Undesirable events during electronic cigarette use prior to the implementation of Article 20 of the European Union Tobacco Products Directive: Findings from the EUREST-PLUS ITC Europe Surveys
Notice bibliographique
Résumé
Introduction Article 20 of the European Union (EU) Tobacco Products Directive (TPD) sets forth provisions on electronic cigarette (e-cigarette) product regulation, such as child-proof packaging and protection against e-liquid refilling without leakage. The aim of the current study was to examine frequencies and correlates of experiencing undesirable events during e-cigarette use related to e-cigarette product design parameters prior to the implementation of the EU TPD. Material and Methods The EUREST-PLUS ITC Europe Wave 1 survey was conducted with adult cigarette smokers from June to September 2016 across Germany, Greece, Hungary, Poland, Romania, Spain. Results Among our sample of adult cigarette smokers, one in five reported having ever used e-cigarettes. Prevalence of current e-cigarette use (daily or weekly) was very low (1.5%). Major undesirable events, such as battery exploding or catching fire (1.9%) or overheating (11%) were uncommon. Among those reporting at least monthly use and whose usual/current brand has a tank that you fill with liquids, 18.8% experienced spilling during refill and 18.5% experienced e-liquid leaking during use. Nearly one-quarter reported that the e-liquid cap was easy for a child to open. Conclusions In light of the EU TPD establishing standards around e-cigarette design parameters to mitigate undesirable events and risks during e-cigarette use, these findings further support the need for its implementation, as well as for monitoring undesirable events experienced during e-cigarette use once Article 20 is fully implemented. Acknowledgements EUREST-PLUS is a Horizon2020 project conducted by researchers throughout Europe from both the six participating countries as well as other institution partners within Europe and abroad. Partnering organizations include the European Network on Smoking Prevention (Belgium), Kings College London (United Kingdom), German Cancer Research Centre (Germany), University of Maastricht (The Netherlands), University of Athens (Greece), Aer Pur Romania (Romania), European Respiratory Society (Switzerland), the University of Waterloo (Canada), the Catalan Institute of Oncology (Catalonia, Spain), Smoking or Health Hungarian Foundation (Hungary), Health Promotion Foundation (Poland), University of Crete (Greece), and Kantar Public Brussels (Belgium). Funding The EUREST-PLUS Project takes place with the financial support of the European Commission, Horizon 2020 HCO-6-2015 program (EUREST-PLUS: 681109; C. Vardavas) and the University of Waterloo (GT. Fong). Additional support was provided to the University of Waterloo by the Canadian Institutes of Health Research (FDN-148477). GT. Fong was supported by a Senior Investigator Grant from the Ontario Institute for Cancer Research. E. Fernández is partly supported by Ministry of Universities and Research, Government of Catalonia (2017SGR139) and by the Instituto Carlos III and co-funded by the European Regional Development Fund (FEDER) (INT16/00211 and INT17/00103), Government of Spain.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».