IDU Peer Recruitment Dynamics and Network Structure in Respondent Driven Sampling

描述（由申请人提供）：本研究旨在深入评估在注射吸毒者（IDUs）中实施被调查者驱动抽样（RDS）时的同伴招募动态和被调查者的多层次社会网络。RDS是HIV研究和监测中非常流行的一种招募工具和抽样方法。这项研究的结果将有助于更好地理解和改进RDS统计模型的性能，这些模型允许对艾滋病毒传播高风险的隐藏人群（如注射吸毒者、男男性行为者和商业性工作者）进行无偏人口估计。早期的RDS统计模型是基于关于同伴招聘过程和潜在社会网络结构的强大但没有证据的假设。随着RDS在不同背景下对各种人群的应用越来越多，人们对RDS统计推断模型的有效性产生了严重的怀疑，因为在实施过程中要满足这些假设存在挑战，而且最近发现，从最广泛使用的模型中得出的人口估计远不如普遍认为的准确。一小群研究人员现在正在开发新的模型，这些模型对违反假设的情况不那么敏感，或者基于更现实但仍然有些理想化的招聘动态，需要准确报告网络规模和组成。此外，RDS文献中最显著的差距是未能解决高风险人群社会网络的复杂性以及影响同伴转诊行为和网络信息报告的因素。为了解决这些问题及其对RDS统计模型性能的影响，我们建议实现以下目标：1)使用RDS招募IDUs样本，同时对招募个体进行社会网络研究；2)了解影响同伴招聘意向决策的因素、招聘尝试的动态、招生流失以及同伴招聘过程中影响因素随时间的变化；3)了解注射吸毒者多层次社会网络（注射风险网络、意向和实际同伴招募网络、最终招募网络成员）的组成和结构，以及它们之间的关联。我们建议在康涅狄格州哈特福德招募典型的RDS样本500名注射者。在招募和2个月的随访中，全面的社会网络调查将产生超过500名参与者的网络数据，并允许在IDU样本中绘制多个网络。这些数据将用于自我中心和社会计量网络分析，以更好地了解在RDS实施背景下IDUs复杂的社会网络结构。60个定性深度访谈将评估idu的实际同伴招聘经历，以及与RDS同伴招聘过程相关的多层次社会网络组成和结构的变化。计算机模拟也将用于评估潜在的假设违反的敏感性。

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.