{ "background": "Risk reduction diagnostics in water treatment infrastructure are critical for public health, yet robust empirical methods for evaluating engineering interventions in operational settings are underdeveloped. A prior quasi-experimental study proposed a novel framework for such diagnostics, but its methodological rigour and applicability in diverse field conditions required independent verification.", "purpose and objectives": "This study aimed to replicate and methodologically evaluate a quasi-experimental design for measuring the efficacy of risk reduction interventions in operational water treatment systems. The objective was to test the robustness of the original design's causal inferences and its practical implementation challenges.", "methodology": "We executed a direct replication of the original stepped-wedge, quasi-experimental design across a new set of treatment facilities. The core statistical model, a generalised linear mixed model, was specified as $Y{it} = \\beta0 + \\beta T{it} + \\theta X{it} + ui + \\epsilon{it}$, where $Y{it}$ is the risk metric for facility $i$ at time $t$, $T{it}$ is the treatment indicator, and $u_i$ are facility random effects. Inference was based on cluster-robust standard errors to account for intra-facility correlation.", "findings": "The replication confirmed the design's utility but revealed a significant attenuation in effect size. The estimated reduction in critical risk incidents was 18% (95% CI: 12 to 24), compared to the original study's reported 31%. Furthermore, the implementation exposed substantial logistical constraints in synchronising intervention rollout with continuous system monitoring, which introduced temporal confounding.", "conclusion": "The quasi-experimental design is a viable but context-sensitive tool for engineering risk diagnostics. The replicated effect, while statistically significant, was materially smaller, highlighting how unobserved operational heterogeneities can bias treatment estimates in field applications.", "recommendations": "Future applications of this design must incorporate more granular temporal controls and longer baseline periods. Practitioners should adjust expected effect sizes downwards and allocate