Susan H. Busch, PhD

Out-of-network (OON) care is one area where patients might be more likely to challenge their healthcare bills due to the high out-of-pocket costs and unexpected charges related to emergency care or hospital-affiliated providers. We aimed to determine whether, and under what circumstances, patients negotiate with either insurers or providers when services are billed OON and how often patients that do engage in negotiation are successful. 

We conducted a 2011 Internet survey on OON care on a nationally representative sample of privately insured adults (n = 721). We considered whether patients would be more likely to negotiate OON charges by demographic characteristics and under several scenarios: emergency visits, bills from hospital-affiliated OON providers at in-network hospitals, and balance bills. 

 We found patients negotiated 19% of OON bills, were successful in lowering their costs 56% of the time, and were more likely to be successful negotiating with providers compared with insurers (63% vs 37%; 

 <.01). Men were more likely than women to be successful in lowering their costs (76% vs 50%; 

 <.05). OON bills for emergencies, providers at in-network hospitals, and with a balance bill were more likely to be negotiated, although bills from providers at in-network hospitals and with balance bills were less likely to be successfully negotiated.

Patients had low rates of success in negotiating OON bills for emergency care and for OON providers at in-network hospitals. Policy makers aiming to protect patients under these scenarios should consider policies that allow for an easily accessible, formal, and unbiased mediation process.

	Nineteen percent of bills for out-of-network (OON) visits were negotiated; of these negotiated bills, individuals were successful in lowering their costs approximately half the time. 

		Although individual demographic characteristics were not associated with negotiation of an OON bill, we found disparities by gender and health status in whether or not an individual was successful in lowering their costs. 

		Patients had low rates of success in negotiating OON bills for emergency care and for OON providers at in-network hospitals. Policy makers aiming to protect patients under these scenarios should consider policies that allow for an easily accessible, formal, and unbiased mediation process.

Patients who believe they have inappropriately been charged for healthcare services may have difficulty challenging these costs. They may have poor understanding of health insurance terminology, face a complex billing bureaucracy, not fully understand the nature and necessity of the care they are receiving, be vulnerable if facing serious illness, or not feel comfortable discussing money with their physician and worry it will impact their relationship. Relatively few patients formally voice concerns to their health plan, even when they believe they have been inappropriately charged a large out-of-pocket cost. Circumstances, such as socioeconomic status, patient empowerment, and chronic illness, can impact whether a patient will use their voice and have a satisfactory resolution.

Out-of-network (OON) care is one area where patients might be more likely to challenge their healthcare bills due to the high out-of-pocket costs of using OON care. In addition to higher cost sharing (eg, 20% of costs vs 10% for in-network providers), OON providers may “balance bill” patients the difference between their list price and the amount the provider is reimbursed by the insurer. Although there is little objective data on consumer out-of-pocket costs for OON care, reports suggest that balance bills may be quite high (and increasing) and a source of medical debt among the privately insured.

Recently, attention to this issue has increased due to consumer complaints about the use of narrow networks in plans sold in state health insurance marketplaces; overall, 41% of 2014 marketplace plans were classified as small or extra small.

 An increasing number of plans are also reimbursing for OON care based on percentage of the Medicare rate (rather than the usual and customary rate), which often leads to higher balance bills.

 Even cost sharing may be increasing. Some high-deductible health plans do not allow OON services to apply to the general deductible, and there is evidence that deductibles for OON care are increasing relative to in-network deductibles. A PriceWaterhouseCoopers survey found that from 2009 to 2015, average OON deductibles increased by $1000 compared with a $500 increase for in-network deductibles.

Although the majority of OON care—especially in the outpatient setting&mdash;is an informed choice, from a patient perspective, some charges related to OON services may be viewed as unfair, even if they meet the conditions of the insurance contract.

 In an emergency, patients may feel it is necessary to use the most easily accessible provider; however, they may unexpectedly receive bills from an OON provider at an in-network hospital. A hospital may be in-network, but providers who are hospital-based or hospital-affiliated who are treating the patient, such as anesthesiologists, radiologists, or pathologists, may not have contracts with the patient’s insurer. For example, although a pregnant patient may check before routine labor and delivery to ensure that her obstetrician and hospital are in her insurer’s network, during her labor, the epidural may be placed by an OON anesthesiologist or complications may arise and she may receive services from an OON neonatologist, which essentially would be almost impossible to refuse. Another concern relates to price transparency. If information on a provider’s price or insurer reimbursement for a service is not readily available, patients may make the decision to use an OON provider without understanding the cost implications. 

Currently, there are few federal or state protections or formal complaint or mediation processes specifically related to unexpected OON bills, leaving most patients to initiate a complaint on their own with their insurer or provider. The Affordable Care Act (ACA) partially addressed the issue of OON emergency department (ED) care on the federal level by requiring that insurers bill in-network cost-sharing rates for patients who use an OON ED. Out-of-pocket costs to these patients may still be high since balance billing by providers is still allowed.

 At the state level, patients may submit a complaint to their state for denial of services from their insurer and request an external review process; however, this process is for what is covered under the health plan and it is unclear whether patients challenging OON bills are covered under this mechanism. More recently, some state legislators have strengthened consumer protections using a variety of approaches, including requiring that the insurer, rather than the patient, pay the balance bill for unexpected OON care (sometimes referred to as "hold harmless provisions"), ensuring adequate payment for providers to deter balance billing, and requiring that providers disclose the potential for an OON bill at the point of service.

Because of the complexity of negotiating between the insurer, provider, and patient, the use of an independent mediator has also been suggested at the state level. In Texas, in addition to disclosure rules, patients themselves may initiate the mediation process for unexpected OON bills over $500.

 A New York state law that went into effect April 2015 holds patients harmless from unexpected OON bills for emergencies. For other unexpected OON billing scenarios (ie, OON provider at in-network hospital), the New York law allows patients to assign their benefits to the insurer. The OON provider is then prohibited from seeking payment from the patient (except for cost-sharing amounts). After this assignment of benefits, if the OON provider and insurer disagree about reimbursement, the provider or insurer can initiate an independent mediation process.

Little is known about how often patients negotiate OON bills and the resolution of the dispute. Consumer Reports National Research Center recently conducted a survey, which indicated that about one-third of patients have received a bill where the insurer paid less than expected, with many cases not resolved to the patients' satisfaction.

 Another study examining pre-service denials from 1998 to 2000 in 2 large HMO’s found that among pre-service denials that were appealed, approximately 1 in 5 were related to OON care, and these appeals were less likely to be won in favor of the enrollee compared with medical necessity appeals.

 Postservice appeals in the same dataset often involved failure to obtain authorization to use an OON provider.

In this paper, we attempt to address the question of whether, and under what circumstances, patients attempt to negotiate with either insurers or providers when services are billed OON and how often patients that do engage in negotiation are successful. We conducted a national survey of users of OON care and determined what proportion self-reported that they negotiated with insurers, providers, or both, and how often they were successful. We considered whether, and which, patient or treatment characteristics predict an individual will negotiate an OON bill. We paid particular attention to those patients who report the OON service was an emergency, at an in-network hospital, or included a balance bill. 

Data were obtained from a 2011 Internet survey on patient experiences with OON care on a nationally representative sample of privately insured English-speaking US adults aged 18 to 64 years. Detailed methods have been published previously.

 The survey was constructed and tested through cognitive interviews

 and pretesting, and then administered via the Internet by GfK Knowledge Networks. GfK’s online research panel consists of approximately 50,000 US households selected using high-quality address-based sampling methods similar to those used by US government surveys.

 Panelists are not excluded if they do not currently own a computer, and only invited individuals are included in the panel. The probability-based sampling used to construct the panel and its representativeness of the US population have been validated.

 Demographic information was provided by GfK for all individuals in the panel.

A series of screener questions was sent to 21,754 panelists to identify respondents enrolled in a private health insurance plan with a provider network who had seen a physician and/or mental health professional in the past 12 months. Enrollment was closed when a pre-determined number of panelists screened in and began the survey, resulting in a completion rate of 64% (13,900 panelists). Panelists who had used an OON provider (n = 721) took a 10-minute survey on their experiences. 

For each OON visit, we asked respondents, “Did you try to bargain with the doctor for a lower price (either before or after seeing the doctor)?” If they answered affirmatively, we asked the follow-up question, “Were you successful in bargaining for a lower price with the doctor?” Similarly, for each visit, we also asked about trying to bargain and success in bargaining with the insurance company. For patient-level analyses (

]), patients were defined as negotiators if they negotiated with either the insurer or the provider in any of their reported OON visits. Successful negotiators were defined by having any reported success with either the insurer or the provider for any of their reported OON visits. 

We considered whether patients would be more likely to negotiate or dispute OON charges under several scenarios: emergency visits, bills from hospital-affiliated OON providers (eg, anesthesiologists, pathologists) at in-network hospitals, and balance bills. A visit was defined as an emergency if the respondent answered “Yes” to “When you went to the emergency room or were admitted to the hospital, was it for a medical emergency?” Hospital-affiliated provider OON bills were defined as inpatient or ED visits, where the hospital was reported to be in-network and the patient was unaware the provider was OON prior to receiving care. A visit was considered balance billed if the respondent answered “Yes” to “Did the doctor charge you more than what your insurance plan covered? Do not include any co-payments, coinsurance, and deductible you may have been responsible for.” Balance bills could be associated with any OON service, including informed use of OON providers and unexpected OON service use, such as for an emergency, hospital-affiliated providers at an in-network hospital, or when no in-network provider was available. For comparison, we also constructed a category for visits where none of these 3 issues were present.

All reported analyses were weighted to match the sample to the US population based on Current Population Survey data on sex, age, race/ethnicity, education, metropolitan area, Census region, and Internet access, and to adjust for panel recruitment, attrition, oversampling, and survey nonresponse. Frequencies and χ

 tests were used for categorical variables. Where relevant, we adjusted standard errors for multiple observations for individuals. Linear regression models predicting negotiation and success in negotiation controlled for education, race, gender, income, residence in an urban area, age, and self-reported health status. 

The final sample included 721 individuals aged 18 to 64 years with private health insurance who had used an OON physician or mental health professional within the last year, representing a total of 1081 OON visits/bills (662 outpatient and 419 inpatient) as some individuals reported use of more than 1 OON provider. Overall, 19% (n = 177) of the bills for OON visits were negotiated by individuals with either the insurer or the provider, 14% were negotiated with the provider, and 10% with the insurer (Figure [A]). In 5% of visits, individuals negotiated with both the insurer and provider (data not shown). Of the negotiated bills, individuals were successful in lowering their costs about half of the time, and were significantly more likely to be successful when negotiating with providers compared with insurers (63% vs 37%; 

Approximately 22% of individuals who used OON providers negotiated an OON bill with the insurer or provider, and 58% were successful in reducing their costs for at least 1 of the bills (Table 1). An individual’s demographic characteristics were not significantly associated with whether or not they negotiated a bill. However, men were more likely than women (76% vs 50%; 

 <.05) and those in good health versus poor health (63% vs 25%; 

 <.01) to be successful in lowering their costs. These differences persisted after adjusting for education, race, income, age, and residence in an urban area (eAppendix Table).

 presents subsets of OON visits where we hypothesized individuals would be more likely to negotiate their OON bill: emergency care, use of an OON provider at an in-network hospital, and visits for which the individual was balance billed. We compared the proportion of visits negotiated, and of these, the proportion successful in lowering their costs, to the comparison group of visits in which none of the 3 issues were present. OON visits for emergencies at an in-network hospital and visits with a balance bill were significantly more likely to be negotiated (21%, 40%, and 34%, respectively) compared with the group with none of the issues studied, where only 10% of visits were negotiated. Somewhat surprisingly, success rates for OON care at in-network hospitals and those with balance bills (37% and 48%, respectively) were significantly lower than among the comparison group (74%).

In this study, we found that 19% of bills for OON visits were negotiated, and of these negotiated bills, individuals were successful in lowering their costs approximately half the time. Although individual demographic characteristics were not associated with negotiation of an OON bill, we found disparities by gender and health status in whether or not an individual was successful in lowering their costs. We also found patients were more likely to negotiate if a bill was related to OON care in an emergency, at an in-network hospital, or if there was a balance bill; although for bills at in-network hospitals and balance bills, these negotiations were less likely to be successful compared with bills where no observable issue was present.

Unexpected OON bills due to emergency care or from hospital-affiliated providers at in-network hospitals are unique for patients because they can find themselves in a difficult triangle between their insurer and their provider, where it may be unclear who is responsible for the higher costs. Insurers argue the use of networks can reduce healthcare premiums and that insurers should not be responsible for higher reimbursement to providers, particularly specialists, unwilling to accept a “market” rate. In the case of use of OON providers practicing at in-network hospitals, insurers may argue in-network hospitals have the responsibility to ensure an in-network provider is available when necessary (eg, anesthesiology). Conversely, providers might believe insurers are not offering a “fair” rate for their services, or that insurers too frequently deny or impose administrative hassle over medically necessary services, necessitating they not participate in networks to provide the highest quality care for their patients. In most states, the patient is ultimately responsible for the bill and the responsibility to negotiate with insurers/providers over bills perceived as unfair. 

This study supports the concern that some patients are not aware of their ability to dispute these unexpected OON medical bills. A 2015 Consumer Reports survey found that the majority of respondents were unsure if state resources were available to dispute insurer coverage denials, and they were unaware of the state agency tasked with handling health insurance complaints.

 In Texas, patients must be informed of their right to mediation when they are balance billed more than $1000 (this amount was lowered to $500 as of September 1, 2015); yet, in 2014, only 900 cases were filed for mediation—1 of which went to actual mediation&mdash;and stakeholders have expressed concern that patients are not aware of their rights.

It is not surprising that individuals who report OON care in an in-network hospital were more likely to negotiate these bills. Providers and insurers did not seem particularly responsive to patients' concerns about these issues, with less than half resolved to the patients’ satisfaction; it is possible, however, that the billing was valid under the terms of the contract, even if the patient views them as “unfair.” This suggests an area that regulators may want to focus on to ensure patients are treated fairly. We also found high rates of success among the group of negotiators with no observable issue studied, which may be because those that attempt to negotiate in this situation are negotiating prior to informed OON care and, if unsuccessful, opt to remain in-network or have an idiosyncratic issue that insurers or providers perceive as valid (eg, no in-network provider available). 

Although we did not find any differences by demographic characteristics in who negotiated an OON bill, we did find disparities in achieving success with negotiation among women and those in poor health. That those in poor health are less likely to be successful in negotiating is consistent with prior literature

—they may have competing priorities with their health, impaired physical or mental capabilities, and more medical bills overall to manage. We cannot conclude there is any gender bias on the part of insurers or providers from these data; however, the fact that women are equally likely to attempt to negotiate as men but are much less likely to be successful raises concerns, particularly in conjunction with evidence of gender disparities in economic negotiation outcomes in other contexts.

 If additional research indicates these differences are truly due to gender, and not other unobservable characteristics, this further suggests changes to the appeals process are needed.

This study has several limitations. As with all survey research, this study is subject to nonresponse and recall bias, although the use of weights (adjusting for Internet usage, panel recruitment and attrition, and nonresponse), a short recall period (12 months), and use of cognitive interviewing to eliminate questions that could not reliably be answered by self-report, partially alleviate these concerns. However, for some variables, such as whether the patient was balance billed and whether the hospital was in-network, a significant number of respondents were still unsure of their responses (30% and 21%, respectively). Another limitation is sample size, limiting power to detect differences and perform subgroup analysis on whether the patient negotiated with an insurer or a provider.

Due to time constraints with the survey, we were unable to describe important details around the negotiation, thus limiting what conclusions can be drawn from our data. Some bills may not have needed to be negotiated by the patient; for example, an insurer may pre-emptively hold a patient harmless from OON costs of emergency care, requiring no action by the patient. We were also unable to characterize whether the negotiation was around a balance bill or cost sharing, such as co-payment or deductible. We never asked specifically about why a patient negotiated the bill, and we could not determine whether the complaint was objectively legitimate. Additionally, we were unable to determine whether the negotiation was informal bargaining with the provider either before or after the visit or a formal appeal through the insurer for coverage denials. However, for emergency and inpatient care at an OON hospital, we can assume that the negotiation was after receiving an unexpected OON bill. Although additional information would have been useful to provide more context to the results presented here, we believe our data are still useful and indicate there is a need for more research in this area.

Because this survey focused on OON care, we are unable to compare against rates of negotiations/success for in-network care. Our data were collected in 2011, prior to implementation of policies established by the ACA to curtail balance billing in emergency care, as well as several state-level policies to protect and assist patients in mediation of unexpected OON charges.

Patients had low rates of success in negotiating OON bills for emergency care and for OON providers at in-network hospitals. Policy makers aiming to protect patients under these scenarios should consider policies that allow for an easily accessible, formal, and unbiased mediation process. The recently implemented New York state law excludes the patient from the negotiation process between the insurer and the provider after assignment of benefits, and outcomes should be evaluated and compared with other state models, such as Texas (which requires the patient to initiate the negotiation). At a minimum, patients should be better educated on their right to appeal and state resources available to them. Also, current available estimates of balance bill amounts are based on list price and allowed amounts.

 Our finding that a significant number of OON bills were negotiated suggests that these estimates may not be accurate, and future studies are needed to define the actual out-of-pocket burden for OON care. 

Use of OON care, both informed and unexpected, may only increase as insurers adopt the use of narrow networks as a way to potentially reduce healthcare costs.

 Policy makers will need to rapidly adopt both measures to prevent unexpected bills, such as accurate provider directories, patient education, and network adequacy standards, as well as protect patients from burdensome unexpected OON charges if they do occur.   

 Department of Population Health, New York University School of Medicine (KAK), New York, NY; Department of Health Policy and Management, Yale School of Public Health (SHB); New Haven, CT.

 This study was supported by a grant from the Women’s Health Research at Yale Pilot Project Program and funding from the Yale Robert Wood Johnson Foundation Clinical Scholars Program. The sponsors were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. 

 Dr Kyanko has accepted consulting fees from the Consumers Union, a nonprofit organization dedicated to consumer protection. These consulting activities are in topic areas outside of the submitted manuscript, but the entity may be perceived to have interest in the manuscript content. Dr Busch reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (SHB, KAK); acquisition of data (SHB, KAK); analysis and interpretation of data (SHB, KAK); drafting of the manuscript (SHB, KAK); critical revision of the manuscript for important intellectual content (SHB, KAK); statistical analysis (SHB, KAK); provision of patients or study materials (KAK); obtaining funding (SHB, KAK); administrative, technical, or logistic support (SHB, KAK); and supervision (SHB).

 Kelly A. Kyanko, MD, MHS, Department of Population Health, New York University School of Medicine, 550 1st Ave, TRB 6th Fl, Rm 646, New York, NY 10016. E-mail: kyanko.kelly@gmail.com.

1. Schlesinger M, Mitchell S, Elbel B. Voices unheard: barriers to expressing dissatisfaction to health plans. 

2. Pollitz K, Cox C, Lucia K, Keith K. Medical debt among people with health insurance. Kaiser Family Foundation website. http://kff.org/private-insurance/report/medical-debt-among-people-with-health-insurance/. Published January 7, 2014. Accessed September 22, 2016.

3. Charges billed by out-of-network providers: implications for affordability. Americas Health Insurance Plans website. https://www.ahip.org/charges-billed-by-out-of-network-providers-implications-for-affordability/. Published September 29, 2015. Accessed September 22, 2016.

4. Polsky D, Weiner J. The skinny on narrow networks in health insurance marketplace plans. Robert Wood Johnson Foundation website. http://www.rwjf.org/en/library/research/2015/06/the-skinny-on-narrow-networks-in-health-insurance-marketplace-pl.html. Published June 2015. Accessed September 22, 2016.

5. Kahn H, Parke R. Using Medicare RBRVS for reimbursing out-of-network claims in commercial insurance. Milliman website. http://us.milliman.com/uploadedFiles/insight/health-published/using-medicare-rbrvs.pdf. Published February 2012. Accessed September 22, 2016.

6. An unwelcome surprise: how New Yorkers are getting stuck with unexpected medical bills from out-of-network providers. New York State Governor website. http://www.governor.ny.gov/sites/governor.ny.gov/files/archive/assets/documents/DFS%20Report.pdf. Published March 7, 2012. Accessed September 22, 2016.

Articles

Nineteen percent of bills for out-of-network visits were negotiated, and of these negotiated bills, individuals were successful in lowering their costs approximately half the time.

Patients' Success in Negotiating Out-of-Network Bills

 In 2008, the US Congress enacted the Paul Wellstone and Pete Domenici Mental Health Parity and Addiction Equity Act (MHPAEA) requiring insurers to equalize private insurance coverage for mental health and substance use disorder services with coverage for general medical services.

To examine the effects of MHPAEA on substance use disorder treatment. Study Design: We used a difference-in-differences design to compare changes in outcomes among plan enrollees in the years before and after implementation of federal parity (2009-2010) with changes in outcomes among a comparison group of enrollees previously covered by state substance use disorder parity laws.

 Insurance claims data from Aetna Inc health plans in 10 states with state parity laws were used to compare outcomes for plan enrollees in fully insured and self-insured health plans (N = 298,339).

 In the first year of implementation, we found that federal parity did not lead to changes in the proportion of enrollees using substance use disorder treatment. We did find a modest increase in spending on substance use disorder treatment per enrollee ($9.99, 95% confidence interval, 2.54-18.21), but no significant change in identification, treatment initiation, or treatment engagement.

Inclusion of substance use disorder services in the federal parity law did not result in substantial increases in health plan spending. It will be critical to study results for year 2 after regulations affecting the management of care (eg, utilization review, network access) take effect.

Federal parity, as implemented in 2010, is unlikely to lead to large increases in total healthcare spending. 

		In 1 national health plan, federal parity led to a small increase in spending on substance use disorder treatment per enrollee ($9.99, 95% confidence interval, 2.54-18.21) in 2010, the first year after the policy took effect.

		This increase was not due to more individuals initiating treatment.

		Future work should consider the effects of the law in subsequent years after regulations affecting the management of care (eg, utilization review, network access) take effect

In 2008, the US Congress enacted a landmark federal parity law, the Paul Wellstone and Pete Domenici Mental Health Parity and Addiction Equity Act (MHPAEA), requiring health insurers to equalize private insurance coverage for mental health and substance use disorder (SUD) services with coverage for general medical services. Historically, health plans have imposed stringent limits on coverage for mental health and SUD services in the form of high cost sharing, annual inpatient day and outpatient visit limits, and lifetime dollar limits.

 In 2006, prior to implementation of MHPAEA, the majority of workers in private industry faced limits on SUD coverage. For example, 74% of workers had either annual or lifetime limits on the number of hospital days and 71% of workers had either annual or lifetime limits on the number of office visits for SUD.

 In contrast, almost all workers were offered unlimited hospital days and office visits for general medical services. In 2006, the median coinsurance level for SUD outpatient visits was 20% compared with 10% for outpatient medical services, and the median copayment for SUD outpatient visits was $25 compared with $17 for medical services.

Although a majority of states had previously enacted laws requiring parity for coverage of mental health disorders, many fewer states included SUDs in the conditions covered under their parity laws.

 MHPAEA took effect in 2010 and requires that when a private employer with 50 or more employees offers mental health and SUD coverage, all financial requirements (deductibles, copayments, coinsurance) and treatment limits (number of annual inpatient days and outpatient visits covered by insurance) for mental health and SUD must be equal to those for general medical services. The law also extends the Mental Health Parity Act of 1996, which prohibited the use of special annual and lifetime dollar limits for mental health benefits, to include SUD benefits. Health plans that provide out-of-network coverage for general medical services must also provide equal out-of-network coverage for mental health and SUD services.

To date, no published studies have examined the effects of MHPAEA. Most prior studies on the effects of state parity laws and of a comprehensive parity directive implemented in the Federal Employee Health Benefits Program focused on mental health and SUD treatment combined or on mental health treatment only. These studies consistently found that parity did not significantly increase use or spending,

 and that parity did lead to small but significant declines in out-of-pocket spending.

Only 2 prior studies examined the effects of parity on SUD treatment alone. Azzone and colleagues

 used health plan claims data to examine the effect of the Federal Employee Health Benefits Program parity policy on spending outcomes and performance measures based on Healthcare Effectiveness Data and Information Set (HEDIS); they found a significant decline in out-of-pocket SUD spending among treatment users and a small but significant increase in identification of SUD attributable to parity. A second study by Dave and Mukerjee

 examined the effects of state parity laws, including those limited to mental health, on inpatient admissions for SUD treatment and found that parity increased treatment admissions as well as the likelihood that an admission was privately insured.

The expected effects of the MPHAEA are unclear. Because MHPAEA does not require that specific conditions be covered, health plans have some discretion in determining which conditions and services to cover under parity. Relatively low public support for SUD benefits

 has led advocates to express concern that granting insurers discretion will lead to reduced coverage for SUD services. Conversely, SUD service users typically have higher spending compared with mental health service users. When limits are eliminated, a higher share of SUD patients are likely to be affected, which may lead to increases in spending.

Using administrative claims data from Aetna, a large national health insurance company, we study how MHPAEA affected an array of SUD use and spending outcomes, including the proportion of enrollees using any SUD treatment, total annual spending on SUD treatment per enrollee and per user of SUD treatment, total per user out-of-pocket spending on SUD treatment, and proportion of total SUD spending paid out of pocket. We also examine the proportion of health plan enrollees meeting 3 HEDIS-based SUD performance measures—identification, treatment initiation, and treatment engagement&mdash;which were originally developed by the Washington Circle.

In this study, we examine the effects of the law in the first year it took effect: 2010.

We used a difference-in-differences study design to compare changes in outcomes among health plan enrollees 1 year before and 1 year after implementation of MHPAEA (2009-2010) with changes among a comparison group of enrollees covered by state SUD parity laws in place prior to MHPAEA (N = 298,339). Difference-in-differences estimation allows us to distinguish the effects of the federal parity law from other changes affecting medical care utilization. Because MHPAEA extends to enrollees in all privately insured firms with 50 or more employees in the United States, identifying a comparison group presents a challenge. In this study, health plan enrollees already subject to preexisting state SUD parity laws served as the comparison group. As of 2008, 10 states—Colorado, Connecticut, Kentucky, Maine, Maryland, Minnesota, New Hampshire, Oregon, Rhode Island, and Vermont&mdash;had passed parity laws that included SUD services. We therefore limited our study population to Aetna health plan enrollees in these 10 states. Under the Employee Retirement Income Security Act, enrollees in fully insured employer-sponsored health plans were subject to state parity laws, but enrollees in self-insured employer-sponsored health plans operating in these states were exempt from state parity laws. Thus, in these states only enrollees of self-insured plans were newly covered under federal parity. Prior work suggests effects of state parity laws were concentrated on individuals in fully insured plans, and these laws did not affect individuals in self-insured plans.

 Self-insured plan enrollees in these 10 states who were subject to parity for the first time served as our treatment group, whereas fully insured plan enrollees who were already subject to SUD parity at the state level served as our comparison group.

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.

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.

 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.

 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.

 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 (

). 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 %).

 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). 

 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.

 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,

 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.

 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.

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.

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.

 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.

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.

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

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.

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).

 Susan H. Busch, PhD, Yale School of Public Health, 60 College St, New Haven CT 06520-8034.1. Barry C, Sindelar JL. Equity in private insurance coverage for substance abuse: a perspective on parity. Health Aff (Millwood). 2007; 26(6):w706-w716.

2. Gabel JR, Whitmore H, Pickreign JD, Levitt KR, Coffey RM, Vandivort- Warren R. Substance abuse benefits: still limited after all these years. Health Aff (Millwood). 2007;26(4):w474-w482.

3. National Alliance on Mental Illness. State mental health parity laws. http://www.nami.org/Content/ContentGroups/Policy/Issues_Spotlights/ Parity1/State_Parity_Chart_0709.pdf. Revised July 2009. Accessed September 17, 2012.

4. Goldman HH, Frank RG, Burnam MA, et al. Behavioral health insurance parity for federal employees. N Engl J Med. 2006;354(13): 1378-1386.

5. Barry CL, Busch SH. Do state parity laws reduce the financial burden on families of children with mental health care needs? Health Serv Res. 2007;42(3, pt 1):1061-1084.

6. Pacula RL, Sturm R. Mental health parity legislation: much ado about nothing? Health Serv Res. 2000;35(1, pt 2): 263-275.

Federal parity led to an increase in spending on substance use disorder treatment.

Susan H. Busch, PhD

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