Evaluating Public Health Surveillance Systems in Nigeria: A Difference-in-Differences Analysis of Adoption Rates, 2000–2025

Public health surveillance systems are critical for early disease detection and response, yet their adoption and effectiveness in resource-limited settings remain inadequately quantified. Existing evaluations often lack robust counterfactual analysis, limiting causal inference about system performance. This study aimed to rigorously evaluate the causal impact of a national initiative to strengthen integrated disease surveillance and response (IDSR) on system adoption rates across Nigerian states. We employed a quasi-experimental difference-in-differences design. The model, $Y_{it} = \alpha + \beta (Treat_i \times Post_t) + \gamma_i + \delta_t + \epsilon_{it}$, estimated the average treatment effect on the treated (ATT), where $Y_{it}$ is the adoption rate in state $i$ at time $t$. Analyses used longitudinal data from all states, with robust standard errors clustered at the state level. The intervention significantly increased adoption rates by 18.7 percentage points (95% CI: 12.4, 25.0; p