{ "background": "Public health surveillance systems are critical for disease control, yet their operational efficiency in resource-limited settings is poorly quantified. In Uganda, existing evaluations are largely descriptive, lacking robust methods to measure causal impacts of system optimisations on performance metrics. ", "purpose and objectives": "This protocol details a quasi-experimental design to rigorously evaluate the efficiency gains from a targeted optimisation of Uganda's Integrated Disease Surveillance and Response (IDSR) system. The primary objective is to estimate the causal effect of introducing a streamlined digital reporting tool on timeliness and completeness of surveillance reports. ", "methodology": "A controlled interrupted time series analysis will be implemented. Twelve districts will be purposively selected and paired, with one from each pair randomly assigned to early intervention. The core statistical model is a segmented regression: Yt = \0 + \1T + \2Xt + \3TXt + \, where Yt is the weekly reporting completeness, T is time, and Xt indicates intervention phase. Inference will rely on Newey-West robust standard errors to account for autocorrelation. Secondary analyses will assess cost-efficiency. ", "findings": "As a protocol, no empirical findings are presented. The anticipated primary outcome is a quantified improvement in the proportion of complete and timely reports submitted from intervention districts. The analysis is powered to detect a minimum 15-percentage-point increase in report completeness with 90% power. ", "conclusion": "This protocol provides a methodological framework for a rigorous, causal evaluation of surveillance system optimisation. The anticipated findings will move beyond descriptive assessment to provide evidence on the efficacy of a specific intervention. ", "recommendations": "Future research should adopt similar quasi-experimental designs for health systems evaluation. Policy makers should consider phased roll-outs of digital tools to enable robust impact assessment. ", "key words": "surveillance evaluation, quasi-experiment, interrupted time series, health systems research, operational efficiency, digital health", "cont
Nalwoga et al. (Sun,) studied this question.