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

Nidoviruses in Aquatic Organisms - Paradigm of a Nascent Concern

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Nidoviruses in Aquatic Organisms - Paradigm of a Nascent Concern

The extraordinarily genetically complex Nidovirus is an enveloped, single-stranded RNA virus. This diversified group of viruses has a higher degree of RNA recombination due to its large nested arrangements of sub-genomic mRNA. This is considered to be crucial for accelerating the evolutionary process and pathogenesis of the virus resulting in enhancing their adaptation to rapidly changing environment as well as upgrading them to infect a broad range of host. In general, Nidoviruses are an alarming group of pathogens of terrestrial animals but they have, of late, started affecting a wide range of aquatic animals such as fish and shellfish since the last 20 years. The common Nidoviruses reported from aquatic animals are Bafinivirus i.e. White bream virus, Fathead minnow virus, Chinook salmon bafinivirus from fish; Yellow head virus, Gill-associated virus from shrimp; Eriocheir sinensis ronivirus from crab; Harbour seal coronavirus, Beluga whale coronavirus, Bottlenose dolphin coronavirus from marine mammals. The clinical symptoms associated with this virus infection are mainly noticed in the skin, eye, anterior kidney, spleen and liver whereas gill, lymphoid organ, haemopoietic tissue, midgut, cuticular epithelium, heart, haemocytes in shrimps and ecdysal gland in crabs are the primary target organs of Nidoviruses. It has gained considerable importance in the research area of aquatic animal health with the discovery of first fish Nidovirus in cyprinids but the prevalence, host range, route of infection, mode of transmission and diagnosis of the virus are required to be explored in detail to evaluate the significant risk of Nidoviruses and to control the future outbreak of diseases in aquatic animals.</p

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Not AvailableField experiments were conducted with four levels of irrigation and nitrogen on wheat for 2 years (2009–2010 and 2010–2011) to quantify and predict the crop water status using hyperspectral remote sensing. Hyperspectral reflectance in 350–2500 nm range was recorded at five growth stages. Based on highest correlation between relative leaf water content (RLWC) and reflectance in five water bands, the booting stage was identified as the most suitable stage for water stress evaluation. Ten hyperspectral water indices were calculated using the first year booting stage reflectance data and prediction models for RLWC and equivalent water thickness (EWT) based on these ten indices were developed. The prediction models for RLWC based on moisture stress index (MSI), normalized difference infrared index (NDII), normalized difference water index 1640 (NDWI) and normalized multiband drought index (NMDI) were identified as the most precise and accurate models as indicated by different validation statistics. The models developed for EWT based on water band index1640 (WBI), MSI, NDWI1640and NMDI were found to be most suitable and accurate. These indices were found to be insensitive to N stress treatments indicating their ability to detect water deficiency as the cause of plant stress. Thus, the study identified four hyperspectral water indices to assess the wheat crop water status at booting stage and developed their respective predictive models.Not Availabl

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Not AvailableAll the morphometric variables of Amblyaster sirm show significant correlation between them except head lenght with showed correlation coefficient of less than 0.80 with all other morphometric variables. Liner regression between standard length and other morphometric variables showed R2 value of > 0.9 except for HL and BD were the values dropped below 0.75 Pairwise linear regression of log transformed morphometric varibales with log-standard length shows negative allometric growht for HL and positive allometric growth for body depth with standard length. Descriptive statistics for morphometric and meristic characters were found to be in agreement with previous work. Spearman rank correlation shows insignificant relation between meristic characters and standard length. L-W relationship for males and females were established as W=0.000007L3054 and W=0.000001L3370 respectively. Relative conditon factor (Kn) for female (mean=) were found to be higher than males (mean=) throughout the year with no major seasonal fluctuation.Not Availabl

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