This study was conducted to evaluate seasonal patterns of household food insecurity, dietary diversity, and household characteristics on wasting and stunting among children in households followed for 1 year in the drought-prone areas of Sidama, Ethiopia. A cohort study design was employed. Data were collected on the pre-harvest season (March and June) and post-harvest season (September and December) of 2017. We studied 935 children aged 6 to 47 months. At four seasons over a year, we had 3,449 observations from 897 households and 82% (2,816) (95% CI: 80.3–82.9) were food in-secured households. Severe food insecurity was higher in the pre-harvest (March; food scarcity season) which was 69% as compared to 50% of September (P < .001). From 3,312 observations, 43% (1,408) (95% CI: 40.8-44.2) of children were stunted. Stunting showed seasonal variations with 36% (95% CI: 32.5-39.0) in March and 48% (95% CI: 45.0-51.8) in December. Six percent (95% CI: 4.8-6.3) of children were wasted, with higher prevalence in March (8%) as compared to 3% of September (P < .001). Moreover, household characteristics such as poverty level, education, occupation and the household food insecurity and dietary diversity were associated with subsequent wasting or stunting.

Tuberculosis is a leading cause of death among people living with human immunodeficiency virus. In sub-Saharan Africa, tuberculosis accounts for more than 78% of all deaths among people with human immunodeficiency virus. To assess tuberculosis treatment outcome and the associated factors in adult tuberculosis/human immunodeficiency virus co-infected patients in four public hospitals of eastern and southern zone of Tigray region, Ethiopia. Institution based cross-sectional study design was used to examine secondary data from tuberculosis/human immunodeficiency virus co-infected patients attending four public hospitals of eastern and southern zone of Tigray, from January 2009 to August 2011. Systematic random sampling technique was used to select individual patient cards from the respective hospitals. Univariate analysis and multivariate logistic regression modeling was used to assess the impact of each variable in predicting treatment outcome. Out of 342 patients included, 199 (58.2%) patients completed treatment, 43 (12.6%) patients were cured, 88 (25.7%) died, 7 (2%) defaulted, and 5 (1.5%) patients failed treatment. Treatment success rate was around 71%. In the multivariate logistic regression analysis the factors that were strongly associated with unfavorable tuberculosis treatment outcomes were WHO stage IV (AOR=3.2, CI=1.58-6.82, p-value=0.001), age greater than 45 years (AOR=6.08, CI=2.28-16.23) and baseline CD4 count less than 200cells/μL (AOR=6.19, CI=2.28-16.89, p-value=0.001). The rate of treatment success in this study was lower than the rate newly recommended by WHO. Therefore, efforts should be undertaken to improve treatment success rates of both diseases.

Food availability varies seasonally in most rural areas of developing nations, especially in areas affected by drought and climate change, with women being one of the most vulnerable groups. This study aimed to assess the effect of seasonal variation in household food security, adequate dietary diversity, food consumption, and wealth on the nutritional status of women of reproductive age in a rural community in South Ethiopia. Further, the study aimed at identifying associated factors with women’s nutrition status. An open cohort study was conducted from June 2021 to June 2022, with follow-up visits every three months. Anthropometric measurements were carried out along with interviews. Data were analyzed using STATA version 15. Multilevel, multiple linear regressions were employed. Findings revealed that women’s average body mass index (BMI) was 20.4 kg/m² (95% CI: 20.4–20.5). The highest (20.6 kg/m², 95% CI: 20.5–20.8) was observed in December, while the lowest (20.2 kg/m², 95% CI: 20–20.3) occurred in September. During the main postharvest period in December, the household food insecurity score was the lowest (median: 4, Inter quartile range (IQR): 0–9), while the household dietary diversity score (median: 6, IQR: 5–7), and the household food consumption score were the highest (median: 50.5, IQR: 44–70). Factors such as household food security, food consumption, previous season BMI, age, marital status, and membership in safety net programs were identified as determinants of women’s BMI. The study showed the vulnerability of women in drought-prone areas to seasonal undernutrition. We recommend collaborative work among stakeholders to ensure sustainable food access and minimize seasonal food shortages’ effect on women’s nutrition and overall well-being.

Globally, understanding spatial analysis of malnutrition is increasingly recognized. However, our knowledge on spatial clustering of malnutrition after controlling for known risk factors of malnutrition such as wealth status, food insecurity, altitude and maternal characteristics is limited from Ethiopia. Previous studies from southern Ethiopia have shown seasonal patterns of malnutrition, yet they did not evaluate spatial clustering of malnutrition. The aim of this study was to assess whether child stunting and maternal malnutrition were spatially clustered in drought-prone areas after controlling for previously known risk factors of malnutrition. We used a community-based cohort study design for a one-year study period. We used SaTScan software to identify high rates of child stunting and maternal malnutrition clustering. The outcome based was the presence or absence of stunting and maternal malnutrition ([BMI] <18.5 kg/m²). We controlled for previously known predictors of child stunting and maternal malnutrition to evaluate the presence of clustering. We did a logistic regression model with declaring data to be time-series using Stata version 15 for further evaluation of the predictors of spatial clustering. The crude analysis of SaTScan showed that there were areas (clusters) with a higher risk of stunting and maternal malnutrition than in the underlying at risk populations. Stunted children within an identified spatial cluster were more likely to be from poor households, had younger and illiterate mothers, and often the mothers were farmers and housewives. Children identified within the most likely clusters were 1.6 times more at risk of stunting in the unadjusted analysis. Similarly, mothers within the clusters were 2.4 times more at risk of malnutrition in the unadjusted analysis. However, after adjusting for known risk factors such as wealth status, household food insecurity, altitude, maternal age, maternal education, and maternal occupation with SaTScan analysis, we show that child stunting and maternal malnutrition were not spatially clustered. The observed spatial clustering of child stunting and maternal malnutrition before controlling for known risk factors for child stunting and maternal malnutrition could be due to non-random distribution of risk factors such as poverty and maternal characteristics. Moreover, our results indicated the need for geographically targeted nutritional interventions in a drought-prone area.

Introduction: Early identification of high-risk pregnancies is crucial, yet conventional approaches often miss complex clinical and contextual factors. This study measured prevalence and determinants in Southern Ethiopia and applied explainable machine learning as a clinical decision-support tool, linking prediction to actionable digital health insights. Methods: We conducted a retrospective cohort study of 3,954 mother–infant pairs using routine records from pregnancy to the postpartum. Five supervised machine learning algorithms - Logistic Regression, Random Forest, Support Vector Machine, an Artificial Neural Network and XGBoost were developed and validated. Performance was assessed using AUC, specificity, sensitivity, calibration, and F1-score. SHAP-based analysis enhanced interpretability, revealing the contribution of each predictor at both individual and cohort levels, supporting practical integration into digital maternal health systems. Results: Obstetric complications occurred 16.3% of mothers, with higher incidence in rural settings. XGBoost achieved the highest predictive performance (AUC 0.86). Key predictors identified young maternal age, unplanned pregnancy, low education, previous complications, inadequate antenatal care, anemia, hypertension and long travel distance to health facilities. Conclusions: Obstetric complications remain common in Southern Ethiopia. By applying explainable machine learning, this study not only predicts high-risk pregnancies with high accuracy but also provides actionable insights for clinical decision-making and digital health implementation.

Background/Objectives: Anemia and undernutrition are severe public health concerns in Ethiopia. These are the two most common nutritional disorders in pregnant women and frequently coexist. However, to our knowledge, there is little evidence of the coexistence of anemia and undernutrition among pregnant women. Therefore, this study aimed to examine the prevalence of coexisting anemia and undernutrition (CAU) and associated factors among pregnant women. Methods: A community-based cross-sectional study was conducted from 1 to 25 June 2024, on 515 pregnant women in the Hawela Lida district of Sidama, Ethiopia. We utilized a multi-stage sampling method to choose eligible study participants. A pre-tested and structured questionnaire was used to collect data via the online Open Data Kit mobile tool. We controlled the effect of confounders and clustering by using a multi-level mixed-effect modified Poisson regression analysis model. Results: The prevalence of CAU among pregnant women was 25.4% (95% CI: 21.9–28.9). The prevalence of CAU was associated with household food insecurity (adjusted prevalence ratio [APR]: 2.17; 95% CI: 1.43–3.28), training on model family (APR: 0.66; 95% CI: 0.45–0.96), inadequate dietary diversity (APR: 1.51; 95% CI: 1.18–1.95), and having poor knowledge of nutrition (APR: 1.55; 95% CI: 1.06–2.26) at individual levels. Low community-level women’s autonomy (APR: 6.19; 95% CI: 3.42–11.22) and community-level road accessibility (APR: 0.65; 95% CI: 0.43–0.98) were the identified determinants of CAU at the community level. Conclusions: One in four pregnant women had CAU in the study area. Household food insecurity, inadequate dietary diversity, and poor nutrition knowledge were associated with an increased likelihood of CAU, while participation in model family training and improved road accessibility were associated with reduced CAU. We have also indicated that low community-level women’s autonomy significantly increased the risk of CAU. Therefore, inter-sectorial collaboration should be required to comprehensively address CAU’s determinants at different levels. Additionally, any CAU prevention and intervention programs should provide model family training explicitly targeting women with poor nutritional knowledge and low autonomy in healthcare decision-making.