From Networks to Populations: The Development and Application of Respondent-Driven Sampling Among IDUs and Latino Gay Men

Abstract

One of the challenges in studying HIV-risk behaviors among gay men is gathering information from a non-biased sample, as traditional probability sampling methods cannot be applied in gay populations. Respondent-Driven Sampling (RDS) has been proposed as a reliable and bias-free method to recruit “hidden” populations, such as gay men. The aim of this study is to assess the feasibility and effectiveness of RDS to sample Latino gay men and transgender persons. This was carried out when we used RDS to recruit participants into a study that investigated community involvement on HIV/AIDS sexual risk behaviors among Latino gay and bisexual men, and transgender (male-to-female) persons in Chicago and San Francisco. The population coverage of RDS was then compared to simulated time-location sampling (TLS). Recruitment differences were observed across cities, but the samples were comparable. RDS showed broader population coverage than TLS, especially among individuals at high risk for HIV.