A Comparison of Methods for Classifying and Modeling Respondents Who Endorse Multiple Racial/Ethnic Categories: A Health Care Experience Application. Issue 6 (June 2019)

• Record Type: Journal Article
• Authors: Klein, David J.; Elliott, Marc N.; Haviland, Amelia M.; Morrison, Peter A.; Orr, Nate; Gaillot, Sarah; Weech-Maldonado, Robert
• Is Part Of: Medical care. Volume 57:Issue 6(2019)
• Publication Date: 2019-06
• Subjects: multiracial -- disparity -- CAHPS -- methodology -- survey; Economics, Medical -- Periodicals; Insurance, Health -- Periodicals; Santé, Services de -- Administration -- Périodiques; Soins médicaux -- Périodiques; Medical economics -- Periodicals; Health insurance -- Periodicals; Medical economics -- United States -- Periodicals; Health insurance -- United States -- Periodicals; Comprehensive Health Care -- Periodicals; Personal Health Services -- Periodicals; Gezondheidszorg; Économie de la santé -- Périodiques; Santé, Services de -- Périodiques; Health insurance; Medical economics; United States; Periodicals; 362.10973
• DOI: 10.1097/MLR.0000000000001012
• Languages: English
• ISSNs: 0025-7079

Abstract

Abstract : 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. Abstract : Supplemental Digital Content is available in the text.