Measuring Sexual and Gender Minority Populations in Health Surveillance

Purpose: Sexual and gender minorities (SGMs) are underrepresented and information about SGMs is difficult to locate in national health surveillance data, and this limits identification and resolution of SGM health disparities. It is also not known how measures of sexual orientation and transgender-inclusive gender identity in health surveillance compare with best practice recommendations. This article reviews and summarizes the publicly available, English language, large-scale, rigorously sampled, national, international, and regional data sources that include sexual orientation or transgender-inclusive gender identity and compares measures with best practice guidelines.

Methods: A systematic review was undertaken of national, international, state, and regional health surveillance data sources. Data sources that measured sexual orientation or transgender-inclusive gender identity and met seven inclusion criteria were included.

Results: Forty-three publicly accessible national, international, and regional data sources included measures of sexual orientation and transgender-inclusive gender identity and health. For each data source, sampling design, sample characteristics, study years, survey questions, contact persons, and data access links are provided. Few data sources met best practice recommendations for SGM measurement: 14% measured all three dimensions of sexual orientation (identity, behavior, attraction) as recommended by the Sexual Minority Assessment Research Team. No data sources measured transgender-inclusive gender identity according to the Gender Identity in U.S. Surveillance-recommended two-step method of measuring sex assigned at birth and current gender identity.

Conclusions: This article provides a much needed detailed summary of extant health surveillance data sources that can be used to inform research about health risks and disparities among SGM populations. Future recommendations are for more rigorous measurement and oversampling to advance what is known about SGM health disparities and guide development of interventions to reduce disparities.