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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Nkiruka C. Atuegwu & Cheryl Oncken & Reinhard C. Laubenbacher & Mario F. Perez & Eric M. Mortensen, 2020. "Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach," IJERPH, MDPI, vol. 17(19), pages 1-16, October.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:19:p:7271-:d:423858
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    References listed on IDEAS

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    Cited by:

    1. Wei Fang & Ying Liu & Chun Xu & Xingguang Luo & Kesheng Wang, 2024. "Feature Selection and Machine Learning Approaches in Prediction of Current E-Cigarette Use Among U.S. Adults in 2022," IJERPH, MDPI, vol. 21(11), pages 1-14, November.
    2. Nkiruka C. Atuegwu & Mark D. Litt & Suchitra Krishnan-Sarin & Reinhard C. Laubenbacher & Mario F. Perez & Eric M. Mortensen, 2021. "E-Cigarette Use in Young Adult Never Cigarette Smokers with Disabilities: Results from the Behavioral Risk Factor Surveillance System Survey," IJERPH, MDPI, vol. 18(10), pages 1-13, May.
    3. Zidian Xie & Francisco Cartujano-Barrera & Paula Cupertino & Dongmei Li, 2022. "Cross-Sectional Associations of Self-Reported Social/Emotional Support and Life Satisfaction with Smoking and Vaping Status in Adults," IJERPH, MDPI, vol. 19(17), pages 1-10, August.

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