A groundbreaking report by the National Academy of Sciences on how to best gather information on gender and sexuality says gender identity, not biological sex, should be the default data collected and reported.
The report, released this month, provides recommendations to the National Institutes of Health for how to measure gender and sexuality throughout different health contexts, including in research, surveys, healthcare administration and clinical practice. Made up of 27 different branches, the NIH is the nation’s agency for medical research, as well as the largest funder of biomedical research in the world. Its global influence puts it in a unique position to model meaningful data collection on gender and sexual minority populations.
Accurate data collection is essential to understanding disparities across marginalized populations. Integrating these new metrics will help research programs better evaluate the specific health needs of LGBTQ+ populations.
LGBTQ+ communities have rarely been represented in general population surveys. This is the first rigorous study of how or when data on sex, gender or sexuality should be collected. Without a universal standard, data cannot be combined or compared accurately. For instance, one survey may have asked about gender and another about sex, and thus they ended up measuring different things.
The report, funded by the Department of Health and Human Services, includes suggested language for survey questions, which data should be collected in which context, and extensive research on privacy concerns and how to minimize potential harm while collecting data. Nancy Bates, retired senior methodologist for survey research at the U.S. Census Bureau and co-chair of the committee that wrote the report, told The 19th in email that the nine-member committee was made up of researchers who identified across the gender and sexuality spectrum, specifically including two-spirit (a general term to describe the variety of culture-specific Indigenous genders and sexualities), intersex and gender nonconforming identities.