Cost-effectiveness of Investment in End-of-Life Home Care to Enable Death in Community Settings
Bibliographic record
Abstract
BACKGROUND: Many people with terminal illness prefer to die in home-like settings-including care homes, hospices, or palliative care units-rather than an acute care hospital. Home-based palliative care services can increase the likelihood of death in a community setting, but the provision of these services may increase costs relative to usual care. OBJECTIVE: The aim of this study was to estimate the incremental cost per community death for persons enrolled in end-of-life home care in Ontario, Canada, who died between 2011 and 2015. METHODS: Using a population-based cohort of 50,068 older adults, we determined the total cost of care in the last 90 days of life, as well as the incremental cost to achieve an additional community death for persons enrolled in end-of-life home care, in comparison with propensity score-matched individuals under usual care (ie, did not receive home care services in the last 90 days of life). RESULTS: Recipients of end-of-life home care were nearly 3 times more likely to experience a community death than individuals not receiving home care services, and the incremental cost to achieve an additional community death through the provision of end-of-life home care was CAN$995 (95% confidence interval: -$547 to $2392). CONCLUSION: Results suggest that a modest investment in end-of-life home care has the potential to improve the dying experience of community-dwelling older adults by enabling fewer deaths in acute care hospitals.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".