Methodological Evaluation and Time-Series Forecasting of Clinical Outcomes in Senegalese District Hospital Systems: A Systematic Review

"background": "District hospital systems are critical for healthcare delivery in Senegal, yet there is a lack of consolidated evidence on the methodological rigour of studies evaluating their clinical outcomes and the application of forecasting models to inform system management. ", "purpose and objectives": "This systematic review aims to critically appraise the methodologies used in studies of clinical outcomes within Senegalese district hospitals and to synthesise evidence on the application and performance of time-series forecasting models in this context. ", "methodology": "A systematic search of multiple electronic databases was conducted following PRISMA guidelines. Studies were screened against pre-defined eligibility criteria. Data were extracted on study design, clinical outcomes, forecasting methodologies, and model performance metrics. Quality assessment was performed using appropriate tools for observational and modelling studies. ", "findings": "Of the studies utilising forecasting models, a predominant theme was the reliance on autoregressive integrated moving average (ARIMA) frameworks, typically specified as Xt = \ + \1 X{t-1 + \1 -1 + \. However, model performance was frequently suboptimal; a key finding was that forecast uncertainty, as indicated by wide prediction intervals, was substantially underestimated in over 60% of applicable studies, compromising their utility for operational planning. ", "conclusion": "The methodological quality of existing evaluations is heterogeneous. While forecasting is recognised as a valuable tool, current applications often lack the sophistication and validation required for robust health system decision-making in this setting. ", "recommendations": "Future research should prioritise the development and validation of hybrid models that incorporate both clinical and operational covariates. Mandatory reporting of prediction interval coverage probabilities and external validation on held-out data are essential to improve forecasting utility. ", "key words": "systematic review, health systems research, forecasting models, clinical outcomes, district hospitals, Senegal, ARIMA, methodological evaluation", "contribution statement": "This review provides the first methodological synthesis and critique of time-series forecasting applications for clinical outcomes in