Methodological Evaluation and Risk Reduction Analysis of Public Health Surveillance Systems in Nigeria: A Difference-in-Differences Modelling Approach

Public health surveillance systems are critical for early detection and response to disease outbreaks, yet their methodological evaluation, particularly regarding attributable risk reduction, remains underdeveloped in many resource-limited settings. This study aimed to conduct a rigorous methodological evaluation of enhanced surveillance systems and quantify their causal effect on reducing public health risks. We employed a quasi-experimental difference-in-differences (DiD) design, analysing longitudinal data from intervention and matched control regions. The primary model was specified as Yᵢₜ = β₀ + β₁ (Treatᵢ × Postₜ) + β₂ Treatᵢ + β₃ Postₜ + εᵢₜ, where Yᵢₜ is the composite risk score. Inference was based on cluster-robust standard errors. The DiD estimator (β₁) was -0.18 (95% CI: -0.31 to -0.05), indicating a statistically significant 18% reduction in the composite risk score attributable to the enhanced surveillance intervention. The parallel trends assumption was validated using pre-intervention data. The enhanced surveillance methodology demonstrated a significant causal effect in mitigating public health risks, confirming the value of targeted system strengthening. Policy should prioritise the scale-up of the evaluated surveillance components. Future evaluations should adopt quasi-experimental designs to robustly estimate programme impact. surveillance evaluation, impact assessment, causal inference, quasi-experimental design, health security This paper provides novel empirical evidence of the causal risk reduction attributable to an enhanced surveillance system, employing a DiD model not previously used for this specific evaluative purpose in the region.