Characterizing Heated Tobacco Product Use Among Adult Cigarette Smokers and Nicotine Vaping Product Users in the 2018 ITC Four Country Smoking & Vaping Survey
Bibliographic record
Abstract
INTRODUCTION: Heated tobacco products (HTP) have diversified global tobacco markets, and user characteristics remain understudied. This study evaluated sociodemographic characteristics, nicotine-related perceptions, and behaviors of current HTP users within a sample of adult (18+ years) nicotine users across four countries. AIMS AND METHODS: Data were from current smokers or nicotine vaping product (NVP; known as "e-cigarettes") users from Canada, England, the United States, and Australia (n = 11 421) who participated in the 2018 ITC Four Country Smoking and Vaping Survey. Current (at-least-monthly) HTP users were characterized (n = 441), and weighted multivariable logistic regressions examined correlates of HTP use. RESULTS: Compared to nonusers, current HTP users were younger (mean age: 44.4 vs 31.0 years; p < .001) and had higher socioeconomic status (p < .001). A majority of current HTP users used HTPs nondaily (daily: 40.3% vs nondaily: 59.7%). Most HTP users concurrently used both cigarettes and NVPs (90.5%). Among concurrent cigarette-HTP-NVP users, 36.2% used all three products daily. Use of other combusted tobacco products (cigars, cigarillos, pipe, waterpipe/hookah), cannabis, and binge drinking were each associated with current HTP use. HTP use was more common among smokers intending to quit within 6 months or reporting a quit attempt in the past 18 months, and vapers who had experienced negative side effects. CONCLUSION: HTP users in this sample tended to be younger and more affluent. Most reported concurrent use of multiple nicotine products and other substances. Those cigarette smokers who used HTPs appeared more interested in smoking cessation, while some characteristics of concurrent HTP-NVP users were suggestive of dissatisfaction with NVPs. IMPLICATIONS: Few studies have scrutinized characteristics of HTP early adopters in emerging markets. Our results indicate that in 2018, characteristics of established nicotine users who adopted HTP use in four high-income Western countries mirror those of HTP users in East Asian markets (South Korea and Japan) where HTPs are popular. HTP users reported high levels of concurrent use of noncigarette-combusted tobacco products (e.g., cigars, pipe tobacco). These findings point to the need for future longitudinal studies of HTP use given the implications of those use patterns on the harm reduction potential of HTPs. HTP user characteristics may yield important information to consider in regulation of these products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".