School of Engineering, Tufts University, Medford, MA, USA E-mail:
School of Engineering, Tufts University, Medford, MA, USA.
J Water Health. 2024 Sep;22(9):1606-1617. doi: 10.2166/wh.2024.376. Epub 2024 Aug 21.
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.
当安全饮用水有限时，推荐使用家庭水处理（HWT）。为了了解 HWT 采用的决定因素，我们在海地不同地区进行了一项包含 650 户家庭的横断面调查。数据收集了 71 个人口统计学和社会心理因素以及 2 个结果（自我报告和确认的 HWT 使用）。数据转化为九个类别中的 169 个可能的采用决定因素。我们使用逻辑回归评估了决定因素，并且由于机器学习方法越来越多地被使用，我们还使用了随机森林分析。总体而言，376 名（58%）受访者自我报告了处理或购买水，123 名（19%）受访者家中储存的水中有残留氯。逻辑回归和机器学习分析都具有很高的准确性（接受者操作特征曲线下的面积（AUC）：0.77-0.82），模型中最强的决定因素是人口统计学和社会经济学、风险信念以及 WASH 实践类别。可以影响这些决定因素的方法可以为海地的 HWT 推广提供信息。建议增加 HWT 产品的获取渠道，为受紧急情况影响的人群提供现金和水处理教育，并在未来的调查中关注已知的采用决定因素。我们发现，回归和机器学习方法都需要有经验、深思熟虑和训练有素的分析师来确保有意义的结果，并在此讨论分析方法的优缺点。
