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）：估计难以到达的人群的大小对于公共卫生和公共政策中的许多问题很重要。在艾滋病毒/艾滋病研究中，人口规模的估计尤其迫在很紧迫，因为对最高危人群的大小的可靠估计（ - 药物注射器，女性性工作者和与男性发生性关系的男性）对于理解和控制流行病的传播至关重要。不幸的是，当前的统计方法无法应对这一挑战。缺乏有关这些最高风险群体大小的及时，准确的信息，这是对艾滋病毒预防计划的设计和评估的关键障碍。这项研究的目的是改善网络扩展方法，这是一种有希望的统计方法，用于估计难以到达的组的大小。网络扩大估计来自收集的有关一般人群随机样本的个人网络的调查数据，并且比其他方法具有重要的优势，以估算难以到达的组的大小：1）可以轻松地在各国和时间进行标准化，因为它需要随机的一般人群的随机样本，也许是世界上最广泛使用的抽样设计； 2）它可以在同一数据收集中产生许多目标人群的尺寸估计，而许多替代方法需要为每个感兴趣的人群收集不同的数据； 3）它可以部分自动化，因为它很容易应用于已知大小的种群。然而，尽管世界各地的研究人员和政府的使用越来越多，但对扩展方法的统计基础知之甚少，而关键的实施问题仍未得到解决。这项研究将通过数学建模，计算机模拟以及对现有扩大数据集的分析的结合来实现，这将使研究人员能够收集有关难以到达的组的更准确，更有用的信息。此外，实现这些目标所需的统计发展将使我们从采样数据中了解完整网络的一般能力。因此，该项目结合了有关网络中采样的基础研究，并为全球努力遏制艾滋病毒/艾滋病流行而做出了重要贡献。