title: Imputing Missing Race/Ethnicity in Pediatric Electronic Health Records: Reducing Bias with Use of U.S. Census Location and Surname Data.
abstract: OBJECTIVE
  To assess the utility of imputing race/ethnicity using U.S. Census race/ethnicity, residential address, and surname information compared to standard missing data methods in a pediatric cohort.
  
  
  METHODS
  Electronic health record data from 30 pediatric practices with known race/ethnicity.
  
  
  METHODS
  In a simulation experiment, we constructed dichotomous and continuous outcomes with pre-specified associations with known race/ethnicity. Bias was introduced by nonrandomly setting race/ethnicity to missing. We compared typical methods for handling missing race/ethnicity (multiple imputation alone with clinical factors, complete case analysis, indicator variables) to multiple imputation incorporating surname and address information.
  
  
  RESULTS
  Imputation using U.S. Census information reduced bias for both continuous and dichotomous outcomes.
  
  
  CONCLUSIONS
  The new method reduces bias when race/ethnicity is partially, nonrandomly missing.
authors.affiliation: The Children's Hospital of Philadelphia, Philadelphia, PA.
authors.forename: Robert W
authors.initials: RW
authors.lastname: Grundmeier
authors.affiliation: The Children's Hospital of Philadelphia, Philadelphia, PA.
authors.forename: Lihai
authors.initials: L
authors.lastname: Song
authors.affiliation: The Children's Hospital of Philadelphia, Philadelphia, PA.
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Ramos
authors.affiliation: The Children's Hospital of Philadelphia, Philadelphia, PA.
authors.forename: Alexander G
authors.initials: AG
authors.lastname: Fiks
authors.affiliation: RAND Corporation, Santa Monica, CA.
authors.forename: Marc N
authors.initials: MN
authors.lastname: Elliott
authors.affiliation: RAND Corporation, Santa Monica, CA.
authors.forename: Allen
authors.initials: A
authors.lastname: Fremont
authors.affiliation: DARTNet Institute, University of Colorado Denver, Aurora, CO.
authors.forename: Wilson
authors.initials: W
authors.lastname: Pace
authors.affiliation: University of Vermont, Burlington, VT.
authors.forename: Richard C
authors.initials: RC
authors.lastname: Wasserman
authors.affiliation: Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
authors.forename: Russell
authors.initials: R
authors.lastname: Localio
mesh_terms: D000293:Adolescent; D001741:Black or African American / Q000706:statistics & numerical data; D000367:Age Factors; D001249:Asthma / Q000208:ethnology; D001289:Attention Deficit Disorder with Hyperactivity / Q000208:ethnology; D015982:Bias; D019352:Censuses; D002648:Child; D002675:Child, Preschool; D003625:Data Collection / Q000379:methods*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005006:Ethnicity / Q000706:statistics & numerical data*; D005260:Female; D006630:Hispanic or Latino / Q000706:statistics & numerical data; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D009275:Names; D044469:Racial Groups / Q000706:statistics & numerical data*; D012107:Research Design; D012737:Sex Factors; D012959:Socioeconomic Factors; D014481:United States
keywords: Multiple imputation; U.S. Census location and surname data; health disparities; race and ethnicity