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

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

Intrapartum-related neonatal encephalopathy incidence and impairment at regional and global levels for 2010 with trends from 1990.

BACKGROUND: Intrapartum hypoxic events ("birth asphyxia") may result in stillbirth, neonatal or postneonatal mortality, and impairment. Systematic morbidity estimates for the burden of impairment outcomes are currently limited. Neonatal encephalopathy (NE) following an intrapartum hypoxic event is a strong predictor of long-term impairment. METHODS: Linear regression modeling was conducted on data identified through systematic reviews to estimate NE incidence and time trends for 184 countries. Meta-analyses were undertaken to estimate the risk of NE by sex of the newborn, neonatal case fatality rate, and impairment risk. A compartmental model estimated postneonatal survivors of NE, depending on access to care, and then the proportion of survivors with impairment. Separate modeling for the Global Burden of Disease 2010 (GBD2010) study estimated disability adjusted life years (DALYs), years of life with disability (YLDs), and years of life lost (YLLs) attributed to intrapartum-related events. RESULTS: In 2010, 1.15 million babies (uncertainty range: 0.89-1.60 million; 8.5 cases per 1,000 live births) were estimated to have developed NE associated with intrapartum events, with 96% born in low- and middle-income countries, as compared with 1.60 million in 1990 (11.7 cases per 1,000 live births). An estimated 287,000 (181,000-440,000) neonates with NE died in 2010; 233,000 (163,000-342,000) survived with moderate or severe neurodevelopmental impairment; and 181,000 (82,000-319,000) had mild impairment. In GBD2010, intrapartum-related conditions comprised 50.2 million DALYs (2.4% of total) and 6.1 million YLDs. CONCLUSION: Intrapartum-related conditions are a large global burden, mostly due to high mortality in low-income countries. Universal coverage of obstetric care and neonatal resuscitation would prevent most of these deaths and disabilities. Rates of impairment are highest in middle-income countries where neonatal intensive care was more recently introduced, but quality may be poor. In settings without neonatal intensive care, the impairment rate is low due to high mortality, which is relevant for the scale-up of basic neonatal resuscitation

Single nucleotide variant detection in Jaffrabadi buffalo (<i>Bubalus bubalis</i>) using high-throughput targeted sequencing

<div><p>The water buffalo is among the most important livestock species of southern Asia, contributing greatly to the ecosystem and rural livelihood of the region. The identification of large-scale single nucleotide polymorphisms in this species would greatly facilitate our understanding of the genetic basis of economically important traits such as milk production, fertility traits and general health traits. The present study investigated the cost-effective method of exome capture and single nucleotide variant (SNV) identification from genomic DNA of Jaffrabadi buffalo using biotin-labelled cDNA as probes. Sequencing of enriched fragments generated 608 Mb of data, which was mapped to a <i>Bos taurus</i> genome assembly followed by variant calling and annotation. Furthermore, 393 coding SNVs were identified, leading to 143 non-synonymous substitutions (nsSNVs) in 75 genes. Of the 75 nsSNV-containing genes, four matched the genes that have previously been reported to be potentially associated with economically important traits such as milk production and meat production. Furthermore, functional annotation using gene ontology (GO) enrichment identified categories such as glutamate receptor activity (GO: 0008066) enriched in the fertility trait samples. These results provide a framework for the application of cost-effective methods of target capture in SNV detection from non-model organisms such as the water buffalo.</p></div

Functional and Structural Connectivity Between the Perigenual Anterior Cingulate and Amygdala in Bipolar Disorder

Objective: Abnormalities in the morphology and function of two gray matter structures central to emotional processing, the perigenual anterior cingulate cortex (pACC) and amygdala, have consistently been reported in bipolar disorder (BD). Evidence implicates abnormalities in their connectivity in BD. This study investigates the potential disruptions in pACC-amygdala functional connectivity and associated abnormalities in white matter that provides structural connections between the two brain regions in BD. Methods: Thirty-three individuals with BD and 31 healthy comparison subjects (HC) participated in a scanning session during which functional magnetic resonance imaging (fMRI) during processing of face stimuli and diffusion tensor imaging (DTI) were performed. The strength of pACC-amygdala functional connections was compared between BD and HC groups, and associations between these functional connectivity measures from the fMRI scans and regional fractional anisotropy (FA) from the DTI scans were assessed. Results: Functional connectivity was decreased between the pACC and amygdala in the BD group compared with HC group, during the processing of fearful and happy faces (p < .005). Moreover, a significant positive association between pACC-amygdala functional coupling and FA in ventrofrontal white matter, including the region of the uncinate fasciculus, was identified (p < .005). Conclusion: This study provides evidence for abnormalities in pACC-amygdala functional connectivity during emotional processing in BD. The significant association between pACC-amygdala functional connectivity and the structural integrity of white matter that contains pACC-amygdala connections suggest that disruptions in white matter connectivity may contribute to disturbances in the coordinated responses of the pACC and amygdala during emotional processing in BD.National Institute of Mental Health (NIH)[R01MH69747]National Institute of Mental Health (NIH)National Institute of Mental Health (NIH)National Institute of Mental Health (NIH)[R01MH070902]National Institute of Mental Health (NIH)[T32MH1427G]National Institute of Mental Health (NIH)Clinical and Translational Science Award (CTSA), NIH National Center[UL1 RR0249139]Clinical and Translational Science Award (CTSA), NIH National CenterDepartment of Veterans Affairs Career Development (HPB), Merit Review (HPB) and Research Enhancement Award Program (REAP)Department of Veterans Affairs Career Development (HPB), Merit Review (HPB) and Research Enhancement Award Program (REAP)National Alliance for Research in Schizophrenia and Depression (Great Neck, New York)National Alliance for Research in Schizophrenia and Depression (Great Neck, New York)Attias Family FoundationAttias Family FoundationMarcia Simon KaplanMarcia Simon KaplanEthel F. Donaghzre Women`s Investigator ProgramEthel F. Donaghzre Women`s Investigator ProgramKlingenstein FoundationKlingenstein FoundationHoward Hughes Medical Institute FellowshipHoward Hughes Medical Institute Fellowshi