Abstract
This paper describes the clinical, reproductive, sociodemographic, and health care setting characteristics of a diverse transmasculine patient population compared to cisgender patients among a cohort treated with hysterectomy in a large health care system in the United States (US) South from 2014 to 2017. Additionally, we compare a computational phenotype (CP) for identifying transmasculine patients against a self-reported gender identity variable from the electronic health record (EHR). CPs are algorithms of medical codes and provider notes used to identify sub-populations not otherwise well-specified in EHR data. Without accurate identification of transgender patients, researchers are unable to fully address health issues in transgender populations. A CP for identifying transmasculine patients was generated using manual abstraction of provider notes, diagnostic codes, and prescription data. The CP identified 35 transmasculine hysterectomy patients compared to 1,822 cisgender women treated with hysterectomy for non-cancerous conditions. The 2017 EHR gender variable identified only 16 (45.7%) of the 35 patients from our CP method as transmasculine. Transmasculine patients (n=35) were younger and had higher reports of chronic pelvic pain than cisgender patients (n=1,822). Transmasculine patients were all treated in academic medical centers while cisgender patients were mostly treated at community hospitals. We found that an algorithm of diagnosis codes, keyword text-strings and testosterone prescriptions identified more transmasculine patients treated with hysterectomy than the self-reported gender identity variable.