Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Wide-ranging differences among the relative body weight gains in Asian seabass fed pelleted feeds compared to control.

<p>Total percentage of body weight gain after the consumption of three different pelleted feeds (Feeds B-D) relative to frozen baitfish (Feed A) during a two-month period (107–168 days post-hatch). Post-ANOVA Tukey's multiple comparison test was performed to compare the mean values of every group. Error bars are plotted with 95% confidence intervals from the mean. Significantly different means are noted with *—P_adj < 0.05; **—P_adj < 0.01; ***—P_adj < 0.001; and ****—P_adj < 0.0001.</p

Increased lipid deposition observed in two of the three pellet-fed groups compared to controls with the exception of Group C.

<p>Panel (A-D): cross sections of posterior liver stained with H/E of Groups (A-D) respectively; Green arrows = hepatocytes containing lipid droplet. Panel (a-d); 40x magnification, scale bar = 50μm. Panel E: Quantitative analysis of mean hepatocyte diameter detected within Groups (A-D); Post ANOVA Dunnett’s multiple comparisons test was performed between the mean of each group with the mean of the control group. Means significantly different from Group A are noted with (*—P_adj < 0.05; **—P_adj < 0.01; ***—P_adj < 0.001; ****—P_adj < 0.0001).</p

Positive correlation of fatty acids between fillets of groups and their respective feeds.

<p>Only area % of TFA of large-sized pelleted feeds were used for correlation. Pearson’s correlation coefficient (r) was used to determine the correlation relationship. TFA = Total Fatty Acids.</p

Gene Set Enrichment Analysis (GSEA) of KEGG pathway comparing pellet-fed groups (B, C and D) against baitfish-fed control (Group A).

<p>By using <i>Danio rerio</i> as reference model, (A) genes that are involved in Biosynthesis of Unsaturated fatty acid pathway, (B) genes that are involved in steroid biosynthesis pathway were compared. Significantly enriched genes are noted with *—P < 0.05. Out of those *, genes that are differentially expressed as well are marked with *—P_fdr < 0.05. Error bars are mean ± SEM.</p

Different sets of significantly expressed transcripts (SETs) with limited overlaps were found when the liver-derived transcriptome of pellet-fed groups (B-D) were compared.

<p>(A) Venn diagram showing 36 SETs uniformly different in all three commercial vs. commercial feed comparisons. (B) Heatmap showing the complexity of hepatic expression caused by having different dietary intake. Different clusters of genes are represented by symbols (!, @, #, $, %, ^).</p

Gene ontology enrichment of significantly up-regulated transcripts between the commercial groups (B-D).

<p>Significantly up-regulated transcripts of were analyzed by singular enrichment analysis (agriGO). Results of individual comparisons were presented in a three by three plot. Out of the many ‘Biological Processes’ found to be significantly enriched, those related to metabolism and growth were highlighted and compared upon.</p

Formation of unique clusters of fillet fatty acid profile after consuming different pelleted feeds (Feed B-D) and frozen baitfish (Feed A).

<p>The PCA plot generated by Partek® Genomic Suite (v6.6) is based on covariance matrix of 21 fatty acids (area % of total fatty acids) per sample. Coloured balls represent individual samples fed with different feed types.</p

The gastrointestinal tract of Asian seabass is influenced by the different feeds.

<p>External circumference of serosa (Panel A), mucosal height (Panel B), muscularis layer thickness (Panel C) and goblet cell number (Panel D) were measured from sections of the mid gut of 168 dph-old Asian seabass fed frozen baitfish (Group A) or pelleted commercial feeds (Groups B-D) for 61 days. Values are represented as percentages over control (A) value. Post ANOVA Dunnett’s multiple comparisons test was performed between the mean of each group with the mean of the control group. Means significantly different from Group A are noted with *—P_adj < 0.05; **—P_adj < 0.01; ***—P_adj < 0.001; or ****—P_adj < 0.0001. Representative examples of the mid-gut histological sections are shown on the right (Panels E&G–Group A; F&H–Group C). Panel E: Blue line = External circumference of serosa (S); Panel G: red = high of Mucosa (mu), purple = Thickness of muscularis layer (m), green = Goblet cells (gc); Panels E&F - 5x magnification; Panels G&H - 10x magnification. Panels E&H; scale bar = 100μm.</p

Schematic overview of the experimental design.

<p>Five large tanks containing six individual canvases of two metric ton volume each per tank were set up. Asian seabass siblings (103 dph) from a pairwise cross performed with two brooders selected for increased growth rate were tagged, measured, photographed and placed into the canvases at 75 individuals per canvas initial density. Following a short adaptation period, frozen baitfish (Feed A, control) and five commercial pelleted growout feeds (Feed B-F) were fed to their respective groups at an average body weight (BW)/day for 61 days; Group A: 6.1%; Group B, C, D, E: 2.2%; Group F: 1.8%. The amount of feed was adjusted on a weekly basis based on BW measurements of 15 individuals per canvas. At 139 dph, all the fish were measured and 25 individuals were randomly removed to reduce density. During the 147–151 dph period, gradual change in pellet size fed to fishes in groups B, C, D and F. At the end of the experiment all individuals were measured and photographed and samples were collected for the different analyses. [Only fish from Groups A-D were subjected to detailed analysis, whereas the other two groups (E&F) were excluded due to technical reasons.]</p