Abstract
BACKGROUND: Environmental factors have been identified as significant risk factors for scrub typhus. However, the impact of inorganic compounds such as greenhouse gases and air pollutants on the incidence of scrub typhus has not been evaluated. METHODS: Our study investigated the correlation between greenhouse gases, air pollutants from the global atmospheric emissions database (2005-2018), and reported cases of scrub typhus from the Public Health Science Data Center. First, an early warning method was applied to estimate the epidemic threshold and the grading intensity threshold. Second, four statistical methods were used to assess the correlation and lag effects across different age groups and epidemic periods. Deep learning algorithms were employed to evaluate the predictive effect of environmental factors on the incidence of scrub typhus. RESULTS: Using the Moving Epidemic Method (MEM) and Treed Distributed Lag Non-Linear Model (TDLNM), we found that the period from April to September is the epidemic season for scrub typhus in China. During this period, BC, CH(4), NH(3) and PM(10) all reach key windows during their respective early warning lag periods. Interaction effects showed that increased CO exposure during the 0-2-month period led to an increased magnitude of the PM(10) effect during the 3-7-month period. The Quantile-based G Computation (qgcomp) model revealed age-specific differences in susceptibility to environmental factors. In the Bayesian Kernel Machine Regression (BKMR) model, we identified NO(x) (RR(max) (95% CI) = 103.14 (70.40, 135.87)) and NMVOC as the risk environmental factors for young adults, while CH(4) (RR(max) (95% CI) = 20.94 (9.26, 32.63)) was significantly associated with scrub typhus incidence in younger populations. For the elderly, N(2)O and NO(x) (RR(max) (95% CI) = 30.23 (13.78, 46.68)) were identified as susceptibility factors for scrub typhus. The Weighted Quantile Sum (WQS) model revealed a significant risk effect of NO(x) on scrub typhus during periods of low risk, which are often overlooked (OR (95% CI) = 0.40 (0.23, 0.58)). During periods of medium to high risk, Convolutional Neural Networks (CNN) showed that environmental factors performed well in predicting the incidence of scrub typhus. CONCLUSIONS: We found that most greenhouse gases and air pollutants increase the risk of contracting scrub typhus, mainly driven by CH(4), NO(x), and NMVOC. Among these, the primary high-level pollutants have long-term lag effects during the epidemic period. The correlation between environmental factors and scrub typhus incidence varies significantly across different age groups and risk periods. Among them, middle-aged and young individuals are more susceptible to the effects of exposure to mixed air pollutants. CNN algorithm can help develop a comprehensive early warning system for scrub typhus. These findings may have important implications for guiding effective public health interventions in the future. The primary interventions should focus on controlling greenhouse gas emissions and reducing air pollutants, which can, in turn, be used to support infectious disease monitoring systems through environmental monitoring. Moreover, given the cross-sectional approach of our study, these findings need to be confirmed through additional cohort studies.