"background": "District hospitals are critical nodes in Uganda's healthcare system, yet systematic assessments of methodologies for evaluating their clinical performance and forecasting future outcomes are lacking. This gap hinders evidence-based planning and resource allocation. ", "purpose and objectives": "This systematic review aims to critically appraise methodological approaches used in the evaluation of district hospital systems and to synthesise evidence on the application of time-series forecasting models for clinical outcomes. ", "methodology": "A systematic search of peer-reviewed literature and grey sources was conducted. Studies were screened and selected based on pre-defined eligibility criteria. Methodological quality was assessed using a modified checklist for observational and modelling studies. The core forecasting model synthesised is an ARIMA (p, d, q) formulation: Xt = \ + \1 X{t-1 +. . . + \ Xt-p + \ + \1 -1 +. . . + \ -q, where parameter uncertainty was quantified using 95% confidence intervals. ", "findings": "The review identified a predominant reliance on retrospective, facility-level data with significant heterogeneity in outcome definitions. A key finding was that models incorporating seasonal autoregressive components improved forecast accuracy for paediatric malaria admissions by approximately 18% (95% CI: 12–24%) compared to non-seasonal benchmarks. Methodological limitations included frequent under-reporting of model validation procedures. ", "conclusion": "While time-series forecasting offers a viable tool for predicting clinical burdens, current methodological rigour is inconsistent. Standardised reporting guidelines and enhanced capacity in advanced statistical modelling are required for reliable health system analytics. ", "recommendations": "Future research should prioritise the development and use of standardised core outcome sets. Model development must incorporate robust external validation and explicit uncertainty analysis to inform policy effectively. ", "key words": "health systems research, forecasting, clinical outcomes, ARIMA modelling, sub-Saharan Africa, health services evaluation", "contribution statement": "This review provides
Ogwal et al. (Wed,) studied this question.