Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Abstract

Summary Background Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. Methods The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specific mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in different components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950. Findings Globally, 18·7% (95% uncertainty interval 18·4–19·0) of deaths were registered in 1950 and that proportion has been steadily increasing since, with 58·8% (58·2–59·3) of all deaths being registered in 2015. At the global level, between 1950 and 2017, life expectancy increased from 48·1 years (46·5–49·6) to 70·5 years (70·1–70·8) for men and from 52·9 years (51·7–54·0) to 75·6 years (75·3–75·9) for women. Despite this overall progress, there remains substantial variation in life expectancy at birth in 2017, which ranges from 49·1 years (46·5–51·7) for men in the Central African Republic to 87·6 years (86·9–88·1) among women in Singapore. The greatest progress across age groups was for children younger than 5 years; under-5 mortality dropped from 216·0 deaths (196·3–238·1) per 1000 livebirths in 1950 to 38·9 deaths (35·6–42·83) per 1000 livebirths in 2017, with huge reductions acro...

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

“…Using an ensemble modeling framework that fed into a Bayesian generalized linear mixed-effects model with a correlated space-time random effect and 1,000 draws from an approximate posterior distribution, we generated annual prevalence estimates for overweight and wasting on a 5 × 5-km grid over 105 LMICs from 2000 to 2017 and aggregated these to administrative and national levels (Supplementary Table 2). Countries were selected for inclusion in this study using the socio-demographic index (SDI), a summary measure of development that combines education, fertility and poverty 47 . Selected countries were in the low, lower-middle and middle SDI quintiles, with several exceptions (Supplementary Table 2).…”

“…This projection scheme is analogous to the methods used in the Global Burden of Disease 2017 study 47 for measurement of progress and projected attainment of health-related Sustainable Development Goals. Our projections are based on the assumption that areas will sustain the current AROC, and the precision of the AROC estimates is dependent on the level of uncertainty emanating from the estimation of annual prevalence.…”

Global, Regional, National, and Subnational Big Data to Inform Health Equity Research: Perspectives from the Global Burden of Disease Study 2017

“…Six modeling approaches were used to assess cause-specific mortality, with the Cause of Death Ensemble Model (CODEm) generating estimates for the vast majority of causes. 2 Deaths were tabulated by location, age group (<1, 1-4, 5-9, …, 90-94, and 95+), sex, year, and by cause. The cause list developed by GBD and used for this analysis has been widely used for cause of death analyses, 2 and is arranged hierarchically in four levels.…”

“…7,10,19 Estimates of observed disease burden were compared with expected burden, or a country's anticipated burden based on its development status. 2 Figure 1 shows the number and causes of death by five-year age groups at the global level in 1990. There were clear patterns, with more infectious diseases causing deaths in younger age groups compared with a greater burden of non-communicable diseases at later ages.…”

“…The GBD has evolved over time; it is now produced annually by the Institute for Health Metrics and Eval-uation at the University of Washington in Seattle, WA, with more than 3,600 collaborators in 140 countries. [2][3][4][5][6][7][8][9][10][11][12][13][14][15] GBD 2017 used a comprehensive approach to report causes of death with garbage redistribution; systemically and simultaneously estimated disease incidence, prevalence, and exposure to risks and injuries; and used statistical models to pool data, adjust for bias, and incorporate covariates. [2][3][4][5][6][7][8][9][10][11][12][13][14][15] It used several metrics to report results on health loss related to specific diseases, injuries, and risk factors: deaths and death rates, years of life lost due to premature mortality (YLLs), prevalence and prevalence rates for sequelae, years lived with disability (YLDs), Health Adjusted…”