Racial Differences in Health Care Transitions and Hospice Use at the End of Life
Bibliographic record
Abstract
Abstract Background: Although the fragmentation of end-of-life care has been well documented, previous research has not examined racial and ethnic differences in transitions in care and hospice use at the end of life. Design and Subjects: Retrospective cohort study among 649,477 Medicare beneficiaries who died between July 2011 and December 2011. Measurements: Sankey diagrams and heatmaps to visualize the health care transitions across race/ethnic groups. Among hospice enrollees, we examined racial/ethnic differences in hospice use patterns, including length of hospice enrollment and disenrollment rate. Results: The mean number of care transitions within the last six months of life was 2.9 transitions (standard deviation [SD] = 2.7) for whites, 3.4 transitions (SD = 3.2) for African Americans, 2.8 transitions (SD = 3.0) for Hispanics, and 2.4 transitions (SD = 2.7) for Asian Americans. After adjusting for age and sex, having at least four transitions was significantly more common for African Americans (39.2%; 95% confidence interval [CI]: 38.8–39.6%) compared with whites (32.5%, 95% CI: 32.3–32.6%), and less common among Hispanics (31.2%, 95% CI: 30.4–32.0%), and Asian Americans (26.5%, 95% CI: 25.5–27.5%). Having no care transition was significantly more common for Asian Americans (33.0%, 95% CI: 32.0–34.1%) and Hispanics (28.8%, 95% CI: 28.0–29.6%), compared with African Americans (19.2%, 95% CI: 18.9–19.5%) and whites (18.9%, 95% CI: 18.8–19.0%). Among hospice users, whites, African Americans, and Hispanics had similar length of hospice enrollment, which was significantly longer than that of Asian Americans. Nonwhite patients were significantly more likely than white patients to experience hospice disenrollment. Conclusions: Racial/ethnic differences in patterns of end-of-life care are marked. Future studies to understand why such patterns exist are warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".