Background Domestic violence among HIV discordant couples poses significant public health challenges, affecting treatment adherence and HIV transmission risks. This study examined factors contributing to domestic violence among HIV discordant couples in Kicukiro District, Rwanda. Methods A cross-sectional study was conducted among 384 HIV discordant couples from eleven health centers using stratified systematic sampling. Data were collected through structured face-to-face interviews and analyzed using descriptive statistics and bivariate analysis. Results Domestic violence prevalence was 41.1% physical violence, 34.2% sexual coercion, and 52.3% emotional abuse. Key socio-demographic risk factors included female gender (56% vs. 29% males, p<0.001), older age (61% in ≥55 years vs. 32% in 18-24 years, p=0.004), unemployment (55% vs. 34% formal employment, p=0.014), and financial hardship (63% vs. 25% comfortable situations, p=0.002). Behavioral factors included alcohol use (58% vs. 38%, p=0.021), substance abuse (62% vs. 35%, p<0.001), and poor conflict resolution (72% vs. 25%, p<0.001). Contextual factors like hostile HIV disclosure reactions (68% vs. 34%, p<0.001) and HIV-related stigma (60% vs. 35%, p<0.001) significantly increased violence risk. Conclusions Domestic violence among HIV discordant couples is multifactorial, driven by socio-economic, behavioral, and HIV-related factors. Integrated interventions addressing economic empowerment, conflict resolution skills, stigma reduction, and couple-centered counseling are urgently needed. Show less

Objective Diabetes poses significant public health challenges, with many individuals remaining undiagnosed and at risk of complications. This study aimed to evaluate the performance of decision tree ensemble methods for predicting diabetes onset using the Framingham Heart Study Teaching Dataset and to explore sex-specific risk patterns relevant to AI-driven interventions. Methods We analyzed data from 11,627 participants, incorporating demographics, vital signs, smoking status, medication use, and laboratory measures. Random Forest classifiers were developed to predict diabetes incidence at approximately 6-year (Period 2) and 12-year (Period 3) follow-ups. Class imbalance was addressed using undersampling, oversampling, and the Synthetic Minority Over-sampling Technique (SMOTE). Results The models demonstrated robust performance, achieving an Area Under the Curve (AUC) of 0.856 in Period 2, and moderate predictive ability in Period 3 (AUC = 0.732 in males, 0.786 in females). Key predictors included glucose level, BMI, systolic blood pressure, age, and heart rate. Notably, differences emerged in predictive accuracy between men and women, suggesting potential sex-specific vulnerabilities that merit further study. Conclusion Machine learning approaches, particularly Random Forests, show promise for medium- and long-term diabetes risk prediction, supporting early identification and intervention efforts. Future work should focus on hyperparameter tuning and explainability techniques, such as SHapley Additive exPlanations (SHAP) values, to improve model precision, interpretability, and fairness. Equity-focused strategies remain critical to ensure AI-driven tools benefit diverse populations and do not exacerbate existing disparities in diabetes care. Show less