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

Kinetic Study of Yellow Fever 17DD Viral Infection in Gallus gallus domesticus Embryos

Submitted by sandra infurna ([email protected]) on 2016-06-23T19:15:34Z No. of bitstreams: 1 pedropaulo_manso_etal_IOC_2016.PDF: 3837298 bytes, checksum: 6c380180580b1760d8c32cb01f332b2c (MD5)Approved for entry into archive by sandra infurna ([email protected]) on 2016-06-23T19:27:36Z (GMT) No. of bitstreams: 1 pedropaulo_manso_etal_IOC_2016.PDF: 3837298 bytes, checksum: 6c380180580b1760d8c32cb01f332b2c (MD5)Made available in DSpace on 2016-06-23T19:27:36Z (GMT). No. of bitstreams: 1 pedropaulo_manso_etal_IOC_2016.PDF: 3837298 bytes, checksum: 6c380180580b1760d8c32cb01f332b2c (MD5) Previous issue date: 2016Submitted by Angelo Silva ([email protected]) on 2016-07-07T11:16:48Z No. of bitstreams: 3 pedropaulo_manso_etal_IOC_2016.PDF.txt: 41790 bytes, checksum: 34550c22d039d8923094561748811b01 (MD5) pedropaulo_manso_etal_IOC_2016.PDF: 3837298 bytes, checksum: 6c380180580b1760d8c32cb01f332b2c (MD5) license.txt: 2991 bytes, checksum: 5a560609d32a3863062d77ff32785d58 (MD5)Approved for entry into archive by sandra infurna ([email protected]) on 2016-07-07T12:00:32Z (GMT) No. of bitstreams: 3 license.txt: 2991 bytes, checksum: 5a560609d32a3863062d77ff32785d58 (MD5) pedropaulo_manso_etal_IOC_2016.PDF: 3837298 bytes, checksum: 6c380180580b1760d8c32cb01f332b2c (MD5) pedropaulo_manso_etal_IOC_2016.PDF.txt: 41790 bytes, checksum: 34550c22d039d8923094561748811b01 (MD5)Made available in DSpace on 2016-07-07T12:00:32Z (GMT). No. of bitstreams: 3 license.txt: 2991 bytes, checksum: 5a560609d32a3863062d77ff32785d58 (MD5) pedropaulo_manso_etal_IOC_2016.PDF: 3837298 bytes, checksum: 6c380180580b1760d8c32cb01f332b2c (MD5) pedropaulo_manso_etal_IOC_2016.PDF.txt: 41790 bytes, checksum: 34550c22d039d8923094561748811b01 (MD5) Previous issue date: 2016Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Patologia. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Patologia. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Biologia Molecular de Flavivírus. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Patologia. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Patologia. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Patologia. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Patologia. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Patologia. Rio de Janeiro, RJ, Brasil / Universidade Federal do Estado do Rio de Janeiro. UNIRIO. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Biologia Molecular de Flavivírus. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Instituto de Tecnologia em Imunobiológicos. Laboratório de Tecnologia. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Biologia Molecular de Flavivírus. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Patologia. Rio de Janeiro, RJ, Brasil.Yellow fever continues to be an important epidemiological problem in Africa and South America even though the disease can be controlled by vaccination. The vaccine has been produced since 1937 and is based on YFV 17DD chicken embryo infection. However, little is known about the histopathological background of virus infection and replication in this model. Here we show by morphological and molecular methods (brightfield and confocal microscopies, immunofluorescence, nested-PCR and sequencing) the kinetics of YFV 17DD infection in chicken embryos with 9 days of development, encompassing 24 to 96 hours post infection. Our principal findings indicate that the main cells involved in virus production are myoblasts with a mesenchymal shape, which also are the first cells to express virus proteins in Gallus gallus embryos at 48 hours after infection. At 72 hours post infection, we observed an increase of infected cells in embryos. Many sites are thus affected in the infection sequence, especially the skeletal muscle. We were also able to confirm an increase of nervous system infection at 96 hours post infection. Our data contribute to the comprehension of the pathogenesis of YF 17DD virus infection in Gallus gallus embryos

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<p>Infected nervous tissue cells in the brain (A, B); Detail of infected cells in the brain congested with virus proteins (C,D). Yellow fever virus proteins in green, nuclei stained with DAPI in blue.</p

Confocal microscopy analysis of nervous system in <i>Gallus gallus domesticus</i> 96 hpi with Yellow Fever 17DD virus.

<p>Infected neurons in the spinal cord (A); infected cells in nerve bundles (B); infected cells in the dorsal root ganglion (C); infected fibroblastoid cells in the meninges (D). Yellow fever virus proteins in green, nuclei stained with DAPI in blue.</p

Confocal microscopy analysis of embryos of <i>Gallus gallus domesticus</i> 48 hpi with Yellow Fever 17DD virus.

<p>Mesenchymal cells in leg skeletal muscle (A) and in heart (B). Yellow fever virus in green, nuclei stained with DAPI in blue.</p

Brightfield microscopy analysis of <i>Gallus gallus domesticus</i> 72hpi with Yellow Fever 17DD virus.

<p>Apoptotic corpuscles in muscle bundles (A), and in the kidney tubular epithelium (B). Apoptotic nuclei are indicated by black arrow (→). Hematoxylin and eosin stain.</p

Detection of YF 17DD viral RNA by nested-PCR analysis.

<p>The RNA was extracted from formalin-fixed paraffin-embedded tissues. YF 17DD amplicons were assayed by means of agarose gel electrophoresis. The above figure show samples from a chicken embryo infected for 72 hours, and the bottom figure 96 hours. The lanes correspond to the following specimens: (1) empty; (2) and (3) head; (4) and (5) legs; (6) and (7) wings; lanes from (8) to (15) trunks; (16) and (17) vitelline membrane; (18) and (19) chorioallantoic membrane; from (20) to (23) negative control (water-inoculated animals). Even-numbered lanes indicate samples submitted to amplification of genomic RNA whereas odd-numbered lanes indicate samples submitted to amplification of the replicative intermediate RNA. The molecular length markers are indicated on the left of the figure. The black arrow indicates the 156bp amplicon obtained from the amplification of YF 17DD RNA.</p

Confocal microscopy analysis of <i>Gallus gallus domesticus</i> 72 hpi with Yellow Fever 17DD virus.

<p>Infected skeletal muscle bundles (A). The viral protein form clusters in the cytoplasm and follow the muscular striations; Infected muscular fibers and mesenchymal cells adhering to infected and uninfected muscular fibers (B); Infected muscular cells in the heart (C). Infected kidney tubular epithelium cells (D). Yellow fever virus proteins in green, nuclei stained with DAPI in blue and desmin in red.</p

Brightfield microscopy analysis of <i>Gallus gallus domesticus</i> 96hpi with Yellow Fever 17DD virus.

<p>Apoptotic bodies in (A) muscular bundles; (B) heart cells; (C) kidney tubular epithelium; (D) gizzard muscle; (E) lung parenchyma; and (F) brain cells. Apoptotic nuclei are indicated by black arrow (→). Hematoxylin and eosin stain.</p