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A Spatial, Social and Environmental Study of Tuberculosis in China Using Statistical and GIS Technology

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State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
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University of Chinese Academy of Sciences, Beijing 100049, China
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Zhejiang-CAS Application Center for Geoinformatics, Zhejiang 314100, China
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The Chinese Center for Disease Control and Prevention, Beijing 102206, China
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Center for Airborne Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
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Author to whom correspondence should be addressed.
Academic Editor: Paul B. Tchounwou
Int. J. Environ. Res. Public Health 2015, 12(2), 1425-1448; https://doi.org/10.3390/ijerph120201425
Received: 10 December 2014 / Accepted: 20 January 2015 / Published: 27 January 2015
Tuberculosis (TB) remains a major public health problem in China, and its incidence shows certain regional disparities. Systematic investigations of the social and environmental factors influencing TB are necessary for the prevention and control of the disease. Data on cases were obtained from the Chinese Center for Disease and Prevention. Social and environmental variables were tabulated to investigate the latent factor structure of the data using exploratory factor analysis (EFA). Partial least square path modeling (PLS-PM) was used to analyze the complex causal relationship and hysteresis effects between the factors and TB prevalence. A geographically weighted regression (GWR) model was used to explore the local association between factors and TB prevalence. EFA and PLS-PM indicated significant associations between TB prevalence and its latent factors. Altitude, longitude, climate, and education burden played an important role; primary industry employment, population density, air quality, and economic level had hysteresis with different lag time; health service and unemployment played a limited role but had limited hysteresis. Additionally, the GWR model showed that each latent factor had different effects on TB prevalence in different areas. It is necessary to formulate regional measures and strategies for TB control and prevention in China according to the local regional effects of specific factors. View Full-Text
Keywords: tuberculosis; partial least squares path model; geographically weighted regression tuberculosis; partial least squares path model; geographically weighted regression
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Sun, W.; Gong, J.; Zhou, J.; Zhao, Y.; Tan, J.; Ibrahim, A.N.; Zhou, Y. A Spatial, Social and Environmental Study of Tuberculosis in China Using Statistical and GIS Technology. Int. J. Environ. Res. Public Health 2015, 12, 1425-1448.

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