Global burden of 292 causes of death in 204 countries and territories and 660 subnational locations, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023
| dc.contributor.author | Naghavi, Mohsen | en |
| dc.contributor.author | Kyu, Hmwe Hmwe | en |
| dc.contributor.author | Bhoomadevi, A. | en |
| dc.contributor.author | Aalipour, Mohammad Amin | en |
| dc.contributor.author | Aalruz, Hasan | en |
| dc.contributor.author | Ababneh, Hazim S. | en |
| dc.contributor.author | Abafita, Bedru J. | en |
| dc.contributor.author | Abaraogu, Ukachukwu O. | en |
| dc.contributor.author | Abbafati, Cristiana | en |
| dc.contributor.author | Abbasi, Madineh | en |
| dc.contributor.author | Abbaspour, Faezeh | en |
| dc.contributor.author | Abbastabar, Hedayat | en |
| dc.contributor.author | Abd Al Magied, Abdallah H.A. | en |
| dc.contributor.author | Abd ElHafeez, Samar | en |
| dc.contributor.author | Abdalla, Ashraf Nabiel | en |
| dc.contributor.author | Abdalla, Mohammed Altigani | en |
| dc.contributor.author | Abdallah, Emad M. | en |
| dc.contributor.author | Abdeeq, Barkhad Aden | en |
| dc.contributor.author | Abdel Razeq, Nadin M.I. | en |
| dc.contributor.author | Abdelgalil, Ahmed Abdelrahman | en |
| dc.contributor.author | Abdel-Hameed, Reda | en |
| dc.contributor.author | Abdelmasseh, Michael | en |
| dc.contributor.author | Abdelnabi, Mahmoud | en |
| dc.contributor.author | Abdel-Rahman, Wael M. | en |
| dc.contributor.author | Abdous, Arman | en |
| dc.contributor.author | Abdrabou, Mostafa M. | en |
| dc.contributor.author | Abdul Aziz, Jeza Muhamad | en |
| dc.contributor.author | Abdulah, Deldar Morad | en |
| dc.contributor.author | Abdullahi, Auwal | en |
| dc.contributor.author | Abdul-Rahman, Toufik | en |
| dc.contributor.author | Abebe Getahun, Habtamu | en |
| dc.contributor.author | Abedi, Aidin | en |
| dc.contributor.author | Abedi, Armita | en |
| dc.contributor.author | Abedi, Parisa | en |
| dc.contributor.author | Abejew, Asrat Agalu | en |
| dc.contributor.author | Abeldaño Zuñiga, Roberto Ariel | en |
| dc.contributor.author | Abid, Shehab Uddin Al | en |
| dc.contributor.author | Abidi, Syed Hani | en |
| dc.contributor.author | Abie, Alemwork | en |
| dc.contributor.author | Abiodun, Olugbenga Olusola | en |
| dc.contributor.author | Aboagye, Richard Gyan | en |
| dc.contributor.author | Abohashem, Shady | en |
| dc.contributor.author | Adhikary, Ripon Kumar | en |
| dc.contributor.author | Ahmad, Danish | en |
| dc.contributor.author | Alene, Kefyalew Addis | en |
| dc.contributor.author | Asgary, Saeed | en |
| dc.contributor.author | Bagheri, Nasser | en |
| dc.contributor.author | Burns, Richard A. | en |
| dc.contributor.author | Kamal, Md Moustafa | en |
| dc.contributor.author | Kinfu, Yohannes | en |
| dc.date.accessioned | 2025-12-23T20:40:29Z | |
| dc.date.available | 2025-12-23T20:40:29Z | |
| dc.date.issued | 2025-10-18 | en |
| dc.description.abstract | Background Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. Methods GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. Findings The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6–47·0) in 1990 to 63·4 years (63·1–63·7) in 2023. For males, mean age increased from 45·4 years (45·1–45·7) to 61·2 years (60·7–61·6), and for females it increased from 48·5 years (48·1–48·8) to 65·9 years (65·5–66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9–81·0) and for males 74·8 years (74·8–74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5–38·4) for females and 35·6 years (35·2–35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. Interpretation We examined global mortality patterns over the past three decades, highlighting—with enhanced estimation methods—the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. Funding Gates Foundation. | en |
| dc.description.sponsorship | Research reported in this publication was supported by the Gates Foundation (OPP1152504); Queensland Department of Health, Australia; UK Department of Health and Social Care; the Norwegian Institute of Public Health; St Jude Children's Research Hospital; and the New Zealand Ministry of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. Collection of these data was made possible by the U.S. Agency for International Development (USAID) under the terms of cooperative agreement GPO-A-00–08–000_D3–00. The opinions expressed are those of the authors and do not necessarily reflect the views of USAID or the United States government. 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. 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 U.S. government. 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 Prof Candace Currie and the Data Bank Manager for the 1997/98 survey was Prof Bente Wold, whereas for the following survey Prof Oddrun Samdal was the Databank Manager. A list of principal investigators in each country can be found at http://www.hbsc.org. This manuscript is based on data collected and shared by the International Vaccine Institute (IVI) from an original study it conducted with support from the Bill and Melinda Gates Foundation (BMGF). This analysis is based on the Canadian Heart Health Database 1986–92, which contains anonymised data collected in a coordinated series of Heart Health Surveys carried out in the ten Provinces of Canada between 1986 and 1992. The database was constructed by the Conference of Principal Investigators of Provincial Heart Health Programs from survey questions and clinical measures which were common to all surveys. All computations on these microdata were prepared by IHME and the responsibility for the use and interpretation of these data is entirely that of the author(s). The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with license number SLN2019–8-64, 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. This analysis is based on Statistics Canada Microdata file International Adult Literacy Skills Survey (Canada), 2003; International Adult Literacy Survey, 1994–1998; Adult Literacy and Life Skills Survey, 2003 which contains anonymised data collected in the 2003 Bermuda Adult Literacy and Life Skills Survey. All computations on these microdata were prepared by IHME and the responsibility for the use and interpretation of these data is entirely that of the authors. This paper uses data from the American Samoa 2004 STEPS survey, implemented by Department of Health (American Samoa) and Monash University (Australia) with the support of WHO. Collection of these data was made possible by the U.S. Agency for International Development (USAID) under the terms of cooperative agreement GPO-A-00–08–000_D3–00. The opinions expressed are those of the authors and do not necessarily reflect the views of USAID or the United States government. This paper uses data from the Botswana 2007 and 2014 STEPS surveys, implemented by Ministry of Health (Botswana) with the support of WHO. This paper uses data from the Cameroon 2003 STEPS survey, implemented by Health of Populations in Transition (HoPiT) Research Group (Cameroon) and Ministry of Public Health (Cameroon) with the support of WHO. This paper uses data from the ZambiA&Mdash;Lusaka 2008 STEPS survey, implemented by Ministry of Health (Zambia) with the support of WHO. This paper uses data from the Uruguay 2006 and 2013–2014 STEPS surveys, implemented by Ministry of Health (Uruguay) with the support of WHO. This paper uses data from the Tokelau 2005 STEPS survey, implemented by Tokelau Department of Health, Fiji School of Medicine with the support of WHO. This paper uses data from the Chad—Ville de N’Djamena 2008 STEPS survey, implemented by Ministry of Public Health (Chad) with the support of WHO. This paper uses data from the Seychelles 2004 STEPS survey, implemented by Ministry of Health (Seychelles) with the support of WHO. This paper uses data from the Sierra Leone 2009 STEPS survey, implemented by Ministry of Health and Sanitation (Sierra Leone) with the support of WHO. This paper uses data from the Nauru 2004 and 2015–2016 STEPS surveys, implemented by Ministry of Health (Nauru) with the support of WHO. This paper uses data from the Niger 2007 STEPS survey, implemented by Ministry of Health (Niger) with the support of WHO. This paper uses data from the Malawi 2009 and 2017 STEPS surveys, implemented by Ministry of Health (Malawi) with the support of WHO. This paper uses data from the MauritaniA&Mdash;Nouakchott 2006 STEPS survey, implemented by Ministry of Health (Mauritania) with the support of WHO. This paper uses data from the Mozambique 2005 STEPS survey, implemented by Ministry of Health (Mozambique) with the support of WHO. This paper uses data from the Mongolia 2005, 2009, and 2013 STEPS surveys, implemented by Ministry of Health (Mongolia) with the support of WHO. This paper uses data from the Madagascar—Antananarivo and Toliara 2005 STEPS survey, implemented by Ministry of Health and Family Planning (Madagascar) with the support of WHO. This paper uses data from the Laos—Viangchan 2008 STEPS survey, implemented by Ministry of Health (Laos) with the support of WHO. This paper uses data from the Kuwait 2006 and 2014 STEPS surveys, implemented by Ministry of Health (Kuwait) with the support of WHO. This paper uses data from the Kiribati 2004–2006 and 2016 STEPS surveys, implemented by Ministry of Health and Medical Services (Kiribati) with the support of WHO. This paper uses data from the Gabon—Estuaire 2009 STEPS survey, implemented by Ministry of Health and Public Hygiene (Gabon) with the support of WHO. This paper uses data from the MicronesiA&Mdash;Pohnpei 2002 STEPS survey, implemented by Centre for Physical Activity and Health, University of Sydney (Australia), Department of Health and Social Affairs (Micronesia), Fiji School of Medicine, Micronesia Human Resources Development Center, Pohnpei State Department of Health Services with the support of WHO. This paper uses data from the Fiji 2002 STEPS survey, implemented by Fiji School of Medicine, Menzies Center for Population Health Research, University of Tasmania (Australia), Ministry of Health (Fiji) with the support of WHO. This paper uses data from the Eritrea 2004 and 2010 STEPS surveys, implemented by Ministry of Health (Eritrea) with the support of WHO. This paper uses data from the AlgeriA&Mdash;Setif and Mostaganem 2003 STEPS survey, implemented by Ministry of Health, Population and Hospital Reform (Algeria) with the support of WHO. This paper uses data from the Congo—Brazzaville 2004 STEPS survey, implemented by Ministry of Health and Population (Congo) with the support of WHO. This paper uses data from the Democratic Republic of the Congo—Kinshasa 2005 STEPS survey, implemented by the Ministry of Public Health (Congo, DR) with the support of WHO. This paper uses data from the Cote D’Ivoire—Lagunes 2005 STEPS survey, implemented by Ministry of Health and Public Hygiene (Cote D’Ivoire) with the support of WHO. This paper uses data from the Bhutan—Thimphu 2007 STEPS survey, implemented by Ministry of Health (Bhutan) with the support of WHO. This paper uses data from the Benin—Littoral 2007 STEPS survey, implemented by Ministry of Health (Benin) with the support of WHO. This paper uses data from the Benin 2008 and 2015 STEPS surveys, implemented by Ministry of Health (Benin) with the support of WHO. This analysis is based on Statistics Canada Microdata file, product 62M0004XCB, which contains anonymised data collected in the Survey of Household Spending for the year 2009. All computations on these microdata were prepared by IHME and the responsibility for the use and interpretation of these data is entirely that of the author(s). 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 study is based on data from Eurostat, Malta European Health Interview Survey 2008 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This paper uses data from the Qatar 2012 STEPS survey, implemented by Supreme Council of Health (Qatar) with the support of WHO. This paper uses data from the Libya 2009 STEPS survey, implemented by Secretariat of Health and Environment (Libya) with the support of WHO. This paper uses data from the Palestine 2010–2011 STEPS survey, implemented by Ministry of Health (Palestine) with the support of WHO. This paper contains information licensed under the Open Government Licence Canada. https://open.canada.ca/en/open-government-licence-canada. This paper uses data from the Bangladesh 2009–2010 STEPS survey, implemented by Ministry of Health and Family Welfare (Bangladesh), Bangladesh Society of Medicine with the support of WHO. This paper uses data from the MicronesiA&Mdash;Chuuk 2006 STEPS survey, implemented by Department of Health and Social Affairs (Micronesia), Chuuk Department of Health Services (Micronesia) with the support of WHO. This paper uses data from the Cambodia 2010 STEPS survey, implemented by Ministry of Health (Cambodia) with the support of WHO. This paper uses data from the Solomon Islands 2005–2006 STEPS survey, implemented by Ministry of Health and Medical Services (Solomon Islands) with the support of WHO. This paper uses data from the Togo 2010–2011 STEPS survey, implemented by Ministry of Health (Togo) with the support of WHO. This paper uses data from the EthiopiA&Mdash;Addis Ababa 2006 STEPS survey, implemented by School of Public Health, Addis Ababa University (Ethiopia) with the support of WHO. This paper uses data from the Fiji 2011 STEPS survey, implemented by Ministry of Health (Fiji) with the support of WHO. This paper uses data from the Lesotho 2012 STEPS survey, implemented by Ministry of Health and Social Welfare (Lesotho) with the support of WHO. This paper uses data from the Barbados 2007 STEPS survey, implemented by Ministry of Health (Barbados) with the support of WHO. This paper uses data from the Cape Verde 2007 STEPS survey, implemented by Ministry of Health, National Statistics Office with the support of WHO. This paper uses data from the Central African Republic—Bangui 2010 STEPS survey, implemented by Ministry of Health and Population (Central African Republic) with the support of WHO. This paper uses data from the Comoros 2011 STEPS survey, implemented by Ministry of Health (Comoros) with the support of WHO. This paper uses data from the Gambia 2010 STEPS survey, implemented by Ministry of Health and Social Welfare (Gambia) with the support of WHO. This paper uses data from the Guinea 2009 STEPS survey, implemented by Ministry of Public Health and Hygiene (Guinea) with the support of WHO. This paper uses data from the Liberia 2011 STEPS survey, implemented by Ministry of Health and Social Welfare (Liberia) with the support of WHO. This paper uses data from the Maldives 2011 STEPS survey, implemented by Health Protection Agency (Maldives) with the support of WHO. This paper uses data from the Mali 2007 STEPS survey, implemented by Ministry of Health (Mali) with the support of WHO. This paper uses data from the Marshall Islands 2002 STEPS survey, implemented by Ministry of Health (Marshall Islands) with the support of WHO. This paper uses data from the MicronesiA&Mdash;Pohnpei 2008 STEPS survey, implemented by FSM Department of Health and Social Affairs, Pohnpei State Department of Health Services with the support of WHO. This paper uses data from the Sao Tome and Principe 2008 and 2019 STEPS surveys, implemented by Ministry of Health (Sao Tome and Principe) with the support of WHO. This paper uses data from the Sri Lanka 2006, 2014–2015, and 2019 STEPS surveys, implemented by Ministry of Health (Sri Lanka) with the support of WHO. This paper uses data from the Swaziland 2007 and 2014 STEPS surveys, implemented by Ministry of Health (Swaziland) with the support of WHO. This paper uses data from the Tanzania 2012 STEPS survey, implemented by National Institute for Medical Research (Tanzania) with the support of WHO. This paper uses data from the Tonga 2004, 2011–2012, and 2017 STEPS surveys, implemented by Ministry of Health (Tonga) with the support of WHO. This paper uses data from the Vanuatu 2005 and 2011 STEPS surveys, implemented by Ministry of Health (Vanuatu) with the support of WHO. This paper uses data from the Virgin Islands, British 2009 STEPS survey, implemented by Ministry of Health and Social Development (British Virgin Islands) with the support of WHO. This paper uses data from the French Polynesia 2010 STEPS survey, implemented by Ministry of Health (French Polynesia) with the support of WHO. This research used data from the National Health Survey 2003. The author is grateful to the Ministry of Health, Survey copyright owner, allowing him to have the database. All results of the study are those of the author and in no way committed to the Ministry. This study is based on data from Eurostat, Slovenia European Health Interview Survey 2007–2008 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This paper uses data from the Cook Islands 2003–2004 and 2013–2015 STEPS surveys, implemented by Ministry of Health (Cook Islands) with the support of WHO. This paper uses data from the TanzaniA&Mdash;Zanzibar 2011 STEPS survey, implemented by Ministry of Health (Zanzibar) with the support of WHO. This research used data from the National Health Survey 2009–2010. The author is grateful to the Ministry of Health, Survey copyright owner, allowing him 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. This study is based on data from Eurostat, Slovakia European Health Interview Survey 2009 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). 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.w1.611, 10.6103/SHARE.w2.611, 10.6103/SHARE.w3.611, 10.6103/SHARE.w4.611, 10.6103/SHARE.w5.611, and 10.6103/SHARE.w6.611), see Börsch-Supan et al (2013) for methodological details. 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 U.S. 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 (see www.share-project.org). This paper uses data from the Rwanda 2012–2013 STEPS survey, implemented by Ministry of Health (Rwanda) with the support of WHO. 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 Prof Candace Currie and the Data Bank Manager for the 1997/98 survey was Prof Bente Wold, whereas for the following survey Prof 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 (SAGE). This study is based on data from Eurostat, Latvia European Health Interview Survey 2008 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Romania European Health Interview Survey 2008 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This paper uses data from the Moldova 2013 and 2021 STEPS surveys, implemented by Ministry of Health (Moldova) with the support of WHO. This paper uses data from the Cayman Islands 2012 STEPS survey, implemented by Ministry of Health, Environment, Youth, Sports and Culture (Cayman Islands) with the support of WHO. This paper uses data from the Grenada 2010–2011 STEPS survey, implemented by Ministry of Health (Grenada) with the support of WHO. This paper uses data from the Nepal 2012–2013 STEPS survey, implemented by the Nepal Health Research Council with the support of WHO. This publication uses data provided by Statistics Botswana. This paper uses data from the Namibia 2005 STEPS survey, implemented by the Ministry of Health with the support of WHO. Researchers interested in using TILDA data may access the data for free from the following sites: Irish Social Science Data Archive (ISSDA) at University College Dublin http://www.ucd.ie/issda/data/tilda/; Interuniversity Consortium for Political and Social Research (ICPSR) at the University of Michigan http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/34315. Data for this research were accessed via the Irish Social Science Data Archive (www.ucd.ie/issda). The original creators bear no responsibility for analysis or interpretation of them. This analysis is based on the Statistics Canada Canadian Community Health Survey Microdata File which contains anonymised data collected in the 2013–2014 Canadian Community Health Survey. All computations, use and interpretation of these data are entirely that of IHME. This paper uses data from the Kenya 2015 STEPS survey, implemented by Kenya National Bureau of Statistics, Ministry of Health (Kenya) with the support of WHO. 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). This paper uses data from the Bhutan 2014 and 2019 STEPS surveys, implemented by Ministry of Health (Bhutan) with the support of WHO. This paper uses data from the Uganda 2014 STEPS survey, implemented by Ministry of Health (Uganda) with the support of WHO. This paper uses data from the Timor-Leste 2014 STEPS survey, implemented by Ministry of Health (Timor-Leste) with the support of WHO. The CRELES project (Costa Rican Longevity and Healthy Aging Study) is a longitudinal study by the University of Costa Rica's Centro Centroamericano de Población 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 U.S. National Institute on Aging (grant R01AG031716). The study Principal Investigators are Luis Rosero-Bixby and William H. Dow, and co-Principal Investigators Xinia Fernández and Gilbert Brenes. This paper uses data from the GhanA&Mdash;Greater Accra Region 2006 STEPS survey, implemented by Ghana Health Service with the support of WHO. The CRELES project (Costa Rican Longevity and Healthy Aging Study) is a longitudinal study by the University of Costa Rica's Centro Centroamericano de Población 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 U.S. National Institute on Aging (grant R01AG031716). The study Principal Investigators are Luis Rosero-Bixby and William H. Dow, and co-Principal Investigators Xinia Fernández and Gilbert Brenes. This paper uses data from the Myanmar 2014 STEPS survey, implemented by Ministry of Health (Myanmar) with the support of WHO. HBSC is an international study carried out in collaboration with WHO/EURO. The International Coordinator of the 2013/2014 surveys was Prof Candace Currie and the Data Bank Manager was Prof Oddrun Samdal. A list of principal investigators in each country can be found at http://www.hbsc.org. The Canada Health Measures Survey 2016–2017 contains information licensed under the Open Government License Canada. This research used information from the Health Surveys for epidemiological surveillance of the Undersecretary of Public Health. The author thanks the Ministry of Health of Chile, having allowed them to have access to the database. All the results obtained from the study or research are the responsibility of the author and in no way compromise that institution. In this paper use is made of data of the DNB Household Survey administered by Centerdata (Tilburg University, The Netherlands). Those who carried out the original collection and analysis of the Jamaica Survey of Living Conditions bear no responsibility for their further analysis or interpretation. This paper uses data from China Family Panel Studies (CFPS), funded by 985 Program of Peking University and carried out by the Institute of Social Science Survey of Peking University. This paper uses data from the Vietnam 2009 and 2015 STEPS surveys, implemented by Ministry of Health (Vietnam) with the support of WHO. This paper uses data from the Pakistan 2013–2014 STEPS survey, implemented by Ministry of National Health Services, Regulation and Coordination, Pakistan Health Research Council with the support of WHO. This paper uses data from WHO's Study on Global Ageing and Adult Health (SAGE). SAGE is supported by the US National Institute on Aging through Interagency Agreements OGHA 04034785; YA1323–08-CN-0020; Y1-AG-1005–0) and through research grants R01-AG034479 and R21-AG034263. Adapted from Statistics Canada, Canada Tobacco, Alcohol and Drugs Survey 2015. This does not constitute an endorsement by Statistics Canada of this product. This study is based in part on data from Eurostat, European Union Labor Force Survey, 1992–2016. The responsibility for all conclusions drawn from the data lies entirely with the authors. This study is based on data from Eurostat, Belgium Health Interview Survey 2008 and 2009. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Cyprus Health Interview Survey 2008–2009. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Czech Republic European Health Interview Survey 2006–2009 and 2013–2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Estonia European Health Interview Survey 2006–2007 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Greece European Health Interview Survey 2009 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based in part on data from Eurostat, Poland European Health Interview Survey 2009. The responsibility for all conclusions drawn from the data lies entirely with the authors. This study is based in part on data from Eurostat, Spain European Health Interview Survey 2009–2010. The responsibility for all conclusions drawn from the data lies entirely with the authors. This study is based in part on data from Eurostat, France European Health Interview Survey 2008. The responsibility for all conclusions drawn from the data lies entirely with the authors. The responsibility for analysis and processing is that of the authors and not ISTAT. This paper uses data from the Lebanon 2016–2017 STEPS survey, implemented by Ministry of Public Health (Lebanon) with the support of WHO. This paper uses data from the Zambia 2017 STEPS survey, implemented by Ministry of Health (Zambia) with the support of WHO. This paper uses data from the Armenia 2016 STEPS survey, implemented by Ministry of Health (Armenia), National Institute of Health with the support of WHO. This paper uses data from the Belarus 2016–2017 STEPS survey, implemented by Republican Scientific and Practical Center of Medical Technologies, Informatization, Management and Economics of Public Health (Belarus) with the support of WHO. This paper uses data from the Iraq 2015 STEPS survey, implemented by Ministry of Health (Iraq) with the support of WHO. This paper uses data from the Brunei 2015–2016 STEPS survey, implemented by Ministry of Health (Brunei) with the support of WHO. This paper uses data from the Samoa 2002 and 2013 STEPS surveys, implemented by Ministry of Health (Samoa) with the support of WHO. The data are from China Family Panel Studies (CFPS), funded by 985 Program of Peking University and carried out by the Institute of Social Science Survey of Peking University. This paper uses data from the Algeria 2016–2017 STEPS survey, implemented by Ministry of Health (Algeria) with the support of WHO. This paper uses data from the Azerbaijan 2017 STEPS survey, implemented by Ministry of Health (Azerbaijan) with the support of WHO. This paper uses data from the Kyrgyzstan 2013 STEPS survey, implemented by Ministry of Health (Kyrgyzstan) with the support of WHO. This paper uses data from the Laos 2013 STEPS survey, implemented by Ministry of Health (Laos) with the support of WHO. This paper uses data from the MicronesiA&Mdash;Kosrae 2009 STEPS survey, implemented by FSM Department of Health and Social Affairs with the support of WHO. This paper uses data from the MicronesiA&Mdash;Yap 2009 STEPS survey, implemented by Ministry of Health and Social Affairs (Micronesia) with the support of WHO. This paper uses data from the Palau 2011–2013 and 2016 STEPS surveys, implemented by Ministry of Health (Palau) with the support of WHO. This paper uses data from the Tajikistan 2016 STEPS survey, implemented by Ministry of Health (Tajikistan) with the support of WHO. This paper uses data from the Tokelau 2014 STEPS survey, implemented by the Department of Health with the support of WHO. This paper uses data from the Sudan 2016 STEPS survey, implemented by the Ministry of Health with the support of WHO. This paper uses data from the Morocco 2017 STEPS survey, implemented by the Ministry of Health with the support of WHO. This paper uses data from the Georgia 2016 STEPS survey, implemented by the National Center for Disease Control and Public Health with the support of WHO. This paper uses data from the Guyana 2016 STEPS survey, implemented by the Ministry of Public Health with the support of WHO. The MHAS (Mexican Health and Aging Study) is partly sponsored by the National Institutes of Health/National Institute on Aging (grant number NIH R01AG018016) and the INEGI in Mexico. Data files and documentation are public use and available at www.MHASweb.org. The Irish Longitudinal study on Ageing (TILDA) Wave 4, 2016 was accessed via the Irish Social Science Data Archive—www.ucd.ie/issda. The harmonised dataset was downloaded from the GDD website (Global Dietary Database. The Estonian National Dietary Survey 2014. https://www.globaldietarydatabase.org/management/microdata-surveys/657, Aug 28, 2020]. The harmonisation of the dataset was performed by the data owner (The Estonian National Dietary Survey 2014 (RTU2014), 2014, National Institute for Health Development], and the overall process was overseen by EFSA (European Food Safety Authority. EFSA Comprehensive European Food Consumption Database. http://www.efsa.europa.eu/en/food-consumption/comprehensive-database] and GDD. This paper uses data from the Bahamas 2011–2012 STEPS survey, implemented by the Ministry of Health with the support of WHO. This paper uses data from the Central African Republic—Bangui and Ombella M’Poko 2017 STEPS survey, implemented by the Ministry of Health and Population with the support of WHO. This paper uses data from the MicronesiA&Mdash;Chuuk STEPS 2016 survey, implemented by the Federated States of Micronesia Department of Health and Social Affairs, Department of Health Services of the State of Chuuk, FSM with the support of WHO. This paper uses data from the Tuvalu 2015 STEPS survey, implemented by the Ministry of Health with the support of WHO. This paper uses data from the Solomon Islands 2015 STEPS survey, implemented by the Ministry of Health with the support of WHO. This paper uses data from the Mali—Kati, Ouéléssébougou, Koulikoro, Ségou and Bamako District 2013 STEPS survey, implemented by the Ministry of Health with the support of WHO. This paper uses data from the Marshall Islands 2017–2018 STEPS survey, implemented by the Ministry of Health and Human Services with the support of WHO. This research is based on data from the National Health Interview Survey of the National Center for Health Statistics. The analyses, interpretations, and conclusions of this paper are the author's own. The NCHS is responsible only for the initial data. This paper uses data from the Nepal 2019 STEPS survey, implemented by Nepal Health Research Council, Ministry of Health and Population with the support of WHO. This paper uses data from the Bangladesh 2018 STEPS survey, implemented by the National Institute of Preventive and Social Medicine with the support of WHO. The harmonised dataset was downloaded from the GDD website (Global Dietary Database. Nutrition and Nutritional Status of Children under 5 years in Bulgaria [NUTRICHILD] 2007. https://www.globaldietarydatabase.org/management/microdata-surveys/649, accessed Aug 28, 2020). The harmonisation of the dataset was jointly performed by the data owner (Nutrition and Nutritional Status of Children under 5 years in Bulgaria [NUTRICHILD], 2007) and EFSA (European Food Safety Authority. EFSA Comprehensive European Food Consumption Database. http://www.efsa.europa.eu/en/food-consumption/comprehensive-database), and the overall process was overseen by EFSA and GDD. The harmonised dataset was downloaded from the GDD website (Global Dietary Database. Canadian Community Health Survey—Nutrition [CCHS-Nutrition], 2015. https://www.globaldietarydatabase.org/management/microdata-surveys/650; accessed Aug 28, 2020). The harmonisation of the original dataset was performed by GDD. The data was adapted from Statistics Canada, Canadian Community Health Survey: Public Use Microdata File, 2015/2016 (Statistics Canada. Canadian Community Health Survey—Nutrition [CCHS-Nutrition], 2015); this does not constitute an endorsement by Statistics Canada of this product. The data is used under the terms of the Statistics Canada Open Licence (Statistics Canada. Statistics Canada Open License. https://www.statcan.gc.ca/eng/reference/licence). The harmonised dataset was downloaded from the GDD website (Global Dietary Database. Compilation of existing individual food consumption data collected within the most recent national dietary surveys in Europe (SK-MON) 2008. https://www.globaldietarydatabase.org/management/microdata-surveys/652; Sept 21, 2020). The harmonisation of the dataset was jointly performed by the data owner (National nutrition survey in Slovakia (NDS), 2008, Food Research Institute and Public Health Authority) and EFSA (European Food Safety Authority. EFSA Comprehensive European Food Consumption Database. http://www.efsa.europa.eu/en/food-consumption/comprehensive-database), and the overall process was overseen by EFSA. The harmonised dataset was downloaded from the GDD website (Global Dietary Database. National dietary survey in adults in Sweden, Riksmaten adults 2010–2011. https://www.globaldietarydatabase.org/management/microdata-surveys/174; accessed Sept 23, 2020). The harmonisation of the dataset was performed by the data owner (National dietary survey in adults in Sweden, Riksmaten adults 2010–11, Swedish Food Agency), and the overall process was overseen by EFSA (European Food Safety Authority. EFSA Comprehensive European Food Consumption Database. http://www.efsa.europa.eu/en/food-consumption/comprehensive-database) and GDD. The harmonised dataset was downloaded from the GDD website (Global Dietary Database. DIETA-PILOT Survey Adults, Children 2012. https://www.globaldietarydatabase.org/management/microdata-surveys/661. accessed 10/7/20). The harmonisation of the dataset was performed by the data owner (DIETA-PILOT Survey, 2012, Dunarea de Jos University of Galaţi, Romania), and the overall process was overseen by EFSA (European Food Safety Authority. EFSA Comprehensive European Food Consumption Database. http://www.efsa.europa.eu/en/food-consumption/comprehensive-database) and GDD. This paper uses data from the Afghanistan 2018 STEPS survey, implemented by Ministry of Public Health with the support of WHO. This paper uses data from the Ecuador 2018 STEPS survey, implemented by Ministry of Public Health with the support of WHO. This paper uses data from the Generations and Gender Programme (www.ggp-i.org). The Generations and Gender Programme has received funding from the European Commission, its Consortium Board Members and National Funding Bodies which are gratefully acknowledged. The harmonised dataset was downloaded from the GDD website (Global Dietary Database. National Survey of Food Intake and Nutritional Status (NSFIN) 2004. https://www.globaldietarydatabase.org/management/microdata-surveys/648, accessed Aug 28, 2020). The harmonisation of the dataset was jointly performed by the data owner (National Survey of Food Intake and Nutritional Status (NSFIN), National Nutrition Monitoring in Bulgaria, 2004) and EFSA (European Food Safety Authority. EFSA Comprehensive European Food Consumption Database. http://www.efsa.europa.eu/en/food-consumption/comprehensive-database), and the overall process was overseen by EFSA and GDD. The harmonised dataset was downloaded from the GDD website (Global Dietary Database. Mabat Youth—First Israeli National Health and Nutrition Survey in 7th-12th grade students 2003–2004. https://www.globaldietarydatabase.org/management/microdata-surveys/180; accessed Sept 17, 2020). The harmonisation of the dataset was jointly performed by the data owner (MABAT Youth First Israeli National Health and Nutrition Survey in 7th-12th grade students 2003–2004. Israel Center for Disease Control, Ministry of Health, State of Israel) and GDD, and the overall process was overseen by GDD. VACS data are owned by the Government of Cote d’Ivoire and made available by the Centers for Disease Control and Prevention through a Data Use Agreement. This paper uses data from the Mongolia 2019 STEPS survey, implemented by the Ministry of Health, Public Health Institute with the support of WHO. This paper uses data from the Jordan 2019 STEPS survey, implemented by the Ministry of Health with the support of WHO. This paper uses data from the Turkmenistan 2018 STEPS survey, implemented by the Ministry of Health and Medical Industry with the support of WHO. This study is based on data from Eurostat, Austria European Health Interview Survey 2006–2007 and 2013–2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Belgium European Health Interview Survey 2013 and 2013–2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Cyprus European Health Interview Survey 2013–2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Germany European Health Interview Survey 2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Denmark European Health Interview Survey 2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Spain European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Finland European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, France European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Hungary European Health Interview Survey 2008 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Croatia European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Ireland European Health Interview Survey 2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Iceland European Health Interview Survey 2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Italy European Health Interview Survey 2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Lithuania European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Netherlands European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Norway European Health Interview Survey 2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Poland European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Portugal European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Sweden European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, European Union Statistics on Income and Living Conditions, Cross-sectional Data Collection 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, and 2020. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, European Union Statistics on Income and Living Conditions, Longitudinal Data Collection 2005, 2006, and 2007. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This paper uses data from the Ukraine 2019 STEPS survey, implemented by the Ministry of Health with the support of WHO. This study is based on data from Eurostat, United Kingdom European Health Interview Survey 2013. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Luxembourg European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This research uses data from the study on “Understanding the Lives of Adolescents and Young Adults (UDAYA) in Bihar and Uttar Pradesh” which was collected by the Population Council. Data collection funded by Bill & Melinda Gates Foundation and the David and Lucile Packard Foundation. Data for the Seychelles Heart Study IV was provided by the Global Dietary Database and Tufts University in association with the Ministry of Health and University of Lausanne. HBSC is an international study carried out in collaboration with WHO/EURO. The International Coordinator of the 2017/2018 surveys was Prof Jo Inchley and the Data Bank Manager was Prof Oddrun Samdal. A list of principal investigators in each country can be found at http://www.hbsc.org. This paper uses data from the Global School-Based Student Health Survey (GSHS). GSHS is supported by WHO and the US Centers for Disease Control and Prevention. This paper uses data from the Bolivia 2019 STEPS survey, implemented by the Ministry of Health with the support of WHO. This paper uses data from the Cabo Verde 2020 STEPS survey, implemented by the Ministry of Health, National Institute of Statistics with the support of WHO. This paper uses data from the WHO Well-being of Older People Study (WOPS) a Study on Global AGEing and Adult Health (SAGE) sub-study. Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Number R01AG044917. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This paper uses data from the Saint Lucia 2019 STEPS survey, implemented by the Ministry of Health with the support of WHO. This paper uses data from the Viet Nam 2021 STEPS survey, implemented by the Ministry of Health with the support of WHO. Parts of this material are based on data and information provided by the Canadian Institute for Health Information. However, the analyses, conclusions, opinions and statements expressed herein are those of the author and not those of the Canadian Institute for Health information. The views and opinions of the authors expressed herein do not necessarily state or reflect those of ECDC. The accuracy of the authors’ statistical analysis and the findings they report are not the responsibility of ECDC. ECDC is not responsible for the conclusions or opinions drawn from the data provided. ECDC is not responsible for the correctness of the data and for data management, data merging and data collation after provision of the data. ECDC shall not be held liable for improper or incorrect use of the data. Editorial note: The Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations. | en |
| dc.description.status | Peer-reviewed | en |
| dc.format.extent | 62 | en |
| dc.identifier.issn | 0140-6736 | en |
| dc.identifier.other | PubMed:41092928 | en |
| dc.identifier.other | ORCID:/0000-0002-7750-4341/work/197545840 | en |
| dc.identifier.other | ORCID:/0000-0002-5441-6146/work/197545990 | en |
| dc.identifier.other | ORCID:/0000-0001-7891-3756/work/197546044 | en |
| dc.identifier.scopus | 105019266565 | en |
| dc.identifier.uri | https://hdl.handle.net/1885/733797043 | |
| dc.language.iso | en | en |
| dc.provenance | This is an Open Access article under the CC BY 4.0 | en |
| dc.rights | © 2025 The Author(s) | en |
| dc.source | The Lancet | en |
| dc.title | Global burden of 292 causes of death in 204 countries and territories and 660 subnational locations, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023 | en |
| dc.type | Journal article | en |
| dspace.entity.type | Publication | en |
| local.bibliographicCitation.lastpage | 1872 | en |
| local.bibliographicCitation.startpage | 1811 | en |
| local.contributor.affiliation | Naghavi, Mohsen; University of Washington | en |
| local.contributor.affiliation | Kyu, Hmwe Hmwe; University of Washington | en |
| local.contributor.affiliation | Bhoomadevi, A.; Amity University, Noida | en |
| local.contributor.affiliation | Aalipour, Mohammad Amin; Shahid Beheshti University of Medical Sciences | en |
| local.contributor.affiliation | Aalruz, Hasan; Al-Zaytoonah University of Jordan | en |
| local.contributor.affiliation | Ababneh, Hazim S.; Massachusetts General Hospital | en |
| local.contributor.affiliation | Abafita, Bedru J.; University of Tasmania | en |
| local.contributor.affiliation | Abaraogu, Ukachukwu O.; University of the West of Scotland | en |
| local.contributor.affiliation | Abbafati, Cristiana; University of Rome La Sapienza | en |
| local.contributor.affiliation | Abbasi, Madineh; Tabriz University of Medical Sciences | en |
| local.contributor.affiliation | Abbaspour, Faezeh; University of California at San Francisco | en |
| local.contributor.affiliation | Abbastabar, Hedayat; Tehran University of Medical Sciences | en |
| local.contributor.affiliation | Abd Al Magied, Abdallah H.A.; Ajman University | en |
| local.contributor.affiliation | Abd ElHafeez, Samar; Alexandria University | en |
| local.contributor.affiliation | Abdalla, Ashraf Nabiel; Umm Al-Qura University | en |
| local.contributor.affiliation | Abdalla, Mohammed Altigani; Hull York Medical School | en |
| local.contributor.affiliation | Abdallah, Emad M.; Qassim University | en |
| local.contributor.affiliation | Abdeeq, Barkhad Aden; Save the Children | en |
| local.contributor.affiliation | Abdel Razeq, Nadin M.I.; University of Jordan | en |
| local.contributor.affiliation | Abdelgalil, Ahmed Abdelrahman; King Saud University | en |
| local.contributor.affiliation | Abdel-Hameed, Reda; University of Hail | en |
| local.contributor.affiliation | Abdelmasseh, Michael; Marshall University | en |
| local.contributor.affiliation | Abdelnabi, Mahmoud; Mayo Clinic Scottsdale, AZ | en |
| local.contributor.affiliation | Abdel-Rahman, Wael M.; University of Sharjah | en |
| local.contributor.affiliation | Abdous, Arman; Islamic Azad University | en |
| local.contributor.affiliation | Abdrabou, Mostafa M.; Cairo University | en |
| local.contributor.affiliation | Abdul Aziz, Jeza Muhamad; Komar University of Science and Technology | en |
| local.contributor.affiliation | Abdulah, Deldar Morad; University of Dohuk | en |
| local.contributor.affiliation | Abdullahi, Auwal; Bayero University | en |
| local.contributor.affiliation | Abdul-Rahman, Toufik; Toufik's World Medical Association | en |
| local.contributor.affiliation | Abebe Getahun, Habtamu; Gondar University | en |
| local.contributor.affiliation | Abedi, Aidin; University of Southern California | en |
| local.contributor.affiliation | Abedi, Armita; Zanjan University of Medical Sciences | en |
| local.contributor.affiliation | Abedi, Parisa; Yale University | en |
| local.contributor.affiliation | Abejew, Asrat Agalu; Bahar Dar University | en |
| local.contributor.affiliation | Abeldaño Zuñiga, Roberto Ariel; University of Sierra Sur | en |
| local.contributor.affiliation | Abid, Shehab Uddin Al; University of Oxford | en |
| local.contributor.affiliation | Abidi, Syed Hani; Nazarbayev University | en |
| local.contributor.affiliation | Abie, Alemwork; Bahar Dar University | en |
| local.contributor.affiliation | Abiodun, Olugbenga Olusola; Federal Medical Centre | en |
| local.contributor.affiliation | Aboagye, Richard Gyan; University of Health and Allied Sciences | en |
| local.contributor.affiliation | Abohashem, Shady; Massachusetts General Hospital | en |
| local.contributor.affiliation | Adhikary, Ripon Kumar; The Australian National University | en |
| local.contributor.affiliation | Ahmad, Danish; Australian Primary Health Care Research Institute, National Centre for Epidemiology and Population Health, ANU College of Law, Governance and Policy, The Australian National University | en |
| local.contributor.affiliation | Alene, Kefyalew Addis; Curtin University | en |
| local.contributor.affiliation | Asgary, Saeed; Shahid Beheshti University of Medical Sciences | en |
| local.contributor.affiliation | Bagheri, Nasser; Australian Primary Health Care Research Institute, National Centre for Epidemiology and Population Health, ANU College of Law, Governance and Policy, The Australian National University | en |
| local.contributor.affiliation | Burns, Richard A.; National Centre for Epidemiology and Population Health, ANU College of Law, Governance and Policy, The Australian National University | en |
| local.contributor.affiliation | Kamal, Md Moustafa; University of Sydney | en |
| local.contributor.affiliation | Kinfu, Yohannes; The Pacific Community | en |
| local.identifier.citationvolume | 406 | en |
| local.identifier.doi | 10.1016/S0140-6736(25)01917-8 | en |
| local.identifier.pure | 8025f9cb-987b-4826-ab7e-3f8825ea16c5 | en |
| local.identifier.url | https://www.scopus.com/pages/publications/105019266565 | en |
| local.type.status | Published | en |
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