Does early palliative care reduce end-of-life hospital costs? A propensity-score matched, population-based, cohort study.
Bibliographic record
Abstract
12006 Background: Few studies describe how early versus late palliative care affects end-of-life health services costs. The aim of this study was to investigate the impact of early vs not-early palliative care among cancer decedents on the combined costs of receiving aggressive care (ED/hospitalization) and supportive care (home care/physician home visit). Methods: Using linked administrative databases, we created a retrospective cohort of cancer decedents between 2004 -2014 in Ontario, Canada. We identified those who received “early” palliative care (palliative care service used in the hospital or community 12 to 6 months before death [exposure]). We used propensity score matching to identify a control group of “not-early” palliative care, hard matched on age, sex, cancer type and stage. The propensity score included region, year, treatment, etc. We examined differences in median costs (including hospital, ED, physician, and home care costs) between pairs in the last month of life. Results: We identified 144,306 cancer decedents, of which 37% received early palliative care in the exposure period. After propensity score matching, we created 36,238 pairs of decedents who received early and not-early palliative care. After matching the early and not-early groups had equal distributions of age, sex, cancer type (24% lung cancer) and stage (25% stage 3 or 4). Among those who received early palliative care, 56.3% used hospital in-patient care in the last month, whereas 66.7% of the control group (not-early palliative care) used in-patient care; considering only inpatient hospital costs, those receiving early palliative care used a median of $2,894 in the last month of life compared to the control group of $5,311 (p < 0.001). Overall median costs in the last month of life for patients in the early palliative care vs the control group was $11,129 vs. $10,598 (p < 0.001). Conclusions: In our population-based, propensity-score matched, cohort study of cancer decedents, receiving early palliative care reduced the median overall health system costs, especially via avoiding hospitalizations in the last month of life.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".