A Machine Learning Approach Reveals Distinct Predictors of Vaping Dependence for Adolescent Daily and Non-Daily Vapers in the COVID-19 Era

Abstract

Since 2016, there has been a substantial rise in e-cigarette (vaping) dependence among young people. In this prospective cohort study, we aimed to identify the different predictors of vaping dependence over 3 months among adolescents who were baseline daily and non-daily vapers. We recruited ever-vaping Canadian residents aged 16–25 years on social media platforms and asked them to complete a baseline survey in November 2020. A validated vaping dependence score (0–23) summing up their responses to nine questions was calculated at the 3-month follow-up survey. Separate lasso regression models were developed to identify predictors of higher 3-month vaping dependence score among baseline daily and non-daily vapers. Of the 1172 participants, 643 (54.9%) were daily vapers with a mean age of 19.6 ± 2.6 years and 76.4% (n = 895) of them being female. The two models achieved adequate predictive performance. Place of last vape purchase, number of days a pod lasts, and the frequency of nicotine-containing vaping were the most important predictors for dependence among daily vapers, while race, sexual orientation and reporting treatment for heart disease were the most important predictors in non-daily vapers. These findings have implications for vaping control policies that target adolescents at different stages of vape use.

Table 2 Fallacy in Descriptive Epidemiology: Bringing Machine Learning to the Table

“…Indeed, ML has been increasingly applied in health research, [ 7 , 8 , 9 , 10 ] although a systematic justification of using these methods for descriptive epidemiology purposes and an analytical pipeline to do so are currently lacking. Specifically, substance use appears to be an area with lots of interest in establishing ML as a routine statistical method; this may be attributed to the increasingly complex public health environments and large volume of data that have become available [ 11 , 12 ].…”