The impact of vaping and smoking on nicotine intake and toxicant exposure among youth in England compared with youth in North America
Bibliographic record
Abstract
Background: Youth vaping prevalence varies across countries and may be related to differing regulations/products. The emergence of cheap disposable vapes and high-concentration nicotine salts heightened concerns related to youth's ease of access, dependence and potential health risks. Objectives: We examined youth in England versus Canada and the United States and: how patterns of vaping/smoking varied, given the countries' different regulatory frameworks nicotine and potential toxicant exposure in youth who vape, smoke or do neither in youth who use salt and free-base nicotine respiratory symptom reporting. Design, methods, setting and participants: = 201. Past-week users and past 30-day non-users were tested. Interventions: None, comparisons based on vaping/smoking status. Main outcome measures: Objective 1: Vape flavours, nicotine concentration, product types, brands used. Objectives 2 and 3: Urinary biomarkers, normalised for creatinine; tobacco-specific nitrosamine NNK (NNAL); volatile organic compounds (VOCs): acrolein (3HPMA), acrylamide (2CaHEMA), acrylonitrile (2CyEMA), benzene (PhMA), toluene (BzMA), xylene (24MPhMA); nicotine: cotinine, trans-3'-hydroxycotinine (3-HC), total nicotine equivalents. Objective 4: Self-reporting any of 5 past-week respiratory symptoms (e.g. cough and dyspnoea). Results: Objective 1: Usual flavours were unchanged after 2020 United States pod-based vape flavour restrictions. Youth used exempt brands/products. Simultaneously, disposable vape use increased. In England, in 2022, 48% of 16- to 29-year-olds who vaped in past 30 days used Elf Bar brands, mainly for subjective responses (e.g. flavour/taste), rather than quitting smoking. Nicotine concentrations varied cross-country. Objectives 1, 2 and 3: Compared to smoking tobacco (exclusive or alongside vaping), exclusive vaping was associated with: similar nicotine exposure (those using nicotine salts had higher levels of nicotine metabolites vs. free-base/unknown); lower exposure to NNK, acrolein, acrylamide and acrylonitrile, but higher toluene exposure (than dual use). Compared with not vaping/smoking, exclusive vaping was associated with similar exposure to acrolein and acrylonitrile and higher exposure to toluene and acrylamide (past 24-hour sensitivity analysis). Benzene and xylene biomarkers were detected in < 5% of urine samples. Some country-level biomarker differences were observed. Objective 4: Vaping was associated with higher respiratory symptom reporting than not vaping/smoking. Youth who smoked and vaped had higher odds of symptoms than those only vaping. Using fruit, multiple or 'other' flavours was associated with higher odds of symptoms than tobacco flavours. Nicotine salt use was frequently unknown but may be associated with symptoms. Limitations: Recall, misunderstandings and misreporting are possible. A subset of biomarkers was included, not all potential confounders were assessed and categorisation into vaping/smoking groups based on past-week behaviour does not fully account for past smoking exposure. Conclusions: Pod flavour restrictions were ineffective. Youth were increasingly using disposable vapes containing nicotine salts. Those who vape were exposed to lower levels of toxicants than those who smoke, but a few toxicants were higher compared to youth who did not vape/smoke. Self-reported past-week respiratory symptoms were also higher in those who vaped than those not vaping/smoking and were related to flavours. Future work: The rapidly evolving nicotine vape market needs ongoing survey/biomarker research. Funding: This synopsis presents independent research funded by the National Institute for Health and Care Research (NIHR) Public Health Research programme as award number NIHR130292.
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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 | 0.000 | 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 | 0.001 | 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".