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Julia Haller, Yang Dai, Philip Storey, Lisa Hark, Laura Pizzi, Benjamin Leiby, Ann Murchison; Developing Best Practices for Analyzing Real-World Data in Vision Care: Comparison of Approaches for Imputing Ethnicity from a High Volume Urban Vision Clinic. Invest. Ophthalmol. Vis. Sci. 2013;54(15):1551. doi: https://doi.org/.
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© ARVO (1962-2015); The Authors (2016-present)
To compare methodologies for determining ethnicity from clinical and administrative data in an urban ophthalmology clinic.
Using data from 19,165 patients with self-reported ethnicity and home address, we compared the accuracy of three methodologies for imputing ethnicity of patients enrolled into a retrospective chart review research study: 1) surname analysis based on tabulation from the 2000 U.S. Census 2) geocoding analysis based on block coding from the 2010 U.S. Census 3) a previously published approach involving combination of surname and geocoding.
Overall agreement of imputed and self-reported ethnicity was fair for surname analysis (kappa=0.23), moderate for geocoding (kappa=0.58), and strong for the combined model (kappa=0.76). Surname analysis was able to determine Asian ethnicity (sensitivity (SE) 80%; positive predictive value (PPV) 77%) and Latino ethnicity (SE 78%; PPV 68%) with reasonable accuracy but had poor reliability for Caucasians (SE 12%; PPV 92%) and African-Americans (SE 96%; PPV 47%). Geocoding was able to determine African-American ethnicity (SE 74%; PPV 89%) and Caucasian ethnicity (SE 91%; PPV 70%) with reasonable accuracy, but had poor reliability for Asians (SE 10%; PPV 26%) and Latinos (SE 35%; PPV 41%). The Bayesian approach determined African-American (SE 84%; PPV 94%), Caucasian (SE 92%; PPV 82%), Asian (SE 83%; PPV 79%) and Latino (SE 77%; PPV 71%) ethnicity with the highest accuracy of the three methods.
A methodology combining surname analysis and geocoded Census tract data to determine ethnicity is a valid and accurate means of imputing African-American, Caucasian, Asian and Latino ethnicity. The combined approach is superior to the other methods tested and is ideally suited for research purposes of real-world clinical and administrative data in an ophthalmology setting.
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