Evaluation of alcohol policy control measures is key
Notice bibliographique
Résumé
In Lithuania, large income inequalities may also be linked to increasing health inequalities 1, 8, caused by an interaction between alcohol use, socio-economic status and rapid economic changes. To explore this further, the planned evaluation of alcohol control measures in this country 9 should include more analyses stratified by socio-economic strata. This could either be achieved at the individual level (e.g. using available national surveys and linking to national health databases) or at the population level, by using data on the economic wealth of different regions—readily available from national mortality and morbidity databases—for stratification purposes. There is an urgent need to further study the impact of Lithuania's natural experiment on alcohol control policy measures, as well as to inform national stakeholders of the results of these efforts. Further, by disseminating the findings of such studies in the international literature, other researchers may be inspired to conduct similar, much-needed research in this area. Over time, the public health emergency in Lithuania may serve as an exemplar for other small countries—countries that do not necessarily have the capacity to conduct such in-depth multi-dimensional studies themselves—of the effectiveness of the alcohol control policies adopted in Lithuania. Lastly, as Jasilionis pointed out, other external causes of mortality, such as deaths by suicide, have been declining at a much slower rate than alcohol-related traffic deaths 10. This could, in part, be due to the fact that individuals with an alcohol use disorder (AUD) are unlikely to recover as a result of population-level alcohol policies. Another explanation might be that such policies only have an effect on the prevalence of AUDs in the long term, and are therefore not immediately reflected in mortality statistics. Given that individuals with an AUD have a two- to threefold higher risk of dying by suicide compared to those without an AUD 11, this hypothesis could explain why other external causes of mortality are declining at a much slower rate. This is a line of research that should be explored, as lag times of risk factors on various disease and mortality outcomes are important in considering impacts, and to avoid raising unrealistic expectations. In conclusion, evaluations of alcohol control policy are key, and future policies need to be held against a standard where they are not only effective in reducing alcohol-attributable harm, but also in reducing health inequalities 12. J.B. and C. F.-B. are staff members of the WHO Regional Office for Europe. The authors alone are responsible for the views expressed in this publication and these do not necessarily represent the decisions or the stated policy of the World Health Organization. J.R. acknowledge funding from the Canadian Institutes of Health Research's Institute of Neurosciences, Mental Health and Addiction (Canadian Research Initiative on Substance Misuse Ontario Node GrantSMN-13950). The Institute of Neurosciences, Mental Health and Addiction is one of the Institutes of CIHR.
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) | 0,001 | 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 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 ».