Effect of Early Palliative Care on End-of-Life Health Care Costs: A Population-Based, Propensity Score–Matched Cohort Study
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate the impact of early versus not-early palliative care among cancer decedents on end-of-life health care costs. METHODS: Using linked administrative databases, we created a retrospective cohort of cancer decedents between 2004 and 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 at diagnosis. We examined differences in average health system costs (including hospital, emergency department, physician, and home care costs) between groups in the last month of life. RESULTS: We identified 144,306 cancer decedents, of which 37% received early palliative care. After matching, we created 36,238 pairs of decedents who received early and not-early (control) palliative care; there were balanced distributions of age, sex, cancer type (24% lung cancer), and stage (25% stage III and IV). Overall, 56.3% of early group versus 66.7% of control group used inpatient care in the last month ( P < .001). Considering inpatient hospital costs in the last month of life, the early group used an average (±standard deviation) of $7,105 (±$10,710) versus the control group of $9,370 (±$13,685; P < .001). Overall average costs (±standard deviation) in the last month of life for patients in the early versus control group was $12,753 (±$10,868) versus $14,147 (±$14,288; P < .001). CONCLUSION: Receiving early palliative care reduced average health system costs in the last month of life, especially via avoided hospitalizations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".