Political orientation and traffic deaths: An ecological analysis
Bibliographic record
Abstract
Americans have some of the highest traffic fatality risks and the most polarized national politics compared to other democracies. This study tested whether traffic fatality risks in the United States are associated with state-level presidential voting patterns. In this cross-sectional ecological analysis, we examined state-level popularity of the Republican presidential candidate with traffic fatality data from the National Highway Traffic Safety Administration. The analysis covered seven presidential elections from 1996 to 2020. Sensitivity analyses examined voter turnout as a non-partisan predictor, three health behaviors as negative control outcomes, and three medical causes of death as positive control outcomes. A higher share of votes for the Republican presidential candidate in 2020 was correlated with higher traffic fatality risks (r = 0.63, p < 0.001). On average, a 1% increase in Republican popularity was associated with 20 extra traffic deaths per state annually (95% confidence interval = 13 to 28). This pattern replicated in six prior presidential elections and persisted across all ages, both sexes, diverse road users, and irrespective of alcohol influence. American states with a higher share of votes for the Republican presidential candidate tend to have higher traffic fatality risks. Clinicians, patients, and policymakers living in Republican states should be extra careful driving and at other times traveling on public roads in the community.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".