13 research outputs found

    [Molecular genetic and genomic approaches to studying evolution and adaptation in birds] ΠœΠΎΠ»Π΅ΠΊΡƒΠ»ΡΡ€Π½ΠΎ-гСнСтичСскиС ΠΈ Π³Π΅Π½ΠΎΠΌΠ½Ρ‹Π΅ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄Ρ‹ ΠΊ ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΡŽ ΡΠ²ΠΎΠ»ΡŽΡ†ΠΈΠΈ ΠΈ Π°Π΄Π°ΠΏΡ‚Π°Ρ†ΠΈΠΈ ΠΏΡ‚ΠΈΡ†

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    The review summarizes information on the advances in molecular genetic and genomic approaches to elucidate the main points in the evolutionary history of birds (class Aves) adapted to a wide variety of habitats. Π’ ΠΎΠ±Π·ΠΎΡ€Π΅ ΠΎΠ±ΠΎΠ±Ρ‰Π΅Π½Ρ‹ свСдСния ΠΎ возмоТностях молСкулярно-гСнСтичСских ΠΈ Π³Π΅Π½ΠΎΠΌΠ½Ρ‹Ρ… ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ΠΎΠ² для выяснСния основных ΠΌΠΎΠΌΠ΅Π½Ρ‚ΠΎΠ² Π² ΡΠ²ΠΎΠ»ΡŽΡ†ΠΈΠΎΠ½Π½ΠΎΠΉ истории ΠΏΡ‚ΠΈΡ† (класс Aves), Π°Π΄Π°ΠΏΡ‚ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… ΠΊ самым Ρ€Π°Π·Π½ΠΎΠΎΠ±Ρ€Π°Π·Π½Ρ‹ΠΌ условиям обитания

    [Genomic assessment of breeding bulls] ГСномная ΠΎΡ†Π΅Π½ΠΊΠ° ΠΏΠ»Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ… Π±Ρ‹ΠΊΠΎΠ²

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    The review considers aspects of genomic assessment of breeding bulls based on the use of molecular genetic markers and, in particular, SNP markers for determining the breeding value of animals. Π’ ΠΎΠ±Π·ΠΎΡ€Π΅ рассмотрСны аспСкты Π³Π΅Π½ΠΎΠΌΠ½ΠΎΠΉ ΠΎΡ†Π΅Π½ΠΊΠΈ ΠΏΠ»Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ… Π±Ρ‹ΠΊΠΎΠ² Π½Π° основС использования молСкулярно-гСнСтичСских ΠΌΠ°Ρ€ΠΊΠ΅Ρ€ΠΎΠ² ΠΈ, Π² частности, SNP-ΠΌΠ°Ρ€ΠΊΠ΅Ρ€ΠΎΠ² для опрСдСлСния ΠΏΠ»Π΅ΠΌΠ΅Π½Π½ΠΎΠΉ цСнности ΠΆΠΈΠ²ΠΎΡ‚Π½Ρ‹Ρ…

    [Towards advanced biotechnological developments to realize the genetic potential of egg-type poultry] НаправлСния соврСмСнных биотСхнологичСских Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΎΠΊ для Ρ€Π΅Π°Π»ΠΈΠ·Π°Ρ†ΠΈΠΈ гСнСтичСского ΠΏΠΎΡ‚Π΅Π½Ρ†ΠΈΠ°Π»Π° яичной ΠΏΡ‚ΠΈΡ†Ρ‹

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    Realization of the genetic potential of laying hens makes it feasible to achieve the maximum possible yield of egg products against, while using effective feed compositions and various feed additives. Implementation of molecular genetic technologies for the analysis of intestinal microbiota and the expression of key genes for productivity and resistance is an important tool in studying mechanisms of the effects of feed preparations on microorganism of birds. Within the framework of the project for the development of modern biotechnologies to assess gene expression, we carried out an experiment to assess influence of human recombinant interferon alpha-2b on the expression of genes for productivity and immunity in laying hens. A positive effect of the additive on the immune system of birds and the effectiveness of molecular genetic technologies for assessing the expression of key genes and the use of the studied additives in feeding of egg-type poultry have been shown. РСализация гСнСтичСского ΠΏΠΎΡ‚Π΅Π½Ρ†ΠΈΠ°Π»Π° ΠΊΡƒΡ€-Π½Π΅ΡΡƒΡˆΠ΅ΠΊ позволяСт Π΄ΠΎΡΡ‚ΠΈΠ³Π°Ρ‚ΡŒ максимально Π²ΠΎΠ·ΠΌΠΎΠΆΠ½Ρ‹ΠΉ Π²Ρ‹Ρ…ΠΎΠ΄ яичной ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ†ΠΈΠΈ Π½Π° Ρ„ΠΎΠ½Π΅ примСнСния эффСктивных ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡ†ΠΈΠΉ ΠΊΠΎΡ€ΠΌΠΎΠ² ΠΈ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… ΠΊΠΎΡ€ΠΌΠΎΠ²Ρ‹Ρ… Π΄ΠΎΠ±Π°Π²ΠΎΠΊ. Π’Π½Π΅Π΄Ρ€Π΅Π½ΠΈΠ΅ молСкулярно-гСнСтичСских Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ для Π°Π½Π°Π»ΠΈΠ·Π° ΠΌΠΈΠΊΡ€ΠΎΠ±ΠΈΠΎΡ‚Ρ‹ ΠΊΠΈΡˆΠ΅Ρ‡Π½ΠΈΠΊΠ° ΠΈ экспрСссии ΠΊΠ»ΡŽΡ‡Π΅Π²Ρ‹Ρ… Π³Π΅Π½ΠΎΠ² продуктивности ΠΈ рСзистСнтности являСтся Π²Π°ΠΆΠ½Ρ‹ΠΌ инструмСнтом Π² исслСдовании ΠΌΠ΅Ρ…Π°Π½ΠΈΠ·ΠΌΠΎΠ² воздСйствия ΠΊΠΎΡ€ΠΌΠΎΠ²Ρ‹Ρ… ΠΏΡ€Π΅ΠΏΠ°Ρ€Π°Ρ‚ΠΎΠ² Π½Π° ΠΌΠ°ΠΊΡ€ΠΎΠΎΡ€Π³Π°Π½ΠΈΠ·ΠΌ ΠΏΡ‚ΠΈΡ†Ρ‹. Π’ Ρ€Π°ΠΌΠΊΠ°Ρ… ΠΏΡ€ΠΎΠ΅ΠΊΡ‚Π° ΠΏΠΎ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ΅ соврСмСнных Π±ΠΈΠΎΡ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ для ΠΎΡ†Π΅Π½ΠΊΠΈ экспрСссии Π³Π΅Π½ΠΎΠ² Π½Π°ΠΌΠΈ осущСствлСн экспСримСнт ΠΏΠΎ ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΡŽ чСловСчСского Ρ€Π΅ΠΊΠΎΠΌΠ±ΠΈΠ½Π°Π½Ρ‚Π½ΠΎΠ³ΠΎ ΠΈΠ½Ρ‚Π΅Ρ€Ρ„Π΅Ρ€ΠΎΠ½Π° Π°Π»ΡŒΡ„Π°-2b Π½Π° ΡΠΊΡΠΏΡ€Π΅ΡΡΠΈΡŽ Π³Π΅Π½ΠΎΠ² продуктивности ΠΈ ΠΈΠΌΠΌΡƒΠ½ΠΈΡ‚Π΅Ρ‚Π° Ρƒ ΠΊΡƒΡ€-Π½Π΅ΡΡƒΡˆΠ΅ΠΊ. ΠŸΠΎΠΊΠ°Π·Π°Π½Ρ‹ ΠΏΠΎΠ»ΠΎΠΆΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠ΅ влияниС ΠΏΡ€Π΅ΠΏΠ°Ρ€Π°Ρ‚Π° Π½Π° ΠΈΠΌΠΌΡƒΠ½Π½ΡƒΡŽ систСму ΠΏΡ‚ΠΈΡ† ΠΈ ΡΡ„Ρ„Π΅ΠΊΡ‚ΠΈΠ²Π½ΠΎΡΡ‚ΡŒ молСкулярно-гСнСтичСских Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ для ΠΎΡ†Π΅Π½ΠΊΠΈ экспрСссии ΠΊΠ»ΡŽΡ‡Π΅Π²Ρ‹Ρ… Π³Π΅Π½ΠΎΠ² ΠΈ использования ΠΈΠ·ΡƒΡ‡Π°Π΅ΠΌΡ‹Ρ… Π΄ΠΎΠ±Π°Π²ΠΎΠΊ Π² ΠΊΠΎΡ€ΠΌΠ»Π΅Π½ΠΈΠΈ яичной ΠΏΡ‚ΠΈΡ†Ρ‹

    [Genetic variation of the NCAPG-LCORL locus in chickens of local breeds based on SNP genotyping data] ГСнСтичСская ΠΈΠ·ΠΌΠ΅Π½Ρ‡ΠΈΠ²ΠΎΡΡ‚ΡŒ локуса NCAPG-LCORL Ρƒ ΠΊΡƒΡ€ Π»ΠΎΠΊΠ°Π»ΡŒΠ½Ρ‹Ρ… ΠΏΠΎΡ€ΠΎΠ΄ Π½Π° основС Π΄Π°Π½Π½Ρ‹Ρ… SNP-гСнотипирования

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    Using SNP analysis, genomic variation of the NCAPG-LCORL locus in chickens of 49 gene pool breeds and crossbreds from the Genetic Collection of Rare and Endangered Chicken Breeds was analyzed. Genotyping was performed using an Illumina Chicken 60K SNP iSelect BeadChip. As a result of SNP scanning, five significant SNPs were identified in the NCAPG-LCORL region in all breeds and crossbreds of the analyzed groups of chickens for GGA4. Cluster analysis of admixture models revealed a subdivision of individuals according to their origin at K = 5. Chickens of the egg and meat types formed two separate clusters, which is consistent with the results of genotype frequencies. When analyzing genetic differentiation between groups of chickens with different utility types on the basis of pairwise FST values, significant differences (p < 0.05) were found for the group of egg-type chickens in comparison with meat-type (0.330), dual purpose (meat-egg, 0.178), game (0.225 ) and dual purpose (egg-meat, 0.237) chickens, as well as for meat-type relative to fancy chickens (0.153). The results showed that the compared groups differ genetically from each other, which is confirmed by the data on genotype frequencies. The population specificity of the linkage disequilibrium structure at the NCAPG-LCORL locus was revealed for 11 chicken breeds. Π’ Ρ…ΠΎΠ΄Π΅ исслСдования с ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ Π°Π½Π°Π»ΠΈΠ·Π° ΠΎΠ΄Π½ΠΎΠ½ΡƒΠΊΠ»Π΅ΠΎΡ‚ΠΈΠ΄Π½ΠΎΠ³ΠΎ ΠΏΠΎΠ»ΠΈΠΌΠΎΡ€Ρ„ΠΈΠ·ΠΌΠ° (SNP) Π±Ρ‹Π»Π° ΠΏΡ€ΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Π° гСномная ΠΈΠ·ΠΌΠ΅Π½Ρ‡ΠΈΠ²ΠΎΡΡ‚ΡŒ локуса NCAPG-LCORL Ρƒ ΠΊΡƒΡ€ 49 Π³Π΅Π½ΠΎΡ„ΠΎΠ½Π΄Π½Ρ‹Ρ… ΠΏΠΎΡ€ΠΎΠ΄ ΠΈ Π³ΠΈΠ±Ρ€ΠΈΠ΄Π½Ρ‹Ρ… Ρ„ΠΎΡ€ΠΌ ΠΈΠ· «ГСнСтичСской ΠΊΠΎΠ»Π»Π΅ΠΊΡ†ΠΈΠΈ Ρ€Π΅Π΄ΠΊΠΈΡ… ΠΈ ΠΈΡΡ‡Π΅Π·Π°ΡŽΡ‰ΠΈΡ… ΠΏΠΎΡ€ΠΎΠ΄ ΠΊΡƒΡ€Β». Π“Π΅Π½ΠΎΡ‚ΠΈΠΏΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ с ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ Ρ‡ΠΈΠΏΠ° Illumina Chicken 60K SNP iSelect BeadChip. Π’ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π΅ SNP-сканирования Ρƒ всСх ΠΏΠΎΡ€ΠΎΠ΄ ΠΈ Π³ΠΈΠ±Ρ€ΠΈΠ΄ΠΎΠ² Π°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΡƒΠ΅ΠΌΡ‹Ρ… Π³Ρ€ΡƒΠΏΠΏ ΠΊΡƒΡ€ Π½Π° GGA4 Π² Ρ€Π΅Π³ΠΈΠΎΠ½Π΅, Π²ΠΊΠ»ΡŽΡ‡Π°ΡŽΡ‰Π΅ΠΌ NCAPG-LCORL, ΠΈ Π² области рядом с этим Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠΌ ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½ΠΎ ΠΏΡΡ‚ΡŒ Π·Π½Π°Ρ‡ΠΈΠΌΡ‹Ρ… SNPs, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°Ρ‚Π°ΠΌΠΈ для сСлСкции с ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ ΠΌΠ°Ρ€ΠΊΠ΅Ρ€ΠΎΠ² (MAS). ΠšΠ»Π°ΡΡ‚Π΅Ρ€Π½Ρ‹ΠΉ Π°Π½Π°Π»ΠΈΠ· адмикс-ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΎΠ±Π½Π°Ρ€ΡƒΠΆΠΈΠ» Ρ€Π°Π·Π΄Π΅Π»Π΅Π½ΠΈΠ΅ особСй соотвСтствСнно ΠΈΡ… ΠΏΡ€ΠΎΠΈΡΡ…ΠΎΠΆΠ΄Π΅Π½ΠΈΡŽ ΠΏΡ€ΠΈ К=5. ΠšΡƒΡ€Ρ‹ яичного ΠΈ мясного направлСния продуктивности сформировали Π΄Π²Π° обособлСнных кластСра, Ρ‡Ρ‚ΠΎ согласуСтся с Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π°ΠΌΠΈ частот Π³Π΅Π½ΠΎΡ‚ΠΈΠΏΠΎΠ². ΠŸΡ€ΠΈ Π°Π½Π°Π»ΠΈΠ·Π΅ гСнСтичСской Π΄ΠΈΡ„Ρ„Π΅Ρ€Π΅Π½Ρ†ΠΈΠ°Ρ†ΠΈΠΈ ΠΌΠ΅ΠΆΠ΄Ρƒ Π³Ρ€ΡƒΠΏΠΏΠ°ΠΌΠΈ ΠΊΡƒΡ€ Ρ€Π°Π·Π»ΠΈΡ‡Π½ΠΎΠ³ΠΎ направлСния продуктивности Π½Π° основС ΠΏΠΎΠΏΠ°Ρ€Π½Ρ‹Ρ… FST-Π·Π½Π°Ρ‡Π΅Π½ΠΈΠΉ ΠΎΡ‚ΠΌΠ΅Ρ‡Π΅Π½Ρ‹ достовСрныС различия (p < 0,05) для Π³Ρ€ΡƒΠΏΠΏΡ‹ ΠΊΡƒΡ€ яичного направлСния продуктивности Π² сравнСнии с мясными (0,330), мясо-яичными (0,178), Π±ΠΎΠΉΡ†ΠΎΠ²Ρ‹ΠΌΠΈ (0,225) ΠΈ яично-мясными (0,237), Π° Ρ‚Π°ΠΊΠΆΠ΅ для ΠΊΡƒΡ€ мясного направлСния продуктивности ΠΎΡ‚Π½ΠΎΡΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎ Π΄Π΅ΠΊΠΎΡ€Π°Ρ‚ΠΈΠ²Π½Ρ‹Ρ… (0,153). Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, Ρ‡Ρ‚ΠΎ сравниваСмыС Π³Ρ€ΡƒΠΏΠΏΡ‹ ΠΎΡ‚Π»ΠΈΡ‡Π°ΡŽΡ‚ΡΡ гСнСтичСски Π΄Ρ€ΡƒΠ³ ΠΎΡ‚ Π΄Ρ€ΡƒΠ³Π°, Ρ‡Ρ‚ΠΎ подтвСрТдаСтся Π΄Π°Π½Π½Ρ‹ΠΌΠΈ ΠΎ частотах Π³Π΅Π½ΠΎΡ‚ΠΈΠΏΠΎΠ². ВыявлСна популяционная ΡΠΏΠ΅Ρ†ΠΈΡ„ΠΈΡ‡Π½ΠΎΡΡ‚ΡŒ структуры нСравновСсия ΠΏΠΎ ΡΡ†Π΅ΠΏΠ»Π΅Π½ΠΈΡŽ (LD) ΠΏΠΎ локусу NCAPG-LCORL для 11 ΠΏΠΎΡ€ΠΎΠ΄ ΠΊΡƒΡ€

    Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

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    Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62Β·8% [95% UI 60Β·5–65Β·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5Β·1% [0Β·9–9Β·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4Β·66 million (3Β·98–5Β·50) global deaths in children younger than 5 years in 2021 compared with 5Β·21 million (4Β·50–6Β·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15Β·9 million (14Β·7–17Β·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22Β·7 years (20Β·8–24Β·8), from 49Β·0 years (46Β·7–51Β·3) to 71Β·7 years (70Β·9–72Β·5). Global life expectancy at birth declined by 1Β·6 years (1Β·0–2Β·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15Β·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7Β·89 billion (7Β·67–8Β·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39Β·5% [28Β·4–52Β·7]) and south Asia (26Β·3% [9Β·0–44Β·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92Β·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. Funding: Bill & Melinda Gates Foundation

    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

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    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 modelβ€”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 estimatesβ€”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 2Β·5th and 97Β·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 mortalityβ€”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 94Β·0 deaths (95% UI 89Β·2–100Β·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 (271Β·0 deaths [250Β·1–290Β·7] per 100 000 population) and Latin America and the Caribbean (195Β·4 deaths [182Β·1–211Β·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48Β·1 deaths [47Β·4–48Β·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23Β·2 deaths [16Β·3–37Β·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 1Β·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 8Β·3 years (6Β·7–9Β·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0Β·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3Β·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
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