Address correspondence to Donna D. McAlpine, Ph.D., Assistant Professor, School of Public Health, University of Minnesota, 420 Delaware Street SE, MMC 729, Minneapolis, MN 55455. Timothy J. Beebe, Ph.D., is with the Mayo Clinic College of Medicine, MN. Michael Davern, Ph.D., and Kathleen Thiede Call, Ph.D., are with the Division of Health Services Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN.
Agreement between Self-Reported and Administrative Race and Ethnicity Data among Medicaid Enrollees in Minnesota
Article first published online: 16 AUG 2007
Health Services Research
Volume 42, Issue 6p2, pages 2373–2388, December 2007
How to Cite
McAlpine, D. D., Beebe, T. J., Davern, M. and Call, K. T. (2007), Agreement between Self-Reported and Administrative Race and Ethnicity Data among Medicaid Enrollees in Minnesota. Health Services Research, 42: 2373–2388. doi: 10.1111/j.1475-6773.2007.00771.x
- Issue published online: 16 AUG 2007
- Article first published online: 16 AUG 2007
- Race and ethnicity;
- administrative data
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.