Kim Nayoung, Loh Wei-Yin, McCarthy Danielle E
Center for Tobacco Research and Intervention, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America.
Department of Statistics, University of Wisconsin, Madison, Wisconsin, United States of America.
PLOS Glob Public Health. 2021 Dec 8;1(12):e0000060. doi: 10.1371/journal.pgph.0000060. eCollection 2021.
Adolescents are particularly vulnerable to tobacco initiation and escalation. Identifying factors associated with adolescent tobacco susceptibility and use can guide tobacco prevention efforts. Novel machine learning (ML) approaches efficiently identify interactive relations among factors of tobacco risks and identify high-risk subpopulations that may benefit from targeted prevention interventions. Nationally representative cross-sectional 2013-2017 Global Youth Tobacco Survey (GYTS) data from 97 countries (28 high-income and 69 low-and middle-income countries) from 342,481 adolescents aged 13-15 years (weighted N = 52,817,455) were analyzed using ML regression tree models, accounting for sampling weights. Predictors included demographics (sex, age), geography (region, country-income), and self-reported exposure to tobacco marketing, secondhand smoke, and tobacco control policies. 11.9% (95% CI 11.1%-12.6%) of tobacco-naïve adolescents were susceptible to tobacco use and 11.7% (11.0%-12.5%) of adolescents reported using any tobacco product (cigarettes, other smoked tobacco, smokeless tobacco) in the past 30 days. Regression tree models found that exposure or receptivity to tobacco industry promotions and secondhand smoke exposure predicted increased risks of susceptibility and use, while support for smoke-free air policies predicted decreased risks of tobacco susceptibility and use. Anti-tobacco school education and health warning messages on product packs predicted susceptibility or use, but their protective effects were not evident across all adolescent subgroups. Sex, region, and country-income moderated the effects of tobacco promotion and control factors on susceptibility or use, showing higher rates of susceptibility and use in males and high-income countries, Africa and the Americas (susceptibility), and Europe and Southeast Asia (use). Tobacco policy-related factors robustly predicted both tobacco susceptibility and use in global adolescents, and interacted with adolescent characteristics and other environments in complex ways that stratified adolescents based on their tobacco risk. These findings emphasize the importance of efficient ML modeling of interactions in tobacco risk prediction and suggest a role for targeted prevention strategies for high-risk adolescents.
青少年特别容易开始吸烟并增加吸烟量。识别与青少年烟草易感性和吸烟行为相关的因素可以指导烟草预防工作。新颖的机器学习（ML）方法能够有效地识别烟草风险因素之间的交互关系，并识别出可能从有针对性的预防干预措施中受益的高危亚人群。利用ML回归树模型对来自97个国家（28个高收入国家和69个中低收入国家）的342,481名13 - 15岁青少年（加权N = 52,817,455）的具有全国代表性的2013 - 2017年全球青少年烟草调查（GYTS）数据进行了分析，并考虑了抽样权重。预测因素包括人口统计学特征（性别、年龄）、地理位置（地区、国家收入）以及自我报告的接触烟草营销、二手烟和烟草控制政策的情况。11.9%（95%置信区间11.1% - 12.6%）的未接触过烟草的青少年易患烟草使用问题，11.7%（11.0% - 12.5%）的青少年报告在过去30天内使用过任何烟草产品（香烟、其他烟熏烟草、无烟烟草）。回归树模型发现，接触或接受烟草行业促销活动以及接触二手烟会增加易感性和使用烟草的风险，而支持无烟空气政策则会降低烟草易感性和使用烟草的风险。反烟草学校教育和产品包装上的健康警示信息可预测易感性或使用情况，但其保护作用在所有青少年亚组中并不明显。性别、地区和国家收入调节了烟草促销和控制因素对易感性或使用情况的影响，显示男性以及高收入国家、非洲和美洲（易感性方面）、欧洲和东南亚（使用方面）的易感性和使用率较高。与烟草政策相关的因素有力地预测了全球青少年的烟草易感性和使用情况，并以复杂的方式与青少年特征和其他环境相互作用，根据青少年的烟草风险对其进行分层。这些发现强调了在烟草风险预测中对交互作用进行有效ML建模的重要性，并表明针对高危青少年的有针对性预防策略具有重要作用。
