State or market? How to effectively decrease alcohol-related crash fatalities and injuries
Bibliographic record
Abstract
BACKGROUND: It is estimated that more than 270 000 people die yearly in alcohol-related crashes globally. To tackle this burden, government interventions, such as laws which restrict blood alcohol concentration (BAC) levels and increase penalties for drunk drivers, have been implemented. The introduction of private-sector measures, such as ridesharing, is regarded as alternatives to reduce drunk driving and related sequelae. However, it is unclear whether state and private efforts complement each other to reduce this public health challenge. METHODS: We conducted interrupted time-series analyses using weekly alcohol-related traffic fatalities and injuries per 1 000 000 population in three urban conglomerates (Santiago, Valparaíso and Concepción) in Chile for the period 2010-2017. We selected cities in which two state interventions-the 'zero tolerance law' (ZTL), which decreased BAC, and the 'Emilia law' (EL), which increased penalties for drunk drivers-were implemented to decrease alcohol-related crashes, and where Uber ridesharing was launched. RESULTS: In Santiago, the ZTL was associated with a 29.1% decrease (95% CI 1.2 to 70.2), the EL with a 41.0% decrease (95% CI 5.5 to 93.2) and Uber with a non-significant 28.0% decrease (95% CI -6.4 to 78.5) in the level of weekly alcohol-related traffic fatalities and injuries per 1 000 000 population series. In Concepción, the EL was associated with a 28.9% reduction (95% CI 4.3 to 62.7) in the level of the same outcome. In Valparaíso, the ZTL had a -0.01 decrease (95% CI -0.02 to -0.00) in the trend of weekly alcohol-related crashes per 1 000 000 population series. CONCLUSION: In Chile, concomitant decreases of alcohol-related crashes were observed after two state interventions were implemented but not with the introduction of Uber. Relationships between public policy interventions, ridesharing and motor vehicle alcohol-related crashes differ between cities and over time, which might reflect differences in specific local characteristics.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".