Community-Level Disadvantage of Adults With Firearm- vs Motor Vehicle–Related Injuries
Bibliographic record
Abstract
Importance: Motor vehicle crash (MVC) and firearm injuries are 2 of the top 3 mechanisms of adult injury-related deaths in the US. Objective: To understand the differing associations between community-level disadvantage and firearm vs MVC injuries to inform mechanism-specific prevention strategies and appropriate postdischarge resource allocation. Design, Setting, and Participants: This multicenter cross-sectional study analyzed prospectively collected data from the American College of Surgeons (ACS) Firearm Study. Included patients were treated either for firearm injury between March 1, 2021, and February 28, 2022, or for MVC-related injuries between January 1 and December 31, 2021, at 1 of 128 participating ACS trauma centers. Exposures: Community distress. Main outcome and Measure: Odds of presenting with a firearm as compared with MVC injury based on levels of community distress, as measured by the Distressed Communities Index (DCI) and categorized in quintiles. Results: A total of 62 981 patients were included (mean [SD] age, 42.9 [17.7] years; 42 388 male [67.3%]; 17 737 Black [28.2%], 9052 Hispanic [14.4%], 36 425 White [57.8%]) from 104 trauma centers. By type, there were 53 474 patients treated for MVC injuries and 9507 treated for firearm injuries. Patients with firearm injuries were younger (median [IQR] age, 31.0 [24.0-40.0] years vs 41.0 [29.0-58.0] years); more likely to be male (7892 of 9507 [83.0%] vs 34 496 of 53 474 [64.5%]), identified as Black (5486 of 9507 [57.7%] vs 12 251 of 53 474 [22.9%]), and Medicaid insured or uninsured (6819 of 9507 [71.7%] vs 21 310 of 53 474 [39.9%]); and had a higher DCI score (median [IQR] score, 74.0 [53.2-94.8] vs 58.0 [33.0-83.0]) than MVC injured patients. Among admitted patients, the odds of presenting with a firearm injury compared with MVC injury were 1.50 (95% CI, 1.35-1.66) times higher for patients living in the most distressed vs least distressed ZIP codes. After controlling for age, sex, race, ethnicity, and payer type, the DCI components associated with the highest adjusted odds of presenting with a firearm injury were a high housing vacancy rate (OR, 1.11; 95% CI, 1.04-1.19) and high poverty rate (OR, 1.17; 95% CI, 1.10-1.24). Among patients sustaining firearm injuries patients, 4333 (54.3%) received no referrals for postdischarge rehabilitation, home health, or psychosocial services. Conclusions and Relevance: In this cross-sectional study of adults with firearm- and motor vehicle-related injuries, we found that patients from highly distressed communities had higher odds of presenting to a trauma center with a firearm injury as opposed to an MVC injury. With two-thirds of firearm injury survivors treated at trauma centers being discharged without psychosocial services, community-level measures of disadvantage may be useful for allocating postdischarge care resources to patients with the greatest need.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".