Interactive effects of meteorological factors and ambient air pollutants on influenza incidences 2019–2022 in Huaian, China

• Published in: Infectious disease modelling Vol. 10; no. 4; pp. 1384 - 1397
• Main Authors: Wang, Xiaomeng, Hu, Jianli, Wang, Zhiming, Cai, Yongli, He, Daihai
• Format: Journal Article
• Language: English
• Published: China Elsevier B.V 01.12.2025; KeAi Publishing; KeAi Communications Co., Ltd
• Subjects: Ambient air pollutants; Influenza; Interactive effects; Meteorological factors; Ambient air pollutants; Meteorological factors; Interactive effects; Influenza
• ISSN: 2468-0427; 2468-2152; 2468-0427
• DOI: 10.1016/j.idm.2025.07.010

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 PM2.5(Fine Particulate Matter) and temperature and influenza.The low concentration of PM2.5 and O3(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 PM2.5 and O3 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 PM2.5 exposure was 15.90 %, equivalent to 1456 cases. AF of the high concentration of O3 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 PM2.5 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 PM2.5, the risk of influenza increases with increasing concentration but decreases at the high concentrations. At the high concentration of O3, the risk of influenza increases rapidly. 15.90 % of influenza cases may be attributed to the high concentration of PM2.5, 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 PM2.5 on influenza incidence, and outdoor exposure time should be minimized in low temperature and high PM2.5 weather.