Evidence map›Paper›PMID 41152820›Full record

ArticleBMC public health2025

Bayesian joint spatiotemporal modelling of primary infection and reinfection of HFMD at county level in Jiangsu, China, 2009-2023.

Weijia Wang, Hong Ji, Yifan Tang, Hongfei Zhu, Wendong Liu, Kai Wang, Liguo Zhu, Chengxiu Ling, Changjun Bao, Ying Wang

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 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. 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

10 authors.

Weijia Wang *Academy of Pharmacy, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
Hong Ji *Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China.
Yifan Tang *Academy of Pharmacy, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
Hongfei Zhu *School of Public Health, Fudan University, Shanghai, 200032, China.
Wendong LiuDepartment of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China.
Kai WangAcademy of Pharmacy, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
Liguo ZhuDepartment of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China.
Chengxiu LingAcademy of Pharmacy, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China. Chengxiu.Ling@xjtlu.edu.cn.
Changjun BaoDepartment of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China. bao2000_cn@163.com.
Ying WangSchool of Public Health, Fudan University, Shanghai, 200032, China. wangying1013@fudan.edu.cn.

Funding

National Key Research and Development Program of China 2024YFC2310403Xi'an Jiaotong-Liverpool University RDF-TP- 0023Xi'an Jiaotong-Liverpool University RDF-TP-0030
6 · The paper itself

Abstract

Over the past decade, multiple outbreaks of hand, foot, and mouth disease (HFMD) have occurred in East Asia, especially in China. It is crucial to understand the distribution pattern and risk factors of HFMD while also studying the corresponding characteristics of reinfections. This paper aims to jointly analyze the spatiotemporal distribution and influential factors of primary infection and reinfection of HFMD in Jiangsu province, China, under the Bayesian framework. Using county-level monthly HFMD counts from 2009 to 2023, we proposed four spatiotemporal hierarchical models with latent effects shared in the reinfection sub-model to evaluate the influence of air pollution, meteorological factors, and demographic characteristics on primary infection and reinfection of HFMD. The integrated nested Laplace approximation (INLA) approach estimated model parameters and quantified the spatial and temporal random effects. The optimal model with spatial, temporal, and spatiotemporal interaction effects indicated a significant positive influence of NO[Formula: see text], wind speed, relative humidity, and solar radiation, as well as a significant negative effect of PM[Formula: see text], O[Formula: see text], temperature above 27 °C, precipitation, and COVID-19, on both infections. Scattered status and critical primary infection have a significant positive effect on primary infection and reinfection. Positive sharing coefficients revealed similar spatiotemporal patterns of both types of infection. Beyond these fixed effects, non-linear analyses further highlighted comparable exposure-response patterns for primary infection and reinfection, with notable thresholds around 50-70 [Formula: see text]g/m[Formula: see text] for PM[Formula: see text], 90 [Formula: see text]g/m[Formula: see text] for O[Formula: see text], 80 [Formula: see text]g/m[Formula: see text] for NO[Formula: see text], 20 °C for temperature, and 55% for relative humidity. Our findings reveal distinct drivers of primary infection and reinfection but also similar spatiotemporal patterns, suggesting the need for both targeted protection of vulnerable groups and integrated surveillance strategies.

Indexed as

Hand, Foot and Mouth DiseaseReinfectionBayes TheoremChildChild, PreschoolChinaCOVID-19FemaleHumansInfantMaleRisk FactorsSpatio-Temporal AnalysisHand, foot, and mouth disease (HFMD)Integrated nested Laplace approximation (INLA)Primary infection and reinfectionSpatiotemporal joint model

Identifiers

PMID41152820
PMCPMC12570623

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.