title: Definition of race and ethnicity in older people in Medicare and Medicaid.
abstract: BACKGROUND
  Race and ethnicity are important predictors of health care access and outcomes, but quality of their documentation in the healthcare system is often problematic.
  
  
  OBJECTIVE
  To study the agreement between Medicare and Medicaid descriptions of race and ethnicity in older beneficiaries.
  
  
  METHODS
  Quasiexperimental design in a natural practice setting.
  
  
  METHODS
  New Jersey.
  
  
  METHODS
  153,241 dually enrolled participants in Medicare and Medicaid.
  
  
  METHODS
  Agreement rates between administrative databases on recipients' race and ethnicity.
  
  
  RESULTS
  Agreement between Medicare and Medicaid on the recipients' race and ethnicity was modest (kappa = .58; 95% CI, .57-.58) for men and women alike and across different age groups. Depending on whether Medicare or Medicaid was used as the reference standard, the relative agreement rates for race and ethnic group assignments varied. For example, using Medicare as the reference, the relative agreement rate was 84% for whites, 74% for blacks, 61% for others, 23% for Hispanics, and only 5% for Asians. Using Medicaid as the reference, a different pattern emerged. However, such gradients of agreement rates across racial groups were observed in both programs. Medicare and Medicaid reported different percentages of all race and ethnicity groups, with Medicaid reporting greater proportions of White and Black beneficiaries, and Medicare reporting greater proportions of Hispanic, Asian, and Other groups.
  
  
  CONCLUSIONS
  Depiction of race and ethnicity data in large government health insurance programs is approximate at best and often contradictory from one program to another. This can impede efforts to study the relationship between these important characteristics and health care utilization and outcomes.
authors.affiliation: Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
authors.forename: C X
authors.initials: CX
authors.lastname: Pan
authors.forename: R J
authors.initials: RJ
authors.lastname: Glynn
authors.forename: H
authors.initials: H
authors.lastname: Mogun
authors.forename: I
authors.initials: I
authors.lastname: Choodnovskiy
authors.forename: J
authors.initials: J
authors.lastname: Avorn
mesh_terms: D000368:Aged / Q000706:statistics & numerical data*; D000369:Aged, 80 and over / Q000706:statistics & numerical data*; D005006:Ethnicity / Q000145:classification* / Q000706:statistics & numerical data; D005260:Female; D016330:Frail Elderly / Q000706:statistics & numerical data; D006801:Humans; D007256:Information Systems / Q000592:standards*; D008297:Male; D008484:Medicaid / Q000706:statistics & numerical data*; D006278:Medicare / Q000706:statistics & numerical data*; D009515:New Jersey; D044469:Racial Groups / Q000145:classification*; D014481:United States