The Impact of Socioeconomic Status on Place of Death Among Patients Receiving Home Palliative Care in Toronto, Canada: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Socioeconomic disparities in home death have been noted in the literature. Home-based palliative care increases access to home death and has been suggested as a means to decrease these disparities. Aim: Our study examines the association between socioeconomic status and other demographic factors on place of death in a population receiving home palliative care in Toronto, Canada. Design: This is a retrospective chart review of patients who died between August 2013 and August 2015 when admitted to a home-based palliative care service. Multivariate multinomial regression examined the relationship between the place of death (home, palliative care unit [PCU], or acute care) with age, gender, primary diagnosis, and income quintile. Bivariate logistic regression was fitted to calculate the odds ratio (OR) and probability of preference for home death. Setting/Participants: Patients receiving home-based palliative care services from the Latner Centre for Palliative Care in Toronto, Canada. Results: A total of 2066 patients were included in multivariate analysis. Patients in the lowest income quintile had increased odds of dying in acute care (OR = 2.41, P < .001) or dying in PCU (OR = 1.64, P = .008) than patients in highest income quintile. Patients in the next lowest income quintiles 2 and 3 were also more likely to die in acute care. The rate of preference for home death was significantly lower in the lowest income quintile (OR = 0.47, P = .0047). Conclusions: Patients in lower income quintiles are less likely to die at home, despite receiving home-based palliative care, although they may also be less likely to prefer home death.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".