Drivers obesity and road crash risks in the United States
Bibliographic record
Abstract
We assessed obesity trends in US drivers involved in fatal crashes since 1999 and distinguished whether crash risk factors were different between obese and non-obese drivers. We included drivers of passenger cars involved in fatal traffic crashes between January 1, 1999 and December 31, 2012. Obesity was classified according to the World Health Organization guidelines and profiled between 1999 and 2012 using adjusted prevalence ratio (aPR) from log-binomial regression models. Differences in crash risks (e.g., fatality, drunk-driving, seat-belt non-use) between obese and non-obese drivers were estimated as adjusted odds ratio (aOR) using logistic regression models. A total of 753,024 US drivers were involved in fatal crashes, of whom obesity information was available in 534,887. About 56% (n=299,078) were driving passenger cars. The prevalence of class I obesity increased from 10% in 1999 to 14% in 2012 (aPR=1.50, 95% confidence intervals [95%CI]=1.42-1.58), class II obesity from 3% to 5% (aPR=2.22, 95%CI=2.05-3.01), and class III obesity from 1% to 2% (aPR=2.65; 95%CI=2.27-3.10). Compared to non-obese controls, obese drivers had significantly higher risks for fatality (1.10≤aOR≤1.47), seat-belt non-use (1.00≤aOR≤1.21), need for extrication (1.01≤aOR≤1.23), and ambulance transport time ≥30min (1.01≤aOR≤1.28). Compared to non-obese controls, obese drivers were less likely to drink-drive (0.41≤aOR≤0.72) and speed ≥65mph (0.78≤aOR≤0.93).. The rising national prevalence of obesity extends to US drivers involved in fatal crashes and indicates the need to improve seat-belt use, vehicle design, and post-crash care for this vulnerable population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".