June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Developing Best Practices for Analyzing Real-World Data in Vision Care: Comparison of Approaches for Imputing Ethnicity from a High Volume Urban Vision Clinic
Author Affiliations & Notes
  • Julia Haller
    Ophthalmology, Wills Eye Institute, Philadelphia, PA
    Thomas Jefferson University, Philadelphia, PA
  • Yang Dai
    Ophthalmology, Wills Eye Institute, Philadelphia, PA
    Thomas Jefferson University, Philadelphia, PA
  • Philip Storey
    Ophthalmology, Wills Eye Institute, Philadelphia, PA
  • Lisa Hark
    Ophthalmology, Wills Eye Institute, Philadelphia, PA
    Thomas Jefferson University, Philadelphia, PA
  • Laura Pizzi
    Ophthalmology, Wills Eye Institute, Philadelphia, PA
    Thomas Jefferson University, Philadelphia, PA
  • Benjamin Leiby
    Ophthalmology, Wills Eye Institute, Philadelphia, PA
    Thomas Jefferson University, Philadelphia, PA
  • Ann Murchison
    Ophthalmology, Wills Eye Institute, Philadelphia, PA
    Thomas Jefferson University, Philadelphia, PA
  • Footnotes
    Commercial Relationships Julia Haller, Allergan (F), Advanced Cell Technology (C), Regeneron (C), Merck (C), Second Sight (C), KalVista (C), ThromboGenics (C), Optimedica (I); Yang Dai, None; Philip Storey, None; Lisa Hark, None; Laura Pizzi, None; Benjamin Leiby, None; Ann Murchison, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 1551. doi:https://doi.org/
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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)

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Abstract

Purpose: To compare methodologies for determining ethnicity from clinical and administrative data in an urban ophthalmology clinic.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: 459 clinical (human) or epidemiologic studies: biostatistics/epidemiology methodology • 468 clinical research methodology • 463 clinical (human) or epidemiologic studies: prevalence/incidence  
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