Health and economic impacts of key alcohol policy options
Bibliographic record
Abstract
Alcohol policies have significant potential to curb alcohol-related harms, improve health, increase productivity, reduce crime and violence, and cut government expenditure. The WHO Global Strategy to reduce the harmful use of alcohol provides a menu of policy options based on international consensus, which the OECD has used as a starting point in identifying a set of policies to be assessed in an economic analysis based on a computer simulation approach. The policies assessed in three country settings – Canada, the Czech Republic and Germany – include price policies, regulation and enforcement policies, education programmes and health care interventions. The results of the OECD analyses show that brief interventions in primary care, typically targeting high-risk drinkers, and tax increases, which affect all drinkers, have the potential to generate large health gains. The impacts of regulation and enforcement policies as well as other health care interventions are more dependent on the setting and mode of implementation, while school-based programmes show less promise. Alcohol policies have the potential to prevent alcohol-related disabilities and injuries in hundreds of thousands of working-age people in the countries examined, with major potential gains in their productivity. Most alcohol policies are estimated to cut health care expenditures to the extent that their implementation costs would be more than offset. Health care interventions and enforcement of drinking-and-driving restrictions are more expensive policies, but they still have very favourable cost-effectiveness profiles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".