Evidence map›Paper›PMID 40200162›Full record

ArticleBMC infectious diseases2025

Epidemiological trends of hepatitis C incidence and death in Mainland China between 2004 and 2018 and its predictions to 2030.

Guo Tian, Yang Zheng, Yinghua He, Can Chen, Xiaobao Zhang, Yuxia Du, Shigui Yang, Tianan Jiang, Lanjuan Li

Abstract read
In one paragraph

Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

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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

9 authors.

Guo TianState 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, National Medical Center for Infectious Diseases, Zhejiang University School of Medicine, 79 Qingchun Rd, Hangzhou City, 310003, China.
Yang ZhengState 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, National Medical Center for Infectious Diseases, Zhejiang University School of Medicine, 79 Qingchun Rd, Hangzhou City, 310003, China.
Yinghua HeDepartment of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Can ChenDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Xiaobao ZhangDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Yuxia DuDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Shigui YangDepartment of Emergency Medicine, Second Affiliated Hospital, Department of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Tianan JiangZhejiang Provincial Key Laboratory for Drug Evaluation and Clinical Research, Hangzhou, Zhejiang, 310014, China.
Lanjuan 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, National Medical Center for Infectious Diseases, Zhejiang University School of Medicine, 79 Qingchun Rd, Hangzhou City, 310003, China. ljli@zju.edu.cn.

Funding

This study was supported by the Fundamental Research Funds for the Central Universities, grant No. 2022ZFJH003. 2022ZFJH003
6 · The paper itself

Abstract

introductionThis study aimed to evaluate the epidemiological trends and spatial-temporal distribution of hepatitis C virus (HCV) in mainland China.

methodsHCV monthly incidence surveillance data from 2004 to 2018 was mainly available from the Public Health Sciences Data Center of China. Five models (Bayesian age-period-cohort, BAPC; Auto-regressive integrated moving average, Auto_arima; Exponential smoothing, ETS; Prophet; Earth) were used to forecast the incidence. Temporal-spatial scanning analysis was conducted to visualize the grading of hepatitis C incidence in mainland China.

resultsThis study observed 180 months of data from January 2004 and December 2018. A total of 2,278,280 hepatitis C cases and 1771 deaths were reported, with an average annual reported incidence rate of 11.24/100,000. The BAPC model indicated that the indexes (mean absolute error, MAE; mean absolute percentage error, MAPE; mean squared error, MSE; root mean square error, RMSE) in the BAPC model were better than those of the other models (4.33 × 10

conclusionsIn summary, this study showed that the incidence of HCV in mainland China continued to be high, and BAPC model suggested that this trend will continue until at least 2030. In order to effectively control the HCV epidemic, it was necessary to strengthen blood safety management, expand screening for high-risk population, improve public awareness of HCV, and provide timely and effective antiviral treatment in potentially high-risk areas.

Indexed as

Hepatitis CBayes TheoremChinaFemaleForecastingHepacivirusHumansIncidenceMaleMiddle AgedSpatio-Temporal AnalysisChinaEpidemiologyHepatitis C virusIncidence

Identifiers

PMID40200162
PMCPMC11980347

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.