Sexual orientation and gender identity inequities in cervical cancer screening by race and ethnicity.

Background: In the United States, inequities in preventive health behaviors such as cervical cancer screening have been documented. Sexual orientation, gender identity, and race/ethnicity all individually contribute to such disparities. However, little work has investigated their joint impact on screening behavior.

Methods: Using sampling weighted data from the 2016 and 2018 Behavioral Risk Factor Surveillance System, we assessed differences in two metrics via chi-square statistics: 1) lifetime uptake, and 2) up-to-date cervical cancer screening by sexual orientation and gender identity, within and across racial/ethnic classifications.

Results: Within all races, individuals who identify as members of sexual and gender minority (SGM) communities reported higher rates of never being screened (except for Black transgender men) than straight or cisgender individuals (p < 0.0001). [*START* Across all races, the Asian/Pacific Islander transgender population (32.4%; weighted n (w.n.) = 1,313) had the lowest proportion of lifetime screening, followed by the Asian/Pacific Islander gay/lesbian (53.0%, w.n. = 21,771), Hispanic transgender (58.7%; w.n. = 24,780), Asian/Pacific Islander bisexual (61.8%, w.n. = 54,524), and Hispanic gay/lesbian (69.6%, w.n. = 125,781) populations. *END*] Straight or cisgender Non-Hispanic White (w.n. = 40,664,476) individuals had the highest proportion of lifetime screening (97.7% and 97.5%, respectively). However, among individuals who had been screened at least once in their lifetime, identifying as SGM was not associated with a decreased proportion of up-to-date screening within or between races.

Conclusions: Due to small sample sizes, especially among Asian/Pacific Islander and Hispanic populations, confidence intervals were wide. Heterogeneity in screening participation by SGM status within and across racial/ethnic groups were observed.

Impact: These screening disparities reveal the need to disaggregate data to account for intersecting identities and for studies with larger sample sizes to increase estimate reliability.