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Japanese Encephalitis Risk and Contextual Risk Factors in Southwest China: A Bayesian Hierarchical Spatial and Spatiotemporal Analysis

Author

Listed:
  • Xing Zhao

    (West China School of Public Health, Sichuan University, Chengdu 610041, China
    Department of Biostatistics, School of Public Health, University of Washington,Seattle, WA 98195, USA)

  • Mingqin Cao

    (School of Public Health, Xinjiang Medical University, Urumqi 830011, China)

  • Hai-Huan Feng

    (West China School of Public Health, Sichuan University, Chengdu 610041, China)

  • Heng Fan

    (West China School of Public Health, Sichuan University, Chengdu 610041, China)

  • Fei Chen

    (West China School of Public Health, Sichuan University, Chengdu 610041, China)

  • Zijian Feng

    (Office for Disease Control and Emergency Response, Chinese Center for Disease Control and Prevention (China CDC), Beijing 102206, China)

  • Xiaosong Li

    (West China School of Public Health, Sichuan University, Chengdu 610041, China)

  • Xiao-Hua Zhou

    (Department of Biostatistics, School of Public Health, University of Washington,Seattle, WA 98195, USA
    HSR&D Center of Excellence, VA Puget Sound Health Care System, Seattle, WA 98101, USA)

Abstract

It is valuable to study the spatiotemporal pattern of Japanese encephalitis (JE) and its association with the contextual risk factors in southwest China, which is the most endemic area in China. Using data from 2004 to 2009, we applied GISmapping and spatial autocorrelation analysis to analyze reported incidence data of JE in 438 counties in southwest China, finding that JE cases were not randomly distributed, and a Bayesian hierarchical spatiotemporal model identified the east part of southwest China as a high risk area. Meanwhile, the Bayesian hierarchical spatial model in 2006 demonstrated a statistically significant association between JE and the agricultural and climatic variables, including the proportion of rural population, the pig-to-human ratio, the monthly precipitation and the monthly mean minimum and maximum temperatures. Particular emphasis was placed on the time-lagged effect for climatic factors. The regression method and the Spearman correlation analysis both identified a two-month lag for the precipitation, while the regression method found a one-month lag for temperature. The results show that the high risk area in the east part of southwest China may be connected to the agricultural and climatic factors. The routine surveillance and the allocation of health resources should be given more attention in this area. Moreover, the meteorological variables might be considered as possible predictors of JE in southwest China.

Suggested Citation

  • Xing Zhao & Mingqin Cao & Hai-Huan Feng & Heng Fan & Fei Chen & Zijian Feng & Xiaosong Li & Xiao-Hua Zhou, 2014. "Japanese Encephalitis Risk and Contextual Risk Factors in Southwest China: A Bayesian Hierarchical Spatial and Spatiotemporal Analysis," IJERPH, MDPI, vol. 11(4), pages 1-17, April.
    • Handle: RePEc:gam:jijerp:v:11:y:2014:i:4:p:4201-4217:d:35112
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    1. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. Hualiang Lin & Liuqing Yang & Qiyong Liu & Tong Wang & Sarah Hossain & Suzanne Ho & Linwei Tian, 2012. "Time series analysis of Japanese encephalitis and weather in Linyi City, China," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 57(2), pages 289-296, April.
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    1. Shaobai Zhang & Wenbiao Hu & Xin Qi & Guihua Zhuang, 2018. "How Socio-Environmental Factors Are Associated with Japanese Encephalitis in Shaanxi, China—A Bayesian Spatial Analysis," IJERPH, MDPI, vol. 15(4), pages 1-13, March.

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