Citation

Keralis JM, Javanmardi M, Khanna S, Dwivedi P, Huang D, Tasdizen T, Nguyen QC. BMC Public Health 2020; 20(1): e215.

Affiliation

Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, 4200 Valley Dr. #2242, College Park, MD, 20742, USA.

Copyright

(Copyright © 2020, Holtzbrinck Springer Nature Publishing Group - BMC)

DOI

10.1186/s12889-020-8300-1

PMID

32050938

Abstract

BACKGROUND: The built environment is a structural determinant of health and has been shown to influence health expenditures, behaviors, and outcomes. Traditional methods of assessing built environment characteristics are time-consuming and difficult to combine or compare. Google Street View (GSV) images represent a large, publicly available data source that can be used to create indicators of characteristics of the physical environment with machine learning techniques. The aim of this study is to use GSV images to measure the association of built environment features with health-related behaviors and outcomes at the census tract level.

METHODS: We used computer vision techniques to derive built environment indicators from approximately 31 million GSV images at 7.8 million intersections. Associations between derived indicators and health-related behaviors and outcomes on the census-tract level were assessed using multivariate regression models, controlling for demographic factors and socioeconomic position. Statistical significance was assessed at the α = 0.05 level.

RESULTS: Single lane roads were associated with increased diabetes and obesity, while non-single-family home buildings were associated with decreased obesity, diabetes and inactivity. Street greenness was associated with decreased prevalence of physical and mental distress, as well as decreased binge drinking, but with increased obesity. Socioeconomic disadvantage was negatively associated with binge drinking prevalence and positively associated with all other health-related behaviors and outcomes.

CONCLUSIONS: Structural determinants of health such as the built environment can influence population health. Our study suggests that higher levels of urban development have mixed effects on health and adds further evidence that socioeconomic distress has adverse impacts on multiple physical and mental health outcomes.

Language: en

Keywords

Built environment; Computer vision; Google Street View; Machine learning; Structural determinants of health