Yale Cancer Center, 333 Cedar Street, WWW 205, New Haven, CT, 06511, USA.
Yale School of Medicine, New Haven, CT, USA.
J Med Syst. 2024 Oct 12;48(1):96. doi: 10.1007/s10916-024-02111-w.
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.
关于跨性别和性别多样化(TGD)人群健康的数据很少。研究人员越来越多地转向保险索赔数据来调查 TGD 人群的疾病负担。由于索赔不包括性别自我认同或方式(即 TGD 或非 TGD),研究人员已经开发了算法,试图使用诊断、程序和处方代码来识别 TGD 个体,有时还推断出生时的性别和性别。基于索赔的算法引入了尚未在数据信息学、流行病学或卫生服务研究中解决的认识论和伦理复杂性。我们讨论了基于索赔的算法在识别和分类 TGD 人群方面的影响,包括延续顺性别偏见和忽视 TGD 个体的自我认同。我们使用认识正义的框架,概述了在进行基于索赔的 TGD 健康研究时应考虑的伦理问题,并提供了一些建议,以尽量减少对 TGD 个体和社区的伤害并最大限度地提高其利益。