Public health surveillance systems in Kenya are essential for monitoring infectious diseases such as malaria and tuberculosis (TB). However, their effectiveness varies across different regions and levels of administration. A multilevel regression model will be employed to analyse data collected from various healthcare facilities in Kenya. The model will account for both fixed effects (e. g. , facility-level characteristics) and random effects (e. g. , regional variations). The analysis revealed that facility-level infrastructure significantly influenced clinical outcomes, with a moderate effect size. Our multilevel regression approach provides valuable insights into the performance of surveillance systems in Kenya, offering a refined method for future evaluations. Future studies should consider incorporating additional variables to enhance model accuracy and address potential biases. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Kinyua et al. (Mon,) studied this question.
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