title: Using the Bayesian Improved Surname Geocoding Method (BISG) to create a working classification of race and ethnicity in a diverse managed care population: a validation study.
abstract: OBJECTIVE
  To validate classification of race/ethnicity based on the Bayesian Improved Surname Geocoding method (BISG) and assess variations in validity by gender and age.
  
  
  METHODS
  Secondary data on members of Kaiser Permanente Georgia, an integrated managed care organization, through 2010.
  
  
  METHODS
  For 191,494 members with self-reported race/ethnicity, probabilities for belonging to each of six race/ethnicity categories predicted from the BISG algorithm were used to assign individuals to a race/ethnicity category over a range of cutoffs greater than a probability of 0.50. Overall as well as gender- and age-stratified sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Receiver operating characteristic (ROC) curves were generated and used to identify optimal cutoffs for race/ethnicity assignment.
  
  
  RESULTS
  The overall cutoffs for assignment that optimized sensitivity and specificity ranged from 0.50 to 0.57 for the four main racial/ethnic categories (White, Black, Asian/Pacific Islander, Hispanic). Corresponding sensitivity, specificity, PPV, and NPV ranged from 64.4 to 81.4 percent, 80.8 to 99.7 percent, 75.0 to 91.6 percent, and 79.4 to 98.0 percent, respectively. Accuracy of assignment was better among males and individuals of 65 years or older.
  
  
  CONCLUSIONS
  BISG may be useful for classifying race/ethnicity of health plan members when needed for health care studies.
authors.affiliation: Center for Health Research-Southeast, Kaiser Permanente, Atlanta, GA.
authors.forename: Dzifa
authors.initials: D
authors.lastname: Adjaye-Gbewonyo
authors.forename: Robert A
authors.initials: RA
authors.lastname: Bednarczyk
authors.forename: Robert L
authors.initials: RL
authors.lastname: Davis
authors.forename: Saad B
authors.initials: SB
authors.lastname: Omer
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001499:Bayes Theorem; D001719:Birth Certificates; D002648:Child; D002675:Child, Preschool; D005260:Female; D006302:Health Services Research; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008329:Managed Care Programs; D008875:Middle Aged; D009275:Names; D044469:Racial Groups / Q000145:classification*; D012680:Sensitivity and Specificity; D014481:United States
keywords: Race/ethnicity; geocoding; health plans; imputation and indirect estimation; surname analysis