The impact of vaping and smoking on nicotine intake and toxicant exposure among youth in England compared with youth in North America
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».