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      Comparative Effectiveness of Different Treatment Pathways for Opioid Use Disorder

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          Abstract

          This comparative effectiveness research study examines associations between opioid use disorder treatment pathways and overdose and opioid-related acute care use as proxies for opioid use disorder recurrence.

          Key Points

          Question

          What is the real-world effectiveness of different treatment pathways for opioid use disorder?

          Findings

          In this comparative effectiveness research study of 40 885 adults with opioid use disorder that compared 6 different treatment pathways, only treatment with buprenorphine or methadone was associated with reduced risk of overdose and serious opioid-related acute care use compared with no treatment during 3 and 12 months of follow-up.

          Meaning

          Methadone and buprenorphine were associated with reduced overdose and opioid-related morbidity compared with opioid antagonist therapy, inpatient treatment, or intensive outpatient behavioral interventions and may be used as first-line treatments for opioid use disorder.

          Abstract

          Importance

          Although clinical trials demonstrate the superior effectiveness of medication for opioid use disorder (MOUD) compared with nonpharmacologic treatment, national data on the comparative effectiveness of real-world treatment pathways are lacking.

          Objective

          To examine associations between opioid use disorder (OUD) treatment pathways and overdose and opioid-related acute care use as proxies for OUD recurrence.

          Design, Setting, and Participants

          This retrospective comparative effectiveness research study assessed deidentified claims from the OptumLabs Data Warehouse from individuals aged 16 years or older with OUD and commercial or Medicare Advantage coverage. Opioid use disorder was identified based on 1 or more inpatient or 2 or more outpatient claims for OUD diagnosis codes within 3 months of each other; 1 or more claims for OUD plus diagnosis codes for opioid-related overdose, injection-related infection, or inpatient detoxification or residential services; or MOUD claims between January 1, 2015, and September 30, 2017. Data analysis was performed from April 1, 2018, to June 30, 2019.

          Exposures

          One of 6 mutually exclusive treatment pathways, including (1) no treatment, (2) inpatient detoxification or residential services, (3) intensive behavioral health, (4) buprenorphine or methadone, (5) naltrexone, and (6) nonintensive behavioral health.

          Main Outcomes and Measures

          Opioid-related overdose or serious acute care use during 3 and 12 months after initial treatment.

          Results

          A total of 40 885 individuals with OUD (mean [SD] age, 47.73 [17.25] years; 22 172 [54.2%] male; 30 332 [74.2%] white) were identified. For OUD treatment, 24 258 (59.3%) received nonintensive behavioral health, 6455 (15.8%) received inpatient detoxification or residential services, 5123 (12.5%) received MOUD treatment with buprenorphine or methadone, 1970 (4.8%) received intensive behavioral health, and 963 (2.4%) received MOUD treatment with naltrexone. During 3-month follow-up, 707 participants (1.7%) experienced an overdose, and 773 (1.9%) had serious opioid-related acute care use. Only treatment with buprenorphine or methadone was associated with a reduced risk of overdose during 3-month (adjusted hazard ratio [AHR], 0.24; 95% CI, 0.14-0.41) and 12-month (AHR, 0.41; 95% CI, 0.31-0.55) follow-up. Treatment with buprenorphine or methadone was also associated with reduction in serious opioid-related acute care use during 3-month (AHR, 0.68; 95% CI, 0.47-0.99) and 12-month (AHR, 0.74; 95% CI, 0.58-0.95) follow-up.

          Conclusions and Relevance

          Treatment with buprenorphine or methadone was associated with reductions in overdose and serious opioid-related acute care use compared with other treatments. Strategies to address the underuse of MOUD are needed.

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          Most cited references35

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          Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

          Hude Quan, Vijaya Sundararajan, Patricia Halfon … (2005)
          Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.
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            Mortality risk during and after opioid substitution treatment: systematic review and meta-analysis of cohort studies

            Luis Sordo, Gregorio Barrio, María J. Bravo … (2017)
            Objective To compare the risk for all cause and overdose mortality in people with opioid dependence during and after substitution treatment with methadone or buprenorphine and to characterise trends in risk of mortality after initiation and cessation of treatment. Design Systematic review and meta-analysis. Data sources Medline, Embase, PsycINFO, and LILACS to September 2016. Study selection Prospective or retrospective cohort studies in people with opioid dependence that reported deaths from all causes or overdose during follow-up periods in and out of opioid substitution treatment with methadone or buprenorphine. Data extraction and synthesis Two independent reviewers performed data extraction and assessed study quality. Mortality rates in and out of treatment were jointly combined across methadone or buprenorphine cohorts by using multivariate random effects meta-analysis. Results There were 19 eligible cohorts, following 122 885 people treated with methadone over 1.3-13.9 years and 15 831 people treated with buprenorphine over 1.1-4.5 years. Pooled all cause mortality rates were 11.3 and 36.1 per 1000 person years in and out of methadone treatment (unadjusted out-to-in rate ratio 3.20, 95% confidence interval 2.65 to 3.86) and reduced to 4.3 and 9.5 in and out of buprenorphine treatment (2.20, 1.34 to 3.61). In pooled trend analysis, all cause mortality dropped sharply over the first four weeks of methadone treatment and decreased gradually two weeks after leaving treatment. All cause mortality remained stable during induction and remaining time on buprenorphine treatment. Overdose mortality evolved similarly, with pooled overdose mortality rates of 2.6 and 12.7 per 1000 person years in and out of methadone treatment (unadjusted out-to-in rate ratio 4.80, 2.90 to 7.96) and 1.4 and 4.6 in and out of buprenorphine treatment. Conclusions Retention in methadone and buprenorphine treatment is associated with substantial reductions in the risk for all cause and overdose mortality in people dependent on opioids. The induction phase onto methadone treatment and the time immediately after leaving treatment with both drugs are periods of particularly increased mortality risk, which should be dealt with by both public health and clinical strategies to mitigate such risk. These findings are potentially important, but further research must be conducted to properly account for potential confounding and selection bias in comparisons of mortality risk between opioid substitution treatments, as well as throughout periods in and out of each treatment.
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              Medication for Opioid Use Disorder After Nonfatal Opioid Overdose and Association With Mortality

              Sarah Bagley, Ziming Xuan, Na Wang … (2018)
              Opioid overdose survivors have an increased risk for death. Whether use of medications for opioid use disorder (MOUD) after overdose is associated with mortality is not known.
              Article 0 comments Cited 269 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): JAMA Netw Open
                Journal ID (iso-abbrev): JAMA Netw Open
                Title: JAMA Network Open
                Publisher: American Medical Association
                ISSN (Electronic): 2574-3805
                Publication date (Electronic): 5 February 2020
                Publication date Collection: February 2020
                Publication date (Corrected, electronic): 31 May 2024
                Publication date PMC-release: 5 February 2020
                Volume: 3
                Issue: 2
                Electronic Location Identifier: e1920622
                Affiliations
                [1 ]Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Boston
                [2 ]Department of Medicine, Harvard Medical School, Boston, Massachusetts
                [3 ]Clinical Addiction Research and Education Unit, Boston Medical Center, Boston, Massachusetts
                [4 ]Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
                [5 ]Integrated Programs, OptumLabs Inc, Cambridge, Massachusetts
                [6 ]Department of Research, OptumLabs, Minnetonka, Minnesota
                [7 ]Department of Research, OptumLabs, Cambridge, Massachusetts
                [8 ]Department of Research, Optum Behavioral Health, Cambridge, Massachusetts
                [9 ]Department of Medicare and Retirement, United Healthcare, Minnetonka, Minnesota
                Author notes
                Article Information
                Accepted for Publication: December 12, 2019.
                Published: February 5, 2020. doi:10.1001/jamanetworkopen.2019.20622
                Correction: This article was corrected on May 31, 2024, to fix errors in the captions of Figures 1 and 2.
                Open Access: This is an open access article distributed under the terms of the CC-BY-NC-ND License. © 2020 Wakeman SE et al. JAMA Network Open.
                Corresponding Author: Sarah E. Wakeman, MD, Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, 55 Fruit St, Founders 880, Boston, MA 02114 ( swakeman@ 123456partners.org ).
                Author Contributions: Drs Wakeman and Sanghavi had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Wakeman, Larochelle, Ameli, Chaisson, Crown, Azocar, Sanghavi.
                Acquisition, analysis, or interpretation of data: Wakeman, Larochelle, Ameli, Chaisson, McPheeters, Crown, Azocar.
                Drafting of the manuscript: Wakeman, Ameli, Crown, Azocar.
                Critical revision of the manuscript for important intellectual content: Wakeman, Larochelle, Ameli, Chaisson, McPheeters, Crown, Sanghavi.
                Statistical analysis: Ameli, McPheeters, Crown.
                Administrative, technical, or material support: Chaisson, McPheeters, Azocar.
                Supervision: Chaisson, Sanghavi.
                Conflict of Interest Disclosures: Dr Wakeman reported receiving personal fees from OptumLabs during the conduct of the study. Dr Ameli reported receiving grants from OptumLabs during the conduct of the study. Ms Chaisson, Mr McPheeters, and Dr Azocar reported receiving salary support from OptumLabs during the conduct of the study. Dr Azocar also reported receiving salary support from United Health Group outside the submitted work. Dr Sanghavi reported being an employee of United Health Group. No other disclosures were reported.
                Funding/Support: This study was supported by grant K23DA042168 from Boston Medical Center, grant 1UL1TR001430 from the National Institute on Drug Abuse and the National Center for Advancing Translational Sciences, National Institutes of Health, grant U01CE002780 from the Centers for Disease Control and Prevention, grant HHSF2232009100006I from the US Food and Drug Administration, grant G1799ONDCP06B from the Office of National Drug Control Policy/University of Baltimore, a Boston University School of Medicine Department of Medicine Career Investment Award (Dr Larochelle) and by Massachusetts General Hospital, grant 1R01DA044526-01A1 from the National Institutes of Health, grant 3UG1DA015831-17S2 from the National Institute on Drug Abuse, grant 1H79TI081442-01 from the Substance Abuse and Mental Health Services Administration, and the Laura and John Arnold Foundation (Dr Wakeman).
                Role of the Funder/Sponsor: The funding sources reviewed the manuscript but had no role in the design and conduct of the study; interpretation of the data; preparation, or approval of the manuscript; and decision to submit the manuscript for publication.
                Article
                Publisher ID: zoi190774
                DOI: 10.1001/jamanetworkopen.2019.20622
                PMC ID: 11143463
                PubMed ID: 32022884
                SO-VID: 3501e8db-d6a5-491a-b434-85d64ef4f0f6
                Copyright © Copyright 2020 Wakeman SE et al. JAMA Network Open.
                License:

                This is an open access article distributed under the terms of the CC-BY-NC-ND License.

                History
                Date received : 9 September 2019
                Date accepted : 10 December 2019
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                Categories
                Subject: Research
                Subject: Original Investigation
                Subject: Online Only
                Subject: Substance Use and Addiction

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