Customize your JAMA Network experience by selecting one or more topics from the list below.
Frazier W, Cochran G, Lo-Ciganic W, et al. Medication-Assisted Treatment and Opioid Use Before and After Overdose in Pennsylvania Medicaid. JAMA. 2017;318(8):750–752. doi:10.1001/jama.2017.7818
For every fatal opioid overdose, there are approximately 30 nonfatal overdoses. Nonfatal overdoses that receive medical attention represent intervention opportunities for clinicians to mitigate risk by reducing opioid prescribing or advocating addiction treatment. Studies evaluating commercially insured patients suggest these potential interventions are underutilized. For example, a 2000-2012 study1 reported high rates of opioid prescribing for patients even after they had sustained a nonfatal opioid overdose. Another study2 of patients with opioid use disorder (OUD) showed low rates of buprenorphine treatment after hospitalization for overdose. However, little is known about how opioid prescribing and medication-assisted treatment (MAT) changes from before to after overdose among Medicaid enrollees, who have a 3-times higher risk of opioid overdose.3 We used data from a large Medicaid program to compare (1) prescription opioid use, (2) duration of opioid use, and (3) rates of MAT (buprenorphine, methadone, or naltrexone) among enrollees before and after an overdose event.4
This study was deemed exempt by the University of Pittsburgh institutional review board. We conducted a retrospective cohort analysis using 2008-2013 claims data for all Pennsylvania Medicaid enrollees aged 12 to 64 years with a heroin or prescription opioid overdose who were identified using International Classification of Diseases, Ninth Revision, codes (965.00-965.02, 965.09, E.850.1-E.850.2) in inpatient, outpatient, and professional claims. We included patients with 6 months of continuous enrollment in Medicaid before and after the overdose claim (limiting our analyses to nonfatal opioid overdoses). We measured all nonparenteral prescription opioid use in pharmacy claims. We used Current Procedural Terminology codes (H0020, J1230) in professional claims to capture methadone dispensed for OUD in an opioid treatment program (as opposed to prescriptions for pain management). We used National Drug Codes in pharmacy claims to identify OUD-approved buprenorphine and naltrexone. We used a logistic regression model with generalized estimating equations for correlated data to estimate differences from before to after overdose in prescription opioid use (any use and receipt of ≥90 cumulative days duration) and differences in receiving MAT (overall and each medication separately). Analyses were stratified by overdose type (prescription opioid vs heroin) and conducted using SAS (SAS Institute), version 9.4, and STATA (StataCorp), version 14. We considered 2-sided P values less than .05 to be statistically significant and reported 95% CIs for differences of proportions.
Of the 13 670 patients with an overdose event, 6013 (44%) were continuously enrolled 6 months before and after overdose (2068 with a heroin overdose and 3945 with a prescription opioid overdose). Among enrollees with a heroin overdose or a prescription opioid overdose, the mean ages were 32.6 years and 35.5 years; 48.4% and 61.6% were female; and 82.7% and 50.9% had a OUD diagnosis, respectively (Table 1). Any filled opioid prescription decreased after overdose from 43.2% to 39.7% after heroin overdose (difference, −3.5 percentage points [95% CI, −5.9 to −1.1], P = .005), and from 66.1% before to 59.6% after prescription opioid overdose (difference, −6.5 percentage points [95% CI −8.0 to −5.0], P < .001) (Table 2). The percentage of enrollees with 90 days or more duration of prescription opioids decreased in the heroin group (from 10.5% to 9.0%; difference, −1.5 percentage points [95% CI, −2.7 to −0.3], P = .01) and the prescription opioid group (from 32.5% to 28.3%; difference, −4.1 percentage points [95% CI −5.3 to −3.0], P < .001). MAT increased after heroin overdose from 29.5% to 33.0% (difference, 3.6 percentage points [95% CI, 1.4 to 5.8], P = .002) and after prescription opioid overdose from 13.5% to 15.1% (difference, 1.6 percentage points [95% CI, 0.7 to 2.5], P = .001).
Despite receiving medical attention for an overdose, these patients in Pennsylvania Medicaid continued to have persistently high prescription opioid use, with only slight increases in MAT engagement, signaling a relatively weak health system response to a life-threatening event. Several interventions have been shown to reduce overdose risk, including trigger notifications to clinicians for patients treated for overdose5 and emergency department–initiated naloxone education and distribution.6 Study limitations include the focus on 1 state, more stable Medicaid coverage due to the continuous enrollment criteria, and use of claims data, which only track overdoses receiving medical attention, may have limited sensitivity to detect overdoses, and may underestimate opioid use by only tracking prescriptions filled.
Accepted for Publication: May 31, 2017.
Corresponding Author: Julie M. Donohue, PhD, Department of Health Policy and Management, University of Pittsburgh Graduate School of Public Health, 130 DeSoto St, Crabtree Hall, A635, Pittsburgh, PA 15261 (email@example.com).
Author Contributions: Drs Frazier and Donohue had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: All authors.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Frazier, Cochran, Gordon, Donohue.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Lo-Ciganic, Chang.
Obtained funding: Cochran, Donohue.
Administrative, technical, or material support: Frazier, Lo-Ciganic, Gordon, Donohue.
Supervision: Gordon, Donohue.
Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.
Funding/Support: This work was supported by grant U01CE002496 from the Centers for Disease Control and Prevention (CDC) and an intergovernmental agreement between the Pennsylvania Department of Human Services (PADHS) and the University of Pittsburgh.
Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and decision to submit the manuscript for publication. The CDC had no role in the preparation, review, or approval of the manuscript. PADHS reviewed and approved the manuscript prior to publication per the terms of the data use agreement.
Disclaimer: The contents represent the views of the authors only and not necessarily those of the department of Veterans Affairs or the US government.