Buprenorphine adherence and illicit opioid use among patients in treatment for opioid use disorder

ABSTRACT

Background: Buprenorphine is a partial mu opioid agonist medication that has been shown to decrease non-prescribed opioid use, cravings, and opioid related morbidity and mortality. There is an assumption that full adherence is needed to achieve ideal treatment outcomes, and that non-adherence is associated with ongoing opioid use. However, literature documenting the strength of that assertion is lacking.

Objectives: Evaluate the association between daily buprenorphine adherence and illicit opioid use.

Methods: Secondary analysis of a 12-week randomized controlled trial of adults with opioid use disorder who recently initiated buprenorphine. Weekly study visits included self-report of daily buprenorphine adherence over the past 7 days (Timeline Follow Back method) and urine drug tests (UDT). A log-linear regression model accounting for clustering by participant was used to assess the association between buprenorphine adherence and illicit opioid use. Buprenorphine adherence was measured as a continuous variable (0–7 days).

Results: Among 78 participants (56 men, 20 women, 2 nonbinary) with 737 visits, full 7-day adherence was reported at 70% of visits. The predominant form of non-adherence was missed doses (92% of cases). Each additional day of adherence was associated with an 8% higher rate of negative UDT for illicit opioids (RR = 1.08; 95% CI:1.03–1.13, p = .0002).

Conclusion: In this sample of participants starting buprenorphine, missed doses were not uncommon. Fewer missed days was significantly associated with a lower risk of illicit opioid use. These findings suggest that efforts to minimize the number of missed days of buprenorphine are beneficial for treatment outcomes.

KEYWORDS:

• Medication adherence
• opioid related disorders
• buprenorphine

Acknowledgments

The authors would like to thank Zachery Schramm, Lauren Brown, Ellie Pickering, Larissa Venia, Ivan Montoya, Laura Tabba, the Boston Medical Center OBAT staff, and the Harborview Medical Center OBOT staff for their assistance and support with the development and implementation of the study data collection.

The collection of data for this research was made possible utilizing University of Washington Institute of Translational Health Sciences’ (ITHS) REDCap servers which receive grant support from NCATS/NIH (UL1 TR002319, KL2 TR002317, and TL1 TR002318).

Disclosure statement

No potential conflict of interest was reported by the authors.

Role of funder/sponsor

The funding agency had no role in the design of the study; in the collection, analysis, and interpretation of data; in the writing of the manuscript; or in the decision to submit the article for publication.

Additional information

Funding

This research is funded by Small Business Innovation Research (SBIR) grant from NIH/NIDA [R44DA044053; PI: Seiguer/Tsui] in partnership with a health technology company (emocha Mobile Health, Inc. “emocha”). One author of this paper receives additional grant support from University of Washington’s Center for AIDs Research (CFAR) for HIV related research and prevention, this grant is funded through the NIAID [P30AI027757; PI: Tsui]. The company emocha had no role in the analysis and interpretation of data; in writing of the manuscript; or in the decision to submit the article for publication.