Public health surveillance systems in Kenya are crucial for monitoring infectious diseases, but their effectiveness varies among different regions and populations. The study employs a fixed effects regression model to analyse healthcare expenditure and disease prevalence over time in different regions. Uncertainty is quantified through robust standard errors. Panel data reveal significant variations in the cost-effectiveness of surveillance systems, with urban areas outperforming rural ones by an average of 20% in terms of infection control. The fixed effects model provides a nuanced understanding of how public health investments can be optimised for better disease management across Kenya's diverse regions. Investment priorities should shift towards underserved areas to achieve equitable and cost-effective surveillance coverage, informed by the identified disparities. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Charles et al. (Sun,) studied this question.
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