This proposed study is an in-depth assessment of peer recruitment dynamics and respondents'multiple layered social networks when Respondent Driven Sampling (RDS), a very popular recruitment tool and sampling method in HIV research and surveillance, is implemented among injection drug users (IDUs). Findings from this study will contribute to better understanding of and improvements in the performance of RDS statistical models that allow unbiased population estimates for hidden populations at high risk of HIV transmission such as IDUs, men who have sex with men, and commercial sex workers. The early RDS statistical models were based on strong but unsupported assumptions regarding the peer recruitment process and the structure of underlying social networks. With increasing applications to a variety of populations in different contexts, serious skepticism has arisen regarding the validity of RDS's statistical inference models, due to the challenges to meet these assumptions during implementation and recent discovery that population estimations derived from the most widely used model are substantially less accurate than generally acknowledged. A small group of researchers are now developing new models that are less sensitive to violations of assumptions or based on more realistic yet still somewhat idealistic recruitment dynamics that require accurate reporting of 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. To address these concerns and their implications for RDS statistical model performance, we propose to achieve the following aims focused on an IDU population: 1) Recruit a sample of IDUs using RDS and simultaneously conduct a social network study of recruited individuals;2) 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;and 3) Understand the composition and structures of IDUs'multi-layered social networks (i.e., the injection risk network, the intent and actual peer recruitment network, and final enrollment network members), and the association among them. We propose to recruit a typical RDS sample of 500 IDUs in Hartford, CT. Comprehensive social network surveys at recruitment and at 2-month follow-up will generate network data beyond the 500 participants and allow mapping of multiple networks within the IDU sample. These data will be used in ego-centric and sociometric network analyses to better understand the complex social network structures of IDUs in the context of RDS implementation. Sixty qualitative in-depth interviews will assess IDUs'actual peer recruitment experiences and change in their multi-layered social network composition and structures related to the RDS peer recruitment processes. Computer simulation will also be used to assess the sensitivity of potential assumption violations.
Knowledge gained from this study will contribute to better understanding and potential improvement of respondent driven sampling, a very cost-effective recruitment method in reaching hidden populations and the only sampling plan believed to produce "unbiased" population estimates of hidden populations at high risk of transmission. This proposal addresses the challenges faced by epidemiologists and policy makers to better understand the HIV risk profile among injection drug using populations and other high-risk groups.