Impact of PM2.5 and ozone on incidence of influenza in Shijiazhuang, China: a time-series study

Environ Sci Pollut Res Int. 2023 Jan;30(4):10426-10443. doi: 10.1007/s11356-022-22814-2. Epub 2022 Sep 8.

Abstract

Most of the studies are focused on influenza and meteorological factors for influenza. There are still few studies focused on the relationship between pollution factors and influenza, and the results are not consistent. This study conducted distributed lag nonlinear model and attributable risk on the relationship between influenza and pollution factors, aiming to quantify the association and provide a basis for the prevention of influenza and the formulation of relevant policies. Environmental data in Shijiazhuang from 2014 to 2019, as well as the data on hospital-confirmed influenza, were collected. When the concentration of PM2.5 was the highest (621 μg/m3), the relative risk was the highest (RR: 2.39, 95% CI: 1.10-5.17). For extremely high concentration PM2.5 (348 μg/m3), analysis of cumulative lag effect showed statistical significance from cumulative lag0-1 to lag0-6 day, and the minimum cumulative lag effect appeared in lag0-2 (RR: 0.760, 95% CI: 0.655-0.882). In terms of ozone, the RR value was 2.28(1.19,4.38), when O3 concentration was 310 μg/m3, and the RR was 1.65(1.26,2.15), when O3 concentration was 0 μg/m3. The RR of this lag effect increased with the increase of lag days, and reached the maximum at lag0-7 days, RR and 95% CI of slightly low concentration and extremely high concentration were 1.217(1.108,1.337) and 1.440(1.012,2.047), respectively. Stratified analysis showed that there was little difference in gender, but in different age groups, the cumulative lag effect of these two pollutants on influenza was significantly different. Our study found a non-linear relationship between two pollutants and influenza; slightly low concentrations were more associated with contaminant-related influenza. Health workers should encourage patients to get the influenza vaccine and wear masks when going out during flu seasons.

Keywords: Attribution risk; Distributed lag nonlinear model; Influenza; Ozone; PM2.5; Relative risk; Time-series study.

MeSH terms

  • Air Pollutants* / analysis
  • Air Pollution* / analysis
  • China / epidemiology
  • Environmental Exposure / analysis
  • Environmental Pollutants* / analysis
  • Humans
  • Incidence
  • Influenza Vaccines* / analysis
  • Influenza, Human* / epidemiology
  • Ozone* / analysis
  • Particulate Matter / analysis
  • Risk Factors

Substances

  • Ozone
  • Air Pollutants
  • Particulate Matter
  • Influenza Vaccines
  • Environmental Pollutants