Classifying national drinking patterns in Europe between 2000 and 2019: A clustering approach using comparable exposure data
Notice bibliographique
Résumé
Abstract Background and aims Previously identified national drinking patterns in Europe lack comparability and might be no longer be valid due to changes in economic conditions and policy frameworks. We aimed to identify the most recent alcohol drinking patterns in Europe based on comparable alcohol exposure indicators using a data‐driven approach, as well as identifying temporal changes and establishing empirical links between these patterns and indicators of alcohol‐related harm. Design Data from the World Health Organization's monitoring system on alcohol exposure indicators were used. Repeated cross‐sectional hierarchical cluster analyses were applied. Differences in alcohol‐attributable harm between clusters of countries were analyzed via linear regression. Setting European Union countries, plus Iceland, Norway and Ukraine, for 2000, 2010, 2015 and 2019. Participants/Cases Observations consisted of annual country data, at four different time points for alcohol exposure. Harm indicators were only included for 2019. Measurements Alcohol exposure indicators included alcohol per capita consumption (APC), beverage‐specific consumption and prevalence of drinking status indicators (lifetime abstainers, current drinkers, former drinkers and heavy episodic drinking). Alcohol‐attributable harm was measured using age‐standardized alcohol‐attributable Disability‐Adjusted Life Years (DALYs) lost and deaths per 100 000 people. Findings The same six clusters were identified in 2019, 2015 and 2010, mainly characterized by type of alcoholic beverage and prevalence drinking status indicators, with geographical interpretation. Two‐thirds of the countries remained in the same cluster over time, with one additional cluster identified in 2000, characterized by low APC. The most recent drinking patterns were shown to be significantly associated with alcohol‐attributable deaths and DALY rates. Compared with wine‐drinking countries, the mortality rate per 100 000 people was significantly higher in Eastern Europe with high spirits and ‘other’ beverage consumption [ = 90, 95% confidence interval (CI) = 55–126], and in Eastern Europe with high lifetime abstainers and high spirits consumption ( = 42, 95% CI = 4–78). Conclusions European drinking patterns appear to be clustered by level of beverage‐specific consumption, with heavy episodic drinkers, current drinkers and lifetime abstainers being distinguishing factors between clusters. Despite the overall stability of the clusters over time, some countries shifted between drinking patterns from 2000 to 2019. Overall, patterns of drinking in the European Union seem to be stable and partly determined by geographical proximity.
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 | 0,000 | 0,000 |
| 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 ».