Table 1.

Summary of Three Main Definitions and Methods of Disparities Measurement with Examples, Pros, and Cons

Definition	Method	Example	Pros	Cons
Residual direct effect	Compare mean white health care use with mean health care use in a counterfactual group that is similar to the white group in all aspects except having racial/ethnic minority identity	Logit results for models of any reported ambulatory visits by year 1997 (reported as odds ratios with whites as comparison group). Black OR = 0.46 (0.35, 0.59); Latino OR = 0.7 (0.56, 0.89) (Guevara et al. 2006)	Readily interpreted from standard regression output. Identifies independent effect of race	Adjusts for racial/ethnic differences in care that are due to SES variables such as income, insurance, and education
Disparities equal to unadjusted means	Compare mean white health care use with mean health care use of racial/ethnic minority group	Percentage of people who had an office-based or outpatient department visit in the calendar year, by race and ethnicity, 2007. White 75.3% versus black 65.7% versus Hispanic 59.1% versus Asian 62.7% (AHRQ 2010)	Easily computable and understandable comparison	Does not account for health status and age differences between populations
Institute of Medicine (IOM) definition	Compare mean white health care use with mean health care use of a counterfactual group that has the SES characteristics and racial/ethnic identity of the racial/ethnic group but the health status of the white group	Predicted probability of having any mental health care use in the last year, 2007. 15.7% Whites versus 7.0% blacks versus 8.3% Latinos (Cook et al. 2010)	Accounts for racial/ethnic differences by health status, age, and gender	Uses a rank and replace prediction method that is more computationally intensive than other methods