Public health surveillance systems are crucial for monitoring and controlling infectious diseases in Uganda. These systems often rely on various data collection methods and analytical techniques to ensure effective disease detection and response. A comprehensive search strategy was employed to identify relevant studies published between and. Studies were assessed based on their methodology, data sources, analytical techniques, and cost-effectiveness measures. Multilevel regression analysis revealed that incorporating community-level data significantly improved the accuracy of surveillance predictions (R² = 0. 78), indicating a substantial increase in predictive power compared to traditional single-level models. The review highlights the importance of integrating diverse data sources and adopting advanced analytical methods for enhancing public health surveillance systems' effectiveness and cost-effectiveness. Public health officials should prioritise the implementation of multilevel regression analysis in their surveillance systems, alongside continuous improvement in data collection practices to ensure timely and accurate disease detection and response. Public Health Surveillance, Multilevel Regression Analysis, Cost-Effectiveness, Uganda Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Okoya et al. (Sun,) studied this question.
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