A Time-Series Forecasting Model for the Cost-Effectiveness Evaluation of District Hospital Systems in Senegal

{ "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 \ (B) \ (Bˢ) \ᵈ\D yt = \ (B) \ (Bˢ) \ + \ 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,