Predictors of Buprenorphine Adoption in Methadone and non- Methadone Treatment Settings Lori J. Ducharme, Ph.D. Hannah K. Knudsen, Ph.D. Paul M. Roman, Ph.D. University of Georgia

Organizational Resources & Evidence-Based Practice Much recent research has examined the links between organizational resources and the adoption of evidence- based practices, including pharmacotherapies, in addiction treatment. Among the predictors of medication adoption are measures of: –program quality (e.g., accreditation), –workforce professionalism (e.g., employing physicians, counselor certification and education), –philosophical orientation (e.g., 12 step vs. medical model programs), and –caseload characteristics (e.g., diagnostic groups). However, less attention has been paid to program revenue streams as potential predictors.

Organizational Resources and Medication Adoption Understanding program resources demands a more nuanced consideration of revenue sources than simply program ownership (i.e., “public” vs. “private”). The extent of program reliance on commercial insurance has been positively associated with the adoption of pharmacotherapies such as naltrexone and disulfiram. By contrast, programs that are more dependent on Medicaid and other public revenue streams have been less likely to adopt pharmacotherapies. In these analyses, we examine the relative contribution of these revenue streams to models predicting program adoption of buprenorphine.

Data Sources Data were collected in face-to-face interviews with administrators of 991 community-based treatment programs. Pooled data from 3 samples are used: –Nationally representative sample of 401 private-sector treatment units –Nationally representative sample of 350 public-sector treatment units –240 units affiliated with NIDA’s Clinical Trials Network Organizational data were collected in 2003-’04. Data on adoption of buprenorphine were collected via brief telephone follow-ups at 6 months after onsite visit.

Descriptive Statistics for Study Sample Treatment programs in sampleN=991 Adopted buprenorphine at 6-month follow-up13.2% Government operated11.0% For-profit16.6% Accredited52.4% Methadone unit18.4% Offers detox services26.6% Employs physician(s)43.5% % master’s level counselors (mean)44.5% Program size (in FTEs, mean)33.9 % primary opiate clients (mean)22.3% % revenues from Medicaid (mean)13.1% % revenues from commercial insurance (mean)15.8%

Bivariate Associations: Organizational Characteristics and Buprenorphine Adoption Adopters (13.2% of 991) Non-Adopters (86.8% of 991) Government operated * 2.6%13.2% For-profit20.0%16.5% Accredited * 79.1%50.0% Methadone unit * 30.7%17.1% Offers detox services * 50.9%22.1% Employs physician(s) * 63.5%41.3% % master’s level counselors (mean) * 50.8%43.9% Program size (in FTEs, mean) * % primary opiate clients (mean) * 34.9%20.4% % revenues from Medicaid (mean)11.3%13.7% % revenues from commercial insurance (mean) * 29.8%13.6% * indicates p<.05

Multivariate logistic regression predicting buprenorphine adoption (n=831) Model 1 Odds ratios Model 2 Odds ratios Government owned.248*.253* For-profit Accredited1.780*1.549 Methadone unit Offers detox2.875**1.996* % Primary opiate clients1.015**1.018** Employs physician(s) (p=.06) % Master’s counselors1.950*1.598 FTEs (log transformed) % Medicaid revenues * % commercial insurance revenues ** *p<.05, **p<.01

Interpretation of Results Both models show the influence of a number of organizational characteristics on the adoption of buprenorphine. The addition of revenue variables in Model 2 attenuates the significance of the effects for accreditation and the percentage of Master’s level counselors. Each of the revenue variables significantly predicted the likelihood of buprenorphine adoption in this sample. –A standard deviation (20.5%) increase in the percentage of revenues obtained from Medicaid decreases the odds of buprenorphine adoption by 27.9% (27.9=100*[e (-.016)(20.5) -1]). –A standard deviation (23.2%) increase in the percentage of revenues obtained from commercial insurance increases the odds of buprenorphine adoption by 44.9% (44.9=100*[e (.016)(23.2) -1]).

Results, cont’d The proportion of clients with primary opiate dependence also remains a significant predictor in Model 2, net of the effects of revenue sources. –A standard deviation (26.8%) increase in the proportion of primary opiate clients increases the odds of buprenorphine adoption by 57.7% (57.7 = 100*[e (.017)(26.8) -1]). Net of the effects of revenue sources and caseload, buprenorphine adoption was twice as likely among facilities offering detox services, and 75% less likely among government-owned programs. Employing at least one physician has a positive effect that approaches statistical significance (p=.062).

Implications for OTPs OTPs have been viewed as being at a “disadvantage” relative to other modalities regarding the adoption of buprenorphine, due to the restrictive regulatory requirements for dispensing bup/nx in OTP settings. In the bivariate analyses, OTPs were more likely than other programs to have adopted bup/nx. However, multivariate models found that, net of other organizational variables and revenue sources, OTPs were neither more nor less likely to have adopted buprenorphine. On the one hand, these findings suggest that OTPs are not disadvantaged by key organizational or resource issues to a greater extent than other modalities. On the other hand, the lack of resource differences suggests that adoption of bup/nx in OTPs could be significantly greater if the regulatory restrictions were lessened.

In general, the inverse association between Medicaid and buprenorphine adoption is due to the lack of inclusion (through 2004) on Medicaid formularies. –However, Medicaid may also be an indicator of the socioeconomic status of a program’s caseload. Conversely, the positive association between commercial insurance and buprenorphine adoption may indicate a greater likelihood of reimbursement, or may be serving as a proxy for socioeconomic status of program clients. In either case, programs with a relatively more affluent client base are likely to have greater access to resources that facilitate the adoption of pharmacotherapies and other resource-intensive technologies. Further research is needed on the regulatory and policy environments in which organizational decisions about service offerings are enmeshed. Future Directions

The authors gratefully acknowledge the support of research funding from the National Institute on Drug Abuse (R01DA13110 and R01DA14482). For more information and related publications, visit the National Treatment Center Study website at