Evidence map›Paper›PMID 28514276›Full record

ArticleAIDS (London, England)2017

Sexual network drivers of HIV and herpes simplex virus type 2 transmission.

Ryosuke Omori, Laith J Abu-Raddad

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in AIDS (London, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 8 pooled it
–field-weighted citation impact
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

31 citing papers in PubMed, 8 syntheses or guidelines pooled it.

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  4. Global epidemiology ofSexually transmitted infections · 2021
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  6. Epidemiology ofJournal of global health · 2019
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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

2 authors.

Ryosuke OmoriaDivision of Bioinformatics, Research Center for Zoonosis Control, Hokkaido University, Sapporo, Hokkaido, Japan bInfectious Disease Epidemiology Group, Weill Cornell Medicine, Qatar, Cornell University, Qatar Foundation, Education City, Doha, Qatar cDepartment of Healthcare Policy and Research, Weill Cornell Medicine, Cornell University, New York City, New York, USA dJST, PRESTO, Kawaguchi, Saitama, Japan eCollege of Public Health, Hamad bin Khalifa University, Doha, Qatar.
Laith J Abu-Raddad

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesHIV and herpes simplex virus type 2 (HSV-2) infections are sexually transmitted and propagate in sexual networks. Using mathematical modeling, we aimed to quantify effects of key network statistics on infection transmission, and extent to which HSV-2 prevalence can be a proxy of HIV prevalence. DESIGN/

methodsAn individual-based simulation model was constructed to describe sex partnering and infection transmission, and was parameterized with representative natural history, transmission, and sexual behavior data. Correlations were assessed on model outcomes (HIV/HSV-2 prevalences) and multiple linear regressions were conducted to estimate adjusted associations and effect sizes.

resultsHIV prevalence was one-third or less of HSV-2 prevalence. HIV and HSV-2 prevalences were associated with a Spearman's rank correlation coefficient of 0.64 (95% confidence interval: 0.58-0.69). Collinearities among network statistics were detected, most notably between concurrency versus mean and variance of number of partners. Controlling for confounding, unmarried mean/variance of number of partners (or alternatively concurrency) were the strongest predictors of HIV prevalence. Meanwhile, unmarried/married mean/variance of number of partners (or alternatively concurrency), and clustering coefficient were the strongest predictors of HSV-2 prevalence. HSV-2 prevalence was a strong predictor of HIV prevalence by proxying effects of network statistics.

conclusionNetwork statistics produced similar and differential effects on HIV/HSV-2 transmission, and explained most of the variation in HIV and HSV-2 prevalences. HIV prevalence reflected primarily mean and variance of number of partners, but HSV-2 prevalence was affected by a range of network statistics. HSV-2 prevalence (as a proxy) can forecast a population's HIV epidemic potential, thereby informing interventions.

Indexed as

Disease Transmission, InfectiousSexual BehaviorComputer SimulationFemaleHerpes GenitalisHIV InfectionsHumansMalePrevalence

Identifiers

PMID28514276
PMCPMC5508852

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.