Evidence map›Paper›PMID 38195403›Full record

ArticleBMC infectious diseases2024

Identification of associated risk factors for serological distribution of hepatitis B virus via machine learning models.

Ning Yao, Yang Liu, Jiawei Xu, Qing Wang, Quanhua Zhou, Yue Wang, Dong Yi, Yazhou Wu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.5field-weighted citation impact, top 18% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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

8 authors at 1 institution in 1 country.

Ning Yao *Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
Yang Liu *Chongqing Center for Disease Control and Prevention, NO.8 Changjiang 2nd Street, Yuzhong District, Chongqing, 400042, China.
Jiawei Xu *Chongqing Center for Disease Control and Prevention, NO.8 Changjiang 2nd Street, Yuzhong District, Chongqing, 400042, China.
Qing WangChongqing Center for Disease Control and Prevention, NO.8 Changjiang 2nd Street, Yuzhong District, Chongqing, 400042, China.
Quanhua ZhouChongqing Center for Disease Control and Prevention, NO.8 Changjiang 2nd Street, Yuzhong District, Chongqing, 400042, China.
Yue WangChongqing Center for Disease Control and Prevention, NO.8 Changjiang 2nd Street, Yuzhong District, Chongqing, 400042, China.
Dong YiDepartment of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China. yidong@tmmu.edu.cn.
Yazhou WuDepartment of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China. asiawu@tmmu.edu.cn.
Army Medical University · CN

Funding

Chongqing Health Commission and Chongqing Science & Technology Commission 2020FYYX040National Natural Science Foundation of China 82173621
6 · The paper itself

Abstract

backgroundThe provincial-level sero-survey was launched to learn the updated seroprevalence of hepatitis B virus (HBV) infection in the general population aged 1-69 years in Chongqing and to assess the risk factors for HBV infection to effectively screen persons with chronic hepatitis B (CHB).

methodsA total of 1828 individuals aged 1-69 years were investigated, and hepatitis B surface antigen (HBsAg), antibody to HBsAg (HBsAb), and antibody to B core antigen (HBcAb) were detected. Logistic regression and three machine learning (ML) algorithms, including random forest (RF), support vector machine (SVM), and stochastic gradient boosting (SGB), were developed for analysis.

resultsThe HBsAg prevalence of the total population was 3.83%, and among persons aged 1-14 years and 15-69 years, it was 0.24% and 4.89%, respectively. A large figure of 95.18% (770/809) of adults was unaware of their occult HBV infection. Age, region, and immunization history were found to be statistically associated with HBcAb prevalence with a logistic regression model. The prediction accuracies were 0.717, 0.727, and 0.725 for the proposed RF, SVM, and SGB models, respectively.

conclusionsThe logistic regression integrated with ML models could helpfully screen the risk factors for HBV infection and identify high-risk populations with CHB.

Indexed as

Hepatitis BHepatitis B, ChronicAdultHepatitis B AntibodiesHepatitis B Surface AntigensHepatitis B virusHumansMachine LearningRisk FactorsSeroepidemiologic StudiesHepatitis B AntibodiesHepatitis B Surface AntigensHepatitis BMachine learningSero-epidemiologyVaccine

Identifiers

PMID38195403
PMCPMC10775609
OpenAlexW4390731438

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.