Atuegwu Nkiruka C, Mortensen Eric M, Krishnan-Sarin Suchitra, Laubenbacher Reinhard C, Litt Mark D
Department of Medicine, University of Connecticut School of Medicine, Farmington, CT 06030, USA.
Department of Psychiatry, Yale University School of Medicine, Connecticut Mental Health Center, 34 Park Street, New Haven, CT 06519, USA.
Prev Med Rep. 2023 Feb 13;32:102148. doi: 10.1016/j.pmedr.2023.102148. eCollection 2023 Apr.
The use of electronic nicotine delivery systems (ENDS) is increasing among young adults. However, there are few studies regarding predictors of ENDS initiation in tobacco-naive young adults. Identifying the risk and protective factors of ENDS initiation that are specific to tobacco-naive young adults will enable the creation of targeted policies and prevention programs. This study used machine learning (ML) to create predictive models, identify risk and protective factors for ENDS initiation for tobacco-naive young adults, and the relationship between these predictors and the prediction of ENDS initiation. We used nationally representative data of tobacco-naive young adults in the U.S drawn from the Population Assessment of Tobacco and Health (PATH) longitudinal cohort survey. Respondents were young adults (18-24 years) who had never used any tobacco products in Wave 4 and who completed Waves 4 and 5 interviews. ML techniques were used to create models and determine predictors at 1-year follow-up from Wave 4 data. Among the 2,746 tobacco-naive young adults at baseline, 309 initiated ENDS use at 1-year follow-up. The top five prospective predictors of ENDS initiation were susceptibility to ENDS, increased days of physical exercise specifically designed to strengthen muscles, frequency of social media use, marijuana use and susceptibility to cigarettes. This study identified previously unreported and emerging predictors of ENDS initiation that warrant further investigation and provided comprehensive information on the predictors of ENDS initiation. Furthermore, this study showed that ML is a promising technique that can aid ENDS monitoring and prevention programs.
电子尼古丁传送系统（ENDS）在年轻成年人中的使用正在增加。然而，关于从未接触过烟草的年轻成年人开始使用ENDS的预测因素的研究很少。确定从未接触过烟草的年轻成年人中开始使用ENDS的风险和保护因素，将有助于制定有针对性的政策和预防计划。本研究使用机器学习（ML）来创建预测模型，确定从未接触过烟草的年轻成年人开始使用ENDS的风险和保护因素，以及这些预测因素与开始使用ENDS的预测之间的关系。我们使用了来自美国烟草与健康人口评估（PATH）纵向队列调查的具有全国代表性的从未接触过烟草的年轻成年人数据。受访者是在第4波调查中从未使用过任何烟草产品且完成了第4波和第5波访谈的年轻成年人（18 - 24岁）。ML技术用于根据第4波数据创建模型并在1年随访时确定预测因素。在基线时的2746名从未接触过烟草的年轻成年人中，有309人在1年随访时开始使用ENDS。开始使用ENDS的前五个前瞻性预测因素是对ENDS的易感性、专门为增强肌肉而进行的体育锻炼天数增加、社交媒体使用频率、大麻使用以及对香烟的易感性。本研究确定了以前未报告的和新出现的开始使用ENDS的预测因素，这些因素值得进一步研究，并提供了关于开始使用ENDS的预测因素的全面信息。此外，本研究表明ML是一种有前景的技术，可以帮助ENDS监测和预防计划。
