Lagging effects and prediction of pollutants and their interaction modifiers on influenza in northeastern China

Abstract

Abstract Background Previous studies have typically explored the daily lagged relationship between influenza and meteorology, with little seasonal exploration of the monthly lagged relationship, interaction and multiple prediction between influenza and pollution. Methods Our researchers collected ILI cases data from 2005–2018 as well as meteorological and contaminative factors from 2015–2018 for the Northeast region. We developed a generalized additive model (GAM) with a maximum lag of 6 months to evaluate the lagged and interaction effects of pollution factors on the cases of Influenza. Multiple complex regression analysis was then performed after applying lasso regression to screen environmental factors in the Northeast, along with the relationship at different levels by quantile regression. Results There has been a yearly upward trend in influenza outbreaks in the northeastern regions. Overall low levels of NO2 during January, March, and June may be the most suspected air pollutant contributing to excessive influenza incidence. Relative risk of influenza under PM2.5 exposure increased with increasing PM2.5 concentration in the Age15-24 group with a lag of 0–6 months (ERR1.08(95% CI:0.10,2.07). In the quantitative analysis of the interaction model, PM10 at the level of 100–120 µg/m3, PM2.5 at the level of 60–80 µg/m3, and NO2 at the level of 60 µg/m3 or more had the greatest effect on the onset of influenza. The GPR model was better in prediction. Conclusions The air pollutant NO2 increased the risk of influenza with a cumulative lag. We should focus on winter and spring pollution monitoring and influenza prediction modeling.