Tokelau Tonga Tuvalu Vanuatu Viet Nam WP Total

2,193,190 16,743 19,094 3,561,507 • • 273 6,197 • • 126,606 18,380,947 361 338 2,277,727 1,393 341,050 647 1,685 1,180 466,371 2,230 • 328,735 33 716 2,581 141,974 897 470,288 301 • 416,729 625 • 932 105 212 49,775

4,135,956 26,842 33,686 6,488,040 • • 491 10,440 • • 240,857 34,998,795 645 606 4,017,903 2,603 572,305 1,104 2,713 2,150 864,669 3,347 • 601,609 58 1,245 4,791 245,286 1,577 789,054 560 • 741,642 1,181 • 1,561 174 399 94,215

2,967,127 25,425 114,567 14,754,816 • • 744 15,822 • • 535,641 15,834,637 1,038 64,245 3,876,478 6,828 798,834 1,279 3,664 5,594 44,416 4,534 • 486,423 13 1,191 15,957 775,788 2,425 578,695 862 • 1,486,332 2,592 • 2,989 104 584 320,292

5,595,461 40,762 202,114 26,879,023 • • 1,338 26,655 • • 1,019,011 30,150,416 1,856 115,141 6,838,094 12,753 1,340,499 2,181 5,901 10,193 82,349 6,806 • 890,189 23 2,071 29,620 1,340,312 4,266 970,940 1,602 • 2,645,189 4,901 • 5,008 172 1,096 606,251

28,811,441

53,886,502

42,729,935

78,832,195

Population data are from the UN 2004 revision (medium variant) of the World Population Prospects4 (for non-UN members, the CIA World Factbook55 was used).

Expenditure data are in 2002 USD and derived from the WHO World Health Report.2

Totals for expenditure per person are means of the mean per person expenditure per country.

a. Conversion between USD and ID based on official exchange rates in 2002.

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Mean health expenditure Health expenditure for diabetes in 2025 (‘000) per person with diabetes in 2007 USD ID US Dollars (USD) International Dollars (ID) R=2 R=2 R=2 R=3 R=2 R=3 2,369 577 59 89 • • 372 142 • • 44 2,634 85 0 741 19 223 354 217 47 534 869 • 1,521 549 626 44 46 152 1,058 60 • 132 95 • 143 109 87 38

3,205 876 351 371 • • 1,014 363 • • 185 2,269 245 80 1,261 93 522 700 472 221 51 1,768 • 2,251 219 1,041 270 254 412 1,302 171 • 470 395 • 459 107 239 247

3,178,633 33,535 31,764 5,175,082 • • 393 8,885 • • 208,057 18,799,147 644 438 3,182,402 2,477 596,702 841 2,411 2,253 769,478 3,626 • 436,634 40 1,102 5,026 249,455 1,362 804,791 605 • 597,424 1,155 • 1,207 167 391 91,572

6,130,269 54,241 56,669 9,658,294 • • 715 15,194 • • 396,667 36,654,941 1,164 788 5,732,761 4,622 1,016,024 1,435 3,912 4,126 1,446,903 5,485 • 818,961 71 1,935 9,342 433,976 2,389 1,391,185 1,124 • 1,083,762 2,207 • 2,021 279 736 173,188

4,300,317 50,926 190,586 21,439,625 • • 1,071 22,686 • • 880,240 16,194,904 1,852 83,249 5,416,151 12,136 1,397,644 1,663 5,244 10,681 73,284 7,374 • 646,079 16 1,833 31,071 1,363,094 3,684 990,305 1,733 • 2,130,814 4,790 • 3,874 165 1,076 589,244

8,293,532 82,370 340,013 40,012,932 • • 1,947 38,794 • • 1,678,206 31,577,137 3,350 149,709 9,756,623 22,650 2,379,815 2,836 8,508 19,559 137,800 11,153 • 1,211,801 28 3,218 57,753 2,371,368 6,461 1,711,870 3,216 • 3,865,419 9,156 • 6,485 275 2,025 1,114,430

439

684

34,187,702

65,105,388

55,857,411

104,880,442











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Table 5.10 Cost-effectiveness of interventions for preventing and treating diabetes and its complications in developing regions Intervention

Cost/QALY (2001 USD)



East Asia and the Pacific







Europe and Latin America and Central Asia the Caribbean

Level 1 Glycaemic control in people with HbA1c higher than 9 percent Cost saving Cost saving Cost saving Blood pressure control in people with pressure higher than 160/95 mmHg Cost saving Cost saving Cost saving Foot care in people with a high risk of ulcers Cost saving Cost saving Cost saving Level 2 Preconception care for women of reproductive age Cost saving Cost saving Cost saving Lifestyle interventions for preventing type 2 diabetes 80 100 130 Influenza vaccinations among the elderly for type 2 diabetes 220 290 360 Annual eye examination 420 560 700 Smoking cessation 870 1,170 1,450 ACE inhibitor use for people with diabetes 620 830 1,020 Level 3 Metformin intervention for preventing type 2 diabetes 2,180 2,930 3,630 Cholesterol control for people with total cholesterol higher than 200 milligrams/decilitre 4,420 5,940 7,350 Intensive glycaemic control for people with HbA1c higher than 8 percent 2,410 3,230 4,000 Screening for undiagnosed diabetes 5,140 6,910 8,550 Annual screening for microalbuminuria 3,310 4,450 5,510

a. Feasibility was assessed based on difficulty of reaching the intervention population (the capacity of the healthcare system to deliver an intervention to the targeted population), technical complexity (the level of medical technologies or expertise needed for implementing an intervention), capital intensity (the amount of capital required for an intervention), and cultural acceptability (appropriateness of an intervention in terms of social norms and/or religious beliefs). ++++ indicates feasible for all four aspects, +++ indicates feasible for three of the four, ++ indicates feasible for two of the four, and + indicates feasible for one of the four.

b. Implementing priority was assessed by combining the cost-effectiveness of an intervention and its implementation feasibility; 1 represents the highest priority and 3 represents the lowest priority.

Source: World Bank, 200640

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Cost/QALY (2001 USD)



Middle East and South Asia Sub-Saharan Africa Implementing priorityb North Africa Feasibilitya

Cost saving Cost saving Cost saving ++++ 1 Cost saving Cost saving Cost saving ++++ 1 Cost saving Cost saving Cost saving ++++ 1 Cost saving Cost saving Cost saving ++ 2 110 60 60 ++ 2 310 180 160 ++++ 2 590 350 320 ++ 2 1,230 730 660 ++ 2 870 510 460 +++ 2 3,080 1,820 1,640 ++ 3 6,240 3,680 3,330 +++ 3 3,400 2,000 1,810 ++ 3 7,260 4,280 3,870 ++ 3 4,680 2,760 2,500 ++ 3









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hapter 6 presents a description of the pattern of use of diabetes therapies, both pharmacological and dietary, in as many countries as data are available. There appears to be a very wide range of prescribing habits across the world. Whilst some of these differences are easily explicable in terms of different prevalences of type 1 and type 2 diabetes, and others are attributable to differences in study methodology, there is a clear need to investigate further how prescribing habits can be brought into closer alignment with treatment guidelines.

and a link between depression and diabetes has been recognized. Chapter 8 discusses the metabolic syndrome and the IDF definition of this condition. The metabolic syndrome is now thought to be a key driver of the modern day epidemics of diabetes and CVD. Its cause is uncertain, but lifestyle change, focussing on increased physical activity and healthy eating to achieve weight loss, is an essential component of its treatment.

This chapter also looks at the barriers to insulin access and diabetes care in three sub-Saharan African countries and identifies the factors that have to be present for a solution to succeed. Chapter 7 focuses on the association between mental health, antipsychotic drugs and hyperglycaemia. The chronically mentally ill are a high-risk population for diseases such as diabetes and cardiovascular disease due to a number of factors. These include suboptimal access to medical services, a propensity to smoking, poor diet, sedentary lifestyle and obesity. Evidence is mounting for the association between antipsychotic medication and the development of diabetes, THE CHALLENGES

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CHAPTER 6 ACCESS TO INSULIN, MEDICATION, AND DIABETES SUPPLIES

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Continuous accessibility to insulin and other diabetes supplies is still a major problem in many developing countries.

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ne of the major breakthroughs in medical sciences of the last century was the discovery of insulin in 1921. This discovery meant that people with diabetes who were insulin-treated survived the acute effects of the disease. Eighty-five years after its discovery, people around the world are still dying because they cannot access insulin, which is classified by WHO as an essential drug. Continuous accessibility to insulin is still a major problem in many developing countries especially those in sub-Saharan Africa such that there are reports of premature deaths due to the chronic lack of access to insulin in some of these countries.

and socio-economic differences, and as it was felt that they were representative countries for sub-Saharan Africa. An assessment was carried out in these three countries to see how a sustainable solution could be found to the issues of access to insulin and proper diabetes care under extreme conditions of scarce resources in the health sector.

Chapter 6.1 captures the pattern of use of diabetes therapies, both pharmacological and dietary, in countries where there are data. Describing the use of hypoglycaemic treatments is valuable in gaining an understanding of how therapies are actually used in practice, as against the advice given in various published guidelines. Chapter 6.2 examines the barriers to insulin access and diabetes care in Mali, Mozambique and Zambia. These countries were chosen due to their geographical, historical ACCESS TO INSULIN, MEDICATION, AND DIABETES SUPPLIES

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he treatment of diabetes includes both lifestyle changes and pharmacological therapy. Whilst type 1 diabetes mandates insulin use from the time of diagnosis, the management of type 2 diabetes begins with lifestyle advice. If glycaemic control is inadequate, oral hypoglycaemic medication is added, and insulin is added for those in whom oral therapy is insufficient to achieve glycaemic targets. Each drug can be used alone or in a variety of combinations with others. The patterns of use of different therapies within a population can be influenced by many factors including the availability of medication, beliefs among healthcare professionals about the value, efficacy and practicality of different therapies, the phenotype of the person with diabetes (particularly in regard to obesity), and the costs, which include not only the drug cost, but also the costs of associated education for the person with diabetes (specifically with regard to insulin use). Describing the use of hypoglycaemic treatments is valuable in gaining an understanding of how therapies are actually used in practice, as against the advice given in various published guidelines. DIABETES MEDICATION USE: INTERNATIONAL PRESCRIPTION PATTERNS

Chapter6Fin.indd 273

The aim of this report is to present a description of the pattern of use of diabetes therapies, both pharmacological and dietary, in as many countries as data are available.

The data have been collected from a variety of sources, including published articles, government and institution reports, national pharmacy databases and personally communicated information. Searches of Medline were undertaken using the major Medical Subject Headings (MeSH) categories of ‘diabetes mellitus (epidemiology)’, ‘diabetes mellitus (diet therapy)’, ‘diabetes mellitus (drug therapy)’. Further articles were accessed in Medline using ‘related articles’.

The studies were grouped into six main types. Each study type has its specific advantages and limitations. 1. National prescription databases

A number of countries maintain databases of all prescriptions that are presented to pharmacists through national or government health schemes. These provide very accurate CHAPTER 6

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data covering large numbers of individuals. National prescription databases generally include the vast majority of prescriptions for oral hypoglycaemic agents (OHAs) and insulin used for a whole country in a given time period.

use. The Finnish data for 1995 have been previously published, briefly noting therapy category according to type 1 or 2 diabetes2, although type was not classified for a quarter of those on treatment.

There are two principal types of database: the first has linkage to individual persons, so that numbers of people using any specific single medication, or combination of drugs can be ascertained. The second type of prescription database is not linked to persons, but indicates the number of prescriptions that have been presented for each drug, or the overall amount of drug provided/prescribed, without specifying the number of people involved.

In most of the studies based on counting numbers of prescriptions for each drug (without linking to individuals), the data are reported as the defined daily dose per 1,000 persons in the general population per day (ddd/1000 persons/day). This system is based on a definition for each drug of the usual daily dose, and its accuracy will depend on the applicability of this estimate to the population in question. (The WHO definition of the defined daily dose is the assumed average maintenance dose per day for a drug used, normally in monotherapy, for its main indication in adults.)

These databases do not have any information on type of diabetes, nor on the number of persons with diabetes who are not on pharmacological treatment. When interpreting these data sources, a figure of, for example, 20% of the diabetic population using insulin indicates that 20% of all people using pharmacological glucose lowering therapies are on insulin. Those on only dietary management are excluded. Furthermore, it is not possible to determine the proportion of insulin users who have type 1 or type 2 diabetes. There is no absolute certainty that national databases include all persons using any particular medicine. Each national system will have its own peculiarities, so that some prescriptions may not necessarily be included, as they are too expensive to be included on the government scheme, or are so cheap that they can be purchased more cheaply outside the government scheme. Allowances have not been made for such variations. There were two general formats in which national prescription data were available. In the most detailed (Australia, Denmark, Finland, France), records were linked to persons so that number/proportions of persons on insulin only, OHA only, or combination were available. The data indicated the extent of overlap of categories, so that, either directly or indirectly, persons using both oral agents and insulin concurrently (or sequentially within the one-year time frame) could be determined. The French data1, and Australian data (Health Insurance Commission) specifically indicated the number (or proportion) of persons using combination therapy, whereas for Denmark and Finland, the usage of combination therapy was estimated from the difference between those using any therapy, and the sum of insulin use and oral medication 274

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The ddd classification is calculated by addition: 1. Total ddd of all OHA medication = the sum of ddd for each type of OHA 2. The ddd of all diabetic medication = sum of OHA ddd + sum of insulin ddd It is tempting to infer that the ddd of all diabetic medication is an estimate of drug-treated diabetes. However, each person will be counted for each drug, when, in reality, many people are on more than one drug. Thus, for the United Kingdom (UK), the prescription rate for 2003 of 37.5 ddd/1000 p/day is nearly twice the estimate of total prevalence of diagnosed diabetes of 2.1% using the United Kingdom General Practice Research Database (GPRD). Describing combination therapy was not possible for these studies. Prescription data from four countries were available only from sub-samples of the population, and are not necessarily representative of the treatment patterns nationally. Irish data came from the one-third of the population specifically eligible (older age, or lower income) for the subsidized health insurance3, and Belgian data were derived from a national 2% sample of private pharmacies4,5. A small sample of records (1,100 persons) from a South African prescription database was the only report of this nature from Africa6. A report from Bahrain used prescription data from persons attending a large sample of the country’s primary healthcare centres to document patterns of treatment7. 2. Population-based data

Data on treatment derived from population-based reports are included for 14 countries. These studies used self-report data to ascertain treatment categories, which may have DIABETES ATLAS THIRD EDITION

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affected accuracy. The collection of data on treatment was frequently a secondary objective of such reports. The population-based reports were of four general groups: national, regional, health insurance, and occupational. Unlike the prescription-based data, the population-based reports almost always had some form of age restriction on participants, but the exclusion of younger persons with diabetes is likely to affect treatment estimates minimally, as the vast majority of those with diabetes are adults. Differentiation between type 1 and type 2 diabetes was not provided in most of the population-based reports.

6. Inpatient Records

Only two studies have been included which describe treatment for hospital inpatients14,15. Treatment profiles from these populations are not likely to be representative of the general diabetic population, and will be biased towards those with serious complications.

3. Primary care

The preferred format for presenting treatment data was assignment of persons to one of the mutually exclusive categories of dietary treatment alone, OHA use alone, insulin alone, or OHA/insulin combination. This classification was used by the largest number of reports.

The largest group of studies was for persons with diabetes selected because of their attendance for care. Most of these were attending primary care clinics. There was a large range in numbers of such participants, the primary objective of the reports, and the extent to which the results could reasonably be extrapolated beyond the study population. The studies were almost invariably retrospective reviews of case records. Both primary and secondary care patients were recruited from six South American countries (Argentina, Brazil, Chile, Colombia, Paraguay, Uruguay)8, to assess management among the QUALIDIAB participants, but data have not yet been published or analysed by country of health facility site, so that only the broadest description is available from the data.

For those reports detailing treatment by rates (ddd/1000p/ day), no measure of overlap of OHA and insulin use was available, so that total use represents the addition of insulin and OHA prescriptions, and, as described above, is likely to be an overestimate of the proportion using medication for diabetes. Similarly, for each of insulin and OHA use, the ddd/ 1000p is likely to be an overestimate of persons using that type of medication, as many individuals use more than one type of insulin and many use more than one type of OHA. Data from these studies relating to individual classes of OHA (e.g. sulfonylureas or metformin) are likely to be accurate estimates of the numbers of people using these drugs.

4. Secondary Care

Dietary treatment

The majority of the persons recruited from specialized diabetes clinics, for whom there are reported treatment data, were participants of the DiabCare Asia study9. The study’s objective was assessment of many aspects of diabetes control among the 12 participating countries, involving 230 centres (ranging from one centre in Bangladesh to 34 centres in the Philippines). Individual data for India10 and Taiwan11 including treatment have been published.

For some studies, the percentage of participants on dietary treatment alone (i.e. not on pharmacological therapy for diabetes) was explicitly stated. In others, however, it was not. In such cases, when the sum of all those reported to be on pharmacological treatment was less than 99% of the study sample, and where it was not stated that those on diet alone were excluded from the population, it was assumed that the remainder were on dietary treatment alone.

5. Diabetes Registry

Diabetes registries attempt to register all people known to have diabetes, and living within a certain area or obtaining care from a single provider. Data are usually completed by healthcare professionals (rather than based on self-report by people with diabetes), and information on medication has been extracted from the registers. Most registries attempt to include all people with diabetes, but the Canadian Vascular Protection Registry only enrolled people with diabetes if they also had cardiovascular disease12, and the Ontario (Canada)13 register only had data for people aged 65 years and older. DIABETES MEDICATION USE: INTERNATIONAL PRESCRIPTION PATTERNS

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Oral hypoglycaemic agents All studies provided specific data relating to use of OHAs. Where data were available, this was broken down into different classes of OHA. Data on use of combinations of OHAs were either available in the source material, or have been calculated from the degree of overlap apparent from the data. For example, a statement that 50% of those on OHAs were on metformin, and that 70% were on sulfonylureas indicates that 20% were on metformin-sulfonylurea combinations. CHAPTER 6

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Insulin and insulin-OHA combinations Most studies reported data on the use of insulin. It should be noted that in many studies, there was no information on the proportion of the population that had type 1 diabetes, and so the figures presented should be interpreted cautiously. While some studies differentiated between those on insulin alone, and those on insulin-OHA combination therapy, others did not. In some circumstances combination use could be calculated. For example, if dietary treatment was 10%, any OHA use 70%, and any insulin use 30%, OHA/insulin combination can be calculated as 10%.

Table 6.1 presents the data relating to the broad categories of insulin and oral medications, or dietary treatment without medication for 100 reports.

Insulin use About 35% of the studies indicated the percentage of persons with type 1 diabetes. Of these, the median percentage was 7%, and the highest rate was nearly 20%, for Finland2. For those reports that differentiated between type 1 and type 2 diabetes, the percentage of those with insulin-treated type 2 diabetes could be derived. The median use was 20% and ranged from 1% (inpatients, Argentina) to 40% (outpatient clinic, Mexico16). The greatest use of insulin (>30% of persons) for those with type 2 diabetes generally occurred in studies based on secondary care recruitment. Low usage of insulin in type 2 diabetes (<10%) was reported for the DiabCare study countries of Indonesia and Malaysia17, South Asian (but not the white) UK patients 18, Argentinian inpatients15, and the Italian cohort from 198619. For the 65 studies which did not differentiate the type of diabetes, median insulin use was 25%, and was greatest for those aged under 45 years (50-70%)20, and for the prescriptionbased reports for northern Europe (Belgium4,5, Denmark21, Finland2, Germany22 and Sweden23). Lower insulin use (<15%) in general appeared to be more common for developing countries (West Indies, India) and particularly for population-based (Mexico, Thailand), or elderly cohorts (>65 years; Canada13).

majority of reports not based only on prescription rates, and varied markedly, from about 50% to 1% (median 15%). Almost all of those with rates less than 6% were from the DiabCare Asia countries9,17, or with secondary care recruitment, or from less developed countries. The highest levels of dietary treatment (>30%) were generally reports from developed countries, although the sample of studies from less developed countries was markedly biased to secondary source recruitment.

Oral hypoglycaemic agents Oral medication use had been recorded for nearly all the reports, and median use (OHA only or insulin-OHA combination) was about 70%. The highest proportion of OHA use (>90%) was from the Bahrain national prescriptionbased report7 and a population-based report from India24. There was, in general, a preponderance of less developed countries among those studies with OHA use for more than 70% of persons. The only reports for which OHA use was less than 30% of persons were those aged less than 45 years20. Data on combination insulin-OHA use were available for about two-thirds of studies in Table 6.1. For the 70 reports with data, the median use of such was about 7%. High combination usage tended to be associated with more recent reports, high non-type 1 insulin use, and secondary care recruitment.

Prescription rates Table 6.2 provides data indicating prescription rates of OHA and insulin, as population rates, for 10 European countries. Comparisons between countries and temporal trends for each country can be assessed. The average annual rate of increase of use of diabetes medication was 6.8% (6.5% for insulin; 7.0% for OHA). This progressive rise in medication use could have been due to an increasing prevalence of diabetes and/or more aggressive treatment. The relative proportions of insulin and OHA use thus had hardly changed. There were considerable differences between countries for the most recent year with data available (between 2000 and 2003). Total prescriptions rates varied between 28 (Denmark) and 58 (Finland) ddd/1000p/day. The proportion of defined daily doses that were prescribed for insulin varied from 16.3% (Portugal) to 51.2% (Sweden).

Comparative use of specific medications Diet therapy The percentage on ‘dietary therapy only’ was available for the 276

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The comparative use of specific oral medications is indicated in Tables 6.3 and 6.4. For Table 6.3 the rates represent the DIABETES ATLAS THIRD EDITION

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proportion of persons using OHA who are taking the specific medication (except for the two South African reports6,25, and Irish data3,26 and the USA report of national volume of prescriptions 27, for which data represent prescription numbers, and proportion of prescriptions). In Table 6.4 the data are for prescription rates (as ddd/1000 persons/day), with proportion of the total OHA ddd prescribed as each medication. Sulfonylureas were more commonly prescribed than metformin, except for several of the most recent reports. From Table 6.3, there were only four reports, for which more persons were taking metformin than sulfonylureas: Australia, Health Insurance Commission; Finland Insurance Agency; Germany22,28,29, and all relate to the last five years. Other reports included in Table 6.3 also demonstrate an increase in metformin use. The Denmark national data (Danish Medicines Agency) show the total use of metformin to have increased from 30% to 60% of OHA between 1997 and 2003. Increases in metformin use over time were also apparent in data from the USA (where metformin only became available in the mid-1990s)27,30 and Trinidad and Tobago31. The reports listed in Table 6.4 indicate overall and specific OHA usage, as prescription rates. There are considerable differences between countries, with a two-fold range in total OHA use for the most recent year (2002-03) between lowest and highest use (Denmark, 17.2 ddd/1000p/day and Portugal, 37.5 ddd/1000p/day). Usage of sulfonylureas was greater than of metformin for all countries, but the rate of increase of metformin use over time was far greater than of sulfonylurea use. The median annual increase in per capita metformin consumption was 18%, whereas the average increase in nonmetformin OHA prescription was 4%.

Type of insulin use There was a paucity of data and reports concerning types of insulin being used/prescribed, with most of the data coming from Europe. Among the six developed countries with data (see Table 6.5), there were some clear differences in prescription practice. Fast acting insulins were being used considerably less in Spain than the other countries. Since the data on insulin type include both type 1 and type 2 diabetes, the less frequent use of fast acting insulin in Spain32 (as well as in the small study from the United Republic of Tanzania33) may be partly explained by the lower prevalence of type 1 diabetes than in northern European countries and Australia. DIABETES MEDICATION USE: INTERNATIONAL PRESCRIPTION PATTERNS

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Although intermediate insulins account for a higher proportion of all insulin prescribed in Spain than in Sweden or Germany34, the absolute rate of insulin prescription is relatively low in Spain, so that the absolute rate of intermediate insulin prescription is still considerably lower in Spain than in Sweden or Germany.

Significant limitations remain in interpreting these data on the pharmacological treatment of diabetes. Many of the studies were designed to answer other questions, and the information on medication use was very limited. Even for prescription database studies, in which the information on drug use is likely to be highly accurate, the limitations of not knowing either the type of diabetes, or the numbers of individuals on nonpharmacological treatment are significant. Nevertheless, it is possible to make some conclusions, particularly on the use of metformin. It seems very clear that the use of metformin has increased over recent years. This is no doubt a result of its value both in terms of weight control and in prevention of cardiovascular disease, as a result of the United Kingdom Prospective Diabetes Study (UKPDS) reports. However, metformin still does not appear to be the most commonly used first line agent. In most reports, the percentage of individuals on metformin alone was much lower than the percentage on sulfonylureas alone or on combination OHA, suggesting that metformin is mainly used as an add-on to sulfonylureas. The fact that combination therapy (sulfonylurea plus metformin) was widely used (in up to 67% of those on OHAs) indicates that metformin sideeffects are not a major barrier to its use, nor an explanation of the relatively low rates of metformin monotherapy. In summary, there appears to be a very wide range of prescribing habits across the world. Whilst some of these differences are easily explicable in terms of different prevalences of type 1 and type 2 diabetes, and others are attributable to differences in study methodology, there is a clear need to investigate further how prescribing habits can be brought into closer alignment with treatment guidelines.

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TABLE 6.1 Classification of diabetes medication use: insulin, oral, diet, oral/insulin combination

REGION

COUNTRY/TERRITORY

AUTHOR

STUDY TYPE

AFR

Kenya South Africa Tanzania. United Republic of

EMME

Bahrain United Arab Emirates Belgium

Otieno et al35 Truter6 Gulam-Abbas et al14 Gulam-Abbas36 Neuhann et al33 Al Khaja et al7 Malik et al37 Donker et al20 Donker et al20 Sartor and Walckiers5 and Walckiers et al4 Donker et al20 Donker et al20 Danish Medicines Agency21 Danish Medicines Agency21 Donker et al20 Donker et al20 Vides et al38 Reunanen et al2 Social Insurance Institute39 Le Floch et al40 Ricordeau et al1 Meisinger et al28 Meisinger et al28 Ott et al22 Rothenbacher et al41 Gikas et al42 Katsilambros et al43 Barry et al3 and Tilson26 Stern et al44 Ciardullo et al45 De Berardis et al46 De Berardis et al46 Di Cianni et al47 Muggeo et al19 Donker et al20 Donker et al20 Donker et al20 Donker et al20 Sender Palacios et al48 Farnkvist and Lundman23 Abbott et al18 Abbott et al18 Abbott et al18 Hippisley-Cox and Pringle49 Hippisley-Cox and Pringle50 Mulnier51 Harvey et al52 Gulliford et al53 Hennis et al54 Hackam et al12 Shah et al13 Swaby et al55 Wilks et al56 Wilks et al56 Aguilar-Salinas et al57

Secondary care; regional Prescription database; national. private insurance Inpatient Secondary care; regional Secondary care; regional Prescription database; national Population-based Primary care; national. aged ≥ 45 years Primary care; national. aged <45 years Prescription database; national Primary care; national. aged ≥ 45 years Primary care; national. aged <45 years Prescription database; national Prescription database; national Primary care; national. aged ≥ 45 years Primary care; national. aged <45 years Diabetes register; regional Prescription database; national Prescription database; national Primary care; national Prescription database; national Population-based; regional Population-based; regional Primary/ secondary care; national Primary care; regional Population survey; regional Population survey; regional Prescription database; national Population-based; occupation Primary care; regional Secondary care; national Primary care; national Prescriptions & primary/ secondary clinics; regional Prescriptions & primary/ secondary clinics; regional Primary care; national. aged ≥ 45 years Primary care; national. aged <45 years Primary care; national. aged ≥ 45 years Primary care; national. aged <45 years Primary care; regional Diabetes register; regional Population-based Population-based Population-based Primary care; regional Primary care; regional Primary care; national Multisource; regional Primary/ secondary care; national Population-based; national Secondary care; regional Diabetes register; regional Primary care: national Primary care; regional Secondary care; regional Population-based; national

EUR

Croatia Denmark England Estonia Finland France Germany

Greece Ireland Israel Italy

Netherlands Spain

Sweden United Kingdom (White European) United Kingdom (South Asian) United Kingdom (African Caribbean) United Kingdom

NA

Wales Barbados Canada Jamaica

Mexico

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DIABETES ATLAS THIRD EDITION

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YEAR

PERSONS (n)

TYPE 1 (%)

DIET ONLY (%)

OHA ONLY (%)

INSULIN ONLY (%)

OHA AND INSULIN (%)

1998 1996 1997-1998 1997-2005 1996-1998 1998-1999 1999-2000 1999-2000 1999-2000 1990 1999-2000 1999-2000 2003 1997 1997 1997 1993-1995 1995 2003 1996-1997 1999 1999-2001 1989-1990 2002-2004 2000 2002 1990 2002 1996 1998 1998-1999 1998-1999 1994 1986 1996-1997 1996-1997 1986 1986 2002 1996-1997 1994-1996 1994-1996 1994-1996 2003 2000 2001 1998 1991 1988-1992 2001-2003 1999 1999 1995 1995 2000

288 1,100 92 2,165 474 1,459 699 4,034 252 884 2,825 131 124,811 87,731 1,192 180 181 116,224 181,894 7,391 est. 1.8 million 170 235 2,488 1,065 245 490 est. 50,000 289 4,610 2,658 779 4,503 5,873 2,693 295 6,881 345 1,495 5,143 13,409 1,866 371 8,626 5,849 24,348 8,877 690 736 1,871 est. 250,000 est. 550 247 190 2,878

13.5 NSt NSt NSt 15.8 EX NSt NSt NSt NSt NSt NSt NSt NSt NSt NSt 10.0 19.8 NSt 8.9 NSt NSt NSt EX EX 1.2 NSt NSt NSt EX EX EX 3.2 2.8 NSt NSt NSt NSt 3.8 13.0 10.8 7.3 4.9 8.8 EX NSt 14.8 NSt 2.0 NSt NSt NSt NSt NSt NSt

4.9 EX NSt 7.8 NSt EX 32.0 15.4 14.3 EX 21.9 19.8 EX EX 25.8 11.7 19.9 EX EX 6.9 EX 17.8 34.9 12.5 32.0 17.0 29.6 EX 54.0 31.3 15.5 19.5 10.2 12.4 13.8 8.1 25.4 18.3 30.8 19.0 30.9 23.3 19.9 31.3 34.1 24.6 28.6 10.4 17.0 21.7 40.0 NSt 6.1 2.6 21.7

61.8 64.4 80.4a 66.5 NSt 88.8 59.0 63.7 32.5 62.0 54.8 22.9 59.9 58.6 56.0 18.9 50.3 52.1 57.8 64.3 81.2 53.8 47.2 40.6 44.0 69.0 55.1 81.9 41.9 68.5 61.5 65.2 63.0 78.3 57.9 26.8 55.2 20.0 49.0 40.0 46.5 67.0 62.7 NSt 44.9 49.0 44.8 77.8 70.9 62.9 46.0 NSt 87.4 45.8 69.4a

23.6 30.5 NSt 18.1 41.4b 7.1 8.0b 12.8 46.0 30.0 13.4 51.1 30.4 37.5 16.9 67.2 23.2 31.9 24.3 10.6 14.5 16.6 9.8 26.3 13.0 14.0b 15.3b 15.0 3.5 0.3b 13.5 9.6 25.4 5.5 22.4 62.0 16.0 58.0 16.3 29.0 22.7b 9.6b 17.6b NSt 11.3 19.9 26.5b 11.7b 11.3 7.6 11.0 14.0b 6.5b 51.6b 5.8b

9.7 5.2 NSt 7.7 NSt 4.0 NSt 8.2 7.1 8.0 9.9 6.1 9.7 3.9 1.2 2.2 6.6 16.0 17.9 18.4 4.3 11.8 8.1 20.6 11.0 NSt NSt 3.2 0.7 NSt 9.5 5.7 1.5 3.7 6.2 3.1 3.4 3.8 3.8 12.0 NSt NSt NSt NSt 9.7 6.5 NSt NSt 0.8 7.8 3.0 NSt NSt NSt NSt

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TABLE 6.1 Classification of diabetes medication use: insulin, oral, diet, oral/insulin combination

REGION

COUNTRY/TERRITORY

AUTHOR

STUDY TYPE

Mexico Tortola (British Virgin Islands) Trinidad and Tobago

Bautista-Martinez et al16 Gulliford et al53 Gulliford et al53 Mahabir and Gulliford31 Mahabir and Gulliford31 Mahabir and Gulliford31 CDC58 CDC58 Cohen et al30 Cohen et al30 Karter et al59 Karter et al60 Koro et al61 Koro et al61 Gagliardino et al15 Perez-Cardona et al62 Perez-Cardona et al63 Perez-Cardona et al64 Perez-Cardona et al65 Perez-Cardona et al65 Gagliardino et al8 Chuang17 Mohan et al66 Raheja et al10 Rema et al24 Chuang17 Chuang17 Chuang et al9 Davis67 Health Insurance Commission68 Kemp et al69 Chuang17 Chuang17 Chuang17 Chuang17 Coppell et al70 Simmons et al71 Simmons et al71 Simmons et al71 Chuang17 Chuang17 Chang et al72 Chuang et al11 Chuang17 Aekplakorn et al73 Chuang17 Chuang17

Secondary care; regional Primary/ secondary care; national Primary/ secondary care; national Primary/ secondary care; insurance public Primary/ secondary care; insurance public Primary/ secondary care; insurance public Population-based; national Population-based; national Population survey; insurance private Population survey; insurance private Diabetes register; insurance private Population survey; insurance private Population-based; national Population-based; national Inpatient Population-based; national Population-based; insurance private Population-based; insurance public Population-based; insurance private Population-based; insurance public Primary/ secondary; international Secondary care; national Secondary care; regional Secondary care; national Population-based; regional Secondary care; national Secondary care; national Secondary care; international Population-based; regional Prescription database; national Population-based; national Secondary care; national Secondary care; national Secondary care; national Secondary care; national Diabetes register; regional Population-based; regional Population-based. regional Population-based. regional Secondary care; national Primary/ secondary care; national Population-based; national Secondary care; national Secondary care; national Population-based; national Secondary care; national Secondary care; national

United States of America

SACA

Argentina Puerto Rico

SEA

South America Bangladesh India

WP

Sri Lanka 12 countries Australia

China Indonesia Korea Malaysia New Zealand New Zealand (European) New Zealand (Maori) New Zealand (Pacific Islander) Philippines Singapore Taiwan

Thailand Viet Nam

a. b. est. Ex NSt

280

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Includes unknown percentage also using insulin Includes unknown percentage also using OHA Estimated number of persons with diabetes (from rates indicated in data) Excluded Not stated

DIABETES ATLAS THIRD EDITION

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YEAR

PERSONS (n)

TYPE 1 (%)

DIET ONLY (%)

OHA ONLY (%)

INSULIN ONLY (%)

OHA AND INSULIN (%)

1999 1991 1991 1993 1998 2003 1997 2003 1997 2000 1996-1997 2000-2001 1988-1994 1999-2000 1996 1999 1997-1998 1997-1998 2000 2000 1999 1998 pre-1997 1998 2001 1998 1998 1998 1993-1996 2004 1999-2000 1998 1998 1998 1998 2003-2004 1992-1994 1992-1994 1992-1994 1998 1998 2000 1998 1998 2000 1998 1998

570 180 791 690 1,579 1,952 est. 10.2 million est. 13.7 million 81,324 177,718 24,312 11,922 1,215 372 117 244 22,424 38,139 23,903 49,197 11,425 1,565 9,873 2,269 442 2,228 1,183 21,817 1,426 433,072 475 2,414 2,086 946 1,013 3,599 176 176 495 2,713 1,667 764 2,439 2,435 est. 245 2,332 1,235

11.2 NSt NSt NSt NSt NSt NSt NSt EX EX 4.8 NSt EX EX 7.0 NSt NSt NSt NSt NSt 9.1 0.2 EX 7.6 EX 7.6 3.6 4.4 9.3 NSt 7.2 5.6 2.0 3.8 5.0 9.4 NSt NSt NSt 3.8 8.2 NSt NSt 2.7 NSt 3.8 7.3

EX 9.4 8.5 5.8 13.9 6.7 17.6 15.3 32.5 33.5 22.8 7.6 27.4 20.2 36.0 NSt NSt NSt NSt NSt 14.0 17.8 1.2 4.5 5.9 4.0 13.0 5.2 29.0 EX 29.7 3.1 5.1 3.6 2.6 29.4 NSt NSt NSt 4.5 8.2 NSt 1.5 1.5 NSt 2.2 2.3

52.3 72.2 79.1 82.3 74.0 78.0 49.0 56.9 43.4 47.6 52.6 62.5 45.4 52.5 56.3 NSta 59.4a 36.4a 46.1a 47.1a 60.2 60.3 71.6 53.9 81.0 53.9 72.1 70.8 50.9 73.1 52.9 67.3 88.1 61.2 82.6 44.5 52.0a 64.0a 71.0a 70.2 70.7 78.9a 74.7 74.7 81.9a 75.5 73.5

47.7b 18.3b 12.4b 11.9b 12.1b 15.3b 22.5 15.3 18.3 13.0 24.6b 18.1 24.2 16.4 7.7b 34.6b 29.1b 27,8b 16.4b 29.7b 25.8b 14.4 27.2b 21.8 3.8 21.9 11.4 14.3 17.8 18.5 14.1 17.0 4.6 29.7 10.8 18.2 28.0b 19.0b 15.0b 15.2 15.0 14.7b 13.7 13.6 2.8b 11.8 8.9

NSt NSt NSt NSt NSt NSt 10.8 12.4 5.8 5.9 NSt 11.8 3.1 11.0 NSt NSt NSt NSt NSt NSt NSt 7.5 NSt 19.8 9.3 19.8 3.0 9.3 2.4 8.4 3.4 10.9 2.1 5.0 3.7 7.9 NSt NSt NSt 10.0 6.1 NS 10.2 10.2 NSt 10.5 15.1

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TABLE 6.2 Comparative European national prescription rates: insulin and oral medication

COUNTRY/TERRITORY

AUTHOR

Belgium

Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Joost and Mengel34 Joost and Mengel34 Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Del Pozo et al32 Del Pozo et al32 Melander74 Melander74

Denmark England Finland Germany

Italy Portugal Slovakia Spain

Sweden

282

YEAR

PRESCRIPTION RATE ddd/1000p/day

OHA RATE ddd/1000p/day

1997 2000 1994 2003 1994 2003 1994 2003 1994 2002 1994 2003 2000 2003 2000 2002 1998 2000 1994 2002 1989 1998 1994 2003

27.6 34.7 14.0 28.0 16.3 37.5 29.4 57.9 32.1 55.8 30.5 50.4 29.8 39.0 10.0 44.8 34.2 31.6 22.7 46.7 16.0 32.7 27.2 41.0

19.4 24.7 7.6 17.2 9.3 23.2 18.2 37.7 21.1 32.6 20.7 28.3 20.3 29.6 32.4 37.5 25.8 22.3 15.6 33.5 11.2 23.2 13.0 20.0

ddd/1000 p/day

defined daily doses/1000 persons of the whole population/day

OHA

oral hypoglycaemic agent

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INSULIN RATE ddd/1000p/day

INSULIN PROPORTION (%)

8.2 10.0 6.4 11.0 7.0 14.3 11.2 20.2 11.0 23.2 9.8 22.1 9.5 9.4 6.4 7.3 8.6 9.3 7.0 13.2 4.8 9.5 14.2 21.0

29.8 28.9 45.7 39.3 42.9 38.3 38.1 34.8 34.3 41.6 32.0 43.8 32.0 24.1 64.0 16.3 25.1 29.4 30.9 28.3 30.2 28.9 52.2 51.2

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TOTAL

ANNUAL CHANGE RATE (AVERAGE) (%) OHA

INSULIN

7.9

8.4

6.9

8.0

9.5

6.2

9.7

10.7

8.3

7.8

8.4

6.8

7.2

5.6

9.8

5.7

3.5

9.5

9.4

13.5

-0.5

111.7

7.6

6.7

-3.9

-7.0

4.0

21.6

22.6

19.1

9.3

9.6

8.7

4.7

4.9

4.4

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TABLE 6.3 Proportion of people (or prescriptions) using different oral hypoglycaemic agents, among all those using oral hypoglycaemic agents

REGION

COUNTRY/TERRITORY

AUTHOR

AFR

South Africa

EMME EUR

Tanzania. United Republic of Bahrain Denmark

Steyn et al25 Truter6 Truter6 Gulam-Abbas et al36 Al Khaja et al7 Danish Medicines Agency21 Danish Medicines Agency21 Vides et al38 Social Insurance Institute39 Le Floch et al40 Rothenbacher et al41 Meisinger et al28 Meisinger et al28 Meisinger et al28 Meisinger et al28 Ott et al22 Barry et al3 and Tilson26 Ciardullo et al45 Hippisley-Cox and Pringle50 Mulnier51 Hennis et al54 Gulliford et al53 Wilks et al56 Gulliford et al53 Gulliford et al53 Mahabir and Gulliford31 Mahabir and Gulliford31 Mahabir and Gulliford31 Wysowski et al27 Wysowski et al27 Wysowski et al27 Cohen et al30 Cohen et al30 Chuang17 Chuang17 Chuang17 Chuang et al9 Davis et al75 Health Insurance Commission68 Chuang17 Chuang17 Chuang17 Chuang17 Chuang17 Chuang17 Chuang17 Chuang17 Chuang17

Estonia Finland France Germany

Ireland Italy United Kingdom NA

Barbados Jamaica Tortola (British Virgin Islands) Trinidad and Tobago

United States of America

SEA

WP

Bangladesh India Sri Lanka Asia (DiabCare) Australia China Indonesia Korea Malaysia Philippines Singapore Taiwan Thailand Viet Nam

a. b. s. NA NSt

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YEAR

PERSONS

1998 1996 1996 2005 1998-1999 1997 2003 1993-1995 2003 1996-1997 2000 1984-1985 1989-1990 1994-1995 1999-2000 2002-2004 2002 1998 2000 2001 1988-1992 1991 1995 1991 1991 1993 1998 2003 1990 1996 2001 1997 2000 1998 1998 1998 1998 2001 2004 1998 1998 1998 1998 1998 1998 2002 1998 1998

249s 9,078s 708 1,608 1,358 54,848 86,818 104 137,663 6,078 586 44 130 129 111 1,617 est. 380,000s 3,156 2,626 11,924 378 537 314 130 626 568 1,169 1,523 23.4 millionb 41.0 millionb 91.7 millionb 40,011 95,079 1,062 1,642 888 16,217 727 352,928 1,886 1,881 626 874 2,176 1,279 2,066 2,031 1,094

Nearly all combination therapy was of metformin with sulfonylurea Numbers of prescriptions; proportions are of prescriptions. For year 2001 the combination represents specific metformin/sulfonylurea mix Prescriptions; proportions are of prescriptions Not applicable Not stated

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nts COMBINATION MEDICATION SULFONYLUREAS ONLY (%)

METFORMIN ONLY (%)

OTHER (%)

(IF SPECIFIED)a (%)

63.2 63.1 61.3 16.2 59.8 66.4 37.0 96.2 25.2 41.8 41.8 100.0 96.9 53.5 29.7 14.1 51.0 48.0 44.9 30.9 62.1 90.5 46.2 84.6 97.0 70.1 75.7 17.3 100.0 79.8 42.7 57.6 37.0 50.7 41.7 38.2 35.5 44.8 20.9 26.5 40.5 40.7 33.1 36.6 35.4 18.5 34.0 48.4

35.1 33.3 65.0 13.3 5.4 4.0 23.8 3.8 28.9 23.1 34.5 NSt 0.8 17.1 31.5 32.7 44.5 7.8 22.0 22.6 5.4 5.4 4.1 7.7 2.9 3.7 4.4 13.8 0.0 19.0 32.7 15.0 22.0 16.9 9.7 22.0 1.7 24.6 37.7 13.7 12.0 8.3 9.5 15.5 10.9 14.5 9.0 2.8

1.7 3.6 0.0 3.6 0.0 3.9 5.5 NSt 5.9 NSt NSt 0.0 0.0 0.0 0.0 10.0 4.6 9.8 0.4 0.5 0.0 4.1 0.0 7.7 0.2 NSt NSt NSt 0.0 1.2 19.6 7.3 10.8 0.0 0.4 0.0 12.2 0.0 1.9 4.9 0.4 5.8 0.1 4.2 0.6 0.0 0.9 3.9

NA NA 0.3 66.9 34.8 25.8 33.7 NSt 40.1 35.1 23.6 0.0 2.3 29.5 38.7 43.2 NA 34.4 32.6 46.1 32.6 NSt 49.7 NSt NSt 26.2 19.9 68.9 NSt NSt 4.9 20.2 30.1 32.5 48.2 39.9 50.5 30.6 39.4 54.8 47.1 45.2 57.3 43.7 52.9 66.9 57.2 44.9

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TABLE 6.4 Prescription rates of oral hypoglycaemic agents within the whole population, and proportionate use of individual oral hypoglycaemic agents

COUNTRY/TERRITORY

AUTHOR

Belgium

Sartor and Walckiers5 and Walckiers et al4 Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Joost and Mengel34 Joost and Mengel34 Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Melander74 Del Pozo et al32 Del Pozo et al32 Apoteket AB76 Apoteket AB76 Melander74 Melander74

Denmark England Finland Germany

Italy Portugal Slovakia Spain

Sweden

ddd/1000 p/day

YEAR

PRESCRIPTION RATES (ddd/1000p/day)

1990 1997 2000 1994 2003 1994 2003 1994 2003 1994 2002 1994 2003 2000 2003 2000 2002 1998 2000 1994 2002 1989 1998 2000 2004 1994 2003

6.6 19.4 24.6 7.6 17.2 9.3 23.2 18.2 37.7 21.1 32.6 18.6 32.9 20.3 29.6 32.4 37.5 25.8 22.3 15.6 33.5 11.2 23.3 16.8 21.2 13.0 20.0

defined daily doses/1000 persons of the whole population/day Nst Not stated

TABLE 6.5 Comparative use of type of insulin

COUNTRY/TERRITORY

REPORT

Australia Australiaa Denmarka Denmarka Finlanda Germany Jamaica South Africab Spain

Health Insurance Commission68 Health Insurance Commission68 Danish Medicines Agency21 Danish Medicines Agency21 Social Insurance Institute39 Joost and Mengel34 Swaby et al55 Truter6 Del Pozo32 Del Pozo32 Apoteket AB76 Apoteket AB76 Neuhann et al33

Sweden Tanzania, United Republic ofb

a. b. (r)

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YEAR 2003 2004 1997 2003 2002 2003 1999 1996 1989 1998 2000 2004 1996-1998

The total number of persons is less than the sum of those on each type of insulin, as many people use more than one type of insulin Data are for small samples; all other reports describe total use over 12-month interval ddd/1000 p/day DIABETES ATLAS THIRD EDITION

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SULFONYLUREAS (%)

METFORMIN (%)

OTHER (%)

68.5 66.2 61.1 85.5 67.4 74.2 56.8 84.6 62.6 90.5 67.2 90.4 55.6 78.7 69.6 75.6 70.7 78.3 73.1 94.2 84.2 93.1 78.4 64.0 40.9 83.1 54.0

31.5 33.8 38.9 14.5 32.6 25.8 43.2 15.4 37.4 9.5 32.8 9.6 37.4 21.3 30.4 24.4 29.3 21.7 26.9 5.8 15.8 6.3 8.4 31.7 49.2 16.9 46.0

NSt NSt NSt NSt NSt NSt NSt NSt NSt NSt NSt NSt 7.0 NSt NSt NSt NSt NSt NSt NSt NSt 0.6 13.2 4.3 9.9 NSt NSt

NON-METFORMIN CHANGE (ANNUAL) (%)

13.4

13.6

19.8

20.4

17.2

17.4

19.7

20.2

23.3

24.6

23.9

25.1

27.7

28.3

18.0

18.4

3.5

4.0

24.8

26.8

12.1

12.7

18.4

18.7

17.2

17.7

SCRIPTS (s), PERSONS (p), RATES (r)

FAST ACTING

INTERMEDIATE

BIPHASIC

ULTRALONG

550,427 (s) 149,210 (p) 36,299 (p) 50,056 (p) 76,839 (p) 22 (r) 80 (p) 4,387 (s) 5 (r) 10 (r) 19 (r) 22 (r) 196 (p)

157,210 66,138 19,601 25,398 37,682 9 0 1,215 0 1 7 8 10

151,027 67,683 31,036 39,253 67,638 4 0 1,470 3 6 7 6 118

221,212 72,195 9,583 16,682 14,963 8 67 1,619 0 3 5 6 58

20,978 7,869 0 0 8,969 2 13 83 1 0 0 3 •

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METFORMIN VOLUME CHANGE (ANNUAL) (%)

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nsulin is vital for the survival of people with type 1 diabetes and is necessary for some people with type 2 diabetes77. Also of central importance are the means to administer the treatment such as syringe and needles, the means to monitor the response to insulin such as blood and urine tests, supportive care and education, and an understanding of how diabetes impacts the life and work of the individual. Some 85 years after its discovery, insulin is still not available on an uninterrupted basis in many parts of sub-Saharan Africa (SSA)78-80 . Very little primary data exist on type 1 diabetes in SSA and most information is based on anecdote. The Rapid Assessment Protocol for Insulin Access (RAPIA) was developed81 in order to clearly assess the barriers to insulin access and proper diabetes care, and to attempt to improve these. For the purpose of this section, insulin-requiring diabetes has been defined as diabetes diagnosed before age 30 and with insulin treatment being commenced within one month of diagnosis. This term is used instead of the more common 288

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term ‘type 1 diabetes’, both because scarcity of ketone testing makes for difficulties with the term ‘ketosis-prone’, and because of differences in the spectrum of insulin-requiring diabetes between Africans and Caucasians82,83.

Selection of countries Mali, Mozambique and Zambia are three countries located in sub-Saharan Africa. They are all defined as Highly Indebted Poor Countries (HIPC) by the World Bank on the basis that the demands for debt repayment heavily exceed their ability to generate income, and as a consequence, programmes of social investment including health are suffering. An assessment of the barriers to insulin access and diabetes care, using the RAPIA, was carried out in these three countries by the International Insulin Foundation (IIF). These countries were chosen due to their geographical, historical and socio-economic differences, and as it was felt that they were representative countries for subSaharan Africa, with one being affected by civil war, two by famine and the HIV/AIDS epidemic. Implementing the DIABETES ATLAS THIRD EDITION

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RAPIA in these three HIPCs was to see how a sustainable solution could be found to the issues of access to insulin and proper diabetes care under extreme conditions of scarce resources in the health sector. In addition as the main burden of disease in these countries is still communicable diseases such as malaria, respiratory infections, diarrhoeal diseases and HIV/AIDS, the aim of the RAPIA and the IIF’s work was also to raise awareness about diabetes in these countries. Mozambique was chosen as a pilot country due to strong local support and availability of funding to develop and implement the RAPIA. Strong support and interest from the diabetes associations and Ministries of Health in Zambia and Mali lead to the implementation of the protocol in these countries.

Aim The assessment was carried out in three different locations in each country — the capital city, an urban area and a predominantly rural area. The aim was to see if the problems in different areas of the country varied due to their geographical and socio-economic situation. The Project Coordinator together with a team of local interviewers from the respective national diabetes associations (Mozambique and Zambia), and a local non-governmental organization (NGO) and the diabetes association (Mali) carried out the RAPIA in these three countries in collaboration with the respective Ministries of Health. The topics covered by RAPIA included: • Health service structure and functioning with regards to procurement of medicines and diabetes management • Diabetes policies, written and enacted • Reported and observed practice for diabetes management • Availability of insulin, syringes and monitoring equipment • Price of insulin, syringes and monitoring equipment • Existence of distribution networks for insulin • Insulin supply-related knowledge and attitudes amongst people with diabetes and their carers • Other problems that hamper the access to proper insulin and care *All currency amounts are stated in USD for ease of comparison and were converted from other currencies at the time of reporting (Mozambique September 2003; Zambia April 2004; Mali December 2004).

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Insulin A lack of data at various levels of the health system led to many problems, especially with regards to quantification of needs for insulin. For example in Mozambique, 77% of the insulin imported nationally remained in the capital city. Insulin is purchased by governments using tenders (Mozambique and Zambia), from local wholesalers (Mozambique and Zambia) or, in Mali, directly with a wholesaler based in France. Tendering and bulk purchasing can substantially reduce insulin prices, but problems with quantification lead to unnecessarily high prices being paid for insulin. In 2003 the government of Zambia purchased 21% of its insulin from local suppliers rather than by national tender, representing 32% of total insulin cost. Had the 10,260 additional vials been purchased through the tender process, this would have saved USD38,000*, or around 15% of the total. The drawback of tenders is that delivery of insulin can take several months compared to a matter of days when it is bought locally. Insulin was exempt from any taxes and duties in Mozambique and Zambia while insulin in Mali is subject to 2.5% duty. Mali, Mozambique and Zambia’s Essential Drug List all listed both regular and slow acting human100 IU/ml insulin. Insulin is supposed to be available at hospitals and at the smaller Referral Health Centres in each of the three countries. During the assessments, however, insulin was present at some Referral Health Centres only in Zambia. In Mozambique insulin was present at most hospitals and in Mali only the two national referral level hospitals in the capital city had insulin available at the time of the study. In both Mozambique and Zambia the Central Medical Stores were the main supplier of insulin to the public sector. In Mali private wholesalers sold directly to the public sector, although in August 2004 a purchase of insulin by the Central Medical Stores was destined to the public sector, the first in two or three years. Figure 6.1 shows the different average purchasing prices of insulin at different levels of the healthcare system. Mozambique and Zambia have instigated measures to allow people with diabetes to receive free or subsidized insulin. However these are not standardized and are unclear to CHAPTER 6

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FIGURE 6.1 Average purchasing prices per vial of 100 IU insulin (USD) in Mali, Mozambique and Zambia

Average purchasing price (USD) 20.0 18.0 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0 Central Medical Store Mali

Public Sector Mozambique

Private Sector

Syringes Syringes are vital for the delivery of insulin. In all countries syringes had some form of Value Added Tax (VAT) applied to their cost, and also were rarely available in the public sector as they were not ordered by Central Medical Stores or facilities. In consequence, people with diabetes in rural areas had the most difficulty accessing them. There was substantial variation reported in the costs of syringes by people with diabetes (see Table 6.6), and in the duration of single use syringes (range of changing syringe: daily to every three weeks, median four days).

Testing materials Another financial hurdle for people with diabetes is the cost of their testing. For example children and the elderly in Zambia had their diabetes monitored for free. Others paid anywhere from USD1.06 to USD51.06 per month for their monitoring costs, depending on whether they had one or more urine and/ or blood tests per month or their own blood glucose meter. CHAPTER 6

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Patient (private sector)

Zambia

individuals. In Mali no such assistance exists and people with diabetes need to bear the total cost of their insulin.

290

Patient (public sector)

This financial barrier is coupled with problems of availability of these tools, reagents and consumables (see Table 6.7). The reason for this was primarily financial, with health facilities lacking the appropriate budget for purchasing these tools.

Many people were initially diagnosed with diabetes only following the onset of complications, primarily due to the lack of diagnostic tools and healthcare worker training. From previous studies, it has been claimed that the life expectancy of a child with newly diagnosed type 1 diabetes in much of sub-Saharan Africa may be as short as one year84,85. From different interviews and site visits, and using aggregate data of different registers, an estimate of prevalence for all age groups for the different areas studied in the three countries was calculated (see Table 6.8). It is seen that there are substantial differences in prevalence between different countries, and between urban and rural areas within countries. These differences are most marked in Mali and least obvious in Zambia. While some of the DIABETES ATLAS THIRD EDITION

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FIGURE 6.2 Differences in calculated average life expectancies for people with insulin-requiring diabetes from different areas of Mali, Mozambique and Zambia Life expectancy (years)

0-14 years

Life expectancy (years)

30

30

25

25

20

20

15

15

10

10

5

5

15+ years

0

0 National Mali

Capital city

Urban area

Mozambique

Rural area

Zambia

differences in prevalence in Mali are consequent upon people with diabetes travelling to the capital city for their care, this is much less so in Mozambique, where a six-fold urban-rural difference in prevalence was found.

National

Capital city

Urban area

Rural area

It was not possible to estimate regional differences in life expectancy in Mali because people with diabetes commonly travelled to the capital city for treatment.

It should be noted that 3.3% of adults in Mali is estimated to have diabetes in 2007, as shown in Chapter 1 (see Table 1.7). The numbers shown in Table 6.8 are for both adults and children, but highlight that very few people with diabetes in these countries are actually diagnosed, that the health systems lack many resources and that access to insulin is difficult.

Zambia had the highest life expectancy (11 years), but in Mozambique and in Mali, the onset of insulin-requiring diabetes in childhood will mean an average life expectancy of 12 months and of 30 months respectively. Zambia showed substantially less variation between different areas of the country compared to Mozambique. Thus, despite similar national indices of poverty, the newly presenting diabetic child can expect around 5-10 times better prognosis in Zambia than in Mozambique, a difference likely to be accounted for by differences in the organization of healthcare. These differences are closely linked to the availability of insulin, syringes and testing materials, but factors such as healthcare worker training and guidelines, costs for the person with diabetes, and the advocacy role of the diabetes associations also impact the outcome for a person with insulin-requiring diabetes.

In Figure 6.2, the National series shows the overall life expectancy in the country taking into account the difference in urban and rural populations. It was calculated by taking the total population, minus the population of the capital city, and seeing the proportion of urban and rural dwellers in each country. The value for life expectancy for the urban and rural areas was then applied to these values.

One key aim of the IIF is to promote and support the development of national diabetes programmes, which address the identified problems during the RAPIA assessment and provide sustainable solutions. This work contributes to the WHO AFRO and IDF African Region African Declaration

Using the estimated national prevalence rates in Table 6.8, and assuming incidence rates from studies in Nigeria86 and Tanzania87, estimates were made for life expectancy (see Figure 6.2).

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RAPID ASSESSMENT PROTOCOL FOR INSULIN ACCESS The RAPIA is a series of assessment tools aimed at investigating different levels of the health system. It looks at the availability, price and distribution of insulin and related supplies. By looking at the entire health system the RAPIA is able to identify problems that hamper access to proper insulin and care. This information is gathered through specific questionnaires (see Table 6.9), site visits, document reviews and discussions81,88. The RAPIA is divided into three levels (see Table 6.9): Macro level: aimed at the Ministries of Health, Finance and Trade, national diabetes associations, educators, Central Medical Stores and private wholesalers of medicines and medical equipment.

The meso and micro levels are carried out in three distinct geographical locations – the capital city, a large city or urban area and a predominantly rural area to represent different geographical and economic situations. Each interview had as its main aim to obtain the person’s perspective on the problems people with diabetes face in gaining access to insulin and diabetes care in the given country. Through overlap in the areas of questioning and by using other sources of information (site visits, discussions and document reviews), the information gathered was validated by cross-checking and triangulation between different sources and types of information.

Meso level: designed to focus on Regional and District Health Offices, health facilities including pharmacies and laboratories, as well as private clinics. Micro level: comprises interviews with healthcare workers, traditional healers and people with diabetes.

on Diabetes which calls on governments, NGOs, donors, industry, healthcare providers and all partners and stakeholders in diabetes to ensure:

but one of the necessities is information on the size and scope of the problem of diabetes. The role of a strong diabetes association is also essential in pushing this forward.

• Adequate, appropriate and affordable medications and supplies for people with diabetes

Development of national diabetes programmes

• Earlier detection and optimal quality of care of diabetes • Effective efforts to create healthier environments and prevent diabetes Such initiatives need to be supported and promoted by governments and stakeholders at a national and international level in order to ensure that people with diabetes benefit from these. The work carried out by the IIF in these three countries indicate that a combination of factors have to be in place in order for a comprehensive solution to succeed. These include:

Once information about health system performance has been collected, the development of a national diabetes programme/policy is needed in order to ensure continuity and guiding principles. These should help establish a comprehensive organization of the health system able to provide care for a chronic condition. The appropriate infrastructure is vital within the system to supply medicines, provide diagnostics, and the tools within health facilities to provide appropriate care. The national programme should also include such elements as prevention of diabetes and complications, and should address the issues of accessibility and affordability of medicines and care. Diabetes training

Political will

Based on the IIF’s experience, strong political will is necessary in order for a national diabetes programme to be established. This political will can be generated through different means, 292

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In parallel diabetes training for healthcare workers needs to be implemented, both for those in training and those already practising. This training needs to include both clinical aspects of diabetes and how to manage a long-term condition. DIABETES ATLAS THIRD EDITION

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Diabetes education and empowerment

Healthcare workers and the diabetes association need to work in conjunction with the community at large towards education for people with diabetes and empowerment in order to ensure proper adherence to treatment. By addressing these different elements, governments can not only ensure the survival of those with type 1 diabetes, but also improve the health system for the care of people with type 2 diabetes, which could act as a model for other non-communicable diseases.

The RAPIA provides information on the different barriers that exist in a given country with regards to access to essential elements needed for the diagnosis, care and management of people with diabetes. The work of the IIF is not only in documenting the problems, but to also work in close collaboration with local stakeholders and international agencies to develop feasible solutions.

the diabetes associations, Ministries of Health and local NGOs in Mali, Mozambique and Zambia have been able to formulate clear action plans based on a sound assessment. The IIF and the IDF African Region have assisted these three countries in developing and implementing the recommendations put forward after the RAPIA assessments. This model of assessing the health system, developing country specific recommendations and following this with implementation should be a gold standard applicable to other non-communicable diseases beyond diabetes. This will ensure that real needs are being tackled and that appropriate means are used to address the problems in the health system identified by the RAPIA. Note: the data are correct at the time of the RAPIA assessment in each of these countries.

The RAPIA tool empowers local stakeholders by involving them in all the steps of the assessment. By using the RAPIA MANAGING INSULIN-REQUIRING DIABETES IN SUB-SAHARAN AFRICA

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TABLE 6.6 Comparison of the price range per syringe (USD) in Mali, Mozambique and Zambia

COUNTRY

PRICE RANGE PER SYRINGE (USD)

Mali Mozambique Zambia

0.20-0.60 0.04-0.20 0.15-1.50

TABLE 6.7 Availability of various diagnostic tools in Mali, Mozambique and Zambia at different health facilities visited COUNTRY

Mali Mozambique Zambia

URINE GLUCOSE STRIPS (%)

KETONE STRIPS (%)

BLOOD GLUCOSE METER (%)

PRESENCE OF A SPECTROPHOTOMETER OR OTHER LABORATORY EQUIPMENT FOR BLOOD ANALYSES (%)

Urban

Rural

Urban

Rural

Urban

Rural

Urban

Rural

67 21 64

0 0 56

40 9 54

0 0 38

67 38 51

30 0 63

50 15 10

0 0 6

TABLE 6.8 Estimated prevalence of insulin requiring diabetes in areas studied in Mali, Mozambique and Zambia

LOCATION

PREVALENCE PER 100,000 POPULATION OF INSULIN-REQUIRING DIABETES

MALI

NATIONAL (extrapolation) Bamako (capital city) Sikasso (urban area) Timbuktu (geographically inaccessible area) Kadiolo (rural area) Douentza (rural area)

MOZAMBIQUE

NATIONAL (extrapolation) Maputo (capital city) Beira (urban area) Lichinga (rural area)

ZAMBIA

NATIONAL (extrapolation) Lusaka Province (capital city and surrounding province) Copperbelt Province. (urban area) Eastern Province (rural area)

3.9 27.9 1.8 2.9 1.4 0.2 3.5 9.1 5.0 1.4 12.0 18.0 12.6 9.5

As interviews, site visits and aggregate data of different registers were used in these calculations, the following assumptions were made: • Patients attended clinics for their diabetes care once a month • No overlap in patients from different facilities within the same area

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TABLE 6.9 The different questionnaires that comprise the RAPIA

LEVEL MACRO

ISSUES ADDRESSED IN EACH RAPIA QUESTIONNAIRE Ministry of Health

Ministry of Trade Ministry of Finance

Private sector National diabetes association Central Medical Store

MESO

Regional Health Organization Hospitals, clinics, health centres, etc

Laboratory Pharmacy MICRO

Health workers and traditional healers

People with diabetes

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Organization of delivery of diabetes care Resources available for diabetes and insulin National Programmes for diabetes and insulin Pricing of insulin Distribution of insulin Funding for insulin and diabetes Insulin tendering and purchase Trade issues (laws, barriers to trade) Trade infrastructure Funding of health system Taxes on insulin Funding for insulin and diabetes Pricing of insulin Distribution of insulin Issues with diabetes and insulin Insulin tendering and purchase Insulin distribution and storage Insulin pricing Issues with diabetes and insulin Organization of care for people with diabetes Treatment and management of people with diabetes Access to appropriate tools to diagnose and treat patients Infrastructure present and/or lacking for insulin provision Infrastructure present and/or lacking for proper diagnosis and follow-up Insulin distribution and storage Insulin pricing Problems encountered in diagnosis and treatment of patients Training Infrastructure present and/or lacking Tools present and/or lacking Diagnosis Access to treatment Cost of treatment

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CHAPTER 7 MENTAL HEALTH, ANTIPSYCHOTIC DRUGS AND HYPERGLYCAEMIA

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It has been estimated that people with diabetes are twice as likely as the general population to suffer from depression.

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he chronically mentally ill are a high-risk population for diseases such as diabetes and cardiovascular disease due to a number of factors. These include suboptimal access to medical services, a propensity to smoking, poor diet, sedentary lifestyle and obesity1,2. There is increasing information regarding the association between diabetes and psychotic disorders. However, the true prevalence of diabetes in this population may be underestimated due to inconsistencies in surveillance protocols. Evidence is also mounting for the association between antipsychotic medication and the development of diabetes, and a link between depression and diabetes has been recognized.

There is an increasing awareness of the link between diabetes, both type 1 and type 2, and depression. But which one leads to the other or affects its course and MENTAL HEALTH, ANTIPSYCHOTIC DRUGS AND HYPERGLYCAEMIA

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outcome has not yet been fully elucidated. Nevertheless, it has been estimated that people with diabetes are twice as likely as the general population to suffer from depression, with the risk being higher in women than in men3,4. Depending on the definition used the prevalence of depression among people with diabetes ranges from 8.5 to 32.5%5-7. People with poorly controlled diabetes are more likely to have depression7. This may be because depression leads to problems with adherence to medication and diet, and affects quality of life8,9. Depression also seems to be a factor in increasing the risk of developing diabetes-related complications10, and also increased mortality11. Having diabetes and depression may also be associated with higher risk of suicide, with some reports of a 10-fold increased risk of suicide and suicidal ideation12,13. Thus the combination of diabetes and depression appears to be associated with a poorer quality of life, and with increased morbidity and mortality.

Type 2 diabetes and schizophrenia There has been a purported association between abnormal CHAPTER 7

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FIGURE 7.1 Pharmacological agents implicated in the development of diabetes mellitus

Destruction of beta cells Streptozotocin

Inhibition of insulin secretion Thiazide diuretics Calcium channel antagonists Phenytoin Pentamidine Cyclosporin A

DIABETES MELLITUS Sympathetic stimulation/ blockade α, β agonists/ antagonists Xanthines

Impaired insulin action Corticosteroids Oral contraceptive pill Anabolic steroids

Unknown mechanisms Nalidixic acid, Rifampicin, Isoniazid, Phenothiazines

glucose tolerance and psychiatric disorders that goes back almost 100 years14, and by the 1960s and 1970s it was recognized that phenothiazines (drugs used to treat schizophrenia) were associated with hyperglycaemia15. More recently there have been numerous reports of diabetes occurring among people with psychotic disorders. However, these studies, many of which have been retrospective and of variable size, have not always been controlled for potential confounders such as age, race, gender, family history and obesity. In addition, many studies have not been controlled for use of antipsychotic drugs, which themselves, especially some of the newer, atypical agents, have been implicated in the development of diabetes. Furthermore, it has been suggested that the apparent difference in risk between medications may partly be attributed to a greater degree of vigilance with more glucose monitoring being done among those taking atypical antipsychotics such as clozapine or olanzapine, as opposed to older drugs such as risperidone16. Other shortfalls include differences in whether subjects were screened before or after initiating medication and the type of test(s) used for diagnosing diabetes17. 300

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As an illustration of the link between schizophrenia and diabetes, a study from the USA 18 reported a diabetes prevalence of 15% among those with schizophrenia, compared to 3% from the general population. Increasing age and African American background were highlighted as being important risk factors. Notably, much of this study predated the widespread use of atypical antipsychotics.

A wide variety of antipsychotic drugs has been used in the treatment of schizophrenia and mood disorders. Antipsychotic drugs are split into first and second generation, or typical and atypical antipsychotics (see Table 7.1). Second generation drugs have only been around for some 20 years. First generation agents used to be commonly prescribed for the treatment of schizophrenia, but have now been largely superseded by atypical antipsychotics. Second generation or atypical antipsychotic drugs have been favoured due to a better side effect profile.

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TABLE 7.1 Examples of some first and second generation antipsychotic agents

FIRST GENERATION ANTIPSYCHOTIC DRUGS Chlorpromazine Fluphenazine Thioridazine Haloperidol Droperidol Flupenthixol Zuclopenthixol

SECOND GENERATION OR ATYPICAL ANTIPSYCHOTIC DRUGS Olanzapine Clozapine Risperidone Quetiapine Aripiprazole Ziprasidone Amisulpride

Drug induced hyperglycaemia A variety of drugs has been implicated in precipitating diabetes mellitus (see Figure 7.1). Case reports linking atypical antipsychotics with hyperglycaemia have appeared for some years going back to 1994 for clozapine19, 1998-99 for olanzapine20 and 1999 for quetiapine21. Reports linking older antipsychotic drugs such as chlorpromazine to increased blood glucose levels22 and aggravation of existing diabetes23 go back to the 1950s and 1960s24. A study set in 1999 reviewed the records of 38,632 outpatients with schizophrenia receiving either atypical (58.6%) or typical antipsychotics (41.4%)25. The overall prevalence of diabetes was high at 19%, and in those under the age of 60, there was an increased risk of developing diabetes for those who were on clozapine, olanzapine and quetiapine (but not risperidone), compared to those on other antipsychotic agents. A study, using the UK-based General Practice Research Database (GPRD)26, showed that the incidence of diabetes was higher among users of atypical than conventional MENTAL HEALTH, ANTIPSYCHOTIC DRUGS AND HYPERGLYCAEMIA

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antipsychotics (10/1,000 person years for clozapine; 5.4/1,000 for risperidone; 5.1/1,000 for conventional agents). Compared to those not on any antipsychotic treatment (and adjusted for age, sex and use of drugs known to affect glucose metabolism), olanzapine was an independent risk factor for diabetes, but risperidone and conventional agents were not (olanzapine odds ratio (OR) 5.8 (2.0-16.7); risperidone OR 2.2 (0.9-5.2); and conventional agents OR 1.4 (1.1-1.7)). When compared to those treated with conventional agents, olanzapine, but not risperidone, was again an independent risk factor. A retrospective study examined claims data from a health plan of nearly two million members from 1997 to 200027. Over 16,000 people with psychosis were identified, and those treated with antipsychotics (n=6,582) were compared to those not treated with antipsychotics (n=10,296). Major depression was the most common diagnosis in both groups (76% versus 38%). Treatment with olanzapine was a significant predictor of the development of diabetes, but other antipsychotics showed no association with diabetes. However, this finding was not supported by another similar study28. In another study of a huge claims database, which CHAPTER 7

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FIGURE 7.2

FIGURE 7.3

Factors associated with higher morbidity and mortality among people with psychotic conditions

ADA Consensus monitoring protocol for patients initiating antipsychotic medication38

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handles 300 million prescription claims per year for over 50 million members in the USA29, those on antipsychotic medications were 3-4 times more likely to develop diabetes (after adjusting for age and sex), compared to the general population. Of note, there was no difference in the risk of developing diabetes between those treated with typical and atypical drugs. A meta-analysis of 14 studies (10 retrospective cohort and four case control studies) included 256,000 people with schizophrenia or related disorders30. Comparisons between atypical and conventional antipsychotics revealed a strong association between atypical agents and the development of diabetes (clozapine OR 1.37 (1.25-1.52), olanzapine OR 1.26 (1.10-1.46) and risperidone OR 1.07 (1.00-1.13)). Although there was a trend towards diabetes development for quetiapine, results did not reach statistical significance. When compared to those not receiving antipsychotics, there was a significantly increased risk of developing diabetes for clozapine (OR 7.44 (1.59-34.75)) and a borderline significant increase in risk for olanzapine (OR 2.31 (0.985.46)). Head to head comparisons showed a possible increase in diabetes risk associated with olanzapine, when 302

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Personal / family history

X

X

Weight (BMI)

X

Waist circumference

X

Blood pressure

X

X

X

Fasting plasma glucose

X

X

X

Fasting lipid profile

X

X

X

X

X

X

X X

compared to risperidone (OR 1.13 (0.99-1.28)), to clozapine (OR 1.41 (1.08-1.83)), and to quetiapine (OR 1.17 (1.001.37)). Although the vast majority of cases of diabetes associated with antipsychotic drugs are type 2 diabetes, there are a number of case reports linking these drugs with diabetic ketoacidosis (DKA) 31. DKA is a life-threatening, acute metabolic disturbance, which is usually associated with type 1, not type 2, diabetes. Although there are many studies examining the link between antipsychotic use and development of diabetes they tend to have similar limitations. That is, they are often retrospective, not randomized control studies and do not always take into account risk factors such as race, body mass index (BMI) and family history. Other factors include not allowing for lack of compliance32, which is a problem in this population, changing medications during the study period and detection bias33.

Potential causes of drug-induced hyperglycaemia The specific biochemical links between psychosis, DIABETES ATLAS THIRD EDITION

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antipsychotic drugs and diabetes remain poorly understood. However, it is abundantly clear that people with psychoses such as schizophrenia have markedly suboptimal lifestyles (see Figure 7.2). They also have lower standards of living, being less educated and more likely to suffer from poverty and unstable living conditions18. They do not exercise as much; their diet is poor and they have high rates of smoking 34,35. Studies examining diet found a lower consumption of dietary fibre, fruit and vegetables compared to a matched control group. They also appear to consume more sugar and fat, which seem to be independently associated with the severity of symptoms particularly in schizophrenia36. Poor dietary choices may however, also be related to the use of antipsychotic medication which increases appetite37.

therefore careful consideration is required when choosing the best drug for the patient and the likely non-psychiatric consequences of using that drug. It is somewhat concerning that although there has been an increasing number of reports published on the link between antipsychotic drugs and diabetes, a survey of psychiatrists in the USA found that 49% did not recognize diabetes as a potential metabolic complication of atypical antipsychotic medication41. More work is required to explain a possible link between depression and diabetes. As with diabetes and psychosis, the combination of diabetes and depression leads to greater morbidity and mortality than the general population and therefore treatment needs to follow a multi-disciplinary approach.

The American Diabetes Association (ADA) has issued a consensus statement outlining screening, which should be done on commencement of antipsychotic therapy, and periodically thereafter38 (see Figure 7.3). Other recommendations have been for testing to be done at the initiation of therapy, and then repeated at four months and annually thereafter39. An Australian Consensus working group has addressed the practical problems of screening this population, including taking into account the ethnicity of the person and recommends finger-prick testing and the use of a modified OGTT where necessary, where standard investigations are not possible40. Monitoring should also be individually tailored together with an emphasis on modification of lifestyle factors such as diet and exercise.

Healthcare professionals should be aware that people with a psychotic illness are at higher risk of developing diabetes and other metabolic disturbances. This risk is derived from a disordered lifestyle, use of antipsychotic medication, and possibly because of intrinsic pathophysiological effects of mental illness. Management of these people has to not only encompass mental illness but general health issues and therefore treatment should be multi-disciplinary. Medical practitioners need to be aware that drugs used for psychosis have differing risk profiles for weight gain, hyperglycaemia and other metabolic disturbances and MENTAL HEALTH, ANTIPSYCHOTIC DRUGS AND HYPERGLYCAEMIA

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CHAPTER 8 THE METABOLIC SYNDROME

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The metabolic syndrome is now thought to be a key driver of the modern day epidemics of diabetes and cardiovascular disease. 306

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The metabolic syndrome, the clustering of visceral (abdominal) obesity, dyslipidaemia, hyperglycaemia and hypertension, is a major public health challenge worldwide as more and more people fall victim to the syndrome1,2. The syndrome is not benign as it is associated with a substantially elevated risk not only of type 2 diabetes (five-fold) but also of cardiovascular disease (CVD) (twoto three-fold)1. The metabolic syndrome is now thought to be a key driver of the modern day epidemics of diabetes and CVD1. Its increasing occurrence could possibly reverse the gains that have been made in many communities and nations through recent declining CVD morbidity and mortality. The metabolic syndrome is not a new condition. Its description goes back at least 80 years being first described in the 1920s by Kylin3, a Swedish physician. This cluster of CVD risk factors has had a number of names including Deadly Quartet, Syndrome X, Syndrome X plus, and Insulin Resistance Syndrome1 but metabolic syndrome is likely to remain the popular choice for the foreseeable future. Numerous definitions for the metabolic syndrome THE METABOLIC SYNDROME

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have surfaced and this has caused considerable confusion, just as has the variety of names used to describe the syndrome4.

The cause of the metabolic syndrome remains poorly understood, and likely to be complex and multifactorial1. Insulin resistance and abdominal obesity have received the most attention as the putative underlying features most likely to explain the frequently observed clustering of the other components. However, further research is required to determine which factors are central to the development of this syndrome. Several different factors are probably involved, many related to sedentary lifestyle but clearly, genetic factors also play a role.

Confusion arising from various definitions Since its initial description, several definitions of the syndrome have emerged. Each of these definitions used differing sets of criteria, the combination of which either reflected contrasting views on pathogenic mechanisms or clinical usefulness. The use of these definitions to conduct CHAPTER 8

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research into the metabolic syndrome in diverse populations resulted in wide ranging prevalence rates, inconsistencies and confusion, and spurred on the vigorous debate regarding how the metabolic syndrome should be defined.

perspective, the ATP III definition was probably the most practical for alerting healthcare professionals to those at highest risk1,8.

Of the various attempts, the WHO definition5 and two others, the European Group for the Study of Insulin Resistance (EGIR)6 and National Cholesterol Education Program – Third Adult Treatment Panel (NCEP ATP III)7 were the main ones in use. Each of these agreed on the essential components of obesity, hyperglycaemia, dyslipidaemia and hypertension. However, the definitions differed in the cutoff points used for each component, and the way in which the components were combined. This has led to considerable confusion and it has been particularly apparent in attempts to compare the burden in different populations1,2.

It was because of this confusion and the need to take into account ethnic differences that the International Diabetes Federation (IDF) embarked on the process of arriving at an urgently needed consensus on a new global definition of the metabolic syndrome (see Table 8.1). This has now been published on the web9 and in both The Lancet8 and recently in more detail, in Diabetic Medicine10.

One of the major issues that these three definitions failed to address was the inherent ethnic differences in measurements of obesity (body mass index and waist circumference). It was also uncertain which of the definitions best predicted those at risk of CVD and diabetes, although from a clinical 308

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The new IDF definition recognizes the mounting evidence that visceral adiposity is common to each of the components of the metabolic syndrome although it does not necessarily imply an aetiological link. Thus, an excessive waist circumference (demonstrated to be a good proxy measurement for visceral adiposity) is now a necessary requirement for the metabolic syndrome. This is based on the strong evidence linking waist circumference with cardiovascular disease and the other metabolic syndrome components, and the likelihood that central obesity is an DIABETES ATLAS THIRD EDITION

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FIGURE 8.1 Guide to measuring waist circumference

Inferior margin of the ribs Waist Anterior superior iliac crest

Waist circumference should be measured in a horizontal plane, midway between the inferior margin of the ribs and the anterior superior iliac crest. Data show that if BMI is greater than 30 kg/m2, waist circumference is highly likely to be above the diagnostic cut-points for the metabolic syndrome, and measurement is not necessary.

early step in the aetiological cascade leading to the full metabolic syndrome 1. The optimal technique for its measurement is shown in Figure 8.1. The waist circumference cut-off selected was the same as that used by EGIR and lower than the main ATP III recommendations, because most available data suggest an increase in other cardiovascular disease risk factors in Europids when the waist circumference rises above 94 cm in men and 80 cm in women1. Since it is clear that the levels of obesity at which the risk of other morbidities begins to rise varies between population groups1,11, ethnic-specific waist circumference cut-offs have been incorporated into the definition (see Table 8.1). The cut-offs have been based on available data linking waist circumference to other components of the metabolic syndrome in different populations12,13. The levels of the other variables were as described by ATP III, except that the most recent diagnostic level from the American Diabetes Association (ADA) for impaired fasting glucose (5.6 mmol/L [100 mg/dL]) was used14. Although the new definition will still miss substantial THE METABOLIC SYNDROME

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numbers of people with impaired glucose tolerance (because an oral glucose tolerance test is not required), it retains the simplicity of the instrument, particularly in a primary healthcare setting. In the short time since the new definition has been made available, a number of publications have reported the prevalence, and these are shown in Table 8.2. The IDF consensus report10 also includes recommendations for future research into components not currently included in the core definition of the metabolic syndrome. These factors should be combined with assessment of cardiovascular disease outcome and development of diabetes so better predictors can be developed. The IDF consensus report also highlights strategies for the treatment of the metabolic syndrome and its components. It addresses both clinical and research needs and: • provides a simple entry point for primary care physicians to diagnose the metabolic syndrome; • provides an accessible, diagnostic tool suitable for worldwide use, taking into account ethnic differences in CHAPTER 8

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TABLE 8.1 The International Diabetes Federation (IDF) definition of the metabolic syndrome9,10

ACCORDING TO THE IDF DEFINITION, FOR A PERSON TO BE DEFINED AS HAVING THE METABOLIC SYNDROME, HE/SHE MUST HAVE: Central obesity (defined as waist circumference): ≥ 94cm for Europid men and ≥ 80cm for Europid women ≥ 90cm for men and ≥ 80cm for women for those of South and South-East Asian, Japanese, and ethnic South and Central American origins

plus any two of the following four factors: • raised triglycerides: ≥ 1.7mmol/L • reduced HDL-cholesterol: <1.03mmol/L in males and <1.29mmol/L in females, or specific treatment for these lipid abnormalities • raised blood pressure: systolic BP ≥130 or diastolic BP ≥85mm Hg, or treatment of previously diagnosed hypertension • impaired fasting glycaemia (IFG): fasting plasma glucose ≥5.6 mmol/L, or previously diagnosed type 2 diabetes

waist circumference and associated type 2 diabetes and CVD risk; and • establishes a comprehensive ‘platinum standard’ list of additional criteria that should be included in epidemiological studies and other research into the metabolic syndrome.

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The IDF definition should provide researchers with a common platform for investigating the syndrome and its consequences. It provides for the first time a useful practical global tool that will draw attention to healthcare professionals of the metabolic consequences of obesity. The new definition serves a useful purpose to focus on people, in both the community and clinical settings, who are at high risk of developing CVD and type 2 diabetes, and are likely to benefit from (lifestyle) interventions.

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TABLE 8.2 Prevalence of the metabolic syndrome, according to the IDF definition

COUNTRY/TERRITORY

DATA USED

AGE

SAMPLE SIZE MEN

Australia Germany Greece Korea, Republic of Mexico Peru Spain United Kingdom United States of America USA (Mexican-American) USA (non-Hispanic white)

Adams et al, 200515 Zimmet et al, 20054 Rathmann et al, 200616 Athyros et al, 200517 Park et al, 200618 Guerrero-Romero et al, 200519 Lorenzo et al, 200620 Lorenzo et al, 200620 Lorenzo et al, 200620 Lawlor et al, 200621 Ford, 200522 Lorenzo et al, 200620 Lorenzo et al, 200620

18+ 25+ 55-74 18+ 20-80 30-64 35-64 35-64 35-64 60-79 20+ 35-64 35-64

4,060 11,247 1,373 9,669 6,824 700 1,990* 346* 2,540* 3,589 3,601 1,150* 1,323*

26.4 • 57.0 • 13.5 • 54.4 26.0 27.7 40.7 46.3 38.3

PREVALENCE (%) WOMEN TOTAL 15.7 • 46.0 • 15.0 • 61.0 28.1 33.6 47.5 37.1 41.0 28.8

• 29.1 • 43.4 • 22.3 • • • • 39.1 • •

* People with diabetes not included in this study

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he mission statement of the International Diabetes Federation (IDF) has been revised and expanded to include the prevention of diabetes as well as the promotion of diabetes care and its cure. Some of the rationale for this is provided in Chapter 10 but, in part, this revision is a reflection of the accumulating evidence for the efficacy of specific interventions directed at the primary prevention of type 2 diabetes as well as the likelihood that, unless we act decisively, and act now, the health systems of developed and developing countries will be overwhelmed by the fast growing numbers of people affected by diabetes and its complications.

the strong evidence base for the primary prevention of type 2 diabetes will slow down and, eventually, reverse the hitherto inexorable rise in the burden of type 2 diabetes. It enthusiastically advocates this for developed countries but does so even more strongly for developing countries.

The growth in numbers of people with type 1 diabetes is well documented. At the present time, there is no evidence base for the primary prevention of this important but numerically smaller sub-type of diabetes. To date, no large scale trials have been successful in preventing type 1 diabetes, although there is suggestive evidence from small studies that some drugs may prove to be useful in preventing or delaying its onset. Chapter 9 focuses on the primary prevention of type 2 diabetes, and looks to the future – a future in which the practical application, in clinical and public health practice, of PREVENTION AND ACTION

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PART 3

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CHAPTER 9 PREVENTION AND DIABETES: POSSIBILITIES FOR SUCCESS AND CONSEQUENCES OF INACTION

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Lifestyle modification with moderate but consistent physical activity and a healthy diet helps to prevent type 2 diabetes in high-risk groups.

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The scope of this chapter is the primary prevention of type 2 diabetes. Although the prevention of diabetic complications is of critical importance when diabetes is already established, this is part of therapy and management. This is firmly in the domain of the diabetes multidisciplinary team – with, at its centre, the person with diabetes and the family. It is the concern, by and large, of health services and the effectiveness with which complications can be averted is largely determined by access to these services, the quality of these services, the rapport that develops between the person with diabetes and the health professionals concerned, the quality of education, motivation for personal behavioural change and the availability of appropriate diabetes medication and supplies. The prevention of diabetes itself, on the other hand, has wider connotations throughout society. Health services have a part to play in identifying ‘at risk’ individuals, and advocating and supporting one or more of the effective person-based interventions. However, in terms of preventing type 2 diabetes (by preventing obesity, for example), policies in education, transport, the form of our physical environment, PREVENTION AND DIABETES: POSSIBILITIES FOR SUCCESS AND CONSEQUENCES OF INACTION

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leisure facilities, work, nutrition, food labelling and pricing, institutional catering and many more behavioural influences all have a part to play. This chapter explores the potential for success in the prevention of type 2 diabetes and some of the consequences of failure.

The evidence base for the primary prevention of type 2 diabetes, at least in people who are classified as having impaired glucose tolerance (IGT) is clear and overwhelming1. The notion that weight loss, where appropriate, and increased physical activity is beneficial in such persons has long been a belief and is now supported by randomized controlled trial (RCT) studies in many countries. In addition, certain specific pharmacological interventions have also been proven to be effective. The recent RCTs carried out in China2, Finland3, the USA4, Japan5 and India6 have conclusively shown that lifestyle interventions in people with IGT can prevent, or at least delay, the transition to type 2 diabetes. These interventions investigated weight loss and increased physical activity in the overweight and obese3,4 but also lifestyle management CHAPTER 9

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FIGURE 9.1

In touch with: an advocate for prevention

Cumulative incidence of diabetes (%) in the Indian Diabetes Prevention Programme*

Diabetes is preventable. Lifestyle modification with moderate but consistent physical activity and diet modification helps to prevent type 2 diabetes in high-risk groups. Type 2 diabetes once considered a disorder of the affluent is now seen among people of all walks of life. It affects the rich and the poor, the fat and the lean, the young and the old alike. Dr A Ramachandran and his colleagues at the Diabetes Research Centre (DRC) in Chennai, India, undertook a major research study on primary prevention of diabetes in the Indian population, who are genetically susceptible, highly insulin resistant and have a lean body mass index. People who had impaired glucose tolerance (IGT) were targeted.

Cumulative incidence of diabetes (%) 60 50 40 30 20 10 0 0

6

12

18

24

Control group Metformin (MET) Lifestyle modification + metformin Lifestyle modification (LSM)

30

36

42

Time taken to develop diabetes ( months)

*Cumulative incidence of diabetes, calculated using the Cox proportional hazards model. The number of subjects who underwent an annual OGTT was 484, 403 and 345 at 12, 24 and 30/36 months, respectively. The p values for relative risk reduction were as follows: LSM = 0.018, LSM + MET= 0.022, MET = 0.029. LSM and LSM + MET showed identical results, therefore, the graphs overlap. Source: Ramachandran et al, 20066

in those who were not obese2. The pharmacological interventions investigated were medication with the biguanide drug metformin4 or with the alpha-glucosidase inhibitor acarbose7 or with sibutramine8 or, in those with a history of gestational diabetes, troglitazone9. The main features and conclusions of these trials are summarized in Table 9.1. In the Diabetes Prevention Program (DPP) in the US the risk reduction effect following lifestyle changes were much more apparent than intervention with metformin, which was also significant. In the Indian DPP, the effect of following lifestyle changes and intervention with metformin produced similar results as combining both, and did not show an additional benefit (see Figure 9. 1). Not shown in Table 9.1 is the fact that, for the interventions shown, the number needed to treat (NNT) over three years ranged from 2.25 (Da Qing) to 36 (in the IGT group of the XENDOS study). That is, the number of people needed to be treated with these interventions over three years to avoid one person with IGT progressing to type 2 diabetes ranged from 2.25 to 3610. 318

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Dr VJ Krishnamurti, 54, a senior professor at an institute of technology, walked in reluctantly to the prevention camp organized by DRC in May 2001. He walked in with lots of questions and the research team could sense his apprehension. His attitude was one of “I know everything and what more do you have to tell me?”. On screening he was found to have IGT.

In a recent systematic review and meta analysis of these RCTs of lifestyle interventions in type 2 diabetes11, lifestyle education reduced two-hour plasma glucose by a mean 0.84 mmol/l (95% CI 0.39-1.29) over one year and the incidence of type 2 diabetes by approximately 50% (RR 0.5, 95% CI 0.440.69) compared with the control group over the same period of time. No publication bias was evident in this body of evidence. The cost-effectiveness of preventing type 2 diabetes or, more generally, of preventing cardiovascular disease (CVD) in people who are at high risk (such as those with diabetes) has been investigated. The results are highly encouraging. A recent study12 concluded that lifestyle intervention was cost effective in all age groups and cost-saving in those aged 2544 years. Metformin intervention, while cost effective in younger age groups, was not cost effective in those aged over 65 years. In low-income countries preventing diabetes by means of lifestyle intervention is likely to be highly cost effective. Other methods to reduce the risk of cardiovascular disease in people at high risk are also attractive from the humanitarian and economic points of view. Another study13 found that a DIABETES ATLAS THIRD EDITION

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The research team had a testing time explaining that although he was not diabetic, he may eventually develop the condition if not prevented. The stages leading to diabetes were explained, and the physician of the team emphasized the need for intervention as a preventive measure. The social worker and the dietitian spent time with him to elucidate details on his diet and other habits. He was a regular walker, but did not practise any dietary restrictions. “It came as a rude shock to me when I was first told that I had impaired glucose tolerance and that I may become diabetic if I didn’t prevent it then,” recalled Dr Krishnamurti. “I am a health buff and thought that I was taking proper care of my health. I was quite sceptical and found it hard to believe when the research team told me that diabetes is preventable with proper lifestyle modification.” Dr Krishnamurti’s response to the changes in his diet pattern suggested by the dietician was guarded. However, the dietician was effective in reducing his calorie intake from 2120 to 1565 calories in a year, achieved in phases

number of population-based and person-based interventions to lower blood pressure would be cost effective in all regions of the world. Some of the results are shown in Table 9.2. Costs in international dollars (ID) per Disability Adjusted Life Years (DALY) saved range from below ID200 in countries such as Egypt, Iraq, Pakistan and Yemen to around ID1,500 in the wealthier countries of Europe, North America and Australasia. As has recently been pointed out14, there is little direct (i.e. RCT) evidence that people at high risk selected specifically on the basis of other risk factors (those with a family history of diabetes, for example) can benefit from these (or other) interventions. Also, with the exception of the STOP-NIDDM trial15, none of the trials listed in Table 9.1 have directly demonstrated a reduction in risk of cardiovascular disease consequent upon the reduction in risk of transition to type 2 diabetes. (Although the Diabetes Prevention Program has demonstrated a reduction in surrogate risk factors16). However, it could logically be argued that those with other risk factors should benefit and that cardiovascular risk should be reduced if the progression to diabetes can be delayed. Additional to this possible benefit is that the early diagnosis PREVENTION AND DIABETES: POSSIBILITIES FOR SUCCESS AND CONSEQUENCES OF INACTION

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and upon repeated counselling by telephone. He reduced his intake of fatty and fried foods, and started eating more vegetables. At the end of one year in the prevention programme, Dr Krishnamurti was able to see visible changes such as weight reduction, and his blood sugars returned to normal. This was a motivation for him to continue the preventive measures suggested by the research team. “My apprehensions and reluctance were laid to rest at the end of the year when I could see changes in my weight and blood sugar level,” said Dr Krishnamurti. “From then on there was no looking back for me and I have become an advocate of prevention of diabetes to my colleagues and friends. I strictly follow the dietician’s advice, and I am thankful and full of appreciation for the Diabetes Research Centre for having helped me to prevent diabetes.” Dr VJ Krishnamurti is a pseudonym.

of type 2 diabetes enables surveillance of the development of microvascular complications to be initiated and their treatment to be started if this is available and appropriate. If these preventive goals are realized, the potential health benefits, both to the individual and to society, are enormous. As a result, the global profile of diabetes would be transformed.

If we do nothing to prevent type 2 diabetes then the numbers of people predicted to be affected by diabetes will reach the figures listed in Chapter 1, or perhaps more. Even though methods for estimating the future prevalence of diabetes are becoming more sophisticated, no amount of methodological sophistication can overcome all of the problems inherent in making future predictions of disease occurrence. Experience from the past suggests that such predictions are often underestimates. A major problem in making these future predictions now is the uncertainty relating to future levels of obesity. We know that the prevalence of obesity is increasing but we do not know (and have no reliable means of estimating) how large the increases will be. CHAPTER 9

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MAIN FEATURES OF THE PROGRAMME FOR THE PREVENTION OF TYPE 2 DIABETES IN FINLAND (2003-2010)

• A population strategy: aimed at promoting the health of the entire population by means of nutritional interventions and increased physical activity. Comprising society-orientated measures and action targeting individuals with the aim of preventing obesity. • A high-risk strategy: individual-orientated measures targeted at individuals at particularly high risk of developing type 2 diabetes. A systematic approach for identifying, educating and monitoring people at risk. • A strategy for the early diagnosis and management of existing type 2 diabetes: this aims to bring these people into the sphere of systematic treatment thus preventing the development of diabetic complications. It offers practical guidance for intensive lifestyle management.

Adapted from ‘Programme for the Prevention of Type 2 Diabetes in Finland 2003 2010’, Finnish Diabetes Association, Finland, 2003. The programme is available in English at www.diabetes.fi.

Despite this uncertainty, doing nothing is clearly not a viable option. The declaration of the Diabetes in Asia meeting in Colombo, Sri Lanka in 2002 (featured in the second edition of the Diabetes Atlas) called for “programmes [for primary prevention] which must be tailored to local circumstances in order to be effective”. These programmes have already commenced in a few countries. The main features of a particularly prominent example, The Programme for the Prevention of Type 2 Diabetes, in Finland are summarized in this chapter. In economic terms, the consequences of inaction are likely to be disastrous - disastrous for national economic wellbeing, for public health and social services, and for individuals and families. As with most phenomena such as these, the blow will fall particularly heavily on developing countries, on the poor in these countries in particular, and on the poorer sections of the developed countries. In developing countries like India, which has the largest number of people with diabetes, the burden is mainly on families and individuals, who bear the expenses for diabetes treatment. There are indications that the cost of diabetes care is increasing with time. It is indeed, a major healthcare burden for the nation too.

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In Chapter 5, the economic impacts of diabetes and its complications are set out in stark detail. The total sum that will be spent on treating diabetes and its complications and on preventing diabetes in 2007 is estimated to be at least USD232 billion, increasing to over USD302 billion in 2025. In middle-income countries around half of the medical expenditure devoted to diabetes is spent on the treatment of the acute life-threatening effects of the condition such as hyperglycaemia. The remainder is divided between general medical care and the specific measures necessary to identify and treat complications. While much can be done to improve outcome for individuals and to reduce these costs by the more effective management of the acute phases of the condition and the longer term effects, the most substantial economic benefit is likely to be realized only when the onset of diabetes itself can be prevented or at least substantially delayed. With many of the proven effective treatments for diabetic complications either unavailable or unaffordable in developing countries and in the poorer sections of many developed countries, the benefits of better management of established diabetes may be unrealizable, at least with current resources.

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IN TOUCH WITH A 45-year old man with diabetes in India had developed a wound on his foot which would not heal. His condition grew worse with pain; there was discharge from the wound and an offensive smell. For a living he ran a vegetable stall in the local market. People stopped buying from his stall because of the smell from his foot. There was no unemployment benefit and he had no health insurance. A visit to the doctor cost him 45 rupees each time and he believed that the amputation which he needed would cost him 15,000 rupees. In desperation, he went down to the local railway track and allowed the wheels of the next train to cut off his foot. The article did not say what happened to him after that.

Source: New Indian Express, 19 February 2000

Out-of-pocket expenses for diabetes care are known often to be severe when a member or members of the family is found to have diabetes. A recent re-examination, using the same methodology as the original study17, of the amount poorer Indian families who opt for private care have to pay for diabetes care showed that the original proportion of 25% of family income has increased to 34% from 1998 to 200518. The heavy financial burden of diabetes, when added to other aspects such as anxiety, physical pain and loss of livelihood, can be devastating to individuals and families, particularly when state support or personal health insurance is not available.

Predicting the impact of primary prevention programmes on the incidence and prevalence of type 2 diabetes in future years has even more difficulties than predicting the future of diabetes in the ‘do nothing’ scenario. The extent to which the potential for success will be realised depends upon a number of factors: • The extent to which the spectacular results of the explanatory RCTs summarized in Table 9.1 can be replicated in pragmatic RCTs and in the ‘real world’ of clinical public health practice. PREVENTION AND DIABETES: POSSIBILITIES FOR SUCCESS AND CONSEQUENCES OF INACTION

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• The extent to which transition to type 2 diabetes is prevented as opposed to being delayed. • The extent to which the micro- and, particularly, the macrovascular complications of diabetes can be modified by this prevention or delay. • The extent to which other factors, as yet unknown, contribute to a change in the epidemiology of the condition. In the context of the explanatory RCT, transition to type 2 diabetes can be reduced by as much as 50% over three years. The annual rate of progression from IGT to type 2 diabetes in observational studies varies from 3%-13%10,11 and, in the control arms of the DPP and Finnish Diabetes Prevention Study (DPS) were, respectively, 12% and 6% at the end of the first years, increasing to 28% and 20% at three years. Public health interventions (as distinct from RCT interventions) could not be expected to decrease these rates by as much as 50% but, even if they decreased this conversion rate by 10% or 25% the future maps of type 2 diabetes would be very different. A more likely scenario is that public health interventions CHAPTER 9

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FIGURE 9.2 Kaplan-Meier survival plots for total cardiovascular mortality*

Survival probability 1.00 0.95 0.90 0.85 0.80 0.75 0.70 0.65 0.60 0.55 0.50 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Simulated years 10 year delay 5 year delay 3 year delay 1 year delay No delay

*Assumes no delay in the onset of diabetes and a one, three, five and 10-year delay in onset. Source: McEwan et al19

will delay the transition from IGT to type 2 diabetes rather than prevent it entirely. A ‘first pass’ in predicting the extent to which such preventive programmes will change future experience of diabetes and its complications in one country (the United Kingdom) can be provided by modelling a number of ‘what if’ scenarios. The DiabetesForecaster model19 investigates the impact of delaying the onset of type 2 diabetes on the rate of progression to micro- and macrovascular complications and death, and estimates the impact on direct financial healthcare costs and qualityadjusted life years (QALYs) of delayed onset. Running the model over a 40-year time horizon for 1,000 people in the absence of any preventive intervention predicts just under 500 coronary heart disease (CHD) events, approximately 450 stroke events and 350 cardiovascularrelated deaths. Running the model once more, this time with a delay of 11 years in the transition to type 2 diabetes (as predicted in DPP simulation studies), decreased the number of predicted CHD, stroke and fatal cardiovascular events by 22%, 25% and 20% respectively. Figure 9.2 shows the effects on total cardiovascular disease mortality of delaying the onset of type 2 diabetes by one, three, five or 10 years compared with no delay. 322

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The cost savings and QALY improvements relating to macrovascular complications when running the model over this time horizon resulted in a mean, total discounted cost decrease of GBP1,200 per subject from a mean of GBP11,500 per subject assuming no intervention. Discounted QALYS per subject were 12 assuming no intervention, increasing by approximately one for lifestyle intervention (that is, an important increase in expected length and/or quality of life). Clearly these are modelled estimates and take no account of the cost of introducing these interventions on a community scale. However, they do suggest that the gains in adverse outcomes averted, healthcare resources available for other uses and improvements in the quality of life will be real and worthwhile striving for.

Although the above emphasizes that we now have sufficient firm evidence on the prevention of type 2 diabetes to be active, both in clinical and public health practice to act, this does not mean that we should cease to understand better the phenomena leading to type 2 DIABETES ATLAS THIRD EDITION

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diabetes in susceptible people. Two particularly fruitful areas for further research are the effects of early nutrition and the effects of external stresses on the nervous system of adults and, perhaps particularly, on the young developing individual. These areas may be interconnected as has been recently suggested20.

activity and maintaining a health body weight or, where necessary, reducing body weight will be effective and costeffective. Individual interventions to reduce risk of type 2 diabetes and cardiovascular disease in those at high risk will also work. Prevention strategies, therefore, must be at least two-fold:

As people move from place to place, as with migration from rural to urban settings, they experience stress and the physiological effects of this, through a combined effect of the nervous system and endocrine system, can influence adversely the standard risk factors for cardiovascular disease. For some time it has been proposed that maternal nutrition can influence early fetal development sufficiently to confer additional risk for the development of type 2 diabetes in adult life. It seems that early environment may be influential in increasing the susceptibility of the individual to the adverse effects of external stresses20.

• Population-based measures to encourage active lives and healthy weight maintenance; and

We need not wait until this new knowledge is available. This and other more thorough reviews of the evidence on prevention suggest strongly that population-based measures aimed at maintaining or increasing physical PREVENTION AND DIABETES: POSSIBILITIES FOR SUCCESS AND CONSEQUENCES OF INACTION

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• Individually focussed measures to identify those at high risk and reduce that risk. As specified in the 2002 Colombo declaration, these population-based programmes must be tailored to local circumstances in order to be effective. The individually focussed measures, similarly, must adopt the most acceptable and affordable means of identification (possibly through risk assessment questionnaires such as that used in the Finnish national programme) followed by appropriate biochemical investigation and therapeutic measures to manage established diabetes if present, or ‘pre-diabetes’ (impaired glucose tolerance or fasting hyperglycaemia). CHAPTER 9

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EXAMPLES OF PREVENTION PROGRAMMES Cite des Palmiers Health District, Douala, Cameroon

Fleurbaix-Laventie Ville Santé Study, France

• Health education campaign with posters, stickers, handouts, flipcharts • Target groups identified, including health facilities, schools, churches, social and cultural groups • Health education sessions held • Traditional healers involved • Raised awareness and increased knowledge of diabetes and obesity, including among traditional healers • Increased number of people presenting for voluntary screening • Demand from other districts for similar health promotion initiatives

• 12-year programme to reduce childhood obesity • Comparison with control towns • Prevalence of obesity stabilized (at 11%) compared with increase in control towns (11% in 1992 and 18% in 2004) • Larger programme (EPODE) set up as a result • Local stakeholders will deliver consistent messages to families • Keys to success are “concrete, visible, sustainable and local actions, the involvement of all local players” The website for the programme is www.villesante.com. Source: Borys and Raffin, 200622

Source: Tuo-uo Kpu, 200621

Practical examples of programmes already established to increase awareness, encourage healthier lifestyles and, in most instances, identify and manage individuals at high risk are shown in this chapter. We cannot afford the ‘do-nothing’ option. Our own and our children’s futures depend on it. As is often said at IDF: “The time has come to act…NOW!”

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TABLE 9.1 Recent trials relevant to the primary prevention of type 2 diabetes

TRIAL

INCIDENCE (%) OF PROGRESSION FROM IGT TO TYPE 2 DIABETES (INTERVENTION VS CONTROL)

INTERVENTION

44 vs 66 14 vs 30 14 vs 29 32 vs 42 32 vs 42 6 vs 9 3 vs 9 39 vs 55

Lifestyle Troglitazone Lifestyle Lifestyle Acarbose Xenical Lifestyle Lifestyle

Da Qing2 TRIPOD9 DPP4 DPS, Finland3 STOP-NIDDM7 XENDOS8 Kosaka et al5 Indian DPP6

Adapted from Davies et al, 200410

TABLE 9.2 Cost-effectiveness of a combined preventive strategy by region* COST (X106)

DALY-SAVED

COST/DALY-SAVED

ID

(X105)

ID

733 543

18 17

400 320

Algeria, Nigeria, Ghana Botswana, Eritrea, Uganda, United Republic of Tanzania

12,783 2,056 298

91 58 5

1,410 350 650

Canada, Cuba, United States of America Argentina, Colombia, Mexico Bolivia, Ecuador, Guatemala, Haiti

Eastern Mediterranean and Middle East Low adult and child mortality

789

21

380

High adult and child mortality

952

52

180

Iran, Libyan Arab Jamahiriya, Saudi Arabia, Tunisia Egypt, Iraq, Pakistan, Yemen

15,474 N/A 4,198

99 88 176

1,570 310 240

Belgium, Denmark, Italy, Spain, Bulgaria, Poland, Turkey Estonia, Hungary, Russia, Ukraine

South-East Asia Low adult and child mortality High adult and child mortality

733 2,994

20 95

360 310

Sri Lanka Bangladesh, India,

Western Pacific Very low adult and child mortality Low adult and child mortality

N/A 6,072

42 158

1,320 388

Australia, Japan, Singapore China, Philippines, Republic of Korea, Samoa

REGION Africa High adult and child mortality Very high adult and high child mortality

The Americas Very low adult and child mortality Low adult and child mortality High adult and child mortality

Europe Very low adult and child mortality United Kingdom Low adult and child mortality High adult and low child mortality

EXAMPLE NATIONS

*Mean Expected Annual Costs (2000 ID), Disability Adjusted Life Years saved, and Costs/DALY-saved for a combined programme of salt-reduction, mass media health education, and four-drug therapy for all citizens with CVD risk > 25%. N/A

not available

Adapted from Murray et al, 200313

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CHAPTER 10 FROM VISION TO ACTION

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Diabetes is a global problem and needs a global solution

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The new mission statement of the International Diabetes Federation (IDF) to ‘promote diabetes care, prevention and a cure worldwide’ signalled its readiness to become more active on the world scene in a broader range of issues without foregoing any of its commitment to be a strong advocate for people with diabetes. The third edition of the Diabetes Atlas provides stark new figures on the extent of the global burden of diabetes, and its release at the 19th World Diabetes Congress in December 2006 in Cape Town has provided an invaluable opportunity to highlight the need to continue the fight against diabetes. The data confirm beyond all doubt that the diabetes epidemic is real and that its magnitude is larger than previous projections had anticipated. There are now over 240 million adults with diabetes worldwide, representing 6% of the adult population and the numbers are increasing by seven million per year. It is projected that by 2025 there will be nearly 380 million adults worldwide living with diabetes. Far from being regarded simply as a risk factor for cardiovascular disease, diabetes is responsible for as many FROM VISION TO ACTION

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deaths as HIV/AIDS annually (3.8 million) and clearly deserves greater recognition as a major disease in itself. The data indicate that diabetes is responsible for a million lower limb amputations each year and is a major cause of kidney failure and blindness. Its direct healthcare costs are huge and its indirect costs much greater. The Diabetes Atlas reveals that the major burden of diabetes falls on the developing world where it threatens not only to subvert the gains of economic development but also the gains brought about by international humanitarian programmes addressing the UN Millennium Development Goals. Furthermore, the data show that type 2 diabetes is not only a disease of the elderly but that it is now affecting younger age groups. Even children and adolescents are being diagnosed with this form of diabetes and far from being a simple ‘touch of sugar’, type 2 diabetes in the young is proving to be as serious as type 1 diabetes. In Japan type 2 diabetes in young adolescents is now four to eight times more common than type 1 diabetes. Microvascular complications such as retinopathy are as frequent and as severe as in type 1 diabetes, and the risks for nephropathy and cardiovascular disease are both significantly greater. CHAPTER 10

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What has changed and what is driving the diabetes epidemic? The answers are complex. Some reflect factors we cannot change (genetic, ethnic differences, ageing) while others are clearly environmental and involve changes in diet, decreased physical activity, increases in overweight and obesity as well as profound changes in our living environment which include changes in work practices, globalization, urbanization, town planning, transport, schooling, sport, and the development of mega-cities. These genetic and environmental risk factors collide especially in indigenous peoples (e.g. native US, Canadian and Mexican Indians, Australian Aboriginal people, Torres Strait Islanders), where diabetes occurs in 50% or more of adults aged over 35 years. The very existence of some indigenous populations is threatened and it is a race against time to turn this epidemic around in these populations. The debate over the causes is complex and encompasses areas of personal responsibility over health (e.g. “no one forces people to over-eat”) as well as societal or governmental responsibilities for deleterious changes to 330

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our living environment. It is clear that people do need to accept greater responsibility for their wellbeing but at the same time whole-of-government action is needed to create the conditions for a healthy living environment. Governments have been slow to recognize that chronic diseases are better prevented rather than treated. The failure of the World Health Assembly Resolution on ‘Diabetes’ and on ‘Diet, Physical Activity and Health’, and of the World Health Report on ‘Preventing Chronic Diseases: a vital investment’ to stem the diabetes epidemic has led to the realization that the diabetes world must participate in the debate, take a leadership role, and be part of the solution and not simply accept the present unsatisfactory situation which exists. For the first time, there are now evidence-based cost-effective strategies to reduce or prevent diabetes complications and evidence that much of type 2 diabetes can be prevented. Public health strategies to improve nutrition, prevent overweight and obesity, increase physical activity and reduce smoking can prevent not only diabetes but many of the chronic diseases.

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WORLD DIABETES DAY World Diabetes Day is an annual global awareness campaign which focuses on issues that would help prevent diabetes and its complications.

World Diabetes Day Not only must IDF lead in turning the epidemic of type 2 diabetes around but it must also become a stronger advocate for all people with all types of diabetes. With the burgeoning numbers of adults with diabetes, children and adolescents have to compete for limited resources. Strong advocacy for the rights of children with diabetes has never been more needed. To this end World Diabetes Day 2007 will focus on the needs of children with diabetes. The Diabetes Atlas estimates that the incidence of type 1 diabetes in children is increasing at the rate of approximately 3% per year, with many countries reporting a higher rate of increase in the very young (under five years). Worldwide there are approximately 440,000 children with type 1 diabetes under the age of 15 years; about half live in the developing world and about a quarter in the poorest 50 countries where people have to live on less than USD1 a day. The actual prevalence of childhood diabetes may well be significantly greater than these current estimates indicate as many children with diabetes in developing countries die without the correct diagnosis being made or because of lack FROM VISION TO ACTION

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of access to insulin. In such developing countries, access to insulin is determined not only by the ability of the family to pay for lifesaving insulin which often retails for over USD20 a vial, but also by availability of insulin and distribution networks throughout the country. The lifespan of children in rural areas is less than in urban areas not only because of greater poverty, but also because of less access to expert help and because rural distribution networks of insulin are less reliable. The world must no longer quietly accept that children with diabetes die needlessly because their diabetes had not been diagnosed or because insulin is not available or is not affordable.

Unite for Diabetes IDF’s role as a global advocate for people with diabetes will be enhanced by recently being accredited as a nongovernmental organization to the United Nations. At the time of writing this, the outcome of the campaign for a United Nations Resolution on Diabetes has not yet been decided but, whatever the outcome of the campaign, what is clear is that the diabetes world has realized the need to join forces, to ‘unite for diabetes’ and to call for increased recognition of diabetes as a major global disease. CHAPTER 10

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UNITE FOR DIABETES The blue circle, developed as part of the IDF-led campaign for a United Nations Resolution, will provide diabetes with a visual symbol which hopefully in time will become as readily recognized as the red ribbon of AIDS.

By putting diabetes on the global agenda with this campaign, IDF is working towards a successful outcome which would result in: • Raised global awareness of diabetes • Increased recognition of the humanitarian, social and economic burden of diabetes • Individual nations making diabetes a health priority • Widespread implementation of cost-effective strategies for the prevention of diabetic complications • Development of affordable public-health strategies for the prevention of diabetes • Recognition of ‘special needs’ groups (children, the elderly, indigenous peoples, migrant people from developing nations, and women during pregnancy) • More research towards a cure for diabetes

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The epidemic of diabetes is one of the most serious challenges facing the modern world. It is largely a hidden, silent epidemic causing much hardship, but it has not as yet received serious consideration from the world community. Prevention of diabetes is essential, as millions, especially in low- and middle-income countries, develop the disease each year. To do nothing is not an option and is morally indefensible. Cost-effective strategies for the prevention of diabetes are available. However, because they require changes in diet, physical activity and lifestyle, they will require whole-ofgovernment implementation, rather than unilateral action by the governmental agency responsible for health, in order to change nutrition, public behaviour and the environment we live in. Diabetes is a global problem and needs a global solution. Much needs to be done. But progress can only be made if strategies are based on accurate data and are evidencebased. The Diabetes Atlas provides health planners with the evidence needed to translate IDF’s vision into action. DIABETES ATLAS THIRD EDITION

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APPENDICES METHODOLOGY

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334

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he search for data was limited to studies published after 1979. This cut-off was chosen as data collected prior to 1980 may no longer reflect the current prevalence of diabetes. Selection of articles was limited to those published preMarch 2006. The Medline database and internet were used for the literature search. Systematic searches were conducted for each country using the following search formulae: 1. Country name (all the countries of the world were entered for separate searches) together with ‘diabetes’ or ‘impaired glucose tolerance’ and ‘prevalence’ or ‘incidence’; and

geographical region were contacted and requested to provide information on the prevalence of diabetes for countries within their region. In addition, IDF member associations in each member country were asked about relevant data. In the absence of data for a country, the member association was further asked to comment on the use of data from another country (see section on Extrapolation below).

The search obtained data in a variety of forms such as prevalence studies, registry reports, hospital statistics, government estimates, etc. Studies for a particular country were included based on their level of reliability. The following factors were taken into account when assessing a study’s level of reliability:

2. ‘NIDDM’ or ‘IDDM’ or ‘non-insulin-dependent diabetes mellitus’ or ‘insulin-dependent diabetes mellitus’ or ‘Type 1 • The year of the study — more recent studies were preferred. diabetes’ or ‘Type 2 diabetes’, combined with ‘prevalence’ • The screening method used — the oral glucose tolerance test (OGTT) was the preferred method of screening, followed or ‘incidence’. by two-hour blood glucose (2hBG) alone, then the fasting blood glucose (FBG) alone, and then self-report (SR). Relevant citations from each article were also obtained. A number of other avenues were explored in the search for • Sample size — studies with larger sample sizes and higher relevant data. Diabetes researchers in each major IDF response rates were preferred. METHODOLOGY FOR CHAPTER 1.1

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EXAMPLES OF MODELLED AND PUBLISHED DIABETES PREVALENCES FIGURE A1.1

FIGURE A1.2

Jordanian males and females combined (urban)

Chinese males

Prevalence (%)

Prevalence (%) 8

30

7

25

6

20

5 4

15

3

10 2

5

1

0

0

Age group

25-29

30-39

Smoothed curve

40-49

50-59

60-69

70-79

Published data

When more than one study was available for a country, and there was no clear superiority of one over the other, the results from the available studies were averaged, and then applied to the national population.

Age group 25-34

35-44

Smoothed curve

45-54

55-64

65-74

Published data

exercise, diet, and socio-economic factors often result in significant differences in diabetes prevalence rates. Therefore, for low- and middle-income economies (except those of the former socialist economies in Europe), the urban and rural rates were calculated and numbers reported separately.

Extrapolation If there were no data available for a particular country, prevalence rates from a published study from the socioeconomically, ethnically, and geographically most similar country were applied to that country’s age and sex-specific (and in the case of low/middle-income countries, urban/ rural-specific) population distribution. Socio-economic comparisons were based on gross national product (GNP) per capita. Ethnic comparisons were based on ethnicity data from the CIA World Factbook 20051.

Urban: rural prevalence

For studies reporting on a mixed urban and rural population, but where no data were provided as to the urban/rural distribution of the survey population, the available age and gender specific data were assigned to the population so as to produce a 2:1 urban:rural ratio in diabetes prevalence.

In countries with low or middle-income economies, differences between urban and rural populations in levels of physical

For countries where only urban or only rural data were available, the 2:1 ratio was used to calculate the prevalence of diabetes in

If a dataset did not provide sex-specific data, the data were disaggregated and assigned 50% to females and 50% to males.

336

The economies were defined according to the 1997 GNP per capita, calculated using the World Bank Atlas method2. Lowand middle-income economies had a GNP per capita of less than USD9,655, and high-income economies had a GNP per capita of USD9,655 or more. If the above conditions for different urban and rural diabetes prevalences applied, then for countries where available studies showed prevalences separately for urban and rural populations, these rates were applied to the national urban and rural populations.

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FIGURE A1.3

FIGURE A1.4

Bolivian females

Indian females

25

Prevalence (%)

Prevalence (%) 35 30

20 25

15

20 15

10

10

5 5

0

0 Age group 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69

Smoothed curve

Published data

Age group 20-29

30-39

Smoothed curve

40-49

50-59

60-69

70-79

Published data

the other segment of the population. No urban:rural difference was used for IGT prevalence, unless the data for that country indicated a prevalence difference to be present.

distributions, and not for the likely changes in lifestyle and obesity, which may tend to increase diabetes prevalence. Thus, the figures may be an underestimate.

Known diabetes

The prevalence rate (PR) of diabetes and IGT for each country was then calculated using the formula:

Studies from several countries — Canada, France, Germany, Israel, Italy, Netherlands, New Zealand, Norway — only provided data on self-reported diabetes. To account for undiagnosed diabetes, the prevalence of diabetes for Canada was multiplied by a factor of 1.5, in accordance with findings from the USA3, and for the other countries doubled, based on data from a number of countries4-8.

A list of the world’s countries and 2007 and 2025 population distribution estimates was obtained from the United Nations Population Division9. The age- and sex-specific prevalence rates (obtained from the logistic regression — see below) were applied to the corresponding age and sex population distribution for the years 2007 and 2025 for each country. This method for estimating figures for 2025 only takes into account changes in age, sex and urban/rural population METHODOLOGY FOR CHAPTER 1.1

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PR (for those people 20-79 years) = Total number of expected cases (20-79) Total country population (20-79) Where: Total number of expected cases of diabetes, or IGT, in the 20-79 year range = the sum of each age and gender (and urban/rural) specific number, as derived according to the earlier description. Following calculation of the PR, the expected number of people with diabetes and IGT within the country was reported separately for males and females, according to age groups (2039, 40-59, 60-79), and in those low- and middle-income economies (only for diabetes), according to residence in urban and rural areas. APPENDIX 1.1

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For countries without available age and gender distribution descriptions i.e. those with populations of less than 100,000 for the year 2000, (and Taiwan), for which data are not provided9, the total world population distribution was applied to the 2005 population as indicated in the CIA World Factbook 20051. For Andorra, Liechtenstein, Monaco and San Marino, the total developed world population was applied. Populations for all these countries for 2007 were obtained by applying the annual increase for one year, and for 2025, by assuming an unchanged proportion of the world (or developed world) from 2005 to 2025. The countries/territories without UN population data that are included are: Andorra, Anguilla, Antigua and Barbuda, Aruba, Bermuda, British Virgin Islands, Cayman Islands, Dominica, Grenada, Cook Islands, Kiribati, Liechtenstein, Marshall Islands, Monaco, Nauru, Niue, Palau, Saint Kitts and Nevis, San Marino, Seychelles, Taiwan, Tokelau, Tuvalu.

In addition to calculating the national rates, a prevalence for each country and region, adjusted to the world population, was calculated by applying for each country that country’s age- and sex-specific rates to a notional population of that country’s population size, but with the world population age and gender distribution for 20–79 years (for 2007 and 2025). This was done to facilitate comparison of rates between countries and regions, and this adjustment to the world population noted whenever it was used. For each region the prevalence adjusted to the world population was calculated by the summation of the number of persons for each member country with the condition, if each country’s world population adjusted prevalence were applied to that country, and the sum divided by the total regional population (20-79 years).

For each country, data for both diabetes and IGT are presented for people in the 20-79 age group. Most of the datasets used did not contain data for all age groups in the 20-79 year age bracket. In order to fill in missing data and to ensure a smooth relationship between prevalence and age, logistic regression was performed on those datasets that contained four or more datapoints. Observed data were entered into an SPSS spreadsheet under the following columns: age (mid-age of each age group), 338

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weight (number of people without or with diabetes, or IGT, for each age group), and diabetes or IGT (0 = no, 1 = yes). The age specific prevalence (or case numbers, when provided) was used to obtain the weighting in the following manner: If 3.6% of 1,000 participants of a particular age group had the condition (diabetes or IGT), the weighting for having the condition would be 36, and for not having the condition, 964. Following this, the variable age2 (age x age) was calculated, to enable the model to contain a quadratic term, so that the end model could include the possibility of flattening or reducing prevalence for the oldest age groups. A binary logistic regression was then performed using diabetes or IGT as the dependent variable and age and age2 as the covariates, to produce parameter estimates for the intercept, B and C. This provided the values for each of the 12 five-year groups (20-24, 25-29, …75-79) for the following equation: y = Intercept + (B x age) + (C x age2) The age specific prevalence (for the five-year age group) was then calculated as (ey/(ey+1)). The total numbers of persons with diabetes and IGT for each country were then calculated by applying the calculated age specific prevalence rates to the demographic data from the United Nations Population Prospects9. An upper limit of age was necessary for the logistic regression process, and 79 years was the limit chosen. When original datasets contained the age group 65+, the assumption was made that this age group was 65-74. If a dataset contained the age group 60+, the assumption was that this age group was 60-79, unless all previous age group data were in 10-year groups, in which case a 60-69 year limit was applied. No age groups with the youngest members being over 79 years were included, but persons over 80 years were included if part of an age group 75-84 years. Where the data were available, five-year age bands were chosen instead of 10-year age bands as they provided 12 datapoints in the 60 years age range which gave a smoother relationship between age and diabetes prevalence. Figures A1.1 to A1.4 illustrate how the published age specific data could be converted by using the described methodology into a smoothed curve with respect to age. DIABETES ATLAS THIRD EDITION

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Medline search was undertaken for each complication: retinopathy, neuropathy, nephropathy, coronary heart disease, stroke and amputations. Six keywords were used: Diabetic Retinopathy/ep, Diabetes Mellitus/Co [Complications], Diabetic Nephropathies/ep [Epidemiology], Amputations/sn [statistical & Numerical Data], Diabetic Neuropathies/ep [Epidemiology], Diabetic Foot Complications/co [Complications]. For CHD and stroke a combination of key words were used: cardiovascular disease, coronary disease, diabetic angiopathies, myocardial infarction, myocardial ischemia, cerebrovascular disorders, cerebrovascular accident, prevalence and diabetes mellitus. A final search, for countries without data, was performed using the keyword ‘Diabetes Mellitus’, along with each country name. The references of each paper selected for inclusion were reviewed and relevant articles from the reference lists were also reviewed. In addition, each IDF member country was contacted and asked to provide recent papers or unpublished reports on each complication. METHODOLOGY FOR CHAPTER 1.3

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Studies with ≥100 participants were included. Where studies presented results for subgroups (e.g. type 1 or type 2 diabetes) separately, only results for subgroups with ≥100 participants were included. Where more than one study was available for a country, preference was given to larger and population-based studies, those published after 1989, and those with the fewest restrictions (e.g. on age). Where complete articles were not available or were not translated into English, details from abstracts were used. Where possible the authors of the abstracts were contacted to verify and complete missing details. Studies were excluded in instances where details from the abstracts were insufficient and the authors could not be located. Unpublished studies which were the only available data for a country have been included. Prevalences are reported for cardiovascular disease, nephropathy, neuropathy and retinopathy, while incidence and prevalence are reported for lower extremity amputations (with preference given to incidence studies). Where amputation studies presented data as a time trend, the most recent data have been included. APPENDIX 1.2

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Where possible, the age ranges of the populations are reported. Where the age range of the population was not available, the mean or median age is reported. Where a publication only recorded the age range as groups in a Table, the lower and upper age groups are presented as the age range. Diagnostic criteria for each complication are recorded, as variation in definitions can affect the prevalences reported. Diagnostic criteria for neuropathy were divided into five categories: clinical, quantitative sensory testing, electrophysiology, autonomic function tests and questionnaire to general practitioners. Where validated clinical scores were used, the name of the scale is recorded. Retinopathy was defined as the presence of at least microaneurysms or haemorrhages or exudates. The types of retinal examination are listed. Criteria for nephropathy are listed (type of measurement and diagnostic values used).

included, as they do not reflect the general diabetic population of the country. There are some important differences between the section on diabetic complications (Chapter 1.3) and the section on diabetes prevalence (Chapter 1.1). The total numbers of individuals within a country who may have complications are not estimated, nor is a national prevalence. Furthermore, data have not been projected from one country onto other countries. There are two reasons for these differences: first, such calculations require knowledge of the age and sex structure of both the original study population, and of the target (national diabetic) population. In most cases, neither of these is known. Secondly, many studies are clinic based, and so their generalizability is limited.

Five types of studies were included: population based, register, clinic (primary care), clinic (secondary care), and clinic (primary and secondary care). For amputation studies reporting incidence, the sources of the amputation data are presented. The sources used for these studies were registers, hospital records, operating theatre records, limb fitting centres, hospital discharge records and hospital discharge codes. Amputations were recorded as first or all amputations. The lowest (most distal) level of amputation included in each study, and the total numbers of amputees (rather than amputations) are reported. In most of the incidence studies, the authors calculated amputation incidence using the recorded number of amputations and an estimated figure for the total diabetic population. In two studies, a cohort design was used in which a defined population was followed over time allowing a more accurate calculation of amputation incidence. For some countries, results from more than one study are presented. This is usually because they cover different aspects of the diabetic population such as type 1, type 2, undiagnosed diabetes and previously diagnosed diabetes. Furthermore, since studies vary considerably in design, the presentation of two or more studies can help to build a broad picture of the prevalence of a complication. Studies of small minority groups from populations, e.g. Pima Indians, have not been 340

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he following systematic searches were performed to identify sources of published data for the rates of type 1 diabetes in childhood: 1. Medline was accessed using OVID restricted to human studies published since 1980 and using (exp registries OR exp incidence OR exp prevalence) AND exp diabetes mellitus, insulin-dependent AND exp with the /ep [Epidemiology] sub-heading. If a country was not indexed in Medline then it was included in the search as a text word.

The following criteria were used, although not necessarily in the order shown, to select the most suitable studies in countries with a number of available studies:

2. PubMed using the Boolean search terms (incidence OR prevalence) AND diabetes AND .

• More recent studies, preferably covering periods into the 1990s. • Studies with widest coverage within the country. • Studies providing rates for the target age range of 0-14 years. • Studies providing sex-specific rates for the 0-4, 5-9 and 1014 year age groups.

3. Published abstracts from recent international meetings including those in the Institute for Scientific Information (ISI) Proceedings were also searched.

If necessary the numerators and denominators of rates from a number of registers within a country were combined to obtain pooled rates.

The titles and abstracts of all articles were reviewed and those likely to provide incidence or prevalence rates were obtained. The reference lists of articles were also scanned to check for further relevant publications. No restrictions were placed on the language of published articles.

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The majority of studies found by the literature search provided incidence rates rather than prevalence rates. An estimate of the number of cases in each country was obtained by multiplying the population projections in each of six age/sex APPENDIX 2

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subgroups (males or females aged 0-4, 5-9 or 10-14 years) by the corresponding estimated prevalence rate. Prevalence rates in each age group were obtained by averaging cumulative incidence rates for the five individual years in the age group. For example, the prevalence in the 5-9 age group was obtained as an average of: Prevalence (age 5) = 5* (0-4 year incidence rate) + 0.5*(5-9 year incidence rate)

low and any adjustment for mortality is unlikely to have much impact. In less developed countries, which often have poorly estimated incidence rates based on small numbers, the application of an adjustment for mortality was not felt to be justified. In many African countries estimates of numbers of cases were derived directly from reported prevalence rates (usually extrapolated from other countries), rather than indirectly through incidence rates and in this situation no adjustment for mortality was required.

Prevalence (age 6) = 5* (0-4 year incidence rate) + 1.5*(5-9 year incidence rate) Prevalence (age 7) = 5* (0-4 year incidence rate) + 2.5*(5-9 year incidence rate) Prevalence (age 8) = 5* (0-4 year incidence rate) + 3.5*(5-9 year incidence rate) Prevalence (age 9) = 5* (0-4 year incidence rate) + 4.5*(5-9 year incidence rate) In a few countries that reported age-specific rates pooled for boys and girls, the rates were taken to apply to both boys and girls. The incidence rate is not uniform in the 0-14 year age group but rather it tends to be lower in young ages and increases to a peak usually in the 10-14 year age group. For countries in which age-specific rates were not available, a single multiplier to convert incidence rates to prevalence rates was derived as the median multiplier for the 65 countries for which age- and sex-specific incidence rates were available. Equal-sized populations in each age-sex subgroup were assumed in this calculation. The resulting prevalence to incidence ratio of 6.2 was therefore employed to convert incidence rates to prevalence rates in all countries in which age-specific incidence rates were unavailable. Using an assumption that the mean age at onset of diabetes occurring before the 15th birthday was 8.5 years, a similar conversion factor of 6.5 was derived in the second edition of the Diabetes Atlas, as the mean duration of diabetes in the 0-14 year age range. This method of estimating prevalence from incidence assumes that the effects of mortality are minimal. In developed countries, which tend to have high quality incidence data, mortality rates amongst diabetic children are 342

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he estimates in Tables 5.1-5.9 were created by formula, using country-by-country estimates of diabetes prevalence by age and sex, population size by age and sex, total healthcare expenditures by age and sex, and the ratio of expenditures per person with diabetes to expenditures per person without diabetes, matched for age and sex. None of these inputs are known with certainty in industrialized countries and, in the rest of the world, no direct measurements have ever been published. One parameter, the diabetes cost ratio, is known by 10-year categories of age and sex only for a US population; assumptions about the relative magnitudes of expenditures for persons without diabetes were also based on US data. Although IDF and WHO are sponsoring studies that will obtain estimates from more settings, the estimates presented here rely on limited information.

The expenditures displayed in Tables 5.1 through 5.9 are the estimated total health expenditures caused by diabetes. Initial data on per capita total health expenditures by country were obtained from Annex Table 6 to the WHO World Health Report for 200410. WHO defines ‘total health expenditure’ to include all expenditures for medical care regardless of who METHODOLOGY FOR CHAPTER 5

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paid for them. The WHO definition also includes expenditures for public health programmes, water supply and hygiene activities, nutritional support activities, education, training, and research — but only when these activities intentionally and primarily address a health problem. The WHO definition excludes the unpaid care-giving of relatives and others, and the opportunity costs of this care-giving, including loss of paid employment. It also excludes other opportunity costs, such as loss of educational opportunities for children who must stay home to care for disabled parents. A significant portion of healthcare spending in the poorest countries comes from governmental programmes and from external donors, who focus on communicable and parasitic diseases rather than on diabetes and cardiovascular disease (despite diabetes causing as many deaths as HIV/AIDS). The IDF estimates of expenditures for diabetes in poor countries may therefore be exaggerated.

In each country, expenditures for persons without diabetes APPENDIX 3

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were estimated for 42 subpopulations based on sex and fiveyear strata of age, ranging from age zero to ages greater than 100. Because data on total health expenditures by age and sex are very rare, even for developed countries, estimates of population-wide total health expenditures per capita published by WHO for the year 200210 were used. Per capita WHO expenditure estimates were divided into two components: a portion that was assumed to vary with ageand sex-specific mortality (about 80% of total expenditures) and a portion (about 20%) that was assumed to be constant within each age- and sex-subgroup. Total expenditures per subgroup were calculated as the sum of (a) constant expenditures multiplied by subgroup size and (b) the product of mortality-related expenditures and the predicted number of annual deaths in the subgroup. Reliable mortality statistics by country are not universally available so each country’s mortality rates were assumed to match the rates published for its WHO Demographic Group (N=14)11. Because mortality rates vary more widely with age than medical care expenditures do, rates by sex and five-year age group were transformed using a log function, ln(3.00 + mortality rate). Subgroup mortality rates were further modified to account for the fact that only about half the children who die in countries with high and very high childhood mortality receive medical care, and for the generally lower average expenditures for conditions that cause death in childhood. The 20% of annual expenditures that were assumed not to vary with mortality were adjusted by age and sex to account for natural differences in medical care utilization, such as the higher use of medical care services by women of child-bearing age. The resulting estimates were then fine-tuned by approximately equalizing, for each age and sex subgroup, the ratio of total per capita medical care expenditures predicted via these methods for the US population to the per capita medical care expenditures observed in a US sample of persons who did not have diabetes. (The sample without diabetes were members of the Kaiser Permanente medical care programme in the United States, selected and analyzed by one of the authors [GN].) Per capita expenditures for men were further adjusted to maintain the male/female ratios to expenditures found in the Kaiser Permanente data. The resulting relative distributions of per capita health expenditures by age and sex, when multiplied by the population in each subgroup, yielded for each country an aggregate total health expenditure. 344

APPENDIX 3

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To ensure that average total per capita expenditures in each country still matched the estimates published by WHO, aggregate expenditures were compared to the WHO estimates for 2002, country by country. Expenditures were adjusted up or down so that the estimated countrywide expenditure equalled the country’s WHO-published expenditure for 2002. This result was then increased to account for population growth since 2002, the year of the WHO estimates, by dividing the UN medium-variant projected population for each country in either 2007 or 2025, as appropriate, by the population assumed in WHO estimates for 2002, and multiplying by the resulting ratio.

The diabetes expenditure ratio, R The Tables give alternative estimates for values of a parameter called R, which is the ratio of all medical care expenditures for persons with diabetes to all medical care expenditures for age- and sex-matched persons who do not have diabetes. By comparing the total expenditures of matched persons with and without diabetes, the expenditures that diabetes causes can be isolated. Because R varies from country to country and over time, the Tables show results for likely lower and upper bounds of R, R=2 and R=3. The present analysis attempts to improve on the groundbreaking estimates calculated for the second edition of the Diabetes Atlas, by explicitly accounting for demographic variation in R. R is quite sensitive to age and sex, and countries differ markedly in the age structures of their populations. In industrialized countries, R is higher in younger age groups because younger persons without diabetes do not usually incur large medical expenditures. Conversely, R is lower at older ages because old persons without diabetes use substantial medical care. Younger men without diabetes also use less medical care than younger women in industrialized countries. When the single global R’s used in the second edition of the Atlas were replaced with age- and sex-specific R’s, most country estimates of expenditures for diabetes increased, often quite substantially. Global expenditures nearly doubled. This is an encouraging result because IDF’s earlier results appeared to underestimate true diabetes care expenditures, when compared with published national studies. To obtain an empirical basis for age- and sex-specific values of R, authors [GN and JBB] affiliated with Kaiser Permanente Northwest Region (KPNW), a large not-for-profit pre-paid DIABETES ATLAS THIRD EDITION

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medical care system in the United States, calculated ratios from this organization’s large diabetes registry. The mean R for all KPNW registrants aged 20-79 for 2004 was 2.066 for women and 2.088 for men. To create age by sex distributions of R’s for standard R’s with a population-weighted mean of 2.0 and 3.0, the KPNW distributions of ratios were adjusted up and down. Table A3.1 displays these observed and adjusted ratios and the numbers of subjects that contributed data. Because of low sample sizes in the age groups between 20 and 50, R’s for these ages were estimated en bloc. R undoubtedly varies among and within countries. In addition, values of R may be decreasing, at least in industrialized countries. Earlier studies from the USA reported mean R’s of 2.6 in 199212 and 2.4 in 199413, much higher than the R’s of 2.07 and 2.09 described above for KPNW in 2004. A recently published German study reported an R for sick-fund reimbursed medical care expenditures of 2.014. There are several reasons why a lowering could be underway. One is that persons with type 2 diabetes are being diagnosed sooner, which means that the average person with diabetes will have fewer and fewer costly complications. One US study showed that R is lower (~2) during the first six years after diagnosis15. Additionally, the control of risk factors for diabetic complications (hyperglycaemia, hypertension, dyslipidaemia) has been improving in developed countries, as has the use of classes of drugs (aspirin, statins, ACEinhibitors, other antihypertensives) that are known to be highly effective in preventing cardiovascular complications. This means that the incidence of diabetic complications is probably decreasing, which also reduces average medical care expenditures. Finally, effective drugs in each of the classes used in diabetes are now less expensive because they are off-patent, which further lowers treatment expenditures (when generic drugs are used). Do the age and sex patterns of diabetes treatment expenditures in industrialized countries like the US and Germany accurately describe the rest of the world? Expenditure patterns in low- and middle-income countries are not yet known. One study in China of relatively wealthy patients of endocrinologists reported an overall R of 2.516. A study in Taiwan reported a ratio of 4.3 but this estimate is high because it is not age- or sex-adjusted17. Studies supported by IDF, WHO and the World Diabetes Foundation will yield more data soon. The first of these studies, in Shanghai and Iran, should have results by 2007. METHODOLOGY FOR CHAPTER 5

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Computational details For this third edition of the Diabetes Atlas, data and calculations for each country were broken down into 10-year age-sex subgroups, starting with age 20-29 and ending with age 7079. (Persons aged less than 20 or more than 79 years were omitted because data on the prevalence of diabetes in these age groups are lacking for most countries.) Expenditures were calculated for each subgroup, one at a time, for men and for women, using a different value of R for each subgroup. The subgroup expenditures were then combined, first within sex, and then combining the sexes, weighting each subgroup’s contribution to the total by the proportion of the country’s diabetic population that fell into each age-sex subgroup. Specifically, countrywide and per capita expenditures of medical care in 2007 were estimated by combining data describing: 1. the estimated current prevalences of diabetes in 2007 (Pas, as estimated in Chapter 1 from epidemiologic studies); 2. estimated 2007 populations (Nas, based on United Nations projections, median fertility variant18 or, for non-UN members, the CIA World Factbook1); 3. total current healthcare budgets in 2002 (Cas, obtained from WHO estimates and projections19); and 4. ratios (Ras) of medical care expenditures for persons with diabetes compared to persons without diabetes. All these data were divided into age deciles (a=1–6), by sex (s=1,2). The formula used to calculate the expenditures of medical care for diabetes in each country was: 2

6

s =1

a =1

D = C {∑ ∑ ( N as / N ) P ( R as

as

− 1) /[

P (R as

as

− 1) + 1] }

where D = the total expenditure of care for diabetes in a country C = the estimated annual budget for all healthcare in the country in 2002 Nas = the total population of persons, in each age and sex subgroup, projected for a country in 2007 N = the total population of the country of all ages Pas = the prevalence of diabetes in the country, by age and sex Ras = the ratios of expenditures for persons with diabetes to persons without diabetes, by age and sex, and where a is an indicator for age decile (20-29, 30-39, …70-79), and b is an indicator for sex (men, women). APPENDIX 3

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This formula corrects for the fact that per capita health expenditures per person with diabetes include expenditures caused by many conditions, not just diabetes. The formula yields the costs caused by diabetes.

Projection to 2025 Estimates of expenditures in 2025 differ from estimates for 2007 only as a result of projected changes in population structure (total size, sex, age, and percent urban). Expected growth in diabetes incidence is not included. Also ignored are increases in medical care expenditures due to economic growth and/or relative inflation in prices for medical care. For these reasons, these projections underestimate future diabetes expenditures.

US and international dollars Expenditures are shown both in US dollars (USD) and international dollars (ID), valued as of the year 2002, the most recent year for which national healthcare expenditure data for all countries are currently available. (Projected expenditures in 2025 are also shown in 2002 dollars.) Expenditures in USD estimate the amount of internationally traded currency that 346

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is spent for diabetes care. These expenditures can be used to compare how much individuals and institutions paid or will pay for diabetes care. A unit of internationally traded currency can buy many more goods and services in some countries than in others. Converting USD to ID corrects for such differences, which economists call differences in purchasing power. Expenditure estimates in ID can be used to compare the amounts of diabetes care that countries actually produce. The market-basket studies from which ID multipliers are calculated involve a wide range of products and services. These multipliers might not be accurate for the medical care sectors of some countries. For example, healthcare workers in many poor countries are said to be underpaid relative to workers in other occupations in the same country. If so, the true difference between USD and ID estimates might be greater than is reported here. On the other hand, medicines and medical supplies are often imported and, in many lowincome countries, medicines are taxed upon entry. Some manufacturers of diabetes medicines lower their wholesale prices to poor countries, but shortages and black-market distribution can erode these efforts. Consequently, estimates DIABETES ATLAS THIRD EDITION

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TABLE A3.1 Diabetes expenditure ratios (R) by age and sex* WOMEN

AGE (YEARS) 20-49 50-59 60-69 70-79

MEN

KPNW R**

ADJ. R=2***

ADJ. R=3***

KPNW SAMPLE SIZE

2.23 2.30 2.11 1.70

2.15 2.22 2.03 1.62

3.15 3.22 3.03 2.62

1,145 1,996 2,031 1,774

KPNW R** 2.74 2.23 2.03 1.57

ADJ. R=2***

ADJ. R=3***

KPNW SAMPLE SIZE

2.66 2.15 1.95 1.50

3.66 3.15 2.95 2.50

1,108 2,239 2,356 1,901

*Ratio of total medical care expenditures for persons with diagnosed diabetes divided by total medical care expenditures of persons not diagnosed with diabetes. **Source: Kaiser Permanente Northwest Region, 2004 ***Calculated so that the mean R in all age groups equalled 2 or 3 when weighted by the KPNW population sizes in each age group.

in ID could overestimate the amount of medicine that can be purchased in poorer countries.

Summary of limitations Expenditure estimates derived by the methods described above have many limitations. First, they depend on estimates of population size, diabetes prevalence, aggregate health expenditures and rates of mortality that are imperfect. Second, they depend on assumptions whose accuracy has not been confirmed in most of the world. For example, the data used to calculate R and to adjust mean per capita expenditures for age and sex came from a single country, the USA, and from a single medical care system within that country. Almost nothing is known about R or about general medical expenditure patterns by age and sex in poor and middle-income countries. It was also assumed that estimates of R derived from persons with diagnosed diabetes apply to persons with undiagnosed diabetes, which could be wrong. And it was assumed that estimates of R derived from data on medical care can be generalized to apply to all money expended for health purposes, the only definition of expenditure for which there are estimates for every country. This may be especially inaccurate in low-income countries, METHODOLOGY FOR CHAPTER 5

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which receive large portions of their health budgets from external donors, who generally want to focus their giving on public health initiatives and infectious disease. Finally, purchasing power parities estimated for general baskets of goods and services may not describe purchasing power medicines and medical care, increasing the uncertainty of the estimates in international dollars. These limitations mean that the estimates here for most countries will be very imprecise. Nevertheless, some conclusions shine through clearly. Diabetes causes huge amounts of spending and loss. Wealthy countries do almost all the spending. Low- and middle-income countries bear most of the loss. Better diabetes treatment would be costeffective everywhere.

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GLOSSARY, ACRONYMS & REFERENCES

GLOSSARY A acanthosis nigricans (AN) A skin disease characterized by grey-black warty patches usually situated in the armpit or groin or on elbows or knees and sometimes associated with cancer in the abdomen. albumin Albumin is the protein of the highest concentration in plasma, and transports many small molecules in the blood. Because albumin is synthesized by the liver, decreased serum albumin may result from liver disease. It can also result from kidney disease, which allows albumin to escape into the urine. albuminuria The presence of albumin in the urine that is usually a symptom of kidney disease but sometimes a response to other diseases or physiological disturbances of benign nature. See microalbuminuria. atherosclerosis Hardening and thickening of the walls of the arteries as a result of deposits of atheroma (fatty material) on their inner lining. This buildup of atheroma may slow down or stop blood flow.

B beta cells Beta cells are found in the islets of Langerhans in the pancreas. They produce and release insulin. body mass index (BMI) A key index for assessing body weight in relation to height. Body mass index (BMI) is calculated by dividing weight in kilograms (kg) by the square of height in metres (m). A person is considered obese when BMI is 30 and above.

C C-peptide C-peptide is a sub-unit of the hormone insulin. The C-peptide level may be measured in a person with type 2 diabetes to see if any insulin is still being produced by the body. It may also be measured in the evaluation of hypoglycemia (low blood sugar) to see if too much insulin is being produced by the person. cardiovascular disease (CVD) Cardiovascular diseases are defined as diseases and injuries of the circulatory system: the heart, the blood vessels of the heart and the system of blood vessels throughout the body and to (and in) the brain. Stroke is the result of a blood

354

time lost due to premature mortality. One DALY can be thought of as one lost year of ‘healthy’ life and the burden of disease as a measurement of the gap between current health status and an ideal situation where everyone lives into old age free of disease and disability.

flow problem within, or leading to, the brain and is considered a form of CVD. coronary heart disease (CHD) Any disease of the heart caused by coronary artery disease, although it usually refers to heart attack and angina.

D diabetes mellitus (DM) Diabetes mellitus is a chronic condition that arises when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin produced. This causes hyperglycaemia (an abnormally high concentration of glucose in the blood), which seriously damages many of the body’s systems, especially the blood vessels and nerves. There are two basic forms of diabetes: type 1 (requiring insulin for survival) and type 2 (requiring insulin for metabolic control). People with type 1 diabetes do not produce enough insulin. People with type 2 diabetes produce insulin but cannot use it effectively. diabetic complications Diabetic complications are chronic conditions caused by diabetes. They include retinopathy (eye disease), nephropathy (kidney disease), neuropathy (nerve disease), cardiovascular disease (disease of the circulatory system), foot ulceration and amputation. These complications can be prevented by timely treatment. Public and professional awareness of the risk factors for, and symptoms of, diabetes are an important step towards the control and prevention of complications. diabetic ketoacidosis (DKA) Also called diabetic coma. It indicates very high blood sugar level which requires emergency treatment. Ketoacidocis occurs because of lack of insulin. Without insulin, the body uses stored fat instead of glucose for energy, and acidic waste products called ketones are produced, which build up in the blood, causing ketoacidosis. Its symptoms include nausea and vomiting, which can lead to loss of water, stomach pain, and deep and rapid breathing. Other signs are a flushed face, dry skin and mouth, fruity breath odour, rapid and weak pulse, and low blood pressure. If the person is not given fluids and insulin right away, ketoacidosis can lead to coma and even death. Disability Adjusted Life Year (DALY) The Disability Adjusted Life Year or DALY is a health gap measure that extends the concept of potential years of life lost due to premature death to include equivalent years of ‘healthy’ life lost by virtue of being in states of poor health or disability. The DALY combines in one measure the time lived with disability and the

dyslipidaemia It indicates abnormalities of the lipid metabolism and is often associated with insulin resistance in type 2 diabetes.

E epidemiology The branch of medicine which deals with the incidence, distribution and possible control of disease and other health-related factors.

F Foot ulceration A foot ulcer is a break in the skin or a deep sore that can occur in people with diabetes because of nerve and/or vessel damage to the foot. Foot ulceration and amputation are among the most costly diabetic complications. Diabetes is the most common cause of amputation that is not the result of accident.

G gestational diabetes mellitus (GDM) A carbohydrate intolerance of varying degrees of severity with onset or first recognition during pregnancy. Gestational diabetes develops during some cases of pregnancy, but usually disappears when pregnancy is over. However, women who have had gestational diabetes are at greater risk of developing type 2 diabetes at a later stage in their lives. glucose Also called dextrose. The main sugar the body produces from proteins, fats and carbohydrates. Glucose is the major source of energy for living cells and is carried to each cell through the bloodstream. However, the cells cannot use glucose without the help of insulin. glycosylated haemoglobin (HbA1c) Haemoglobin to which glucose is bound. Glycosylated haemoglobin is tested to monitor the long-term control of diabetes mellitus. The level of glycosylated haemoglobin is increased in the red blood cells of persons with poorly controlled diabetes mellitus. Since the glucose stays attached to haemoglobin for the life of the red blood cell (normally about 120 days), the level of glycosylated haemoglobin reflects the average blood glucose level over the past three months.

DIABETES ATLAS THIRD EDITION





Glycosylated haemoglobin is also known as glycohaemoglobin or as haemoglobin A1C (the main frac tion of glycosylated haemoglobin).

H HbA1c See glycosylated haemoglobin hyperglycaemia A raised level of glucose in the blood; a sign that diabetes is out of control. Many things can cause hyperglycaemia. It occurs when the body does not have enough insulin or cannot use the insulin it does have to turn glucose into energy. Signs of hyperglycaemia are great thirst, dry mouth and need to urinate often. For people with t ype 1 diabetes, hyperglycaemia may lead to diabetic ketoacidosis. hypertension Very high blood pressure; this can cause health problems such as heart attacks and strokes. hypoglycaemia Too low a level of glucose in the blood. This occurs when a person with diabetes has injected too much insulin, eaten too little food, or has exercised without extra food. A person with hypoglycaemia may feel nervous, shaky, weak, or sweaty, and have a headache, blurred vision and hunger. Taking small amounts of sugar, sweet juice, or food with sugar will usually help the person feel better within 10-15 minutes.

I impaired fasting glucose (IFG) Raised fasting levels of glucose. impaired glucose tolerance (IGT) Blood glucose levels that are higher than normal, but below the level of a person with diabetes. Individuals with IGT are at high risk of progressing to type 2 diabetes, although such progression is not inevitable, and approximately 30% of individuals with IGT will return to normal glucose tolerance. In addition to carrying a risk of future diabetes, IGT is also a risk factor for future cardiovascular disease. incidence It indicates how often a disease occurs. More precisely, it corresponds to the number of new cases of a disease among a certain group of people for a certain period of time. insulin A hormone whose main action is to enable body cells to absorb glucose from the blood

GLOSSARY

and use it for energy. Insulin is produced by the beta cells of the islets of Langerhans in the pancreas.

The three kinds of macrovascular disease are: coronary heart disease, cerebrovascular disease and peripheral vascular disease.

insulin resistance (IR) A state in which a given level of insulin produces a less than expected biological effect.

metformin Metformin is used alone or with other medications, including insulin, to treat type 2 diabetes. Metformin helps to control the amount of glucose in the blood. It decreases the amount of glucose absorbed from food and the amount of glucose made by the liver. Metformin also increases the body’s response to insulin. Metformin is not used to treat type 1 diabetes.

ischaemic heart disease The term ‘ischaemic’ means that an organ, in this case the heart muscle, has not received enough blood and oxygen. People with this condition have weakened heart pumps, either due to previous heart attacks or due to current blockages of the coronary arteries. islets of Langerhans Named after Paul Langerhans, the German scientist who discovered them in 1869, these clusters of cells are located in the pancreas. They produce and secrete hormones that help the body break down and use food. There are five types of cells in an islet: alpha cells, which produce glucagon; beta cells, which produce insulin; delta cells, which produce somatostaton; and PP cells and D1 cells, about which little is known.

K ketones Chemicals that the body produces when there is not enough insulin in the blood and it must break down fat for its energy. Without insulin, ketones build up in the blood and then pass into urine so that the body can dispose of them. See diabetic ketoacidosis. ketonuria The presence of excess ketone bodies in the urine in conditions, such as diabetes mellitus, involving reduced or disturbed carbohydrate metabolism. ketosis A condition of having ketones build up in body tissues and fluids. The signs of ketosis are nausea, vomiting and stomach pain. Ketosis can lead to ketoacidosis.

M macula The part of the retina in the eye used for reading and seeing fine detail. macular oedema Swelling of the macula macrovascular disease Disease of the large blood vessels that may occur in people who have had diabetes for a long time. Fat and blood clots build up in the large blood vessels and stick to the vessel walls.

microalbuminuria Albuminuria characterized by a relatively low rate of urinary excretion of albumin typically between 30 and 300 milligrams per 24-hour period. Albuminuria is a typical finding of disorders such as diabetic nephropathy. microvascular disease Disease of the smallest blood vessels that may occur in people who have had diabetes for a long time. The walls of the vessels become abnormally thick but weak. Therefore, they bleed, leak protein and slow the flow of blood through the body.

N nephropathy Diabetic nephropathy (kidney damage) results in large amounts of urine protein and hypertension, and progressively leads to kidney failure. Diabetes is also the leading cause of nephropathy. Nephropathy can be detected by testing for traces of protein in the urine. neuropathy Diabetic neuropathy refers to damage to the nerve fibres caused by diabetes. It is the most common diabetic complication of a microvascular nature. Hyperglycaemia is a significant risk factor which can cause diabetic neuropathy. Diabetic neuropathy is a major cause of impotence in men with diabetes. NHANES The National Health and Nutrition Examination Survey (NHANES) is a survey conducted by the National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention. This survey has been designed to collect information about the health and diet of people in the USA.

O oral hypoglycaemic agent (OHA) Drugs that lower the level of glucose in the blood. They work for some people with type

355

GLOSSARY 2 diabetes if their pancreas still produces some insulin. They can help the body in several ways such as causing the cells in the pancreas to release more insulin. All oral hypoglycaemic agents belong to a class of drugs known as sulfonylureas.

P pancreas The pancreas is an organ situated behind the lower part of the stomach which produces insulin. peripheral neuropathy A disease or degenerative state of the peripheral nerves in which motor, sensory, or vasomotor nerve fibers may be affected, and which is marked by muscle weakness and atrophy, pain and numbness. polycystic ovary syndrome (PCOS) Polycystic ovary syndrome is an accumulation of many incompletely developed follicles in the ovaries. This condition is characterized by irregular menstrual cycles, scanty or absent menses, multiple small cysts on the ovaries (polycystic ovaries), excessive amounts of facial or body hair (hirsutism) and infertility. Many women who have this condition also have diabetes with insulin resistance. pre-eclampsia A toxic condition developing in late pregnancy that is characterized by a sudden rise in blood pressure, excessive gain in weight, generalized oedema, albuminuria, severe headache and visual disturbances. prevalence The number of people in a given group or population who are reported to have a disease at any point in time.

Q quality-adjusted life years (QALY) A quality-adjusted life year (QALY) takes into account both quantity and the quality of life generated by healthcare interventions. It is the arithmetic product of life expectancy and a measure of the quality of the remaining life years. A QALY places a weight on time in different health states. A year of perfect health is worth 1; however, a year of less than perfect health life expectancy is worth less than 1. QALYs provide a common currency to assess the extent of the benefits gained from a variety of interventions in terms of healthrelated quality of life and survival for the patient.

356



ACRONYMS

R

A

retinopathy Retinopathy is a disease of the retina of the eye which may cause visual impairment and blindness.

A/CR albumin/creatinine ratio ADA American Diabetes Association ADM atypical diabetes mellitus AER albumin excretion rate AFI amniotic fluid insulin AFR African Region AIDS acquired immunodeficiency syndrome AN acanthosis nigricans

S spectrophotometer A device which measures the amount of ultraviolet light absorbed by a substance. stroke A sudden loss of function in part of the brain as a result of the interruption of its blood supply by a blocked or burst artery. sulfonylureas Any of several hypoglycaemic compounds (as glipizide and tolbutamide) related to the sulfonamides and used in the oral treatment of type 2 diabetes.

T transient ischaemic attacks ‘Mini strokes’ that produce stroke-like symptoms and signs which clear completely within 24 hours. These attacks are strong predictors of stroke. tropical and malnutrition diabetes Diabetes mellitus associated with chronic malnutrition and, sometimes, chronic pancreatitis. Whether tropical diabetes exists as a distinct entity is under debate. Also called malnutrition-related diabetes. type 1 diabetes Type 1 diabetes mellitus develops most frequently in children and adolescents. About 10% of people with diabetes have type 1. The symptoms of type 1 vary in intensity. Symptoms include excessive thirst, excessive passing of urine, weight loss and lack of energy. Insulin is a life-sustaining medication for people with type 1 diabetes. They require daily insulin injections for survival. type 2 diabetes Type 2 diabetes mellitus is much more common than type 1, and occurs mainly in adults although it is now also increasingly found in children and adolescents. The symptoms of type 1, in a less marked form, may also affect people with type 2. Some people with type 2, however, have no early symptoms and are only diagnosed several years after the onset of the condition, when various diabetic complications are already present. People with type 2 may require oral hypoglycaemic drugs and may also need insulin injections.

B BMI body mass index (kg/m2) BW birth weight

C CABG coronary artery bypass graft CBVD cerebrovascular disease CCF congestive cardiac failure CDC Centers for Disease Control CHD coronary heart disease CHF congestive heart failure CI confidence interval CIA Central Intelligence Agency CVD cardiovascular disease

D DALY Disability Adjusted Life Years DCCT Diabetes Control and Complications Trial ddd/1000 person/day defined daily doses/1000 persons of the whole population/day DECODA Diabetes Epidemiology: Collaborative Analysis Of Diagnostic Criteria in Asia DECODE Diabetes Epidemiology: Collaborative Analysis Of Diagnostic Criteria in Europe DiabCare Diabetes Care study DiaMond Diabetes Mondiale study DKA diabetic ketoacidosis DM diabetes mellitus DNI diabetic neuropathy index DPP Diabetes Prevention Program DPS Diabetes Prevention Study

E ECG electrocardiogram EGIR European Group for the Study of Insulin Resistance EMME Eastern Mediterranean and Middle East Region EPDS Edinburgh Postnatal Depression Scale EPODE Ensemble, prévenons l’obésité des enfants

DIABETES ATLAS THIRD EDITION





ACRONYMS N

T TIA transient ischaemic attack 2hBG two-hour blood glucose TRIPOD Troglitazone in the Prevention of Diabetes

GDM gestational diabetes mellitus GDP gross domestic product GNP gross national product GPRD United Kingdom General Practice Research Database

N/A not available NA not applicable NA North American Region NCEP ATP III National Cholesterol Education Program – Third Adult Treatment Panel NDS neuropathy disability score NGO non-governmental organization NHANES National Health and Nutrition Examination Survey NIDDM non-insulin dependent diabetes mellitus NNT number needed to treat ns not significant NSP neuropathy symptom profile NSS neuropathy symptom score NSt Not stated

H

O

VAT Value Added Tax VPT vibration perception threshold 

est estimated number of persons with diabetes EUR European Region EURODIAB Europe and Diabetes study EX Excluded

F FBG fasting blood glucose FCG fasting capillary glucose FPG fasting plasma glucose

G

HAPO Hyperglycemia and Adverse Pregnancy Outcome study HbA1c Glycosylated haemoglobin A1c HIPC Highly Indebted Poor Countries HIV human immunodeficiency virus

I ID International dollar IDDM insulin-dependent diabetes mellitus IDF International Diabetes Federation IFG impaired fasting glucose IGT impaired glucose tolerance IIF International Insulin Foundation IR insulin resistance

K KDM known diabetes

L LADA latent autoimmune diabetes mellitus in adults LGA large for gestational age LSM lifestyle modification

M MMWR Morbidity and Mortality Weekly Report MeSH Medical Subject Headings MET Metformin MI myocardial infarction

ACRONYMS

U UK United Kingdom UKPDS United Kingdom Prospective Diabetes Study UN United Nations UnDM undiagnosed diabetes USA United States of America USD United States Dollar

V

OGTT oral glucose tolerance test OHA oral hypoglycaemic agents OR odds ratio

W

P

WDF World Diabetes Foundation WHO World Health Organization WP Western Pacific Region

PCOS polycystic ovary syndrome PGDM pre-gestational diabetes mellitus PR prevalence rate PTCA percutaneous transluminal coronary angioplasty

X

Q

XENDOS Xenical in the prevention of diabetes in obese subjects study

QALYs quality-adjusted life years QUALIDIAB Quality of diabetes care network

R RAPIA Rapid Assessment Protocol for Insulin Access RBG random blood glucose RCT randomized clinical trial

S SACA South and Central American Region SEA South-East Asian Region SGA small for gestational age SMR standardized mortality ratio SPSS Statistical Package for the Social Sciences SR self-report SSA sub-Saharan Africa STOP-NIDDM Study to Prevent Non-InsulinDependent Diabetes Mellitus

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REFERENCES CHAPTER 6 76. National Corporation of Swedish Pharmacies (Apoteket AB). Personal communication. 2004. 77. Zimmet PZ, Corrie C, Ekoe JM, Shaw JE. Classification of diabetes mellitus and other categories of glucose intolerance. In DeFronzo RA, Ferrannini E, Keen H, and Zimmet P, editors. International Textbook of Diabetes Mellitus. Chichester, England: John Wiley and Sons Ltd; 2004. p3-14. 78. McLarty D, Swai ABM, Alberti K.G.M.M. Insulin availability in Africa: an insoluble problem ? International Diabetes Digest 1994; 5: 15-17. 79. Deeb L, Tan M, Alberti K. Insulin availability among International Diabetes Federation Member Associations. Diabetes Care 1994; 17 (3): 220-223. 80. Savage A. The insulin dilemma: a survey of insulin treatment in the tropics. International Diabetes Digest 1994; 5: 19-20. 81. Beran D, Yudkin JS, de Courten M. Assessing health systems for insulin-required diabetes in sub-Saharan Africa: developing a ‘Rapid Assessment Protocol for Insulin Access’. BMC Health Serv Res 2006; 6: 17. 82. Mauvais-Jarvis F, Sobngwi E, Porcher R, Riveline JP, Kevorkian JP, Vaisse C et al. Ketosis-prone Type 2 diabetes in patients of sub-Saharan African origin: clinical pathophysiology and natural history of beta-cell dysfunction and insulin resistance. Diabetes 2004; 53: 645-653. 83. Swai A, Lutale J, McLarty D. Diabetes in tropical Africa: a prospective study, 1981-7 I. characteristics of newly presenting patients in Dar es Salaam, Tanzania, 1981-7. BMJ 1990; 300: 1103-1106. 84. Makame MH. Childhood diabetes, insulin, and Africa. DERI (Diabetes Epidemiology Research International) Study Group. Diabet Med 1992; 9 (6): 571-573. 85. Castle WM, Wicks AC. A follow-up of 93 newly diagnosed African diabetics for 6 years. Diabetologia 1980; 18 (2): 121-123. 86. Olatunbosun ST, Ojo PO, Fineberg NS, Bella AF, Department of Medicine University of Illinois. Prevalence of diabetes mellitus and impaired glucose tolerance in a group of urban adults in Nigeria. J Natl Med Assoc 1998; 90 (5): 293-301. 87. McLarty DG, Swai AB, Kitange HM, Masuki G, Mtinangi BL, Kilima PM et al. Prevalence of diabetes and impaired glucose tolerance in rural Tanzania. Lancet 1989; 1 (8643): 871-875. 88. Beran D, Yudkin JS, de Courten M. Access to care for patients with insulin-requiring diabetes in developing countries. Diabetes Care 2005; 28: 2136-2140.

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Dixon L, Postrado L, Delahanty J, Fischer PJ, Lehman A. The association of medical comorbidity in schizophrenia with poor physical and mental health. J Nerv Ment Dis 1999; 187 (8): 496-502. 2. Fontaine KR, Heo M, Harrigan EP, Shear CL, Lakshminarayanan M, Casey DE et al. Estimating the consequences of anti-psychotic induced weight gain on health and mortality rate. Psychiatry Res 2001; 101 (3): 277-288. 3. Anderson RJ, Freedland KE, Clouse RE, Lustman PJ. The prevalence of comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care 2001; 24 (6): 1069-1078. 4. Hermanns N, Kulzer B, Krichbaum M, Kubiak T, Haak T. Affective and anxiety disorders in a German sample of diabetic patients: prevalence, comorbidity and risk factors. Diabet Med 2005; 22 (3): 293-300. 5. Goodnick PJ, Henry JH, Buki VM. Treatment of depression in patients with diabetes mellitus. J Clin Psychiatry 1995; 56 (4): 128-136. 6. Gavard JA, Lustman PJ, Clouse RE. Prevalence of depression in adults with diabetes. An epidemiological evaluation. Diabetes Care 1993; 16 (8): 1167-1178. 7. Lustman PJ, Anderson RJ, Freedland KE, de Groot M, Carney RM, Clouse RE. Depression and poor glycemic control: a meta-analytic review of the literature. Diabetes Care 2000; 23 (7): 934942. 8. Lustman PJ, Clouse RE. Depression in diabetic patients: the relationship between mood and glycemic control. J Diabetes Complications 2005; 19 (2): 113-122. 9. Lin EH, Katon W, Von Korff M, Rutter C, Simon GE, Oliver M et al. Relationship of depression and diabetes self-care, medication adherence, and preventive care. Diabetes Care 2004; 27 (9): 21542160. 10. de Groot M, Anderson R, Freedland KE, Clouse RE, Lustman PJ. Association of depression and diabetes complications: a meta-analysis. Psychosom Med 2001; 63 (4): 619-630.

11. Katon WJ, Rutter C, Simon G, Lin EH, Ludman E, Ciechanowski P et al. The association of comorbid depression with mortality in patients with type 2 diabetes. Diabetes Care 2005; 28 (11): 2668-2672. 12. Goldston DB, Kovacs M, Ho VY, Parrone PL, Stiffler L. Suicidal ideation and suicide attempts among youth with insulin-dependent diabetes mellitus. J Am Acad Child Adolesc Psychiatry 1994; 33 (2): 240-246. 13. Goldston DB, Kelley AE, Reboussin DM, Daniel SS, Smith JA, Schwartz RP et al. Suicidal ideation and behavior and noncompliance with the medical regimen among diabetic adolescents. J Am Acad Child Adolesc Psychiatry 1997; 36 (11): 1528-1536. 14. Kooy F. Hyperglycemia in mental disorders. Brain 1919; 42: 214-288. 15. Proakis AG, Mennear JH, Miya TS, Borowitz JL. Phenothiazine-induced hyperglycemia: relation to CNS and adrenal effects. Proc Soc Exp Biol Med 1971; 137 (4): 1385-1388. 16. Citrome L, Jaffe A, Levine J, Allingham B, Robinson J. Relationship between antipsychotic medication treatment and new cases of diabetes among psychiatric inpatients. Psychiatr Serv 2004; 55 (9): 1006-1013. 17. Bushe C, Holt R. Prevalence of diabetes and impaired glucose tolerance in patients with schizophrenia. Br J Psychiatry 2004; 47 Suppl: S67-S71. 18. Dixon L, Weiden P, Delahanty J, Goldberg R, Postrado L, Lucksted A et al. Prevalence and correlates of diabetes in national schizophrenia samples. Schizophr Bull 2000; 26 (4): 903-912. 19. Kamran A, Doraiswamy PM, Jane JL, Hammett EB, Dunn L. Severe hyperglycemia associated with high doses of clozapine. Am J Psychiatry 1994; 151 (9): 1395. 20. Fertig MK, Brooks VG, Shelton PS, English CW. Hyperglycemia associated with olanzapine. J Clin Psychiatry 1998; 59 (12): 687-689. 21. Sobel M, Jaggers ED, Franz MA. New-onset diabetes mellitus associated with the initiation of quetiapine treatment. J Clin Psychiatry 1999; 60 (8): 556-557. 22. Arneson GA. Phenothiazine Derivatives and Glucose Metabolism. J Neuropsychiatr 1964; 5: 181-185. 23. Hiles B. Hyperglycemia and glycosuria following chlorpromazine therapy. Jama 1956; 162: 1651. 24. Schwarz L, Munoz R. Blood sugar levels in patients treated with chlorpromazine. Am J Psychiatry 1968; 125 (2): 253-255. 25. Sernyak MJ, Leslie DL, Alarcon RD, Losonczy MF, Rosenheck R. Association of diabetes mellitus with use of atypical neuroleptics in the treatment of schizophrenia. Am J Psychiatry 2002; 159 (4): 561-566. 26. Koro CE, Fedder DO, L’Italien GJ, Weiss SS, Magder LS, Kreyenbuhl J et al. Assessment of independent ef fect of olanzapine and risperidone on risk of diabetes among patients with schizophrenia: population based nested case-control study. BMJ 2002; 325 (7358): 243. 27. Gianfrancesco F, White R, Wang RH, Nasrallah HA. Antipsychotic-induced type 2 diabetes: evidence from a large health plan database. J Clin Psychopharmacol 2003; 23 (4): 328-335.

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REFERENCES CHAPTER 7 28. Ostbye T, Curtis LH, Masselink LE, Hutchison S, Wright A, Dans PE et al. Atypical antipsychotic drugs and diabetes mellitus in a large outpatient population: a retrospective cohort study. Pharmacoepidemiol Drug Saf 2005; 159 (4): 362366. 29. Buse JB, Cavazzoni P, Hornbuckle K, Hutchins D, Breier A, Jovanovic L. A retrospective cohort study of diabetes mellitus and antipsychotic treatment in the United States. J Clin Epidemiol 2003; 56 (2): 164-170. 30. Newcomer JW, Tuomari V, Carson W, L’Italien GJ. Diabetes Incidence among patients treated with atypical agents: a systematic review of the literature and meta-analysis. American Psychiatric Association, Atlanta, Georgia, USA: Poster; 2005. 31. Seaburg HL, McLendon BM, Doraiswamy PM. Olanzapine-associated severe hyperglycemia, ketonuria, and acidosis: case report and review of literature. Pharmacotherapy 2001; 21 (11): 14481454. 32. Lacro JP, Dunn LB, Dolder CR, Leckband SG, Jeste DV. Prevalence of and risk factors for medication nonadherence in patients with schizophrenia: a comprehensive review of recent literature. J Clin Psychiatry 2002; 63 (10): 892-909. 33. Compton MT, Hill HA. Potential for detection bias in the association between olanzapine and diabetes. J Clin Psychiatry 2004; 65 (2): 274; author reply 275. 34. Peet M. Diet, diabetes and schizophrenia: review and hypothesis. Br J Psychiatry 2004; 47 Suppl: S102-S105. 35. McCreadie RG. Diet, smoking and cardiovascular risk in people with schizophrenia: descriptive study. Br J Psychiatry 2003; 183: 534-539. 36. Stokes C, Peet M. Dietar y sugar and polyunsaturated fatty acid consumption as predictors of severity of schizophrenia symptoms. Nutr Neurosci 2004; 7 (4): 247-249. 37. Gothelf D, Falk B, Singer P, Kairi M, Phillip M, Zigel L et al. Weight gain associated with increased food intake and low habitual activity levels in male adolescent schizophrenic inpatients treated with olanzapine. Am J Psychiatry 2002; 159 (6): 1055-1057. 38. Anonymous. Consensus development conference on antipsychotic drugs and obesity and diabetes. Diabetes Care 2004; 27 (2): 596601. 39. Expert Group. ‘Schizophrenia and Diabetes 2003’ Expert Consensus Meeting, Dublin, 3-4 October 2003: consensus summary. Br J Psychiatry 2004; 47 Suppl: S112-S114. 40. Lambert TJ, Chapman LH. Diabetes, psychotic disorders and antipsychotic therapy: a consensus statement. Med J Aust 2004; 181 (10): 544-548. 41. Newcomer JW, Nasrallah HA, Loebel AD. The Atypical Antipsychotic Therapy and Metabolic Issues National Survey: practice patterns and k n ow l e d g e o f ps ychiat r is t s . J Clin Psychopharmacol 2004; 24 Suppl 1 (5): 1-6.

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Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet 2005; 365 (9468): 1415-28. 2. Cameron AJ, Shaw JE, Zimmet PZ. The metabolic syndrome: prevalence in worldwide populations. Endocrinol Metab Clin North Am 2004; 33 (2): 351-75. 3. Kylin E. Studien uber das Hypertonie-Hyperglyka “mie-Hyperurika” miesyndrom. Zentralblatt fuer Innere Medizin 1923; 44: 105-127. 4. Zimmet P, Alberti G, Shaw J. Mainstreaming the metabolic syndrome: a definitive definition. Med J Aust 2005; 183 (4): 175-176. 5. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 1998; 15 (7): 539-553. 6. Balkau B, Charles MA. Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR). Diabet Med 1999; 16 (5): 442443. 7. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). Jama 2001; 285 (19): 2486-2497. 8. Alberti KG, Zimmet P, Shaw J. The metabolic syndrome—a new worldwide definition. Lancet 2005; 366 (9491): 1059-1062. 9. The IDF consensus worldwide definition of the metabolic syndrome. International Diabetes Federation.http://www.idf.org/webdata/docs/ IDF_Metasyndrome_definition.pdf Accessed May 2006. 10. Alberti KG, Zimmet P, Shaw J. Metabolic syndrome—a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med 2006; 23 (5): 469-480. 11. WHO Expert Consultation. Appropriate bodymass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004; 363: 157-163.

12. Tan CE, Ma S, Wai D, Chew SK, Tai ES. Can we apply the National Cholesterol Education Program Adult Treatment Panel definition of the metabolic syndrome to Asians? Diabetes Care 2004; 27 (5): 1182-1186. 13. Snehalatha C, Viswanathan V, Ramachandran A. Cutoff values for normal anthropometric variables in asian Indian adults. Diabetes Care 2003; 26 (5): 1380-1384. 14. Genuth S, Alberti KG, Bennett P, Buse J, DeFronzo R, Kahn R et al. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 2003; 26 (11): 3160-3167. 15. Adams RJ, Appleton S, Wilson DH, Taylor AW, Dal Grande E, Chittleborough C et al. Population comparison of two clinical approaches to the metabolic syndrome: implications of the new International Diabetes Federation consensus definition. Diabetes Care 2005; 28 (11): 27772779. 16. Rathmann W, Haastert B, Icks A, Giani G, Holle R, Koenig W et al. Prevalence of the metabolic syndrome in the elderly population according to IDF, WHO, and NCEP definitions and associations with C-reactive protein: the KORA Survey 2000. Diabetes Care 2006; 29 (2): 461. 17. Athyros VG, Ganotakis ES, Elisaf M, Mikhailidis DP. The prevalence of the metabolic syndrome using the National Cholesterol Educational Program and International Diabetes Federation definitions. Curr Med Res Opin 2005; 21 (8): 11571159. 18. Park HS, Lee SY, Kim SM, Han JH, Kim DJ. Prevalence of the metabolic syndrome among Korean adults according to the criteria of the International Diabetes Federation. Diabetes Care 2006; 29 (4): 933-934. 19. Guerrero-Romero F, Rodriguez-Moran M. Concordance between the 2005 International Diabetes Federation definition for diagnosing metabolic syndrome with the National Cholesterol Education Program Adult Treatment Panel III and the World Health Organization definitions. Diabetes Care 2005; 28 (10): 25882589. 20. Lorenzo C, Serrano-Rios M, Martinez-Larrad MT, Gonzalez-Sanchez JL, Seclen S, Villena A et al. Geographic variations of the International Diabetes Federation and the National Cholesterol Education Program-Adult Treatment Panel III definitions of the metabolic syndrome in nondiabetic subjects. Diabetes Care 2006; 29 (3): 685-691. 21. Lawlor DA, Smith GD, Ebrahim S. Does the new International Diabetes Federation definition of the metabolic syndrome predict CHD any more strongly than older definitions? Findings from the British Women’s Heart and Health Study. Diabetologia 2006; 49 (1): 41-48. 22. Ford ES. Prevalence of the metabolic syndrome defined by the International Diabetes Federation among adults in the U.S. Diabetes Care 2005; 28 (11): 2745-2749.

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APPENDICES 2 diabetes persists in the TRIPOD cohort eight months after stopping troglitazone. Diabetes 2001; 50 Suppl 2: A81. Davies MJ, Tringham JR, Troughton J, Khunti KK. Prevention of Type 2 diabetes mellitus. A review of the evidence and its application in a UK setting. Diabet Med 2004; 21 (5): 403-414. Despres JP, Lemieux I, Prud’homme D. Treatment of obesity: need to focus on high risk abdominally obese patients. BMJ 2001; 322 (7288): 716-720. Herman WH, Hoerger TJ, Brandle M, Hicks K, Sorensen S, Zhang P et al. The cost-effectiveness of lifestyle modification or metformin in preventing type 2 diabetes in adults with impaired glucose tolerance. Ann Intern Med 2005; 142 (5): 323-332. Murray CJ, Lauer JA, Hutubessy RC, Niessen L, Tomijima N, Rodgers A et al. Effectiveness and costs of interventions to lower systolic blood pressure and cholesterol: a global and regional analysis on reduction of cardiovascular-disease risk. Lancet 2003; 361 (9359): 717-725. Tuomilehto J. Modeling of primary prevention of the development of type 2 diabetes. Przegl Lek 2006; 63 Suppl 4: 3-6. Chiasson JL, Josse RG, Gomis R, Hanefeld M, Karasik A, Laakso M et al. Acarbose treatment and the risk of cardiovascular disease and hypertension in patients with impaired glucose tolerance: the STOP-NIDDM trial. JAMA 2003; 290 (4): 486-494. Ratner R, Goldberg R, Haffner S, Marcovina S, Orchard T, Fowler S et al. Impact of intensive lifest yle and met formin therapy on cardiovascular disease risk factors in the diabetes prevention program. Diabetes Care 2005; 28 (4): 888-894. Shobhana R, Rama RP, Lavanya A, Williams R, Vijay V, Ramachandran A. Expenditure on health care incurred by diabetic subjects in a developing country--a study from southern India. Diabetes Res Clin Pract. 2000; 48 (1): 37-42. Ramachandran A, Ramachandran S, Snehalatha C, Augustine C, Murugesan N, Viswanathan V et al. Increasing Expenditure On Health Care Incurred By Diabetic Subjects In A Developing Country -- A Study From India. In press. McEwan P, Williams R, Bergenheim K, Peters JR, Currie CJ. Economic benefits of delaying the onset of type 2 diabetes. Unpublished. Phillips DI, Jones A. Fetal programming of autonomic and HPA function: do people who were small babies have enhanced stress responses? J Physiol 2006; 572(Pt 1): 45-50. Tuo-uo Kpu L. Creating awareness on diabetes and its risk factors in the Cite des Palmiers Health District, Douala. 19th IDF World Diabetes Congress 2006 Abstract Book. Diabet Med 2006; 23 Special Suppl. Borys JM, Raffin S. Preventing non-communicable diseases: an integrated community approach. Diabetes Voice 2006; 51 (1): 41-43.

1. Central Intelligence Agency. The World Factbook. CIA. 2005. 2. The World Bank. Development Report - Knowledge for Development 1998/99. New York, USA: Oxford University Press; 1999. 3. Harris MI, Flegal KM, Cowie CC, Eberhardt MS, Goldstein DE, Little RR et al. Prevalence of diabetes, impaired fasting glucose, and impaired glucose tolerance in U.S. adults. The Third National Health and Nutrition Examination Survey, 1988-1994. Diabetes Care 1998; 21 (4): 518524. 4. Gourdy P, Ruidavets JB, Ferrieres J, Ducimetiere P, Amouyel P, Arveiler D et al. Prevalence of type 2 diabetes and impaired fasting glucose in the middle-aged population of three French regions - The MONICA study 1995-97. Diabetes Metab 2001; 27 (3): 347-358. 5. Rathmann W, Haastert B, Icks A, Lowel H, Meisinger C, Holle R et al. High prevalence of undiagnosed diabetes mellitus in Southern Germany: target populations for efficient screening. The KORA survey 2000. Diabetologia 2003; 46 (2): 182-189. 6. Mooy J, Grootenhuis P, de Vries H, Valkenburg H, Bouter L, Kostense P et al. Prevalence and determinants of glucose intolerance in a Dutch Caucasian population. The Hoorn study. Diabetes Care 1995; 18: 1270-1273. 7. Eliasson M, Lindahl B, Lundberg V, Stegmayr B. Diabetes and obesity in Northern Sweden: occurrence and risk factors for stroke and myocardial infarction. Scand J Public Health Suppl 2003; 61: 70-77. 8. Dunstan DW, Zimmet PZ, Welborn TA, de Courten MP, Cameron AJ, Sicree RA et al. The rising prevalence of diabetes and impaired glucose tolerance: the Australian Diabetes, Obesity and Lifestyle Study. Diabetes Care 2002; 25 (5): 829834. 9. United Nations, Population Division. World Population Prospects: The 2004 Revision. Geneva: United Nations; 2005. 10. World Health Organization. The World Health Report 2004. Geneva, Switzerland: World Health Organization; 2004. 11. World Health Organization. Projections of mortality and burden of disease to 2030. World Health Organization Global Burden of Disease Project, 2006. 12. Rubin RJ, Altman WM, Mendelson DN. Health care expenditures for people with diabetes mellitus, 1992. J Clin Endocrinol Metab 1994; 78 (4): 809a-809f. 13. Selby JV, Ray GT, Zhang D, Colby CJ. Excess costs of medical care for patients with diabetes in a managed care population. Diabetes Care 1997; 20 (9): 1396-1402. 14. Koster I, von Ferber L, Ihle P, Schubert I, Hauner H. The cost burden of diabetes mellitus: the evidence from Germany—the CoDiM Study. Diabetologia 2006; 49: 1498-1504. 15. Nichols GA, Glauber HS, Brown JB. Type 2 diabetes: Incremental medical care costs during the first eight years preceding diagnosis. Diabetes Care 2000; 23: 1654-1659. 16. Chen X, Tank L, Tan A, Zhao L, Hu C. Economic

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THE WORLD DIABETES FOUNDATION

Her Royal Highness Princess Benedikte of Denmark Patron of the World Diabetes Foundation MEMBERS OF THE BOARD

Chairman Pierre Lefèbvre Vice Chairman Leif Fenger Jensen from 1/4 -2006 Members Ida Nicolaisen Ib Bygbjerg Lars Rebien Sørensen Kaushik Ramaiya Managing Director Anil Kapur from 1/4- 2006

Diabetes is already a major public health problem in the developing world and regarded as a major cause of premature mortality and morbidity. It is amongst the leading causes of blindness, renal failure, heart attacks, strokes and limb amputations. Due to a compromised immune system, bacterial and fungal infections are also common and pose a health hazard for people with diabetes. Poor and disadvantaged people tend to be diagnosed later, have less access to treatment and consequently suffer more acute and late complications, limiting productivity and increasing economic burden. Effective intervention reduces the health and economic burden of diabetes. This requires focus on prevention – primary prevention – promoting healthy living, and secondary prevention reducing the burden of complications by early diagnosis and proper care. There is an urgent need for a multi-sectoral approach in which governments, non-governmental organizations (NGOs), the health industry, national associations, healthcare providers and people with diabetes can play a role in providing at least minimum standards of care that would help those affected maintain the best possible quality of life. This is precisely what the World Diabetes Foundation (WDF) is aiming for. DEVELOPING SUSTAINABLE SOLUTIONS FOR DIABETES CARE IN THE DEVELOPING COUNTRIES The World Diabetes Foundation aims to address and potentially limit the diabetes epidemic by bringing diabetes higher on the global healthcare agenda as well as fund sustainable projects in awareness, primary prevention, building healthcare capacity, and improving access to diabetes care in the poorest countries. The World Diabetes Foundation acts as a catalyst to build sustainable relations between different

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stakeholders to ensure that individual project initiatives live on even after the specific project funding has ceased. The World Diabetes Foundation focuses on the following areas: • • • • •

Awareness about diabetes Prevention of diabetes and its complications Education and training for people with diabetes and healthcare professionals Access to essential medicines in diabetes Detection, treatment and monitoring of diabetes

A CATALYTIC PARTNER It is very important to the World Diabetes Foundation that its funds are directed to people with the greatest burden and most need: namely for diabetes projects in the developing countries. The strategy is to act as a catalyst - help others do more - making a much greater impact than the Foundation’s size would suggest. The WDF seeks partnerships with established organizations in the areas of health, diabetes and development aid to build on existing structure and resources that help bring diabetes higher on the global healthcare agenda. Through these partnerships we aim to raise global awareness of diabetes and help find the resources to address and potentially limit the epidemic. WDF has established project-related partnerships with organizations such as the World Health Organization (WHO), International Diabetes Federation (IDF), Danish International Development Assistance (DANIDA), The Insulin Foundation, the German NGO Humanitäre CubaHilfe, the Spanish foundation Fundación para la Diabetes, local diabetes associations, the Ministries of Health in various countries, DIABETES ATLAS THIRD EDITION





leading diabetes research institutions and WHO collaborating centres. WDF, established in 2002 through a commitment of 500 million Danish Kroner over ten years by Novo Nordisk A/S, is registered as an independent trust and governed by a board of six experts in the field of diabetes, access to health and development assistance. The Foundation is currently chaired by Professor Pierre Lefèbvre, who is also President of IDF. WDF raises funds from other sources to support specific projects ensuring a multiplier effect; for every dollar spent the Foundation is able to raise approximately three US dollars in cash or kind from other sources. The World Diabetes Foundation supports Diabetes Action Now, a global collaboration project between WHO and IDF. The initiative represents the major part of the future WHO diabetes programme. Working with WHO at a national level, we have launched a diabetes project in Vietnam. Based on a community approach to prevention, control and management of diabetes, the project aims to improve the quality of diabetes care in Vietnam.

WDF currently supports 86 projects in the developing countries. The projects funded by the Foundation will in the coming 3-4 years directly influence the diabetes treatment, awareness and advocacy of potentially 35,840,000 people in the developing countries FOLLOW UP TO ENSURE SUCCESS The World Diabetes Foundation has developed a number of general procedures for monitoring and evaluating the projects. The monitoring process is now organized into a system, which determines the need for precise monitoring activities for each individual project done on the basis of assessment of the project’s size, complexity and duration.

The project aims to provide and promote standardized clinical guidelines for diabetes care to improve the quality of care given to people living with diabetes in Sub-Sahara Africa. The guidelines have been finalized and will soon be distributed and implemented throughout the region.

It is important for us to have realistic expectations regarding the results and outcome of the projects. Experience shows that not all projects will fare as planned and hoped for - not out of ill will, but often due to factors beyond the control of project management and WDF. These factors include but are not limited to - the political and personal commitment to the activities, transfer of trained staff, changes in policy or unrest in the country in question. These and other such factors will affect and hamper implementation and sustainability of our projects despite our comprehensive efforts to pre-qualify projects before granting support. In order to minimize chances of failure and maximize the likelihood of success and sustainability, we ensure strong local commitment to the activities; we work with highly competent organizations and project leaders; we focus on close dialogue in our partnerships and we support our partners to address problems and to drive positive project processes further.

The Memorandum of Understanding that was signed with DANIDA in 2002 materialized into collaboration on three projects.

For information on the World Diabetes Foundation and WDF-funded projects please visit: www.worlddiabetesfoundation.org

There are also a number of projects with IDF to build capacity and raise awareness of diabetes. One example is the Clinical Practice Guidelines in sub-Saharan Africa.

WORLD DIABETES FOUNDATION

WDF promotes the cause of primary prevention and proactively seeks projects that target health promotion for the general population, and particularly for school children. The new focus area has been introduced as – The Coming Generation.

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