A Methodological Evaluation and Time-Series Forecasting Model for Risk Reduction in Rwandan Community Health Centre Systems

"background": "Community health centres are critical nodes in Rwanda's healthcare system, yet their operational resilience is challenged by fluctuating demand and supply chain vulnerabilities. A robust, predictive methodology for quantifying systemic risk is lacking. ", "purpose and objectives": "This study aimed to develop and methodologically evaluate a novel time-series forecasting model to measure and predict risk reduction in the operational continuity of community health centres. ", "methodology": "We conducted an intervention study using longitudinal, facility-level data on stock-outs, patient attendance, and referral rates. The core analytical framework was an autoregressive integrated moving average with exogenous variables (ARIMAX) model, specified as yt = \ + =1^{p\ yt-i + =1^q\ -j + =1^r\ Xt, k + \, where Xt represents intervention covariates. Model performance was assessed via rolling-origin forecast evaluation and robust standard errors. ", "findings": "The ARIMAX (2, 1, 1) model demonstrated superior forecasting accuracy against benchmarks, reducing one-step-ahead forecast error for essential medicine stock-out risk by 34% (95% CI: 28 to 40). The inclusion of community health worker deployment density as an exogenous variable was a significant predictor of reduced operational risk. ", "conclusion": "The proposed forecasting model provides a validated methodological tool for proactively quantifying risk in decentralised health systems, demonstrating significant predictive utility. ", "recommendations": "Health system planners should integrate predictive, model-based risk assessments into routine supply chain management and resource allocation decisions for community health centres. ", "key words": "health systems resilience, predictive modelling, supply chain management, ARIMAX, operational research, public health", "contribution statement": "This paper provides the first application of a tailored ARIMAX forecasting framework to quantify dynamic risk in a community-based health system, offering a