"background": "Public health surveillance is a cornerstone of effective disease control, yet the cost-effectiveness of such systems in low-resource settings remains inadequately assessed. In Uganda, diverse surveillance methodologies have been implemented, but a comprehensive methodological evaluation and predictive economic analysis are lacking. ", "purpose and objectives": "This systematic review aimed to critically evaluate methodological approaches for assessing public health surveillance and to develop a time-series forecasting model to project the cost-effectiveness of these systems in the Ugandan context. ", "methodology": "A systematic review of peer-reviewed and grey literature was conducted. Eligible studies were those describing or evaluating surveillance systems. Methodological quality was appraised using a modified framework. A novel forecasting model was developed, integrating historical cost and outcome data to project future cost-effectiveness. The core model is an ARIMA (p, d, q) formulation: yt = \ + \1 y{t-1 +. . . + \ yt-p + \1 -1 +. . . + \ -q + \, where yₜ represents the cost-effectiveness ratio at time t. Uncertainty was quantified using 95% prediction intervals. ", "findings": "The methodological review revealed significant heterogeneity in evaluation frameworks, with only 28% of studies employing a standardised economic evaluation. The forecasting model, applied to sentinel surveillance data, indicated a likely improvement in cost-effectiveness over a five-year horizon, with a projected 15-22% reduction in cost per disability-adjusted life year (DALY) averted, though predictions were sensitive to assumed disease incidence rates (95% PI: 10% to 30% reduction). ", "conclusion": "Current evaluations of surveillance systems exhibit inconsistent methodologies, hindering comparative analysis. The proposed time-series model offers a replicable tool for forecasting economic efficiency, supporting more strategic resource allocation. ", "recommendations": "Implement standardised cost-effectiveness analysis guidelines for surveillance. Integrate the forecasting
Akello et al. (Wed,) studied this question.
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