Network-based epidemiology for hidden and hard-to-reach populations

摘要：隐藏的或难以触及的人群，如性工作者、男男性行为者或 注射毒品的人承受着不成比例的沉重的不良健康后果负担，但他们中 很难研究。这些群体的成员通常在社会上被污名化或在法律上被定为犯罪，所以潜在的 受试者不能直接列举，随机抽样通常是不可能的。出于这个原因，研究人员 开发了依赖于追踪个人之间的社交网络链接的调查技术。最多的 目前流行的方法是受访者驱动抽样(RDS)，它在流行病学和临床中得到了广泛的应用 关于艾滋病毒、乙肝病毒、丙型肝炎病毒和梅毒、烟草、酒精和非法药物使用以及获得治疗的研究。RDS 美国疾病控制与预防中心也使用它进行艾滋病毒监测，在国际上也被联合国艾滋病规划署/世卫组织使用。值得注意的是， 这些示例中包含的大多数网络信息要么被丢弃，要么被错误地应用于标准 从RDS数据估计目标人群特征的方法。方法论研究 RDS侧重于两个相关的推理目标：社交网络特征(例如，聚类，程度， 中心性)和人口数量(例如艾滋病毒流行率、总人口规模)，但准确估计 高风险隐藏和难以接触到的人群中的网络结构、发病率和风险因素 这是公共卫生领域一个尚未解决的重大问题。在这份提案中，我概述了一项计划，以制定严格的方法来 来自隐藏和难以接触到的人群的社会联系追踪设计的社会流行病学。中的关键见解 这项工作是，RDS揭示了关于可使用的目标人群社交网络的结构信息 以显著改进流行病学推断。我将首先展示现有的统计方法 分析链接跟踪研究的数据依赖于不切实际的假设，忽视了重要的可观测数据，以及 得出的估计存在严重的偏差。通过严格描述观察到的和缺失的网络 对于每个采样过程的数据，我将提供统计和数学工具，使研究人员能够利用 RDS透露的网络数据。该方法可以准确估计人口平均水平(例如艾滋病毒 流行率)，使用网络回归评估与流行病学结果相关的风险因素， 隐藏的人口规模估计，以及风险和健康结果的地理空间测绘。这种基于网络的 透视是对RDS既定方法的根本背离，并有可能带来革命性的 流行病学家从隐蔽和难以触及的风险群体调查中收集和分析数据的方式。 最后，我将开发免费的、开源的、基于Web的软件，用于设计和分析RDS研究，这些软件将 世界上任何地方的任何人都可以使用。这些思想在中国的实证研究中的初步应用 现实世界中的风险人群已经取得了令人振奋的成果。拟议的工作具有创新性，因为它 利用以前被忽视的每项RDS研究收集的网络信息，并将其显著用于 提高公共卫生重点风险人群人群水平估计的准确性和精确度。

Abstract: Hidden or hard-to-reach populations such as sex workers, men who have sex with men, or people who inject drugs suffer from a disproportionately high burden of adverse health outcomes, but are the most difficult to study. Members of these groups are often socially stigmatized or legally criminalized, so potential subjects are not directly enumerable and random sampling is usually impossible. For this reason, researchers have developed survey techniques that rely on tracing social network links between individuals. The most popular technique is respondent-driven sampling (RDS), and it is widely used in epidemiological and clinical research on HIV, HBV, HCV, and syphilis, tobacco, alcohol, and illicit drug use, and access to treatment. RDS is also used by the CDC for HIV surveillance in the US, and by UNAIDS/WHO internationally. Remarkably, most of the network information contained in these samples is either discarded or misapplied in standard approaches to estimate characteristics of the target population from RDS data. Methodological research on RDS is focused on two related inferential targets: social network characteristics (e.g. clustering, degree, centrality) and population-level quantities (e.g. HIV prevalence, total population size), but accurate estimation of network structures, disease rates, and risk factors in high-risk hidden and hard-to-reach populations remains a major unsolved problem in public health. In this proposal I outline a plan to develop rigorous methodology for social epidemiology from social link-tracing designs in hidden and hard-to-reach populations. The key insight in this work is that RDS reveals structural information about the target population social network that can be used to dramatically improve epidemiological inference. I will begin by showing that existing statistical approaches to analyzing data from link-tracing studies rely on unrealistic assumptions, neglect important observable data, and produce estimates that suffer from serious bias. By rigorously characterizing the observed and missing network data for each sampling process, I will provide statistical and mathematical tools that allow researchers leverage the network data revealed by RDS. The approach allows accurate estimation of population averages (e.g. HIV prevalence), assessment of risk factors associated with epidemiological outcomes using network regression, hidden population size estimation, and geospatial mapping of risk and health outcomes. This network-based perspective is a radical departure from established approaches to RDS and has the potential to revolutionize the way epidemiologists collect and analyze data from surveys of hidden and hard-to-reach risk groups. Finally, I will develop free, open-source, web-based software for design and analysis of RDS studies that will be available to anyone anywhere in the world. Preliminary application of these ideas to empirical studies in real-world risk populations has already yielded promising results. The proposed work is innovative because it leverages previously neglected network information collected by every RDS study and uses it to dramatically improve the accuracy and precision of population-level estimates for key risk populations in public health.