Health Services Usage in Patients Receiving Buprenorphine for Opioid Use Disorder or Long-Term Opioid Therapy for Chronic Pain: Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Patients using buprenorphine for opioid use disorder (OUD) or long-term opioid therapy for chronic pain are at risk for poor outcomes if care is interrupted. Both treatments are highly regulated, with prepandemic requirements for in-person care. COVID-19 may have resulted in barriers to accessing in-person care through disruptions in care delivery. However, there were also opportunities for improved access to telemedicine visits through policy changes. OBJECTIVE: This study aims to evaluate changes in health care and telemedicine use during the COVID-19 pandemic among patients using buprenorphine for OUD and long-term opioid therapy for chronic pain. METHODS: We used administrative claims data for commercially insured and Medicare Advantage patients from the OptumLabs Data Warehouse. We included patients using buprenorphine for OUD or long-term opioid therapy for chronic pain compared to patients with another chronic condition without similar prescribing restrictions: serious mental illness. We evaluated changes in in-person and telemedicine care by comparing rates of services by physician specialty, type of service, and the percentage of visits through telemedicine. Changes in usage were measured using a difference-in-differences approach with Poisson regression. The results are presented as incident rate ratios (IRR). RESULTS: We found declines in in-person visits in April 2020 across the buprenorphine, chronic opioids, and serious mental illness cohorts. The largest declines were for specialties that rely on in-person treatment, such as emergency medicine (IRR range 0.60-0.62), orthopedics (IRR 0.48-0.52), cardiology (IRR 0.64-0.78), and oncology (IRR 0.77-0.81). In contrast, there were smaller declines for specialties that could more easily transition to telemedicine, namely family practice (IRR 0.80-0.92), mental health (IRR 0.92-1.01), and pain medicine (IRR 0.87-1.08). The percentage of telemedicine visits for these specialties ranged from 30% to 51% in the period. There were also large declines for specific services, including emergency medicine (IRR 0.53-0.89), physical therapy (IRR 0.24-0.72), and new office visits (IRR 0.38-0.64). By January 2022, usage was similar to prepandemic levels, but the percentage of telemedicine visits remained elevated for family practice (10%-14%), mental health (34%-43%), and pain medicine (11%-15%) through January 2022. The results were similar across the cohorts, although in April 2020 there was a modest decrease (IRR 0.87) for pain medicine in the serious mental illness cohort, but the differences were not significant for the buprenorphine (IRR 1.08) and chronic opioids (IRR: 0.99) cohorts. CONCLUSIONS: These findings highlight the value of telemedicine to maintain access among people at risk for poor outcomes if care is interrupted. While flexibilities in the regulation of telemedicine services that arose during the pandemic have been temporarily extended multiple times, they are set to expire in 2025 without further action. Making these changes to telemedicine regulation permanent may benefit vulnerable patient populations who face access to care challenges.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".