Spatiotemporal Heterogeneity in Human Schistosoma japonicum Infection at Village Level in Hubei Province, China

Juan Qiu, Rendong Li, Ying Xiao, Jing Xia, Hong Zhu, Yingnan Niu, Duan Huang, Qihui Shao, Ying Cui and Yong Wang
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Juan Qiu: Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China
Rendong Li: Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China
Ying Xiao: Hubei Center for Disease Control and Prevention, Hubei Provincial Academy of Preventive Medicine, Wuhan 430079, China
Jing Xia: Hubei Center for Disease Control and Prevention, Hubei Provincial Academy of Preventive Medicine, Wuhan 430079, China
Hong Zhu: Hubei Center for Disease Control and Prevention, Hubei Provincial Academy of Preventive Medicine, Wuhan 430079, China
Yingnan Niu: College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Duan Huang: Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China
Qihui Shao: Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China
Ying Cui: Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China
Yong Wang: State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

IJERPH, 2019, vol. 16, issue 12, 1-11

Abstract: The spatiotemporal dynamics of Schistosoma japonicum , combined with temporal heterogeneity among regions of different epidemic areal-types from a microscale viewpoint might capture the local change dynamics and thus aid in optimizing the combinations of precise schistosomiasis control measures. The prevalence data on schistosomiasis infection from 2007 to 2012 in the 30 most endemic counties of Hubei Province, Central China, were appended to the village-level administrative division polygon layer. Anselin local Moran’s I, a retrospective space–time scan statistic and a multilevel-growth model analysis framework, was used to investigate the spatiotemporal pattern of schistosomiasis resident infection rate (RIR) at the village level and how natural geographical environment influence the schistosomiasis RIR over time. Two spatiotemporal high-risk clusters and continuous high-rate clusters were identified mainly in the embankment region across flooding areas of lakes connected with the Yangze and Hanjiang Rivers. Moreover, 12 other clusters and outlier evolution modes were detected to be scattered across the continuous high-rate clusters. Villages located in embankment region had the highest initial values and most rapidly reduced RIRs over time, followed by villages located in marshland-and-lake regions and finally by villages located in hilly region. Moreover, initial RIR values and rates of change did significantly vary ( p < 0.001 and p < 0.001, respectively) irrespective of their epidemic areal-type. These local spatiotemporal heterogeneities could contribute to the formulation of distinct control strategies based on local transmission dynamics and be applied in other endemic areas of schistosomiasis.

Keywords: Schistosoma japonicum; spatiotemporal heterogeneity; microscale; spatial analysis; multilevel growth model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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