There were significant increases in rectal chlamydia and syphilis diagnoses when comparing the periods directly before and after PrEP initiation. However, only 28% of individuals had an increase in STIs between periods. Although risk compensation appears to be present for a segment of PrEP users, the majority of individuals either maintain or decrease their sexual risk following PrEP initiation.

Objectives. To quantify sexual orientation and gender identity (SOGI) disparities in incidence of HIV, other sexually transmitted infections (STIs), and viral hepatitis. Methods. We performed a records-based study of 19 933 patients visiting a federally qualified health center in Los Angeles, California, between November 2016 and October 2017 that examined HIV, STIs, and viral hepatitis incidence proportions. We created multivariable logistic regression models to examine the association between incidence proportions and SOGI among people living with HIV and HIV-negative patients. Results. Among those who were HIV-negative at baseline (n = 16 757), 29% tested positive for any STI during the study period, compared with 38% of people living with HIV. Stratified by birth sex, STI positivity was 32% among men and 11% among women. By SOGI, STI positivity was 35% among gay and bisexual cisgender men, 15% among heterosexual cisgender men, 11% among cisgender women, 25% among transgender women, 13% among gay and bisexual transgender men, 3% among heterosexual transgender men, and 26% among nonbinary people. Conclusions. Stratifying by SOGI highlighted disparities that are obscured when stratifying by birth sex. Public Health Implications. To monitor and reduce disparities, health jurisdictions should include SOGI data with infectious disease reporting.

Purpose of Review We provided an overview of sampling methods for hard-to-reach populations and guidance on implementing one of the most popular approaches: respondent-driven sampling (RDS). Recent Findings Limitations related to generating a sampling frame for marginalized populations can make them “hard-to-reach” when conducting population health research. Data analyzed from non-probability-based or convenience samples may produce estimates that are biased or not generalizable to the target population. In RDS and time-location sampling (TLS), factors that influence inclusion can be estimated and accounted for in an effort to generate representative samples. RDS is particularly equipped to reach the most hidden members of hard-to-reach populations. Summary TLS, RDS, or a combination can provide a rigorous method to identify and recruit samples from hard-to-reach populations and more generalizable estimates of population characteristics. Researchers interested in sampling hard-to-reach populations should expand their toolkits to include these methods.