Combined short-term exposure to meteorological, pollution factors and pertussis in different groups from Jining, China

Abstract Background Previous studies have typically explored daily lagged relationships among pertussis and meteorology, with little assessment of effect and interaction among pollutants mixtures. Methods Our researchers collected pertussis cases data from 2017–2022 as well as meteorological and contaminative factors for the Jining region. Firstly, we reported the application of the moving epidemic method (MEM) to estimate epidemic threshold and intensity level. Then we developed a weighted quantile sum (WQS) regression and bayesian kernel machine regression (BKMR) model to assess single, multiple effects and interaction of meteorological and pollution factors on pertussis cases for different sex, delayed and epidemic threshold groups. Results There has been a yearly upward trend in the incidence of pertussis in Jining regions. High prevalence threshold years were in 2018–2019, the epidemic peak was mainly concentrated in 32 weeks. Totally, pertussis infections disease was separately 2.1% (95% CI: 1.3, 2.8 and 1.1% (95% CI: 0.3, 1.9) higher per decile increase in temperature and SO2. And pertussis infections disease was 1.1% lower per decile increase in humidity. In the different stratified analyses, air pressure was a strong negative effect in males and in the lagged 11–20 days group, with 7.3% and 14.7%, respectively. SO2 had a relatively weak positive effect in males, females and the group after 20 days lag, ranging from 0.5 to 0.6%. The main positive effectors affecting the onset of disease at low and high threshold levels were O3 and SO2, respectively, while the negative effectors were SO2 and CO, respectively. Conclusions This is the first mathematically based study of seasonal threshold of pertussis in China, which allows accurate estimation of epidemic level. Our findings support that short-term exposure to pollutants is the risk factor for pertussis. We should concentrate on pollutants monitoring and effect modeling.