Identification of Transgender and Gender-Diverse Individuals in the All of Us Research Program, 2017–2022.

Objectives. To develop computable phenotype algorithms to identify a transgender and gender-diverse (TGD) cohort by using diverse data sources in All of Us, a national community-engaged program to facilitate health equity in the United States by partnering with 1 million participants. Methods. We id...

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Detalles Bibliográficos
Publicado en:American Journal of Public Health Vol. 115; no. 8; pp. 1278 - 1288
Autores principales: Shi, Fanghui, Yang, Xueying, Cai, Ruilie, Zhang, Jiajia, Harrison, Sayward E., Qiao, Shan, Frary, Sarah Grace, Li, Xiaoming
Formato: Artículo
Publicado: American Public Health Association Aug2025
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Acceso en línea:Ver este registro en EBSCOhost
Descripción
Sumario:Objectives. To develop computable phenotype algorithms to identify a transgender and gender-diverse (TGD) cohort by using diverse data sources in All of Us, a national community-engaged program to facilitate health equity in the United States by partnering with 1 million participants. Methods. We identified TGD individuals in All of Us by applying inclusion criteria based on conditions, laboratory measurements, or medications related to being TGD in electronic health record data or confirmed survey responses, using participant data collected between May 31, 2017, and July 1, 2022. Results. Of 413 457 participants, we identified 4781 (1.2%) as TGD. Participants aged 18 to 29 years (26.1% vs 8.2%), who were bisexual (20.7% vs 3.5%), with annual income of less than $25 000 (35.9% vs 24.7%), and with housing security concerns (31.9% vs 16.0%) accounted for a larger proportion of TGD individuals than non-TGD individuals. Conclusions. Combining survey and electronic health record data enables the identification of TGD individuals who have been missed by previous studies that used survey data alone in All of Us to explore health disparities in TGD people.