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

Discutindo a educação ambiental no cotidiano escolar: desenvolvimento de projetos na escola formação inicial e continuada de professores

A presente pesquisa buscou discutir como a Educação Ambiental (EA) vem sendo trabalhada, no Ensino Fundamental e como os docentes desta escola compreendem e vem inserindo a EA no cotidiano escolar., em uma escola estadual do município de Tangará da Serra/MT, Brasil. Para tanto, realizou-se entrevistas com os professores que fazem parte de um projeto interdisciplinar de EA na escola pesquisada. Verificou-se que o projeto da escola não vem conseguindo alcançar os objetivos propostos por: desconhecimento do mesmo, pelos professores; formação deficiente dos professores, não entendimento da EA como processo de ensino-aprendizagem, falta de recursos didáticos, planejamento inadequado das atividades. A partir dessa constatação, procurou-se debater a impossibilidade de tratar do tema fora do trabalho interdisciplinar, bem como, e principalmente, a importância de um estudo mais aprofundado de EA, vinculando teoria e prática, tanto na formação docente, como em projetos escolares, a fim de fugir do tradicional vínculo “EA e ecologia, lixo e horta”.Facultad de Humanidades y Ciencias de la Educació

Personal Exposure and Inhaled Dose Estimation of Air Pollutants during Travel between Albany, NY and Boston, MA

Out of eight deaths caused worldwide, one death is caused due to air pollution exposure, making it one of the top global killers. Personal exposure measurement for real-time monitoring has been used for inhaled dose estimation during various modes of workplace commuting. However, dose-exposure studies during long commutes are scarce and more information on inhaled doses is needed. This study focuses on personal exposures to size-fractionated particulate matter (PM1, PM2.5, PM4, PM7, PM10, TSP) and black carbon (BC) inside a bus traveling more than 270 kms on a highway between Albany, NY and Boston, MA. Measurements were also made indoors, outdoors, and while walking in each city. Mean PM (PM1, PM2.5, PM4, PM7, PM10, TSP) and mean BC concentrations were calculated to estimate the inhaled exposure dose. The highest average PM2.5 and PM10 exposures concentrations were 30 ± 12 and 111 ± 193 µg/m3, respectively, during Boston to Albany. Notably, personal exposure to BC on a bus from Albany to Boston (5483 ± 2099 ng/m3) was the highest measured during any commute. The average inhaled dose for PM2.5 during commutes ranged from 0.018 µg/km to 0.371 µg/km. Exposure concentrations in indoor settings (average PM2.5 = 37 ± 55 µg/m3, PM10 = 78 ± 82 µg/m3, BC = 5695 ± 1774 ng/m3) were higher than those in outdoor environments. Carpeted flooring, cooking, and vacuuming all tended to increase the indoor particulate level. A high BC concentration (1583 ± 1004 ng/m3) was measured during walking. Typical concentration profiles in long-haul journeys are presented

A review of environmental occurrence, toxicity, biotransformation and biomonitoring of volatile organic compounds

Volatile organic compounds (VOCs) encompass hundreds of high production volume chemicals that have been used in a wide range of household and industrial products. Widespread use of products that contain VOCs resulted in their ubiquitous occurrence in the environment, with elevated concentrations frequently found in indoor environments. Human exposure to VOCs is pervasive and has been a topic of concern, due to the mutagenic, neurotoxic, genotoxic, and carcinogenic potentials of these chemicals. Although several previous articles described toxic effects of VOCs, relatively less is known on their human exposure and body burdens. VOCs have been determined in human breath condensate, blood, feces, and urine. This review updates the information on the environmental occurrence, toxicity, sources and pathways of human exposure, metabolism and elimination, and biomonitoring of exposure to VOCs. Indoor air is a major source of human exposure to VOCs. Higher atmospheric concentrations of VOCs have been reported in Asian countries than in North American and European countries. Elevated concentrations of four widely studied VOCs namely, benzene, toluene, ethylbenzene and xylene (BTEX) were reported in air from newly constructed or renovated homes (1.3–350 μg/m3) and e-waste workshops (2.45–3,10,000 μg/m3). BTEX were also found in consumer products such as shoe polish, whiteout, leather cleaner and ink at notable concentrations (e.g., ~92,600 μg/g). Traditional methods of exposure assessment of VOCs entailed measurement of these chemicals in indoor air and determination of inhalation exposure dose. SStudies on VOC exposure assessment mainly focused on occupationally exposed individuals. Recent developments in biomonitoring of urinary metabolites of VOCs present accurate assessment of exposures and internal body burdens. Biomonitoring studies of VOCs offer novel biomarkers for the assessment of airway inflammation, lung injury, neurological disorders, immune dysfunction and cancers in populations. Considering the very high production volume (at billions of pounds annually), known toxicity, and widespread human exposures, significance of VOCs in eliciting adverse health effects in populations will be a subject of increasing public health concern for years to come

Spatial distribution of dissolved neodymium and ε_Nd in the Bay of Bengal: role of particulate matter and mixing of water masses

The concentration and isotope composition of dissolved Nd have been measured in the water column along an 87°E transect (GIO1 section of International GEOTRACES Program) in the Bay of Bengal (BoB) to investigate the effect of water mass mixing and Nd release from particulate matter in determining these properties. The concentration of Nd in surface waters of the BoB shows a North–South decreasing non-linear trend (∼46 to ∼22 pmol/kg) with salinity, whereas its depth profiles typically show a high value in surface waters, a minimum (∼15 to ∼23 pmol/kg) in the shallow subsurface (∼50–200 m), followed by a gradual increase with depth. The Nd concentration of BoB waters is generally higher than that in nearby oceanic basins. On the other hand, the εNd values in the BoB are less radiogenic compared to those reported for other regions of the global oceans (except Baffin Bay, the North Atlantic Subpolar Gyre and the Niger delta margin), and show a greater variation in the upper water column. Surface waters of the southernmost profile (∼6°N) show a more radiogenic εNd value ∼−8, which decreases to −15 in the northernmost profile (∼20°N), close to the values for dissolved and particulate phases of the Ganga–Brahmaputra (G–B) Rivers. This latitudinal trend is most likely a result of variations in mixing proportion between the more radiogenic Indonesian Throughflow surface waters (IW) and unradiogenic BoB low salinity water; the former’s signature being more clearly discernible in surface waters of the two southernmost profiles (∼6°N and ∼8.5°N). Attempts to balance the Nd budget in the water column based on an inversion model, suggest that in addition to water masses other source(s) is required, the strength of which is estimated to vary from 1% to 65% of the measured Nd concentration. The calculations also show that the εNd of this additional source(s) has to be in the range of ∼−16 ± 2, typical of G–B river sediments. These observations, coupled with the North–South distribution of dissolved Nd and εNd, indicate that this additional source is release from particulate phases supplied by the G–B river system. The calculations also bring out the presence of “hot-spots” of Nd release (excess Nd) near the sediment–water interface along the northern slope of the bay, indicating supply of Nd from continental margin sediments. This study underscores the significant role of dissolved/particulate Nd from the Ganga–Brahmaputra river system in contributing to the dissolved Nd budget of the global oceans

Distribution of Laboratory Parameters in Trauma Population

Background: Biochemical laboratory investigations help plan optimum management and communication in short- as well as long-term outcome to trauma victims. Objective: To assess the status of real-time values of biochemical laboratory investigations of different trauma patients and their association with overall mortality. Materials and Methods: Data based on prospective, observational registry of “Towards Improved Trauma Care Outcomes” (TITCO) from four Indian city hospitals. Hemoglobin, hematocrit, random blood sugar, blood urea nitrogen (BUN), and serum creatinine of patients on admission were recorded. Logistic regression was applied with all biochemical investigation as independent variable and overall mortality as dependent variable. Results: Among 17047 trauma patients, 3456 with available laboratory result details were considered for this study. Overall mortality was 20% (range 14%–21%). For the higher laboratory results, value mortality was 21%–70%, with highest death (70%) for higher hemoglobin patients, followed by hematocrit (44%) and then creatinine (43%). Odds of high hemoglobin compared to normal were 15.20; odds of higher and lower of normal creatinine were 3.80 and 1.65 and for BUN were 2.17 and 1.92, respectively. Gender-wise significant difference was in overall female mortality (29%)% compared males (18%). Similar differences were replicated with results of each laboratory tests. Conclusion: The study ascertained the composite additional explanatory values of laboratory parameters in predicting outcome among injured patients in our population from Indian settings