Using Multiple Data Sources to Improve Respondent Driven Sampling Estimation

使用多个数据源改进受访者驱动的抽样估计

• 批准号: 8084696
• 负责人: Giovanna M Merli
• 金额: $ 32.56万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-15 至 2015-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8084696
• 关键词: AIDS/HIV problem; Accounting; Address; Centers for Disease Control and Prevention (U.S.); Characteristics; China; Clinic; Communicable Diseases; Communities; Data; Data Analyses; Data Collection; Data Sources; Development; Effectiveness; Evaluation; Female; Geography; Guidelines; HIV; Health; Health behavior; Infection; Injecting drug user; Interview; Investments; Laboratories; Lead; Methodology; Methods; Modeling; Participant; Population; Population Group; Prevalence; Preventive; Probability; Probability Samples; Procedures; Process; Production; Protocols documentation; Province; Recruitment Activity; Research; Research Personnel; Respondent; Rest; Risk Behaviors; Running; Sampling; Sampling Studies; Scheme; Sexually Transmitted Diseases; Simulate; Social Network; Statistical Models; Structure; Surveys; Techniques; base; design; disorder risk; high risk; improved; intravenous drug user; men who have sex with men; population health; sex; simulation; virtual

DESCRIPTION (provided by applicant): Using Multiple Data Sources to Improve Respondent Driven Sampling Estimation ABSTRACT: This study addresses efforts to obtain valid estimates of the prevalence of sexually transmitted disease (STD) infection and risky and preventive health behaviors in a hidden population, female sex workers in China. We take advantage of multiple observations schemas to improve the utility of Respondent Driven Sampling (RDS). RDS is an increasingly popular sampling method used to recruit samples of hidden populations with the aim to provide a probability-based inferential structure for representations of populations such as injection drug users, sex workers, men who have sex with men and population groups whose status characteristics are not likely to be revealed by omnibus survey research because they are rare and socially stigmatized and/or illegal. RDS capitalizes on the social network structure of the hidden population to identify and interview participants. Its validity rests on stringent theoretical assumptions about the referral practices of participants to new participants and the structure of the underlying network that are not observed. Despite significant investments by CDC and similar organizations in RDS, we have few empirical evaluations of its effectiveness at keeping its representation promise. Here we propose to improve RDS for representation of female sex workers in China by moving considerations regarding real-world referral processes from the theoretical to the empirical realms. We accomplish this with a combination of analyses of data we have recently collected through two RDS studies and a venue-based sampling approach in Shanghai and Liuzhou (Guangxi Province). We use this overlapping data collection to observe the social network information embedded in the RDS recruitment process and to realistically simulate RDS settings in order to develop improved RDS estimates adaptive to the observed network referral process. We distill guidelines for researchers using RDS methods on needed steps to improve RDS estimation for representation of other hidden populations. PUBLIC HEALTH RELEVANCE: The main aims of this research are to obtain valid estimates of the prevalence of sexually transmitted disease (STD) infection and risk behaviors in a hidden population, female sex workers in China, sampled with different strategies including Respondent Driven Sampling (RDS) and to improve RDS methodology and procedures using data collected as part of this multiple data collection effort. This will lead to the production of more accurate information on this population, a better understanding of its impact on the larger population health dynamics, and guidelines for researchers using RDS on steps to improve RDS estimation for representation of other hidden populations.

描述（由申请人提供）：使用多个数据源来改善受访者驱动的抽样估计摘要：本研究旨在解决对性传播疾病（STD）感染（STD）感染（STD）感染的有效估计以及隐藏人群中的风险和预防性健康行为的努力中国的性工作者。我们利用多个观测模式来改善受访者驱动抽样（RDS）的效用。 RDS是一种越来越流行的抽样方法综合调查研究不太可能揭示，因为它们罕见并且受到社会污名化和/或非法。 RD会利用隐藏人群的社交网络结构来识别和采访参与者。它的有效性取决于对参与者对新参与者的转诊实践以及未观察到的基础网络的结构的严格理论假设。尽管CDC和类似组织在RDS上进行了巨大的投资，但我们对其在保持其代表性承诺方面的有效性几乎没有经验评估。在这里，我们建议通过将关于现实世界推荐过程的考虑从理论到经验领域的考虑来改善中国女性性工作者的代表。我们最近通过两项RDS研究和在上海和Liuzhou（Guangxi Province）中收集了一系列数据来完成此操作。我们使用此重叠数据收集来观察RDS招聘过程中嵌入的社交网络信息，并实际模拟RDS设置，以开发改进的RDS估计值适应观察到的网络推荐过程。我们使用RDS方法根据所需的步骤来提高研究人员的指南，以改善RDS估计以表示其他隐藏人群的代表。 公共卫生相关性：这项研究的主要目的是获得有关隐藏人口中性行为传播疾病（STD）感染（STD）感染和风险行为的有效估计，中国的女性性工作者，并采样了不同的策略，包括受访者驱动的采样（RDS） ）并使用收集的数据作为此多个数据收集工作的一部分来改进RDS方法和程序。这将导致有关该人群的更准确信息的生产，更好地理解其对较大人群健康动态的影响，以及针对研究人员使用RDS的指南，以改善RDS估算的步骤，以改善其他隐藏人群的代表。