Are all deaths recorded equally? The impact of hospice care on risk-adjusted mortality
Bibliographic record
Abstract
BACKGROUND: Hospice care provides dignity and comfort at the end of life. While patients transferred to hospice die, they are often not recorded as in-hospital deaths in trauma registries or in some administrative discharge data. Mortality rates for the purpose of database research, performance improvement, or public reporting may therefore be artificially low. The current study sought to determine the impact of discharges to hospice on risk-adjusted mortality for trauma deaths reported to the Trauma Quality Improvement Program. METHODS: Performance from Trauma Quality Improvement Program centers in 2011 was evaluated using risk-adjusted mortality with observed-to-expected mortality ratios derived from a logistic regression model. The impact of discharge to hospice on performance was measured by determining changes in performance if hospice cases were treated as survivors rather than deaths. Differences between groups were compared by nonparametric Wilcoxon rank-sum test. RESULTS: From the 167 centers with 126,259 injured patients, there were 8,862 deaths: 746 (8.4%) were discharged to a hospice, and the remainder was counted as in-hospital deaths. Overall, 106 centers (63.5%) reported at least one discharge to hospice, with the proportion of deaths ranging from 1.6% to 57%. Logistic regression demonstrated that age greater than 70 years (odds ratio [OR], 4.3; 95% confidence interval [CI], 3.5-5.1), male sex (OR, 0.7; 95% CI, 0.6-0.8), nonblack race (OR, 1.9; 95% CI, 1.3-2.7), noncommercial insurance (OR, 1.4; 95% CI, 1.1-1.7), and comorbidity counts greater than 2 (OR, 1.3; 95% CI, 1.1-1.6) were associated with hospice care. If patients transferred to a hospice were treated as survivors in the estimation of risk-adjusted mortality, 34 centers (20%) would have a change in status. Changes would be in both directions for average-performing centers, while high-performing centers would seem worse and poor-performing centers would seem better. For centers that reported hospice deaths, the relative risk-adjusted mortality decreased by 8.8% for every 10% increase in the proportion of deaths recorded as discharged to a hospice. CONCLUSION: Given the large variation in the proportion of deaths recorded as discharged to a hospice rather than as in-hospital deaths, there is the potential for significant distortion of actual performance. Failure to consider this potential may misguide efforts directing performance improvement, research, and national reporting. Discharges to a hospice should be included with in-hospital deaths when reporting risk-adjusted mortality.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".