-
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.
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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
All rights reserved. Publications of the World Health
Organization can beobtained from Marketing and Dissemination, World
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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
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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
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Chapters within.
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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
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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
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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
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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
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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
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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
4 Comparative Quantification of Health Risks
-
“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
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
Christopher J.L. Murray et al. 5
-
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
6 Comparative Quantification of Health Risks
-
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
Christopher J.L. Murray et al. 7
-
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
8 Comparative Quantification of Health Risks
-
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
Christopher J.L. Murray et al. 9
-
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
10 Comparative Quantification of Health Risks
-
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
Christopher J.L. Murray et al. 11
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-
12 Comparative Quantification of Health Risks
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, ,
Christopher J.L. Murray et al. 13
-
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.
14 Comparative Quantification of Health Risks
-
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.
Christopher J.L. Murray et al. 15
-
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
( )( )