Framework for maternal morbidity and mortality interventions in Ethiopia: a systematic review protocol.

BACKGROUND: Maternal morbidity and mortality remain critical public health challenges in Ethiopia with limited evidence on the effectiveness of interventions and health promotion strategies. A scoping review of the existing literature on maternal morbidity and mortality interventions and health promotion in Ethiopia can provide a comprehensive overview of the current evidence, identify research gaps and establish a framework for successful maternal morbidity and mortality interventions. OBJECTIVE: The systematic review seeks to assess the existing literature on maternal morbidity and mortality interventions in Ethiopia to develop an evidence-based framework for effective interventions. METHOD: The methodology for this study adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols guidelines for systematic review protocol. A comprehensive search strategy will be devised, in compliance with the highly sensitive search guidelines of Cochrane, which will involve using both snowball methods to identify relevant articles and searching electronic databases using specific key search terms. The following databases will be searched for studies to be included in the systematic review: MEDLINE (via PubMed), Embase, Scopus, Google Scholar, Web of Science, Science Direct and African Journals Online (AJOL).The search will be restricted to English language publications starting from January 2010 to May 2023. In a comprehensive review process, independent reviewers will meticulously assess titles, abstracts and full texts of studies, ensuring alignment with predetermined inclusion and exclusion criteria at each stage of selection.Quality evaluation instruments appropriate for each research design will be used to assess the quality of the selected studies. The findings from the included studies will be analysed and summarised using a narrative synthesis approach. ETHICS AND DISSEMINATION: Since this systematic review is based on the reviewing of existing literature and will not involve the collection of primary data, ethical approval is not required. The results will be disseminated through peer-reviewed publication and conference presentations. PROSPERO REGISTRATION NUMBER: CRD42023420990.

Time to recovery and its predictors among under five children in outpatient therapeutic feeding programme in Borena zone, Southern Ethiopia: a retrospective cohort study.

OBJECTIVES: This study aimed to assess the time to recovery and its predictors among 6-59 months aged children treated at an outpatient therapeutic feeding programme in Borena zone. DESIGN: A retrospective cohort study. SETTING: Facility based; 23 treatment sites included in this study. PARTICIPANTS: Among the cohorts of 601 children aged 6-59 months enrolled from July 2019 to June 2021, records of 590 children were selected using systematic random sampling. Transfers and incomplete records were excluded. PRIMARY AND SECONDARY OUTCOME MEASURES: Time to recovery was a main outcome while its predictors were secondary outcomes. RESULTS: The median recovery time was 49 days (95% CI=49 to 52) with a recovery rate of 79.8% (95% CI=76.4 to 83.0). Absence of comorbidity (adjusted HR, AHR=1.72, 95% CI=1.08 to 2.73), referral way by trained mothers on screening (AHR=1.91, 95% CI=1.25 to 2.91), new admission (AHR=1.59, 95% CI=1.05 to 2.41) and adequate Plumpy'Nut provision (AHR=2.10, 95% CI=1.72 to 2.56) were significantly associated with time to recovery. It is also found that being from a distance ≥30 min to treatment site lowers a chance of recovery by 27% (AHR=0.73, 95% CI=0.60 to 0.89). CONCLUSIONS: The findings showed that a time to recovery was within an acceptable range. Incidence of recovery is enhanced with early case detection, proper management, nearby service, new admissions, provision of adequate Plumpy'Nut and enabling mothers to screen their own children for acute malnutrition. However, we did not observe a statistically significant association among breastfeeding status, type of health facility, wasting type, vaccination and routine medications. Service providers should improve adherence to treatment protocols, defaulter tracing, community outreach and timely case identification.

Boosting the Performance of Artificial Intelligence-Driven Models in Predicting COVID-19 Mortality in Ethiopia.

Like other nations around the world, Ethiopia has suffered negative effects from COVID-19. The objective of this study was to predict COVID-19 mortality using Artificial Intelligence (AI)-driven models. Two-year daily recorded data related to COVID-19 were trained and tested to predict mortality using machine learning algorithms. Normalization of features, sensitivity analysis for feature selection, modelling of AI-driven models, and comparing the boosting model with single AI-driven models were the main activities performed in this study. Prediction of COVID-19 mortality was conducted using a combination of four dominant feature variables, and hence, the best determination of coefficient (DC) of AdaBoost, KNN, ANN-6, and SVM in the prediction process were 0.9422, 0.8618, 0.8629, and 0.7171, respectively. The Boosting model improved the performance of the individual AI-driven models KNN, SVM, and ANN-6 by 7.94, 22.51, and 8.02 percent, respectively, at the verification stage using the testing dataset. This suggests that the boosting model has the best performance for prediction of COVID-19 mortality in Ethiopia. As a result, it suggests a promising potential performance of boosting ensemble model to be applied in predicting mortality and cases from similarly recorded daily data to predict mortality due to COVID-19 in other parts of the world.

Artificial Intelligence-Driven Ensemble Model for Predicting Mortality Due to COVID-19 in East Africa.

East Africa was not exempt from the devastating effects of COVID-19, which led to the nearly complete cessation of social and economic activities worldwide. The objective of this study was to predict mortality due to COVID-19 using an artificial intelligence-driven ensemble model in East Africa. The dataset, which spans two years, was divided into training and verification datasets. To predict the mortality, three steps were conducted, which included a sensitivity analysis, the modelling of four single AI-driven models, and development of four ensemble models. Four dominant input variables were selected to conduct the single models. Hence, the coefficients of determination of ANFIS, FFNN, SVM, and MLR were 0.9273, 0.8586, 0.8490, and 0.7956, respectively. The non-linear ensemble approaches performed better than the linear approaches, and the ANFIS ensemble was the best-performing ensemble approach that boosted the predicting performance of the single AI-driven models. This fact revealed the promising capability of ensemble models for predicting the daily mortality due to COVID-19 in other parts of the globe.

The burden of injury in Central, Eastern, and Western European sub-region: a systematic analysis from the Global Burden of Disease 2019 Study.

BACKGROUND: Injury remains a major concern to public health in the European region. Previous iterations of the Global Burden of Disease (GBD) study showed wide variation in injury death and disability adjusted life year (DALY) rates across Europe, indicating injury inequality gaps between sub-regions and countries. The objectives of this study were to: 1) compare GBD 2019 estimates on injury mortality and DALYs across European sub-regions and countries by cause-of-injury category and sex; 2) examine changes in injury DALY rates over a 20 year-period by cause-of-injury category, sub-region and country; and 3) assess inequalities in injury mortality and DALY rates across the countries. METHODS: We performed a secondary database descriptive study using the GBD 2019 results on injuries in 44 European countries from 2000 to 2019. Inequality in DALY rates between these countries was assessed by calculating the DALY rate ratio between the highest-ranking country and lowest-ranking country in each year. RESULTS: In 2019, in Eastern Europe 80 [95% uncertainty interval (UI): 71 to 89] people per 100,000 died from injuries; twice as high compared to Central Europe (38 injury deaths per 100,000; 95% UI 34 to 42) and three times as high compared to Western Europe (27 injury deaths per 100,000; 95%UI 25 to 28). The injury DALY rates showed less pronounced differences between Eastern (5129 DALYs per 100,000; 95% UI: 4547 to 5864), Central (2940 DALYs per 100,000; 95% UI: 2452 to 3546) and Western Europe (1782 DALYs per 100,000; 95% UI: 1523 to 2115). Injury DALY rate was lowest in Italy (1489 DALYs per 100,000) and highest in Ukraine (5553 DALYs per 100,000). The difference in injury DALY rates by country was larger for males compared to females. The DALY rate ratio was highest in 2005, with DALY rate in the lowest-ranking country (Russian Federation) 6.0 times higher compared to the highest-ranking country (Malta). After 2005, the DALY rate ratio between the lowest- and the highest-ranking country gradually decreased to 3.7 in 2019. CONCLUSIONS: Injury mortality and DALY rates were highest in Eastern Europe and lowest in Western Europe, although differences in injury DALY rates declined rapidly, particularly in the past decade. The injury DALY rate ratio of highest- and lowest-ranking country declined from 2005 onwards, indicating declining inequalities in injuries between European countries.

Estimating global injuries morbidity and mortality: methods and data used in the Global Burden of Disease 2017 study.

BACKGROUND: While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. METHODS: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. RESULTS: GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. CONCLUSIONS: GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future.