Jean de Dieu Harerimana

Background: Diarrhoeal disease remains a leading cause of morbidity and mortality among children under five years (U5) in Rwanda, contributing substantially to healthcare utilisation and imposing considerable economic burdens on households and the health system. National estimates indicate that approximately 14.3% of children under five experienced diarrhoea, and diarrhoea has been reported as the third leading cause of death in this age group. Climate variability may influence diarrhoeal disease risk, but evidence across Rwanda’s diverse ecological zones remains limited. This study examined sector-level associations between climatic variables and U5 diarrhoeal incidence in Rwanda. Methods: Monthly counts of U5 diarrhoeal cases reported by health facilities across 416 administrative sectors in Rwanda from January 2015 to December 2024 were analysed together with satellite-derived climate data. Spatio-temporal statistical models were used to evaluate associations between standardised climate variables and diarrhoeal incidence rates while accounting for geographic and seasonal variation. Model comparison relied on both goodness-of-fit metrics and out-of-sample predictive diagnostics. Results: Diarrhoeal incidence showed clear spatial and seasonal patterns, with persistently higher rates in northern and eastern Rwanda. In the final model, maximum temperatures were positively associated with increased diarrhoeal incidence, with a one-standard-deviation increase corresponding to a 5.6% rise in the estimated incidence rate ratio (RR = 1.056; 95% CrI: 1.02–1.09). Relative humidity showed a protective association (RR ≈ 0.919; 95% CrI: 0.89–0.94), while rainfall showed limited immediate effects. Conclusion: Under-five diarrhoeal incidence in Rwanda showed marked spatial and seasonal variability. Maximum temperature was positively associated with diarrhoeal incidence rates, while relative humidity showed an inverse association that may partly reflect unmeasured confounding by drought, water access, and WASH infrastructure. External validation is needed before the modelling framework can be applied operationally. These findings highlight the importance of preparedness during hotter periods and strengthened prevention and surveillance efforts in high-risk areas.

Background: Diarrheal disease remains a major and persistent cause of morbidity and mortality in Rwanda, with substantial seasonal surges that strain routine services; however, transparent and operationally interpretable national forecasting has been underused for age-stratified burden. Methods: We analyzed the Rwanda Health Management Information System (HMIS) monthly diarrhea case counts (January 2015–December 2025), stratified by age group (under-five and five-and-above), and developed validated Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasts for January 2026–December 2027. Stationarity was assessed using the Augmented Dickey–Fuller test and addressed through differencing. Candidate models were selected via rolling 5-fold cross-validation: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Mean Absolute Percentage Error (MAPE) and confirmed via Ljung–Box residual diagnostics, and benchmarked against seasonal naïve, Exponential Smoothing State-Space (ETS), and Seasonal-Trend decomposition using Loess (STL) + drift reference models. Results: Rwanda recorded 6,309,098 diarrhea cases during 2015–2025, with 49.2% among under-fives; while absolute counts were higher in those aged ≥5 years, risk remained consistently higher in under-fives (91.7–229.5 per 1000) than in those ≥5 years (17.9–34.3 per 1000). Both series showed strong annual seasonality with recurrent peaks in August–November, and forecasts suggest this pattern will persist through 2026–2027. Conclusions: These findings suggest a provisional seasonal (pre-peak, peak, and post-peak) preparedness framework and age-differentiated planning signals, underscoring that burden and risk are not inter changeable across age groups.

Rwanda's community-based health insurance (CBHI) has achieved near-universal enrollment, yet inequities in healthcare use remain. Understanding whether coverage translates into equitable utilization is critical for advancing universal health coverage (UHC). This study aims to examine how social protection complements health insurance in promoting equitable healthcare access. To examine the relationship between insurance coverage and healthcare utilization in sectors targeted by the Vision Umurenge Programme (VUP), assess socioeconomic inequities, and evaluate the complementary role of social protection. This study analyzed cross-sectional data from 15,039 households in VUP sectors using the 2023-2024 Seventh Integrated Household Living Conditions Survey. Socioeconomic inequality was measured using Erreygers-corrected concentration indices and need-standardized horizontal inequity analysis. Insurance effects were estimated using survey weighted logistic regression, propensity score matching, and doubly robust inverse-probability-weighted regression adjustment. Insurance coverage was 85.8%, yet only 25.5% reported any formal healthcare utilization in the 12 months preceding the survey, including outpatient visits, inpatient admissions and preventive services. Utilization showed stronger pro-rich concentration (E = +0.066) than coverage (E = +0.026) and need-standardized analysis confirmed residual inequity. Insurance increased utilization by +14.6% points in regression models and ~+12% points in causal estimators. Participation in VUP components, particularly direct support, was consistently associated with higher service use. In a high coverage setting, persistent pro-rich inequities highlight the role of non-financial barriers such as indirect costs and service readiness. Layering social protection with insurance and strengthening primary care delivery is critical to convert nominal coverage into equitable healthcare access.