Youth Vaping During the Early Stages of the COVID-19 Pandemic Period: Adjusted Annual Changes in Vaping Between the Pre-COVID and Initial COVID-Lockdown Waves of the COMPASS Study
Notice bibliographique
Résumé
BACKGROUND: Adolescence is a critical period for vaping onset. The purpose of this article was to examine the effect of the early stages of the COVID-19 pandemic period on youth vaping. METHODS: We used 3-year linked data from the COMPASS study, including 7585 Canadian (Quebec, Ontario) adolescents from which 1949 completed all three survey waves (pre-COVID-19 [2018, 2019] and online [2020] during the early pandemic period [May-July 2020]) and provided vaping data. Structural equation modeling (SEM) and difference-in-difference (DD) models were used to estimate pre-COVID-19 to initial COVID-19 pandemic period change (2019-2020) in vaping (monthly, weekly, daily) compared with 2018-2019 change to adjust for age-related effects. Models were adjusted for age of entry into the cohort and sociodemographic characteristics. RESULTS: In the SEM and DD models, the proportion of youth who were monthly and weekly vaping increased from 2018 to 2019 but decreased from 2019 to 2020; daily vaping increased across all waves. However, for all vaping outcomes modeled, the expected increases from the pre-COVID-19 wave (2019) to the initial COVID-19 period wave (2020) were lesser relative to the changes seen across the 2018 to 2019 waves. CONCLUSION: The early stages of the COVID-19 pandemic period appear to be associated with a reduction in the proportion of youth who were monthly and weekly vapers in our adjusted longitudinal models. While daily vaping increased over this same period of time, the magnitude of the increase in our adjusted longitudinal models appears attenuated by the early stages of the pandemic. IMPLICATIONS: This large prospective study of youth that included pre-pandemic data is unique in that we were able to identify that the early stages of the COVID-19 pandemic period was associated with a reduction in the proportion of youth who were monthly and weekly vapers in our adjusted longitudinal models. Conversely, the proportion of youth who were daily vaping increased over this same period of time, but the magnitude of the increase appears smaller than expected during the early stages of the pandemic in our adjusted longitudinal models. This study provides novel robust evidence that the patterns of vaping most aligned with onset and progression (i.e., monthly and weekly use) appear attenuated during the initial pandemic period.
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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 | 0,006 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».