The case for gender considerate tobacco control policies in Albania
Notice bibliographique
Résumé
Background: Tobacco use is a serious health concern in Albania. While the prevalence of tobacco smoking has traditionally been higher for men, the increasing prevalence of smoking for women is becoming a concern. The 2007 Tobacco Control policy mandated smoke-free indoor spaces, banned various forms of tobacco advertising, required written health warnings on packaging and levied excise taxes on cigarette sales. Since smoking behavior varies among different demographic groups, each group's response to a uniform policy will differ, blunting the effectiveness of these efforts as a result. This study examines various socioeconomic, demographic and behavioral factors affecting both the likelihood and frequency of smoking in Albanian households in order to provide insights on targeting various populations more effectively. Methods: The study utilizes data from Albanian 2008-09 and 2017-18 Demographic and Health Surveys consisting of adults aged 15-49 years. The outcome variable includes respondents' current tobacco smoking behaviour and its frequency. The exposure variables include respondents' sociodemographic and lifestyle characteristics. We use a two-level random intercept model with the two-stage residual inclusion estimation method to determine the association between outcome and exposure variables. By including a time variable, we capture the change in smoking behavior during the 2009-2018 period. We also extend the analysis by assessing the differential influence of gender on the likelihood of smoking, both by income quintiles and education. Results: The results suggest that the likelihood of smoking decreased by 23% in 2017-18 compared to 2008-09, after controlling for various socioeconomic and demographic factors. Tobacco smoking is also found to be linked to alcohol consumption, with binge drinkers 59% more likely to smoke tobacco compared to moderate drinkers. We also found significant inter-quintile and inter-educational differences in smoking practices within each gender category. While the likelihood of tobacco smoking decreases with increasing wealth and educational attainment among men, the opposite (for wealth) or more involved (for educational attainment) patterns are true among women. Conclusions: To further enhance the effectiveness of the current Tobacco Control policy, the Government of Albania should target various demographic groups (such as poor males, rich and educated females) in a differentiated fashion.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,000 |
| 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 ».