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Spatial-Temporal Epidemiology of Tuberculosis in Mainland China: An Analysis Based on Bayesian Theory

Author

Listed:
  • Kai Cao

    (School of Public Health, Capital Medical University, No. 10 Xitoutiao, You’anmen Wai, Fengtai District, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
    Beijing Ophthalmology & Visual Science Key Lab., Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
    These authors contributed equally to this work.)

  • Kun Yang

    (School of Public Health, Capital Medical University, No. 10 Xitoutiao, You’anmen Wai, Fengtai District, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
    These authors contributed equally to this work.)

  • Chao Wang

    (School of Public Health, Capital Medical University, No. 10 Xitoutiao, You’anmen Wai, Fengtai District, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
    Department of Statistics and Information, Beijing Centers for Disease Control and Prevention, No 16, Hepingli Middle Street, Dongcheng District, Beijing 100013, China)

  • Jin Guo

    (School of Public Health, Capital Medical University, No. 10 Xitoutiao, You’anmen Wai, Fengtai District, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China)

  • Lixin Tao

    (School of Public Health, Capital Medical University, No. 10 Xitoutiao, You’anmen Wai, Fengtai District, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China)

  • Qingrong Liu

    (School of Public Health, Capital Medical University, No. 10 Xitoutiao, You’anmen Wai, Fengtai District, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China)

  • Mahara Gehendra

    (School of Public Health, Capital Medical University, No. 10 Xitoutiao, You’anmen Wai, Fengtai District, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China)

  • Yingjie Zhang

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

  • Xiuhua Guo

    (School of Public Health, Capital Medical University, No. 10 Xitoutiao, You’anmen Wai, Fengtai District, Beijing 100069, China
    Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China)

Abstract

Objective : To explore the spatial-temporal interaction effect within a Bayesian framework and to probe the ecological influential factors for tuberculosis. Methods : Six different statistical models containing parameters of time, space, spatial-temporal interaction and their combination were constructed based on a Bayesian framework. The optimum model was selected according to the deviance information criterion (DIC) value. Coefficients of climate variables were then estimated using the best fitting model. Results : The model containing spatial-temporal interaction parameter was the best fitting one, with the smallest DIC value (−4,508,660). Ecological analysis results showed the relative risks (RRs) of average temperature, rainfall, wind speed, humidity, and air pressure were 1.00324 (95% CI, 1.00150–1.00550), 1.01010 (95% CI, 1.01007–1.01013), 0.83518 (95% CI, 0.93732–0.96138), 0.97496 (95% CI, 0.97181–1.01386), and 1.01007 (95% CI, 1.01003–1.01011), respectively. Conclusions : The spatial-temporal interaction was statistically meaningful and the prevalence of tuberculosis was influenced by the time and space interaction effect. Average temperature, rainfall, wind speed, and air pressure influenced tuberculosis. Average humidity had no influence on tuberculosis.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:5:p:469-:d:69451
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    References listed on IDEAS

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    1. R. B. Millar & S. McKechnie, 2014. "A one-step-ahead pseudo-DIC for comparison of Bayesian state-space models," Biometrics, The International Biometric Society, vol. 70(4), pages 972-980, December.
    2. 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.
    3. Yun-Xia Liu & Chun-Kun Pang & Yanxun Liu & Xiu-Bin Sun & Xin-Xu Li & Shi-Wen Jiang & Fuzhong Xue, 2015. "Association between Multidrug-Resistant Tuberculosis and Risk Factors in China: Applying Partial Least Squares Path Modeling," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-13, May.
    4. Xia Zhang & Hongyan Jia & Fei Liu & Liping Pan & Aiying Xing & Shuxiang Gu & Boping Du & Qi Sun & Rongrong Wei & Zongde Zhang, 2013. "Prevalence and Risk Factors for Latent Tuberculosis Infection among Health Care Workers in China: A Cross-Sectional Study," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-6, June.
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    Cited by:

    1. Peter Congdon, 2016. "Spatiotemporal Frameworks for Infectious Disease Diffusion and Epidemiology," IJERPH, MDPI, vol. 13(12), pages 1-4, December.
    2. Yang Li & Yi Hu & Mikael Mansjö & Qi Zhao & Weili Jiang & Solomon Ghebremichael & Sven Hoffner & Biao Xu, 2018. "The Epidemiological Significance and Temporal Stability of Mycobacterial Interspersed Repetitive Units-Variable Number of Tandem Repeats-Based Method Applied to Mycobacterium tuberculosis in China," IJERPH, MDPI, vol. 15(4), pages 1-11, April.
    3. 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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