School of Public Health

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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.

Note: Prior to July 1, 2007, the School of Public Health was named the College of Health & Human Performance.

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Author: search.filters.author.Nguyen, Quynh C.Subject: search.filters.subject.Google Street View

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Now showing 1 - 4 of 4
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    Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States
    (MDPI, 2020-05-22) Phan, Lynn; Yu, Weijun; Keralis, Jessica M.; Mukhija, Krishay; Dwivedi, Pallavi; Brunisholz, Kimberly D.; Javanmardi, Mehran; Tasdizen, Tolga; Nguyen, Quynh C.
    Previous studies have demonstrated that there is a high possibility that the presence of certain built environment characteristics can influence health outcomes, especially those related to obesity and physical activity. We examined the associations between select neighborhood built environment indicators (crosswalks, non-single family home buildings, single-lane roads, and visible wires), and health outcomes, including obesity, diabetes, cardiovascular disease, and premature mortality, at the state level. We utilized 31,247,167 images collected from Google Street View to create indicators for neighborhood built environment characteristics using deep learning techniques. Adjusted linear regression models were used to estimate the associations between aggregated built environment indicators and state-level health outcomes. Our results indicated that the presence of a crosswalk was associated with reductions in obesity and premature mortality. Visible wires were associated with increased obesity, decreased physical activity, and increases in premature mortality, diabetes mortality, and cardiovascular mortality (however, these results were not significant). Non-single family homes were associated with decreased diabetes and premature mortality, as well as increased physical activity and park and recreational access. Single-lane roads were associated with increased obesity and decreased park access. The findings of our study demonstrated that built environment features may be associated with a variety of adverse health outcomes.
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    Google Street View-Derived Neighborhood Characteristics in California Associated with Coronary Heart Disease, Hypertension, Diabetes
    (MDPI, 2021-10-03) Nguyen, Thu T.; Nguyen, Quynh C.; Rubinsky, Anna D.; Tasdizen, Tolga; Deligani, Amir Hossein Nazem; Dwivedi, Pallavi; Whitaker, Ross; Fields, Jessica D.; DeRouen, Mindy C.; Mane, Heran; Lyles, Courtney R.; Brunisholz, Kim D.; Bibbins-Domingo, Kirsten
    Characteristics of the neighborhood built environment influence health and health behavior. Google Street View (GSV) images may facilitate measures of the neighborhood environment that are meaningful, practical, and adaptable to any geographic boundary. We used GSV images and computer vision to characterize neighborhood environments (green streets, visible utility wires, and dilapidated buildings) and examined cross-sectional associations with chronic health outcomes among patients from the University of California, San Francisco Health system with outpatient visits from 2015 to 2017. Logistic regression models were adjusted for patient age, sex, marital status, race/ethnicity, insurance status, English as preferred language, assignment of a primary care provider, and neighborhood socioeconomic status of the census tract in which the patient resided. Among 214,163 patients residing in California, those living in communities in the highest tertile of green streets had 16–29% lower prevalence of coronary artery disease, hypertension, and diabetes compared to those living in communities in the lowest tertile. Conversely, a higher presence of visible utility wires overhead was associated with 10–26% more coronary artery disease and hypertension, and a higher presence of dilapidated buildings was associated with 12–20% greater prevalence of coronary artery disease, hypertension, and diabetes. GSV images and computer vision models can be used to understand contextual factors influencing patient health outcomes and inform structural and place-based interventions to promote population health.
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    Google Street View Images as Predictors of Patient Health Outcomes, 2017–2019
    (MDPI, 2022-01-27) Nguyen, Quynh C.; Belnap, Tom; Dwivedi, Pallavi; Deligani, Amir Hossein Nazem; Kumar, Abhinav; Li, Dapeng; Whitaker, Ross; Keralis, Jessica; Mane, Heran; Yue, Xiaohe; Nguyen, Thu T.; Tasdizen, Tolga; Brunisholz, Kim D.
    Collecting neighborhood data can both be time- and resource-intensive, especially across broad geographies. In this study, we leveraged 1.4 million publicly available Google Street View (GSV) images from Utah to construct indicators of the neighborhood built environment and evaluate their associations with 2017–2019 health outcomes of approximately one-third of the population living in Utah. The use of electronic medical records allows for the assessment of associations between neighborhood characteristics and individual-level health outcomes while controlling for predisposing factors, which distinguishes this study from previous GSV studies that were ecological in nature. Among 938,085 adult patients, we found that individuals living in communities in the highest tertiles of green streets and non-single-family homes have 10–27% lower diabetes, uncontrolled diabetes, hypertension, and obesity, but higher substance use disorders—controlling for age, White race, Hispanic ethnicity, religion, marital status, health insurance, and area deprivation index. Conversely, the presence of visible utility wires overhead was associated with 5–10% more diabetes, uncontrolled diabetes, hypertension, obesity, and substance use disorders. Our study found that non-single-family and green streets were related to a lower prevalence of chronic conditions, while visible utility wires and single-lane roads were connected with a higher burden of chronic conditions. These contextual characteristics can better help healthcare organizations understand the drivers of their patients’ health by further considering patients’ residential environments, which present both risks and resources.
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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
    (Springer Nature, 2020-02-12) Keralis, Jessica M.; Javanmardi, Mehran; Khanna, Sahil; Dwivedi, Pallavi; Huang, Dina; Tasdizen, Tolga; Nguyen, Quynh C.
    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.