使用两种分析方法:逻辑回归和机器学习,分析海地采用家庭水处理方法的决定因素。

IF 2.5 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
Journal of water and health Pub Date : 2024-09-01 Epub Date: 2024-08-21 DOI:10.2166/wh.2024.376
Camille Heylen, Diona Antoine, Michael Ritter, Jean Marcel Casimir, Neil Van Dine, Jean Jackendy, Alice Leung, Dustin Wright, Daniele Lantagne
{"title":"使用两种分析方法:逻辑回归和机器学习,分析海地采用家庭水处理方法的决定因素。","authors":"Camille Heylen, Diona Antoine, Michael Ritter, Jean Marcel Casimir, Neil Van Dine, Jean Jackendy, Alice Leung, Dustin Wright, Daniele Lantagne","doi":"10.2166/wh.2024.376","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":17436,"journal":{"name":"Journal of water and health","volume":"22 9","pages":"1606-1617"},"PeriodicalIF":2.5000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Determinants of adoption of household water treatment in Haiti using two analysis methods: logistic regression and machine learning.\",\"authors\":\"Camille Heylen, Diona Antoine, Michael Ritter, Jean Marcel Casimir, Neil Van Dine, Jean Jackendy, Alice Leung, Dustin Wright, Daniele Lantagne\",\"doi\":\"10.2166/wh.2024.376\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":17436,\"journal\":{\"name\":\"Journal of water and health\",\"volume\":\"22 9\",\"pages\":\"1606-1617\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of water and health\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.2166/wh.2024.376\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/8/21 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of water and health","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.2166/wh.2024.376","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/21 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

当安全饮用水有限时,建议采用家庭水处理技术(HWT)。为了解采用 HWT 的决定因素,我们对海地不同地区的 650 个家庭进行了横断面调查。我们收集了 71 个人口和社会心理因素以及 2 个结果(自我报告和确认使用 HWT)的数据。数据被转化为 9 个类别中 169 个可能的采用决定因素。我们使用逻辑回归法评估了决定因素,随着机器学习方法的使用越来越多,我们还使用了随机森林分析法。总体而言,376 名受访者(58%)自称对水进行过处理或购买过水,123 名受访者(19%)的家庭储水中含有余氯。逻辑回归和机器学习分析的准确率都很高(接收者工作特征曲线下的面积(AUC)为 0.77-0.82),而机器学习分析的准确率则在 0.77-0.82 之间: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、风险信念和讲卫生运动实践类别。可影响的决定因素为在海地推广 HWT 提供了依据。建议增加使用 HWT 产品的机会,向受紧急情况影响的人群提供现金和水处理教育,并在未来的调查中重点关注采用 HWT 的已知决定因素。我们发现回归和机器学习方法都需要知情、深思熟虑和训练有素的分析人员,以确保得出有意义的结果,并在此讨论分析方法的优点/缺点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determinants of adoption of household water treatment in Haiti using two analysis methods: logistic regression and machine learning.

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of water and health
Journal of water and health 环境科学-环境科学
CiteScore
3.60
自引率
8.70%
发文量
110
审稿时长
18-36 weeks
期刊介绍: Journal of Water and Health is a peer-reviewed journal devoted to the dissemination of information on the health implications and control of waterborne microorganisms and chemical substances in the broadest sense for developing and developed countries worldwide. This is to include microbial toxins, chemical quality and the aesthetic qualities of water.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信