Quantifying Balance Billing for Out-of-Network Behavioral Health Care in Employer-Sponsored Insurance
Abstract
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.
HIGHLIGHTS
•
About half of study families using out-of-network behavioral health services in a given year were responsible for out-of-network provider charges (i.e., balance billing), beyond what the insurer and patient were contractually required to pay.
•
Among family-years with these additional behavioral health expenditures, half the families had annual balance-billing levels over $175.
•
Higher balance billing was experienced by families enrolled in carve-out plans and in health maintenance organizations.
Balance billing occurs when patients receive a bill to cover the difference between what the provider charged and what private insurers and patients are contractually obliged to pay (1). This practice has been documented for medical care (2–6). The practice of balance billing can be financially devastating to patients; however, little is known about balance billing in the context of behavioral health care (e.g., specialty mental health services and addiction treatment).
The literature points to two scenarios involving behavioral health balance billing. Recent evidence identified high rates of inaccurate network status listings on insurance physician directory pages (7), which may lead to patients unknowingly getting care from out-of-network providers. Alternatively, because narrow networks (less than 25% of providers in a market) are more common for behavioral health care than for primary care (8), patients are more likely to go out of network when using behavioral health providers than when using general health care providers (9) and thus are more likely to receive balance billing.
Although estimates of behavioral health balance billing have not been published, evidence points to high out-of-network cost-sharing for behavioral health services. Researchers observe that patients with chronic mental health conditions had significantly higher average out-of-network cost-sharing, compared with patients with congestive health failure and patients with diabetes (10). Other studies have shown that prices (what insurers pay) and cost-sharing (e.g., patient copayments, coinsurance, and deductibles) for out-of-network psychotherapy (11) and other behavioral health services (12) have increased in recent years, even as cost-sharing for many other out-of-network services has decreased (13). The study reported here built on this literature by adding calculations of out-of-network behavioral health balance-billing amounts—which is one component of total out-of-network cost-sharing—from a national sample of commercial claims.
Here, balance billing was calculated as the difference between the provider charge amount and the amount allowed by the insurer plus patient cost-sharing. This provided a measure of what patients were potentially charged as balance billing. Our analyses also contribute to the literature by aggregating claims into membership units (called “families”—i.e., the main policy holder, or subscriber, and the subscriber’s spouse and any dependents) and examining differences in behavioral health balance-billing levels across family characteristics. We posed two research questions: How are balance-billing levels for behavioral health care distributed among a commercially insured sample? Do family-years with greater financial resources and education have higher levels of balance billing, compared with other family-years, when the analysis controls for plan and employer characteristics and provider supply?
Methods
Data
We leveraged a database linking employer-sponsored behavioral health claims and enrollment data from 2011 to 2014, as well as plan, employer, and provider network information, provided by a national managed behavioral health organization (representing all 50 U.S. states and territories) (14, 15) with self-reported subscriber demographic data provided by Optum Insight and hospital supply by state from the Area Health Resource File provided by the Health Resources and Services Administration (16).
The claims data included records from carve-in plans (i.e., plans that administer both general medical and behavioral health benefits) and carve-out plans (i.e., plans that administer behavioral health benefits separately from the general medical benefits, which are covered through a separate contract with the employer, often through a different vendor). The records provided information on patient utilization (with indicators of whether services were provided in network or out of network) and diagnoses. Claims data also included the amount charged by the provider, the amount the plan ultimately paid, and the amount the patient owed through his or her cost-sharing (copayment, deductibles, coinsurance, etc.). These data were used to create the study outcomes.
A study ID in the enrollment data allowed us to map individual enrollees to a subscriber. This established membership within family units in a given year (creating a unit, called “family-year” hereafter) and also provided information about subscriber age, relationship status, dependents, and behavioral health diagnoses across family members.
We linked a subset of the claims to subscriber socioeconomic characteristics drawn from Optum consumer marketing data. These included subscribers’ highest level of educational attainment, income and net worth, and race-ethnicity and language. The unit of analysis was the family-year.
Study Samples
The study sample was selected from among 363,048 family-years with any out-of-network behavioral health expenditures recorded in the employer-sponsored insurance claims in a given year. (A figure detailing exclusion criteria is included in an online supplement to this article.) In brief, because family-year health care expenditure–related outcomes (such as balance billing) were expected to be dependent, in part, on household composition, the first set of exclusion criteria dropped family-years with undetermined, not continuously enrolled, or changing family composition during a year. We also excluded families with any members residing outside the 50 U.S. states. Families were excluded if they were enrolled in plans that did not cover behavioral health services, were retiree or supplemental plans, or were not a calendar-year plan. Finally, families were excluded if any member had negative expenditures, if their only behavioral health diagnosis was dementia, or if they were missing claims data. Because 35% (N=68,659) of the remaining 196,034 family-years had complete data (whereas the other 65% were missing Optum Insight data), we used two samples in our analysis. We examined unadjusted balance billing among the 196,034 family-year sample (hereafter called the full sample) and the 68,659 family-year sample with nonmissing socioeconomic data (hereafter called the SES sample). We examined adjusted balance billing in the SES sample.
Outcomes
Our measure of balance billing used claims expenditure fields. Balance billing was calculated by identifying the “submitted amount” (i.e., the total dollar amount that the provider charged) and subtracting the “allowed amount” (i.e., the amount that the insurer and the patient agree to pay), as well as any discounts made available to the insurer by the provider (observed in less than 5% of the sample). Balance-billing amounts were adjusted by using 2014 inflation factors. The outcome variables measured balance billing in three ways: the total level of balance billing per family-year, the level of balance billing per family member, and the total level of balance billing per family-year as a proportion of the family’s total out-of-network out-of-pocket expenditures (for example, for copays and deductibles).
Family-Year Characteristics
To test the study hypotheses, we used Optum Insight data variables measuring the subscribers’ income and net worth, educational attainment, and race-ethnicity and language. The categories reported in this analysis were created by Optum Insight to preserve statistical deidentification. We also controlled for subscriber age and other family-level characteristics by using demographic information derived from the claims data, including subscriber relationship status, number of dependents, number of behavioral health diagnoses in the family, and the presence of 16 behavioral health diagnosis categories.
Plan and Employer Characteristics
The plan-level covariate indicated whether plans were a carve-out (versus a carve-in) or a health maintenance organization (HMO) (versus a preferred provider organization). State-level measures of total inpatient beds (from the Area Health Resource File) (16) and state-level measures of the behavioral health provider network in managed behavioral health organizations (MBHOs) (aggregating Ph.D.s, M.D.s, M.S.W.s and R.N.s) per 10,000 people were also used as covariates. The following employer-level covariates were controlled for in our models: size, U.S. census region of employer’s headquarters, and employer industry, defined by North American Industry Classification codes (covariates are described in greater detail elsewhere [17]).
Statistical Analysis
We report the distribution of family-year characteristics among both the full and the SES samples. We also present descriptive statistics (mean, standard deviation, median, 75th percentile, and 90th percentile) of balance-billing outcomes—overall and among family-years with any balance billing, and in both the full and the SES samples.
Our multivariate analysis was conducted with the SES sample. We used a two-part model framework to calculate the probability of having any balance billing (using logistic regression) among all family-years, the level of balance billing among those who had any balance billing (using a generalized linear model gamma regression), and the level of balance billing among all family-years (18). We report the marginal effects, calculated by using the margins command in Stata, to indicate the average difference in probability or level of balance billing between a given subscriber or family-level characteristic value, compared with the reference value. We report 95% confidence intervals (CIs).
Sensitivity Analyses
To account for potential multiplicative effects of having more than one diagnosis on balance billing, we repeated the main multivariate analysis by using a model that distinguished between having a particular diagnosis by itself and having that particular diagnosis together with at least one other diagnosis. To test the possibility that our balance-billing estimates were driven by extreme values, we repeated all our main analyses with samples that excluded family-years with balance-billing values in the top 99.5th percentile of the distributions for inpatient, intermediate, and outpatient care. This resulted in a modified full sample of 190,072 family-years and a modified SES sample of 66,648 family-years. For family-years with additional plan information (57%), we repeated analyses among the 104,134 family-years for whom plan out-of-network coverage could be confirmed. Finally, we conducted a sensitivity analysis to determine how well the results in the SES sample generalized to the full sample. We did this by comparing the results from a parsimonious model (excluding the SES variables) in the SES sample to the results of the same model in the full sample. This work was judged exempt by the University of California, Los Angeles, Institutional Review Board.
Results
Distribution of Family-Year Characteristics
Table 1 shows that distributions of plan, state provider supply, and family-year characteristics were similar among the 196,034 family-years in the full sample and the 68,659 family-years in the SES sample. In the full sample, enrollment was higher in carve-in plans and in HMOs. Most subscribers were middle-aged, married to a spouse of a different gender, and had dependents (data not shown). Just under half of the family-years had a member with a diagnosis of depression. Other common diagnoses included adjustment disorder (37%), generalized anxiety disorder (37%), and bipolar disorder (25%). A majority of family-years (62%) had multiple behavioral health conditions diagnosed. Although these diagnoses could apply to a single family member or to multiple family members, for the vast majority of family-years, a single family member was associated with the behavioral health diagnoses.
| Full sample (N=196,034) | SES sample (N=68,659) | |||
|---|---|---|---|---|
| Characteristic | N | % | N | % |
| Plan | ||||
| Carve-out status | 55,773 | 28 | 5,032 | 7 |
| Carve-in status | 140,261 | 72 | 63,627 | 93 |
| Health maintenance organization (HMO) | 138,020 | 70 | 61,000 | 89 |
| Non-HMO | 58,014 | 29 | 7,659 | 11 |
| Provider supply per 10,000 people (M±SD) | ||||
| Short-term hospital beds | 23±5 | 23±4 | ||
| Psychiatric hospitals | .02±.009 | .02±.01 | ||
| In-network behavioral health providers (M.D., Ph.D., M.S.W., and R.N.) | 7±8 | 7±7 | ||
| Subscriber age | ||||
| 18–24 | 1,404 | 1 | 461 | 1 |
| 25–34 | 25,167 | 13 | 9,657 | 14 |
| 35–44 | 55,148 | 28 | 20,497 | 30 |
| 45–54 | 68,313 | 35 | 23,727 | 35 |
| 55–64 | 39,300 | 20 | 12,117 | 18 |
| ≥65 | 6,702 | 3 | 2,200 | 3 |
| Subscriber relationship status | ||||
| Single | 68,037 | 35 | 23,972 | 35 |
| Domestic partner, different gender | 1,749 | 1 | 808 | 1 |
| Domestic partner, same gender | 1,336 | 1 | 563 | 1 |
| Spouse, different gender | 124,271 | 63 | 43,172 | 63 |
| Spouse, same gender | 641 | .3 | 144 | .2 |
| N of unique diagnoses in familyb | ||||
| 1 | 74,031 | 38 | 25,968 | 38 |
| 2 | 47,317 | 24 | 16,485 | 24 |
| 3 | 29,907 | 15 | 10,290 | 15 |
| ≥4 | 44,779 | 23 | 15,916 | 23 |
| Diagnosis | ||||
| Adjustment disorder | 72,890 | 37 | 25,893 | 38 |
| Posttraumatic stress disorder | 11,439 | 6 | 3,983 | 6 |
| Generalized anxiety disorder | 72,880 | 37 | 26,038 | 38 |
| Obsessive-compulsive disorder | 7,327 | 4 | 2,514 | 4 |
| Panic disorder | 10,817 | 6 | 3,844 | 6 |
| Phobia disorder | 7,067 | 4 | 2,527 | 4 |
| Attention-deficit hyperactivity disorder | 32,910 | 17 | 11,646 | 17 |
| Other child behavioral health disorder | 39,800 | 20 | 14,037 | 20 |
| Pervasive developmental disorder | 6,559 | 3 | 2,253 | 3 |
| Bipolar disorder | 49,412 | 25 | 17,186 | 25 |
| Depression | 93,539 | 48 | 32,081 | 47 |
| Personality disorder | 3,468 | 2 | 1,201 | 2 |
| Schizophrenia | 10,595 | 5 | 4,139 | 6 |
| Alcohol use disorder | 14,943 | 8 | 5,393 | 8 |
| Drug use disorder | 20,260 | 10 | 7,062 | 10 |
| Other behavioral health disorder | 34,116 | 17 | 12,000 | 17 |
| N of dependents, by dependent age group (M±SD)c | ||||
| <5 | .2±.5 | .2±.5 | .5 | |
| 6–11 | .3±.7 | .3±.7 | .7 | |
| 12–17 | .4±.7 | .4±.7 | .7 | |
| 18–25 | .4±.7 | .4±.7 | .7 | |
| Subscriber level of educational attainment | ||||
| High school or lower | 8,770 | 13 | ||
| Some college | 22,541 | 33 | ||
| Associate’s degree | 7,749 | 11 | ||
| Bachelor’s degree or higher | 29,599 | 43 | ||
| Subscriber income; net worth ($) | ||||
| <75,000; ≤25,000 | 4,949 | 7 | ||
| <75,000; >25,000–100,000 | 3,360 | 5 | ||
| <75,000; >100,000 | 5,541 | 8 | ||
| 75,000–150,000; <100,000 | 4,269 | 6 | ||
| 75,000–150,000; 150,000–250,000 | 5,571 | 8 | ||
| 75,000–150,000; >250,000 | 10,155 | 15 | ||
| >150,000; <500,000 | 7,591 | 11 | ||
| >150,000; ≥500,000 | 13,522 | 20 | ||
| Unknown; <150,000 | 4,941 | 7 | ||
| Unknown; 25,000–100,000 | 8,760 | 13 | ||
| Subscriber race-ethnicity, language | ||||
| Asian, English | 1,281 | 2 | ||
| Asian, other language | 877 | 1 | ||
| Black, any language | 3,294 | 5 | ||
| Hispanic, English | 2,385 | 3 | ||
| Hispanic, other language | 2,074 | 3 | ||
| Other, any language | 3,438 | 5 | ||
| White, any language | 55,310 | 81 | ||
a
The SES sample included family-years with nonmissing socioeconomic information.
b
If a family-year had multiple instances of the same diagnostic category, only one additional diagnosis was counted. For example, a family-year with multiple claims for depression but no other claims had a count of one diagnosis. Multiple diagnoses could be either for a single person in the family or for more than one person in the family.
c
Dependents were counted here if they were not the subscriber and not the subscriber’s spouse or domestic partner.
In the SES sample, over half of family-years had a subscriber with a college degree (Table 1). One-third of subscribers earned between $75,000 and $150,000, and another third earned over $150,000. A substantial proportion of family-years had a subscriber who was Hispanic, Black, or Asian.
Unadjusted Balance-Billing Levels
Table 2 describes, for the full sample, the distribution of balance billing among all family-years and among family-years with balance billing over the 4 study years. Among the 97,979 (50%) of family-years with any balance billing, mean total family-year balance billing was $861 (mean balance billing per family member, $381; balance billing as a proportion of family-year out-of-network out-of-pocket expenditures, 33%). Balance-billing values in the SES sample were similar (see table in online supplement).
| Percentile | |||||
|---|---|---|---|---|---|
| Balance billing | M | SD | Median | 75th | 90th |
| Among all family-years (N=196,034) | |||||
| Total for family-year ($) | 430 | 2,460 | 0 | 175 | 802 |
| Per member in family-year ($) | 191 | 1,233 | 0 | 64 | 323 |
| As a proportion of family out-of-network out-of-pocket expenditures (%) | 17 | 25 | 0 | 29 | 55 |
| Among family-years with any balance billing (N=97,979) | |||||
| Total for family-year ($) | 861 | 3,500 | 175 | 600 | 1,684 |
| Per member in family-year ($) | 381 | 1,723 | 64 | 236 | 738 |
| As a proportion of family out-of-network out-of-pocket expenditures (%) | 33 | 26 | 28 | 49 | 70 |
a
Dollar amounts were adjusted for inflation to 2014 values.
Differences in Adjusted Balance-Billing Levels by Family-Year Characteristics
Table 3 presents the adjusted average differences in balance billing for each predictor in the model, relative to the reference group. Total family-year balance billing was substantially higher for families enrolled in carve-out plans ($523) and for families enrolled in HMO plans ($156). Both differences were driven by differences in the probability of any balance billing, and the difference for carve-out enrollment was additionally driven by the level in balance billing among those with any balance billing.
| Difference in level of balance billing | ||||||
|---|---|---|---|---|---|---|
| Difference in probability of any balance billing (N=68,659)b | Among family-years with any balance billing (N=32,777)c | Among all family-years (N=68,659)d | ||||
| Percentage | ||||||
| Variable | points | 95% CI | $ | 95% CI | $ | 95% CI |
| Plan characteristic | ||||||
| Carve-out status (reference: carve-in status) | 27* | 25, 29 | 430* | 169, 691 | 523* | 340, 705 |
| Health maintenance organization (HMO) (reference: non-HMO) | 15* | 13, 17 | 46 | –120, 212 | 156* | 75, 237 |
| Provider supply | ||||||
| Short-term hospital beds per 10,000 people | .0003 | –.1, .1 | 16* | 2, 30 | 8* | .7, 15 |
| Psychiatric hospitals per 10,000 people | 1.2 | –47, 49 | –2,004 | –13, 166 | –947 | –3,128, 1,234 |
| In-network behavioral health providers (M.D., Ph.D., M.S.W., and R.N.) per 10,000 people | .01 | –.05, .08 | –16* | –21, –11 | –8* | –10, –5 |
| Subscriber’s highest level of educational attainment (reference: bachelor’s degree or higher) | ||||||
| High school or lower | –4.6* | –6.0, –3.2 | –380* | –503, –257 | –224* | –284, –163 |
| Some college | –4.5* | –5.5, –3.6 | –304* | –400, –207 | –188* | –236, –139 |
| Associate’s degree | –3.7* | –4.9, –2.4 | –285* | –398, –172 | –172* | –228, –116 |
| Subscriber income; net worth ($) (reference: <75,000; ≤25,000) | ||||||
| <75,000; 25,000–100,000 | –1.7 | –3.8, .5 | –24 | –224, 175 | –26 | –124, 70 |
| <75,000; ≥100,000 | –1.5 | –3.4, .5 | 70 | –121, 261 | 20 | –73, 114 |
| 75,000–150,000; <100,000 | –4.1* | –6.0, –1.9 | 65 | –133, 263 | –7 | –102, 87 |
| 75,000–150,000; 100,000–250,000 | –1.8 | –3.7, .1 | –71 | –244, 102 | –49 | –134, 35 |
| 75,000–150,000; >250,000 | –1.9* | –3.7, –.1 | –71 | –234, 91 | –50 | –130, 29 |
| >150,000; <500,000 | –2.0* | –3.9, –.1 | 23 | –154, 199 | –7 | –93, 80 |
| >150,000; ≥500,000 | 1.2 | –.7, 3.1 | 21 | –150, 192 | 21 | –64, 107 |
| Subscriber race-ethnicity, language (reference: White, any language) | ||||||
| Asian, English | –1.5 | –4.1, 1.2 | 26 | –234, 286 | –.7 | –124, 123 |
| Asian, other language | –1.7 | –4.9, 1.5 | 124 | –214, 461 | 42 | –117, 202 |
| Black, any language | –1.4 | –3.2, –.4 | 31 | –140,203 | 3 | –79, 84 |
| Hispanic, English | –1.2 | –3.2, .8 | –8 | –193, 177 | –14 | –102, 74 |
| Hispanic, other language | –.7 | –2.9, 1.5 | 172 | –62, 406 | 75 | –38, 188 |
| Other, any language | .7 | –1.0, 2.4 | 29 | –125, 183 | 21 | –56, 97 |
| Subscriber age (reference: 45–54) | ||||||
| 18–24 | –3.2 | –8.7, .4 | –557* | –789, –325 | –290* | –398, 183 |
| 25–34 | –3.2* | –4.5, –1.9 | –258* | –367, –148 | –148* | –201, –96 |
| 35–44 | –1.1* | –2.1, –.009 | –96* | –191, –2 | –57* | –103, –10 |
| 55–64 | –.2 | –1.3, .9 | –53 | –158, 51 | –28 | –79, –24 |
| ≥65 | –8.3* | –10.6, –6.0 | 6 | –263, 274 | –78 | –192, –36 |
| Subscriber relationship status (reference: single) | ||||||
| Domestic partner, different gender | .9 | –2.5, 4.3 | –112 | –409, 185 | –45 | –191, 100 |
| Domestic partner, same gender | .02 | –4.0, 4.1 | 259 | –231, 750 | 123 | –114, 359 |
| Spouse, different gender | 1.0* | .07, 1.9 | –51 | –135, –34 | –16 | –56, 25 |
| Spouse, same gender | –3.3 | –11.2, 4.5 | –316 | –852, 219 | –170 | –412, 71 |
| N of dependents (reference: 0) | ||||||
| 1 | 1.5* | 1.0, 2.2 | 44 | –28, 116 | 34 | –1, 69 |
| 2 | 1.8* | 1.2, 2.4 | 7 | –48, 61 | 19 | –8, 46 |
| 3 | 1.3* | .8, 1.9 | –37 | –86, 12 | –6 | –30, 18 |
| ≥4 | –.04 | –.6, .5 | –45 | –96, 6 | –22 | –47, 3 |
| Diagnosis (reference: absence of indicated diagnosis) | ||||||
| Adjustment disorder | 4.0* | 3.1, 4.8 | 20* | –54, 94 | 45* | 8, 82 |
| Posttraumatic stress disorder | –.01 | –1.7, 1.7 | 267* | 83, 450 | 127* | 38, 217 |
| Generalized anxiety disorder | 3.4* | 2.6, 4.2 | 186* | 111, 262 | 120* | 83, 158 |
| Obsessive-compulsive disorder | 7.7* | 5.7, 9.7 | 167* | –22, 357 | 158* | 53, 263 |
| Panic disorder | –.2 | –1.9, 1.5 | –103* | –242, –37 | –51 | –118, 17 |
| Phobia disorder | 5.6* | 3.6, 7.6 | 191 | –14, 395 | 149* | 40, 259 |
| Attention-deficit hyperactivity disorder | 3.3* | 1.1, 5.4 | –109 | –280, 62 | –25 | –112, 61 |
| Other child behavioral health disorder | 5.4* | 3.3, 7.4 | 14 | –162, 190 | 54 | –37, 146 |
| Pervasive developmental disorder | 8.0* | 5.9, 10.1 | 1,111* | 738, 1,485 | 678* | 469, 887 |
| Bipolar disorder | 1.8* | .8, 2.7 | 83 | –4, 170 | 56* | 12, 99 |
| Depression | 3.6* | 3.0, 4.6 | 119* | 45, 194 | 89* | 52, 125 |
| Personality disorder | 1.0 | –1.8, 3.9 | 299 | –18, 617 | 154 | –4, 312 |
| Schizophrenia | –9.1* | –11.0, –7.9 | –162* | –296, –27 | –148* | –205, –91 |
| Alcohol use disorder | –.3 | –1.3, 1.9 | 930* | 709, 1,150 | 447* | 339, 555 |
| Drug use disorder | 6.3* | 4.8, 7.8 | 2,182* | 1,849, 2,516 | 1,190* | 1,010, 1,370 |
| Other behavioral health disorder | 3.0* | 2.0, 4.0 | 157* | 59, 254 | 104* | 54, 153 |
a
All models controlled for subscriber employer characteristics (size, industry, region, etc.), whether or not the plans were a health maintenance organization, unknown income and net worth of <$150,000, unknown income and net worth of ≥$150,000, and calendar year. Regressions used a two-part model for total family-year balance billing, and marginal effects were generated by using the margins command in Stata.
b
Part 1 of the two-part model used logistic regression to determine the probability of having any balance billing.
c
Part 2 of the two-part model used a gamma regression to determine the average difference in the level of balance billing among the family-years with any balance billing in the socioeconomic status (SES) sample.
d
The model combining parts 1 and 2 calculated the average difference in the level of balance billing among all family-years in the SES sample—not conditional on whether they had any balance billing.
*p<.05.
Looking at the state provider-supply predictors, higher numbers of short-term hospital beds were associated with significantly higher balance billing ($8), and higher numbers of in-network behavioral health providers were associated with significantly lower balance billing (–$8) levels.
Several family-year characteristics were significant predictors as well. Balance billing was between $172 and $224 lower for subscribers without a bachelor’s degree (Table 3), compared with subscribers with a bachelor’s degree or a higher level of educational attainment. This was attributable to both a lower probability of any balance billing and to lower levels among those with any balance billing. As shown in Table 3, total family-year balance billing was significantly lower (between $28 and $290 lower) for family-years with subscribers in the three younger age groups, compared with subscribers ages 45–54 years old.
Table 3 also shows associations between behavioral health diagnoses and balance billing. All but four diagnoses (panic disorder, attention-deficit hyperactivity disorder, other childhood disorders, and personality disorders) were significantly associated with balance billing. Among diagnoses with significant associations, all but one (schizophrenia) was associated with a higher level of balance billing, compared with not having the diagnosis. Adjusted differences in balance billing ranged from $45 (adjustment disorder) to $447 (alcohol use disorder) and $1,190 (drug use disorder). (A table in the online supplement shows that differences in balance billing per family member were similar to differences in total family-year balance billing.)
Sensitivity Analyses
Controlling for whether members in a family-year had each of the 16 diagnoses by itself or together with at least one of the other 15 diagnoses did not change the magnitude, direction, or significance of most model predictors (see table in online supplement). Excluding family-years with extreme balance-billing values resulted in mean±SD conditional balance billing of $741±$2,710 in the modified full sample (see table in online supplement), and the direction and significance of most predictors in the multivariate models aligned with the main results (see tables in online supplement). Among family-years with confirmed out-of-network coverage status, mean conditional balance billing was $777±$3,099, about $100 lower than in the full sample, and median conditional balance billing was $166, about $10 lower than in the full sample. The multivariate results for this sample closely resembled the main model results. Finally, the multivariate results of the parsimonious model were nearly identical between the full sample and the SES sample.
Discussion
This analysis documented additional cost-sharing burden (i.e., balance billing) for behavioral health services in a national sample of commercially insured families who used out-of-network behavioral health services. It found that half of families submitting a claim for out-of-network behavioral health services in a given year had any balance billing; that among those with balance billing, half the families had annual balance-billing levels over $175; and that higher total family-year balance billing was experienced by families enrolled in carve-out plans as well as by those enrolled in HMOs, compared with other families.
Several limitations require mention. First, administrative data may include data entry errors. Excluding family-years with the highest balance-billing values did not lead to substantially different results, but underestimates are also possible. Second, we were missing SES data for a substantial portion of families with out-of-network behavioral health care use. The SES sample and the full sample had similar distributions of predictor variables and similar regression results for a parsimonious model, but we cannot confirm whether the SES data were missing at random. Third, this analysis focused on balance billing among providers who submitted claims to the insurer and did not capture the full out-of-pocket burden for persons seeing behavioral health providers who did not accept any insurance. Fourth, state-level provider supply measures may obscure the actual supply in a particular region in the state.
With respect to external validity, our data represented claims for one large national MBHO and might not generalize to other organizations offering employer-sponsored insurance or other forms of insurance. Differences in plan generosity or network size could affect balance billing. Previous publications have described financial requirements (copayments, coinsurance, etc.) for these and other plans offered by the MBHO, which can help readers assess the generalizability of this MBHO’s plan generosity (19, 20). However, because of the wide reach of the study MBHO, data on this MBHO alone merit inquiry. The age of the data may also reduce external validity.
Our study found that one in two sample families were asked to cover the difference between what their provider charged and what the plan agreed to pay and what the family owed as cost-sharing. Among them, the families in the top 25th percentile had annual balance-billing values over $500, even after the analysis excluded the most extreme balance-billing values. Those facing the most extreme values (N=9,322 sample family-years) saw balance-billing levels nearly three times as high.
A national survey of household well-being, fielded by the Federal Reserve, suggested that for many families, an unexpected bill over $500 would involve financial hardship, including increased debt, deferred necessities, and so forth (21). As noted above, this financial burden may be further compounded by bills from providers who do not accept insurance, and such bills cannot be measured by using insurance claims data. It is also important to note that the families who did not receive balance billing may have had higher cost-sharing for out-of-network behavioral health care via coinsurance and deductibles, for example, although characterizing this cost-sharing was beyond the scope of the analysis.
Higher balance billing was noted in carve-out plans, compared with carve-in plans. Carve-out plans have become less ubiquitous following the Mental Health Parity and Addiction Equity Act, which was passed before this study was conducted (14). Given the sample’s heavy concentration of family-years enrolled in HMOs, which typically have narrower networks (22) and often do not reimburse for out-of-network care, the finding that only half of family-years had any balance billing may be counterintuitive. However, the adjusted analysis found that HMOs were associated with substantially higher out-of-network balance billing. Additionally, a post hoc analysis found that 75% of claims for HMO plans in our sample had a nonzero plan pay amount. Higher balance billing in HMO plans was consistent with the finding that balance billing was higher for families in states with smaller networks (i.e., smaller provider supply). Policies that increase the supply of providers who accept insurance and, simultaneously, disincentivize narrow insurance networks may indirectly reduce balance-billing burden on households while also improving access to behavioral health care.
Conclusions
This is the first national study of balance billing in out-of-network behavioral health claims, a topic of interest given that families frequently need to use out-of-network providers to obtain behavioral health care. The analysis found that about half of family-years with employer-sponsored insurance claims for out-of-network behavioral health services had some balance-billing amount. Balance-billing levels, which were substantially higher for families enrolled in carve-out and HMO plans, may be burdensome for many families and may reduce use of needed behavioral health services.
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Information & Authors
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History
Received: 18 March 2021
Revision received: 11 August 2021
Revision received: 16 November 2021
Revision received: 6 December 2021
Accepted: 16 December 2021
Published online: 23 March 2022
Published in print: September 01, 2022
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Funding Information
This work was supported by grant 1R01MH117013-01 from the National Institute of Mental Health.Dr. Azocar is an employee of Optum–UnitedHealth Group and receives compensation in the form of salary and stock. The other authors report no financial relationships with commercial interests.
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