Global Status Of Drink-Driving-Related Fatality Reporting: Availability And Its Relationship With Use And Other Alcohol Mortality Patterns
Bibliographic record
Abstract
Introduction: According to the World Health Organization one in five road traffic fatalities are related to impaired driving involving drunk-driving (DD), drugs, illicit substances as well as prescription medications. The proportion of DD-related road fatality (or DDRF %) is the recommended indicator in setting road safety goals for DD prevention. The literature however is relatively silent about the availability of DDRF % in high-income (HIC), medium-income (MIC) and low-income (LIC) countries. Objective: The study assessed the availability of DDRF % in different countries and particularly with respect to their income and alcohol use patterns. This research is in line with Qatar National Research Strategy pillar H.E.1.8 (prevention of motor vehicle crashes and injuries) and the Grand Challenges Qatar. Methods: Availability of DDRF % was extracted from the two recent global status reports on road safety (2009) and on alcohol and health (2011) and assessed with respect to country income and alcohol use patterns. Results: Report on road safety included more DDRF% than report on alcohol and health (n=90 vs. 27). DDRF% was significantly (P<0.01) more available in high-income countries (77%, n=30/39) versus middle income countries (52%, n=47/90) and low-income countries (28%, n=13/46). DDRF% distribution ranged from 5% to over 40%, however, we noted no differences between country's income status and the distribution of DDRF%. We did observe that DDRF%s were not available in the high-income countries of the Eastern Mediterranean Region. DDRF%s were available in 88% countries with high consumption (<20% abstainers) One in two countries with moderate consumption did not report DDRF%. Conclusions: Data on the contribution of DD to fatality rates are generally inadequate, especially in HICs of Eastern Mediterranean region, MICs and LICs. These findings indicate the need to strengthen road safety data collection on DD, drugs and medication use while driving in above mentioned countries.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".