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

Economic Viability and Socio-Environmental Impacts of Solar Home Systems for Off-Grid Rural Electrification in Bangladesh

This study conducted a questionnaire-led survey to explore the financial feasibility and socio-environmental impacts of stand-alone solar home systems (SHS) through stratified random sampling. Based on the above consideration, fifteen cases of studies of various watt peak (Wp) capacities have been investigated to evaluate the economic viability of solar home systems. The results revealed that most of the cases have positive net present value (NPV) and low payback periods, with an internal rate of return (IRR) value ranging from 16% to 131%, which signifies a high rate of investment exchange. Solar home systems are economically profitable for micro-enterprises and households with low-income generation activities as opposed to the households using it only for lighting. The study found that solar home systems with a capacity above 30 Wp are the most economically viable option, which can also avoid 6.15 to 7.34 tonnes of CO2 emissions during the 20 years of life-cycle, while providing different applications including lighting, recreation, information, health, and economic benefits

First field evaluation of the optimized CE marked Abbott protocol for HIV RNA testing on dried blood spot in a routine clinical setting in Vietnam

International audienceViral load (VL) monitoring of HIV-infected patients in decentralized areas is limited due to logistic constraints. Dried Blood Spots (DBS) offer the opportunity to collect samples in remote area which can be easily transferred and tested at a central laboratory. The MOVIDA (Monitoring Of Viral load In Decentralized Area) project evaluated the performance of VL measurements on DBS using the new CE marked optimized Abbott protocol

Correlation between plasma and DBS viral load measurements.

<p>The dashed line represents the threshold of 3 log copies/mL defining virological failure. The solid line represents the line of slope 1 displaying perfect concordance. Correlation: 0.95 (p<0.001).</p

Bland-Altman analysis to evaluate concordance between plasma and DBS viral load measurements.

<p>The solid line is the mean difference between plasma and DBS viral load measurements and the dashed line is 95% confidence interval of the difference.</p

Sensitivity and specificity of viral load measurements on DBS compared to plasma at the threshold of 1000 copies/mL.

<p>Sensitivity and specificity of viral load measurements on DBS compared to plasma at the threshold of 1000 copies/mL.</p

Distribution of plasma and DBS VL measurements.

<p>Distribution of plasma and DBS VL measurements.</p

Aetiology and Potential Animal Exposure in Central Nervous System Infections in Vietnam.

An estimated 73% of emerging infections are zoonotic in origin, with animal contact and encroachment on their habitats increasing the risk of spill-over events. In Vietnam, close exposure to a wide range of animals and animal products can lead to acquisition of zoonotic pathogens, a number of which cause central nervous system (CNS) infections. However, studies show the aetiology of CNS infections remains unknown in around half of cases. We used samples and data from hospitalised patients with CNS infections, enrolled into the Vietnam Initiative on Zoonotic Infections multicentre study, to determine the association between aetiology and animal contact including those in whom the cause was unknown. Among 933 patients, a pathogen or an antibody response to it was identified in 291 (31.2%, 95% CI 28.3-34.3%). The most common pathogens were Streptococcus suis (n = 91 (9.8%, 8.0-11.9%)) and Japanese encephalitis virus (JEV) (n = 72 (7.7%, 6.1-9.7%)). Commonly reported animal contact included keeping, raising or handling (n = 364 (39.0%, 35.9-42.2%)) and handling, cooking or consuming raw meat, blood or viscera in the 2 weeks prior to symptom onset (n = 371 (39.8%, 36.6-43.0%)), with the latter most commonly from pigs (n = 343 (36.9%, 33.8-40.1%). There was no association between an unknown aetiology and exposure to animals in a multivariate logistic regression. Further testing for unknown or undetected pathogens may increase diagnostic yield, however, given the high proportion of zoonotic pathogens and the presence of risk factors, increasing public awareness about zoonoses and preventive measures can be considered