Restricting alcohol marketing to reduce alcohol consumption: A systematic review of the empirical evidence for one of the ‘best buys’
Bibliographic record
Abstract
BACKGROUND AND AIMS: Even though a ban of alcohol marketing has been declared a 'best buy' of alcohol control policy, comprehensive systematic reviews on its effectiveness to reduce consumption are lacking. The aim of this paper was to systematically review the evidence for effects of total and partial bans of alcohol marketing on alcohol consumption. METHODS: This descriptive systematic review sought to include all empirical studies that explored how changes in the regulation of alcohol marketing impact on alcohol consumption. The search was conducted between October and December 2022 considering various scientific databases (Web of Science, PsycINFO, MEDLINE, Embase) as well as Google and Google Scholar. The titles and abstracts of a total of 2572 records were screened. Of the 26 studies included in the full text screening, 11 studies were finally included in this review. Changes in consumption in relation to marketing bans were determined based on significance testing in primary studies. Four risk of bias domains (confounding, selection bias, information bias and reporting bias) were assessed. RESULTS: Seven studies examined changes in marketing restrictions in one location (New Zealand, Thailand, Canadian provinces, Spain, Norway). In the remaining studies, between 17 and 45 locations were studied (mostly high-income countries from Europe and North America). Of the 11 studies identified, six studies reported null findings. Studies reporting lower alcohol consumption following marketing restrictions were of moderate, serious and critical risk of bias. Two studies with low and moderate risk of bias found increasing alcohol consumption post marketing bans. Overall, there was insufficient evidence to conclude that alcohol marketing bans reduce alcohol consumption. CONCLUSIONS: The available empirical evidence does not support the claim of alcohol marketing bans constituting a best buy for reducing alcohol consumption.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".