The role of alcohol control policies in the reversal of alcohol consumption levels and resulting attributable harms in China
Bibliographic record
Abstract
Yearly adult per capita consumption of alcohol in China between 2016 and 2019 decreased by 2.4 L of pure alcohol, or 33%. According to the World Health Organization, this decrease in consumption was accompanied by reductions in alcohol-attributable mortality of 23% between 2015 and 2019. This paper examines the contribution of alcohol control policies in China to these public health gains. A systematic search of the literature was conducted on alcohol control policies and their effectiveness in China as part of a larger search of all countries in WHO Western Pacific Region. In addition to articles on empirical evidence on the impact of such alcohol control policies, we also searched for reviews. The plausibility of changes of traditional alcohol control policies (taxation increases, availability restrictions, restriction on advertisement and marketing, drink-driving laws, screening and brief interventions) in explaining reductions of consumption levels and attributable mortality rates was explored. There was some progress in the successful implementation of strict drink-driving policies, which could explain reductions in traffic injuries, including fatalities. Other traditional alcohol control policies seem to have played a minimal role in reducing alcohol consumption and attributable harms during the time period 2016-2019. However, an anti-corruption campaign was extensive enough to have substantially contributed to these reductions. The campaign prohibited the consumption of alcoholic beverages in everyday life of government officials and thus contributed to a de-normalization of alcohol. While this anti-corruption campaign was the only policy to potentially explain marked decreases in levels of alcohol consumption and attributable mortality, more detailed research is required to determine exactly how the campaign achieved these decreases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".