Expanding Public Insurance Eligibility Increases Substance Use Treatment Provider Acceptance of Public Insurance and Increases Adolescent Access to Treatment

Sarah Hamersma and Johanna Catherine Maclean

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KEY FINDINGS
• Substance use disorder treatment providers are more likely to accept public coverage when adolescent eligibility expands.
• Providers increase acceptance of the Children’s Health Insurance Program (CHIP) when child public insurance expands but do not generally increase Medicaid acceptance.
• Non-profit and government providers are more likely than private providers to have increased their acceptance of CHIP.
• Adolescents get more treatment from non-profit providers when coverage expands.

Substance use disorders (SUDs) are chronic conditions experienced by both children and adults that impose costs on individuals, their families, and society at large. Childhood SUD treatment can change the trajectory of that child’s life. It supports public health and reduces overall costs to individuals and society for current and future generations. A key barrier to SUD treatment-seeking is inability to pay and lack of insurance coverage.1 Expanding public insurance access through Medicaid and the Children’s Health Insurance Program (CHIP) may encourage childhood SUD treatment. However, this can only happen if SUD treatment providers accept this new coverage.  This brief summarizes the results of our recently published study. We evaluated how SUD treatment providers’ acceptance of public coverage changed with the expansion of older-child Medicaid and CHIP from 1997 to 2011. We also examined trends in the number of children treated. 

Income Eligibility Limits for Adolescents Doubled in Many States
Medicaid, introduced in the 1960s, is the primary insurer for low-income, non-elderly individuals in the U.S. Since the 1980s, Medicaid eligibility thresholds have been gradually expanding for targeted groups like pregnant women and children, eventually surpassing the Federal Poverty Line (FPL) in many states. Implemented in 1997, CHIP offered states federal funding to insure less-poor children through block grants.  States can use CHIP funding to expand children’s coverage through directly expanding Medicaid eligibility or through creating distinct CHIP programs. Over our study period (1997 to 2011), states expanded Medicaid programs, developed CHIP programs, and set income eligibility thresholds for both programs in multiple ways. Figure 1 shows the expansion of eligibility thresholds for public insurance targeting children by U.S. state between 1997 and 2011. By 2011, over 43 million children had been insured by one of these programs.2 SUD treatment is provided under all Medicaid programs and nearly all CHIP programs over our study period.

Data Source: See published paper for details on children’s public insurance threshold level sources (including Alaska and Hawaii). 

Public Insurance Expansions Increased Acceptance of Some Public Insurance
Figure 2 shows change in providers’ acceptance of Medicaid and CHIP when the eligibility threshold increases from 100% to 200% FPL for 6-18-year-olds. We found no evidence that increased thresholds increased providers’ acceptance of Medicaid. However, a threshold boost of 100% FPL increased acceptance of CHIP by about 10% for non-profit and government providers. Increased acceptance of CHIP may be driven by higher federal match rates than those under Medicaid or provider anticipation of a less disadvantaged clientele. CHIP may also be a more manageable program to expand since the newly eligible are limited to one specific group: children. Because a provider’s Medicaid participation involves accepting all ages, it may be less responsive to an eligibility change affecting only children.  

Notes:  Estimates are from the published study. N=40,996 for-profit provider-years, N=86,578 non-profit provider-years, and N=19,911 government provider-years.  Bold numbers indicate statistical significance at 95% or higher confidence.

Public Insurance Expansions Slightly Increased Child SUD Treatment by Non-profits
Figure 3 displays the estimated effects of insurance expansions on the quantity of SUD treatments provided to children.  Outcomes include the presence of a specialized child program (binary), treatment of 10 or more children (binary), and the number of children treated (continuous, including zeros). We expected that providers might increase treatment of children given their increased access to public insurance. However, there is no evidence of large effects on treatment quantity, with only about a 5% increase that seems confined to non-profit providers. Capacity constraints—in terms of space and skilled labor—may temper the expected increase.

Notes: Estimates are from the published study.  Bold number indicates statistical significance at 95% or higher confidence.  Sample sizes are N=47,243 for-profit, N=100,492 non-profit, and N=23,849 government providers (with some sample loss in second and third columns). 

Public Health Insurance Is Essential for Adolescents’ Access to SUD Treatment
These findings are timely as Medicaid and CHIP have faced uncertainty. There have been calls from lawmakers to convert Medicaid from an entitlement to a block-grant program, which will reduce the number of enrollees.3 The status of SUD treatment as an ‘essential benefit’ in most insurance plans under the Affordable Care Act has been challenged4 as has ongoing funding of CHIP.5

Recent research has shown that access to public insurance early in life improves health, reduces healthcare and social service use, and supports social outcomes across the life course.6 Our study suggests that public insurance expansions targeting children may also improve access to SUD treatment. While this coverage does not close the gap between need and treatment, protecting and expanding access to public health insurance for underserved children is one piece of a larger strategy to improve care for this vulnerable population.

Data and Methods
Our analysis leveraged expansions of public insurance eligibility for children ages 6 to 18 through state Medicaid programs and CHIP over the period 1997 to 2011. We linked these to provider data from the National Survey of Substance Abuse Treatment Services (N-SSATS), which contains government data on the near-universe of SUD treatment facilities in the U.S. Our merged data allowed us to estimate linear regression models to estimate the relationship between higher income eligibility thresholds and provider outcomes. The analyses control for state characteristics, provider fixed-effects, and year fixed-effects to isolate the effect of the public health insurance policy change. Additional details are provided in the published article.

References

  1. Substance Abuse and Mental Health Services Administration & Center for Behavioral Health Statistics and Quality. (2019). Results from the 2018 national survey on drug use and health: Detailed tables. https://www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHDetailedTabs2018R2/NSDUHDetailedTabs2018.pdf   
  2. Centers for Medicare & Medicaid Services. 2012. FY 2012 number of children ever enrolled in Medicaid and CHIP. Baltimore, MD: Centers for Medicare & Medicaid Services.
  3. Goodman-Bacon, A.J., & Nikpay, S.S. (2017). Per capita caps in Medicaid—lessons from the past.” New England Journal of Medicine, 376(11), 1005-1007.
  4. Congressional Budget Office. (2017). American Health Care Act. Washington, DC: Congressional Budget Office.
  5. Kaiser Family Foundation. (2018). Summary of the 2018 CHIP funding extension. https://www.kff.org/medicaid/fact-sheet/summary-of-the-2018-chip-funding-extension/
  6. Goodman-Bacon, A. (2016). The long-run effects of childhood insurance coverage: Medicaid implementation, adult health, and labor market outcomes. NBER Working Paper Series. Cambridge, MA: National Bureau of Economic Research.

Acknowledgments
The author is an affiliate of the Lerner Center for Public Health Promotion and the Center for Aging and Policy Studies, which receives funding from the National Institute on Aging (grant # 1P30AG066583). The authors thank Shannon Monnat and Megan Ray for edits made to previous drafts of this brief.

About the Authors
Sarah Hamersma (sehamers@syr.edu) is Associate Professor of Public Administration and International Affairs and a Senior Research Associate with the Center for Policy Research in the Maxwell School at Syracuse University. Johanna Catherine Maclean (catherine.maclean@temple.edu) is an Associate Professor of Economics at Temple University, a Research Associate at the National Bureau of Economic Research, and a Research Affiliate at the Institute of Labor Economics.