Validation of an Administrative Algorithm for Transgender and Gender Diverse Persons against Self-Report Data in Electronic Health Records

Citation

Streed, Carl G., Jr.; King, Dana; Grasso, Chris; Reisner, Sari L.; Mayer, Kenneth H.; Jasuja, Guneet K.; Poteat, Tonia; Mukherjee, Monica; Shapira-Daniels, Ayelet; & Cabral, Howard, et al. (2023). Validation of an Administrative Algorithm for Transgender and Gender Diverse Persons against Self-Report Data in Electronic Health Records. Journal of the American Medical Informatics Association, 30(6), 1047-1055. PMCID: PMC10198536

Abstract

OBJECTIVE: To adapt and validate an algorithm to ascertain transgender and gender diverse (TGD) patients within electronic health record (EHR) data.
METHODS: Using 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.
RESULTS: Within 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).
CONCLUSIONS: In 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.

URL

http://dx.doi.org/10.1093/jamia/ocad039

Reference Type

Journal Article

Year Published

2023

Journal Title

Journal of the American Medical Informatics Association

Author(s)

Streed, Carl G., Jr.
King, Dana
Grasso, Chris
Reisner, Sari L.
Mayer, Kenneth H.
Jasuja, Guneet K.
Poteat, Tonia
Mukherjee, Monica
Shapira-Daniels, Ayelet
Cabral, Howard
Tangpricha, Vin
Paasche-Orlow, Michael K.
Benjamin, Emelia J.

Article Type

Regular

PMCID

PMC10198536

Continent/Country

United States of America

State

Massachusetts

Race/Ethnicity

Black
White
Asian
Native American

Sex/Gender

Gender Non-Binary
Transgender Men
Transgender Women

ORCiD

Poteat - 0000-0001-6541-3787