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Patterns and Quality of Buprenorphine Opioid Agonist Treatment in a Large Medicaid Program
Abstract
OBJECTIVES: Use of buprenorphine - an effective treatment for opioid use disorders (OUDs) - has increased rapidly in recent years and is often financed by Medicaid. We investigated predictors of buprenorphine treatment, patterns of care, and quality of care in a large state Medicaid program. METHODS: Data from Pennsylvania Medicaid from 2007 to 2012 provided information regarding diagnoses, demographic characteristics, enrollment, and use of inpatient and outpatient services, and prescription drugs. We identified adult enrollees using buprenorphine, and examined prevalence of OUD diagnosis and patterns of use (duration and dose) and quality of care (physician visits, receipt of behavioral health counseling, urine drug screens, and other prescription drug use). We use a mixed logistic regression model to examine enrollee characteristics associated with buprenorphine use. RESULTS: The share of enrollees with OUD filling prescriptions for buprenorphine increased from 2985 (9.8%) to 12,691 (25.2%) from 2007 to 2012. Between 26.2 and 32.0% of enrollees using buprenorphine had no diagnosis of OUD, depending on the year. Only 60.1% of enrollees with buprenorphine use received at least one urine drug screen, 41.0% had behavioral health counseling services, and 34.7 and 38.0% had other opioid and benzodiazepine claims, respectively, concomitant with buprenorphine use. Quality of care was lower among those with no OUD diagnosis recorded. The mean daily doses of buprenorphine decreased over time. We found wide variation in likelihood of buprenorphine use among those with OUD based upon age, sex, and race. CONCLUSIONS: Increases in buprenorphine treatment in a Medicaid population were observed across time; however, increases varied by age, sex, and rate, and the quality of care received seemed to be generally poor. The quality of the provision of buprenorphine treatment occurring in Medicaid populations should be further explored.
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“patients often drop out of medication treatment [50, 51].”
From Translating economic evaluations into financing strategies for implementing evidence-based practices (2021) · cites this paper as [50]
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“it remains underutilized”
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Metadata
- Authors
- Gordon, A. J., Lo-Ciganic, W.-H., Cochran, G., Gellad, W. F., Cathers, T., Kelley, D., & Donohue, J. M.
- Affiliations
- University of Arizona (US)
- VA Pittsburgh Healthcare System (US)
- University of Pittsburgh (US)
- Pennsylvania Department of Human Services (US)
- Corresponding author
- Gordon, Adam J.
- Venue
- Journal of Addiction Medicine
- Published
- 2015-11
- Volume
- 9
- Issue
- 6
- Pages
- 470-477
- ISSN
- 1932-0620 (ISSN-L)
- OpenAlex
- W1891063291
- Cited by
- 76
- References
- 35
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 8 · 2023: 7 · 2022: 5 · 2021: 15 · 2020: 10 · 2019: 11 · 2018: 8 · 2017: 4 · 2016: 2 · 2015: 2
- FWCI
- 5.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Addiction Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Veterans Affairs
- Pennsylvania Department of Human Services
- Centers for Disease Control and Prevention (U01CE002496)
- VA Pittsburgh Healthcare System
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Opioid Use Disorder Treatment
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Healthcare Policy and Management
- Keywords
- buprenorphine, opioid use disorder, Medicaid, quality of care, patterns of care, urine drug testing, benzodiazepine use, prescription drug use, racial disparities, sex disparities, age disparities
- MeSH terms
- Adolescent, Adult, Analgesics, Opioid, Buprenorphine, Female, Humans, Male, Medicaid, Middle Aged, Opioid-Related Disorders, Quality of Health Care, United States, Logistic Models, Prescription Drugs, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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