Abstract
Background: Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. Methods: GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk–outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk–outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk–outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. Findings: The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51–12·1) deaths (19·2% [16·9–21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12–9·31) deaths (15·4% [14·6–16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253–350) DALYs (11·6% [10·3–13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0–9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10–24 years, alcohol use for those aged 25–49 years, and high systolic blood pressure for those aged 50–74 years and 75 years and older. Interpretation: Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public. Funding: Bill & Melinda Gates Foundation.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1223-1249 |
| Number of pages | 27 |
| Journal | The Lancet |
| Volume | 396 |
| Issue number | 10258 |
| DOIs | |
| State | Published - Oct 17 2020 |
ASJC Scopus subject areas
- General Medicine
Online availability
- 10.1016/S0140-6736(20)30752-2License: CC BY
Library availability
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In: The Lancet, Vol. 396, No. 10258, 17.10.2020, p. 1223-1249.
Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Global burden of 87 risk factors in 204 countries and territories, 1990–2019
T2 - a systematic analysis for the Global Burden of Disease Study 2019
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N1 - Research reported in this publication was supported by the Bill & Melinda Gates Foundation; Bloomberg Philanthropies; the University of Melbourne; Queensland Department of Health, Australia; the National Health and Medical Research Council, Australia; Public Health England; the Norwegian Institute of Public Health; St Jude Children's Research Hospital; the Cardiovascular Medical Research and Education Fund; the National Institute on Ageing of the National Institutes of Health (NIH; award P30AG047845); and the National Institute of Mental Health of the NIH (award R01MH110163). The content is solely the responsibility of the authors and does not necessary represent the official views of the funders. Data for this research was provided by MEASURE Evaluation, funded by the United States Agency for International Development (USAID). Views expressed do not necessarily reflect those of USAID, the US Government, or MEASURE Evaluation. This research used data from the Chile National Health Survey 2003 and 2009\u201310. The authors are grateful to the Ministry of Health, the survey copyright owner, for allowing them to have the database. All results of the study are those of the authors and in no way committed to the Ministry. This research used data from China Family Panel Studies, funded by 985 Program of Peking University and carried out by the Institute of Social Science Survey of Peking University. The Costa Rican Longevity and Healthy Aging Study project is a longitudinal study by the University of Costa Rica's Centro Centroamericano de Poblaci\u00F3n and Instituto de Investigaciones en Salud, in collaboration with the University of California at Berkeley. The original pre-1945 cohort was funded by the Wellcome Trust (grant 072406), and the 1945-1955 Retirement Cohort was funded by the US National Institute on Aging (grant R01AG031716). The Principal Investigators are Luis Rosero-Bixby and William H Dow, and co-Principal Investigators Xinia Fern\u00E1ndez and Gilbert Brenes. This paper uses data from Eurostat. The responsibility for all conclusions drawn from the data lies entirely with the authors. The Health Behaviour in School-Aged Children study is an international study carried out in collaboration with WHO/EURO. The International Coordinator of the 1997\u201398, 2001\u201302, 2005\u201306, and 2009\u201310 surveys was Candace Currie and the Data Bank Manager for the 1997\u201398 survey was Bente Wold, whereas for the following survey Oddrun Samdal was the Databank Manager. A list of principal investigators in each country can be found at http://www.hbsc.org. This paper uses data from the WHO Study on global AGEing and adult health. Researchers interested in using Irish Longitudinal Study on Ageing data can access the data for free from the following sites: Irish Social Science Data Archive at University College Dublin (http://www.ucd.ie/issda/data/tilda/); Interuniversity Consortium for Political and Social Research at the University of Michigan (http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/34315). Data used in this paper come from the 2009\u201310 Ghana Socioeconomic Panel Study Survey, which is a nationally representative survey of more than 5000 households in Ghana. The survey is a joint effort undertaken by the Institute of Statistical, Social and Economic Research (ISSER) at the University of Ghana, and the Economic Growth Centre (EGC) at Yale University. It was funded by EGC. At the same time, ISSER and the EGC are not responsible for the estimations reported by the analysts. The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with license number SLN2014-3-170, after subjecting data to processing aiming to preserve the confidentiality of individual data in accordance with the General Statistics Law, 2000. The researchers are solely responsible for the conclusions and inferences drawn upon available data. The authors thank the Russia Longitudinal Monitoring Survey, RLMS-HSE, conducted by the National Research University Higher School of Economics and ZAO Demoscope together with Carolina Population Center, University of North Carolina at Chapel Hill, and the Institute of Sociology, RAS for making data available. This paper uses data from the Armenia 2016, Bangladesh 2009\u201310, Belarus 2016\u201317, Benin 2015, Bhutan 2014, Iraq 2015, Kuwait 2006 and 2014, Libya 2009, Malawi 2009, Moldova 2013, and Sudan 2016 STEP Surveys, all implemented with the support of WHO. This paper uses data from Survey of Health, Ageing and Retirement in Europe (SHARE) Waves 1 (DOI:10.6103/SHARE.w1.700), 2 (10.6103/SHARE.w2.700), 3 (10.6103/SHARE.w3.700), 4 (10.6103/SHARE.w4.700), 5 (10.6103/SHARE.w5.700), 6 (10.6103/SHARE.w6.700), and 7 (10.6103/SHARE.w7.700); see B\u00F6rsch-Supan and colleagues (2013) for methodological details. The SHARE data collection has been funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812), FP7 (SHARE-PREP: GA number 211909, SHARE-LEAP: GA number 227822, SHARE M4: GA number 261982), and Horizon 2020 (SHARE-DEV3: GA number 676536, SERISS: GA number 654221) and by DG Employment, Social Affairs & Inclusion. Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the US National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C) and from various national funding sources is gratefully acknowledged (www.share-project.org). The United States Aging, Demographics, and Memory Study is a supplement to the Health and Retirement Study, which is sponsored by the National Institute of Aging (grant number NIA U01AG009740). It was conducted jointly by Duke University and the University of Michigan. This paper uses data from Add Health, a programme project designed by J Richard Udry, Peter S Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due to Ronald R Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website. No direct support was received from grant P01-HD31921 for this analysis. This paper uses data from the NIH. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This analysis uses data or information from the Longitudinal Aging Study in India (LASI) Pilot microdata and documentation. The development and release of the LASI Pilot Study was funded by the National Institute on Aging, a division of the NIH (R21AG032572, R03AG043052, and R01 AG030153). L G Abreu acknowledges support from Coordena\u00E7\u00E3o de Aperfei\u00E7oamento de Pessoal de N\u00EDvel Superior, Brazil (finance Code 001) and Conselho Nacional de Desenvolvimento Cient\u00EDfico e Tecnol\u00F3gico. L Abu-Raddad acknowledges the support of Qatar National Research Fund (NPRP 9-040-3-008). O Adetokunboh acknowledges the South African Department of Science and Innovation, and National Research Foundation. A Agrawal acknowledges support from the Wellcome Trust DBT India Alliance Senior Fellowship. R Akinyemi acknowledges support by Grant U01HG010273 from the NIH as part of the H3Africa Consortium and support by the FLAIR fellowship funded by the UK Royal Society and the African Academy of Sciences. S Aljunid acknowledges the Department of Health Policy and Management, Faculty of Public Health, Kuwait University and International Centre for Casemix and Clinical Coding, Faculty of Medicine, and the National University of Malaysia for the approval and support to participate in this research project. T B\u00E4rnighausen acknowledges support from the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. A Badawi acknowledges support from the Public Health Agency of Canada. G Britton acknowledges support from the Sistema Nacional de Investigaci\u00F3n of SENACYT, Panam\u00E1. J J Carrero acknowledges support from the Swedish Research Council (2019-01059). F Caravlho acknowledges support from UID/MULTI/04378/2019 and UID/QUI/50006/2019 with funding from FCT/MCTES through national funds. A Cohen acknowledges support from Health Effects Institute, Boston. V M Costa acknowledges support from Grant SFRH/BHD/110001/2015, received by Portuguese national funds through Funda\u00E7\u00E3o para a Ci\u00EAncia e Tecnologia, IP, under the Norma Transit\u00F3ria DL57/2016/CP1334/CT0006. M DeLang acknowledges the NASA grant number NNX16AQ30G and NIOSH grant number T42-OH008673 for supporting the work to estimate global ozone. H Erskine acknowledges support from the Australian National Health and Medical Research Council Early Career Fellowship (APP1137969) and is employed by the Queensland Centre for Mental Health Research, which receives core funding from Queensland Health. E Fernandes acknowledges support from UID/MULTI/04378/2019 and UID/QUI/50006/2019 with funding from FCT/MCTES through national funds. A Ferrari acknowledges support from a National Health and Medical Research Council Early Career Fellowship Grant (APP1121516) and the Queensland Centre for Mental Health Research, which receives funding from the Queensland Department of Health. M Ferreira acknowledges support from a National Health and Medical Council of Australia fellowship. M Freitas acknowledges financial support from the EU (FEDER funds through COMPETE POCI-01-0145-FEDER-029248), and National Funds (FCT, Funda\u00E7\u00E3o para a Ci\u00EAncia e Tecnologia) through project PTDC/NAN-MAT/29248/2017. M Ausloos, C Herteliu, and A Pana acknowledge partial support by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. C Herteliu acknowledges partial support by a grant co-funded by European Fund for Regional Development through Operational Program for Competitiveness, Project ID P_40_382. P Hoogar acknowledges the Bio Cultural Studies, Manipal Academy of Higher Education, and Manipal and Centre for Holistic Development and Research, Kalaghatgi. B-F Hwang acknowledges support from China Medical University (107-Z-04), Taichung, Taiwan. N Ikeda acknowledges support from The Japan Society for the Promotion of Science (grant number 15K08762 and 18H03063). S M S Islam acknowledges support from the National Heart Foundation of Australia and Deakin University. M Jakovljevic acknowledges grant OI175014 of the Ministry of Education Science and Technological Development of the Republic of Serbia for supporting the Serbian portion of this GBD study. P Jeemon supported by a Clinical and Public Health intermediate fellowship (grant number IA/CPHI/14/1/501497) from the Wellcome Trust-Department of Biotechnology, India Alliance (2015\u201320). O John acknowledges receiving the UIPA scholarship from UNSW, Sydney. S V Katikireddi acknowledges funding from a NRS Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_12017/13 & MC_UU_12017/15), and the Scottish Government Chief Scientist Office (SPHSU13 & SPHSU15). C Kieling acknowledges employment with the Conselho Nacional de Desenvolvimento Cient\u00EDfico e Tecnol\u00F3gico (a Brazilian public funding agency) and being a UK Academy of Medical Sciences Newton Advanced Fellow. Y J Kim acknowledges support from the Research Management Centre, Xiamen University Malaysia, XMUMRF/2018-C2/ITCM/0001. K Krishan acknowledges support from UGC Centre of Advanced Study (CAS II) awarded to the Department of Anthropology, Panjab University, Chandigarh, India. B Lacey acknowledges support from the NIHR Oxford Biomedical Research Centre and the BHF Centre of Research Excellence, Oxford. T Lallukka acknowledges support from the Academy of Finland (grant number 319200). I Landires is member of the Sistema Nacional de Investigaci\u00F3n, which is supported by the Secretar\u00EDa Nacional de Ciencia, Tecnolog\u00EDa e Innovaci\u00F3n, Panam\u00E1. S Langan acknowledges support from a Wellcome Trust Senior Clinical Fellowship (205039/Z/16/Z). J Lazarus acknowledges support from a Spanish Ministry of Science, Innovation and Universities Miguel Servet grant (Instituto de Salud Carlos III/ESF, EU [CP18/00074]). S Lorkowski acknowledges support from the German Federal Ministry of Education and Research (nutriCARD, grant agreement number 01EA1808A). A M Mantilla-Herrera is affiliated with the Queensland Centre for Mental Health Research which receives core funding from the Department of Health, Queensland Government. R Martin is grateful to Washington University for additional institutional support. J McGrath acknowledges support from the Danish National Research Foundation (Niels Bohr Professorship), and the Queensland Health Department (via West Moreton HHS). W Mendoza acknowledges Population and Development department at the United Nations Population Fund Country Office in Peru, which does not necessarily endorse this study. U Mueller acknowledges support from the German National Cohort Study BMBF grant number 01ER1801D. S Nomura acknowledges support from the Ministry of Education, Culture, Sports, Science, and Technology of Japan (18K10082). A Ortiz acknowledges support from ISCIII PI19/00815, DTS18/00032, ISCIII-RETIC REDinREN RD016/0009 Fondos FEDER, FRIAT, Comunidad de Madrid B2017/BMD-3686 CIFRA2-CM; these funding sources had no role in the writing of the manuscript or the decision to submit it for publication. S Patten acknowledges support from the Cuthbertson and Fischer Chair in Pediatric Mental Health at the University of Calgary. G Patton acknowledges support from a National Health & Medical Research Council Fellowship. M R Phillips acknowledges support in part by the National Natural Science Foundation of China (number 81371502 and 81761128031). A Raggi, D Sattin, and S Schiavolin acknowledge support by a grant from the Italian Ministry of Health (Ricerca Corrente, Fondazione Istituto Neurologico C Besta, Linea 4 - Outcome Research: dagli Indicatori alle Raccomandazioni Cliniche). D Ribeiro acknowledges the financial support from the EU (FEDER funds through the Operational Competitiveness Program [COMPETE]; POCI-01-0145-FEDER-029253). D C Ribeiro acknowledges support from The Sir Charles Hercus Health Research Fellowship (number 18/111) Health Research Council of New Zealand. P Sachdev acknowledges support from the National Health and Medical Research Council of Australia Program Grant. A M Samy acknowledges support from a fellowship from the Egyptian Fulbright Mission Program. D Santomauro acknowledges affiliation with the Queensland Centre for Mental Health Research, which receives core funding from the Department of Health. M Santric-Milicevic acknowledges support from the Ministry of Education, Science and Technological Development of the Republic of Serbia (contract number 175087). R Sarmiento acknowledges support from the Applied and Environmental Sciences University in Bogota, Colombia and Carlos III Institute of Health Madrid Spain. A Schutte acknowledges support from the South African National Research Foundation SARChI Chair initiative (GUN 86895) and the South African Medical Research Council. M Serre acknowledges the NASA grant number NNX16AQ30G for supporting the work to estimate global ozone. S T Skou acknowledges support from a grant from Region Zealand (Exercise First) and a grant from the European Research Council under the EU's Horizon 2020 research and innovation programme (grant agreement number 801790). J B Soriano acknowledges support by Centro de Investigaci\u00F3n en Red de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain. R Tabar\u00E9s-Seisdedos acknowledges support in part by the national grant PI17/00719 from ISCIII-FEDER. N Taveira acknowledges partial support from the European & Developing Countries Clinical Trials Partnership, EU (LIFE project, reference RIA2016MC-1615). M Tonelli acknowledges support from the David Freeze chair in health services research, University of Calgary. S Tyrovolas acknowledges support from the Foundation for Education and European Culture, the Sara Borrell postdoctoral program (reference number CD15/00019 from the Instituto de Salud Carlos III), and the Fondos Europeo de Desarrollo Regional. M Wei acknowledges support from the National Institute on Aging at the NIH (K23AG056638). J J West acknowledges support from NASA grant number NNX16AQ30G and NIOSH number T42-OH008673. Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.
PY - 2020/10/17
Y1 - 2020/10/17
N2 - Background: Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. Methods: GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk–outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk–outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk–outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. Findings: The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51–12·1) deaths (19·2% [16·9–21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12–9·31) deaths (15·4% [14·6–16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253–350) DALYs (11·6% [10·3–13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0–9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10–24 years, alcohol use for those aged 25–49 years, and high systolic blood pressure for those aged 50–74 years and 75 years and older. Interpretation: Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public. Funding: Bill & Melinda Gates Foundation.
AB - Background: Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. Methods: GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk–outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk–outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk–outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. Findings: The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51–12·1) deaths (19·2% [16·9–21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12–9·31) deaths (15·4% [14·6–16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253–350) DALYs (11·6% [10·3–13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0–9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10–24 years, alcohol use for those aged 25–49 years, and high systolic blood pressure for those aged 50–74 years and 75 years and older. Interpretation: Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public. Funding: Bill & Melinda Gates Foundation.
UR - https://www.scopus.com/pages/publications/85092451099
UR - https://www.scopus.com/pages/publications/85092451099#tab=citedBy
U2 - 10.1016/S0140-6736(20)30752-2
DO - 10.1016/S0140-6736(20)30752-2
M3 - Article
C2 - 33069327
AN - SCOPUS:85092451099
SN - 0140-6736
VL - 396
SP - 1223
EP - 1249
JO - The Lancet
JF - The Lancet
IS - 10258
ER -