{ "background": "Evaluating the impact of interventions within public health surveillance systems is critical for evidence-based policy. Quasi-experimental designs (QEDs) offer a practical alternative to randomised controlled trials in operational settings, yet their methodological application and rigour in assessing yield improvement in such systems require systematic assessment.", "purpose and objectives": "This systematic review aims to critically appraise the application of QEDs in evaluating yield improvement within Kenya's public health surveillance systems, focusing on methodological choices, analytical robustness, and the validity of causal inferences drawn.", "methodology": "A systematic search of multiple electronic databases was conducted following PRISMA guidelines. Eligible studies employed QEDs (e.g., difference-in-differences, interrupted time series, regression discontinuity) to evaluate surveillance interventions. Data were extracted on design, statistical methods, and validity threats. A key model assessed was the two-way fixed effects specification: $Y{it} = \\beta0 + \\beta1 (Treati \\times Postt) + \\alphai + \\gammat + \\epsilon{it}$, where robust standard errors were clustered at the facility level.", "findings": "Of the 27 included studies, interrupted time series was the most frequently employed design (52%). A predominant theme was the inadequate reporting of sensitivity analyses to test the parallel trends assumption in difference-in-differences models. Only 33% of studies using this design conducted such tests, substantially weakening causal claims.", "conclusion": "While QEDs are increasingly used, their application is often methodologically incomplete, compromising the strength of evidence for decision-making. There is a pressing need for enhanced methodological rigour in this applied field.", "recommendations": "Future evaluations should pre-specify design and analysis plans, rigorously test and report on key identifying assumptions, and employ complementary designs to triangulate findings. Capacity building in advanced causal inference methods for local researchers is essential.", "key words": "quasi-experimental design, causal inference, surveillance systems,