Association between opioid use disorder and palliative care: a cohort study using linked health administrative data in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: People with opioid use disorder (OUD) are at risk of premature death and can benefit from palliative care. We sought to compare palliative care provision for decedents with and without OUD. METHODS: We conducted a cohort study using health administrative databases in Ontario, Canada, to identify people who died between July 1, 2015, and Dec. 31, 2021. The exposure was OUD, defined as having emergency department visits, hospital admissions, or pharmacologic treatments suggestive of OUD within 3 years of death. Our primary outcome was receipt of 1 or more palliative care services during the last 90 days before death. Secondary outcomes included setting, initiation, and intensity of palliative care. We conducted a secondary analysis excluding sudden deaths (e.g., opioid toxicity, injury). RESULTS: Of 679 840 decedents, 11 200 (1.6%) had OUD. Compared with people without OUD, those with OUD died at a younger age and were more likely to live in neighbourhoods with high marginalization indices. We found people with OUD were less likely to receive palliative care at the end of their lives (adjusted relative risk [RR] 0.84, 95% confidence interval [CI] 0.82-0.86), but this difference did not exist after excluding people who died suddenly (adjusted RR 0.99, 95% CI 0.96-1.01). People with OUD were less likely to receive palliative care in clinics and their homes regardless of cause of death. INTERPRETATION: Opioid use disorder can be a chronic, life-limiting illness, and people with OUD are less likely to receive palliative care in communities during the 90 days before death. Health care providers should receive training in palliative care and addiction medicine to support people with OUD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".