60 research outputs found
Diversity of indigenous arbuscular mycorrhizal fungi in rhizosphere of upland rice (Oryza sativa L.) varieties in Southwest Nigeria
Article Details: Received: 2020-02-05 | Accepted: 2020-05-07 | Available online: 2020-06-30https://doi.org/10.15414/afz.2020.23.02.42-48 Arbuscular mycorrhizal fungi (AMF) have the potential to increase crop productivity and play a key role in the functioning and sustainability of most agroecosystems. However, limited information is available on the divervisity of AMF associated with upland rice varieties in Southwest Nigeria. Field survey was conducted to investigate colonization and diversity of AMF in 13 upland rice varieties commonly grown in Southwest Nigeria. Root and soil samples were collected from rice fields in 2012. The results showed natural root colonization of all the rice varieties by AMF with highest root colonization in ITA 157and Ofada. The spore densities retrieved from the different rhizospheres were relatively high, varying from 13 spores in UORW 111 to 174 spores in Ofada with a mean of 67.6 spores per 20 g dry soil. Glomus was observed to be the most abundant AMF genus. Funneliformis mosseae was the most frequently occurring AMF species (96.2%) with relative density (RD) of 32.2%, followed by Glomus intraradices, Claroideoglomus etunicatum, and Glomus clareium. This study showed that AMF naturally colonized the roots of these rice varieties and diversity of different AMF genera in rice rhizosphere. This study will help draw attention to natural colonization of AMF in rice producing areas of Nigeria that can influence future possibility of using inocula of the dominant AMF species in upland rice cultivation.Keywords: Arbuscular mycorrhizal fungi, community structure, diversity, upland rice, spore densityReferences ADEYEMI, N.O. et al. (2020). Effect of commercial arbuscular mycorrhizal fungi inoculant on growth and yield of soybean under controlled and natural field conditions. Journal of Plant Nutrition, 43(4), 487–499, DOI: https://doi.org/10.1080/019041 67.2019.1685101 ADEYEMI, N.O. et al. (2019). Identification and relative abundance of native arbuscular mycorrhizal fungi associated with oil-seed crops and maize (Zea mays L.) in derived savannah of Nigeria. Acta fytotechn zootechn, 22(3), 84–89. DOI: https://doi.org/10.15414/afz.2019.22.03.84-89 ADEYEMI, N. et al. (2017). Yield and yield attributes responses of soybean (Glycine max L. Merrill) to elevated CO2 and arbuscular mycorrhizal fungi inoculation in the humid transitory rainforest. Notulae Scientia Biologicae, 9(2), 233–241. DOI: https://doi.org/10.15835/nsb9210002 BARBER, N.A. et al. (2013). Linking agricultural practices, mycorrhizal fungi, and traits mediating plant-insect interactions. Ecol Appl, 23(7), 1519–1530.BŁASZKOWSKI, J. (2012) Glomeromycota. Kraków: W. Szafer Institute of Botany, Polish Academy of Sciences. BOUYOUCOS, G.H. (1951). A recalibration of the hydrometer method for testing mechanical analysis of soils. Agronomy Journal, 43,434–438.BRUNDRETT, M.C. and TEDERSOO, L. (2018) Evolutionary history of mycorrhizal symbioses and global host plant diversity. New Phytol, 220,1108–1115. CAMPOS-SORIANO, L. et al. (2010). Activation of basal defense mechanisms of rice plants by Glomus intraradices does not affect the arbuscular mycorrhizal symbiosis. New Phytol, 188(2), 597–614. CHEN, M. et al. (2018) Beneficial services of arbuscular mycorrhizal fungi – from ecology to application. Frontiers in Plant Science, 9. DOI: https://doi.org/10.3389/fpls.2018.01270DAVISON, J. et al. (2015). Global assessment of arbuscular mycorrhizal fungus diversity reveals very low endemism. Science, 349, 970–973. DE ANDRADE-JÚNIOR, J.A. et al. (2018) Fixação de carbono em sistemas agroecológicos na região do Vale do São Patrício, Goiás. Científica – Multidiscip J, 5, 85–98. DE MOURA, J.B. et al. (2018) Taxa de colonização micorrízica sob diferentes sistemas de cultivo no cerrado em cana-deaçúcar. Diálogos & Ciência, 2, 60–66. GIANINAZZI, S. et al. (2010). Agroecology: The key role of arbuscular mycorrhizas in ecosystem services. Mycorrhiza, 20(8), 519–530. INVAM (2018). International culture collection of (vesicular) arbuscular mycorrhizal fungi. Morgantown: West Virginia University. HAZARD, C. et al. (2013). The role of local environment and geographical distance in determining community composition of arbuscular mycorrhizal fungi at the landscape scale. The ISME Journal, 7, 498–508. JIANG, Y.N. et al. (2017). Plants transfer lipids to sustain colonization by mutualistic mycorrhizal and parasitic fungi. Science, 356, 1172–1175. JOHNSON, N.C. (2010). Resource stoichiometry elucidates the structure and function of arbuscular mycorrhizas across scales. New Phytol, 185(3), 631–647. LEKBERG, Y. and KOIDE, R.T. (2005). Is plant performance limited by abundance of arbuscular mycorrhizal fungi? A metaanalysis of studies published between 1988 and 2003. New Phytol, 168(1). LIN, X. et al. (2012). Long-term balanced fertilization decreases arbuscular mycorrhizal fungal diversity in an arable soil in north China revealed by 454 pyrosequencing. Environmental Science & Technology, 46, 5764–5771. LUGINBUEHL, L.H. et al. (2017). Fatty acids in arbuscular mycorrhizal fungi are synthesized by the host plant. Science, 356, 1175–1178. LUMINI, E. et al. (2011). Different farming and water regimes in Italian rice fields affect arbuscular mycorrhizal fungal soil communities. Ecol Appl, 21(5), 1696–1707.OEHL, F. et al. (2010). Soil type and land use intensity determine the composition of arbuscular mycorrhizal fungal communities. Soil Biology and Biochemistry, 42, 724–738. OEHL, F. et al. (2017) Diversity and biogeography of arbuscular mycorrhizal fungi in agricultural soils. Biol Fertil Soils, (53), 777–797. PEYRET-GUZZON, M. et al. (2016). Arbuscular mycorrhizal fungal communities and Rhizophagus irregularis populations shift in response to short term ploughing and fertilisation in a buffer strip. Mycorrhiza, 26, 33–46. PHILLIPS, J.M. and HAYMAN, D.S. (1970). Improved procedures for clearing roots and staining parasitic and vesicular-arbuscular mycorrhizal fungi for rapid assessment of infection. Trans Br Mycol Soc., 55,158–IN18. PIVATO, B. et al. (2007). Medicago species affect the community composition of arbuscular myccorhizal fungi associated with roots. New Phytologist 176, 197–210. RILLIG, M.C. and MUMMEY, D.L. (2006). Mycorrhizas and soil structure. New Phytol, 171(1), 41–53. SILVA-FLORES, P. et al. (2019) Factors affecting arbuscular mycorrhizal fungi spore density in the Chilean Mediterraneantype ecosystem. J Soil Sci Plant Nutr, 19, 42–50. SMITH, S.E. and READ, D.J. (2008). Mycorrhizal symbiosis. 3rd ed., New York: Academic Press. SNOECK, D. et al. (2010). Temporal changes in VAM fungi in the cocoa agroforestry systems of central Cameroon. Agroforestry Syst., 78, 323–328. SOUZA, B.R. et al. (2016) Arbuscular mycorrhizal fungi as indicative of soil quality in conservation systems in the region of vale do São Patrício, Goiás. Int J Curr Res, 8, 43307–43311.VALLINO, et al. (2014). Rice flooding negatively impacts root branching and arbuscular mycorrhizal colonization, but not fungal viability. Plant Cell Environ, 37(3), 557–572. VAN Der HEIJDEN, M.G. et al. (2015) Mycorrhizal ecology and evolution: the past, the present, and the future. New Phytol 205, 1406–1423. VENTURA, M.V.A, et al. (2018) Influence of arbuscular mycorrhizal fungi in the establishment of pre-broken sugar cane. Poljoprivreda i Sumarstvo, 64,149–157. WALKLEY, A., and BLACK, I.A. (1934). An examination of Degtjareff method for determining soil organic matter and proposed modification of the chromic acid in soil analysis.1. Experimental soil science, 79, 459–465. WANG, Y.T. et al. (2015). Community dynamics of arbuscular mycorrhizal fungi in high-input and intensively irrigated rice cultivation systems. Appl Environ Microbiol, 81(8), 2958–2965. ZHANG, S.J. et al. (2015). Is resource allocation and grain yield of rice altered by inoculation with arbuscular mycorrhizal fungi? J Plant Ecol, 8(4), 436–448
PHYSIOLOGICAL AND YIELD RESPONSE OF SOME UPLAND RICE VARIETIES TO RE-WATERING AFTER IMPOSED SOIL MOISTURE STRESS
A pot experiment was conducted in the Screen house of Federal University of Agriculture, Abeokuta, October, 2011 (late dry season) to study drought recovery ability of 13 upland rice varieties exposed to soil moisture stress (20 days) at three growth stages (vegetative, reproductive and grain filling stage). The experiment was in completely randomized design, with three replicates. Under moisture stress significantly higher growth recovery, more erect canopy and flatter leaf surface were obtained in all the rice varieties at vegetative growth stage than other growth stages with increasing duration of re-watering. Under stress condition NERICA 4 maintained a significantly higher leaf area (27.50 cm2 and 40.18 cm2), plant height (53.45 cm and 67.62 cm) and number of tillers (1.67 and 1.67), but with a depressed number of leaf, slanted leaf posture and curved leaf especially during the later stage of its growth (Reproductive and grain filling stage respectively). It could be concluded that NERICA 4 had higher recovery ability than other rice varieties in drought prone upland ecology
ROOT RESPONSE OF SOME SELECTED RICE VARIETIES TO SOIL MOISTURE STRESS AT DIFFERENT PHENOLOGICAL STAGES
Physiological adjustment in plant root system is a determinant for survival and crop productivity in situation of moisture stress. A screen house experiment was conducted to access response of rice roots to moisture stress. Thirteen varieties of rice comprising six NERICAs, WAB 56-104, CG 14, ART26-3-1-B, AC 103549, MOROBEREKAN, ART19-25-1-B and a local check (OFADA) were subjected to twenty-day moisture stress once at each phenological stage. Results indicated that root growth generally showed preference over shoot growth. Moisture stress did not affect root volume (RV), deep root numbers (DRN), root dry weight (RDW) and root depth (RD) of all the rice varieties at reproductive stage. CG14 however recorded 67.6% increase in RD at this stage while NERICA 3, CG14 and OFADA recorded an increase in root depth: shoot length. At vegetative and grain filling stages, RV, DRN, RDW, RD, and RMC were significantly (p< 0.05) increased by moisture stress in most rice varieties. NERICA2, NERICA7, ART26-3-1-B, MOROBEREKAN and WAB56-104 however recorded 54%, 76.5%, 72.7%, 57.1%, and 56.3% significant reduction in DRN respectively at vegetative stage. Correlation analysis showed that plant height, leaf area, and number of tillers depend highly on, RD, RV, RDW and deep root weight. Therefore, attention should be focused on these parameters in selection for moisture stress tolerance in rice
HIV-1 Drug Resistance Emergence among Breastfeeding Infants Born to HIV-Infected Mothers during a Single-Arm Trial of Triple-Antiretroviral Prophylaxis for Prevention of Mother-To-Child Transmission: A Secondary Analysis
Analysis of a substudy of the Kisumu breastfeeding trial by Clement Zeh and colleagues reveals the emergence of HIV drug resistance in HIV-positive infants born to HIV-infected mothers treated with antiretroviral drugs
Track D Social Science, Human Rights and Political Science
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138414/1/jia218442.pd
Track E Implementation Science, Health Systems and Economics
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138412/1/jia218443.pd
Global, regional, and national prevalence and mortality burden of sickle cell disease, 2000-2021: a systematic analysis from the Global Burden of Disease Study 2021
BACKGROUND: Previous global analyses, with known underdiagnosis and single cause per death attribution systems, provide only a small insight into the suspected high population health effect of sickle cell disease. Completed as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, this study delivers a comprehensive global assessment of prevalence of sickle cell disease and mortality burden by age and sex for 204 countries and territories from 2000 to 2021. METHODS: We estimated cause-specific sickle cell disease mortality using standardised GBD approaches, in which each death is assigned to a single underlying cause, to estimate mortality rates from the International Classification of Diseases (ICD)-coded vital registration, surveillance, and verbal autopsy data. In parallel, our goal was to estimate a more accurate account of sickle cell disease health burden using four types of epidemiological data on sickle cell disease: birth incidence, age-specific prevalence, with-condition mortality (total deaths), and excess mortality (excess deaths). Systematic reviews, supplemented with ICD-coded hospital discharge and insurance claims data, informed this modelling approach. We employed DisMod-MR 2.1 to triangulate between these measures-borrowing strength from predictive covariates and across age, time, and geography-and generated internally consistent estimates of incidence, prevalence, and mortality for three distinct genotypes of sickle cell disease: homozygous sickle cell disease and severe sickle cell β-thalassaemia, sickle-haemoglobin C disease, and mild sickle cell β-thalassaemia. Summing the three models yielded final estimates of incidence at birth, prevalence by age and sex, and total sickle cell disease mortality, the latter of which was compared directly against cause-specific mortality estimates to evaluate differences in mortality burden assessment and implications for the Sustainable Development Goals (SDGs). FINDINGS: Between 2000 and 2021, national incidence rates of sickle cell disease were relatively stable, but total births of babies with sickle cell disease increased globally by 13·7% (95% uncertainty interval 11·1-16·5), to 515 000 (425 000-614 000), primarily due to population growth in the Caribbean and western and central sub-Saharan Africa. The number of people living with sickle cell disease globally increased by 41·4% (38·3-44·9), from 5·46 million (4·62-6·45) in 2000 to 7·74 million (6·51-9·2) in 2021. We estimated 34 400 (25 000-45 200) cause-specific all-age deaths globally in 2021, but total sickle cell disease mortality burden was nearly 11-times higher at 376 000 (303 000-467 000). In children younger than 5 years, there were 81 100 (58 800-108 000) deaths, ranking total sickle cell disease mortality as 12th (compared to 40th for cause-specific sickle cell disease mortality) across all causes estimated by the GBD in 2021. INTERPRETATION: Our findings show a strikingly high contribution of sickle cell disease to all-cause mortality that is not apparent when each death is assigned to only a single cause. Sickle cell disease mortality burden is highest in children, especially in countries with the greatest under-5 mortality rates. Without comprehensive strategies to address morbidity and mortality associated with sickle cell disease, attainment of SDG 3.1, 3.2, and 3.4 is uncertain. Widespread data gaps and correspondingly high uncertainty in the estimates highlight the urgent need for routine and sustained surveillance efforts, further research to assess the contribution of conditions associated with sickle cell disease, and widespread deployment of evidence-based prevention and treatment for those with sickle cell disease. FUNDING: Bill & Melinda Gates Foundation
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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026
Background
The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic; characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic; and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness.
Methods
In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need.
Findings
In 2019, at the onset of the COVID-19 pandemic, US7·3 trillion (95% UI 7·2–7·4) in 2019; 293·7 times the 43·1 billion in development assistance was provided to maintain or improve health. The pandemic led to an unprecedented increase in development assistance targeted towards health; in 2020 and 2021, 37·8 billion was provided for the health-related COVID-19 response. Although the support for pandemic preparedness is 12·2% of the recommended target by the High-Level Independent Panel (HLIP), the support provided for the health-related COVID-19 response is 252·2% of the recommended target. Additionally, projected spending estimates suggest that between 2022 and 2026, governments in 17 (95% UI 11–21) of the 137 LMICs will observe an increase in national government health spending equivalent to an addition of 1% of GDP, as recommended by the HLIP.
Interpretation
There was an unprecedented scale-up in DAH in 2020 and 2021. We have a unique opportunity at this time to sustain funding for crucial global health functions, including pandemic preparedness. However, historical patterns of underfunding of pandemic preparedness suggest that deliberate effort must be made to ensure funding is maintained
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Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
Background
Detailed, comprehensive, and timely reporting on population health by underlying causes of disability and premature death is crucial to understanding and responding to complex patterns of disease and injury burden over time and across age groups, sexes, and locations. The availability of disease burden estimates can promote evidence-based interventions that enable public health researchers, policy makers, and other professionals to implement strategies that can mitigate diseases. It can also facilitate more rigorous monitoring of progress towards national and international health targets, such as the Sustainable Development Goals. For three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has filled that need. A global network of collaborators contributed to the production of GBD 2021 by providing, reviewing, and analysing all available data. GBD estimates are updated routinely with additional data and refined analytical methods. GBD 2021 presents, for the first time, estimates of health loss due to the COVID-19 pandemic.
Methods
The GBD 2021 disease and injury burden analysis estimated years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries using 100 983 data sources. Data were extracted from vital registration systems, verbal autopsies, censuses, household surveys, disease-specific registries, health service contact data, and other sources. YLDs were calculated by multiplying cause-age-sex-location-year-specific prevalence of sequelae by their respective disability weights, for each disease and injury. YLLs were calculated by multiplying cause-age-sex-location-year-specific deaths by the standard life expectancy at the age that death occurred. DALYs were calculated by summing YLDs and YLLs. HALE estimates were produced using YLDs per capita and age-specific mortality rates by location, age, sex, year, and cause. 95% uncertainty intervals (UIs) were generated for all final estimates as the 2·5th and 97·5th percentiles values of 500 draws. Uncertainty was propagated at each step of the estimation process. Counts and age-standardised rates were calculated globally, for seven super-regions, 21 regions, 204 countries and territories (including 21 countries with subnational locations), and 811 subnational locations, from 1990 to 2021. Here we report data for 2010 to 2021 to highlight trends in disease burden over the past decade and through the first 2 years of the COVID-19 pandemic.
Findings
Global DALYs increased from 2·63 billion (95% UI 2·44–2·85) in 2010 to 2·88 billion (2·64–3·15) in 2021 for all causes combined. Much of this increase in the number of DALYs was due to population growth and ageing, as indicated by a decrease in global age-standardised all-cause DALY rates of 14·2% (95% UI 10·7–17·3) between 2010 and 2019. Notably, however, this decrease in rates reversed during the first 2 years of the COVID-19 pandemic, with increases in global age-standardised all-cause DALY rates since 2019 of 4·1% (1·8–6·3) in 2020 and 7·2% (4·7–10·0) in 2021. In 2021, COVID-19 was the leading cause of DALYs globally (212·0 million [198·0–234·5] DALYs), followed by ischaemic heart disease (188·3 million [176·7–198·3]), neonatal disorders (186·3 million [162·3–214·9]), and stroke (160·4 million [148·0–171·7]). However, notable health gains were seen among other leading communicable, maternal, neonatal, and nutritional (CMNN) diseases. Globally between 2010 and 2021, the age-standardised DALY rates for HIV/AIDS decreased by 47·8% (43·3–51·7) and for diarrhoeal diseases decreased by 47·0% (39·9–52·9). Non-communicable diseases contributed 1·73 billion (95% UI 1·54–1·94) DALYs in 2021, with a decrease in age-standardised DALY rates since 2010 of 6·4% (95% UI 3·5–9·5). Between 2010 and 2021, among the 25 leading Level 3 causes, age-standardised DALY rates increased most substantially for anxiety disorders (16·7% [14·0–19·8]), depressive disorders (16·4% [11·9–21·3]), and diabetes (14·0% [10·0–17·4]). Age-standardised DALY rates due to injuries decreased globally by 24·0% (20·7–27·2) between 2010 and 2021, although improvements were not uniform across locations, ages, and sexes. Globally, HALE at birth improved slightly, from 61·3 years (58·6–63·6) in 2010 to 62·2 years (59·4–64·7) in 2021. However, despite this overall increase, HALE decreased by 2·2% (1·6–2·9) between 2019 and 2021.
Interpretation
Putting the COVID-19 pandemic in the context of a mutually exclusive and collectively exhaustive list of causes of health loss is crucial to understanding its impact and ensuring that health funding and policy address needs at both local and global levels through cost-effective and evidence-based interventions. A global epidemiological transition remains underway. Our findings suggest that prioritising non-communicable disease prevention and treatment policies, as well as strengthening health systems, continues to be crucially important. The progress on reducing the burden of CMNN diseases must not stall; although global trends are improving, the burden of CMNN diseases remains unacceptably high. Evidence-based interventions will help save the lives of young children and mothers and improve the overall health and economic conditions of societies across the world. Governments and multilateral organisations should prioritise pandemic preparedness planning alongside efforts to reduce the burden of diseases and injuries that will strain resources in the coming decades
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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
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