Prediction, lag and mixture effects of meteorology and pollutants on the incidence of pulmonary tuberculosis in Jining City, China.

BACKGROUND: The burden of pulmonary tuberculosis in China continues to increase, and the potential impact of environmental changes warrants serious attention. While the association between meteorological factors and pulmonary tuberculosis has garnered increasing interest, relatively few studies have examined the effects of air pollutants on the disease. Leveraging real-world evidence, this study aims to investigate the potential long-term effects of exposure to both meteorological variables and air pollutants on the incidence of various forms of pulmonary tuberculosis.

METHODS: We obtained daily data on meteorological factors and air pollutants from National Oceanic and Atmospheric Administration (2014–2022), and pulmonary tuberculosis counts from Jining Center for Disease Control and Prevention (2009–2022). We used different time series (Single-factor Seasonal Autoregressive Integrated Moving Average (SARIMA) model, Holt-Winters model and Generalized Autoregressive conditional heteroskedasticity model (GARCH) models) and machine learning models to construct predictive models of pulmonary tuberculosis, followed by distributional lag nonlinear modelling (DLNM) to explore the chronic effects of meteorological conditions and pollutant exposure on the risk of pulmonary tuberculosis among different age and gender subgroups. Bayesian kernel machine regression (BKMR) models were used to screen pollutant drivers for different classifications of pulmonary tuberculosis.

RESULTS: SARIMA and GARCH models demonstrate different advantages in capturing variations in disease incidence rates. Extremely low levels of PMand very high levels of SOhad a hazardous effect on pulmonary tuberculosis at the maximum number of lagged days (22 d) with a relative risk (RR) (95% CI): 1.186 (1.045, 1.345) and 1.591 (1.186, 2.135), respectively. Patients under 12 years of age exhibited heightened sensitivity to elevated levels of PM₁₀, while females demonstrated greater susceptibility to the pollutant compared to males. SO₂ emerged as the primary environmental driver associated with pulmonary tuberculosis cases that were either bacteria-negative or lacked sputum test results. In contrast, PM₁₀ was identified as the main environmental factor influencing non-sputum and culture-positive pulmonary tuberculosis cases.

CONCLUSIONS: Different time series models can predict disease incidence rates by capturing fluctuations across various temporal scales. Long-term exposure to air pollutants such as SO₂ and PM₁₀ has been shown to increase susceptibility to pulmonary tuberculosis, exerting significant lagged effects over time. Notably, individuals of younger age and those with different subtypes of pulmonary tuberculosis display varying degrees of sensitivity to specific pollutants.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-026-26257-z.