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Validation of an administrative algorithm for transgender and gender diverse persons against self-report data in electronic health records

Validation of an administrative algorithm for transgender and gender diverse persons against... ObjectiveTo adapt and validate an algorithm to ascertain transgender and gender diverse (TGD) patients within electronic health record (EHR) data.MethodsUsing a previously unvalidated algorithm of identifying TGD persons within administrative claims data in a multistep, hierarchical process, we validated this algorithm in an EHR data set with self-reported gender identity.ResultsWithin an EHR data set of 52 746 adults with self-reported gender identity (gold standard) a previously unvalidated algorithm to identify TGD persons via TGD-related diagnosis and procedure codes, and gender-affirming hormone therapy prescription data had a sensitivity of 87.3% (95% confidence interval [CI] 86.4–88.2), specificity of 98.7% (95% CI 98.6–98.8), positive predictive value (PPV) of 88.7% (95% CI 87.9–89.4), and negative predictive value (NPV) of 98.5% (95% CI 98.4–98.6). The area under the curve (AUC) was 0.930 (95% CI 0.925–0.935). Steps to further categorize patients as presumably TGD men versus women based on prescription data performed well: sensitivity of 97.6%, specificity of 92.7%, PPV of 93.2%, and NPV of 97.4%. The AUC was 0.95 (95% CI 0.94–0.96).ConclusionsIn the absence of self-reported gender identity data, an algorithm to identify TGD patients in administrative data using TGD-related diagnosis and procedure codes, and gender-affirming hormone prescriptions performs well. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Medical Informatics Association Oxford University Press

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References (42)

Publisher
Oxford University Press
Copyright
© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com
ISSN
1067-5027
eISSN
1527-974X
DOI
10.1093/jamia/ocad039
Publisher site
See Article on Publisher Site

Abstract

ObjectiveTo adapt and validate an algorithm to ascertain transgender and gender diverse (TGD) patients within electronic health record (EHR) data.MethodsUsing a previously unvalidated algorithm of identifying TGD persons within administrative claims data in a multistep, hierarchical process, we validated this algorithm in an EHR data set with self-reported gender identity.ResultsWithin an EHR data set of 52 746 adults with self-reported gender identity (gold standard) a previously unvalidated algorithm to identify TGD persons via TGD-related diagnosis and procedure codes, and gender-affirming hormone therapy prescription data had a sensitivity of 87.3% (95% confidence interval [CI] 86.4–88.2), specificity of 98.7% (95% CI 98.6–98.8), positive predictive value (PPV) of 88.7% (95% CI 87.9–89.4), and negative predictive value (NPV) of 98.5% (95% CI 98.4–98.6). The area under the curve (AUC) was 0.930 (95% CI 0.925–0.935). Steps to further categorize patients as presumably TGD men versus women based on prescription data performed well: sensitivity of 97.6%, specificity of 92.7%, PPV of 93.2%, and NPV of 97.4%. The AUC was 0.95 (95% CI 0.94–0.96).ConclusionsIn the absence of self-reported gender identity data, an algorithm to identify TGD patients in administrative data using TGD-related diagnosis and procedure codes, and gender-affirming hormone prescriptions performs well.

Journal

Journal of the American Medical Informatics AssociationOxford University Press

Published: Mar 15, 2023

Keywords: transgender; gender identity; diagnosis codes; electronic health record

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