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

Phenotypic characteristics and sexual behavior of Sennar Jacks ( Equus asinus)

This study was carried out with an objective of evaluating the breeding soundness and sexual behavior of Sennar jackasses. The physical characteristics such as the body weight, height at wither, body condition, and age of the jacks was measured and recorded. The characteristics of sexual behavior as determined by score of the sex derive, and the time taken for erection, first mount and intromission were assessed in the presence of estrous Jennies and recorded before semen collection. The body weight of the jacks range between119 and 380 kg, with mean body condition score of 7 for the breeding and 4.8 for the working jacks. The mean (±SE) of height at wither, the chest girth and the head-tail length were118±5.54 cm, 124.4±2.2 cm, 150.3±1.0 cm, respectively. The frequency of mounting without erection and with erection, time to full erection, time to ejaculation and number of ejaculatorythrust were in the order of 4.62±1.93 and 1.9±0.88, 13.45±7.02 minutes, 23.03±7.78 minutes, 4.21±0.61, respectively. Libido score was mostly (89.5%) strong during the 160 collections. The longest time to ejaculation was 58 minutes. The most frequent sexual behavior was sniffing of the vulva, flehmen, and mounting with or without erection. Flehmen responses and mounts without erection greatly varied among the jacks.The most notable difference from Abyssinian donkeys was the libido, which was relatively stronger in Sennar donkeys. However, they usually took longer time for ejaculation, which is also a common behavior among donkeys as compared to stallions. This study confirms that body characteristics of Sennar donkeys with respect to size are better than other donkey types, and semen collection and AI are feasible procedures for breed improvement in Sennar donkeys.Keywords: Sennar donkeys, Body morphometry, Sexual behavior, Jacks, Gonda

Joint Angle-Frequency Estimation Using Multi-Resolution Esprit

Multi-resolution ESPRIT is an extension of the ESPRIT direction finding algorithm to antenna arrays with multiple baselines. A short (half wavelength) baseline is necessary to avoid aliasing, a long baseline is preferred for accuracy. The MR-ESPRIT algorithm allows to combine both estimates. The same algorithm can be used for multi-resolution frequency estimation, by combining two different sampling frequencies. We show how this can be used to construct a joint angle-frequency estimator. 1 INTRODUCTION Since its derivation in 1983, the ESPRIT algorithm [1] has been used for direction-of-arrival estimation, harmonic analysis, frequency estimation, delay estimation, and combinations thereof. In essence, the algorithm makes use of a single shift invariance structure present in the array response vector a(`), where ` = e j¯ , and ¯ is a phase shift to be estimated. In narrowband direction-of-arrival estimation, the phase shift is due to the difference in arrival times of the wavefront a..

Experimental Analysis Of Antenna Coupling For High-Resolution Doa Estimation Algorithms

In array signal processing, high-resolution parameter estimation algorithms are known to be sensitive to phase, amplitude and mutual coupling distortions. In this paper, we present experimental results showing that these high-resolution estimation methods can achieve their theoretically expected performances only if the non-ideal array behavior is appropriately modelled and compensated. Using a simple previously proposed distortion compensation technique, we show that it is possible to improve the estimation error considerably, and that actual array response modelling and compensation is indeed an essential element of any high-resolution DOA estimation method. 1. INTRODUCTION Many computer simulation results confirm the superior performance of high resolution DOA estimation algorithms such as ESPRIT [1,2] and MUSIC [3,4]. In actual arrays, distortions caused by non ideal behaviors (antenna gain, phase and mutual coupling errors) degrade the expected performances of these methods drast..

Economic, environmental and social indicators of sustainability among smallholders in Ethiopia: Based on tool for agroecological performance evaluation data

This data article is a result of research conducted by a multidisciplinary team of researchers with the aim of analyzing agroecological transition and performance of agroecology in Ethiopia. It was conducted in four districts of Oromia and Southern Nations, Nationalities and People's (SNNP) regional states - Fedis district (East Hararghe Zone) and Miesso district (West Hararghe Zone) from the Oromia region, and Kindo Koysha district (Wolaita Zone) and Meskan district (Gurage Zone) of SNNP region. The rationale behind generating this dataset lies on the fact that there is scanty empirical evidence on the multidimensional performance of agroecology in the country. Available evidence only provides data on limited indicators of sustainability. Hence, there is a lack of comprehensive data on the economic, environmental and social indicators of sustainability and agroecological transition in the context of smallholder farming systems in the country. To fill this gap, the Food and Agriculture Organization of the United Nations (FAO) commissioned a consultancy project that employed the Tool for Agroecological Performance Evaluation (TAPE) to assess several dimensions and indicators of agroecological transitions and generate globally comparable data. A random sample of 619 farms were selected from 12 Kebeles (i.e., the lowest administrative unit), and trained enumerators gathered primary data based on a modified TAPE questionnaire using Kobo Toolbox. Participation of smallholders was on a voluntary basis and informed consent was obtained from the respondents. The survey questionnaire contained information on basic socio-economic and demographic characteristics, access to services and infrastructure, livelihood and Income-Generating Activities (IGAs), social and ecological indicators. Data on the 10 elements of agroecology was also collected. The collected data were entered into a STATA software, cleaned and analyzed through descriptive and inferential statistics. The outputs were summarized in Tables, Charts and Graphs. Since the data contained in this data article are disaggregated by study district, categories of agroecological transition, production typology and land size groups, this can foster the promotion of specific projects and programs that can address expressed needs of smallholder farmers. It can also facilitate agro-ecological based implementation of development interventions to encourage agroecological transition, sustainable development and food systems. The dataset can also enable researchers, practitioners and other decision-makers to make comparative analysis on the economic, environmental and social dimensions of sustainability. The analyzed data is provided in this data article. The raw data used to prepare figures is provided as a supplementary material. A copy of the questionnaire, raw dataset, and description of variables are available online on Mendeley Data

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

BackgroundRegular, 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.MethodsThe 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.FindingsThe 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.InterpretationLong-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