Quynh C Nguyen1, Jessica M Keralis1, Pallavi Dwivedi1, Amanda E Ng1, Mehran Javanmardi2, Sahil Khanna3, Yuru Huang1, Kimberly D Brunisholz4, Abhinav Kumar5, Tolga Tasdizen2. 1. 1068 Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, USA. 2. 14434 Department of Electrical and Computer Engineering, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA. 3. Electrical and Computer Engineering Department and Robert H. Smith School of Business, University of Maryland, College Park, MD, USA. 4. 7061 Intermountain Healthcare Delivery Institute, Intermountain Healthcare, Murray, UT, USA. 5. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.
Abstract
OBJECTIVES: Built environments can affect health, but data in many geographic areas are limited. We used a big data source to create national indicators of neighborhood quality and assess their associations with health. METHODS: We leveraged computer vision and Google Street View images accessed from December 15, 2017, through July 17, 2018, to detect features of the built environment (presence of a crosswalk, non-single-family home, single-lane roads, and visible utility wires) for 2916 US counties. We used multivariate linear regression models to determine associations between features of the built environment and county-level health outcomes (prevalence of adult obesity, prevalence of diabetes, physical inactivity, frequent physical and mental distress, poor or fair self-rated health, and premature death [in years of potential life lost]). RESULTS: Compared with counties with the least number of crosswalks, counties with the most crosswalks were associated with decreases of 1.3%, 2.7%, and 1.3% of adult obesity, physical inactivity, and fair or poor self-rated health, respectively, and 477 fewer years of potential life lost before age 75 (per 100 000 population). The presence of non-single-family homes was associated with lower levels of all health outcomes except for premature death. The presence of single-lane roads was associated with an increase in physical inactivity, frequent physical distress, and fair or poor self-rated health. Visible utility wires were associated with increases in adult obesity, diabetes, physical and mental distress, and fair or poor self-rated health. CONCLUSIONS: The use of computer vision and big data image sources makes possible national studies of the built environment's effects on health, producing data and results that may inform national and local decision-making.
OBJECTIVES: Built environments can affect health, but data in many geographic areas are limited. We used a big data source to create national indicators of neighborhood quality and assess their associations with health. METHODS: We leveraged computer vision and Google Street View images accessed from December 15, 2017, through July 17, 2018, to detect features of the built environment (presence of a crosswalk, non-single-family home, single-lane roads, and visible utility wires) for 2916 US counties. We used multivariate linear regression models to determine associations between features of the built environment and county-level health outcomes (prevalence of adult obesity, prevalence of diabetes, physical inactivity, frequent physical and mental distress, poor or fair self-rated health, and premature death [in years of potential life lost]). RESULTS: Compared with counties with the least number of crosswalks, counties with the most crosswalks were associated with decreases of 1.3%, 2.7%, and 1.3% of adult obesity, physical inactivity, and fair or poor self-rated health, respectively, and 477 fewer years of potential life lost before age 75 (per 100 000 population). The presence of non-single-family homes was associated with lower levels of all health outcomes except for premature death. The presence of single-lane roads was associated with an increase in physical inactivity, frequent physical distress, and fair or poor self-rated health. Visible utility wires were associated with increases in adult obesity, diabetes, physical and mental distress, and fair or poor self-rated health. CONCLUSIONS: The use of computer vision and big data image sources makes possible national studies of the built environment's effects on health, producing data and results that may inform national and local decision-making.
Entities:
Keywords:
GIS; big data; built environment; computer vision; machine learning; structural determinants of health
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