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

Author

Listed:
  • Wenyi Sun

    (State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Jianhua Gong

    (State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
    Zhejiang-CAS Application Center for Geoinformatics, Zhejiang 314100, China)

  • Jieping Zhou

    (State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
    Zhejiang-CAS Application Center for Geoinformatics, Zhejiang 314100, China)

  • Yanlin Zhao

    (The Chinese Center for Disease Control and Prevention, Beijing 102206, China)

  • Junxiang Tan

    (Center for Airborne Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China)

  • Abdoul Nasser Ibrahim

    (State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
    Zhejiang-CAS Application Center for Geoinformatics, Zhejiang 314100, China)

  • Yang Zhou

    (The Chinese Center for Disease Control and Prevention, Beijing 102206, China)

Abstract

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.

Suggested Citation

  • Wenyi Sun & Jianhua Gong & Jieping Zhou & Yanlin Zhao & Junxiang Tan & Abdoul Nasser Ibrahim & Yang Zhou, 2015. "A Spatial, Social and Environmental Study of Tuberculosis in China Using Statistical and GIS Technology," IJERPH, MDPI, vol. 12(2), pages 1-24, January.
    • Handle: RePEc:gam:jijerp:v:12:y:2015:i:2:p:1425-1448:d:45151
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    References listed on IDEAS

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    1. Herman Wold, 1980. "Model Construction and Evaluation When Theoretical Knowledge Is Scarce," NBER Chapters, in: Evaluation of Econometric Models, pages 47-74, National Bureau of Economic Research, Inc.
    2. Myers, W.P. & Westenhouse, J.L. & Flood, J. & Riley, L.W., 2006. "An ecological study of tuberculosis transmission in California," American Journal of Public Health, American Public Health Association, vol. 96(4), pages 685-690.
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    Cited by:

    1. Kai Cao & Kun Yang & Chao Wang & Jin Guo & Lixin Tao & Qingrong Liu & Mahara Gehendra & Yingjie Zhang & Xiuhua Guo, 2016. "Spatial-Temporal Epidemiology of Tuberculosis in Mainland China: An Analysis Based on Bayesian Theory," IJERPH, MDPI, vol. 13(5), pages 1-8, May.
    2. Zongyuan Xia & Bo Tang & Long Qin & Huiguo Zhang & Xijian Hu, 2023. "Spatially Dependent Bayesian Modeling of Geostatistics Data and Its Application for Tuberculosis (TB) in China," Mathematics, MDPI, vol. 11(19), pages 1-15, October.
    3. Gehendra Mahara & Chao Wang & Kun Yang & Sipeng Chen & Jin Guo & Qi Gao & Wei Wang & Quanyi Wang & Xiuhua Guo, 2016. "The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models," IJERPH, MDPI, vol. 13(11), pages 1-15, November.
    4. Lan Li & Yuliang Xi & Fu Ren, 2016. "Spatio-Temporal Distribution Characteristics and Trajectory Similarity Analysis of Tuberculosis in Beijing, China," IJERPH, MDPI, vol. 13(3), pages 1-17, March.
    5. Ying Mao & Rongxin He & Bin Zhu & Jinlin Liu & Ning Zhang, 2020. "Notifiable Respiratory Infectious Diseases in China: A Spatial–Temporal Epidemiology Analysis," IJERPH, MDPI, vol. 17(7), pages 1-15, March.

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