Evidence map›Paper›PMID 28946852›Full record

ArticleBMC infectious diseases2017

Spatio-temporal clustering analysis and its determinants of hand, foot and mouth disease in Hunan, China, 2009-2015.

Xinrui Wu, Shixiong Hu, Abuaku Benjamin Kwaku, Qi Li, Kaiwei Luo, Ying Zhou, Hongzhuan Tan

Abstract read
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Article in BMC infectious diseases, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

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24citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

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  18. Transmission dynamics of brucellosis: Mathematical modelling and applications in China.Computational and structural biotechnology journal · 2020
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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xinrui WuDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, Hunan, 410008, People's Republic of China.
Shixiong HuHunan Center for Disease Control and Prevention, Changsha, Hunan, 410078, People's Republic of China.
Abuaku Benjamin KwakuDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, Hunan, 410008, People's Republic of China.
Qi LiDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, Hunan, 410008, People's Republic of China.
Kaiwei LuoHunan Center for Disease Control and Prevention, Changsha, Hunan, 410078, People's Republic of China.
Ying ZhouDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, Hunan, 410008, People's Republic of China.
Hongzhuan TanDepartment of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, Hunan, 410008, People's Republic of China. tanhz99@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHand, foot and mouth disease (HFMD) is one of the highest reported infectious diseases with several outbreaks across the world. This study aimed at describing epidemiological characteristics, investigating spatio-temporal clustering changes, and identifying determinant factors in different clustering areas of HFMD.

methodsDescriptive statistics was used to evaluate the epidemic characteristics of HFMD from 2009 to 2015. Spatial autocorrelation and spatio-temporal cluster analysis were used to explore the spatial temporal patterns. An autologistic regression model was employed to explore determinants of HFMD clustering.

resultsThe incidence rates of HFMD ranged from 54.31/10 million to 318.06/10 million between 2009 and 2015 in Hunan. Cases were mainly prevalent in children aged 5 years and even younger, with an average male-to-female sex ratio of 1.66, and two epidemic periods in each year. Clustering areas gathered in the northern regions in 2009 and in the central regions from 2010 to 2012. They moved to central-southern regions in 2013 and 2014 and central-western regions in 2015. The significant risk factors of HFMD clusters were rainfall (OR = 2.187), temperature (OR = 4.329) and humidity (OR = 2.070). The protect factor was wind speed (OR = 0.258).

conclusionsThe HFMD incidence from 2009 to 2015 in Hunan showed a new spatiotemporal clustering tendency, with the shifting trend of clustering areas toward south and west. Meteorological factors showed a strong association with HFMD clustering, which may assist in predicting future spatial-temporal clusters.

Indexed as

Spatio-Temporal AnalysisChildChild, PreschoolChinaCluster AnalysisDisease OutbreaksEpidemicsFemaleHand, Foot and Mouth DiseaseHumansHumidityIncidenceInfantMaleMeteorological ConceptsRisk FactorsAutologistic regression modelDeterminantsHFMDSpatial autocorrelationSpatio-temporal analysisTendency analysis

Identifiers

PMID28946852
PMCPMC5613322

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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.