Improving Data-Driven Methods to Identify and Categorize Transgender Individuals by Gender in Insurance Claims Data.

ABSTRACT: Purpose: Prior algorithms enabled the identification and gender categorization of transgender people in insurance claims databases in which sex and gender are not simultaneously captured. However, these methods have been unable to categorize the gender of a large proportion of their samples. We improve upon these methods to identify the gender of a larger proportion of transgender people in insurance claims data. Methods: Using 2001-2019 Optum's Clinformatics^® Data Mart insurance claims data, we adapted prior algorithms by combining diagnosis, procedure, and pharmacy claims to (1) identify a transgender sample; and (2) stratify the sample by gender category (trans feminine and nonbinary [TFN], trans masculine and nonbinary [TMN], unclassified). We use

SUBMITTER: Hughto JMW 

PROVIDER: S-EPMC9150133 | biostudies-literature | 2022 May-Jun

REPOSITORIES: biostudies-literature