Abstract
{ "background": "Public health surveillance systems in Nigeria face persistent challenges regarding data reliability, which undermines effective outbreak response and health policy formulation. Existing evaluations often lack rigorous, experimental designs to isolate and measure the specific effect of interventions on system performance.", "purpose and objectives": "This study aimed to methodologically evaluate a quasi-experimental intervention designed to measure and improve the reliability of integrated disease surveillance and response (IDSR) systems. The primary objective was to quantify the intervention's effect on key reliability metrics, including data completeness, timeliness, and concordance.", "methodology": "We employed a controlled, quasi-experimental design with staggered implementation across multiple states. The intervention comprised a standardised training module, integrated digital reporting tools, and structured feedback loops. Reliability was assessed using a difference-in-differences model: $Y{it} = \\beta0 + \\beta1 (\\text{Treatment}{it}) + \\beta2 (\\text{Time}{t}) + \\beta3 (\\text{Treatment}{it} \\times \\text{Time}{t}) + \\epsilon{it}$, where $Y_{it}$ represents reliability outcomes for unit $i$ at time $t$. Inference was based on cluster-robust standard errors.", "findings": "The intervention yielded a statistically significant positive effect on data completeness, with an estimated increase of 22.4 percentage points (95% CI: 18.1 to 26.7) in the intervention group relative to controls. Improvements in timeliness and concordance were also observed, though effect sizes were more heterogeneous across regions.", "conclusion": "The quasi-experimental framework provides a robust methodological approach for quantifying the impact of interventions on surveillance system reliability. The positive results demonstrate the potential of structured, multi-component interventions to enhance data quality.", "recommendations": "We recommend the adoption of this methodological framework for future surveillance system evaluations. Policymakers should consider scaling the specific intervention components, with adaptations for regional heterogeneity,