Regulating Opioid Supply Through Insurance Coverage
Bibliographic record
Abstract
Responding to an opioid crisis in Canada, policy makers have implemented supply-side interventions seldom used in the US, regulating insurance reimbursement to discourage the prescribing of specified opioids. Using national databases of all opioids dispensed through provincial pharmaceutical programs and of opioid hospitalizations from January 2006 through March 2017, we found that requiring physicians to obtain prior authorization for patients to receive reimbursement for OxyContin prescriptions substantially reduced OxyContin fills, particularly among opioid-naive patients; it also reduced overall opioid prescriptions, suggesting limited substitution. "Grandfathering" OxyNeo (an abuse-resistant OxyContin variant), allowing previous OxyContin patients to obtain OxyNeo, increased OxyNeo fills but had no detectable effect on total opioid prescriptions, which points to substantial opioid substitution among chronic users of prescription opioids. We found no effects of regulatory changes on opioid-related hospitalizations. These results suggest that restrictions on pharmaceutical formularies can reduce fills of targeted opioids with the additional benefit of altering treatment of opioid-naive and other patients differently. Canadian policy makers may wish to extend such regulations to more provincial formularies and private insurers, and policy makers in the US and elsewhere could fruitfully follow suit.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".