ArticleEpidemiology and infection2016
Exploratory social network analysis and gene sequencing in people who inject drugs infected with hepatitis C virus.
Article in Epidemiology and infection, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed, 11 citations in OpenAlex.
- Achieving Hepatitis C Micro-Elimination in Chinese Injecting Drug Users: A Dynamic Network Modeling Study.Infectious diseases and therapy · 2025Article
- Widespread hepatitis C virus transmission network among people who inject drugs in Kenya.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2024Article
- Feasibility of dried blood spot for hepatitis C diagnosis in vulnerable subjects and people living in remote areas from Brazil.BMC infectious diseases · 2022Article
- Hepatitis C Resistance-Associated Substitutions Among People Who Inject Drugs Treated With Direct-Acting Antiviral-Containing Regimens.Open forum infectious diseases · 2021Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Social networks facilitate the transmission of hepatitis C virus (HCV) in people who inject drugs (PWID). The aim of this study was to assess how certain network structural characteristics are related to HCV infections in PWID and to determine the most susceptible individuals for HCV transmission in a network of PWID. PWID (N = 80) from central China were recruited from a previous follow-up case-control study. Demographic and behavioural information was obtained from a computerized database for each group. HCV RNA was extracted from blood specimens. Sequences were used to construct a phylogenetic tree and to determine genetic distances. Socio-metric social links were established between participants. Network measures were calculated using UCINET. Three HCV genotypes were identified, covering five subtypes. The density of the social networks for the whole sample (N = 80), case group (n = 31) and control group (n = 49) was 0.038, 0.054 and 0.008, respectively. PWID infected with HCV were in frequent contact with others within their group. There were four pairs of nodes with genotypic distances of 0.000 that were identified and clustered in subtypes 6a and 1b; each subject pair was linked and found in one clique. Three of the five most active nodes were infected with HCV. These three nodes served as a bridge, contributing to the connection of other nodes. These findings identify susceptible individuals for HCV transmission in PWID based on their frequent contact with others in the network. These results provide data that could be used for modelling HCV transmission patterns and in public health policies.
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Registered trials
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