Air pollution’s numerical, spatial, and temporal heterogeneous impacts on childhood hand, foot and mouth disease: a multi-model county-level study from China

Abstract

Background

While stationary links between childhood hand, foot and mouth disease (HFMD) and air pollution are known, a comprehensive study on their heterogeneous relationships (nonstationarity), jointly considering numerical, temporal and spatial dimensions, has not been reported.

Methods

Monthly HFMD incidence and air pollution data were collected at the county level from Sichuan-Chongqing, China (2009–2011), alongside meteorological and social environmental covariates. Key influential factors were identified using random forest (RF) under the stationary assumption. Factors’ numerically, temporally, and spatially heterogeneous relationships with HFMD were assessed using generalized additive model (GAM) and geographically and temporally weighted regression (GTWR).

Results

Our findings highlighted the relatively higher stationary contributions of fine particulate matter (PM_2.5) and ozone (O₃) to HFMD incidence across Sichuan-Chongqing counties. We further uncovered heterogeneous impacts of PM_2.5 and O₃ from three nonstationary perspectives. Numerically, PM_2.5 showed an inverse ‘V’-shaped relationship with HFMD incidence, while O₃ exhibited a complex pattern, with increased HFMD incidence at low PM_2.5 and moderate O₃ concentrations. Temporally, PM_2.5’s impact peaked in autumn and was weakest in spring, whereas O₃’s effect was strongest in summer. Spatially, hotspot mapping revealed high-risk clusters for PM_2.5 impact across all seasons, with notable geographical variations, and for O₃ in spring, summer, and autumn, concentrated in specific regions of Sichuan-Chongqing.

Conclusions

This study underscores the nuanced and three-perspective heterogeneous influences of air pollution on HFMD in small areas, emphasizing the need for differentiated, localized, and time-sensitive prevention and control strategies to enhance the precision of dynamic early warnings and predictive models for HFMD and other infectious diseases, particularly in the fields of environmental and spatial epidemiology.