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Article

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

by 1,2,3,†, 1,2,†, 1,2,4, 1,2, 1,2, 1,2, 1,2, 5 and 1,2,*
1
School of Public Health, Capital Medical University, No. 10 Xitoutiao, You’anmen Wai, Fengtai District, Beijing 100069, China
2
Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
3
Beijing Ophthalmology & Visual Science Key Lab., Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
4
Department of Statistics and Information, Beijing Centers for Disease Control and Prevention, No 16, Hepingli Middle Street, Dongcheng District, Beijing 100013, China
5
Chinese Center for Disease Control and Prevention, Beijing 102206, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Peter Congdon
Int. J. Environ. Res. Public Health 2016, 13(5), 469; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph13050469
Received: 27 February 2016 / Revised: 6 April 2016 / Accepted: 27 April 2016 / Published: 5 May 2016
(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)
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. View Full-Text
Keywords: tuberculosis; Bayesian theory; spatial-temporal interaction; ecological factors tuberculosis; Bayesian theory; spatial-temporal interaction; ecological factors
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MDPI and ACS Style

Cao, K.; Yang, K.; Wang, C.; Guo, J.; Tao, L.; Liu, Q.; Gehendra, M.; Zhang, Y.; Guo, X. Spatial-Temporal Epidemiology of Tuberculosis in Mainland China: An Analysis Based on Bayesian Theory. Int. J. Environ. Res. Public Health 2016, 13, 469. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph13050469

AMA Style

Cao K, Yang K, Wang C, Guo J, Tao L, Liu Q, Gehendra M, Zhang Y, Guo X. Spatial-Temporal Epidemiology of Tuberculosis in Mainland China: An Analysis Based on Bayesian Theory. International Journal of Environmental Research and Public Health. 2016; 13(5):469. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph13050469

Chicago/Turabian Style

Cao, Kai, Kun Yang, Chao Wang, Jin Guo, Lixin Tao, Qingrong Liu, Mahara Gehendra, Yingjie Zhang, and Xiuhua Guo. 2016. "Spatial-Temporal Epidemiology of Tuberculosis in Mainland China: An Analysis Based on Bayesian Theory" International Journal of Environmental Research and Public Health 13, no. 5: 469. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph13050469

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