Alcohol control policy and changes in alcohol‐related traffic harm
Bibliographic record
Abstract
AIMS: To study the impact of alcohol control policy measures (i.e. increases in taxation, restrictions on availability, including minimum purchasing age regulations, legislation on drink driving and advertisement bans) on alcohol-related traffic harm in Lithuania between January 2004 and February 2019. DESIGN: Analyses of trend data on the proportion of alcohol-related collisions and crashes, injury and mortality, adjusting for secular trends, seasonality, periods of alcohol control measure implementation and economic development. Generalized additive mixed models were used. Multiple sensitivity analyses were conducted. SETTING: Lithuania. CASES: Monthly number of alcohol-related cases of traffic collisions and crashes, injuries and deaths. INTERVENTIONS AND COMPARATORS: Periods of time during which new alcohol control measures were implemented and/or augmented compared to periods when they were not. MEASUREMENTS: Monthly data for 2004 to 2019 from routine statistics of the Lithuanian Road Police Service. FINDINGS: All indicators decreased consistently and significantly after the implementation of alcohol control measures, including increased taxation, reduction of availability and a ban on advertisement, starting in 2014. On average, each implemented policy measure permanently reduced the proportion of alcohol-attributable crashes by 0.55% [95% confidence interval (CI) = 0.21-0.90%; P = 0.002], the proportion of alcohol-attributable injuries by 0.60% (95% CI = 0.24-0.97%; P = 0.001) and the proportion of alcohol-attributable deaths by 0.13% (95% CI = 0.10-0.15%; P < 0.001). CONCLUSIONS: Alcohol control policy measures, including measures to reduce overall level of alcohol consumption, were associated with a marked decrease in alcohol-related traffic harm.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".