According to three approaches to measuring disparities, Hispanic-White disparities for two broad indicators of health care use have grown over the period 1996-1997 to 2004-2005 while Black-White disparities have remained roughly constant.
While all three definitions of disparities tell the same basic story, we find important differences between the definitions in terms of the magnitudes of disparities and changes over time. Adjusting for differences in health status using the IOM method reduces the magnitude of Hispanic-White disparities compared to unadjusted trends. Further adjusting for SES, health insurance, and all other variables in the RDE method reduces the magnitude of Hispanic-White disparities even more and also reduces Black-White disparities.
The SES of Hispanics improved between 1996 and 2005 in a number of indicators. Disparities between Hispanics and Whites would have been slightly worse had these SES improvements not occurred, leaving open the question of why Hispanics are faring badly in comparison to Whites.
Hispanics experience continuing high levels of uninsurance and underinsurance (Alegria et al., 2007
; Kaiser Commission on Medicaid and the Uninsured [KCMU], 2000
), and continuing problems with access to care due to language and cultural barriers. These factors impede Hispanics’ access and may be having even a greater effect in more recent years. Also, increasing levels of immigration by Hispanic populations may contribute to increasing disparities in ways not captured by the SES variables in our model. The percentage of the U.S. population that was foreign born from Latin America increased 35% between 1996 and 2003 (4.6% to 6.2%, respectively; Hansen & Faber, 1997
; Larsen, 2004
). The foreign born are younger, more likely to live in poverty (Larsen, 2004
), more likely to be uninsured (KCMU, 2004
), and are likely to be disadvantaged in other ways as well.
The absence of improvement against disparities for Blacks and the worsening situation for Hispanics must be regarded as disheartening in light of the massive amount of research and policy attention devoted to disparities in the past 10 years. In broad terms, solutions for disparities could come from the eventual improvement of the SES of minorities in relation to Whites or from a reconfigured health care system that diminish the effect of SES on use in comparison to factors related to health status. Slight improvements in the relative SES of minorities have occurred over this time period, but progress on this front will inevitably be slow. For the measures examined in this analysis, we found no evidence that the health care system was becoming more responsive to health status in comparison to SES.
Trends analysis is limited by data. The 1996 and 1997 MEPS do not have health status data beyond age, sex, functional limitation, and self-reported health status. Beginning in 2000, the MEPS included more measures of health status. To assess the impact of this exclusion on the IOM and RDE estimates, we reestimated 2004-2005 predictions using SF-12, BMI, and 11 health condition measures. This sensitivity test did not significantly change IOM-based estimates or RDE-based estimates of any doctor visit, but it did slightly increase RDE-based estimates of Hispanic and Black medical expenditure. RDE-based medical expenditure disparities may therefore be overestimated within each time period. However, we expect, though cannot be certain, that RDE-based expenditure disparities would have similarly decreased in 1996-1997 given the same set of variables, thereby leaving disparity trends relatively unchanged. Precision of comparisons can improve over time as more years of data become available with more extensive measures of key variables.
Health insurers and government health care programs have recently begun to track disparities for the purpose of monitoring provider and system performance, and to identify interventions that improve the health and health care of minorities. For example, nine of the nation’s largest health insurers are collecting and analyzing data by race/ethnicity on the quality of diabetes care for the National Health Plan Collaborative to Reduce Disparities and Improve Quality in Diabetes Care (Center for Health Care Strategies, 2004
). Measuring trends in disparities further increases in importance as pay for performance initiatives may tie provider payments to the reduction of racial disparities. The recently implemented Massachusetts health reform package mandating health insurance for all Massachusetts residents includes an initiative that would make hospital rate increases contingent on reducing health care disparities (State of Massachusetts, 2006
Our article compares several approaches for measuring disparities and assesses trends in disparities. We compare what we regard to be current best practice—implementing the definition of disparity proposed by the IOM with a rigorous method of adjustment for health status—with an approach based on unadjusted means and an approach based on estimated race/ethnicity coefficients.
Empirical researchers studying disparities at a point in time or trends in disparities must choose a definition of disparity to employ in their work. We recognize that all three definitions have value, and our most fundamental recommendation is that the choice of definition be explicit.
From a practical standpoint, all three approaches are straightforward to implement. The AHRQ means-based definition is very easy. Once a researcher estimates an empirical model of health care use, the IOM and RDE definitions are within reach. We adjusted for health status in this article in an innovative way to come to the IOM measure, but there are other easier methods for approximating the mediating effect of SES and other variables in a nonlinear model of health care use, such as omitting these variables altogether from the model (Balsa, Cao, & McGuire, 2007
). The important feature of the IOM definition is the counting of mediation for nonhealth status variables, not the method for quantifying the mediation.
Each definition calls attention to unfair differences in a distinct way, and the choice of definition to employ should be guided by the underlying objective of the investigation. We believe an unadjusted comparison of means is less informative about fairness and, therefore, less useful for policy. The coefficient-based RDE measure picks up racial/ethnic differences not due to other factors in the model; therefore, its interpretation across studies is fluid, depending on what else the researcher is able to measure and include. The RDE measure also runs the risk of implying an absence of racial/ethnic disparities if there is no significant race coefficient. A researcher focused on the causes of group differences and most interested in the effect of race as distinct from SES or other system factors may prefer the RDE approach.
When the goal of a study is to quantify disparities (or change in disparities), we believe the IOM approach is the right one. The definition of unfair differences—differences not due to health status or need—we believe, captures what most researchers and policy makers are concerned about. The IOM approach captures the reality that race and ethnicity matter in direct and in indirect ways affecting access to health care and that all of these are legitimate concerns about the way our health care system serves members of different groups.