Associations between trees and grass presence with childhood asthma prevalence using deep learning image segmentation and a novel green view index.

Journal: Environmental pollution (Barking, Essex : 1987)
Published Date:

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' 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 <2.5 μm (PM) was a modifier. An IQR increase in GVI 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 GVI 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 PM exposure level, the negative association between trees and asthma, and the positive association between grass and asthma were observed only in low PM exposure levels (≤median: 56.23 μg/m). 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, PM may modify the associations of trees and grass with childhood asthma.

Authors

  • Hongyao Yu
    Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
  • Yang Zhou
    State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou, China.
  • Ruoyu Wang
    Institute of Public Health and Wellbeing, University of Essex, Essex, UK.
  • Zhengmin Qian
    Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO, 63104, USA.
  • Luke D Knibbs
    Department of Epidemiology and Biostatistics, School of Public Health, The University of Queensland, Brisbane, Australia.
  • Bin Jalaludin
    Centre for Air Quality and Health Research and Evaluation, Glebe, NSW, 2037, Australia; IIngham Institute for Applied Medial Research, University of New South Wales, Sydney, 2170, Australia.
  • Mario Schootman
    Department of Clinical Analytics, System Data & Analytics, SSM Health, Saint Louis, MO, 63132, USA.
  • Stephen Edward McMillin
    School of Social Work, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO, 63104, USA.
  • Steven W Howard
    Department of Health Management and Policy, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO, 63104, USA.
  • Li-Zi Lin
    Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
  • Peien Zhou
    Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
  • Li-Wen Hu
    Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
  • Ru-Qing Liu
    Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
  • Bo-Yi Yang
    Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
  • Gongbo Chen
    Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
  • Xiao-Wen Zeng
    Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
  • Wenru Feng
    Department of Environmental Health, Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China.
  • Mingdeng Xiang
    State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou, 510655, China.
  • Guang-Hui Dong
    Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, China.