Rescheduling alcohol marketing bans within the World Health Organization menu of policy options
Notice bibliographique
Résumé
We appreciate the critical comment made by our colleague Dr Sally Casswell [1]. As pointed out in her critique, the impact of marketing restrictions may not be comparable to the effects of pricing policies and availability restrictions. Casswell acknowledges that ‘ensuring a real change as a result of policy intervention’ is difficult to establish for marketing restrictions, summarizing a key finding of our systematic review [2]. We agree that marketing plays a crucial role for the alcohol industry, we endorse any measures that effectively reduce the exposure of the population to marketing and we advocate for more nuanced approaches to evaluate the effectiveness of marketing bans. Although we agree with most of the points raised by Dr Casswell, we disagree with the argument put forward regarding partial marketing bans. As partial marketing bans may not necessarily result in a reduction of marketing exposure in the population, Dr Casswell argues that we should not have included partial bans in our review. Considering partial bans appears to limit her confidence in our conclusion, namely that we found insufficient evidence to support the World Health Organization (WHO) assertion that alcohol marketing restrictions constitute a ‘best buy’. We are responding to this criticism with two arguments. First, the latest iteration of this ‘best buy’ adopted by the World Health Assembly in 2023 states ‘Enact and enforce bans or comprehensive restrictions on exposure to alcohol advertising (across multiple types of media)’ [3], whereas the earlier Global Action Plan referred to ‘Restricting or banning alcohol advertising and promotions’ [4]. Therefore, we argue that partial bans can be considered a ‘best buy’ based on official definitions. Second, we have identified five studies that evaluated complete marketing bans [5-9]. However, only one study found a reduction in alcohol consumption following policy implementation [7]. Therefore, our conclusion would not have been different if we had focused exclusively on complete bans. Our work does not question the relevance of marketing restrictions for public health. However, we challenge the categorisation of alcohol marketing bans as a ‘best buy’, which gives pricing, availability policies and marketing restrictions equal priority based on cost-effectiveness and ease of implementation [4]. However, a measure cannot be called cost-effective if there is no evidence for effectiveness. Moreover, it may not be easy to implement bans on marketing because the industry often finds ways to circumvent them, and full enforcement will affect the cost-effectiveness further. Finally, the time scale of effect from bans is not clear [10]. In conclusion, labelling marketing restriction as ‘best buy’ can create false expectations for policymakers. Currently, it is suggested that alcohol marketing restrictions or bans ‘generate an extra year of healthy life for a cost that falls below the average annual income or gross domestic product per person’ [4], which clearly does not align with available real-world evidence. It is important to note that the WHO menu of policy options is expected to be updated with emerging evidence; therefore, we propose rescheduling marketing restrictions into policies not characterized by demonstrated cost-effectiveness. None. Unrelated to the present work, J.M. has worked as consultant for public health agencies and has received honoraria for presentations/workshops/manuscripts funded by various public health agencies.
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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 | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| 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 | 0,001 |
| 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.
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