Association between geospatial access to trauma center care and motor vehicle crash mortality in the United States
Bibliographic record
Abstract
BACKGROUND: Motor vehicle crashes (MVCs) are a leading cause of preventable trauma death in the United States. Access to trauma center care is highly variable nationwide. The objective of this study was to measure the association between geospatial access to trauma center care and MVC mortality. METHODS: This was a population-based study of MVC-related deaths that occurred in 3,141 US counties (2017-2020). American College of Surgeons and state-verified Level I to III trauma centers were mapped. Geospatial network analysis estimated the ground transport time to the nearest trauma center from the population-weighted centroid for each county. In this way, the exposure was the predicted access time to trauma center care for each county population. Hierarchical negative binomial regression measured the risk-adjusted association between predicted access time and MVC mortality, adjusting for population demographics, rurality, access to trauma resources, and state traffic safety laws. RESULTS: We identified 92,398 crash fatalities over the 4-year study period. Trauma centers mapped included 217 Level I, 343 Level II, and 495 Level III trauma centers. The median county predicted access time was 47 minutes (interquartile range, 26-71 minutes). Median county MVC mortality was 12.5 deaths/100,000 person-years (interquartile range, 7.4-20.3 deaths/100,000 person-years). After risk-adjustment, longer predicted access times were significantly associated with higher rates of MVC mortality (>60 minutes vs. <15 minutes; mortality rate ratio 1.36; 95% confidence interval, 1.31-1.40). This relationship was significantly more pronounced in urban/suburban vs. rural/wilderness counties ( p for interaction, <0.001). County access to trauma center care explained 16% of observed state-level variation in MVC mortality. CONCLUSION: Geospatial access to trauma center care is significantly associated with MVC mortality and contributes meaningfully to between-state differences in road traffic deaths. Efforts to improve trauma system organization should prioritize access to trauma center care to minimize crash fatalities. LEVEL OF EVIDENCE: Prognostic and Epidemiological, Level III.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".