Introduction The ability to track improvement against racial/ethnic disparities in mental health care is hindered by the varying methods and disparity definitions used in previous research. Data Nationally representative sample of Whites, Blacks, and Latinos from the 2002–2006 MEPS. Dependent variables are total, outpatient, and prescription drug mental health care expenditure. Methods Rank- and propensity score-based methods concordant with the IOM definition of healthcare disparities were compared with commonly used disparities methods. To implement the IOM definition, we modeled expenditures using a two-part GLM, adjusted distributions of need variables, and predicted expenditures for each racial/ethnic group. Findings Racial/ethnic disparities were significant for all expenditure measures. Disparity estimates from the IOM-concordant methods were similar to one another but greater than a method using the residual effect of race/ethnicity. Black-White and Latino-White disparities were found for any expenditure in each category and Latino-White disparities were significant in expenditure conditional on use. Conclusions Findings of disparities in access among Blacks and disparities in access and expenditures after initiation among Latinos suggest the need for continued policy efforts targeting disparities reduction. In these data, the propensity score-based method and the rank-and-replace method were precise and adequate methods of implementing the IOM definition of disparity.

Five of the six univariate natural exponential families (NEF) with quadratic variance functions (QVF), meaning their variances are at most quadratic functions of their means, are the Normal, Poisson, Gamma, Binomial, and Negative Binomial distributions. The sixth is the NEF-CHS, the NEF generated by convolved Hyperbolic Secant distributions. These six NEF-QVFs and their relatives are unified in this paper and in the main diagram, Figure 1, which connects NEFs with many other named distributions, including Pearson's families of conjugate distributions (Inverted Gamma, Beta, F, and Skewed-t), conjugate mixtures (including two Polya urn schemes, with types I and II sampling), and conditional distributions (including the Hypergeometric and Negative Hypergeometric). Limit laws that relate these distributions are indicated by solid arrows in Figure 1.