title: Health plan administrative records versus birth certificate records: quality of race and ethnicity information in children.
abstract: BACKGROUND
  To understand racial and ethnic disparities in health care utilization and their potential underlying causes, valid information on race and ethnicity is necessary. However, the validity of pediatric race and ethnicity information in administrative records from large integrated health care systems using electronic medical records is largely unknown.
  
  
  METHODS
  Information on race and ethnicity of 325,810 children born between 1998-2008 was extracted from health plan administrative records and compared to birth certificate records. Positive predictive values (PPV) were calculated for correct classification of race and ethnicity in administrative records compared to birth certificate records.
  
  
  RESULTS
  Misclassification of ethnicity and race in administrative records occurred in 23.1% and 33.6% children, respectively; the majority due to missing ethnicity (48.3%) and race (40.9%) information. Misclassification was most common in children of minority groups. PPV for White, Black, Asian/Pacific Islander, American Indian/Alaskan Native, multiple and other was 89.3%, 86.6%, 73.8%, 18.2%, 51.8% and 1.2%, respectively. PPV for Hispanic ethnicity was 95.6%. Racial and ethnic information improved with increasing number of medical visits. Subgroup analyses comparing racial classification between non-Hispanics and Hispanics showed White, Black and Asian race was more accurate among non-Hispanics than Hispanics.
  
  
  CONCLUSIONS
  In children, race and ethnicity information from administrative records has significant limitations in accurately identifying small minority groups. These results suggest that the quality of racial information obtained from administrative records may benefit from additional supplementation by birth certificate data.
authors.affiliation: Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.
authors.forename: Ning
authors.initials: N
authors.lastname: Smith
authors.forename: Rajan L
authors.initials: RL
authors.lastname: Iyer
authors.forename: Annette
authors.initials: A
authors.lastname: Langer-Gould
authors.forename: Darios T
authors.initials: DT
authors.lastname: Getahun
authors.forename: Daniel
authors.initials: D
authors.lastname: Strickland
authors.forename: Steven J
authors.initials: SJ
authors.lastname: Jacobsen
authors.forename: Wansu
authors.initials: W
authors.lastname: Chen
authors.forename: Stephen F
authors.initials: SF
authors.lastname: Derose
authors.forename: Corinna
authors.initials: C
authors.lastname: Koebnick
mesh_terms: D001719:Birth Certificates; D002140:California; D002648:Child; D002675:Child, Preschool; D016001:Confidence Intervals; D003695:Delivery of Health Care / Q000706:statistics & numerical data; D005006:Ethnicity / Q000706:statistics & numerical data*; D005260:Female; D006285:Health Planning; D054625:Healthcare Disparities; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data*; D016017:Odds Ratio; D011237:Predictive Value of Tests; D011786:Quality Control; D044469:Racial Groups / Q000706:statistics & numerical data*