Determinants of adoption of household water treatment in Haiti using two analysis methods: logistic regression and machine learning.

Abstract

Household water treatment (HWT) is recommended when safe drinking water is limited. To understand determinants of HWT adoption, we conducted a cross-sectional survey with 650 households across different regions in Haiti. Data were collected on 71 demographic and psychosocial factors and 2 outcomes (self-reported and confirmed HWT use). Data were transformed into 169 possible determinants of adoption across nine categories. We assessed determinants using logistic regression and, as machine learning methods are increasingly used, random forest analyses. Overall, 376 (58%) respondents self-reported treating or purchasing water, and 123 (19%) respondents had residual chlorine in stored household water. Both logistic regression and machine learning analyses had high accuracy (area under the receiver operating characteristic curve (AUC): 0.77-0.82), and the strongest determinants in models were in the demographics and socioeconomics, risk belief, and WASH practice categories. Determinants that can be influenced inform HWT promotion in Haiti. It is recommended to increase access to HWT products, provide cash and education on water treatment to emergency-impacted populations, and focus future surveys on known determinants of adoption. We found both regression and machine learning methods need informed, thoughtful, and trained analysts to ensure meaningful results and discuss the benefits/drawbacks of analysis methods herein.