Indirect Estimation of Race/Ethnicity for Survey Respondents Who Do Not Report Race/Ethnicity

Background: Researchers are increasingly interested in measuring race/ethnicity, but some survey respondents skip race/ethnicity items. Objectives: The main objectives of this study were to investigate the extent to which racial/ethnic groups differ in skipping race/ethnicity survey items, the degree to which this reflects reluctance to disclose race/ethnicity, and the utility of imputing missing race/ethnicity. Research Design: We applied a previously developed method for imputing race/ethnicity from administrative data (Medicare Bayesian Improved Surname and Geocoding 2.0) to data from a national survey where race/ethnicity was usually self-reported, but was sometimes missing. A linear mixed-effects regression model predicted the probability of self-reporting race/ethnicity from imputed racial/ethnic probabilities. Subjects: In total, 508,497 Medicare beneficiaries responding to the 2013–2014 Medicare Consumer Assessment of Healthcare Providers and Systems surveys were included in this study. Measures: Self-reported race/ethnicity and estimated racial/ethnic probabilities. Results: Black beneficiaries were most likely to not self-report their race/ethnicity (6.6%), followed by Hispanic (4.7%) and Asian/Pacific Islander (4.7%) beneficiaries. Non-Hispanic whites were the least likely to skip these items (3.2%). The 3.7% overall rate of missingness is similar to adjacent demographic items. General patterns of item missingness rather than a specific reluctance to disclose race/ethnicity appears to explain the elevated rate of missing race/ethnicity among Asian/Pacific Islander and Hispanic beneficiaries and most but not all among Black beneficiaries. Adding imputed cases to the data set did not substantially alter the estimated overall racial/ethnic distribution, but it did modestly increase sample size and statistical power. Conclusions: It may be worthwhile to impute race/ethnicity when this information is unavailable in survey data sets due to item nonresponse, especially when missingness is high.

[1]  Noel J. Purcell,et al.  Postcensal Estimates for Local Areas (or Domains) , 1980 .

[2]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[3]  Carl-Erik Särndal,et al.  Generalized Raking Procedures in Survey Sampling , 1993 .

[4]  K. Collins.  Minority health. , 1995, Bulletin of the New York Academy of Medicine.

[5]  M. Elliott,et al.  Differences in CAHPS adult survey reports and ratings by race and ethnicity: an analysis of the National CAHPS benchmarking data 1.0. , 2001, Health services research.

[6]  M. Elliott,et al.  A new method for estimating race/ethnicity and associated disparities where administrative records lack self-reported race/ethnicity. , 2008, Health services research.

[7]  Daniel F McCaffrey,et al.  Power of tests for a dichotomous independent variable measured with error. , 2008, Health services research.

[8]  D. McCaffrey,et al.  Using the Census Bureau’s surname list to improve estimates of race/ethnicity and associated disparities , 2009, Health Services and Outcomes Research Methodology.

[9]  A. Zaslavsky,et al.  Understanding nonresponse to the 2007 Medicare CAHPS survey. , 2011, The Gerontologist.

[10]  Steven C Martino,et al.  Reporting CAHPS and HEDIS data by race/ethnicity for Medicare beneficiaries. , 2013, Health Services Research.

[11]  David M. Quinn,et al.  Science Achievement Gaps by Gender and Race/Ethnicity in Elementary and Middle School , 2015 .

[12]  Steven C Martino,et al.  Racial/Ethnic Disparities in Medicare Beneficiaries’ Care Coordination Experiences , 2016, Medical care.

[13]  L. Egede,et al.  Achieving Equity in an Evolving Healthcare System: Opportunities and Challenges. , 2016, The American journal of the medical sciences.

[14]  Differences in Receipt of Three Preventive Health Care Services by Race/Ethnicity in Medicare Advantage Plans: Tracking the Impact of Pay for Performance, 2010 and 2013 , 2016, Preventing chronic disease.

[15]  Using First Name Information to Improve Race and Ethnicity Classification , 2016 .

[16]  J. Salemi,et al.  Trends and Correlates of Disparities in Alcohol-Related Mortality Between Hispanics and Non-Hispanic Whites in the United States, 1999 to 2014. , 2016, Alcoholism, clinical and experimental research.

[17]  D. Himmelstein,et al.  Racial and Ethnic Disparities in Mental Health Care for Children and Young Adults , 2016, International journal of health services : planning, administration, evaluation.

[18]  Paula M. Cuccaro,et al.  Racial and Ethnic Disparities in ADHD Diagnosis and Treatment , 2016, Pediatrics.

[19]  Michael Nilsson,et al.  Physical Activity for Cognitive and Mental Health in Youth: A Systematic Review of Mechanisms , 2016, Pediatrics.

[20]  Ioan Voicu.  Using First Name Information to Improve Race and Ethnicity Classification , 2016 .

[21]  Evaluating race and Hispanic origin responses of Medicaid participants using census data , 2018, Statistical Journal of the IAOS.