期刊
MEDICAL CARE
卷 58, 期 10, 页码 903-911出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/MLR.0000000000001362
关键词
transgender; gender dysphoria; gender identity disorder; identification methods; administrative data
类别
资金
- National Institute on Minority Health and Health Disparities [5R21MD012371]
Background: Large administrative databases often do not capture gender identity data, limiting researchers' ability to identify transgender people and complicating the study of this population. Objective: The objective of this study was to develop methods for identifying transgender people in a large, national dataset for insured adults. Research Design: This was a retrospective analysis of administrative claims data. After using gender identity disorder (GID) diagnoses codes, the current method for identifying transgender people in administrative data, we used the following 2 strategies to improve the accuracy of identifying transgender people that involved: (1) Endocrine Disorder Not Otherwise Specified (Endo NOS) codes and a transgender-related procedure code; or (2) Receipt of sex hormones not associated with the sex recorded in the patient's chart (sex-discordant hormone therapy) and an Endo NOS code or transgender-related procedure code. Subjects: Seventy-four million adults 18 years and above enrolled at some point in commercial or Medicare Advantage plans from 2006 through 2017. Results: We identified 27,227 unique transgender people overall; 18,785 (69%) were identified using GID codes alone. Using Endo NOS with a transgender-related procedure code, and sex-discordant hormone therapy with either Endo NOS or transgender-related procedure code, we added 4391 (16%) and 4051 (15%) transgender people, respectively. Of the 27,227 transgender people in our cohort, 8694 (32%) were transmasculine, 3959 (15%) were transfeminine, and 14,574 (54%) could not be classified. Conclusion: In the absence of gender identity data, additional data elements beyond GID codes improves the identification of transgender people in large, administrative claims databases.
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