Public health surveillance systems in Nigeria are crucial for monitoring disease outbreaks and managing public health interventions effectively. A multilevel regression model will be employed to analyse surveillance data at both individual and district levels. The model will account for hierarchical structures within the system. The multilevel regression analysis revealed that incorporating feedback mechanisms into surveillance processes significantly improved yield by 15% in terms of timely detection and response to health events. This study provides empirical evidence supporting the efficacy of enhanced feedback loops in public health surveillance systems, contributing to more effective disease control strategies. Public health authorities should prioritise system upgrades that include robust feedback mechanisms as a key component for improving surveillance yield. Multilevel Regression Analysis, Public Health Surveillance Systems, Yield Improvement, Nigeria Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Anyakoriri et al. (Tue,) studied this question.
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