Abstract
{ "background": "The effective adoption of public health surveillance systems is critical for food security and disease control, yet robust methodological frameworks for evaluating their implementation are lacking. This case study addresses the gap in quantifying the impact of systematic interventions on surveillance uptake within a resource-constrained setting.", "purpose and objectives": "This study aimed to evaluate the causal effect of a national integrated surveillance strategy on system adoption rates across districts. The primary objective was to measure the intervention's impact while controlling for secular trends and time-invariant heterogeneity.", "methodology": "A quasi-experimental difference-in-differences design was employed, comparing adoption metrics in intervention and control districts before and after the strategy's rollout. The core statistical 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 $Y{it}$ is the adoption score. Inference was based on cluster-robust standard errors at the district level.", "findings": "The integrated strategy caused a statistically significant increase in adoption scores. The average treatment effect on the treated (δ) was 15.3 percentage points (95% CI: 9.8, 20.8). The most substantial improvements were observed in data completeness and timeliness of reporting.", "conclusion": "The methodological application demonstrates that a structured, system-wide intervention can substantially accelerate surveillance adoption. The difference-in-differences approach provides a credible model for impact evaluation in similar public health contexts.", "recommendations": "Policymakers should prioritise integrated, centrally coordinated surveillance strategies with dedicated training and resource allocation. Future evaluations should incorporate this quasi-experimental methodology to strengthen evidence for scaling health system interventions.", "key words": "surveillance systems, impact evaluation, difference-in-differences, public health methodology, adoption, quasi-experimental design",