Identifying emerging predictors for adolescent electronic nicotine delivery systems use: A machine learning analysis of the Population Assessment of Tobacco and Health Study.

Abstract

Intervention strategies to prevent adolescents from using electronic nicotine delivery systems (ENDS) should be based on robust predictors of ENDS use that may differ from predictors of conventional cigarette use. Literature points to the need for uncovering emerging predictors of ENDS use. This study identified emerging predictors of adolescent ENDS use using machine learning (ML) techniques. We analyzed nationally representative multi-wave longitudinal survey data (2013-2018) drawn from the Population Assessment of Tobacco and Health Study. A sample of adolescents (12-17 years) who never used any tobacco products at baseline and completed Wave 2 (n = 7958), Wave 3 (n = 6260) and Wave 4 (n = 4544) were analyzed. We developed a supervised ML prediction model using the penalized logistic regression to assess self-reported past-month ENDS use (i.e., current use) at Waves 2-4 based on the variables measured at the previous wave. We then extracted important predictors from each model. The penalized logistic regression models showed suitable capability to discriminate between ENDS uses and non-uses at each wave based on the area under the receiver operating characteristic curve and the area under the precision-recall curve. Interestingly, social media use emerged as an important variable in predicting adolescent ENDS use. ML models appear to be a promising method to identify unique population-level predictors for U.S. adolescent ENDS use behaviors. More research is warranted to investigate emerging predictors of ENDS use and experimentally examine the mechanism by which these emerging predictors affect ENDS use behavior across different spectrum of populations.