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 $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.