Evidence map›Paper›PMID 40186148›Full record

ArticleBMC public health2025

Spatial and temporal distribution patterns and factors influencing hepatitis B in China: a geo-epidemiological study.

Kang Fang, Na Cheng, Chuang Nie, Wentao Song, Yunkang Zhao, Jie Pan, Qi Yin, Jiwei Zheng, Qinglin Chen, Tianxin Xiang

Abstract read
In one paragraph

Article in BMC public health, 2025. 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
–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

7 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

10 authors.

Kang Fang *State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, National Innovation Platform for Industry-Education Integration in Vaccine Research, Xiamen University, Xiamen, 361102, Fujian, China.
Na Cheng *Jiangxi Provincial Key Laboratory of Prevention and Treatment of Infectious Diseases, Jiangxi Medical Center for Critical Public Health Events, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330052, Jiangxi, China.
Chuang Nie *Jiangxi Provincial Key Laboratory of Prevention and Treatment of Infectious Diseases, Jiangxi Medical Center for Critical Public Health Events, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330052, Jiangxi, China.
Wentao SongState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, National Innovation Platform for Industry-Education Integration in Vaccine Research, Xiamen University, Xiamen, 361102, Fujian, China.
Yunkang ZhaoState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, National Innovation Platform for Industry-Education Integration in Vaccine Research, Xiamen University, Xiamen, 361102, Fujian, China.
Jie PanState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, National Innovation Platform for Industry-Education Integration in Vaccine Research, Xiamen University, Xiamen, 361102, Fujian, China.
Qi YinState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, National Innovation Platform for Industry-Education Integration in Vaccine Research, Xiamen University, Xiamen, 361102, Fujian, China.
Jiwei ZhengState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, National Innovation Platform for Industry-Education Integration in Vaccine Research, Xiamen University, Xiamen, 361102, Fujian, China.
Qinglin ChenState Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, School of Public Health, National Innovation Platform for Industry-Education Integration in Vaccine Research, Xiamen University, Xiamen, 361102, Fujian, China.
Tianxin XiangJiangxi Provincial Key Laboratory of Prevention and Treatment of Infectious Diseases, Jiangxi Medical Center for Critical Public Health Events, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330052, Jiangxi, China. ndyfy02258@ncu.edu.cn.

Funding

the National Natural Science Foundation of China 82360130the Natural Science Foundation of Jiangxi Province 20202BAB206008the Provincial Key Research and Development Program of Jiangxi 20232BBG70020
6 · The paper itself

Abstract

backgroundChina is a country with an extremely high disease burden of hepatitis B. Spatiotemporal analysis of hepatitis B from a socioeconomic perspective is of great significance for reducing the disease burden, but there is still a relative lack of research.

methodsThe age-period-cohort model and spatial distribution maps describe the three-dimensional distribution characteristics of hepatitis B. Spatial autocorrelation analysis and spatiotemporal scanning were used to analyze the spatiotemporal distribution characteristics. The random forest algorithm was used to screen the potential influencing factors. The geographic detector model was used to analyze the interaction patterns of variables. Finally, a geographically and temporally weighted regression model was established to analyze the effects of variables on the incidence rate of hepatitis B at different spatiotemporal scales.

resultsFrom 2004 to 2023, a total of 20,376,898 cases of hepatitis B were reported in China. The incidence rate of hepatitis B decreased at a rate of 3.31% per year, and hepatitis B vaccination has led to this downward trend, accompanied by a significant birth cohort effect. And it shows an aggregated characteristic, which highlights the inequality of geographical distribution. Stronger explanations for the incidence of hepatitis B were found for the number of people at the end of each year (q = 0.1949; where q value refers to the explanatory ability of the independent variable for the dependent variable) and the proportion of rural population (q = 0.1895), with an even stronger explanation for the interaction (q = 0.5366). The magnitude and direction of the effect of factors influencing hepatitis B also varied in different regions, and the effect of each factor on the incidence of hepatitis B was not an independent event.

conclusionsThe later people are born, the lower the incidence of hepatitis B. The northwest and southwest regions are the main hotspots, but there is a tendency to spread to southern China. The number of beds in medical institutions should be increased in densely populated areas, and economic development should be accelerated in sparsely populated areas. Hepatitis B prevention and control should be prioritized in geographic hotspots, coupled with enhanced awareness campaigns in rural areas and catch-up vaccination programs targeting high-risk populations.

Indexed as

Hepatitis BAdolescentAdultAgedChildChild, PreschoolChinaFemaleHumansIncidenceInfantMaleMiddle AgedRisk FactorsSpatio-Temporal AnalysisYoung AdultHepatitis BRisk factorsSpatiotemporal distributionSpatiotemporal heterogeneityTransmission patterns

Identifiers

PMID40186148
PMCPMC11971818

What OpenQuestion holds

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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 OpenQuestion graph.