Associations between Short-Term Exposure to Ambient Air Pollution and Influenza: An Individual-Level Case-Crossover Study in Guangzhou, China

Background: Influenza imposes a heavy burden on public health. Little is known, however, of the associations between detailed measures of exposure to ambient air pollution and influenza at an individual level. Objective: We examined individual-level associations between six criteria air pollutants and influenza using case-crossover design. Methods: In this individual-level time-stratified case-crossover study, we linked influenza cases collected by the Guangzhou Center for Disease Control and Prevention from 1 January 2013 to 31 December 2019 with individual residence-level exposure to particulate matter (PM2.5 and PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and carbon monoxide (CO). The exposures were estimated for the day of onset of influenza symptoms (lag 0), 1–7 d before the onset (lags 1–7), as well as an 8-d moving average (lag07), using a random forest model and linked to study participants’ home addresses. Conditional logistic regression was developed to investigate the associations between short-term exposure to air pollution and influenza, adjusting for mean temperature, relative humidity, public holidays, population mobility, and community influenza susceptibility. Results: N=108,479 eligible cases were identified in our study. Every 10-μg/m3 increase in exposure to PM2.5, PM10, NO2, and CO and every 5-μg/m3 increase in SO2 over 8-d moving average (lag07) was associated with higher risk of influenza with a relative risk (RR) of 1.028 (95% CI: 1.018, 1.038), 1.041 (95% CI: 1.032, 1.049), 1.169 (95% CI: 1.151, 1.188), 1.004 (95% CI: 1.003, 1.006), and 1.134 (95% CI: 1.107, 1.163), respectively. There was a negative association between O3 and influenza with a RR of 0.878 (95% CI: 0.866, 0.890). Conclusions: Our findings suggest that short-term exposure to air pollution, except for O3, is associated with greater risk for influenza. Further studies are necessary to decipher underlying mechanisms and design preventive interventions and policies. https://doi.org/10.1289/EHP12145