Population impact of fine particulate matter on tuberculosis risk in China: a causal inference

Abstract

Abstract Background Previous studies have suggested the potential association between air pollution and tuberculosis incidence, but this association remains inconclusive and evidence to assess causality is particularly lacking. We aimed to draw causal inference between fine particulate matter less than 2.5 μm in diameter (PM2.5) and tuberculosis in China. Methods Granger causality (GC) inference was performed within vector autoregressive models at levels and/or first-differences using annual national aggregated data during 1982–2019, annual provincial aggregated data during 1982–2019 and monthly provincial aggregated data during 2004–2018. Convergent cross-mapping (CCM) approach was used to determine the backbone nonlinear causal association based on the monthly provincial aggregated data during 2004–2018. Moreover, distributed lag nonlinear model (DLNM) was applied to quantify the causal effects. Results GC tests identified PM2.5 driving tuberculosis dynamics at national and provincial levels in Granger sense. Empirical dynamic modeling provided the CCM causal intensity of PM2.5 effect on tuberculosis at provincial level and demonstrated that PM2.5 had a positive effect on tuberculosis incidence. Then, DLNM estimation demonstrated that the PM2.5 exposure driven tuberculosis risk was concentration- and time-dependent in a nonlinear manner. This result still held in the multi-pollutant model. Conclusions Causal inference showed that PM2.5 exposure driving tuberculosis, which showing a concentration gradient change. Air pollutant control may have potential public health benefit of decreasing tuberculosis burden.

Keywords

• PM2.5
• Tuberculosis
• Causality
• Empirical dynamic modeling
• Eco-driver