Login / Signup

Latent Classes of Tobacco and Cannabis Use among Youth and Young Adults in the United States.

Delvon T MattinglyMichael R ElliottNancy L Fleischer
Published in: Substance use & misuse (2023)
Background: Research characterizing patterns of tobacco and cannabis use by product type and route of administration among youth and young adults (YAs) is limited. Methods: We conducted latent class analysis of tobacco and cannabis use (i.e., cigarettes, electronic nicotine delivery systems (ENDS), cigars, blunts, cannabis vaping, and other cannabis use (without blunting/vaping)) among youth (ages 15-17) and YAs (ages 18-24) who used at least one product in the past 30 days, using data from the Population Assessment of Tobacco and Health Study (Wave 4, 2016-2017). We used multinomial logistic regression models to examine associations between sociodemographic characteristics and use classes. Results: The latent use classes for youth included cigarettes (2.5%), ENDS (2.6%), blunts (2.5%), other cannabis (6.3%), ENDS + cannabis vaping (2.7%), and cigarettes + cigars + other cannabis (1.5%), while the latent use classes for YAs included cigarettes (11.7%), ENDS (3.9%), blunts (5.3%), other cannabis (7.0%), cigarettes + cigars (8.2%), and cigarettes + ENDS + cannabis vaping (4.9%). We compared use classes to never/former use for youth (82.0%) and YAs (59.0%) and found that they differed by each sociodemographic characteristic. For example, non-Hispanic Black YAs had higher odds of cigarettes + cigar use compared to non-Hispanic White YAs, whereas racial/ethnic minority youth and YAs had lower odds of other dual/poly use groups compared to their non-Hispanic White counterparts. Conclusions: We observed differences in use classes by sociodemographic characteristics for youth and YAs. Health professionals must consider tobacco and cannabis use patterns when implementing prevention and cessation interventions.
Keyphrases
  • young adults
  • smoking cessation
  • mental health
  • physical activity
  • replacement therapy
  • healthcare
  • african american
  • public health
  • childhood cancer
  • big data
  • machine learning