Developing and Validating a Computable Phenotype for the Identification of Transgender and Gender Nonconforming Individuals and Subgroups

Abstract

Transgender and gender nonconforming (TGNC) individuals face significant marginalization, stigma, and discrimination. Under-reporting of TGNC individuals is common since they are often unwilling to self-identify. Meanwhile, the rapid adoption of electronic health record (EHR) systems has made large-scale, longitudinal real-world clinical data available to research and provided a unique opportunity to identify TGNC individuals using their EHRs, contributing to a promising routine health surveillance approach. Built upon existing work, we developed and validated a computable phenotype (CP) algorithm for identifying TGNC individuals and their natal sex (i.e., male-to-female or female-to-male) using both structured EHR data and unstructured clinical notes. Our CP algorithm achieved a 0.955 F1-score on the training data and a perfect F1-score on the independent testing data. Consistent with the literature, we observed an increasing percentage of TGNC individuals and a disproportionate burden of adverse health outcomes, especially sexually transmitted infections and mental health distress, in this population.

Implementation of Gender Identity and Assigned Sex at Birth Data Collection in Electronic Health Records: Where Are We Now?

“…Rule-based algorithms and computable phenotypes that draw on a broad spectrum of structured data including diagnostic codes, procedure codes, medication orders, as well as a range of transgender and nonbinary-related keywords labeled in clinical notes have been generated to identify more comprehensive TGNB patient cohorts and associated health outcomes [ 23 , 37 , 38 , 39 ]. Machine learning and natural language processing (NLP) techniques may also operationalize critical information in clinical notes, rectifying current TGNB population health research gaps and relieving pressure on both patients and providers to disclose or collect SO/GI demographic data [ 40 ].…”

How good gender be identified from heart rate data? Evaluation using ALLSTAR Heart rate variability big data analysis

“…discriminate from text data such as names [10,11], and discriminate from heartbeats sounds [11]. Those methods for gender identi cation based on physical characteristics have also been applied to transgender identi cation [12]. In this study, we con rmed how much gender can be identi ed using Holter ECG database.…”

The OneFlorida Data Trust: a centralized, translational research data infrastructure of statewide scope

“…We have confronted this problem through, for example, the development of transgender and gender nonconforming computable phenotypes that leverage data besides the demographics table in the PCORnet CDM. 10 …”

“… 27 Researchers have also used the Data Trust to develop computable phenotypes, including resistant hypertension, type 1 diabetes mellitus, and transgender and gender nonconforming individuals. 10 , 28 , 29 In addition, the Data Trust has played key roles in several, large studies carried out in PCORnet. 30–34 Also, to confront the COVID-19 pandemic, PCORnet partnered with the Centers for Disease Control and Prevention (CDC) on a COVID-19 healthcare data initiative, 35 with 5 OneFlorida partners participating.…”