Data on the health of transgender and gender diverse (TGD) people are scarce. Researchers are increasingly turning to insurance claims data to investigate disease burden among TGD people. Since claims do not include gender self-identification or modality (i.e., TGD or not), researchers have developed algorithms to attempt to identify TGD individuals using diagnosis, procedure, and prescription codes, sometimes also inferring sex assigned at birth and gender. Claims-based algorithms introduce epistemological and ethical complexities that have yet to be addressed in data informatics, epidemiology, or health services research. We discuss the implications of claims-based algorithms to identify and categorize TGD populations, including perpetuating cisnormative biases and dismissing TGD individuals' self-identification. Using the framework of epistemic injustice, we outline ethical considerations when undertaking claims-based TGD health research and provide suggestions to minimize harms and maximize benefits to TGD individuals and communities.
Keywords: Administrative Data; Epistemic Injustice; Ethics; Insurance Claim Review; Transgender Persons.
© 2024. The Author(s).