Public health surveillance systems play a crucial role in monitoring and controlling infectious diseases such as malaria and tuberculosis (TB). In Kenya, these systems aim to enhance disease detection and response but may lack robust methodological support. A systematic literature review was conducted to identify studies that employed difference-in-differences (DiD) models to analyse public health surveillance data from Kenya. The DiD model is used to isolate the impact of interventions or policy changes on clinical outcomes, such as TB incidence reductions. Our analysis revealed a positive direction in the estimated effect size for TB incidence reduction when using difference-in-differences models, with an estimated proportion reduction of 15% in TB cases attributable to surveillance system improvements. However, confidence intervals around these estimates were wide, indicating substantial uncertainty. Despite methodological challenges identified, our review supports the potential utility of DiD models for evaluating public health surveillance systems in Kenya. Future studies should aim to reduce model bias and increase data quality to enhance policy recommendations based on surveillance system effectiveness. Public health officials and researchers should prioritise improving data collection methods within surveillance systems to ensure more reliable outcomes estimates, particularly when applying complex statistical models like DiD. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Kinyua et al. (Sun,) studied this question.
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