A time-series analysis on generalized additive model for atmospheric pollen concentration and the number of visits of allergic conjunctivitis, Beijing, China

Sheng, Weixuan; Liu, Aizhu; Peng, Hong; Wang, Jia; Guan, Lei

A time-series analysis on generalized additive model for atmospheric pollen concentration and the number of visits of allergic conjunctivitis, Beijing, China

2022

Sheng, Weixuan | Liu, Aizhu | Peng, Hong | Wang, Jia | Guan, Lei

The objective of this study is to investigate the correlation between atmospheric pollen concentration and daily visits for allergic conjunctivitis. Daily counts of outpatient visits for allergic conjunctivitis, atmospheric pollen concentration, and meteorological data during pollen season of 2018 and 2019 were collected from Beijing Shijitan Hospital, China. A time-series analysis on generalized additive model with Poisson distribution was used to estimate the relationship between pollen concentration and visits for allergic conjunctivitis, after controlling for the time trend, weather variables, day of the week, and holiday effect. The RStudio was used to generate Spearman correlation coefficients and then to plot the lag-response curves for specific and incremental cumulative effects of relative risk (RR). There was a moderate positive correlation between pollen concentration and visits for allergic conjunctivitis, and Spearman’s correlation coefficient was 0.521 in 2018 and 0.515 in 2019 (P<0.01). The specific cumulative effect peak associated with per 10 grains/kmm² increases of atmospheric pollen concentration was within 0 day, and the lag time was 8 days(2018, 2019). The incremental cumulative effect peak associated with per 10 grains/kmm² increases of atmospheric pollen concentration occurred on lag day 10 (2018) and lag day 8 (2019), and the lag time was 14 days (2018) and 20 days (2019). Elevated concentrations of pollen increase the risk of allergic conjunctivitis with a time lag effect.

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