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Spatial, temporal, and spatiotemporal analysis of mumps in Guangxi Province, China, 2005–2016
BACKGROUND: The resurgence of mumps around the world occurs frequently in recent years. As the country with the largest number of cases in the world, the status of mumps epidemics in China is not yet clear. This study, taking the relatively serious epidemic province of Guangxi as the example, aimed...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6090846/ https://www.ncbi.nlm.nih.gov/pubmed/30068308 http://dx.doi.org/10.1186/s12879-018-3240-4 |
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author | Yu, Guoqi Yang, Rencong Wei, Yi Yu, Dongmei Zhai, Wenwen Cai, Jiansheng Long, Bingshuang Chen, Shiyi Tang, Jiexia Zhong, Ge Qin, Jian |
author_facet | Yu, Guoqi Yang, Rencong Wei, Yi Yu, Dongmei Zhai, Wenwen Cai, Jiansheng Long, Bingshuang Chen, Shiyi Tang, Jiexia Zhong, Ge Qin, Jian |
author_sort | Yu, Guoqi |
collection | PubMed |
description | BACKGROUND: The resurgence of mumps around the world occurs frequently in recent years. As the country with the largest number of cases in the world, the status of mumps epidemics in China is not yet clear. This study, taking the relatively serious epidemic province of Guangxi as the example, aimed to examine the spatiotemporal pattern and epidemiological characteristics of mumps, and provide a scientific basis for the effective control of this disease and formulation of related health policies. METHODS: Geographic information system (GIS)-based spatiotemporal analyses, including spatial autocorrelation analysis, Kulldorff’s purely spatial and space-time scan statistics, were applied to detect the location and extent of mumps high-risk areas. Spatial empirical Bayesian (SEB) was performed to smoothen the rate for eliminating the instability of small-area data. RESULTS: A total of 208,470 cases were reported during 2005 and 2016 in Guangxi. Despite the fluctuations in 2006 and 2011, the overall mumps epidemic continued to decline. Bimodal seasonal distribution (mainly from April to July) were found and students aged 5–9 years were high-incidence groups. Though results of the global spatial autocorrelation based on the annual incidence largely varied, the spatial distribution of the average annual incidence of mumps was nonrandom with the significant Moran’s I. Spatial cluster analysis detected high-value clusters, mainly located in the western, northern and central parts of Guangxi. Spatiotemporal scan statistics identified almost the same high-risk areas, and the aggregation time was mainly concentrated in 2009–2012. CONCLUSION: The incidence of mumps in Guangxi exhibited spatial heterogeneity in 2005–2016. Several spatial and spatiotemporal clusters were identified in this study, which might assist the local government to develop targeted health strategies, allocate health resources reasonably and increase the efficiency of disease prevention. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12879-018-3240-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6090846 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-60908462018-08-17 Spatial, temporal, and spatiotemporal analysis of mumps in Guangxi Province, China, 2005–2016 Yu, Guoqi Yang, Rencong Wei, Yi Yu, Dongmei Zhai, Wenwen Cai, Jiansheng Long, Bingshuang Chen, Shiyi Tang, Jiexia Zhong, Ge Qin, Jian BMC Infect Dis Research Article BACKGROUND: The resurgence of mumps around the world occurs frequently in recent years. As the country with the largest number of cases in the world, the status of mumps epidemics in China is not yet clear. This study, taking the relatively serious epidemic province of Guangxi as the example, aimed to examine the spatiotemporal pattern and epidemiological characteristics of mumps, and provide a scientific basis for the effective control of this disease and formulation of related health policies. METHODS: Geographic information system (GIS)-based spatiotemporal analyses, including spatial autocorrelation analysis, Kulldorff’s purely spatial and space-time scan statistics, were applied to detect the location and extent of mumps high-risk areas. Spatial empirical Bayesian (SEB) was performed to smoothen the rate for eliminating the instability of small-area data. RESULTS: A total of 208,470 cases were reported during 2005 and 2016 in Guangxi. Despite the fluctuations in 2006 and 2011, the overall mumps epidemic continued to decline. Bimodal seasonal distribution (mainly from April to July) were found and students aged 5–9 years were high-incidence groups. Though results of the global spatial autocorrelation based on the annual incidence largely varied, the spatial distribution of the average annual incidence of mumps was nonrandom with the significant Moran’s I. Spatial cluster analysis detected high-value clusters, mainly located in the western, northern and central parts of Guangxi. Spatiotemporal scan statistics identified almost the same high-risk areas, and the aggregation time was mainly concentrated in 2009–2012. CONCLUSION: The incidence of mumps in Guangxi exhibited spatial heterogeneity in 2005–2016. Several spatial and spatiotemporal clusters were identified in this study, which might assist the local government to develop targeted health strategies, allocate health resources reasonably and increase the efficiency of disease prevention. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12879-018-3240-4) contains supplementary material, which is available to authorized users. BioMed Central 2018-08-02 /pmc/articles/PMC6090846/ /pubmed/30068308 http://dx.doi.org/10.1186/s12879-018-3240-4 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Yu, Guoqi Yang, Rencong Wei, Yi Yu, Dongmei Zhai, Wenwen Cai, Jiansheng Long, Bingshuang Chen, Shiyi Tang, Jiexia Zhong, Ge Qin, Jian Spatial, temporal, and spatiotemporal analysis of mumps in Guangxi Province, China, 2005–2016 |
title | Spatial, temporal, and spatiotemporal analysis of mumps in Guangxi Province, China, 2005–2016 |
title_full | Spatial, temporal, and spatiotemporal analysis of mumps in Guangxi Province, China, 2005–2016 |
title_fullStr | Spatial, temporal, and spatiotemporal analysis of mumps in Guangxi Province, China, 2005–2016 |
title_full_unstemmed | Spatial, temporal, and spatiotemporal analysis of mumps in Guangxi Province, China, 2005–2016 |
title_short | Spatial, temporal, and spatiotemporal analysis of mumps in Guangxi Province, China, 2005–2016 |
title_sort | spatial, temporal, and spatiotemporal analysis of mumps in guangxi province, china, 2005–2016 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6090846/ https://www.ncbi.nlm.nih.gov/pubmed/30068308 http://dx.doi.org/10.1186/s12879-018-3240-4 |
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