A Spatio-temporal Bayesian model to estimate risk and influencing factors related to tuberculosis in Chongqing, China, 2014–2020

Abstract

Background Tuberculosis (TB) is a serious infectious disease that is one of the leading causes of death worldwide. This study aimed to investigate the spatial and temporal distribution patterns and potential influencing factors of TB incidence risk, and to provide a scientific basis for the prevention and control of TB. Methods We collected reported cases of TB in 38 districts and counties in Chongqing from 2014 to 2020 and data on environment, population characteristics and economic factors during the same period. By constructing a Bayesian spatio-temporal model, we explored the spatio-temporal distribution pattern of TB incidence risk and potential influencing factors, identified key areas and key populations affected by TB, compared the spatio-temporal distribution characteristics of TB in populations with different characteristics, and explored the differences in the influence of various social and environmental factors. Results The high-risk areas for TB incidence in Chongqing from 2014 to 2020 were mainly concentrated in southeastern and northeastern regions of Chongqing, and the overall relative risk (RR) of TB showed a decreasing trend during the study period, while RR of TB in main urban area and southeast of Chongqing showed an increasing trend. The RR of TB was relatively high in the main urban area for the female population and the population aged 0–29 years, and the RR of TB for the population aged 30–44 years in the main urban area and the population aged 60 years or older in southeast of Chongqing had an increasing trend, respectively. For each 1 μg/m3 increase in SO2 and 1% increase in the number of low-income per 1000 non-agricultural households (LINA per 1000 persons), the RR of TB increased by 0.35% (95% CI: 0.08–0.61%) and 0.07% (95% CI: 0.05–0.10%), respectively. And LINA per 1000 persons had the greatest impact on the female population and the over 60 years old age group. Although each 1% increase in urbanization rate (UR) was associated with 0.15% (95% CI: 0.11–0.17%) reduction in the RR of TB in the whole population, the RR increased by 0.18% (95% CI: 0.16–0.21%) in the female population and 0.37% (95% CI: 0.34–0.45%) in the 0–29 age group. Conclusion This study showed that high-risk areas for TB were concentrated in the southeastern and northeastern regions of Chongqing, and that the elderly population was a key population for TB incidence. There were spatial and temporal differences in the incidence of TB in populations with different characteristics, and various socio-environmental factors had different effects on different populations. Local governments should focus on areas and populations at high risk of TB and develop targeted prevention interventions based on the characteristics of different populations.

Paradoxical tuberculosis-associated immune reconstitution inflammatory syndrome in initiating ART among HIV-Infected patients in China-risk factors and management

“…Several clinical risk factors, including lower CD4 + T-cell counts, CD4/CD8 ratio, higher viral load (VL), and shorter intervals between ATT and ART, have been associated with the development of paradoxical TB-IRIS [ 13 , 14 ]. However, it’s noteworthy that not all PLHIV with TB under similar conditions develop paradoxical TB-IRIS [ 15 – 17 ]. Furthermore, in the current era, the introduction of integrase strand transfer inhibitor (INSTI)-based ART regimens, known for their rapid recovery of CD4 + T-cell counts and VL reduction, raises questions regarding the potential increase in the incidence of paradoxical TB-IRIS.…”

A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology

“…Bayesian spatiotemporal models have been applied to study tuberculosis in various countries, including India [134], China [135][136][137], Kenya [138], Indonesia [139], Ethiopia [140]. Srinivasan and Venkatesan [134] examined disease rates among Indian states, assessing neighbourhood tuberculosis infections and their causes using Bayesian models with a CAR approach for spatial and temporal pattern changes in two survey periods.…”

Spatiotemporally Comparative Analysis of HIV, Pulmonary Tuberculosis, HIV-Pulmonary Tuberculosis Coinfection in Jiangsu Province, China

“… 11 Chen et al reported that the high-risk areas of TB were distributed in the southeast and northeast of Chongqing, China, which might be related to the more aging and empty-nesting population and lower economic levels. 12 The Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome (HIV/AIDS) epidemic affects the transmission dynamic of TB. 13 There is a strong synergy between HIV and TB infection.…”