The effect of out-of-pocket costs on medications for opioid use disorder and overdose: A scoping review
Bibliographic record
Abstract
BACKGROUND: The opioid epidemic is a major public health crisis in Canada and elsewhere. The increase in opioid prescriptions is a major contributor to this crisis. Medications for opioid use disorder (OUD) and overdose are effective and lifesaving treatments. Often, patients do not have adequate insurance coverage (or uninsured) for medications for OUD and have to pay out of pocket (OOP). OOP costs (OOPCs) result in financial burdens among patients, limiting their access to medications for OUD, and overdose. OBJECTIVES: To identify the evidence on (1) the OOPCs of medications for OUD and overdose, and (2) the effect of insurance coverage (or being uninsured) and corresponding OOPCs on medications for OUD initiation, retention, and discontinuation. METHODS: This scoping review was conducted in accordance with methodological guidance from the Joanna Briggs Institute. The literature search aimed to identify peer-reviewed publications in English in MEDLINE, Embase, and CINAHL, which were searched from inception to March 22, 2024. Two reviewers independently completed title, abstract, and full-text screening against inclusion criteria. Data extracted were used to describe the body of literature using descriptive and qualitative approaches. RESULTS: Out of the 2003 search results, a total of ten studies met the inclusion criteria and were included in the review. Uninsured patients have paid higher OOPCs compared to private or publicly insured patients. Among privately insured patients with OUD, greater OOPC may result in poor retention of buprenorphine. The risk of discontinuation was higher with the buprenorphine/naloxone tablet compared with the sublingual buprenorphine/naloxone film. Generic substitution or providing coverage for these medications being dispensed from community pharmacies can potentially minimize the burden of OOPCs and improve access. CONCLUSION: The literature highlights beneficiaries of private/commercial health plans experience a substantial burden of OOPCs, creating barriers to treatment initiation, retention, and adherence to medications for OUD.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".