Trends and correlates of concurrent opioid and benzodiazepine and/or gabapentinoid use among Ontario nursing home residents
Bibliographic record
Abstract
BACKGROUND: A concern with long-term opioid use is the increased risk arising when opioids are used concurrently with drugs that can potentiate their associated adverse effects. The drugs most often encountered are benzodiazepines (BZDs) and gabapentinoids. Our study objectives were to examine trends in the concurrent use of opioids and BZDs, or gabapentinoids, in a Canadian nursing home population over an 11-year period, and current resident-level correlates of this concurrent use. METHODS: We conducted a population-based, repeated cross-sectional study among Ontario nursing home residents (>65 years) dispensed opioids between April 2009 and February 2020. For the last study year, we examined cross-sectional associations between resident characteristics and concurrent use of opioids with BZDs or gabapentinoids. Linked data on nursing home residents from clinical and health administrative databases was used. The yearly proportions of residents who were dispensed an opioid concurrently with a BZD or gabapentinoid were plotted with percent change derived from log-binomial regression models. Separate modified Poisson regression models estimated resident-level correlates of concurrent use of opioids with BZDs or gabapentinoids. RESULTS: Over the study period, among residents dispensed an opioid there was a 53.2% relative decrease (30.7% to 14.4%) in concurrent BZD and a 505.4% relative increase (4.4% to 26.6%) in concurrent gabapentinoid use. In adjusted models, increasing age and worsening cognition were inversely associated with the concurrent use of both classes, but most other significantly related covariates were unique to each drug class (e.g., sex and anxiety disorders for BZD, pain severity and presence of pain-related conditions for gabapentinoids). CONCLUSIONS: Co-administration of BZDs or gabapentinoids in Ontario nursing home residents dispensed opioids remains common, but the pattern of co-use has changed over time. Observed covariates of concurrent use in 2019/20 suggest distinct but overlapping resident populations requiring consideration of the relative risks versus benefits of this co-use and monitoring for potential harm.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".