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Estimating Hidden Population Sizes with Venue Based Sampling

Extensions of the Generalized Network Scale-up Estimator

Verdery, Ashton M.a; Weir, Sharonb; Reynolds, Zahrab; Mulholland, Graceb; Edwards, Jessie K.b

doi: 10.1097/EDE.0000000000001059
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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. Recent work 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.

aDepartment of Sociology and Criminology, The Pennsylvania State University

bDepartment of Epidemiology, The Gillings School of Global Public Health, The University of North Carolina at Chapel Hill

Conflicts of Interest: The authors report no conflict of interest.

Sources of Financial Support: We wish to thank the MeSH Consortium for their support in this work. The MeSH Consortium is funded by the Bill & Melinda Gates Foundation (BMGF-OPP1120138). This work was also supported by the Population Research Institute (R24-HD041025) and the Institute for CyberScience at the Pennsylvania State University, as well as by the U.S. Agency for International Development (USAID) under the terms of MEASURE Evaluation cooperative agreement AID-OAA-L-14-00004 at the Carolina Population Center and the University of North Carolina at Chapel Hill. Views expressed are not necessarily those of USAID or the United States government.

Data and Code Availability Statement: All data and code analyzed in this manuscript are provided in a zipped file included with the supplementary materials.

Corresponding Author: Ashton M. Verdery, Oswald Tower, The Pennsylvania State University, University Park, PA 16801, Phone: 814-863-5385, Email: amv5430@psu.edu

This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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