Public health surveillance systems are critical for monitoring diseases and managing outbreaks efficiently in Uganda. A multilevel regression model was used to analyse data from various levels of the surveillance system (national, regional, and district), accounting for both fixed and random effects. Uncertainty around regression coefficients was estimated using robust standard errors. The analysis revealed a significant positive correlation between investment in surveillance infrastructure and reduced healthcare costs per case detected, with a 15% reduction in health expenditure attributed to timely detection of outbreaks. Public health surveillance systems in Uganda have demonstrated cost-effectiveness in reducing healthcare costs associated with disease outbreaks. Investment in upgrading surveillance technology at district levels and strengthening data management practices is recommended for further enhancing the system's efficiency. public health, surveillance systems, multilevel regression analysis, cost-effectiveness, Uganda Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Besigye et al. (Sat,) studied this question.
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