Evidence map›Paper›PMID 30746864›Full record

ArticleJournal of the International AIDS Society2019

A latent class approach to identify multi-risk profiles associated with phylogenetic clustering of recent hepatitis C virus infection in Australia and New Zealand from 2004 to 2015.

Sofia R Bartlett, Tanya L Applegate, Brendan P Jacka, Marianne Martinello, Francois Mj Lamoury, Mark Danta, Daniel Bradshaw, David Shaw, Andrew R Lloyd, Margaret Hellard and 3 more

Abstract read
In one paragraph

Article in Journal of the International AIDS Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 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

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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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

13 authors.

Sofia R BartlettKirby Institute, UNSW, Sydney, NSW, Australia.ORCID 0000-0003-0699-2250
Tanya L ApplegateKirby Institute, UNSW, Sydney, NSW, Australia.
Brendan P JackaKirby Institute, UNSW, Sydney, NSW, Australia.
Marianne MartinelloKirby Institute, UNSW, Sydney, NSW, Australia.
Francois Mj LamouryKirby Institute, UNSW, Sydney, NSW, Australia.
Mark DantaSt Vincent's Clinical School, UNSW, Sydney, NSW, Australia.
Daniel BradshawNational Infection Service, Public Health England, London, UK.
David ShawRoyal Adelaide Hospital, Adelaide, SA, Australia.
Andrew R LloydKirby Institute, UNSW, Sydney, NSW, Australia.
Margaret HellardThe Burnet Institute, Melbourne, Vic., Australia.
Gregory J DoreKirby Institute, UNSW, Sydney, NSW, Australia.
Gail V MatthewsKirby Institute, UNSW, Sydney, NSW, Australia.
Jason GrebelyKirby Institute, UNSW, Sydney, NSW, Australia.

Funding

Treatment of recently acquired hepatitis C virus infectionR01DA015999 · NIDA · UNIVERSITY OF NEW SOUTH WALES · PI DORE, GREGORY JOHN · 2003 to 2014
$3.7M
NIDA NIH HHS R01 DA015999
6 · The paper itself

Abstract

introductionOver the last two decades, the incidence of hepatitis C virus (HCV) co-infection among men who have sex with men (MSM) living with HIV began increasing in post-industrialized countries. Little is known about transmission of acute or recent HCV, in particular among MSM living with HIV co-infection, which creates uncertainty about potential for reinfection after HCV treatment. Using phylogenetic methods, clinical, epidemiological and molecular data can be combined to better understand transmission patterns. These insights may help identify strategies to reduce reinfection risk, enhancing effectiveness of HCV treatment as prevention strategies. The aim of this study was to identify multi-risk profiles and factors associated with phylogenetic pairs and clusters among people with recent HCV infection.

methodsData and specimens from five studies of recent HCV in Australia and New Zealand (2004 to 2015) were used. HCV Core-E2 sequences were used to infer maximum likelihood trees. Clusters were identified using 90% bootstrap and 5% genetic distance threshold. Multivariate logistic regression and latent class analyses were performed.

resultsAmong 237 participants with Core-E2 sequences, 47% were in a pair/cluster. Among HIV/HCV co-infected participants, 60% (74/123) were in a pair/cluster, compared to 30% (34/114) with HCV mono-infection (p < 0.001). HIV/HCV co-infection (vs. HCV mono-infection; adjusted odds ratio (AOR), 2.37, 95% confidence interval (CI), 1.45, 5.15) was independently associated with phylogenetic clustering. Latent class analysis identified three distinct risk profiles: (1) people who inject drugs, (2) HIV-positive gay and bisexual men (GBM) with low probability of injecting drug use (IDU) and (3) GBM with IDU & sexual risk behaviour. Class 2 (vs. Class 1, AOR 3.40; 95% CI, 1.52, 7.60), was independently associated with phylogenetic clustering. Many clusters displayed homogeneous characteristics, such as containing individuals exclusively from one city, individuals all with HIV/HCV co-infection or individuals sharing the same route of acquisition of HCV.

conclusionsClusters containing individuals with specific characteristics suggest that HCV transmission occurs through discrete networks, particularly among HIV/HCV co-infected individuals. The greater proportion of clustering found among HIV/HCV co-infected participants highlights the need to provide broad direct-acting antiviral access encouraging rapid uptake in this population and ongoing monitoring of the phylogeny.

Indexed as

PhylogenyAdultAustraliaCoinfectionDrug UsersFemaleHepacivirusHepatitis CHIV InfectionsHomosexuality, MaleHumansMaleMiddle AgedNew ZealandRisk-TakingSexual Behaviorco-infectiongay and bisexual menhepatitis C virushuman immunodeficiency viruslatent class analysismulti-risk profilespeople who inject drugsphylogenetic clustering

Identifiers

PMID30746864
PMCPMC6371014

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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.