Alkali metal ions transfer across the water/1,2-dichloroethane interface facilitated by a series of crown ethers

National Basic Research Program of China [2011CB933700, 2012CB932900]; National Science Foundation of China [NSFC21061120456, 21021002, 20973142]; National Project 985 of High Education; Chinese GovernmentThe facilitated transfer of alkali metal ions (Li+ and Na+) across the water/1,2-dichloroethane (W/1,2-DCE) interface was studied by using a series of crown ethers as ionophores: 4'-ethynylbenzo-15-crown-5-ether (L1), 3',6'-diethynylbenzo-15-crown-5-ether (L2) and 4',5'-diethynylbenzo-15-crown-5-ether (L3). Cyclic voltammetry was employed to study the electrochemical behaviour of the facilitated ion transfer across the W/1,2-DCE interface supported at the tip of a micropipette. The diffusion coefficients of the ionophores in the 1,2-DCE phase were determined, while the metal-ligand complexes formed by these ions with all the ionophores were obtained to be in a 1 : 1 stoichiometric ratio. The association constants, log beta degrees, for complexes LiL1(+), LiL2(+), LiL3(+), NaL1(+), NaL2(+) and NaL3(+) were calculated to be 3.3, 4.2, 4.0, 2.1, 3.5 and 2.2, respectively. The theoretical calculations have shown that the conjugated constituent groups on the benzene ring have an essential effect on the spatial structures of the crown ether rings, which determine the supramolecular interaction between the ions and ionophores

Non-communicable diseases in Ethiopia: policy and strategy gaps in the reduction of behavioral risk factors

Introduction: Non-communicable diseases (NCDs) are the leading cause of death worldwide. Over 80% of NCD deaths occur in developing countries. Four modifiable behaviors, namely tobacco use, consumption of unhealthy diet, physical inactivity, and the harmful use of alcohol, contribute to 80% of the NCD burden. Studies show that the vast majority of NCDs can be prevented through behavioral risk-reduction interventions. Properly executed, the interventions could lead to a decrease in the burden of NCDs, ranging from a 30% drop in the prevalence of cancer to a 75% reduction in cardiovascular diseases. This study examined the policy and strategy gaps in the reduction of the modifiable NCD behavioral risk factors in Ethiopia to inform and guide policy-makers and other stakeholders. Methodology: This study used a data triangulation methodology with a sequential, explanatory, mixed-method design conducted in two stages. The authors carried out quantitative analysis on the prevalence and distribution of behavioral risk factors from the Ethiopia NCD STEPwise approach to surveillance (STEPS) survey. Qualitative data on national policies and strategies complemented the analysis of the progress made so far and the existing gaps. Results and Discussion: Ethiopia has made substantial progress in responding to the NCD epidemic by developing a health sector NCD strategic action plan, generating evidence, and setting time-bound national targets on NCD behavioral risk factors. Activities mainly aimed at reducing tobacco use, such as implementation of the ratified WHO Framework Convention on Tobacco Control (FCTC), using evidence of the Global Adult Tobacco Survey (GATS), and the articulation of legislative measures are ongoing. On this paper our analysis reveals policy and strategy gaps, status in law enforcement, social mobilization, and awareness creation to reduce the major behavioral risk factors. Conclusions: NCDs share common risk factors and risk reduction strategies creates an opportunity for an effective response. However, the national response still needs more effort to have a sufficient impact on the prevention of NCDs in Ethiopia. Thus, there is an urgent need for the country to develop and implement targeted strategies for each behavioral risk factor and design functional, multisectoral coordination. There is also a need for establishing sustainable financial mechanisms, such as increasing program budgets and levying ‘sin taxes,’ to support the NCD prevention and control program. Ethiop. J. Health Dev. 2019; 33(4):259-268] Key words: NCDs, behavioral risk factors, policy, strategy, multisectoral coordination, Ethiopi

Non-communicable Diseases in Ethiopia: Disease burden, gaps in health care delivery and strategic directions.

Abstract Introduction: In Ethiopia, non-communicable diseases (NCDs) cause 42% of deaths, of which 27% are premature deaths before 70 years of age. The Disability Adjusted Life Years (DALYs) increased from below 20% in 1990 to 69% in 2015. With no action, Ethiopia will be the first among the most populous nations in Africa to experience dramatic burden of premature deaths and disability from NCDs by 2040. However, the national response to NCDs remains fragmented with the total health spending per capita for NCDs still insignificant. The focus of this paper is highlighting the burden of NCDs in Ethiopia and analyzing one of the two major WHO recommended policy issues; the status of integrated management of NCDs, in Ethiopia. NCDs are complex conditions influenced by a range of individual, social and economic factors, including our perceptions and behavior. Also, NCDs tend to be easily overlooked by individuals and policy makers due to their silent nature. Thus, effectively addressing NCDs requires a fresher look into a range of health system issues, including how health services are organized and delivered.Methods: A mixed method approach with quantitative and qualitative data was used. Quantitative data was obtained through analysis of the global burden of diseases study, WHO-STEPs survey, Ethiopian SARA study and the national essential NCD drug survey. This was supplemented by qualitative data through review of a range of documents, including the national NCD policies and strategies and global and regional commitments.Results and discussion: In 2015, NCDs were the leading causes of age-standardized death rate (causing 711 deaths per 100,000 people (95% UI: 468.8–1036.2) and DALYs. The national estimates of the prevalence of NCD metabolic risk factors showed high rates of raised blood pressure (16%), hyperglycemia (5.9%), hypercholesterolemia (5.6%), overweight (5.2%) and Obesity (1.2%). Prevalence of 3-5 risk factors constituting a metabolic syndrome was 4.4%. Data availability on NCD morbidity and mortality is limited. While there are encouraging actions on NCDs in terms of political commitment, lot of gaps as shown by limited availability of resources for NCDs, NCD prevention and treatment services at the primary health care (PHC) level. Shortage of essential NCD drugs and diagnostic facilities and lack of treatment guidelines are major challenges. There is a need to re-orient the national health system to ensure recognition of the NCD burden and sustain political commitment, allocate sufficient funding and improve organization and delivery of NCD services at PHC level. [Ethiop. J. Health Dev. 2018;32 (3):00-000]Key words: Non-communicable diseases, health-system re-orientation, NCD burden, metabolic risk factors, Service delivery, Primary Health Car

Tobacco use and its predictors among Ethiopian adults: A further analysis of Ethiopian NCD STEPS survey-2015

Background: Consuming any form of tobacco is one of the leading causes of preventable morbidity and mortality. Tobacco smoking has been identified as one of the major risk factors for NCDs, including cardiovascular, chronic respiratory diseases, and different cancers. Although there is national information on magnitude of tobacco use, to date there is limited nationally representative data on factors associated with tobacco use. The aim of this study is to assess the distribution and predicators of tobacco use in Ethiopian adult population between 15 -59.Objectives: The main aim of this study was to assess the prevalence of tobacco use and its predictor in Ethiopia.Methods: A cross-sectional population based study design was employed among population age from 15- 69 years. A stratified, three-stage cluster sampling was used to identify the study subjects. Households in each cluster were selected using simple random sampling method. The sampling frame was based on the population and housing census conducted for Ethiopia in 2007. Data was collected using WHO NCD STEPS questionnaire; current tobacco use of any type was taken as the dependent variable. Five hundred thirteen enumeration areas (EAs) as primary sampling units (PSUs) (404 rural and 109 urban) were selected with probability proportionate to size, followed by selection of households as a secondary sampling units (SSUs). A total of 10,260 households were selected from the 513 EAs (20 households per EA). Eligible individuals were selected from households using Kish method (a pre-assigned table of random numbers to find the person to be interviewed). Descriptive statistics using frequency table, mean, median, interquartile range and standard deviations were computed. Step wise logistic regression was used to analyse the predictors of tobacco use. An Estimator of 95% confidence interval was used both for computing descriptive statistics as well testing associations using logistic regression.Results: The prevalence of tobacco use (all tobacco products) was 4.2%. The mean age (± SD) of starting tobacco use was 21(7) years. The mean frequency of tobacco use was 2 times per day. Hierarchical Logistic regression analysis revealed that participants in age groups 30-44 years, and 60-69 years were less likely to use any tobacco type compared to younger age group of15-29 years. Heavy episodic drinking, AOR 2.46 [95% CI= 1.4 – 4.5], and khat chewing, AOR 4.71[95%= 2.26 – 9.8], were independently associated with tobacco use.Conclusion and recommendations: The overall prevalence of tobacco use was relatively higher in males. Factors associated with tobacco use were heavy episodic drinking and khat chewing. Although tobacco use is an important risk factor for different disease on its own, the additional use of these substances exposes individuals to increased risk of NCDs. The findings warrant the need to implement existing anti-tobacco laws in the country, enhance anti-tobacco awareness raising efforts, and implement interventions to help current tobacco users, focusing attention more on regions with high rates of tobacco use and males. Key words: Ethiopia, NCDs, Predictors, Risk factors, Tobacco use, WHO STEP

Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100:a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BackgroundAccurate assessments of current and future fertility—including overall trends and changing population age structures across countries and regions—are essential to help plan for the profound social, economic, environmental, and geopolitical challenges that these changes will bring. Estimates and projections of fertility are necessary to inform policies involving resource and health-care needs, labour supply, education, gender equality, and family planning and support. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 produced up-to-date and comprehensive demographic assessments of key fertility indicators at global, regional, and national levels from 1950 to 2021 and forecast fertility metrics to 2100 based on a reference scenario and key policy-dependent alternative scenarios.MethodsTo estimate fertility indicators from 1950 to 2021, mixed-effects regression models and spatiotemporal Gaussian process regression were used to synthesise data from 8709 country-years of vital and sample registrations, 1455 surveys and censuses, and 150 other sources, and to generate age-specific fertility rates (ASFRs) for 5-year age groups from age 10 years to 54 years. ASFRs were summed across age groups to produce estimates of total fertility rate (TFR). Livebirths were calculated by multiplying ASFR and age-specific female population, then summing across ages 10–54 years. To forecast future fertility up to 2100, our Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR. CCF50 was modelled using an ensemble approach in which three sub-models (with two, three, and four covariates variously consisting of female educational attainment, contraceptive met need, population density in habitable areas, and under-5 mortality) were given equal weights, and analyses were conducted utilising the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. To capture time-series trends in CCF50 not explained by these covariates, we used a first-order autoregressive model on the residual term. CCF50 as a proportion of each 5-year ASFR was predicted using a linear mixed-effects model with fixed-effects covariates (female educational attainment and contraceptive met need) and random intercepts for geographical regions. Projected TFRs were then computed for each calendar year as the sum of single-year ASFRs across age groups. The reference forecast is our estimate of the most likely fertility future given the model, past fertility, forecasts of covariates, and historical relationships between covariates and fertility. We additionally produced forecasts for multiple alternative scenarios in each location: the UN Sustainable Development Goal (SDG) for education is achieved by 2030; the contraceptive met need SDG is achieved by 2030; pro-natal policies are enacted to create supportive environments for those who give birth; and the previous three scenarios combined. Uncertainty from past data inputs and model estimation was propagated throughout analyses by taking 1000 draws for past and present fertility estimates and 500 draws for future forecasts from the estimated distribution for each metric, with 95% uncertainty intervals (UIs) given as the 2·5 and 97·5 percentiles of the draws. To evaluate the forecasting performance of our model and others, we computed skill values—a metric assessing gain in forecasting accuracy—by comparing predicted versus observed ASFRs from the past 15 years (2007–21). A positive skill metric indicates that the model being evaluated performs better than the baseline model (here, a simplified model holding 2007 values constant in the future), and a negative metric indicates that the evaluated model performs worse than baseline.FindingsDuring the period from 1950 to 2021, global TFR more than halved, from 4·84 (95% UI 4·63–5·06) to 2·23 (2·09–2·38). Global annual livebirths peaked in 2016 at 142 million (95% UI 137–147), declining to 129 million (121–138) in 2021. Fertility rates declined in all countries and territories since 1950, with TFR remaining above 2·1—canonically considered replacement-level fertility—in 94 (46·1%) countries and territories in 2021. This included 44 of 46 countries in sub-Saharan Africa, which was the super-region with the largest share of livebirths in 2021 (29·2% [28·7–29·6]). 47 countries and territories in which lowest estimated fertility between 1950 and 2021 was below replacement experienced one or more subsequent years with higher fertility; only three of these locations rebounded above replacement levels. Future fertility rates were projected to continue to decline worldwide, reaching a global TFR of 1·83 (1·59–2·08) in 2050 and 1·59 (1·25–1·96) in 2100 under the reference scenario. The number of countries and territories with fertility rates remaining above replacement was forecast to be 49 (24·0%) in 2050 and only six (2·9%) in 2100, with three of these six countries included in the 2021 World Bank-defined low-income group, all located in the GBD super-region of sub-Saharan Africa. The proportion of livebirths occurring in sub-Saharan Africa was forecast to increase to more than half of the world's livebirths in 2100, to 41·3% (39·6–43·1) in 2050 and 54·3% (47·1–59·5) in 2100. The share of livebirths was projected to decline between 2021 and 2100 in most of the six other super-regions—decreasing, for example, in south Asia from 24·8% (23·7–25·8) in 2021 to 16·7% (14·3–19·1) in 2050 and 7·1% (4·4–10·1) in 2100—but was forecast to increase modestly in the north Africa and Middle East and high-income super-regions. Forecast estimates for the alternative combined scenario suggest that meeting SDG targets for education and contraceptive met need, as well as implementing pro-natal policies, would result in global TFRs of 1·65 (1·40–1·92) in 2050 and 1·62 (1·35–1·95) in 2100. The forecasting skill metric values for the IHME model were positive across all age groups, indicating that the model is better than the constant prediction.InterpretationFertility is declining globally, with rates in more than half of all countries and territories in 2021 below replacement level. Trends since 2000 show considerable heterogeneity in the steepness of declines, and only a small number of countries experienced even a slight fertility rebound after their lowest observed rate, with none reaching replacement level. Additionally, the distribution of livebirths across the globe is shifting, with a greater proportion occurring in the lowest-income countries. Future fertility rates will continue to decline worldwide and will remain low even under successful implementation of pro-natal policies. These changes will have far-reaching economic and societal consequences due to ageing populations and declining workforces in higher-income countries, combined with an increasing share of livebirths among the already poorest regions of the world.FundingBill & 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

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.FundingBill &amp; Melinda Gates Foundation.<br/

Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study

Background: Many causes of vision impairment can be prevented or treated. With an ageing global population, the demands for eye health services are increasing. We estimated the prevalence and relative contribution of avoidable causes of blindness and vision impairment globally from 1990 to 2020. We aimed to compare the results with the World Health Assembly Global Action Plan (WHA GAP) target of a 25% global reduction from 2010 to 2019 in avoidable vision impairment, defined as cataract and undercorrected refractive error. Methods: We did a systematic review and meta-analysis of population-based surveys of eye disease from January, 1980, to October, 2018. We fitted hierarchical models to estimate prevalence (with 95% uncertainty intervals [UIs]) of moderate and severe vision impairment (MSVI; presenting visual acuity from <6/18 to 3/60) and blindness (<3/60 or less than 10° visual field around central fixation) by cause, age, region, and year. Because of data sparsity at younger ages, our analysis focused on adults aged 50 years and older. Findings: Global crude prevalence of avoidable vision impairment and blindness in adults aged 50 years and older did not change between 2010 and 2019 (percentage change −0·2% [95% UI −1·5 to 1·0]; 2019 prevalence 9·58 cases per 1000 people [95% IU 8·51 to 10·8], 2010 prevalence 96·0 cases per 1000 people [86·0 to 107·0]). Age-standardised prevalence of avoidable blindness decreased by −15·4% [–16·8 to −14·3], while avoidable MSVI showed no change (0·5% [–0·8 to 1·6]). However, the number of cases increased for both avoidable blindness (10·8% [8·9 to 12·4]) and MSVI (31·5% [30·0 to 33·1]). The leading global causes of blindness in those aged 50 years and older in 2020 were cataract (15·2 million cases [9% IU 12·7–18·0]), followed by glaucoma (3·6 million cases [2·8–4·4]), undercorrected refractive error (2·3 million cases [1·8–2·8]), age-related macular degeneration (1·8 million cases [1·3–2·4]), and diabetic retinopathy (0·86 million cases [0·59–1·23]). Leading causes of MSVI were undercorrected refractive error (86·1 million cases [74·2–101·0]) and cataract (78·8 million cases [67·2–91·4]). Interpretation: Results suggest eye care services contributed to the observed reduction of age-standardised rates of avoidable blindness but not of MSVI, and that the target in an ageing global population was not reached. Funding: Brien Holden Vision Institute, Fondation Théa, The Fred Hollows Foundation, Bill & Melinda Gates Foundation, Lions Clubs International Foundation, Sightsavers International, and University of Heidelberg

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

Analysing land use land cover (LULC) dynamics by using remote sensing and GIS techniques: the case of Dukem town, Oromia special zone

Urbanization increases the proportion of non-agricultural workforce, and changes in land use land cover from agricultural to non-agricultural pattern continuously through time. This study designed to analyse land use land cover (LULC) dynamics in Dukem town using remote sensing & GIS techniques between 2003 and 2019 years. The Primary data sources were collected through observations and secondary data sources were collected using remotely sensed satellite images. The key results of this study revealed that built-up area augmented severely from 698.06 ha (16.45%) in 2003 to 3,091.67 hectare (72.81%) in 2019. Specifically, agricultural land extremely decreased in all years from 2,764.37 hectare (65.12%) in 2003 to 999.05 hectare (23.53%) in 2019. Hence, accordingly, this incident reduced agricultural land and increased built areas dramatically