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The role of health insurance on treatment for opioid use disorders: Evidence from the Affordable Care Act Medicaid expansion
Accepted Manuscript Title: The Role of Health Insurance on Treatment for Opioid Use Disorders: Evidence from the Affordable Care Act Medicaid Expansion Author: Ang´elica Meinhofer Allison E. Witman PII: DOI: Reference:
S0167-6296(17)31153-0 https://doi.org/doi:10.1016/j.jhealeco.2018.06.004 JHE 2127
To appear in:
Journal of Health Economics
Received date: Revised date: Accepted date:
31-12-2017 5-6-2018 8-6-2018
Please cite this article as: Ang´elica Meinhofer, Allison E. Witman, The Role of Health Insurance on Treatment for Opioid Use Disorders: Evidence from the Affordable Care Act Medicaid Expansion, (2018), https://doi.org/10.1016/j.jhealeco.2018.06.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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The Role of Health Insurance on Treatment for Opioid Use Disorders: Evidence from the Affordable Care Act Medicaid Expansion Allison E. Witmanb
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Ang´elica Meinhofera∗
RTI International, 3040 East Cornwallis Road, P.O. Box 12194, Research Triangle Park, NC 27709, United States
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University of North Carolina Wilmington, 601 S. College Road, Wilmington, NC 28043-5920, United States
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Abstract
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We estimate the effect of health insurance coverage on opioid use disorder treatment utilization and availability by exploiting cross-state variation in effective dates of Medicaid expansions under the Affordable Care Act. Using a difference-in-differences design, we find that aggregate opioid admissions to specialty treatment facilities increased 18% in expansion states, most of which involved outpatient medication-assisted treatment (MAT). Opioid admissions from Medicaid beneficiaries increased 113% without crowding out admissions from individuals with other health insurance. These effects appeared to be driven by market entry of select MAT providers and by greater acceptance of Medicaid payments among existing MAT providers. Moreover, effects were largest in expansion states with comprehensive MAT coverage. Our findings suggest that Medicaid expansions resulted in substantial utilization and availability gains to clinically efficacious and cost-effective pharmacological treatments, implying potential benefits of expanding Medicaid to non-expansion states and extending MAT coverage. Keywords: opioid use disorder, treatment, Medicaid Expansions JEL Codes: I11, I12, I13, I18
∗ Corresponding author. E-mail addresses: [email protected] (A. Meinhofer), [email protected] (A. Witman). We thank Susan Haber, Stephanie Kissam, Jesse Hinde, Erin Mallonee, and Michelle Marcus for helpful comments and suggestions. All errors are our own.
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1
Introduction
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The United States is in the midst of an opioid overdose epidemic that began to develop during the early 2000s and that has now become the largest and fastest growing drug problem in the country. In 2016, opioids were involved in more than
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42,000 overdose deaths, surpassing any year on record (Centers for Disease Control and Prevention, 2017). The overdose epidemic has been characterized by a shift
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from the abuse of legally manufactured prescription opioids to the abuse of illegally manufactured “street” opioids such as heroin and illicitly-made fentanyl (Rudd et al.,
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2016). Previous work has suggested that select supply-reduction efforts in the market for prescription opioids, along with a natural progression of addiction to higher-
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potency opioids and a greater supply of low-cost “street” opioids have contributed to the shift (Alpert et al., 2017; Cicero and Ellis, 2015; Compton et al., 2016; Ruhm,
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2018). This transition unveils the pivotal role of interventions with the potential to reduce the abuse of both prescription and “street” opioids among existing users
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with varying addiction severities. One such intervention is treatment for opioid use
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disorders (OUD), a demand-reduction effort that helps existing users recover from opioid addiction and improve their health and social functioning. Treatment for OUD entails detoxification and rehabilitation services. Detoxification produces stabilization by treating acute opioid withdrawal symptoms, while rehabilitation produces long-term recovery through behavioral health and other support services once stabilization is achieved. Services can be delivered in outpatient, inpatient, or residential settings and occasionally involve medication-assisted treatment (MAT).1 Opioid agonist MAT with methadone or buprenorphine is the most 1
Methadone, buprenorphine, and naltrexone are the only FDA-approved MATs for OUD. Methadone and buprenorphine are opioid agonists, while naltrexone is an opioid antagonist.
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effective treatment for OUD (Jones et al., 2015; Abraham et al., 2017; Schuckit, 2016; Volkow et al., 2014), more than doubling illicit opioid abstinence outcomes in randomized controlled trials (Connery, 2015; Whelan and Remski, 2012). Yet, those
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in need often fail to receive any treatment, let alone MAT. Between 2009-2013, about 80% of individuals with an OUD did not receive treatment in the prior year (Saloner
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and Karthikeyan, 2015). Availability and utilization barriers are partially responsible for the treatment gap. Availability barriers include paucity of licensed providers,
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capacity constraints, and stringent regulation, while utilization barriers include treatment costs and limited health insurance coverage (Jones et al., 2015; Volkow et al.,
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2014; American Society of Addiction Medicine, 2013; Wen et al., 2013). We study the role of health insurance coverage on OUD treatment utilization and
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availability by estimating the effect of Medicaid expansions under the Affordable Care Act (ACA). The ACA, the most significant expansion of health insurance since the passage of Medicare and Medicaid in 1965, was expected to narrow the treatment gap
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through regulatory changes and increases in insurance coverage (Substance Abuse
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and Mental Health Services Administration, 2014b; Ali et al., 2016). Regulatory
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changes defined substance use disorder (SUD) services as one of ten essential health benefits that insurers, including most Medicaid plans, must cover and offer at parity to other medical benefits. Coverage of specific services, however, could vary by state (Abraham et al., 2017). As for increases in insurance coverage, gains among individuals with SUD were primarily expected to occur via Medicaid expansions to adults with incomes up to 138% of the federal poverty level (Buck, 2011), a group with higher rates of SUD and unmet treatment needs than previously-enrolled beneficiaries (Busch et al., 2013). Medicaid expansions were originally intended to apply nationally, however, a Supreme Court ruling in 2012 made these optional to states. As of 2017, Medicaid expansions have become effective in thirty-one states 3
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and the District of Columbia. The effect of Medicaid expansions on OUD treatment utilization and availability is uncertain a priori. Health insurance coverage should reduce out-of-pocket treat-
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ment costs, and in turn, increase aggregate treatment demand. This, however, need not always be the case as some newly-enrolled Medicaid beneficiaries may have orig-
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inally been receiving treatment financed through alternative payment sources (e.g. private insurance, block grants), or may be unwilling or unable to obtain treatment.
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We study utilization with aggregate opioid admissions to specialty treatment facilities. We also categorize admissions by service, medical setting, and health insurance.
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Increases in treatment demand from insurance expansions of the magnitude of the ACA, especially via Medicaid expansions, might be large enough to induce supply
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responses affecting treatment availability (Finkelstein, 2007). We study availability by considering OUD treatment provider market entry or exit, changes in the scope of services offered, and acceptance of Medicaid payments. Our identification strategy
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relies on administrative data and a difference-in-differences design that exploits cross-
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state variation in effective dates of Medicaid expansions. We also exploit cross-state
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variation in Medicaid coverage of detoxification and opioid agonist MATs.2 We find that Medicaid expansions resulted in significant utilization gains. Aggregate opioid admissions to specialty treatment facilities increased 18% in expansion states. Nearly all of this effect was driven by admissions from Medicaid beneficiaries, which increased 113% without crowding out admissions from individuals with other health insurance. However, per-capita magnitudes suggest that only about two thirds of the increase in admissions from Medicaid beneficiaries resulted in utilization gains. The remainder was offset by declines in admissions from uninsured individuals 2
Variation in our measure of opioid agonist MAT coverage reflects that some states cover both methadone and buprenorphine, while others only cover buprenorphine.
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financing treatment through alternative payment sources. We also find that utilization gains were highly heterogenous by service, medical setting, and state coverage. Admissions to outpatient settings for rehabilitation services involving MAT increased
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substantially, while those for more intensive and expensive services such as detoxification in residential or inpatient settings were unchanged. Moreover, utilization
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gains were largest in expansion states with comprehensive MAT coverage.
As for availability, we find greater Medicaid acceptance and market entry among
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select MAT providers, but no changes in the scope of services offered. Opioid treatment programs (OTPs) accepting Medicaid payments increased 17%, suggesting that
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some OTPs incurred the costs of meeting state Medicaid requirements and interfacing with additional payers to become Medicaid providers. This supply response improved
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MAT availability for newly-enrolled and possibly previously-enrolled Medicaid beneficiaries as OTPs are specifically licensed to dispense methadone and buprenorphine. There was no evidence of OTP market entry, plausibly due to high fixed costs of
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OTP licensure. There was, however, market entry of Drug Addiction Treatment Act
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2000 (DATA2000) providers, who are licensed to prescribe buprenorphine subject to
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patient limits. Consistent with utilization gains, availability gains were largest in expansion states with comprehensive MAT coverage. This study provides the most comprehensive evidence to date of the early effects of Medicaid expansions on treatment for opioid use disorders, and contributes to the growing literature on the effects of the Affordable Care Act on treatment for substance use disorders (Maclean and Saloner, 2017; Clemans-Cope et al., 2017; Wen et al., 2017; Antwi et al., 2015). Our findings coincide with previous studies showing that Medicaid expansions increased treatment admissions for Medicaid beneficiaries with SUD but that were subject to offsetting declines in uninsured individuals (Maclean and Saloner, 2017). However, our findings imply larger effects 5
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for Medicaid beneficiaries with OUD along with smaller offsetting declines in uninsured individuals, resulting in significant utilization gains. These utilization gains point at Medicaid’s key role in addressing the opioid epidemic through reductions
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in the treatment gap. Our findings also coincide with previous studies showing that market-wide changes in health insurance coverage can induce supply responses
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(Finkelstein, 2007). To our knowledge, we provide the first estimates of the effect of Medicaid expansions on supply responses among substance use treatment providers.
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We are also the first to document heterogeneity by service, medical setting, and state coverage. Overall, we find that Medicaid expansions resulted in substantial utiliza-
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tion and availability gains to clinically efficacious and cost-effective pharmacological treatments, especially in expansion states with comprehensive MAT coverage.
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The paper proceeds as follows. Section 2 provides an overview of the OUD treatment system, the Affordable Care Act, previous literature, and outlines the conceptual framework. Section 3 describes data sources and the empirical strategy.
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Section 4 presents results and Section 5 discusses policy implications and concludes.
Background
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2 2.1
Treatment for Opioid Use Disorders
Treatment for OUD entails detoxification and rehabilitation services. Detoxification services treat the physical and psychological withdrawal symptoms that arise with the abrupt discontinuation of opioids after long-term use and occasionally involve stabilization and tapering with opioid agonist MAT methadone or buprenorphine. Rehabilitation services produce long-term recovery through behavioral therapy and other support services once stabilization is achieved, and occasionally involve an opi6
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oid abstinence approach with opioid antagonist MAT naltrexone or an opioid maintenance approach with opioid agonist MAT methadone or buprenorphine (Schuckit, 2016; Jones et al., 2015).3 OUD treatment services are generally delivered in outpa-
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tient, inpatient, or residential specialty settings. However, a minority of services are also delivered in non-specialty settings such as primary care or physician offices.
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Treatment for OUD is most effective when it combines behavioral therapy with MAT, especially opioid agonist MAT (Connery, 2015; Abraham et al., 2017; Schuckit,
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2016; Jones et al., 2015).4 Methadone, buprenorphine, and naltrexone are costeffective and offer relapse prevention, however, these differ significantly in their phar-
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macokinetic and pharmacodynamic properties, which in turn, affect toxicity and efficacy (Connery, 2015). MATs also differ in their delivery and licensing requirements.
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While naltrexone can be prescribed by anyone licensed to prescribe medications, methadone and buprenorphine are subject to additional licensing requirements. The two licenses for opioid agonist MAT providers are opioid treatment program licenses
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(OTP) which authorize methadone and buprenorphine dispensing from OTP facil-
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ities, and Drug Addiction Treatment Act 2000 licenses (DATA2000 waivers) which
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authorize buprenorphine prescribing or dispensing subject to patient limits (i.e. 30, 100, and recently 275 patients) in various medical settings, including specialty and non-specialty settings.5 Put differently, methadone cannot be prescribed and may only be dispensed at specialty facilities with an OTP-license.6 Buprenorphine may 3
An opioid maintenance approach with opioid agonist MATs methadone or buprenorphine is generally used on patients who are unable to stay abstinent from opioids but still want to improve their condition. An opioid abstinence approach with opioid antagonist MAT naltrexone is generally used on patients who are highly motivated to stay abstinent from opioids (Schuckit, 2016). 4 Oral naltrexone demonstrates poor adherence and increased mortality rates (Connery, 2015). 5 https://www.samhsa.gov/medication-assisted-treatment/buprenorphine-waiver-management https://www.samhsa.gov/medication-assisted-treatment/opioid-treatment-programs 6 Practitioners working at OTP facilities may choose to obtain a DATA2000-license in order to prescribe rather than dispense buprenorphine.
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be dispensed at specialty facilities with an OTP license, or may be dispensed and prescribed at specialty or non-specialty facilities with a DATA2000-licensed practi-
The Affordable Care Act
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2.2
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tioners but subject to patient limits.
The ACA transformed the behavioral health landscape by increasing health insur-
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ance coverage and by regulating plan coverage of mental health and SUD treatment benefits. Increases in health insurance coverage were primarily obtained through
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exchange plan premium tax credits for individuals with incomes between 100-400% percent of the federal poverty level and the expansion of Medicaid eligibility to individuals with incomes up to 138% of the federal poverty level.7 Among individuals
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with SUD, increases in insurance coverage were primarily expected to occur via Medicaid expansions (Buck, 2011). Regulation of plan coverage guaranteed newly-
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enrolled Medicaid beneficiaries SUD treatment coverage and that such coverage be
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offered at parity to other medical benefits. The ACA defined mental health and SUD treatment as one of ten essential health benefits, effectively requiring insurers
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to offer some form of coverage for these services. State Medicaid programs were free to define which specific conditions and treatments to cover as long as they used generally recognized independent standards for defining mental health and SUD benefits (Centers for Medicare and Medicaid Services, 2017).8 Building on the Mental Health 7
Cost-sharing subsidies were also available for certain health insurance exchange plans purchased by individuals between 100-250% of the federal poverty level. Other ACA changes affecting health insurance coverage included requiring most Americans to purchase health insurance (individual mandate), allowing individuals to remain on their parents’ private plans until age 26, and ending discrimination on the basis of pre-existing conditions. 8 Essential health benefits apply to Medicaid managed care organizations, coverage provided by Medicaid alternative benefit plans, and Children’s Health Insurance Programs. Essential health benefit requirements do not apply to non-alternative benefit fee-for-service Medicaid plans offered to previously-eligible groups. Our identification strategy uses individuals gaining coverage under the
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Parity and Addiction Equity Act of 2008, the ACA also extended the requirement that coverage for SUD treatment be no more restrictive than coverage for other medical services to all insurers, including most Medicaid plans (Centers for Medicare and
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Medicaid Services, 2017; Zur et al., 2017).9
Medicaid expansions were originally intended to apply nationally, however, a
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Supreme Court ruling in 2012 made expansions optional to states. As of 2017, thirty-one states and the District of Columbia have expanded Medicaid under the
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ACA (six in 2010-11, twenty-one in 2014, and five in 2015-16), resulting in substantial increases in the Medicaid population. Increases in the Medicaid population could be
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attributable to both newly-eligible individuals as well as previously-eligible individuals enrolling in Medicaid due to increased awareness and the individual mandate, i.e.,
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the “woodwork effect” (Frean et al., 2017). Since the woodwork effect could occur in both expansion and non-expansion states, its impact will likely be differenced out in difference-in-differences models that compare expansion states to non-expansion
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states. Therefore, our estimates will be primarily identified from newly-eligible Med-
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icaid beneficiaries and interpreted as the impact of gaining Medicaid coverage that
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was subject to new ACA requirements. Medicaid expansion; therefore, our estimates incorporate the effects of the essential health benefits. 9 ACA Medicaid expansion coverage is required to comply with parity requirements. Parity requirements apply to Medicaid managed care organizations, coverage provided by Medicaid alternative benefit plans, and Children’s Health Insurance Programs. Parity requirements do not apply to non-alternative benefit fee-for-service Medicaid plans offered to previously-eligible groups, although the Centers for Medicare and Medicaid Services encourage states to meet parity requirements in all plans. Our identification strategy uses individuals gaining coverage under the Medicaid expansion; therefore, our estimates incorporate the effects of the parity requirement.
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2.3
Conceptual Framework
Market-wide changes in health insurance may induce both demand and supply re-
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sponses, making the effect of Medicaid expansions on OUD treatment utilization and availability theoretically ambiguous. All else equal, health insurance coverage should
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reduce out-of-pocket treatment costs, and in turn, increase aggregate treatment demand. This, however, need not always be the case as some newly-enrolled Medicaid
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beneficiaries may have been originally receiving OUD treatment financed through alternative payment sources such as other government payments (e.g. block grants)
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or private insurance (e.g. crowd out). Treatment demand could even decrease among newly-enrolled beneficiaries originally receiving OUD treatment if Medicaid coverage
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is less generous than previous payment sources. As for newly-enrolled beneficiaries not originally in OUD treatment, demand need not increase among those unable or unwilling to obtain treatment. For instance, individuals may be unable to obtain
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OUD treatment due to limited transportation, other commitments (e.g. childcare,
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employment), if Medicaid does not cover a preferred service, if providers do not accept Medicaid payments, or if providers do not have the capacity to meet increases
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in treatment demand. Moreover, individuals may be unwilling to obtain OUD treatment if they do not want to stop using opioids, fear stigma, rather seek mental health treatment, or do not perceive a need for OUD treatment (Ali et al., 2015a; Maclean and Saloner, 2017). However, if health insurance coverage increases individuals’ perceived need for treatment after utilizing other medical services, treatment demand may increase.
Supply responses to market-wide changes in health insurance can affect OUD treatment availability and utilization, and may primarily occur through two interrelated pathways: fixed-costs and spillovers (Finkelstein, 2007). The fixed-costs path10
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way conjectures that if increases in aggregate treatment demand are large enough, providers may incur the fixed costs of adaptation, capacity enhancement, or market entry. For instance, providers may adapt by adopting newer evidence-based prac-
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tices or by accepting Medicaid payments; may enhance capacity by hiring additional staff; or may enter the market by becoming licensed to provide OUD treatment
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services. These supply responses are likely to increase treatment availability and utilization. In contrast, supply responses may reduce treatment availability and
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utilization through provider market exit, which may occur due to increased competition or if some facilities are unable adopt technology needed to bill Medicaid.
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The spillovers pathway conjectures that changes in insurance coverage for one set of patients my cause spillovers to other patients. This could manifest as providers
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adjusting their patient care or practice style in response to Medicaid expansions, which would affect all patients served. This could also manifest through changes in treatment availability such as those generated via the fixed-costs pathway, which
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can affect not only newly-enrolled Medicaid beneficiaries but also previously-enrolled
Previous Literature
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2.4
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Medicaid beneficiaries along with non-Medicaid populations.
Evidence of the effect of Medicaid expansions on treatment for OUD is scarce. Previous studies focus on prescribing and spending on MAT with buprenorphine or naltrexone in outpatient settings using the Medicaid State Drug Utilization Files. Using a difference-in-differences design, Wen et al. (2017) find that prescribing and spending on buprenorphine increased by 70% and 50%, respectively. Using an observational design, Clemans-Cope et al. (2017) find that national Medicaid spending on buprenorphine, naltrexone, and naloxone increased by 136% between 2011 and 2016,
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with expansion states driving most of this increase. These studies, however, only capture a small fraction of the market for OUD treatment services. A related study considers substance use disorder (SUD) treatment. Using a difference-in-differences
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approach, Maclean and Saloner (2017) find no effect on aggregate SUD treatment admissions, but do find that admissions financed by Medicaid and from Medicaid ben-
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eficiaries increased by 57% and 58%, respectively. These findings, however, might not be representative for the OUD population as the majority of the SUD population is
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comprised of individuals with alcohol use disorders. Considering that the population with OUD faces unique availability and utilization barriers to treatment, and that
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it represents only 10% of the population with SUD but over 60% of drug overdose deaths (Substance Abuse and Mental Health Services Administration, 2017; Rudd
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et al., 2016), focusing on the market for OUD treatment services is critical.10 We seek to build upon previous studies by making four main contributions. First, we provide comprehensive evidence of the effect of Medicaid expansions on OUD
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treatment utilization and availability by considering and distinguishing between a
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wide set of services and medical settings. Such distinction may uncover potential
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treatment effect heterogeneity, which is important for understanding efficacy and cost implications of the expansions. Second, we consider the question of supply responses, which has yet to be examined. Specifically, we examine changes in treatment availability that could result from provider market entry or exit, changes in the scope of services offered, and acceptance of Medicaid payments. Third, we account for cross10
Two somewhat related papers examine the impact of other insurance expansions on hospitalizations for SUD. Antwi et al. (2015) find no change in inpatient discharges for SUD from the ACA’s rule allowing young adults to remain on their parents’ health insurance until age 26 in a differencein-difference framework; however, they do document changes in the composition of payers for SUD admissions. SUD admissions financed by private payers increased while those financed by Medicare and Medicaid declined. Meara et al. (2014) document a small decline in inpatient discharges for SUD from the Massachusetts health reform in a difference-in-differences design.
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state variation in Medicaid coverage of essential OUD treatment services. We focus on detoxification to capture intensive services, and on opioid agonist MAT to capture efficacious services. Since Medicaid covers buprenorphine in all states, variation in
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MAT coverage reflects that some states cover both methadone and buprenorphine, while others only cover buprenorphine. Understanding the differential effect for states
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with methadone coverage is key as methadone and buprenorphine vary in terms of licensing, delivery, costs, and efficacy. Relative to buprenorphine, methadone is less
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expensive and more efficacious, yet also more regulated (Connery, 2015; Whelan and Remski, 2012; Mattick et al., 2008). Moreover, as buprenorphine has a dose “ceiling”
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and methadone does not, methadone is generally better for treating users with more severe OUD (Whelan and Remski, 2012). Finally, most previous studies have focused
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on partial effects by considering Medicaid beneficiaries alone, which does not capture offsetting effects. We contribute by also focusing on aggregate effects, which are not only relevant for assessing Medicaid expansions’ efficiency, but also their potential
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to narrow the treatment gap and curb the opioid overdose epidemic.11
Empirical Strategy
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3 3.1
Data Sources
We rely on administrative data from 2007 to 2016, with the exception of treatment admissions data which is available up to 2015. Data are converted into state-year counts and divided by 2015 Census estimates of the population aged 18 years or older. Outcome variables measure OUD treatment utilization, OUD treatment availability, and opioid agonist MAT availability. Treatment variables measure effective dates 11
Maclean and Saloner (2017) consider aggregate effects for the SUD population.
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of Medicaid expansions and Medicaid coverage of detoxification and opioid agonist MAT. Finally, control variables measure economic conditions and prescription opioid
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supply-reduction efforts. Outcome Variables
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OUD treatment utilization is measured as opioid admissions to specialty treatment
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facilities. Admissions are drawn from the Substance Abuse and Mental Health Services Administration’s (SAMHSA) Treatment Episode Data Set Admissions (TEDSA), which attempts to capture all admissions to publicly funded treatment facilities.
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TEDS-A is comprised of a “Minimum” dataset reported by all states and a “Supplemental” dataset reported by some states. Data are available up to 2015 and the
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unit of observation is an admission. The sample is restricted to admissions with any mention of opioids among individuals aged 18 years or older. Admissions are
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categorized into services (detoxification, rehabilitation, and MAT), medical setting (inpatient, outpatient, and residential), and health insurance (Medicaid, other in-
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surance, and uninsured). One limitation of TEDS-A is that health insurance is part
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of the “Supplemental” dataset, and thus, not reported by all states.12 Even among some reporting states, there are missing values for a subset of admissions in a subset of years. To get around this issue, we follow a similar approach as Maclean and Saloner (2017) and assign a missing value to any state-year count with underlying non-reporting of 25% or more. States with 3 or more missing state-year counts (either due to full non-reporting or to partial reporting with underlying non-reporting 12
We originally also categorized admissions by “payment source” which may not always coincide with “health insurance.” However, since “payment source” is also part of the “Supplemental” dataset but is especially underreported, we opted to fully drop these unreliable estimates.
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of 25% or more) are removed.13 Substance, service, and medical setting variables are part of the “Minimum” dataset, and thus, trivially affected by this selection criteria. Another limitation of TEDS-A is that admissions do not represent individuals, im-
treatment and in individuals re-entering treatment within a year.
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plying that variation in admissions reflects both changes in individuals first entering
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OUD treatment availability is measured as the number of specialty treatment facilities offering OUD treatment services. Facilities are drawn from the National
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Survey of Substance Abuse Treatment Services (N-SSATS), an annual census of substance use treatment facilities administered by SAMHSA. The N-SSATS collects
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information on the services offered and utilization of those services. Data are available up to 2016 and the unit of observation is a facility. The sample is restricted
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to facilities offering detoxification for OUD, methadone, naltrexone, or buprenorphine, or that are federally-certified as opioid treatment programs. One limitation is that N-SSATS fails to properly capture providers at non-specialty settings. To
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get around this issue, we also measure availability with the number of DATA2000
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licenses, which authorize buprenorphine prescribing or dispensing in specialty and
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non-specialty settings. Licenses are drawn from SAMHSA’s DATA2000-Certified Physicians database, which tracks the number of physicians, nurse practitioners, and physician assistants receiving a 30-, 100-, and 275-patient limit license in a given year. We use data from 2007 to 2016 and the unit of observation is a state, year, and patient limit. DATA2000 licenses are converted into state-year patient capacity counts.14 A limitation of this data is that it captures the state the provider was 13
Estimates are robust to different cutoffs (e.g. 20%, 15%), or to the alternative approach where states with three or more missing state-year counts are kept in the analysis. We confirm the external validity of insurance estimates by comparing aggregate effects from “Supplemental” dataset states to aggregate effects from “Minimum” dataset states. 14 Capacity= 30×N30,t + 100×N100,t + 275×N275,t , where Np,t is total licenses with patient limit p in year t.
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licensed in when receiving the DATA2000 waiver. In some cases, this may not be where the provider is currently practicing. Additionally, the data does not properly capture the date in which providers who were previously licensed to dispense
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buprenorphine had their waiver relinquished, revoked, or expired.15
Opioid agonist MAT availability is measured as methadone and buprenorphine
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grams. Grams are drawn from the Drug Enforcement Administration’s (DEA) Automation of Reports and Consolidated Orders System (ARCOS), which tracks total
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drug grams distributed to dispensers. Data are available up to 2016 and the unit of observation is a state, year, drug, and provider. Grams are converted into mor-
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phine milligram equivalents as described in the Appendix (see Table 7). The sample is restricted to methadone and buprenorphine grams distributed to OTPs, which
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captures MAT offered under an OTP license. To capture MAT offered under a DATA2000 license, MAT availability is also measured as outpatient buprenorphine prescriptions financed by Medicaid. Prescriptions are drawn from the Medicaid State
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Drug Utilization Files (SDUF) and identified following a similar approach as Wen
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et al. (2017). Data are available up to 2016 and the unit of observation is a state,
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quarter, and national drug code. Prescriptions are converted into state-year counts. 3.1.2
Treatment Variables
Medicaid expansion dates are obtained from the Henry J. Kaiser Family Foundation (see Tables 8 and 9). Although Medicaid expansions were scheduled for January 1, 15
Note that SAMHSA presently knows, based on whether a provider has lost or given up their DEA license, whether a previously DATA2000-licensed provider continues to be DATA2000-licensed as of today. However, currently SAMHSA’s database does not properly capture historical exit dates. To assess the importance of this limitation, we analyzed state-year counts based on all providers ever receiving a DATA2000 license (used to estimate main findings) and on providers currently DATA2000-licensed, the former which is subject to a slight overcount of licenses in later years and the latter to a slight undercount of licenses in earlier years. Estimates are robust regardless.
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2014, states could expand eligibility as early as 2010. California, Connecticut, Minnesota, New Jersey, Washington, and the District of Columbia expanded early and in multiple occasions. For these states, we assigned the expansion date that increased
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enrollment of non-disabled adults the most. In robustness checks, we exclude early expansion states and estimates are robust (see Section A.1 in the Appendix).
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Data on Medicaid coverage of OUD treatment services are drawn from the Medicaid and CHIP Payment and Access Commission (MACPAC). MACPAC identified
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mental health and SUD services offered through state plan authority by reviewing Medicaid state plans, provider manuals, billing manuals, fee schedules, and other
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publicly available official documents. The report did not include waiver services or additional services provided by managed care organizations, unless the managed
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care organization served all beneficiaries in the state. This primary source (Medicaid and CHIP Payment and Access Commission, 2016) was consistent with other sources (American Society of Addiction Medicine, 2013; Substance Abuse and Mental Health
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Services Administration, 2014a). We focus on Medicaid coverage of detoxification
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treatment in residential and inpatient settings, and of opioid agonist MAT. Since
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Medicaid covers buprenorphine in all states, variation in opioid agonist MAT coverage reflects that some states cover both methadone and buprenorphine, while others only cover buprenorphine. 3.1.3
Control Variables
To retrieve causal estimates of the effect of health insurance coverage on outcomes, other outcome determinants that are correlated with health insurance coverage must be controlled for. To that end, we control for a number of variables directly affecting the market for opioids at the state-level. Specifically, we control for pain clinic laws
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and prescription drug monitoring program (PDMP) implementation and mandates to account for supply-reduction efforts in the market for prescription opioids (Popovici et al., 2018; Ali et al., 2017; Meinhofer, 2016, 2017; Bao et al., 2016; Dowell et al.,
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2016). Additionally, we control for the unemployment and poverty rates to account for economic conditions (Hollingsworth et al., 2017). In robustness checks, we also
cr
consider other variables indirectly affecting the market for opioids at the state-level (e.g. recreational marijuana laws) or directly affecting the market for opioids at
us
the nation-level (e.g. increases in illicitly-made fentanyl availability, re-scheduling of hydrocodone combination products). Estimates are generally robust to these controls
an
(see Section A.1 in the Appendix).
The effective dates of PDMP implementation, PDMP use mandates, and pain
M
clinic laws were collected and verified through a number of sources. These sources include PDMP administrators, PDMP websites, the National Alliance for Model State Laws, the PDMP Training and Technical Assistance Center at Brandeis University,
d
the Prescription Drug Abuse Policy System, and the Policy Surveillance Program at
te
Temple University. Effective dates can be found in the Appendix (see Table 10).
Ac ce p
Unemployment rates for each state are obtained from the Bureau of Labor Statistics’ Local Area Unemployment Statistics. The percentage of individuals below 100 percent of the federal poverty level is calculated using Current Population Survey data downloaded from the Integrated Public Use Microdata Series (IPUMS). We obtain state-year poverty rates using the methodology recommended by IPUMS.16 16
An overview of how to calculate official poverty rates using the IPUMS CPS data is here: https://cps.ipums.org/cps/poverty notes.shtml
18
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3.2
Econometric Approach
The identification strategy is to estimate a difference-in-differences specification that
ip t
exploits cross-state variation in effective dates of Medicaid expansions and in Medicaid coverage of different OUD treatment services. Equation 1 estimates the average
cr
effect of the expansions (γ) and is indexed by state s and year t. Yst is the outcome variable and is equal to counts per 10,000 persons aged 18 years or older. Expst is
us
the treatment variable and is equal to one if state s expanded Medicaid in year t and zero otherwise. Ss are state fixed effects and control for state-specific time invariant
an
determinants of Yst . Tt are year fixed effects and control for year-specific state invariant determinants of Yst . Despite these controls, threats to identification might
M
remain if other determinants of Yst changed differentially over time among treatment states. In an effort to minimize potential threats, a vector of control variables Xst that account for economic conditions, pain clinic laws, and PDMP implementation
d
and mandates is incorporated into all specifications (see Section 3.1.3). Section A.1
te
in the Appendix tests the robustness of γ to changes in time window (+/- 3 years),
Ac ce p
the exclusion of early expansion states, and the inclusion of additional controls.
Yst = α + γExpst + θXst + Ss + Tt + st
(1)
Yst = α + γExpst + β1 Expst ×M ATs + β2 Expst ×Detoxs + θXst + Ss + Tt + st (2)
Yst = α +
q X
λj Est (j = t − k) + θXst + Ss + Tt + st
(3)
j=−m
19
Page 19 of 62
Equation 1 fails to account for cross-state variation in Medicaid coverage of different OUD treatment services. This represents an important limitation as the effect of health insurance on availability and utilization of OUD treatment services will be
ip t
directly affected by coverage of such services. In an effort to circumvent this limitation, Equation 2 estimates the differential effect of the expansions by accounting
cr
for cross-state variation in Medicaid coverage of opioid agonist MAT and of detoxification treatment (see Section 3.1.2). M ATs is equal to one if Medicaid in state s
covers detoxification treatment and zero otherwise.
us
covers methadone and zero otherwise. Detoxs is equal to one if Medicaid in state s
an
Difference-in-differences estimates are only valid under the assumptions of parallel trends and no policy endogeneity. To examine whether these assumptions appear to
M
hold and determine whether there is a dynamic treatment effect, graphical evidence from estimates in Equation 3 is presented. This evidence is based on an event study approach that controls for m leads and q lags of the treatment, captured in the
d
dummy variables Est (j = t − k), where k is the time of expansion in state s. The
te
reference group is j = 0, the year right before the expansion became effective. A
Ac ce p
test validating the identifying assumptions is if λj is statistically not different from zero for all j < 0 (this need not be the case for j > 0). More generally, detecting a trend or an irregular jump prior to the time of expansions should raise concerns regarding the validity of γ. Graphical evidence based on the raw data is presented in the Appendix (see Section A.3). In all specifications, standard errors are clustered at the state level (Bertrand et al., 2004).
20
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4
Results OUD Treatment Utilization
ip t
4.1
In Table 1 we present estimates of the effect of Medicaid expansions on OUD treat-
cr
ment utilization. We measure OUD treatment utilization as the aggregate number of opioid admissions to specialty treatment facilities per 10,000 persons. We also cate-
us
gorize aggregate opioid admissions into services received, medical setting, and health insurance.17 In Panel A we report the average effect of the expansions as specified in
an
Equation 1, and in Panel B we report the differential effect of the expansions by state coverage of detoxification and opioid agonist MAT as specified in Equation 2. To
M
assess the credibility of these estimates, we present event study plots in Figure 1.18 We find that aggregate opioid admissions increased 18% (γ = 5.4) in expansion states. Nearly all of this effect was driven by admissions from Medicaid beneficiaries,
d
which increased 113% (γ = 8.7) without crowding out admissions from individuals
te
with other health insurance. Nonetheless, per-capita magnitudes suggest that only about two thirds of the increase in admissions from Medicaid beneficiaries resulted
Ac ce p
in utilization gains (5.4/8.7). The remainder was offset by declines in admissions from uninsured individuals (γ = −3.7). These offsetting effects are consistent with some reductions in treatment financing through other payment sources, which might occur if expansions were accompanied by declines in state government spending or in out-of-pocket spending on OUD treatment. Alternatively, these offsetting effects are also consistent with some treatment financing through other payment sources among newly-enrolled beneficiaries, which might occur if Medicaid does not cover a 17
All categories in “medical setting” and “health insurance” are mutually exclusive and exhaustive. Except for MAT, all categories in “services received” are mutually exclusive and exhaustive. 18 See Figures 6 and 7 in the Appendix for non-parametric graphical evidence.
21
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preferred service or if a provider does not accept Medicaid payments. We also find significant service and setting heterogeneity. Utilization gains were driven by increases in admissions to outpatient settings for rehabilitation services
ip t
(γ = 5.3), over half of which involved MAT (γ = 3.4). There was limited evidence of an average effect for admissions to inpatient or residential settings, or for detox-
cr
ification services. This service heterogeneity appeared to be partially explained by differential Medicaid coverage of OUD services across states. Utilization gains to
us
OUD services were larger and occurred almost exclusively in expansion states with Medicaid coverage of such services. Specifically, admissions for rehabilitation services
an
involving MAT increased 105% (γ + β1 = 7.4) in expansion states with comprehensive MAT coverage. Even admissions for detoxification services were relatively larger
OUD Treatment Availability
d
4.2
M
(β1 = 2.2) in expansion states with detoxification coverage.
te
Market-wide changes in health insurance coverage may induce supply responses affecting OUD treatment availability. In Table 2 we present estimates of the effect of
Ac ce p
Medicaid expansions on three types of supply responses: provider market entry or exit defined as providers obtaining OTP or DATA2000 licenses; changes in the scope of services offered defined as providers offering MAT or detoxification services; and Medicaid acceptance defined as providers accepting or not accepting Medicaid payments. In column (1) the dependent variable is the capacity of DATA2000-licensed physicians per 10,000 persons and in columns (2)-(10) is the number of specialty treatment facilities offering OUD services per 10,000 persons. In Panel A we report the average effect of the expansions as specified in Equation 1 and in Panel B we report the differential effect of the expansions by state coverage of detoxification and
22
Page 22 of 62
opioid agonist MAT as specified in Equation 2. To assess the credibility of these estimates, we present event study plots in Figure 2.19 We find some evidence of provider market entry. The capacity of DATA2000-
ip t
licensed physicians increased 17% (γ = 10.8), improving buprenorphine availability. There was no such effect for OTPs, possibly due to higher fixed costs of obtain-
cr
ing these licenses. As for the scope of services, we find no changes in the number of specialty treatment facilities offering detoxification, naltrexone, methadone, or
us
buprenorphine. Nonetheless, increases in DATA2000 licenses suggest that changes in the scope of services might have still occurred either at non-specialty treatment
an
settings, or at specialty treatment settings already offering buprenorphine through intensive margin increases in DATA2000-licensed physicians or in patient limits. Fi-
M
nally, we find evidence of increased Medicaid acceptance. OTPs accepting Medicaid payments increased 17% (γ = 0.008) while those not accepting Medicaid decreased by a comparable magnitude (γ = 0.009), suggesting a switch in Medicaid acceptance
d
among existing OTPs. This switch possibly broadened MAT availability among Med-
te
icaid beneficiaries. In Table 3 and Figure 3, we examine this possibility and find that
Ac ce p
OTPs accepting Medicaid and offering naltrexone, methadone, or buprenorphine increased by the same magnitude for all three MATs (γ = 0.007). Availability gains primarily occurred in states with comprehensive MAT coverage. Increases in the capacity of DATA2000-licensed physicians were driven by states with methadone MAT coverage (γ + β1 = 18.2). Likewise, increases in OTP Medicaid acceptance were driven by these states (γ + β1 = 0.013). These changes in OUD treatment availability are consistent with changes in OUD treatment utilization in Section 4.1, and suggest that Medicaid expansions induced supply responses enhancing MAT availability and thus, MAT utilization for newly-enrolled Medicaid 19
See Figures 8 and 11 in the Appendix for non-parametric graphical evidence.
23
Page 23 of 62
beneficiaries. Further, the nature of supply responses suggests that expansions might have resulted in spillovers to other patients. The entry of DATA2000 physicians can spill over to patients needing OUD treatment, regardless of coverage. Similarly, OTP
Opioid Agonist MAT Availability
cr
4.3
ip t
Medicaid acceptance can spill over to previously-enrolled Medicaid beneficiaries.
us
In Table 4 we present estimates of the effect of Medicaid expansions on opioid agonist MAT availability. In columns (1)-(3) the outcome variable is the aggregate
an
distribution of buprenorphine and methadone grams at OTPs per 10,000 persons and in column (4) is the number of outpatient buprenorphine prescriptions financed by Medicaid per 10,000 persons.20 In general, prescriptions primarily capture MAT
M
prescribing under a DATA2000 license at specialty and non-specialty treatment settings, and grams capture MAT dispensing under an OTP license at the OTP. In
d
Panel A we report the average effect of the expansions as specified in Equation 1
te
and in Panel B we report the differential effect of the expansions by state coverage of detoxification and opioid agonist MAT as specified in Equation 2. To assess the
Ac ce p
credibility of these estimates, we present event study plots in Figure 4.21 We find substantial increases in opioid agonist MAT availability. Aggregate gram distribution at OTPs increased 17% (γ = 456) and Medicaid prescriptions increased 104% (γ = 129), suggesting that both OTP and DATA2000 providers contributed to MAT utilization gains documented in Section 4.1.22 As before, increases 20
Due to different datasets, prescriptions apply to the Medicaid population only and grams apply to the whole population. Despite not being quite comparable, these variables are comprehensive of opioid agonist MAT availability at different settings and providers. 21 See Figure 12 in the Appendix for non-parametric graphical evidence. 22 Our prescription estimates -104%- are somewhat bigger than those in Wen et al. (2017) -70%due to our larger sample window and an increasing treatment effect on prescribing over time.
24
Page 24 of 62
in methadone and buprenorphine grams (γ + β1 = 786) and buprenorphine prescriptions (γ + β1 = 164) were largest in expansion states with comprehensive MAT coverage. As for states with limited MAT coverage, there is statistically insignificant
ip t
evidence that buprenorphine prescriptions increased 83% (γ = 102). These findings, along with those in Sections 4.1 and 4.2, suggest that OTPs increased opioid ago-
cr
nist MAT availability through intensive margin changes. OTPs, the sole providers of methadone and historically known as methadone providers, also engaged in new
us
practice styles by increasing buprenorphine dispensing and possibly prescribing. Although an OTP license authorizes methadone and buprenorphine dispensing from
an
an OTP, it is plausible that some providers working at OTPs incurred the fixed costs of obtaining a DATA2000 license, which authorizes buprenorphine prescribing.
M
However, increases in buprenorphine prescribing in states with limited MAT coverage suggest that not all increases in MAT availability occurred at OTPs, but also at
Conclusion
Ac ce p
5
te
changes.
d
other specialty and even non-specialty treatment settings through intensive margin
We provide the most comprehensive evidence to date of the early effects of Medicaid expansions on the market for OUD treatment and document four key findings. First, Medicaid expansions resulted in substantial OUD treatment utilization gains. Utilization gains were attributable to dramatic increases in opioid treatment admissions from Medicaid beneficiaries but were subject to some offsetting declines in admissions from uninsured individuals. These offsetting effects are consistent with reductions in treatment financing through other payment sources, which might occur if expansions were accompanied by declines in state general revenue spending or in out-of-pocket 25
Page 25 of 62
spending on OUD treatment. Alternatively, these offsetting effects are consistent with treatment financing through other payment sources among newly-enrolled beneficiaries, which might occur if Medicaid does not cover a preferred service or if a
ip t
provider does not accept Medicaid. Notably, there was no crowd out of other health insurance. These findings suggest that Medicaid expansions have potential to curb
cr
the opioid epidemic through reductions in the treatment gap and shed light on the importance of preserving government spending on treatment to fund care for the
us
remaining uninsured or for key services not covered by Medicaid.
Another key finding is that OUD treatment utilization gains were heterogenous
an
by service and medical setting. Admissions to outpatient settings for rehabilitation services involving MAT increased substantially, while those for detoxification services
M
in residential or inpatient settings were unchanged. Some of this heterogeneity may be partially explained by capacity constrains at residential or inpatient settings, along with specific features of the Medicaid program. Medicaid generally requires
d
that services be physician-directed and prohibits federal payments for services at
te
residential or inpatient facilities with more than 16 beds (Buck, 2011). These findings
Ac ce p
hold important policy implications in terms of efficacy and costs as MAT is considered the most effective clinical approach to treatment for OUD and services in outpatient settings are generally less expensive than those in residential or inpatient settings. A third key finding is that changes in OUD treatment utilization were accompanied by supply responses affecting OUD treatment availability. There was some evidence of market entry by DATA2000-licensed providers. Despite no evidence of market entry by OTP-licensed providers, plausibly due to higher fixed costs of obtaining these licenses, OTPs were largely responsible for MAT availability gains through intensive margin increases in MAT dispensing and greater acceptance of Medicaid payments. One explanation for greater OTP Medicaid acceptance is more com26
Page 26 of 62
prehensive Medicaid coverage of OUD services resulting from parity requirements, coupled with expected declines in state general revenue spending on OUD treatment (Buck, 2011). These findings suggest that Medicaid expansions induced supply re-
and possibly previously-enrolled Medicaid beneficiaries.
ip t
sponses enhancing MAT availability and thus, MAT utilization for newly-enrolled
cr
Finally, we find that MAT utilization and availability gains were largest in expansion states with methadone coverage. Although it is not surprising that OTP supplier
us
responses would occur in these states, since by law OTPs are the sole providers of methadone and primarily offer methadone, it is surprising that DATA2000 supplier
an
responses would. One possible explanation for these findings is competition between OTPs and other providers. Another is that other providers, especially those
M
at non-specialty treatment settings, may more easily refer difficult patients with severe OUD to OTPs instead of having to treat them themselves, increasing provider willingness to enter the market for OUD treatment. Alternatively, OTPs could be
d
seeking revenue expansion through additional services. While an OTP license autho-
te
rizes the use of methadone and buprenorphine, it requires that these be dispensed
Ac ce p
at the OTP, which can be burdensome for patients regularly visiting the facility to obtain medications. As a DATA2000 license authorizes buprenorphine prescribing, it is plausible that some physicians working at OTPs incurred the fixed costs of obtaining DATA2000 licenses to attract patients needing greater flexibility. Moreover, many Medicaid state plans require proof of counseling prior to authorization of buprenorphine coverage. OTPs must offer counseling services, and thus, are in a better position to attract buprenorphine patients seeking integrated care. As for states without methadone coverage, there was limited evidence of availability or utilization gains at specialty settings. Nonetheless, there were some increases in buprenorphine prescribing, possibly at non-specialty settings. These findings imply potential bene27
Page 27 of 62
fits of extending Medicaid coverage of methadone, as this coverage appears to induce supply responses that enhance MAT utilization and availability. This study has several limitations. First, since our measure of treatment utiliza-
ip t
tion captures admissions rather than individuals, we cannot establish the degree to which utilization gains reflect extensive or intensive margin increases.23 Second, our
cr
estimates of treatment utilization by health insurance status are based on data from twenty-nine states, and thus, may not be generalizable. Third, although we consider
us
OUD treatment in specialty settings, along with measures of buprenorphine treatment, which can be offered in non-specialty settings, we do not fully capture OUD
an
treatment in non-specialty settings. Moreover, we do not capture all relevant forms of behavioral health services, including mental health treatment, which is more often
M
sought than SUD treatment among individuals with SUD (Ali et al., 2015b). Medicaid has the potential to play an important role in addressing the opioid crisis, especially as many of the states that declined to expand Medicaid have high
d
rates of opioid abuse (Abraham et al., 2017). Moreover, many expansion states with
te
high rates of opioid abuse do not cover methadone despite its proved effectiveness
Ac ce p
(U.S. Department of Health and Human Services, Office of the Surgeon General, 2016). Overall, Medicaid expansions resulted in substantial utilization and availability gains to clinically efficacious and cost-effective pharmacological treatments. This, along with the finding that gains were largest in expansion states with comprehensive MAT coverage suggests that both Medicaid expansions and methadone coverage might be important for reducing the treatment gap.
Funding
23 That is, whether increases in treatment utilization reflect more individuals getting treatment for the first or same individuals getting treatment multiple times.
28
Page 28 of 62
This research did not receive any specific grant from funding agencies in the public,
ip t
commercial, or not-for-profit sectors.
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cr
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Ac ce p
use disorder treatment. Medical care 55 (4), 336–341. Wen, H., J. M. Hockenberry, and J. R. Cummings (2015). The effect of medical marijuana laws on adolescent and adult use of marijuana, alcohol, and other substances. Journal of health economics 42, 64–80. Whelan, P. J. and K. Remski (2012). Buprenorphine vs methadone treatment: A review of evidence in both developed and developing worlds. Journal of neurosciences in rural practice 3 (1), 45. Zur, J., M. Musumeci, and R. Garfield (2017). Medicaid’s role in financing behavioral
36
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health services for low-income individuals. Technical report, The Henry J. Kaiser
Ac ce p
te
d
M
an
us
cr
ip t
Family Foundation.
37
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Services Received
−3
−2
−1
1
Year −3
−2
−1
1
2
3 Rehabilitation
MAT
2
3
Detoxification
cr
Year
ip t
−5
−5
0
0
5
5
10
10
15
15
Aggregate Treatment Admissions
Health Insurance
−5
−10
an
0
0
10
5
20
10
30
us
40
15
Medical Setting
−3
−2
−1
1
2
3
−3
Year Residential
Inpatient
Medicaid
−1
1
2
Year Other Insurance
Uninsured
M
Outpatient
−2
Figure 1: Effect of Medicaid Expansions on Opioid Treatment Admissions
Ac ce p
te
d
Notes: Estimates and 95% confidence intervals are based on the event study approach in Equation 3. The reference year is j = 0, the period right before the Medicaid expansion. Opioid treatment admissions of the population aged 18 and older are drawn from the 2007-2015 TEDS-A (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000.
38
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ip t
cr
.02
−1
1
DATA2000
2
−.02
−.02 −.04
3
−3
−2
an
−2
us
0
0 −3
−.04
−30
−20
−10
0
10
.02
20
30
.04
Scope of Services .04
Market Entry or Exit
OTPs
Methadone
1
2
Naltrexone
3 Detox
.02 0
−1
1 Yes
2
3
No
−.04
−.02 −2
te
−3
d
−.04
−.02
0
.02
M
.04
Non−OTP Medicaid Acceptance
.04
OTP Medicaid Acceptance
−1
Buprenorphine
−3
−2
−1
1 Yes
2
3
No
Ac ce p
Figure 2: Effect of Medicaid Expansions on OUD Treatment Providers Notes: Estimates and 95% confidence intervals are based on the event study approach in Equation 3. The reference year is j = 0, the period right before the Medicaid expansion. Facilities are drawn from the 2007-2016 N-SSATS and DATA2000 physician capacity from the 2007-2016 SAMHSA database (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Coefficients for DATA2000 waivers are axis 1 and treatment facilities are axis 2.
39
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−3
−2
−1
Methadone
ip t
1 Buprenorphine
2
an
−.02
−.01
−.01
0
0
us
.01
.01
.02
cr
OTPs Not Accepting Medicaid
.02
OTPs Accepting Medicaid
3
−3
Naltrexone
−2
−1
Methadone
2
3
Naltrexone
Non−OTPs Not Accepting Medicaid
−3
−2
−1
1
2
3
−.01 −.02
−3
Naltrexone
−2
−1 Buprenorphine
1
2
3
Naltrexone
Ac ce p
Buprenorphine
te
−.01
0
d
0
.01
.01
.02
M
.03
.02
Non−OTPs Accepting Medicaid
1 Buprenorphine
Figure 3: Effect of Medicaid Expansions on OUD Treatment Providers
Notes: Estimates and 95% confidence intervals are based on the event study approach in Equation 3. The reference year is j = 0, the period right before the Medicaid expansion. Facilities are drawn from the 2007-2016 N-SSATS (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000.
40
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−3
−2
−1
1
Year Methadone
2
3
Buprenorphine
an
Aggregate
us
−500
0
cr
500
ip t
1000
1500
MAT Gram Distribution at OTPs
−2
Ac ce p
−3
te
−100
0
d
100
200
M
300
400
MAT Prescriptions Financed by Medicaid
−1
1
2
3
Year Buprenorphine
Figure 4: Effect of Medicaid Expansions on Opioid Agonist MAT Availability Notes: Estimates and 95% confidence intervals are based on the event study approach in Equation 3. The reference year is j = 0, the period right before the Medicaid expansion. MAT dispensing is measured as aggregate methadone and buprenorphine gram distribution at opioid treatment programs, and MAT prescribing as outpatient buprenorphine prescriptions financed by Medicaid. Grams are drawn from the 2007-2016 ARCOS Report 5 and prescriptions from the 2010-2016 Medicaid SDUF (see Section 3.1). Grams are converted into morphine gram equivalents (see Table 7 in the Appendix). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000.
41
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42
Page 42 of 62
Ac ce p
0.2 21.42 430 49 Yes Yes Yes
0.2 7.05 377 43 Yes Yes Yes
0.1 1.06 430 49 Yes Yes Yes
0.3 12.76 430 49 Yes Yes Yes
us
an
M
-2.7 (1.9) 1.7 (1.7) 4.3 (1.7)**
0.3
1.1 (0.9)
cr
0.2 7.72 243 28 Yes Yes Yes
7.4 (5.7) 14.8 (8.8) -11.7 (10.5)
0.1
8.7 (4.6)*
0.2 5.62 243 28 Yes Yes Yes
0.7 (1.9) -1.1 (1.6) -0.1 (1.5)
0.2
-0.0 (1.0)
0.2 13.92 243 28 Yes Yes Yes
-2.3 (3.6) -4.9 (3.3) 2.4 (3.3)
0.2
-3.7 (2.6)
Health Insurance Medicaid Other Uninsured (8) (9) (10)
ip t
Notes: Opioid treatment admissions of the population aged 18 and older are drawn from the 2007-2015 TEDS-A (see Section 3.1). Health insurance is part of the TEDS-A “Supplemental” dataset, and thus, not reported by all states. Categories in medical setting and health insurance are mutually exclusive and exhaustive. Except for MAT, categories in services received are mutually exclusive and exhaustive. Counts are divided by 2015 Census estimates of the population aged 18 years or older and multiplied by 10,000. The unit of analysis is a state-year. Estimates in Panel A are based on Equation 1 and estimates in Panel B are based on Equation 2. Control variables include PDMP implementation, PDMP mandates, pain clinic laws, the unemployment rate, and the percent of state residents in poverty. The baseline mean captures outcomes 2 years prior to expansions for treated states. State clustered standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
0.1 8.96 430 49 Yes Yes Yes
0.2 16.55 430 49 Yes Yes Yes
0.3 30.38 430 49 Yes Yes Yes
R2 M ean N Clusters Y ear F.E. State F.E. Controls
d
0.4 (0.4) -0.2 (0.3) -0.3 (0.4)
0.5 (3.4) 6.8 (3.9)* -1.7 (5.2)
te
0.1
0.0 (0.2)
0.2
4.3 (2.1)**
R2 0.2 0.1 0.2 0.1 Panel B: Differential Effect of Medicaid Expansions Exp -1.9 -1.6 -0.3 0.2 (4.5) (1.3) (3.8) (3.5) Exp×M AT 8.4 0.5 7.9 7.2 (4.4)* (1.3) (3.9)** (3.6)* Exp×Detox 2.2 2.2 -0.0 -3.3 (5.1) (1.2)* (5.0) (5.2)
Services Received Detox. Rehab. MAT (1) (2) (3) (4) Panel A: Average Effect of Medicaid Expansions Exp 5.4 0.1 5.3 3.4 (2.2)** (0.6) (2.1)** (2.0)*
Aggregate
Medical Setting Outpat. Inpat. Resid. (5) (6) (7)
Table 1: Effect of Medicaid Expansions on Opioid Treatment Admissions Per 10,000 Persons
43
Page 43 of 62
d
te
0.771 65.4 510 51 Yes Yes Yes
0.190 0.062 510 51 Yes Yes Yes
0.169 0.060 510 51 Yes Yes Yes
0.552 0.144 510 51 Yes Yes Yes
0.599 0.158 510 51 Yes Yes Yes
0.570 0.054 357 51 Yes Yes Yes
0.285 0.047 510 51 Yes Yes Yes
cr
0.188 0.014 510 51 Yes Yes Yes
-0.007 (0.004)* -0.009 (0.004)** 0.006 (0.004)
ip t
0.543 0.118 510 51 Yes Yes Yes
-0.005 (0.016) 0.017 (0.012) -0.001 (0.011)
0.003 (0.005) 0.010 (0.004)** -0.003 (0.006)
0.153
-0.009 (0.004)**
0.217 0.048 510 51 Yes Yes Yes
-0.009 (0.009) 0.007 (0.006) -0.001 (0.006)
0.212
-0.005 (0.005)
Notes: Facilities are drawn from the 2007-2016 N-SSATS and DATA2000 physician capacity from the 2007-2016 SAMHSA database (see Section 3.1). Opioid detox is available for 2010-2016. Counts are divided by 2015 Census estimates of the population aged 18 years or older and multiplied by 10,000. The unit of analysis is a state-year. Estimates in Panel A are based on Equation 1 and estimates in Panel B are based on Equation 2. Control variables include PDMP implementation, PDMP mandates, pain clinic laws, the unemployment rate, and the percent of state residents in poverty. OTP = opioid treatment program. The baseline mean captures outcomes 2 years prior to expansions for treated states. State clustered standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
R2 M ean N Clusters Y earF.E. StateF.E. Controls
0.537
0.005 (0.008)
0.260
0.008 (0.002)***
Medicaid Acceptance Yes No OTP Non-OTP OTP Non-OTP (7) (8) (9) (10)
us
an
-0.003 (0.005)
-0.000 (0.010)
M
Detox (6)
Nalt. (5)
R2 0.764 0.188 0.169 0.545 0.589 0.569 Panel B: Differential Effect of Medicaid Expansions Exp 3.8 -0.004 -0.001 -0.013 -0.017 -0.005 (11.5) (0.004) (0.003) (0.016) (0.017) (0.007) Exp×M AT 14.4 0.001 -0.001 0.020 0.028 0.003 (10.2) (0.005) (0.005) (0.012) (0.012)** (0.005) Exp×Detox -4.5 0.002 -0.001 0.005 -0.004 0.000 (9.4) (0.006) (0.005) (0.013) (0.012) (0.004)
DATA2000 OTP (1) (2) Panel A: Average Effect of Medicaid Exp 10.8 -0.002 (5.4)** (0.004)
Meth. Bupre. (3) (4) Expansions -0.002 0.004 (0.004) (0.008)
Ac ce p
Market Entry or Exit
Scope of Services Offered
Table 2: Effect of Medicaid Expansions on OUD Treatment Providers Per 10,000 Persons
44
Page 44 of 62
Ac ce p
0.288 0.045 510 51 Yes Yes Yes
0.259 0.027 510 51 Yes Yes Yes
0.200 0.017 510 51 Yes Yes Yes
0.184 0.014 510 51 Yes Yes Yes
0.147 0.010 510 51 Yes Yes Yes
us
0.092 0.004 510 51 Yes Yes Yes
0.517 0.094 510 51 Yes Yes Yes
-0.005 (0.016) 0.018 (0.012) -0.011 (0.011)
0.509
ip t
0.452 0.072 510 51 Yes Yes Yes
-0.002 (0.011) 0.011 (0.009) 0.002 (0.009)
0.448
0.007 0.001 (0.006) (0.009)
cr
0.000 (0.002) -0.004 (0.002)** 0.003 (0.002)*
-0.006 (0.004) -0.005 (0.004) 0.004 (0.003)
an
M
0.057
0.126
-0.001 (0.002)
Nalt. (6)
0.292 0.032 510 51 Yes Yes Yes
-0.003 (0.005) 0.004 (0.004) -0.002 (0.004)
0.290
0.205 0.041 510 51 Yes Yes Yes
-0.010 (0.010) 0.006 (0.007) -0.002 (0.006)
0.201
-0.002 -0.007 (0.004) (0.005)
Notes: Facilities are drawn from the 2007-2016 N-SSATS (see Section 3.1). Counts are divided by 2015 Census estimates of the population aged 18 years or older and multiplied by 10,000. The unit of analysis is a state-year. Estimates in Panel A are based on Equation 1 and estimates in Panel B are based on Equation 2. Control variables include PDMP implementation, PDMP mandates, pain clinic laws, the unemployment rate, and the percent of state residents in poverty. OTP = opioid treatment program. The baseline mean captures outcomes 2 years prior to expansions for treated states. State clustered standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
R2 M ean N Clusters Y ear F.E. State F.E. Controls
d
No Bupre. (5)
-0.009 -0.006 (0.004)** (0.003)**
Meth. (4)
R2 0.258 0.243 0.185 0.149 Panel B: Differential Effect of Medicaid Expansions Exp 0.005 0.000 0.000 -0.006 (0.005) (0.006) (0.006) (0.004)* Exp×M AT 0.008 0.008 0.006 -0.009 (0.004)** (0.004)* (0.005) (0.004)** Exp×Detox -0.006 0.001 0.003 0.006 (0.005) (0.006) (0.006) (0.004)
te
Yes Meth. Bupre. Nalt. (1) (2) (3) Panel A: Average Effect of Medicaid Expansions Exp 0.007 0.007 0.007 (0.002)*** (0.002)*** (0.002)***
OTP Medicaid Acceptance
Non-OTP Medicaid Acceptance Yes No Bupre. Nalt. Bupre. Nalt. (7) (8) (9) (10)
Table 3: Effect of Medicaid Expansions on OUD Treatment Providers Per 10,000 Persons
45
Page 45 of 62
Ac ce p
d
0.42 2655.97 510 51 Yes Yes Yes
0.48 2602.48 510 51 Yes Yes Yes
0.18 53.48 510 51 Yes Yes Yes
cr
ip t
0.39 123.92 354 51 Yes Yes Yes
102.12 (79.44) 62.10 (84.07) -18.78 (74.64)
0.38
129.07 (38.25)***
Notes: MAT dispensing is measured as aggregate methadone and buprenorphine grams purchased by opioid treatment programs (OTP), and MAT prescribing as buprenorphine prescriptions financed by Medicaid. Grams are drawn from the 2007-2016 ARCOS Report 5 and prescriptions from the 2010-2016 Medicaid State Drug Utilization Data (see Section 3.1). Counts are divided by 2015 Census estimates of the population aged 18 years or older and multiplied by 10,000. Grams are converted into morphine gram equivalents (see Table 7 in the Appendix). The unit of analysis is a state-year. Estimates in Panel A are based on Equation 1 and estimates in Panel B are based on Equation 2. Control variables include PDMP implementation, PDMP mandates, pain clinic laws, the unemployment rate, and the percent of state residents in poverty. The baseline mean captures outcomes 2 years prior to expansions for treated states. State clustered standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
R2 M ean N Clusters Y ear F.E. State F.E. Controls
us
an
M
R2 0.38 0.44 0.15 Panel B: Differential Effect of Medicaid Expansions Exp -250.59 -88.11 -162.48 (438.77) (311.98) (245.17) Exp×M AT 1037.70 755.75 281.96 (422.26)** (283.16)** (226.73) Exp×Detox -1.20 -229.69 228.48 (508.41) (329.77) (280.06)
te
MAT Dispensing OTP Gram Distribution Aggregate Methadone Buprenorphine (1) (2) (3) Panel A: Average Effect of Medicaid Expansions Exp 455.84 281.11 174.73 (236.26)* (170.10) (107.37)
MAT Prescribing Medicaid-financed Prescriptions Buprenorphine (4)
Table 4: Effect of Medicaid Expansions on Opioid Agonist MAT Availability Per 10,000 Persons
A
APPENDIX Robustness
ip t
A.1
In Tables 5 and 6 we investigate the robustness of our main findings to a number
cr
of alternative specifications. In row (1) we include baseline estimates of the average effect of Medicaid expansions reported in preceding sections. In row (2) we drop early
us
expansion states (CT, CA, DC, MN, NJ, WA), which expanded in multiple occasions, to assess whether our selection of early expansion dates is driving main estimates. In
an
row (3), we trim the time window to -/+ 3 years since Medicaid expansions to assess their average effect during the first three years while minimizing compositional effect
M
bias from the staggered implementation of expansions (i.e. not all treated states are observed throughout the same lags and leads). Finally, in rows (4) to (6) we test the sensitivity of main findings to the inclusion of other potential confounders while
d
maintaining our original set of control variables.24
te
In rows (4) and (5) we consider two nation-level developments in the market for opioids occurring around 2014 that might have changed differentially among expan-
Ac ce p
sion states, in which case year fixed effects alone would not suffice. Specifically, in row (4) we account for increases in illicitly-made fentanyl availability (Drug Enforcement Administration, 2015) with the per capita rate of law enforcement seizures of fentanyl, and in row (5) for the re-scheduling of hydrocodone combination products from schedule III to schedule II (Drug Enforcement Administration, 2014; Jones et al., 2016) with the per capita rate of hydrocodone grams. In row (6) we account for cross-state variation in effective dates of recreational marijuana laws. Although our 24
Each row accounts for the original set of control variables in addition to the potential confounder specified in that row.
46
Page 46 of 62
original set of controls include a host of state-level policies directly affecting the market for opioids, policies affecting the market for other drugs might indirectly affect the market for opioids and bias main findings. Marijuana liberalization policies are
ip t
a relevant example as previous work suggests that marijuana can be a substitute for opioids in select contexts (Bradford et al., 2018; Livingston et al., 2017; Wen et al.,
cr
2015; Powell et al., 2015). Recreational marijuana laws are of special concern since, alike Medicaid Expansions, many states adopted them around 2014 (see Table 11).
us
If Medicaid expansions occurred in states that also moved toward recreational marijuana nearly simultaneously, our estimates might be subject to downward bias.25
Ac ce p
te
d
M
an
We find that estimates are generally robust to these alternative specifications.
25
The theoretical relationship between marijuana liberalization policies and opioid use is that marijuana might be a substitute for opioids. Therefore, marijuana liberalization policies might reduce opioid use. If marijuana liberalization policies are positively associated with Medicaid expansions, then failure to account for them should result in a downward bias of our estimates.
47
Page 47 of 62
48
Page 48 of 62
Ac ce p
5.3 (2.0)** 5.5 (2.2)** 5.4 (2.2)** 5.2 (2.3)**
(3) +/ − 3 Y ears
(4) F entanyl
(5) Hydrocodone
(6) M J Legal
-0.1 (0.6)
0.1 (0.6)
0.1 (0.6)
d
5.3 (2.2)**
5.3 (2.1)** 3.5 (2.2)
3.2 (2.0)
4.4 (2.2)*
4.3 (2.1)**
4.4 (2.1)**
4.2 (2.0)**
4.9 (2.8)*
1.1 (0.9)
1.0 (0.8)
1.1 (0.8)
1.2 (1.2)
0.0 (0.2)
ip t
9.3 (5.1)*
8.7 (4.6)*
8.2 (4.7)*
8.3 (4.4)*
8.9 (4.8)*
-0.7 (0.9)
-0.1 (1.0)
-0.1 (1.0)
-0.2 (1.0)
0.3 (0.9)
-4.5 (2.6)
-3.6 (2.6)
-3.3 (2.7)
-3.0 (2.4)
-3.6 (2.7)
Health Insurance Medicaid Other Uninsured (8) (9) (10) 8.7 -0.0 -3.7 (4.6)* (1.0) (2.6)
cr
0.8 (0.8)
us
0.0 (0.2)
0.0 (0.2)
0.1 (0.2)
0.1 (0.2)
Medical Setting Outpat. Inpat. Resid. (5) (6) (7) 4.3 0.0 1.1 (2.1)** (0.2) (0.9)
an
M
3.0 (1.9)
4.4 (2.8)
5.4 3.4 (2.1)** (2.0)*
5.1 (2.0)**
5.9 (2.8)**
te
0.2 (0.6)
0.3 (0.7)
Services Received Detox. Rehab. MAT (2) (3) (4) 0.1 5.3 3.4 (0.6) (2.1)** (2.0)*
Notes: Opioid treatment admissions of the population aged 18 and older are drawn from the 2007-2015 TEDS-A (see Section 3.1). Health insurance is part of the TEDS-A “Supplemental” dataset, and thus, not reported by all states. Categories in medical setting and health insurance are mutually exclusive and exhaustive. Except for MAT, categories in services received are mutually exclusive and exhaustive. Counts are divided by 2015 Census estimates of the population aged 18 years or older and multiplied by 10,000. The unit of analysis is a state-year. All specifications account for state and year fixed effects, along with main controls, including PDMP implementation, PDMP mandates, pain clinic laws, the unemployment rate, and the percent of state residents in poverty. State clustered standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
6.2 (2.9)**
(1) 5.4 (2.2)**
(2) Early Exp.
(1) Baseline
Aggregate
Table 5: Effect of Medicaid Expansions on Opioid Treatment Admissions, Alternative Specifications
49
Page 49 of 62
Ac ce p
-0.002 (0.004)
-0.002 (0.004)
-0.002 (0.004)
-0.000 (0.003)
d
0.005 (0.008)
0.005 (0.008)
0.004 (0.008)
0.004 (0.007)
0.004 (0.010)
cr
0.008 (0.002)***
0.008 (0.002)***
0.008 (0.002)***
0.007 (0.002)***
0.008 (0.003)***
-0.009 (0.004)**
-0.009 (0.004)**
-0.009 (0.004)**
-0.008 (0.003)**
-0.006 (0.002)**
-0.004 (0.005)
-0.005 (0.005)
-0.006 (0.005)
-0.004 (0.005)
-0.008 (0.005)
Medicaid Acceptance Yes No OTP Non-OTP OTP Non-OTP (7) (8) (9) (10) 0.008 0.005 -0.009 -0.005 (0.002)*** (0.008) (0.004)** (0.005)
us
-0.002 0.003 0.001 -0.003 (0.004) (0.008) (0.010) (0.005)
-0.003 0.004 -0.000 -0.003 (0.004) (0.008) (0.010) (0.005)
an
M
-0.003 0.000 -0.004 -0.003 (0.004) (0.008) (0.010) (0.005)
-0.001 0.003 0.001 -0.001 (0.003) (0.007) (0.008) (0.005)
0.002 0.005 0.002 -0.003 (0.002) (0.010) (0.012) (0.005)
Meth. Bupre. Nalt. Detox (3) (4) (5) (6) -0.002 0.004 -0.000 -0.003 (0.004) (0.008) (0.010) (0.005)
te
0.002 (0.002)
OTP (2) -0.002 (0.004)
Scope of Services Offered
ip t
Notes: Facilities are drawn from the 2007-2016 N-SSATS and DATA2000 physician capacity from the 2007-2016 SAMHSA database (see Section 3.1). Opioid detox is available for 2010-2016. Counts are divided by 2015 Census estimates of the population aged 18 years or older and multiplied by 10,000. The unit of analysis is a state-year. All specifications account for state and year fixed effects, along with main controls, including PDMP implementation, PDMP mandates, pain clinic laws, the unemployment rate, and the percent of state residents in poverty. OTP = opioid treatment program. State clustered standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
10.0 (5.4)*
(6) M J Legal
6.5 (5.2)
(4) F entanyl 11.0 (5.3)**
9.6 (4.9)*
(3) +/ − 3 Y ears
(5) Hydrocodone
12.3 (6.4)*
(2) Early Exp.
(1) Baseline
DATA2000 (1) 10.8 (5.4)**
Market Entry or Exit
Table 6: Effect of Medicaid Expansions on OUD Treatment Providers, Alternative Specifications
A.2
Supportive Tables
Drug Methadone (30-90) Methadone (90-300) Methadone (>300) Hydrocodone Hydromorphone Hydromorphone Meperidine Meperidine Buprenorphine Fentanyl
Route Oral Oral Oral Oral Oral IV Oral IV IV IV
cr
Mg 20 0.36 10 1 30 10 200 100 0.4 0.43
us
Route Oral Transdermal Oral IV Oral IV Oral IV Sublingual Transdermal
an
Drug Oxycodone Fentanyl Oxymorphone Oxymorphone Morphine Morphine Codeine Codeine Buprenorphine Buprenorphine
ip t
Table 7: Opioid Dose Equivalence Mg 7.5 5 3.75 30 7.5 1.5 300 100 0.3 0.1
Ac ce p
te
d
M
Notes: Table 7 was drawn from McPherson (2009) and used to convert different active ingredients in the opioid class into morphine gram equivalents. Note that the equivalence changes not only by active ingredient, but also by formulation and strength. Since ARCOS data provides total gram information by active ingredient, but does not specify formulation or strength, this study assumes methadone (90-300) oral doses and buprenorphine sublingual doses.
50
Page 50 of 62
51
Page 51 of 62
d
te
Meth. Yes Yes Yes Yes No Yes No Yes Yes No No No Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes No Yes
cr
Meth. Yes No No Yes No No Yes Yes Yes No No Yes No Yes No Yes No No No No Yes Yes Yes Yes No
ip t
Date 8/15/2014 9/1/2015 2/1/2015 1/1/2015 1/1/2016 7/1/2016 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
us
State New Hampshire Alaska Indiana Pennsylvania Montana Louisiana Alabama Florida Georgia Idaho Kansas Maine Mississippi Missouri Nebraska North Carolina Oklahoma South Carolina South Dakota Tennessee Texas Utah Virginia Wisconsin Wyoming
an
M
Detox Yes Yes Yes Yes No No No Yes Yes Yes Yes Yes No Yes Yes Yes No Yes No No No No Yes Yes No No
Bupr. Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Detox No Yes Yes Yes Yes Yes No No No Yes Yes Yes Yes Yes No Yes Yes Yes No Yes Yes No No Yes Yes
Notes: Expansion dates were obtained from The Henry J. Kaiser Family Foundation (2012, 2017) and service coverage from Medicaid and CHIP Payment and Access Commission (2016) and Substance Abuse and Mental Health Services Administration (2014a). California, Connecticut, Minnesota, New Jersey, Washington, and D.C. expanded Medicaid early and in stages. Because initial expansions among some of these states occurred via a Section 1115 Waiver, which could be more limited than the ACA Option by providing less generous benefits or capping enrollment, we assigned dates based on enrollment numbers (see Table 9).
Date 4/1/2010 7/1/2010 3/1/2011 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 4/1/2014
Ac ce p
State Connecticut Washington, D.C. Minnesota Arizona Arkansas California Colorado Delaware Hawaii Illinois Iowa Kentucky Maryland Massachusetts Nevada New Jersey New Mexico New York North Dakota Ohio Oregon Rhode Island Vermont Washington West Virginia Michigan
Bupr. Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Table 8: Assigned Medicaid Expansion Dates and Medicaid Service Coverage
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Enrollment Increase 248,321 2,198,407 123,415 38,856 173,725 173,725 85,658 107,524 422,440 6,055 424,460 50,751 50,751 3,005
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X X
X
X
X X
Date Assigned
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Notes: Increases in the number of non-disabled adults enrolled in Medicaid were calculated by comparing enrollment in the fiscal year before and after expansion. Enrollment data for fiscal years 2009 to 2012 are collected from enrollment reports published by the Centers for Medicare and Medicaid services (Centers for Medicare and Medicaid Services, 2013, 2015). Enrollment data for fiscal year 2013 are from Medicaid and CHIP Payment and Access Commission (2016). Enrollment data for fiscal year 2014 are from the The Henry J. Kaiser Family Foundation (2014). Washington, D.C. expanded Medicaid twice in 2010. Minnesota expanded Medicaid twice in 2011. For these states, we cannot calculate the increase in enrollment from the two expansions separately because adult enrollment figures are provided annually. Early expansion dates for California and Minnesota were validated using state documents (Research and Analytic Studies Division, 2017; Research Department, 2016).
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Ac ce p State California California Connecticut Connecticut Minnesota Minnesota Minnesota New Jersey New Jersey Washington Washington Washington, D.C. Washington, D.C. Washington, D.C.
Expansion Dates 7/1/2011 1/1/2014 4/1/2010 1/1/2014 3/1/2011 8/1/2011 1/1/2014 4/14/2011 1/1/2014 1/3/2011 1/1/2014 7/1/2010 12/1/2010 1/1/2014
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Table 9: Increase in Medicaid Enrollment of Non-Disabled Adults for Early Expansion States
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State Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming
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PDMP Mandates N/A N/A 2016 2016 2015 2012 2013 N/A 2014 2011 2010 N/A 2015 2016 N/A N/A 2013 N/A 2017 2013 2015 N/A 2012 2017 N/A
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PDMP Operations 2012 2011 1997 2014 2011 2005 1973 2007 2007 2006 1991 2011 1973 1979 2008 2011 2006 1982 1995 2009 2006 2011 1996 2013 2004
Pain Clinic Laws N/A N/A N/A N/A N/A N/A N/A N/A N/A 2011 N/A N/A N/A N/A N/A N/A 2012 2009 N/A N/A N/A N/A 2012 N/A N/A
Notes: Table is based on data collected by the author from PDMP administrators and other official sources (National Alliance for Model State Drug Laws, 2014; PDMP Center of Excellence, 2016; The Policy Surveillance Program: A LawAtlas Project, 2011).
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PDMP Mandates N/A 2017 N/A 2015 2016 N/A 2015 N/A N/A 2018 N/A N/A N/A N/A N/A N/A 2012 2014 2017 2018 2014 N/A N/A N/A N/A
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State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri
PDMP Operations 2006 2011 2008 2013 1939 2007 2008 2012 2011 2013 1943 1998 1957 1994 2009 2011 1999 2008 2004 2013 1992 1988 2010 2005 N/A
Pain Clinic Laws 2013 N/A N/A N/A N/A N/A N/A N/A 2010 2013 N/A N/A N/A N/A N/A N/A 2012 2005 N/A N/A N/A N/A N/A 2012 N/A
Table 10: Effective Dates of PDMP Operations, PDMP Mandates, and Pain Clinic Laws
Table 11: Recreational Marijuana Laws Effective Date 2015 2016 2012 2015 2017 2015 2017 2016 2018 2012
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State Alaska California Colorado District of Columbia Maine Massachusetts Nevada Oregon Vermont Washington
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Notes: Dates are drawn from ProCon (https://marijuana.procon.org/view.resource.php?resourceID=006868).
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A.3
Non-Parametric Graphical Evidence
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80
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40 20 0 2007
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Figure 5: Effect of Medicaid Expansions on Opioid Treatment Admissions
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te
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Notes: Opioid treatment admissions of the population aged 18 and older are drawn from the 2007-2015 TEDS-A (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity.
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2011
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25 20 15 10
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Figure 6: Effect of Medicaid Expansions on Opioid Treatment Admissions
Notes: Opioid treatment admissions of the population aged 18 and older are drawn from the 2007-2015 TEDS-A (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity.
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2011
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16 14 12 10
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Figure 7: Effect of Medicaid Expansions on Opioid Treatment Admissions
Notes: Opioid treatment admissions of the population aged 18 and older are drawn from the 20072016 TEDS-A (see Section 3.1). Health insurance is part of the TEDS-A “Supplemental” dataset, and thus, not reported by all states. State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity.
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OTPs .1
2009
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.2
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.05
.1
an
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Count (10,000 Persons)
.1 .08 .06 .04 .02
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.1 .08 .06 .02
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Detox Availability Count (10,000 Persons)
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.25 .2 .15 .1 .05
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.08 .06 .02
.04
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80 100 120 60 40 20
Count (10,000 Persons)
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Capacity of DATA 2000 Certified Physicians
2013
2015
Year Mid
Control
Early
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Figure 8: Effect of Medicaid Expansions on OUD Treatment Providers
Notes: DATA2000 capacity is drawn from SAMHSA and specialty treatment facilities are drawn from 2007-2016 N-SSATS (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity. OTP = opioid treatment program.
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Non−OTPs Accepting Medicaid
2009
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cr 2007
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an
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.15 .05
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.06 .05 .04 .03
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.05
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.1
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.08 .06 .04 .02 0
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.2
OTPs Accepting Medicaid
2007
2009
2011
Year
Early
2015
Mid
Control
Early
Mid
Ac ce p
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2013
Year
Figure 9: Effect of Medicaid Expansions on OUD Treatment Providers
Notes: Specialty treatment facilities are drawn from 2007-2016 N-SSATS (see Section 3.1). Stateyear counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity. OTP = opioid treatment program.
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.01
2007
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2007
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0
.005
Count (10,000 Persons)
.03 .025 .02 .015 .01 .005
Count (10,000 Persons)
Nalt. OTPs Not Accepting Medicaid
.015
Bupr. OTPs Not Accepting Medicaid
2011
Year Early
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.04
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an
.03 .02 .01
Count (10,000 Persons)
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.05
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Count (10,000 Persons)
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te
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d
Nalt. OTPs Accepting Medicaid Count (10,000 Persons)
2015
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Control
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Year
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2015
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Control
Early
Mid
Figure 10: Effect of Medicaid Expansions on OUD Treatment Providers
Notes: Specialty treatment facilities are drawn from the 2007-2016 N-SSATS (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity. OTP = opioid treatment program.
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.05 2009
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2013
Year
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Count (10,000 Persons)
.01 2007
Count (10,000 Persons)
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Nalt. Non−OTPs Not Accepting Medicaid
.05
Bupr. Non−OTPs Not Accepting Medicaid
2007
2009
2011
2013
2015
Year Control
Early
Mid
Ac ce p
Figure 11: Effect of Medicaid Expansions on OUD Treatment Providers
Notes: Specialty treatment facilities are drawn from the 2007-2016 N-SSATS (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity. OTP = opioid treatment program.
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Buprenorphine Gram Distribution at OTPs
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Prescriptions (10,000 Persons)
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Early
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Ac ce p
Figure 12: Effect of Medicaid Expansions on Opioid Agonist MAT Availability
Notes: MAT dispensing is measured as methadone and buprenorphine gram distribution at opioid treatment programs, and MAT prescribing as outpatient buprenorphine prescriptions financed by Medicaid. Grams are drawn from the 2007-2016 ARCOS Report 5 and prescriptions from the 2010-2016 Medicaid SDUF (see Section 3.1). Grams are converted into morphine gram equivalents (see Table 7 in the Appendix). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity.
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S0167-6296(17)31153-0 https://doi.org/doi:10.1016/j.jhealeco.2018.06.004 JHE 2127
To appear in:
Journal of Health Economics
Received date: Revised date: Accepted date:
31-12-2017 5-6-2018 8-6-2018
Please cite this article as: Ang´elica Meinhofer, Allison E. Witman, The Role of Health Insurance on Treatment for Opioid Use Disorders: Evidence from the Affordable Care Act Medicaid Expansion, (2018), https://doi.org/10.1016/j.jhealeco.2018.06.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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The Role of Health Insurance on Treatment for Opioid Use Disorders: Evidence from the Affordable Care Act Medicaid Expansion Allison E. Witmanb
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Ang´elica Meinhofera∗
RTI International, 3040 East Cornwallis Road, P.O. Box 12194, Research Triangle Park, NC 27709, United States
b
University of North Carolina Wilmington, 601 S. College Road, Wilmington, NC 28043-5920, United States
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a
Abstract
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We estimate the effect of health insurance coverage on opioid use disorder treatment utilization and availability by exploiting cross-state variation in effective dates of Medicaid expansions under the Affordable Care Act. Using a difference-in-differences design, we find that aggregate opioid admissions to specialty treatment facilities increased 18% in expansion states, most of which involved outpatient medication-assisted treatment (MAT). Opioid admissions from Medicaid beneficiaries increased 113% without crowding out admissions from individuals with other health insurance. These effects appeared to be driven by market entry of select MAT providers and by greater acceptance of Medicaid payments among existing MAT providers. Moreover, effects were largest in expansion states with comprehensive MAT coverage. Our findings suggest that Medicaid expansions resulted in substantial utilization and availability gains to clinically efficacious and cost-effective pharmacological treatments, implying potential benefits of expanding Medicaid to non-expansion states and extending MAT coverage. Keywords: opioid use disorder, treatment, Medicaid Expansions JEL Codes: I11, I12, I13, I18
∗ Corresponding author. E-mail addresses: [email protected] (A. Meinhofer), [email protected] (A. Witman). We thank Susan Haber, Stephanie Kissam, Jesse Hinde, Erin Mallonee, and Michelle Marcus for helpful comments and suggestions. All errors are our own.
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1
Introduction
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The United States is in the midst of an opioid overdose epidemic that began to develop during the early 2000s and that has now become the largest and fastest growing drug problem in the country. In 2016, opioids were involved in more than
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42,000 overdose deaths, surpassing any year on record (Centers for Disease Control and Prevention, 2017). The overdose epidemic has been characterized by a shift
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from the abuse of legally manufactured prescription opioids to the abuse of illegally manufactured “street” opioids such as heroin and illicitly-made fentanyl (Rudd et al.,
an
2016). Previous work has suggested that select supply-reduction efforts in the market for prescription opioids, along with a natural progression of addiction to higher-
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potency opioids and a greater supply of low-cost “street” opioids have contributed to the shift (Alpert et al., 2017; Cicero and Ellis, 2015; Compton et al., 2016; Ruhm,
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2018). This transition unveils the pivotal role of interventions with the potential to reduce the abuse of both prescription and “street” opioids among existing users
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with varying addiction severities. One such intervention is treatment for opioid use
Ac ce p
disorders (OUD), a demand-reduction effort that helps existing users recover from opioid addiction and improve their health and social functioning. Treatment for OUD entails detoxification and rehabilitation services. Detoxification produces stabilization by treating acute opioid withdrawal symptoms, while rehabilitation produces long-term recovery through behavioral health and other support services once stabilization is achieved. Services can be delivered in outpatient, inpatient, or residential settings and occasionally involve medication-assisted treatment (MAT).1 Opioid agonist MAT with methadone or buprenorphine is the most 1
Methadone, buprenorphine, and naltrexone are the only FDA-approved MATs for OUD. Methadone and buprenorphine are opioid agonists, while naltrexone is an opioid antagonist.
2
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effective treatment for OUD (Jones et al., 2015; Abraham et al., 2017; Schuckit, 2016; Volkow et al., 2014), more than doubling illicit opioid abstinence outcomes in randomized controlled trials (Connery, 2015; Whelan and Remski, 2012). Yet, those
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in need often fail to receive any treatment, let alone MAT. Between 2009-2013, about 80% of individuals with an OUD did not receive treatment in the prior year (Saloner
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and Karthikeyan, 2015). Availability and utilization barriers are partially responsible for the treatment gap. Availability barriers include paucity of licensed providers,
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capacity constraints, and stringent regulation, while utilization barriers include treatment costs and limited health insurance coverage (Jones et al., 2015; Volkow et al.,
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2014; American Society of Addiction Medicine, 2013; Wen et al., 2013). We study the role of health insurance coverage on OUD treatment utilization and
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availability by estimating the effect of Medicaid expansions under the Affordable Care Act (ACA). The ACA, the most significant expansion of health insurance since the passage of Medicare and Medicaid in 1965, was expected to narrow the treatment gap
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through regulatory changes and increases in insurance coverage (Substance Abuse
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and Mental Health Services Administration, 2014b; Ali et al., 2016). Regulatory
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changes defined substance use disorder (SUD) services as one of ten essential health benefits that insurers, including most Medicaid plans, must cover and offer at parity to other medical benefits. Coverage of specific services, however, could vary by state (Abraham et al., 2017). As for increases in insurance coverage, gains among individuals with SUD were primarily expected to occur via Medicaid expansions to adults with incomes up to 138% of the federal poverty level (Buck, 2011), a group with higher rates of SUD and unmet treatment needs than previously-enrolled beneficiaries (Busch et al., 2013). Medicaid expansions were originally intended to apply nationally, however, a Supreme Court ruling in 2012 made these optional to states. As of 2017, Medicaid expansions have become effective in thirty-one states 3
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and the District of Columbia. The effect of Medicaid expansions on OUD treatment utilization and availability is uncertain a priori. Health insurance coverage should reduce out-of-pocket treat-
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ment costs, and in turn, increase aggregate treatment demand. This, however, need not always be the case as some newly-enrolled Medicaid beneficiaries may have orig-
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inally been receiving treatment financed through alternative payment sources (e.g. private insurance, block grants), or may be unwilling or unable to obtain treatment.
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We study utilization with aggregate opioid admissions to specialty treatment facilities. We also categorize admissions by service, medical setting, and health insurance.
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Increases in treatment demand from insurance expansions of the magnitude of the ACA, especially via Medicaid expansions, might be large enough to induce supply
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responses affecting treatment availability (Finkelstein, 2007). We study availability by considering OUD treatment provider market entry or exit, changes in the scope of services offered, and acceptance of Medicaid payments. Our identification strategy
d
relies on administrative data and a difference-in-differences design that exploits cross-
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state variation in effective dates of Medicaid expansions. We also exploit cross-state
Ac ce p
variation in Medicaid coverage of detoxification and opioid agonist MATs.2 We find that Medicaid expansions resulted in significant utilization gains. Aggregate opioid admissions to specialty treatment facilities increased 18% in expansion states. Nearly all of this effect was driven by admissions from Medicaid beneficiaries, which increased 113% without crowding out admissions from individuals with other health insurance. However, per-capita magnitudes suggest that only about two thirds of the increase in admissions from Medicaid beneficiaries resulted in utilization gains. The remainder was offset by declines in admissions from uninsured individuals 2
Variation in our measure of opioid agonist MAT coverage reflects that some states cover both methadone and buprenorphine, while others only cover buprenorphine.
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financing treatment through alternative payment sources. We also find that utilization gains were highly heterogenous by service, medical setting, and state coverage. Admissions to outpatient settings for rehabilitation services involving MAT increased
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substantially, while those for more intensive and expensive services such as detoxification in residential or inpatient settings were unchanged. Moreover, utilization
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gains were largest in expansion states with comprehensive MAT coverage.
As for availability, we find greater Medicaid acceptance and market entry among
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select MAT providers, but no changes in the scope of services offered. Opioid treatment programs (OTPs) accepting Medicaid payments increased 17%, suggesting that
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some OTPs incurred the costs of meeting state Medicaid requirements and interfacing with additional payers to become Medicaid providers. This supply response improved
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MAT availability for newly-enrolled and possibly previously-enrolled Medicaid beneficiaries as OTPs are specifically licensed to dispense methadone and buprenorphine. There was no evidence of OTP market entry, plausibly due to high fixed costs of
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OTP licensure. There was, however, market entry of Drug Addiction Treatment Act
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2000 (DATA2000) providers, who are licensed to prescribe buprenorphine subject to
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patient limits. Consistent with utilization gains, availability gains were largest in expansion states with comprehensive MAT coverage. This study provides the most comprehensive evidence to date of the early effects of Medicaid expansions on treatment for opioid use disorders, and contributes to the growing literature on the effects of the Affordable Care Act on treatment for substance use disorders (Maclean and Saloner, 2017; Clemans-Cope et al., 2017; Wen et al., 2017; Antwi et al., 2015). Our findings coincide with previous studies showing that Medicaid expansions increased treatment admissions for Medicaid beneficiaries with SUD but that were subject to offsetting declines in uninsured individuals (Maclean and Saloner, 2017). However, our findings imply larger effects 5
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for Medicaid beneficiaries with OUD along with smaller offsetting declines in uninsured individuals, resulting in significant utilization gains. These utilization gains point at Medicaid’s key role in addressing the opioid epidemic through reductions
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in the treatment gap. Our findings also coincide with previous studies showing that market-wide changes in health insurance coverage can induce supply responses
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(Finkelstein, 2007). To our knowledge, we provide the first estimates of the effect of Medicaid expansions on supply responses among substance use treatment providers.
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We are also the first to document heterogeneity by service, medical setting, and state coverage. Overall, we find that Medicaid expansions resulted in substantial utiliza-
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tion and availability gains to clinically efficacious and cost-effective pharmacological treatments, especially in expansion states with comprehensive MAT coverage.
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The paper proceeds as follows. Section 2 provides an overview of the OUD treatment system, the Affordable Care Act, previous literature, and outlines the conceptual framework. Section 3 describes data sources and the empirical strategy.
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Section 4 presents results and Section 5 discusses policy implications and concludes.
Background
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2 2.1
Treatment for Opioid Use Disorders
Treatment for OUD entails detoxification and rehabilitation services. Detoxification services treat the physical and psychological withdrawal symptoms that arise with the abrupt discontinuation of opioids after long-term use and occasionally involve stabilization and tapering with opioid agonist MAT methadone or buprenorphine. Rehabilitation services produce long-term recovery through behavioral therapy and other support services once stabilization is achieved, and occasionally involve an opi6
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oid abstinence approach with opioid antagonist MAT naltrexone or an opioid maintenance approach with opioid agonist MAT methadone or buprenorphine (Schuckit, 2016; Jones et al., 2015).3 OUD treatment services are generally delivered in outpa-
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tient, inpatient, or residential specialty settings. However, a minority of services are also delivered in non-specialty settings such as primary care or physician offices.
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Treatment for OUD is most effective when it combines behavioral therapy with MAT, especially opioid agonist MAT (Connery, 2015; Abraham et al., 2017; Schuckit,
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2016; Jones et al., 2015).4 Methadone, buprenorphine, and naltrexone are costeffective and offer relapse prevention, however, these differ significantly in their phar-
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macokinetic and pharmacodynamic properties, which in turn, affect toxicity and efficacy (Connery, 2015). MATs also differ in their delivery and licensing requirements.
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While naltrexone can be prescribed by anyone licensed to prescribe medications, methadone and buprenorphine are subject to additional licensing requirements. The two licenses for opioid agonist MAT providers are opioid treatment program licenses
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(OTP) which authorize methadone and buprenorphine dispensing from OTP facil-
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ities, and Drug Addiction Treatment Act 2000 licenses (DATA2000 waivers) which
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authorize buprenorphine prescribing or dispensing subject to patient limits (i.e. 30, 100, and recently 275 patients) in various medical settings, including specialty and non-specialty settings.5 Put differently, methadone cannot be prescribed and may only be dispensed at specialty facilities with an OTP-license.6 Buprenorphine may 3
An opioid maintenance approach with opioid agonist MATs methadone or buprenorphine is generally used on patients who are unable to stay abstinent from opioids but still want to improve their condition. An opioid abstinence approach with opioid antagonist MAT naltrexone is generally used on patients who are highly motivated to stay abstinent from opioids (Schuckit, 2016). 4 Oral naltrexone demonstrates poor adherence and increased mortality rates (Connery, 2015). 5 https://www.samhsa.gov/medication-assisted-treatment/buprenorphine-waiver-management https://www.samhsa.gov/medication-assisted-treatment/opioid-treatment-programs 6 Practitioners working at OTP facilities may choose to obtain a DATA2000-license in order to prescribe rather than dispense buprenorphine.
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be dispensed at specialty facilities with an OTP license, or may be dispensed and prescribed at specialty or non-specialty facilities with a DATA2000-licensed practi-
The Affordable Care Act
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2.2
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tioners but subject to patient limits.
The ACA transformed the behavioral health landscape by increasing health insur-
us
ance coverage and by regulating plan coverage of mental health and SUD treatment benefits. Increases in health insurance coverage were primarily obtained through
an
exchange plan premium tax credits for individuals with incomes between 100-400% percent of the federal poverty level and the expansion of Medicaid eligibility to individuals with incomes up to 138% of the federal poverty level.7 Among individuals
M
with SUD, increases in insurance coverage were primarily expected to occur via Medicaid expansions (Buck, 2011). Regulation of plan coverage guaranteed newly-
d
enrolled Medicaid beneficiaries SUD treatment coverage and that such coverage be
te
offered at parity to other medical benefits. The ACA defined mental health and SUD treatment as one of ten essential health benefits, effectively requiring insurers
Ac ce p
to offer some form of coverage for these services. State Medicaid programs were free to define which specific conditions and treatments to cover as long as they used generally recognized independent standards for defining mental health and SUD benefits (Centers for Medicare and Medicaid Services, 2017).8 Building on the Mental Health 7
Cost-sharing subsidies were also available for certain health insurance exchange plans purchased by individuals between 100-250% of the federal poverty level. Other ACA changes affecting health insurance coverage included requiring most Americans to purchase health insurance (individual mandate), allowing individuals to remain on their parents’ private plans until age 26, and ending discrimination on the basis of pre-existing conditions. 8 Essential health benefits apply to Medicaid managed care organizations, coverage provided by Medicaid alternative benefit plans, and Children’s Health Insurance Programs. Essential health benefit requirements do not apply to non-alternative benefit fee-for-service Medicaid plans offered to previously-eligible groups. Our identification strategy uses individuals gaining coverage under the
8
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Parity and Addiction Equity Act of 2008, the ACA also extended the requirement that coverage for SUD treatment be no more restrictive than coverage for other medical services to all insurers, including most Medicaid plans (Centers for Medicare and
ip t
Medicaid Services, 2017; Zur et al., 2017).9
Medicaid expansions were originally intended to apply nationally, however, a
cr
Supreme Court ruling in 2012 made expansions optional to states. As of 2017, thirty-one states and the District of Columbia have expanded Medicaid under the
us
ACA (six in 2010-11, twenty-one in 2014, and five in 2015-16), resulting in substantial increases in the Medicaid population. Increases in the Medicaid population could be
an
attributable to both newly-eligible individuals as well as previously-eligible individuals enrolling in Medicaid due to increased awareness and the individual mandate, i.e.,
M
the “woodwork effect” (Frean et al., 2017). Since the woodwork effect could occur in both expansion and non-expansion states, its impact will likely be differenced out in difference-in-differences models that compare expansion states to non-expansion
d
states. Therefore, our estimates will be primarily identified from newly-eligible Med-
te
icaid beneficiaries and interpreted as the impact of gaining Medicaid coverage that
Ac ce p
was subject to new ACA requirements. Medicaid expansion; therefore, our estimates incorporate the effects of the essential health benefits. 9 ACA Medicaid expansion coverage is required to comply with parity requirements. Parity requirements apply to Medicaid managed care organizations, coverage provided by Medicaid alternative benefit plans, and Children’s Health Insurance Programs. Parity requirements do not apply to non-alternative benefit fee-for-service Medicaid plans offered to previously-eligible groups, although the Centers for Medicare and Medicaid Services encourage states to meet parity requirements in all plans. Our identification strategy uses individuals gaining coverage under the Medicaid expansion; therefore, our estimates incorporate the effects of the parity requirement.
9
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2.3
Conceptual Framework
Market-wide changes in health insurance may induce both demand and supply re-
ip t
sponses, making the effect of Medicaid expansions on OUD treatment utilization and availability theoretically ambiguous. All else equal, health insurance coverage should
cr
reduce out-of-pocket treatment costs, and in turn, increase aggregate treatment demand. This, however, need not always be the case as some newly-enrolled Medicaid
us
beneficiaries may have been originally receiving OUD treatment financed through alternative payment sources such as other government payments (e.g. block grants)
an
or private insurance (e.g. crowd out). Treatment demand could even decrease among newly-enrolled beneficiaries originally receiving OUD treatment if Medicaid coverage
M
is less generous than previous payment sources. As for newly-enrolled beneficiaries not originally in OUD treatment, demand need not increase among those unable or unwilling to obtain treatment. For instance, individuals may be unable to obtain
d
OUD treatment due to limited transportation, other commitments (e.g. childcare,
te
employment), if Medicaid does not cover a preferred service, if providers do not accept Medicaid payments, or if providers do not have the capacity to meet increases
Ac ce p
in treatment demand. Moreover, individuals may be unwilling to obtain OUD treatment if they do not want to stop using opioids, fear stigma, rather seek mental health treatment, or do not perceive a need for OUD treatment (Ali et al., 2015a; Maclean and Saloner, 2017). However, if health insurance coverage increases individuals’ perceived need for treatment after utilizing other medical services, treatment demand may increase.
Supply responses to market-wide changes in health insurance can affect OUD treatment availability and utilization, and may primarily occur through two interrelated pathways: fixed-costs and spillovers (Finkelstein, 2007). The fixed-costs path10
Page 10 of 62
way conjectures that if increases in aggregate treatment demand are large enough, providers may incur the fixed costs of adaptation, capacity enhancement, or market entry. For instance, providers may adapt by adopting newer evidence-based prac-
ip t
tices or by accepting Medicaid payments; may enhance capacity by hiring additional staff; or may enter the market by becoming licensed to provide OUD treatment
cr
services. These supply responses are likely to increase treatment availability and utilization. In contrast, supply responses may reduce treatment availability and
us
utilization through provider market exit, which may occur due to increased competition or if some facilities are unable adopt technology needed to bill Medicaid.
an
The spillovers pathway conjectures that changes in insurance coverage for one set of patients my cause spillovers to other patients. This could manifest as providers
M
adjusting their patient care or practice style in response to Medicaid expansions, which would affect all patients served. This could also manifest through changes in treatment availability such as those generated via the fixed-costs pathway, which
d
can affect not only newly-enrolled Medicaid beneficiaries but also previously-enrolled
Previous Literature
Ac ce p
2.4
te
Medicaid beneficiaries along with non-Medicaid populations.
Evidence of the effect of Medicaid expansions on treatment for OUD is scarce. Previous studies focus on prescribing and spending on MAT with buprenorphine or naltrexone in outpatient settings using the Medicaid State Drug Utilization Files. Using a difference-in-differences design, Wen et al. (2017) find that prescribing and spending on buprenorphine increased by 70% and 50%, respectively. Using an observational design, Clemans-Cope et al. (2017) find that national Medicaid spending on buprenorphine, naltrexone, and naloxone increased by 136% between 2011 and 2016,
11
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with expansion states driving most of this increase. These studies, however, only capture a small fraction of the market for OUD treatment services. A related study considers substance use disorder (SUD) treatment. Using a difference-in-differences
ip t
approach, Maclean and Saloner (2017) find no effect on aggregate SUD treatment admissions, but do find that admissions financed by Medicaid and from Medicaid ben-
cr
eficiaries increased by 57% and 58%, respectively. These findings, however, might not be representative for the OUD population as the majority of the SUD population is
us
comprised of individuals with alcohol use disorders. Considering that the population with OUD faces unique availability and utilization barriers to treatment, and that
an
it represents only 10% of the population with SUD but over 60% of drug overdose deaths (Substance Abuse and Mental Health Services Administration, 2017; Rudd
M
et al., 2016), focusing on the market for OUD treatment services is critical.10 We seek to build upon previous studies by making four main contributions. First, we provide comprehensive evidence of the effect of Medicaid expansions on OUD
d
treatment utilization and availability by considering and distinguishing between a
te
wide set of services and medical settings. Such distinction may uncover potential
Ac ce p
treatment effect heterogeneity, which is important for understanding efficacy and cost implications of the expansions. Second, we consider the question of supply responses, which has yet to be examined. Specifically, we examine changes in treatment availability that could result from provider market entry or exit, changes in the scope of services offered, and acceptance of Medicaid payments. Third, we account for cross10
Two somewhat related papers examine the impact of other insurance expansions on hospitalizations for SUD. Antwi et al. (2015) find no change in inpatient discharges for SUD from the ACA’s rule allowing young adults to remain on their parents’ health insurance until age 26 in a differencein-difference framework; however, they do document changes in the composition of payers for SUD admissions. SUD admissions financed by private payers increased while those financed by Medicare and Medicaid declined. Meara et al. (2014) document a small decline in inpatient discharges for SUD from the Massachusetts health reform in a difference-in-differences design.
12
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state variation in Medicaid coverage of essential OUD treatment services. We focus on detoxification to capture intensive services, and on opioid agonist MAT to capture efficacious services. Since Medicaid covers buprenorphine in all states, variation in
ip t
MAT coverage reflects that some states cover both methadone and buprenorphine, while others only cover buprenorphine. Understanding the differential effect for states
cr
with methadone coverage is key as methadone and buprenorphine vary in terms of licensing, delivery, costs, and efficacy. Relative to buprenorphine, methadone is less
us
expensive and more efficacious, yet also more regulated (Connery, 2015; Whelan and Remski, 2012; Mattick et al., 2008). Moreover, as buprenorphine has a dose “ceiling”
an
and methadone does not, methadone is generally better for treating users with more severe OUD (Whelan and Remski, 2012). Finally, most previous studies have focused
M
on partial effects by considering Medicaid beneficiaries alone, which does not capture offsetting effects. We contribute by also focusing on aggregate effects, which are not only relevant for assessing Medicaid expansions’ efficiency, but also their potential
te
d
to narrow the treatment gap and curb the opioid overdose epidemic.11
Empirical Strategy
Ac ce p
3 3.1
Data Sources
We rely on administrative data from 2007 to 2016, with the exception of treatment admissions data which is available up to 2015. Data are converted into state-year counts and divided by 2015 Census estimates of the population aged 18 years or older. Outcome variables measure OUD treatment utilization, OUD treatment availability, and opioid agonist MAT availability. Treatment variables measure effective dates 11
Maclean and Saloner (2017) consider aggregate effects for the SUD population.
13
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of Medicaid expansions and Medicaid coverage of detoxification and opioid agonist MAT. Finally, control variables measure economic conditions and prescription opioid
3.1.1
ip t
supply-reduction efforts. Outcome Variables
cr
OUD treatment utilization is measured as opioid admissions to specialty treatment
us
facilities. Admissions are drawn from the Substance Abuse and Mental Health Services Administration’s (SAMHSA) Treatment Episode Data Set Admissions (TEDSA), which attempts to capture all admissions to publicly funded treatment facilities.
an
TEDS-A is comprised of a “Minimum” dataset reported by all states and a “Supplemental” dataset reported by some states. Data are available up to 2015 and the
M
unit of observation is an admission. The sample is restricted to admissions with any mention of opioids among individuals aged 18 years or older. Admissions are
d
categorized into services (detoxification, rehabilitation, and MAT), medical setting (inpatient, outpatient, and residential), and health insurance (Medicaid, other in-
te
surance, and uninsured). One limitation of TEDS-A is that health insurance is part
Ac ce p
of the “Supplemental” dataset, and thus, not reported by all states.12 Even among some reporting states, there are missing values for a subset of admissions in a subset of years. To get around this issue, we follow a similar approach as Maclean and Saloner (2017) and assign a missing value to any state-year count with underlying non-reporting of 25% or more. States with 3 or more missing state-year counts (either due to full non-reporting or to partial reporting with underlying non-reporting 12
We originally also categorized admissions by “payment source” which may not always coincide with “health insurance.” However, since “payment source” is also part of the “Supplemental” dataset but is especially underreported, we opted to fully drop these unreliable estimates.
14
Page 14 of 62
of 25% or more) are removed.13 Substance, service, and medical setting variables are part of the “Minimum” dataset, and thus, trivially affected by this selection criteria. Another limitation of TEDS-A is that admissions do not represent individuals, im-
treatment and in individuals re-entering treatment within a year.
ip t
plying that variation in admissions reflects both changes in individuals first entering
cr
OUD treatment availability is measured as the number of specialty treatment facilities offering OUD treatment services. Facilities are drawn from the National
us
Survey of Substance Abuse Treatment Services (N-SSATS), an annual census of substance use treatment facilities administered by SAMHSA. The N-SSATS collects
an
information on the services offered and utilization of those services. Data are available up to 2016 and the unit of observation is a facility. The sample is restricted
M
to facilities offering detoxification for OUD, methadone, naltrexone, or buprenorphine, or that are federally-certified as opioid treatment programs. One limitation is that N-SSATS fails to properly capture providers at non-specialty settings. To
d
get around this issue, we also measure availability with the number of DATA2000
te
licenses, which authorize buprenorphine prescribing or dispensing in specialty and
Ac ce p
non-specialty settings. Licenses are drawn from SAMHSA’s DATA2000-Certified Physicians database, which tracks the number of physicians, nurse practitioners, and physician assistants receiving a 30-, 100-, and 275-patient limit license in a given year. We use data from 2007 to 2016 and the unit of observation is a state, year, and patient limit. DATA2000 licenses are converted into state-year patient capacity counts.14 A limitation of this data is that it captures the state the provider was 13
Estimates are robust to different cutoffs (e.g. 20%, 15%), or to the alternative approach where states with three or more missing state-year counts are kept in the analysis. We confirm the external validity of insurance estimates by comparing aggregate effects from “Supplemental” dataset states to aggregate effects from “Minimum” dataset states. 14 Capacity= 30×N30,t + 100×N100,t + 275×N275,t , where Np,t is total licenses with patient limit p in year t.
15
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licensed in when receiving the DATA2000 waiver. In some cases, this may not be where the provider is currently practicing. Additionally, the data does not properly capture the date in which providers who were previously licensed to dispense
ip t
buprenorphine had their waiver relinquished, revoked, or expired.15
Opioid agonist MAT availability is measured as methadone and buprenorphine
cr
grams. Grams are drawn from the Drug Enforcement Administration’s (DEA) Automation of Reports and Consolidated Orders System (ARCOS), which tracks total
us
drug grams distributed to dispensers. Data are available up to 2016 and the unit of observation is a state, year, drug, and provider. Grams are converted into mor-
an
phine milligram equivalents as described in the Appendix (see Table 7). The sample is restricted to methadone and buprenorphine grams distributed to OTPs, which
M
captures MAT offered under an OTP license. To capture MAT offered under a DATA2000 license, MAT availability is also measured as outpatient buprenorphine prescriptions financed by Medicaid. Prescriptions are drawn from the Medicaid State
d
Drug Utilization Files (SDUF) and identified following a similar approach as Wen
te
et al. (2017). Data are available up to 2016 and the unit of observation is a state,
Ac ce p
quarter, and national drug code. Prescriptions are converted into state-year counts. 3.1.2
Treatment Variables
Medicaid expansion dates are obtained from the Henry J. Kaiser Family Foundation (see Tables 8 and 9). Although Medicaid expansions were scheduled for January 1, 15
Note that SAMHSA presently knows, based on whether a provider has lost or given up their DEA license, whether a previously DATA2000-licensed provider continues to be DATA2000-licensed as of today. However, currently SAMHSA’s database does not properly capture historical exit dates. To assess the importance of this limitation, we analyzed state-year counts based on all providers ever receiving a DATA2000 license (used to estimate main findings) and on providers currently DATA2000-licensed, the former which is subject to a slight overcount of licenses in later years and the latter to a slight undercount of licenses in earlier years. Estimates are robust regardless.
16
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2014, states could expand eligibility as early as 2010. California, Connecticut, Minnesota, New Jersey, Washington, and the District of Columbia expanded early and in multiple occasions. For these states, we assigned the expansion date that increased
ip t
enrollment of non-disabled adults the most. In robustness checks, we exclude early expansion states and estimates are robust (see Section A.1 in the Appendix).
cr
Data on Medicaid coverage of OUD treatment services are drawn from the Medicaid and CHIP Payment and Access Commission (MACPAC). MACPAC identified
us
mental health and SUD services offered through state plan authority by reviewing Medicaid state plans, provider manuals, billing manuals, fee schedules, and other
an
publicly available official documents. The report did not include waiver services or additional services provided by managed care organizations, unless the managed
M
care organization served all beneficiaries in the state. This primary source (Medicaid and CHIP Payment and Access Commission, 2016) was consistent with other sources (American Society of Addiction Medicine, 2013; Substance Abuse and Mental Health
d
Services Administration, 2014a). We focus on Medicaid coverage of detoxification
te
treatment in residential and inpatient settings, and of opioid agonist MAT. Since
Ac ce p
Medicaid covers buprenorphine in all states, variation in opioid agonist MAT coverage reflects that some states cover both methadone and buprenorphine, while others only cover buprenorphine. 3.1.3
Control Variables
To retrieve causal estimates of the effect of health insurance coverage on outcomes, other outcome determinants that are correlated with health insurance coverage must be controlled for. To that end, we control for a number of variables directly affecting the market for opioids at the state-level. Specifically, we control for pain clinic laws
17
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and prescription drug monitoring program (PDMP) implementation and mandates to account for supply-reduction efforts in the market for prescription opioids (Popovici et al., 2018; Ali et al., 2017; Meinhofer, 2016, 2017; Bao et al., 2016; Dowell et al.,
ip t
2016). Additionally, we control for the unemployment and poverty rates to account for economic conditions (Hollingsworth et al., 2017). In robustness checks, we also
cr
consider other variables indirectly affecting the market for opioids at the state-level (e.g. recreational marijuana laws) or directly affecting the market for opioids at
us
the nation-level (e.g. increases in illicitly-made fentanyl availability, re-scheduling of hydrocodone combination products). Estimates are generally robust to these controls
an
(see Section A.1 in the Appendix).
The effective dates of PDMP implementation, PDMP use mandates, and pain
M
clinic laws were collected and verified through a number of sources. These sources include PDMP administrators, PDMP websites, the National Alliance for Model State Laws, the PDMP Training and Technical Assistance Center at Brandeis University,
d
the Prescription Drug Abuse Policy System, and the Policy Surveillance Program at
te
Temple University. Effective dates can be found in the Appendix (see Table 10).
Ac ce p
Unemployment rates for each state are obtained from the Bureau of Labor Statistics’ Local Area Unemployment Statistics. The percentage of individuals below 100 percent of the federal poverty level is calculated using Current Population Survey data downloaded from the Integrated Public Use Microdata Series (IPUMS). We obtain state-year poverty rates using the methodology recommended by IPUMS.16 16
An overview of how to calculate official poverty rates using the IPUMS CPS data is here: https://cps.ipums.org/cps/poverty notes.shtml
18
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3.2
Econometric Approach
The identification strategy is to estimate a difference-in-differences specification that
ip t
exploits cross-state variation in effective dates of Medicaid expansions and in Medicaid coverage of different OUD treatment services. Equation 1 estimates the average
cr
effect of the expansions (γ) and is indexed by state s and year t. Yst is the outcome variable and is equal to counts per 10,000 persons aged 18 years or older. Expst is
us
the treatment variable and is equal to one if state s expanded Medicaid in year t and zero otherwise. Ss are state fixed effects and control for state-specific time invariant
an
determinants of Yst . Tt are year fixed effects and control for year-specific state invariant determinants of Yst . Despite these controls, threats to identification might
M
remain if other determinants of Yst changed differentially over time among treatment states. In an effort to minimize potential threats, a vector of control variables Xst that account for economic conditions, pain clinic laws, and PDMP implementation
d
and mandates is incorporated into all specifications (see Section 3.1.3). Section A.1
te
in the Appendix tests the robustness of γ to changes in time window (+/- 3 years),
Ac ce p
the exclusion of early expansion states, and the inclusion of additional controls.
Yst = α + γExpst + θXst + Ss + Tt + st
(1)
Yst = α + γExpst + β1 Expst ×M ATs + β2 Expst ×Detoxs + θXst + Ss + Tt + st (2)
Yst = α +
q X
λj Est (j = t − k) + θXst + Ss + Tt + st
(3)
j=−m
19
Page 19 of 62
Equation 1 fails to account for cross-state variation in Medicaid coverage of different OUD treatment services. This represents an important limitation as the effect of health insurance on availability and utilization of OUD treatment services will be
ip t
directly affected by coverage of such services. In an effort to circumvent this limitation, Equation 2 estimates the differential effect of the expansions by accounting
cr
for cross-state variation in Medicaid coverage of opioid agonist MAT and of detoxification treatment (see Section 3.1.2). M ATs is equal to one if Medicaid in state s
covers detoxification treatment and zero otherwise.
us
covers methadone and zero otherwise. Detoxs is equal to one if Medicaid in state s
an
Difference-in-differences estimates are only valid under the assumptions of parallel trends and no policy endogeneity. To examine whether these assumptions appear to
M
hold and determine whether there is a dynamic treatment effect, graphical evidence from estimates in Equation 3 is presented. This evidence is based on an event study approach that controls for m leads and q lags of the treatment, captured in the
d
dummy variables Est (j = t − k), where k is the time of expansion in state s. The
te
reference group is j = 0, the year right before the expansion became effective. A
Ac ce p
test validating the identifying assumptions is if λj is statistically not different from zero for all j < 0 (this need not be the case for j > 0). More generally, detecting a trend or an irregular jump prior to the time of expansions should raise concerns regarding the validity of γ. Graphical evidence based on the raw data is presented in the Appendix (see Section A.3). In all specifications, standard errors are clustered at the state level (Bertrand et al., 2004).
20
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4
Results OUD Treatment Utilization
ip t
4.1
In Table 1 we present estimates of the effect of Medicaid expansions on OUD treat-
cr
ment utilization. We measure OUD treatment utilization as the aggregate number of opioid admissions to specialty treatment facilities per 10,000 persons. We also cate-
us
gorize aggregate opioid admissions into services received, medical setting, and health insurance.17 In Panel A we report the average effect of the expansions as specified in
an
Equation 1, and in Panel B we report the differential effect of the expansions by state coverage of detoxification and opioid agonist MAT as specified in Equation 2. To
M
assess the credibility of these estimates, we present event study plots in Figure 1.18 We find that aggregate opioid admissions increased 18% (γ = 5.4) in expansion states. Nearly all of this effect was driven by admissions from Medicaid beneficiaries,
d
which increased 113% (γ = 8.7) without crowding out admissions from individuals
te
with other health insurance. Nonetheless, per-capita magnitudes suggest that only about two thirds of the increase in admissions from Medicaid beneficiaries resulted
Ac ce p
in utilization gains (5.4/8.7). The remainder was offset by declines in admissions from uninsured individuals (γ = −3.7). These offsetting effects are consistent with some reductions in treatment financing through other payment sources, which might occur if expansions were accompanied by declines in state government spending or in out-of-pocket spending on OUD treatment. Alternatively, these offsetting effects are also consistent with some treatment financing through other payment sources among newly-enrolled beneficiaries, which might occur if Medicaid does not cover a 17
All categories in “medical setting” and “health insurance” are mutually exclusive and exhaustive. Except for MAT, all categories in “services received” are mutually exclusive and exhaustive. 18 See Figures 6 and 7 in the Appendix for non-parametric graphical evidence.
21
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preferred service or if a provider does not accept Medicaid payments. We also find significant service and setting heterogeneity. Utilization gains were driven by increases in admissions to outpatient settings for rehabilitation services
ip t
(γ = 5.3), over half of which involved MAT (γ = 3.4). There was limited evidence of an average effect for admissions to inpatient or residential settings, or for detox-
cr
ification services. This service heterogeneity appeared to be partially explained by differential Medicaid coverage of OUD services across states. Utilization gains to
us
OUD services were larger and occurred almost exclusively in expansion states with Medicaid coverage of such services. Specifically, admissions for rehabilitation services
an
involving MAT increased 105% (γ + β1 = 7.4) in expansion states with comprehensive MAT coverage. Even admissions for detoxification services were relatively larger
OUD Treatment Availability
d
4.2
M
(β1 = 2.2) in expansion states with detoxification coverage.
te
Market-wide changes in health insurance coverage may induce supply responses affecting OUD treatment availability. In Table 2 we present estimates of the effect of
Ac ce p
Medicaid expansions on three types of supply responses: provider market entry or exit defined as providers obtaining OTP or DATA2000 licenses; changes in the scope of services offered defined as providers offering MAT or detoxification services; and Medicaid acceptance defined as providers accepting or not accepting Medicaid payments. In column (1) the dependent variable is the capacity of DATA2000-licensed physicians per 10,000 persons and in columns (2)-(10) is the number of specialty treatment facilities offering OUD services per 10,000 persons. In Panel A we report the average effect of the expansions as specified in Equation 1 and in Panel B we report the differential effect of the expansions by state coverage of detoxification and
22
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opioid agonist MAT as specified in Equation 2. To assess the credibility of these estimates, we present event study plots in Figure 2.19 We find some evidence of provider market entry. The capacity of DATA2000-
ip t
licensed physicians increased 17% (γ = 10.8), improving buprenorphine availability. There was no such effect for OTPs, possibly due to higher fixed costs of obtain-
cr
ing these licenses. As for the scope of services, we find no changes in the number of specialty treatment facilities offering detoxification, naltrexone, methadone, or
us
buprenorphine. Nonetheless, increases in DATA2000 licenses suggest that changes in the scope of services might have still occurred either at non-specialty treatment
an
settings, or at specialty treatment settings already offering buprenorphine through intensive margin increases in DATA2000-licensed physicians or in patient limits. Fi-
M
nally, we find evidence of increased Medicaid acceptance. OTPs accepting Medicaid payments increased 17% (γ = 0.008) while those not accepting Medicaid decreased by a comparable magnitude (γ = 0.009), suggesting a switch in Medicaid acceptance
d
among existing OTPs. This switch possibly broadened MAT availability among Med-
te
icaid beneficiaries. In Table 3 and Figure 3, we examine this possibility and find that
Ac ce p
OTPs accepting Medicaid and offering naltrexone, methadone, or buprenorphine increased by the same magnitude for all three MATs (γ = 0.007). Availability gains primarily occurred in states with comprehensive MAT coverage. Increases in the capacity of DATA2000-licensed physicians were driven by states with methadone MAT coverage (γ + β1 = 18.2). Likewise, increases in OTP Medicaid acceptance were driven by these states (γ + β1 = 0.013). These changes in OUD treatment availability are consistent with changes in OUD treatment utilization in Section 4.1, and suggest that Medicaid expansions induced supply responses enhancing MAT availability and thus, MAT utilization for newly-enrolled Medicaid 19
See Figures 8 and 11 in the Appendix for non-parametric graphical evidence.
23
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beneficiaries. Further, the nature of supply responses suggests that expansions might have resulted in spillovers to other patients. The entry of DATA2000 physicians can spill over to patients needing OUD treatment, regardless of coverage. Similarly, OTP
Opioid Agonist MAT Availability
cr
4.3
ip t
Medicaid acceptance can spill over to previously-enrolled Medicaid beneficiaries.
us
In Table 4 we present estimates of the effect of Medicaid expansions on opioid agonist MAT availability. In columns (1)-(3) the outcome variable is the aggregate
an
distribution of buprenorphine and methadone grams at OTPs per 10,000 persons and in column (4) is the number of outpatient buprenorphine prescriptions financed by Medicaid per 10,000 persons.20 In general, prescriptions primarily capture MAT
M
prescribing under a DATA2000 license at specialty and non-specialty treatment settings, and grams capture MAT dispensing under an OTP license at the OTP. In
d
Panel A we report the average effect of the expansions as specified in Equation 1
te
and in Panel B we report the differential effect of the expansions by state coverage of detoxification and opioid agonist MAT as specified in Equation 2. To assess the
Ac ce p
credibility of these estimates, we present event study plots in Figure 4.21 We find substantial increases in opioid agonist MAT availability. Aggregate gram distribution at OTPs increased 17% (γ = 456) and Medicaid prescriptions increased 104% (γ = 129), suggesting that both OTP and DATA2000 providers contributed to MAT utilization gains documented in Section 4.1.22 As before, increases 20
Due to different datasets, prescriptions apply to the Medicaid population only and grams apply to the whole population. Despite not being quite comparable, these variables are comprehensive of opioid agonist MAT availability at different settings and providers. 21 See Figure 12 in the Appendix for non-parametric graphical evidence. 22 Our prescription estimates -104%- are somewhat bigger than those in Wen et al. (2017) -70%due to our larger sample window and an increasing treatment effect on prescribing over time.
24
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in methadone and buprenorphine grams (γ + β1 = 786) and buprenorphine prescriptions (γ + β1 = 164) were largest in expansion states with comprehensive MAT coverage. As for states with limited MAT coverage, there is statistically insignificant
ip t
evidence that buprenorphine prescriptions increased 83% (γ = 102). These findings, along with those in Sections 4.1 and 4.2, suggest that OTPs increased opioid ago-
cr
nist MAT availability through intensive margin changes. OTPs, the sole providers of methadone and historically known as methadone providers, also engaged in new
us
practice styles by increasing buprenorphine dispensing and possibly prescribing. Although an OTP license authorizes methadone and buprenorphine dispensing from
an
an OTP, it is plausible that some providers working at OTPs incurred the fixed costs of obtaining a DATA2000 license, which authorizes buprenorphine prescribing.
M
However, increases in buprenorphine prescribing in states with limited MAT coverage suggest that not all increases in MAT availability occurred at OTPs, but also at
Conclusion
Ac ce p
5
te
changes.
d
other specialty and even non-specialty treatment settings through intensive margin
We provide the most comprehensive evidence to date of the early effects of Medicaid expansions on the market for OUD treatment and document four key findings. First, Medicaid expansions resulted in substantial OUD treatment utilization gains. Utilization gains were attributable to dramatic increases in opioid treatment admissions from Medicaid beneficiaries but were subject to some offsetting declines in admissions from uninsured individuals. These offsetting effects are consistent with reductions in treatment financing through other payment sources, which might occur if expansions were accompanied by declines in state general revenue spending or in out-of-pocket 25
Page 25 of 62
spending on OUD treatment. Alternatively, these offsetting effects are consistent with treatment financing through other payment sources among newly-enrolled beneficiaries, which might occur if Medicaid does not cover a preferred service or if a
ip t
provider does not accept Medicaid. Notably, there was no crowd out of other health insurance. These findings suggest that Medicaid expansions have potential to curb
cr
the opioid epidemic through reductions in the treatment gap and shed light on the importance of preserving government spending on treatment to fund care for the
us
remaining uninsured or for key services not covered by Medicaid.
Another key finding is that OUD treatment utilization gains were heterogenous
an
by service and medical setting. Admissions to outpatient settings for rehabilitation services involving MAT increased substantially, while those for detoxification services
M
in residential or inpatient settings were unchanged. Some of this heterogeneity may be partially explained by capacity constrains at residential or inpatient settings, along with specific features of the Medicaid program. Medicaid generally requires
d
that services be physician-directed and prohibits federal payments for services at
te
residential or inpatient facilities with more than 16 beds (Buck, 2011). These findings
Ac ce p
hold important policy implications in terms of efficacy and costs as MAT is considered the most effective clinical approach to treatment for OUD and services in outpatient settings are generally less expensive than those in residential or inpatient settings. A third key finding is that changes in OUD treatment utilization were accompanied by supply responses affecting OUD treatment availability. There was some evidence of market entry by DATA2000-licensed providers. Despite no evidence of market entry by OTP-licensed providers, plausibly due to higher fixed costs of obtaining these licenses, OTPs were largely responsible for MAT availability gains through intensive margin increases in MAT dispensing and greater acceptance of Medicaid payments. One explanation for greater OTP Medicaid acceptance is more com26
Page 26 of 62
prehensive Medicaid coverage of OUD services resulting from parity requirements, coupled with expected declines in state general revenue spending on OUD treatment (Buck, 2011). These findings suggest that Medicaid expansions induced supply re-
and possibly previously-enrolled Medicaid beneficiaries.
ip t
sponses enhancing MAT availability and thus, MAT utilization for newly-enrolled
cr
Finally, we find that MAT utilization and availability gains were largest in expansion states with methadone coverage. Although it is not surprising that OTP supplier
us
responses would occur in these states, since by law OTPs are the sole providers of methadone and primarily offer methadone, it is surprising that DATA2000 supplier
an
responses would. One possible explanation for these findings is competition between OTPs and other providers. Another is that other providers, especially those
M
at non-specialty treatment settings, may more easily refer difficult patients with severe OUD to OTPs instead of having to treat them themselves, increasing provider willingness to enter the market for OUD treatment. Alternatively, OTPs could be
d
seeking revenue expansion through additional services. While an OTP license autho-
te
rizes the use of methadone and buprenorphine, it requires that these be dispensed
Ac ce p
at the OTP, which can be burdensome for patients regularly visiting the facility to obtain medications. As a DATA2000 license authorizes buprenorphine prescribing, it is plausible that some physicians working at OTPs incurred the fixed costs of obtaining DATA2000 licenses to attract patients needing greater flexibility. Moreover, many Medicaid state plans require proof of counseling prior to authorization of buprenorphine coverage. OTPs must offer counseling services, and thus, are in a better position to attract buprenorphine patients seeking integrated care. As for states without methadone coverage, there was limited evidence of availability or utilization gains at specialty settings. Nonetheless, there were some increases in buprenorphine prescribing, possibly at non-specialty settings. These findings imply potential bene27
Page 27 of 62
fits of extending Medicaid coverage of methadone, as this coverage appears to induce supply responses that enhance MAT utilization and availability. This study has several limitations. First, since our measure of treatment utiliza-
ip t
tion captures admissions rather than individuals, we cannot establish the degree to which utilization gains reflect extensive or intensive margin increases.23 Second, our
cr
estimates of treatment utilization by health insurance status are based on data from twenty-nine states, and thus, may not be generalizable. Third, although we consider
us
OUD treatment in specialty settings, along with measures of buprenorphine treatment, which can be offered in non-specialty settings, we do not fully capture OUD
an
treatment in non-specialty settings. Moreover, we do not capture all relevant forms of behavioral health services, including mental health treatment, which is more often
M
sought than SUD treatment among individuals with SUD (Ali et al., 2015b). Medicaid has the potential to play an important role in addressing the opioid crisis, especially as many of the states that declined to expand Medicaid have high
d
rates of opioid abuse (Abraham et al., 2017). Moreover, many expansion states with
te
high rates of opioid abuse do not cover methadone despite its proved effectiveness
Ac ce p
(U.S. Department of Health and Human Services, Office of the Surgeon General, 2016). Overall, Medicaid expansions resulted in substantial utilization and availability gains to clinically efficacious and cost-effective pharmacological treatments. This, along with the finding that gains were largest in expansion states with comprehensive MAT coverage suggests that both Medicaid expansions and methadone coverage might be important for reducing the treatment gap.
Funding
23 That is, whether increases in treatment utilization reflect more individuals getting treatment for the first or same individuals getting treatment multiple times.
28
Page 28 of 62
This research did not receive any specific grant from funding agencies in the public,
ip t
commercial, or not-for-profit sectors.
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cr
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an
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Services Received
−3
−2
−1
1
Year −3
−2
−1
1
2
3 Rehabilitation
MAT
2
3
Detoxification
cr
Year
ip t
−5
−5
0
0
5
5
10
10
15
15
Aggregate Treatment Admissions
Health Insurance
−5
−10
an
0
0
10
5
20
10
30
us
40
15
Medical Setting
−3
−2
−1
1
2
3
−3
Year Residential
Inpatient
Medicaid
−1
1
2
Year Other Insurance
Uninsured
M
Outpatient
−2
Figure 1: Effect of Medicaid Expansions on Opioid Treatment Admissions
Ac ce p
te
d
Notes: Estimates and 95% confidence intervals are based on the event study approach in Equation 3. The reference year is j = 0, the period right before the Medicaid expansion. Opioid treatment admissions of the population aged 18 and older are drawn from the 2007-2015 TEDS-A (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000.
38
Page 38 of 62
ip t
cr
.02
−1
1
DATA2000
2
−.02
−.02 −.04
3
−3
−2
an
−2
us
0
0 −3
−.04
−30
−20
−10
0
10
.02
20
30
.04
Scope of Services .04
Market Entry or Exit
OTPs
Methadone
1
2
Naltrexone
3 Detox
.02 0
−1
1 Yes
2
3
No
−.04
−.02 −2
te
−3
d
−.04
−.02
0
.02
M
.04
Non−OTP Medicaid Acceptance
.04
OTP Medicaid Acceptance
−1
Buprenorphine
−3
−2
−1
1 Yes
2
3
No
Ac ce p
Figure 2: Effect of Medicaid Expansions on OUD Treatment Providers Notes: Estimates and 95% confidence intervals are based on the event study approach in Equation 3. The reference year is j = 0, the period right before the Medicaid expansion. Facilities are drawn from the 2007-2016 N-SSATS and DATA2000 physician capacity from the 2007-2016 SAMHSA database (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Coefficients for DATA2000 waivers are axis 1 and treatment facilities are axis 2.
39
Page 39 of 62
−3
−2
−1
Methadone
ip t
1 Buprenorphine
2
an
−.02
−.01
−.01
0
0
us
.01
.01
.02
cr
OTPs Not Accepting Medicaid
.02
OTPs Accepting Medicaid
3
−3
Naltrexone
−2
−1
Methadone
2
3
Naltrexone
Non−OTPs Not Accepting Medicaid
−3
−2
−1
1
2
3
−.01 −.02
−3
Naltrexone
−2
−1 Buprenorphine
1
2
3
Naltrexone
Ac ce p
Buprenorphine
te
−.01
0
d
0
.01
.01
.02
M
.03
.02
Non−OTPs Accepting Medicaid
1 Buprenorphine
Figure 3: Effect of Medicaid Expansions on OUD Treatment Providers
Notes: Estimates and 95% confidence intervals are based on the event study approach in Equation 3. The reference year is j = 0, the period right before the Medicaid expansion. Facilities are drawn from the 2007-2016 N-SSATS (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000.
40
Page 40 of 62
−3
−2
−1
1
Year Methadone
2
3
Buprenorphine
an
Aggregate
us
−500
0
cr
500
ip t
1000
1500
MAT Gram Distribution at OTPs
−2
Ac ce p
−3
te
−100
0
d
100
200
M
300
400
MAT Prescriptions Financed by Medicaid
−1
1
2
3
Year Buprenorphine
Figure 4: Effect of Medicaid Expansions on Opioid Agonist MAT Availability Notes: Estimates and 95% confidence intervals are based on the event study approach in Equation 3. The reference year is j = 0, the period right before the Medicaid expansion. MAT dispensing is measured as aggregate methadone and buprenorphine gram distribution at opioid treatment programs, and MAT prescribing as outpatient buprenorphine prescriptions financed by Medicaid. Grams are drawn from the 2007-2016 ARCOS Report 5 and prescriptions from the 2010-2016 Medicaid SDUF (see Section 3.1). Grams are converted into morphine gram equivalents (see Table 7 in the Appendix). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000.
41
Page 41 of 62
42
Page 42 of 62
Ac ce p
0.2 21.42 430 49 Yes Yes Yes
0.2 7.05 377 43 Yes Yes Yes
0.1 1.06 430 49 Yes Yes Yes
0.3 12.76 430 49 Yes Yes Yes
us
an
M
-2.7 (1.9) 1.7 (1.7) 4.3 (1.7)**
0.3
1.1 (0.9)
cr
0.2 7.72 243 28 Yes Yes Yes
7.4 (5.7) 14.8 (8.8) -11.7 (10.5)
0.1
8.7 (4.6)*
0.2 5.62 243 28 Yes Yes Yes
0.7 (1.9) -1.1 (1.6) -0.1 (1.5)
0.2
-0.0 (1.0)
0.2 13.92 243 28 Yes Yes Yes
-2.3 (3.6) -4.9 (3.3) 2.4 (3.3)
0.2
-3.7 (2.6)
Health Insurance Medicaid Other Uninsured (8) (9) (10)
ip t
Notes: Opioid treatment admissions of the population aged 18 and older are drawn from the 2007-2015 TEDS-A (see Section 3.1). Health insurance is part of the TEDS-A “Supplemental” dataset, and thus, not reported by all states. Categories in medical setting and health insurance are mutually exclusive and exhaustive. Except for MAT, categories in services received are mutually exclusive and exhaustive. Counts are divided by 2015 Census estimates of the population aged 18 years or older and multiplied by 10,000. The unit of analysis is a state-year. Estimates in Panel A are based on Equation 1 and estimates in Panel B are based on Equation 2. Control variables include PDMP implementation, PDMP mandates, pain clinic laws, the unemployment rate, and the percent of state residents in poverty. The baseline mean captures outcomes 2 years prior to expansions for treated states. State clustered standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
0.1 8.96 430 49 Yes Yes Yes
0.2 16.55 430 49 Yes Yes Yes
0.3 30.38 430 49 Yes Yes Yes
R2 M ean N Clusters Y ear F.E. State F.E. Controls
d
0.4 (0.4) -0.2 (0.3) -0.3 (0.4)
0.5 (3.4) 6.8 (3.9)* -1.7 (5.2)
te
0.1
0.0 (0.2)
0.2
4.3 (2.1)**
R2 0.2 0.1 0.2 0.1 Panel B: Differential Effect of Medicaid Expansions Exp -1.9 -1.6 -0.3 0.2 (4.5) (1.3) (3.8) (3.5) Exp×M AT 8.4 0.5 7.9 7.2 (4.4)* (1.3) (3.9)** (3.6)* Exp×Detox 2.2 2.2 -0.0 -3.3 (5.1) (1.2)* (5.0) (5.2)
Services Received Detox. Rehab. MAT (1) (2) (3) (4) Panel A: Average Effect of Medicaid Expansions Exp 5.4 0.1 5.3 3.4 (2.2)** (0.6) (2.1)** (2.0)*
Aggregate
Medical Setting Outpat. Inpat. Resid. (5) (6) (7)
Table 1: Effect of Medicaid Expansions on Opioid Treatment Admissions Per 10,000 Persons
43
Page 43 of 62
d
te
0.771 65.4 510 51 Yes Yes Yes
0.190 0.062 510 51 Yes Yes Yes
0.169 0.060 510 51 Yes Yes Yes
0.552 0.144 510 51 Yes Yes Yes
0.599 0.158 510 51 Yes Yes Yes
0.570 0.054 357 51 Yes Yes Yes
0.285 0.047 510 51 Yes Yes Yes
cr
0.188 0.014 510 51 Yes Yes Yes
-0.007 (0.004)* -0.009 (0.004)** 0.006 (0.004)
ip t
0.543 0.118 510 51 Yes Yes Yes
-0.005 (0.016) 0.017 (0.012) -0.001 (0.011)
0.003 (0.005) 0.010 (0.004)** -0.003 (0.006)
0.153
-0.009 (0.004)**
0.217 0.048 510 51 Yes Yes Yes
-0.009 (0.009) 0.007 (0.006) -0.001 (0.006)
0.212
-0.005 (0.005)
Notes: Facilities are drawn from the 2007-2016 N-SSATS and DATA2000 physician capacity from the 2007-2016 SAMHSA database (see Section 3.1). Opioid detox is available for 2010-2016. Counts are divided by 2015 Census estimates of the population aged 18 years or older and multiplied by 10,000. The unit of analysis is a state-year. Estimates in Panel A are based on Equation 1 and estimates in Panel B are based on Equation 2. Control variables include PDMP implementation, PDMP mandates, pain clinic laws, the unemployment rate, and the percent of state residents in poverty. OTP = opioid treatment program. The baseline mean captures outcomes 2 years prior to expansions for treated states. State clustered standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
R2 M ean N Clusters Y earF.E. StateF.E. Controls
0.537
0.005 (0.008)
0.260
0.008 (0.002)***
Medicaid Acceptance Yes No OTP Non-OTP OTP Non-OTP (7) (8) (9) (10)
us
an
-0.003 (0.005)
-0.000 (0.010)
M
Detox (6)
Nalt. (5)
R2 0.764 0.188 0.169 0.545 0.589 0.569 Panel B: Differential Effect of Medicaid Expansions Exp 3.8 -0.004 -0.001 -0.013 -0.017 -0.005 (11.5) (0.004) (0.003) (0.016) (0.017) (0.007) Exp×M AT 14.4 0.001 -0.001 0.020 0.028 0.003 (10.2) (0.005) (0.005) (0.012) (0.012)** (0.005) Exp×Detox -4.5 0.002 -0.001 0.005 -0.004 0.000 (9.4) (0.006) (0.005) (0.013) (0.012) (0.004)
DATA2000 OTP (1) (2) Panel A: Average Effect of Medicaid Exp 10.8 -0.002 (5.4)** (0.004)
Meth. Bupre. (3) (4) Expansions -0.002 0.004 (0.004) (0.008)
Ac ce p
Market Entry or Exit
Scope of Services Offered
Table 2: Effect of Medicaid Expansions on OUD Treatment Providers Per 10,000 Persons
44
Page 44 of 62
Ac ce p
0.288 0.045 510 51 Yes Yes Yes
0.259 0.027 510 51 Yes Yes Yes
0.200 0.017 510 51 Yes Yes Yes
0.184 0.014 510 51 Yes Yes Yes
0.147 0.010 510 51 Yes Yes Yes
us
0.092 0.004 510 51 Yes Yes Yes
0.517 0.094 510 51 Yes Yes Yes
-0.005 (0.016) 0.018 (0.012) -0.011 (0.011)
0.509
ip t
0.452 0.072 510 51 Yes Yes Yes
-0.002 (0.011) 0.011 (0.009) 0.002 (0.009)
0.448
0.007 0.001 (0.006) (0.009)
cr
0.000 (0.002) -0.004 (0.002)** 0.003 (0.002)*
-0.006 (0.004) -0.005 (0.004) 0.004 (0.003)
an
M
0.057
0.126
-0.001 (0.002)
Nalt. (6)
0.292 0.032 510 51 Yes Yes Yes
-0.003 (0.005) 0.004 (0.004) -0.002 (0.004)
0.290
0.205 0.041 510 51 Yes Yes Yes
-0.010 (0.010) 0.006 (0.007) -0.002 (0.006)
0.201
-0.002 -0.007 (0.004) (0.005)
Notes: Facilities are drawn from the 2007-2016 N-SSATS (see Section 3.1). Counts are divided by 2015 Census estimates of the population aged 18 years or older and multiplied by 10,000. The unit of analysis is a state-year. Estimates in Panel A are based on Equation 1 and estimates in Panel B are based on Equation 2. Control variables include PDMP implementation, PDMP mandates, pain clinic laws, the unemployment rate, and the percent of state residents in poverty. OTP = opioid treatment program. The baseline mean captures outcomes 2 years prior to expansions for treated states. State clustered standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
R2 M ean N Clusters Y ear F.E. State F.E. Controls
d
No Bupre. (5)
-0.009 -0.006 (0.004)** (0.003)**
Meth. (4)
R2 0.258 0.243 0.185 0.149 Panel B: Differential Effect of Medicaid Expansions Exp 0.005 0.000 0.000 -0.006 (0.005) (0.006) (0.006) (0.004)* Exp×M AT 0.008 0.008 0.006 -0.009 (0.004)** (0.004)* (0.005) (0.004)** Exp×Detox -0.006 0.001 0.003 0.006 (0.005) (0.006) (0.006) (0.004)
te
Yes Meth. Bupre. Nalt. (1) (2) (3) Panel A: Average Effect of Medicaid Expansions Exp 0.007 0.007 0.007 (0.002)*** (0.002)*** (0.002)***
OTP Medicaid Acceptance
Non-OTP Medicaid Acceptance Yes No Bupre. Nalt. Bupre. Nalt. (7) (8) (9) (10)
Table 3: Effect of Medicaid Expansions on OUD Treatment Providers Per 10,000 Persons
45
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Ac ce p
d
0.42 2655.97 510 51 Yes Yes Yes
0.48 2602.48 510 51 Yes Yes Yes
0.18 53.48 510 51 Yes Yes Yes
cr
ip t
0.39 123.92 354 51 Yes Yes Yes
102.12 (79.44) 62.10 (84.07) -18.78 (74.64)
0.38
129.07 (38.25)***
Notes: MAT dispensing is measured as aggregate methadone and buprenorphine grams purchased by opioid treatment programs (OTP), and MAT prescribing as buprenorphine prescriptions financed by Medicaid. Grams are drawn from the 2007-2016 ARCOS Report 5 and prescriptions from the 2010-2016 Medicaid State Drug Utilization Data (see Section 3.1). Counts are divided by 2015 Census estimates of the population aged 18 years or older and multiplied by 10,000. Grams are converted into morphine gram equivalents (see Table 7 in the Appendix). The unit of analysis is a state-year. Estimates in Panel A are based on Equation 1 and estimates in Panel B are based on Equation 2. Control variables include PDMP implementation, PDMP mandates, pain clinic laws, the unemployment rate, and the percent of state residents in poverty. The baseline mean captures outcomes 2 years prior to expansions for treated states. State clustered standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
R2 M ean N Clusters Y ear F.E. State F.E. Controls
us
an
M
R2 0.38 0.44 0.15 Panel B: Differential Effect of Medicaid Expansions Exp -250.59 -88.11 -162.48 (438.77) (311.98) (245.17) Exp×M AT 1037.70 755.75 281.96 (422.26)** (283.16)** (226.73) Exp×Detox -1.20 -229.69 228.48 (508.41) (329.77) (280.06)
te
MAT Dispensing OTP Gram Distribution Aggregate Methadone Buprenorphine (1) (2) (3) Panel A: Average Effect of Medicaid Expansions Exp 455.84 281.11 174.73 (236.26)* (170.10) (107.37)
MAT Prescribing Medicaid-financed Prescriptions Buprenorphine (4)
Table 4: Effect of Medicaid Expansions on Opioid Agonist MAT Availability Per 10,000 Persons
A
APPENDIX Robustness
ip t
A.1
In Tables 5 and 6 we investigate the robustness of our main findings to a number
cr
of alternative specifications. In row (1) we include baseline estimates of the average effect of Medicaid expansions reported in preceding sections. In row (2) we drop early
us
expansion states (CT, CA, DC, MN, NJ, WA), which expanded in multiple occasions, to assess whether our selection of early expansion dates is driving main estimates. In
an
row (3), we trim the time window to -/+ 3 years since Medicaid expansions to assess their average effect during the first three years while minimizing compositional effect
M
bias from the staggered implementation of expansions (i.e. not all treated states are observed throughout the same lags and leads). Finally, in rows (4) to (6) we test the sensitivity of main findings to the inclusion of other potential confounders while
d
maintaining our original set of control variables.24
te
In rows (4) and (5) we consider two nation-level developments in the market for opioids occurring around 2014 that might have changed differentially among expan-
Ac ce p
sion states, in which case year fixed effects alone would not suffice. Specifically, in row (4) we account for increases in illicitly-made fentanyl availability (Drug Enforcement Administration, 2015) with the per capita rate of law enforcement seizures of fentanyl, and in row (5) for the re-scheduling of hydrocodone combination products from schedule III to schedule II (Drug Enforcement Administration, 2014; Jones et al., 2016) with the per capita rate of hydrocodone grams. In row (6) we account for cross-state variation in effective dates of recreational marijuana laws. Although our 24
Each row accounts for the original set of control variables in addition to the potential confounder specified in that row.
46
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original set of controls include a host of state-level policies directly affecting the market for opioids, policies affecting the market for other drugs might indirectly affect the market for opioids and bias main findings. Marijuana liberalization policies are
ip t
a relevant example as previous work suggests that marijuana can be a substitute for opioids in select contexts (Bradford et al., 2018; Livingston et al., 2017; Wen et al.,
cr
2015; Powell et al., 2015). Recreational marijuana laws are of special concern since, alike Medicaid Expansions, many states adopted them around 2014 (see Table 11).
us
If Medicaid expansions occurred in states that also moved toward recreational marijuana nearly simultaneously, our estimates might be subject to downward bias.25
Ac ce p
te
d
M
an
We find that estimates are generally robust to these alternative specifications.
25
The theoretical relationship between marijuana liberalization policies and opioid use is that marijuana might be a substitute for opioids. Therefore, marijuana liberalization policies might reduce opioid use. If marijuana liberalization policies are positively associated with Medicaid expansions, then failure to account for them should result in a downward bias of our estimates.
47
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48
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Ac ce p
5.3 (2.0)** 5.5 (2.2)** 5.4 (2.2)** 5.2 (2.3)**
(3) +/ − 3 Y ears
(4) F entanyl
(5) Hydrocodone
(6) M J Legal
-0.1 (0.6)
0.1 (0.6)
0.1 (0.6)
d
5.3 (2.2)**
5.3 (2.1)** 3.5 (2.2)
3.2 (2.0)
4.4 (2.2)*
4.3 (2.1)**
4.4 (2.1)**
4.2 (2.0)**
4.9 (2.8)*
1.1 (0.9)
1.0 (0.8)
1.1 (0.8)
1.2 (1.2)
0.0 (0.2)
ip t
9.3 (5.1)*
8.7 (4.6)*
8.2 (4.7)*
8.3 (4.4)*
8.9 (4.8)*
-0.7 (0.9)
-0.1 (1.0)
-0.1 (1.0)
-0.2 (1.0)
0.3 (0.9)
-4.5 (2.6)
-3.6 (2.6)
-3.3 (2.7)
-3.0 (2.4)
-3.6 (2.7)
Health Insurance Medicaid Other Uninsured (8) (9) (10) 8.7 -0.0 -3.7 (4.6)* (1.0) (2.6)
cr
0.8 (0.8)
us
0.0 (0.2)
0.0 (0.2)
0.1 (0.2)
0.1 (0.2)
Medical Setting Outpat. Inpat. Resid. (5) (6) (7) 4.3 0.0 1.1 (2.1)** (0.2) (0.9)
an
M
3.0 (1.9)
4.4 (2.8)
5.4 3.4 (2.1)** (2.0)*
5.1 (2.0)**
5.9 (2.8)**
te
0.2 (0.6)
0.3 (0.7)
Services Received Detox. Rehab. MAT (2) (3) (4) 0.1 5.3 3.4 (0.6) (2.1)** (2.0)*
Notes: Opioid treatment admissions of the population aged 18 and older are drawn from the 2007-2015 TEDS-A (see Section 3.1). Health insurance is part of the TEDS-A “Supplemental” dataset, and thus, not reported by all states. Categories in medical setting and health insurance are mutually exclusive and exhaustive. Except for MAT, categories in services received are mutually exclusive and exhaustive. Counts are divided by 2015 Census estimates of the population aged 18 years or older and multiplied by 10,000. The unit of analysis is a state-year. All specifications account for state and year fixed effects, along with main controls, including PDMP implementation, PDMP mandates, pain clinic laws, the unemployment rate, and the percent of state residents in poverty. State clustered standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
6.2 (2.9)**
(1) 5.4 (2.2)**
(2) Early Exp.
(1) Baseline
Aggregate
Table 5: Effect of Medicaid Expansions on Opioid Treatment Admissions, Alternative Specifications
49
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Ac ce p
-0.002 (0.004)
-0.002 (0.004)
-0.002 (0.004)
-0.000 (0.003)
d
0.005 (0.008)
0.005 (0.008)
0.004 (0.008)
0.004 (0.007)
0.004 (0.010)
cr
0.008 (0.002)***
0.008 (0.002)***
0.008 (0.002)***
0.007 (0.002)***
0.008 (0.003)***
-0.009 (0.004)**
-0.009 (0.004)**
-0.009 (0.004)**
-0.008 (0.003)**
-0.006 (0.002)**
-0.004 (0.005)
-0.005 (0.005)
-0.006 (0.005)
-0.004 (0.005)
-0.008 (0.005)
Medicaid Acceptance Yes No OTP Non-OTP OTP Non-OTP (7) (8) (9) (10) 0.008 0.005 -0.009 -0.005 (0.002)*** (0.008) (0.004)** (0.005)
us
-0.002 0.003 0.001 -0.003 (0.004) (0.008) (0.010) (0.005)
-0.003 0.004 -0.000 -0.003 (0.004) (0.008) (0.010) (0.005)
an
M
-0.003 0.000 -0.004 -0.003 (0.004) (0.008) (0.010) (0.005)
-0.001 0.003 0.001 -0.001 (0.003) (0.007) (0.008) (0.005)
0.002 0.005 0.002 -0.003 (0.002) (0.010) (0.012) (0.005)
Meth. Bupre. Nalt. Detox (3) (4) (5) (6) -0.002 0.004 -0.000 -0.003 (0.004) (0.008) (0.010) (0.005)
te
0.002 (0.002)
OTP (2) -0.002 (0.004)
Scope of Services Offered
ip t
Notes: Facilities are drawn from the 2007-2016 N-SSATS and DATA2000 physician capacity from the 2007-2016 SAMHSA database (see Section 3.1). Opioid detox is available for 2010-2016. Counts are divided by 2015 Census estimates of the population aged 18 years or older and multiplied by 10,000. The unit of analysis is a state-year. All specifications account for state and year fixed effects, along with main controls, including PDMP implementation, PDMP mandates, pain clinic laws, the unemployment rate, and the percent of state residents in poverty. OTP = opioid treatment program. State clustered standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
10.0 (5.4)*
(6) M J Legal
6.5 (5.2)
(4) F entanyl 11.0 (5.3)**
9.6 (4.9)*
(3) +/ − 3 Y ears
(5) Hydrocodone
12.3 (6.4)*
(2) Early Exp.
(1) Baseline
DATA2000 (1) 10.8 (5.4)**
Market Entry or Exit
Table 6: Effect of Medicaid Expansions on OUD Treatment Providers, Alternative Specifications
A.2
Supportive Tables
Drug Methadone (30-90) Methadone (90-300) Methadone (>300) Hydrocodone Hydromorphone Hydromorphone Meperidine Meperidine Buprenorphine Fentanyl
Route Oral Oral Oral Oral Oral IV Oral IV IV IV
cr
Mg 20 0.36 10 1 30 10 200 100 0.4 0.43
us
Route Oral Transdermal Oral IV Oral IV Oral IV Sublingual Transdermal
an
Drug Oxycodone Fentanyl Oxymorphone Oxymorphone Morphine Morphine Codeine Codeine Buprenorphine Buprenorphine
ip t
Table 7: Opioid Dose Equivalence Mg 7.5 5 3.75 30 7.5 1.5 300 100 0.3 0.1
Ac ce p
te
d
M
Notes: Table 7 was drawn from McPherson (2009) and used to convert different active ingredients in the opioid class into morphine gram equivalents. Note that the equivalence changes not only by active ingredient, but also by formulation and strength. Since ARCOS data provides total gram information by active ingredient, but does not specify formulation or strength, this study assumes methadone (90-300) oral doses and buprenorphine sublingual doses.
50
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51
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d
te
Meth. Yes Yes Yes Yes No Yes No Yes Yes No No No Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes No Yes
cr
Meth. Yes No No Yes No No Yes Yes Yes No No Yes No Yes No Yes No No No No Yes Yes Yes Yes No
ip t
Date 8/15/2014 9/1/2015 2/1/2015 1/1/2015 1/1/2016 7/1/2016 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
us
State New Hampshire Alaska Indiana Pennsylvania Montana Louisiana Alabama Florida Georgia Idaho Kansas Maine Mississippi Missouri Nebraska North Carolina Oklahoma South Carolina South Dakota Tennessee Texas Utah Virginia Wisconsin Wyoming
an
M
Detox Yes Yes Yes Yes No No No Yes Yes Yes Yes Yes No Yes Yes Yes No Yes No No No No Yes Yes No No
Bupr. Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Detox No Yes Yes Yes Yes Yes No No No Yes Yes Yes Yes Yes No Yes Yes Yes No Yes Yes No No Yes Yes
Notes: Expansion dates were obtained from The Henry J. Kaiser Family Foundation (2012, 2017) and service coverage from Medicaid and CHIP Payment and Access Commission (2016) and Substance Abuse and Mental Health Services Administration (2014a). California, Connecticut, Minnesota, New Jersey, Washington, and D.C. expanded Medicaid early and in stages. Because initial expansions among some of these states occurred via a Section 1115 Waiver, which could be more limited than the ACA Option by providing less generous benefits or capping enrollment, we assigned dates based on enrollment numbers (see Table 9).
Date 4/1/2010 7/1/2010 3/1/2011 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 1/1/2014 4/1/2014
Ac ce p
State Connecticut Washington, D.C. Minnesota Arizona Arkansas California Colorado Delaware Hawaii Illinois Iowa Kentucky Maryland Massachusetts Nevada New Jersey New Mexico New York North Dakota Ohio Oregon Rhode Island Vermont Washington West Virginia Michigan
Bupr. Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Table 8: Assigned Medicaid Expansion Dates and Medicaid Service Coverage
52
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an
M
Enrollment Increase 248,321 2,198,407 123,415 38,856 173,725 173,725 85,658 107,524 422,440 6,055 424,460 50,751 50,751 3,005
cr
X X
X
X
X X
Date Assigned
us
Notes: Increases in the number of non-disabled adults enrolled in Medicaid were calculated by comparing enrollment in the fiscal year before and after expansion. Enrollment data for fiscal years 2009 to 2012 are collected from enrollment reports published by the Centers for Medicare and Medicaid services (Centers for Medicare and Medicaid Services, 2013, 2015). Enrollment data for fiscal year 2013 are from Medicaid and CHIP Payment and Access Commission (2016). Enrollment data for fiscal year 2014 are from the The Henry J. Kaiser Family Foundation (2014). Washington, D.C. expanded Medicaid twice in 2010. Minnesota expanded Medicaid twice in 2011. For these states, we cannot calculate the increase in enrollment from the two expansions separately because adult enrollment figures are provided annually. Early expansion dates for California and Minnesota were validated using state documents (Research and Analytic Studies Division, 2017; Research Department, 2016).
d
te
Ac ce p State California California Connecticut Connecticut Minnesota Minnesota Minnesota New Jersey New Jersey Washington Washington Washington, D.C. Washington, D.C. Washington, D.C.
Expansion Dates 7/1/2011 1/1/2014 4/1/2010 1/1/2014 3/1/2011 8/1/2011 1/1/2014 4/14/2011 1/1/2014 1/3/2011 1/1/2014 7/1/2010 12/1/2010 1/1/2014
ip t
Table 9: Increase in Medicaid Enrollment of Non-Disabled Adults for Early Expansion States
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us
an
M
State Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming
cr
PDMP Mandates N/A N/A 2016 2016 2015 2012 2013 N/A 2014 2011 2010 N/A 2015 2016 N/A N/A 2013 N/A 2017 2013 2015 N/A 2012 2017 N/A
ip t
PDMP Operations 2012 2011 1997 2014 2011 2005 1973 2007 2007 2006 1991 2011 1973 1979 2008 2011 2006 1982 1995 2009 2006 2011 1996 2013 2004
Pain Clinic Laws N/A N/A N/A N/A N/A N/A N/A N/A N/A 2011 N/A N/A N/A N/A N/A N/A 2012 2009 N/A N/A N/A N/A 2012 N/A N/A
Notes: Table is based on data collected by the author from PDMP administrators and other official sources (National Alliance for Model State Drug Laws, 2014; PDMP Center of Excellence, 2016; The Policy Surveillance Program: A LawAtlas Project, 2011).
d
te
PDMP Mandates N/A 2017 N/A 2015 2016 N/A 2015 N/A N/A 2018 N/A N/A N/A N/A N/A N/A 2012 2014 2017 2018 2014 N/A N/A N/A N/A
Ac ce p
State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri
PDMP Operations 2006 2011 2008 2013 1939 2007 2008 2012 2011 2013 1943 1998 1957 1994 2009 2011 1999 2008 2004 2013 1992 1988 2010 2005 N/A
Pain Clinic Laws 2013 N/A N/A N/A N/A N/A N/A N/A 2010 2013 N/A N/A N/A N/A N/A N/A 2012 2005 N/A N/A N/A N/A N/A 2012 N/A
Table 10: Effective Dates of PDMP Operations, PDMP Mandates, and Pain Clinic Laws
Table 11: Recreational Marijuana Laws Effective Date 2015 2016 2012 2015 2017 2015 2017 2016 2018 2012
us
cr
ip t
State Alaska California Colorado District of Columbia Maine Massachusetts Nevada Oregon Vermont Washington
Ac ce p
te
d
M
an
Notes: Dates are drawn from ProCon (https://marijuana.procon.org/view.resource.php?resourceID=006868).
54
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A.3
Non-Parametric Graphical Evidence
ip t
80
cr
60
us
40 20 0 2007
2009
2011
2013
an
Admissions (10,000 Persons)
Aggregate Treatment Admissions
2015
Year
Early
Mid
M
Control
Figure 5: Effect of Medicaid Expansions on Opioid Treatment Admissions
Ac ce p
te
d
Notes: Opioid treatment admissions of the population aged 18 and older are drawn from the 2007-2015 TEDS-A (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity.
55
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2011
2013
25 20 15 10
2015
2007
2009
Year Control
Early
Mid
Control
te
Ac ce p 0
2009
2011
2013
2015
us
40 30
an 10
2009
Control
Early
2013
2015
Early
Mid
6
Inpatient
2007
2009
Year
Control
2011
Year
4
d
20 15 10 5
Admissions (10,000 Persons)
Medication Assisted Treatment
2007
Mid
2
Mid
2007
Admissions (10,000 Persons)
Early
20
Admissions (10,000 Persons)
2015
M
2013
Year Control
Early
0
2011
2015
0
60 50 40 30 20
2009
2013
Outpatient
10
Admissions (10,000 Persons)
Rehabilitation
2007
2011
Year
ip t
2009
cr
2007
5
5
10
15
Admissions (10,000 Persons)
20
Residential
0
Admissions (10,000 Persons)
Detoxification
2011
2013
2015
Year Mid
Control
Early
Mid
Figure 6: Effect of Medicaid Expansions on Opioid Treatment Admissions
Notes: Opioid treatment admissions of the population aged 18 and older are drawn from the 2007-2015 TEDS-A (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity.
56
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2011
2013
ip t
cr
16 14 12 10
2015
2007
Control
2009
2011
2013
2015
Year
Early
an
Year
us
2009
8
Admissions (10,000 Persons)
20 15 10 2007
6
25
Uninsured
5
Admissions (10,000 Persons)
Medicaid Insurance
Mid
Control
Early
Mid
M
7 6 5 4
2009
te
2007
d
3 2
Admissions (10,000 Persons)
Other Insurance
2011
2013
2015
Year Early
Mid
Ac ce p
Control
Figure 7: Effect of Medicaid Expansions on Opioid Treatment Admissions
Notes: Opioid treatment admissions of the population aged 18 and older are drawn from the 20072016 TEDS-A (see Section 3.1). Health insurance is part of the TEDS-A “Supplemental” dataset, and thus, not reported by all states. State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity.
57
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OTPs .1
2009
2011
2013
2015
2007
2009
2011
Year Early
Mid
Control
.2
Mid
2007
2009
2011
2013
2015
Year Control
M
.05
.1
an
.15
Count (10,000 Persons)
.1 .08 .06 .04 .02
Count (10,000 Persons)
Early
Buprenorphine Availability
.12
Methadone Availability
Early
Mid
2007
2009
2011
2013
Control
Early
Early
Mid
.1 .08 .06 .02
2015
2007
2009
2011
Year
Control
2015
.04
te
Ac ce p 2009
2013
Detox Availability Count (10,000 Persons)
d
.25 .2 .15 .1 .05
2007
2011
Year
Naltrexone Availability Count (10,000 Persons)
2015
Year
us
Control
2013
cr
2007
ip t
.08 .06 .02
.04
Count (10,000 Persons)
80 100 120 60 40 20
Count (10,000 Persons)
.12
Capacity of DATA 2000 Certified Physicians
2013
2015
Year Mid
Control
Early
Mid
Figure 8: Effect of Medicaid Expansions on OUD Treatment Providers
Notes: DATA2000 capacity is drawn from SAMHSA and specialty treatment facilities are drawn from 2007-2016 N-SSATS (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity. OTP = opioid treatment program.
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ip t
Non−OTPs Accepting Medicaid
2009
2011
2013
2015
cr 2007
Control
Early
2009
2011
an
Year
us
.15 .05
2007
Mid
2013
2015
Year
Control
Early
Mid
2007
2009
2011
te 2013
2015
.06 .05 .04 .03
.01
d
.02
.03
.04
M
Count (10,000 Persons)
.07
Non−OTPs Not Accepting Medicaid
.05
OTPs Not Accepting Medicaid Count (10,000 Persons)
.1
Count (10,000 Persons)
.08 .06 .04 .02 0
Count (10,000 Persons)
.2
OTPs Accepting Medicaid
2007
2009
2011
Year
Early
2015
Mid
Control
Early
Mid
Ac ce p
Control
2013
Year
Figure 9: Effect of Medicaid Expansions on OUD Treatment Providers
Notes: Specialty treatment facilities are drawn from 2007-2016 N-SSATS (see Section 3.1). Stateyear counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity. OTP = opioid treatment program.
59
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ip t
.01
2007
2009
2011
2013
2015
2007
2009
cr
0
.005
Count (10,000 Persons)
.03 .025 .02 .015 .01 .005
Count (10,000 Persons)
Nalt. OTPs Not Accepting Medicaid
.015
Bupr. OTPs Not Accepting Medicaid
2011
Year Early
Mid
Control
.04
Mid
0
M
an
.03 .02 .01
Count (10,000 Persons)
.04 .03 .02
Count (10,000 Persons)
.01
Early
Bupr. OTPs Accepting Medicaid
.05
Methadone OTPs Not Accepting Medicaid
2007
2009
2011
2013
2015
Year Control
Early
Mid
2007
2007
2009
2011
2013
2011
Control
Early
2015
Early
Mid
.04
.06
.08
Methadone OTPs Accepting Medicaid
2015
2007
2009
2011
Year
Control
2013
Year
.02
Count (10,000 Persons)
2009
0
0
.01
Ac ce p
.02
te
.03
d
Nalt. OTPs Accepting Medicaid Count (10,000 Persons)
2015
us
Control
2013
Year
2013
2015
Year Mid
Control
Early
Mid
Figure 10: Effect of Medicaid Expansions on OUD Treatment Providers
Notes: Specialty treatment facilities are drawn from the 2007-2016 N-SSATS (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity. OTP = opioid treatment program.
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cr
.05 2009
2011
2013
2015
2007
Control
Early
2009
2011
an
Year
us
.04 .02
.03
Count (10,000 Persons)
.04 .03 .02
Mid
Control
2015
Early
Mid
0
d
.05 2007
2009
2011
2013
Control
2015
te
Year
Early
Mid
.1
.1
M
Count (10,000 Persons)
.15
Nalt. Non−OTPs Accepting Medicaid
.15
Bupr. Non−OTPs Accepting Medicaid
2013
Year
.05
Count (10,000 Persons)
.01 2007
Count (10,000 Persons)
ip t
Nalt. Non−OTPs Not Accepting Medicaid
.05
Bupr. Non−OTPs Not Accepting Medicaid
2007
2009
2011
2013
2015
Year Control
Early
Mid
Ac ce p
Figure 11: Effect of Medicaid Expansions on OUD Treatment Providers
Notes: Specialty treatment facilities are drawn from the 2007-2016 N-SSATS (see Section 3.1). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity. OTP = opioid treatment program.
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ip t
500 400
cr
300
2010
2011
2012
2013
2014
2015
2016
2007
Year
2011
2013
2015
Mid
Control
Early
Mid
an
Early
3000
M
4000
5000
Methadone Gram Distribution at OTPs
2009
2011
te
2007
d
2000
Grams (10,000 Persons)
2009
Year
1000
Control
us
200 100 0
100
200
300
Grams (10,000 Persons)
400
Buprenorphine Gram Distribution at OTPs
0
Prescriptions (10,000 Persons)
Buprenorphine Prescribing
2013
2015
Year
Control
Early
Mid
Ac ce p
Figure 12: Effect of Medicaid Expansions on Opioid Agonist MAT Availability
Notes: MAT dispensing is measured as methadone and buprenorphine gram distribution at opioid treatment programs, and MAT prescribing as outpatient buprenorphine prescriptions financed by Medicaid. Grams are drawn from the 2007-2016 ARCOS Report 5 and prescriptions from the 2010-2016 Medicaid SDUF (see Section 3.1). Grams are converted into morphine gram equivalents (see Table 7 in the Appendix). State-year counts are divided by 2015 Census estimates of the population aged 18 and older and multiplied by 10,000. Outcomes are classified into states expanding before 2014 “Early,” states expanding in 2014 “Mid,” states expanding after 2014 “Late,” and non-expansion states “Control.” The “Late” group is excluded for clarity.
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