IDU Peer Recruitment Dynamics and Network Structure in Respondent Driven Sampling

受访者驱动抽样中的 IDU 同伴招募动态和网络结构

• 批准号: 8239508
• 负责人: JIANGHONG LI
• 金额: $ 57.73万
• 依托单位: INSTITUTE FOR COMMUNITY RESEARCH
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-03-15 至 2014-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8239508
• 关键词: AIDS/HIV problem; Address; Affect; Baseline Surveys; Behavior; Centers for Disease Control and Prevention (U.S.); Cities; Complex; Computer Simulation; Connecticut; Country; Data; Decision Making; Development; Drug usage; Ego; Employee Strikes; Enrollment; Epidemiologist; Failure; HIV; Health behavior; Individual; Information Networks; Injecting drug user; Injection of therapeutic agent; Intention; Interdisciplinary Study; Interview; Knowledge; Link; Literature; Maps; Measurement; Methodology; Methods; Modeling; Participant; Performance; Policy Maker; Population; Population Heterogeneity; Prevention; Procedures; Process; Prostitution; Recruitment Activity; Reporting; Research; Research Personnel; Respondent; Risk; Risk Estimate; Sampling; Social Network; Statistical Models; Structure; Surveys; Time; base; behavioral/social science; cost effective; cost effectiveness; design; direct application; disorder prevention; experience; follow-up; high risk; improved; innovation; meetings; member; men who have sex with men; peer; peer influence; public health relevance; social; social science research; tool; transmission process

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.

描述（由申请方提供）：本拟议研究是在注射吸毒者（IDUs）中实施应答者驱动抽样（RDS）（一种非常流行的HIV研究和监测招募工具和抽样方法）时，对同伴招募动态和应答者的多层社交网络进行的深入评估。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，该模型允许对HIV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）进行无偏的人口估计。 早期的RDS统计模型是基于强有力的，但不支持的假设，对同行招聘过程和结构的基础社交网络。随着越来越多的应用到各种人群在不同的情况下，严重的怀疑已经出现了关于RDS的统计推断模型的有效性，由于挑战，以满足这些假设在实施过程中，最近发现，人口估计来自最广泛使用的模型是大大低于公认的准确性。一小群研究人员正在开发新的模型，这些模型对违反假设的行为不太敏感，或者基于更现实但仍有点理想化的招聘动态，需要准确报告网络规模和组成。此外，RDS文献中最引人注目的差距是未能解决高危人群社交网络的复杂性以及影响同伴转介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招募一个IDU样本，同时对招募的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，随着同龄人招募的进行，随着时间的推移，注册损耗和影响力的变化;以及3）理解IDUs的多层社交网络的组成和结构（即，注射风险网络、意图和实际对等招募网络、以及最终注册网络成员），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500名注射吸毒者的典型RDS样本。在招募时和2个月随访时进行全面的社会网络调查，将产生超过500名参与者的网络数据，并允许绘制注射吸毒者样本内的多个网络。这些数据将用于以自我为中心的社会网络分析，以更好地了解在RDS实施的背景下，注射吸毒者的复杂社会网络结构。60个定性深入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。计算机模拟也将被用来评估潜在的假设违反的敏感性。 公共卫生关系：从这项研究中获得的知识将有助于更好地理解和潜在的改进受访者驱动的抽样，一个非常具有成本效益的招募方法，达到隐藏的人口和唯一的抽样计划，相信产生“无偏”的人口估计隐藏的高风险的传播。该提案解决了流行病学家和决策者在更好地了解注射吸毒人群和其他高危群体中艾滋病毒风险状况方面所面临的挑战。