Evidence map›Paper›PMID 31299014›Full record

ArticleEpidemiology (Cambridge, Mass.)2019

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

Ashton M Verdery, Sharon Weir, Zahra Reynolds, Grace Mulholland, Jessie K Edwards

Open access · hybridAbstract read
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.4field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 14 citations in OpenAlex.

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  7. Thirty Years of The Network Scale-up Method.Journal of the American Statistical Association · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Ashton M VerderyFrom the Department of Sociology and Criminology, The Pennsylvania State University, University Park, PA.
Sharon WeirDepartment of Epidemiology, The Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC.
Zahra ReynoldsDepartment of Epidemiology, The Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC.
Grace MulhollandDepartment of Epidemiology, The Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC.
Jessie K EdwardsDepartment of Epidemiology, The Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC.
Pennsylvania State University · US

Funding

The Population Research Institute: Development CoreP2CHD041025 · NICHD · PENNSYLVANIA STATE UNIVERSITY, THE · PI Brian Thiede · 2016 to 2026
$9.0M
NICHD NIH HHS P2C HD041025
6 · The paper itself

Abstract

backgroundResearchers 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.

methodsWe 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.

resultsThe 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.

conclusionsKey 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.

Indexed as

Population DensityStatistics as TopicDrug UsersHealth PolicyHIV InfectionsHumansPolicy MakingRisk FactorsSexual and Gender MinoritiesSex WorkersSubstance Abuse, Intravenous

Identifiers

PMID31299014
PMCPMC6768707
OpenAlexW2961078843

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.