Tobacco and nicotine product use patterns and trends among United States youth and young adults (2021–2024)
Bibliographic record
Abstract
Objective: To examine patterns of tobacco and nicotine product use, identify changes in usage trends, and determine the frequency of use across exclusive, dual, and polyuse patterns and the influence of sociodemographic variables among United States youth and young adults. Methods: Data were collected from a repeated, cross-sectional survey of United States young people (aged 15-24 years) from 2021 to 2024. Joinpoint regression was used to determine quarter-year percentage change in usage trends. Chi-square tests and multinomial logistic regression were applied to 2024 data to assess associations with sociodemographic variables. Results: The most common usage pattern was dual use of e-cigarettes and combustible tobacco products. Quarter-year percentage change for dual use of e-cigarettes and oral nicotine pouches increased by 14.75 % from Quarter 3 2023 to Quarter 3 2024. Quarter-year percentage change for polyuse of e-cigarettes, combustible tobacco products, and oral nicotine pouches increased by 6.82 % from Quarter 3 2022 to Quarter 3 2024. Use patterns varied by age, gender identity, race and ethnicity, and perceived financial status. Conclusions: Results indicate significant changes in dual and polyuse of tobacco and nicotine products. These shifts underscore the importance of ongoing surveillance and targeted interventions to mitigate health risks and reduce the burdens of nicotine addiction in this vulnerable population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".