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Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis

BACKGROUND: Tuberculosis (TB) is the notifiable infectious disease with the second highest incidence in the Qinghai province, a province with poor primary health care infrastructure. Understanding the spatial distribution of TB and related environmental factors is necessary for developing effective...

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Autores principales: Rao, Hua-Xiang, Zhang, Xi, Zhao, Lei, Yu, Juan, Ren, Wen, Zhang, Xue-Lei, Ma, Yong-Cheng, Shi, Yan, Ma, Bin-Zhong, Wang, Xiang, Wei, Zhen, Wang, Hua-Fang, Qiu, Li-Xia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4890510/
https://www.ncbi.nlm.nih.gov/pubmed/27251154
http://dx.doi.org/10.1186/s40249-016-0139-4
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author Rao, Hua-Xiang
Zhang, Xi
Zhao, Lei
Yu, Juan
Ren, Wen
Zhang, Xue-Lei
Ma, Yong-Cheng
Shi, Yan
Ma, Bin-Zhong
Wang, Xiang
Wei, Zhen
Wang, Hua-Fang
Qiu, Li-Xia
author_facet Rao, Hua-Xiang
Zhang, Xi
Zhao, Lei
Yu, Juan
Ren, Wen
Zhang, Xue-Lei
Ma, Yong-Cheng
Shi, Yan
Ma, Bin-Zhong
Wang, Xiang
Wei, Zhen
Wang, Hua-Fang
Qiu, Li-Xia
author_sort Rao, Hua-Xiang
collection PubMed
description BACKGROUND: Tuberculosis (TB) is the notifiable infectious disease with the second highest incidence in the Qinghai province, a province with poor primary health care infrastructure. Understanding the spatial distribution of TB and related environmental factors is necessary for developing effective strategies to control and further eliminate TB. METHODS: Our TB incidence data and meteorological data were extracted from the China Information System of Disease Control and Prevention and statistical yearbooks, respectively. We calculated the global and local Moran’s I by using spatial autocorrelation analysis to detect the spatial clustering of TB incidence each year. A spatial panel data model was applied to examine the associations of meteorological factors with TB incidence after adjustment of spatial individual effects and spatial autocorrelation. RESULTS: The Local Moran’s I method detected 11 counties with a significantly high-high spatial clustering (average annual incidence: 294/100 000) and 17 counties with a significantly low-low spatial clustering (average annual incidence: 68/100 000) of TB annual incidence within the examined five-year period; the global Moran’s I values ranged from 0.40 to 0.58 (all P-values < 0.05). The TB incidence was positively associated with the temperature, precipitation, and wind speed (all P-values < 0.05), which were confirmed by the spatial panel data model. Each 10 °C, 2 cm, and 1 m/s increase in temperature, precipitation, and wind speed associated with 9 % and 3 % decrements and a 7 % increment in the TB incidence, respectively. CONCLUSIONS: High TB incidence areas were mainly concentrated in south-western Qinghai, while low TB incidence areas clustered in eastern and north-western Qinghai. Areas with low temperature and precipitation and with strong wind speeds tended to have higher TB incidences. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40249-016-0139-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-48905102016-06-03 Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis Rao, Hua-Xiang Zhang, Xi Zhao, Lei Yu, Juan Ren, Wen Zhang, Xue-Lei Ma, Yong-Cheng Shi, Yan Ma, Bin-Zhong Wang, Xiang Wei, Zhen Wang, Hua-Fang Qiu, Li-Xia Infect Dis Poverty Research Article BACKGROUND: Tuberculosis (TB) is the notifiable infectious disease with the second highest incidence in the Qinghai province, a province with poor primary health care infrastructure. Understanding the spatial distribution of TB and related environmental factors is necessary for developing effective strategies to control and further eliminate TB. METHODS: Our TB incidence data and meteorological data were extracted from the China Information System of Disease Control and Prevention and statistical yearbooks, respectively. We calculated the global and local Moran’s I by using spatial autocorrelation analysis to detect the spatial clustering of TB incidence each year. A spatial panel data model was applied to examine the associations of meteorological factors with TB incidence after adjustment of spatial individual effects and spatial autocorrelation. RESULTS: The Local Moran’s I method detected 11 counties with a significantly high-high spatial clustering (average annual incidence: 294/100 000) and 17 counties with a significantly low-low spatial clustering (average annual incidence: 68/100 000) of TB annual incidence within the examined five-year period; the global Moran’s I values ranged from 0.40 to 0.58 (all P-values < 0.05). The TB incidence was positively associated with the temperature, precipitation, and wind speed (all P-values < 0.05), which were confirmed by the spatial panel data model. Each 10 °C, 2 cm, and 1 m/s increase in temperature, precipitation, and wind speed associated with 9 % and 3 % decrements and a 7 % increment in the TB incidence, respectively. CONCLUSIONS: High TB incidence areas were mainly concentrated in south-western Qinghai, while low TB incidence areas clustered in eastern and north-western Qinghai. Areas with low temperature and precipitation and with strong wind speeds tended to have higher TB incidences. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40249-016-0139-4) contains supplementary material, which is available to authorized users. BioMed Central 2016-06-02 /pmc/articles/PMC4890510/ /pubmed/27251154 http://dx.doi.org/10.1186/s40249-016-0139-4 Text en © Rao et al. 2016 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
Rao, Hua-Xiang
Zhang, Xi
Zhao, Lei
Yu, Juan
Ren, Wen
Zhang, Xue-Lei
Ma, Yong-Cheng
Shi, Yan
Ma, Bin-Zhong
Wang, Xiang
Wei, Zhen
Wang, Hua-Fang
Qiu, Li-Xia
Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis
title Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis
title_full Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis
title_fullStr Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis
title_full_unstemmed Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis
title_short Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis
title_sort spatial transmission and meteorological determinants of tuberculosis incidence in qinghai province, china: a spatial clustering panel analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4890510/
https://www.ncbi.nlm.nih.gov/pubmed/27251154
http://dx.doi.org/10.1186/s40249-016-0139-4
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