Replication of a Field Trial for Pathogen Reduction Diagnostics in Tanzanian Water Treatment Systems

Pathogen reduction in decentralised water treatment is a persistent challenge in sub-Saharan Africa. Previous research proposed a diagnostic protocol for evaluating treatment efficacy, but its robustness across varied operational conditions required independent validation. This study aimed to replicate a field trial of a diagnostic protocol for assessing pathogen reduction in decentralised water treatment systems, with the objective of evaluating its methodological consistency and practical applicability in a real-world setting. A randomised field trial was conducted across multiple small-scale treatment facilities. The replication employed the original diagnostic protocol, measuring log-reduction values (LRVs) for key microbial indicators. Statistical analysis used a mixed-effects model: LRV₈₉ = ₀ + ₁ X₈₉ + uⱼ + ₈₉, where uⱼ represents facility-level random effects. Robust standard errors were calculated to account for heteroskedasticity. The replication confirmed the protocol's utility but revealed a 15% lower mean LRV for Escherichia coli compared to the original study, with a 95% confidence interval for the difference ranging from -0. 8 to -0. 3 log units. Operational variability, particularly in coagulant dosing, was identified as a critical factor influencing diagnostic outcomes. The diagnostic protocol is a viable tool, but its results are sensitive to site-specific operational practices, indicating a need for contextual interpretation beyond standardised application. Future applications of the protocol should incorporate real-time monitoring of operational parameters. Practitioners should use its findings as part of a broader risk assessment framework, not as a standalone performance metric. water treatment, pathogen reduction, field trial, replication study, diagnostic protocol, Tanzania This replication provides an independent, empirical evaluation of a previously proposed diagnostic method, offering a critical assessment of its reliability and generating a novel dataset on treatment performance under typical operational conditions.