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dc.contributor.authorWang, Haidongspa
dc.contributor.authoralcalde rabanal, jacqueline elizabethspa
dc.contributor.authorAntonio, Carl Abelardospa
dc.contributor.authorAlvis-Guzmán, Nelsonspa
dc.contributor.authorAmini-Rarani, Mostafaspa
dc.contributor.authorAndrei, Catalina Lilianaspa
dc.contributor.authorBabaee, Ebrahimspa
dc.contributor.authorBarker-Collo, Lynspa
dc.contributor.authorBisignano, Catherinespa
dc.contributor.authorNikolaevich Briko, Andreyspa
dc.contributor.authorDahlawi, Saadspa
dc.contributor.authorDaryani, Ahmadspa
dc.contributor.authorGallus, Silvanospa
dc.contributor.authorGitimoghaddam, Mojganspa
dc.contributor.authorHassankhani, Hadispa
dc.contributor.authorHouseh, Mowafaspa
dc.contributor.authorkamiab, zahraspa
dc.contributor.authorKhazaei, Salmanspa
dc.contributor.authorKosen, Soewartaspa
dc.contributor.authorLinn, Shaispa
dc.contributor.authorMahasha, Phetolespa
dc.contributor.authorMoghadaszadeh Ahrabi, Masoudspa
dc.contributor.authorMohammadpourhodki, Rezaspa
dc.contributor.authorSamad, Zainabspa
dc.contributor.authorSantric Milicevic, Milenaspa
dc.contributor.authorShaheen, Amira Aspa
dc.contributor.authorSharma, Rajeshspa
dc.contributor.authorTopouzis, Fotisspa
dc.contributor.authorUnnikrishnan, Bhaskaranspa
dc.contributor.authorValli, Alessandrospa
dc.contributor.authorWiangkham, Taweewatspa
dc.contributor.authorYoon, Seok-Junspa
dc.contributor.authoryusefzadeh, hasanspa
dc.contributor.authorZiapour, Arashspa
dc.date.accessioned2021-04-09T15:00:43Z
dc.date.available2021-04-09T15:00:43Z
dc.date.issued2020-10-17
dc.identifier.issn01406736spa
dc.identifier.issn1474547Xspa
dc.identifier.urihttps://hdl.handle.net/11323/8115spa
dc.description.abstractBackground 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 agespecific 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 monitoringspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.sourceThe Lancetspa
dc.subjectHIVspa
dc.subjectFertilityspa
dc.subjectMortalityspa
dc.titleGlobal age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019: a comprehensive demographic analysis for the Global Burden of Disease Study 2019spa
dc.typeArtículo de revistaspa
dc.source.urlhttps://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30977-6/fulltextspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1016/S0140-6736(20)30977-6spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.relation.references1 UN Statistics Division. National statistical offices. https://unstats. un.org/home/nso_sites (accessed Oct 1, 2019).spa
dc.relation.references2 Organisation for Economic Co-operation and Development. Society data. http://data.oecd.org/society.htm (accessed Jan 25, 2020).spa
dc.relation.references3 Eurostat. Population: demography, population projections, census, asylum & migration—overview. https://ec.europa.eu/eurostat/web/ population/overview (accessed Jan 25, 2020)spa
dc.relation.references4 WHO. Disease burden and mortality estimates. http://www.who. int/healthinfo/global_burden_disease/estimates/en (accessed Jan 25, 2020).spa
dc.relation.references5 US Census Bureau. International programs, international data base. https://www.census.gov/programs-surveys/internationalprograms/about/idb.html (accessed Oct 2, 2019).spa
dc.relation.references6 UN Department of Economic and Social Affairs. Population Division. World population prospects: highlights. 2019. https://population.un.org/wpp/Publications/Files/ WPP2019_10KeyFindings.pdf (accessed Sept 24, 2019)spa
dc.relation.references7 Lee RD, Reher DS. Introduction: the landscape of demographic transition and its aftermath. Popul Dev Rev 2011; 37: 1–7.spa
dc.relation.references8 Kirk D. Demographic transition theory. Popul Stud (Camb) 1996; 50: 361–87.spa
dc.relation.references9 Bloom DE, Canning D. How demographic change can bolster economic performance in developing countries. World Econ 2003; 4: 1–14.spa
dc.relation.references10 Bloom DE. 7 billion and counting. Science 2011; 333: 562–69spa
dc.relation.references11 Dyson T. The role of the demographic transition in the process of urbanization. Popul Dev Rev 2011; 37 (suppl 1): 34–54spa
dc.relation.references12 Murphy M. Long-term effects of the demographic transition on family and kinship networks in Britain. Popul Dev Rev 2011; 37 (suppl 1): 55–80.spa
dc.relation.references13 Reher DS. Economic and social implications of the demographic transition. Popul Dev Rev 2011; 37: 11–33.spa
dc.relation.references14 Dicker D, Nguyen G, Abate D, et al. Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392: 1684–735.spa
dc.relation.references15 Murray CJL, Callender CSKH, Kulikoff XR, et al. Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392: 1995–2051.spa
dc.relation.references16 Murray CJL, Rajaratnam JK, Marcus J, Laakso T, Lopez AD. What can we conclude from death registration? Improved methods for evaluating completeness. PLoS Med 2010; 7: e1000262spa
dc.relation.references17 Wang H, Abajobir AA, Abate KH, et al. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017; 390: 1084–150.spa
dc.relation.references18 Wang H, Naghavi M, Allen C, et al. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016; 388: 1459–544.spa
dc.relation.references19 Stevens GA, Alkema L, Black RE, et al. Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement. Lancet 2016; 388: e19–23.spa
dc.relation.references20 Obermeyer Z, Rajaratnam JK, Park CH, et al. Measuring adult mortality using sibling survival: a new analytical method and new results for 44 countries, 1974–2006. PLoS Med 2010; 7: e1000260.spa
dc.relation.references21 Coale AJ. Demeny, Paul George. Regional model life tables and stable populations. Princeton, NJ: Princeton University Press, 1966.spa
dc.relation.references22 UN. Model life tables for developing countries. 1982. https://www. un.org/en/development/desa/population/publications/manual/ model/life-tables.asp (accessed Jan 24, 2020)spa
dc.relation.references23 Wilmoth J, Zureick S, Canudas-Romo V, Inoue M, Sawyer C. A flexible two-dimensional mortality model for use in indirect estimation. Popul Stud (Camb) 2012; 66: 1–28.spa
dc.relation.references24 Wang H, Dwyer-Lindgren L, Lofgren KT, et al. Age-specific and sex-specific mortality in 187 countries, 1970–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012; 380: 2071–94.spa
dc.relation.references25 Eaton JW, Brown T, Puckett R, et al. The Estimation and Projection Package Age-Sex Model and the r-hybrid model: new tools for estimating HIV incidence trends in sub-Saharan Africa. AIDS 2019; 33 (suppl 3): S235–44.spa
dc.relation.references26 GBD 2019 Diseases, Injuries, and Impairments Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396: 1204–22spa
dc.relation.references27 Roth GA, Abate D, Abate KH, et al. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392: 1736–88spa
dc.relation.references28 Wheldon MC, Raftery AE, Clark SJ, Gerland P. Reconstructing past populations with uncertainty from fragmentary data. J Am Stat Assoc 2013; 108: 96–110.spa
dc.relation.references29 Murray CJL, Ferguson BD, Lopez AD, Guillot M, Salomon JA, Ahmad O. Modified logit life table system: principles, empirical validation, and application. Popul Stud 2003; 57: 165–82.spa
dc.relation.references30 UNHCR. Global report 2018. Geneva, Switzerland: The UN Refugee Agency, 2018.spa
dc.relation.references31 Kyu HH, Abate D, Abate KH, et al. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392: 1859–922.spa
dc.relation.references32 Zheng P, Aravkin AY, Barber R, Sorensen RJD, Murray CJL. Trimmed constrained mixed effects models: formulations and algorithms. arXiv 2019; published online Sept 24. http://arxiv.org/ abs/1909.10700 (preprint).spa
dc.relation.references33 Zaidi B, Morgan SP. The second demographic transition theory: a review and appraisal. Annu Rev Sociol 2017; 43: 473–92.spa
dc.relation.references34 Gakidou E, Cowling K, Lozano R, Murray CJ. Increased educational attainment and its effect on child mortality in 175 countries between 1970 and 2009: a systematic analysis. Lancet 2010; 376: 959–74.spa
dc.relation.references35 Cutler DM, Lleras-Muney A. Understanding differences in health behaviors by education. J Health Econ 2010; 29: 1–28.spa
dc.relation.references36 Baird S, Friedman J, Schady N. Aggregate income shocks and infant mortality in the developing world. Rev Econ Stat 2010; 93: 847–56.spa
dc.relation.references37 Brinda EM, Rajkumar AP, Enemark U. Association between gender inequality index and child mortality rates: a cross-national study of 138 countries. BMC Public Health 2015; 15: 97spa
dc.relation.references38 Marphatia AA, Cole TJ, Grijalva-Eternod C, Wells JCK. Associations of gender inequality with child malnutrition and mortality across 96 countries. Glob Health Epidemiol Genom 2016; 1: e6.spa
dc.relation.references39 Anderson S, Ray D. Missing women: age and disease. Rev Econ Stud 2010; 77: 1262–300.spa
dc.relation.references40 Wynes S, Nicholas KA. The climate mitigation gap: education and government recommendations miss the most effective individual actions. Environ Res Lett 2017; 12: 074024.spa
dc.relation.references41 Scutchfield FD, Keck CW. Deaths of despair: why? What to do? Am J Public Health 2017; 107: 1564–65.spa
dc.relation.references42 Bloom DE, Chatterji S, Kowal P, et al. Macroeconomic implications of population ageing and selected policy responses. Lancet 2015; 385: 649–57.spa
dc.relation.references43 Bloom DE, Canning D, Fink G. Implications of population ageing for economic growth. Oxf Rev Econ Policy 2010; 26: 583–612.spa
dc.relation.references44 Bloom DE, Williamson JG. Demographic transitions and economic miracles in emerging Asia. World Bank Econ Rev 1998; 12: 419–55.spa
dc.relation.references45 UN. Global compact for migration. United Nations, 2018. https://refugeesmigrants.un.org/migration-compact (accessed Sept 28, 2019).spa
dc.relation.references46 Malak N, Rahman MM, Yip TA. Baby bonus, anyone? Examining heterogeneous responses to a pro-natalist policy. J Popul Econ 2019; 32: 1205–46.spa
dc.relation.references47 UN Department of Economic and Social Affairs. Population Division. United Nations expert group meeting on policy responses to low fertility. New York; Nov 2–3, 2015. https://www.un.org/en/ development/desa/population/events/expert-group/24/index.asp (accessed Jan 28, 2020).spa
dc.relation.references48 Feng W, Gu B, Cai Y. The end of China’s one-child policy. Stud Fam Plann 2016; 47: 83–86spa
dc.relation.references49 Caldwell JC. Routes to low mortality in poor countries. Popul Dev Rev 1986; 12: 171–220.spa
dc.relation.references50 Palloni A. Fertility and mortality decline in Latin America. Ann Am Acad Pol Soc Sci 1990; 510: 126–44.spa
dc.relation.references51 UNICEF. Under-five mortality. UNICEF data. September, 2019. https://data.unicef.org/topic/child-survival/under-five-mortality (accessed Jan 28, 2020).spa
dc.relation.references52 UN Department of Economic and Social Affairs. Population Division. World Population Prospects 2019: data booklet. New York, NY: United Nations, 2019.spa
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