Abstract
Aim: Popularity of electronic cigarettes (i.e. e-cigarettes) is soaring in Canada. Understanding person-level correlates of current e-cigarette use (vaping) is crucial to guide tobacco policy, but prior studies have not fully identified these correlates due to model overfitting caused by multicollinearity. This study addressed this issue by using classification tree, a machine learning algorithm. Methods: This population-based cross-sectional study used the Canadian Tobacco, Alcohol, and Drugs Survey (CTADS) from 2017 that targeted residents aged 15 or older. Forty-six person-level characteristics were first screened in a logistic mixed-effects regression procedure for their strength in predicting vaper type (current vs. former vaper) among people who reported to have ever vaped. A 9:1 ratio was used to randomly split the data into a training set and a validation set. A classification tree model was developed using the cross-validation method on the training set using the selected predictors and assessed on the validation set using sensitivity, specificity and accuracy. Results: Of the 3,059 people with an experience of vaping, the average age was 24.4 years (standard deviation = 11.0), with 41.9% of them being female and 8.5% of them being aboriginal. There were 556 (18.2%) current vapers. The classification tree model performed relatively well and suggested attraction to e-cigarette flavors was the most important correlate of current vaping, followed by young age (< 18) and believing vaping to be less harmful to oneself than cigarette smoking. Conclusions: People who vape due to flavors are associated with very high risk of becoming current vapers. The findings of this study provide evidence that supports the ongoing ban on flavored vaping products in the US and suggests a similar regulatory intervention may be effective in Canada.
Highlights
Canada and the US have recently witnessed exponential growth of e-cigarette use, raising worldwide public health concern of a new nicotine epidemic [1, 2]
Prior studies have suggested a set of characteristics that may be unique to current vapers, including female, younger age, use of a certain type of vaping device as well as initiating vaping due to attraction to flavors and lower cost [11,12,13,14,15]
We applied machine learning to data collected from a nationally representative sample of Canadians aged 15+ with vaping experience to understand correlates of the current use of the device
Summary
Canada and the US have recently witnessed exponential growth of e-cigarette use (vaping), raising worldwide public health concern of a new nicotine epidemic [1, 2]. Prior studies have suggested a set of characteristics that may be unique to current vapers, including female, younger age, use of a certain type of vaping device as well as initiating vaping due to attraction to flavors and lower cost [11,12,13,14,15] These results were yielded primarily by regression where multicollinearity is a concern. As person-level variables are usually correlated, e.g., younger people are attracted to e-cigarette flavors and are more likely to perceive vaping to have lower risks [16], it is difficult to isolate a set of independent predictors of current vaping using just regression This issue warrants the use of more advanced statistical techniques
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