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基于场所抽样的隐藏人口规模估计:广义网络扩展估计器的扩展。

Estimating Hidden Population Sizes with Venue-based Sampling: Extensions of the Generalized Network Scale-up Estimator.

机构信息

From the Department of Sociology and Criminology, The Pennsylvania State University, University Park, PA.

Department of Epidemiology, The Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC.

出版信息

Epidemiology. 2019 Nov;30(6):901-910. doi: 10.1097/EDE.0000000000001059.

Abstract

BACKGROUND

Researchers use a variety of population size estimation methods to determine the sizes of key populations at elevated risk of human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS), an important step in quantifying epidemic impact, advocating for high-risk groups, and planning, implementing, and monitoring prevention, care, and treatment programs. Conventional procedures often use information about sample respondents' social network contacts to estimate the sizes of key populations of interest. A recent study proposes a generalized network scale-up method that combines two samples-a traditional sample of the general population and a link-tracing sample of the hidden population-and produces more accurate results with fewer assumptions than conventional approaches.

METHODS

We extended the generalized network scale-up method from link-tracing samples to samples collected with venue-based sampling designs popular in sampling key populations at risk of HIV. Our method obviates the need for a traditional sample of the general population, as long as the size of the venue-attending population is approximately known. We tested the venue-based generalized network scale-up method in a comprehensive simulation evaluation framework.

RESULTS

The venue-based generalized network scale-up method provided accurate and efficient estimates of key population sizes, even when few members of the key population were sampled, yielding average biases below ±6% except when false-positive reporting error is high. It relies on limited assumptions and, in our tests, was robust to numerous threats to inference.

CONCLUSIONS

Key population size estimation is vital to the successful implementation of efforts to combat HIV/AIDS. Venue-based network scale-up approaches offer another tool that researchers and policymakers can apply to these problems.

摘要

背景

研究人员使用各种人口规模估计方法来确定处于人类免疫缺陷病毒(HIV)/获得性免疫缺陷综合征(AIDS)风险升高的关键人群的规模,这是量化流行影响、倡导高危人群以及规划、实施和监测预防、护理和治疗计划的重要步骤。常规程序通常使用有关样本受访者社会网络联系人的信息来估计感兴趣的关键人群的规模。最近的一项研究提出了一种广义网络扩展方法,该方法结合了两个样本-传统的一般人群样本和隐藏人群的链接追踪样本-并比传统方法具有更少的假设和更准确的结果。

方法

我们将广义网络扩展方法从链接追踪样本扩展到基于场所的抽样设计中收集的样本,该设计在 HIV 风险高危关键人群抽样中很流行。只要大致了解场所参与人群的规模,我们的方法就不需要传统的一般人群样本。我们在综合模拟评估框架中测试了基于场所的广义网络扩展方法。

结果

基于场所的广义网络扩展方法提供了关键人群规模的准确和高效估计,即使对关键人群的少数成员进行了抽样,平均偏差也低于±6%,除非假阳性报告错误率很高。它依赖于有限的假设,并且在我们的测试中,对许多推理威胁具有鲁棒性。

结论

关键人群规模估计对于成功实施抗击 HIV/AIDS 的努力至关重要。基于场所的网络扩展方法为研究人员和政策制定者提供了另一种可用于解决这些问题的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6174/6784771/d9f0540e9ae8/ede-30-901-g002.jpg

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