{ "background": "District hospital systems in sub-Saharan Africa face persistent challenges in resource allocation and financial sustainability. Robust, predictive tools for evaluating their cost-effectiveness are lacking, hindering evidence-based policy and management.", "purpose and objectives": "This study aimed to develop and validate a novel time-series forecasting model to measure and predict the cost-effectiveness of district hospital systems, using Senegal as a case study.", "methodology": "We conducted an intervention study using longitudinal administrative data from a nationally representative panel of district hospitals. The core forecasting model is a seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), specified as $\\phi(B)\\Phi(B^s)\\nabla^d\\nablas^D yt = \\theta(B)\\Theta(B^s)\\epsilont + \\beta Xt$, where $yt$ is the cost-effectiveness ratio and $Xt$ includes intervention covariates. Model fit was assessed using AIC and out-of-sample forecasting accuracy; uncertainty was quantified with 95% prediction intervals.", "findings": "The SARIMAX(1,1,1)(0,1,1)12 model demonstrated strong predictive validity. A one-unit increase in outpatient utilisation rate was associated with a 7.3% improvement in the forecasted cost-effectiveness ratio (95% PI: 5.1% to 9.5%). Forecasts indicated that systemic interventions targeting supply chain efficiency could yield the most significant cost-effectiveness gains.", "conclusion": "The developed model provides a statistically robust tool for forecasting cost-effectiveness, enabling proactive resource management and policy simulation for district health systems.", "recommendations": "Health policymakers should integrate predictive modelling into hospital performance reviews. Future research should apply this model to other health system levels and contexts to assess generalisability.", "key words": "health economics, forecasting, SARIMAX, health systems strengthening, resource allocation, predictive modelling", "contribution statement": "This paper provides a novel methodological framework for the predictive evaluation of health system cost-effectiveness,