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Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment

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) im...

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Autores principales: Keralis, Jessica M., Javanmardi, Mehran, Khanna, Sahil, Dwivedi, Pallavi, Huang, Dina, Tasdizen, Tolga, Nguyen, Quynh C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7017447/
https://www.ncbi.nlm.nih.gov/pubmed/32050938
http://dx.doi.org/10.1186/s12889-020-8300-1
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author Keralis, Jessica M.
Javanmardi, Mehran
Khanna, Sahil
Dwivedi, Pallavi
Huang, Dina
Tasdizen, Tolga
Nguyen, Quynh C.
author_facet Keralis, Jessica M.
Javanmardi, Mehran
Khanna, Sahil
Dwivedi, Pallavi
Huang, Dina
Tasdizen, Tolga
Nguyen, Quynh C.
author_sort Keralis, Jessica M.
collection PubMed
description 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.
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spelling pubmed-70174472020-02-20 Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment Keralis, Jessica M. Javanmardi, Mehran Khanna, Sahil Dwivedi, Pallavi Huang, Dina Tasdizen, Tolga Nguyen, Quynh C. BMC Public Health Research Article 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. BioMed Central 2020-02-12 /pmc/articles/PMC7017447/ /pubmed/32050938 http://dx.doi.org/10.1186/s12889-020-8300-1 Text en © The Author(s). 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Keralis, Jessica M.
Javanmardi, Mehran
Khanna, Sahil
Dwivedi, Pallavi
Huang, Dina
Tasdizen, Tolga
Nguyen, Quynh C.
Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment
title Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment
title_full Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment
title_fullStr Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment
title_full_unstemmed Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment
title_short Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment
title_sort health and the built environment in united states cities: measuring associations using google street view-derived indicators of the built environment
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7017447/
https://www.ncbi.nlm.nih.gov/pubmed/32050938
http://dx.doi.org/10.1186/s12889-020-8300-1
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