An evaluation of the impact of a national Minimum Unit Price on alcohol policy on alcohol behaviours
Bibliographic record
Abstract
BACKGROUND: In 2018, Scotland pioneered national legislation which set a Minimum Unit Price (MUP) of 50 pence (∼US$0.64, €0.59) per unit of UK alcohol sold (8 g/10 ml). To inform policy development, we examine the policy effect using the Alcohol Use Disorders Identification Test (AUDIT-C), employing longitudinal data for over 17 200 individuals. METHODS: The effect of MUP on AUDIT-C scores is inferred by employing difference-in-difference regression. Pre- and post-intervention alcohol behaviours of individuals from Scotland are compared to a matched 'control' from England. Drinking at hazardous and harmful levels could be identified, as well as the frequency of alcohol consumption, number of drinks and heavy episodic drinking. Estimates adjust for demographic, socioeconomic and health characteristics. Potential inequalities by gender, age and household income are examined. RESULTS: MUP led to an estimated 5.3% reduction in the number of drinks consumed on drinking occasions, though a statistically significant effect on overall reported AUDIT-C scores or drinking at hazardous levels was not detected, with few differential effects for subgroups. CONCLUSIONS: Differences in the findings of this research compared to other studies may be explained by differences in population coverage collected in the survey data, compared to more comprehensive, population-wide administrative data, as well as sample attrition.
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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 | 0.026 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".