Opioid Deprescribing Among Residents of Long-Term Care Homes in Ontario: Characteristics and Outcomes
Bibliographic record
Abstract
Background and Objectives Residents of long-term care (LTC) homes in Ontario, Canada are older, frailer, and have more complex care needs than their community dwelling counterparts. In addition, they often have multiple comorbidities and potentially painful conditions. As a result, they are often prescribed opioids, which may result in medication-related patient safety events. Opioid deprescribing may be a strategy for mitigating adverse events. Therefore, the purpose of this study was to examine opioid deprescribing among residents of LTC homes in Ontario. Methods We conducted a population-based retrospective cohort study using the routinely collected health administrative databases held at ICES (formerly the Institute for Clinical Evaluative Sciences). The cohort consisted of residents of LTC homes in Ontario prescribed long-term opioid therapy (a continuous prescription for an opioid for 90 days or longer) between April 1, 2014, and March 31, 2016. We allowed a two-year look back window to collect data about baseline characteristics for residents, and a two-year follow-up period to examine outcomes. We used a three-level exposure variable: (1) no discontinuation, (2) discontinuation for 30-119 days, and (3) discontinuation for 120 days or longer. Outcomes included unplanned acute healthcare use, all-cause mortality, and functional measures. Multivariate modeling was used to determine the associations between opioid deprescribing and outcomes. Results There were 26,592 residents prescribed long-term opioid therapy between April 1, 2014, and March 31, 2016, and 4,299 (16.2%) had opioids discontinued during the follow-up period: 2,852 (66.3%) short term, and 1,447 (33.7%) long-term. Short term opioid deprescribing was associated with a 13% reduced risk for death, and short and long-term opioid deprescribing were associated with an increased risk for unplanned acute healthcare use. Conclusions Over 20% of residents were prescribed long-term opioid therapy, and opioid deprescribing was sustained in very few residents. Healthcare providers must be informed about the adverse outcomes associated with opioid deprescribing and long-term opioid therapy to support resident needs and preferences related to the interprofessional plan of care. Findings from this study will be used to inform safe, quality, resident-centered care, direct future research, inform the development of opioid deprescribing guidelines, and inform health policy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".