Incarceration is associated with higher mortality after trauma: An unreported health care disparity
Bibliographic record
Abstract
BACKGROUND: While the United States has the highest incarceration rate worldwide, at nearly 1% of the adult population (more than 2 million people), insights regarding health disparities in this population remain limited. This retrospective cohort study represents the largest national database analysis of incarcerated trauma patients to date and investigates whether incarceration status is an independent risk factor for poor outcomes after trauma for US adults. METHODS: We analyzed data from the National Trauma Data Bank from 2017 to 2018. Using multilevel logistic regression, we measured risk-adjusted associations between incarceration status (assessed by International Classification of Diseases, Tenth Revision , location codes) and trauma outcomes: mortality, any in-hospital complications, aggregate major complications, and failure to rescue. We report odds ratios and 95% confidence intervals, adjusting for demographics, transfer status, insurance, comorbidities, injury mechanism, injury severity, and presenting vitals. A secondary analysis was performed using nearest neighbor matching with a 2:1 ratio of nonincarcerated to incarcerated patients, followed by multilevel logistic regression. RESULTS: There were 12,888 incarcerated patients and 1,654,254 nonincarcerated patients. Incarcerated patients were younger (median, 36 vs. 55 years), more likely to be male (94.9% vs. 60.5%), Black (27.9% vs. 13.9%), and Hispanic (15.7% vs. 11.5%) and presented more frequently with minor injuries (Injury Severity Score, <9; 65.4% vs. 48.9%) and with stabbings and other blunt events as mechanisms of injury. Although unadjusted mortality was lower for incarcerated patients, after adjustment, they were significantly more likely to die (adjusted odds ratio (AOR), 1.42 [1.19-1.68]), which was consistent in the matched analysis (AOR, 1.19 [1.03-1.36]). Incarcerated patients were, conversely, less likely to suffer any in-hospital complication (AOR, 0.76 [0.68-0.85]; matched AOR, 0.88 [0.81-0.97]). CONCLUSION: Our study redemonstrated that incarcerated trauma patients' demographics and injuries differ significantly from nonincarcerated patients. Furthermore, incarceration was an independent risk factor for mortality, a previously unreported disparity. This highlights the need for improved data collection regarding incarceration status and national prospective investigations. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level III.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".