A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of - PDF Document

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Articles A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010 Stephen S Lim‡, Theo Vos, Abraham D Flaxman, Goodarz Danaei, Kenji Shibuya, Heather Adair-Rohani*, Markus Amann*, H Ross Anderson*, Kathryn G Andrews*, Martin Aryee*, Charles Atkinson*, Loraine J Bacchus*, Adil N Bahalim*, Kalpana Balakrishnan*, John Balmes*, Suzanne Barker-Collo*, Amanda Baxter*, Michelle L Bell*, Jed D Blore*, Fiona Blyth*, Carissa Bonner*, Guilherme Borges*, Rupert Bourne*, Michel Boussinesq*, Michael Brauer*, Peter Brooks*, Nigel G Bruce*, Bert Brunekreef*, Claire Bryan-Hancock*, Chiara Bucello*, Rachelle Buchbinder*, Fiona Bull*, Richard T Burnett*, Tim E Byers*, Bianca Calabria*, Jonathan Carapetis*, Emily Carnahan*, Zoe Chafe*, Fiona Charlson*, Honglei Chen*, Jian Shen Chen*, Andrew Tai-Ann Cheng*, Jennifer Christine Child*, Aaron Cohen*, K Ellicott Colson*, Benjamin C Cowie*, Sarah Darby*, Susan Darling*, Adrian Davis*, Louisa Degenhardt*, Frank Dentener*, Don C Des Jarlais*, Karen Devries*, Mukesh Dherani*, Eric L Ding*, E Ray Dorsey*, Tim Driscoll*, Karen Edmond*, Suad Eltahir Ali*, Rebecca E Engell*, Patricia J Erwin*, Saman Fahimi*, Gail Falder*, Farshad Farzadfar*, Alize Ferrari*, Mariel M Finucane*, Seth Flaxman*, Francis Gerry R Fowkes*, Greg Freedman*, Michael K Freeman*, Emmanuela Gakidou*, Santu Ghosh*, Edward Giovannucci*, Gerhard Gmel*, Kathryn Graham*, Rebecca Grainger*, Bridget Grant*, David Gunnell*, Hialy R Gutierrez*, Wayne Hall*, Hans W Hoek*, Anthony Hogan*, H Dean Hosgood III*, Damian Hoy*, Howard Hu*, Bryan J Hubbell*, Sally J Hutchings*, Sydney E Ibeanusi*, Gemma L Jacklyn*, Rashmi Jasrasaria*, Jost B Jonas*, Haidong Kan*, John A Kanis*, Nicholas Kassebaum*, Norito Kawakami*, Young-Ho Khang*, Shahab Khatibzadeh*, Jon-Paul Khoo*, Cindy Kok*, Francine Laden*, Ratilal Lalloo*, Qing Lan*, Tim Lathlean*, Janet L Leasher*, James Leigh*, Yang Li*, John Kent Lin*, Steven E Lipshultz*, Stephanie London*, Rafael Lozano*, Yuan Lu*, Joelle Mak*, Reza Malekzadeh*, Leslie Mallinger*, Wagner Marcenes*, Lyn March*, Robin Marks*, Randall Martin*, Paul McGale*, John McGrath*, Sumi Mehta*, George A Mensah*, Tony R Merriman*, Renata Micha*, Catherine Michaud*, Vinod Mishra*, Khayriyyah Mohd Hanafi ah*, Ali A Mokdad*, Lidia Morawska*, Dariush Mozaff arian*, Tasha Murphy*, Mohsen Naghavi*, Bruce Neal*, Paul K Nelson*, Joan Miquel Nolla*, Rosana Norman*, Casey Olives*, Saad B Omer*, Jessica Orchard*, Richard Osborne*, Bart Ostro*, Andrew Page*, Kiran D Pandey*, Charles D H Parry*, Erin Passmore*, Jayadeep Patra*, Neil Pearce*, Pamela M Pelizzari*, Max Petzold*, Michael R Phillips*, Dan Pope*, C Arden Pope III*, John Powles*, Mayuree Rao*, Homie Razavi*, Eva A Rehfuess*, Jürgen T Rehm*, Beate Ritz*, Frederick P Rivara*, Thomas Roberts*, Carolyn Robinson*, Jose A Rodriguez-Portales*, Isabelle Romieu*, Robin Room*, Lisa C Rosenfeld*, Ananya Roy*, Lesley Rushton*, Joshua A Salomon*, Uchechukwu Sampson*, Lidia Sanchez-Riera*, Ella Sanman*, Amir Sapkota*, Soraya Seedat*, Peilin Shi*, Kevin Shield*, Rupak Shivakoti*, Gitanjali M Singh*, David A Sleet*, Emma Smith*, Kirk R Smith*, Nicolas J C Stapelberg*, Kyle Steenland*, Heidi Stöckl*, Lars Jacob Stovner*, Kurt Straif*, Lahn Straney*, George D Thurston*, Jimmy H Tran*, Rita Van Dingenen*, Aaron van Donkelaar*, J Lennert Veerman*, Lakshmi Vijayakumar*, Robert Weintraub*, Myrna M Weissman*, Richard A White*, Harvey Whiteford*, Steven T Wiersma*, James D Wilkinson*, Hywel C Williams*, Warwick Williams*, Nicholas Wilson*, Anthony D Woolf*, Paul Yip*, Jan M Zielinski*, Alan D Lopez†, Christopher J L Murray†, Majid Ezzati† Summary Background Quantifi cation of the disease burden caused by diff erent risks informs prevention by providing an account of health loss diff erent to that provided by a disease-by-disease analysis. No complete revision of global disease burden caused by risk factors has been done since a comparative risk assessment in 2000, and no previous analysis has assessed changes in burden attributable to risk factors over time. Lancet 2012; 380: 2224–60 See Comment pages 2053, 2054, 2055, 2058, 2060, 2062, and 2063 See Special Report page 2067 See Articles pages 2071, 2095, 2129, 2144, 2163, and 2197 Methods We estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent eff ects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010. We estimated exposure distributions for each year, region, sex, and age group, and relative risks per unit of exposure by systematically reviewing and synthesising published and unpublished data. We used these estimates, together with estimates of cause-specifi c deaths and DALYs from the Global Burden of Disease Study 2010, to calculate the burden attributable to each risk factor exposure compared with the theoretical-minimum-risk exposure. We incorporated uncertainty in disease burden, relative risks, and exposures into our estimates of attributable burden. *Author listed alphabetically †Joint senior authors ‡Corresponding author See Online for appendix For interactive versions of fi gures 3, 4, and 6 see http:// healthmetricsandevaluation.org/ gbd/visualizations/regional Findings In 2010, the three leading risk factors for global disease burden were high blood pressure (7·0% [95% uncertainty interval 6·2–7·7] of global DALYs), tobacco smoking including second-hand smoke (6·3% [5·5–7·0]), and alcohol use (5·5% [5·0–5·9]). In 1990, the leading risks were childhood underweight (7·9% [6·8–9·4]), household air pollution from solid fuels (HAP; 7·0% [5·6–8·3]), and tobacco smoking including second-hand smoke (6·1% [5·4–6·8]). Dietary risk factors and physical inactivity collectively accounted for 10·0% (95% UI 9·2–10·8) of global DALYs in 2010, with the most prominent dietary risks being diets low in fruits and those high in sodium. Several risks that primarily aff ect childhood communicable diseases, including unimproved water and sanitation and childhood micronutrient defi ciencies, fell in rank between 1990 and 2010, with unimproved water Institute for Health Metrics and Evaluation (S S Lim PhD, A D Flaxman PhD, K G Andrews MPH, C Atkinson BS, E Carnahan BA, K E Colson BA, R E Engell BA, G Freedman BA, M K Freeman BA, E Gakidou PhD, R Jasrasaria BA, 2224 www.thelancet.comVol 380 December 15/22/29, 2012

  2. Articles and sanitation accounting for 0·9% (0·4–1·6) of global DALYs in 2010. However, in most of sub-Saharan Africa childhood underweight, HAP, and non-exclusive and discontinued breastfeeding were the leading risks in 2010, while HAP was the leading risk in south Asia. The leading risk factor in Eastern Europe, most of Latin America, and southern sub-Saharan Africa in 2010 was alcohol use; in most of Asia, North Africa and Middle East, and central Europe it was high blood pressure. Despite declines, tobacco smoking including second-hand smoke remained the leading risk in high-income north America and western Europe. High body-mass index has increased globally and it is the leading risk in Australasia and southern Latin America, and also ranks high in other high-income regions, North Africa and Middle East, and Oceania. Prof R Lozano MD, L Mallinger MPH, A A Mokdad MD, T Murphy PhD, M Naghavi PhD, T Roberts BA, L C Rosenfeld MPH, E Sanman BS, L Straney PhD, Prof C J L Murray MD), Department of Anesthesiology and Pain Medicine (N Kassebaum MD), University of Washington, Seattle, WA, USA (C Olives PhD, Prof F P Rivara MD); Queensland Centre for Mental Health Research (A Baxter MPH, J-P Khoo MBBS, A Ferrari BPsySc, Prof H Whiteford MBBS), School of Population Health (Prof T Vos PhD, F Charlson MPH, A Page PhD, Prof A D Lopez PhD, J D Blore PhD, R Norman PhD), Brain Institute (Prof J McGrath MD), University of Queensland, Brisbane, QLD, Australia (Prof W Hall PhD, J L Veerman PhD); Department of Biostatistics (M M Finucane PhD), Department of Epidemiology (S Khatibzadeh MD, P Shi PhD), School of Public Health (G Danaei MD, E L Ding ScD, Prof E Giovannucci MD, F Laden ScD, J K Lin AB, Y Lu MS, R Micha PhD, D Mozaff arian MD, M Rao BA, Prof J A Salomon PhD, G M Singh PhD, R A White MA), Medical School (D Mozaff arian), Harvard University, Boston, MA, USA; Department of Global Health Policy (Prof K Shibuya MD), University of Tokyo, Tokyo, Japan (Prof N Kawakami MD); School of Public Health (Prof J Balmes MD), University of California, Berkeley, Berkeley, CA, USA (H Adair-Rohani MPH, Z Chafe MPH, Prof K R Smith PhD, J H Tran MA); International Institute for Applied Systems Analysis, Laxenburg, Austria (M Amann PhD); St George's, University of London, London, UK (Prof H R Anderson MD); Bloomberg School of Public Health (K Mohd Hanafi ah MSPH), School of Medicine (M Aryee PhD), Johns Hopkins University, Baltimore, MD, USA (E R Dorsey MD, R Shivakoti BA); London School of Hygiene and Tropical Medicine, London, UK (L J Bacchus PhD, J C Child MSc, K Devries PhD, K Edmond PhD, G Falder MSc, J Mak MSc, Prof N Pearce PhD, H Stöckl PhD); Independent Consultant, Geneva, Switzerland (A N Bahalim MEng); Sri Ramachandra University, Chennai, India Interpretation Worldwide, the contribution of diff erent risk factors to disease burden has changed substantially, with a shift away from risks for communicable diseases in children towards those for non-communicable diseases in adults. These changes are related to the ageing population, decreased mortality among children younger than 5 years, changes in cause-of-death composition, and changes in risk factor exposures. New evidence has led to changes in the magnitude of key risks including unimproved water and sanitation, vitamin A and zinc defi ciencies, and ambient particulate matter pollution. The extent to which the epidemiological shift has occurred and what the leading risks currently are varies greatly across regions. In much of sub-Saharan Africa, the leading risks are still those associated with poverty and those that aff ect children. Funding Bill & Melinda Gates Foundation. Introduction Measurement of the burden of diseases and injuries is a crucial input into health policy. Equally as important, is a comparative assessment of the contribution of potentially modifi able risk factors for these diseases and injuries. The attribution of disease burden to various risk factors provides a diff erent account compared with disease-by- disease analysis of the key drivers of patterns and trends in health. It is essential for informing prevention of disease and injury. Understanding the contribution of risk factors to disease burden has motivated several comparative studies in the past few decades. The seminal work of Doll and Peto1 provided a comparative assessment of the importance of diff erent exposures, particularly tobacco smoking, in causing cancer. Peto and colleagues2 subsequently estimated the eff ects of tobacco smoking on mortality in developed countries since 1950. Although these risk factor-specifi c or cause-specifi c analyses are useful for policy, a more comprehensive global assessment of burden of disease attributable to risk factors can strengthen the basis for action to reduce disease burden and promote health. The Global Burden of Disease Study (GBD) 1990 provided the fi rst global and regional comparative assessment of mortality and disability- adjusted life-years (DALYs) attributable to ten major risk factors.3 However, diff erent epidemiological traditions for diff erent risks limited the comparability of the results. Subsequently, Murray and Lopez4 proposed a framework for global comparative risk assessment, which laid the basis for assessment of 26 risks in 2000.5–7 Since this work, WHO has provided estimates for some risks by the same methods but with updated exposures and some updates of the eff ect sizes for each risk.8 Analyses have also been done for specifi c clusters of diseases, like cancers,9 or clusters of risk factors, like maternal and child under- nutrition.10 National comparative risk assess ments (including in Australia, Iran, Japan, Mexico, South Africa, Thailand, USA, and Vietnam) have also been undertaken with similar approaches.11–16 GBD 2010 provides an opportunity to re-assess the evidence for exposure and eff ect sizes of risks for a broad set of risk factors by use of a common framework and methods. Particularly, since this work was done in parallel with a complete re-assessment of the burden of diseases and injuries in 1990 and 2010, for the fi rst time changes in burden of disease attributable to diff erent risk factors can be analysed over time with comparable methods. Since uncertainty has been estimated for each disease or injury outcome,17,18 the comparative risk assessment for GBD 2010 has also enabled us to incorporate uncertainty into the fi nal estimates. We describe the general approach and high-level fi ndings for comparison of the importance of 67 risk factors and clusters of risk factors, globally and for 21 regions of the world, over the past two decades. Methods Overview The basic approach for the GBD 2010 comparative risk assessment is to calculate the proportion of deaths or disease burden caused by specifi c risk factors—eg, ischaemic heart disease caused by increased blood pressure—holding other independent factors unchanged. These calculations were done for 20 age groups, both sexes, and 187 countries and for 1990, 2005 (results for 2005 not shown, available from authors on request),and 2010. We present aggregated results for 21 regions. Table 1 shows the included risk factors and their organisation into a hierarchy with three levels. Level 1 risks are clusters of risk factors that are related by mechanism, biology, or potential policy intervention. Most risks are presented at level 2. For occupational carcinogens, a third level is included to provide additional detail about specifi c carcinogens. For suboptimal breastfeeding we www.thelancet.comVol 380 December 15/22/29, 2012 2225

  3. Articles also include a third level to distinguish between non- exclusive breastfeeding during the fi rst 6 months and discontinued breastfeeding from 6 to 23 months. We calculated burden attributable to all (67) risk factors and clusters of risk factors except for physiological risks and air pollution. These two clusters present analytical challenges for computation of the aggregate burden. For example, the eff ects of high body-mass index are partly mediated through high blood pressure, high total cholesterol, and high fasting plasma glucose, and household air pollution from solid fuels (wood, crop, residues, animal dung, charcoal, and coal) contributes to ambient particulate matter pollution. We ranked results for 43 risk factors and clusters of risk factors, grouping together occupational carcinogens, non-exclusive and discontinued breastfeeding, and tobacco smoking with second-hand smoke on the basis of common exposure sources. Our estimation of disease burden attributable to a risk factor had fi ve steps: 1) selection of risk–outcome pairs to be included in the analysis based on criteria about causal associations; 2) estimation of distributions of exposure to each risk factor in the population; 3) estimation of etiological eff ect sizes, often relative risk per unit of exposure for each risk–outcome pair; 4) choice of an alternative (counterfactual) exposure distribution to which the current exposure distribution is compared. We selected an optimum exposure distribution, termed the theoretical-minimum-risk exposure distribution for this purpose; and 5) computation of burden attributable to each risk factor, including uncertainty from all sources. Further details about the data and methods used for specifi c risk factors are available on request. sources to estimate risk factor exposure distributions in 1990 and 2010. Strategies to identify data sources included searching survey databases such as the WHO Global Database on Child Growth and Malnutrition, searching general citation databases such as Google Scholar and PubMed, manual searching of reference lists of articles and conference abstracts, and contacting experts in the relevant fi elds. Data sources included censuses, health examination and nutrition surveys, and community-based epidemiological studies. Because data for risk factor exposure are often incomplete or missing for many populations, models were used to generate a complete set of current exposure distributions for risk factors for each country and for both years, including uncertainty. Table 1 shows for each risk factor the main sources of data and the modelling approach used for estimation of present risk factor exposure distributions. Briefl y, risk factor models were designed to use available data and information for expo sures in countries, for several years, and for diff erent age groups to generate estimates for all countries, for both years, and for all relevant age groups. Estimation of exposure was done with statistical models that used predictors such as time, geography, and other variables that were relevant to the exposure of interest— eg, income per person. For each risk factor, we tested a wide array of covariates for prediction of exposure distributions, drawing from covariates included in databases created or collated at the Institute for Health Metrics and Evaluation for GBD 2010. If relevant, the model also included age. Finally, each analysis accounted for important study charac- teristics such as national versus subnational represen- tativeness, and the measures and instruments used for measuring exposure. In addition to this general approach, specifi c methods were used for some risk factors. For tobacco including second-hand smoke, much scientifi c literature exists about alternative methods to estimate cumulative exposure, based on the premise that present prevalence and consumption data do not take into account likely variations in duration and intensity of smoking. In this case, we used the method of Peto and Lopez,2 which uses lung cancer mortality as a marker (ie, smoking impact ratio) of cumulative population exposure to smoking for cancers and chronic respiratory disease. We used epidemiological data to estimate lung cancer mortality in non-smokers separately for China, other countries in the high-income Asia Pacifi c region, and all remaining countries.119,120 For all other outcomes, we used 10-year lagged tobacco smoking prevalence. We also applied an approach analogous to the smoking impact ratio for occupational exposure to asbestos, for which we used mesothelioma mortality, separately estimated, as a marker of asbestos exposure. For ambient particulate matter pollution, two com- plete, high resolution estimates exist of the concen tration of particulate matter smaller than 2·5 μm in aerodynamic (Prof K Balakrishnan PhD, S Ghosh MS); University of Auckland, Auckland, New Zealand (S Barker-Collo PhD); Yale University, New Haven, CT, USA (Prof M L Bell PhD); School of Public Health (J Leigh MBBS, T Driscoll PhD), Department of Rheumatology, Northern Clinical School (E Smith PhD), Institute of Bone and Joint Research (J S Chen PhD, Prof L March MD, L Sanchez-Riera MD, N Wilson PhD), University of Sydney, Sydney, NSW, Australia (F Blyth PhD, C Bonner MPH, S Darling MPH, G L Jacklyn MPH, J Orchard MPH, E Passmore MPH); National Institute of Psychiatry, Mexico City, Mexico (Prof G Borges ScD); National Autonomous University, Mexico City, Mexico (Prof G Borges); Vision and Eye Research Unit, Anglia Ruskin University, Cambridge, UK (Prof R Bourne MD); Institut de Recherche pour le Développement, Martinique, France (M Boussinesq MD); University of British Columbia, Vancouver, BC, Canada (Prof M Brauer ScD); Department of Pediatrics (R Weintraub MBBS), Centre for Health Policy, Programs and Economics (Prof L Degenhardt PhD), School of Population Health (Prof R Room PhD), University of Melbourne, Melbourne, VIC, Australia (Prof P Brooks MD, Prof R Marks MBBS); University of Liverpool, Liverpool, UK (Prof N G Bruce MBBS, M Dherani PhD, D Pope PhD); Insititute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands (Prof B Brunekreef PhD); Flinders University, Adelaide, Selection of risk–outcome pairs The inclusion criteria for each risk–outcome pair that we applied were: 1) the likely importance of a risk factor to disease burden or policy based on previous work; 2) availability of suffi cient data and methods to enable estimation of exposure distributions by country for at least one of the study periods (1990 and 2010); 3) suffi cient evidence for causal eff ects based on high-quality epidemi- ological studies in which the fi ndings were unlikely to be caused by bias or chance, analogous to the criteria used for assessment of carcinogens with con vincing or probable evidence (panel). Suffi cient data to estimate outcome-specifi c etiological eff ect sizes per unit of exposure were also needed; and 4) evidence to support generalisability of eff ect sizes to populations other than those included in the available epidemiological studies or satisfactory models for extrapolating them. Table 1 shows the risk–outcome pairs that were included in the fi nal analysis, on the basis of these criteria. SA, Australia (C Bryan-Hancock BPsych, T Lathlean MA); National Drug and Alcohol Research Centre (B Calabria BPsyc, Prof L Degenhardt, P K Nelson MHSc), University of New South Wales, Sydney, NSW, Australia (C Bucello BPsyc); Cabrini Institute, Malvern, VIC, Australia (Prof R Buchbinder MBBS); Monash University, Melbourne, VIC, Australia (Prof R Buchbinder, D Hoy PhD); Telethon Institute for Child Health Research, Centre for Child Health Research (Prof J Carapetis MBBS), Distribution of exposure to each risk factor For most risk factors, a systematic search was done to identify published and, when possible, unpublished data 2226 www.thelancet.comVol 380 December 15/22/29, 2012

  4. Articles Source of relative risks Theoretical- minimum-risk exposure distribution Exposure defi nition Outcomes Subgroup Main data sources for exposure Exposure estimation method 1. Unimproved water and sanitation 1.1. Unimproved water source (unprotected wells or springs, vendor-provided water, tanker trucks, surface water, and other unspecifi ed sources) Intestinal infectious diseases All ages Population surveys and censuses Spatiotemporal Gaussian process regression19–21 Proportion of households using an unimproved water source New meta-analysis All households use an improved water source (household connection, a public tap or standpipe, a tubewell or borehole, a protected well or spring, or rainwater collection) All households use improved sanitation (public sewers, septic systems, fl ush or pour-fl ush facilities, ventilated improved latrines, simple pit latrines with squatting slabs, and composting toilets) Intestinal infectious diseases All ages Population surveys and censuses Spatiotemporal Gaussian process regression19–21 Proportion of households using unimproved sanitation (traditional latrines, open latrines without squatting slabs, bucket latrines, hanging latrines, open defecation or no facilities, and other unspecifi ed facilities) 1.2. Unimproved sanitation New meta-analysis 2. Air pollution 2.1. Ambient particulate matter pollution Ambient concentration of particles with an aerodynamic diameter smaller than 2·5 μm, measured in μg/m3 Surface monitor measurements, aerosol optical depth from satellites, and TM5 global atmospheric chemistry transport model22–26 5·8–8·8 μg/m³ Integrated exposure– response curve Age <5 years for lower respiratory tract infection; ≥25 years for all others Lower respiratory infections; trachea, bronchus, and lung cancers; IHD; cerebrovascular disease; COPD Average of satellite and chemistry transport estimates, calibrated to surface monitor measurements Mixed eff ect regression Integrated exposure– response curve for lower respiratory tract infection, IHD, and stroke; new meta-analysis for cataracts, COPD, and lung cancer Jerrett and colleagues27 Age <5 years for lower respiratory tract infection; ≥25 years for all others Population surveys and censuses All households using clean fuels for cooking (vented gas, electricity) Lower respiratory infections; trachea, bronchus, and lung cancers; IHD; cerebrovascular disease; COPD; cataracts 2.2. Household air pollution from solid fuels Proportion of households using solid fuels for cooking (coal, wood, charcoal, dung, and agricultural residues) COPD Age ≥25 years TM5 global atmospheric chemistry transport model22–24 2.3. Ambient ozone pollution Ambient concentrations of ozone in air, measured in parts per billion TM5 global atmospheric chemistry transport model22–24 33·3–41·9 parts per billion 3. Other environmental risks 3.1. Residential radon Mixed eff ect regression 10 Bq/m³ Darby and colleagues28 Residential radon, measured in Bq/m3 Trachea, bronchus, and lung cancers All ages Direct household measurements from surveys Examination surveys and epidemiological studies DisMod 3 Bone lead level expected from age- specifi c cumulative exposure to blood lead of 0·09652 μmol/L29 <15 years for intellectual disability; ≥25 years for all others 3.2. Lead exposure Blood lead (measured in μg/dL) and bone lead (measured in μg/g) Intellectual disability; systolic blood pressure, which has eff ects on: RHD; IHD; ischaemic stroke; haemorrhagic and other non-ischaemic stroke; HHD; aortic aneurysm; the aggregate of cardiomyopathy and myocarditis and endocarditis; the aggregate of atrial fi brillation and fl utter, PVD, other CVD; CKD Lanphear and colleagues,30 Navas-Acien and colleagues31 (Continues on next page) www.thelancet.comVol 380 December 15/22/29, 2012 2227

  5. Articles Source of relative risks Theoretical- minimum-risk exposure distribution Exposure defi nition Outcomes Subgroup Main data sources for exposure Exposure estimation method (Continued from previous page) 4. Child and maternal undernutrition 4.1. Suboptimal breastfeeding 4.1.1. Non- exclusive breastfeeding predominant, partial, or no breastfeeding 4.1.2. Discontinued breastfeeding breastfeeding Intestinal infectious diseases; the aggregate of lower respiratory infections, upper respiratory infections, and otitis media Intestinal infectious diseases Age 0–5 months Population surveys Spatiotemporal Gaussian process regression19–21 Spatiotemporal Gaussian process regression19–21 Spatiotemporal Gaussian process regression19–21 Proportion of children younger than 6 months with Lamberti and colleagues,32 Black and colleagues10 Lamberti and colleagues,32 Black and colleagues10 Black and colleagues10 All children exclusively breastfed for fi rst 6 months Continued breastfeeding until 2 years Proportion of children aged 6–23 months with discontinued Age 6–23 months Population surveys Age <5 years Examination surveys and epidemiological studies 4.2. Childhood underweight Proportion of children less than –3 SDs, –3 to –2 SDs, and –2 to –1 SDs of the WHO 2006 standard weight-for-age curve Intestinal infectious diseases; measles; malaria; the aggregate of lower respiratory infections, upper respiratory infections, and otitis media; protein–energy malnutrition The aggregate of maternal haemorrhage and maternal sepsis; iron-defi ciency anaemia Intestinal infectious diseases; measles; vitamin A defi ciency Proportion of the WHO 2006 reference population in each SD range All ages Examination surveys and epidemiological studies Mixed eff ect regression Country-specifi c Stoltzfus and colleagues33 4.3. Iron defi ciency Haemoglobin, measure in g/L Age 6 months to 5 years Examination surveys and epidemiological studies DisMod 3 No childhood vitamin A defi ciency Imdad and colleagues,34,35 adjusted for background prevalence Yakoob and colleagues,36 adjusted for background prevalence 4.4. Vitamin A defi ciency Proportion of children with serum retinol concentration <70 μmol/L Intestinal infectious diseases; lower respiratory infections Age 1–4 years Food and Agricultural Organization food balance sheets Mixed eff ect regression No inadequate zinc intake 4.5. Zinc defi ciency Proportion of the population with inadequate zinc intake based on estimated mean daily amount of absorbable zinc per head in the food supply compared with mean physiological requirements 5. Tobacco smoking, including second-hand smoke 5.1. Tobacco smoking cancers and chronic respiratory disease, 10-year lagged tobacco smoking prevalence for all other causes including cardiovascular diseases CoDEM37 Age ≥25 years Mortality data including vital registration, verbal autopsy, and population surveys for smoking prevalence Tuberculosis; oesophageal cancer; nasopharynx cancer; pancreatic cancer; kidney and other urinary organ cancers; bladder cancer; stomach cancer; leukaemia; liver cancer; trachea, bronchus, and lung cancers; cervical cancer; colon and rectal cancer; mouth cancer; diabetes mellitus; IHD; cerebrovascular disease; the aggregate of HHD, atrial fi brillation and fl utter, aortic aneurysm, PVD, and other CVD; COPD; the aggregate of pneumoconiosis, asthma, other interstitial lung disease, and other chronic respiratory diseases The aggregate of lower respiratory infections, upper respiratory infections, and otitis media; trachea, bronchus, and lung cancers; IHD; cerebrovascular disease Smoking impact ratio for No tobacco smoking Re-analysis of the Cancer Prevention Study 238–40 Population surveys Spatiotemporal Gaussian process regression19–21 Age <5 years for the aggregate of lower respiratory infections, upper respiratory infections, and otitis media, age ≥25 years for all others 5.2. Second- hand smoke Proportion of children and non-smoking adults reporting exposure to second-hand smoke No second-hand smoke exposure US Department of Health and Human Services,41 Oono and colleagues,42 Jones and colleagues43,44 (Continues on next page) 2228 www.thelancet.comVol 380 December 15/22/29, 2012

  6. Articles Source of relative risks Theoretical- minimum-risk exposure distribution Exposure defi nition Outcomes Subgroup Main data sources for exposure Exposure estimation method (Continued from previous page) 6. Alcohol and drug use 6.1. Alcohol use Mixed eff ect regression45 Population surveys, alcohol sales, production, and other economic statistics All ages for alcohol use disorders, transport injuries, and interpersonal violence; ≥15 years for all others Tuberculosis; lower respiratory infections; oesophageal cancer; the aggregate of mouth cancer, nasopharynx cancer, cancer of other part of pharynx and oropharynx; liver cancer; larynx cancer; breast cancer; colon and rectum cancers; diabetes mellitus; IHD; ischaemic stroke; haemorrhagic and other non- ischaemic stroke; HHD; atrial fi brillation and fl utter; cirrhosis of the liver; pancreatitis; epilepsy; transport injuries; the aggregate of falls, drowning, fi re, heat, and hot substances, poisonings, exposure to mechanical forces, intentional self-harm, and interpersonal violence; alcohol use disorders Drug use disorders; schizophrenia; HIV/AIDS; the aggregate of acute hepatitis B, liver cancer secondary to hepatitis B, and cirrhosis of the liver secondary to hepatitis B; the aggregate of acute hepatitis C, liver cancer secondary to hepatitis C, and cirrhosis of the liver secondary to hepatitis C; intentional self-harm Average consumption of pure alcohol (measure in g/day) and proportion of the population reporting binge consumption of 0·06 kg or more of pure alcohol on a single occasion No alcohol consumption Published studies46–59 New meta- analyses, published studies60,61 DisMod 3 No use of cannabis, opioid, or amphetamines, no use of injecting drugs All ages Population surveys, registries, and indirect measures 6.2. Drug use Proportion of the population reporting use of cannabis, opioids, and amphetamines, proportion of the population reporting use of injecting drugs 7. Physiological risk factors 7.1. High fasting plasma glucose Fasting plasma glucose, measured in mmol/L Diabetes mellitus; IHD; cerebrovascular disease; CKD; tuberculosis Age ≥25 years Examination surveys and epidemiological studies Bayesian hierarchical regression62 Mean 4·9–5·3 mmol/L (SD 0·3 mmol/L) Meta- regression of pooled prospective studies63–66 Meta- regression of pooled prospective studies68,69 Meta- regression of pooled prospective studies71–73 7.2. High total cholesterol Total cholesterol, measured in mmol/L IHD; ischaemic stroke Age ≥25 years Examination surveys and epidemiological studies Bayesian hierarchical regression67 Mean 3·8–4·0 mmol/L (SD 0·9 mmol/L) Age ≥25 years Examination surveys and epidemiological studies Bayesian hierarchical regression70 7.3. High blood pressure Systolic blood pressure, measured in mm Hg RHD; IHD; ischaemic stroke, haemorrhagic and other non- ischaemic stroke; HHD; the aggregate of cardiomyopathy and myocarditis and endocarditis; the aggregate of atrial fi brillation and fl utter, PVD, and other CVD; aortic aneurysm; CKD Oesophageal cancer; gallbladder and biliary tract cancer; pancreatic cancer; kidney and other urinary organ cancers; breast cancer; uterine cancer; colon and rectum cancers; diabetes mellitus; IHD; ischaemic stroke; HHD; the aggregate of cardiomyopathy and myocarditis and endocarditis; the aggregate of atrial fi brillation and fl utter, PVD, and other CVD; CKD; osteoarthritis; low back pain Mean 110–115 mm Hg (SD 6 mm Hg) Age ≥25 years Examination surveys and epidemiological studies Bayesian hierarchical regression74 7.4. High body- mass index Body-mass index, measured in kg/m2 Mean 21·0–23·0 kg/m² (SD 1 kg/m²) Meta- regression of pooled prospective studies75–78 (Continues on next page) www.thelancet.comVol 380 December 15/22/29, 2012 2229

  7. Articles Source of relative risks Theoretical- minimum-risk exposure distribution Exposure defi nition Outcomes Subgroup Main data sources for exposure Exposure estimation method (Continued from previous page) 7.5. Low bone mineral density Standardised bone mineral density measured at the femoral neck Hip fracture falls; non-hip fracture falls Age ≥50 years Examination surveys and epidemiological studies DisMod 3 90th percentile of NHANES-III cohort79 by age Johnell and colleagues80 8. Dietery risk factors and physical inactivity 8.1. Diet low in fruits frozen, cooked, canned, or dried but excluding fruit juices and salted or pickled fruits) Age ≥25 years Nutrition and health surveys DisMod 3 Mean 300 g/day (SD 30 g/day) New meta- analysis, published studies81,82 The aggregate of oesophageal cancer, mouth cancer, the aggregate of nasopharynx cancer, cancer of other part of pharynx and oropharynx, and larynx cancer; trachea, bronchus, and lung cancers; IHD; ischaemic stroke; haemorrhagic and other non-ischaemic stroke The aggregate of mouth cancer, nasopharynx cancer, cancer of other part of pharynx and oropharynx, and larynx cancer; IHD; ischaemic stroke; haemorrhagic and other non-ischaemic stroke Dietary intake of fruits (fresh, Age ≥25 years Nutrition and health surveys DisMod 3 Mean 400 g/day (SD 30 g/day) New meta- analysis, He and colleagues81 8.2. Diet low in vegetables Dietary intake of vegetables (fresh, frozen, cooked, canned, or dried vegetables including legumes but excluding salted or pickled, juices, nuts and seeds, and starchy vegetables such as potatoes or corn) Dietary intake of whole grains (bran, germ, and endosperm in their natural proportions) from breakfast cereals, bread, rice, pasta, biscuits, muffi ns, tortillas, pancakes, and others Dietary intake of nut and seed foods including, for example, peanut butter Dietary intake of milk including non-fat, low-fat, and full-fat milk but excluding soya milk and other plant derivatives 8.3. Diet low in whole grains Diabetes mellitus; IHD; cerebrovascular disease Age ≥25 year Nutrition and health surveys DisMod 3 Mean 125 g/day (SD 12·5 g/day) Mellen and colleagues,83 de Munter and colleagues84 Kelly and colleagues85 IHD Age ≥25 years Nutrition and health surveys DisMod 3 Mean 114 g per week (SD 11·4 g per week) Mean 450 g/day (SD 45 g/day) 8.4. Diet low in nuts and seeds Colon and rectum cancers Age ≥25 years Nutrition and health surveys DisMod 3 World Cancer Research Fund and American Institute for Cancer Research82 World Cancer Research Fund and American Institute for Cancer Research,82 published studies86,87 World Cancer Research Fund and American Institute for Cancer Research,82 Micha and colleagues87 New meta- analysis 8.5. Diet low in milk 8.6. Diet high in red meat Dietary intake of red meat (beef, pork, lamb, and goat but excluding poultry, fi sh, eggs, and all processed meats) Colon and rectum cancers; diabetes mellitus Age ≥25 years Nutrition and health surveys DisMod 3 Mean 100 g per week (SD 10 g per week) Colon and rectum cancers; diabetes mellitus; IHD Age ≥25 years Nutrition and health surveys DisMod 3 No dietary intake of processed meat 8.7. Diet high in processed meat Dietary intake of meat preserved by smoking, curing, salting, or addition of chemical preservatives, including bacon, salami, sausages, or deli or luncheon meats like ham, turkey, and pastrami Age ≥25 years Nutrition and health surveys DisMod 3 No dietary intake of sugar-sweetened beverages Dietary intake of beverages with ≥50 kcal per 226·8 g serving, including carbonated beverages, sodas, energy drinks, fruit drinks but excluding 100% fruit and vegetable juices Diabetes mellitus and body-mass index with subsequent eff ects on: oesophageal cancer; gallbladder and biliary tract cancer; pancreatic cancer; kidney and other urinary organ cancers; breast cancer; uterine cancer; colon and rectum cancers; diabetes mellitus; IHD; ischaemic stroke; HHD; the aggregate of cardiomyopathy and myocarditis and endocarditis; the aggregate of atrial fi brillation and fl utter, PVD, and other CVD; CKD; osteoarthritis; low back pain 8.8. Diet high in sugar- sweetened beverages (Continues on next page) 2230 www.thelancet.comVol 380 December 15/22/29, 2012

  8. Articles Source of relative risks Exposure defi nition Outcomes Subgroup Main data sources for exposure Exposure estimation method Theoretical- minimum-risk exposure distribution (Continued from previous page) Colon and rectum cancers; IHD Age ≥25 years Nutrition and health surveys DisMod 3 Mean of 30 g/day (SD 3 g/day) World Cancer Research Fund and American Institute for Cancer Research,82 Pereira and colleagues88 World Cancer Research Fund and American Institute for Cancer Research,82 Cho and colleagues89 Updated published review of Mozaff arian and colleagues90 Jakobsen and colleagues,91 Mozaff arian and colleagues92 8.9. Diet low in fi bre Dietary intake of fi bre from all sources including fruits, vegetables, grains, legumes, and pulses Colon and rectum cancers; prostate cancer Age ≥25 years Nutrition and health surveys DisMod 3 Mean of 1200 mg/day (SD 120 mg/day) 8.10. Diet low in calcium Dietary intake of calcium from all sources, including milk, yogurt, and cheese Death caused by IHD Age ≥25 years Nutrition and health surveys DisMod 3 250 mg/day 8.11. Diet low in seafood omega-3 fatty acids Dietary intake of eicosapentaenoic acid and docosahexaenoic acid, measured in mg/day IHD Age ≥25 years Nutrition and health surveys DisMod 3 Substitution of present saturated fatty acid intake up to a mean intake of polyunsaturated fatty acids of 12% of energy (SD 1·2%) Mean of 0·5% of energy (SD 0·05%) Dietary intake of omega-6 fatty acids from all sources, mainly liquid vegetable oils, including soybean oil, corn oil, and saffl ower oil 8.12. Diet low in polyunsaturated fatty acids Dietary intake of trans fat from all sources, mainly partially hydrogenated vegetable oils and ruminant products 24 h urinary sodium, measured in mg/day IHD Age ≥25 years Nutrition and health surveys DisMod 3 Mozaff arian and colleagues93 8.13. Diet high in trans fatty acids Age ≥25 years Nutrition and health surveys DisMod 3 Mean of 1000 mg/day (SD 100 mg/day) Re-analysis of observational studies for stomach cancer and randomised studies for blood pressure lowering82,94 8.14. Diet high in sodium Stomach cancer; systolic blood pressure which has eff ects on: RHD; IHD; ischaemic stroke, haemorrhagic and other non-ischaemic stroke; HHD; the aggregate of cardiomyopathy and myocarditis and endocarditis; the aggregate of atrial fi brillation and fl utter, PVD, and other CVD; aortic aneurysm; CKD Breast cancer; colon and rectum cancers; diabetes mellitus; IHD; ischaemic stroke Age ≥25 years Population surveys DisMod 3 All individuals are highly active (level 3) Danaei and colleagues11 Proportion of the population in categories of physical activity: level 0, <600 MET-minutes per week (inactive); level 1, 600–3999 MET-minutes per week (low-active); level 2, 4000– 7999 MET-minutes per week (moderately active); and level 3, ≥8000 MET-minutes per week (highly active) 8.15. Physical inactivity and low physical activity* 9. Occupational risk factors 9.1. Occupational carcinogens 9.1.1. Occupational exposure to asbestos Age ≥15 years Vital registration mortality data, asbestos production, import, and export statistics Spatiotemporal Gaussian process regression19–21 Ovarian cancer; other neoplasms; larynx cancer; trachea, bronchus, and lung cancers Cumulative exposure to asbestos using mesothelioma in a smoking impact ratio analogue No exposure to asbestos Published studies95–98 (Continues on next page) www.thelancet.comVol 380 December 15/22/29, 2012 2231

  9. Articles Source of relative risks Theoretical- minimum-risk exposure distribution Exposure defi nition Outcomes Subgroup Main data sources for exposure Exposure estimation method (Continued from previous page) 9.1.2. Occupational exposure to arsenic Proportion of population ever exposed (by taking into account worker turnover)99,100 based on distribution of the population in nine industries† Proportion of population ever exposed (by taking into account worker turnover)99,100 based on distribution of the population in nine industries† Proportion of population ever exposed (by taking into account worker turnover)99,100 based on distribution of the population in nine industries† Proportion of population ever exposed (by taking into account worker turnover)99,100 based on distribution of the population in nine industries† Proportion of population ever exposed (by taking into account worker turnover)99,100 based on distribution of the population in nine industries† Proportion of population ever exposed (by taking into account worker turnover)99,100 based on distribution of the population in nine industries† Proportion of population ever exposed (by taking into account worker turnover)99,100 based on distribution of the population in nine industries† Proportion of population ever exposed (by taking into account worker turnover)99,100 based on distribution of the population in nine industries† Proportion of population ever exposed (by taking into account worker turnover)99,100 based on distribution of the population in nine industries† Proportion of population ever exposed (by taking into account worker turnover)99,100 based on distribution of the population in nine industries† Spatiotemporal Gaussian process regression19–21 Lee-Feldstein101 Trachea, bronchus, and lung cancers Age ≥15 years Labour force surveys, censuses, and International Information System on Occupational Exposure to Carcinogens Labour force surveys, censuses, and International Information System on Occupational Exposure to Carcinogens Labour force surveys, censuses, and International Information System on Occupational Exposure to Carcinogens Labour force surveys, censuses, and International Information System on Occupational Exposure to Carcinogens Labour force surveys, censuses, and International Information System on Occupational Exposure to Carcinogens Labour force surveys, censuses, and International Information System on Occupational Exposure to Carcinogens Labour force surveys, censuses, and International Information System on Occupational Exposure to Carcinogens Labour force surveys, censuses, and International Information System on Occupational Exposure to Carcinogens Labour force surveys, censuses, and International Information System on Occupational Exposure to Carcinogens Labour force surveys, censuses, and International Information System on Occupational Exposure to Carcinogens No occupational exposure to carcinogens Spatiotemporal Gaussian process regression19–21 Leukaemia Age ≥15 years Khalade and colleagues102 9.1.3. Occupational exposure to benzene No occupational exposure to carcinogens Trachea, bronchus, and lung cancers Age ≥15 years Spatiotemporal Gaussian process regression19–21 Schubauer- Berigan and colleagues103 9.1.4. Occupational exposure to beryllium No occupational exposure to carcinogens Spatiotemporal Gaussian process regression19–21 Hutchings and colleagues95 9.1.5. Occupational exposure to cadmium Trachea, bronchus, and lung cancers Age ≥15 years No occupational exposure to carcinogens Spatiotemporal Gaussian process regression19–21 9.1.6. Occupational exposure to chromium Trachea, bronchus, and lung cancers Age ≥15 years No occupational exposure to carcinogens Rosenman and colleagues104 Trachea, bronchus and lung cancers Age ≥15 years Spatiotemporal Gaussian process regression19–21 Lipsett and colleagues105 9.1.7 Occupational exposure to diesel engine exhaust 9.1.8. Occupational exposure to second-hand smoke 9.1.9. Occupational exposure to formaldehyde No occupational exposure to carcinogens Spatiotemporal Gaussian process regression19–21 Stayner and colleagues106 Trachea, bronchus, and lung cancers Age ≥15 years No occupational exposure to carcinogens Spatiotemporal Gaussian process regression19–21 Leukaemia; nasopharynx cancer Age ≥15 years No occupational exposure to carcinogens Collins and colleagues,107 Hauptmann and colleagues108 Grimsrud and colleagues109,110 Trachea, bronchus, and lung cancers Age ≥15 years Spatiotemporal Gaussian process regression19–21 9.1.10. Occupational exposure to nickel No occupational exposure to carcinogens Spatiotemporal Gaussian process regression19–21 Armstrong and colleagues111 9.1.11. Occupational exposure to polycyclic aromatic hydrocarbons 9.1.12. Occupational exposure to silica Trachea, bronchus, and lung cancers Age ≥15 years No occupational exposure to carcinogens Spatiotemporal Gaussian process regression19–21 Proportion of population ever exposed (by taking into account worker turnover)99,100 based on distribution of the population in nine industries† Proportion of population ever exposed (by taking into account worker turnover)99,100 based on distribution of the population in nine industries† Trachea, bronchus, and lung cancers Age ≥15 years Labour force surveys, censuses, and International Information System on Occupational Exposure to Carcinogens Labour force surveys, censuses, and International Information System on Occupational Exposure to Carcinogens No occupational exposure to carcinogens Kurihara and colleagues112 Larynx cancer Age ≥15 years Spatiotemporal Gaussian process regression19–21 Soskolne and colleagues113 9.1.13. Occupational exposure to sulphuric acid No occupational exposure to carcinogens (Continues on next page) 2232 www.thelancet.comVol 380 December 15/22/29, 2012

  10. Articles Theoretical- minimum-risk exposure distribution Source of relative risks Exposure defi nition Outcomes Subgroup Main data sources for exposure Exposure estimation method (Continued from previous page) Asthma Age ≥15 years Labour force surveys and censuses Spatiotemporal Gaussian process regression19–21 Proportion of population exposed based on distribution of the population in eight occupational groups (professional, technical, and related workers; administrative and managerial workers; clerical and related workers; sales workers; service workers; agriculture, animal husbandry, and forestry workers, fi shermen and hunters; production and related workers; and transport equipment operators and labourers) Proportion of population exposed based on distribution of the population in nine industries† 9.2. Occupational asthmagens Published studies114–116 Background asthmagen exposures COPD Age ≥15 years Labour force surveys and censuses Spatiotemporal Gaussian process regression19–21 New meta- analysis 9.3. Occupational particulate matter, gases, and fumes 9.4. Occupational noise No occupational exposure to particulates, gases, or fumes Hearing loss Age ≥15 years Labour force surveys and censuses Spatiotemporal Gaussian process regression19–21 Spatiotemporal Gaussian process regression19–21 Spatiotemporal Gaussian process regression19–21 Proportion of population exposed based on distribution of the population in nine industries† Fatal occupational injury Background noise exposure New meta- analysis ·· Age ≥15 years International Labour Organization injury database 9.5. Occupational risk factors for injuries 9.6. Occupational low back pain ·· Five injury deaths per 1 000 000 person-years Low back pain Age ≥15 years Labour force surveys and censuses Proportion of population exposed based on distribution of the population in eight occupational groups (professional, technical, and related workers; administrative and managerial workers; clerical and related workers; sales workers; service workers; agriculture, animal husbandry, and forestry workers, fi shermen and hunters; production and related workers; and transport equipment operators and labourers) New meta- analysis All individuals have the ergonomic factors of clerical and related workers 10. Sexual abuse and violence 10.1. Childhood sexual abuse* Alcohol use disorders, unipolar depressive disorders, intentional self-harm All ages Population surveys and epidemiological studies DisMod 3 No childhood sexual abuse New meta- analysis Proportion of the population who have ever experienced childhood sexual abuse, defi ned as the experience with an older person of unwanted non-contact, contact abuse, or intercourse, when aged 15 years or younger Proportion of the population who have ever experienced one or more acts of physical or sexual violence by a present or former partner since age 15 years Population surveys and epidemiological studies DisMod 3 No intimate partner violence New meta- analysis, Beydoun and colleagues117 Age 15–49 years for abortion, ≥15 years for all others Abortion, unipolar depressive disorders, intentional self-harm, interpersonal violence 10.2. Intimate partner violence* IHD=ischaemic heart disease. COPD=chronic obstructive pulmonary disease. CVD=cardiovascular and circulatory diseases. RHD=rheumatic heart disease. PVD=peripheral vascular disease. CKD=chronic kidney disease. HHD=hypertensive heart disease *Not assessed for 1990 because of absence of exposure data. †Agriculture, hunting, forestry, and fi shing; mining and quarrying; wholesale and retail trade and restaurants and hotels; manufacturing; electricity, gas, and water; transport, storage, and communication; construction; fi nancing, insurance, real estate, and business services; and community, social, and personal services. Table 1: Risk factors included, exposure variables, theoretical-minimum-risk exposure distributions, and outcomes aff ected www.thelancet.comVol 380 December 15/22/29, 2012 2233

  11. Articles diameter (PM2·5) in ambient air: TM5 estimates—based on a nested three-dimensional global atmospheric chem- istry transport model—which simulates both particulate matter and ozone at a high spatial resolution;22,23,121 and satellite-based estimates, which are based on satellite observations of aerosol optical depth, a measure of light extinction by aerosols in the total atmospheric column.25 TM5 and satellite-based estimates of PM2·5, measured in μg/m³, were averaged at a 0·1° × 0·1° grid cell resolution (equivalent to roughly 11 km × 11 km at the equator) and linked to available measures of PM2·5 from ground-based monitors. We used a regression model with the average of TM5 and satellite-based estimates as the predictor to estimate ground-based PM2·5 for all grid cells.26 For ozone, we relied solely on the TM5 model. Few population-based surveys have measured zinc defi ciency based on serum zinc concentration;122 however, intervention trials show a benefi t of zinc supplementation for reduction of diarrhoeaand lower respiratory infections in populations that have high zinc defi ciency.10 Because of the paucity of data for serum zinc concentrations, we measured zinc defi ciency at the population level on the basis of dietary sources of zinc, expanding on previous work of the International Zinc Nutrition Consultative Group.123 This approach uses national food balance sheets produced by the UN Food and Agriculture Organization to estimate a country-specifi c mean fractional absorption of zinc. The estimated mean daily per person amount of absorbable zinc in the food supply was compared with the mean physiological requirements of the population to calculate the percentage of the population with inadequate zinc intake. University of Western Australia, Perth, WA, Australia (Prof F Bull PhD); Health Canada, Ottawa, ON, Canada (R T Burnett PhD, J M Zielinski PhD); Colorado School of Public Health, Aurora, CO, USA (Prof T E Byers MD); National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA (H Chen PhD, S London MD); Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan (Prof A T-A Cheng MD); Health Eff ects Institute, Boston, MA, USA (A Cohen MPH); Victorian Infectious Diseases Reference Laboratory, Melbourne, VIC, Australia (B C Cowie MBBS); Clinical Trial Services Unit (P McGale PhD), University of Oxford, Oxford, UK (Prof S Darby PhD); MRC Hearing and Communication Group, Eff ects of risk factors on disease outcomes Table 1 shows the sources of eff ect sizes per unit of exposure for each risk factor. Some eff ect sizes were based on meta-analyses of epidemiological studies. For several risk factors without recent systematic reviews or for which evidence had not recently been synthesised, new meta- analyses were done as part of GBD 2010. We used eff ect sizes that had been adjusted for measured confounders but not for factors along the causal pathway. For example, eff ect sizes for body-mass index were not adjusted for blood pressure. For some risk–outcome pairs, evidence is only available for the relative risk (RR) of morbidity or mortality. In these cases, we assumed that the reported RR would apply equally to morbidity or mortality, unless evidence suggested a diff er ential eff ect. For example, studies of ambient particulate matter pollution suggest a smaller eff ect on incidence of cardio vascular and respiratory disease than on mortality;124–126 the published work on consumption of seafood omega-3 fatty acids suggests an eff ect on ischaemic heart disease mortality but not on incidence of ischaemic heart disease.90 Evidence for the RR of diarrhoea from unimproved water and sanitation is complicated by the complexity of available epidemiological studies, since the comparison groups varied greatly between studies. The comparison group used varied widely. For example, some studies compare an improved water source (eg, piped water) with an unimproved water source (eg, river water); in other studies the comparison is between two diff erent types of improved water source (eg, piped water vs a protected well). Furthermore, studies often examine a combination of water, sanitation, and hygiene inter- ventions. Previous reviews have yielded confl icting results about the magnitude of the eff ect sizes.127–131 We re-examined the epidemiological evidence for the eff ects of water and sanitation by reviewing the relation between water, sanitation and hygiene, and diarrhoea, starting with previous reviews.128–131 We did a meta- regression of 119 studies that was designed to adjust for intervention and baseline group characteristics. First, we compared indicator variables for each of the intervention components (improved sanitation, hygiene, point-of-use water treatment, source water treatment, and piped water) with a reference category (improved water source). Second, we also included indicator variables for the base- line characteristics—ie, whether the baseline was an unimproved or improved water source or sanitation—as covariates to account for the heterogeneous control groups. Our analysis showed a signifi cant eff ect of both improved water and improved sanitation compared with unimproved water and sanitation; we did not note a Manchester, UK (Prof A Davis PhD); European Commission, Joint Research Centre, Brussels, Belgium (F Dentener PhD, R Van Dingenen PhD); Beth Israel Medical Center, New York City, NY, USA (D C Des Jarlais PhD); Federal Ministry of Health, Panel: The World Cancer Research Fund grading system118 Convincing evidence Evidence based on epidemiological studies showing consistent associations between exposure and disease, with little or no evidence to the contrary. The available evidence is based on a substantial number of studies including prospective observational studies and where relevant, randomised controlled trials of suffi cient size, duration, and quality showing consistent eff ects. The association should be biologically plausible. Probable evidence Evidence based on epidemiological studies showing fairly consistent associations between exposure and disease, but for which there are perceived shortcomings in the available evidence or some evidence to the contrary, which precludes a more defi nite judgment. Shortcomings in the evidence may be any of the following: insuffi cient duration of trials (or studies); insuffi cient trials (or studies) available; inadequate sample sizes; or incomplete follow-up. Laboratory evidence is usually supportive. The association should be biologically plausible. Possible evidence Evidence based mainly on fi ndings from case-control and cross-sectional studies. Insuffi cient randomised controlled trials, observational studies, or non-randomised controlled trials are available. Evidence based on non-epidemiological studies, such as clinical and laboratory investigations, is supportive. More trials are needed to support the tentative associations, which should be biologically plausible. Insuffi cient evidence Evidence based on fi ndings of a few studies which are suggestive, but insuffi cient to establish an association between exposure and disease. Little or no evidence is available from randomised controlled trials. More well-designed research is needed to support the tentative associations. 2234 www.thelancet.comVol 380 December 15/22/29, 2012

  12. Articles signifi cantly greater eff ect of piped water or point-of-use or source water treatment compared with improved water. Particulate matter smaller than 2·5 μm is a common useful indicator of the risk associated with exposure to a mixture of pollutants from diverse sources and in diff erent environments, including ambient particulate matter pollution from transportation, wind-blown dust, burning of bio mass, and industrial sources; second-hand smoke; burning of biomass and coal for household energy; and active smoking.132,133 However, existing studies cover only small concentration ranges—for example, ambient particulate matter pollution studies have been restricted to yearly average concentrations of particulate matter smaller than 2·5 μm of roughly 5 μg/m3 to 30 μg/m3,134–137 but much higher concentrations of ambient particulate matter have been recorded in polluted cities in Asia and elsewhere. The relation between concentration of small particulate matter and risk of disease is probably non-linear.132,133 To inform estimates of risk across the full range of concentrations, we used the approach of Pope and colleagues132 and integrated epidemiological evidence for the hazardous eff ects of particulate matter at diff erent concentrations from diff erent sources and environments. Methods for estimation of the integrated exposure– response curves for each cause are described elsewhere.138 Briefl y, we compiled study-level estimates of the RR of mortality associated with any or all of ambient air pollution, second-hand smoke, household air pollution, and active smoking for the following outcomes: ischaemic heart disease, stroke, lung cancer, chronic obstructive pulmonary disease, and acute lower respiratory tract infection in children. We evaluated several non-linear functions with up to three parameters for fi tting the integrated exposure– response relation and assessed them by calculation of the root mean squared error. An exponential decay with a power of concentration was the functional form that provided the best fi t for all fi ve outcomes. The integrated exposure–response curve was then used to generate eff ect sizes specifi c to the amount of ambient particulate matter smaller than 2·5 μm for each population. For ischaemic heart disease and stroke, evidence shows that household air pollution aff ects intermediate outcomes, such as blood pressure,139 but not clinical events. For acute lower respiratory tract infection, the integrated exposure– response curve enabled us to extrapolate beyond the partial exposure–response measured in the RESPIRE trial.140 For eff ects of household air pollution on chronic obstructive pul monary disease and lung cancer we use the eff ect size based on new systematic reviews and meta-analyses. Several dietary factors aff ect ischaemic heart disease and stroke, including consumption of fruits, vegetables, nuts and seeds, whole grains, processed meat, polyunsaturated fats, and seafood omega-3 fatty acids.81,83,85,87,90–92,141,142 We updated earlier systematic reviews and meta-analyses for fruits, vegetables, and seafood omega-3 fatty acids, which included both observational and intervention studies if available. A systematic review143 of randomised clinical trials of supplementation with seafood omega-3 fatty acids reported non-signifi cant eff ects on several outcomes, and a signifi cant eff ect for mortality from ischaemic heart disease—the primary outcome in GBD 2010. In view of this fi nding, we tested whether a signifi cant diff erence exists between the randomised clinical trials of seafood omega-3 fatty acid supplementation and observational studies of seafood-omega 3 fatty acid intake. The eff ect of seafood omega-3 fatty acids tended to be lower in randomised controlled trials than in observational studies, however, this diff erence was not statistically signifi cant (p=0·057). Therefore, we used the eff ect size based on the combination of randomised clinical trials and observational studies but also did a sensitivity analysis with the eff ect size based on randomised clinical trials. Estimates of the RR associated with dietary risk factors are based largely on observational studies that control for age, sex, and other cardiovascular risk factors. However, some early observational studies do not fully control for other dietary components. Protective dietary risk factors such as consumption of fruits, vegetables, and whole grains, tend to be positively correlated with each other and negatively correlated with harmful dietary risk factors such as consumption of processed meat. There- fore, RRs estimated for single risk factors in observational studies could overestimate the protective or harmful eff ect of that risk factor. In eff ect, the partially adjusted RR will include some of the eff ects associated with other correlated diet components, particularly since the exposure measure for dietary risk factors is energy adjusted to a standard calorie intake. To examine this issue, we did further empirical assessments using studies of dietary patterns and randomised controlled feeding studies. Studies of dietary patterns144–148 have estimated the eff ects of benefi cial diets (prudent or Mediterranean diets) and harmful diets (western diets); these studies capture the overall eff ects of diff erences in dietary components. For example, a prudent diet has lots of fruits, vegetables, fi sh, and whole grains. For each of the dietary pattern studies we com- puted the estimated RR for dietary pattern groups with the RRs from the meta-analyses of single dietary risk factors, the reported diff erences in dietary intake, and assuming a multiplicative relation between RRs for individual components. Results of this internal validation study show that overall, estimation of the eff ect of dietary pattern based on the RRs reported for single risk factors was much the same as the eff ect reported in the study; across four large cohort studies of seven dietary patterns the average ratio for the estimated RR reduction compared with the measured RR reduction was 0·98. In addition to the dietary pattern studies, we also investigated the evidence for the eff ects of dietary risk factors from randomised controlled feeding studies, such as DASH149 and OmniHeart,150 which measured the eff ect of dietary changes on blood pressure and LDL cholesterol. We used meta-regression to estimate the pooled eff ect of Khartoum, Sudan (S Eltahir Ali Mcs); Mayo Clinic, Rochester, MN, USA (P J Erwin MLS); Institute of Public Health (J Powles MBBS), Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK (S Fahimi MD); Digestive Disease Research Center (Prof R Malekzadeh MD), Tehran University of Medical Sciences, Tehran, Iran (F Farzadfar MD); Carnegie Mellon University, Pittsburgh, PA, USA (S Flaxman BA); University of Edinburgh, Edinburgh, Scotland, UK (Prof F G R Fowkes FRCPE); Addiction Info Switzerland, Lausanne, Switzerland (Prof G Gmel PhD); Centre for Addiction and Mental Health, Toronto, ON, Canada (K Graham PhD, Prof J T Rehm PhD, K Shield MHSc); University of Otago, Dunedin, New Zealand (R Grainger PhD, T R Merriman PhD); Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute (Q Lan PhD), National Institutes of Health, Bethesda, MD, USA (R Grainger, B Grant PhD); University of Bristol, Bristol, UK (Prof D Gunnell DSc); College of Physicians and Surgeons (Prof M M Weissman PhD), Mailman School of Public Health, Columbia University, New York City, NY, USA (H R Gutierrez BS, Prof M M Weissman); Parnassia Psychiatric Institute, The Hague, Netherlands (Prof H W Hoek MD); Australian National University, Canberra, ACT, Australia (A Hogan PhD); Albert Einstein College of Medicine, Yeshiva University, New York City, NY, USA (H D Hosgood III PhD); Dalla Lana School of Public Health , University of Toronto, Toronto, ON, Canada (Prof H Hu MD); US Environmental Protection Agency, Washington, DC, USA (B J Hubbell PhD); MRC-HPA Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK (Prof M Ezzati PhD, S J Hutchings BSc, L Rushton PhD); University of Port Harcourt, Port Harcourt, www.thelancet.comVol 380 December 15/22/29, 2012 2235

  13. Articles as the independent variable with an age intercept (RR=1) at age 110 years. Uncertainty in the RR was generated by simulation analyses.152 The causal association between a risk factor and a disease outcome is often informed by a wider body of evidence than epidemiological studies of RRs for specifi c measures of exposure, especially when disease-specifi c and age- specifi c RRs are needed. For example, although smoking is an established cause of cardiovascular diseases, when cohorts are analysed in fi ne age groups, the 95% CI for the eff ect of smoking on stroke spans 1·0 in several age groups.38 Similarly, randomised supplementation were designed to detect eff ects on total mortality.36,153 Re-analysis of the same trials for disease- specifi c outcomes, which is necessary to extrapo late eff ects to populations with diff erent causes of death, reduced their statistical power and gave 95% CIs that spanned 1·0. To use the broad evidence while accounting for the uncertainty of the subgroup RRs, we included in the uncertainty analysis all draws of the RR distribution, including those that show a protective eff ect as long as the overall relation for the risk factor across all ages is signifi cant. In other cases, if there are diff erent degrees of exposure for a risk factor, in some exposure categories the RR might not be signifi cant. We have included draws from these posterior distributions if the mean values show a dose–response relation. To fairly represent the extent of our epidemiological knowledge, we have included in the uncertainty analysis draws from the posterior distribution for those exposure categories that show a protective eff ect. fruits, vegetables, nuts and seeds, whole grains, fi sh, and dietary fi bre on systolic blood pressure and LDL cholesterol, based on all random ised controlled feeding studies (six treatment groups from three studies for blood pressure and six treatment groups from two studies for cholesterol). When translated into an eff ect using the RRs of blood pressure and cholesterol for ischaemic heart disease, the average ratio of the estimated to measured RR reduction was 1·07 for all components and 0·85 when excluding fi sh, which has mechanisms additional to lowering blood pressure and cholesterol.151 These two supplementary analyses suggest that the RRs estimated in the meta-analyses of single dietary risk factors are unlikely to be signifi cantly biased because of residual confounding due to other diet components. Pooled epidemiological studies of cardiovascular disease risks show that the RR decreases with age, and that the inverse age association is roughly log-linear. Based on a pooled analysis of several risk factors (high blood pressure, high fasting plasma glucose, high total cholesterol, and tobacco smoking), the age at which the RR reaches 1 is often between 100 and 120 years. We therefore estimated age-specifi c RRs for all cardiovascular risk factors by meta-regression of available data with logRR as the dependent variable and median age at event Nigeria (S E Ibeanusi MBBS); Department of Ophthalmology, Medical Faculty Mannheim of the Ruprecht Karls University Heidelberg, Heidelberg, Germany (Prof J B Jonas MD); Fudan University, Shanghai, China (H Kan MD); University of Sheffi eld, Sheffi eld, UK (Prof J A Kanis MD); Department of Preventive Medicine, University of Ulsan College of Medicine, Seoul, South Korea (Prof Y-H Khang MD); Spinal Cord Injury Network, Glebe, New Zealand (C Kok PhD); School of Dentistry and Oral Health (Prof R Lalloo PhD), Population and Social Health Research Program (Prof R Lalloo), Griffi th University, Brisbane, QLD, trials of zinc Australia (N J C Stapelberg MBBS); Nova Southeastern University, Fort Lauderdale, FL, USA (J L Leasher OD); Critical Care and Trauma Division (Y Li MBBS), The George Institute for Global Health, Sydney, NSW, Australia (Prof B Neal PhD); Miller School of Medicine, University of Miami, Miami, FL, USA (Prof S E Lipshultz MD, Prof J D Wilkinson MD); Queen Mary University of London, Disability-adjusted life-years (%) London, UK Physiological risk factors High blood pressure High total cholesterol High body-mass index High fasting plasma glucose Alcohol use Tobacco smoking, including second-hand smoke Dietary risk factors and physical inactivity Diet low in nuts and seeds Physical inactivity and low physical activity Diet low in fruits Diet low in seafood omega-3 fatty acids Diet low in whole grains Diet high in sodium Diet high in processed meat Diet low in vegetables Diet low in fi bre Diet low in polyunsaturated fatty acids Diet high in trans fatty acids Diet high in sugar-sweetened beverages Air pollution Ambient particulate matter pollution Household air pollution from solid fuels Other environmental risks Lead exposure (Prof W Marcenes PhD); Dalhousie University, Halifax, NS, Canada (Prof R Martin PhD, A van Donkelaar PhD); Global Alliance for Clean Cookstoves, Washington, DC, USA (S Mehta PhD); Department of Medicine, University of Cape Town, Cape Town, South Africa (Prof G A Mensah MD); Agricultural University of Athens, Athens, Greece (R Micha); China Medical Board, Boston, MA, USA (C Michaud MD); United Nations Population Division, New York City, NY, USA (V Mishra PhD); Queensland Univeristy of Technology, Brisbane, QLD, Australia (Prof L Morawska PhD); Rheumatology Department (Prof J M Nolla MD), Institut d’Investigacio Biomedica de Bellvitge, Hospital Universitari de Bellvitge, Barcelona, Spain (L Sanchez-Riera); School of Public Health (S B Omer MBBS, K Steenland PhD), School of Medicine, Emory University, Atlanta, GA, USA (S B Omer); Deakin University, Melbourne, 53% 29% 23% 16% 33% 31% Theoretical-minimum-risk exposure distributions for counterfactual comparison In the comparative risk assessment framework, disease burden attributable to risk factors is calculated with reference to an alternative (counterfactual) distribution of exposure; in GBD 2010, we used an optimal exposure distribution (in terms of eff ect on population health), termed the theoretical-minimum-risk exposure distri- bution. For several risk factors, such as tobacco smoking, the choice of theoretical-minimum-risk exposure dis- tribution is clear—ie, 100% of the population being lifelong non-smokers. However, for many of the other risk factors zero exposure is not possible (eg, blood pressure), or the lowest amount of exposure that is still benefi cial is not yet established. In these cases the theoretical-minimum-risk exposure distribution was informed by two consider ations: the availability of convincing evidence from epidemiological studies that support a continuous reduc tion in risk of disease to the chosen distribution; and a distribution that is theoretically possible at the population level (table 1). For some risk factors, new evidence has resulted in a revision of the theoretical-minimum-risk exposure distribution compared to the previous comparative risk assessment. For example, the previous distribution for systolic blood pressure was a mean of 115 mm Hg (SD 6).6 However, subsequent randomised trials154 of blood 40% 31% 30% 22% 17% 17% 13% 12% 11% 9% 9% 2% 22% 18% 4% VIC, Australia (Prof R Osborne PhD); California Environmental Protection Agency, Sacramento, CA, USA Table 2: Proportion of ischaemic heart disease disability-adjusted life-years attributable to individual risk factors, worldwide, 2010 2236 www.thelancet.comVol 380 December 15/22/29, 2012

  14. Articles pressure-lowering medication suggest that the benefi ts of lowering blood pressure could continue to 110 mm Hg or lower. On this basis, we changed the theoretical-minimum- risk exposure distribution to a mean of 110–115 mm Hg (SD 6). For other exposures, the distribution was increased because of data from new epidemiological studies75— eg, for mean body-mass index we used 21–23 kg/m², compared with 21 kg/m² used previously. For ambient particulate matter pollution, we did a sensitivity analysis with an alternative theoretical- minimum-risk exposure distribution that included the eff ect of regional dust particulate matter. We did so because although particulate exposure from dust could theoretically be reduced, it would probably be prohibitively expensive and could only be done over a very long period. This factor is particularly relevant in areas with high amounts of dust—eg, deserts. Dusty grid cells were identifi ed as those with an ambient air concentration of PM2·5 of 36 μg/m³ or more and where the dust fraction from the TM5 chemical transport model was 50% or more. attributable fraction for risk factors for each age, sex, year, and cause according to the following formula: (B Ostro PhD); The World Bank, Washington DC, USA (K D Pandey PhD); South African Medical Research Council, Cape Town, South Africa (Prof C D H Parry PhD); St Michael Hospital, Toronto, ON, Canada (J Patra PhD); Centers for Medicare and Medicaid Services, Baltimore, MD, USA (P M Pelizzari MPH); Centre for Applied Biostatistics, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden (Prof M Petzold PhD); Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China (Prof M R Phillips MD); Brigham Young University, Provo, UT, USA (Prof C A Pope III PhD); Center for Disease Analysis, Louisville, CO, USA (H Razavi PhD); Ludwig Maximilians Universität Munich, Munich, Germany (E A Rehfuess PhD); University of California, Los Angeles, Los Angeles, CA, USA (Prof B Ritz MD); University of California, San Francisco, San Francisco, CA, USA (C Robinson BS); P Universidad Católica de Chile, Santiago, Chile (Prof J A Rodriguez-Portales MD); International Agency for Research on Cancer, Lyon, France (I Romieu MD, K Straif MD); Centre for Alcohol Policy Research, Turning Point Alcohol & Drug Centre, Fitzroy, SA, Australia (Prof R Room); Environmental and Occupational Health Sciences, University of Medicine and Dentistry of New Jersey, Newark, NJ, USA (A Roy ScD); Vanderbilt University, Nashville, TN, USA (Prof U Sampson MD); School of Public Health, University of Maryland, Baltimore, MD, USA (A Sapkota PhD); Stellenbosch University, Stellenbosch, South Africa (Prof S Seedat PhD); National Center for Injury Prevention and Control (D A Sleet PhD), Centers for Disease Control and Prevention, Baltimore, MD, USA (S T Wiersma MD); Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway (Prof L J Stovner PhD); New York University, New York City, NY, USA (Prof G D Thurston ScD); Voluntary Health Services, R ? ? PAF=1– 1–PAFr r=1 Where r is each individual risk factor, and R is the number of risk factors. This approach assumes that risk factors are independent—ie, it does not account for mediation, exposure correlation, or eff ect size modifi cation that might exist between risk factors in a cluster.155 To represent uncertainty in the estimates we used simulation analysis to take 1000 draws from the posterior distribution of exposure, RR, and each relevant outcome for each age, sex, country, year. We accounted for the correlation structure of uncertainty (ie, whether exposure in a country, age group, and sex is high or low might be related to whether it is high or low in other subgroups) by use of the same draw of exposure across diff erent outcomes and the same draw of RR across country, age, and sex subgroups when the RR does not vary by country, age, or sex. We otherwise assumed that the uncertainties in exposure, RR, and underlying burden attributable to the outcome were independent. We computed the mean deaths and DALYs attributable to each risk factor and risk factor cluster from the 1000 draws. The 95% uncertainty intervals (95% UI) were calculated as the 2·5th and 97·5th percentiles of the 1000 draws. We also computed the mean rank and 95% UI for the 43 risk factors included in the ranking list. The mean of the ranks for a risk factor was not necessarily equivalent to the rank of the mean deaths or mean DALYs attributable to the risk factor. Mortality and disease burden attributable to individual and clusters of risk factors We calculated the burden attributable to risk factors with continuous exposure by comparing the present distri- bution of exposure to the theoretical-minimum-risk exposure distribution for each age group, sex, year (1990 and 2010), and cause according to the following formula: m m RR x ? ?P1 x ? ?dx RR x ? ?P2 x ? ?dx – PAF= x=0 x=0 m RR x ? ?P1 x ? ?dx x=0 Role of the funding source The sponsor of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had fi nal responsibility for the decision to submit for publication. Where PAF is the population attributable fraction (burden attributable to risk factor), RR(x) is the RR at exposure level x, P1(x) is the (measured or estimated) population distribution of exposure, P2(x) is the counter- factual distribution of exposure (ie, the theoretical- minimum-risk exposure distribution), and m the maxi mum exposure level.4 Burden attributable to categorical exposures was calculated by comparing exposure categories to a reference category for each age, sex, year, and cause according to the following formula: Results Quantifi cation of risk factors in this analysis represents the eff ects of each individual risk factor, holding all other independent factors constant. The eff ects of multiple risk factors are not a simple addition of the individual eff ects and are often smaller than their sums,156 especially for cardiovascular diseases, which are aff ected by several risk factors (eg, table 2). The sum of the individual eff ects of just the metabolic risk factors at the global level is 121% and the summation of all the risks is greater than 400%. We estimated global attributable mortality and DALYs with uncertainty for 1990, and 2010, for each of the 67 risk factors and clusters of risk factors (table 3, 4 ). The appendix shows full results by region, year, age, and sex for attributable deaths and DALYs. Because of the interest in n PiRRi–1 ? ? PAF= i=1 n PiRRi–1 ? ?+1 i=1 Where RRi is the RR for exposure category i, Pi is the fraction of the population in exposure category i, and n is the number of exposure categories.4 We calculated the burden attributable to clusters of risk factors by computing the combined population www.thelancet.comVol 380 December 15/22/29, 2012 2237

  15. Articles Men Women Both sexes 1990 2010 1990 2010 1990 2010 Unimproved water and sanitation 365 244 (18 940–662 551) 147 857 (10 566–282 890) 252 779 (8032–480 822) ·· 1 549 448 (1 345 894–1 752 880) 2 309 166 (1 720 246–2 824 893) 77 087 (25 256–134 021) 109 224 (91 805–131 511) ·· 171 097 (6841–326 262) 59 463 (3880–120 264) 123 255 (2924–242 588) 350 629 (17 531–638 433) 140 150 (10 042–271 546) 244 207 (7348–460 913) 166 379 (6690–326 989) 56 663 (3604–115 704) 120 851 (3104–242 452) 715 873 (36 817–1 279 220) 288 007 (20 641–553 293) 496 986 (15 380–927 845) ·· 2 910 161 (2 546 184–3 286 508) 4 579 715 (3 717 711–5 365 013) 143 362 (47 539–251 885) 209 923 (177 673–243 565) ·· 337 476 (13 150–648 205) 116 126 (7518–233 136) 244 106 (6027–478 186) Unimproved water source Unimproved sanitation Air pollution Ambient particulate matter pollution Household air pollution from solid fuels Ambient ozone pollution ·· ·· ·· ·· 1 850 428 (1 614 010–2 082 474) 1 900 443 (1 378 832–2 518 572) 86 335 (30 551–153 776) 426 280 (341 744–541 465) 70 014 (9140–154 460) 356 266 (292 587–435 046) 739 863 (570 560–909 248) 293 449 (175 623–429 772) 257 771 (143 116–382 459) 35 678 (3475–79 940) 458 639 (366 866–561 352) 32 287 (21 925–37 449) 63 291 (32 070–104 030) 52 390 (9382–105 728) 4 507 059 (3 757 779–5 092 460) 4 251 424 (3 503 674–4 850 554) 255 634 (191 587–314 541) 3 249 978 (3 004 655–3 488 393) 3 140 109 (2 902 204–3 376 483) 109 420 (82 297–152 421) 1 360 712 (1 166 992–1 559 747) 2 270 549 (1 889 651–2 655 411) 66 274 (22 424–116 663) 100 699 (82 720–119 745) ·· 1 373 113 (1 187 639–1 563 793) 1 645 956 (1 265 509–2 089 785) 66 100 (21 362–115 225) 346 751 (281 555–413 370) 28 978 (4098–64 387) 317 772 (265 722–376 431) 698 442 (569 013–832 012) 251 368 (155 884–359 651) 218 117 (126 383–319 470) 33 251 (3091–73 804) 401 478 (325 516–484 452) 87 321 (62 505–107 021) 56 472 (28 192–91 464) 44 940 (7696–87 711) 1 790 228 (1 278 666–2 094 260) 1 443 924 (920 763–1 743 849) 346 304 (252 702–439 439) 1 768 073 (1 588 197–1 935 072) 1 720 059 (1 541 469–1 886 125) 48 385 (36 780–64 303) 3 223 540 (2 828 854–3 619 148) 3 546 399 (2 679 627–4 516 722) 152 434 (52 272–267 431) 773 030 (640 893–929 935) 98 992 (13 133–215 237) 674 038 (575 858–779 314) 1 438 305 (1 175 257–1 713 103) 544 817 (338 453–775 077) 475 888 (272 493–684 422) 68 929 (6445–153 290) 860 117 (715 742–1 033 573) 119 608 (93 261–139 985) 119 762 (61 723–191 846) 97 330 (17 575–190 527) 6 297 287 (5 395 769–7 006 942) 5 695 349 (4 755 779–6 421 611) 601 938 (447 705–745 328) 5 018 051 (4 680 954–5 321 362) 4 860 168 (4 533 106–5 153 283) 157 805 (124 639–209 873) Other environmental risks Residential radon 100 699 (82 720–119 745) 1 668 365 (1 396 689–1 986 532) 581 921 (370 598–814 551) 505 849 (302 585–720 858) 76 073 (7809–165 395) 1 065 774 (898 859–1 299 715) 128 675 (92 036–156 884) 168 203 (80 696–298 163) 132 071 (23 716–253 841) 1 649 238 (1 380 504–2 144 408) 1 244 106 (961 356–1 781 819) 405 132 (310 224–500 100) 1 394 778 (1 245 021–1 545 612) 1 374 578 (1 223 155–1 522 080) 21 895 (15 984–31 023) Lead exposure 109 224 (91 805–131 511) 1 805 224 (1 479 043–2 219 888) 693 103 (427 028–972 440) 612 059 (354 236–875 230) 81 044 (8643–178 237) 1 198 178 (997 627–1 484 105) 39 409 (30 677–47 108) 181 151 (85 775–341 439) 143 518 (27 797–276 850) 3 680 571 (3 213 427–4 229 530) 3 332 192 (2 871 957–3 840 033) 348 378 (273 555–425 310) 2 367 579 (2 201 233–2 555 818) 2 325 747 (2 153 733–2 512 207) 46 682 (33 063–78 398) 209 923 (177 673–243 565) 3 473 589 (2 906 896–4 175 138) 1 275 024 (802 142–1 772 745) 1 117 908 (663 274–1 576 633) 157 117 (16 188–341 702) 2 263 952 (1 927 356–2 735 821) 168 084 (130 444–197 085) 349 354 (170 504–632 149) 275 590 (51 274–529 451) 5 329 808 (4 778 526–6 049 296) 4 576 298 (4 068 753–5 312 438) 753 510 (585 131–912 313) 3 762 356 (3 508 021–4 030 022) 3 700 324 (3 451 511–3 967 436) 68 577 (50 706–102 395) Child and maternal undernutrition Suboptimal breastfeeding Non-exclusive breastfeeding Discontinued breastfeeding Childhood underweight Iron defi ciency Vitamin A defi ciency Zinc defi ciency Tobacco smoking (including second-hand smoke) Tobacco smoking Second-hand smoke Alcohol and drug use Alcohol use Drug use Physiological risk factors High fasting plasma glucose 1 051 401 (865 949–1 250 550) 936 749 (767 684–1 128 051) 3 412 588 (3 089 548–3 769 223) 887 047 (698 599–1 079 235) 52 816 (43 822–69 605) 1 749 058 (1 455 169–2 039 206) 961 614 (714 774–1 236 023) 4 750 581 (4 272 529–5 273 576) 1 632 766 (1 328 501–1 941 988) 103 440 (67 743–124 596) 1 052 773 (881 704–1 230 327) 1 009 172 (829 163–1 218 442) 3 880 598 (3 559 634–4 250 099) 1 076 502 (878 065–1 286 482) 50 455 (40 408–62 110) 1 607 214 (1 367 465–1 839 764) 1 057 196 (793 595–1 350 633) 4 645 279 (4 198 029–5 092 003) 1 738 466 (1 454 008–2 036 059) 84 146 (57 863–102 441) 2 104 174 (1 797 633–2 401 170) 1 945 920 (1 625 929–2 318 054) 7 293 185 (6 701 203–7 859 894) 1 963 549 (1 590 282–2 345 133) 103 270 (90 672–124 230) 3 356 271 (2 917 520–3 782 483) 2 018 811 (1 572 853–2 479 097) 9 395 860 (8 579 630–10 147 805) 3 371 232 (2 817 774–3 951 127) 187 586 (140 636–219 906) (Continues on next page) High total cholesterol High blood pressure High body-mass index Low bone mineral density 2238 www.thelancet.comVol 380 December 15/22/29, 2012

  16. Articles Men Women Both sexes 1990 2010 1990 2010 1990 2010 (Continued from previous page) Dietary risk factors and physical inactivity Diet low in fruits 4 473 276 (4 110 262–4 852 556) 2 013 415 (1 570 347–2 435 112) 779 747 (535 472–1 041 517) 649 676 (503 984–787 057) 1 041 726 (667 481–1 349 266) 34 838 (10 464–58 211) 13 888 (3859–23 763) 397 198 (85 536–688 905) 100 250 (69 485–134 139) 333 603 (149 007–521 712) 48 975 (32 814–66 562) 576 646 (418 376–735 746) 248 677 (117 929–381 787) 202 725 (144 395–260 843) 1 197 713 (776 962–1 589 448) ·· 6 687 621 (6 172 230–7 206 283) 2 837 481 (2 203 651–3 414 649) 1 017 500 (687 787–1 378 721) 963 640 (748 116–1 162 721) 1 389 433 (890 869–1 817 734) 54 093 (16 106–91 527) 21 330 (6175–37 340) 473 562 (103 608–842 923) 161 042 (111 700–219 563) 441 895 (201 062–693 234) 76 413 (51 653–103 188) 793 650 (574 241–1 010 930) 306 296 (140 873–473 149) 293 087 (209 155–371 284) 1 732 870 (1 122 107–2 301 781) 1 547 833 (1 264 464–1 835 192) 749 857 (580 954–941 322) 92 154 (57 261–127 678) 26 563 (14 454–36 593) 1915 (717–3496) 1542 (618–2706) 114 (44–192) 410 (179–670) 1361 (720–2014) 18 773 (9641–28 714) 17 189 (10 127–23 037) 486 (185–939) 6443 (1616–13 317) 4 057 558 (3 704 325–4 431 571) 1 653 787 (1 269 335–2 006 693) 674 309 (441 649–910 150) 580 600 (447 140–706 303) 872 483 (541 757–1 147 258) 33 312 (9745–57 799) 12 551 (3425–22 054) 334 476 (71 692–584 050) 83 548 (53 949–117 567) 250 541 (111 867–394 088) 33 330 (23 008–43 904) 466 440 (337 205–601 988) 199 388 (95 418–305 733) 164 736 (117 395–211 588) 1 047 642 (666 779–1 397 486) ·· 5 815 748 (5 380 274–6 261 225) 2 064 761 (1 593 495–2 507 876) 779 754 (521 285–1 040 304) 762 171 (592 879–919 709) 1 082 390 (663 158–1 441 054) 46 858 (13 085–80 413) 16 762 (4306–29 007) 367 296 (83 446–637 120) 138 480 (91 257–203 236) 300 994 (134 201–470 634) 49 181 (34 016–63 592) 596 246 (437 287–764 762) 227 307 (108 675–350 194) 222 173 (160 511–283 740) 1 371 438 (878 780–1 834 541) 1 636 107 (1 369 722–1 899 182) 102 250 (68 744–140 097) 25 943 (15 498–37 074) 7047 (3312–9681) 747 (275–1402) 1189 (434–2156) 49 (19–86) 145 (62–245) 570 (295–858) 3413 (1709–5262) 7046 (3935–9630) 245 (97–456) 2702 (743–5679) 8 530 835 (7 907 898–9 150 862) 3 667 202 (2 870 267–4 394 152) 1 454 057 (978 665–1 924 334) 1 230 276 (958 136–1 489 812) 1 914 209 (1 216 363–2 487 874) 68 150 (20 479–114 435) 26 439 (7374–45 232) 731 675 (158 044–1 257 423) 183 799 (127 938–240 028) 584 144 (260 065–914 729) 82 305 (57 324–108 535) 1 043 085 (757 418–1 327 627) 448 065 (213 262–687 396) 367 461 (265 936–467 609) 2 245 355 (1 459 900–2 966 107) ·· 12 503 370 (11 710 741–13 324 770) 4 902 242 (3 818 356–5 881 561) 1 797 254 (1 205 059–2 394 366) 1 725 812 (1 342 896–2 067 224) 2 471 823 (1 559 603–3 226 994) 100 951 (29 728–171 340) 38 092 (10 749–65 727) 840 857 (188 952–1 460 279) 299 521 (212 310–403 716) 742 888 (334 379–1 166 933) 125 594 (88 323–164 800) 1 389 896 (1 010 300–1 781 401) 533 603 (245 096–820 854) 515 260 (371 081–649 451) 3 104 308 (2 016 734–4 105 019) 3 183 940 (2 657 204–3 718 963) 852 107 (659 652–1 062 443) 118 097 (77 249–160 431) 33 610 (20 317–43 647) 2662 (1011–4860) 2731 (1111–4811) 163 (65–276) 555 (249–901) 1931 (1140–2799) 22 187 (12 180–33 213) 24 235 (16 094–31 803) 731 (301–1361) 9145 (2449–18 834) (Continues on next page) Diet low in vegetables Diet low in whole grains Diet low in nuts and seeds Diet low in milk Diet high in red meat Diet high in processed meat Diet high in sugar-sweetened beverages Diet low in fi bre Diet low in calcium Diet low in seafood omega-3 fatty acids Diet low in polyunsaturated fatty acids Diet high in trans fatty acids Diet high in sodium Physical inactivity and low physical activity Occupational risk factors 694 403 (541 113–858 435) 55 306 (37 867–80 887) 17 024 (11 044–26 605) 1155 (446–2210) 993 (426–1757) 61 (24–110) 214 (97–370) 729 (431–1133) 10 979 (6241–17 555) 10 171 (6878–15 272) 299 (117–584) 3578 (935–7585) 116 743 (74 642–164 679) 16 766 (11 866–24 842) 6033 (4012–9397) 463 (176–915) 770 (292–1422) 26 (10–47) 74 (33–130) 293 (171–490) 2060 (1180–3422) 3854 (2637–6207) 179 (77–325) 1425 (369–3031) 811 146 (623 674–1 010 107) 72 073 (50 753–101 233) 23 057 (16 939–33 009) 1618 (622–3039) 1764 (741–3085) 87 (35–152) 288 (131–494) 1022 (618–1578) 13 040 (7494–20 486) 14 025 (10 058–19 715) 478 (202–877) 5004 (1331–10 489) Occupational carcinogens Occupational exposure to asbestos Occupational exposure to arsenic Occupational exposure to benzene Occupational exposure to beryllium Occupational exposure to cadmium Occupational exposure to chromium Occupational exposure to diesel engine exhaust Occupational exposure to second-hand smoke Occupational exposure to formaldehyde Occupational exposure to nickel www.thelancet.comVol 380 December 15/22/29, 2012 2239

  17. Articles Men Women Both sexes 1990 2010 1990 2010 1990 2010 (Continued from previous page) Occupational exposure to polycyclic aromatic hydrocarbons Occupational exposure to silica 1638 3092 492 993 2130 4086 (772–2817) 7870 (5154–11 902) 1964 (531–4383) 31 666 (15 305–62 856) 207 366 (92 516–320 244) 0 (0–0) 400 064 (308 482–507 787) 0 (0–0) ·· (1394–5028) 14 205 (8244–19 702) 2606 (718–5761) 25 364 (15 642–48 748) 171 553 (79 656–270 369) 0 (0–0) 460 785 (343 904–618 319) 0 (0–0) 37 429 (21 366–56 607) 37 429 (21 366–56 607) ·· (230–864) 1185 (797–1975) (441–1661) 2072 (1102–2948) 239 (74–509) 8352 (4854–13 425) 47 311 (20 330–77 499) (1018–3613) 9056 (6140–13 213) 2157 (626–4707) 42 151 (24 425–76 872) 275 647 (121 774–429 427) 0 (0–0) 421 275 (329 209–529 004) 0 (0–0) ·· (1909–6567) 16 277 (9875–22 272) 2845 (833–6109) 33 716 (22 844–58 659) 218 864 (100 403–344 633) 0 (0–0) 481 429 (363 778–639 590) 0 (0–0) 238 359 (143 200–325 690) 64 438 (37 339–94 174) 186 365 (92 028–280 059) Occupational exposure to sulphuric acid Occupational asthmagens 193 (55–452) 10 485 (5116–19 129) 68 281 (29 408–112 504) 0 (0–0) 21 211 (16 479–27 705) 0 (0–0) Occupational particulate matter, gases, and fumes Occupational noise Occupational risk factors for injuries 0 (0–0) 20 644 (15 628–27 414) Occupational low back pain Sexual abuse and violence 0 (0–0) ·· 200 930 (113 070–292 802) 27 009 (14 290–43 424) 186 365 (92 028–280 059) Childhood sexual abuse ·· ·· ·· Intimate partner violence ·· ·· ·· No data indicates that attributable deaths were not quantifi ed. Table 3: Deaths attributable to risk factors and risk factor clusters, worldwide ambient particulate matter pollution accounted for 3·1 million (2·7 million to 3·5 million) deathsand 3·1% (2·7–3·4) of global DALYs. For ambient particulate matter pollution, we also did a post-hoc sensitivity analysis excluding the eff ects of dust, which had a small eff ect worldwide—attributable global DALYs decreased by 2%—but large eff ects in north Africa and Middle East. Household air pollution is an important con- tributor to ambient particulate matter pollution; we estimate that it accounted for 16% of the worldwide burden from ambient particulate matter pollution in 2010. The eff ects of ambient ozone pollution, which increases the risk of chronic obstructive pulmonary disease, were smaller than those of household air pollution from solid fuels or ambient particulate matter pollution (0·2 million [0·1 million to 0·3 million] deaths and 0·1% [0·03–0·2] of global DALYs in 2010). For other clusters of risk factors for which we approx- imated the joint eff ects assuming independence, dietary risk factors and physical inactivity were responsible for the largest disease burden: 10·0% (9·2–10·8) of global DALYs in 2010. Of the individual dietary risk factors, the largest attributable burden in 2010 was associated with diets low in fruits (4·9 million [3·8 million to 5·9 million] deaths and 4·2% [3·3–5·0] of global DALYs), followed by diets high in sodium (4·0 million [3·4 million to 4·6 million]; 2·5% [1·7–3·3]), low in nuts and seeds (2·5 million [1·6 million to 3·2 million]; 2·1% [1·3–2·7] ), low in whole grains (1·7 million [1·3 million to 2·1 million]; 1·6% [1·3–1·9]), low in vegetables (1·8 million [1·2 million to the combined eff ects of multiple risk factors, we have approximated the joint eff ects of clusters of risk factors assuming that risk factors included in each cluster are independent. However, risk factors included in a cluster are not necessarily independent; for example, a substantial part of the burden attributable to high body-mass index is mediated through high blood pressure and high fasting plasma glucose. Others act together and risk factor exposures might be correlated at the individual level,155 especially household air pollution and ambient particulate matter pollution, which might have common sources. For these reasons we have not computed the joint eff ects for physiological risk factors or air pollution. However, the combined eff ects of physiological risk factors are probably large, with high blood pressure the leading single risk factor globally, accounting for 9·4 million (95% UI 8·6 million to 10·1 million) deaths and 7·0% (6·2–7·7) of global DALYs in 2010, followed by high body-mass index (3·4 million [2·8 million to 4·0 million deaths] and 3·8% [3·1–4·4] of global DALYs in 2010), high fasting plasma glucose (3·4 million [2·9 million to 3·7 million] deaths and 3·6% [3·1–4·0] of DALYs), high total cholesterol (2·0 million [1·6 million to 2·5 million] deaths and 1·6% [1·3–2·0] of DALYs), and low bone mineral density (0·2 million [0·1 million to 0·2 million] deaths and 0·21% [0·17–0·25] of DALYs). The joint eff ects of air pollution are also likely to be large. Household air pollution from solid fuels accounted for 3·5 million (2·7 million to 4·4 million) deaths and 4·5% (3·4–5·3) of global DALYs in 2010 and Sneha, Chennai, India (Prof L Vijayakumar MBBS); Royal Children’s Hospital and Critical Care and Neurosciences Theme, Murdoch Children’s Research Institute, Melbourne, VIC, Australia (R Weintraub); University of Nottingham, Nottingham, UK (Prof H C Williams PhD); National Acoustic Laboratories, Sydney, NSW, Australia (W Williams PhD); Royal Cornwall Hospital, Truro, UK (Prof A D Woolf MBBS); and Centre for Suicide Research and Prevention, The University of Hong Kong, Hong Kong, China (Prof P Yip PhD) Correspondence to: Dr Stephen S Lim, Institute for Health Metrics and Evaluation, 2301 Fifth Ave, Suite 600, Seattle, WA 98121, USA stevelim@uw.edu For the WHO Global Database on Child Growth and Malnutrition see http://apps. who.int/nutrition/landscape/ search.aspx?dm=52&countries= 2240 www.thelancet.comVol 380 December 15/22/29, 2012

  18. Articles Men Women Both sexes 1990 2010 1990 2010 1990 2010 Unimproved water and sanitation Unimproved water source 27 045 (1409–49 439) 11 075 (792–21 250) 18 610 (593–35 486) ·· 46 667 (40 185–53 381) 94 276 (73 721–113 071) 1409 (460–2456) 2876 (2406–3459) ·· 11 022 (458–21 162) 4080 (266–8172) 7735 (190–15 338) 25 123 (1262–45 792) 10 097 (722–19 424) 17 441 (522–32 889) 10 165 (428–19 650) 3694 (242–7511) 7192 (187–14 099) ·· 29 431 (25 722–33 273) 49 317 (38 818–60 315) 1016 (331–1758) 6617 (5322–7938) 600 (84–1355) 6017 (4915–7231) 82 894 (69 171–98 757) 21 965 (13 717–31 340) 18 850 (10 926–27 569) 3114 (296–6915) 36 045 (29 430–43 394) 28 251 (20 195–39 063) 5098 (2566–8168) 4256 (1131–7821) 41 342 (30 473–48 563) 31 272 (19 859–38 467) 10 070 (7931–12 429) 42 178 (38 018–46 786) 34 188 (30 882–37 479) 7562 (5922–9471) ·· 39 864 (34 103–45 972) 17 721 (13 153–22 508) 73 991 (66 161–81 931) 45 300 (37 218–54 219) 2111 (1627–2618) 52 169 (2700–93 073) 21 172 (1517–40 491) 36 050 (1115–66 871) ·· 81 699 (71 012–92 859) 175 909 (141 870–207 095) 2534 (851–4426) 5365 (4534–6279) ·· 21 187 (866–40 957) 7775 (514–15 705) 14 927 (377–29 705) Unimproved sanitation Air pollution Ambient particulate matter pollution Household air pollution from solid fuels Ambient ozone pollution ·· ·· ·· 46 732 (41 393–52 602) 61 645 (45 944–77 497) 1440 (506–2563) 9434 (7476–12 045) 1514 (191–3383) 7920 (6491–9683) 83 202 (67 963–99 704) 25 572 (15 540–37 260) 22 258 (12 464–32 936) 3314 (324–7377) 41 270 (33 478–50 007) 19 974 (13 595–28 289) 5672 (2904–9348) 4880 (1203–9316) 115 496 (98 595–130 090) 105 635 (88 332–120 347) 9861 (7669–12 312) 119 130 (107 244–130 842) 101 875 (92 405–111 165) 16 248 (12 679–20 132) ·· 49 148 (41 619–57 197) 23 179 (17 148–29 650) 99 566 (88 193–110 943) 48 310 (39 429–57 750) 3105 (2295–3831) 35 032 (29 974–40 402) 81 632 (66 415–96 472) 1125 (375–1990) 2489 (1974–3015) ·· 76 163 (68 086–85 171) 110 962 (86 848–137 813) 2456 (837–4299) 16 051 (13 212–19 503) 2114 (273–4660) 13 936 (11 750–16 327) 166 095 (139 685–193 981) 47 537 (29 868–67 518) 41 108 (23 668–58 913) 6429 (605–14 426) 77 316 (64 497–91 943) 48 225 (33 769–67 592) 10 770 (5625–17 149) 9136 (2458–16 903) 156 838 (136 543–173 057) 136 907 (117 201–153 778) 19 931 (15 707–24 223) 161 308 (146 934–175 282) 136 063 (125 361–146 788) 23 810 (18 780–29 246) ·· 89 012 (77 743–101 390) 40 900 (31 662–50 484) 173 556 (155 939–189 025) 93 609 (77 107–110 600) 5216 (4133–6418) (Continues on next page) Other environmental risks Residential radon Lead exposure 2876 2489 5365 (1974–3015) 164 599 (139 926–192 077) 50 359 (32 186–70 526) 43 601 (26 173–62 072) 6758 (696–14 710) 93 028 (78 656–112 766) 30 390 (22 473–40 703) 14 598 (7068–25 637) 11 709 (2640–22 049) 46 926 (39 634–58 092) 28 784 (21 829–40 090) 18 142 (13 748–22 355) 33 648 (30 426–37 108) 28 479 (25 762–31 372) 4993 (3811–6417) ·· 26 181 (22 243–30 349) 17 006 (13 940–20 640) 63 897 (57 903–70 789) 26 174 (20 911–31 642) 1361 (1102–1686) (2406–3459) 175 366 (146 049–211 406) 59 902 (36 953–84 059) 52 729 (30 540–75 288) 7173 (767–15 819) 104 713 (87 668–128 697) 21 451 (14 947–30 321) 15 689 (7475–29 165) 12 666 (2938–23 883) 104 840 (91 849–119 255) 84 956 (73 038–97 937) 19 884 (14 493–25 591) 88 000 (79 774–96 333) 77 550 (70 706–84 835) 10 178 (7787–13 073) ·· 30 177 (25 148–34 980) 22 519 (18 230–27 029) 73 120 (65 538–81 302) 25 391 (19 752–31 108) 1764 (1448–2208) (4534–6279) 339 965 (289 845–402 489) 110 261 (69 615–153 539) 96 330 (57 274–135 861) 13 931 (1443–30 062) 197 741 (169 224–238 276) 51 841 (37 477–71 202) 30 288 (14 884–54 488) 24 375 (5385–45 685) 151 766 (136 367–169 522) 113 740 (100 454–131 675) 38 026 (28 832–47 544) 121 648 (111 426–132 342) 106 029 (98 299–114 763) 15 171 (11 714–19 369) ·· 56 358 (48 720–65 030) 39 526 (32 704–47 202) 137 017 (124 360–149 366) 51 565 (40 786–62 557) 3125 (2589–3811) Child and maternal undernutrition Suboptimal breastfeeding Non-exclusive breastfeeding Discontinued breastfeeding Childhood underweight Iron defi ciency Vitamin A defi ciency Zinc defi ciency Tobacco smoking (including second-hand smoke) Tobacco smoking Second-hand smoke Alcohol and drug use Alcohol use Drug use Physiological risk factors High fasting plasma glucose High total cholesterol High blood pressure High body-mass index Low bone mineral density www.thelancet.comVol 380 December 15/22/29, 2012 2241

  19. Articles Men Women Both sexes 1990 2010 1990 2010 1990 2010 (Continued from previous page) Dietary risk factors and physical inactivity Diet low in fruits 102 663 (94 539–111 011) 47 979 (37 530–57 842) 18 755 (12 859–24 939) 17 033 (13 513–20 522) 24 918 (16 268–31 946) 818 (248–1366) 642 (306–1014) 10 477 (2801–17 479) 3085 (2120–4151) 8485 (3787–13 262) 1083 (752–1406) 13 620 (9915–17 307) 6185 (2891–9362) 4979 (3571–6413) 26 807 (17 646–35 273) ·· 149 576 (138 035–160 263) 65 523 (51 056–78 959) 24 169 (16 503–32 480) 24 881 (19 486–29 709) 32 615 (21 258–41 958) 1171 (350–1977) 1026 (484–1629) 12 901 (4012–21 421) 4858 (3154–6549) 10 893 (4903–17 191) 1570 (1113–2058) 18 300 (13 267–23 201) 7521 (3455–11 583) 7339 (5240–9300) 37 378 (24 639–49 428) 37 007 (30 583–43 466) 48 317 (38 407–58 677) 2087 (1315–2928) 521 (279–709) 45 (17–84) 52 (21–92) 3 (1–5) 10 (4–16) 32 (17–48) 442 (232–682) 405 (244–544) 17 (6–31) 151 (38–312) 74 611 (68 196–81 173) 32 474 (25 061–39 155) 12 803 (8412–17 503) 12 370 (9625–14 895) 15 607 (9915–20 208) 710 (210–1210) 566 (263–903) 6882 (2340–11 119) 2358 (1586–3484) 4862 (2188–7562) 753 (521–975) 8120 (5900–10 388) 3727 (1788–5709) 3085 (2226–3944) 19 376 (12 521–25 596) ·· 104 757 (97 047–112 535) 38 573 (29 923–46 512) 14 389 (9434–19 284) 15 881 (12 615–18 949) 18 674 (11 716–24 404) 931 (264–1605) 827 (374–1362) 8038 (2932–12 685) 3695 (2356–5255) 5559 (2500–8639) 1019 (720–1319) 9899 (7241–12 596) 4159 (1973–6396) 4253 (3106–5416) 23 852 (15 544–31 682) 32 311 (27 698–37 217) 14 171 (10 344–18 842) 594 (368–855) 132 (61–184) 18 (7–33) 40 (15–72) 1 (0–2) 3 (1–6) 13 (7–21) 81 (42–126) 167 (95–228) 9 (4–16) 64 (18–132) 177 274 (164 710–190 286) 80 453 (63 298–95 763) 31 558 (21 349–41 921) 29 404 (23 097–35 134) 40 525 (26 308–51 741) 1527 (461–2555) 1208 (571–1909) 17 359 (5137–27 949) 5443 (3769–7373) 13 347 (5970–20 751) 1836 (1316–2368) 21 740 (15 869–27 537) 9912 (4655–14 976) 8064 (5893–10 305) 46 183 (30 363–60 604) ·· 254 333 (237 748–270 495) 104 095 (81 833–124 169) 38 559 (26 006–51 658) 40 762 (32 112–48 486) 51 289 (33 482–65 959) 2101 (619–3544) 1853 (870–2946) 20 939 (6982–33 468) 8553 (5823–11 418) 16 452 (7401–25 783) 2590 (1873–3322) 28 199 (20 624–35 974) 11 680 (5360–17 798) 11 592 (8395–14 623) 61 231 (40 124–80 342) 69 318 (58 646–80 182) 62 488 (49 471–76 240) 2681 (1773–3689) 653 (389–840) 63 (24–114) 92 (39–163) 4 (2–6) 13 (6–21) 45 (27–66) 523 (292–789) 572 (386–762) 25 (11–47) 215 (58–443) (Continues on next page) Diet low in vegetables Diet low in whole grains Diet low in nuts and seeds Diet low in milk Diet high in red meat Diet high in processed meat Diet high in sugar- sweetened beverages Diet low in fi bre Diet low in calcium Diet low in seafood omega-3 fatty acids Diet low in polyunsaturated fatty acids Diet high in trans fatty acids Diet high in sodium Physical inactivity and low physical activity Occupational risk factors 42 660 (35 146–50 545) 1346 (917–1958) 362 (236–555) 29 (11–56) 36 (15–64) 2 (1–3) 5 (2–9) 18 (11–28) 278 (158–436) 257 (173–383) 11 (4–20) 90 (24–191) 12 754 (9357–16 658) 412 (284–611) 122 (78–189) 12 (5–24) 28 (11–52) 55 414 (45 312–66 718) 1758 (1220–2477) 484 (354–695) 41 (16–77) 65 (27–112) 2 (1–4) 7 (3–12) 26 (16–40) 332 (192–517) 358 (255–500) 18 (8–32) 128 (34–266) Occupational carcinogens Occupational exposure to asbestos Occupational exposure to arsenic Occupational exposure to benzene Occupational exposure to beryllium Occupational exposure to cadmium Occupational exposure to chromium Occupational exposure to diesel engine exhaust Occupational exposure to second-hand smoke Occupational exposure to formaldehyde Occupational exposure to nickel 1 (0–1) 2 (1–3) 8 (4–13) 54 (31–88) 100 (69–162) 7 (3–13) 37 (10–79) 2242 www.thelancet.comVol 380 December 15/22/29, 2012

  20. Articles Men Women Both sexes 1990 2010 1990 2010 1990 2010 (Continued from previous page) Occupational exposure to polycyclic aromatic hydrocarbons Occupational exposure to silica Occupational exposure to sulphuric acid Occupational asthmagens 41 73 13 23 54 96 (19–71) (33–119) (6–23) (10–39) (26–92) (45–156) 199 333 31 49 230 382 (129–297) (199–463) (21–52) (26–71) (154–328) (239–526) 52 66 5 6 57 71 (14–114) 1467 (874–2439) 6808 (3162–10 425) 1936 (1149–3103) 20 175 (15 588–25 639) 10 929 (7340–15 116) ·· (19–143) 1359 (917–2153) 6682 (3293–10 311) 2284 (1348–3649) 22 434 (16 711–29 943) 13 471 (8968–18 945) 3588 (2669–4679) 3588 (2669–4679) ·· (1–12) (2–13) (16–122) 2129 (1419–3222) 9552 (4385–14 636) 2869 (1698–4582) 21 265 (16 644–26 702) 17 841 (11 846–24 945) ·· (21–152) 2020 (1441–2871) 9142 (4377–14 250) 3451 (2072–5574) 23 444 (17 736–30 904) 21 750 (14 492–30 533) 23 519 (17 961–30 322) 7833 (5964–10 005) 16 794 (11 373–23 087) 662 661 (366–1062) 2745 (1216–4406) 933 (550–1489) 1090 (836–1437) 6912 (4487–9835) (407–994) 2460 (1105–4025) 1167 (696–1870) 1010 (771–1331) 8279 (5502–11 602) 19 931 (14 524–26 397) 4244 (3082–5533) 16 794 (11 373–23 087) Occupational particulate matter, gases, and fumes Occupational noise Occupational risk factors for injuries Occupational low back pain Sexual abuse and violence ·· Childhood sexual abuse ·· ·· ·· Intimate partner violence ·· ·· ·· No data indicates that attributable disability-adjusted life-years were not quantifi ed. Total disability-adjusted life-years (in 1000s) in 1990 were 1 360 569 for men, 1 142 032 for women, and 2 502 601 for both. In 2010, they were 1 370 177 for men, 1 120 208 for women, and 2 490 385 for both. Table 4: Disability-adjusted life-years (1000s) attributable to risk factors and risk factor clusters, worldwide burden with second-hand smoke, and alcohol use which accounted for 4·9 million (4·5 million to 5·2 million) deaths and 5·5% (5·0–5·9) of global DALYs in 2010. Of the remaining risk factor clusters, occupational risk factors accounted for 0·9 million (0·7 million to 1·1 million) deaths and 2·5% (2·0–3·0) of global DALYs in 2010, followed by sexual abuse and violence (0·2 million [0·1 million to 0·3 million] deaths and 0·9% [0·7–1·2] DALYs), unimproved water and sanitation, (0·3 million [0 to 0·6 million] deaths and 0·9% [0·04–1·6] DALYs), and other environmental risks (0·7 million [0·6 million to 0·9 million] deaths and 0·6% [0·5–0·8] DALYs). The rest of the results section refers to the 43 risk factors and clusters of risk factors in the rank list. The predominance of non-communicable disease risks in 2010 highlights the global epidemiological transition that has occurred since 1990 (fi gures 1, 2, 3). In 1990, the leading risks were childhood underweight (7·9% [6·8–9·4] of global DALYs), household air pollution from solid fuels (7·0% [5·6–8·3]), and tobacco smoking including second-hand smoke (6·1% [5·4-6·8]), high blood pressure (5·5% [4·9–6·0]), and suboptimal breast feeding (4·4% [2·8–6·1]). With the exception of house hold air pollution, which is a signifi cant contributor to childhood lower respiratory tract infections, the fi ve leading risk factors in 2010 (high 2·3 million]; 1·5% [1·0–2·1]), and low in seafood omega-3 fatty acids (1·4 million [1·0 million to 1·8 million]; 1·1% [0·8–1·5]). Our sensitivity analysis of omega-3 fatty acids using relative risks from randomised trials reduced the attributable burden by more than half, to 0·6 million (–0·6 million to 1·7 million) deaths, and 0·5% (–0·5 to 1·4) of global DALYs in 2010. Physical inactivity and low physical activity accounted for 3·2 million (2·7 million to 3·7 million) deaths, and 2·8% (2·4–3·2) of DALYs in 2010. Child and maternal undernutrition was responsible for the next largest attributable burden of the risk factor clusters (1·4 million [1·2 million to 1·7 million] deaths; 6·7% [5·7–7·7] of global DALYs in 2010), with childhood underweight the largest individual contributor (0·9 million [0·7 million to 1·0 million]; 3·1% [2·6–3·7]), followed by iron defi ciency (0·1 million [0·09 million to 0·14 million]; 1·9% [1·4–2·6]), and suboptimal breast feeding (0·5 million [0·3 million to 0·8 million]; 1·9% [1·2–2·7]). Vitamin A and zinc defi ciencies amongst chil dren accounted for less than 0·8% of the disease burden. The burdens of disease attributable to tobacco smoking including second-hand smoke (6·3 million [5·4 million to 7·0 million] deaths and 6·3% [5·5–7·0] of DALYs) as well as alcohol and drug use (5·0 million [4·7 million to 5·3 million] deaths and 6·5% [6·0–7·0] of DALYs) were substantial in 2010. These burdens are mainly driven by active smoking, which accounts for 87% of the combined www.thelancet.comVol 380 December 15/22/29, 2012 2243

  21. Articles A Tobacco smoking, including second-hand smoke Childhood underweight Household air pollution from solid fuels Alcohol use High blood pressure Suboptimal breastfeeding Diet low in fruits Cancer Cardiovascular and circulatory diseases Chronic respiratory diseases Cirrhosis Digestive diseases Neurological disorders Mental and behavioural disorders Diabetes, urogenital, blood, and endocrine Musculoskeletal disorders Other non-communicable diseases HIV/AIDS and tuberculosis Diarrhoea, lower respiratory infections, and other common infectious diseases Neglected tropical diseases and malaria Maternal disorders Neonatal disorders Nutritional deficiencies Other communicable diseases Transport injuries Unintentional injuries Intentional injuries War and disaster Ambient particulate matter pollution High fasting plasma glucose Diet high in sodium High body-mass index Diet low in nuts and seeds High total cholesterol Iron deficiency Occupational risk factors for injuries Diet low in vegetables Unimproved sanitation Diet low in whole grains Vitamin A deficiency Diet low in seafood omega-3 fatty acids B Childhood underweight Household air pollution from solid fuels High blood pressure Suboptimal breastfeeding Tobacco smoking, including second-hand smoke Ambient particulate matter pollution Diet low in fruits Iron deficiency Alcohol use High fasting plasma glucose High body-mass index Diet high in sodium Unimproved sanitation High total cholesterol Diet low in nuts and seeds Vitamin A deficiency Diet low in vegetables Diet low in whole grains Zinc deficiency Unimproved water source C Childhood underweight Household air pollution from solid fuels Tobacco smoking, including second-hand smoke High blood pressure Suboptimal breastfeeding Alcohol use Diet low in fruits Ambient particulate matter pollution High fasting plasma glucose Iron deficiency High body-mass index Diet high in sodium Diet low in nuts and seeds High total cholesterol Unimproved sanitation Diet low in vegetables Figure 1: Burden of disease attributable to 20 leading risk factors in 1990, expressed as a percentage of global disability-adjusted Vitamin A deficiency Diet low in whole grains Zinc deficiency Diet low in seafood omega-3 fatty acids –0·5 0 2 4 6 8 life-years Disability-adjusted life-years (%) For men (A), women (B), and both sexes (C). 2244 www.thelancet.comVol 380 December 15/22/29, 2012

  22. Articles A Tobacco smoking, including second-hand smoke High blood pressure Alcohol use Diet low in fruits Household air pollution from solid fuels High fasting plasma glucose High body-mass index Cancer Cardiovascular and circulatory diseases Chronic respiratory diseases Cirrhosis Digestive diseases Neurological disorders Mental and behavioural disorders Diabetes, urogenital, blood, and endocrine Musculoskeletal disorders Other non-communicable diseases HIV/AIDS and tuberculosis Diarrhoea, lower respiratory infections, and other common infectious diseases Neglected tropical diseases and malaria Maternal disorders Neonatal disorders Nutritional deficiencies Other communicable diseases Transport injuries Unintentional injuries Intentional injuries War and disaster Ambient particulate matter pollution Childhood underweight Diet high in sodium Physical inactivity and low physical activity Diet low in nuts and seeds Suboptimal breastfeeding Diet low in whole grains Diet low in vegetables High total cholesterol Occupational risk factors for injuries Iron deficiency Diet low in seafood omega-3 fatty acids Drug use B High blood pressure Household air pollution from solid fuels High body-mass index Tobacco smoking, including second-hand smoke High fasting plasma glucose Diet low in fruits Childhood underweight Alcohol use Physical inactivity and low physical activity Ambient particulate matter pollution Iron deficiency Diet high in sodium Suboptimal breastfeeding Diet low in nuts and seeds High total cholesterol Intimate partner violence Diet low in whole grains Diet low in vegetables Diet low in seafood omega-3 fatty acids Occupational low-back pain C High blood pressure Tobacco smoking, including second-hand smoke Alcohol use Household air pollution from solid fuels Diet low in fruits High body-mass index High fasting plasma glucose Childhood underweight Ambient particulate matter pollution Physical inactivity and low physical activity Diet high in sodium Diet low in nuts and seeds Iron deficiency Suboptimal breastfeeding High total cholesterol Diet low in whole grains Diet low in vegetables Figure 2: Burden of disease attributable to 20 leading risk factors in 2010, expressed as a percentage of global disability-adjusted life-years For men (A), women (B), and both sexes (C). Diet low in seafood omega-3 fatty acids Drug use Occupational risk factors for injuries –0·5 0 2 4 6 8 Disability-adjusted life-years (%) www.thelancet.comVol 380 December 15/22/29, 2012 2245

  23. Articles zinc defi ciency all decreased substantially between 1990, and 2010. The transition from childhood communicable to non- communicable disease burden is also exemplifi ed by the fall in DALYs caused by household air pollution from solid fuels (despite the rise in its eff ects on cardio- vascular diseases). Although the burden attributable to ambient particulate matter pollution has largely remained unchanged (3·2% [2·8–3·7] of global DALYs in 1990 vs 3·0% [2·6–3·4] in 2010), the contribution of lower respiratory tract infections had fallen sharply by 2010, with chronic diseases of adults being the dominant health outcome caused by this exposure. Figure 4 shows the 95% uncertainty interval in global DALYs attributable to each risk factor and the overall rank for each risk factor. The uncertainty intervals for many risk factors overlap, especially those not in the top fi ve. Unimproved water, unimproved sanitation, vitamin A defi ciency, and zinc defi ciency have large uncertainty, which refl ects the substantial uncertainty in the estimates of etiological eff ect sizes for these risks. blood pressure, tobacco smoking including second- hand smoke, alcohol use, household air pollution, and diets low in fruits) are mainly causes of adult chronic disease, especially cardio vascular diseases and cancers (fi gures 1, 2). The burden of disease attributable to other chronic disease risk factors also increased substantially between 1990 and 2010; for example, the global disease burden attributable to high body-mass index increased from 52 million to 94 million DALYs and that of high fasting plasma glucose increased from 56 million to 89 million DALYs over this period. The rise in global disease burden attributable to chronic disease risk factors has been accompanied by a decrease in the relative importance of risk factors that largely or exclusively cause communicable diseases in children. The global disease burden attributable to childhood under- weight halved between 1990 (7·9% [6·8–9·4] of global DALYs) and 2010 (3·1% [2·6–3·7]; table 3). Although the fraction of disease burden attributable to iron defi ciency fell relatively little, suboptimal breastfeeding, unimproved water, unimproved sanitation, vitamin A defi ciency, and 1990 2010 Mean rank (95% UI) Risk factor Risk factor Mean rank (95% UI) % change (95% UI) 1·1 (1–2) 1 Childhood underweight 1 High blood pressure 1·1 (1–2) 27% (19 to 34) 2·1 (1–4) 2 Household air pollution 2 Smoking (excluding SHS) 1·9 (1–2) 3% (–5 to 11) 2·9 (2–4) 3 Smoking (excluding SHS) 3 Alcohol use 3·0 (2–4) 28% (17 to 39) 4·0 (3–5) 4 High blood pressure 4 Household air pollution 4·7 (3–7) –37% (–44 to –29) 5·4 (3–8) 5 Suboptimal breastfeeding 5 Low fruit 5·0 (4–8) 29% (25 to 34) 5·6 (5–6) 6 Alcohol use 6 High body-mass index 6·1 (4–8) 82% (71 to 95) 7·4 (6–8) 7 Ambient PM pollution 7 High fasting plasma glucose 6·6 (5–8) 58% (43 to 73) 7·4 (6–8) 8 Low fruit 8 Childhood underweight 8·5 (6–11) –61% (–66 to –55) 9·7 (9–12) 9 High fasting plasma glucose 9 Ambient PM pollution 8·9 (7–11) –7% (–13 to –1) 10·9 (9–14) 10 High body-mass index 10 Physical inactivity 9·9 (8–12) 0% (0 to 0) 11·1 (9–15) 11 Iron deficiency 11 High sodium 11·2 (8–15) 33% (27 to 39) 12·3 (9–17) 12 High sodium 12 Low nuts and seeds 12·9 (11–17) 27% (18 to 32) 13·9 (10–19) 13 Low nuts and seeds 13 Iron deficiency 13·5 (11–17) –7% (–11 to –4) 14·1 (11–17) 14 High total cholesterol 14 Suboptimal breastfeeding 13·8 (10–18) –57% (–63 to –51) 16·2 (9–38) 15 Sanitation 15 High total cholesterol 15·2 (12–17) 3% (–13 to 19) 16·7 (13–21) 16 Low vegetables 16 Low whole grains 15·3 (13–17) 39% (32 to 45) 17·1 (10–23) 17 Vitamin A deficiency 17 Low vegetables 15·8 (12–19) 22% (16 to 28) 17·3 (15–20) 18 Low whole grains 18 Low omega-3 18·7 (17–23) 30% (21 to 35) 20·0 (13–29) 19 Zinc deficiency 19 Drug use 20·2 (18–23) 57% (42 to 72) 20 Occupational injury 20·4 (18–23) 12% (–22 to 58) 20·6 (17–25) 20 Low omega-3 21 Occupational low back pain 21·2 (18–25) 22% (11 to 35) 20·8 (18–24) 21 Occupational injury 21·7 (14–34) 22 Unimproved water 22 High processed meat 22·0 (17–31) 22% (2 to 44) 22·6 (19–26) 23 Occupational low back pain 23 Intimate partner violence 23·8 (20–28) 0% (0 to 0) 23·2 (19–29) 24 High processed meat 24 Low fibre 24·4 (19–32) 23% (13 to 33) 24·2 (21–26) 25 Drug use 25 Lead 25·5 (23–29) 160% (143 to 176) 26 Low fibre 30 Lead 26 Sanitation 29 Vitamin A deficiency 31 Zinc deficiency Ascending order in rank Descending order in rank 33 Unimproved water Figure 3: Global risk factor ranks with 95% UI for all ages and sexes combined in 1990, and 2010, and percentage change PM=particulate matter. UI=uncertainty interval. SHS=second-hand smoke. An interactive version of this fi gure is available online at http://healthmetricsandevaluation.org/gbd/visualizations/regional. 2246 www.thelancet.comVol 380 December 15/22/29, 2012

  24. Articles Some risks were quantifi ed for women only—for example, intimate partner violence, which accounted for 1·5% (1·0—2·1) of DALYs among women in 2010. Important diff erences between men and women also exist for disease burden attributable to other risk factors, most notably, for tobacco smoking including second- hand smoke and alcohol use (fi gures 1, 2). These risks cause substantially lower burden in women than in men, because women drink less and in less harmful ways than do men, and fewer smoke or have smoked for a shorter time than have men in most regions.157 In 2010, tobacco smoking including second-hand smoke accounted for 8·4% of worldwide disease burden among men (the leading risk factor) compared with 3·7% among women (fourth highest risk factor). For alcohol use, these sex diff erences were similarly sub stantial: 7·4% (third) versus 3·0% (eighth). The eff ect of occupational risk factors on population health also diff ered between sexes—for example, the fraction of disease burden attributable to occupational risk factors for injuries was 18·5 times higher for men than for women in 2010 (20 175 000 DALYs for men vs 1 090 000 for women). Dietary risk factors had broadly similar eff ects for men and women with the exception of diet low in fruits, for which the fraction of disease burden attributable was 1·5 times largerfor men than for women in 2010 (47 979 000 DALYs for men vs 32 474 000 for women). This eff ect is caused by lower fruit consumption and a larger disease burden from cardiovascular disease in men. Further disaggregation of mortality and disease burden attributable to risk factors reveals several patterns by age group (appendix). Among children younger than 5 years, childhood underweight was the leading risk factor worldwide in 2010 (12·4% [10·4–14·7] of global DALYs), followed by non-exclusive or discontinued breast feeding (7·6% [4·8–10·9]) and household air pollu tion from solid fuels (6·3% [4·4–8·1]). Vitamin A and zinc defi ciencies, unimproved sanitation, and unimproved water each accounted for less than 2% of disease burden in children younger than 5 years. For people aged 15–49 years, the leading risk factor worldwide was alcohol use, followed by tobacco smoking including second-hand smoke, high blood pressure, high body-mass index, diet low in fruits, drug use, and occupational risk factors for injuries. Risk factor rankings in this age group stayed broadly similar between 1990, and 2010, with the exception of iron defi ciency, which dropped from the fourth leading risk factor in 1990, to ninth in 2010. High blood pressure, tobacco smoking including second-hand smoke, alcohol use, and diet low in fruits were all in the top fi ve risk factors for adults aged 50–69 years and adults older than 70 years, in both 1990, and 2010, accounting for a large proportion of disease burden in both age groups. Globally, high blood pressure accounted for more than 20% of all health loss in adults aged 70 years and older in 2010, and around 15% in those 200 Disability-adjusted life-years, 95% uncertainty intervals (millions) 150 100 50 0 0 5 10 15 20 25 30 35 40 45 Rank Figure 4: 95% uncertainty intervals for risk factors ranked by global attributable disability-adjusted life-years, 2010 An interactive version of this fi gure is available online at http://healthmetricsandevaluation.org/gbd/ visualizations/regional aged 50–69 years. Tobacco smoking including second- hand smoke accounted for more than 10% of global disease burden in each of these age groups in 2010. In all 21 regions, and worldwide, a shift has occurred, from risk factors for childhood communicable disease to risk factors for non-communicable disease. The size of this shift and which risk factors account for the largest burden varies highly between regions (fi gure 5, appendix). In central, eastern, and western sub-Saharan Africa, the share of disease burden attributable to childhood underweight, household air pollution from solid fuels, and suboptimal breastfeeding has fallen sub stan tially. However, these risk factors continue to be the leading three causes of disease burden in 2010. The disease burden attributable to hood communicable diseases, such as micronutrient defi ciencies and unimproved water and sanitation, has decreased, both as a proportion of total disease burden and in their rank order: risk factors for some non- communicable diseases and injury accounted for a larger disease burden in 2010. The most notable of these factors were alcohol use and high blood pressure (appendix). Compared with other regions of sub-Saharan Africa, southern sub-Saharan Africa had a more mixed pattern of risk factor burden in 1990 (appendix). In 2010, alcohol use was the leading risk factor in southern sub-Saharan Africa, followed by high blood pressure and high body- mass index (fi gure 6). In addition to high exposure to harmful alcohol use, the eff ects of alcohol were particularly large because it increases the risk of road traffi c and other unintentional and intentional injuries, as well as of tuberculosis,47 all of which are large causes of disease and injury burden in this region. risk factors for child- www.thelancet.comVol 380 December 15/22/29, 2012 2247

  25. Articles sub-Saharan Africa sub-Saharan Africa sub-Saharan Africa sub-Saharan Africa Ranking legend 1–5 16–20 31–35 North Africa and Western Europe Eastern Europe Southern Latin Central Europe Southeast Asia North America Tropical Latin 6–10 21–25 36–40 11–15 26–30 >40 Andean Latin Latin America High-income High-income Central Asia Middle East South Asia Asia Pacific Australasia Caribbean Southern East Asia America Western America America Oceania Central Central Eastern Global Risk factor 1 2 3 4 1 2 2 1 4 1 1 2 1 1 3 6 2 6 5 6 2 1 High blood pressure 2 1 2 1 3 3 3 2 5 2 3 5 3 3 2 3 5 7 12 10 4 2 Tobacco smoking, including second-hand smoke 3 4 4 3 2 4 1 6 1 6 2 1 11 5 8 5 1 5 6 5 3 1 Alcohol use 42 14 23 20 5 11 3 12 7 13 9 1 4 7 2 2 2 18 4 Household air pollution from solid fuels 5 7 7 7 5 6 5 3 7 4 5 10 6 8 5 9 8 8 11 13 6 5 Diet low in fruits 8 3 1 2 4 1 4 9 2 9 4 3 2 2 17 2 3 14 18 15 3 6 High body-mass index 7 6 6 5 7 5 10 8 3 5 7 6 4 4 7 1 6 10 13 11 7 5 High fasting plasma glucose 39 38 37 39 38 38 38 38 23 13 25 18 21 14 4 8 9 1 1 1 8 32 Childhood underweight 9 11 26 14 12 24 14 4 19 11 10 24 7 19 6 32 25 16 14 7 9 27 Ambient particulate matter pollution 4 5 5 6 6 7 7 10 6 8 9 8 5 7 11 7 11 15 15 16 8 10 Physical inactivity and low physical activity 6 10 11 11 9 11 9 7 13 7 6 13 8 15 14 16 13 21 17 18 11 9 Diet high in sodium 11 9 8 8 8 8 8 12 8 15 8 12 9 10 13 13 16 22 16 21 10 12 Diet low in nuts and seeds 20 32 21 35 22 17 21 19 12 12 17 4 12 6 9 11 10 4 4 4 13 14 Iron deficiency 27 24 15 14 16 9 15 13 10 10 4 3 3 3 14 22 Suboptimal breastfeeding 12 8 9 9 10 9 6 13 10 16 14 16 10 16 20 14 19 28 27 30 11 15 High total cholesterol 10 16 16 17 11 12 11 11 14 26 13 17 14 12 15 15 32 24 19 24 12 16 Diet low in whole grains 14 13 12 13 13 10 12 15 20 10 11 14 18 11 16 12 15 23 23 20 17 Diet low in vegetables 16 17 17 15 13 16 16 14 13 17 18 19 15 23 16 17 18 20 23 27 25 25 18 Diet low in seafood omega-3 fatty acids 13 14 10 10 20 13 17 18 16 18 20 11 19 18 22 19 12 19 24 22 19 13 Drug use 24 24 20 25 26 16 25 20 22 23 21 21 23 31 12 22 22 20 22 17 19 20 Occupational risk factors for injuries 15 17 15 23 18 20 24 14 24 17 24 22 20 26 23 17 24 17 21 19 21 15 Occupational low back pain 22 12 14 12 15 18 15 29 9 27 19 15 27 24 25 27 28 31 28 28 22 7 Diet high in processed meat 18 22 23 22 25 21 22 21 26 22 27 19 25 23 21 25 14 18 20 23 23 Intimate partner violence 23 16 18 18 18 19 15 16 16 28 20 18 28 22 22 33 21 33 36 34 36 25 24 Diet low in fibre 38 39 39 41 42 40 40 40 38 30 37 31 32 28 19 18 18 9 8 9 40 25 Unimproved sanitation 23 21 19 24 17 19 23 22 25 24 23 20 26 21 24 30 20 25 26 26 26 20 Lead exposure 19 19 17 20 21 22 18 26 27 21 22 29 24 25 32 23 30 33 30 29 27 24 Diet low in polyunsaturated fatty acids 29 23 24 15 23 28 19 28 21 33 26 27 17 38 28 34 35 37 36 37 28 21 Diet high in trans fatty acids 40 40 38 40 41 41 42 43 37 32 34 34 37 33 30 31 17 11 7 8 29 41 Vitamin A deficiency 34 33 32 28 32 33 31 23 32 28 29 33 31 34 26 33 29 29 29 31 30 29 Occupational particulate matter, gases, and fumes 37 37 36 37 39 39 39 39 29 29 28 25 35 27 31 28 21 13 10 14 39 31 Zinc deficiency 28 31 31 19 33 26 27 37 17 25 32 30 28 20 27 26 26 32 32 34 32 26 Diet high in sugar-sweetened beverages 26 25 22 21 30 25 26 30 30 37 30 26 29 30 29 35 31 26 31 27 28 33 Childhood sexual abuse 41 41 40 38 40 42 41 42 40 31 36 35 30 29 34 24 27 12 9 12 42 34 Unimproved water source 21 20 25 26 24 30 28 25 33 35 35 36 34 32 36 37 38 35 37 33 30 35 Low bone mineral density 33 35 34 36 35 35 35 33 31 34 31 32 36 35 37 36 34 30 33 32 36 33 Occupational noise 31 26 29 31 34 32 34 27 35 38 33 40 38 40 39 41 37 41 42 42 38 37 Occupational carcinogens 25 28 27 29 27 29 30 31 39 39 39 39 40 37 40 39 39 38 39 38 38 34 Diet low in calcium 36 36 41 33 36 43 37 34 43 43 43 43 43 43 35 43 43 42 38 41 39 43 Ambient ozone pollution 32 27 35 27 28 36 33 32 41 41 38 42 41 42 41 42 42 43 43 43 40 36 Residential radon 27 29 30 30 29 34 32 35 42 40 41 41 42 39 42 40 41 39 41 39 41 37 Diet low in milk 35 34 33 34 37 37 36 41 36 36 42 37 39 36 38 29 36 34 35 35 35 42 Occupational asthmagens 30 30 28 32 31 31 29 36 34 42 40 38 33 41 43 38 40 40 40 40 43 31 Diet high in red meat Figure 5: Risk factors ranked by attributable burden of disease, 2010 Regions are ordered by mean life expectancy. No data=attributable disability-adjusted life-years were not quantifi ed. 2248 www.thelancet.comVol 380 December 15/22/29, 2012

  26. Articles almost a quarter of total disease burden. Other risk factors, such as high blood pressure, tobacco smoking including second-hand smoke, high body-mass index, and dietary risks, also feature prominently, underscoring the large underlying burden of cardiovascular disease in the region. In North America, Australasia, southern Latin America, and western Europe, the share of disease burden attributable to tobacco smoking including second-hand smoke has fallen slightly; it has stayed almost constant in central Europe and high-income Asia Pacifi c. Tobacco smoking including second-hand smoke was still the leading risk factor in 2010 in North America and western Europe. Important decreases in disease burden are evident for high blood pressure and total cholesterol in North America, Australasia, and western Europe. High blood pressure is a leading risk for health in high-income Asia Pacifi c (accounting for 8·5% [95% UI 7·1–10·1] of disease burden) and central Europe (18·9% [16·8–20·8]); evidence from individual-level trials of salt and blood pressure and from cross-population studies indicates that this result is likely to be driven partly by high salt consumption in these regions.94,158 Falls in disease burden attributable to tobacco smoking including second-hand smoke, high blood pressure, and high total cholesterol in high-income regions have been partly off set by the increasing burden caused by high body-mass index. In southern Latin America, high body-mass index accounted for almost 10% of overall disease burden in 2010, and is the leading risk factor in southern Latin America and Australasia. Figure 6 summarises these regional patterns, in relation to the proportion of regional burden and attributable DALYs per 1000 people. Regions in fi gure 6 are ordered by mean age of death, a marker of the epidemiological transition. Figure 6 shows the clear transition away from risk factors for childhood communicable disease towards risk factors for non- communicable disease, with increasing mean age at death. This change is apparent from the decrease in burden of disease attributable to undernutrition and unimproved water and sanitation, with increased mean age at death, especially when the eff ect of risks is assessed by DALYs per 1000 people (fi gure 6C, D). A clear general shift occurs towards a larger proportion of overall burden arising from risk factors for non- communicable diseases, particularly metabolic risks and dietary risk factors (fi gure 6A, B). However, the absolute burden of risk factors for non-communicable disease does not increase with increasing mean age at death. Rather, its magnitude is lower in high-income regions than in sub-Saharan Africa and south Asia (fi gure 6C, D), showing the double burden of communicable and non- communicable disease in regions early in the epidemiological transition. Some risk factors deviated from the pattern of the proportional burden (percent of region-specifc DALYs attributable to a risk factor) being closely associated with epidemiological and demographic transition (shift from In south Asia, the rise of risk factors for non- communicable diseases is shown by the substantial increase in the burden attributable to tobacco smoking including second-hand smoke, high blood pressure and other metabolic risk factors, dietary risk factors, and alcohol use. However, household air pollution from solid fuels was, despite decreases, the leading risk factor in 2010. Childhood underweight was still the fourth leading risk factor in 2010, despite its share of disease burden having more than halved from 11·9% [95% UI 10·1–14·4] of DALYs in 1990, to 4·0% [3·2–4·9] in 2010. Other risk factors for communicable disease, such as suboptimal breastfeeding and micronutrient defi ciencies, fell sub stantially in the region as child mortality decreased. In southeast, east, and central Asia, the epidemiological transition was already well advanced in 1990, and by 2010, high blood pressure (which is commonly associated with diets high in sodium as a prominent underlying cause94,158), tobacco smoking including second-hand smoke, and diets low in fruits were all among the fi ve leading risk factors in these regions. The disease burden attributable to childhood underweight and sub optimal breastfeeding had been largely eliminated in east Asia by 2010, although they remain important in southeast Asia. In these three regions, despite decreases, household air pollution from solid fuels was still a leading risk factor in 2010, ranked third in southeast Asia, sixth in east Asia, and 12th in central Asia. Ambient particulate matter pollution accounted for a larger disease burden than did household air pollution in central and east Asia in 2010, although household solid fuels is an important source of ambient particulate matter pollution in these regions. The North Africa and Middle East region also had a large shift from risk factors for communicable to non- communicable diseases. In 2010, risk factors for non- communicable disease almost exclusively dominated the region’s causes of loss of health, with high blood pressure and high body-mass index each accounting for roughly 8% of disease burden, followed by tobacco smoking including second-hand smoke, high fasting plasma glucose, and physical inactivity or low physical activity. Ambient particulate matter pollution (seventh leading risk factor) is a notable cause of disease burden in this region, caused by a combination of polluted cities and dust from the Sahara desert. Alcohol use was an important cause of disease burden in most of Latin America. It was ranked fi rst in central Latin America, fourth in tropical Latin America, and sixth in Andean Latin America in 1990, and fi rst in all these regions in 2010. Risk factors for childhood communicable disease had been largely replaced by those causing non- communicable diseases in these regions by 2010, although household air pollution from solid fuels was still an important risk factor in Andean Latin America in 2010. One of the most notable fi ndings was the eff ect of alcohol use in Eastern Europe, where it accounts for www.thelancet.comVol 380 December 15/22/29, 2012 2249

  27. Articles Child and maternal undernutrition Zinc deficiency Vitamin A deficiency Iron deficiency Childhood underweight Suboptimal breastfeeding Unsafe water and sanitation Unimproved sanitation Unimproved water source Air pollution Ambient ozone pollution Household air pollution Ambient particulate matter pollution A Diet low in seafood omega-3 fatty acids Diet low in fibre Physiological risk factors: High body-mass index High blood pressure High total cholesterol High fasting plasma glucose Low bone mineral density Diet low in calcium Diet high in sugar-sweetened beverages Diet high in sodium Diet low in whole grains Diet low in vegetables Diet high in trans fatty acids Diet high in red meat Diet low in polyunsaturated fatty acids Diet high in processed meat Diet low in nuts and seeds Diet low in milk Diet low in fruits Other environmental risks Residential radon Lead exposure Sexual abuse and violence Intimate partner violence Childhood sexual abuse Occupational risk factors Occupational low-back pain Risk factors for occupational injuries Occupational noise Occupational particulate matter, gases, and fumes Occupational asthmagens Occupational carcinogens Alcohol and drug use Drug use Alcohol use Tobacco smoking, including second-hand smoke Tobacco smoking, including second-hand smoke Dietary risk factors and physical inactivity Physical inactivity and low physical activity B 150 Disability-adjusted life-years (%) 100 50 0 C D 800 Disability-adjusted life-years (per 1000 people) 600 400 200 0 Central Europe High-income Asia Pacific High-income North America Southern Latin America Central Europe Tropical Latin America Central Latin America Southeast Asia Andean Latin America Central Asia Southern sub-Saharan Africa Caribbean South Asia Oceania Western sub-Saharan Africa Western Europe Australasia Western Europe Australasia Eastern Europe Eastern sub-Saharan Africa Central sub-Saharan Africa High-income Asia Pacific High-income North America Southern Latin America Eastern Europe Tropical Latin America Central Latin America Central Asia Southern sub-Saharan Africa South Asia Eastern sub-Saharan Africa Central sub-Saharan Africa Oceania Western sub-Saharan Africa Southeast Asia Andean Latin America Caribbean East Asia East Asia North Africa and Middle East North Africa and Middle East 2250 www.thelancet.comVol 380 December 15/22/29, 2012

  28. Articles These broad global patterns mask enormous regional variation in risks to health. In sub-Saharan Africa, risks such as childhood underweight, household air pollution from solid fuels, and suboptimal breastfeeding continue to cause a disproportionate amount of health burden, despite decreasing. The shift to risk factors for non- communicable disease was clear in east Asia, North Africa and Middle East, and Latin America. This regional heterogeneity underestimates even greater diff erences in exposure to, and health eff ects of, risk factors in national and subnational populations. These diff erences should be further elucidated in country-specifi c analyses using the framework and methods reported here. Our analysis shows the large burden of disease attributable to primary and secondary tobacco smoking and to particulate matter pollution in household and ambient environments. The magnitude of disease burden from particulate matter is substantially higher than estimated in previous comparative risk assessment analyses. For example, ambient particulate matter pollution was estimated in the previous comparative risk assessment7 to account for 0·4% of DALYs in 2000 compared with 3·1% in GBD 2010 based on interpolating our 1990 and 2005 results; for household air pollution from solid fuels the comparison is 2·7% in the previous comparative risk assessment versus 5·3% based on GBD 2010. Several reasons could account for this diff erence. First, accumulation of evidence from epidemiological studies about diseases caused by particulate matter, and the use of an integrated exposure–response curve, has led to the inclusion of more outcomes than before. For example, health eff ects for ischaemic heart disease and stroke were not previously included for household air pollution from solid fuels, and lung cancer was included for coal smoke only. Second, the previous assessment of ambient particulate matter pollution was restricted to medium and large cities. High-resolution satellite data and chemical transport models have enabled us to quantify exposure and burden for all rural and urban populations. Third, the previous assessment of ambient particulate matter pollution did not include additional increments of risk above a concentration of 50 μg/m³ for PM2·5, because of the narrow range of ambient particulate matter pollution epidemiological studies. The use of an integrated exposure–response curve enabled us to estimate a continuous risk function across the full range of particulate matter concentrations, which covers the very high concentrations of ambient particulate matter exposure measured in, for example, parts of east Asia. Our integrated exposure–response curve, however, does not address how diff erent sources of particulate matter interact in terms of eff ects and overlapping exposures. Studies124,159,160 have reported broadly similar eff ect sizes for ambient particulate matter by smoking status (never, former, and current smokers). Other evidence161 shows communicable to non-communicable disease with increasing mean age of death). The proportion of DALYs attributable to tobacco smoking including second-hand smoke was largest in North America—where smoking among women has generally been prevalent for a long time—and central and eastern Europe. Central and eastern Europe and central Asia also had the largest proportion of disease burden attributable to risk factors with large eff ects on cardiovascular diseases, which are disproportionately high in these regions. Exposure to particulate matter from household and ambient sources had the most varied pattern with respect to the epidemiological transition, partly because of the heterogeneous pattern of exposure and the eff ects on both children and adult causes of ill health. Household air pollution from solid fuels accounted for a large proportion of disease burden in central, eastern, and western sub-Saharan Africa and it is a leading risk factor in some Asian regions and Oceania. In central and east Asia in 2010, ambient particulate matter pollution surpassed household air pollution in terms of its burden. Discussion The results of GBD 2010 suggest that the contributions of risk factors to regional and global burden of diseases and injuries has shifted substantially between 1990, and 2010, from risk factors that mainly cause communicable diseases in children to risk factors that mainly cause non- communicable diseases in adults. The proportion of overall disease burden attributable to childhood under- weight—the leading risk factor worldwide in 1990—had more than halved by 2010, making childhood under- weight the eighth risk worldwide, behind six behavioural and physiological risks, and household air pollution from solid fuels. Other risks for child mortality, such as non- exclusive and discontinued breastfeeding, micronutrient defi ciencies, and unimproved water and sanitation, have also fallen. However, child and maternal undernutrition risks collectively still account for almost 7% of disease burden in 2010, with unimproved water and sanitation accounting for almost 1%. Of the non-communicable disease risks, high blood pressure, high body-mass index, high fasting plasma glucose, alcohol use, and dietary risks have increased in relative impor tance. This overall shift has arisen from a combin ation of the age- ing population, substantial achievements in lowering mortality of children aged younger than 5 years, and changes in risk factor exposure. levels reported in Figure 6: Attributable burden for each risk factor As percentage of disability-adjusted life-years in 1990 (A), and 2010 (B), and as disability-adjusted life-years per 1000 people in 1990 (C), and 2010 (D). Regions ordered by mean life expectancy. Burden of disease attributable to individual risk factors are shown sequentially for ease of presentation. In reality, the burden attributable to diff erent risks overlaps because of multicausality and because the eff ects of some risk factors are partly mediated through other, more proximal, risks. An interactive version of this fi gure is available online at http:// healthmetricsandevaluation.org/gbd/visualizations/regional. www.thelancet.comVol 380 December 15/22/29, 2012 2251

  29. Articles quality of implementation. The real burden from water and sanitation could therefore be underestimated if well- implemented household con nect ions and water quality interventions have a larger eff ect than improved water sources alone, and if the combination of poor water and sanitation has a larger eff ect than a sample interaction of individual eff ects. More defi nitive epidemiological evidence is needed to assess the eff ects of low quality versus high quality water, household connections versus improved water sources, and exposure based on travel time to water source.175 Also, we could not include an assessment of personal hygiene because of the paucity of national exposure data. Our fi ndings on the burden of micronutrients are also substantially smaller than those in the previous com- parative risk assessment for 2000 and in estimates for 2004 by Black and colleagues10 in TheLancet's Maternal and Child Undernutrition Series. For example, Black and colleagues estimated 668 000 deaths caused by vitamin A defi ciency in 2004; we estimated a quarter (168 000 deaths) for 2005; for zinc defi ciency, the diff erences are similarly large (453 000vs120 000). These diff erences stem from many sources. First, the estimates of Black and colleagues were based on 10·3 million child deaths worldwide, itself based on WHO estimates of global child deaths for 2004. This estimate is substantially larger than those reported by UNICEF176 and the Institute for Health Metrics and Evaluation177 at the time of Black and colleagues' publication. Large diff erences also exist for cause-specifi c mortality, especially in relation to diarrhoea and lower respiratory tract infections (which can be aff ected by both of these risks) versus malaria (which is not).176 The estimates also diff er because of diff erences in the availability and interpretation of epidemiological evidence for disease outcomes and eff ect sizes. Maternal mortality and malaria as outcomes of vitamin A defi ciency were included in the 2000 comparative risk assessment but they were not included in the present report because recent epidemi- ological evidence did not show a signifi cant eff ect of supplementation on these outcomes. Furthermore, we excluded neonatal vitamin A defi ciency since it is the subject of three ongoing randomised trials. The age at which the eff ects of zinc defi ciency begin was increased from birth in the 2000 comparative risk assessment, to 6 months in 2004,10 and to 12 months in the present analysis based on a reassessment of existing and new supplementation trials. Furthermore, we quantifi ed the proportion of the population who are vitamin A or zinc defi cient instead of classing whole countries as exposed or non-exposed. The evolving epidemiology of exposure to micronutrient defi ciency and the subsequent health eff ects suggests a need to systematically reconsider most single nutrient supplementation for children in preventive strategies to lower child mortality, as suggested by the 2000 comparative risk assessment and later analy- ses.10 Therapeutic zinc supplementation in health-care that the eff ects diminish with increasing exposure for active smoking, a pattern incorporated into our exposure– response curves. We applied the eff ects of ambient particulate matter to both smokers and non-smokers alike to be consistent with the epidemiological evidence that emphasises independent eff ects of ambient particulate matter. The reasons for the independent eff ects of diff erent sources of particulate matter should be further investigated. They might include diff erent compositions of particulate matter by source, or diff erent time patterns of exposure162—eg, exposure to particulate matter from active smoking is characterised by episodic, high doses whereas exposure to ambient particulate matter is more constant over time. These limitations aside, the large attributable burden documented in our analysis represents a major shift in our understanding of disease burden arising from particulate matter and emphasises the need to design alternative fuels for household cooking and heating,163 implement more stringent regulation of vehicle and industrial emissions,164–166 reduce agricultural burning or land clearing by fi re,167 and curb and reverse deforestation and desertifi cation to reduce ambient particulate matter from dust.168–171 A large share of ambient particulate matter in Asia and sub-Saharan Africa originates from solid fuel.172,173 Therefore the two exposures are related, and alternative cooking and heating fuels would have benefi ts for people who currently use solid fuels as well as those who do not, but live in the same community.173 Unimproved water and unimproved sanitation together accounted for 0·9% of DALYs in 2010, compared with 2·1% in 1990. These proportions are substantially smaller than the 6·8%for 1990, and 3·7% for 2000, estimated in previous GBD studies for water, sanitation, and hygiene combined.3,7 The relatively small burden estimated for 2010 is partly related to decreases in diarrhoeal disease mortality since 1990, and partly to diff erences in the distributions of deaths by underlying cause of death. We have also done an updated meta-analysis of quasi experi- mental and experimental studies. Historical demographic analyses suggest that the introduction of piped water into cities in the late 19th and early 20th centuries had a large benefi cial eff ect on mortality.174 However, our re-analysis both when restricted to experimental studies and when also including quasi experimental studies did not detect a signifi cantly improved eff ect of household water connec- tions over improved water sources. Similarly, we did not fi nd a signifi cantly improved eff ect of water quality inter- ventions, consistent with the fi ndings reported by Cairncross and colleagues,128 which showed that masked point-of-use water quality interventions did not have a signifi cant eff ect on self-reported diarrhoea. As a result of this reassessment, we restricted our analysis to improved water and improved sanitation compared with unimproved sources following the MDG defi nitions. However, the interventions used in previous studies might not have achieved their full effi cacy because of the 2252 www.thelancet.comVol 380 December 15/22/29, 2012

  30. Articles settings is feasible, as is iron supplementation during pregnancy.174–179 Our fi ndings support the need for strengthened policy about promotion of optimal breast- feeding practices and nutritional programmes that improve child growth. The estimated number of child deaths caused by underweight has also changed sub- stantially over successive studies: in GBD 1990 it was estimated to be 5·9 million deaths in 1990,180 in the comparative risk assessment study for 2000 as 3·7 million deaths,7 and 1·9 million deaths in 2004.10 In GBD 2010 we estimated 2·3 million deaths for 1990 and 0·9 million deaths for 2010. The evolution of estimates for deaths caused by childhood under weight is because of improvements in assessment of the population at risk. These improvements come from systematic analysis of the available data on underweight, a major modifi cation of RRs after the change in the WHO standard in 2006, and diff erences in estimates of total and cause-specifi c mortality. We have also assessed the burden attributable to childhood wasting and childhood stunting. These analyses produce quite similar fi nd ings, for example, worldwide, childhood wasting accounted for 0·7 million deaths in 2010, and childhood stunting for 0·9 million deaths, compared with 0·9 million deaths for childhood under weight (the eff ects of these risks cannot be added). The global burden of disease attributable to tobacco smoking including second-hand smoke has changed little, with decreases in high-income regions off set by increases in regions such as southeast Asia and, to a lesser extent, east and south Asia. The burden attributable to alcohol use has increased substantially in eastern Europe since 1990, mainly because of a rise in the eff ects of heavy drinking on cardiovascular diseases.181 The high burden in eastern Europe was also identifi ed in the 2000 comparative risk assessment but the data for patterns of alcohol consumption and their eff ects were weaker, whereas now they are supported by more surveys and epidemiological studies.182 High blood pressure, high body-mass index, and high fasting plasma glucose are leading risk factors for disease worldwide, with blood pressure having large eff ects on population health in all regions, including low- income regions in sub-Saharan Africa and south Asia. This fi nding is consistent with previous comparative risk assessment analyses. The disease burden in south Asia and sub-Saharan Africa, caused by increased blood pressure,70 has increased its absolute and relative importance in risk factor rankings. The large burden of high blood pressure emphasises the importance of implementing both population-wide and high-risk approaches to reduction of blood pressure.183,184 The worldwide increase in body-mass index and blood glucose is of particular concern in view of the absence of eff ective interventions.62,74 In contrast to these risks, the burden of high total cholesterol is lower than that estimated in the 2000 comparative risk assessment, because the eff ects on ischaemic stroke were negligible at old ages when data from the Asia-Pacifi c Cohort Studies Collaboration and Prospective Studies Collaboration were pooled,68,185 and because exposure has fallen in high-income countries.67 A recent study estimated that 5·3 million deaths were attributable to physical inactivity in 2008.186 This number, which has been widely quoted and equated with the number of deaths attributable to tobacco smoking,187 used eff ect sizes for all-cause mortality obtained from cohorts of adults mainly from North America and Europe and applied these eff ects to deaths at all ages. This approach not only assumes that the cause distribution is the same in all populations, irrespective of region and age structure, but also extends the eff ects to people younger than those in the cohort study, including to infants and children. In other words, a proportion of deaths from maternal causes, neonatal causes, and children’s infectious diseases and HIV were attributed to physical inactivity.186 The prevalence of inactivity also included people who had sedentary patterns as well as those in the low (insuffi cient) activity group. By contrast, our approach—calculating attributable burden by cause and age group, and accounting for exposure in four categories—estimated substantially fewer attributable deaths: 3·2 million (2·7 million to 3·7 million) in 2010, 56% of what we attribute to tobacco smoking when second-hand smoke is excluded. This discrepancy shows the importance of comparable risk factor assessments and the importance of estimation of attributable burden taking into account diff erences in underlying disease and injury patterns across populations. We have expanded the set of components of diet included from a combined category of fruits and vegetables in the 2000 comparative risk assessment to 15 components in GBD 2010; together these dietary risk factors account for a tenth of global disease burden. Of the dietary risk factors, the aetiological eff ect sizes for sodium, polyunsaturated fatty acids replacing saturated fatty acids, and seafood omega-3 fatty acids were informed fully (for sodium) or partly by randomised controlled trials. Disease burden attributable to diet high in sodium was a third of that for high blood pressure. The theoretical-minimum-risk exposure distribution was selected on the basis of values reported in randomised trials; studies of populations with low prevalence of cardiovascular disease suggest that benefi ts are likely to continue to lower levels.158 The large attributable burden for dietary risk factors such as diets low in fruits, vegetables, whole grains, nuts and seeds, and seafood omega-3 fatty acids might surprise some readers. The large burden is caused by both high exposure—eg, low intake of fruits in many regions—and large eff ect sizes. We did supplementary analyses using information from studies of dietary patterns and randomised controlled feeding studies to examine the robustness of the eff ect sizes used in GBD 2010. The fi ndings of these supplementary analyses were consistent with those from the meta-analyses of single risk factors. www.thelancet.comVol 380 December 15/22/29, 2012 2253

  31. Articles in applications of our methods to individual countries and shows the importance of surveillance of national risk factors as a crucial component of national health information systems. More importantly, for some risk factors we have less direct measures of exposure than for others. For example, for household air pollution from solid fuels we measured exposure on the basis of household fuel use rather than personal exposure to particulate matter; for other risks, such as blood pressure, we have direct biological measurements of exposure. Second, the presence of residual confounding in the estimates of eff ect sizes cannot be defi nitively ruled out, particularly for those without evidence from intervention studies, either because they have not yet been done or the risk is not amenable to intervention. For example, no large-scale trials have been done of interventions for high body-mass index that measured cause-specifi c deaths although eff ects on disease incidence have been investigated in trials.193 Observational studies of the eff ect sizes for body-mass index have controlled for some potential confounders.75–77 As noted, the pooled eff ect of risks and interventions trends towards the null result over time;189,190 the implication being that risks for which only a few studies have been done might have their eff ect overestimated compared with risks for which a large body of evidence exists. Third, with the exception of risk factors for which much evidence has been accumulated across diverse populations and age groups, such as the metabolic risks, uncertainty remains as to the extent to which eff ect sizes are generalisable to diff erent populations. Similarly, the large body of epidemiological evidence for cardiovascular risk factors shows a relation between age and the eff ect size of risk factors for cardiovascular disease. Such age- related changes might be present for other outcomes. Fourth, we have combined epidemiological evidence for eff ect sizes using studies across diff erent periods, which could mask underlying temporal changes in risk; no data presently exist to enable an examination of the extent to which eff ect sizes might change over time. Fifth, we have excluded risks for which insuffi cient information exists to enable estimation of exposure, or for which the evidence of eff ect sizes is scarce. This approach excludes several risk–outcome pairs that have been previously included in global and regional assessments of risk factor attributable burden, such as unsafe sex and global climate change. Unsafe sexual practices were included in the 2000 comparative risk assessment but we excluded it because of the absence of robust estimates of exposure or available approaches to determine the proportion of HIV infection that is attributable to unsafe sexual practices by country over time. If quantifi able, unsafe sexual practices would probably account for a large fraction of global health burden; the direct burden of HIV is 3·3% of DALYs in 2010; other sexually transmitted infections account for 0·4% of DALYs. Similarly, we have been unable to However, we stress that these results should still be interpreted with caution, particularly because of the debate surrounding the eff ects of seafood omega-3 fatty acids.143,188 Empirical assessments show that the pooled eff ect of risks and interventions trends towards a null result over time189,190 and this pattern could apply to seafood omega-3 fatty acids since the earlier, primarily observational eff ect sizes tended to show a larger eff ect than did the more recent randomised controlled trials. Because the diff erence between results of observational studies and randomised controlled trials is not statis tically signifi cant we have quantifi ed the attributable burden by use of the combined eff ect size. However, the validity of this approach could change as new evidence accumulates. Also, evidence from randomised controlled trials does not exist for several of the dietary components with a large attributable burden—fruits, vegetables, and nuts and seeds—although, as previously noted, evidence from randomised controlled trials does exist for inter mediate outcomes. Further work is needed to confi rm the eff ect size of dietary components and to establish to what degree the benefi ts continue, preferably through intervention studies of fatal and non-fatal events. The extended analysis of components of diet does not include saturated fat beyond its replacement by polyunsaturated fats. Ecological studies suggest that saturated fat intake is a signifi cant risk factor for mortality from ischaemic heart disease.191 However, observational studies indicate that there might be no benefi ts if saturated fat reduction is associated with an increase in carbohydrates,91 which is also supported by the absence of benefi ts from a low fat diet in the Women’s Health Initiative.192 Together with data for seafood omega-3 fatty acids, these fi ndings show the complexity of the relation between dietary fat and health and suggest that the traditional health education message focused on lowering saturated fat alone needs to be expanded greatly to encompass several other key components of diet, including increased consumption of healthy foods that are presently missing from most diets. The strengths of our study include a more comprehensive set of risk factors than any previous global or national analysis, consistent analyses in 1990, and 2010, which enables assessment of changes in risk factor burden, the incorporation of substantially more data for risk-factor exposure, improved methods to deal with missing and incomparable data, strong emphasis on comparability of methods related to exposure, disease outcomes, and eff ect sizes, and use of theoretical-minimum-risk exposure distribution as the consistent alternative exposure distri- bution with which current exposures are compared. Like all population-based analysis, our study also has some limitations. First, despite the massive improvement in the availability of exposure data and methods, exposure estimates for many risk factors are aff ected by data limitations, especially for 2010, since fewer data could be included. This limitation will become even more salient 2254 www.thelancet.comVol 380 December 15/22/29, 2012

  32. Articles control for confounding in observational studies of late initiation of breastfeeding, which is associated with an increased risk of neonatal mortality. Infants who might too ill or weak to breastfeed are more likely to die. In our analysis, we could not assess low birthweight as an outcome for maternal iron defi ciency, despite evidence from random ised trials. Similarly, we could not assess low birthweight as an outcome for maternal alcohol use. Low birthweight was not a disease outcome in GBD 2010 but is associated with an increased risk of neonatal mortality. We excluded several other risk–outcome pairs that had insuffi cient evidence to estimate eff ect sizes or that had substantial potential of residual confounding— eg, the eff ect of addictive drugs (cannabis, amphetamines, and opioids) on unintentional and inten tional injuries; or the eff ects of intimate partner violence, on HIV or other sexually transmitted infections. Sixth, we included few risks that aff ected three of the leading communicable diseases—HIV/AIDs, tuberculosis, and malaria (beyond deaths in childhood). Overall, we have not included risks for 126 of the 241 most detailed causes included in the GBD, which account for 26·3% of global disease health burden. This shortcoming emphasises the need for a more deliberate research focus to identify and quantify risk factors for the outcomes for which there are presently no risks or few large risks. Seventh, we have quantifi ed the attributable burden of risk factors, holding all other independent factors constant. For clusters of risk factors we have approx- imated the joint eff ects, assuming that risk factors within each cluster are independent. A more accurate quantifi cation of the joint eff ects of multiple risk factors is an important area for future research. Finally, it is important to stress that the size of the attributable risk factor burden does not equal priority for action since prioritisation also depends on availability, cost, and eff ectiveness of inter vention strategies to reduce exposures to these risks. Public policy to improve the health of populations will be more eff ective if it addresses the major causes of disease burden. Even small reductions of population exposure to large risks will yield substantial health gains.194 The principal comprehensive and comparable scientifi c assessment of disease burden caused by diff erent risk factors is that it provides the evidence base for informing discussion about policy. Coupled with evidence of their present burden, most of the leading risk factors, except high body-mass index and high fasting plasma glucose, have decreased in at least some regions and countries, showing that substantial reduction of their eff ect through targeted prevention strategies is feasible. If predictions about huge increases in disease burden worldwide are to be proved wrong, then countries, with appropriate global public health leadership, must urgently implement measures to control exposure to leading hazards, particularly risks for non-communicable diseases. Contributors CJLM, SSL, and ME wrote the fi rst draft. SSL, TV, AF, GD, KS, ADL, CJLM, and ME revised the report. ME, CJLM, and ADL designed the study and provided overall guidance. SSL, EC, GF, CA, ESa, KA, REE, and LCR did comparative analyses of risk factors. All other authors developed the estimates of risk-specifi c exposure, theoretical-minimum-risk exposure distribution, and RR inputs, and checked and interpreted results. Confl icts of interest A Davis is employed by the NHS on works for the UK Dept of Health as lead adviser on audiology. E R Dorsey has been a consultant for Medtronic and Lundbeck and has received grant support from Lundbeck and Prana Biotechnology. M Ezzati chaired a session and gave a talk at the World Cardiology Congress (WCC), with travel cost reimbursed by the World Heart Federation. At the WCC, he also gave a talk at a session organised by Pepsico with no fi nancial remuneration. G A Mensah is a former employee of PepsiCo. D Mozaff arian has received: ad hoc travel reimbursement and/or honoraria for one-time specifi c presentations on diet and cardiometabolic diseases from Nutrition Impact (9/10), the International Life Sciences Institute (12/10), Bunge (11/11), Pollock Institute (3/12), and Quaker Oats (4/12; modest); and Unilever’s North America Scientifi c Advisory Board (modest). B Neal is the Chair of the Australian Division of World Action on Salt and Health. He has consulted to Roche and Takeda. He has received lecture fees, travel fees, or reimbursements from Abbott, Amgen, AstraZeneca, George Clinical, GlaxoSmithKline, Novartis, PepsiCo, Pfi zer, Pharmacy Guild of Australia, Roche, Sanofi -Aventis, Seervier, and Tanabe. He holds research support from the Australian Food and Grocery Council, Bupa Australia, Johnson and Johnson, Merck Schering-Plough, Roche, Servier, and United Healthcare Group. He is not employed by a commercial entity and has no equity ownership or stock options, patents or royalties, or any other fi nancial or non-fi nancial support that might be viewed as a confl ict of interest. L Rushton received honorarium for board membership of the European Centre for Ecotoxicology and Toxicology of Chemicals and research grants to Imperial College London (as PI) from the European Chemical Industry Council and CONCAWE. Acknowledgments We thank the countless individuals who have contributed to the Global Burden of Disease Study 2010 in various capacities. We specifi cally acknowledge the important contribution to this work from multiple staff members of the World Health Organization. We also thank the following organisations that hosted consultations during the fi nal stages of the analytical process, providing valuable feedback about the results and the data to improve the study’s fi ndings overall: Pan American Health Organization; Eastern Mediterranean Regional Offi ce of WHO; UNAIDS; Ministry of Health, Brazil; China Centers for Disease Control; and the University of Zambia. We thank Regina Guthold, Jördis Ott, Annette Pruss-Ustun, and Gretchen A Stevens for their collaboration and input into the analyses and estimates. Finally, we acknowledge the extensive support from all staff members at the Institute for Health Metrics and Evaluation and specifi cally thank: James Bullard, Andrew Ernst, and Serkan Yalcin for their tireless support of the computational infrastructure required to produce the results; Linda A Ettinger for her expert administrative support to facilitate communication and coordination amongst the authors; Peter Speyer, Abigail McLain, Katherine Leach-Kemon, and Eden Stork for their persistent and valuable work to gain access to and catalogue as much data as possible to inform the estimates; and Erin C Mullany for her systematic eff orts in organising drafts of papers, formatting correspondence with expert groups, and preparing the fi nal manuscript. J Balmes, Z Chafe, and K R Smith acknowledge that their aspects of the research were also supported by USEPA and the Shell Foundation, neither of which had any role in design, data collection, analysis, interpretation, or decisions related to publication. R Bourne acknowledges Institutional Support: Vision & Eye Research Unit, Postgraduate Medical Institute, Anglia Ruskin University, Cambridge, UK. Funding support: Fight for Sight (Dr Hans and Mrs Gertrude Hirsch award). R Buchbinder was partially supported by an Australian National Health and Medical Research Council Practitioner Fellowship, Monash University, and Cabrini Health. A J Cohen received support from the Health Eff ects Institute and The William and Flora Hewlett advantage of doing a www.thelancet.comVol 380 December 15/22/29, 2012 2255

  33. Articles Foundation. S Darby was supported by Cancer Research UK. L Degenhardt was supported by an Australian National Health and Medical Research Council Senior Research Fellowship. T Driscoll was supported in part by funding from the National Occupational Health and Safety Commission (now Safework Australia). K M Hanafi ah’s work for the GBD hepatitis C prevalence study was funded partly by Johns Hopkins Vaccine Initiative Scholarship and partly by WHO. H W Hoek acknowledges the support of: the Parnassia Psychiatric Institute, The Hague, Netherlands; the Department of Psychiatry, University Medical Center Groningen, University of Groningen, Netherlands; and the Department of Epidemiology, Columbia University, New York, USA. D Hoy was supported by the Bill & Melinda Gates Foundation and the Australian National Health and Medical Research Council. N Kawakami notes that data used in the study was collected through support from the following grants: The World Mental Health Japan is supported by the Grant for Research on Psychiatric and Neurological Diseases and Mental Health (H13-SHOGAI-023, H14-TOKUBETSU-026, H16-KOKORO-013) from the Japan Ministry of Health, Labour, and Welfare. He thanks staff members, fi led coordinators, and interviewers of the WMH Japan 2002–2004 Survey. Q Lan was supported in part by the Intramural Research Program of the NIH (National Cancer Institute). S London is supported by the Division of Intramural Research, National Institute of Environmental Health Sciences, USA. T R Merriman acknowledges the Health Research Council of New Zealand. B Neal was supported in his contribution to this work by an Australian Research Council Future Fellowship and a National Health and Medical Research Council of Australia Senior Research Fellowship. C Olives was supported in his contribution to this work by an Australian Research Council Future Fellowship and a National Health and Medical Research Council of Australia Senior Research Fellowship. E A Rehfuess acknowledges fi nancial support from the Munich Centre of Health Sciences. R Room’s position at the University of Melbourne and Turning Point Alcohol and Drug Centre is funded by the Foundation for Alcohol Research and Education and the Victorian Department of Health. J A Salomon received support from the Burke Global Health Fellowship while working on this study. U Sampson was supported in part by: The Harold Amos Medical Faculty Development Award of the Robert Wood Johnson Foundation; The Vanderbilt Clinical and Translational Scholars Award. L Sanchez-Riera acknowledges the Spanish Society of Rheumatology for their funds. S Seedat is supported by the South African Research Chairs Initiative, hosted by the Department of Science and Technology and the National Research Foundation. G D Thurston was supported in part by grant ES00260 from the National Institute of Environmental Health Sciences. J M Zielinski acknowledges institutional support from: Health Canada, University of Ottawa, and WHO (International Radon Project). M Ezzati’s research is supported by a Strategic Award from the UK Medical Research Council (MRC) and by the National Institute for Health Research Comprehensive Biomedical research Centre at Imperial College Healthcare NHS Trust. Work on micronutrient defi ciencies was supported by the Nutrition Impact Model Study (NIMS) funded by the Bill & Melinda Gates Foundation. The GBD Osteoporosis Expert Group was supported by the Spanish Rheumatology Association, Institute of Bone and Joint Research, University of Sydney. 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