Impact of minimum unit pricing on alcohol-related hospital outcomes: systematic review
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of minimum unit pricing (MUP) on the primary outcome of alcohol-related hospitalisation, and secondary outcomes of length of stay, hospital mortality and alcohol-related liver disease in hospital. DESIGN: Databases MEDLINE, Embase, Scopus, APA Psycinfo, CINAHL Plus and Cochrane Reviews were searched from 1 January 2011 to 11 November 2022. Inclusion criteria were studies evaluating the impact of minimum pricing policies, and we excluded non-minimum pricing policies or studies without alcohol-related hospital outcomes. The Effective Public Health Practice Project tool was used to assess risk of bias, and the Bradford Hill Criteria were used to infer causality for outcome measures. SETTING: MUP sets a legally required floor price per unit of alcohol and is estimated to reduce alcohol-attributable healthcare burden. PARTICIPANT: All studies meeting inclusion criteria from any country INTERVENTION: Minimum pricing policy of alcohol PRIMARY AND SECONDARY OUTCOME MEASURES: RESULTS: 22 studies met inclusion criteria; 6 natural experiments and 16 modelling studies. Countries included Australia, Canada, England, Northern Ireland, Ireland, Scotland, South Africa and Wales. Modelling studies estimated that MUP could reduce alcohol-related admissions by 3%-10% annually and the majority of real-world studies demonstrated that acute alcohol-related admissions responded immediately and reduced by 2%-9%, and chronic alcohol-related admissions lagged by 2-3 years and reduced by 4%-9% annually. Minimum pricing could target the heaviest consumers from the most deprived groups who tend to be at greatest risk of alcohol harms, and in so doing has the potential to reduce health inequalities. Using the Bradford Hill Criteria, we inferred a 'moderate-to-strong' causal link that MUP could reduce alcohol-related hospitalisation. CONCLUSIONS: Natural studies were consistent with minimum pricing modelling studies and showed that this policy could reduce alcohol-related hospitalisation and health inequalities. PROSPERO REGISTRATION NUMBER: CRD42021274023.
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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 | 0.011 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".