Racial/ethnic disparity trends in children's mental health care access and expenditures from 2002 to 2007.

Noting significant racial/ethnic disparities in mental health care, the U.S. Surgeon General promoted a vision for reducing these disparities in 2001 (U.S. Department of Health and Human Services 2001). Evidence indicated that African American children were less likely than white children to use mental health services, even after adjustment for socioeconomic, family, and regional factors (Cuffe et al. 1995; Cunningham and Freiman 1996; Zahner and Daskalakis 1997). Limited findings for Latino children were mixed. Puerto Ricans received less mental health care than mainland Latino children (Leaf et al. 1996), and Latino children reported fewer lifetime counseling visits than whites (Pumariega et al. 1998). On the other hand, Latino children with both mental health disorder and impairment had less unmet need for care than white children (Flisher et al. 1997). Since then, other studies have emerged documenting disparities in children's mental health care, finding black–white disparities in psychotropic drug use nationwide (Chen and Chang 2002) and in the Medicaid population (Zito et al. 2005), and Latino–white and black–white disparities in psychotropic drug use in the child welfare population (Raghavan et al. 2005). Latino–white and black–white disparities were also found in children's antidepressant use (Kirby, Hudson, and Miller 2010) and stimulant use (Olfson et al. 2003; Hudson, Miller, and Kirby 2007). To our knowledge, previous studies have not assessed disparities in children's overall mental health care expenditures or outpatient mental health care use, or tracked trends in children's mental health care use and spending over time. To understand how disparities in children's mental health care use have changed since the Surgeon General's report, we use the Andersen behavioral model of health care utilization as a conceptual framework (Andersen 1995), recognizing the importance of predisposing factors (e.g., age and gender), enabling factors (e.g., income, education, and insurance), and need-based factors (e.g., mental health status and comorbid physical health status) on mental health care utilization. The IOM Definition of Health Care Disparities Health care disparities have been measured using multiple methods and disparity definitions, including assessing differences in unadjusted means as is done in the National Health care Disparities Reports (AHRQ 2008b), interpreting race coefficients in models that increasingly add available covariates in a regression context (Fiscella et al. 2002; Trivedi et al. 2005; Vaccarino et al. 2005; Guevara et al. 2006), and model-based estimations of disparities that adhere to the Institute of Medicine (IOM) definition of health care disparities (McGuire et al. 2006; Cook, McGuire, and Miranda 2007; Cook et al. 2010b). We use the latter approach in this article, building on the IOM definition put forth in Unequal Treatment (IOM 2002), that defines disparity to be any difference in health care not due to clinical appropriateness, need, or patient preferences for health care services (see Figure 1). Figure 1 The Institute of Medicine Definition of Racial/Ethnic Health Care Disparities Ideally, implementation of the IOM definition of racial/ethnic health care disparities in national health care datasets requires the identification of survey variables that match the constructs of clinical appropriateness, need, and patient preferences. To compute disparities, an analyst would adjust for differences due to clinical appropriateness, need, and patient preferences but include differences due to other variables (Cook, McGuire, and Zaslavsky 2012). Mental and physical health status variables are strong proxies for need for services, as are age and sex given large differences across these categories in prevalence of mental illness (Merikangas et al. 2010; Kessler et al. 2012). Differences due to patient preferences (e.g., perceived value of medical care and tolerance of risk) should also be adjusted for to the extent they are available in the data. Clinical appropriateness may be more difficult to account for in studies that use observational data, and we are unable to account for this in our analysis. Although clinical appropriateness, need, and patient preference variables should be equalized or adjusted across racial/ethnic groups according to the IOM definition, differences due to the operation of health care systems and the legal and regulatory climate should be considered part of the disparity (see Figure 1). In survey data, differences due to measures of socioeconomic status (SES) (e.g., income and education) can be considered to fit into the operation of health care systems category. For example, if lower SES families are less able to navigate the health care system or pay for their children's mental health care, and racial/ethnic minority families are disproportionately represented in lower SES categories, then the operation of the health care system may be affecting disparities through SES. Differences due to discrimination should also be considered to be part of the disparity according to the IOM definition (see Figure 1). In regression models of survey data, the independent effect of race/ethnicity on health care can be considered a proxy for discrimination (National Research Council 2004). Using Longitudinal Data to Assess Disparities in Mental Health Care Analysis of longitudinal data can identify underlying care-seeking behaviors and correlates of treatment behaviors that drive disparities in mental health care, fulfilling in part the need for research that is more directly translatable into disparities reduction policies (Alegria 2009). Unlike cross-sectional data analysis, longitudinal data analysis can distinguish whether disparities in any use of mental health care are due to whites’ greater initiation of care or whites’ longer treatment episodes. The policy implications will differ depending on the result. Also important, individual characteristics in cross-sectional data can be endogenous to the decision to seek treatment. For example, being insured may affect whether an individual seeks treatment, but once treatment is initiated, children may be more likely to be insured (either because providers encourage enrolling children in public programs or parents choose to enroll their children), thus biasing the effect of insurance on treatment downward. In longitudinal data, patient characteristics can be observed in the time period prior to treatment initiation, reflecting the circumstances of individuals when deciding to seek treatment. New Contribution This study measures recent national trends in disparities in children's mental health care and is the first study that we are aware of to measure disparities in children's use of outpatient mental health care. In addition, this article makes two important contributions over and above the prior literature. First, we ground our method of measuring disparities conceptually in the IOM definition of racial/ethnic health care disparities. Second, we capitalize on longitudinal panel data from the Medical Expenditure Panel Surveys (MEPS) and model the initiation of treatment among youth with unmet need for mental health care. This analysis allows us to identify mechanisms associated with disparities in initiation of mental health care.