Association of children wheezing diseases with meteorological and environmental factors in Suzhou, China

Abstract

Wheezing diseases are one of the major chronic respiratory diseases in children. To explore the effects of meteorological and environmental factors on the prevalence of children wheezing diseases, clinical data of children hospitalized with wheezing diseases in Suzhou, China from 2013 to 2017 were collected. Meteorological and environmental factors from 2013 to 2017 were obtained from the local Meteorological Bureau and Environmental Protection Bureau. Relationships between wheezing diseases and meteorological and environmental factors were evaluated using Pearson's correlation and multivariate regression analysis. An autoregressive integrated moving average (ARIMA) model was used to estimate the effects of meteorological and environmental variables on children wheezing diseases. Children wheezing diseases were frequently presented in infants less than 12 months old (1897/2655, 58.28%), and the hospitalization rate was highest in winter (1024/3255, 31.46%). In pathogen-positive specimens, the top three pathogens were respiratory syncytial virus (21.35%), human rhinovirus (16.28%) and mycoplasma pneumoniae (10.47%). The seasonality of wheezing children number showed a distinctive winter peak. Children wheezing diseases were negatively correlated with average temperature (P < 0.001, r = - 0.598). The ARIMA (1,0,0)(0,0,0)₁₂ model could be used to predict temperature changes associated wheezing diseases. Meteorological and environmental factors were associated with the number of hospitalized children with wheezing diseases and can be used as early warning indicators for the occurrence of wheezing diseases and prevalence of virus.

Figure 1

Distribution of pathogens in children with wheezing disease. RSV, respiratory syncytial virus; HRV, human rhinovirus; MP, mycoplasma pneumoniae; HBoV, human bocavirus; Pinf, parainfluenza virus; Inf, influenza virus; ADV, adenovirus; HMPV, human metapneumovirus. Figures were generated using Adobe Illustrator version CC 2018 (https://www.adobe.com/cn/products/illustrator.html).

Figure 2

Monthly distribution of meteorological (A) and environmental (B) factors among children hospitalized with wheezing diseases. (A) Mean temperature, relative humidity, total rainfall, total sunshine, wind velocity and wheezing children number from 2013 to 2017 in Suzhou. (B) PM_2.5, PM₁₀, O₃, NO₂, SO₂, CO and wheezing children number from 2013 to 2017 in Suzhou. Figures were generated using Adobe Illustrator version CC 2018 (https://www.adobe.com/cn/products/illustrator.html).

Figure 3

Correlation between meteorological, environmental factors and wheezing diseases in children (Pearson correlation analysis) (A) mean temperature; (B) total rainfall; (C) PM_2.5; (D) PM₁₀; (E) NO₂; (F) CO; (G) O₃. Figures were generated using GraphPad Prism version 5 (https://www.graphpad.com/scientific-software/prism/) and Adobe Illustrator version CC 2018 (https://www.adobe.com/cn/products/illustrator.html).

Figure 4

ARIMA (1,0,0)(1,1,0)₁₂ model with mean temperature as the covariate. Good agreement was found between observed and predicted wheezing diseases incidence. LCL, lower confidence interval; UCL, upper confidence interval. Figures were generated using Adobe Illustrator version CC 2018 (https://www.adobe.com/cn/products/illustrator.html).