Comparative Quantification of Health R...Comparative Quantification of Health Risks Global and Regional Burden of Disease Attributable toSelected Major Risk Factors Volume 1 Edited

Comparative Quantificationof Health Risks

Global and Regional Burden of DiseaseAttributable to Selected Major

Risk Factors

Volume 1

Edited by

Majid Ezzati, Alan D. Lopez, Anthony Rodgersand Christopher J.L. Murray

World Health OrganizationGeneva

alikText BoxPlease see the Table of Contents for access to the entire publication.

WHO Library Cataloguing-in-Publication Data

Comparative quantification of health risks : global and regional burden of diseaseattributable to selected major risk factors / edited by Majid Ezzati . . . [et al.].

2 v. + v.3 in 1 CD-ROM.

Contents: vol. 1, Childhood and maternal undernutrition—Other nutrition-related risk factors and physical activity—Addictive substances—vol. 2, Sexual and reproductive health—Environmental and occupationalrisks—Other selected risks—Distribution of risks by poverty—Data analysisand results—Multi-risk assessment.—Annex tables CD-ROM, Populationattributable fractions, mortality and disease burden for selected major riskfactors.

1. Risk factors 2. Cost of illness 3. Risk assessment4. Comparative study I. Ezzati, Majid. II. Title: Global and regionalburden of disease attributable to selected major risk factors.

ISBN 92 4 158031 3 (NLM Classification: WA 105)

© World Health Organization 2004

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The designations employed and the presentation of the material in this publica-tion do not imply the expression of any opinion whatsoever on the part of theWorld Health Organization concerning the legal status of any country, territory,city or area or of its authorities, or concerning the delimitation of its frontiersor boundaries. Dotted lines on maps represent approximate border lines forwhich there may not yet be full agreement.

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The World Health Organization does not warrant that the information containedin this publication is complete and correct and shall not be liable for any damagesincurred as a result of its use.

Typeset in Hong KongPrinted in Switzerland

Contents

Volume 1

List of authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

Foreword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviiRichard Peto

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xixMajid Ezzati, Alan D. Lopez, Anthony Rodgers and Christopher J.L. Murray

1. Comparative quantification of health risks: conceptual framework and methodological issues . . . . . . . . . . . . . . . . . . . . . 1Christopher J.L. Murray, Majid Ezzati, Alan D. Lopez, Anthony Rodgers and Stephen Vander Hoorn

Childhood and maternal undernutrition

2. Childhood and maternal underweight . . . . . . . . . . . . . . . . . . . . 39Steven M. Fishman, Laura E. Caulfield, Mercedes de Onis,Monika Blössner, Adnan A. Hyder, Luke Mullany and Robert E. Black

3. Iron deficiency anaemia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163Rebecca J. Stoltzfus, Luke Mullany and Robert E. Black

4. Vitamin A deficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211Amy L. Rice, Keith P. West Jr. and Robert E. Black

5. Zinc deficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257Laura E. Caulfield and Robert E. Black

Other nutrition-related risk factors and physical inactivity

6. High blood pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281Carlene M.M. Lawes, Stephen Vander Hoorn, Malcolm R. Law, Paul Elliott, Stephen MacMahon and Anthony Rodgers

7. High cholesterol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391Carlene M.M. Lawes, Stephen Vander Hoorn,Malcolm R. Law and Anthony Rodgers

alikText BoxPlease click in the blue boxes below to access the Chapters within.

8. Overweight and obesity (high body mass index) . . . . . . . . . . . 497W. Philip T. James, Rachel Jackson-Leach, Cliona Ni Mhurchu, Eleni Kalamara, Maryam Shayeghi, Neville J. Rigby, Chizuru Nishida and Anthony Rodgers

9. Low fruit and vegetable consumption . . . . . . . . . . . . . . . . . . . 597Karen Lock, Joceline Pomerleau, Louise Causer and Martin McKee

10. Physical inactivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729Fiona C. Bull, Timothy P. Armstrong, Tracy Dixon, Sandra Ham, Andrea Neiman and Michael Pratt

Addictive substances

11. Smoking and oral tobacco use . . . . . . . . . . . . . . . . . . . . . . . . . 883Majid Ezzati and Alan D. Lopez

12. Alcohol use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 959Jürgen Rehm, Robin Room, Maristela Monteiro, Gerhard Gmel, Kathryn Graham, Nina Rehn, Christopher T. Sempos, Ulrich Frick and David Jernigan

13. Illicit drug use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1109Louisa Degenhardt, Wayne Hall, Matthew Warner-Smith and Michael Lynskey

Volume 2

Sexual and reproductive health

14. Unsafe sex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1177Emma Slaymaker, Neff Walker, Basia Zaba and Martine Collumbien

15. Non-use and use of ineffective methods of contraception . . . 1255Martine Collumbien, Makeda Gerressu and John Cleland

Environmental and occupational risk factors

16. Unsafe water, sanitation and hygiene . . . . . . . . . . . . . . . . . . 1321Annette Prüss-Üstün, David Kay, Lorna Fewtrell and Jamie Bartram

17. Urban air pollution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1353Aaron J. Cohen, H. Ross Anderson, Bart Ostro, Kiran Dev Pandey, Michal Krzyzanowski, Nino Künzli, Kersten Gutschmidt, C. Arden Pope III, Isabelle Romieu,Jonathan M. Samet and Kirk R. Smith

18. Indoor air pollution from household use of solid fuels . . . . . 1435Kirk R. Smith, Sumi Mehta and Mirjam Maeusezahl-Feuz

iv Comparative Quantification of Health Risks

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19. Lead exposure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1495Annette Prüss-Üstün, Lorna Fewtrell, Philip J. Landrigan and José Luis Ayuso-Mateos

20. Global climate change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1543Anthony McMichael, Diarmid Campbell-Lendrum, Sari Kovats, Sally Edwards, Paul Wilkinson, Theresa Wilson, Robert Nicholls, Simon Hales, Frank Tanser, David Le Sueur, Michael Schlesinger and Natasha Andronova

21. Selected occupational risk factors . . . . . . . . . . . . . . . . . . . . . 1651Marisol Concha-Barrientos, Deborah Imel Nelson, Timothy Driscoll, N. Kyle Steenland, Laura Punnett, Marilyn Fingerhut, Annette Prüss-Üstün, James Leigh, Sang Woo Tak and Carlos Corvalan

Other selected risk factors

22. Contaminated injections in health care settings . . . . . . . . . . . 1803Anja M. Hauri, Gregory L. Armstrong and Yvan J.F. Hutin

23. Child sexual abuse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1851Gavin Andrews, Justine Corry, Tim Slade, Cathy Issakidis and Heather Swanston

Distribution of risk factors by poverty

24. Distribution of risks by poverty . . . . . . . . . . . . . . . . . . . . . . 1941Tony Blakely, Simon Hales, Charlotte Kieft, Nick Wilson and Alistair Woodward

Data analysis and results

25. Estimating attributable burden of disease from exposure andhazard data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2129Stephen Vander Hoorn, Majid Ezzati, Anthony Rodgers, Alan D. Lopez and Christopher J.L. Murray

26. Mortality and burden of disease attributable to individual risk factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2141Majid Ezzati, Anthony Rodgers, Alan D. Lopez, Stephen Vander Hoorn and Christopher J.L. Murray

Multi-risk factor assessment

27. Potential health gains from reducing multiple risk factors . . 2167Majid Ezzati, Stephen Vander Hoorn, Anthony Rodgers, Alan D. Lopez, Colin D. Mathers and Christopher J.L. Murray

Contents v

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28. Effects of multiple interventions . . . . . . . . . . . . . . . . . . . . . . 2191James Robins, Miguel Hernan and Uwe Siebert

29. Conclusions and directions for future research . . . . . . . . . . . 2231Alan D. Lopez, Majid Ezzati, Anthony Rodgers, Stephen Vander Hoorn and Christopher J.L. Murray

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2235

CD-ROM

Annex tables

Population attributable fractions, mortality and disease burden forselected major risk factors

vi Comparative Quantification of Health Risks

http://whqlibdoc.who.int/publications/2004/9241580348_eng_Volume2.pdfhttp://whqlibdoc.who.int/publications/2004/9241580348_eng_Index.pdfhttp://whqlibdoc.who.int/publications/2004/9241580348_eng_Annexes.pdf

List of authors

H. Ross Anderson, St George’s Hospital Medical School, University ofLondon, London, England ([email protected]).

Gavin Andrews, WHO Collaborating Centre for Evidence in MentalHealth Policy, St Vincent’s Hospital, Sydney, Australia([email protected]).

Natasha Andronova, Department of Atmospheric Sciences, Universityof Illinois at Urbana-Champaign,Urbana, IL, USA([email protected]).

Gregory L. Armstrong, Division of Viral Hepatitis, Centers for DiseaseControl and Prevention, Atlanta, GA, USA ([email protected]).

Timothy P. Armstrong, Chronic Diseases and Health Promotion,World Health Organization, Geneva, Switzerland([email protected]).

José Luis Ayuso-Mateos, Servicio de Psiquiatria, Hospital Universitariode la Princesa, Madrid, Spain ([email protected]).

Jamie Bartram, Protection of the Human Environment, World HealthOrganization, Geneva, Switzerland ([email protected]).

Robert E. Black, Department of International Health, Johns HopkinsBloomberg School of Public Health, Baltimore, MD, USA([email protected]).

Tony Blakely, Department of Public Health, Wellington School ofMedicine and Health Sciences, University of Otago, Wellington,New Zealand ([email protected]).

Monika Blössner, Nutrition for Health and Development, WorldHealth Organization, Geneva, Switzerland ([email protected]).

Fiona C. Bull, School of Sport & Exercise Sciences, LoughboroughUniversity, Loughborough, Leicestershire, England([email protected]).

Diarmid Campbell-Lendrum, Protection of the Human Environment,World Health Organization, Geneva, Switzerland([email protected]).

Laura E. Caulfield, Department of International Health, Center forHuman Nutrition, Johns Hopkins Bloomberg School of PublicHealth, Baltimore, MD, USA ([email protected]).

Louise Causer, European Centre on the Health of Societies inTransition, Department of Public Health and Policy, London Schoolof Hygiene and Tropical Medicine, European London, England.

John Cleland, Centre for Population Studies, London School ofHygiene and Tropical Medicine, London, England([email protected]).

Aaron J. Cohen, Health Effects Institute, Boston, MA, USA([email protected]).

Martine Collumbien, Centre for Population Studies, London School ofHygiene and Tropical Medicine, London, England([email protected]).

Marisol Concha-Barrientos, Asociación Chilena de Seguridad,Gerencia de Salud, Santiago, Chile ([email protected]).

Justine Corry, WHO Collaborating Centre for Evidence in MentalHealth Policy, St Vincent’s Hospital, Sydney, Australia([email protected]).

Carlos Corvalan, Protection of the Human Environment, WorldHealth Organization, Geneva, Switzerland ([email protected]).

Mercedes de Onis, Nutrition for Health and Development, WorldHealth Organization, Geneva, Switzerland ([email protected]).

Louisa Degenhardt, National Drug and Alcohol Research Centre,University of New South Wales, Sydney, Australia([email protected])

Tracy Dixon, Cardiovascular Disease, Diabetes and Risk FactorMonitoring Unit, Australian Institute of Health and Welfare,Canberra, Australia ([email protected]).

Timothy Driscoll, School of Public Health, University of Sydney,Sydney, Australia ([email protected]).

Sally Edwards, Department of Epidemiology and Population Health,London School of Hygiene and Tropical Medicine, London, England([email protected]).

Paul Elliot, Department of Epidemiology and Public Health, ImperialCollege of Science Technology and Medicine, London, England([email protected]).

Majid Ezzati, Harvard School of Public Health, Boston, MA, USA([email protected]).

viii Comparative Quantification of Health Risks

Lorna Fewtrell, Centre for Research into Environment and Health,University of Wales, Aberystwyth, Wales ([email protected]).

Marilyn A. Fingerhut, National Institute for Occupational Safety andHealth, Washington, DC, USA ([email protected]).

Steven M. Fishman, Department of International Health, Center forHuman Nutrition, Johns Hopkins Bloomberg School of PublicHealth, Baltimore, MD, USA ([email protected]).

Ulrich Frick, Addiction Research Institute, Zurich, Switzerland([email protected]).

Makeda Gerressu, Department of Primary Care and PopulationSciences, Centre for Sexual Health and HIV Research, Royal Freeand University College Medical School, London, England([email protected]).

Gerhard Gmel, Swiss Institute for the Prevention of Alcohol and DrugProblems, Lausanne, Switzerland ([email protected]).

Kathryn Graham, Centre for Addiction and Mental Health, London,Canada ([email protected]).

Kersten Gutschmidt, Protection of the Human Environment, WorldHealth Organization, Geneva, Switzerland ([email protected]).

Simon Hales, Department of Public Health, Wellington School ofMedicine and Health Sciences, University of Otago, Wellington,New Zealand ([email protected]).

Wayne Hall, Institute for Molecular Biosciences, University ofQueensland, Brisbane, Queensland, Australia([email protected]).

Sandra Ham, Centers for Disease Control and Prevention, Division ofNutrition and Physical Activity, National Center for ChronicDisease Prevention and Health Promotion, Atlanta, GA, USA([email protected]).

Anja M. Hauri, Staatliches Untersuchungsamt Hessen, Dillenburg,Germany ([email protected]).

Miguel Hernan, Harvard School of Public Health, Boston, MA, USA([email protected]).

Yvan J.F. Hutin, Essential Health Technology, World HealthOrganization, Geneva, Switzerland ([email protected]).

Adnan A. Hyder, Department of International Health, Health Systems and Management Program, Johns Hopkins BloombergSchool of Public Health, Baltimore, MD, USA ([email protected]).

List of authors ix

Cathy Issakidis, WHO Collaborating Centre for Evidence in MentalHealth Policy, St Vincent’s Hospital, Sydney, Australia([email protected]).

Rachel Jackson-Leach, International Obesity Task Force, London,England ([email protected]; [email protected]).

W. Philip T. James, International Obesity Task Force, London, England([email protected]).

David Jernigan, Health Policy Institute, Georgetown University,Washington, DC, USA ([email protected]).

Eleni Kalamara, International Obesity Task Force, London, England([email protected]).

David Kay, Centre for Research into Environment and Health,University of Wales, Aberystwyth, Wales([email protected]).

Charlotte Kieft, Department of Public Health, Wellington School ofMedicine and Health Sciences, University of Otago, Wellington,New Zealand.

Sari Kovats, Department of Epidemiology and Population Health,London School of Hygiene and Tropical Medicine, London, England([email protected]).

Michal Krzyzanowski, European Center for Environment and Health,World Health Organization, Bonn, Germany([email protected]).

Nino Künzli, Division of Occupational and Environmental Health,Keck School of Medicine, University of Southern California, LosAngeles, CA, USA (e-mail: [email protected]).

Philip J. Landrigan, Department of Community & PreventiveMedicine, Mount Sinai School of Medicine, New York, NY, USA([email protected]).

Malcolm R. Law, Department of Environmental and PreventiveMedicine, Wolfson Institute of Preventive Medicine, Barts and TheLondon, Queen Mary’s School of Medicine and Dentistry, London,England ([email protected]).

Carlene M.M. Lawes, Clinical Trials Research Unit, University ofAuckland, Auckland, New Zealand ([email protected]).

David Le Sueur (Deceased), Medical Research Council, Durban, SouthAfrica. Deceased.

x Comparative Quantification of Health Risks

James Leigh, Centre for Occupational and Environmental Health,School of Public Health, University of Sydney, Sydney, Australia([email protected]).

Karen Lock, European Centre on the Health of Societies in Transition,Department of Public Health and Policy, London School of Hygieneand Tropical Medicine, London, England ([email protected]).

Alan D. Lopez, School of Population Health, University of Queensland,Brisbane, Queensland, Australia (e-mail: [email protected]).

Michael Lynskey, National Drug and Alcohol Research Centre,University of New South Wales, Sydney, Australia.

Stephen MacMahon, Institute for International Health, University ofSydney, Sydney, Australia ([email protected]).

Mirjam Maeusezahl-Feuz, Division of Epidemiology and InfectiousDiseases, Swiss Federal Office of Public Health, Bern, Switzerland([email protected]).

Colin D. Mathers, Measurement and Health Information, WorldHealth Organization, Geneva, Switzerland ([email protected]).

Martin McKee, European Centre on the Health of Societies inTransition, Department of Public Health and Policy, London Schoolof Hygiene and Tropical Medicine, London, England([email protected]).

Anthony McMichael, National Centre for Epidemiology andPopulation Health, Australian National University, Canberra,Australia ([email protected]).

Sumi Mehta, Health Effects Institute, Boston, MA, USA([email protected]).

Maristela Monteiro, Pan American Health Organization, Washington,DC, USA ([email protected]).

Luke Mullany, Department of International Health, Disease Preventionand Control Program, Johns Hopkins Bloomberg School of PublicHealth, Baltimore, MD, USA ([email protected]).

Christopher J.L. Murray, Harvard University Initiative for GlobalHealth, Cambridge, MA, USA ([email protected]).

Andrea Neiman, Division of Nutrition and Physical Activity, NationalCenter for Chronic Disease Prevention and Health Promotion,Centers for Disease Control and Prevention, Atlanta, GA, USA([email protected]).

Deborah Imel Nelson, School of Civil Engineering and EnvironmentalScience, University of Oklahoma, Norman, OK, USA ([email protected]).

List of authors xi

Cliona Ni Mhurchu, Clinical Trials Research Unit, University ofAuckland, Auckland, New Zealand([email protected]).

Robert Nicholls, Flood Hazards Research Centre, University ofMiddlesex, Enfield, England ([email protected]).

Chizuru Nishida, Nutrition for Health and Development, WorldHealth Organization, Geneva, Switzerland ([email protected]).

Bart Ostro, California Environmental Protection Agency, Office ofEnvironmental Health Hazard Assessment, Oakland, CA, USA([email protected]).

Kiran Dev Pandey, World Bank, DECRG-IE, Washington, DC, USA([email protected]).

Richard Peto, Clinical Trial Service Unit and Epidemiological StudiesUnit, Medical Sciences Division, University of Oxford, Oxford,England ([email protected]).

Joceline Pomerleau, European Centre on the Health of Societies inTransition, Department of Public Health and Policy, London Schoolof Hygiene and Tropical Medicine, London, England([email protected]).

Arden Pope, Department of Economics, Brigham Young University,Provo, UT, USA ([email protected]).

Michael Pratt, Division of Nutrition and Physical Activity, NationalCenter for Chronic Disease Prevention and Health Promotion,Centers for Disease Control and Prevention, Atlanta, GA, USA([email protected]).

Annette Prüss-Üstün, Protection of the Human Environment, WorldHealth Organization, Geneva, Switzerland ([email protected]).

Laura Punnett, University of Massachusetts, Lowell, MA, USA([email protected]).

Jürgen Rehm, Centre for Addiction and Mental Health, Toronto,Canada ([email protected]).

Nina Rehn, UNODC—United Nations Office on Drugs and Crime,Viet Nam Field Office, Hanoi, Vietnam ([email protected]).

Amy L. Rice, Department of International Health, Center for HumanNutrition, Johns Hopkins Bloomberg School of Public Health,Baltimore, MD, USA ([email protected]).

Neville J. Rigby, International Obesity Task Force, London, England([email protected]).

xii Comparative Quantification of Health Risks

James Robins, Harvard School of Public Health, Boston, MA, USA([email protected]).

Anthony Rodgers, Clinical Trials Research Unit, University ofAuckland, Auckland, New Zealand ([email protected]).

Isabelle Romieu, National Institute for Public Health, Cuernavaca,Mexico ([email protected]).

Robin Room, Centre for Social Research on Alcohol and Drugs,Stockholm University, Stockholm, Sweden([email protected]).

Jonathan M. Samet, Department of Epidemiology, Johns HopkinsBloomberg School of Public Health, Baltimore, MD, USA([email protected]).

Michael Schlesinger, Department of Atmospheric Sciences, Universityof Illinois at Urbana-Champaign, Urbana, IL, USA([email protected]).

Christopher T. Sempos, University at Buffalo, Buffalo, NY, USA([email protected]).

Maryam Shayeghi, International Obesity Task Force, London, England([email protected]).

Uwe Siebert, Institute for Technology Assessment, MassachusettsGeneral Hospital, Boston, MA, USA ([email protected]).

Tim Slade, WHO Collaborating Centre for Evidence in Mental HealthPolicy, St Vincent’s Hospital, Sydney, Australia([email protected]).

Emma Slaymaker, Centre for Population Studies, London School ofHygiene and Tropical Medicine, London, England([email protected]).

Kirk R. Smith, School of Public Health, University of California,Berkeley, CA, USA ([email protected]).

N. Kyle Steenland, Rollins School of Public Health, Emory University,Atlanta, GA, USA ([email protected]).

Rebecca J. Stoltzfus, Division of Nutritional Sciences, CornellUniversity, Ithaca, NY, USA ([email protected]).

Heather Swanston, WHO Collaborating Centre for Evidence inMental Health Policy, St Vincent’s Hospital, Sydney, Australia([email protected]).

Sang Woo Tak, University of Massachusetts, Lowell, MA, USA([email protected]).

List of authors xiii

Frank Tanser, Africa Centre for Population Studies and ReproductiveHealth, Mtubatuba, South Africa ([email protected]).

Stephen Vander Hoorn, Clinical Trials Research Unit, University ofAuckland, Auckland, New Zealand ([email protected]).

Neff Walker, Joint United Nations Programme on HIV/AIDS(UNAIDS), Geneva, Switzerland ([email protected]).

Matthew Warner-Smith, National Drug and Alcohol Research Centre,University of New South Wales, Sydney, Australia.

Keith P. West Jr, Department of International Health, Center forHuman Nutrition, Johns Hopkins Bloomberg School of PublicHealth, Baltimore, MD, USA ([email protected]).

Paul Wilkinson, Department of Public Health and Policy, LondonSchool of Hygiene and Tropical Medicine, England([email protected]).

Nick Wilson Department of Public Health, Wellington School ofMedicine and Health Sciences, University of Otago, Wellington,New Zealand ([email protected]).

Theresa Wilson, University of Middlesex, Enfield, England([email protected]).

Alistair Woodward, School of Population Health, University ofAuckland, Auckland, New Zealand ([email protected]).

Basia Zaba, Centre for Population Studies, London School of Hygieneand Tropical Medicine London, England ([email protected]).

xiv Comparative Quantification of Health Risks

Acknowledgements

This book is the product of more than four years of collaborative effortinvolving a large group of scientists across the world. This collaboration,led by the World Health Organization (WHO) and known as the com-parative risk assessment (CRA) project, is one of the largest and mostcomprehensive research projects ever undertaken by WHO. The success-ful completion of the project, and this book, would not have been possi-ble without great effort from a number of individuals and organizations.

More than 150 scientists, experts on the various risk factors andmethodological aspects, reviewed the chapters in this book with greatcare, many of them multiple times, to ensure the scientific integrity, com-pleteness and plausibility of the material and the conclusions. Many indi-viduals and organizations donated their unpublished data to the projectto fill some of the existing data gaps. Stephen Vander Hoorn played akey role in the design of the conceptual and methodological frameworkfor the analysis. The literally millions of calculations leading to thedisease burden estimates reported in these volumes are the result of hisinvaluable technical and statistical abilities and perseverance. Our col-leagues David Evans, Emmanuela Gakidou, Mie Inoue, Carlene Lawes,Rafael Lozano, Doris Ma Fat, Susan Piccolo, Chalapati Rao, JoshuaSalomon, Kenji Shibuya and Niels Tomijima, provided substantial moti-vational and intellectual support and assistance with the Global Burdenof Disease databases. We are especially grateful to Marie-Claude vonRulach for her extraordinary efforts in helping to manage the networkof contributors and the collaborators meetings, and for secretarialsupport. Technical discussions on burden of disease methodology withColin Mathers and Claudia Stein contributed greatly to improving thescientific basis of the estimates.

The final editing and production of a book of this magnitude, withcontributions from numerous authors, is a challenging and complex task. Kaarina Klint and Kai Lashley have managed that process withgreat care and commitment. Editorial assistance from StanislavaNikolova, Anna Moore and Margaret Squadrani is highly appreciated.Our thanks also to our technical and copy editors Andrew Colborne,Heidi Mattock, Gillian Stanbridge and Frank Theakston for their efforts to ensure consistency of style across chapters originally writtenwith varied disciplinary and personal styles. Colin Mathers, David Evans

and Ties Boerma generously and efficiently coordinated the productionprocess in its final stages.

The initial concept of the cover artwork was developed by SaiedEzzati; the final arrangement of the cover design was done by MarkForrest and Emmanuela Gakidou.

The research published in this book forms part of the larger GlobalBurden of Disease 2000 project, funded by a grant from the NationalInstitute on Aging (PO1-AG17625). Their support is gratefully acknowledged.

xvi Comparative Quantification of Health Risks

Foreword

During the twentieth century reliable cause-specific mortality statisticsbecame available for many countries, culminating in the Global Burdenof Disease project which, during the 1990s, provided estimates for dif-ferent regions of the world (with, obviously, varying degrees of reliabil-ity) of the numbers of deaths due to major diseases, and of the amountsof “disability-adjusted” loss of healthy life from those diseases. Thepresent study goes further, and seeks to estimate the amounts of deathand disability due to the main avoidable causes of those diseases. Its pre-liminary conclusions underlay the 250-page World Health Organizationreport on “Reducing Risks” (2002), the aim of which was to summa-rize, for the first time, the amount of death and disability in each of 14subregions of the world that is attributable not to particular diseases,but to particular avoidable risk factors.

Such attributions of causality throw up, of course, many more diffi-culties than were encountered in the previous studies of the GlobalBurden of Disease, which merely tried to classify deaths by the one maindisease (or type of accident or violence) that underlay them. For, onedeath may have several avoidable causes. For example, if a poorly nour-ished child dies of measles, should “the cause” be thought of as expo-sure to the virus, or as the lack of measles vaccination (in that child orin the community), or as the poor diet (low in protein, energy and certainmicronutrients) that prevented recovery from the illness? The mostappropriate answer, if we want to prevent such deaths, is that each ofthese factors should be thought of as “a cause” of a certain proportionof the childhood deaths from measles. That is what the authors of theWorld health report 2002 tried to do, and in the present much moredetailed series of monographs they explain to the interested (or disputa-tive) reader much more about their main conclusions, and about howthey reached those conclusions. This is important, because over the yearssome of the conclusions may need to be revised, as more detailed studiesare undertaken or as exposure and disease patterns evolve.

For many decades it has been recognized increasingly clearly by thoseconcerned with global health that much can affordably be achieved evenin relatively poor countries if resources are directed to the major diseasesof childhood and early adult life, and more recently the affordable avoid-ability of much other adult mortality and morbidity has been recognized

(see The Health of Adults in the Developing World). Ten years ago, the1993 World Bank report, Investing in health (together with its com-panion volume, Disease Control Priorities in Developing Countries) wasextremely influential in consolidating these ideas and getting themaccepted, and acted upon, by the major international economic institutions.

But, any such cost-effectiveness calculations require, among otherthings, reliable estimates of effectiveness, and the present report goesfurther than any other in providing estimates of just how much mortal-ity and morbidity could be avoided by addressing particular causes ofdisease. In many parts of the world (the main exceptions being wherepolitical disruption or HIV predominate) the risk of premature death hasbeen reduced by more than half over the past few decades, and prema-ture death can be halved again over the next few decades if the majorintervention options are pursued to control disease and injury, and theircauses.

This book will greatly facilitate such progress. It is well organized,stimulating and is an important part of a political and scientific processthat is already preventing many millions of deaths a year, and willprevent many more millions of deaths a year in the future.

Sir Richard PetoProfessor of Medical Statistics and Epidemiology

University of Oxford, England

xviii Comparative Quantification of Health Risks

A clear understanding of the role and relative magnitude of diseases,injuries and their underlying causes—and effective and affordable inter-ventions to reduce them—should guide policies and programmes forhealth development. Over the centuries, the health of populations hasimproved because science has helped us understand the main causes ofdisease affecting large populations, and how technologies or programmescan be delivered to reduce hazards among those affected or at risk.

While the monitoring and analysis of diseases and mortality in pop-ulations has been largely undertaken by actuaries and demographers,much of the work on causes of disease has emanated from research infields such as epidemiology, toxicology and physiology, which focus on micro-level analysis. By its very nature, this research has quantifiedhazards in the study population, with its specific characteristics. Thisbody of knowledge has had tremendous application in reducing theestablished causes of disease, from smoking to iodine deficiency, in manypopulations. The broader, policy-relevant issue of population effects ofexposure to risks, however, has remained under-explored relative to ourdocumentation of established diseases. Thus, while there has beendecades of epidemiological research into the leading causes of manymajor diseases, from childhood diarrhoea to ischaemic heart disease,there have been few attempts to estimate the population-level effects ofvarious exposures, either for specific countries and groups of countries,or for the world and its major regions.

During the last quarter of the twentieth century, a number of workshave addressed both the methodological and empirical aspects of population-wide effects of major causes of diseases. Examples includethe development of cancer risk models and methods to forecast the healthof ageing populations based on their causal determinants, and the estimates of mortality due to risk factors such as smoking, asbestos and childhood malnutrition. This gradual establishment of “risk assess-ment” or “risk quantification” has been driven partly by the academiccuriosity of individual researchers and partly by the demands of regula-tory agencies and public policy for better quantitative evidence on thehealth implications of certain risk exposures.

This book provides a comprehensive assessment of the health effectscaused by a range of exposures that are known to be hazardous to human

Preface

health. Its origins lie in the expressed need by policy and advocacy groupsfor comparable data on risk factor exposure and effects in populations.The only previous attempt to quantify risk factor burden worldwide, theGlobal Burden of Disease (GBD) 1990 project, was affected by a lack ofconceptual and methodological comparability across risk factors; theanalysis of each risk was constrained by its own disciplinary tradition.It nonetheless stimulated debate about the crucial role of risk factorassessment as a cornerstone of the evidence base for public health action:for instance, the leading risk factor in 1990, malnutrition, accounted forsubstantially more disease burden worldwide than the leading cause ofdisease at that time, acute lower respiratory infections.

A key concern of the current work on risks to health is to provide adegree of conceptual and methodological consistency and comparabilityacross risk factors. The results reported in this book, therefore, differ ina number of important ways from those of GBD 1990: a new analyticalframework and consistent set of definitions on “risk factor exposure”have been used to enhance comparability; the number of exposuresassessed has more than doubled; and the analyses have benefited frommore recent and thorough research into causality and geographical vari-ations in population exposures and health effects.

The scope of risks to health studied in this book covers many of themost important hazards to health addressed by various fields of scien-tific enquiry. Arguably, there are hundreds of risk exposures that areharmful to health; and there are important implications for better under-standing the disease burden they cause across the world. We haveselected only a relatively small number of exposures for quantificationin this book, largely determined by the availability of scientific researchabout their prevalence and health effects in different parts of the world.It was also important to make choices about the definition of each riskfactor. Given the close interrelationships among diet, exercise and phy-siological risks on the one hand, or among water, sanitation and per-sonal hygiene on the other, the exact definition of what a “risk factor”is, itself requires careful attention. That a particular risk factor likedietary fat intake does not appear in this book does not, of course, implythat it is of limited relevance; or that exposure to lead has been assessedseparately from urban air pollution does not override their close link-ages. Rather, we have limited ourselves to risk factors for which therewas good potential for satisfactory quantification of population expo-sure distributions and health effects using the existing scientific evidenceand available data, and for which intervention strategies are available ormight be envisioned to modify their impact on disease burden.

The chapters in Volumes 1 and 2 of this book fall into two broad categories: those that address specific risk factors, and those that provideconceptual, methodological or empirical links across risks. The bookbegins with a description of some of the important conceptual andmethodological issues in quantifying risk factor burden in a consistent

xx Comparative Quantification of Health Risks

and comparable framework. This is followed by twenty-two chapters,organized under six broad sections, each of which present the back-ground and the scientific evidence and empirical findings for individualrisks. These are followed by an attempt to quantify the distributions ofsome risks by poverty levels. While much is known about the relation-ship between poverty and health, it is undoubtedly too complex andpopulation-specific to be adequately assessed in a single quantificationeffort. The research reported here is therefore limited to a simplemapping of risks by poverty, based on existing data. Following the riskfactor chapters, the calculus of estimating the burden of disease attri-butable to each risk factor from exposure and hazard data is presented,followed by a chapter that summarizes the results for individual riskfactors.

Many policies and programmes affect multiple risks simultaneously,motivating an assessment of the disease burden from multiple riskfactors. The focus on joint exposures and hazards is particularly impor-tant because diseases and injuries are almost always caused by multiplerisk factors, which may act together on disease processes, or have effectsmediated through each other. We have therefore included two chapterson the joint effects of multiple risk factor exposures. The final chapterof the book provides conclusions and recommendations for futureresearch, based on the analytical findings presented in the book, as wellas the gaps in data and scientific knowledge that increased uncertaintyin quantifying risk factor burden reported here.

The specific risk factor chapters have been grouped according to clus-ters of exposures likely to be of similar scientific or policy interest.Volume 1 begins with four chapters on childhood and maternal under-nutrition, which collectively cause a significant proportion of the child-hood infectious disease burden worldwide. With substantial reductionsin child mortality over the past few decades in many countries, the focusof scientific enquiry has progressively moved to improving our under-standing of the causes of disease and injury among adults. The next fivechapters address the various distal (e.g. exercise), more proximal (e.g.overweight and obesity), and physiological (e.g. suboptimal cholesterollevels) risks that are clustered together under the label of nutrition andphysical activity. The last section in Volume 1 and the first section inVolume 2, addictive substances and sexual and reproductive health,include the major lifestyle and behavioural risks that are widespread inmany societies and, despite being the subject of scientific enquiry andpublic health intervention for decades, present a range of complexitiesin risk quantification.

The risk factors that are a part of the physical environment of house-holds (e.g. indoor air pollution from household solid fuel use), commu-nities (e.g. urban air pollution), or specific subgroups (e.g. occupationalrisk factors) are the next group of risks assessed in Volume 2. The nexttwo chapters, childhood sexual abuse and contaminated medical injec-

Preface xxi

tions, do not fall into any of the above broad categories and are pre-sented independently. These two chapters, each representing a risk factorthat affects multiple important diseases, illustrate the potential for riskassessment as an analytical tool for improving the public health evidencebase across a wide spectrum of health concerns.

In each of the specific risk factor chapters, the authors have provideda definition of the risk factor and introduced an “exposure variable” thatbest reflects the distribution of hazards in the population. The complex-ity of disease causation mechanisms (e.g. sexual behaviour and sexuallytransmitted infections), and the limitations posed by available data andepidemiological studies (e.g. physical inactivity or indoor smoke fromsolid fuels) have been important factors in the choice of exposure variable. Coupled with this is the choice of a “theoretical-minimum-risk population exposure distribution”, which can serve as a consistent base-line for assessing attributable disease burden across difference risks. For some risks such as smoking or childhood abuse, the theoretical-minimum-risk population exposure distribution is obviously zero expo-sure for the whole population; for others the choice of baseline exposuredistribution is less obvious, either because zero exposure is not definable(e.g. blood pressure) or because it may not lead to the lowest risk levelin some populations (e.g. alcohol). Each chapter includes current esti-mates of exposure distributions by age and sex for 14 epidemiologicalsubregions. The chapters also examine in detail the evidence for healthoutcomes, including the evidence for causality and the estimates ofhazard (disease-specific) associated with each level of exposure. Eachchapter then concludes with summary results of the burden of diseaseand injury in 2000 attributable to the risk factor, and when possible usingexisting evidence and knowledge, estimates of projected future exposureto the risk.

The CD-ROM attached contains detailed tables on the various com-ponents of disease burden (i.e. deaths, years of life lost [YLL] due to pre-mature mortality, and disability-adjusted life years [DALYs]) attributableto each risk factor by age, sex and the 14 epidemiological subregions ofthe world used by the World Health Organization (WHO) in the Worldhealth report 2002. The 191 Member States of WHO were divided intofive mortality strata on the basis of their levels of child mortality (underfive years of age) and 15–59-year-old male mortality. When these mor-tality strata are applied to the six WHO regions, they produce 14 epi-demiological subregions, which are used throughout this book (Table 1).

This book is the culmination of over four years of scientific enquiryand data collection, collectively known as the comparative risk assessment (CRA) project, coordinated by WHO and involving over 100scientists worldwide. The book is also one of the several planned outputsof the GBD 2000 project which includes multiple analytical and empir-ical perspectives on global population health. The importance of the collaborative effort in the CRA project goes beyond having leading

xxii Comparative Quantification of Health Risks

Preface xxiii

Table 1 The 14 GBD epidemiological subregions

WHO Mortality region stratuma Countries

AFR D Algeria, Angola, Benin, Burkina Faso, Cameroon, Cape Verde, Chad,Comoros, Equatorial Guinea, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Madagascar, Mali, Mauritania, Mauritius, Niger, Nigeria,Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Togo

E Botswana, Burundi, Central African Republic, Congo, Côte d’Ivoire,Democratic Republic of the Congo, Eritrea, Ethiopia, Kenya, Lesotho,Malawi, Mozambique, Namibia, Rwanda, South Africa, Swaziland,Uganda, United Republic of Tanzania, Zambia, Zimbabwe

AMR A Canada, Cuba, United States of America

B Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Brazil,Chile, Colombia, Costa Rica, Dominica, Dominican Republic, El Salvador, Grenada, Guyana, Honduras, Jamaica, Mexico, Panama,Paraguay, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Uruguay, Venezuela

D Bolivia, Ecuador, Guatemala, Haiti, Nicaragua, Peru

EMR B Bahrain, Cyprus, Iran (Islamic Republic of ), Jordan, Kuwait, Lebanon,Libyan Arab Jamahiriya, Oman, Qatar, Saudi Arabia, Syrian Arab Republic, Tunisia, United Arab Emirates

D Afghanistan, Djibouti, Egypt, Iraq, Morocco, Pakistan, Somalia, Sudan,Yemen

EUR A Andorra, Austria, Belgium, Croatia, Czech Republic, Denmark, Finland,France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg,Malta, Monaco, Netherlands, Norway, Portugal, San Marino, Slovenia,Spain, Sweden, Switzerland, United Kingdom

B Albania, Armenia, Azerbaijan, Bosnia and Herzegovina, Bulgaria,Georgia, Kyrgyzstan, Poland, Romania, Serbia and Montenegro,Slovakia, Tajikistan, The former Yugoslav Republic of Macedonia, Turkey,Turkmenistan, Uzbekistan

C Belarus, Estonia, Hungary, Kazakhstan, Latvia, Lithuania, Republic ofMoldova, Russian Federation, Ukraine

SEAR B Indonesia, Sri Lanka, Thailand

D Bangladesh, Bhutan, Democratic People’s Republic of Korea, India,Maldives, Myanmar, Nepal

WPR A Australia, Brunei Darussalam, Japan, New Zealand, Singapore

B Cambodia, China, Cook Islands, Fiji, Kiribati, Lao People’s DemocraticRepublic, Malaysia, Marshall Islands, Micronesia (Federated States of),Mongolia, Nauru, Niue, Palau, Papua New Guinea, Philippines, Republicof Korea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, Viet Nam

a A: very low child mortality and very low adult mortality; B: low child mortality and low adult mortality;C: low child mortality and high adult mortality; D: high child mortality and high adult mortality; E: highchild mortality and very high adult mortality. High-mortality developing subregions: AFR-D, AFR-E, AMR-D, EMR-D and SEAR-D. Low-mortality developing subregions: AMR-B, EMR-B, SEAR-B, WPR-B.Developed subregions: AMR-A, EUR-A, EUR-B, EUR-C and WPR-A. This classification has no officialstatus and is for analytical purposes only.

researchers for multiple risk factors working simultaneously on the sameproject. Rather, the interactions of these researchers, with a core networkof scientists applying a common analytical framework and methods, hasensured greater consistency and comparability in using and evaluatingscientific evidence across risks. As a result, our understanding of the com-parative extent of disease burden caused by various exposures world-wide has advanced, and key areas of scientific enquiry necessary to betterinform policies to reduce risks have been elucidated. Health advocatesand those entrusted with policy and programme development to promotebetter health now have a more comparable empirical assessment of thehazards to health worldwide, and thus a firmer basis for public healthaction. We hope that the methodological and empirical findings reportedin these volumes will indeed serve as the stimulus for global, regionaland national policy action to reduce key hazards to health for decadesto come.

Majid EzzatiAlan D. Lopez

Anthony RodgersChristopher J.L. Murray

xxiv Comparative Quantification of Health Risks

1. IntroductionDetailed description of the level and distribution of diseases and injuries,and their causes are important inputs to strategies for improving popu-lation health. Data on disease or injury outcomes alone, such as deathor hospitalization, tend to focus on the need for palliative or curativeservices. Reliable and comparable analysis of risks to health, on the otherhand, is key for preventing disease and injury. A substantial body ofwork has focused on the quantification of causes of mortality, and more recently, the burden of disease (Murray and Lopez 1997; Preston1976). Analysis of morbidity and mortality due to risk factors, however,has frequently been conducted in the context of methodological traditions of individual risk factors and in a limited number of settings(Kunzli et al. 2000; Leigh et al. 1999; McGinnis and Foege 1993; Peto et al. 1992; Single et al. 1999; Smith 2000; Smith et al. 1999; Willet 2002). The principal conclusions of this body of work are asfollows:

• Causal attribution of morbidity and mortality to risk factors has beenestimated relative to zero or some other constant level of populationexposure. This single, constant baseline, although illustrating the total

Chapter 1

Comparative quantification of health risks: conceptualframework and methodological issues

Christopher J.L. Murray, Majid Ezzati,Alan D. Lopez, Anthony Rodgers and Stephen Vander Hoorn

Previously published: Copyright © 2003, Murray et al.; licensee BioMed Central Ltd. Thisis an Open Access article: verbatim copying and redistribution of this article are permit-ted in all media for any purpose, provided this notice is preserved along with the article’soriginal URL—http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=156894. Popu-lation Health Metrics, 2003, 1:1.

magnitude of the risk, does not provide visions of population healthunder other alternative exposure distribution scenarios.

• Intermediate stages and interactions in the causal process have notbeen considered in the causal attribution calculations. As a result,attributable burden could be calculated only for those riskfactor–disease combinations for which epidemiological studies hadbeen conducted (often limited to individual risks).

• Causal attribution has often taken place using exposure and/oroutcome at one point in time or over an arbitrary period of time (fornotable exceptions see the works of Manton and colleagues [Mantonet al. 1993b, 1994; Yashin et al. 1986] and Robins [Robins 1986,1987, 1999a, 1999b; Robins and Greenland 1991; Robins et al.1999]). Such “counting” of adverse events (such as death) has notbeen able to clearly distinguish between those cases that would nothave occurred in the absence of the risk factor and those where occur-rence would have been delayed. More generally, this approach isunable to consider the accumulated effects of time-varying exposureto a risk factor—in the form of years of life lost prematurely or livedwith disability.

• The outcome has been morbidity or mortality due to specific disease(s)without conversion to a comparable unit, making comparison amongdifferent diseases and/or risk factors difficult.

To allow the assessment of risk factors in a unified framework whileacknowledging risk-factor specific characteristics, the comparative riskassessment (CRA) module of the Global Burden of Disease (GBD) 2000study is a systematic evaluation of the changes in population healthwhich would result from modifying the population distribution of expo-sure to a risk factor or a group of risk factors (Murray and Lopez 1999).This unified framework for describing population exposure to riskfactors and their consequences for population health is an important stepin linking the growing interest in the causal determinants of health acrossa variety of public health disciplines from natural, physical, and medicalsciences to the social sciences and humanities. In particular, in the CRA framework:

• The burden of disease due to the observed exposure distribution in apopulation is compared with the burden from a hypothetical distrib-ution or series of distributions, rather than a single reference level suchas the non-exposed population.

• Multiple stages in the causal network of interactions among riskfactor(s) and disease outcome are considered to allow making infer-ences about combinations of risk factors for which epidemiologicalstudies have not been conducted, including the joint effects of changesin multiple risk factors.

2 Comparative Quantification of Health Risks

• The health loss due to risk factor(s) is calculated as a time-indexed“stream” of disease burden due to a time-indexed “stream” of exposure.

• The burden of disease and injury is converted into a summary measureof population health, which allows comparing fatal and non-fatal out-comes, also taking into account severity and duration.

It is important to emphasize that risk assessment, as defined above, is distinct from intervention analysis, whose purpose is to estimate thebenefits of a given intervention or group of interventions in a specificpopulation and at a specific time. Rather, risk assessment aims atmapping alternative population health scenarios to changes in distribu-tion of exposure to risk factors over time, irrespective of whether expo-sure change is achievable using existing interventions. Therefore, whileintervention analysis is a valuable input into cost-effectiveness studies,risk assessment contributes to assessing research and policy options forreducing disease burden by changing population exposure to risk factors.

Summary measures of population health (SMPH) and their use in burden of disease analysis are discussed elsewhere (Murray 1996;Murray et al. 2002). The next three sections of this chapter address the conceptual basis and methodological issues for the remaining three points above. We then discuss the sources and quantification ofuncertainty.

2. Causal attribution of SMPH to risk factors

Mathers et al. (2002) describe two traditions for causal attribution ofhealth determinants, outcomes, or states: categorical attribution andcounterfactual analysis. In categorical attribution, an event such as deathis attributed to a single cause (such as a disease or risk factor) or groupof causes according to a defined set of rules (hence 100% of the eventis attributed to the single cause or group of causes). The InternationalClassification of Disease system’s (ICD) attribution of causes of death(WHO 1992) and attribution of some injuries to alcohol or occupationalconditions are examples of categorical attribution. In counterfactualanalysis, the contribution of one or a group of diseases, injuries or riskfactors to a summary measure of population health is estimated by com-paring the current or future levels of the summary measure with the levelsthat would be expected under some alternative hypothetical scenario,including the absence of or reduction in the disease(s) or risk factor(s)of interest. This hypothetical scenario is referred to as the counterfactual(see Maldonado and Greenland 2002 for a discussion of conceptual andmethodological issues in the use of counterfactuals).

In theory, causal attribution of a summary measure to risk factors canbe done using both categorical and counterfactual approaches. For

Christopher J.L. Murray et al. 3

example, categorical attribution has been used in attribution of diseasesand injuries to occupational risk factors in occupational health registries(Leigh et al. 1999) and attribution of motor vehicle accidents to alcoholconsumption. In general however, categorical attribution of SMPH torisk factors overlooks the fact that many diseases have multiple causes(Rothman 1976). The epidemiological literature has commonly used thecounterfactual approach for the attribution of a summary measure to arisk factor, and compared mortality or disability from the current dis-tribution of exposure to the risk factor to that expected under an alter-native exposure scenario.

The dominant counterfactual exposure distribution in these studieshas been zero exposure for the whole population (or a fixed non-zerolevel where zero is not possible such as the case of blood pressure whendefined as presence or absence of hypertension). The basic statisticobtained in this approach is the population attributable fraction (PAF)defined as the proportional reduction in disease or death that wouldoccur if exposure to the risk factor were reduced to zero, ceteris paribus(Cole and MacMahon 1971; Eide and Heuch 2001; Greenland 1984;Levin 1953; MacMahon and Pugh 1970; Miettinen 1974; Ouellet et al.1979; Rockhill et al. 1998; Uter and Pfahlberg 2001).1 The attributablemortality, incidence or burden of disease due to the risk factor, AB, isthen given as AB = PAF ¥ B where B is the total burden of disease froma specific cause or group of causes affected by the risk factor with a relative risk of RR:

(1a)

The exposed population may itself be divided into multiple categoriesbased on the level or length of exposure, each with its own relative risk.With multiple (n) exposure categories, the PAF is given by the following generalized form:

(1b)

Although choosing zero as the reference exposure may be useful forsome purposes, it is a restricting assumption for others. The contribu-tion of a risk factor to disease or death can alternatively be estimated bycomparing the disease burden due to the observed exposure distributionin a population with that from another distribution (rather than a singlereference level such as non-exposed) as described by the generalized

PAFP RR

P RR

i ii

n

i ii

n=

-( )

-( ) +

=

=

Â

Â

1

1 1

1

1

PAFP RR

P RR=

-( )-( ) +

11 1


“potential impact fraction” equation (Drescher and Becher 1997; Eideand Heuch 2001; Walter 1980).

(2a)

where RR(x) is the relative risk at exposure level x, P(x) is the popula-tion distribution of exposure, P¢(x) is the counterfactual distribution ofexposure, and m the maximum exposure level. The first and second termsin the numerator of Equation 2a therefore represent the total exposure-weighted risk of mortality or disease in the population under current andcounterfactual exposure distributions. The corresponding relationshipwhen exposure is described as a discrete variable with n levels is givenby:

(2b)

In addition to relaxing the assumption of the no-exposure group asthe reference, analysis based on a broader range of distributions has theadvantage of allowing multiple comparisons with multiple counterfac-tual scenarios. Equation 2a can be further generalized to consider coun-terfactual relative risks (i.e. relative risk may depend on other risks, new technology, medical services, etc.). For example the relative risk ofinjuries as a result of alcohol consumption may depend on road condi-tions and traffic law enforcement. Similarly, people employed in the sameoccupation may have different risks of occupational injuries because ofdifferent safety measures. Therefore, a more general form of Equation2a is given by:

(2c)

2.1 Counterfactual exposure distributions

Various criteria may determine the choice of the counterfactual exposuredistributions. Greenland (2002) has discussed some of the criteria for the choice of counterfactuals, arguing that the counterfactuals should

PIF

RR x P x dx RR x P x dx

RR x P x dx

x

m

x

m

x

m=

( ) ( ) - ¢( ) ¢( )

( ) ( )

= =

=

Ú Ú

Ú0 0

0

PIFP RR P RR

P RR

i ii

n

i ii

n

i ii

n=

- ¢= =

=

Â Â

Â1 1

1

PIF

RR x P x dx RR x P x dx

RR x P x dx

x

m

x

m

x

m=

( ) ( ) - ( ) ¢( )

( ) ( )

= =

=

Ú Ú

Ú0 0

0


be limited to actions that can be implemented (e.g. anti-smoking cam-paigns), rather than the effects of removing the outcomes targeted bythose actions (e.g. smoking cessation) because, in practice, the imple-mentation of counterfactuals for one risk factor or disease may affectother risks. The solution to Greenland’s concern, however, is better ana-lytical techniques for estimating joint risk factor effects, rather thanabandoning non-intervention-based counterfactuals which, as argued byMathers et al. (2002), is a limiting view. Estimating the contributions ofrisk factors to disease burden and the benefits of their removal, even inthe absence of known interventions, can provide an understanding oftheir role in population health and visions of population health underdifferent scenarios of risk factor exposure. This knowledge of risk factoreffects can provide valuable input into public health policies and prior-ities, as well as research and development.

Murray and Lopez (1999) introduced a taxonomy of counterfactualexposure distributions that, in addition to identifying the size of risk,provides a mapping to policy implementation options. These categoriesinclude the exposure distributions corresponding to theoretical minimumrisk, plausible minimum risk, feasible minimum risk and cost-effectiveminimum risk. Theoretical minimum risk refers to the exposure distrib-ution that would result in the lowest population risk, irrespective ofwhether currently attainable in practice. Plausible minimum refers to adistribution which is imaginable, and feasible minimum is one that hasbeen observed in some population. Finally, cost-effective minimum con-siders the cost of exposure reduction (through the set of known cost-effective interventions) as an additional criterion for choosing thealternative exposure scenario.

In addition to illustrating the total magnitude of disease burden dueto a risk factor, the theoretical-minimum-risk distribution (or the currentdifference between theoretical and plausible or feasible risk levels) canguide research and development resources towards those risk factors forwhich the mechanisms of reduction (i.e. interventions) are currentlyunderdeveloped. For example, if the reduction in the burden of diseasedue to improved medical injection safety is high and the methods for riskreduction are well-known, so that plausible/feasible and theoreticalminima are identical, then current policy may have to be focused on theimplementation of such methods. On the other hand, if there are largedifferences between plausible/feasible and theoretical minima risk levelsfor blood lipids or body mass index (BMI) (Powles and Day 2002), thenresearch on reduction methods and their implementation should beencouraged. For this reason the total magnitude of the burden of diseasedue to a risk factor, as illustrated by the theoretical minimum, providesa tool for considering alternative visions of population health and settingresearch and implementation priorities.

Biological principles as well as considerations of equity would neces-sitate that, although the exposure distribution for theoretical minimum


risk may depend on age and sex, it should in general be independent ofgeographical region or population. Exceptions to this are, however,unavoidable. An example would be the case of alcohol consumption,which in limited quantities and when drunk in certain patterns has ben-eficial effects on cardiovascular mortality, but is always harmful for otherdiseases such as cancers and accidents (Puddey et al. 1999). In this case,the composition of the causes of death as well as drinking patterns in aregion would determine the theoretical-minimum-risk distribution. In apopulation where cardiovascular diseases are a dominant cause of mor-tality, the theoretical-minimum-risk exposure distribution may be non-zero with moderate drinking patterns, whereas in a population withbinge drinking and a large burden from injuries the theoretical minimumwould be zero. Feasible and cost-effective distributions, on the otherhand, may vary across populations based on the current distribution ofthe burden of disease and the resources and institutions available forexposure reduction.

The above categories of counterfactual exposure distributions arebased on the burden of disease in the population as a whole. Counter-factual exposure distributions may also be considered based on other criteria. For example, a counterfactual distribution based on equitywould be one in which the highest exposure group (or the group withthe highest burden of disease) would be shifted towards low exposurevalues. Further, such equitable counterfactual distributions for each riskfactor may themselves be categorized into theoretical (most equitable),plausible, feasible and cost-effective as described above. Similarly, acounterfactual distribution that focuses on the most susceptible groupsin the population is one that gives additional weight to lowering theexposure of this group. Therefore, by permitting comparison of diseaseburden under multiple exposure distributions based on a range of crite-ria—including, but not limited to, implementation and cost, equity andresearch prioritization—relaxing the assumption of a constant exposurebaseline provides an effective policy and planning tool.

2.2 Exposure distribution for theoretical minimum risk

In one taxonomy, risk factors such as those in the GBD project (Ezzatiet al. 2002; see also the risk factor chapters in this book) can be broadlyclassified as physiological, behavioural, environmental and socioeco-nomic. Some general principles that guide the choice of theoretical-minimum-risk exposure distribution for each category are:

1. Physiological risk factors: This group includes those factors that arephysiological attributes of humans, such as blood pressure or bloodlipids, and at some level result in increased risk. Since these factorsare necessary to sustain life, their “exposure–response” relationshipis J-shaped or U-shaped, and the theoretical-minimum-risk distribu-tion is non-zero. For such risk factors, the choice of optimal exposure


needs to be based on empirical evidence from different scientific dis-ciplines. For example, epidemiological research on blood pressure andcholesterol have illustrated a monotonically increasing dose–responserelationship for mortality even at low levels of these risk factors (Chenet al. 1991; Eastern Stroke and Coronary Heart Disease Collabora-tive Research Group 1998; MacMahon et al. 1990; ProspectiveStudies Collaboration 1995). But, given the role of these factors insustaining life, this relationship must flatten and reverse at some level.In the blood pressure and cholesterol assessment, a theoretical-minimum-risk exposure distribution with a mean of 115mmHg forsystolic blood pressure and 3.8mmol/l for total cholesterol (each witha small standard deviation) were used (Ezzati et al. 2002). This dis-tribution corresponds to the lowest levels at which the dose–responserelationship has been characterized in meta-analyses of cohort studies(Chen et al. 1991; Eastern Stroke and Coronary Heart Disease Col-laborative Research Group 1998; MacMahon et al. 1990; ProspectiveStudies Collaboration 1995). Further, these levels of blood pressureand cholesterol are consistent with levels seen in populations whichhave low levels of cardiovascular disease, such as the YanomamoIndians (Carvalho et al. 1989) and rural populations in China (He etal. 1991a, 1991b), Papua New Guinea (Barnes 1965; Carvalho et al.1989), and Africa (Mann et al. 1964). Although meta-analyses of ran-domized clinical trials have indicated that blood pressure and choles-terol levels may be lowered substantially with no adverse effects(LaRosa et al. 1999; Pignone et al. 2000), it is difficult to justify an optimal exposure distribution lower than that measured in population-based studies, since lower levels in individuals may becaused by factors such as pre-existing disease. Arguments from evolutionary biology would also support the choice of a lower boundon the optimal distribution based on historical survival of populationswho are not substantially exposed to factors that raise blood pressureor cholesterol.

2. Behavioural risk factors: The exposure–response relationship for thisgroup of risk factors may be monotonically increasing or J-shaped.For risk factors with a monotonic exposure–response relationship,such as smoking, the optimal exposure would be zero unless there arephysical constraints that make zero risk unattainable. For example inthe case of blood transfusion, there may be a lower bound on thesafety of the blood supply process even using the best monitoring technology. With a J-shaped or U-shaped exposure–response rela-tionship, the minimum risk would occur at the turning point of theexposure–response curve. An example of this is alcohol consumptionin adult populations with high cardiovascular disease rates, sincemoderate consumption may result in a reduction in ischaemic heartdisease (IHD) in some age groups (Corrao et al. 2000). With a


J-shaped exposure–response curve, similar to physiological riskfactors, empirical evidence would have to be used to determine thetheoretical minimum risk.

Finally, some behavioural risks are expressed as the absence of protective factors such as physical inactivity or low fruit and veget-able intake. In such cases, optimal exposure would be the level atwhich the benefits of these factors would no longer continue. With amonotonic exposure–response relationship or without detailedknowledge about a possible turning point, the theoretical-minimum-risk exposure distribution should be chosen based on empirical evi-dence about the highest theoretically sustainable levels of intake orexposure (for example very active life style or a purely vegetariandiet).

3. Environmental risk factors: The toxicity of most environmental riskfactors is best described as a monotonically increasing function ofexposure (potentially with some threshold). Therefore, the theoreti-cal-minimum-risk exposure distribution for this group would be thelowest physically achievable level of exposure, such as backgroundparticulate matter concentration due to dust.

4. Socioeconomic “risk factors”: Socioeconomic status and factors—such as income (including levels and distribution) and associated levelsof poverty and inequality, education, the existence of social supportnetworks, etc.—are important determinants of health, often throughtheir effects on other risk factors. The effects of each of these factorson health are, however, highly dependent on other socioeconomicvariables as well as the policy context, including accessibility andeffectiveness of health and welfare systems. For this reason, the theoretical-minimum-risk exposure distribution, even if meaningfullydefined, is likely to change over time and space depending on a largenumber of other factors. Given this heterogeneity, the effects of socio-economic variables are best assessed relative to counterfactual distri-butions defined based on policy and intervention options in specifictimes and settings, as discussed by Greenland (2002).

3. Risk quantification modelsPrediction implicitly assumes the use of a conceptual model which infersthe value of the variable of interest at a point in time or space based onknowledge from a different time, or another location. Predictive modelscan be divided along a continuum between aggregate and structural cat-egories. A completely aggregate model uses the previous trend of the vari-able of interest as the basis for predicting its future value. A structuralmodel, on the other hand, identifies the components—and the relation-ships among them—of the “system” that determines the variable of inter-est. It then uses the knowledge of the system for predicting the value of


the variable of interest. Most predictive models lie between the twoextremes and use a combination of aggregate and structural modelling.2

Consider for example predicting the future population of a city or thefuture ambient concentration of a pollutant. An aggregate model wouldextrapolate the historical levels to predict future values. Even in this casethe model may include some structural elements. For example, the modelmay use a specific functional form—linear, exponential, quadratic or logarithmic—for extrapolation which involves an assumption about theunderlying system. A structural model, in the case of population predic-tion would consider the age structure of the population, fertility (whichitself may be modelled using data on education and family planning programmes), public health variables and rural–urban migration (whichitself can be modelled using economic variables). In the case of air pollution, a structural model may consider demographic variables (themselves modelled as above), the structure of the economy (manu-facturing, agriculture or service), the current manufacturing and trans-portation technology and effects of research and development on newtechnology, the demand for private vehicles, the price of energy and theatmospheric chemistry of pollution. Once again, in both examples themodels may include some aggregation of variables by using historicaltrends to predict the future values of individual variables in the system,such as funding for family planning or research and development of newtechnologies.

The comparative advantage of structural and aggregate models lies inthe balance between theoretical precision and data requirement. Struc-tural models offer the potential for more robust predictions, especiallywhen the underlying system is complex and highly sensitive to one ormore of its components. In such cases, a shift in some of the system vari-ables can introduce large changes in the outcome, which may be missedby extrapolation (such as the discovery of antibiotics and infectiousdisease trends or the change in tuberculosis mortality after the HIV epi-demic). Aggregate models, on the other hand, require considerably lessknowledge of the system components and the relationships among them.These models can therefore provide more reliable estimates when suchinformation is not available, especially when the system is not very sen-sitive to inputs.

3.1 Models for risk factor–disease relationship

Using the above aggregate, structural taxonomy, it is also possible toclassify models that are used to predict changes in death or disease as aresult of changes in exposure to underlying risk factors. Murray andLopez (1999) described a “causal-web” which includes the various distal(such as socioeconomic), proximal (behavioural or environmental) andphysiological and patho-physiological causes of disease, as shown inFigure 1.1. While different disciplinary traditions—from social sciences


and humanities, to the physical, natural and biomedical sciences—havefocused on individual components or stages of these relationships, in asingle multi-layer causal model with interactions the term “risk factor”can be used for any of the causal determinants of health (Mathers et al.2002; Yerushalmy and Palmer 1959).3 For example, poverty, location ofhousing, lack of access to clean water and sanitation, and the existenceof a specific pathogen in water can all be considered the causes of diar-rhoeal diseases, providing a more complete framework for assessment ofinterventions and policy options. Similarly, education and occupation,diet, smoking, air pollution, physical activity, BMI and blood pressureare some of the risk factors at various levels of causality for cardiovas-cular diseases.

Compared to a causal-web, Equations 1 and 2 that use relative riskestimates from epidemiological methods (e.g. the Cox proportionalhazard or other regression models) lie further towards aggregate model-ling. In general, in such methods, relative risks are estimated so that theyincorporate the aggregation of the various underlying relationship(ideally, but not always, controlling for the appropriate confoundingvariables)4 without considering intermediate relationships as separatecausal stages. On the other hand, if specified and estimated correctly,considering the complete set of causal pathways which include multiple


Figure 1.1 Simplified schema for a causal-web illustrating various levelsof disease causation

Note: Feedback from outcomes to preceding layers may also exist. For example, individuals or societiesmay modify their risk behaviour based on health outcomes.

D1

D2

D3

P1

P2

P3

PA1

PA2

PA3

O1

O2

Distal causes Proximal causes

Physiological and patho-physiological

causes Outcomes

risk factors will allow making inferences about combinations of riskfactors and risk factor levels for which direct epidemiological studies maynot be available.

As discussed earlier, the appropriateness of the two approaches to estimation of attributable burden depends on the specific risk factor(s),outcomes and available data. For example, the relationship betweensmoking and lung cancer has been shown to be highly dependent onsmoking intensity and duration which, with appropriate indicators ofpast smoking (Peto et al. 1992), can be readily estimated using the rel-ative risk approach of Equations 1 and 2. Consider, on the other hand,the relationship among age, socioeconomic status and occupation,behavioural risk factors (such as smoking, alcohol consumption, diet,physical activity), physiological variables (such as blood pressure andcholesterol level) and IHD shown in Figure 1.2. Given the multiplecomplex interactions, IHD risk may be best predicted using a structural(causal-web) approach, especially when some risk factors vary simulta-


Figure 1.2 A possible causal diagram based on established relationshipsfor estimating the incidence of ischaemic heart disease

DBP Diastolic blood pressure.

Note: Other interactions may also be possible.

IHD

Type IIdiabetes

LDL-chol

DBP

Physicalactivity

Fatintake

BMI

Alcohol

Age

Education

Income

Smoking

neously, such as smoking, alcohol and diet, requiring joint counterfac-tual distributions. Using a multi-risk model would also allow consider-ing situations for which direct epidemiological studies may not have beenconducted, such as the effects of physical activity on those people whohave diets different from the study group or those who take medicine tolower blood pressure.

The health effects of global climate change provide another examplewhere a structural approach to risk assessment may be appropriate. Economic activities (including manufacturing, agriculture and forest use,transportation and domestic energy use) affect the emissions of green-house gases (GHG). Changes in precipitation, temperature and othermeteorological variables due to atmospheric GHG accumulation alterregional ecology, which in turn results in changes in agricultural pro-ductivity, quantity and quality of water, dynamics of disease vectors andother determinants of disease. All these effects are in turn modulated by local economic activities, land-use patterns and income (Patz et al.2000; Reiter 2001; Rogers and Randolph 2000). A model based on theatmospheric physics/chemistry of GHG emissions and accumulation,climate models, plant and vector ecology and human activity mightprovide the optimal basis for the prediction of the health effects ofclimate change.5

SPECIFYING THE CAUSAL-WEB

Assuming for the moment no temporal dimension in the relationshipbetween the different variables in the causal system (temporal aspects arediscussed below), each layer of a causal-web may be characterized by theequation:

(3a)6

where Xn is the vector of the variables in the nth layer of the causal-web(which can be causal or output such as D, P, PA, or O using the nota-tion of Figure 1.1); f is the functional form connecting the (n - 1)th layerto the nth layer; B is a matrix of coefficients for f which itself may bedependent on the variables in the (n - 1)th and nth layers (Xn-1 and Xn)7(as well as time as we discuss below).

The attributable fraction of disease or mortality due to a single riskfactor in the causal-web is then obtained by integrating the outcome (O)over the current (P(x)) and counterfactual (P¢(x)) population distribu-tions of exposure, as for Equation 2.

(4)AF P x P x

P x

=

( ) - ( )

( )( ) ¢( )

( )

Ú Ú

Ú

O x O x

O x

X B X X Xn n n nf= ( )( )- -1 1, ,


3.2 Joint risk factor changes

The attributable fraction relationships described in Equations 1 and 2are based on individual risk factors. Disease and mortality are howeveroften affected by multiple, and at times correlated, risk factors (Rothman1976; Walter 1980). Estimating the joint effects of multiple distal andproximal risks is particularly important because many factors actthrough other, intermediate, factors (Murray and Lopez 1999;Yerushalmy and Palmer 1959), or in combination with other risks. It istherefore important to consider how the burden of disease may changewith simultaneous variations in multiple risk factors. Analysis of jointrisk factor changes implicitly acknowledges that the disease causationmechanism involves multiple factors, and is therefore suited to a causal-web framework, with P(x) and P¢(x) in Equation 4 being the joint dis-tributions of the vector of risk factors, x. Alternatively, when usingEquations 1 or 2, knowledge of the distribution of all relevant riskfactors and the relative risk for each risk factor, estimated at the appro-priate level of the remaining risk factors,8 is required. Therefore, in Equa-tion 2a, RR and P may represent joint risks and exposure distributionsfor multiple risk factors (Eide and Heuch 2001). In this case, the esti-mates from Equations 2a and 4 may in theory be identical.

ADDITIVITY OF ATTRIBUTABLE FRACTION

Many users of risk assessment desire information characterized by addi-tive decomposition. In other words, users would like to know what frac-tion of the disease burden is related to any risk factor or group of riskfactors, independent of the changes in other risk factors. As discussed byMathers et al. (2002), additive decomposition is a property of categori-cal attribution and, in general, not of counterfactual attribution becausemany diseases are caused by the interaction of multiple risk factors actingsimultaneously and therefore can be avoided by eliminating any of thesefactors (Rothman 1976; Rothman and Greenland 1998; Yerushalmy andPalmer 1959). Consider for example infant and child mortality due toacute respiratory infections (ARI), which are especially high among mal-nourished children, as a result of exposure to indoor smoke from solidfuels (Rice et al. 2000; Smith et al. 2000). In this case, removal of eitherrisk factor can reduce mortality, some of which can therefore be attrib-uted to both factors. Similarly the risk of mortality due to cardiovascu-lar diseases among some of those who are exposed to smoking, lowphysical activity and poor diet may be reduced by elimination of anycombination of these risk factors. Counterfactual causal attribution ofdisease and injury to individual risk factors does not normally allowadditive decomposition and the sum of attributable fractions or burdensfor a single disease due to multiple risk factors is therefore theoreticallyunbounded.


Although epidemiologically unavoidable and conceptually acceptable,the lack of additivity presents additional policy complexity and impliesgreat caution is necessary when communicating and interpreting the estimates of attributable fraction and burden. With multiple attribution,the reduction of one risk factor would seem to make other, equallyimportant risk factors potentially irrelevant from the perspective with alimited scope on quantification. At the same time multi-causality offersopportunities to tailor prevention based on availability and cost of interventions. It also necessitates the development of methods to quan-tify the effects of joint counterfactual distributions for multiple riskfactors.

4. Temporal dimensions of the riskfactor–disease relationship

Both exposure to a risk factor and the health outcomes due to exposureinclude a time dimension. This can be described by a modified versionof Equation 3 in which exposure and outcome as well as the model para-meters (B) are dependent on time. In the following two sections we con-sider the temporal characteristics of exposure and health outcomes,respectively.

4.1 Temporal characteristics of exposure

With the exception of acute hazards (e.g. injury risk factors) exposureto a risk factor affects disease over a time period. As a result, the distri-butional transition between any two exposure distributions includes atemporal dimension as illustrated schematically in Figure 1.3. The tran-sition path is of little importance if exposure changes over a short timeinterval, especially relative to the time required for the effect of expo-sure on disease. Over long time periods, however, there is sufficient timefor contributions from the intermediate exposure values, and the actualpath of transition may be as important as the initial and final distribu-tions in determining the disease burden associated with change in expo-sure. For example, the effects of reducing the prevalence of smoking orexposure to an occupational carcinogen by half in a population wouldbe markedly different if the change takes place immediately, graduallyover a twenty-year period or after twenty years. Therefore, the healtheffects of exposure to many risk factors depend on the complete profileof exposure over time, and may be further accompanied by a time lagfrom the period of exposure. Also, for some risk factors there may becomplete or partial reversibility, with the role of past exposure gradu-ally declining.

To capture the effects of exposure profiles over time, we begin by considering the role of temporal dimensions of exposure at the level of individuals (or groups of individuals with similar exposure) beforeconsidering the whole population.


Suppose that at time T the relative risk of a disease, RR, for individ-uals exposed to a risk factor (compared to the non-exposed group)depends on the complete profile or stream of exposure between time T0and T, denoted by x(t), with some lag, L, between exposure and effect.Then, there is some function, f(x), which can be used to describe the contribution of exposure at any point in time between T0 and T to therelative risk (RR). In mathematical notation:

(5)9

The quantity �TT0f(x(t - L)dx is an equivalent exposure10 between T0 and

T and is dependent on: i) the profile of exposure (i.e. level of exposureat any point in time) described by x(t); and ii) the contribution of pre-vious exposure to current hazard characterized by f(x), an accumulativerisk function.11 Some common forms for the accumulative risk function,f(x), are given in Table 1.1.

RR x t RR f x t L dtTT

T

T

( )( )

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Comparative Quantification of Health R...Comparative Quantification of Health Risks Global and Regional Burden of Disease Attributable toSelected Major Risk Factors Volume 1 Edited