Using name lists to infer Asian racial/ethnic subgroups in the healthcare setting.
BACKGROUND
Many clinical data sources used to assess health disparities lack Asian subgroup information, but do include patient names.


OBJECTIVE
This project validates Asian surname and given name lists for identifying Asian subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese) in clinical records.


METHODS
We used 205,000 electronic medical records from the Palo Alto Medical Foundation, a multipayer, outpatient healthcare organization in Northern California, containing patient self-identified race/ethnicity information.


METHODS
Name lists were used to infer racial/ethnic subgroup for patients with self-identified race/ethnicity data. Using self-identification as the "gold standard," sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of classification by name were calculated. Clinical outcomes (obesity and hypertension) were compared for name-identified versus self-identified racial/ethnic groups.


RESULTS
With classification using surname and given name, the overall sensitivities ranged from 0.45 to 0.76 for the 6 racial/ethnic groups when no race data are available, and 0.40 to 0.79 when the broad racial classification of "Asian" is known. Specificities ranged from 0.99 to 1.00. PPV and NPV depended on the prevalence of Asians in the population. The lists performed better for men than women and better for persons aged 65 and older. Clinical outcomes were very similar for name-identified and self-identified racial/ethnic groups.


CONCLUSIONS
In a clinical setting with a high prevalence of Asian Americans, name-identified and self-identified racial/ethnic groups had similar clinical characteristics. Asian name lists may be a valid substitute for identifying Asian subgroups when self-identification is unavailable.
Wong
Eric C
EC
Palo Alto Medical Foundation Research Institute, 795 El Camino Real, Palo Alto, CA 94301, USA. wonge@pamfri.org
Palaniappan
Latha P
LP
Lauderdale
Diane S
DS
D000328:Adult; D000367:Age Factors; D000368:Aged; D001213:Asian / Q000145:classification / Q000706:statistics & numerical data*; D002140:California / Q000453:epidemiology; D005006:Ethnicity / Q000145:classification* / Q000706:statistics & numerical data*; D005260:Female; D006751:Hospital Information Systems / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008499:Medical Records / Q000706:statistics & numerical data*; D008875:Middle Aged; D009275:Names; D010355:Patient Identification Systems; D012737:Sex Factors; D011795:Surveys and Questionnaires; D055815:Young Adult