Abstract
Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019. Interpretation: Over the past 20 years, fertility rates have been dropping steadily and life expectancy has been increasing, with few exceptions. Much of this change follows historical patterns linking social and economic determinants, such as those captured by the GBD Socio-demographic Index, with demographic outcomes. More recently, several countries have experienced a combination of low fertility and stagnating improvement in mortality rates, pushing more populations into the late stages of the demographic transition. Tracking demographic change and the emergence of new patterns will be essential for global health monitoring. Funding: Bill & Melinda Gates Foundation.
Original language | English |
---|---|
Pages (from-to) | 1160-1203 |
Number of pages | 44 |
Journal | The Lancet |
Volume | 396 |
Issue number | 10258 |
DOIs | |
Publication status | Published - 17 Oct 2020 |
Externally published | Yes |
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In: The Lancet, Vol. 396, No. 10258, 17.10.2020, p. 1160-1203.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950–2019
T2 - a comprehensive demographic analysis for the Global Burden of Disease Study 2019
AU - GBD 2019 Demographics Collaborators
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AU - Machado, D. B.
AU - Cislaghi, B.
AU - Salman, O. M.
AU - Karanikolos, M.
AU - McKee, M.
AU - Abbas, K. M.
AU - Brady, O. J.
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AU - Cummins, S.
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AU - Masoumi, S.
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AU - Taherkhani, A.
AU - Adabi, M.
AU - Abbasifard, M.
AU - Bazmandegan, G.
AU - Kamiab, Z.
AU - Vakilian, A.
AU - Anjomshoa, M.
AU - Mokari, A.
AU - Sabour, S.
AU - Shahbaz, M.
AU - Saeedi, R.
AU - Ahmadieh, H.
AU - Yousefinezhadi, T.
AU - Haj-Mirzaian, A.
AU - Nikbakhsh, R.
AU - Safi, S.
AU - Asgari, S.
AU - Irvani, S. N.
AU - Jahanmehr, N.
AU - Ramezanzadeh, K.
AU - Abbasi-Kangevari, M.
AU - Khayamzadeh, M.
AU - Abbastabar, H.
AU - Shirkoohi, R.
AU - Fazlzadeh, M.
AU - Janjani, H.
AU - Hosseini, M.
AU - Mansournia, M.
AU - Tohidinik, H.
AU - Bakhtiari, A.
AU - Fazaeli, A.
AU - Mousavi, S.
AU - Hasanzadeh, A.
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AU - Malekzadeh, R.
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AU - Salimzadeh, H.
AU - Sepanlou, S. G.
AU - Afarideh, M.
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AU - Ghajar, A.
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AU - Mohamadi, E.
AU - Rahimi-Movaghar, A.
AU - Rahim, F.
AU - Eskandarieh, S.
AU - Sahraian, M.
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AU - Farzadfar, F.
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AU - Pishgar, F.
AU - Saeedi Moghaddam, S.
AU - Shabani, M.
AU - Zarafshan, H.
AU - Abolhassani, H.
AU - Hafezi-Nejad, N.
AU - Heidari-Soureshjani, R.
AU - Abdollahi, M.
AU - Farahmand, M.
AU - Salamati, P.
AU - Mehrabi Nasab, E.
AU - Tajdini, M.
AU - Aghamir, S.
AU - Mirzaei, R.
AU - Dibaji Forooshani, Z.
AU - Khater, M. M.
AU - Abd-Allah, F.
AU - Abdelalim, A.
AU - Abualhasan, A.
AU - El-Jaafary, S. I.
AU - Hassan, A.
AU - Elsharkawy, A.
AU - Khater, A. M.
AU - Elhabashy, H. R.
AU - Salem, M. R.R.
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AU - Shrime, M. G.
AU - Abedi, A.
AU - Doshi, C. P.
AU - Abegaz, K. H.
AU - Geberemariyam, B. S.
AU - Aynalem, Y. A.
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AU - Aboyans, V.
AU - Abrams, E. M.
AU - Gitimoghaddam, M.
AU - Kissoon, N.
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AU - Ribeiro, A. P.
AU - Malta, D. C.
AU - Gomez, R. S.
AU - Abreu, L. G.
AU - Abrigo, M. R.M.
AU - Almulhim, A. M.
AU - Dahlawi, S. M.A.
AU - Pottoo, F. H.
AU - Menezes, R. G.
AU - Alanzi, T. M.
AU - Alumran, A. K.
AU - Abu Haimed, A. K.
AU - Madadin, M.
AU - Alanezi, F. M.
AU - Abu-Gharbieh, E.
AU - Saddik, B.
AU - Abu-Raddad, L. J.
AU - Samy, A. M.
AU - El Nahas, N.
AU - Shalash, A. S.
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AU - Kamath, A. M.
AU - Kassebaum, N. J.
AU - Aravkin, A. Y.
AU - Kochhar, S.
AU - Sorensen, R. J.D.
AU - Afshin, A.
AU - Burkart, K.
AU - Cromwell, E. A.
AU - Dandona, L.
AU - Dharmaratne, S. D.
AU - Gakidou, E.
AU - Hay, S. I.
AU - Kyu, H. H.
AU - Lopez, A. D.
AU - Lozano, R.
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AU - Mokdad, A. H.
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AU - Pigott, D. M.
AU - Reiner, R. C.
AU - Roth, G. A.
AU - Stanaway, J. D.
AU - Vollset, S.
AU - Vos, T.
AU - Wang, H.
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AU - Kalani, R.
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AU - Cho, D. Y.
AU - Kneib, C. J.
AU - Crowe, C. S.
AU - Massenburg, B. B.
AU - Morrison, S. D.
AU - Acebedo, A.
AU - Adelson, J. D.
AU - Agesa, K. M.
AU - Alam, T.
AU - Albertson, S. B.
AU - Anderson, J. A.
AU - Antony, C. M.
AU - Ashbaugh, C.
AU - Assmus, M.
AU - Azhar, G.
AU - Balassyano, S.
AU - Bannick, M. S.
AU - Barthelemy, C. M.
AU - Bender, R. G.
AU - Wang, J.
AU - Chang, A. Y.
AU - Zhang, J.
AU - Ahmed, M. B.
AU - Ali, S.
AU - Khan, M.
AU - Gesesew, H. A.
AU - Islam, M.
AU - Chang, A. Y.
AU - Nguyen, D. N.
AU - Nguyen, D. N.
AU - Singh, P.
AU - Sharma, R.
AU - Wei, J.
AU - Xie, Y.
AU - Zaidi, S.
AU - Zhang, J.
AU - Zhang, Y.
N1 - Funding Information: Research reported in this publication was supported by the Bill & Melinda Gates Foundation, the University of Melbourne, Public Health England, the Norwegian Institute of Public Health, the National Institute on Aging of the National Institutes of Health (NIH; award P30AG047845), and the National Institute of Mental Health of the NIH (award R01MH110163). This analysis uses data or information from the Longitudinal Aging Study in India (LASI) pilot micro data and documentation. The development and release of the LASI pilot study was funded by the National Institute on Aging of the NIH (R21AG032572, R03AG043052, and R01AG030153). 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. Data were included from the Russia Longitudinal Monitoring survey, 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, Russia Academy of Sciences. 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. L G Abreu acknowledges support from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazil; finance code 001) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, a Brazilian funding agency). O Adetokunboh acknowledges the South African Department of Science and Innovation and the National Research Foundation. A Agrawal acknowledges the Wellcome Trust DBT India Alliance Senior Fellowship. 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, National University of Malaysia for the approval and support to participate in this research project. 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. T Bärnighausen 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. D Bennett acknowledges support by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the UK Department of Health and Social Care. J J Carrero was supported by the Swedish Research Council (2019-01059). F Caravlho and E Fernandes acknowledge support from UID/MULTI/04378/2019 and UID/QUI/50006/2019 with funding from FCT/MCTES through national funds. V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia, IP, under the Norma Transitória DL57/2016/CP1334/CT0006. K Deribe acknowledges support by the Wellcome Trust (201900/Z/16/Z) as part of his International Intermediate Fellowship. M Ferreira acknowledges her research fellowship from the National Health and Medical Research Council (NHMRC) of Australia. C Flohr acknowledges support by the NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust. M Freitas acknowledges financial support from the EU (European Regional Development Fund [FEDER] funds through COMPETE POCI-01-0145-FEDER-029248) and National Funds (Fundação para a Ciência e Tecnologia) through project PTDC/NAN-MAT/29248/2017. C Herteliu was partially supported by a grant co-funded by FEDER through Operational Competitiveness Program (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. 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). Y J Kim was supported by Research Management Office, Xiamen University Malaysia (XMUMRF/2018-C2/ITCM/0001). K Krishan is supported by the UCG Centre of Advanced Study awarded to the Department of Anthropology, Panjab University, Chandigarh, India. M Kumar acknowledges the Fogarty International Centre and NIH for the K43 award (K43 TW 010716-03). B Lacey acknowledges support from the NIHR Oxford Biomedical Research Centre and the BHF Centre of Research Excellence, Oxford. J V Lazarus was supported by a Spanish Ministry of Science, Innovation and Universities Miguel Servet grant (Instituto de Salud Carlos III [ISCIII]/ESF, the EU [CP18/00074]). J McGrath acknowledges support by the Danish National Research Foundation (Niels Bohr Professorship). W Mendoza acknowledges the Population and Development department at the UN Population Fund Country Office in Peru, which does not necessarily endorse this study. George Milne acknowledges support provided by The University of Western Australia. I Moreno Velásquez acknowledges support by the Sistema Nacional de Investigación (SENACYT, Panamá). U O Mueller acknowledges funding by the German National Cohort Study (BMBF grant number 01ER1801D). S Nomura acknowledges the Ministry of Education, Culture, Sports, Science, and Technology of Japan (18K10082). A Ortiz was supported by 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. G C Patton was supported by a NHMRC fellowship. A Raggi, D Sattin, and S Schiavolin were supported 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). A M Samy was supported by a fellowship from the Egyptian Fulbright Mission Program. D F Santomauro is affiliated with the Queensland Centre for Mental Health Research, which receives core funding from the Queensland Department of Health. M M Santric-Milicevic acknowledges the Ministry of Education, Science and Technological Development of the Republic of Serbia (contract number 175087). A Sheikh was supported by Health Data Research UK. J B Soriano is funded by the Centro de Investigación en Red de Enfermedades Respiratorias, ISCIII. R Tabarés-Seisdedos was supported in part by the national grant PI17/00719 from ISCIII–FEDER. S Tadakamadla was supported by an NHMRC Early Career Fellowship. N Taveira was partially supported by the European & Developing Countries Clinical Trials Partnership, the EU (LIFE project, reference RIA2016MC-1615). M Tonelli acknowledges the David Freeze Chair in Health Services Research at the University of Calgary. C S Wiysonge was supported by the South African Medical Research Council. S B Zaman received a scholarship from the Australian Government research training programme in support of his academic career. Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations. Funding Information: Research reported in this publication was supported by the Bill & Melinda Gates Foundation, the University of Melbourne, Public Health England, the Norwegian Institute of Public Health, the National Institute on Aging of the National Institutes of Health (NIH; award P30AG047845), and the National Institute of Mental Health of the NIH (award R01MH110163). This analysis uses data or information from the Longitudinal Aging Study in India (LASI) pilot micro data and documentation. The development and release of the LASI pilot study was funded by the National Institute on Aging of the NIH (R21AG032572, R03AG043052, and R01AG030153). 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. Data were included from the Russia Longitudinal Monitoring survey, 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, Russia Academy of Sciences. 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. L G Abreu acknowledges support from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazil; finance code 001) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, a Brazilian funding agency). O Adetokunboh acknowledges the South African Department of Science and Innovation and the National Research Foundation. A Agrawal acknowledges the Wellcome Trust DBT India Alliance Senior Fellowship. 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, National University of Malaysia for the approval and support to participate in this research project. 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. T Bärnighausen 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. D Bennett acknowledges support by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the UK Department of Health and Social Care. J J Carrero was supported by the Swedish Research Council (2019-01059). F Caravlho and E Fernandes acknowledge support from UID/MULTI/04378/2019 and UID/QUI/50006/2019 with funding from FCT/MCTES through national funds. V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia, IP, under the Norma Transitória DL57/2016/CP1334/CT0006. K Deribe acknowledges support by the Wellcome Trust (201900/Z/16/Z) as part of his International Intermediate Fellowship. M Ferreira acknowledges her research fellowship from the National Health and Medical Research Council (NHMRC) of Australia. C Flohr acknowledges support by the NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust. M Freitas acknowledges financial support from the EU (European Regional Development Fund [FEDER] funds through COMPETE POCI-01-0145-FEDER-029248) and National Funds (Fundação para a Ciência e Tecnologia) through project PTDC/NAN-MAT/29248/2017. C Herteliu was partially supported by a grant co-funded by FEDER through Operational Competitiveness Program (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. 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). Y J Kim was supported by Research Management Office, Xiamen University Malaysia (XMUMRF/2018-C2/ITCM/0001). K Krishan is supported by the UCG Centre of Advanced Study awarded to the Department of Anthropology, Panjab University, Chandigarh, India. M Kumar acknowledges the Fogarty International Centre and NIH for the K43 award (K43 TW 010716-03). B Lacey acknowledges support from the NIHR Oxford Biomedical Research Centre and the BHF Centre of Research Excellence, Oxford. J V Lazarus was supported by a Spanish Ministry of Science, Innovation and Universities Miguel Servet grant (Instituto de Salud Carlos III [ISCIII]/ESF, the EU [CP18/00074]). J McGrath acknowledges support by the Danish National Research Foundation (Niels Bohr Professorship). W Mendoza acknowledges the Population and Development department at the UN Population Fund Country Office in Peru, which does not necessarily endorse this study. George Milne acknowledges support provided by The University of Western Australia. I Moreno Velásquez acknowledges support by the Sistema Nacional de Investigación (SENACYT, Panamá). U O Mueller acknowledges funding by the German National Cohort Study (BMBF grant number 01ER1801D). S Nomura acknowledges the Ministry of Education, Culture, Sports, Science, and Technology of Japan (18K10082). A Ortiz was supported by 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. G C Patton was supported by a NHMRC fellowship. A Raggi, D Sattin, and S Schiavolin were supported 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). A M Samy was supported by a fellowship from the Egyptian Fulbright Mission Program. D F Santomauro is affiliated with the Queensland Centre for Mental Health Research, which receives core funding from the Queensland Department of Health. M M Santric-Milicevic acknowledges the Ministry of Education, Science and Technological Development of the Republic of Serbia (contract number 175087). A Sheikh was supported by Health Data Research UK. J B Soriano is funded by the Centro de Investigación en Red de Enfermedades Respiratorias, ISCIII. R Tabarés-Seisdedos was supported in part by the national grant PI17/00719 from ISCIII–FEDER. S Tadakamadla was supported by an NHMRC Early Career Fellowship. N Taveira was partially supported by the European & Developing Countries Clinical Trials Partnership, the EU (LIFE project, reference RIA2016MC-1615). M Tonelli acknowledges the David Freeze Chair in Health Services Research at the University of Calgary. C S Wiysonge was supported by the South African Medical Research Council. S B Zaman received a scholarship from the Australian Government research training programme in support of his academic career. Publisher Copyright: © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
PY - 2020/10/17
Y1 - 2020/10/17
N2 - Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019. Interpretation: Over the past 20 years, fertility rates have been dropping steadily and life expectancy has been increasing, with few exceptions. Much of this change follows historical patterns linking social and economic determinants, such as those captured by the GBD Socio-demographic Index, with demographic outcomes. More recently, several countries have experienced a combination of low fertility and stagnating improvement in mortality rates, pushing more populations into the late stages of the demographic transition. Tracking demographic change and the emergence of new patterns will be essential for global health monitoring. Funding: Bill & Melinda Gates Foundation.
AB - Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019. Interpretation: Over the past 20 years, fertility rates have been dropping steadily and life expectancy has been increasing, with few exceptions. Much of this change follows historical patterns linking social and economic determinants, such as those captured by the GBD Socio-demographic Index, with demographic outcomes. More recently, several countries have experienced a combination of low fertility and stagnating improvement in mortality rates, pushing more populations into the late stages of the demographic transition. Tracking demographic change and the emergence of new patterns will be essential for global health monitoring. Funding: Bill & Melinda Gates Foundation.
UR - http://www.scopus.com/inward/record.url?scp=85092447915&partnerID=8YFLogxK
U2 - 10.1016/S0140-6736(20)30977-6
DO - 10.1016/S0140-6736(20)30977-6
M3 - Article
C2 - 33069325
AN - SCOPUS:85092447915
SN - 0140-6736
VL - 396
SP - 1160
EP - 1203
JO - The Lancet
JF - The Lancet
IS - 10258
ER -