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

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.

Effect of ambient temperature on incidence of tuberculosis and effect modification by meteorological factors in Jinan, China during 2012-2015: A time-series analysis

“…The higher WS could accelerate ventilation, dilute the concentration of bacteria and help reduce the risk of becoming infected. Although another study indicated that areas with stronger wind speeds tend to have a higher infection risk, our study findings were supported by the findings of Kai Cao (Cao et al, 2016). As has been found in a few other studies (Cao et al, 2016, Koh et al, 2013, Rao et al, 2016, the low SD would raise the risk of TB.…”

“…(Jinan Statistical Bureau, 2018) In recent years, Jinan's annual average temperature has gradually increased and extreme weather events have occurred frequently with extreme T max recorded 41.7 ℃ and extreme T min recorded -17 ℃ (Wang et al, 2016). In 2017, the number of high temperature (daily maximum temperature ≥ 35 ℃) days reached 30, which was the most numerous days for nearly two decades.…”

Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009–2016

“…A large number of studies on the spatial and temporal distribution of TB have demonstrated that TB has a highly complex dynamics and is spatially heterogeneous at the provincial, national, and international levels during certain periods of time; however, the variations in small area are always be ignored by using a relatively large scale [3][4][5][6]. In our previous study, the Moran's I spatial autocorrelation analysis method was used to analyze the TB incidence data from 2009 to 2013 in Qinghai and found that the distribution of TB in this province was not random [7].…”

Spatiotemporal Frameworks for Infectious Disease Diffusion and Epidemiology

“…They find links to economic development and identify endemically high-risk regions in western China. Cao et al [34] identify a similar spatial pattern for TB incidence in Chinese provinces and find links to climatic variables. Moise et al [35] find that impacts on malaria incidence of seasonality and elevation may be modified by high population density and economic activity patterns (such as intensive subsistence farming practice) which affect exposure.…”