Injection Drug User (IDU) Peer Recruitment Dynamics and Network Structure in Respondent Driven Sampling (RDS)
Respondent Driven Sampling (RDS) has gained its popularity internationally for its cost effectiveness in reaching hidden populations and claimed ability to make unbiased population estimates. However, RDS inference models were based on strong assumptions regarding peer recruitment processes and the structure of underlying social networks, which have not yet been empirically scrutinized. We conducted a mixed method intensive social network study in Hartford, CT and recruit a typical RDS sample of 524 injection drug users (IDUs). Comprehensive social network surveys before and after actual peer recruitment, location information, as well as 60 in-depth interviews will be analyzed to assess IDUs’ peer recruitment intentions, actual experiences, and real world contextual factors associated with coupon distribution and return success. This study is the first attempt to scrutinize an RDS sample as complex multiple-layered networks linked by different social ties specifically related to RDS sample recruitment progresses using mixed methods. Findings will have direct application to development of improved RDS estimators; or to assess performance of existing estimators. This Study addresses the challenges faced by epidemiologists and policy makers to better understand the HIV risk profile among injection drug using populations and other hidden high-risk groups.
Since RDS was first developed in the mid-1990’s, this innovative and powerful methodology has been rapidly and widely applied in HIV/AIDS research, surveillance, and prevention efforts internationally. The strong demand for RDS is primarily due to its cost-effectiveness as a recruitment tool and the lack of satisfactory alternative sampling design and inference in hidden populations. However, the initial RDS statistical models were based on strong but unsupported assumptions regarding peer recruitment processes and the structure of underlying social networks. With its increasing applications to a variety of populations in different contexts, serious skepticism has arisen regarding the validity of RDS’s statistical inference models, given the challenges to meet the underlying assumptions during implementation and recent discovery
that population estimations derived from the most widely used model are substantially less accurate than generally acknowledged. Even the recently improved models are still based on somewhat idealistic recruitment dynamics and require accurate reporting of social network size and composition. Furthermore, the most striking gap in the RDS literature is the failure to address the complexity of the social networks of high-risk populations and factors affecting peer referral behavior and network information reporting. The network members successfully recruited into the study might not actually be representative of their eligible network members reported on surveys, which will undermine the accuracy of estimations derived from current RDS models.
Project Staff and collaborators
Staff Contact: JiangHong Li, M.D., M.S.
(860) 278-2044 ext. 297
Project Staff: ICR Margaret R. Weeks, Ph.D.Co-Investigator Gayatri Moorthi, Ph.D. Project Coordinator/Ethnographer Chiekwu Obidoa, Ph.D. Research Associate Heather Mosher, Ph.D. Ethnographer Gregory Palmer Outreach Interviewer
Eduardo Robles Outreach Interviewer
University of Southern California Thomas Valente, Ph.D. Co-Investigator
Yale University Robert Heimer, Ph.D. Co-Investigator
Recruit a sample of IDUs using RDS and simultaneously conduct a social network study of recruited individuals
Understand factors that influence peer recruitment intention decision making, dynamics of recruitment attempts, enrollment attrition and changes in influences over time as peer recruitment proceeds.
Understand the composition and structures of IDUs’ multi-layered social networks (i.e., the injection risk network, the intention and actual peer recruitment network, and final enrollment network members), and the associations among them.
The proposed study is the first attempt to scrutinize an RDS sample as complex multiple-layered networks linked by different social ties specifically related to RDS sample recruitment processes using mixed methods. Findings from this study will have direct application to development of improved RDS estimators or to assess performance of existing estimators needed to improve population risk estimates.