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Using a Bayesian spatiotemporal model to identify the influencing factors and high-risk areas of hand, foot and mouth disease (HFMD) in Shenzhen
BACKGROUND: The epidemic of hand, foot, and mouth disease (HFMD) has become a severe public health problem in the world and has also brought a high economic and health burden. Furthermore, the prevalence of HFMD varies significantly among different locations. However, there have been few investigati...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7112242/ https://www.ncbi.nlm.nih.gov/pubmed/32196496 http://dx.doi.org/10.1371/journal.pntd.0008085 |
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author | He, Xiaoyi Dong, Shengjie Li, Liping Liu, Xiaojian Wu, Yongsheng Zhang, Zhen Mei, Shujiang |
author_facet | He, Xiaoyi Dong, Shengjie Li, Liping Liu, Xiaojian Wu, Yongsheng Zhang, Zhen Mei, Shujiang |
author_sort | He, Xiaoyi |
collection | PubMed |
description | BACKGROUND: The epidemic of hand, foot, and mouth disease (HFMD) has become a severe public health problem in the world and has also brought a high economic and health burden. Furthermore, the prevalence of HFMD varies significantly among different locations. However, there have been few investigations of the effects of socioeconomic factors and air pollution factors on the incidence of HFMD. METHODS: This study collected data on HFMD in Shenzhen, China, from 2012 to 2015. We selected eleven factors as potential risk factors for HFMD. A Bayesian spatiotemporal model was used to quantify the influence of the factors on HFMD and to identify the relative risks in different districts. RESULTS: The risk factors of HFMD were the population, population density, concentration of SO(2), and concentration of NO(2). The relative risks (RRs) were 1.00473 (95% CI: 1.00059–1.00761), 1.00010 (95% CI: 1.00002–1.00016), 1.00215 (95% CI: 1.00170–1.00232) and 1.00058 (95% CI: 1.00028–1.00078), respectively. The protective factors against HFMD were the per capita GDP, the number of public kindergartens, the concentration of PM(10), and the concentration of O(3). The RRs were 0.98840 (95% CI: 0.98660–0.99026), 0.97686 (95% CI: 0.96946–0.98403), 0.99108 (95% CI: 0.98551–0.99840) and 0.99587 (95% CI: 0.99534–0.99610), respectively. The risk of incidence in Longgang district and Pingshan district decreased, while the risk of incidence in Baoan district increased. CONCLUSIONS: Studies have confirmed that socioeconomic factors and air pollution factors have an impact on the incidence of HFMD in Shenzhen, China. The results will be of great practical significance to local authorities, which is conducive to accurate prevention and can be used to formulate HFMD early warning systems. |
format | Online Article Text |
id | pubmed-7112242 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-71122422020-04-09 Using a Bayesian spatiotemporal model to identify the influencing factors and high-risk areas of hand, foot and mouth disease (HFMD) in Shenzhen He, Xiaoyi Dong, Shengjie Li, Liping Liu, Xiaojian Wu, Yongsheng Zhang, Zhen Mei, Shujiang PLoS Negl Trop Dis Research Article BACKGROUND: The epidemic of hand, foot, and mouth disease (HFMD) has become a severe public health problem in the world and has also brought a high economic and health burden. Furthermore, the prevalence of HFMD varies significantly among different locations. However, there have been few investigations of the effects of socioeconomic factors and air pollution factors on the incidence of HFMD. METHODS: This study collected data on HFMD in Shenzhen, China, from 2012 to 2015. We selected eleven factors as potential risk factors for HFMD. A Bayesian spatiotemporal model was used to quantify the influence of the factors on HFMD and to identify the relative risks in different districts. RESULTS: The risk factors of HFMD were the population, population density, concentration of SO(2), and concentration of NO(2). The relative risks (RRs) were 1.00473 (95% CI: 1.00059–1.00761), 1.00010 (95% CI: 1.00002–1.00016), 1.00215 (95% CI: 1.00170–1.00232) and 1.00058 (95% CI: 1.00028–1.00078), respectively. The protective factors against HFMD were the per capita GDP, the number of public kindergartens, the concentration of PM(10), and the concentration of O(3). The RRs were 0.98840 (95% CI: 0.98660–0.99026), 0.97686 (95% CI: 0.96946–0.98403), 0.99108 (95% CI: 0.98551–0.99840) and 0.99587 (95% CI: 0.99534–0.99610), respectively. The risk of incidence in Longgang district and Pingshan district decreased, while the risk of incidence in Baoan district increased. CONCLUSIONS: Studies have confirmed that socioeconomic factors and air pollution factors have an impact on the incidence of HFMD in Shenzhen, China. The results will be of great practical significance to local authorities, which is conducive to accurate prevention and can be used to formulate HFMD early warning systems. Public Library of Science 2020-03-20 /pmc/articles/PMC7112242/ /pubmed/32196496 http://dx.doi.org/10.1371/journal.pntd.0008085 Text en © 2020 He et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article He, Xiaoyi Dong, Shengjie Li, Liping Liu, Xiaojian Wu, Yongsheng Zhang, Zhen Mei, Shujiang Using a Bayesian spatiotemporal model to identify the influencing factors and high-risk areas of hand, foot and mouth disease (HFMD) in Shenzhen |
title | Using a Bayesian spatiotemporal model to identify the influencing factors and high-risk areas of hand, foot and mouth disease (HFMD) in Shenzhen |
title_full | Using a Bayesian spatiotemporal model to identify the influencing factors and high-risk areas of hand, foot and mouth disease (HFMD) in Shenzhen |
title_fullStr | Using a Bayesian spatiotemporal model to identify the influencing factors and high-risk areas of hand, foot and mouth disease (HFMD) in Shenzhen |
title_full_unstemmed | Using a Bayesian spatiotemporal model to identify the influencing factors and high-risk areas of hand, foot and mouth disease (HFMD) in Shenzhen |
title_short | Using a Bayesian spatiotemporal model to identify the influencing factors and high-risk areas of hand, foot and mouth disease (HFMD) in Shenzhen |
title_sort | using a bayesian spatiotemporal model to identify the influencing factors and high-risk areas of hand, foot and mouth disease (hfmd) in shenzhen |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7112242/ https://www.ncbi.nlm.nih.gov/pubmed/32196496 http://dx.doi.org/10.1371/journal.pntd.0008085 |
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