Evaluating Public Health Surveillance Systems in Kenya: A Cost-Effectiveness Panel Data Analysis

Public health surveillance systems are crucial for monitoring disease outbreaks and managing healthcare resources in Kenya. However, their effectiveness varies significantly across regions. A cost-effectiveness panel-data estimation approach was employed to analyse data from multiple Kenyan regions. The study utilised a fixed effects model with robust standard errors for accounting for potential unobserved heterogeneity. The analysis revealed that the surveillance systems in high-density urban areas were more effective in terms of disease detection and response, with an estimated cost-effectiveness ratio (CEA) of $100 per detected case. However, lower resource investment was required in rural settings to achieve similar outcomes, indicating a need for targeted funding. The findings underscore the importance of adapting surveillance system resources to regional needs, particularly in densely populated urban areas and sparsely populated rural regions. Policy makers should prioritise investments that balance cost-effectiveness with disease detection rates across different geographic locations within Kenya. Public health surveillance systems, Cost-effectiveness analysis, Panel data, Fixed effects model, Resource allocation Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.