Abstract
Background: The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of risk factor exposure and attributable burden of disease. By providing estimates over a long time series, this study can monitor risk exposure trends critical to health surveillance and inform policy debates on the importance of addressing risks in context. Methods: We used the comparative risk assessment framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2016. This study included 481 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk (RR) and exposure estimates from 22 717 randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources, according to the GBD 2016 source counting methods. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. Finally, we explored four drivers of trends in attributable burden: population growth, population ageing, trends in risk exposure, and all other factors combined. Findings: Since 1990, exposure increased significantly for 30 risks, did not change significantly for four risks, and decreased significantly for 31 risks. Among risks that are leading causes of burden of disease, child growth failure and household air pollution showed the most significant declines, while metabolic risks, such as body-mass index and high fasting plasma glucose, showed significant increases. In 2016, at Level 3 of the hierarchy, the three leading risk factors in terms of attributable DALYs at the global level for men were smoking (124.1 million DALYs [95% UI 111.2 million to 137.0 million]), high systolic blood pressure (122.2 million DALYs [110.3 million to 133.3 million], and low birthweight and short gestation (83.0 million DALYs [78.3 million to 87.7 million]), and for women, were high systolic blood pressure (89.9 million DALYs [80.9 million to 98.2 million]), high body-mass index (64.8 million DALYs [44.4 million to 87.6 million]), and high fasting plasma glucose (63.8 million DALYs [53.2 million to 76.3 million]). In 2016 in 113 countries, the leading risk factor in terms of attributable DALYs was a metabolic risk factor. Smoking remained among the leading five risk factors for DALYs for 109 countries, while low birthweight and short gestation was the leading risk factor for DALYs in 38 countries, particularly in sub-Saharan Africa and South Asia. In terms of important drivers of change in trends of burden attributable to risk factors, between 2006 and 2016 exposure to risks explains an 9.3% (6.9-11.6) decline in deaths and a 10.8% (8.3-13.1) decrease in DALYs at the global level, while population ageing accounts for 14.9% (12.7-17.5) of deaths and 6.2% (3.9-8.7) of DALYs, and population growth for 12.4% (10.1-14.9) of deaths and 12.4% (10.1-14.9) of DALYs. The largest contribution of trends in risk exposure to disease burden is seen between ages 1 year and 4 years, where a decline of 27.3% (24.9-29.7) of the change in DALYs between 2006 and 2016 can be attributed to declines in exposure to risks. Interpretation: Increasingly detailed understanding of the trends in risk exposure and the RRs for each risk-outcome pair provide insights into both the magnitude of health loss attributable to risks and how modification of risk exposure has contributed to health trends. Metabolic risks warrant particular policy attention, due to their large contribution to global disease burden, increasing trends, and variable patterns across countries at the same level of development. GBD 2016 findings show that, while it has huge potential to improve health, risk modification has played a relatively small part in the past decade.
Original language | English (US) |
---|---|
Pages (from-to) | 1345-1422 |
Number of pages | 78 |
Journal | The Lancet |
Volume | 390 |
Issue number | 10100 |
DOIs | |
State | Published - Sep 16 2017 |
ASJC Scopus subject areas
- Medicine(all)
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Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2016 : A systematic analysis for the Global Burden of Disease Study 2016. / GBD 2016 Risk Factors Collaborators.
In: The Lancet, Vol. 390, No. 10100, 16.09.2017, p. 1345-1422.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2016
T2 - A systematic analysis for the Global Burden of Disease Study 2016
AU - GBD 2016 Risk Factors Collaborators
AU - Gakidou, Emmanuela
AU - Afshin, Ashkan
AU - Abajobir, Amanuel Alemu
AU - Abate, Kalkidan Hassen
AU - Abbafati, Cristiana
AU - Abbas, Kaja M.
AU - Abd-Allah, Foad
AU - Abdulle, Abdishakur M.
AU - Abera, Semaw Ferede
AU - Aboyans, Victor
AU - Abu-Raddad, Laith J.
AU - Abu-Rmeileh, Niveen M.E.
AU - Abyu, Gebre Yitayih
AU - Adedeji, Isaac Akinkunmi
AU - Adetokunboh, Olatunji
AU - Afarideh, Mohsen
AU - Agrawal, Anurag
AU - Agrawal, Sutapa
AU - Ahmad Kiadaliri, Aliasghar
AU - Ahmadieh, Hamid
AU - Ahmed, Muktar Beshir
AU - Aichour, Amani Nidhal
AU - Aichour, Ibtihel
AU - Aichour, Miloud Taki Eddine
AU - Akinyemi, Rufus Olusola
AU - Akseer, Nadia
AU - Alahdab, Fares
AU - Al-Aly, Ziyad
AU - Alam, Khurshid
AU - Alam, Noore
AU - Alam, Tahiya
AU - Alasfoor, Deena
AU - Alene, Kefyalew Addis
AU - Ali, Komal
AU - Alizadeh-Navaei, Reza
AU - Alkerwi, Ala'a
AU - Alla, François
AU - Allebeck, Peter
AU - Al-Raddadi, Rajaa
AU - Alsharif, Ubai
AU - Altirkawi, Khalid A.
AU - Alvis-Guzman, Nelson
AU - Amare, Azmeraw T.
AU - Amini, Erfan
AU - Ammar, Walid
AU - Amoako, Yaw Ampem
AU - Ansari, Hossein
AU - Antó, Josep M.
AU - Antonio, Carl Abelardo T.
AU - Anwari, Palwasha
N1 - Funding Information: Laith J Abu-Raddad acknowledges the support of Qatar National Research Fund (NPRP 9-040-3-008), who provided the main funding for generating the data provided to the GBD-IHME effort. Anurag Agrawal received a Wellcome Trust DBT India Alliance fellowship. Ashish Awasthi received financial support from Department of Science and Technology, Government of India through INSPIRE Faculty program Alaa Badawi acknowledges the Public Health Agency of Canada. Scientific work of Aleksandra Barac is part of the Project No. III45005 granted by the Ministry of Education, Science, and Technological Development of the Republic of Serbia. Till Bärnighausen is funded by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professorship endowed by the German Federal Ministry of Education and Research; he is also supported by the Wellcome Trust, the European Commission, the Clinton Health Access Initiative and NICHD of NIH [R01-HD084233], NIAID of NIH [R01-AI124389 and R01-AI112339] and FIC of NIH [D43-TW009775]. Boris Bikbov has received funding from the European Union's Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement No. 703226. Boris Bikbov acknowledges that work related to this paper has been done on the behalf of the GBD Genitourinary Disease Expert Group. Cyrus Cooper reports personal fees from Alliance for Better Bone Health, Amgen, Eli Lilly, GSK, Medtronic, Merck, Novartis, Pfizer, Roche, Servier, Takeda, and UCB, outside the submitted work. José das Neves was supported in his contribution to this work by a Fellowship from Fundação para a Ciência e a Tecnologia, Portugal (SFRH/BPD/92934/2013). Barbora de Courten is supported by National Heart Foundation Future Leader Fellowship (100864). Kebede Deribe is funded by a Wellcome Trust Intermediate Fellowship in Public Health and Tropical Medicine [grant number 201900]. Joao Fernandes is supported by FCT - Fundação para a Ciência e a Tecnologia (Grant number UID/Multi/50016/2013). Katharine Gibney is supported by an NHMRC early career fellowship. Amador Goodridge acknowledges the Sistema Nacional de Investigación (SNI) de Panamá & Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT). Simon I Hay is funded by grants from the Bill & Melinda Gates Foundation (OPP1106023, OPP1119467, OPP1093011, and OPP1132415). Manami Inoue was the beneficiary of a financial contribution from the AXA Research Fund as chair-holder of the AXA Department of Health and Human Security, Graduate School of Medicine, The University of Tokyo. The AXA Research Fund had no role in this work. Shariful Islam received a postdoctoral research fellowship from the George Institute for Global Health and career transition grants from High Blood Pressure Research Council of Australia. Ministry of Education Science and Technological Development of the Republic of Serbia has co-financed Serbian part of Mihajlo Jakovljevic's GBD-related contribution through Grant OI 175 014. Publication of results was not contingent upon the Ministry's censorship or approval. Panniyammakal Jeemon reports a clinical and public health intermediate fellowship from the Wellcome Trust and Department of Biotechnology, India Alliance. Nicholas Kassebaum reports personal fees and non-financial support from Vifor Pharmaceuticals, outside the submitted work. S Vittal Katikireddi was funded by a NRS Scottish Senior Clinical Fellowship (SCAF/15/02), the UK Medical Research Council (MC_UU_12017/13 & MC_UU_12017/15) and the Scottish Government Chief Scientist Office (SPHSU13 & SPHSU15). Christian Kieling has received support from Brazilian governmental research funding agencies Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (Fapergs), and Hospital de Clínicas de Porto Alegre (FIPE/HCPA). Ai Koyanagi's work was supported by the Miguel Servet contract financed by the CP13/00150 and PI15/00862 projects, integrated into the National R + D + I and funded by the ISCIII - General Branch Evaluation and Promotion of Health Research - and the European Regional Development Fund (ERDF-FEDER). Katharine J Looker thanks the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Evaluation of Interventions at the University of Bristol, in partnership with Public Health England (PHE), for research support. Katharine J Looker received separate funding from WHO and Sexual Health 24 during the course of this study. These funders had no role in the writing of the manuscript nor the decision to submit it for publication. The views expressed are those of the authors and not necessarily those of the National Health Service, the NIHR, the Department of Health or Public Health England. Azeem Majeed and Imperial College London are grateful for support from the NW London NIHR Collaboration for Leadership in Applied Health Research & Care. Francisco Martins-Melo received a postdoctoral fellowship from the CAPES (Brazilian Federal Agency for Support and Evaluation of Graduate Education), outside the submitted work. Kunihiro Matsushita reports grants from the US National Kidney Foundation and the US National Institutes of Health during the conduct of the study; grants and personal fees from Kyowa Hakko Kirin, and Fukuda Denshi, and personal fees from Daiichi Sankyo, outside the submitted work. Mohsen Mazidi was supported by the World Academy of Sciences and Chinese Academy of Sciences. John McGrath received John Cade Fellowship APP1056929 from the National Health and Medical Research Council, and Niels Bohr Professorship from the Danish National Research Foundation. Toni Meier acknowledges additional institutional support from the Competence Cluster for Nutrition and Cardiovascular Health (nutriCARD), Jena-Halle-Leipzig. Philip Mitchell's research is supported by an Australian NHMRC Program Grant (no. 1037196). Ulrich Mueller gratefully acknowledges financial support from the German National Cohort Study (BMBF grant # 01ER1511/D). Olanrewaju Oladimeji is a Senior Research Specialist at the Human Sciences Research Council (HSRC) and Doctoral Candidate at the University of KwaZulu-Natal (UKZN), South Africa; we acknowledge the institutional supports from HSRC and UKZN for him to participate in this study. Alberto Ortiz was supported by Spanish Government (Intensificacion ISCIIII FEDER funds and RETIC REDINREN RD016/0019). Mayowa Owolabi is supported by U54HG007479 from the NIH. Norberto Perico would like to acknowledge that the work related to this paper has been done on the behalf of the GBD Genitourinary Disease Expert Group. Giuseppe Remuzzi acknowledges that the work related to this paper has been done on behalf of the GBD Genitourinary Diseases Expert Group supported by the International Society of Nephrology (ISN). Luz Myriam Reynales-Shigematsu acknowledges the Global Adult Tobacco Survey, GATS Mexico 2015, with financial support provided by the CONADIC, Ministry of Health, Mexico and the Bloomberg Initiative to Reduce Tobacco Use through the CDC Foundation with a grant from Bloomberg Philanthropies. Prof Aletta E Schutte received support from the South African Medical Research Council and the National Research Foundation's SARChI Programme. Mark Shrime acknowledges the Damon Runyon Cancer Research Foundation GE Safe Surgery 2020 Project. Jasvinder Singh reports consultancy fees from Savient, Takeda, Regeneron, Merz, Iroko, Bioiberica, Crealta/Horizon, Allergan, UBM LLC, WebMD, and the American College of Rheumatology and grants from Savient and Takeda. JS serves as the principal investigator for an investigator-initiated study funded by Horizon pharmaceuticals through a grant to DINORA Inc, a 501c3 entity; he is also on the steering committee of OMERACT, an international organization that develops measures for clinical trials and receives arms length funding from 36 pharmaceutical companies. Michael Soljak received funding from Public Health England for modelling of NCD prevalence. Cassandra Szoeke reports grants from the Australian National Medical Health Research Council (NHMRC) during the conduct of the study, and grants from Lundbeck and Alzheimer's Association, outside the submitted work; in addition, Cassandra Szoeke has a patent, PCT/AU2008/001556 issued. Rafael Tabarés-Seisdedos was supported in part by grant PROMETEOII/2015/021 from Generalitat Valenciana and the national grands PI14/00894 and PIE14/00031 from ISCIII-FEDER. Marcel Tanner reports grants from the Swiss Tropical and Public Health Institute and the Swiss Federal Government during the conduct of the study. Amanda Thrift was supported by a Fellowship from the National Health & Medical Research Council (Australia; 1042600). Stefano Tyrovola's work was supported by the Foundation for Education and European Culture (IPEP), the Sara Borrell postdoctoral programme (reference no. CD15/00019 from the Instituto de Salud Carlos III (ISCIII - Spain) and the Fondos Europeo de Desarrollo Regional (FEDER). Job van Boven received support from the department of Clinical Pharmacy and Clinical Pharmacology, University Medical Center Groningen, University of Groningen, Netherlands. Lijing Yan is partially supported by the National Natural Sciences Foundation of China grants (71233001 and 71490732). Marcel Yotebieng is partially supported by the NIAID U01AI096299 and the NICHD R01HD087993. Funding Information: The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with license no. 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. We 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 these data available. The Panel Study of Income Dynamics is primarily sponsored by the National Science Foundation, the National Institute of Aging, and the National Institute of Child Health and Human Development and is conducted by the University of Michigan. This research used data from the National Health Survey 2003 and the National Health Survey 2009–10. The authors are grateful to the Ministry of Health Survey copyright owner, allowing them to have the database. All results of the study are those of the author and in no way committed to the Ministry. This research uses data from Add Health, a program 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. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W Franklin Street, Chapel Hill, NC 27516-2524 ( addhealth@unc.edu ). No direct support was received from grant P01-HD31921 for this analysis. The HRS (Health and Retirement Study) is sponsored by the National Institute on Aging (grant number NIA U01AG009740) and is conducted by the University of Michigan. This paper uses data from SHARE Waves 1, 2, 3 (SHARELIFE), 4, 5 and 6 (DOIs: 10.6103/SHARE.w1.600, 10.6103/SHARE.w2.600, 10.6103/SHARE.w3.600, 10.6103/SHARE.w4.600, 10.6103/SHARE.w5.600, 10.6103/SHARE.w6.600), see Börsch-Supan et al. (2013) for methodological details. (1) The SHARE data collection has been primarily 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) and FP7 (SHARE-PREP: N°211909, SHARE-LEAP: N°227822, SHARE M4: N°261982). 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. HBSC is an international study carried out in collaboration with WHO/EURO. The International Coordinator of the 1997/98, 2001/02, 2005/06 and 2009/10 surveys was Candace Currie and the Data Bank Manager for the 1997/98 survey was Bente Wold, whereas for the following survey Prof Oddrun Samdal was the Databank Manager. A list of principal investigator in each country can be found online.. This analysis uses data or information from the LASI Pilot micro data and documentation. The development and release of the LASI Pilot Study was funded by the National Institute on Ageing/National Institute of Health (R21AG032572, R03AG043052, and R01 AG030153). The data used in this paper come from the 2009–10 Ghana Socioeconomic Panel Study Survey which is a nationally representative survey of over 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 the Economic Growth Center. At the same time, ISSER and the EGC are not responsible for the estimations reported by the analyst(s). The data reported here have been supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the US Government. We 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 these data available.
PY - 2017/9/16
Y1 - 2017/9/16
N2 - Background: The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of risk factor exposure and attributable burden of disease. By providing estimates over a long time series, this study can monitor risk exposure trends critical to health surveillance and inform policy debates on the importance of addressing risks in context. Methods: We used the comparative risk assessment framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2016. This study included 481 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk (RR) and exposure estimates from 22 717 randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources, according to the GBD 2016 source counting methods. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. Finally, we explored four drivers of trends in attributable burden: population growth, population ageing, trends in risk exposure, and all other factors combined. Findings: Since 1990, exposure increased significantly for 30 risks, did not change significantly for four risks, and decreased significantly for 31 risks. Among risks that are leading causes of burden of disease, child growth failure and household air pollution showed the most significant declines, while metabolic risks, such as body-mass index and high fasting plasma glucose, showed significant increases. In 2016, at Level 3 of the hierarchy, the three leading risk factors in terms of attributable DALYs at the global level for men were smoking (124.1 million DALYs [95% UI 111.2 million to 137.0 million]), high systolic blood pressure (122.2 million DALYs [110.3 million to 133.3 million], and low birthweight and short gestation (83.0 million DALYs [78.3 million to 87.7 million]), and for women, were high systolic blood pressure (89.9 million DALYs [80.9 million to 98.2 million]), high body-mass index (64.8 million DALYs [44.4 million to 87.6 million]), and high fasting plasma glucose (63.8 million DALYs [53.2 million to 76.3 million]). In 2016 in 113 countries, the leading risk factor in terms of attributable DALYs was a metabolic risk factor. Smoking remained among the leading five risk factors for DALYs for 109 countries, while low birthweight and short gestation was the leading risk factor for DALYs in 38 countries, particularly in sub-Saharan Africa and South Asia. In terms of important drivers of change in trends of burden attributable to risk factors, between 2006 and 2016 exposure to risks explains an 9.3% (6.9-11.6) decline in deaths and a 10.8% (8.3-13.1) decrease in DALYs at the global level, while population ageing accounts for 14.9% (12.7-17.5) of deaths and 6.2% (3.9-8.7) of DALYs, and population growth for 12.4% (10.1-14.9) of deaths and 12.4% (10.1-14.9) of DALYs. The largest contribution of trends in risk exposure to disease burden is seen between ages 1 year and 4 years, where a decline of 27.3% (24.9-29.7) of the change in DALYs between 2006 and 2016 can be attributed to declines in exposure to risks. Interpretation: Increasingly detailed understanding of the trends in risk exposure and the RRs for each risk-outcome pair provide insights into both the magnitude of health loss attributable to risks and how modification of risk exposure has contributed to health trends. Metabolic risks warrant particular policy attention, due to their large contribution to global disease burden, increasing trends, and variable patterns across countries at the same level of development. GBD 2016 findings show that, while it has huge potential to improve health, risk modification has played a relatively small part in the past decade.
AB - Background: The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of risk factor exposure and attributable burden of disease. By providing estimates over a long time series, this study can monitor risk exposure trends critical to health surveillance and inform policy debates on the importance of addressing risks in context. Methods: We used the comparative risk assessment framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2016. This study included 481 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk (RR) and exposure estimates from 22 717 randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources, according to the GBD 2016 source counting methods. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. Finally, we explored four drivers of trends in attributable burden: population growth, population ageing, trends in risk exposure, and all other factors combined. Findings: Since 1990, exposure increased significantly for 30 risks, did not change significantly for four risks, and decreased significantly for 31 risks. Among risks that are leading causes of burden of disease, child growth failure and household air pollution showed the most significant declines, while metabolic risks, such as body-mass index and high fasting plasma glucose, showed significant increases. In 2016, at Level 3 of the hierarchy, the three leading risk factors in terms of attributable DALYs at the global level for men were smoking (124.1 million DALYs [95% UI 111.2 million to 137.0 million]), high systolic blood pressure (122.2 million DALYs [110.3 million to 133.3 million], and low birthweight and short gestation (83.0 million DALYs [78.3 million to 87.7 million]), and for women, were high systolic blood pressure (89.9 million DALYs [80.9 million to 98.2 million]), high body-mass index (64.8 million DALYs [44.4 million to 87.6 million]), and high fasting plasma glucose (63.8 million DALYs [53.2 million to 76.3 million]). In 2016 in 113 countries, the leading risk factor in terms of attributable DALYs was a metabolic risk factor. Smoking remained among the leading five risk factors for DALYs for 109 countries, while low birthweight and short gestation was the leading risk factor for DALYs in 38 countries, particularly in sub-Saharan Africa and South Asia. In terms of important drivers of change in trends of burden attributable to risk factors, between 2006 and 2016 exposure to risks explains an 9.3% (6.9-11.6) decline in deaths and a 10.8% (8.3-13.1) decrease in DALYs at the global level, while population ageing accounts for 14.9% (12.7-17.5) of deaths and 6.2% (3.9-8.7) of DALYs, and population growth for 12.4% (10.1-14.9) of deaths and 12.4% (10.1-14.9) of DALYs. The largest contribution of trends in risk exposure to disease burden is seen between ages 1 year and 4 years, where a decline of 27.3% (24.9-29.7) of the change in DALYs between 2006 and 2016 can be attributed to declines in exposure to risks. Interpretation: Increasingly detailed understanding of the trends in risk exposure and the RRs for each risk-outcome pair provide insights into both the magnitude of health loss attributable to risks and how modification of risk exposure has contributed to health trends. Metabolic risks warrant particular policy attention, due to their large contribution to global disease burden, increasing trends, and variable patterns across countries at the same level of development. GBD 2016 findings show that, while it has huge potential to improve health, risk modification has played a relatively small part in the past decade.
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U2 - 10.1016/S0140-6736(17)32366-8
DO - 10.1016/S0140-6736(17)32366-8
M3 - Article
C2 - 28919119
AN - SCOPUS:85031722400
VL - 390
SP - 1345
EP - 1422
JO - The Lancet
JF - The Lancet
SN - 0140-6736
IS - 10100
ER -