Comparing methods of racial and ethnic disparities measurement across different settings of mental health care

Abstract

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 to 2006 Medical Expenditure Panel Survey. Dependent variables are total, outpatient, and prescription drug mental health care expenditure.

Methods: Rank- and propensity score-based methods concordant with the Institute of Medicine (IOM) definition of health care 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.

Figure 1

Mental Health Expenditure by Race/Ethnicity Note. Unadjusted differences, two IOM-concordant predictions (rank-and-replace and propensity score-based methods), and RDE predictions are presented. Source: 2002–2006 Medical Expenditure Panel Surveys (MEPS). RDE, residual direct effect of race/ethnicity prediction method based on race/ethnicity coefficient error bars represent 95 percent confidence interval generated from standard estimation using a combination of balanced repeated replication and multiple imputation methods.