Improvements to the network scale-up method for studying hard-to-reach population

研究难以到达人群的网络放大方法的改进

• 批准号: 8554792
• 负责人: Matthew J. Salganik
• 金额: $ 6.34万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-27 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8554792
• 关键词: AIDS prevention; AIDS/HIV problem; Age Distribution; Belief; Birth; Characteristics; Computer Simulation; Country; Data; Data Collection; Data Set; Development; Epidemic; Equilibrium; Evaluation; Female; Foundations; Future; General Population; Goals; Government; Health Policy; Injection of therapeutic agent; Learning; Methods; Persons; Pharmaceutical Preparations; Population; Population Sizes; Populations at Risk; Positioning Attribute; Prevention program; Procedures; Public Health; Public Policy; Reporting; Research; Research Methodology; Research Personnel; Respondent; Risk; Rwanda; Sampling; School Teachers; Statistical Methods; Surveys; Target Populations; Time; Uncertainty; Woman; base; cost; design; improved; interest; mathematical model; men who have sex with men; middle school; public health relevance; research study; response; scale up; sex; social

DESCRIPTION (provided by applicant): Estimating the sizes of hard-to-reach populations is important for many problems in public health and public policy. Population size estimation is particularly pressing in HIV/AIDS research because reliable estimates of the sizes of the most at-risk populations---drug injectors, female sex workers, and men who have sex with men---are critical for understanding and controlling the spread of the epidemic. Unfortunately, current statistical methods are not up to this challenge. The lack of timely and accurate information about the sizes of these most at-risk groups is a critical barrier to the design and evaluation of HIV prevention programs. The goal of this research is to improve the network scale-up method, a promising statistical approach for estimating the sizes of hard-to-reach groups. Network scale-up estimates come from survey data collected about the personal networks of a random sample of the general population, and offers important advantages over other approaches for estimating the sizes of hard-to-reach groups: 1) it can easily be standardized across countries and time because it requires a random sample of the general population, perhaps the most widely used sampling design in the world; 2) it can produce estimates of the sizes of many target populations in the same data collection, whereas many alternative methods require distinct data collections for each population of interest; and 3) it can be partially self-validating because it an easily be applied to populations of known size. However, despite these appeal characteristics and growing use by researchers and governments around the world, the statistical foundations of the scale-up method are poorly understood and key implementation questions remain unanswered. This research, which will be achieved through a combination of mathematical modeling, computer simulation, and the analysis of existing scale-up data sets, will enable researchers to collect more accurate and more useful information about hard-to-reach groups. Further, the statistical developments needed to achieve these aims will enrich our general ability to learn about complete networks from sampled data. Thus, this project combines foundational research about sampling in networks with important contributions to the global effort to contain the HIV/AIDS epidemic.

描述（由申请人提供）：估计难以接触人群的规模对于公共卫生和公共政策中的许多问题都很重要。人口规模估计在艾滋病毒/艾滋病研究中尤为紧迫，因为对最危险人群-注射毒品者、女性性工作者和男男性行为者-的规模的可靠估计对于理解和控制流行病的传播至关重要。不幸的是，目前的统计方法无法应对这一挑战。缺乏关于这些高危人群规模的及时和准确信息是设计和评估艾滋病毒预防方案的一个关键障碍。本研究的目标是改进网络扩展方法，这是一种很有前途的统计方法，用于估计难以接触的群体的规模。网络规模扩大的估计来自于对一般人口随机抽样的个人网络收集的调查数据，与其他估计难以接触群体规模的方法相比，它具有重要的优势：1）它可以很容易地在不同国家和时间标准化，因为它需要一般人口的随机抽样，这可能是世界上最广泛使用的抽样设计; 2）它可以在同一数据集合中产生许多目标群体的大小的估计，而许多替代方法需要针对每个感兴趣的群体的不同数据集合;以及3）它可以部分地自我验证，因为它可以容易地应用于已知大小的群体。然而，尽管有这些吸引力的特点，并且越来越多地被世界各地的研究人员和政府使用，但人们对扩大方法的统计基础知之甚少，关键的实施问题仍然没有答案。这项研究将通过数学建模、计算机模拟和对现有放大数据集的分析相结合来实现，将使研究人员能够收集有关难以接触群体的更准确、更有用的信息。此外，实现这些目标所需的统计发展将丰富我们从采样数据中学习完整网络的一般能力。因此，该项目将关于网络抽样的基础研究与对全球遏制艾滋病毒/艾滋病流行的努力的重要贡献结合起来。