Evaluating the population‐level effects of oxycodone restrictions on prescription opioid utilization in Ontario
Bibliographic record
Abstract
PURPOSE: To investigate the impact of restrictions on access to long acting oxycodone on prescription opioid use and opioid-related harms. METHODS: Administrative health data from Ontario, Canada was used to measure differences in opioids dispensed and emergency department (ED) visits for opioid-related overdose, poisoning, or substance use following provincial restrictions on access to publicly insured OxyContin (February 29, 2012) and OxyNeo (February 28, 2013). This study focused on the cohort of provincial drug insurance eligible people (people 65+ and select low-income populations) who were dispensed oxycodone prior to the restrictions. Difference-in-differences models with a propensity score matched comparison group of people who were dispensed non-oxycodone opioids were used to estimate the main effects. RESULTS: In 6 months following the delisting of OxyContin, milligrams of morphine equivalents (MMEs) per person per week for all opioids fell by an average of 7.5% in people dispensed oxycodone relative to the comparison group, and an average of 13.8% in chronic recipients of oxycodone. In the 6 months following the restrictions on OxyNeo, MMEs per person per week fell by an average of 3.1% in all people dispensed oxycodone, and 25.2% in chronic oxycodone recipients. The decline in oxycodone dispensing among chronic oxycodone recipients corresponded with an increase in dispensing of other opioid formulations, particularly hydromorphone and fentanyl. No important differences were observed for ED visits related to opioid poisoning, overdose, or substance use disorder. CONCLUSIONS: Province-wide restrictions on access to long acting oxycodone had an impact on quantities of all opioids dispensed to chronic recipients of oxycodone, but small impacts on the full population of people dispensed oxycodone; the decline in use was partially offset by increases in use of other publicly-funded opioid formulations. This study suggests that policies limiting access to specific prescription opioids led to overall reductions in publicly funded prescription opioid use, particularly in chronic oxycodone recipients, without immediate evidence of changes in opioid-related ED visits.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".