Palliative End-of-Life Medication Prescribing Rates in Long-Term Care: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Medications are often needed to manage distressing end-of-life symptoms (eg, pain, agitation). OBJECTIVES: In this study, we describe the variation in prescribing rates of symptom relief medications at the end of life among long-term care (LTC) decedents. We evaluate the extent these medications are prescribed in LTC homes and whether prescribing rates of end-of-life symptom management can be used as an indicator of quality end-of-life care. DESIGN: Retrospective cohort study using administrative health data. SETTING AND PARTICIPANTS: LTC decedents in all 626 publicly funded LTC homes in Ontario, Canada, between January 1, 2017, and March 17, 2020. METHODS: For each LTC home, we measured the percent of decedents who received 1+ prescription(s) for a subcutaneous end-of-life symptom management medication ("end-of-life medication") in their last 14 days of life. We then ranked LTC homes into quintiles based on prescribing rates. RESULTS: We identified 55,916 LTC residents who died in LTC. On average, two-thirds of decedents (64.7%) in LTC homes were prescribed at least 1 subcutaneous end-of-life medication in the last 2 weeks of life. Opioids were the most common prescribed medication (overall average prescribing rate of 62.7%). LTC homes in the lowest prescribing quintile had a mean of 37.3% of decedents prescribed an end-of-life medication, and the highest quintile mean was 82.5%. In addition, across these quintiles, the lowest prescribing quintile had a high average (30.3%) of LTC residents transferred out of LTC in the 14 days compared with the highest prescribing quintile (12.7%). CONCLUSIONS AND IMPLICATIONS: Across Ontario's LTC homes, there are large differences in prescribing rates for subcutaneous end-of-life symptom relief medications. Although future work may elucidate why the variability exists, this study provides evidence that administrative data can provide valuable insight into the systemic delivery of end-of-life care.
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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 | 0.003 |
| 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) | 0.001 | 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".