Abstract
{ "background": "Public health surveillance is a cornerstone of effective health systems, yet robust methodological frameworks for evaluating their efficiency and impact, particularly in resource-constrained settings, are underdeveloped.", "purpose and objectives": "This study aimed to develop and apply a novel quasi-experimental methodology to evaluate the efficiency gains of a reformed national public health surveillance system, focusing on timeliness and completeness of reporting.", "methodology": "We employed a difference-in-differences (DiD) design, comparing changes in surveillance outcomes between intervention and control counties before and after system reform. The core model is specified as $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon_{it}$, where $\\delta$ is the causal parameter of interest. Inference was based on cluster-robust standard errors at the county level.", "findings": "The reformed system was associated with a statistically significant 18.4 percentage point increase in the timeliness of weekly disease reporting (95% CI: 12.1 to 24.7). The completeness of case reports also improved markedly, with the DiD estimator indicating a reduction in missing data of 15.7% (p<0.01).", "conclusion": "The application of a DiD model provides credible evidence that the redesigned surveillance system generated substantial and significant efficiency improvements. This confirms the value of the specific technological and procedural interventions implemented.", "recommendations": "Policy makers should institutionalise quasi-experimental evaluation methods for health system investments. The specific reforms analysed here, particularly the integrated digital reporting platform, warrant scaling to all regions.", "key words": "health systems evaluation, quasi-experimental design, impact assessment, health informatics, sub-Saharan Africa", "contribution statement": "This paper provides the first application of a difference-in-differences