Department of Epidemiology and Health Statistics, School of Public Health, North China University of Science and Technology, Tangshan, Peoples' Republic of China.
Department of medical engineering, Air Force Medical Centre, PLA, Beijing, Peoples' Republic of China.
J Glob Health. 2024 Oct 25;14:04234. doi: 10.7189/jogh.14.04234.
Previous studies have typically explored daily lagged relationships among pertussis and meteorology, with little assessment of effect and interaction among pollutants mixtures.
Our researchers collected pertussis cases data from 2017-2022 as well as meteorological and contaminative factors for the Jining region. First, 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.
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% confidence Interval (CI) = 1.3, 2.8) and 1.1% (95% CI = 0.3, 1.9) higher per decile increase in temperature and sulphur dioxide (SO). 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. Sulphur dioxide 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 ozone (O) and SO, respectively, while the negative effectors were SO and carbon monoxide (CO), respectively.
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.
先前的研究通常探讨百日咳与气象之间的逐日滞后关系，而很少评估污染物混合物之间的作用和相互影响。
我们的研究人员收集了 2017 年至 2022 年期间济宁市的百日咳病例数据以及气象和污染因素。首先，我们报告了移动流行方法（MEM）的应用，以估计流行阈值和强度水平。然后，我们开发了加权分位数总和（WQS）回归和贝叶斯核机器回归（BKMR）模型，以评估气象和污染因素对不同性别、滞后和流行阈值组的百日咳病例的单一、多重影响和相互作用。
济宁市百日咳发病率呈逐年上升趋势。高流行阈值年份为 2018-2019 年，流行高峰主要集中在 32 周。总的来说，温度和二氧化硫（SO）每增加一个十分位，百日咳感染病分别增加 2.1%（95%置信区间（CI）=1.3，2.8）和 1.1%（95%CI=0.3，1.9）。湿度每增加一个十分位，百日咳感染病减少 1.1%。在不同的分层分析中，气压在男性和滞后 11-20 天组中是一个强烈的负效应，分别为 7.3%和 14.7%。二氧化硫在男性、女性和滞后 20 天组中具有较弱的正效应，范围为 0.5-0.6%。影响低阈值和高阈值水平疾病发病的主要正效应因子分别为臭氧（O）和 SO，而负效应因子分别为 SO 和一氧化碳（CO）。
这是中国首次对百日咳季节性阈值进行的基于数学的研究，可准确估计流行水平。我们的研究结果支持短期暴露于污染物是百日咳的危险因素。我们应该集中监测污染物并进行效应建模。
