Association Between Racial Marginalization With Direct Health Care Expenditure, Time at Home, and Rehabilitation Access Following Moderate to Severe Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To determine the association between residence in racialized neighborhoods with direct health care expenditure and days at home (DAH) after moderate to severe traumatic brain injury (TBI). BACKGROUND: Differences in ethno-racial background have been associated with health outcome disparities. Much of this prior research was conducted in settings without universal health care coverage. The influence of ethno-racial background on health outcomes after TBI in universal health care settings remains unclear. METHODS: This retrospective multicenter cohort study utilized linked administrative health data to identify adults sustaining moderate to severe TBI between 2009 and 2021. The primary exposure was an area-level index corresponding to the degree of racialized and immigrant populations within neighborhoods of residence (quintile 1-least racialized; quintile 5-most racialized). Coprimary outcomes were direct health care expenditure and DAH 365 days after injury. Secondary outcomes included discharge to rehabilitation and functional independence measure (FIM) scores at rehabilitation discharge. RESULTS: A total of 6188 patients met the inclusion criteria. Patients in the most racialized neighborhoods incurred higher crude and adjusted direct health care costs compared with those in the least racialized neighborhoods. This effect was driven predominantly by physician claims and acute care costs. There were no significant differences in crude or adjusted DAH across quintiles. Access to rehabilitation and discharge FIM scores were comparable for patients residing in different racialized neighborhood quintiles. CONCLUSIONS: Despite differences in health care expenditure, this study found similar home time, access to rehab, and discharge FIM scores for patients with TBI according to racialized neighborhood residence. Recognizing the limitations of area-level indices, our findings suggest equitable care delivery in a publicly funded universal health care environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".