Associations between trees and grass presence with childhood asthma prevalence using deep learning image segmentation and a novel green view index. (1st October 2021)

• Record Type: Journal Article
• Authors: Yu, Hongyao; Zhou, Yang; Wang, Ruoyu; Qian, Zhengmin; Knibbs, Luke D.; Jalaludin, Bin; Schootman, Mario; McMillin, Stephen Edward; Howard, Steven W.; Lin, Li-Zi; Zhou, Peien; Hu, Li-Wen; Liu, Ru-Qing; Yang, Bo-Yi; Chen, Gongbo; Zeng, Xiao-Wen; Feng, Wenru; Xiang, Mingdeng; Dong, Guang-Hui
• Is Part Of: Environmental pollution. Volume 286(2021)
• Publication Date: 2021-10-01
• Subjects: Childhood asthma -- Greenspace -- Green view index; CI confidence interval -- FCN-8s fully convolutional network -- GVI green view index -- IQR interquartile range -- NDVI Normalized Difference Vegetation Index -- OR odds ratio -- PM2.5 particulate matter with an aerodynamic diameter < 2.5 μm; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2021.117582
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Limitations of Normalized Difference Vegetation Index (NDVI) potentially contributed to the inconsistent findings of greenspace exposure and childhood asthma. The aim of this study was to use a novel greenness exposure assessment method, capable of overcoming the limitation of NDVI to determine the extent to which it was associated with asthma prevalence in Chinese children. During 2009–2013, a cross-sectional study of 59, 754 children aged 2–17 years was conducted in northeast China. Tencent street view images surrounding participants&#39; schools were segmented by a deep learning model, and streetscape greenness was extracted. The green view index (GVI) was used to assign exposure and higher value indicates more green coverage. Mixed-effects logistic regression models were used to calculate the adjusted odds of asthma per interquartile range (IQR) increase of GVI for trees and grass. Participants were further stratified to investigate whether particulate matter with an aerodynamic diameter &lt;2.5 μm (PM2.5 ) was a modifier. An IQR increase in GVI800m for trees was associated with lower adjusted odds of doctor-diagnosed asthma (OR: 0.76; 95%CI: 0.72–0.80) and current asthma (OR: 0.82; 95%CI: 0.75–0.89). An IQR increase in GVI800m for grass was associated with higher adjusted odds of doctor-diagnosed asthma (OR: 1.04; 95%CI: 1.00–1.08) and current asthma (OR: 1.08; 95%CI: 1.02–1.14). After stratification by PM2.5 exposure level, the negative association between trees and asthma, and the positive association between grass and asthma were observed only in low PM2.5 exposure levels (≤median: 56.23 μg/m 3 ). Our results suggest that types of vegetation may play a role in the association between greenness exposure and childhood asthma. Exposure to trees may reduce the odds of childhood asthma, whereas exposure to grass may increase the odds. Additionally, PM2.5 may modify the associations of trees and grass with childhood asthma. Graphical abstract: Image 1 Highlights: Green view index is a promising approach to assess exposure to urban green space. Street-level trees are beneficially associated with childhood asthma. Street-level grass is detrimentally associated with childhood asthma.