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Abstract
Background Multiple tobacco and e-cigarette product (MTEP) use, the concurrent use of two or more different types of tobacco and/or e-cigarettes products, is common among young people in the US. Changes in patterns of MTEP use among US youth between 2014 and 2020 were identified and the determinants of MTEP use were examined.
Methods Four years of repeated cross-sectional data from the US National Youth Tobacco Survey of middle and high school students from grade 6 to 12 (Ntotal=77 402). Multigroup latent class analysis (LCA) was applied to the data series to allow for simultaneous identification of MTEP use patterns between 2014 and 2020. Logistic regression was used to predict class membership on demographic and tobacco-related variables.
Findings Over the 7-year period, LCA identified three patterns: minimal/non-users (MNU: ~89.8%), mostly occasional e-cigarette and cigarette users (MOEC: ~9%) and polytobacco users (POLY: ~1.2%). From 2014 to 2020, MNU increased from 86.4% to 92% (p<0.05), while MOEC and POLY decreased from 11.2% to 7.9% and from 2.4% to 0.1%, respectively. The probability of regular e-cigarette use increased from 0 to 2.3% among MNU, 6% to 31.9% among MOEC and 29.6% to 67.6% among POLY (p<0.05). In binomial logistic regression, being male, in high school, non-heterosexual, living with someone who uses tobacco at home, having cognitive difficulties, having lower perceptions of tobacco’s danger and exposure to tobacco marketing were associated with greater odds of MOEC and POLY than MNU.
Conclusions There was an increase in regular e-cigarette use in all three classes, but a corresponding decrease in the proportion of MTEP use. Public health interventions to discourage uptake of e-cigarettes, such as tighter restrictions on marketing to minors, are warranted and there is a need to consider disparities in the determinants of MTEP use.
- Co-substance use
- Advertising and Promotion
- Electronic nicotine delivery devices
- Addiction
- Public policy
Data availability statement
Data are available in a public, open access repository.
Statistics from Altmetric.com
Data availability statement
Data are available in a public, open access repository.
Footnotes
Twitter @TianzeSun, @Brie_Rutherford, @benjohnson235, @CoralGartner, @GaryCKChan
Contributors TS—conceptualisation, study registration, statistical analysis and writing initial draft. GC—conceptualisation, study registration and statistical analysis, writing and review. CCWL—statistical analysis, investigation, writing and review. BNR, BJ, JL, JC, CEG and WDH—writing, review and editing. All authors contributed to the data interpretation, writing and revisions of the report.
Funding GC was funded by a National Health and Medical Research Council (NHMRC) Investigator Grant (GNT1176137). CEG was funded by an NHMRC Centre of Research Excellence Grant. CCWL was funded by NHMRC postgraduate scholarship (GNT2005317). JPC and WDH were funded by the Department of Health, Australia. TS and BNR were funded by postgraduate scholarships provided by the University of Queensland.
Disclaimer The funders had no input into the design, implementation or running of the study, in the writing of the manuscript, or decision to publish.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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