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A Comparison of Methods for Classifying and Modeling Respondents Who Endorse Multiple Racial/Ethnic Categories

A Health Care Experience Application

Klein, David J., MS*; Elliott, Marc N., PhD*; Haviland, Amelia M., PhD†,‡; Morrison, Peter A., PhD§; Orr, Nate, MA*; Gaillot, Sarah, PhD; Weech-Maldonado, Robert, PhD

doi: 10.1097/MLR.0000000000001012
Online Article: Applied Methods
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Background: Race/ethnicity information is vital for measuring disparities across groups, and self-report is the gold standard. Many surveys assign simplified race/ethnicity based on responses to separate questions about Hispanic ethnicity and race and instruct respondents to “check all that apply.” When multiple races are endorsed, standard classification methods either create a single heterogenous multiracial group, or attempt to impute the single choice that would have been selected had only one choice been allowed.

Objectives: To compare 3 options for classifying race/ethnicity: (a) hierarchical, classifying Hispanics as such regardless of racial identification, and grouping together all non-Hispanic multiracial individuals; (b) a newly proposed additive model, retaining all original endorsements plus a multiracial indicator; (c) an all-combinations approach, separately categorizing every observed combination of endorsements.

Research Design: Cross-sectional comparison of racial/ethnic distributions of patient experience scores; using weighted linear regression, we model patient experience by race/ethnicity using 3 classification systems.

Subjects: In total, 259,763 Medicare beneficiaries age 65+ who responded to the 2017 Medicare Consumer Assessments of Healthcare Providers and Systems Survey and reported race/ethnicity.

Measures: Self-reported race/ethnicity, 4 patient experience measures.

Results: Additive and hierarchical models produce similar classifications for non-Hispanic single-race respondents, but differ for Hispanic and multiracial respondents. Relative to the gold standard of the all-combinations model, the additive model better captures ratings of health care experiences and response tendencies that differ by race/ethnicity than does the hierarchical model. Differences between models are smaller with more specific measures.

Conclusions: Additive models of race/ethnicity may afford more useful measures of disparities in health care and other domains. Our results have particular relevance for populations with a higher prevalence of multiracial identification.

*RAND Corporation, Santa Monica, CA

Carnegie Mellon University, Heinz College

RAND Corporation, Pittsburgh, PA

§Morrison & Associates Inc., Nantucket, MA

Division of Consumer Assessment & Plan Performance, Centers for Medicare & Medicaid Services, Baltimore, MD

University of Alabama at Birmingham, Birmingham, AL

Supported by a Centers for Medicare & Medicaid Services contract (No. GS-10F-0275P/ Task Order HHSM-500-2017-00083G).

S.G. is an employee of the sponsoring agency. The remaining authors declare no conflict of interest.

Reprints: Marc N. Elliott, PhD, RAND Corporation, 1776 Main Street, Santa Monica, CA 90401. E-mail: elliott@rand.org.

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