Rescheduling alcohol marketing bans within the World Health Organization menu of policy options
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".