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A time-trend ecological study for identifying flood-sensitive infectious diseases in Guangxi, China from 2005 to 2012
BACKGROUND: Flood-related damage can be very severe and include health effects. Among those health impacts, infectious diseases still represent a significant public health problem in China. However, there have been few studies on the identification of the spectrum of infectious diseases associated w...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7094502/ https://www.ncbi.nlm.nih.gov/pubmed/31306984 http://dx.doi.org/10.1016/j.envres.2019.108577 |
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author | Ding, Guoyong Li, Xiaomei Li, Xuewen Zhang, Baofang Jiang, Baofa Li, Dong Xing, Weijia Liu, Qiyong Liu, Xuena Hou, Haifeng |
author_facet | Ding, Guoyong Li, Xiaomei Li, Xuewen Zhang, Baofang Jiang, Baofa Li, Dong Xing, Weijia Liu, Qiyong Liu, Xuena Hou, Haifeng |
author_sort | Ding, Guoyong |
collection | PubMed |
description | BACKGROUND: Flood-related damage can be very severe and include health effects. Among those health impacts, infectious diseases still represent a significant public health problem in China. However, there have been few studies on the identification of the spectrum of infectious diseases associated with floods in one area. This study aimed to quantitatively identify sensitive infectious diseases associated with floods in Guangxi, China. METHODS: A time-trend ecological design was conducted. A descriptive analysis was first performed to exclude infectious diseases with low incidence from 2005 to 2012 in ten study sites of Guangxi. The Wilcoxon rank-sum test was applied to examine the difference in the ten-day attack rate of infectious diseases between the exposure and control periods with different lagged effects. Negative binomial, zero-inflated Poisson and zero-inflated negative binomial models were used to examine the relationship and odd ratios (ORs) of the risk of floods on infectious diseases of preliminary screening. RESULTS: A total of 417,271 infectious diseases were notified. There were 11 infectious diseases associated with floods in the preliminary screening process for flood-sensitive infectious diseases. The strongest effect was shown with a 0–9 ten-day lag in different infectious diseases. Multivariate analysis showed that floods were significantly associated with an increased the risk of bacillary dysentery (odds ratio (OR) = 1.268, 95% confidence interval (CI): 1.072–1.500), acute haemorrhagic conjunctivitis (AHC, OR = 3.230, 95% CI: 1.976–5.280), influenza A (H(1)N(1)) (OR = 1.808, 95% CI: 1.721–1.901), tuberculosis (OR = 1.200, 95% CI: 1.036–1.391), influenza (OR = 2.614, 95% CI: 1.476–4.629), Japanese encephalitis (OR = 2.334, 95% CI: 1.119–4.865), and leptospirosis (OR = 1.138, 95% CI: 1.075–1.205), respectively. CONCLUSION: The spectrum of infectious diseases which are associated with floods are bacillary dysentery, AHC, influenza A (H(1)N(1)), tuberculosis, influenza, Japanese encephalitis and leptospirosis in Guangxi. Floods can result in differently increased risk of these diseases, and public health action should be taken to control a potential risk of these diseases after floods. |
format | Online Article Text |
id | pubmed-7094502 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-70945022020-03-25 A time-trend ecological study for identifying flood-sensitive infectious diseases in Guangxi, China from 2005 to 2012 Ding, Guoyong Li, Xiaomei Li, Xuewen Zhang, Baofang Jiang, Baofa Li, Dong Xing, Weijia Liu, Qiyong Liu, Xuena Hou, Haifeng Environ Res Article BACKGROUND: Flood-related damage can be very severe and include health effects. Among those health impacts, infectious diseases still represent a significant public health problem in China. However, there have been few studies on the identification of the spectrum of infectious diseases associated with floods in one area. This study aimed to quantitatively identify sensitive infectious diseases associated with floods in Guangxi, China. METHODS: A time-trend ecological design was conducted. A descriptive analysis was first performed to exclude infectious diseases with low incidence from 2005 to 2012 in ten study sites of Guangxi. The Wilcoxon rank-sum test was applied to examine the difference in the ten-day attack rate of infectious diseases between the exposure and control periods with different lagged effects. Negative binomial, zero-inflated Poisson and zero-inflated negative binomial models were used to examine the relationship and odd ratios (ORs) of the risk of floods on infectious diseases of preliminary screening. RESULTS: A total of 417,271 infectious diseases were notified. There were 11 infectious diseases associated with floods in the preliminary screening process for flood-sensitive infectious diseases. The strongest effect was shown with a 0–9 ten-day lag in different infectious diseases. Multivariate analysis showed that floods were significantly associated with an increased the risk of bacillary dysentery (odds ratio (OR) = 1.268, 95% confidence interval (CI): 1.072–1.500), acute haemorrhagic conjunctivitis (AHC, OR = 3.230, 95% CI: 1.976–5.280), influenza A (H(1)N(1)) (OR = 1.808, 95% CI: 1.721–1.901), tuberculosis (OR = 1.200, 95% CI: 1.036–1.391), influenza (OR = 2.614, 95% CI: 1.476–4.629), Japanese encephalitis (OR = 2.334, 95% CI: 1.119–4.865), and leptospirosis (OR = 1.138, 95% CI: 1.075–1.205), respectively. CONCLUSION: The spectrum of infectious diseases which are associated with floods are bacillary dysentery, AHC, influenza A (H(1)N(1)), tuberculosis, influenza, Japanese encephalitis and leptospirosis in Guangxi. Floods can result in differently increased risk of these diseases, and public health action should be taken to control a potential risk of these diseases after floods. Elsevier Inc. 2019-09 2019-07-05 /pmc/articles/PMC7094502/ /pubmed/31306984 http://dx.doi.org/10.1016/j.envres.2019.108577 Text en © 2019 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Ding, Guoyong Li, Xiaomei Li, Xuewen Zhang, Baofang Jiang, Baofa Li, Dong Xing, Weijia Liu, Qiyong Liu, Xuena Hou, Haifeng A time-trend ecological study for identifying flood-sensitive infectious diseases in Guangxi, China from 2005 to 2012 |
title | A time-trend ecological study for identifying flood-sensitive infectious diseases in Guangxi, China from 2005 to 2012 |
title_full | A time-trend ecological study for identifying flood-sensitive infectious diseases in Guangxi, China from 2005 to 2012 |
title_fullStr | A time-trend ecological study for identifying flood-sensitive infectious diseases in Guangxi, China from 2005 to 2012 |
title_full_unstemmed | A time-trend ecological study for identifying flood-sensitive infectious diseases in Guangxi, China from 2005 to 2012 |
title_short | A time-trend ecological study for identifying flood-sensitive infectious diseases in Guangxi, China from 2005 to 2012 |
title_sort | time-trend ecological study for identifying flood-sensitive infectious diseases in guangxi, china from 2005 to 2012 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7094502/ https://www.ncbi.nlm.nih.gov/pubmed/31306984 http://dx.doi.org/10.1016/j.envres.2019.108577 |
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