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Evaluating Methods for Imputing Race and Ethnicity in Electronic Health Record Data.

Sarah Conderino1, Jasmin Divers1,2, John A Dodson1

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|May 27, 2025
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Summary

Non-anonymized Bayesian Improved Surname Geocoding (BISG) offers more accurate race and ethnicity imputation than anonymized methods, especially when data is missing not at random. Chronic disease burden studies showed consistent results across imputation techniques.

Keywords:
Bayesian Improved Surname Geocodingelectronic health recordethnicitymultiple imputation with chained equationsracerandom forest imputation

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Area of Science:

  • Health Informatics
  • Epidemiology
  • Biostatistics

Background:

  • Accurate race and ethnicity data are crucial for understanding chronic disease burden.
  • Electronic Health Records (EHRs) are valuable data sources but often have missing demographic information.
  • Imputation methods aim to fill in missing data, but their performance can vary.

Purpose of the Study:

  • To compare anonymized and non-anonymized imputation methods for race and ethnicity in EHR data.
  • To assess the accuracy and agreement of different imputation techniques.
  • To evaluate the impact of imputation methods on chronic disease burden estimates.

Main Methods:

  • Simulation analyses were conducted using various missing data mechanisms.
  • Imputation methods included Bayesian Improved Surname Geocoding (BISG), single imputation, random forest, and multiple imputation with chained equations (MICE).
  • Performance was measured by sensitivity, precision, accuracy, and Cohen's kappa (κ) agreement with self-reported data. Methods were applied to two EHR datasets.

Main Results:

  • Non-anonymized BISG showed the highest accuracy (66-73%) in simulations.
  • Anonymized methods' agreement decreased when data was missing not at random (MNAR).
  • Despite simulation differences, racial/ethnic distributions and disease burden estimates were consistent across methods in EHR data. All imputation methods improved estimate precision compared to complete case analysis.

Conclusions:

  • Non-anonymized BISG may offer superior racial and ethnic classification, especially under MNAR conditions.
  • Descriptive studies on chronic disease burden may be robust to the choice of imputation method for race and ethnicity.
  • Further research can explore the nuances of imputation in diverse EHR populations.