Decreasing missingness in race and ethnicity data by inclusion of preferred language for mapping to aggregate categories
Our findings support that language-based imputation has the potential to reduce missing race and ethnicity data and may be a helpful tool in quality improvement and research efforts. For health systems where race and ethnicity fields may not be fully detailed or have a high rate of missing data, the use of language may serve as a valuable adjunct in improving the comprehensive picture of a population.
Authors
- Lan N. Ðoàn (ORCID: https://orcid.org/0000-0003-1826-4728)
- Zoe Grabinski (ORCID: https://orcid.org/0000-0002-6397-0069)
- Kar-mun Woo (ORCID: https://orcid.org/0009-0004-6117-5303)
- Stella S. Yi (ORCID: https://orcid.org/0000-0002-9943-2609)
- Farah Kader (ORCID: https://orcid.org/0000-0001-7412-5787)
- David E. Bayer
- Dowin Boatwright
Institutions
- NYU Langone Health (US)
- New York University (US)
Publication Details
- Journal
- Health Affairs Scholar
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1093/haschl/qxaf234
- Primary Topic
- Racial and Ethnic Identity Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00