A Single-Center Study of Adherence to Breast Cancer Screening Mammography Guidelines by Transgender and Non-Binary Patients.

BACKGROUND: Adherence to screening guidelines among transgender and non-binary (TGNB) populations is not well studied. This study examines breast cancer screening patterns among TGNB patients at an urban academic medical center. METHODS: Demographic information, risk factors, and screening mammography were collected. Mammography rates were calculated in populations of interest according to national guidelines, and mammogram person-years were also calculated. Univariate and multivariate logistic regression was performed. RESULTS: Overall, 253 patients were analyzed: 193 transgender women and non-binary people designated male at birth (TGNB DMAB) and 60 transgender men and non-binary people designated female at birth (TGNB DFAB). The median (interquartile range) age was 53.2 years (42.3-62.6). Most patients had no family history of breast cancer (n = 163, 64.4%) and were on hormone therapy (n = 191, 75.5%). Most patients where White (n = 164, 64.8%), employed (n = 113, 44.7%), and had public insurance (n = 128, 50.6%). TGNB DFAB breast screening rates were low, ranging from 2.0 to 50.0%, as were TGNB DMAB screening rates, ranging from 7.1 to 47.6%. The screening rates among the TGNB DFAB and TGNB DMAB groups did not significantly differ from one another. Among TGNB DFAB patients, univariate analyses showed no significant predictors for mammography. Among TGNB DMAB patients, not being on hormone therapy resulted in fewer odds of undergoing mammography. There were no significant findings on multivariate analyses. CONCLUSION: Mammography rates in the TGNB population are lower than institutional and national rates for cisgender patients, which are 77.3% and 66.7-78.4%, respectively. Stage of transition, organs present, hormone therapy, and risk factors should be considered to guide screening.

Building a Cohort of Transgender and Nonbinary Patients from the Electronic Medical Record.

Purpose: Sexual orientation, gender identity, and sex recorded at birth (SOGI) have been routinely excluded from demographic data collection tools, including in electronic medical record (EMR) systems. We assessed the ability of adding structured SOGI data capture to improve identification of transgender and nonbinary (TGNB) patients compared to using only International Classification of Diseases (ICD) codes and text mining and comment on the ethics of these cohort formation methods. Methods: We conducted a retrospective chart review to classify patient gender at a single institution using ICD-10 codes, structured SOGI data, and text mining for patients presenting for care between March 2019 and February 2021. We report each method's overall and segmental positive predictive value (PPV). Results: We queried 1,530,154 EMRs from our institution. Overall, 154,712 contained relevant ICD-10 diagnosis codes, SOGI data fields, or text mining terms; 2964 were manually reviewed. This multipronged approach identified a final 1685 TGNB patient cohort. The initial PPV was 56.8%, with ICD-10 codes, SOGI data, and text mining having PPV of 99.2%, 47.9%, and 62.2%, respectively. Conclusion: This is one of the first studies to use a combination of structured data capture with keyword terms and ICD codes to identify TGNB patients. Our approach revealed that although structured SOGI documentation was <10% in our health system, 1343/1685 (79.7%) of TGNB patients were identified using this method. We recommend that health systems promote patient EMR documentation of SOGI to improve health and wellness among TGNB populations, while centering patient privacy.