Using Name Lists to Infer Asian Racial/Ethnic Subgroups in the Healthcare Setting

Palo Alto Medical Foundation Research Institute, 795 El Camino Real, Palo Alto, CA 94301, USA.
Medical care (Impact Factor: 3.23). 06/2010; 48(6):540-6. DOI: 10.1097/MLR.0b013e3181d559e9
Source: PubMed


Many clinical data sources used to assess health disparities lack Asian subgroup information, but do include patient names.
This project validates Asian surname and given name lists for identifying Asian subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese) in clinical records.
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.
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.
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.
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.

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Available from: Latha Palaniappan,
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    • "We extracted data on demographics, anthropometric measures, physician diagnoses, laboratory results, and medication orders from the EHR. We included persons identified as NHW, Asian Indian, Chinese, Filipino, Hispanic, or Non-Hispanic black (NHB) through self-report (25) (72.7%) or by name analysis (27.3%) (26). Japanese, Korean, and Vietnamese and other racial/ethnic groups were excluded owing to small sample sizes. "
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