Social vulnerability index and firearms: How neighborhood health disparities affect trauma outcomes
Bibliographic record
Abstract
Background: Firearm injuries' association with individual-level socioeconomic risk is well described. Trauma research has suggested that neighborhood level risk factors may be associated with differences in firearm injury outcome. We analyzed the relationship between hospital length of stay (LOS), mortality and neighborhood level social markers from the Center for Disease Control (CDC) Social Vulnerability Index (SVI) after firearm injury. Materials and methods: We used the Healthcare Cost and Utilization Project (HCUP) State Inpatient Database (SID) in 2016 to identify firearm injuries using ICD-10 E-codes. Patient locations were identified at the census tract level. The 2016 CDC SVI was used to evaluate neighborhood level social vulnerability. Logistic and linear multivariable regression were used to evaluate the association between SVI percentile rank, mortality, and LOS. Results: We identified 9,764 cases of firearm injury in our database; 88.2% of individuals were male, and the average age was 33.8 years. Assault was the most common intent, accounting for 4682 (48.0%) of all admissions. Overall, SVI was correlated with the risk of firearm injury, but not associated with either outcome of length of stay or risk of death. Conclusions: While there is significant disparity between SVI and risk of firearm injury, once admitted to the hospital outcomes are similar between low and high-vulnerable populations. To reduce disparities in risk, funding and effort should focus on primary prevention.
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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 | 0.018 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| 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".