The Relationship Between Alcohol Consumption and Fatal Motor Vehicle Injury: High Risk at Low Alcohol Levels
Bibliographic record
Abstract
BACKGROUND: Alcohol consumption causes motor vehicle accident (MVA) injury in a dose-response fashion. However, the relationship between how this risk is different with respect to fatal and nonfatal outcomes is not clear. A meta-analysis has already been completed for alcohol consumption and nonfatal MVA injury, but none exists for fatal injury. Thus, an analysis of the acute dose-response relationship between alcohol and motor vehicle injury death is warranted to generate single occasion- and dose-specific relative risks for the first time. METHODS: A systematic literature review and inverse-variance weighted, random effects meta-analysis were conducted to fill this gap. Fractional polynomial regression was used to model the dose-response relationship. Usual tests of heterogeneity and publication bias were run. RESULTS: Five studies meeting the inclusion criteria of this analysis were selected. At all levels of blood alcohol concentration (BAC), the odds ratio (OR) of fatal motor vehicle injury was significant. Overall, the 5 combined studies yielded an OR of fatal injury of 1.74 (95% CI: 1.43-2.14) for every 0.02% increase in BAC. At 0.08, the legal limit in most countries, the OR was 13.0 (95% CI: 11.1-15.2). CONCLUSIONS: This study is able to definitively show and quantify, for the first time, the significantly increased OR for fatal motor vehicle injury. This analysis showed some evidence of both study heterogeneity and publication bias, likely due to the increased variation we could expect from a small study number. The alcohol-caused fatal motor vehicle injury literature is sparse with respect to dose-response information. More studies investigating this relationship and other injury types are recommended in this area to be able to calculate stable estimates of risk overall and by injury type specifically.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.014 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".