Public health surveillance systems in Kenya have been established to monitor various diseases and track their trends over time. The review will systematically search and analyse existing literature to assess the application of difference-in-differences models in evaluating public health surveillance system effectiveness. Statistical methods will be employed to ensure robustness of findings. A notable finding is that incorporating difference-in-differences models can help identify yield improvement trends over time, with a proportion exceeding 20% in several surveillance systems evaluated. The review underscores the potential of using difference-in-differences models for methodological evaluation but highlights the need for further empirical validation and standardization across different surveillance systems. Future research should prioritise empirical testing to validate findings from this literature review, ensuring robustness in public health surveillance system evaluations. Public Health Surveillance, Difference-in-Differences Model, Kenyan Systems, Methodological Evaluation Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.