Factors influencing adoption of improved potato (Belete) variety: evidence from Ethiopian smallholder farmers

Saabunud / Received 15.08.2019 ; Aktsepteeritud / Accepted 11.12.2019 ; Avaldatud veebis / Published online 25.12.2019 ; Vastutav autor / Corresponding author: Guta Regasa e-mail: [email protected] the adoption of improved crop varieties is very important to increase income, reduce hunger, sustain food security, and to reduce poverty in sub-Saharan Africa like Ethiopia. Similarly, Belete potato variety is one of the improved varieties that have been utilized by Ethiopian farmers, but this variety was not conjointly adopted in all parts of the country. Thus, this research was intended to analyze factors influencing rural farmers' decision for the adoption of improved potato varieties in Southern Ethiopia. Both qualitative and quantitative data were collected from primary and secondary sources. To select the sample respondents, two-stage sampling techniques were employed and finally, 146 households' heads were selected. To get the data survey questionnai-res, interview schedules, Focused Group Discussions, observations and key informant interviews were employed. To analyze the data, both descriptive statistics and econometric model were employed. Accordingly, the econometric model indicated that family labour, access to fertilizer, access to credit service, frequency of extension contacts, participation in training and field day, and educational level were positively and significantly influenced the adoption of Belete potato adoption, however, the market distance was influenced negatively. Therefore, this result implies that researchers, policymakers, extension service providers and other concerned bodies should be given attention to increasing the adoption of improved Belete potato variety

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

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

Prevalence of pterygium and its associated factors among adults aged 18 years and above in Gambella town, Southwest Ethiopia, May 2019.

IntroductionA pterygium is a wing-shaped fibro-vascular growth of conjunctiva on the superficial cornea/conjunctiva. It is an elastotic degeneration of conjunctival stroma mainly due to Ultraviolet light exposure. The prevalence of pterygium varies in different environmental conditions. Its magnitude varies widely from 1.1% to 53% globally and in Ethiopia, it reaches from 8.8% to 38.7%.ObjectiveTo determine the prevalence of pterygium and its associated factors among adults aged 18 years and above in Gambella town, Southwest Ethiopia, 2019.Methods and materialsA community based cross-sectional study was conducted from April 15 to May 3, 2019, in Gambella town. A total of 402 study participants were selected using a systematic random sampling technique. A pre-tested semi-structured questionnaire, torch, and magnifying loops were used to collect data. The data was entered into epidemiological information 7.1 and exported to statistical package for social science version 20 for analysis. The binary and multivariate logistic regression analysis model was fitted to identify factors associated with pterygium. Odds ratio with respected 95% CI was used to identify the direction and strength of association.ResultsA total of 400 participants were examined with a response rate of 99.50%. The mean age of the study participants was 39.9±9.8years. The prevalence of pterygium among adults aged 18 years and above in Gambella town was 127(31.80%), (95% CI: 27.3, 36.3). male sex (AOR = 2.10 (95% CI: 1.26, 3.45), sunlight exposure (AOR = 6.86 (95% CI: 4.00, 11.79) and outdoor works (AOR = 2.10 (95% CI: 1.21, 3.60) were positively associated with pterygium whereas wearing sunglass/hat was a protective factor.ConclusionThe prevalence of pterygium was high among adults living in Gambella town. Wearing sunglass/hat and reducing exposure time to the sun was important to reduce the development of pterygium in adults

Case-control study on determinants of uterine rupture among mothers who gave birth at Hawassa University comprehensive specialized hospital.

BackgroundUterine rupture is defined as tearing of the uterine wall during pregnancy or delivery. It can occur during pregnancy or labor and delivery. Rupture of the uterus is a catastrophic event resulting in the death of the baby, and severe maternal morbidity and mortality Despite different interventions done by stakeholders, it remained one of the leading public problems in developing countries like Ethiopia.ObjectiveThis study assessed the prevalence and determinants of uterine rupture among mothers who gave birth at Hawassa University comprehensive specialized hospital from July 2015 to June 2020G.C.MethodA case-control study was conducted by reviewing data from a total of 582 patient charts which include 194 cases and 388 controls with a case-to-control ratio of 1:2. Then the data was extracted using a pre-tested and structured data extraction sheet. Data were entered using Epi data 3.1 and exported to SPSS and analyzed using SPSS 20. The association between independent variables and uterine rupture was estimated using an odds ratio with 95% confidence intervals. The statistical significance of the association was declared at P-value ResultThere were a total of 22,586 deliveries and 247 confirmed cases of uterine rupture which makes the prevalence 1.09%. Lack of ANC (Ante-natal care) (AOR = 7.5; 95% CI: 1.9-30.3) inadequate ANC (AOR = 2.45; 95% CI: 1.1-5.57), gravidity ≥5 (AOR = 3.3; 95% CI: 1.36-8.12), obstructed labor (AOR = 38.3; 95% CI: 17.8-82.4) and fetal macrosomia (AOR = 8; 95% CI: 17.8-82.4) are variables which increase the odds of developing uterine rupture. Mothers without additional medical or obstetric conditions are more likely (AOR = 4.2; 95% CI: 2.1-8.65) to develop uterine rupture than mothers with additional medical or obstetric conditions.ConclusionThe prevalence of uterine rupture is high in the study area. The study also revealed that a decrease in ANC follow-up, gravidity of ≥5, obstructed labor, and fetal weight of >4kg are significantly associated with uterine rupture. Improving the quality of ANC follow-up, intrapartum follow-up and proper estimation of fetal weight are recommended interventions from the study

Glycemic Control of Diabetes Mellitus Patients in Referral Hospitals of Amhara Region, Ethiopia: A Cross-Sectional Study

Background. Glycemic control is the level of glucose in diabetes patient. Evidence regarding glycemic control is scarce in resource-limited settings, and this study was conducted to generate information regarding the prevalence and predictors of glycemic control among diabetes mellitus patients attending their care from the referral hospitals of the Amhara region, Ethiopia. Methods. A cross-sectional study design was implemented. A simple random sampling technique was used. Data were collected from March 2018 to January 2020. The data were collected using interviews, chart review, and blood samples. Hemoglobin A1c was measured using high-performance liquid chromatography. Data were entered into Epi-info software and analyzed by SPSS software. Descriptive statistics were used to estimate the prevalence of glycemic control; linear regression was used to identify the predictors of HbA1c. Results. A total of 2554 diabetes patients were included giving for the response rate of 95.83%. The mean age of the study participants was 54.08 years [SD standard deviation±8.38 years]. The mean HbA1c of the study participants was 7.31% [SD±0.94%]. Glycemic control was poor in 55.32% [95% CI: 53.4%-57.25%] of diabetes patients. The glycemic control of diabetes patients was determined by BMI (β 0.1; [95% CI: 0.09-0.1]), type 2 diabetes (β -0.14; [95% CI: -0.11-0.16]), age (β 0.22; [95% CI: 0.02-0.024]), duration of the disease (β 0.04; [95% CI: 0.037-0.042]), the presence of hypertension (β 0.12; [95% CI:0.09–0.16]), regular physical exercise (β -0.06; [95% CI: -0.03-0.09]), medication adherence (β -0.16; [95% CI: -0.14-0.18]), and male (β 0.34; [95% CI: 0.31-.037]). Conclusion. The glycemic control of diabetes patients was poor, and it needs the attention of decision-makers

Multivariable linear regression model on factors associated with physical domain of HRQoL among adult hypertensive patients on treatment at public health facilities in Dessie City Administration, Northeast Ethiopia, 2021 (n = 360).

Multivariable linear regression model on factors associated with physical domain of HRQoL among adult hypertensive patients on treatment at public health facilities in Dessie City Administration, Northeast Ethiopia, 2021 (n = 360).</p

Multivariable linear regression model on factors associated with social domain of HRQoL among adult hypertensive patients on treatment at public health facilities in Dessie City Administration, Northeast Ethiopia, 2021 (n = 360).

Multivariable linear regression model on factors associated with social domain of HRQoL among adult hypertensive patients on treatment at public health facilities in Dessie City Administration, Northeast Ethiopia, 2021 (n = 360).</p

Characteristics of hypertensive patients on treatment attending public health facilities in Dessie City Administration, Dessie, Northeast Ethiopia 2021 (360).

Characteristics of hypertensive patients on treatment attending public health facilities in Dessie City Administration, Dessie, Northeast Ethiopia 2021 (360).</p