Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

Nkiruka C. Atuegwu, Cheryl Oncken, Reinhard C. Laubenbacher, Mario F. Perez and Eric M. Mortensen
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Nkiruka C. Atuegwu: Department of Medicine, University of Connecticut School of Medicine, Farmington, CT 06030, USA
Cheryl Oncken: Department of Medicine, University of Connecticut School of Medicine, Farmington, CT 06030, USA
Reinhard C. Laubenbacher: Department of Medicine, University of Florida College of Medicine, Gainesville, FL 32610, USA
Mario F. Perez: Department of Medicine, University of Connecticut School of Medicine, Farmington, CT 06030, USA
Eric M. Mortensen: Department of Medicine, University of Connecticut School of Medicine, Farmington, CT 06030, USA

IJERPH, 2020, vol. 17, issue 19, 1-16

Abstract: E-cigarette use is increasing among young adult never smokers of conventional cigarettes, but the awareness of the factors associated with e-cigarette use in this population is limited. The goal of this work was to use machine learning (ML) algorithms to determine the factors associated with current e-cigarette use among US young adult never cigarette smokers. Young adult (18–34 years) never cigarette smokers from the 2016 and 2017 Behavioral Risk Factor Surveillance System (BRFSS) who reported current or never e-cigarette use were used for the analysis ( n = 79,539). Variables associated with current e-cigarette use were selected by two ML algorithms (Boruta and Least absolute shrinkage and selection operator (LASSO)). Odds ratios were calculated to determine the association between e-cigarette use and the variables selected by the ML algorithms, after adjusting for age, gender and race/ethnicity and incorporating the BRFSS complex design. The prevalence of e-cigarette use varied across states. Factors previously reported in the literature, such as age, race/ethnicity, alcohol use, depression, as well as novel factors associated with e-cigarette use, such as disabilities, obesity, history of diabetes and history of arthritis were identified. These results can be used to generate further hypotheses for research, increase public awareness and help provide targeted e-cigarette education.

Keywords: sole e-cigarette use; never smokers of conventional cigarettes; e-cigarette; young adults; electronic nicotine delivery system; machine learning; vaping; behavioral risk factor surveillance system; Boruta; LASSO (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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