The Mental Health Parity and Addiction Equity Act evaluation study: Impact on specialty behavioral health utilization and expenditures among "carve-out" enrollees

Abstract

Interrupted time series with and without controls was used to evaluate whether the federal Mental Health Parity and Addiction Equity Act (MHPAEA) and its Interim Final Rule increased the probability of specialty behavioral health treatment and levels of utilization and expenditures among patients receiving treatment. Linked insurance claims, eligibility, plan and employer data from 2008 to 2013 were used to estimate segmented regression analyses, allowing for level and slope changes during the transition (2010) and post-MHPAEA (2011-2013) periods. The sample included 1,812,541 individuals ages 27-64 (49,968,367 person-months) in 10,010 Optum "carve-out" plans. Two-part regression models with Generalized Estimating Equations were used to estimate expenditures by payer and outpatient, intermediate and inpatient service use. We found little evidence that MHPAEA increased utilization significantly, but somewhat more robust evidence that costs shifted from patients to plans. Thus the primary impact of MHPAEA among carve-out enrollees may have been a reduction in patient financial burden.

Keywords: Behavioral health; Expenditures; Insurance benefits; Parity; Utilization.

Figure 1

Sample Size Flowchart Note: Final sample size is N=1,812,541 people, corresponding to 63 employers, 10,010 plans, and 49,968,367 person-months.

Figure 2

Adjusted Mean Monthly Total Expenditures (2013 $), Among All Enrollees Notes: Sample is person-months from 2008-13 (N=49,968,367). Estimates from linear regression. Interrupted time series segmented regression analysis controlled for a linear monthly time trend, indicators and splines (measuring respective changes in level and slope) for both the transition and post periods, sex, age group, whether the enrollee was the primary insured person, employer group size category, plan type, state fixed effects, provider supply measures, and seasonality.

Figure 3

Adjusted Mean Monthly Plan Expenditures (2013 $), Among All Enrollees Notes: Sample is person-months from 2008-2013 (N=49,968,367). Estimates from linear regression. Interrupted time series segmented regression analysis controlled for a linear monthly time trend, indicators and splines (measuring respective changes in level and slope) for both the transition and post periods, sex, age group, whether the enrollee was the primary insured person, employer group size category, plan type, state fixed effects, provider supply measures, and seasonality.

Figure 4

Adjusted Mean Monthly Patient Out-of-Pocket Expenditures (2013 $), Among All Enrollees Notes: Sample is person-months from 2008-2013 (N=49,968,367). Estimates from linear regression. Interrupted time series segmented regression analysis controlled for a linear monthly time trend, indicators and splines (measuring respective changes in level and slope) for both the transition and post periods, sex, age group, whether the enrollee was the primary insured person, employer group size category, plan type, state fixed effects, provider supply measures, and seasonality.