Nicotine Exposure From Smoking Tobacco and Vaping Among Adolescents
Notice bibliographique
Résumé
Importance: It remains unknown whether nicotine intake among youths who vape is lower, comparable, or higher than among youths who smoke. Objective: To examine potential differences in biomarkers of exposure to nicotine (1) between adolescents who smoke tobacco, vape, both vape and smoke (dual use), or do not use; (2) between adolescents in 3 countries; and (3) by nicotine content and form in the vaping product last used among adolescents who exclusively vaped. Design, Setting, and Participants: This population-based, observational cross-sectional study invited adolescents aged 16 to 19 years in Canada, England, and the US who had previously completed national surveys to participate in a biomarker study based on their vaping and smoking status. Participants completed questionnaires and self-collected urine samples between September 2019 and January 2022. Analyses were conducted in February 2023 and between January and June 2024. Exposures: Vaping, tobacco smoking, dual use, or no use in the past 7 days. Main Outcomes and Measures: Urine concentration of cotinine, trans-3'-hydroxycotinine (3OH-cotinine), and total nicotine equivalents (TNE-2; molar sum of cotinine and 3OH-cotinine), normalized for creatinine concentration. Results: Among the 364 participants (mean [SD] age, 17.6 [1.1] years; 203 females [55.8%]) who provided usable urine samples and completed questionnaires, no differences in TNE-2 concentration were observed between adolescents who exclusively vaped (n = 73; geometric mean [SD], 3.10 [16.69] nmol/mg creatinine), exclusively smoked (n = 68; geometric mean [SD], 3.78 [18.00] nmol/mg creatinine), or both vaped and smoked (n = 77; geometric mean [SD], 6.07 [19.08] nmol/mg creatinine) in the past week, adjusting for creatinine concentration, age, sex, country, and cannabis use. All vaping and/or smoking groups had higher concentrations of TNE-2 than no use (n = 146; geometric mean [SD], 0.19 [1.14] nmol/mg creatinine; P < .001 for all contrasts). Among adolescents who exclusively vaped (n = 73), TNE-2 concentrations were not significantly different between those who reported using products containing more than 20 mg/mL nicotine (n = 33; geometric mean [SD], 4.35 [18.25] nmol/mg creatinine) and containing 20 mg/mL nicotine or less (n = 28; geometric mean [SD], 5.13 [15.64] nmol/mg creatinine). Reported use of vaping products containing nicotine salts (n = 23) was associated with higher concentration of TNE-2 (geometric mean [SD], 10.78 [18.03] nmol/mg creatinine) than reported use of products without nicotine salts (n = 29; geometric mean [SD], 2.72 [15.42] nmol/ng creatinine; P = .03) or reporting "don't know" (n = 14; geometric mean [SD], 1.55 [15.01] nmol/ng creatinine; P = .009). Similar patterns of exposure were observed for cotinine and 3OH-cotinine. Conclusions and Relevance: This cross-sectional study found that vaping was associated with similar nicotine exposure as smoking among adolescents. Reported use of a nicotine salt product was associated with higher nicotine exposure among those who exclusively vaped, consistent with findings from laboratory and population studies indicating greater dependence for nicotine salt e-cigarettes.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».