Insurance-Related Disparities in Withdrawal of Life Support and Mortality After Spinal Cord Injury
Bibliographic record
Abstract
Importance: Identifying disparities in health outcomes related to modifiable patient factors can improve patient care. Objective: To compare likelihood of withdrawal of life-supporting treatment (WLST) and mortality in patients with complete cervical spinal cord injury (SCI) with different types of insurance. Design, Setting, and Participants: This retrospective cohort study collected data between 2013 and 2020 from 498 trauma centers participating in the Trauma Quality Improvement Program. Participants included adult patients (older than 16 years) with complete cervical SCI. Data were analyzed from November 1, 2023, through May 18, 2024. Exposure: Uninsured or public insurance compared with private insurance. Main Outcomes and Measures: Coprimary outcomes were WLST and mortality. The adjusted odds ratio (aOR) of each outcome was estimated using hierarchical logistic regression. Propensity score matching was used as an alternative analysis to compare public and privately insured patients. Process of care outcomes, including the occurrence of a hospital complication and length of stay, were compared between matched patients. Results: The study included 8421 patients with complete cervical SCI treated across 498 trauma centers (mean [SD] age, 49.1 [20.2] years; 6742 male [80.1%]). Among the 3524 patients with private insurance, 503 had WLST (14.3%) and 756 died (21.5%). Among the 3957 patients with public insurance, 906 had WLST (22.2%) and 1209 died (30.6%). Among the 940 uninsured patients, 156 had WLST (16.6%) and 318 died (33.8%). A significant difference was found between uninsured and privately insured patients in the adjusted odds of WLST (aOR, 1.49; 95% CI, 1.11-2.01) and mortality (aOR, 1.98; 95% CI, 1.50-2.60). Similar results were found in subgroup analyses. Matched public compared with private insurance patients were found to have significantly greater odds of hospital complications (odds ratio, 1.27; 95% CI, 1.14-1.42) and longer hospital stay (mean difference 5.90 days; 95% CI, 4.64-7.20), which was redemonstrated on subgroup analyses. Conclusions and Relevance: Health insurance type was associated with significant differences in the odds of WLST, mortality, hospital complications, and days in hospital among patients with complete cervical SCI in this study. Future work is needed to incorporate patient perspectives and identify strategies to close the quality gap for the large number of patients without private insurance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, 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".