Impact of air pollutants on influenza-like illness outpatient visits under COVID-19 pandemic in the subcenter of Beijing, China.
Xin Yao LianLu XiZhong Song ZhangLi Li YangJuan DuYan CuiHong Jun LiWan Xue ZhangChao WangBei LiuYan Na YangFuqiang CuiQing-Bin LuPublished in: Journal of medical virology (2023)
This study aimed to explore the association between air pollutants and outpatient visits for influenza-like illnesses (ILI) under the coronavirus disease 2019 (COVID-19) stage in the subcenter of Beijing. The data on ILI in the subcenter of Beijing from January 1, 2018 to December 31, 2020 were obtained from the Beijing Influenza Surveillance Network. A generalized additive Poisson model was applied to examine the associations between the concentrations of air pollutants and daily outpatient visits for ILI when controlling meteorological factors and temporal trend. A total of 171 943 ILI patients were included. In the pre-coronavirus disease 2019 (COVID-19) stage, an increased risk of ILI outpatient visits was associated to a high air quality index (AQI) and the high concentrations of particulate matter less than 2.5 (PM 2.5 ), particulate matter 10 (PM 10 ), sulphur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and carbon monoxide (CO), and a low concentration of ozone (O 3 ) on lag0 day and lag1 day, while a higher increased risk of ILI outpatient visits was observed by the air pollutants in the COVID-19 stage on lag0 day. Except for PM 10 , the concentrations of other air pollutants on lag1 day were not significantly associated with an increased risk of ILI outpatient visits during the COVID-19 stage. The findings that air pollutants had enhanced immediate effects and diminished lag-effects on the risk of ILI outpatient visits during the COVID-19 pandemic, which is important for the development of public health and environmental governance strategies.
Keyphrases
- particulate matter
- coronavirus disease
- air pollution
- public health
- sars cov
- heavy metals
- respiratory syndrome coronavirus
- end stage renal disease
- chronic kidney disease
- newly diagnosed
- physical activity
- ejection fraction
- human health
- machine learning
- artificial intelligence
- peritoneal dialysis
- global health
- data analysis