Short-term effects of ambient air pollution on the incidence of influenza in Wuhan, China: A time-series analysis

• Published in: Environmental research 2021-01, Vol.192, p.110327, Article 110327
• Main Authors: Meng, Yongna, Lu, Yuanan, Xiang, Hao, Liu, Suyang
• Format: Article
• Language: English
• Subjects: Air Pollutants - adverse effects; Air Pollutants - analysis; Air pollution; Air Pollution - adverse effects; Air Pollution - analysis; China - epidemiology; Generalized additive model; Humans; Incidence; Infectious diseases; Influenza; Influenza, Human - epidemiology; Nitrogen Dioxide - analysis; Particulate Matter - analysis; Time-series
• ISSN: 0013-9351; 1096-0953
• DOI: 10.1016/j.envres.2020.110327

Background: Evidence suggests that air pollution is associated with many adverse health outcomes such as cardiovascular diseases (CVD), respiratory diseases, cancer, and birth defects. Yet few studies dig into the relationship between air pollution and airborne infectious diseases. Methods: Daily data on influenza incidence were obtained from Hubei Provincial Center for Disease Control and Prevention (Hubei CDC). Data on air pollutants including nitrogen dioxide (NO2), sulfur dioxide (SO2), ground-level ozone (O3), particulate matter (PM) with aerodynamic diameter ≤ 2.5 μm (PM2.5), and PM with aerodynamic diameter ≤ 10 μm (PM10) were retrieved from ten national air sampling stations located at Wuhan. We applied generalized additive model (GAM) to estimate the associations between air pollution and the risk of influenza in Wuhan, China during 2015–2017. Results: In the single-day lag model, the largest effect estimates were observed at lag 0. An increased relative risk (RR) of influenza was significantly associated with a 10 μg/m3 increase in SO2 (RR: 1.099; 95% confidence interval [CI]: 1.011–1.195), NO2 (RR: 1.039; 95% CI: 1.013–1.065), and O3 (RR: 1.005; 95% CI: 0.994–1.016), respectively. In the multi-day lag model, concentrations of SO2, NO2, and O3 were statistically significantly associated with the risk of influenza at lag 0–1. The seasonal analysis suggests that the influence of air pollution on influenza is greater in the cold season as compared in the warm season in the early lag days. The multi-pollutant model indicates that NO2 may be a potential confounder for co-pollutants. Conclusions: Our study shows that air pollution may be associated with the risk of influenza in a broad sense. Therefore, when formulating policies to deal with influenza outbreaks in the future, factors regarding air pollution should be taken into consideration. •The highest effect of air pollution on influenza was at lag 0 in the single-day lag model.•Exposure to SO2, NO2, and O3 significantly increased the risk of influenza at lag 0–1.•The impact of air pollution on influenza is greater in the cold season than warm season.•NO2 may be a confounding factor for co-pollutants.