Health Services Usage in Patients Receiving Buprenorphine for Opioid Use Disorder or Long-Term Opioid Therapy for Chronic Pain: Retrospective Cohort Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».