We found black–white and Latino–white disparities in total, outpatient, and prescription drug mental health care expenditures that were robust to adjusting for need using two different methods. The disparities in reaching the first mental health visit, outpatient visit and filling the first prescription highlight the importance of reducing barriers to access to mental health care for racial/ethnic minorities. On the other hand, we did not find black–white disparities among those using mental health care services, complementing previous findings that African Americans were less likely than whites to fill an antidepressant prescription but were similar to whites in having an adequate trial of antidepressant medication (Harman, Edlund, and Fortney 2004
). These latter results provide preliminary evidence that equitable use of mental health care services for African Americans is being provided once it is initiated, but that Latino–white disparities persist in mental health care use even after accessing care.
Eliminating disparities in any use of mental health care will require intervention at multiple levels, including reducing the numbers of uninsured, changing provider practices, and reducing barriers to care. As in previous studies (Zuvekas and Taliaferro 2003
; McGuire et al. 2006
; Cook, Miranda, and McGuire 2007
;), we found that blacks and Latinos were more likely than whites to be uninsured, and that lack of insurance was a strong negative predictor of any utilization of mental health care. At the provider level, physicians were found to be less likely to identify mental illness among racial/ethnic minority patients compared to white patients, possibly because of the inadequacy of standardized screening and diagnostic instruments to detect mental illness in minority populations (Ryu, Young, and Kwak 2002
), or greater levels of miscommunication between patient and provider because of different language, culture, or communications patterns (Balsa, McGuire, and Meredith 2005
). Blacks and Latinos are more likely than whites to perceive stigma and financial barriers to mental health care, disproportionately endorsing “embarrassment to discuss problems,”“loss of pay from work,” and “concern of losing employment,” as barriers to receiving mental health treatment (Ojeda and McGuire 2006
A limitation of the data is the lack of information in the MEPS regarding preferences for mental health care, a predictor of mental health care use that has been shown to vary significantly by racial/ethnic group (Diala et al. 2001
; Cooper et al. 2003
;), and which should be controlled for in disparity calculations according to the IOM definition of health care disparities. Even if preferences measures were included, their adjustment would be problematic given that patients are rarely fully informed about their clinical options when deciding to access health care (Braddock et al. 1999
; IOM 2002
; Ashton et al. 2003
;), and because preferences may have been influenced by previous experiences with discrimination or with inadequate or inaccessible care (Cooper-Patrick et al. 1997
). Without accounting for the association between preferences, SES, and previous discrimination, the inclusion and adjustment of preferences in disparities models may lead to biased disparity estimates. Another limitation of our data is that the MEPS does not provide adequate sample size to measure disparities among Latino and African American subethnic groups (e.g., Mexican, Puerto Rican, Cuban, Afro-Caribbean) while adjusting for other covariates. Prior research (Alegria et al. 2007
; Jackson et al. 2007
;) found significant mental health service use differences between these subethnic groups, suggesting this more in-depth investigation of disparities would be worthwhile given a larger dataset.
In this paper, we offer two methods of implementing the IOM definition of health care disparities. We advocate for the continued use of IOM-concordant methods because they measure the part of racial/ethnic differences in health care that is most relevant and amenable to change at the health system level, distinct from underlying differences in need characteristics that are beyond the immediate reach of health care. Similar to previous studies (McGuire et al. 2006
; Cook, McGuire, and Zuvekas 2009b
;), we found important differences between IOM-concordant methods and other typically used disparity methods in the magnitude of disparities estimated. IOM-concordant methods estimated black–white disparities to be greater than a comparison of unadjusted means, and Latino–white disparities to be greater than the RDE method. The comparison of the IOM-concordant methods with the RDE prediction method identified a significant mediating role of SES-related factors (education, income, insurance, citizenship, and regional characteristics) for Latinos, but not blacks in these data. Decomposing the contributions of these variables to disparity estimates (results not shown) shows that, for blacks, change in disparity estimates due to adjustments to SES were small in comparison with the main black race effect. For Latinos, the smaller disparities estimated using the RDE prediction method were largely driven by adjustments to the education, insurance status, and citizenship status variables.
We compared the propensity score-based and rank-and-replace implementations of the IOM definition of health care disparities. While both methods are IOM-concordant, adjusting white and minority need distributions to be the same, the resulting distribution differs between the two methods. The rank-and-replace method replaces minority need with that of whites. The propensity score-based method adjusts the need distributions of both races to that of a subset of whites and blacks reflecting their overlap on need characteristics and therefore is more similar to that of blacks and reflects higher need.
These differing counterfactual need distributions can lead to differing disparity estimates because of the nonlinearity of the models. Race and SES effects are modeled additively on the logit (for probabilities) or log (for conditional costs) scale, and when applied to a population with greater baseline level, the retransformed difference is larger on the natural scale (probability or dollars, respectively).
The methods also differ in their use of outcome variables; the rank-and-replace method adjusts the health status distributions as they affect expenditure, while the propensity score-based method adjusts health status distributions ignoring expenditure. Thus, the propensity score-based method provides the same adjustment for both parts of the two-part model and for any desired dependent variables, while the rank-and-replace adjustment differs according to the model and the response.
Each method has appealing methodological features. The counterfactual minority population created by the rank-and-replace method is constructed to match two observable distributions: the marginal health status distribution of whites and the marginal SES distribution of minorities. On the other hand, the counterfactual population created by the propensity score-based method may be more plausible because it conducts comparisons in populations of white and minority individuals selected (by weighting) to have similar distributions of health status characteristics. By explicitly modeling the outcome variable in each part of the two-part model, the rank-and-replace method ensures identical distributions of health status variables as they affect our quantities of interest, while by ignoring the outcome variable in the main adjustment step, the propensity score provides protection against data dredging or manipulation of the model to achieve a desired result. Using both methods provides a check on the sensitivity of estimates to alternative modeling assumptions. In this study, both methods found that fewer mental health care resources, whether overall, outpatient, or prescription drug related, were spent on blacks and Latinos, and that the large part of the disparity was attributable to differences in initial use of these services.