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Open AccessArticle

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

1
Department of Medicine, University of Connecticut School of Medicine, Farmington, CT 06030, USA
2
Department of Medicine, University of Florida College of Medicine, Gainesville, FL 32610, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(19), 7271; https://doi.org/10.3390/ijerph17197271
Received: 17 August 2020 / Revised: 24 September 2020 / Accepted: 28 September 2020 / Published: 5 October 2020
(This article belongs to the Section Health Behavior, Chronic Disease and Health Promotion)
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. View Full-Text
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 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
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MDPI and ACS Style

Atuegwu, N.C.; Oncken, C.; Laubenbacher, R.C.; Perez, M.F.; Mortensen, E.M. Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach. Int. J. Environ. Res. Public Health 2020, 17, 7271. https://doi.org/10.3390/ijerph17197271

AMA Style

Atuegwu NC, Oncken C, Laubenbacher RC, Perez MF, Mortensen EM. Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach. International Journal of Environmental Research and Public Health. 2020; 17(19):7271. https://doi.org/10.3390/ijerph17197271

Chicago/Turabian Style

Atuegwu, Nkiruka C.; Oncken, Cheryl; Laubenbacher, Reinhard C.; Perez, Mario F.; Mortensen, Eric M. 2020. "Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach" Int. J. Environ. Res. Public Health 17, no. 19: 7271. https://doi.org/10.3390/ijerph17197271

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