A Spatio-temporal Bayesian model to estimate risk and evaluate factors related to Tuberculosis in Chongqing, China, 2014-2020

Abstract Background Tuberculosis (TB) is the leading cause of death from bacterial pathogens worldwide. The World Health Organization report on TB in 2020 showed that China had the second-highest burden of TB in the world. The average level of TB cases in Chongqing was higher than that of the whole country and the rate of decline was slower than that of the whole country from 2010 to 2015. we aimed to explore the spatial cluster of different populations' risk of TB and identify the factors associated with TB and provide a reference for the prevention and control of TB. Methods We applied a Bayesian Spatio-temporal model to estimate the TB risk of different populations in Chongqing and analyze the influencing factors of TB risk. Results a total of 170934 TB cases (confirmed cases and clinically diagnosed cases) were reported by the Chongqing Provincial Center of Disease Control (CDC) and Prevention Information System from 2014 to 2020. The incidence rate per 100000 people showed a downward trend from 2014 (82.81/100000) to 2020 (70.19/100000). According to the minimum deviation information criterion (DIC), the Bayesian Spatio-temporal model with spatial effect, temporally effect, and Spatio-temporal interaction was selected as the best model. A downward trend in the relative risk (RR) of TB was observed during the study period, and there was a large difference in the relative risk of different districts and populations. After adding environmental and socio-economic variables to the analysis, We found that SO 2 (µg/m 3 ), urbanization rate (UR)(%), the proportion of people engaged in agriculture (PEIA) (%), and the low-income group per 1000 non-agricultural households (LINA per 1000) had significant effects on the risk of TB. Conclusion This study found spatial clustering of TB incidence risk in the northeast and southeast Chongqing. The high-risk groups were mainly women and the elderly, who were most affected by poverty. Urbanization showed a positive effect on TB risk in the female and young population. Using this information, decision-makers may want to implement focused interventions for the control and prevention of TB in high-risk districts and populations.