Doing Justice: Ethical Considerations Identifying and Researching Transgender and Gender Diverse People in Insurance Claims Data.

Autor: Alpert AB; Yale Cancer Center, 333 Cedar Street, WWW 205, New Haven, CT, 06511, USA. ash.alpert@yale.edu.; Yale School of Medicine, New Haven, CT, USA. ash.alpert@yale.edu.; Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA. ash.alpert@yale.edu.; Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, RI, USA. ash.alpert@yale.edu., Babbs G; Department of Philosophy, University of Rochester, Rochester, NY, USA., Sanaeikia R; Department of Health Policy and Management, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA., Ellison J; Department of Health Policy and Management, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.; Center for Innovative Research On Gender Health Equity, Department of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA., Hughes L; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, USA.; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA., Herington J; Department of Health Humanities and Bioethics, University of Rochester Medical Center, Rochester, NY, USA., Dembroff R; Department of Philosophy, Yale University, New Haven, CT, USA.
Jazyk: angličtina
Zdroj: Journal of medical systems [J Med Syst] 2024 Oct 12; Vol. 48 (1), pp. 96. Date of Electronic Publication: 2024 Oct 12.
DOI: 10.1007/s10916-024-02111-w
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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