UCLA

Collection of gender identity data in national probability-based surveys began in 2014, an important first step toward the inclusion of gender identity measurements in public health surveillance. However, the findings about health disparities from probability-based samples do not align with those from nonprobability samples traditionally used to study transgender populations. These contradictions have yet to be understood fully. In this article, we suggest that the truth about disparities lies somewhere between nonprobability and probability samples. We discuss why generalizability from studies using probability sampling may remain limited for transgender populations and describe potential improvements in sampling methodology for transgender populations.