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<0.001). The effect was heterogeneous, with states receiving concurrent technical support showing a 25.1 percentage point increase, compared to 14.3 in others. The national initiative successfully accelerated surveillance system adoption, but the effect was contingent on the provision of complementary technical support. Policy should prioritise sustained, targeted technical assistance alongside core funding. Future system deployments should incorporate phased, support-intensive roll-out to maximise equity in adoption. health surveillance, impact evaluation, difference-in-differences, health systems strengthening, Nigeria This study provides the first causal evidence, using a quasi-experimental design, on the drivers of surveillance system adoption in the country, introducing a novel application of the difference-in-differences model for health system process metrics.