INCREASING ACCESS TO MEDICATIONS FOR OPIOID USE DISORDERS THROUGH - - PowerPoint PPT Presentation
INCREASING ACCESS TO MEDICATIONS FOR OPIOID USE DISORDERS THROUGH STATE POLICY Amanda J. Abraham, PhD 1 ; Christina Andrews, PhD 2 ; Colleen Grogan, PhD 3 ; Harold Pollack, PhD 3 ; Keith Humphreys, PhD 4 ; Thomas DAunno, PhD 5 ; Peter
1University of Georgia; 2University of South Carolina; 3University of Chicago; 4Stanford University; 5New York University; 6University of Massachusetts & Baystate
Health Supported by NIDA Grant No. R01DA034634 (PI: Friedmann)
¨ 15 year increase in overdose deaths involving
¨ Since 2000 there has been a 200% increase in the rate
¨ Heroin overdose deaths more than tripled in 4 years
¨ In 2014, opioids were involved in 28,647 deaths
Opioid Sales, Admissions for Opioid-Abuse Treatment, and Deaths Due to Overdose in the US, 1999-2010
(Volkow, Frieden, Hyde, & Cha, 2014)
¨ Three major strategies (HHS, 2015; Rudd et al., 2016)
¤ 1. Improve the safety of prescribing practices related
¤ 2. Expand access to and use of naloxone ¤ 3. Increase access to medications for the treatment of
n Medicaid and CHIP
, NIDA, NIAAA, CDC, and SAMHSA
¨ Medications are recommended for the treatment of OUDs in
¤ Oral naltrexone, injectable naltrexone, & buprenorphine
¨ Efficacy of these medications is well-established and they
¤ Amass et al., 2004; Amato et al., 2011a, 2011b Krupitsky et al., 2010; Volkow et al.,
2014
¨ However, data indicate that less than half of specialty
et al., 2013)
¤ Buprenorphine is the most widely prescribed medication in non-OTPs
(32.5%)
¨ SUD treatment system operates under a state-
¨ Primarily funded through SAPT block grant and
¤ About half of treatment programs do not accept
¨ SSAs act relatively autonomously to organize
¤ Determine treatment provider qualifications, payment
¨ In states where Medicaid covers SUD
¨ Wide state variation in organization and
¨ System is characterized by limited
¨ Scant research has examined impact of state policy on
¤ Treatment program’s perceptions of the state policy environment (Knudsen
and Abraham, 2012)
¤ Research relied on data from SSAs that was not linked with program-level
data
n Rieckmann et al., 2011; Rieckmann et al., 2015
¤ Focused on adoption of buprenorphine in Opioid Treatment Programs
(OTPs)
n Andrews et al., 2013
¤ Examined the impact of Medicaid policy on adoption of buprenorphine
n Ducharme and Abraham, 2008
¨ To examine the impact of state policy on
¨ Framework integrates 4 models of innovation adoption in
¤ 1. Government policy n Government rules and regulations ¤ 2. Market factors n Dynamics of supply, demand, and competition ¤ 3. Organizational and management characteristics n Organizational infrastructure and capacity ¤ 4. Sociotechnical factors n Fit between innovation and work needs and characteristics of staff and clients ¤ Control variables
¨ Sixth wave of the National Drug Abuse Treatment
¤ NDATSS is a longitudinal study of SUD treatment programs
¤ Split panel design with replacement sampling to replace
¨ Interviews were completed with program directors
¤ Response rate = 85.5%
¨ Constructed survey weights to account for possible
¨ To measure the state policy environment, surveys
¤ SSAs ¤ State Medicaid Agencies ¤ State Departments of Insurance
¨ SSA and Medicaid surveys
¤ Response rate for the SSA survey = 98% ¤ Response rate for the Medicaid survey = 92%
¨ Multivariate mixed effects logistic regression
¨ Missing data on the program-level
¨ Analyses conducted using Stata 14.1
¨ 3 dichotomous dependent variables
¤ Oral naltrexone ¤ Injectable naltrexone ¤ Buprenorphine ¤ ‘1’ if program currently offered medication ¤ ‘0’ if program did not currently offer medication
¨ Government policy
¤ SSA survey
n SSA allocates funding for each medication (1/0) n SSA level of technical assistance provided to treatment programs
(0 to 7 scale)
n e.g., obtain Medicaid certification, collaborate with FQHCs and
mental health providers, become approved in-network providers within private insurance plans, create electronic health records infrastructure
¤ Medicaid survey
n Current Medicaid plan covers oral naltrexone, injectable
naltrexone, buprenorphine (pre-Medicaid expansion plan) (1/0)
¨ Market factors
¤ Increase in competition in local labor market (1/0) ¤ Number of treatment program in the county ¤ Percentage of clients abusing heroin, prescription opioids
¨ Organizational and Management Characteristics
¤ Program ownership (3 dichotomous variables: private for-
¤ Accredited by JC/CARF (1/0) ¤ Program size (number of clients served), log transformed ¤ Percentage of revenues from private insurance & Medicaid
¨ Sociotechnical factors
¤ Staff professionalism (% staff with Master’s degree) ¤ Program director’s external networks and connections (1 to
n e.g., to what extent do you rely on the following as a way of
finding out about developments in the field? journals and professional publications, memberships in professional associations, etc.
¨ Control variables
¤ High risk clients (% Black, Hispanic, female clients) ¤ Program type (1/0) (Inpatient/Residential; Outpatient)
u Descriptive Statistics u Mixed Effects Logistic Regression Models
% (n)
Oral naltrexone 12.3% (79 of 640) Injectable naltrexone 11.0% (70 of 639) Buprenorphine 28.7% (187 of 652)
% (n) or Mean (SD)
SSA funding for oral naltrexone 12.8% (6 of 47) SSA funding for injectable naltrexone 14.9% (7 of 47) SSA funding for buprenorphine 17.0% (8 of 47) SSA level of technical assistance 4.62 (1.68) Medicaid coverage of oral naltrexone 70.8% (34 of 48) Medicaid coverage of injectable naltrexone 96.0% (48 of 50) Medicaid coverage of buprenorphine 100.0% (51)
% (N) or Mean (SD) Market Factors Increase in competition 34.87% (234) % heroin clients 36.75 (33.05) % prescription opioid clients 31.33 (25.78) Organizational-Management Factors Program ownership Private for-profit Private non-profit Public 23.35% (152) 63.75% (415) 12.90% (84) Accredited by JC/CARF 62.0% (384) Program size (number of clients served, log) 5.75 (1.14) % private insurance revenues 11.22 (19.70) % Medicaid revenues 27.30 (30.46) Sociotechnical Factors Staff professionalism (% Master’s level staff) 37.74 (26.57) Director external networks and connections 3.07 (0.61) Control Variables High risk clients % Black % Hispanic % women 19.36 (23.26) 13.26 (18.61) 38.61 (24.19) Program type Outpatient Inpatient/residential 75.8% (527) 24.2% (168)
Oral Naltrexone OR (95% CI) Injectable Naltrexone OR (95% CI) Buprenorphine OR (95% CI) Government policy SSA funding for medication 2.78** (1.38, 5.60) 2.13* (0.68, 2.59) 2.00* (1.09, 3.67) SSA level of technical support 1.08 (0.82, 1.43) 1.33 (0.84, 1.33) 1.22* (1.00, 1.49) Medicaid coverage of medication 1.23 (0.55, 2.75) 1.05 (0.71, 6.39) 0.66 (0.36, 1.19)+ Market Factors Increase in competition 2.21** (1.25, 3.92) 0.62 (0.36, 1.06) 1.68 (0.94, 3.03) Number of programs in county 100 (1.00, 1.00) 1.00 (1.00, 1.00) 1.00 (0.98, 1.02) % heroin clients 1.00 (0.99, 1.02) 1.01 (1.00, 1.03) 1.01 (1.00, 1.03) % prescription opioid clients 1.01 (0.99, 1.03) 1.00 (0.98, 1.02) 1.00 (0.98, 1.01) Organizational-Management Factors Program ownership Private non-profit Public ownership 3.40* (1.03, 11.19) 3.63** (1.38, 8.10) 1.32 (0.53, 3.30) 0.73 (0.17, 3.11) 1.83 (0.83, 4.03) 1.77 (0.86, 3.66) Accredited by JC/CARF 1.20 (0.54, 2.67) 1.63 (0.65, 4.06) 2.08** (1.12, 3.85) Program size 1.22 (0.91, 1.63) 1.04 (0.74, 1.47) 1.46** (1.16, 1.84) % private insurance revenues 1.02 (1.00, 1.04) 1.01 (0.99, 1.03) 1.01 (1.00, 1.03) % Medicaid revenues 0.99 (0.98, 1.01) 0.98 (0.98, 1.02) 1.00 (0.99, 1.01) Sociotechnical Staff professionalism 0.83 (0.19, 3.59) 4.08* (1.01, 16.46) 0.88 (0.24, 3.23) Director external networks/connections 0.77 (0.45, 1.32) 0.94 (0.48, 1.83) 1.16 (0.75, 1.78) Control variables High risk clients % Black % Hispanic % women 0.98 (0.96, 1.01) 0.99 (0.96, 1.01) 1.01 (1.00, 1.02) 0.99 (0.97, 1.01) 0.99 (0.97, 1.02) 1.01 (0.99, 1.02) 0.99 (0.98, 1.01) 1.00 (0.98, 1.02) 1.00 (0.99, 1.02) Program type Inpatient/Residential 2.99** (1.18, 7.59) 2.92** (1.35, 6.39) 1.03 (0.54, 1.98) *p<.05, **p<.01; +Medicaid places limits on buprenorphine
¨ Government Policy
¤ The odds of adopting all three medications were greater in
¤ The odds of adopting buprenorphine were greater in states
¨ Market, Organizational-Management,and
¤ Largely consistent with prior research
¨ Access to SUD medications for OUDs in specialty
¤ Less than 15% of programs offered naltrexone ¤ Less than 30% offered buprenorphine
¨ Given the historically low rates of OUD medication
¨ Results suggest allocating block grant funding
¤ SAMHSA could set aside funding specifically targeting
n In 2015, SAMHSA awarded 11 grants to states with a goal of
enhancing/expanding capacity to provide medications and recovery support services to individuals with OUDs (MAT-PDOA)
¨ We did not find a relationship between Medicaid coverage and
adoption of OUD medications
¤ Not surprising given the limited role Medicaid has played in SUD
treatment in many states
¨ With Medicaid expansion and changes to SUD treatment coverage
under the ACA, Medicaid is projected to become the largest payer
¤ Oversee contracts for SUD treatment programs and provider
certification and set reimbursement rates and other utilization control mechanisms
¨ Medicaid has the opportunity to be a driver of systems change in
SUD treatment
¤ Role in reforming the mental health delivery system
¨ Four avenues through which Medicaid could shape the SUD
treatment system (Andrews, Grogan, Brennen, Pollack, 2015)
¤ 1. Increase professionalism of the SUD treatment system ¤ 2. Expand alternatives to institute-based care (e.g., place a greater
emphasis wraparound services)
¤ 3. Improve the quality and use of evidence-based practices in SUD
treatment
¤ 4. Facilitate integration of SUD treatment with primary care
¨ However, in states that have opted not to expand Medicaid, the
impact of Medicaid on SUD treatment may remain limited
¨ Data are cross-sectional limiting our ability to draw
¨ Due to missing data on some state-level variables,
¨ Other variables not included in our models may
¨ Wave 7 currently in the field
¤ Longitudinal data to examine quality and accessibility
¨ Site visits in 8 states to gather in-depth data on