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Enregistrement W3136940381 · doi:10.1111/add.15466

Commentary on Peña <i>et al</i>.: The broader public health relevance of understanding and addressing the alcohol harm paradox

2021· letter· en· W3136940381 sur OpenAlexaff
Charlotte Probst, Carolin Kilian

Notice bibliographique

RevueAddiction · 2021
Typeletter
Langueen
DomaineMedicine
ThématiqueSubstance Abuse Treatment and Outcomes
Établissements canadiensCentre for Addiction and Mental Health
Organismes subventionnairesNational Institute on Alcohol Abuse and Alcoholism
Mots-clésHarmLife expectancyPublic healthSocioeconomic statusPoison controlInequalityPopulationEnvironmental healthSuicide preventionInjury preventionMedicinePsychologySocial psychology

Résumé

récupéré en direct d'OpenAlex

Socio-economic inequalities in alcohol-attributable mortality make an important contribution to socio-economic health inequalities overall. A comprehensive approach to reducing socio-economic inequalities in alcohol-related health requires combining the implementation of evidence-based, cost-effective alcohol control policies with broader policy measures that act upon the structural, economic and social root causes of socioeconomic inequalities. The ‘alcohol harm paradox’ is the public health phenomenon that individuals with low socio-economic status (SES) experience greater alcohol-attributable harm despite equal or lower levels of alcohol consumption [1]. The study by Peña et al. [2] is the most recent and potentially most comprehensive effort yet to investigate the role of joint effects between SES and various behavioral risk factors, most importantly alcohol use, as a potential explanation of the alcohol harm paradox. The interaction effects between a low SES and alcohol use that were demonstrated by the authors are not merely useful to explain the alcohol harm paradox; they are probable contributors to severe public health crises of our times, such as the stagnation and decline of life expectancy at birth in the general population of the United States. Seminal research by Case & Deaton [3] has demonstrated that the increases in mortality that are underlying these recent trends are largely driven by an increase in so-called ‘deaths of despair’; that is, deaths from causes that are closely linked to alcohol and drug use (alcohol and drug poisoning, alcoholic liver cirrhosis and suicide). Individuals with low SES are most affected by these increases in mortality. Similarly, inequalities in alcohol-attributable mortality are rising in Europe and constitute an important driver of socio-economic inequality in mortality in many parts of Europe [4]. This underlines the public health importance of understanding and acting upon socio-economic inequalities in alcohol-attributable health above and beyond understanding the alcohol harm paradox. The rise in socio-economic inequalities that can be expected as a consequence of the current COVID-19 pandemic adds urgency to understanding the alcohol harm paradox and the ways in which the high alcohol-attributable burden among those with low SES can be addressed [5]. What options exist to tackle inequalities in alcohol-attributable harm from a public health perspective? Unfortunately, the most cost-effective alcohol control policies, such as taxation, regulation of availability and implementation of screening and brief intervention (SBI) [6], are not well equipped per se to target low SES populations if we do not pay close attention in their implementation [7]. For example, increasing the coverage with SBI may, in fact, exacerbate socio-economic inequalities in health outcomes due to lower health-care access for individuals with low SES [8]. It is therefore important to combine such initiatives with efforts to increase and facilitate health-care access for low SES populations and to ensure that SBI is offered across a wide range of health-care services, including occupational health-care and community health centers. Minimum unit pricing is the policy with the strongest evidence so far on addressing socio-economic inequality in alcohol consumption and alcohol-attributable harm [9, 10]. By setting a floor price on the cheapest alcohol, which is more likely to be purchased by heavy drinkers and drinkers with low SES, minimum unit pricing has been shown to be a promising tool in lowering inequalities in alcohol-attributable harm. Currently, however, only ten countries [11] in the WHO European Region have implemented some form of minimum unit pricing [12]. Even if effective alcohol policies are being implemented, their impact upon health inequality in alcohol-attributable harm is limited, given that the prevalence and average level of drinking are often already lower among those with low SES. Thus, alcohol policies must be accompanied by upstream policy measures that address the root causes of the socio-economic inequalities themselves. Such upstream policies include initiatives for social welfare, universal health-care coverage, quality and equality in education and reducing stigma and social exclusion [13]. Importantly, a ‘health in all policies’ approach should be applied in all policy planning, assessing potential health consequences for the most disadvantaged groups explicitly, rather than focusing upon productivity alone [14]. In conclusion, relying exclusively upon fast-acting downstream interventions that are directed at emerging health consequences will fail to address the underlying causes that give rise to the alcohol-related inequalities in the first place [13]. A comprehensive approach to reducing inequalities in alcohol-related health has to act on several levels, addressing the social determinants of health, relevant behavioral risk factors and health consequences down the line [13]. None. Charlotte Probst: Conceptualization. Carolin Killian: Conceptualization.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,093
Score d'incertitude au seuil0,596

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,194
Tête enseignante GPT0,351
Écart entre enseignants0,158 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations7
Publié2021
Routes d'admission1
Résumé présentoui

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