Abstract:Objective To analyze the spatial-temporal distribution characteristics and influencing factors of the incidence of tuberculosis (TB) in Chinese mainland, and provide scientific basis for relevant departments to formulate policies and guidelines. Methods TB incidence in Chinese mainland from 2017 to 2022 was as the research object, and data of relevant influencing factors were collected. The spatial autocorrelation analysis method was adopted to establish a spatial lag model to explore the spatial-temporal distribution characteristics of TB incidence, and the important influencing factors of TB incidence were screened. Results From 2017 to 2022, TB incidence reported in 31 provinces and cities in Chinese mainland were 60.53/100 000, 59.27/100 000, 55.55/100 000, 47.76/100 000, 45.37/100 000 and 39.76/100 000, respectively, showing a yearly downward trend. Global Moran's Ⅰ analysis showed that TB incidence presented spatial-temporal aggregation. The spatial distribution map and the local indicators of spatial association (LISA) aggregation diagram analysis results for the incidence of reported TB showed a decreasing trend from west to east in TB incidence. In the spatial lag model, the coefficients of 6 insignificant factors shrank to 0, and 6 important factors were screened out: gross domestic product (GDP) per capita (coefficient -0.259), urban unemployment rate (coefficient -0.198), annual sunshine duration (coefficient -0.332), annual mean relative humidity (coefficient -0.433), annual mean NO2 concentration (coefficient -0.263), and annual mean PM10 concentration (coefficient -0.336). Conclusion From 2017 to 2022, TB incidences in Chinese mainland declined year by year, and presented spatial difference and spatial aggregation: high in the east, low in the west, and stable in the middle area. Social economy, climate and air pollution have strong effects on the incidence of TB. Relevant departments should pay more attention to the prevention and treatment of TB in the western region and take targeted preventive measures.