Spatiotemporal trends and drivers of pulmonary tuberculosis incidence in China in the past two decades

Purpose: To analyze the spatiotemporal evolution pattern of tuberculosis incidence in China from 2004 to 2023 and reveal various driving factors. Methods: Using national tuberculosis surveillance data, economic indicators, and environmental information, we employed spatiotemporal econometric models, geographic detectors, and random forests for a comprehensive analysis. Results: Overall, tuberculosis incidence rates in China have been declining; however, significant regional disparities persist. The western region consistently demonstrates higher incidence rates compared to the eastern region. Furthermore, there has been an annual increase in rifampicin-resistant cases from 2017 to 2020. Upon comparing five distinct spatiotemporal econometric models, the spatiotemporal geographically weighted regression (GTWR) model (R² = 0.950) emerged as the most effective, indicating that the impact of various factors exhibits both spatial and temporal variability. Population density (PD) and particulate matter 10 (PM ₁₀ ) concentration were correlated with elevated incidence rates. In contrast, the proportion of the urban population, normalized difference vegetation index (NDVI), and ozone (O ₃ ) concentration were correlated with reduced rates. Geographic detector analysis further identified NDVI, PD, and PM ₁₀ as critical determinants, revealing statistically significant interactive effects among these variables. The random forest model demonstrated a complex, non-linear relationship between various factors and the incidence rate. Conclusions: This study emphasizes the importance of integrating socioeconomic, environmental, and population factors to understand tuberculosis transmission dynamics and provides a strong foundation for developing targeted prevention and control strategies. Funding: The research was supported by the 2024 Sanming City Health and Wellness Science and Technology Innovation Joint Project (No. 2024-S-011) and the Startup Fund for Scientific Research at Fujian Medical University (No.2023QH1286).