Identifying predictors of multi-year cannabis vaping in U.S. Young adults using machine learning.

Journal: Addictive behaviors
PMID: 39341185

Abstract

INTRODUCTION: Increasing number of current cannabis users report using a vaporized form of cannabis and young adults are most likely to vape cannabis. However, the number of studies on cannabis vaping is limited, and predictors of cannabis vaping among U.S. young adults remain unclear. Previous studies on cannabis vaping have known limitations, as they (1) relied heavily on regression-based approaches that often fail to examine complex and non-linear interactive effects, (2) focused on examining cannabis vaping initiation but not on its use over multiple years, and (3) failed to account for recreational cannabis legalization (RCL) status.

Authors

  • Siyoung Choe
    Department of Applied Health Science, Indiana University School of Public Health, 1025 E. 7th St., Bloomington, IN 47405-7109, USA. Electronic address: sichoe@iu.edu.
  • Jon Agley
    Department of Applied Health Science, School of Public Health Bloomington, Indiana University Bloomington, Bloomington, IN, United States.
  • Kit Elam
    Department of Applied Health Science, Indiana University School of Public Health, 1025 E. 7th St., Bloomington, IN 47405-7109, USA. Electronic address: kitelam@iu.edu.
  • Aurelian Bidulescu
    Department of Epidemiology and Biostatistics, Indiana University School of Public Health, 1025 E. 7th St., Bloomington, IN 47405-7109, USA. Electronic address: abidules@iu.edu.
  • Dong-Chul Seo
    Department of Applied Health Science, Indiana University School of Public Health in Bloomington, USA. Electronic address: seo@indiana.edu.

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