Prevalence and risk factors of bovine mastitis in Ambo town of West Shewa Zone, Oromia, Ethiopia

A cross-sectional study was conducted to estimate the prevalence and associatedrisk factors of mastitis in dairy cows from November, 2012 to July, 2013 in Ambotown of West Shewa Zone, Oromia Regional State. Thorough clinical examinationwas made on all lactating cows for evidence of signs of clinical mastitis followedby collection of milk sample for examination of gross changes of milk secretionand screening using California Mastitis Test (CMT). Mastitis was categorized asclinical if lactating cows exhibited clinical features of mastitis, or subclinical basedon degree of coagulation up on examination using CMT. A total of 151 dairy cowswere selected from all volunteer dairy farms in Ambo town of West Shewa Zone ofOromia region state. Sixty three (41.7%) cows had mastitis, of which 9.9% (15/151) were clinical and 31.8% (48/151) were subclinical mastitis cases. The quarter level prevalence was 44.4% (268/604), comprising 9.3% (56/604) clinical and 31.8% (198/604) subclinical forms of mastitis. In addition, 5.5% (33/604) of teats were found to be blind up on clinical examination of udder and teat. The Chi-square (χ2) analysis of intrinsic risk factors revealed significantly (P<0.05) higher prevalence of mastitis in crossbred cattle (47.2%) than indigenous (15.4%), in cattle above 7 years (75%) than less than 2-6 years of age (28%) and cows given more than 4 calves (81.3%) than those with less than 4 calves (31.1%) irrespective to their lactation stage. There was also significantly (P<0.05) higher mastitis prevalence in larger (46.6%) than smaller herds (24.2%) and among the farming systems in semi-intensive (47.1%) and intensive (42.3%) than extensive (8.1%) management system. The present study indicated higher prevalence of mastitis linked with several risk factors. Thus, early diagnosis and regular screening of cows for subclinical mastitis together with treatment of clinical cases are of paramount importance. Moreover, control and prevention strategies should be  designed and implemented with great emphasis given to risk factors to reduce bovine mastitis and its impact on milk production and food security.Keywords: Ambo town, CMT, Dairy Cows, Mastitis, Prevalence, Risk factor

Prevalence, risk factors and antimicrobial susceptibility profile of Salmonella isolated from dogs of Ambo, Bako and Gojo towns of West Shoa, Ethiopia

Salmonella is the most known zoonotic bacterial agent, which produces salmonellosis in animals as wells as in humans. The objectives of this study were to estimate the prevalence, to determine antimicrobial susceptibility and to assess risk factors associated with Salmonella shedding in dogs in selected towns of West Shoa Zone, Oromia, Ethiopia. Using a cross-sectional design, a total of 438 rectal swab samples were collected from randomly selected dogs for isolation and identification of Salmonella using standard procedures. A questionnaire survey was also administered. The results showed that 48 (11.0%, 95%, [CI]: 8.2% - 14.3 %) dogs were positive for Salmonella. The occurrenceof Salmonella was 10.9% (26/238), 11.6% (15/129) and 9.9% (7/71) in Ambo, Bako and Gojo towns respectively. Salmonella shedding was significantly higher in older dogs (15.0%) than younger ones (6.1 %) (P<0.05). The occurrence of Salmonella was not significantly associated with study towns, sex, breed of dogs,educational level of owners, presence of other domestic animals and housing system (P>0.05). Multivariable logistic regression analysis showed that the odds of isolation of Salmonella was significantly higher in old than young dogs (OR): 2.6, 95% CI: 1.3-5.6, P=0.007) and in dogs eating mixed type of feeds (raw animal products, household leftover) than cooked feed (OR = 9.0, 95% CI: 1.2 – 68.1, P=0.033). There was also a significant difference between kebeles of the study areas in that Salmonella isolation was nearly nine times higher in Bako Kebele 02 (P=0.005) as compared to Ambo kebele 03. Salmonella isolates have developed the highest level of resistance for ampicillin (100%), tetracycline  (93.3%) and streptomycin (80.0 %), however, all isolates were susceptible to Norfloxacin. The occurrence of Salmonella, most of which resistant to commonly used antimicrobials, in rectal swab samples show the potential threat dogs may pose to public health in the study area.Keywords: Antimicrobial Resistance; Dog; Ethiopia; Prevalence; Risk factor; Salmonell

Genotypes and their Growing Environments Influence on Physicochemical Qualities of Tef Grain in the Highlands of Ethiopia

አህፅሮት  ከዋና ዋና የብርዕ አገዳ ሰብሎች መካከል አንዱ የሆነው የጤፍ ሰብል  እና ከሱ የሚሰራው እንጀራ ለአብዛኛው የኢትዮጵያ ህዝብ ዋና ምግብ ሆኖ በማገልገል ላይ ይገኛል፡፡ የጤፍ ፍሬ አካላዊ/ቅርፃዊ ይዘት በተለይም ቀለሙ የበላተኛውን ቀልብ በመሳብ፣ የገበያ ዋጋን በመወሰንና በአልሚ ምግብ ይዘቱ ላይ ከፍተኛውን ድርሻ ይወስዳል፡፡ ይሁንና የጤፍ ፍሬ አካላዊና ኬሚካላዊ ባህርያት ልዩነቶች ከጤፉ ዝርያ  ወይም ከሚበቅልበት አካባቢ ተፈጥሯዊ ሁኔታ ጋር የሚዛመድ ወይም የማይዛመድ መሆኑን እስከ አሁን በጥናት አልተረጋገጠም፡፡ በመሆኑም ይህ ጥናት የጤፍ ፍሬ አካለዊና ስነ-ምግባዊ/ኬሚካላዊ/ ይዘት በጤፍ ዝርያዎችና ጤፍ በሚበቅልበት ቦታ ያለው ከባቢያዊ ሁኔታ ሊያደርስ የሚችለውን ተፅዕኖ ውጤት ዳስሷል፡፡ በአስር የተለያዩ ሥነ-ምህዳር (የአየር ፀባይና የአፈር ዓይነት) ባላቸው የመካከለኛውና ሰሜን ምዕራብ ኢትዮጵያ አካባቢዎች እና ዘጠኝ የተለያዩ ባህርያት ያለቸው የጤፍ ዝርያዎች (የፍሬ ቀለማቸው ነጭ የሁኑ ሰባት እና ቀይ ሁለት) ለአንድ ዓመት (በ2009/10 ዓ.ም፣ የመኸር ወቅት) ተዘርተው የአካላዊና ኬሚካላዊ ባህርያታቸው ተጠንቷል፡፡ በጥናቱ መሰረት በአብዛኛው በጤፍ ፍሬ አካላዊም ይሁን ስነ-ኬሚካለዊ ባህርያት ላይ በዝርያዎች ዓይነት፣ በተዘሩበት ቦታ እና ዝርያዎች ከተዘሩበት ቦታ ጋር ባላቸው መስተጋብር መካከል ከፍተኛ ልዩነት (P ≤ 0.01)  አስመዝግበዋል፡፡ የነጭ ጤፍ ዝርያዎች በጤፍ ቀለም መለኪያ መስፈርት ማለትም የቀለም ጥግበት/ምጠት/ (saturation) እና የቀለም ፍካት/ብሩህነት/ (brightnes) ልዩነቶች የመጡት በአብዛኛው በሚበቅሉበት አከባቢ ተፅዕኖ (43.9በመቶ, እና 66.8በመቶ, በቅደም-ተከተል) እና ዝርያዎቹ ከሚበቅሉበት አካባቢ ያላቸው መስተጋበር (33.7በመቶ, እና 24.5በመቶ, በቅደም-ተከተል) ሲሆን የዝርያዎች ልዩነት በተናጥል ያመጣው ለውጥ ግን አነስተኛ (22.5በመቶ, እና 8.7በመቶ, በቅደም-ተከተል) ሆኖ ተገኝቷል፡፡ ጤፉ የተዘራበት አካባቢ የዝናብ መጠን ሲጨምር የፍሬው ቀለም ፍካት የመቀነስ ሁኔታዎች ነበሩት፡፡ በተጨማሪም ጤፍ የተዘራበቸው መሬቶች የአፈር ባህርያት ለምሳሌ ኮምጣጣነት፣ የንጥረ-ነገር ቅይይር ብቃት፣ ካልሽየም፣ ማግኒዝየም እና ፎስፎረስ የመሳሰሉት በቀለም ፍካት ላይ ቀጥተኛ/አወንታዊ እንዲሁም በቀለሙ ጥግበት ላይ አሉታዊ ተፅዕኖ ፈጥረውበታል፡፡ ይሁንና የጤፍ ፍሬ ንጥረ-ነገር ይዘት ከፍሬ ቀለሙ ጋር አጥጋቢ ተዛምዶ እንዳለው ጥናቱ አያሳይም፡፡ ጤፍ የበቀለባቸው ቦታዎች የአፈር ባህርያትና  የአየር ፀባይ ከጤፍ ፍሬ ክብደትና መጠን ጋር ግን ዝምድና እንዳለቸው ይሳያል፡፡ የዝርያዎቹ ባህሪ ከጤፍ ፍሬ ክብደት ይልቅ መጠን ላይ ያላቸው ተፅዕኖ ይጎላል፡፡ ጤፍ የተዘራበት አካበባቢ ከባህር ወለል ከፍታው እና የዝናብ መጠን በጨመረ ቁጥር የጤፍ ፍሬ ክብደት እየጨመረ የመሄድ አዝማሚያ ታይቷል፡፡ ጤፍ የተመረተበት አካባቢ ሁኔታ እንዲሁም ዝርያዎች ከተዘሩበት አካባቢ ጋር ያለው መስተጋብር በጤፍ ፍሬ ንጥረ-ነገር (ፎስፎረስ፣ ፖታሽየም፣ ካልሽየም፣ ማግኒዠየም፣ ሶዲየም፣ አይረን፣ ዚነክ፣ ማንጋኒዝ፣ መዳብ፣ እና ሞሊቢዲነም)  ላይ ያላቸው ተፅዕኖ ዝርያዎቹ በተናጥል ከሚያሳዩት ተፅዕኖ በእጅጉ በልጦ ተገኝቷል፡፡ የጤፍ ፍሬ የቃጫ፣ የቅባት፣ የፕሮቲን እና የሰታርች መጠንም ጤፍ በበቀለበት አካባቢ 70.0በመቶ, 46.9በመቶ, 70.9በመቶ, እና 20.5በመቶ, በቅደም-ተከተል) እና የጤፍ ዝርያዎች ከበቀሉበት አካባቢ ያለው መስተጋብር (28.3በመቶ, 47.3በመቶ, 27.5በመቶ, እና  67.7በመቶ, በቅደም-ተከተል) ከፍተኛውን ልዩነት ያመጡ ሲሆን ዝርያዎቹ በተናጥል (1.7በመቶ. 5.8በመቶ, 1.6በመቶ, እና  11.8በመቶ, በቅደም-ተከተል) እምብዛም ተፅዕኖ አላደረሱም፡፡ የዝርያዎች ከበቀሉበት አካባቢ ጋር ያለው መስተጋብር ለጤፍ ፍሬ ንጥረ-ነገሮች እና ለቃጫ፣ ለቅባት፣ ለፕሮቲን እና ስታርች ያበረከተውን መጠን በትንተና ሲታይ በአስሩም አካባቢዎች አንድ ዝርያ ብቻውን ከሌሎች በልዩነት ገንኖ አልወጣም፡፡ የቀይ ጤፍ ፍሬ በንጥረ-ነገር ይዘቱ ከነጭ ይበልጣል የሚለው አሰተሳሰብ በዚህ ምርምር ውጤት ተቀባይነት አላገኘም፡፡ ይለቁንም ሁለቱም ቀይ የጤፍ ዝርያዎች በስታርች ይዘታቸው ከሁሉም ያነሱ ሁነው ተመዝግበዋል፡፡ በአጠቃላይ የዚህ ምርምር ውጤት የሚያሳየው የጤፍ ፍሬ አካላዊና ኬሚካላዊ ባሕርያት ልዩነቶች የሚመጡት በአብዛኛው የተዘራበት አካባቢ እና ዝርያዎቹ ከተዘሩበት አካባቢ ያለቸው መስተጋብር የፈጠረው መሆኑን ነው፡፡ ስለዚህ በኢትዮጵያውያን ተፈላጊ የሆኑ የጤፍ ፍሬ አካላዊ ባህርያት እና ኬሚካላዊ ይዘት ማሻሻል ይቻል ዘንድ ለሰብሉ ተስማሚ የሆነ አካባቢ፣ የአፈር ኮምጣጣነትን የማስተካከል እና የአፈር ንጥረ-ነገሮችን መምረጥ አስፈላጊ ነው፡፡   Abstract Tef is one of the main cereal crops and its injera is the major staple food for the majority of Ethiopians. Tef grain physical quality especially color is an important attribute influencing preference of consumers, the market prices and nutritional quality. However, the effect of the growing environment and the genotype on its physicochemical quality is not yet investigated. The study was, therefore, aimed at assessing the effects of genotypes (G) and growing environments (E) on physicochemical quality of tef grain. Ten diverse locations and nine tef genotypes were selected based on soil and climatic variability as well as variation in grain color [seven white and two brown). Most of tef grain physicochemical contents significantly (P ≤ 0.01) different between genotype, environment and G x E interaction effects. The environment, wherein tef was grown, accounted for the greatest proportion of variation in S (saturation), and V (brightness) values of the white grain genotypes (16.8%, 43.9%, and 66.8%) and  G x E interaction effects (33.7%, and 24.5%) as compared to genotype alone (22.5%, and8.7%).  Growing areas of greatest precipitation will reduce the brightness value of tef grain.  Soil parameters such as soil pH, Ca, Mg, and P play a positive and negative roles in grain brightness and saturation values of tef, respectively. However, grain minerals had no influential role on the color of tef grain in this study. Tef growing areas tied to both climatic and edaphic factors are critical in governing both grain density and size.  The role of genotype was more influential in the grain size of tef than the grain density. The raise of growing locations altitudes and precipitation increased tef grain density. The environment and genotype by environment interaction  effects accounted a greater proportion of  the variation of grain P, K, Ca, Mg, Na, S, Fe, Zn, B, Mn, Cu, and Mo minerals concentrations, while the genotype effect was relatively low.  The variability of grain fiber, fat, protein, and starch compositions were also due to environment (70.0%, 46.9%, 70.9%, and 20.5%, respectively), and genotype by environment interaction (28.3%, 47.3, 27.5%, and 67.7%, respectively), while genotype played a minor role (1.7%. 5.8%, 1.6%, and 11.8%. respectively). With location by genotype interactions, there was no consistency in the dominance of any single genotype across all 10 locations in most of the tef grain mineral concentration and proximate compositions. The brown grain color genotype superiority in grain mineral and proximate composition is not supported by this research, rather the brown color genotypes were the lowest in grain starch concentration on the majority of the locations in this study. Generally, most physical and chemical quality variables of tef grain were markedly influenced by tef growing environments and their interactions with a minuscule role of genotype. Therefore, selection of suitable teff growing environments and proper soil pH and nutrient management would be so important for harnessing the maximum potentials of tef with the desired physicochemical quality of tef grain in Ethiopia.   &nbsp

Prevalence, risk factors and antibiogram of Escherichia coli isolated from dogs in Ambo, Gojo and Bako towns of Oromia region, Ethiopia

Dogs are a potential reservoir for Escherichia coli and other zoonotic bacterial pathogens posing the risk of infection to humans and other animals. A crosssectional study was used to collect 438 rectal swab samples from apparently healthy dogs of Ambo, Gojo, and Bako towns of West Shewa Zone with the objectives of investigating the prevalence, risk factors and antibiogram of E. coli. A questionnaire survey was administered at the household level to collect data on potential risk factors to acquire E. coli infection. Prevalence was determined by using standard bacteriological culture techniques. Further  confirmation and antimicrobial susceptibility testing of E. coli using the minimum inhibitory concentration (MIC) method were  conducted using Automated Phoenix Machine. The overall prevalence of E. coli was found to be 24.2% (95% confidence interval [CI]:20.26-28.49%). The isolation rate of E. coli was the highest in Bako 31.01%, followed by Gojo 30.99% and Ambo 18.49% towns.  Univariable and multivariable logistic regression analyses revealed that there was a significant association between E. coli prevalence and towns and Kebeles (P<0.05). However, age, sex, the presence of other domestic animals in the household, types of the housing system, educational level of the dogs’ owners, and type of feed provided to the dogs did not significantly correlate with the isolation of E. coli (P > 0.05). E. coli isolates were pan-susceptible to amikacin, ertapenem, imipenem, piperacillin-tazobactam, netilmicin, ampicillin, piperacillin, cefoxitin, ciprofloxacin, levofloxacin, and meropenem. A low level of resistance was found to aztreonam, ceftriaxone, (each 13.95%), cefazolin (22.73%), gentamicin (11.90%), and trimethoprim-sulphamethoxazole (6.97%). E. coli isolates showed multidrug resistance to aztreonam, cefazolin, ceftriaxone, gentamicin, and trimethoprim-sulphamethoxazole (14.0%). This demonstrated E. coli prevalence and moderately low antimicrobial resistance  suggests the possible risk of infection of humans. Therefore, raising public awareness about zoonotic canine disease prevention measures and good hygienic practices are essential.. Keywords: Antibiogram, Dog, Escherichia coli, Ethiopia, Prevalence, Risk facto

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

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

Teff Grain Physical and Chemical Quality Responses to Soil Physicochemical Properties and the Environment

Teff is the only cultivated cereal crop from the genus Eragrostis and it is the major staple food of Ethiopians. In Ethiopia, the quality of teff and its market price are primarily determined by its grain color. The objective of this study was to evaluate the effects of soil physicochemical characteristics across multiple locations in the two main teff growing regions of Amhara and Oromia states in Ethiopia on teff grain color and nutritional quality of a single variety. Grain and soil samples were collected from 24 field sites cultivated with the popular teff variety ‘Quncho’ (DZ-Cr-387/RIL-355). The teff grain samples collected from the 24 locations were evaluated for grain color, proximate composition, amino acid composition, and grain mineral concentration and the soil samples were analyzed for their physicochemical properties. Sample location means were considered different p < 0.05. Teff grain color indices of hue (H), saturation (S), and brightness (V), grain proximate composition, amino acid composition, and mineral concentration differed among locations (p < 0.05). There were significant negative correlations between grain S color value and soil pH, SOC, Ca, Mg, S, and Na. Soils with greater pH, SOC, Ca, Mg, and S generally had lower S values and thus, whiter color teff grains. There were considerable variations in the measured parameters for soil and teff grain physicochemical properties. The results indicated an opportunity for management interventions necessary to obtain uniformity in grain color and chemical composition for the same variety of teff grown in the two major regions in Ethiopia

Seroprevalence and associated risk factors of Brucellosis in dairy cattle in selected towns of West Shewa, Ethiopia

Bovine brucellosis is a contagious disease of cattle causing reproductive failure, loss of milk production and zoonosis worldwide. A cross-sectional epidemiological study was conducted on 816 dairy cattle (449 were cows) from 60 dairy farms to  determine the seroprevalence and associated risk factors of bovine brucellosis in dairy cattle in selected towns of West Shewa, Ethiopia. Sera were collected,  screened by Rose Bengale Plate Test and positive sera were further tested by Complement Fixation Test for confirmation of Brucella seropositivity. Data regarding risk factors were obtained from records and questionnaire. The association of brucellosis with risk factors was analyzed by Chi-square/Fischer’s exact test. The result showed 0.49% (4/816), 0.9% (4/449) and 3.3% (2/60) seroprevalence in cattle, cows alone and at herd level, respectively. Among the risk factors herd size (X2=4.24), history of abortion (OR=8.94) and retained fetal membrane (OR=8.39) showed significant association (p<0.05). The results of questionnaire survey showed 90% of the respondents do not know about brucellosis and use no maternity pen for their cows and 86.7% of them use no safety measure during assisting  delivery and disposing afterbirth materials. The prevalence of bovine brucellosis in the present study was low; however, the risk attributed by a single carrier cow to the public health is high in the presence of lack of awareness and poor hygienic practice. Therefore, at this prevalence level to test and cull seropositive cows at farm level and creating public awareness is suggested.Key words: Brucellosis, Dairy cattle, Seroprevalence, Risk factor, West Shewa Séroprévalence de la brucellose et facteurs de risque associés chez les bovins laitiers, dans certaines villes de l’ouest de Shewa en ÉthiopieLa brucellose bovine est une maladie contagieuse qui est à l’origine de l’infertilité, de la perte de production de lait et de zoonoses, partout dans le monde. Une étude épidémiologique transversale a été réalisée sur 816 bovins laitiers (449 vaches) issus de 60 fermes laitières, dans le but de déterminer la séroprévalence de la  brucellose bovine et les facteurs de risque associés chez les bovins laitiers de certaines villes de l’Ouest de Shewa en Éthiopie. Des sérums ont été prélevés,   étudiés au moyen du test au rose bengale, et les sérums qui se sont révélés positifs ont fait l’objet d’un examen plus approfondi utilisant le test de fixation de  complément pour confirmer la séropositivité à la Brucella. Les données relatives aux facteurs de risque ont été obtenues à partir des dossiers et d’un questionnaire administré. L’association de la brucellose aux facteurs de risque a été analysée en utilisant le test de Chi-carré / méthode exacte de Fischer. Le résultat a montré une séroprévalence de 0,49% (4/816), 0,9% (4/449) et 3,3% (2/60) respectivement chez les bovins, les vaches seules et au niveau du troupeau. Parmi les facteurs de risque, la taille du troupeau (X2 = 4,24), les antécédents d’avortement (OR = 8,94) et la rétention des membranes foetales (OR = 8,39) ont montré une association significative (p <0,05). Les résultats de l’enquête par questionnaire ont révélé que 90% des répondants ne savaient rien de la brucellose et n’avaient pas d’enclos de vêlage pour leurs vaches et que 86,7% d’entre eux n’utilisaient aucune mesure de sécurité pendant la mise-bas et l’élimination du placenta après la mise-bas. Le taux de prévalence de la brucellose bovine dans la présente étude était faible;  cependant, le risque que constitue une seule vache porteuse pour la santé publique est élevé compte tenu du manque de sensibilisation et de l’application de mauvaises pratiques d’hygiène. Par conséquent, avec ce taux de prévalence, il est proposé de procéder au diagnostic et à l’élimination de vaches séropositives au niveau de la ferme et à la sensibilisation du public.Mots-clés : brucellose, bovins laitiers, séroprévalence, facteur de risque, Ouest de Shew

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