Background: Allergic rhinitis (AR) is a severe and the most common chronic allergic disease, affecting 10– 40% of the world population. The effect of air pollutants on AR has been confirmed in clinical experiments.
Purpose: This study aimed to quantify the association between air pollutants and daily outpatient visits for AR in Chongqing, China.
Methods: Based on the data of AR outpatients in the primary urban area of Chongqing from 2016 to 2017, along with the atmospheric pollutants and meteorological data in the same period, the distributed lag nonlinear model (DLNM) and generalized additive model (GAM) were used to analyze the time-series. We examined the effects of the single and double pollutant models with a maximum lag day of 30 days. Effect estimates were described as relative risk (RR) and 95% confidence intervals (CIs) in daily outpatient visits for AR per 10 μg/m³ increases in PM_2.5, PM₁₀, SO₂, NO₂, O₃, and per 1 mg/m³ increase in CO.
Results: A single pollutant’s O₃ level had an immediate positive effect on AR within two days, the relative risks (RR, 95% CI) were 1.066 (1.008– 1.127), 1.057 (1.005– 1.112) and 1.048 (1.002– 1.097). PM_2.5 had a lag effect within 11– 18 days, the max relative risks (RR, 95% CI) were 1.083 (1.010– 1.160). Moreover, O₃, PM_2.5, PM₁₀, SO₂ and NO₂ had significant effects on AR in the two-pollutant model. The RR cumulative effect of PM_2.5 became more pronounced as the concentration increased. The cumulative effect of NO₂ was lesser than PM_2.5.
Conclusion: Air pollutants were associated with the daily outpatient visits for AR, which may have considerable implications for developing tailored health policies and services to prevent AR in Chongqing and even all over the world.
Keywords: allergic rhinitis, air pollution, time-series analysis, generalized additive model, distributed lag nonlinear model