Wang Xiaomeng, Hu Jianli, Wang Zhiming, Cai Yongli, He Daihai
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, PR China.
Jiangsu Provincial Center for Disease Control and Prevention, National Health Commission Key Laboratory of Enteric Pathogenic Microbiology, Nanjing, 210009, PR China.
Infect Dis Model. 2025 Jul 17;10(4):1384-1397. doi: 10.1016/j.idm.2025.07.010. eCollection 2025 Dec.
Influenza is a global public health and economic burden. Its seasonality patterns differ considerably between geographic regions, but the factors underlying these differences are not well characterized.
The data on influenza were obtained from 2019 to 2022 in Huaian. A descriptive study was used to describe the epidemiological characteristics.The DLNM(distributed lag nonlinear model) model was established to further analyze the relationship between influenza cases, meteorological factors and pollutants. In addition, the attribution risk analysis and the interaction analysis further explored the interaction between the attributable risk and meteorological factors of influenza in terms of meteorological factors.
A total of 9205 cases of influenza were reported in Huaian City from 2019 to 2022, Jiangsu province, of which 4938 cases were males and 4267 cases were females.The DLNM results showed an inverted U-shaped relationship between PM(Fine Particulate Matter) and temperature and influenza.The low concentration of PM and O(Ozone) showed decreased risks, and the maximum effect values appeared on the 8th day (RR(Relative Ris) = 0.35,95 %CI(Confidence Interval): 0.25-0.49) and the 2nd day (RR = 0.63,95 %CI: 0.52-0.77). At the high concentration, the cumulative RR values of PM and O reached their maximum on the 8th day (RR = 1.93,95 %CI: 1.47-2.54) and the 9th day (RR = 2.58,95 %CI: 1.63-4.09). The attribution analysis based on DLNM showed that the AF(attributable fraction) value of influenza attributable to the high concentration of PM exposure was 15.90 %, equivalent to 1456 cases. AF of the high concentration of O was 8.12 % (743 cases). The AF of low temperature effect was 30.91 % (2830 cases). The interaction analysis showed that high temperature reduced the influence of PM on the onset of influenza, showing an antagonistic effect (RR = 0.31, 95 %CI: 0.15-0.65), IRR(interaction relative risk) and RERI(interaction relative risk) were 0.17 (95 %CI: 0.08-0.37) and -1.62 (95 %CI: 2.65∼-0.68), respectively.
The results show that low temperature significantly increases the risk of influenza. At the low concentration of PM, the risk of influenza increases with increasing concentration but decreases at the high concentrations. At the high concentration of O, the risk of influenza increases rapidly. 15.90 % of influenza cases may be attributed to the high concentration of PM, equivalent to 1456 cases; temperature-induced cases mainly come from the low-temperature effect, with an AF value of 30.91 %, equivalent to 2830 cases. In addition, high temperature can effectively mitigate the impact of PM on influenza incidence, and outdoor exposure time should be minimized in low temperature and high PM weather.
流感是一项全球性的公共卫生和经济负担。其季节性模式在不同地理区域差异很大，但造成这些差异的因素尚未得到充分描述。
获取了2019年至2022年淮安的流感数据。采用描述性研究来描述流行病学特征。建立分布滞后非线性模型（DLNM）进一步分析流感病例、气象因素和污染物之间的关系。此外，归因风险分析和交互作用分析从气象因素方面进一步探讨了流感归因风险与气象因素之间的交互作用。
江苏省淮安市2019年至2022年共报告9205例流感病例，其中男性4938例，女性4267例。DLNM结果显示，细颗粒物（PM）、温度与流感之间呈倒U形关系。低浓度的PM和臭氧（O）显示风险降低，最大效应值分别出现在第8天（相对危险度（RR）=0.35，95%置信区间（CI）：0.25 - 0.49）和第2天（RR = 0.63，95%CI：0.52 - 0.77）。在高浓度时，PM和O的累积RR值分别在第8天（RR = 1.93，95%CI：1.47 - 2.54）和第9天（RR = 2.58，95%CI：1.63 - 4.09）达到最大值。基于DLNM的归因分析显示，高浓度PM暴露导致的流感归因分数（AF）值为15.90%，相当于1456例。高浓度O的AF为8.12%（743例）。低温效应的AF为30.91%（2830例）。交互作用分析显示，高温降低了PM对流感发病的影响，呈现拮抗作用（RR = 0.31，9
