IDU Peer Recruitment Dynamics and Network Structure in Respondent Driven Sampling

描述(申请人提供)：这项拟议的研究是在注射吸毒者(IDU)中实施受访者驱动抽样(RDS)时，深入评估同伴招募动态和受访者的多层次社会网络。RDS是艾滋病毒研究和监测中非常流行的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的高风险隐藏人群进行无偏见的估计，如注射吸毒者、男男性行为者和商业性工作者。早期的RDS统计模型是基于关于同行招聘过程和基本社交网络结构的强有力但没有得到支持的假设。随着在不同背景下对各种人群的应用越来越多，人们对RDS的统计推断模型的有效性产生了严重的怀疑，这是因为在实施过程中满足这些假设的挑战，以及最近发现，从最广泛使用的模型得出的人口估计值远不如公认的准确。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文献中最显著的缺陷是未能解决高危人群社会网络的复杂性以及影响同伴推荐行为和网络信息报告的因素。为了解决这些担忧及其对RDS统计模型性能的影响，我们建议实现以下目标：1)使用RDS招募注射吸毒者样本，同时对被招募的个人进行社会网络研究；2)了解影响同伴招募意向决策的因素、招募尝试的动态、招生损耗以及随着同伴招募进行而影响随时间的变化；以及3)了解注射吸毒者多层社会网络的组成和结构(即注入风险网络、意图和实际的同伴招募网络、最终招募网络成员)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一个典型的RDS样本，包括500名注射吸毒者。在招聘时和在2个月的后续行动中进行全面的社会网络调查，将产生500名参与者以外的网络数据，并能够绘制国际吸毒单位样本中的多个网络图。这些数据将用于以自我为中心的网络分析和社会计量网络分析，以更好地了解在实施RDS的背景下注射吸毒者的复杂社会网络结构。60个定性的深度访谈将评估注射吸毒者的实际同伴招募经验，以及与RDS同伴招募过程相关的多层社会网络组成和结构的变化。还将使用计算机模拟来评估潜在违反假设的敏感性。 公共卫生相关性：从这项研究中获得的知识将有助于更好地理解和潜在地改进受访者主导的抽样，这是一种接触隐藏人口的非常具有成本效益的招募方法，也是据信能够对高传播风险的隐藏人口进行“无偏见”人口估计的唯一抽样计划。这项建议解决了流行病学家和政策制定者在更好地了解注射吸毒人群和其他高危群体中的艾滋病毒风险概况方面面临的挑战。

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.