ABSTRACT
Objective: To use the spatiotemporal distribution model and INLA algorithm to study the spatiotemporal characteristics and influencing factors of tuberculosis in Shanghai and to provide a theoretical basis for formulating regional tuberculosis epidemic prevention and control measures. Methods: Based on the data of registered pulmonary tuberculosis cases in Shanghai during 2013-2020 derived from the tuberculosis management information system of China Disease Control and Prevention Information System, the hierarchical Bayesian model was adopted to fit the tuberculosis case data, identify the spatiotemporal variation characteristics of tuberculosis, and explore the potential socioeconomic characteristics and other factors related to health services and spatiotemporal characteristics. Results: From 2013 to 2020, 29 281 registered tuberculosis cases were reported in Shanghai, with an average annual incidence of 25.224/100 000. From 2013 to 2020, the incidence trend increased first and then decreased, the highest incidence was reported in 2014 (27.991/100 000). The incidence of tuberculosis in Shanghai is characterized by spatial clustering. Through the spatial characteristics and risk analysis of the reported incidence of tuberculosis, it is found that the high-risk area of tuberculosis in Shanghai is the suburban communities, whereas downtown communities are the low-risk areas. The incidence risk of pulmonary tuberculosis is associated with the gross domestic product per capita (RR=0.48), the number of beds per 10 000 persons (RR=0.56), the normalized vegetation index (RR=0.50), and the night light index (RR=0.80). Conclusions: With the steady progress of tuberculosis prevention and control in the central urban area of Shanghai, special attention should be paid to the prevention and control in the suburbs further to improve the social and economic level in the suburbs and increase the coverage rate of urban green space, to reduce the incidence of tuberculosis and reduce the disease burden of tuberculosis in Shanghai.
