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

Host HLA B*allele-associated multi-clade Gag T-cell recognition correlates with slow HIV-1 disease progression in antiretroviral therapy-naïve Ugandans.

BACKGROUND: Some HIV infected individuals remain asymptomatic for protracted periods of time in the absence of antiretroviral therapy (ART). Virological control, CD4 T cell loss and HIV-specific responses are some of the key interrelated determinants of HIV-1 disease progression. In this study, possible interactions between viral load, CD4 T cell slopes, host genetics and HIV-specific IFN-gamma responses were evaluated in chronically HIV-1-infected adults. METHODOLOGY/PRINCIPAL FINDINGS: Multilevel regression modeling was used to stratify clade A or D HIV-infected individuals into disease progression groups based on CD4 T cell slopes. ELISpot assays were used to quantify the frequency and magnitude of HIV-induced IFN-gamma responses in 7 defined rapid progressors (RPs) and 14 defined slow progressors (SPs) at a single time point. HLA typing was performed using reference strand conformational analysis (RSCA). Although neither the breadth nor the magnitude of the proteome-wide HIV-specific IFN-gamma response correlated with viral load, slow disease progression was associated with over-representation of host immunogenetic protective HLA B* alleles (10 of 14 SPs compared to 0 of 7; p = 0.004, Fisher's Exact) especially B*57 and B*5801, multiclade Gag T-cell targeting (71%, 10 of 14 SPs compared to 14%, 1 of 7 RPs); p = 0.029, Fisher's Exact test and evident virological control (3.65 compared to 5.46 log10 copies/mL in SPs and RPs respectively); p<0.001, unpaired student's t-test CONCLUSIONS: These data are consistent with others that associated protection from HIV disease with inherent host HLA B allele-mediated ability to induce broader Gag T-cell targeting coupled with apparent virological control. These immunogenetic features of Gag-specific immune response which could influence disease progression may provide useful insight in future HIV vaccine design

Current Challenges in the Management of Sepsis in ICUs in Resource-Poor Settings and Suggestions for the Future

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IFNγ/IL-10 Co-producing Cells Dominate the CD4 Response to Malaria in Highly Exposed Children

<div><p>Although evidence suggests that T cells are critical for immunity to malaria, reliable T cell correlates of exposure to and protection from malaria among children living in endemic areas are lacking. We used multiparameter flow cytometry to perform a detailed functional characterization of malaria-specific T cells in 78 four-year-old children enrolled in a longitudinal cohort study in Tororo, Uganda, a highly malaria-endemic region. More than 1800 episodes of malaria were observed in this cohort, with no cases of severe malaria. We quantified production of IFNγ, TNFα, and IL-10 (alone or in combination) by malaria-specific T cells, and analyzed the relationship of this response to past and future malaria incidence. CD4⁺ T cell responses were measurable in nearly all children, with the majority of children having CD4⁺ T cells producing both IFNγ and IL-10 in response to malaria-infected red blood cells. Frequencies of IFNγ/IL10 co-producing CD4⁺ T cells, which express the Th1 transcription factor <i>T-bet</i>, were significantly higher in children with ≥2 prior episodes/year compared to children with <2 episodes/year (<i>P</i><0.001) and inversely correlated with duration since malaria (<i>Rho</i> = −0.39, <i>P</i><0.001). Notably, frequencies of IFNγ/IL10 co-producing cells were not associated with protection from future malaria after controlling for prior malaria incidence. In contrast, children with <2 prior episodes/year were significantly more likely to exhibit antigen-specific production of TNFα without IL-10 (<i>P</i> = 0.003). While TNFα-producing CD4⁺ T cells were not independently associated with future protection, the absence of cells producing this inflammatory cytokine was associated with the phenotype of asymptomatic infection. Together these data indicate that the functional phenotype of the malaria-specific T cell response is heavily influenced by malaria exposure intensity, with IFNγ/IL10 co-producing CD4⁺ T cells dominating this response among highly exposed children. These CD4⁺ T cells may play important modulatory roles in the development of antimalarial immunity.</p></div

Genome Sequence of the Tsetse Fly (Glossina morsitans): Vector of African Trypanosomiasis

Tsetse flies are the sole vectors of human African trypanosomiasis throughout sub-Saharan Africa. Both sexes of adult tsetse feed exclusively on blood and contribute to disease transmission. Notable differences between tsetse and other disease vectors include obligate microbial symbioses, viviparous reproduction, and lactation. Here, we describe the sequence and annotation of the 366-megabase Glossina morsitans morsitans genome. Analysis of the genome and the 12,308 predicted protein–encoding genes led to multiple discoveries, including chromosomal integrations of bacterial (Wolbachia) genome sequences, a family of lactation-specific proteins, reduced complement of host pathogen recognition proteins, and reduced olfaction/chemosensory associated genes. These genome data provide a foundation for research into trypanosomiasis prevention and yield important insights with broad implications for multiple aspects of tsetse biology