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Int. J. Environ. Res. Public Health 2016, 13(5), 469; doi:10.3390/ijerph13050469

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

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
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Peter Congdon
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)
View Full-Text   |   Download PDF [1266 KB, uploaded 5 May 2016]   |  

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. View Full-Text
Keywords: tuberculosis; Bayesian theory; spatial-temporal interaction; ecological factors tuberculosis; Bayesian theory; spatial-temporal interaction; ecological factors
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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.

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