Journal Design Clinical Emerald
African Food Systems Research (Interdisciplinary - incl Agri/Env) | 20 July 2000

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

M, a, r, i, e, A, i, m, e, e, M, u, k, a, m, a, n, a, ,, J, e, a, n, P, a, u, l, N, i, y, o, n, z, i, m, a, ,, J, e, a, n, d, e, D, i, e, u, U, w, i, m, a, n, a
DOI: https://doi.org/10.5281/zenodo.18956399
Health Systems ResiliencePredictive ModellingARIMAXOperational Research
ARIMAX model demonstrated 34% reduction in forecast error for stock-out risk.
Community health worker density was a significant predictor of reduced operational risk.
Methodology enables proactive risk assessment in decentralized health systems.
Framework validated through rolling-origin forecast evaluation with robust standard errors.

Abstract

{ "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 $yt = \\mu + \\sum{i=1}^{p}\\phii y{t-i} + \\sum{j=1}^{q}\\thetaj \\epsilon{t-j} + \\sum{k=1}^{r}\\betak X{t,k} + \\epsilont$, 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