The Impact of the COVID-19 Pandemic on End-of-Life Prescribing in Ontario Nursing Homes.
Bibliographic record
Abstract
ObjectivesOur preliminary work revealed significant variations in the prescribing of end-of-life symptom management medications in nursing homes prior to the onset of the COVID-19 pandemic. In this study, we sought to explore whether the prescribing of end-of-life medications in nursing homes changed with the onset of the pandemic. ApproachThis was a retrospective cohort study of nursing home residents age 65+ who died in Ontario, Canada, divided into two time periods based on death date: pre-COVID-19 (January 1st, 2017 – March 17th, 2020) and during COVID-19 (March 18th, 2020 – March 31st, 2021). Using routinely collected health administrative data and our evidence-based end-of-life medications list, we linked resident data to prescription claims to identify whether residents were prescribed these medications in the last 14 days of life. We grouped homes into quintiles according to the proportion of decedents who received ≥1 prescription and examined changes in prescribing before and during COVID-19. ResultsNursing homes in the lowest prescribing quintile prescribed, on average, 11.5% fewer end-of-life symptom management medications during COVID-19 compared to pre-pandemic. Conversely, homes in the highest quintile prescribed an average of 13.7% more medications during COVID-19. Nursing homes in the lowest quintile had more COVID-19-positive residents (33% of residents) compared to homes in the highest quintile (9% of residents). Additionally, nursing homes in the lowest prescribing quintile spent more time with active COVID-19 outbreaks compared to homes in the highest quintile (mean 72.7 days versus 24.1 days, respectively, standardized difference 0.819). ConclusionThe COVID-19 pandemic has disproportionately impacted nursing homes across Canada. Our findings suggest that nursing homes with low rates of prescribing of end-of-life medications prior to the pandemic had even lower prescribing rates during the pandemic. These homes were also harder hit by COVID-19 infections and outbreaks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".