Evidence map›Paper›PMID 38685009›Full record

ArticleBMC infectious diseases2024

Hepatitis C virus subtype diversity and transmission clusters characteristics among drug users in Zhuhai, South China.

Hongxia Li, Huitao Huang, Wenyan Huang, Man Du, Dongling Long, Guangxian Xu, Wenhua Mei, Kaisong Huang

Open access · goldAbstract read
In one paragraph

Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 30% 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

4 citing papers in PubMed, 2 citations in OpenAlex.

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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

8 authors at 3 institutions in 2 countries.

Hongxia Li *Zhuhai Center for Disease Control and Prevention, Zhuhai, Guangdong, China.
Huitao Huang *Zhuhai Center for Disease Control and Prevention, Zhuhai, Guangdong, China.
Wenyan Huang *Zhuhai Center for Disease Control and Prevention, Zhuhai, Guangdong, China.
Man DuZhuhai Center for Disease Control and Prevention, Zhuhai, Guangdong, China.
Dongling LongZhuhai Center for Disease Control and Prevention, Zhuhai, Guangdong, China.
Guangxian XuGuangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, Guangdong, China. xuguangxian@gdmu.edu.cn.
Wenhua MeiZhuhai Center for Disease Control and Prevention, Zhuhai, Guangdong, China. 1297648405@qq.com.
Kaisong HuangGuangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, Guangdong, China. kaisong@gdmu.edu.cn.
Zhuhai People's Hospital · CNGuangdong Medical College · CNJinan University · CN

Funding

The Guangdong Provincial Science and Technology Program Nos. 2020A1515111009The Science and Technology Planning Project of Guangdong Province No. 2021B1212030002
6 · The paper itself

Abstract

backgroundHepatitis C virus (HCV) infection poses a major public health challenge globally, especially among injecting drug users. China has the world's largest burden of HCV infections. However, little is known about the characteristics of transmission networks among drug user populations. This study aims to investigate the molecular epidemiology and transmission characteristics of HCV infections among drug users in Zhuhai, a bustling port city connecting Mainland China and its Special Administrative Regions.

methodsParticipants enrolled in this study were drug users incarcerated at Zhuhai's drug rehabilitation center in 2015. Their sociodemographic and behavioral information, including gender, promiscuity, drug use method, and so forth, was collected using a standardized questionnaire. Plasmas separated from venous blood were analyzed for HCV infection through ELISA and RT-PCR methods to detect anti-HCV antibodies and HCV RNA. The 5'UTR fragment of the HCV genome was amplified and further sequenced for subtype identifications and phylogenetic analysis. The phylogenetic tree was inferred using the Maximum Likelihood method based on the Tamura-Nei model, and the transmission cluster network was constructed using Cytoscape3.8.0 software with a threshold of 0.015. Binary logistic regression models were employed to assess the factors associated with HCV infection.

resultsThe overall prevalence of HCV infection among drug users was 44.37%, with approximately 19.69% appearing to clear the HCV virus successfully. Binary logistic regression analysis revealed that those aged over 40, engaging in injecting drug use, and being native residents were at heightened risk for HCV infection among drug user cohorts. The predominant HCV subtypes circulating among those drug users were 6a (60.26%), followed by 3b (16.7%), 3a (12.8%), 1b (6.41%) and 1a (3.85%), respectively. Molecular transmission network analysis unveiled the presence of six transmission clusters, with the largest propagation cluster consisting of 41 individuals infected with HCV subtype 6a. Furthermore, distinct transmission clusters involved eight individuals infected with subtype 3b and seven with subtype 3a were also observed.

conclusionThe genetic transmission networks revealed a complex transmission pattern among drug users in Zhuhai, emphasizing the imperative for a targeted and effective intervention strategy to mitigate HCV dissemination. These insights are pivotal for shaping future national policies on HCV screening, treatment, and prevention in port cities.

Indexed as

Drug UsersHepacivirusHepatitis CPhylogenyAdultChinaCluster AnalysisFemaleGenotypeHepatitis C AntibodiesHumansMaleMiddle AgedMolecular EpidemiologyRNA, ViralSubstance Abuse, IntravenousHepatitis C AntibodiesRNA, ViralDrug userHepatitis C virusHigh-risk factorMolecular transmission networkPhylogenetic tree

Identifiers

PMID38685009
PMCPMC11057121
OpenAlexW4396238022

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Registered trials

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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.