Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020-21 COVID-19 pandemic period.22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution.Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5-65·1] decline), and increased during the COVID-19 pandemic period (2020-21; 5·1% [0·9-9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98-5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50-6·01) in 2019. An estimated 131 million (126-137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7-17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8-24·8), from 49·0 years (46·7-51·3) to 71·7 years (70·9-72·5). Global life expectancy at birth declined by 1·6 years (1·0-2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67-8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4-52·7]) and south Asia (26·3% [9·0-44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations.Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic.Bill & Melinda Gates Foundation.

section_content: Understanding mortality and population trends over time and across locations, age groups, and sexes is crucial for planning population-specific public health policies. Age-specific mortality rates can indicate the emergence of new adverse health risks in specific locations, while population counts can inform resource allocation and aid in planning future development. The COVID-19 pandemic has highlighted the importance of demography in understanding disease and injury burden 1 and the roles health policy and infrastructure have in health and demographic outcomes. 1, 2 As the COVID-19 pandemic enters an endemic phase in some locations, demographic indicators can provide important context for understanding and addressing COVID-19, long COVID-19, 3 and the interaction between COVID-19 and other diseases and injuries. Furthermore, demographic trends in the decades before the COVID-19 pandemic and reversals in those trends during the first 2 years of the COVID-19 pandemic (2020-21) can provide insights into potential long-term effects of the pandemic. These shifts in demographic patterns, including in population growth and age distribution, can help policy makers and public health experts better understand how the pandemic has impacted different groups within society and inform strategies for future pandemic preparedness and health-care planning. 

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) is an evolving research effort that quantifies the state of global health. 4 The scope of the study has historically included estimating key demographic metrics and comprehensive health metrics for a set of national and subnational locations that has expanded over time. ] [7] [8] [9] [10] A comprehensive, internally consistent modelling strategy for estimating population and fertility was introduced in GBD 2017, greatly improving the consistency of results. 11 Previously, GBD drew on population estimates from the UN Population Division of the Department of Economic and Social Affairs (UNPD). 12, 13 In GBD 2019, the demographic analysis used population, fertility, and mortality estimates to produce a typology that better helped to specify phases of demographic transition. 10 The GBD demography framework is part of the greater GBD enterprise; thus, it differs from other demographic research initiatives by using estimates of disease and injury burden to inform population and mortality estimates, and vice versa. The GBD 2021 demographic analysis improved on GBD 2019 by using additional data sources and refined methods to generate updated estimates of mortality, life expectancy, and population size at the global, regional, national, and subnational levels for each year from 1950 to 2021. GBD 2021 is the first round to incorporate the COVID-19 pandemic into the modelling process through the estimation of excess mortality due to the pandemic, defined as the net difference between the number of deaths that occurred between 2020 and 2021 and the number of deaths that would be expected over the same period based on previous trends in all-cause mortality. 16 The unified approach to estimate all-cause mortality and excess mortality in GBD 2021 is an innovation in current demographic research methods. This facilitates analysis of the interplay between wider demographic processes and the COVID-19 pandemic. In this iteration of the GBD demographic analysis, we aim to and fertility estimates, the estimation process was run twice: first to generate preliminary numbers, and second to refine all estimates and ensure internal consistency. A detailed description of all methods and analytical flowcharts for all-cause mortality, fertility, and population estimation are available in appendix 1 (sections 2-6, 8). 

This study complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER); 17 a completed GATHER checklist is provided in appendix 1 (section 8). Python (version 3. 8. 17 and 3. 10. 4), Stata (version 15. 1), and R (version 3. 5 and 4. 2) were used for statistical analysis This manuscript was produced with the GBD Collaborator Network and in accordance with the GBD Protocol. 18 An international network of collaborators provides, reviews, and analyses the available data to generate health metrics; the 2021 GBD round drew on the expertise of more than 11 000 collaborators across more than 160 countries and territories.
section_name: Introduction

section_content: The GBD 2021 analysis used a range of data types for mortality and population estimation that were identified from a systematic search of available data from government websites, statistical annuals, demographic compendia, large-scale surveys, and collaborator input; comprehensive details on the sources of input data are available online via the GBD 2021 Sources Tool. Under-5 mortality rates (U5MRs), defined as the probability of death from birth to age 5 years, were estimated using 30 526 location-years of vital registration data (3179 new location-years for GBD 2021 compared with GBD 2019), 10 237 location-years of sample vital registration data, and 1445 other sources (including 57 new surveys, one new census, and ten other new sources; appendix 1 section 8). Adult mortality, defined as the probability of death before age 60 years assuming survival to age 15 years, was estimated using 30 207 location-years of vital registration data (3150 new location-years for GBD 2021 compared with GBD 2019), 1435 location-years of sample vital registration data, 75 censuses, 280 surveys (including 65 sources of household death data and 167 sources of sibling history data), and 24 other sources (appendix 1 section 8). Age-specific mortality was estimated using 43 758 empirical life tables for 1950-2021 (compared with 35 406 in GBD 2019; appendix 1 section 8). Prevalence surveys, antenatal clinic serosurveillance, and vital registration were used to adjust for the impact of the HIV epidemic due to its exceptional impact on agespecific mortality. Fatal discontinuities were accounted for using 2235 location-years from vital registration and 237 other sources (compared with 1812 from vital registration and 174 other sources in GBD 2019). Estimation of excess mortality due to the COVID-19 pandemic utilised an additional 146 139 datapoints of allcause mortality data at either weekly or monthly intervals from vital registration and surveillance reports that were assessed for completeness of registration (compared with our previous excess mortality estimation, 16 GBD 2021 used 1389 additional weeks or months of data). 

Population estimates utilised national and subnational censuses (1277 overall; 25 new), population registries (749 location-years of data), and post-enumeration surveys (161 in total). Additionally, migration data on refugee movements from the UN High Commissioner for Refugees and datasets for select countries (primarily Gulf States and nations in the EU) were used to inform migration estimates.
section_name: Data sources and processing

section_content: GBD 2021 all-cause mortality estimation followed the analytical framework for mortality analysis used in GBD 2019. 10 Point estimates from surveys were generated using both direct and indirect estimation methods for U5MR, while for adult mortality, they were generated from sibling history data with methods that correct for inherent biases such as zero-survivor and recall bias. Time series estimates of the completeness of adult vital registration data were generated using the same modelling process as GBD 2019, which used a combination of five death distribution methods, and point estimates were adjusted accordingly. 

Time series of under-5 and adult mortality without fatal discontinuities were estimated using spatiotemporal Gaussian process regression (ST-GPR), including a biasadjustment process for U5MR, to correct for systematic differences in the data sources and smooth results across time and location. Education, HIV, and lag-distributed income were included as covariates, along with U5MR for adult mortality. These estimates were used as inputs for the GBD relational model life table system with adjustments for older-age mortality to estimate HIV-free age-specific mortality rates. HIV mortality was modelled with a combination of ST-GPR, the Estimation and Projection Package Age-Sex Model, 19 and Spectrum, 20 and subsequently used to produce life tables that included HIV mortality. These abridged life tables were used to generate full life tables by single year age groups with further detailed age groups under the age of 1 year. Sexredistributed and age-redistributed fatal discontinuities by cause were aggregated by age and sex and added to the estimated mortality from the previous step to generate the final all-cause mortality life tables by location, year, sex, and age. We recalculated abridged life tables, including fatal discontinuities for each location, year, and sex combination, and then calculated the final envelope from these abridged life tables. Detailed methods for estimating each mortality component are available in appendix 1 (section 2).
section_name: All-cause mortality estimation

section_content: Excess mortality due to the COVID-19 pandemic in 2020 and 2021 is defined as the observed all-cause mortality minus the mortality that would be expected had the pandemic not occurred, based on historical trends. Excess deaths are those attributed to the COVID-19 pandemic as a whole, both from SARS-CoV-2 infection and from other pandemic-related factors such as deferred care seeking. 21, 22 Excess mortality was calculated using similar methods as in Wang et al (2022), 16 with several key improvements. We included yearly observed deaths from vital registration to supplement daily, weekly, and monthly observed death data. We then used five variants of the spline for weekly seasonal patterns that set the second-to-last knot at 18, 24, 36, 48, or 60 months to allow for more stable trends. To select covariates, we used Rover, a method developed at the Institute for Health Metrics and Evaluation based on Bayesian model averaging. Rover is conceptually similar to the Bayesian model averaging method, which is widely used to explore the parameter space and aggregate estimates across candidate models based on performance metrics. 23 The main difference is that while Bayesian model averaging uses marginal likelihood, Rover focuses on out-of-sample performance. We included covariates pertaining to the COVID-19 pandemic, such as seroprevalence, and background population health metrics, such as the Healthcare Access and Quality Index. 24 With the best model selected, we ran a prediction process using 100 draws for each covariate and 100 draws of estimated coefficients and residuals, estimated from the regressions run at the draw level using draw-level input data on both excess mortality and covariates. Mean values and 95% uncertainty intervals (UIs) were then generated at national, regional, and global levels. Out-of-sample predictive validity testing was conducted based on our final model specification. Complete excess mortality methodology is detailed in appendix 1 (section 2. 8). 

To determine age-specific and sex-specific excess mortality, we estimated all-cause mortality twice: once with data from during the pandemic in 2020 and 2021 included and once without. For location-years with vital registration data from during the pandemic, we computed the difference in estimated age-sex-specific mortality between the two sets of estimates. We then applied this distribution to our excess mortality estimates to calculate age-specific and sex-specific excess mortality. Due to instability in age-sex distributions and implausible patterns, we used the global age-sex distribution for locations with fewer than 75 000 excess deaths, unless otherwise noted (appendix 1 section 2. 8). Other pandemic-related mortality (OPRM) was estimated by calculating the difference between excess mortality and the sum of deaths due directly to COVID-19 infection and indirect deaths due to lower respiratory infections, measles, and pertussis. For locations with a negative OPRM, we adjusted the non-pandemic mortality estimates downward accordingly. We redistributed small discrepancies that remained between the mortality estimates that used vital registration age-sex-specific data from during the pandemic and the non-pandemic mortality estimates plus age-sex-specific excess mortality to ensure that the final mortality estimates including mortality shocks were consistent with observed highquality vital registration data.
section_name: Excess mortality due to the COVID-19 pandemic estimation

section_content: We used the Bayesian hierarchical cohort component model for population projection (BCCMP) from GBD 2019 to produce age-specific population estimates. 10 This method used age-specific fertility estimates from GBD 2021 (appendix 1 section 3), the previously described age-specific mortality estimates, and available census and registry data as inputs. Auxiliary refugee and migration data were used to inform the prior distribution on net migration in countries with substantial migration or reliable data. The model estimates an age-specific 1950 baseline population, age-specific net migration, and age-specific population estimates that are fully consistent with the input fertility and mortality estimates. Complete population estimation methodology is in appendix 1 (section 4).
section_name: Population estimation

section_content: We analysed the relationship between age-specific log mortality rates and SDI using MR-BRT (meta-regression-Bayesian regularised trimmed), 25 a meta-regression programme (appendix 1 section 6. 1). SDI is a composite indicator of a country's lag-distributed income per capita, Completeness is defined as the total number of deaths registered in all VR systems within a super-region during a 5-year period divided by the total number of estimated deaths within that super-region and period, with 100% completeness indicating that all deaths were registered. The size of the datapoints represents the number of estimated deaths. The solid black line shows the global completeness, the dashed black line indicates global completeness, excluding China and India, and other coloured lines indicate GBD super-regions. The green box indicates complete registration (defined as >95%). GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. VR=vital registration. 

average years of schooling, and the total fertility rate in females younger than age 25 years (appendix 1 section 5). MR-BRT defines a linear mixed-effects model with a B-spline specification for the relationship between outcomes of interest and SDI. We used a cubic spline with five knots between 0 and 1, with left-most and rightmost spline segments enforced to be linear, and with slopes matching adjacent interior segments. To ensure that the results were not sensitive to the choice of spline knots, we used a model ensemble of over 50 cubic spline models, as described above. For each model, interior knot placement was randomly generated to be between 0•1 and 0•9, with minimum inter-knot distance of 0•1 and maximum inter-knot distance of 1•0. The final predictions were obtained using the ensemble aggregate over these 50 models. This model was performed separately for each GBD age-sex group. Expected mortality rates for each age-sex group based on SDI were used to estimate expected life expectancy. A similar analysis was done for excess mortality rates due to the COVID-19 pandemic, with the exception that two-degree splines were used.
section_name: Expected mortality based on Socio-demographic Index (SDI) estimation

section_content: We produced estimates for each demographic metric by age-sex-location-year for 25 age groups: early neonatal (0-6 days), late neonatal (7-27 days), 1-5 months, 6-11 months, 12-23 months, 2-4 years, 5-9 years, every 5-year age group up to 95 years, and 95 years and older (fertility estimated for 5-year age groups between ages 10 years and 54 years); for males, females, and all sexes combined; for 204 countries and territories grouped into 21 regions and seven super-regions; and for every year from 1950 to 2021. We also included subnational analyses for 21 countries and territories (Brazil, China, Ethiopia, India, Indonesia, Iran, Italy, Japan, Kenya, Mexico, New Zealand, Nigeria, Norway, Pakistan, the Philippines, Poland, Russia, South Africa, Sweden, the UK, and the USA) and estimates by SDI quintile. All countries and territories were assigned an SDI value ranging from 0 (lowest income and educational attainment and highest fertility) to 100 and then grouped into quintiles from low SDI to high SDI.
section_name: Geographical units, age groups, and time periods

section_content: Uncertainty was propagated throughout the estimation process. For under-5 and adult mortality, ST-GPR generated 1000 draws for every location, year, and sex combination; 1000 draws were also produced for the crude death rate associated with HIV estimates. The 100 draws of excess mortality due to the COVID-19 pandemic were repeated ten times to generate 1000 draws. These draw-level inputs were then used to create 1000 draws of all-cause mortality estimates and draw-level estimates of fatal discontinuities. Mean estimates and 95% UIs (the 25th and 975th ranked values from the 1000 draws) were generated for all demographic metrics using the draw-level estimates. The uncertainty associated with fertility and mortality estimates was included as inputs in the BCCMP model to produce 1000 draws of population estimates.
section_name: Uncertainty analysis

section_content: The funders of this study had no role in study design, data collection, data analysis, data interpretation, or the writing of the report
section_name: Role of the funding source

section_content: This section presents global, regional, and national-level results for key demographic metrics; given space constraints, estimates at the subnational level are presented in appendix 2 and are also available in downloadable form through the GBD Results tool. All subnational locations are listed in appendix 1 (section 8).
section_name: Results

section_content: The proportion of deaths registered in vital registration systems increased substantially at the global level during the study period, from 30•3% in 1975 to a peak of 61•1% in 2016, before declining in subsequent years due to lags in reporting (figure 1 ). Completeness of death registration in vital registration systems varied markedly between regions, however, most progress in completeness was observed in China (where completeness peaked at 71•2% in 2018) and India (where completeness peaked at 80•1% in 2019; appendix 2 table S1 ). The Indian Sample Registration System is considered complete for the sample population it covers. Outside of China and India, progress in death registration has been slow, with only a 10•3 percentage point increase observed in the rest of the world between 1975 and the peak in 2016. This increase was concentrated in north Africa and the Middle East, which improved from 20•6% completeness in 1975 to a peak of 56•0% in 2016. While registration has been complete (defined as >95%) since 1975 for nearly all countries in the high-income super-region and central Europe, eastern Europe, and central Asia, in sub-Saharan Africa peak completeness of only 8•7% was reached in 2008 and completeness has declined since then. Death registration in Latin America and the Caribbean was more variable: countries such as Costa Rica, Cuba, and Argentina have been complete for many years; registration in countries such as Peru and Ecuador has remained around 60-90% complete, and 1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 0 2 0 1 0 2 0 2 0 1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 0 2 0 1 0 2 0 2 0 1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 0 2 0 1 0 2 0 2 0 others, such as Bolivia, continue to lack registration data. At the national level, 96 countries and territories had at least 1 year of complete death registration between 2010 and 2021; 29 countries and territories without complete death registration had at least 1 year of registering more than 75% of deaths; and 47 countries and territories had no vital registration data in the GBD 2021 mortality database. Registration was incomplete or nonexistent in many countries with large numbers of deaths in 2021, especially in sub-Saharan Africa, including Nigeria and Democratic Republic of Congo. In the 2020-21 period, super-regions had varying degrees of lowered completeness indicative of lags in reporting (figure 1 ). 2 ). Conflict and war had a larger impact on mortality rates in males than females. Furthermore, the HIV epidemic had an especially large impact on this age group in sub-Saharan Africa and a lesser impact in southeast Asia, east Asia, and Oceania, with a larger impact on females than males. Additionally, male mortality rates increased in Latin America and the Caribbean during the 2000s, to varying extents in countries such as El Salvador, Peru, Guatemala, Honduras, Mexico, Venezuela, and Brazil (appendix 2 figure S5 ). An increase in male and female mortality was observed in the high-income super-region during the late 2010s, which was most notable in the USA, Canada, and Spain (appendix 2 figure S5 ).
section_name: Civil registration and vital statistics completeness

section_content: During the COVID-19 pandemic in 2020 and 2021, global age-standardised all-cause mortality rates increased by 21•9% (95% UI 13•6-31•1) for males aged 15 years and older compared with 2019 and 16•6% (10•0-23•4) for females in the same age group and time period, reversing trends in mortality observed before the pandemic (appendix 2 table S3 ). In contrast, during 2020 and 2021, global mortality rates for both males and females generally remained constant or further decreased for age groups younger than 15 years (figure 2 ). In particular, between 2019 and 2021, global U5MR decreased by 7•0% (2•3-11•1). This continued reduction in child mortality was consistent across all super-regions (figure 2 ). The distributions are for the mortality sex ratio calculated across all 204 countries and territories included in this study. The boxes represent the middle 50% of the distribution (25th and 75th percentiles), the horizontal line in boxes indicates the mean, and the whiskers show the middle 95% of the distribution (2•5th and 97•5th percentiles). *The ratio of male to female mortality rates, computed by dividing the male mortality rate by the female mortality rate for each age group and year.
section_name: Mortality and life expectancy

section_content: (95 to 101) 62 (59 to 66) San Marino 1 continues All-cause mortality rates differed between sexes, and the extent of this difference varied across age groups and by location. Female mortality was generally lower than male mortality in all age groups, with substantial heterogeneity across countries and territories (figure 3 ). The highest variability in the ratio of male to female mortality rates across countries and territories was found in the 15-39 age groups; although little change in the mortality sex ratio has been observed between locations over time, the ratio generally increased between 1970 and 2021, indicating that the gap between male and female mortality has been increasing, generally driven by mortality rates among females decreasing at a faster rate than among males. Globally in 2021, the mortality rate for males aged 15-39 years was 65•9% (95% UI 56•8-74•7) higher than for females. The widening gap between males and females was also observed for nearly all age groups aged 40 years and older. In the neonatal age groups, the ratio of male to female mortality rates declined slightly over time towards 1, while the variability among countries and territories remained similar. Individuals aged 40 years and older had a consistent pattern of an increasing ratio of male to female mortality rates over time, with increased variability observed among those aged 65 S1 ). Initially, most of this decline could be attributed to declines in both U5MR and the under-5 population in southeast Asia, east Asia, and Oceania (especially China) until a tapering off around the year 2000. After this, the share of the decline attributed to sub-Saharan Africa began to increase, and this pattern continued during 2021 (figure 4 ). The largest number of under-5 deaths was observed in south Asia and sub-Saharan Africa during the pandemic, with south Asia accounting for 25•7% (24•1-27•2) of all deaths in children under 5 years in 2020 and 25 S4 ). An increase in life expectancy during this period was only observed in 32 (15•7%) of 204 countries and territories.
section_name: 98

section_content: We estimated 5•89 million (95% UI S2 ). For seven countries and territories (Taiwan [province of China], Mongolia, Japan, New Zealand, Iceland, Antigua and Barbuda, and Barbados), the all-age excess mortality rate for 2020 and 2021 combined was negative, indicating that fewer deaths occurred in these locations during the first 2 years of the pandemic than what would be expected based on past trends. In 2020, 20 countries and territories had negative excess mortality, while in 2021, only New Zealand and Barbados had negative excess mortality (table 1 ). 

Additionally, we computed age-standardised excess mortality rates to compare the impact of the pandemic across countries and territories while controlling for different population age structures. Age-standardised rates and all-age rates differed substantially, with the highest age-standardised excess mortality rates observed in nations in sub-Saharan Africa, Latin America, and the Middle East (figure 6 ). The lowest age-standardised rates were found in some countries and territories in the Caribbean, east Asia, and Oceania, and some highincome nations. There was substantial variability within all super-regions.
section_name: Excess mortality due to the COVID-19 pandemic

section_content: Between 1950 and 2021, longer life expectancies at birth were generally associated with higher SDI levels (figure 7 In 2020 and 2021 combined, lower age-standardised excess mortality rates due to the COVID-19 pandemic were broadly associated with higher SDI levels, but the association was not consistently strong (figure 8 ). The GBD super-regions with the largest proportion of countries and territories with an excess mortality rate higher than expected based on SDI were central Europe, eastern Europe, and central Asia (26 of 29 nations), Latin America and the Caribbean (21 of 33 nations), and The age structure of populations changed substantially across the globe between 1950 and 2021, with a general shift in the distribution away from younger ages and towards older ages (table 5 ). From 2000 to 2021, the proportion of the population aged younger than 15 years decreased in 196 of 204 countries and territories, with some of the largest declines observed in Saudi Arabia (from 36•0% to 20•1%) and Syria (41•5% to 26•1%). The eight countries in which the proportion of the population aged younger than 15 years did not decline were Angola, Chad, Kazakhstan, Mali, Niger, Nigeria, Russia, and Somalia. During this same period, the proportion of the population aged 65 years and older increased in 175 of 204 countries and territories; some of the largest increases were observed in Japan (from 17•2% to 28•9%) and Puerto Rico (from 11•0% to 22•0%). Three of 204 countries and territories had an increase in the proportion of the population aged younger than 15 years combined with a decline in the proportion of the population aged 65 years and older; these nations (Mali, Nigeria, and Chad) are all located in sub-Saharan Africa. The ratio of the population aged 65 years and older to the population aged less than 15 years increased between 2000 and 2021 in 188 of 204 countries and territories, including all nations within the high-income; Latin America and the Caribbean; south Asia; and southeast Asia, east Asia, and Oceania super-regions (figure 11 ). Some of the largest increases occurred in Japan, Puerto Rico, and South Korea. The countries and territories in which this ratio did not increase were Afghanistan, Benin, Burkina Faso, Burundi, Cameroon, Chad, Democratic Republic of Congo, Guinea, Guinea-Bissau, Kyrgyzstan, Liberia, Mali, Mozambique, Nigeria, Sierra Leone, and South Sudan.
section_name: Estimated mortality versus expected mortality based on SDI

section_name: Discussion

section_content: Our comprehensive set of updated demographic metrics indicate profound changes in the global health landscape during the first 2 years of the COVID-19 pandemic relative to historical trends. Long-term trends of decreasing mortality were superseded by marked increases in mortality rates in age groups older than 15 years during 2020 and 2021; in contrast, mortality in children under 5 years remained largely unaffected by the pandemic and continued to decrease globally. Global life expectancy declined sharply during 2020 and 2021, reversing the longstanding trend of life expectancy improvement. Agestandardised rates demonstrated the pandemic was disproportionately severe in countries within sub-Saharan Africa, the Middle East, south Asia, and Latin America. The COVID-19 pandemic has also highlighted the need for timely and comprehensive data collection and reporting. The development of high-quality civil registration and vital statistics systems has stagnated in many parts of the world due to multifaceted societal, financial, logistical, legislative, and political reasons, with notable exceptions including China, India, and some countries in north Africa and the Middle East. Population growth has slowed globally since 2017, although future declines might not persist at rates similar to those in 2020 and 2021 as the pandemic eases. In contrast, population growth is steady in south Asia and accelerating in sub-Saharan Africa. Increasing populations in many low-income and middle-income locations, combined with a shift in the age distribution away from younger ages and towards older ages, is likely to lead to new social, economic, and political challenges.
section_name: Main findings

section_content: Although the proportion of registered deaths has continuously increased at the global level since 1950, we observed marked variability across GBD super-regions and individual countries and territories. Civil registration and vital statistics are particularly scarce in sub-Saharan Africa; investment in vital registration system development in these nations is recommended to improve the availability of data necessary for accurate health measurements and policy evaluation. The COVID-19 pandemic highlighted the need for accessible and up-todate health data when trying to understand and track emerging global health events. Much uncertainty remains about the true extent of the effect of the pandemic on mortality in countries and territories with minimal to no vital registration data available, which is particularly concerning considering that these countries are potentially the most negatively impacted by the pandemic. With the exception of China, India, and some countries in north Africa and the Middle East, progress in improving the extent of global death registration has slowedperhaps due to a focus on cheaper but less permanent and systematic data collection efforts, such as small-scale and large-scale surveys. Although surveys are an invaluable source of demographic information, investing in more expensive yet comprehensive civil registration and vital statistics systems is crucial to monitor and improve population health. 26 eyond creating and improving civil registration and vital statistics systems, countries and territories without data during the past decade would also benefit from collecting additional data from other sources, such as censuses and nationally representative surveys. 30 countries and territories had no available data on child mortality for the period 2015-21, and 62 countries and territories had no available data on adult mortality. 41 countries and territories had no usable census data between 2010 and 2021, but census data were available before 2000 for these countries. Furthermore, the COVID-19 pandemic interrupted many data collection efforts, such as the USAID Demographic and Health Surveys Program, 27 and national censuses, which are now resuming. 28 Impacts such as these must be resolved to improve future data availability.
section_name: Data availability and gaps

section_content: The COVID-19 pandemic had differential effects on mortality across the lifespan. Life expectancy decreased in every GBD super-region and 84% of countries and territories from 2019 to 2021, but younger age groups were minimally affected. This finding is a welcome contrast to early warnings about potentially devastating impacts of the pandemic on child mortality. 29 Conversely, increases in mortality rates in populations aged 25 years and older were observed on a scale not seen in the previous 70 years. 30 Although the burden of excess deaths and all-age excess mortality rates due to the pandemic was largest in countries in central and eastern Europe, and Latin America, our analysis of age-standardised mortality rates highlights the relative severity of the pandemic's effects on mortality in certain countries within sub-Saharan Africa, the Middle East, south Asia, and Latin America. There was a general association between higher SDI and lower excess mortality, but this association was not particularly strong, and many countries were exceptions to this association, suggesting that at the population level, SDI was not always a strong predictor of excess mortality due to the COVID-19 pandemic in 2020 and 2021. Excess mortality was particularly high in nations such as Bolivia and South Africa when compared with other countries and territories with a similar SDI, which some have argued was in part due to relaxed containment strategies and vaccine hesitancy. 31 Conversely, excess mortality was particularly low in countries such as the Solomon Islands and Bhutan, which might be a reflection of delayed transmission in more isolated nations and of high vaccination rates. 32 4] [35] [36] [37] Reports published as recently as 2023 have shown that since 2021, mortality due to the pandemic has declined, 38, 39 presumably driven by vaccination efforts, public policies, individual behaviour changes, and the emergence of new SARS-CoV-2 variants with lower case-fatality ratios. 40, 41 owever, mortality has increased in some locations, which might be due to lifting of protective restrictions. 42
section_name: Impact of the COVID-19 pandemic

section_content: In the era of the UN Sustainable Development Goals (SDGs), there has been a decline in the global U5MR, which continued during the COVID-19 pandemic. However, progress has varied substantially between countries, and many continue to lag behind SDG targets. mortality was in the 15-39-years age group with much less variability observed in younger and older age groups. These differences go beyond biological explanations and highlight the importance of future efforts to address mortality risks to which males are particularly susceptible due to behavioural factors, war and conflict, occupational hazards, homicide, and suicide. 46, 47 The substantial differences among countries show, however, that it is also important to address mortality risks that predominantly affect women, such as maternal mortality, gender-based violence, and economic disparities. 48, 49 We also found that life expectancy was consistently higher in countries in the Americas, east Asia, and western Europe than countries in sub-Saharan Africa, and this effect was strongly associated with SDI. Although we did not establish causal effects, this finding is supported by many studies showing that social determinants of health are key drivers of mortality, [50] [51] [52] [53] [54] and improving education, economic prosperity, and gender inequalities is vital for continual progress in health outcomes globally. However, notable exceptions regarding the relationship between mortality and SDI indicate that other factors are also involved.
section_name: Long-term mortality trends

section_content: Although the rate of global population growth has plateaued and started to decline since 2017, in lower income countries-primarily in sub-Saharan Africarapid population growth has continued. Thus, much of future population growth will likely occur in the poorest regions. Resource scarcity and rapid infrastructure expansion will be crucial issues to address. 55, 56 These factors, and a history of colonialism, can contribute to political instability. 57, 58 These challenges will require responses from governments and the global community. Furthermore, the concentration of population growth has shifted to locations with the poorest health-ie, locations with the highest child mortality rates. This might lead to challenges in continuing improvement of health outcomes. Outside of these locations, slowing of population growth is widespread. Although most countries and territories had not reached a peak population as of 2021, in 171 of 204 countries and territories a lower rate of natural increase was observed between 2010 and 2019 than between 2000 and 2009. Furthermore, our analysis of population age structures over time indicated a prominent shift towards older ages in most regions and nations. 0] [61] [62] The shift towards a higher ratio of older people to younger people will require greater attention to be paid to labour shortages, health systems strengthening, and evaluation of government policies on retirement and health care. 61, 63, 64 wever, beneficial consequences such as the so-called second demographic dividend of greater personal wealth and investment in human capital might offset some of these challenges. 65 Future research on these topics must seek to understand how changing population dynamics impact health outcomes and systems, and how health interventions can be tailored to address the unique challenges posed by these demographic shifts. Migration is particularly relevant to these challenges. Voluntary emigration from locations with younger adult population bulges to locations in need of more labour to support ageing populations is an open public policy discussion. 66, 67 he level of migration needed to support older age populations is dynamic and is likely to change over time with technological innovations and new public policies. 68 urthermore, environmental constraints in some highincome countries might limit immigration possibilities. Migration of skilled workers out of lower-income countries might consequently worsen these economies. 69, 70 lobal cooperation is necessary, and guidelines such as the UN Global Compact for Safe, Orderly and Regular Migration 71 can help lead this work.
section_name: Population dynamics and age structures

section_content: There are numerous differences in data processing and statistical modelling assumptions between the GBD 2021 estimates reported here and those from other demographic studies that provide important advantages. Excess mortality estimates for 2020 and 2021 have been previously reported in the GBD study and by other institutes. Our previous excess mortality estimates reported 18•2 million (95% UI 17•1-19•6) excess deaths in this study. Estimating mortality during the COVID-19 pandemic was particularly difficult due to many factors including delays in reporting, differing granularity of available data, and political will to provide accurate data. Although our earlier estimates were based on the best available data and methodology at the time, we have made data and modelling improvements that resulted in this lower estimate. We updated to more reliable data sources in some countries that corrected errors in reporting, and included more data up to the end of 2021. Methodologically, we modelled data at the yearly level, and additionally included age-specific detailed projections from our GBD mortality modelling process to inform our non-pandemic counterfactual, which generally led to higher estimates of expected non-pandemic mortality and thus lower excess mortality. 

Our current estimate of global excess mortality during 2020 and 2021 is comparable to the WHO estimate of 14•9 million (95% UI 13•3-16•6) excess deaths, 15 with our mean estimate falling within the uncertainty interval of the WHO estimate and vice versa. Our estimates tend to be higher than those of WHO for sub-Saharan Africa, with the largest differences being 233 000 more deaths in Nigeria and 177 000 more deaths in Ethiopia; and south Asia, with the largest differences being 262 000 more deaths in Pakistan and 171 000 more deaths in Bangladesh. However, our estimate for India was 1•3 million deaths lower than that of WHO, which is the largest discrepancy in this direction. We also estimated 123 000 more excess deaths in China-our results indicated positive excess, whereas WHO estimated negative excess. The largest differences occur in locations for which little or no all-cause mortality data were available for the pandemic period, and thus estimates relied on predictive models. These differences reflect different covariates used for predictions models. Additionally, WHO models and predicts all-cause mortality rates in locations without data, whereas we predict excess mortality rates directly, which leads to different assumptions and functional forms for statistical models. Differences in locations with all-cause mortality data are driven by different data processing steps and different models for expected non-pandemic mortality. 

The latest estimates from UNICEF, published in 2023, reported a global U5MR of 38•1 deaths (95% UI 36•1-42•2) per 1000 livebirths in 2021, 72 which is consistent with our estimate of 35•7 deaths (30•5-42•0) per 1000 livebirths. The mean relative difference at the national level between our 2021 U5MR estimates and those provided by UNICEF is -2•6%, ranging from -58•4% to 111•9%. Similar to our estimates, the UNICEF estimates show a continued decreasing trend in child mortality during the COVID-19 pandemic. Between 1950 and 2019, the mean relative difference between our estimates and UNICEF estimates across countries and territories was -2•0%, ranging from -64•3% to 154•6%. These differences primarily reflect differences in data inclusion, processing, and synthesis. For example, our estimate of mortality in Iran in 2021 is 58•4% lower than that of UNICEF. We included vital registration data from 2021 and our estimates closely match this observed mortality, whereas UNICEF does not include these data, leading to higher estimates. Using the most recent available data suggests our estimates are more reliable. 

Adult mortality estimates at the country level from the 2022 UN World Population Prospects (WPP) report are on average 11•1% lower than our 2021 estimates, 13 which range from 41•8% lower to 289•5% higher. Between 1950 and 2019, the mean relative difference between our adult mortality estimates and those from WPP 2022 was -4•3%, ranging from -64•0% to 229•6%. Differences between WPP 2022 estimates of national life expectancy at birth and those from GBD 2021 are primarily driven by these differences in adult mortality estimates, and variability in child mortality estimates. While locationyears with complete death registration show substantial agreement between estimates, with a mean relative difference of 1•3%, our estimates for 2021 range from 7•8 years lower to 10•1 years higher, and our estimates for years before the COVID-19 pandemic range from 20•4 years lower to 38•4 years higher. The largest discrepancies were due to location-years with large fatal discontinuities or scarcity of high-quality vital registration data. Furthermore, discrepancies between 2021 estimates are highly influenced by the differences in estimation of excess mortality due to the COVID-19 pandemic. As one of the largest differences, our life expectancy estimate for Nigeria in 2021 is 10•1 years higher than the WPP estimate, driven by our estimated 41•8% lower adult mortality. Our adult mortality estimates more closely follow the bulk of the data from sibling-survival histories, and our age-specific mortality estimates rely on a database of 43 758 empirical life tables as opposed to the Coale-Demeny north model life table used by WPP 2022, which has been shown to underperform compared with other modern model life table methods. 73, 74 or further comparison with WPP and as a model validation exercise, we compared estimated age-specific mortality rates and death counts from our analysis and from WPP with those calculated directly from all locationyears of vital registration data deemed to have complete death registration. When comparing our results, we used our population estimates as the denominator to calculate mortality rates from vital registration; similarly, we used WPP population estimates as the denominator for that comparison. Across all location-year-age-sex mortality rates, our estimates had mean absolute error of 0•024, indicating a good fit to the data, along with root mean squared error (RMSE) of 0•52. These were lower than the respective 0•033 and 0•53 calculated for WPP. Similarly, our death count estimates had a mean absolute error of 84•8 and RMSE of 365 compared with a mean absolute error of 222 and RMSE of 1032 for WPP estimates. 

Estimates of the global population from WPP 2022 are similar to that of this study, with an estimated global population of 7•91 billion in 2021, compared with our estimate of 7•89 billion (95 % UI 7•67-8•13). On average in 2021, country-level population estimates were 0•2% lower in GBD 2021 than WPP 2022 and ranged from 34•2% lower to 82•2% higher. For specific ages, differences in the younger than 15 years age group ranged from 48•0% lower to 75•3% higher, while differences in the 65 years and older age group ranged from 36•0% lower to 39•5% higher. The largest relative differences were for locations in which no recent census data were available, and those with substantial net inmigration from other countries.
section_name: Comparisons between GBD 2021 estimates and other estimates

section_content: This research has several limitations. First, estimates continue to be limited by data source availability and scope. COVID-19 showed the crucial need to create more robust vital registration systems that can highlight the differential effects of disease and injury across population subgroups in a timely manner. 93 of 204 countries and territories had no available all-cause mortality data to estimate excess mortality due to the COVID-19 pandemic, which means our estimates in these areas are solely driven by associations with covariates. These locations were largely in regions where the effects of the pandemic were most severe. Furthermore, the scarcity of high-quality civil registration and vital statistics systems to produce reliable data in many low-income and middle-income countries introduces large-scale uncertainty in all demographic estimates. Additionally, population estimates in certain countries rely on modelled projections due to no available recent censuses. Future development of reliable data sources is crucial because estimates improve as the quality of underlying data improves. Subsequent GBD cycles will provide revised estimates after additional data for recent years become available. 

Second, analysis of more granular subpopulations such as subnational areas or by other population characteristics was restricted by data availability. Although our effort represents the most comprehensive global analysis of mortality and population, the estimates presented in this research mask substantial heterogeneity in smaller geographies. This limits the utility of our estimates to provide insights for more targeted interventions, for example, understanding occupational hazards in industrial regions. Improving this aspect of the research requires more comprehensive and detailed data, such as by race, ethnicity, socioeconomic status, and smaller administrative levels, [75] [76] [77] and future work will aim to produce more comprehensive health metrics. 

Third, the GBD demographics approach has not developed an encompassing model to estimate migration together with population, mortality, and fertility. Estimating migration in a model that jointly informs population, mortality, and fertility will not only improve accuracy of population estimates, but also allow assessing and improving corrections for death registration completeness and census coverage. This is crucial in locations with large migration flows, such as the United Arab Emirates and Qatar, where current methods for these corrections might not perform well. 78, 79 The increased importance of migration at present and in the future, especially considering the shifting age structure in many populations, places renewed importance on producing reliable migration estimates. 

Fourth, we assumed a binomial distribution when calculating data variance and did not evaluate other models of distribution. Some of our input data might be overdispersed, resulting in inaccurate estimates of data variance. However, we do not expect that changing our assumptions on the distribution would have a sizeable impact on estimates since the sampling errors on vital registration and civil registration mortality and fertility data are likely to be much smaller than non-sampling errors. In the future, we will consider testing such assumptions. 

Fifth, computational resources did not permit propagation of uncertainty for all covariates throughout the analytical process. While uncertainty from model estimation was accounted for at each stage, such as U5MR, adult mortality, and age-specific mortality rates, uncertainties for some covariates such as lag-distributed income and education were not. Similarly, estimates of coefficients in the COVID-19 excess mortality prediction model did not include uncertainty. Future iterations of GBD will investigate computationally more efficient implementation of current methods and development of new methods to allow for all sources of uncertainty to be included in modelling.
section_name: Limitations

section_content: The COVID-19 pandemic will likely continue to impact estimates of demographic trends in future years due to reporting lags and the persistent effects of the pandemic. Future research should focus on understanding the full demographic impact of the pandemic in 2022 and beyond. Methodologically, we aim to improve our incorporation of excess mortality and COVID-19 direct mortality estimates into the GBD mortality estimation process, rather than post-hoc unification of two separate modelling endeavours. We also plan to develop a standalone migration model and integrate this model into the GBD demographic estimation process. Along with this, we aim to simultaneously estimate mortality and population rather than the current sequentially iterative approach. This would allow the uncertainty in mortality estimates to inform population estimates and vice versa, helping address issues in age, period, and cohort trends that might otherwise arise.
section_name: Future directions

section_content: Tracking long-term health trends and evaluating the impact of the COVID-19 pandemic require accurate global, regional, and national estimates of mortality, life expectancy, and population, because these crucial demographic indicators foundationally underpin our understanding of population health. The comprehensive demographic metrics reported in this study show that marked reversals in adult mortality and life expectancy trends occurred during 2020 and 2021, leading to increased mortality and reduced life expectancy worldwide. This increased mortality did not occur in younger populations: mortality rates in children under 5 years continued to decline globally during the first 2 years of the pandemic, although more equitable and intensified investment is needed to achieve SDG targets in many locations. While global population growth is slowing, geographical distributions and age structures are undergoing fundamental shifts-low-income countries and territories continue to grow, and population structures across the globe are ageing. Nations in the post-pandemic world will need to address emerging health-care, economic, and social challenges with new policies and practices. The development, implementation, and evaluation of these health policies and practices in diverse locations around the world can be informed and guided by the GBD 2021 demographic estimates. Accurate mortality, life expectancy, and population estimates might be even more important to informing policy and practice in a post-pandemic world than in the past. Collectively, the extensive set of demographic estimates reported here represent a valuable global tool for policy evaluation, development, and implementation in diverse locations around the world. Global Initiative; all outside the submitted work. Raul Felipe Palma-Alvarez reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Angelini, Lundbeck, Rubió, Servier, and Takeda; all outside the submitted work. Amy E Peden reports support for the present manuscript from the Australian National Health and Medical Research Council (grant number APP2009306). Manon Pigeolet reports a grant from the Belgian Kids' Fund for Pediatric Research, outside the submitted work. Thomas Pilgrim reports grants or contracts to the institution from Biotronik, Boston Scientific, Edwards Lifesciences, and speakers bureaus, manuscript writing or educational events from Boston Scientific/BTG; outside the submitted work. Katharina S Sunnerhagen reports a leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid as the chair of the scientific committee for the Swedish stroke association; outside the submitted work. Luis M Taborda-Barata reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Sanofi, AstraZeneca, and LETI Laboratories; outside the submitted work. Amanda G Thrift reports grants or contracts paid to their institution from the
section_name: Conclusion

section_name: Acknowledgments

section_content: and Publications Committee on Quality Assurance Agency, dean of Public Health and Preventive Medicine King Edward Medical University, director of Quality Enhancement Cell King Edward Medical University, chief editor of Annals of King Edward Medical University, and Chief Editor History Book King Edward Medical University ; all outside the submitted work. Robert Ancuceanu reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from AbbVie, Sandoz, B Braun, Laropharm, and MagnaPharm, all outside the submitted work. Ruhai Bai reports support for the present manuscript from the Fundamental Research Funds for the Central Universities (grant number 30923011101 ) and the Social Science Fund of Jiangsu Province (grant number 21GLD008 ). Ovidiu Constantin Baltatu reports support for the present manuscript from the National Council for Scientific and Technological Development (grant number 304224/2022-7 ) and Anima Institute -AI research professor fellowship; leadership or a fiduciary role in other board, society, committee or advocacy group, paid or unpaid, as a board member of the Biotechnology Board at São José dos Campos Technology Park and an Academic Ambassador for Afya, outside the submitted work. Michelle L Bell reports grants or contracts from the US Environmental Protection Agency, National Institutes of Health, High Tide Foundation, Health Effects Institute Yale Women Faculty Forum Environmental Defense Fund, Wellcome Trust Foundation, Yale Climate Change and Health Center, Robert Wood Johnson Foundation, and the Hutchinson Postdoctoral Fellowship, all as payments to their institution; consulting fees from Clinique; honoraria for speakers bureaus from Colorado School of Public Health, Duke University, University of Texas, Data4Justice, Korea University, Organization of Teratology Information Specialists, UPenn, Boston University, honoraria for editing duties from IOP Publishing, honoraria for grant review from NIH, Health Canada, PAC-10, UK Research and Innovation, AXA Research Fund Fellowship, and honoraria for external advisory committee from Harvard University and University of Montana ; travel reimbursement from Colorado School of Public Health, University of Texas, Duke University, Boston University, UPenn, Harvard University, American Journal of Public Health; leadership or a fiduciary role in other board, society, committee or advocacy group, unpaid, with the Fifth National Climate Assessment, Lancet Countdown, Johns Hopkins EHE Advisory Board, Harvard external advisory committee for training grant, WHO Global Air Pollution and Health Technical Advisory group, and National Academies Panels and Committees, and paid roles with the US EPA Clean Air Scientific Advisory Committee ; all outside the submitted work. Paulo J G Bettencourt reports other financial or non-financial interests with the Botnar Foundation as project reviewer, outside the submitted work. Pra Bhardwaj reports stock options in Doximity in 2020 and 2021 for being a Doximity fellow, outside the submitted work. Sonu Bhaskar reports grants or contracts from the Japan Society for the Promotion of Science (JSPS) through grants-in-aid for Scientific Research KAKENH and a JSPS International Fellowship ( 2023-25 ); leadership or a fiduciary role in other board, society, committee or advocacy group, paid or unpaid, as chair of the Global Health and Migration Hub Community for Global Health Hub Germany, Berlin, district chair; Diversity, Equity, and Inclusion for Rotary District 9675, Australia; and as editorial board member with Frontiers in Stroke, Frontiers in Neurology, PLOS One, BMC Medical Research Methodology, BMC Neurology, and Frontiers in Public Health; all outside the submitted work. Zulfiqar A Bhutta reports leadership or a fiduciary role in other board, society, committee or advocacy group, paid or unpaid, as member and chair of the Board of Governors of the National Institutes of Health, Pakistan, outside the submitted work. Boris Bikbov reports grants or contracts from the European Commission ; support for attending meetings or travel expenses from the European Renal Association ; an unpaid leadership role in the advocacy group International Society of Nephrology ; and other non-financial interests in Scientific-Tools. org for a public health consultancy; all outside the submitted work. Atanu Biswas reports consulting fees from INTAS Pharmaceuticals, Lupin Pharmaceuticals, and Alkem Laboratories ; and payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Roche Diagnostic ; all outside the submitted work. Edward J Boyko reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational See Online for appendix 3 Articles www. thelancet. comVol 403 May 18, 2024 2051 events from the Korean Diabetes Association, Diabetes Association (Taiwan), and the American Diabetes Association, all outside the submitted work. Márcia Carvalho reports other financial or non-financial interests in LAQV-REQUIMTE and the Faculty of Science and Technology under the scope of the project UIDP/50006/2020, outside the submitted work. Joao Conde reports grants or contracts from the European Research Council (starting grant ERC-StG-2019-848325 ; €1•5 million funding), outside the submitted work. Saswati Das reports leadership or a fiduciary role in other board, society, committee or advocacy group, unpaid, with the Association for Diagnostics and Laboratory Medicine, and the Women in Global Health India Chapter, outside the submitted work. Louisa Degenhardt reports educational grants from Indivior to examine new opioid medications in Australia, outside the submitted work. Andreas K Demetriades reports leadership or a fiduciary role in other board, society, committee or advocacy group, unpaid, with the AO Knowledge Forum Degen Steering Committee, Global Neuro Foundation Board, and the European Association of Neurological Societies board, all outside the submitted work. Susanna J Dunachie reports support for the present manuscript from the UK Fleming Fund at Department of Health and Social Care, Bill & Melinda Gates Foundation, Wellcome Trust, and UK National Institute of Health and Care Research (NIHR) ; grants or contracts from UKRI ( MR/W02067X/1 and MR/W020653/1 ), the US Defense Threat Reduction Agency, Wellcome Drug Resistant Infections Discretionary Award, and UK Department of Health and Social Care ; consulting fees from Scottish Parliament for serving as Scientific Advisor and from Wellcome for serving as funding committee member; participation on the Data Monitoring Committee for UK STABILISE study of BCG Vaccine in COPD; leadership or a fiduciary role in other board, society, committee or advocacy group, paid or unpaid, as a member of the New and Emerging Respiratory Virus Threats Advisory Group, chair of Wellcome SEDRIC subgroup on data standards and harmonisation in antimicrobial resistance, UK, member of the Variant Technical Group for SARS-CoV-2 for UK Health Security Agency, expert adviser to WHO's Global Antimicrobial Resistance Surveillance System, member of WHO Guidelines Development Group on Treatment of Ebola ; all outside the submitted work. Andre Faro reports support for the present manuscript from Coordination of Superior Level Staff Improvement (Brazil), Productivity in Research Scholarship (PQ Scholarship ). Irina Filip and Amir Radfar report support for the present manuscript from Avicenna Medical and Clinical Research Institute. Artem Alekseevich Fomenkov reports support for the present manuscript from Ministry of Science and Higher Education of the Russian Federation (theme number 121050500047-5 ). Lisa M Force reports support for the present manuscript from the Gates Foundation ; grants or contracts from Conquer Cancer Foundation, St Jude Children's Research Hospital, St Baldrick's Foundation, and NIH Loan Repayment Program ; leadership or a fiduciary role in other board, society, committee or advocacy group, unpaid, with the Lancet Oncology International Advisory Board ; all outside the submitted work. Matteo Foschi reports consulting fees as a scientific consultant for Roche and Novartis ; support for attending meetings or travel from Roche, Novartis, Biogen, Merck, and Sanofi ; and a leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid as a member of the MSBase Collaboration Scientific Leadership Group ; outside the submitted work. Richard Charles Franklin reports grants or contracts from Heatwaves in Queensland and Arc Flash Human Factors (Government of Queensland), and Mobile Plant Safety (Agrifutures) ; honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from the World Safety Conference 2022 as the conference convener; support for attending meetings or travel from the Tropical Medicine and Travel Medicine Conference ( 2022, 2023 ) and the Travel Medicine Conference ( Basel 2023 ); a leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid as the Director of Kidsafe, Director of Farmsafe, Director of Auschem, a member of the Governance Committee of ISASH, and Injury Prevention special interest group convenor, Public Health Association of Australia ; outside the submitted work. Emmanuela Gakidou reports support for the present manuscript from the Gates Foundation. Quan Gan reports other financial or non-financial interest in the International Agency for Research on Cancer, WHO; the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer or WHO. Paramjit Singh Gill reports support for the present manuscript from the NIHR as senior investigator with payments to their institution; the views expressed in this publication are those of the authors and not necessarily those of the NIHR or the UK Department of Health and Social Care. Avirup Guha reports grants or contracts from the American Heart Association and Department of Defense ; consulting fees from Pfizer, Novartis, and Myovant; and a leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid, with ZERO Prostate Cancer Health Equity Task Force ; all outside the submitted work. Claudiu Herteliu reports grants or contracts from the Romanian Ministry of Research Innovation and Digitalizatio (project number ID-585-CTR-42-PFE-2021 ), a grant of the European Commission Horizon 4P-CAN ( Personalised Cancer Primary Prevention Research through Citizen Participation and Digitally Enabled Social Innovation), project " Societal and Economic Resilience within multi-hazards environment in Romania " funded by European Union-NextgenerationEU and Romanian Government, under National Recovery and Resilience Plan for Romania (contract number 760050/23. 05. 2023, cod PNRR-C9-I8-CF 267/29. 11. 2022 ), through the Romanian Ministry of Research, Innovation and Digitalization, within Component 9, Investment I8; and project " A better understanding of socio-economic systems using quantitative methods from Physics " funded by European Union-NextgenerationEU and Romanian Government, under National Recovery and Resilience Plan for Romania (contract number 760034/23. 05. 2023, cod PNRR-C9-I8-CF 255/29. 11. 2022 ), through the Romanian Ministry of Research, Innovation and Digitalization, within Component 9, Investment I8; outside the submitted work. Michael Hultström reports support for the present manuscript from Knut och Alice Wallenberg Foundation, Swedish Heart-Lung Foundation, and the Swedish Association for Medicine, all as payments to their institution; payment or honoraria for lectures from the Swedish Society for Anaesthesiology and Intensive Care ; support for attending meetings or travel from the American Physiological Society ; and a leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid, with the American Physiological Society ; all outside the submitted work. Desta Ijo reports grants or contracts from the Ethiopian Public Health Institute (EPHI) ; and consulting fees from EPHI National Data Management Center for Health through their salary; all outside the submitted work. Irena M Illic reports support for the present manuscript from the Serbian Ministry of Education Science and Technological Development (project number 175042, 2011-2023 ). Milena D Illic reports support for the present manuscript from the Serbian Ministry of Education Science and Technological Development (project number 451-03-47/2023-01/200111 ). Sheikh Mohammed Shariful Islam reports grants or contracts from National Health and Medical Research Council through a fellowship, and from the Heart Foundation of Australia through a fellowship and Vanguard Grant, both outside the submitted work. Nahlah Elkudssiah Ismail reports A leadership or fiduciary role in other board, society, committee or advocacy group, unpaid, as a council member of the Malaysian Academy of Pharmacy, outside the submitted work. John S Ji reports a leadership or fiduciary role in other board, society, committee or advocacy group, unpaid, with the WHO Technical Advisory Group Climate Health Ethics, outside the submitted work. Tamas Joo reports support for the present manuscript from Data-Driven Health Division of the National Laboratory for Health Security, National Research, Development and Innovation Office in Hungary (grant number RRF-2. 3. 1-21-2022-00006 ). Jacek Jerzy Jozwiak reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Novartis, Adamed, and Amgen, all outside the submitted work. Sanjay Kalra reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from AstraZeneca, Boehringer Ingleheim, Novo Nordisk, and Sanofi ; and a leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid as president of the Endocrine Society of India and the South Asian Federation of Endocrine Societies ; all outside the submitted work. Srinivasa Vittal Katikireddi reports support for the present manuscript from the Scottish Government Chief Scientist Office ( SPHSU17 ), the UK Medical ATSens ; speaker and consulting fees paid to the institution from Biotronik, Boston Scientific, Edwards Lifesciences, Abbott, Medtronic, Biosensors, and Highlife; participation on a data safety monitoring board or advisory board for the EMPIRE Study (sponsored by Biosensors); receipt of equipment, materials, drugs, medical writing, gifts or other services from ATsens; all outside the submitted work. Constance Dimity reports grants or contracts paid to the University of Newcastle from Valley to Coast Charitable Trust; consulting fees from Sydney North Primary Health Network, HNECC Primary Health Network, SW Sydney Primary Health Network, Australian Department of Health and Aged Care, NSW Health, Royal Australian College of General Practitioners, Dementia Training Australia, Palliative Care Australia, University of Sydney, Monash University, Biogen, Roche, and Medicines Australia ; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Dementia Training Australia, Sydney North Health Network, and In vivo Academy; payment for expert testimony from Legal Aid NSW ; support for attending meetings or travel from the Royal Australian College of General Practitioners and Palliative Care Australia ; a leadership or fiduciary role in other board, society, committee or advocacy group, unpaid as Provost (NSW Faculty, The Royal Australian College of General Practitioners ), vice president of Doctors Reform Society, chair of WONCA Special Interest Group, Ageing and Health, board member of Hunter Postgraduate Medical Institute, paid roles as adjunct professor in the School of Rural Medicine of University of New England, adjunct professor in the School of Nursing and Midwifery of Western Sydney University, clinical professor in the Wicking Dementia Research Education Centre of University of Tasmania, and professor of General Practice at University of Newcastle (until August 2021 ); all outside the submitted work. Luis Felipe Reyes reports consulting fees from GSK, MSD, and Pfizer ; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from GSK, MSD, and Pfizer ; payment for expert testimony from GSK, MSD, and Pfizer ; support for attending meetings or travel from GSK, MSD, and Pfizer ; outside the submitted work. Daniel Cury Ribeiro reports grants or contracts paid to the University of Otago from the Health Research Council (New Zealand 18/111 ), outside the submitted work. Luca Ronfani reports support for the present manuscript from the Italian Ministry of Health ( Ricerca Corrente 34/2017 ), with payments made to the Institute for Maternal and Child Health IRCCS Burlo Garofolo. Andrzej Rynkiewicz reports consulting fees from Ingelheim, and payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Boehringer Ingelheim, Amgen, and Servier; all outside the submitted work. Simona Sacco reports grants or contracts from Novartis and Uriach ; consulting fees from Novartis, Allergan-AbbVie, Teva, Lilly, Lundbeck, Pfizer, NovoNordisk, Abbott, AstraZeneca ; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Novartis, Allergan-AbbVie, Teva, Lilly, Lundbeck, Pfizer, NovoNordisk, Abbott, AstraZeneca ; support for attending meetings or travel from Lilly, Novartis, Teva, Lundbeck ; a leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid as the president elect of the European Stroke Organization, and the second vice-president of the European Headache Federation; and receipt of equipment, materials, drugs, medical writing, gifts or other services from Allergan-AbbVie, NovoNordisk; all outside the submitted work. Juan Sanabria reports support for attending meetings or travel from the Marshall University Medical School ; three patents pending; participation in quality assessment and assurance for surgeries of his Marshall University Department of Surgery ; a leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid with ASTS, SSO, and AASLD; all outside the submitted work. Chinmoy Sarkar reports other financial interests as a Global Health Leadership Fellow from National Academy of Medicine, outside the submitted work. Nikolas Scarmeas reports grants or contracts with Novo Nordisc as the local principal investigator of a recruitment site for multinational, multicenter industry sponsored phase 3 treatment trial for Alzheimer's disease with funding paid to the institution; participation on a data safety monitoring board or advisory board with Albert Einstein College of Medicine ( NIH funded study) as the chair of data safety monitoring board; all outside the submitted work. Benedikt Michael Schaarschmidt reports research grants from Else Kröner-Fresenius Foundatuin, DFG, and PharmaCept; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from AstraZeneca ; support for attending meetings or travel from Bayer AG ; all outside the submitted work. Nilay S Shah reports support for the present manuscript from the National Heart, Lung, and Blood Institute (grant number K23HL157766 ). Amin Sharifan reports leadership or fiduciary roles in other board, society, committee or advocacy group, unpaid as a steering member of the Cochrane Early Career Professionals Network ; and receipt of equipment, materials, drugs, medical writing, gifts or other services from Elsevier; outside the submitted work. Saurab Sharma reports support for the present manuscript from the John J Bonica Postdoctoral Fellowship from the International Association for the Study of Pain (IASP; 2021-23 ); payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from giving an online lecture and a travel grant for delivering a talk conducted by the Pain Education special interest group of the IASP at the World Pain Congress in Toronto (2022 ); support for attending meetings or travel from the International Association for the Study of Pain to attend its biennial meeting in Toronto (September 2022); outside the submitted work. Velizar Shivarov reports one issue patent in Bulgaria and one issue utility model in Bulgaria; restricted stock units for ICON; and other financial interests from Iconplc/PRAHS (salary), outside the submitted work. Sunil Shrestha reports other financial interests from the Graduate Research Merit Scholarship from the School of Pharmacy at Monash University Malaysia, outside the submitted work. João Pedro Silva reports support for the present manuscript from the Portuguese Foundation for Science and Technology. Luís Manuel Lopes Rodrigues Silva reports grants or contracts from CENTRO-04-3559-FSE-000162, Fundo Social Europeu, outside the submitted work. Colin R Simpson reports grants or contracts from Ministry of Business, Innovation, & Employement (New Zealand ), Health Research Council (New Zealand ), Ministry of Health (New Zealand ), UK Medical Research Council, Health Data Research UK, and CSO (UK) ; a leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid with the New Zealand Government Data Ethics Advisory Group as the chair; outside the submitted work. Marco Solmi reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from AbbVie and Otsuka, outside the submitted work. Dan J Stein reports consulting fees from Discovery Vitality, Johnson & Johnson, Kanna, L'Oreal, Lundbeck, Orion, Sanofi, Servier, Takeda, and Vistagen, outside the submitted work. Stefan Stortecky reports grants or contracts paid to their institution from Edwards Lifesciences, Medtronic, Abbott, and Boston Scientific ; consulting fees from Boston Scientific/ BTG and Teleflex; payment or honoraria for lectures, presentations,

section_content: Research reported in this publication was supported by the Gates Foundation, UK Department of Health and Social Care, the Norwegian Institute of Public Health, and the New Zealand Ministry of Health. The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with license no. SLN2019-8-64 and 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 for this research was provided by MEASURE Evaluation, funded by USAID. Views expressed do not necessarily reflect those of USAID, the US Government, or MEASURE Evaluation. 

Editorial note: The Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.
section_name: Acknowledgments

section_name: Data sharing

section_content: To download the data used in these analyses, please visit the GBD 2021 Sources Tool. The statistical code used in GBD 2021 is available online.
section_name: Data sharing

section_name: Population

section_content: The global total population increased annually over the study period, from 2•52 billion (95% UI 2•48-2•58) in 1950 to 6•10 billion (5•98-6•22) in 2000 and 7•89 billion (7•67-8•13) in 2021 (table 5 ). Annual growth in total population fluctuated over the study period, from an annual increase of 46•9 million (41•0-52 •7) from 1950 to 1951 with the highest annual increase of 92•5 million (75•7-106•6) observed between 2008 and 2009 (figure 9 ). After 2009, population growth plateaued, and in 2017, the annual increase in population began to decline. Between 2019 and 2021, this decline accelerated, with annual gains of just 77•0 million (49•4-95•6) from 2019 to 2020 and 69•0 million (50•8-93•2) from 2020 to 2021. These reduced gains include the impact of excess deaths due to the COVID-19 pandemic, therefore the magnitude might not persist as excess mortality declines. The majority of global population growth during the study period is attributed to three GBD super-regions: sub-Saharan Africa; south Asia; and southeast Asia, east Asia, and Oceania. The population of sub-Saharan Africa grew at a steadily increasing rate throughout the study period, contributing 9•1% (7•3-11•0) of the total global population growth from 1950 to 1951, 23•3% (19•4-27•6) from 2000 to 2001, and 39•5% (28•4-52•7) from 2020 to 2021. South Asia contributed 17•1% (13•8-20•6) of the total global population growth from 1950 to 1951, rose to a peak contribution of 32•9% (28•4-37•8) from 1999 to 2000, and remained relatively constant in more recent years, with a contribution of 26•3% (9•0-44•7) from 2020 to 2021. In contrast, the annual growth of the population fluctuated in southeast Asia, east Asia, and Oceania. The contribution of this super-region to annual global population growth was relatively stable up to a peak of 37•3% (30•4-41•8) from 1956 to 1957 and then subsequently decreased, contributing 14•1% (0•0 to 30•2) from 2020 to 2021. Central Europe, eastern Europe, and central Asia contributed little to global population growth, and in fact experienced a decline in population over some periods, with growth from 1950 to 1992, a decline from 1993 to 2006, growth from 2007 to 2018, and a return to population decline in 2019. Population growth was relatively stable in Latin America and the Caribbean and north Africa and the Middle East at the super-regional level during the previous three decades, whereas population growth in the high-income super-region began to decline starting around 2015. 

The majority of countries and territories (154 [75•5%] of 204 countries and territories representing all seven super-regions) had a positive rate of natural increase (calculated as the number of births minus the number of deaths divided by person-years) between 2000 and 2009 followed by a smaller positive rate between 2010 and 2019 (figure 10 ). 26 countries and territories had a rate of natural increase that was positive during both decades and that was larger between 2010 and 2019 than between 2000 and 2009 (figure 10 ). Of these countries and territories, nine were in sub-Saharan Africa, eight were in central Europe, eastern Europe, and central Asia, and five were in the high-income super-region. Seven countries and territories had a positive rate of natural increase between 2000 and 2009 followed by a negative rate of natural increase between 2010 and 2019: Bosnia and Herzegovina, Greece, Japan, North Macedonia, Poland, Portugal, and San Marino (figure 10 ). 

Northern Mariana Islands
section_name: Population

section_content: For list of collaborator affiliations see appendix 3.
section_name: Affiliations

section_content: Please see appendix 1 section 9 for more detailed information about individual author contributions to the research, divided into the following categories: managing the overall research enterprise; writing the first draft of the manuscript; primary responsibility for applying analytical methods to produce estimates; primary responsibility for seeking, cataloguing, extracting, or cleaning data; designing or coding figures and tables; providing data or critical feedback on data sources; developing methods or computational machinery; providing critical feedback on methods or results; drafting the manuscript or revising it critically for important intellectual content; and managing the estimation or publications process. Members of the core research team for this topic area had full access to the underlying data used to generate estimates presented in this article. All other authors had access to and reviewed estimates as part of the research evaluation process, which includes additional stages of formal review. The corresponding and senior authors had full access to the data in the study and final responsibility for the decision to submit for publication.
section_name: Contributors

section_name: Declaration of interests