Quantifying Balance Billing for Out-of-Network Behavioral Health Care in Employer-Sponsored Insurance.

OBJECTIVE: The study estimated balance billing for out-of-network behavioral health claims and described subscriber characteristics associated with higher billing. METHODS: Claims data (2011-2014) from a national managed behavioral health organization's employer-sponsored insurance (N=196,034 family-years with out-of-network behavioral health claims) were used to calculate inflation-adjusted annual balance billing-the submitted amount (charged by provider) minus the allowed amount (insurer agreed to pay plus patient cost-sharing) and any discounts offered by the provider. Among family-years with complete sociodemographic data (N=68,659), regressions modeled balance billing as a function of plan and provider supply, subscriber and family-year, and employer characteristics. A two-part model accounted for family-years without balance billing. RESULTS: Among the 50% of family-years with balance billing, mean±SD balance billing was $861±$3,500 (median, $175; 90th percentile, $1,684). Adjusted analysis found balance billing was higher ($523 higher, 95% confidence interval [CI]=$340, $705) for carve-out versus carve-in plans and for health maintenance organization (HMO) enrollees versus non-HMO enrollees ($156, 95% CI=$75, $237); for subscribers with a bachelor's degree, compared with an associate's degree or with a high school diploma or lower (between $172 [95% CI=$228, $116] and $224 [95% CI=$284, $163] higher, respectively); and for subscribers ages 45-54, compared with those ages 35-44 and 18-24 (between $57 [95% CI=$103, $10] and $290 [95% CI=$398, $183] higher, respectively). Balance billing was lower in states with more in-network providers per capita (-$8, 95% CI=-$10, -$5). CONCLUSIONS: Balance billing for out-of-network behavioral health claims may be burdensome. Expanded behavioral health networks may improve access.

Comparing Gold-standard Copayment and Coinsurance Values From Claims Processing Engines to Values Derived From Behavioral Health Claims Databases.

BACKGROUND: While researchers use patient expenditures in claims data to estimate insurance benefit features, little evidence exists to indicate whether the resulting measures are accurate. OBJECTIVE: To develop and test an algorithm for deriving copayment and coinsurance values from behavioral health claims data. SUBJECTS: Employer-sponsored insurance plans from 2011 to 2013 for a national managed behavioral health organization (MBHO). MEASURES: Twelve benefit features, distinguishing between carve-in and carve-out, in-network and out-of-network, inpatient and outpatient, and copayment and coinsurance, were created. Measures drew from claims (claims-derived measures), and benefit feature data from a claims processing engine database (true measures). STUDY DESIGN: We calculate sensitivity and specificity of the claims-derived measures' ability to accurately determine if a benefit feature was required and for plan-years requiring the benefit feature, the accuracy of the claims-derived measures. Accuracy rates using the minimum, 25th, 50th, 75th, and maximum claims value for a plan-year were compared. PRINCIPAL FINDINGS: Sensitivity (82% or higher for all but 3 benefit features) and specificity (95% or higher for all but 2 benefit features) were relatively high. Accuracy rates were highest using the 75th or maximum claims value, depending on the benefit feature, and ranged from 69% to 99% for all benefit features except for out-of-network inpatient coinsurance. CONCLUSIONS: For most plan-years, claims-derived measures correctly identify required specialty mental health copayments and coinsurance, although the claims-derived measures' accuracy varies across benefit design features. This information should be considered when creating claims-derived benefit features to use for policy analysis.

Banbury Forum Consensus Statement on the Path Forward for Digital Mental Health Treatment.

A major obstacle to mental health treatment for many Americans is accessibility: the United States faces a shortage of mental health providers, resulting in federally designated shortage areas. Although digital mental health treatments (DMHTs) are effective interventions for common mental disorders, they have not been widely adopted by the U.S. health care system. National and international expert stakeholders representing health care organizations, insurance companies and payers, employers, patients, researchers, policy makers, health economists, and DMHT companies and the investment community attended two Banbury Forum meetings. The Banbury Forum reviewed the evidence for DMHTs, identified the challenges to successful and sustainable implementation, investigated the factors that contributed to more successful implementation internationally, and developed the following recommendations: guided DMHTs should be offered to all patients experiencing common mental disorders, DMHT products and services should be reimbursable to support integration into the U.S. health care landscape, and an evidence standards framework should be developed to support decision makers in evaluating DMHTs.

Carve-out plan financial requirements associated with national behavioral health parity.

OBJECTIVES: To examine changes in carve-out financial requirements (copayments, coinsurance, use of deductibles, and out-of-pocket maxima) following the Mental Health Parity and Addiction Equity Act (MHPAEA). DATA SOURCE/STUDY SETTING: Specialty mental health benefit design information for employer-sponsored carve-out plans from a national managed behavioral health organization's claims processing engine (2008-2013). STUDY DESIGN: This pre-post study reports linear and logistic regression as the main analysis. DATA COLLECTION/EXTRACTION METHODS: NA. PRINCIPAL FINDINGS: Copayments for in-network emergency room (-$44.9, 95% CI: -78.3, -11.5; preparity mean: $56.2), outpatient services (eg, individual psychotherapy: -$7.4, 95% CI: -10.5, -4.2; preparity mean: $17.8), and out-of-network coinsurance for emergency room (-11 percentage points, 95% CI: -16.7, -5.4; preparity mean: 38.8 percent) and outpatient (eg, individual psychotherapy: -5.8 percentage points, 95% CI: -10.0, -1.6; preparity mean 41.0 percent) decreased. Probability of family OOP maxima use (29 percentage points, 95% CI: 19.3, 38.6; preparity mean: 36 percent) increased. In-network outpatient coinsurance increased (eg, individual psychotherapy: 4.5 percentage points, 95% CI: 1.1, 7.9; preparity mean: 2.7 percent), as did probability of use of family deductibles (15 percentage points, 95% CI: 6.1, 23.3; preparity mean: 38 percent). CONCLUSIONS: MHPAEA was associated with increased generosity in most financial requirements observed here. However, increased use of deductibles may have reduced generosity for some patients.

Relative Cost Differences of Initial Treatment Strategies for Newly Diagnosed Opioid Use Disorder: A Cohort Study.

BACKGROUND: Relative costs of care among treatment options for opioid use disorder (OUD) are unknown. METHODS: We identified a cohort of 40,885 individuals with a new diagnosis of OUD in a large national de-identified claims database covering commercially insured and Medicare Advantage enrollees. We assigned individuals to 1 of 6 mutually exclusive initial treatment pathways: (1) Inpatient Detox/Rehabilitation Treatment Center; (2) Behavioral Health Intensive, intensive outpatient or Partial Hospitalization Services; (3) Methadone or Buprenorphine; (4) Naltrexone; (5) Behavioral Health Outpatient Services, or; (6) No Treatment. We assessed total costs of care in the initial 90 day treatment period for each strategy using a differences in differences approach controlling for baseline costs. RESULTS: Within 90 days of diagnosis, 94.8% of individuals received treatment, with the initial treatments being: 15.8% for Inpatient Detox/Rehabilitation Treatment Center, 4.8% for Behavioral Health Intensive, Intensive Outpatient or Partial Hospitalization Services, 12.5% for buprenorphine/methadone, 2.4% for naltrexone, and 59.3% for Behavioral Health Outpatient Services. Average unadjusted costs increased from $3250 per member per month (SD $7846) at baseline to $5047 per member per month (SD $11,856) in the 90 day follow-up period. Compared with no treatment, initial 90 day costs were lower for buprenorphine/methadone [Adjusted Difference in Differences Cost Ratio (ADIDCR) 0.65; 95% confidence interval (CI), 0.52-0.80], naltrexone (ADIDCR 0.53; 95% CI, 0.42-0.67), and behavioral health outpatient (ADIDCR 0.54; 95% CI, 0.44-0.66). Costs were higher for inpatient detox (ADIDCR 2.30; 95% CI, 1.88-2.83). CONCLUSION: Improving health system capacity and insurance coverage and incentives for outpatient management of OUD may reduce health care costs.

The Mental Health Parity and Addiction Equity Act evaluation study: Child and adolescent behavioral health service expenditures and utilization.

This study explores possible associations of the Mental Health Parity and Addiction Equity Act (MHPAEA) with child access to behavioral health (BH) services (preimplementation = 2008-2009, transition = 2010, and post = 2011-2013). The study sample included children aged 4-17 years in self-insured "carve-in" plans from large employers. In "carve-ins," BH and medical care are covered through the same insurance plan. The unit of analysis is the person-month (N = 61,823,533). This study employs an interrupted time series model allowing for intercept and slope changes for the transition and postparity periods. Outcomes included total, plan and patient out-of-pocket (OOP) expenditures, and several categories of service utilization. Generalized estimating equations were used to account for clustering. There were significant increases in total and plan expenditures postparity. To illustrate, in July 2012, mean per-member-per-month total expenditures were predicted to be $5.65 without parity but $8.72 with parity. Patient OOP costs did not change significantly. Significant overall increases were seen for utilization of most outpatient services but not intermediate or inpatient services. Our findings suggest that the introduction of MHPAEA was associated with an increase in specialty BH service access for children without a commensurate increase in financial burden for families.

The Role of Behavioral Health Diagnoses in Adverse Selection.

OBJECTIVE: Adverse selection in medical insurance is well documented; however, little is known about the role of behavioral health. This study's objective was to examine the probability of being enrolled in the lowest-deductible plan among commercially insured patients, according to psychiatric diagnosis. METHODS: This cross-sectional study used 2012-2013 benefit design and plan choice data linked to 2011-2012 behavioral health claims for a national sample of individuals (N=116,975) and different family types (couple with at least one dependent, N=59,237; single subscriber with at least one dependent, N=19,066; couple with no dependents, N=40,917) with Optum, UnitedHealth Group "carve-in" plans. Analyses included multiple logistic regressions examining whether the individual (or family) was enrolled in the plan with the lowest deductible as functions of whether individuals (or family members) had any psychiatric diagnosis, the number of psychiatric diagnoses they had, and whether they had individual major psychiatric diagnoses. RESULTS: For individuals, having any psychiatric diagnosis was associated with an increase of about 10% in the probability of being enrolled in the lowest-deductible plan compared with having no psychiatric diagnosis (44.9% vs. 40.7%, p=0.04). Each additional psychiatric diagnosis increased this probability by three percentage points (p=0.02). A diagnosis of depression was associated with the largest increase. CONCLUSIONS: When individuals were offered the choice of a health insurance plan, having a prior psychiatric diagnosis (specifically depression) was associated with being enrolled in the lowest-deductible plans. Individuals with depression may anticipate future expenditures and select plans accordingly.

Comparative Effectiveness of Different Treatment Pathways for Opioid Use Disorder.

Importance: Although clinical trials demonstrate the superior effectiveness of medication for opioid use disorder (MOUD) compared with nonpharmacologic treatment, national data on the comparative effectiveness of real-world treatment pathways are lacking. Objective: To examine associations between opioid use disorder (OUD) treatment pathways and overdose and opioid-related acute care use as proxies for OUD recurrence. Design, Setting, and Participants: This retrospective comparative effectiveness research study assessed deidentified claims from the OptumLabs Data Warehouse from individuals aged 16 years or older with OUD and commercial or Medicare Advantage coverage. Opioid use disorder was identified based on 1 or more inpatient or 2 or more outpatient claims for OUD diagnosis codes within 3 months of each other; 1 or more claims for OUD plus diagnosis codes for opioid-related overdose, injection-related infection, or inpatient detoxification or residential services; or MOUD claims between January 1, 2015, and September 30, 2017. Data analysis was performed from April 1, 2018, to June 30, 2019. Exposures: One of 6 mutually exclusive treatment pathways, including (1) no treatment, (2) inpatient detoxification or residential services, (3) intensive behavioral health, (4) buprenorphine or methadone, (5) naltrexone, and (6) nonintensive behavioral health. Main Outcomes and Measures: Opioid-related overdose or serious acute care use during 3 and 12 months after initial treatment. Results: A total of 40 885 individuals with OUD (mean [SD] age, 47.73 [17.25] years; 22 172 [54.2%] male; 30 332 [74.2%] white) were identified. For OUD treatment, 24 258 (59.3%) received nonintensive behavioral health, 6455 (15.8%) received inpatient detoxification or residential services, 5123 (12.5%) received MOUD treatment with buprenorphine or methadone, 1970 (4.8%) received intensive behavioral health, and 963 (2.4%) received MOUD treatment with naltrexone. During 3-month follow-up, 707 participants (1.7%) experienced an overdose, and 773 (1.9%) had serious opioid-related acute care use. Only treatment with buprenorphine or methadone was associated with a reduced risk of overdose during 3-month (adjusted hazard ratio [AHR], 0.24; 95% CI, 0.14-0.41) and 12-month (AHR, 0.41; 95% CI, 0.31-0.55) follow-up. Treatment with buprenorphine or methadone was also associated with reduction in serious opioid-related acute care use during 3-month (AHR, 0.68; 95% CI, 0.47-0.99) and 12-month (AHR, 0.74; 95% CI, 0.58-0.95) follow-up. Conclusions and Relevance: Treatment with buprenorphine or methadone was associated with reductions in overdose and serious opioid-related acute care use compared with other treatments. Strategies to address the underuse of MOUD are needed.

Private Coverage of Methadone in Outpatient Treatment Programs.

Among the three medications approved for the treatment of opioid use disorder, methadone has been in use for the longest period and has the most extensive evidence base of effectiveness. Yet it remains underutilized as new insurance policies favor access to buprenorphine and neglect to dismantle barriers to obtaining methadone. In the absence of wholesale regulatory change, private insurance carriers should take the lead in expanding access to this medication. We offer several solutions for private payers, including expanding coverage, removing prior authorization, addressing out-of-pocket costs, increasing provider reimbursement, and incentivizing system integration.

What Happens When Employers Switch from a "Carve-Out" to a "Carve-In" Model of Managed Behavioral Health?

BACKGROUND: Since the introduction and soaring popularity of the managed behavioral healthcare (BH) "carve-out" model in the 1980s, policymakers have been concerned with their impact on access. In carve-outs, BH and medical benefits are administered separately. Earlier literature found they reduced intensity of service use while maintaining penetration rates. Recently it has become more common for employers to drop existing carve-out contracts, partly due to the Mental Health Parity and Addiction Equity Act (MHPAEA), which placed a greater administrative burden on carve-outs for parity compliance. Although prior studies focused exclusively on the impact of moving from carve-in to carve-out models, it is now more policy-relevant to understand the effects of the move from carve-out to carve-in, which may not be symmetric. Moreover, the natural experiment resulting from MHPAEA implementation may attenuate concerns about selection bias. STUDY AIMS: This study examines how specialty BH care patterns change when employees and dependents are moved from a "carve-out" plan to a "carve-in" plan. METHODS: Linked insurance claims, eligibility, plan and employer data from 2008-14 were obtained for three Optum( employers who dropped their carve-out contracts but retained their carve-in plans. A longitudinal "difference-in-differences" study design was used to compare changes in BH services use over time among individuals who were: (i) moved to carve-in plans when the employer dropped its carve-out contract (N=177,653); and (ii) enrolled in carve-in plans before and after the transition (N=58,658). Outcomes included total and inpatient expenditures, broken down by plan, patient, and total; outpatient visits for assessment, individual psychotherapy, family psychotherapy, and medication management; and days of structured outpatient care, day treatment, residential care, and acute inpatient care. We pooled person-year observations and estimated regressions including individual fixed effects, year dummies and interactions between indicators for post-transition period and whether transitioned from carve-out to carve-in. RESULTS: Relative to individuals continuously in carve-in plans, those who were transitioned experienced significant increases in inpatient utilization (beta =.02; p=.05) and patient inpatient costs (beta =2.35; p=.01) and decreases in day treatment (beta =-0.01; p=.02). Our conclusions proved robust against potential biases due to differing secular time trends and differential changes in benefits resulting from MHPAEA. DISCUSSION: The increased inpatient utilization associated with switching from carve-out to carve-in plans is consistent with previous literature. Carve-outs may use day treatment to reduce inpatient care so that increased inpatient utilization post-transition reduced demand for day treatment. Limitations include possible selection bias at the employer level; lack of data on medication and generalist use, quality, clinical endpoints and quality of life; and potential lack of generalizability. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE: The reduction in the use of carve-out contracts by private employers associated with MHPAEA implementation likely did not have a net negative impact and may have actually increased access to care among former carve-out enrollees in need of inpatient services. IMPLICATIONS FOR HEALTH POLICIES: Policymakers should consider and evaluate possible unintended consequences of legislation designed to improve access to care. IMPLICATIONS FOR FURTHER RESEARCH: Future work should replicate these analyses with a more representative sample.

The Effects of Three Kinds of Insurance Benefit Design Features on Specialty Mental Health Care Use in Managed Care.

BACKGROUND: Insurance benefit features play a role in determining access to specialty mental health care. Previous research, primarily examining the effects of copayments, coinsurance, and deductibles in a fee-for-service setting, has concluded that specialty mental health use is highly sensitive to changes in financial requirements. Less is known about the effects of other benefit features and the effects of all of these features in a managed care environment. AIMS OF THE STUDY: Determine whether increased generosity of three types of benefit features was associated with increases in specialty mental health use and expenditures in a managed care setting. Secondary analyses investigated whether these associations varied by income level. METHODS: A first-differences design used linked claims, enrollment, and benefit data for 1,242,949 non-elderly adults (aged 18-64) with employer-sponsored insurance, before (2009) and after (2011) national behavioral health parity implementation. The data were provided by a large national managed behavioral health organization. Benefit design features included combined cost sharing from copayment and coinsurance, deductibles, the presence of annual use limits, cost sharing penalties associated with services used without getting required prior-authorization, and provider network. Outcomes included visits/days, total expenditures and patient out-of-pocket expenditures for individual psychotherapy and inpatient use, with separate values for in-network and out-of-network (OON) service use. Ordinary least squares regression was performed on change scores (2011 minus 2009 values) of all outcomes to implement the first-differences study design and normalize distributions of otherwise heavily skewed (towards zero) variables. Regressions stratified by higher income (>=USD75,000) and net worth (>=USD100,000) and lower income/net worth were also conducted. RESULTS: For in-network individual psychotherapy, larger increases in cost sharing from copayment and coinsurance were modestly associated with larger decreases in use and total expenditures (beta_visits=--0.00008, p-value=0.030; beta_total expenditures=USD--0.00629, p-value=0.011), and elimination of treatment limits was associated with larger increases in use (beta=0.09637, p-value=0.002) and total expenditures (beta=USD6.57506, p-value=0.001). These results were observed among all enrollees of plans that covered in-network and out-of-network plans and among a sub-set of these enrollees who did not change plans between 2009 and 2011. Benefit features had fewer associations with inpatient care and OON services. DISCUSSION: Elimination of limits was associated with small average increases in in-network individual psychotherapy utilization and expenditures. Cost sharing sensitivities of individual psychotherapy visits to financial requirements reported here were small, and resembled previous findings based in a managed care setting, which were smaller than findings based on the fee-for-service settings. Cost sharing may not pose a practical barrier to specialty behavioral health for non-elderly adults with employer-sponsored managed care plans. However, the influence of cost sharing may vary by specific healthcare needs, something that should be explored in further research.

Racial/ethnic disparities in specialty behavioral health care treatment patterns and expenditures among commercially insured patients in managed behavioral health care plans.

OBJECTIVE: To document differences among racial/ethnic/gender groups in specialty behavioral health care (BH) utilization/expenditures; examine whether these differences are driven by probability vs intensity of treatment; and identify whether differences are explained by socioeconomic status (SES). DATA SOURCE: The cohort consists of adults continuously enrolled in Optum plans with BH benefits during 2013. STUDY DESIGN: We modeled each outcome using linear regressions among the entire sample stratified by race/ethnicity, language and gender. Then, we estimated logistic regressions of the probability that an enrollee had any spending/use in a given service category (service penetration) and linear regressions of spending/use among the user subpopulation (treatment intensity). Lastly, all analyses were rerun with SES controls. DATA COLLECTION: This study links administrative data from a managed BH organization to a commercial marketing database. PRINCIPAL FINDINGS: We found that in many cases, racial/ethnic minorities had lower specialty BH expenditures/utilization, relative to whites, primarily driven by differences in service penetration. Among women, relative to whites, Asian non-English speakers, Asian English speakers, Hispanic non-English speakers, Hispanic English speakers, and blacks had $106, $95, $90, $48, and $61 less in total expenditures. SES explained racial/ethnic differences in treatment intensity but not service penetration. CONCLUSIONS: In this population, SES was not a major driver of racial/ethnic differences in specialty BH utilization. Future studies should explore the role of other factors not studied here, such as stigma, cultural competence, and geography.

The Mental Health Parity and Addiction Equity Act (MHPAEA) evaluation study: Did parity differentially affect substance use disorder and mental health benefits offered by behavioral healthcare carve-out and carve-in plans?

BACKGROUND: To assess whether implementation of the Mental Health Parity and Addiction Equity Act (MHPAEA) was associated with: 1. Reduced differences in financial requirements (i.e., copayments and coinsurance) for substance use disorder (SUD) versus specialty mental health (MH) care and 2. Reductions in the level of cost-sharing for SUD-specific services. METHODS: MH and SUD copayments and coinsurance, 2008-2013, were obtained from benefits databases for carve-in and carve-out plans from Optum®. Linear regression was used to estimate the association of MHPAEA with differences between MH and SUD care financial requirements among carve-in and carve-out plans. A two-part regression model investigated whether MHPAEA was associated with changes in the use or level of financial requirements for SUD-specific services among carve-out plans. RESULTS: MHPAEA was not associated with significant changes in the difference between SUD and MH copayments or coinsurance levels among either carve-in or carve-out plans. MHPAEA was associated with decreases in the levels of inpatient (in-network: -$51.17; out-of-network: -$34.39) and outpatient (in-network: -$10.26) detox copayments, but increases in the levels of in-network outpatient detox coinsurance (6 percentage points) among all carve-out plans. CONCLUSION: Even if SUD benefits had been historically less generous than MH benefits, SUD financial requirements were already at parity with MH financial requirements by the time MHPAEA was passed, among Optum® plans. MHPAEA's SUD parity mandate reduced cost-sharing for detox services via copayments, but, for outpatient detox, the law simultaneously increased cost-sharing via coinsurance.

The Mental Health Parity and Addiction Equity Act Evaluation Study: Impact on Nonquantitative Treatment Limits for Specialty Behavioral Health Care.

OBJECTIVE: To assess frequency, type, and extent of behavioral health (BH) nonquantitative treatment limits (NQTLs) before and after implementation of the Mental Health Parity and Addiction Equity Act of 2008 (MHPAEA). DATA SOURCES: Secondary administrative data for Optum carve-out and carve-in plans. STUDY DESIGN: Cross-tabulations and "two-part" regression models were estimated to assess associations of parity period with NQTLs. DATA COLLECTION/EXTRACTION METHODS: Optum provided four proprietary BH databases, including 2008-2013 data for 40 carve-out and 385 carve-in employers from Optum's claims processing databases and 2010 data from interviews conducted by Optum's parity compliance team with 49 carve-out employers. PRINCIPAL FINDINGS: Preparity, carve-out plans required preauthorization for in-network inpatient/intermediate care; otherwise coverage was denied. Postparity, 73 percent would review later by request and half charged no penalty for late authorization. Outpatient visit authorization requirements virtually disappeared. For carve-out out-of-network inpatient/intermediate care, and for carve-ins, plans changed penalties to match medical service policies, but this did not necessarily lead to fewer requirements or lower penalties. CONCLUSION: After 2011, MHPAEA was associated with the transformation of BH care management, including much less restrictive preauthorization requirements, especially for in-network care provided by carve-out plans.

Behavioral Health Treatment Patterns Among Employer-Insured Adults in Same- and Different-Gender Marriages and Domestic Partnerships.

OBJECTIVE: This study examined specialty behavioral health treatment patterns among employer-insured adults in same- and different-gender domestic partnerships and marriages. METHODS: The study used behavioral health service claims (2008-2013) from Optum to estimate gender-stratified penetration rates of behavioral health service use by couple type and partnership status among partnered adults ages 18-64 (N=12,727,292 person-years) and levels of use among those with any use (conditional analyses). Least-squares, logistic, and zero-truncated negative binomial regression analyses adjusted for age, employer and plan characteristics, and provider supply and for sociodemographic factors in sensitivity analyses. Generalized estimating equations were used to address within-group correlation of adults clustered in employer groups. RESULTS: Both women and men in same-gender marriages or domestic partnerships had higher rates of behavioral health service use, particularly diagnostic evaluation, individual psychotherapy, and medication management, and those in treatment had, on average, more psychotherapy visits than those in different-gender marriages. Behavioral health treatment patterns were similar between women in same-gender domestic partnerships and same-gender marriages, but they diverged between men in same-gender domestic partnerships and same-gender marriages. Moderation analysis results indicated that adults with same-gender partners living in states with fewer legal protections for lesbian, gay, bisexual, and transgender persons were less likely than adults with same-gender partners in LGBT-friendly states to receive behavioral health treatment. Sensitivity analyses did not affect findings. CONCLUSIONS: Behavioral health treatment patterns varied by couple type, partnership status, and gender. Results highlight the importance of increasing service acceptability and delivering inclusive, culturally relevant behavioral health treatment for lesbian, gay, and bisexual persons.

The Mental Health Parity and Addiction Equity Act Evaluation Study: Impact on Mental Health Financial Requirements among Commercial "Carve-In" Plans.

OBJECTIVE: Did mental health cost-sharing decrease following implementation of the Mental Health Parity and Addiction Equity Act (MHPAEA)? DATA SOURCE: Specialty mental health copayments, coinsurance, and deductibles, 2008-2013, were obtained from benefits databases for "carve-in" plans from a national commercial managed behavioral health organization. STUDY DESIGN: Bivariate and regression-adjusted analyses compare the probability of use and (conditional) level of cost-sharing pre- and postparity. An interaction term is added to compare differential levels of pre- and postparity cost-sharing changes for plans that were and were not already at parity pre-MHPAEA. FINDINGS: Controlling for employer/plan characteristics, MHPAEA is associated with higher intermediate care copayments ($15.9) but lower outpatient ($2.6) copayments among in-network-only plans. Among plans with in- and out-of-network benefits, MHPAEA is associated with lower inpatient ($23.2) and outpatient ($2.5) copayments, but increases in inpatient and intermediate in-network and out-of-network coinsurance (about 1 percentage point). Among the few plans not at parity pre-MHPAEA, changes in use and level of cost-sharing associated with MHPAEA were more dramatic. CONCLUSION: Mixed evidence that MHPAEA led to more generous mental health benefits may stem from the finding that many plans were already at parity pre-MHPAEA. Future policy focus in mental health may shift to slowing growth in cost-sharing for all health services.

Mental Health in the Workplace: A Call to Action Proceedings From the Mental Health in the Workplace-Public Health Summit.

OBJECTIVE: The aim of the study was to declare a call to action to improve mental health in the workplace. METHODS: We convened a public health summit and assembled an Advisory Council consisting of experts in the field of occupational health and safety, workplace wellness, and public policy to offer recommendations for action steps to improve health and well-being of workers. RESULTS: The Advisory Council narrowed the list of ideas to four priority projects. CONCLUSIONS: The recommendations for action include developing a mental health in the workplace (1) "how to" guide, (2) scorecard, (3) recognition program, and (4) executive training.

The Mental Health Parity and Addiction Equity Act evaluation study: Impact on specialty behavioral healthcare utilization and spending among enrollees with substance use disorders.

BACKGROUND: The federal Mental Health Parity and Addiction Equity Act (MHPAEA) sought to eliminate historical disparities between behavioral health and medical health insurance benefits among the commercially insured. This study determines whether MHPAEA was associated with increased BH expenditures and utilization among a population with substance use disorder (SUD) diagnoses. METHODS: Claims and eligibility data from 5,987,776 enrollees, 2008-2013, were obtained from a national, commercial, managed behavioral health organization. An interrupted time series study design with segmented regression analysis estimated time trends of per-member-per-month (PMPM) spending and use before (2008-2009), during (2010), and after (2011-2013) MHPAEA compliance. The study sample contained individuals with drug or alcohol use disorder diagnosis during study period (N=2,716,473 member-month observations). Outcomes included: total, plan, patient out-of-pocket spending; outpatient utilization (assessment/diagnostic evaluation visits; medication management; individual, group and family psychotherapy, and structured outpatient care); intermediate care utilization (day treatment; recovery home and residential); and inpatient utilization. RESULTS: Starting at the beginning of the post-parity period, MHPAEA was associated with increased levels of PMPM total and plan spending ($25.80 [p=0.01]; $28.33 [p=0.00], respectively), as well as the number of PMPM assessment/evaluation, individual psychotherapy, and group psychotherapy visits, and inpatient days (0.01 visits [p=0.01]; 0.02 visits [p=0.01]; 0.01 visits [p=0.03]; 0.01days [p=0.01], respectively). Following these initial level changes, MHPAEA was also associated with monthly increases in PMPM total, plan, and patent out-of-pocket spending ($2.56/month [p=0.00]; $2.25/month [p=0.00]; $0.27 [p=0.03], respectively), as well as structured outpatient visits and inpatient days (0.0012 visits/month [p=0.01]; 0.0012days/month [p=0.00]). CONCLUSION: MHPAEA was associated with modest increases in total, plan, and patient out-of-pocket spending and outpatient and inpatient utilization. These increases, while modest in magnitude, are larger in magnitude than increases detected among a sample of all enrollees (i.e. not only those with SUD diagnoses).

The Mental Health Parity and Addiction Equity Act (MHPAEA) Evaluation Study: Impact on Quantitative Treatment Limits.

OBJECTIVE: The Mental Health Parity and Addiction Equity Act (MHPAEA) significantly changed regulations governing behavioral health benefits for large, commercially insured employers. Pre-MHPAEA, many plans covered only a specific number of behavioral health treatment days or visits; post-MHPAEA, such quantitative treatment limits (QTLs) were allowed only if they were "at parity" with medical-surgical limits. This study assessed MHPAEA's effect on the prevalence of behavioral health QTLs. METHODS: Analyses used 2008-2013 specialty behavioral health benefit design data for Optum large-group plans, both carve-outs (N=2,257 plan-years, corresponding to 1,527 plans and 40 employers) and carve-ins (N=11,644 plan-years, 3,569 plans, and 340 employers). Descriptive statistics were calculated for limits existing at parity implementation, distinguished by accumulation period (annual or lifetime), level of care (inpatient, intermediate, or outpatient), unit (days, visits, or courses), condition, and network level. Proportions of plans using specific limits during the preparity (2008-2009), transition (2010), and postparity (2011-2013) periods were compared with Fisher's exact tests. RESULTS: Preparity, the most common QTLs were annual visit or day limits. Accounting for overlap in limit types, 89% of regular carve-out plans, 90% of in-network-only carve-outs, and 77% of carve-in plans limited outpatient visits; 66% of regular carve-out plans, 74% of in-network-only carve-outs, and 73% of carve-ins limited inpatient or intermediate days. Postparity, QTLs almost entirely disappeared (p<.001). CONCLUSIONS: Before MHPAEA, QTLs were common. Postimplementation, virtually all plans dropped such limits, suggesting that MHPAEA was effective at eliminating QTLs. However, increasing access to behavioral health care will mean going beyond such QTL changes and looking at other areas of benefit management.

The Mental Health Parity and Addiction Equity Act Evaluation Study: Impact on Specialty Behavioral Health Care Utilization and Spending Among Carve-In Enrollees.

OBJECTIVE: The federal Mental Health Parity and Addiction Equity Act (MHPAEA) sought to eliminate historical disparities between insurance coverage for behavioral health (BH) treatment and coverage for medical treatment. Our objective was to evaluate MHPAEA's impact on BH expenditures and utilization among "carve-in" enrollees. METHODS: We received specialty BH insurance claims and eligibility data from Optum, sampling 5,987,776 adults enrolled in self-insured plans from large employers. An interrupted time series study design with segmented regression analysis estimated monthly time trends of per-member spending and use before (2008-2009), during (2010), and after (2011-2013) MHPAEA compliance (N=179,506,951 member-month observations). Outcomes included: total, plan, patient out-of-pocket spending; outpatient utilization (assessment/diagnostic evaluation visits, medication management, individual and family psychotherapy); intermediate care utilization (structured outpatient, day treatment, residential); and inpatient utilization. RESULTS: MHPAEA was associated with increases in monthly per-member total spending, plan spending, assessment/diagnostic evaluation visits [respective immediate increases of: $1.05 (P=0.02); $0.88 (P=0.04); 0.00045 visits (P=0.00)], and individual psychotherapy visits [immediate increase of 0.00578 visits (P=0.00) and additional increases of 0.00017 visits/mo (P=0.03)]. CONCLUSIONS: MHPAEA was associated with modest increases in total and plan spending and outpatient utilization; for example, in July 2012 predicted per-enrollee plan spending was $4.92 without MHPAEA and $6.14 with MHPAEA. Efforts should focus on understanding how other barriers to BH care unaddressed by MHPAEA may affect access/utilization. Future research should evaluate effects produced by the Affordable Care Act's inclusion of BH care as an essential health benefit and expansion of MHPAEA protections to the individual and small group markets.