New opioid use and risk of opioid-related adverse events among adults with intellectual and developmental disabilities in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Individuals with intellectual and developmental disability (IDD) can have a high prevalence of pain, which can be managed with prescription opioids. However, the prevalence of substance use disorder is also high in this population, raising concern about opioid-related adverse events. AIMS: To assess the risk of opioid-related adverse events following opioid initiation among adults with versus without IDD. METHOD: We conducted a population-based, propensity score matched cohort study on all adults starting prescription opioid therapy in Ontario, Canada, between January 2013 and December 2018. The outcomes of interest were opioid toxicity, new opioid use disorder (OUD) diagnosis and dose escalation (≥90 mg morphine or equivalent) in the year after opioid initiation. We used Cox proportional hazards models to assess the association between IDD diagnosis and each outcome. RESULTS: The hazards of opioid toxicity and OUD were significantly higher in those with IDD compared with those without IDD in unmatched analyses (opioid toxicity hazard ratio 3.19, 95% CI 2.81-5.18; OUD hazard ratio 2.36, 95% CI 2.10-2.65), whereas the hazard of dose escalation was significantly lower (hazard ratio 0.76, 95% CI 0.66-0.88). Findings were no longer significant in propensity score matched models for opioid toxicity and dose escalation, whereas the hazard of OUD diagnosis was attenuated substantially in those with IDD (hazard ratio 0.79, 95% CI 0.68-0.91). CONCLUSIONS: IDD diagnosis is not a driver of opioid-related harm. The increased risk we observed is likely driven by various risk factors often present in this population.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".