Julie M Donohue1, Marian P Jarlenski1, Joo Yeon Kim1, Lu Tang2, Katherine Ahrens3, Lindsay Allen4, Anna Austin5, Andrew J Barnes6, Marguerite Burns7, Chung-Chou H Chang8, Sarah Clark9, Evan Cole1, Dushka Crane10, Peter Cunningham6, David Idala11, Stefanie Junker1, Paul Lanier12, Rachel Mauk10, Mary Joan McDuffie13, Shamis Mohamoud11, Nathan Pauly14, Logan Sheets15, Jeffery Talbert16, Kara Zivin17, Adam J Gordon18,19, Susan Kennedy15. 1. Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania. 2. Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania. 3. Public Health Program, Muskie School of Public Service, University of Southern Maine, Portland. 4. Health Policy, Management, and Leadership Department, School of Public Health, West Virginia University, Morgantown. 5. Department of Maternal and Child Health, Gillings School of Global Public Health, University of North Carolina at Chapel Hill. 6. Department of Health Behavior and Policy, School of Medicine, Virginia Commonwealth University, Richmond. 7. Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin, Madison. 8. Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania. 9. Department of Pediatrics, University of Michigan Medical School, Ann Arbor. 10. Ohio Colleges of Medicine Government Resource Center, The Ohio State University, Columbus. 11. The Hilltop Institute, University of Maryland Baltimore County, Baltimore. 12. School of Social Work, University of North Carolina at Chapel Hill. 13. Center for Community Research & Service, Biden School of Public Policy and Administration, University of Delaware, Newark. 14. Health Sciences Center, School of Public Health, Health Affairs Department, School of Public Health, West Virginia University, Morgantown. 15. AcademyHealth, Washington, DC. 16. Division of Biomedical Informatics, College of Medicine, University of Kentucky, Lexington. 17. Department of Psychiatry, University of Michigan Medical School, Ann Arbor. 18. Department of Medicine and Department of Psychiatry, University of Utah School of Medicine, Salt Lake City. 19. Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center, VA Salt Lake City Health Care System, Salt Lake City.
Abstract
Importance: There is limited information about trends in the treatment of opioid use disorder (OUD) among Medicaid enrollees. Objective: To examine the use of medications for OUD and potential indicators of quality of care in multiple states. Design, Setting, and Participants: Exploratory serial cross-sectional study of 1 024 301 Medicaid enrollees in 11 states aged 12 through 64 years (not eligible for Medicare) with International Classification of Diseases, Ninth Revision (ICD-9 or ICD-10) codes for OUD from 2014 through 2018. Each state used generalized estimating equations to estimate associations between enrollee characteristics and outcome measure prevalence, subsequently pooled to generate global estimates using random effects meta-analyses. Exposures: Calendar year, demographic characteristics, eligibility groups, and comorbidities. Main Outcomes and Measures: Use of medications for OUD (buprenorphine, methadone, or naltrexone); potential indicators of good quality (OUD medication continuity for 180 days, behavioral health counseling, urine drug tests); potential indicators of poor quality (prescribing of opioid analgesics and benzodiazepines). Results: In 2018, 41.7% of Medicaid enrollees with OUD were aged 21 through 34 years, 51.2% were female, 76.1% were non-Hispanic White, 50.7% were eligible through Medicaid expansion, and 50.6% had other substance use disorders. Prevalence of OUD increased in these 11 states from 3.3% (290 628 of 8 737 082) in 2014 to 5.0% (527 983 of 10 585 790) in 2018. The pooled prevalence of enrollees with OUD receiving medication treatment increased from 47.8% in 2014 (range across states, 35.3% to 74.5%) to 57.1% in 2018 (range, 45.7% to 71.7%). The overall prevalence of enrollees receiving 180 days of continuous medications for OUD did not significantly change from the 2014-2015 to 2017-2018 periods (-0.01 prevalence difference, 95% CI, -0.03 to 0.02) with state variability in trend (90% prediction interval, -0.08 to 0.06). Non-Hispanic Black enrollees had lower OUD medication use than White enrollees (prevalence ratio [PR], 0.72; 95% CI, 0.64 to 0.81; P < .001; 90% prediction interval, 0.52 to 1.00). Pregnant women had higher use of OUD medications (PR, 1.18; 95% CI, 1.11-1.25; P < .001; 90% prediction interval, 1.01-1.38) and medication continuity (PR, 1.14; 95% CI, 1.10-1.17, P < .001; 90% prediction interval, 1.06-1.22) than did other eligibility groups. Conclusions and Relevance: Among US Medicaid enrollees in 11 states, the prevalence of medication use for treatment of opioid use disorder increased from 2014 through 2018. The pattern in other states requires further research.
Importance: There is limited information about trends in the treatment of opioid use disorder (OUD) among Medicaid enrollees. Objective: To examine the use of medications for OUD and potential indicators of quality of care in multiple states. Design, Setting, and Participants: Exploratory serial cross-sectional study of 1 024 301 Medicaid enrollees in 11 states aged 12 through 64 years (not eligible for Medicare) with International Classification of Diseases, Ninth Revision (ICD-9 or ICD-10) codes for OUD from 2014 through 2018. Each state used generalized estimating equations to estimate associations between enrollee characteristics and outcome measure prevalence, subsequently pooled to generate global estimates using random effects meta-analyses. Exposures: Calendar year, demographic characteristics, eligibility groups, and comorbidities. Main Outcomes and Measures: Use of medications for OUD (buprenorphine, methadone, or naltrexone); potential indicators of good quality (OUD medication continuity for 180 days, behavioral health counseling, urine drug tests); potential indicators of poor quality (prescribing of opioid analgesics and benzodiazepines). Results: In 2018, 41.7% of Medicaid enrollees with OUD were aged 21 through 34 years, 51.2% were female, 76.1% were non-Hispanic White, 50.7% were eligible through Medicaid expansion, and 50.6% had other substance use disorders. Prevalence of OUD increased in these 11 states from 3.3% (290 628 of 8 737 082) in 2014 to 5.0% (527 983 of 10 585 790) in 2018. The pooled prevalence of enrollees with OUD receiving medication treatment increased from 47.8% in 2014 (range across states, 35.3% to 74.5%) to 57.1% in 2018 (range, 45.7% to 71.7%). The overall prevalence of enrollees receiving 180 days of continuous medications for OUD did not significantly change from the 2014-2015 to 2017-2018 periods (-0.01 prevalence difference, 95% CI, -0.03 to 0.02) with state variability in trend (90% prediction interval, -0.08 to 0.06). Non-Hispanic Black enrollees had lower OUD medication use than White enrollees (prevalence ratio [PR], 0.72; 95% CI, 0.64 to 0.81; P < .001; 90% prediction interval, 0.52 to 1.00). Pregnant women had higher use of OUD medications (PR, 1.18; 95% CI, 1.11-1.25; P < .001; 90% prediction interval, 1.01-1.38) and medication continuity (PR, 1.14; 95% CI, 1.10-1.17, P < .001; 90% prediction interval, 1.06-1.22) than did other eligibility groups. Conclusions and Relevance: Among US Medicaid enrollees in 11 states, the prevalence of medication use for treatment of opioid use disorder increased from 2014 through 2018. The pattern in other states requires further research.
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