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Agreement between Self‐Reported and Administrative Race and Ethnicity Data among Medicaid Enrollees in Minnesota
Author(s) -
McAlpine Donna D.,
Beebe Timothy J.,
Davern Michael,
Call Kathleen T.
Publication year - 2007
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/j.1475-6773.2007.00771.x
Subject(s) - medicaid , ethnic group , logistic regression , medicine , health care , race (biology) , odds , odds ratio , demography , american community survey , gerontology , family medicine , environmental health , population , political science , botany , pathology , sociology , law , biology , census
Objective. This paper measures agreement between survey and administrative measures of race/ethnicity for Medicaid enrollees. Level of agreement and the demographic and health‐related characteristics associated with misclassification on the administrative measure are examined. Data Sources. Minnesota Medicaid enrollee files matched to self‐report information from a telephone/mail survey of 4,902 enrollees conducted in 2003. Study Design. Measures of agreement between the two measures of race/ethnicity are computed. Using logistic regression, we also assess whether misclassification of race/ethnicity on administrative files is associated with demographic factors, health status, health care utilization, or ratings of quality of health care. Data Extraction. Race/ethnicity fields from administrative Medicaid files were extracted and merged with self‐report data. Principal Findings. The administrative data correctly classified 94 percent of cases on race/ethnicity. Persons who self‐identified as Hispanic and those whose home language was English had the greater odds (compared with persons who self‐identified as white and those whose home language was not English) of being misclassified in administrative data. Persons classified as unknown/other on administrative data were more likely to self‐identify as white. Conclusions. In this case study in Minnesota, researchers can be reasonably confident that the racial designations on Medicaid administrative data comport with how enrollees self‐identify. Moreover, misclassification is not associated with common measures of health status, utilization, and ratings of quality of care. Further replication is recommended given variation in how race information is collected and coded by Medicaid agencies in different states.