Article

Measuring Trends in Racial/Ethnic Health Care Disparities

Cambridge Health Alliance/Harvard Medical School, Somerville, MA, USA.
Medical Care Research and Review (Impact Factor: 2.57). 10/2008; 66(1):23-48. DOI: 10.1177/1077558708323607
Source: PubMed

ABSTRACT Monitoring disparities over time is complicated by the varying disparity definitions applied in the literature. This study used data from the 1996-2005 Medical Expenditure Panel Survey (MEPS) to compare trends in disparities by three definitions of racial/ethnic disparities and to assess the influence of changes in socioeconomic status (SES) among racial/ethnic minorities on disparity trends. This study prefers the Institute of Medicine's (IOM) definition, which adjusts for health status but allows for mediation of racial/ethnic disparities through SES factors. Black-White disparities in having an office-based or outpatient visit and medical expenditure were roughly constant and Hispanic-White disparities increased for office-based or outpatient visits and for medical expenditure between 1996-1997 and 2004-2005. Estimates based on the independent effect of race/ethnicity were the most conservative accounting of disparities and disparity trends, underlining the importance of the role of SES mediation in the study of trends in disparities.

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    • "The rank and replace and propensity score methods potentially provide an alternative to nonlinear decomposition techniques. Predicting differences with and without adjustment for a variable or set of variables can help to isolate the impact of certain factors on differences in health or health care (Cook, McGuire, and Zuvekas 2009a). For example, in a paper identifying pathways underlying the paradox that immigrants have poorer SES but greater mental health status, Cook et al. (2009b) used the rank and replace technique to identify the independent contribution of a number of factors (discrimination, ethnic identity, intergenerational conflict) on mental health differences between Latino immigrants and U.S.-born Latinos. "
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    ABSTRACT: To review methods of measuring racial/ethnic health care disparities. Identification and tracking of racial/ethnic disparities in health care will be advanced by application of a consistent definition and reliable empirical methods. We have proposed a definition of racial/ethnic health care disparities based in the Institute of Medicine's (IOM) Unequal Treatment report, which defines disparities as all differences except those due to clinical need and preferences. After briefly summarizing the strengths and critiques of this definition, we review methods that have been used to implement it. We discuss practical issues that arise during implementation and expand these methods to identify sources of disparities. We also situate the focus on methods to measure racial/ethnic health care disparities (an endeavor predominant in the United States) within a larger international literature in health outcomes and health care inequality. EMPIRICAL APPLICATION: We compare different methods of implementing the IOM definition on measurement of disparities in any use of mental health care and mental health care expenditures using the 2004-2008 Medical Expenditure Panel Survey. Disparities analysts should be aware of multiple methods available to measure disparities and their differing assumptions. We prefer a method concordant with the IOM definition.
    Health Services Research 02/2012; 47(3 Pt 2):1232-54. DOI:10.1111/j.1475-6773.2012.01387.x · 2.49 Impact Factor
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    • "Model covariates were chosen to assess racial/ethnic differences in medical expenditures while adjusting for the subjects' differences in health status, demographic, regional, health insurance status, and SES characteristics . Interactions between SES and race–ethnicity variables were included to allow for the differential return on these variables by race that has been noted in previous work on the MEPS data (Cook, McGuire, and Zuvekas 2009b). Variables used in the interaction terms were centered by subtracting their mean so that main effects results are readily interpretable (Kraemer and Blasey 2004). "
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    Health Services Research 08/2009; 44(5 Pt 1):1603-21. DOI:10.1111/j.1475-6773.2009.01004.x · 2.49 Impact Factor
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