The effectiveness and cost-effectiveness of population-level policies to reduce alcohol use: A systematic umbrella review
Bibliographic record
Abstract
OBJECTIVE: To systematically review and synthesize evidence from reviews about the effectiveness and cost-effectiveness of population-level policies to reduce alcohol use. METHODS: We searched peer-reviewed literature using eight electronic bibliographic databases, grey literature using two databases, two search engines, and two working paper repositories, and examined references of included studies. At least two reviewers independently screened articles for inclusion, extracted detailed characteristics, and assessed the risk of bias of each included study. We considered all reviews that included studies which quantitatively examined the relationship between alcohol consumption and population-level policies that seek to regulate the public availability and marketing of alcoholic beverages. After screening according to a set of predetermined criteria, we included 32 reviews. SYNTHESIS: We found consistent evidence that addressing alcohol availability (introducing or increasing minimum purchasing age, restrictions on temporal availability, decreasing outlet density, government monopolization) was associated with lower alcohol use; and a general lack of evidence on the associations between alcohol marketing (marketing self-regulation, advertising from government authorities, regulating the volume of advertising from alcohol manufacturers, and introducing warning labels) and alcohol consumption, which precludes any conclusions about these regulations. Additionally, we found scarce evidence about the cost-effectiveness of population-level policies to reduce alcohol use, which is likely due to the relatively low cost of implementation and enforcement of these policies. CONCLUSION: The Government of Ontario began expanding privatized alcohol sales in 2015 with further expansions starting in August 2024. Evidence from reviews suggests that this increase in availability will result in increased alcohol consumption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.195 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".