Randomised Field Trial Methodology for Evaluating Adoption Rates in Kenyan Water Treatment Systems

{ "background": "Evaluating the real-world adoption of engineered water treatment systems in resource-limited settings remains methodologically challenging. Prevailing approaches often rely on self-reported data or short-term surveys, which can overestimate sustained use and provide limited causal insight into factors influencing adoption.", "purpose and objectives": "This article presents a robust methodological framework for conducting randomised field trials to measure longitudinal adoption rates of household water treatment technologies. The objective is to provide a replicable protocol for generating high-quality evidence on technology uptake and its determinants.", "methodology": "The methodology centres on a cluster-randomised controlled trial design. Communities are randomised to receive different intervention packages or support models. Adoption is measured via unannounced direct observation and sensor-based monitoring over a 12-month period. The primary analysis employs a multilevel logistic regression model: \(pij) = \β0 + \β1 Tij + Xij\γ + uj, where pij is the probability of correct use for household i in cluster j, Tij is the treatment assignment, Xij a vector of covariates, and $uj$ a cluster random effect. Robust standard errors are used for inference.", "findings": "As a methodology article, this paper presents no empirical results from a specific trial. However, the proposed framework is designed to detect a minimum detectable effect of a 15-percentage-point difference in correct use between trial arms with 80% power, accounting for an intra-cluster correlation coefficient of 0.05.", "conclusion": "The outlined methodology provides a rigorous, engineering-focused approach to quantifying adoption, moving beyond simplistic binary metrics to assess correct, sustained use of water treatment systems.", "recommendations": "Researchers should integrate objective monitoring technologies with household surveys and allocate sufficient follow-up duration to capture seasonal variations in use. Pilot studies are recommended to refine measurement tools and estimate key parameters for sample size calculations.", "key words":