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...
| Publicado en: | American Journal of Public Health Vol. 115; no. 8; pp. 1278 - 1288 |
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| Autores principales: | , , , , , , , |
| Formato: | Artículo |
| Publicado: |
American Public Health Association
Aug2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=186508417&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 186508417 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00900036 APH jtl: American Journal of Public Health issn: 00900036 maglogo: N pubinfo: dt: Aug2025 vid: 115 iid: 8 pid: 44 pub: American Public Health Association artinfo: ui: 186508417 10.2105/AJPH.2025.308129 ppf: 1278 ppct: 10 formats: tig: atl: Identification of Transgender and Gender-Diverse Individuals in the All of Us Research Program, 2017–2022. aug: au: Shi, Fanghui Yang, Xueying Cai, Ruilie Zhang, Jiajia Harrison, Sayward E. Qiao, Shan Frary, Sarah Grace Li, Xiaoming affil: Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia. Department of Psychology, University of South Carolina College of Arts and Sciences, Columbia. su: United States Gender-nonconforming people Gender identity Socioeconomic factors Medical research Health equity Sociodemographic factors Psychosocial factors American transgender people Descriptive statistics Chi-squared test Longitudinal method Surveys Clinical pathology Electronic health records Software architecture Algorithms Phenotypes sug: subj: Gender-nonconforming people Gender identity Socioeconomic factors Medical research Health equity Sociodemographic factors Psychosocial factors United States Research and Development in the Physical, Engineering, and Life Sciences (except Biotechnology) Computer systems design and related services (except video game design and development) American transgender people Descriptive statistics Chi-squared test Longitudinal method Surveys Clinical pathology Electronic health records Software architecture Algorithms Phenotypes ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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