Evidence map›Paper›PMID 36894824›Full record

ArticleInfectious diseases and therapy2023

Modeling the Impacts of Prevention and Treatment Interventions on Hepatitis C Among People Who Inject Drugs in China.

Kailu Fang, Hong-Liang Wang, Yushi Lin, Shuwen Li, Jie Wu

Open access · goldAbstract read
In one paragraph

Article in Infectious diseases and therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.2field-weighted citation impact, top 22% 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

7 citing papers in PubMed, 6 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

5 authors at 3 institutions in 1 country.

Kailu FangState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China.ORCID http://orcid.org/0000-0003-2808-2071
Hong-Liang WangHepatobiliary and Pancreatic Interventional Treatment Center, Division of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China.ORCID http://orcid.org/0000-0002-1407-0072
Yushi LinState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China.ORCID http://orcid.org/0000-0002-9206-0419
Shuwen LiState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China.ORCID http://orcid.org/0000-0002-7867-908X
Jie WuState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, China. zjwujie@zju.edu.cn.ORCID http://orcid.org/0000-0002-5491-8066
State Key Laboratory of Diagnosis and Treatment of Infectious DiseasesFirst Affiliated Hospital Zhejiang University · CNZhejiang University · CN

Funding

Fundamental Research Funds for the Central Universities 2022ZFJH003Mega-Project of National Science and Technology for the 13th Five-Year Plan of China 2018ZX10715013-003-003National Natural Science Foundation of China 71904170
6 · The paper itself

Abstract

introductionInjection drug use is the main transmission route of hepatitis C virus (HCV) in China. The prevalence of HCV remains high at 40-50% among people who inject drugs (PWID). We developed a mathematical model to predict the impacts of different HCV intervention strategies on the HCV burden in Chinese PWID by 2030.

methodsWe developed a dynamic deterministic mathematical model to simulate the transmission of HCV among PWID in China between 2016 and 2030, using domestic data based on the real cascade of HCV care. We considered various intervention scenarios, including treatment regimens, harm reduction program (HRP) coverage, enhanced testing and referral for treatment.

resultsHCV incidence will exhibit a gradual but slow declining trend from 12,970 in 2016 to 11,761 in 2030 based on current screening and treatment practices among PWID (scenario 1). Scaled-up HCV screening and treatment integrated with HRPs (scenario 8) demonstrated the most substantial reduction in HCV burden, being the only intervention scenario that could achieve the World Health Organization's (WHO's) HCV elimination target. Specifically, the HCV incidence in 2030 is projected to be reduced by 81.42%, and HCV-related deaths are projected to be reduced by 91.94%.

conclusionOur study indicates that achieving WHO elimination targets is an extremely challenging goal that requires substantial improvements in HCV testing and treatment among PWID (scenario S8). The findings suggest that coordinated improvements in testing, treatment, and harm reduction programs could greatly reduce the HCV burden among PWID in China, and urgent policy changes are needed to integrate HCV testing and treatment into existing HRPs.

Indexed as

ChinaHarm reductionHepatitis C virusModelingPeople who inject drugs

Identifiers

PMID36894824
PMCPMC10147892
OpenAlexW4323659981

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.