Spatio-temporal analysis, climate factors for malaria in mainland China, 2004-2013

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• 英文论文题名：Spatio-temporal analysis, climate factors for malaria in mainland China, 2004-2013
• 论文作者：Qiang Huang ; Lin Hu ; Qibing Liao ; Hongjuan Peng
• 英文论文作者：Qiang Huang ; Lin Hu ; Qibing Liao ; Hongjuan Peng ; Department of Pathogen Biology ; Guangdong Provincial Key Laboratory of Tropical Disease Research ; School of Public Health ; Southern Medical University
• 年：2016
• 作者机构：Department of Pathogen Biology, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University;
• 英文论文关键词：malaria ; spatial autocorrelation ; scan statistics ; climate factor
• 会议召开时间：2016-08-08
• 会议录名称：中国动物学会寄生虫学专业委员会第十届全国寄生虫学青年工作者学术研讨会论文摘要集
• 英文会议录名称：The Abstracts of the Tenth Symposium for Yong Scientists of China Society of Parasitology
• 语种：英文
• 分类号：R531.3
• 学会代码：JISC
• 会议名称：中国动物学会寄生虫学专业委员会第十届全国寄生虫学青年工作者学术研讨会
• 会议地点：中国四川成都
• 主办单位：中国动物学会寄生虫学专业青年委员会
• 学会名称：中国动物学会寄生虫学专业委员会
• 页数：1
• 文件大小：156k
• 原文格式：D
• 会议级别：全国

摘要

Objectives: Malaria used to be a devastating and persistent mosquito-borne disease in China. The cases of infection have been sharply decreased to a very low level in recent years. The new transmission pattern is needed to be identified for better prevention and control of malaria. The purpose of this study was to characterize the spatio-temporal heterogeneities of malaria distribution at a provincial level and investigate the association between malaria incidence and some climate factors in China, so as to provide a reference for malaria prevention and control. Methods: The raw malaria incidence was calculated with the reported cases number devided by the permanent resident population of each province, and then smoothed with empirical Bayes model with Geoda 1.6 software. The Global spatial autocorrelations and Local Spatial Autocorrelations were analyzed by geographic information system(GIS) to detect the spatial association in global and local regions. The purely spatial, temporal and spaio-temporal analysis was applied to identify the clusters of space and time with Sa TScan 9.3 software. The spearman correlation analysis was conducted to study the relationship between climate factors and malaria incidence. Results: The descriptive analysis of malaria incidence appears a peak in 2006, and the proportion of imported case had increased from 4.6% in 2004 to 98.1% in 2013. The results of global spatial autocorrelation, local spatial autocorrelation and purely spatial cluster analysis have revealed that malaria was endemic in Hainan, Anhui, and Yunnan up to 2011, and then gradually clustered in Yunnan and Guangxi, sharing the border with Myanmar, Laos, and Vietnam in 2012-2013. The purely temporal clusters were transformed from April-November to May-August. The space-time analysis detected a most likely cluster in Anhui province(2004/6/1-2008/10/31). The correlation coefficients have been gradually decreased during 2004 to 2013, with a different lag effect of climate factors between clusters and non-clusters. Conclusion: From this study, it is concluded that the spatio-temporal distribution of malaria in China has changed from scattering to clustering since 2012, the allocation of public health resources should be adjusted accordingly. The malaria incidence was no longer powerfully influenced by climate factors as before, and different lag effects were observed between clusters and non-clusters. Further studies and early warning system are required to be established to cope with a resurgence of malaria.
Objectives: Malaria used to be a devastating and persistent mosquito-borne disease in China. The cases of infection have been sharply decreased to a very low level in recent years. The new transmission pattern is needed to be identified for better prevention and control of malaria. The purpose of this study was to characterize the spatio-temporal heterogeneities of malaria distribution at a provincial level and investigate the association between malaria incidence and some climate factors in China, so as to provide a reference for malaria prevention and control. Methods: The raw malaria incidence was calculated with the reported cases number devided by the permanent resident population of each province, and then smoothed with empirical Bayes model with Geoda 1.6 software. The Global spatial autocorrelations and Local Spatial Autocorrelations were analyzed by geographic information system(GIS) to detect the spatial association in global and local regions. The purely spatial, temporal and spaio-temporal analysis was applied to identify the clusters of space and time with Sa TScan 9.3 software. The spearman correlation analysis was conducted to study the relationship between climate factors and malaria incidence. Results: The descriptive analysis of malaria incidence appears a peak in 2006, and the proportion of imported case had increased from 4.6% in 2004 to 98.1% in 2013. The results of global spatial autocorrelation, local spatial autocorrelation and purely spatial cluster analysis have revealed that malaria was endemic in Hainan, Anhui, and Yunnan up to 2011, and then gradually clustered in Yunnan and Guangxi, sharing the border with Myanmar, Laos, and Vietnam in 2012-2013. The purely temporal clusters were transformed from April-November to May-August. The space-time analysis detected a most likely cluster in Anhui province(2004/6/1-2008/10/31). The correlation coefficients have been gradually decreased during 2004 to 2013, with a different lag effect of climate factors between clusters and non-clusters. Conclusion: From this study, it is concluded that the spatio-temporal distribution of malaria in China has changed from scattering to clustering since 2012, the allocation of public health resources should be adjusted accordingly. The malaria incidence was no longer powerfully influenced by climate factors as before, and different lag effects were observed between clusters and non-clusters. Further studies and early warning system are required to be established to cope with a resurgence of malaria.

引文