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
Article first published online: 16 JUL 2013
© Health Research and Educational Trust
Health Services Research
Volume 49, Issue 1, pages 268–283, February 2014
How to Cite
Adjaye-Gbewonyo, D., Bednarczyk, R. A., Davis, R. L. and Omer, S. B. (2014), 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. Health Services Research, 49: 268–283. doi: 10.1111/1475-6773.12089
- Issue published online: 17 JAN 2014
- Article first published online: 16 JUL 2013
- Manuscript Accepted: 6 MAY 2013
- imputation and indirect estimation;
- surname analysis;
- health plans
To validate classification of race/ethnicity based on the Bayesian Improved Surname Geocoding method (BISG) and assess variations in validity by gender and age.
Data Sources/Study Setting
Secondary data on members of Kaiser Permanente Georgia, an integrated managed care organization, through 2010.
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.
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.
BISG may be useful for classifying race/ethnicity of health plan members when needed for health care studies.