Associations between air pollution, meteorology, and pulmonary tuberculosis risk in Kashi, China

To explore associations between ambient air pollutants, meteorological factors and pulmonary tuberculosis risk among residents in Kashi, China Data on TB cases were collected from the Data Platform, while data on non-TB controls were retrieved from the hospital electronic medical record system. Exposome-wide association study (ExWAS) was used to explore the association between single factor exposure and TB risk, Adaptive Elastic-net (AENET) penalty regression method was used to construct a multi-factor regression model of TB risk, Extreme Gradient Boosting (XGBoost) method ranked the relative important factors of TB risk . The ExWAS analysis identified eight environmental exposures association with TB risk. Five of the exposures were selected as predictors by the AENET model: particulate matter 10 and 2.5 (PM10, PM2.5), nitrogen dioxide(NO2) (OR = 1.013, OR = 1.008, OR = 1.018, p < 0.0001) were positively associated with the TB risk, while wind velocity and relative humidity (OR = 0.983, OR = 0.944, p < 0.0001) were negatively associated with TB risk. Among environmental factors, the XGBoost model identified PM₁₀ as the most important factor associated with TB. Exposure to PM₁₀, PM₂.₅, NO₂ were positive correlated with TB risk, while wind velocity and relative humidity were negatively correlated. Environmental factors should be prioritized in TB prevention strategies.