Introduction: Pain is a common chief complaint in the emergency department (ED), and there are known disparities in the management of pain among racial/ethnic minorities, women, and older adults. Transgender and gender diverse (TGD) individuals comprise another under-represented patient population in emergency medicine and are also at risk of disparities in care. To measure and evaluate the magnitude of care inequities among TGD individuals, first we need to be able to accurately identify the right cohort and comparison groups. The primary objective of this study was to establish an accurate and generalizable process for identifying TGD patients through the electronic health record (EHR). Secondary objectives included creating and validating a method for matching and comparing of TGD patients to cisgender patients.
Methods: This was a retrospective, observational cohort study that included patients presenting to Mayo Clinic EDs with a chief complaint of abdominal pain across four states (MN, WI, AZ, FL) between July 1, 2018–November 15, 2022. Patients ≥12 years of age were included. Patients’ sex assigned at birth and gender identity was extracted from the EHR via patient-provided registration fields. Two independent investigators independently reviewed each medical record of the identified TGD patient to validate the accuracy of pulled gender identity. Discrepancies were resolved by a third reviewer. Each transgender patient was matched to cisgender GBQ males (gay, bisexual, queer), cisgender LBQ (lesbian, bisexual, queer) females, cisgender heterosexual males, and cisgender heterosexual females using propensity score (PS) matching. We calculated the PS values using a multivariable logistic regression model where being transgender was the outcome, and covariates in the model included age, site, mental health history, and gastrointestinal history.
Results: We initially identified 300 patients as TGD based on electronic data pull. An additional 1,000 patients were also included in the cohort for matching purposes. The agreement between electronic and manual review was 99.9%, and the kappa was 0.998 (95% confidence interval 0.994-1.000). We were able to match patients except for GBQ males due to low numbers. There is a significant difference in age between groups (P <0.001) with GBQ males being older than other groups.
Conclusion: The methodology for identifying transgender and gender diverse patients in the EHR was accurate compared to manual review of gender identity. The TGD patients were able to be well matched, except to GBQ males. This provides a validated method to identify TGD patients in the EHR and further study disparities they may receive in care.
