Prevalence and correlates of negative side effects from vaping nicotine: Findings from the 2020 ITC four country smoking and vaping survey
Bibliographic record
Abstract
INTRODUCTION: This study examined prevalence and correlates of self-reported negative side effects from nicotine vaping product (NVP) use among people who currently or recently vape. METHODS: This cross-sectional study analysed data from 3906 adults (aged 18+ years) from the 2020 ITC Four Country Smoking and Vaping Survey (Canada, US, England and Australia) who reported they had ever smoked cigarettes and were either currently vaping daily/weekly or had vaped in the last month. Participants were asked about experiencing and seeking medical advice for any negative side effects from vaping in the past month. Logistic regressions were used to estimate prevalence and identify correlates. RESULTS: Overall, 87.1 % reported no negative side effects from vaping. The most common side effects were throat irritation (5.8 %), cough (5.5 %), and mouth irritation (4.1 %). The top two that led to seeking medical advice were: mouth irritation (46.8 %) and loss of taste (45.2 %). Those more likely to self-report side effects were younger, male, currently smoking (vs quit), vaping for <6 months (vs >1 year), using disposables or cartridges/pods (vs tanks), using vapes with nicotine (vs without nicotine), using menthol/mint flavour (vs sweet flavour), currently smoking (vs quit), believing vaping causes various diseases (e.g., heart disease), and believing that vaping is equally/more harmful than smoking. CONCLUSION: Negative side effects associated with NVP use were rare and mainly minor in all four countries. Shorter duration of vaping, concurrent smoking while vaping and perceptions of greater vaping harms relative to smoking were associated with more reported negative side effects attributed to vaping.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".