Effective alcohol policies and lifetime abstinence: An analysis of the International Alcohol Control policy index
Bibliographic record
Abstract
INTRODUCTION: Alcohol abstinence remains common among adults globally, although low and middle-income countries are experiencing declines in abstention. The effect of alcohol policies on lifetime abstinence is poorly understood. The International Alcohol Control (IAC) policy index was developed to benchmark and monitor the uptake of effective alcohol policies and has shown strong associations with alcohol per capita consumption and drinking patterns. Uniquely, the index incorporates both policy 'stringency' and 'impact', reflecting policy implementation and enforcement, across effective policies. Here we assessed the association of the IAC policy index with lifetime abstinence in a diverse sample of jurisdictions. METHODS: We conducted a cross-sectional analysis of the relationship between the IAC policy index score, and its components, and lifetime abstinence among adults (15+ years) in 13 high and middle-income jurisdictions. We examined the correlations for each component of the index and stringency and impact separately. RESULTS: Overall, the total IAC policy index scores were positively correlated with lifetime abstinence (r = 0.76), as were both the stringency (r = 0.62) and impact (r = 0.82) scores. Marketing restrictions showed higher correlations with lifetime abstinence than other policy domains (r = 0.80), including restrictions on physical availability, pricing policies and drink-driving prevention. DISCUSSION AND CONCLUSION: Our findings suggest that restricting alcohol marketing could be an important policy for the protection of alcohol abstention. The IAC policy index may be a useful tool to benchmark the performance of alcohol policy in supporting alcohol abstention in high and middle-income countries.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".