The Effects of Federal Parity on Substance Use Disorder Treatment | Page 2

Published Online: January 23, 2014
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
We used de-identified Aetna administrative claims data to conduct this study. These data are from a single insurer, but include employees of many different firms with diverse benefit designs and clearly identifiable information on whether an enrollee is covered by a fully insured or self-insured plan. Importantly, Aetna does not carve out mental health or SUD benefits. We examined enrollee claims 1 year before (2009) and 1 year after (2010) MHPAEA implementation. We included individuals continuously enrolled in Aetna health plans over the full 2-year study period to eliminate variation over time in the underlying population studied. All individuals aged 18 to 62 years in calendar year 2009 were included. We excluded older individuals, who are typically covered by Medicare. We also excluded 135 individuals who switched between fully insured and self-insured plans during the 2-year period. Because most of the state parity laws considered (as well as MHPAEA) do not apply to firms with fewer than 50 employees, individuals employed by these small firms were excluded. Analyses included 162,761 enrollees in self-insured plans and 135,578 enrollees in fully insured plans.


Claims were identified as being SUD related if they met either of 2 criteria. First, we identified enrollees treated for SUD using the first 2 International Classification of Diseases, 9th Revision, Clinical Modification diagnostic codes on each inpatient and outpatient claim using codes for alcohol-or drug-induced mental disorders (291 and 292), alcohol or drug dependence (303 and 304), and nondependent abuse of drugs (excluding tobacco) (305.0 and 305.2-305.9). In addition, any services received in an SUD treatment facility or from a drug or alcohol counselor were considered SUD treatment. We used National Drug Codes to identify prescription medications used specifically to treat SUD (ie, acamprosate, buprenorphine, buprenorphine/naloxone, disulfiram, and naltrexone [oral and sustained release]). Because methadone cannot be prescribed by physicians for the treatment of opioid dependence, we did not include methadone in our analysis. Outcomes in a calendar year were (1) proportion of enrollees using any SUD treatment, (2) total spending on SUD treatment per enrollee, (3) total spending on SUD treatment per user, (4) out-of-pocket spending on SUD treatment per user, and (5) proportion of total SUD spending paid for out of pocket. To calculate an enrollee’s annual total spending, we included all SUD-related inpatient, partial hospitalization, intensive outpatient, and outpatient services, and prescription medications to treat SUD. To calculate an enrollee’s annual out-of-pocket spending, we included the deductible, copayment, and coinsurance for SUD services and prescription medications.

We examined 3 HEDIS-based SUD performance measures. We measured identification as the share of all health plan enrollees who had a new SUD claim within a calendar year. New treatment episodes were those with no SUD treatment during the prior 60 days. We measured treatment initiation as the share of enrollees with a new episode of SUD treatment who initiated treatment within 14 days of their initial diagnosis. Following HEDIS, all patients for whom identification of SUD occurred through a hospital admission were considered to have initiated treatment, but inpatient detoxification services were not considered treatment initiation. We measured treatment engagement as the share of enrollees with a new episode of SUD treatment who received at least 2 SUD services within 30 days of their initial diagnosis. For the treatment engagement measure, multiple services could not occur on the same day. To ensure that we were identifying only new episodes, we did not consider episodes that began during the first 60 days of the calendar year. For both the initiation and engagement measures, we omitted episodes that did not allow for a 30-day follow-up (ie, those that occurred late in the year).

Our explanatory variables were indicators for whether an observation occurred after federal parity implementation (ie, in 2010) and whether the individual was enrolled in a plan newly subject to parity (ie, a self-insured firm). We also controlled for enrollee sex, age (ie, age 18-31, 32-46, 47-62 years in 2009), and state.

Analytic Strategy

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.

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Issue: January 2014
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