Cargando…
Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China
BACKGROUND: The Poyang Lake Region, one of the major epidemic sites of schistosomiasis in China, remains a severe challenge. To improve our understanding of the current endemic status of schistosomiasis and to better control the transmission of the disease in the Poyang Lake Region, it is important...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5341164/ https://www.ncbi.nlm.nih.gov/pubmed/28270197 http://dx.doi.org/10.1186/s13071-017-2059-y |
_version_ | 1782512941631799296 |
---|---|
author | Xia, Congcong Bergquist, Robert Lynn, Henry Hu, Fei Lin, Dandan Hao, Yuwan Li, Shizhu Hu, Yi Zhang, Zhijie |
author_facet | Xia, Congcong Bergquist, Robert Lynn, Henry Hu, Fei Lin, Dandan Hao, Yuwan Li, Shizhu Hu, Yi Zhang, Zhijie |
author_sort | Xia, Congcong |
collection | PubMed |
description | BACKGROUND: The Poyang Lake Region, one of the major epidemic sites of schistosomiasis in China, remains a severe challenge. To improve our understanding of the current endemic status of schistosomiasis and to better control the transmission of the disease in the Poyang Lake Region, it is important to analyse the clustering pattern of schistosomiasis and detect the hotspots of transmission risk. RESULTS: Based on annual surveillance data, at the village level in this region from 2009 to 2014, spatial and temporal cluster analyses were conducted to assess the pattern of schistosomiasis infection risk among humans through purely spatial (Local Moran’s I, Kulldorff and Flexible scan statistic) and space-time scan statistics (Kulldorff). A dramatic decline was found in the infection rate during the study period, which was shown to be maintained at a low level. The number of spatial clusters declined over time and were concentrated in counties around Poyang Lake, including Yugan, Yongxiu, Nanchang, Xingzi, Xinjian, De’an as well as Pengze, situated along the Yangtze River and the most serious area found in this study. Space-time analysis revealed that the clustering time frame appeared between 2009 and 2011 and the most likely cluster with the widest range was particularly concentrated in Pengze County. CONCLUSIONS: This study detected areas at high risk for schistosomiasis both in space and time at the village level from 2009 to 2014 in Poyang Lake Region. The high-risk areas are now more concentrated and mainly distributed at the river inflows Poyang Lake and along Yangtze River in Pengze County. It was assumed that the water projects including reservoirs and a recently breached dyke in this area were partly to blame. This study points out that attempts to reduce the negative effects of water projects in China should focus on the Poyang Lake Region. |
format | Online Article Text |
id | pubmed-5341164 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53411642017-03-10 Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China Xia, Congcong Bergquist, Robert Lynn, Henry Hu, Fei Lin, Dandan Hao, Yuwan Li, Shizhu Hu, Yi Zhang, Zhijie Parasit Vectors Research BACKGROUND: The Poyang Lake Region, one of the major epidemic sites of schistosomiasis in China, remains a severe challenge. To improve our understanding of the current endemic status of schistosomiasis and to better control the transmission of the disease in the Poyang Lake Region, it is important to analyse the clustering pattern of schistosomiasis and detect the hotspots of transmission risk. RESULTS: Based on annual surveillance data, at the village level in this region from 2009 to 2014, spatial and temporal cluster analyses were conducted to assess the pattern of schistosomiasis infection risk among humans through purely spatial (Local Moran’s I, Kulldorff and Flexible scan statistic) and space-time scan statistics (Kulldorff). A dramatic decline was found in the infection rate during the study period, which was shown to be maintained at a low level. The number of spatial clusters declined over time and were concentrated in counties around Poyang Lake, including Yugan, Yongxiu, Nanchang, Xingzi, Xinjian, De’an as well as Pengze, situated along the Yangtze River and the most serious area found in this study. Space-time analysis revealed that the clustering time frame appeared between 2009 and 2011 and the most likely cluster with the widest range was particularly concentrated in Pengze County. CONCLUSIONS: This study detected areas at high risk for schistosomiasis both in space and time at the village level from 2009 to 2014 in Poyang Lake Region. The high-risk areas are now more concentrated and mainly distributed at the river inflows Poyang Lake and along Yangtze River in Pengze County. It was assumed that the water projects including reservoirs and a recently breached dyke in this area were partly to blame. This study points out that attempts to reduce the negative effects of water projects in China should focus on the Poyang Lake Region. BioMed Central 2017-03-08 /pmc/articles/PMC5341164/ /pubmed/28270197 http://dx.doi.org/10.1186/s13071-017-2059-y Text en © The Author(s). 2017 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 Xia, Congcong Bergquist, Robert Lynn, Henry Hu, Fei Lin, Dandan Hao, Yuwan Li, Shizhu Hu, Yi Zhang, Zhijie Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China |
title | Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China |
title_full | Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China |
title_fullStr | Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China |
title_full_unstemmed | Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China |
title_short | Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China |
title_sort | village-based spatio-temporal cluster analysis of the schistosomiasis risk in the poyang lake region, china |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5341164/ https://www.ncbi.nlm.nih.gov/pubmed/28270197 http://dx.doi.org/10.1186/s13071-017-2059-y |
work_keys_str_mv | AT xiacongcong villagebasedspatiotemporalclusteranalysisoftheschistosomiasisriskinthepoyanglakeregionchina AT bergquistrobert villagebasedspatiotemporalclusteranalysisoftheschistosomiasisriskinthepoyanglakeregionchina AT lynnhenry villagebasedspatiotemporalclusteranalysisoftheschistosomiasisriskinthepoyanglakeregionchina AT hufei villagebasedspatiotemporalclusteranalysisoftheschistosomiasisriskinthepoyanglakeregionchina AT lindandan villagebasedspatiotemporalclusteranalysisoftheschistosomiasisriskinthepoyanglakeregionchina AT haoyuwan villagebasedspatiotemporalclusteranalysisoftheschistosomiasisriskinthepoyanglakeregionchina AT lishizhu villagebasedspatiotemporalclusteranalysisoftheschistosomiasisriskinthepoyanglakeregionchina AT huyi villagebasedspatiotemporalclusteranalysisoftheschistosomiasisriskinthepoyanglakeregionchina AT zhangzhijie villagebasedspatiotemporalclusteranalysisoftheschistosomiasisriskinthepoyanglakeregionchina |