Prevalence and patterns of different types of non-cigarette tobacco use in England: a population study
Bibliographic record
Abstract
Abstract Introduction Non-cigarette tobacco smoking increased in England between 2013 and 2023, but little is known about the prevalence of use of specific types of non-cigarette tobacco, including smokeless products, or whether the increasing trend has continued. This study examined recent trends in exclusive non-cigarette tobacco smoking and estimated the prevalence of different smoked and smokeless product use by sociodemographic characteristics, vaping and cigarette smoking status. Methods Data came from the Smoking Toolkit Study, a nationally representative monthly cross-sectional survey of adults (≥18y) in England ( n =94,918; April-2020 to February-2025). We used logistic regression to estimate trends in exclusive non-cigarette tobacco smoking. We further examined the prevalence and correlates of (non-exclusive) use of eight specific smoked and smokeless non-cigarette tobacco products among 8,129 participants surveyed October-2024 to February-2025. Results The prevalence of exclusive non-cigarette tobacco smoking increased from 1.3% [1.1-1.6%] in April-2020 to 2.0% [1.8-2.1%] in August-2022, before declining to 1.2% [1.1-1.5%] by February-2025. Between October-2024 and February-2025, 3.7% [3.2-4.1%] of adults reported any non-cigarette tobacco use (∼1.7 million people). Cigars (1.2%) and waterpipes (0.9%) were the most used products. Use patterns varied: cigars, cigarillos, and pipes were more common among men and those who smoked cigarettes, while waterpipes were more common among younger adults, minority ethnic groups, and people who vaped. Smokeless tobacco use was more common among those who smoked (any form of) tobacco. Conclusions In England, exclusive non-cigarette tobacco smoking has fluctuated in recent years. Patterns of smoked and smokeless tobacco use vary across products and sociodemographic groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".