Evidence map›Paper›PMID 29153265›Full record

ArticleThe Lancet. Infectious diseases2018

Hepatitis C virus treatment as prevention in an extended network of people who inject drugs in the USA: a modelling study.

Alexei Zelenev, Jianghong Li, Alyona Mazhnaya, Sanjay Basu, Frederick L Altice

Open access · greenAbstract read
In one paragraph

Article in The Lancet. Infectious diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed, 2 pooled it
8.7field-weighted citation impact, top 1% 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

59 citing papers in PubMed, 2 syntheses or guidelines pooled it, 100 citations in OpenAlex.

  1. Pooled it
  2. A Review of Network Models for HIV Spread.Journal of acquired immune deficiency syndromes (1999) · 2025
    Pooled it
  3. Article
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  11. Decrease in active hepatitis C infection among people who use drugs in Madrid, Spain, 2017 to 2023: a retrospective study.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2024
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 5 institutions in 3 countries.

Alexei ZelenevDepartment of Internal Medicine, Section of Infectious Diseases, AIDS Program, Yale School of Medicine, New Haven, CT, USA. Electronic address: alexei.zelenev@yale.edu.
Jianghong LiInstitute for Community Research, Hartford, CT, USA.
Alyona MazhnayaICF Alliance for Public Health, Kyiv, Ukraine; Department of Health, Behavior & Society, Johns Hopkins Bloomberg School of Public Health, MD, USA.
Sanjay BasuStanford University School of Medicine, Stanford, CA, USA.
Frederick L AlticeDepartment of Internal Medicine, Section of Infectious Diseases, AIDS Program, Yale School of Medicine, New Haven, CT, USA; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA; Centre of Excellence for Research in AIDS (CERiA), Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
Institute For Community Research · USJohns Hopkins University · USStanford University · USUniversity of Malaya · MYYale University · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Training in the Epidemiology of HPV/Cervical Neoplasia Among HIV+ WomenD43TW000233 · FIC · SUNY DOWNSTATE MEDICAL CENTER · PI DEHOVITZ, JACK A · 1993 to 2015
$10.1M
Expanding Medication Assisted Therapy in Ukraine (ExMAT)R01DA033679 · NIDA · YALE UNIVERSITY · PI ALTICE, FREDERICK LEWIS · 2012 to 2021
$6.4M
YALE BIOMEDICAL SUPERCOMPUTER: NEUROSCIENCES10RR019895 · NCRR · YALE UNIVERSITY · PI WILLIAMS, KENNETH ROBERT · 2004 to 2004
$1.6M
IDU Peer Recruitment Dynamics and Network Structure in Respondent Driven SamplingR01DA031594 · NIDA · INSTITUTE FOR COMMUNITY RESEARCH · PI LI, JIANGHONG · 2011 to 2013
$1.6M
Enhancing Health Outcomes Among HIV+ Substance AbusersK24DA017072 · NIDA · YALE UNIVERSITY · PI ALTICE, FREDERICK LEWIS · 2003 to 2012
$1.5M
Modeling HIV & Substance Abuse: Correcting Sampling Bias and Cost EffectivenessK01DA037826 · NIDA · YALE UNIVERSITY · PI ZELENEV, ALEXEI · 2014 to 2018
$838k
Studying social factors in sodium consumption to reduce hypertension disparitiesK08HL121056 · NHLBI · STANFORD UNIVERSITY · PI BASU, SANJAY · 2014 to 2017
$599k
High Performance Computing Instrumentation for Yale University Biomedical HPC CenS10RR029676 · NCRR · YALE UNIVERSITY · PI BJORNSON, ROBERT DEAN · 2011 to 2011
$588k
Modeling HCV/HIV transmission and Treatment as Prevention in U.S. Networks of People Who Inject DrugsR21DA041953 · NIDA · YALE UNIVERSITY · PI ALTICE, FREDERICK LEWIS · 2016 to 2017
$480k
FIC NIH HHS D43 TW000233NCATS NIH HHS UL1 TR001863NCRR NIH HHS S10 RR019895NCRR NIH HHS S10 RR029676NHLBI NIH HHS K08 HL121056NIDA NIH HHS K01 DA037826NIDA NIH HHS K24 DA017072NIDA NIH HHS R01 DA031594NIDA NIH HHS R01 DA033679NIDA NIH HHS R21 DA041953
6 · The paper itself

Abstract

backgroundChronic infections with hepatitis C virus (HCV) and HIV are highly prevalent in the USA and concentrated in people who inject drugs. Treatment as prevention with highly effective new direct-acting antivirals is a prospective HCV elimination strategy. We used network-based modelling to analyse the effect of this strategy in HCV-infected people who inject drugs in a US city.

methodsFive graph models were fit using data from 1574 people who inject drugs in Hartford, CT, USA. We used a degree-corrected stochastic block model, based on goodness-of-fit, to model networks of injection drug users. We simulated transmission of HCV and HIV through this network with varying levels of HCV treatment coverage (0%, 3%, 6%, 12%, or 24%) and varying baseline HCV prevalence in people who inject drugs (30%, 60%, 75%, or 85%). We compared the effectiveness of seven treatment-as-prevention strategies on reducing HCV prevalence over 10 years and 20 years versus no treatment. The strategies consisted of treatment assigned to either a randomly chosen individual who injects drugs or to an individual with the highest number of injection partners. Additional strategies explored the effects of treating either none, half, or all of the injection partners of the selected individual, as well as a strategy based on respondent-driven recruitment into treatment.

findingsOur model estimates show that at the highest baseline HCV prevalence in people who inject drugs (85%), expansion of treatment coverage does not substantially reduce HCV prevalence for any treatment-as-prevention strategy. However, when baseline HCV prevalence is 60% or lower, treating more than 120 (12%) individuals per 1000 people who inject drugs per year would probably eliminate HCV within 10 years. On average, assigning treatment randomly to individuals who inject drugs is better than targeting individuals with the most injection partners. Treatment-as-prevention strategies that treat additional network members are among the best performing strategies and can enhance less effective strategies that target the degree (ie, the highest number of injection partners) within the network.

interpretationSuccessful HCV treatment as prevention should incorporate the baseline HCV prevalence and will achieve the greatest benefit when coverage is sufficiently expanded.

fundingNational Institute on Drug Abuse.

Indexed as

AdolescentAdultAgedAged, 80 and overAntiviral AgentsChemopreventionCitiesComputer SimulationDisease Transmission, InfectiousFemaleHepatitis CHumansMaleMiddle AgedModels, StatisticalPrevalenceAntiviral Agents

Identifiers

PMID29153265
PMCPMC5860640
OpenAlexW2770394506

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.