The impact of ambient fine particles on influenza transmission and the modification effects of temperature in China: A multi-city study Academic Article uri icon

abstract

  • BACKGROUND:There is good evidence that air pollution is a risk factor for adverse respiratory and vascular health outcomes. However, data are limited as to whether ambient fine particles contribute to the transmission of influenza and if so, how the association is modified by weather conditions. OBJECTIVES:We examined the relationship between ambient PM2.5 and influenza incidence at the national level in China and explored the associations at different temperatures. METHODS:Daily data on concentrations of particulate matter with aerodynamic diameter<2.5μm (PM2.5) and influenza incidence counts were collected in 47 Chinese cities. A Poisson regression model was used to estimate the city-specific PM2.5-influenza association, after controlling for potential confounders. Then, a random-effect meta-analysis was used to pool the effects at national level. In addition, stratified analyses were performed to examine modification effects of ambient temperature. RESULTS:For single lag models, the highest effect of ambient PM2.5 on influenza incidence appeared at lag day 2, with relative risk (RR) of 1.015 (95% confidence interval (CI): 1.004, 1.025) associated with a 10μg/m3 increase in PM2.5. For moving average lag models, the significant association was found at lag 2-3days, with RR of 1.020 (95% CI: 1.006, 1.034). The RR of influenza transmission associated with PM2.5 was higher for cold compared with hot days. Overall, 10.7% of incident influenza cases may result from exposure to ambient PM2.5. CONCLUSIONS:Ambient PM2.5 may increase the risk of exposure to influenza in China especially on cooler days. Control measures to reduce PM2.5 concentrations could potentially also be of benefit in lowering the risk of exposure and subsequent transmission of influenza in China.

authors

  • Chen, Gongbo
  • Zhang, Wenyi
  • Li, Shanshan
  • Zhang, Yongming
  • Williams, Gail
  • Huxley, Rachel
  • Ren, Hongyan
  • Cao, Wei
  • Guo, Yuming

publication date

  • 2017