Evidence map›Paper›PMID 37951862›Full record

ArticleBMC gastroenterology2023

Multi-disciplinary cooperation for the micro-elimination of hepatitis C in China: a hospital-based experience.

Lingling Zheng, Xiaoli Zhang, Yuxia Nian, Wenjuan Zhou, Dan Li, Yong Wu

Open access · goldAbstract read
In one paragraph

Article in BMC gastroenterology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-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

5 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. [Analysis of the results of the Fuxing Program Action for micro-elimination of hepatitis C in Zhuhai].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025
    Article
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

6 authors at 2 institutions in 1 country.

Lingling ZhengDepartment of Prevention and Healthcare, Fujian Medical University Union Hospital, 29 Xinquan Road, Gulou, Fuzhou, Fujian, 350001, P. R. China.
Xiaoli ZhangDepartment of Laboratory Medicine, Fujian Medical University Union Hospital, 29 Xinquan Road, Gulou, Fuzhou, Fujian, 350001, P. R. China.
Yuxia NianDepartment of Prevention and Healthcare, Fujian Medical University Union Hospital, 29 Xinquan Road, Gulou, Fuzhou, Fujian, 350001, P. R. China.
Wenjuan ZhouDepartment of Laboratory Medicine, Fujian Medical University Union Hospital, 29 Xinquan Road, Gulou, Fuzhou, Fujian, 350001, P. R. China.
Dan LiDepartment of Gastroenterology, Fujian Medical University Union Hospital, 29 Xinquan Road, Gulou, Fuzhou, Fujian, 350001, P. R. China.
Yong WuDepartment of Medical Affair, Fujian Medical University Union Hospital, 29 Xinquan Road, Gulou, Fuzhou, Fujian, 350001, P. R. China. wuyong9195@126.com.
Fujian Medical University · CNUnion Hospital · CN

Funding

sponsored by Fujian provincial health technology project No. 2021RKA003
6 · The paper itself

Abstract

backgroundHepatitis C virus (HCV) infection is one of the main causes of liver cancer and imposes an enormous social and economic burden. The blood-borne virus screening policy for preventing iatrogenic infections renders hospitals important for identifying individuals infected with hepatitis C. Therefore, we aimed to investigate the establishment of a multi-disciplinary cooperation model in medical institutions to leverage the screening results of patients with hepatitis C. Our objective is to ensure that patients receive timely and effective diagnosis and treatment, thereby enabling the elimination of hepatitis C by 2030.

methodA multi-disciplinary cooperation model was established in October 2021. This retrospective study was based on the establishment of antibody-positive and HCV RNA-positive patient databases. A Chi-square test was used to compare the HCV RNA confirmation rate in anti-HCV-positive patients, as well as the hepatitis C diagnosis rate and treatment rate in RNA-positive patients before and after the multi-disciplinary cooperation. A multivariable logistic regression was used to analyse the factors affecting the treatment of patients with hepatitis C. In addition, we examined changes in the level of hepatitis C knowledge among medical staff.

resultsAfter the implementation of the multi-disciplinary cooperation model, the RNA confirmation rate of hepatitis C antibody-positive patients increased from 36.426% to 88.737%, the diagnostic accuracy rate of RNA-positive patients increased from 67.456% to 98.113%, and the treatment rate of patients with hepatitis C increased from 12.426% to 58.491%. Significant improvements were observed among the clinicians regarding their ability to understand the characteristics of hepatitis C (93.711% vs. 58.861%), identify people at high risk (94.340% vs. 53.797%), manage patients with hepatitis C after diagnosis (88.679% vs. 67.089%), and effectively treat hepatitis C (84.277% vs. 51.899%). Multi-disciplinary cooperation in medical institutions was the most important factor for patients to undergo HCV treatment (odds ratio: 0.024, 95% confidence interval: 0.007-0.074).

conclusionsThis study showed that the use of a multi-disciplinary cooperation model to utilise the results of HCV antibody screening fully in patients through further tracking, referral, and treatment may facilitate the detection and treatment of patients with hepatitis C and accelerate the elimination of HCV in China.

Indexed as

HepacivirusHepatitis CAntiviral AgentsChinaHepatitis C AntibodiesHospitalsHumansRetrospective StudiesRNAAntiviral AgentsHepatitis C AntibodiesRNAHealth policyHepatitis C virusHospitalMicro-eliminationMulti-disciplinary cooperationPatient follow-up

Identifiers

PMID37951862
PMCPMC10638763
OpenAlexW4388598806

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.