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The American Journal of Managed Care January 2014
Patient-Centered Medical Home Transformation With Payment Reform: Patient Experience Outcomes
Leonie Heyworth, MD, MPH; Asaf Bitton, MD, MPH; Stuart R. Lipsitz, ScD; Thad Schilling, MD, MPH; Gordon D. Schiff, MD; David W. Bates, MD, MSc; and Steven R. Simon, MD, MPH
Process of Care Compliance Is Associated With Fewer Diabetes Complications
Felicia J. Bayer, PhD; Deron Galusha, MS; Martin Slade, MPH; Isabella M. Chu, MPH; Oyebode Taiwo, MBBS, MPH; and Mark R. Cullen, MD
Evidence-Based Guidelines to Determine Follow-up Intervals: A Call for Action
Emilia Javorsky, MPH; Amanda Robinson, MD; and Alexa Boer Kimball, MD, MPH
Electronic Health Risk Assessment Adoption in an Integrated Healthcare System
Diana S. M. Buist, PhD, MPH; Nora Knight Ross, MA; Robert J. Reid, MD, PhD; and David C. Grossman, MD, MPH
Comorbidities and Cardiovascular Disease Risk in Older Breast Cancer Survivors
Reina Haque, PhD, MPH; Marianne Prout, MD; Ann M. Geiger, PhD; Aruna Kamineni, PhD; Soe Soe Thwin, PhD; Chantal Avila, MA; Rebecca A. Silliman, MD, PhD; Virginia Quinn, PhD; and Marianne Ulcickas Yood, DSc
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The Effects of Federal Parity on Substance Use Disorder Treatment
Susan H. Busch, PhD; Andrew J. Epstein, PhD; Michael O. Harhay, MPH; David A. Fiellin, MD; Hyong Un, MD; Deane Leader Jr, DBA, MBA; and Colleen L. Barry, PhD, MPP
Specialist Participation in Healthcare Delivery Transformation: Influence of Patient Self-Referral
Oluseyi Aliu, MD, MS; Gordon Sun, MD, MS; James Burke, MD, MS; Kevin C. Chung, MD, MS; and Matthew M. Davis, MD, MAPP
Optimal Management of Diabetes Among Overweight and Obese Adults
Denison S. Ryan, MPH; Karen J. Coleman, PhD, MS; Jean M. Lawrence, ScD, MPH, MSSA; Teresa N. Harrison, SM; and Kristi Reynolds, PhD, MPH
Why Are Medicare and Commercial Insurance Spending Weakly Correlated?
Laurence C. Baker, PhD; M. Kate Bundorf, PhD; and Daniel P. Kessler, JD, PhD

The Effects of Federal Parity on Substance Use Disorder Treatment

Susan H. Busch, PhD; Andrew J. Epstein, PhD; Michael O. Harhay, MPH; David A. Fiellin, MD; Hyong Un, MD; Deane Leader Jr, DBA, MBA; and Colleen L. Barry, PhD, MPP
Federal parity led to an increase in spending on substance use disorder treatment.
We estimated the effect of federal parity using a difference-in-differences model. For binary outcomes we used logistic regression. For spending outcomes we used a 2-part model to estimate the probability of any SUD use and then estimated spending conditional on any use using a generalized linear model with a log link and gamma distribution, as indicated by the results of a modified Park test.18 To estimate the relationship between parity and share of total spending paid out of pocket, we estimated a fractional logit model, which was implemented as a generalized linear model with a logit link and binomial distribution.19 To facilitate interpretation, we transformed relevant coefficients to the original scale of the outcome using the method of recycled predictions. We calculated confidence intervals using a nonparametric block bootstrap method that accounts for repeat observations for individuals.20 This study was exempted from review by Yale University Institutional Review Board.


We compared characteristics of the self-insured treatment group and fully insured comparison group enrollees in 2009 (Table 1). Self-insured enrollees were significantly more likely to be female and younger, although these differences are not large enough to be clinically meaningful. Although differences were small in absolute terms, self-insured enrollees were 57% more likely than fully insured enrollees to have an SUD diagnosis (1.1 % vs 0.7 %).

Table 2 reports difference-in-differences estimates for the probability of use of SUD treatment and total spending on SUD treatment per enrollee. After accounting for secular trends in the use of SUD treatment, we found no significant difference in the probability of using SUD treatment attributable to MHPAEA. We did find a significant increase of $9.99 (95% confidence interval [CI], $2.54-$18.21) in total spending on SUD treatment per enrollee attributable to MHPAEA, compared with a base rate of $36.51 in the self-insured group. We found no significant difference in total spending on SUD treatment per user, although the point estimate was relatively large ($608). Table 3 indicates that we detected no effect of MHPAEA on out-of-pocket spending on SUD treatment or proportion of spending paid out of pocket among users.

Table 4 summarizes the effects of MHPAEA on the HEDIS- based identification, treatment initiation, and treatment engagement performance measures. We found no significant effect on identification of SUD, treatment initiation, or treatment engagement associated with implementation of MHPAEA.


This study is the first to examine the effects of MHPAEA on SUD treatment use and spending. The new law led to a significant increase in SUD spending of $9.99 per health plan enrollee. The average cost of an employer-sponsored individual health insurance policy in 2010 was $5049,21 suggesting this is a negligible increase. During the congressional debate over passage of MHPAEA, employers and health plans raised the concern that the law would greatly increase healthcare spending.22 This study suggests that, at least related to SUD treatment, this concern was unfounded. Once a controversial and much debated issue, the inclusion of SUD services in parity appears unlikely to affect either health plan profitability or overall rates of insurance coverage.

That we found no change in out-of-pocket SUD spending suggests federal parity did not lead to increases in financial protection for SUD treatments. This result is surprising given data indicating that higher cost sharing for SUD services was common prior to MHPAEA.2 Under parity, 2 potentially offsetting changes to out-of-pocket spending might be expected. First, equalization of cost sharing between general and medical care services might lead to lower cost sharing for SUD services and thus reduce out-of-pocket spending. Second, increases in the number or intensity of services due to reduced out-of-pocket price or elimination of treatment limits might lead to increases in total costs and total out-of-pocket spending. To shed light on the relative importance of these competing effects, we examined the proportion of total costs paid out of pocket. If cost sharing were declining, we would expect the proportion of total costs paid out of pocket to decline as well. That we found no change in the proportion of total costs paid out of pocket suggests that the elimination of treatment limits may be an important cause of increased spending. This would be the effect expected if increases in treatment expenditures result from the elimination of day or visit limits rather than reductions in cost sharing.

We also note that our data included claims for SUD treatments paid for by private insurance. If MHPAEA resulted in a switch from SUD services self-financed by families to services financed by insurance, out-of-pocket spending reported in claims might have increased, but actual out-of-pocket spending by SUD treatment users might have declined. We found no significant increase in SUD identification, treatment initiation, or treatment engagement after parity implementation. We note that these measures only captured whether individuals were more likely to begin treatment and did not measure the duration or the intensity of treatment. Although not definitive, that we found increases in SUD total expenditures of 27% (an increase of $10 with an initial expenditure of $37 per enrollee for the self-insured group, as indicated in Table 2) without similar increases in identification suggests there were increases in treatment duration or treatment intensity after MHPAEA implementation. The point estimate of total spending per SUD treatment user suggests an increase in yearly total spending of approximately $600 per year, although this finding is not statistically significantly different from zero due to imprecision in the estimate caused by the relatively small number of SUD treatment users in our sample. In these data the payment for a physician visit for SUD treatment of low to moderate intensity was approximately $70, suggesting this increased spending may yield 8 additional visits for SUD treatment. Treatment increases may be an important benefit of parity given the level of unmet need associated with SUD and evidence that treatment provides important benefits and reduces social costs.23

Understanding the effects of MHPAEA on SUD treatment is critical because provisions of the Patient Protection and Affordable Care Act will extend the law to the individual and small group insurance markets under state and federal health insurance exchanges beginning in 2014. Substance use disorder services are required as part of the essential health benefit under the Affordable Care Act, although states may differ on the scope of SUD benefits covered.

Strengths of this study include the use of a comparison group to control for secular trends in SUD treatment use, geographic heterogeneity in the study population, and detailed SUD treatment use and spending data. Like all studies, our research is limited by the type of data considered. These data did not include patient interviews or medical chart reviews to confirm information regarding diagnosis or services received, or information on treatments not financed by insurance. There is little reason to expect that these data limitations differentially affected the intervention and comparison groups, suggesting our estimates of changes in use and spending were unbiased. Yet if providers systematically changed recorded diagnoses in response to parity (due to increased coverage for SUD treatments), our results would be biased. In addition, we found little difference in reimbursement levels between the self-insured and fully insured groups.

Another limitation is that MHPAEA may lead to multiple insurance market changes, including declines in the out-ofpocket price of services, increases in supply-side constraints imposed by insurers (ie, prior authorization, referral restrictions), and reduced stigma associated with SUD treatment, which may all affect use and spending. Although our study design allowed us to determine the net effect of parity, we were not able to disentangle these competing mechanisms.

A third consideration is that preexisting state parity laws were not identical to MHPAEA; therefore, fully insured enrollees in our comparison group might have experienced some change in benefits as they moved from being subject to the less comprehensive state parity laws to the more comprehensive MHPAEA in 2010. A fourth limitation is that we did not consider changes in costs for treatment of substance abuse–related medical conditions (eg, alcoholic cirrhosis, hepatitis). A fifth limitation relates to the generalizability of our findings. We evaluated the effects of parity on individuals insured by a single health insurer in 10 states with preexisting state SUD parity laws. Thus our results may not be generalizable to other insurance or population contexts.

Finally, this study examined only the first year after MHPAEA took effect. The interim final regulations of MHPAEA, which were released in February 2010 and took effect for most plans in 2011, prohibited plans from using so-called nonquantitative treatment limits for mental health and SUD benefits unless these limits were comparable to those used for general medical services.24 Nonquantitative treatment limits include medical management standards, prior authorization, utilization review, prescription drug formulary design, standards for provider admission to participate in a network, and provider reimbursement. It is possible that these regulations could lead to different effects of the law; therefore, it is critical for future research to examine use and spending in response to MHPAEA in subsequent years.

Author Affiliations: From Perelman School of Medicine, University of Pennsylvania (AJE, MOH), Philadelphia, PA; Yale School of Public Health (SHB, DAF), New Haven, CT; Yale School of Medicine (DAF), New Haven, CT; Aetna (HU, DL), Hartford, CT; John Hopkins Bloomberg School of Public Health (CLB), Baltimore, MD.

Funding Source: This study was funded by the National Institute on Drug Abuse (grant DA 026414).

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (SHB, AJE, DAF, CLB); acquisition of data (SHB, DL, CLB); analysis and interpretation of data (SHB, AJE, MOH, DAF, HU, DL, CLB); drafting of the manuscript (SHB, CLB); critical revision of the manuscript for important intellectual content (SHB, AJE, MOH, DAF, HU, DL, CLB); statistical analysis (SHB, AJE, MOH, CLB); obtaining funding (SHB, DAF, CLB), administrative, technical, or logistic support (SHB, AJE, MOH, DL, CLB); and supervision (SHB, CLB).

Address correspondence to: Susan H. Busch, PhD, Yale School of Public Health, 60 College St, New Haven CT 06520-8034.
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