Epidemiology & Biostatistics Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/7128

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Subject: search.filters.subject.COVID-19

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Now showing 1 - 4 of 4
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    Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases
    (MDPI, 2020-09-01) Nguyen, Quynh C.; Huang, Yuru; Kumar, Abhinav; Duan, Haoshu; Keralis, Jessica M.; Dwivedi, Pallavi; Meng, Hsien-Wen; Brunisholz, Kimberly D.; Jay, Jonathan; Javanmardi, Mehran; Tasdizen, Tolga
    The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents’ risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.
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    Reducing Anxiety with Nature and Gardening (RANG): Evaluating the Impacts of Gardening and Outdoor Activities on Anxiety among U.S. Adults during the COVID-19 Pandemic
    (MDPI, 2022-04-22) Gerdes, Megan E.; Aistis, Lucy A.; Sachs, Naomi A.; Williams, Marcus; Roberts, Jennifer D.; Rosenberg, Goldstein Rachel E.
    The COVID-19 pandemic impacted mental health. Growing research has identified the mental health benefits of nature contact, including gardening. We used a cross-sectional survey to investigate the association between gardening and other outdoor activities with anxiety among U.S. adults. The RANG (Reducing Anxiety with Nature and Gardening) survey was distributed online from June–September 2020 through social media (Twitter and Facebook) and a national Master Gardeners listserv. Survey questions captured demographics, COVID-19 experiences, gardening, outdoor activities, and anxiety using the Generalized Anxiety Disorder 7-item scale. Data were analyzed using chi-square, Fisher’s exact, and Kruskal–Wallis tests, as well as logistic regression. Among participants, 46% reported anxiety symptoms. Participants who had gardened ≥ 15 years and those gardening > 8 h over two weeks had lower anxiety scores. Spending more time outdoors on weekdays also decreased anxiety scores. After adjusting for covariates, lower odds of anxiety were identified for 50–69 and 70–89-year-olds vs. 18–29-year-olds; males vs. females; and Texas vs. Maryland residents. These findings confirm increased anxiety during the COVID-19 pandemic and suggest that sustained gardening and other outdoor activities could help reduce anxiety.
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    Social Network Analysis on the Mobility of Three Vulnerable Population Subgroups: Domestic Workers, Flight Crews, and Sailors during the COVID-19 Pandemic in Hong Kong
    (MDPI, 2022-06-21) Yu, Weijun; Alipio, Cheryll; Wan, Jia'an; Mane, Heran; Nguyen, Quynh C.
    Background: Domestic workers, flight crews, and sailors are three vulnerable population subgroups who were required to travel due to occupational demand in Hong Kong during the COVID-19 pandemic. Objective: The aim of this study was to explore the social networks among three vulnerable population subgroups and capture temporal changes in their probability of being exposed to SARS-CoV-2 via mobility. Methods: We included 652 COVID-19 cases and utilized Exponential Random Graph Models to build six social networks: one for the cross-sectional cohort, and five for the temporal wave cohorts, respectively. Vertices were the three vulnerable population subgroups. Edges were shared scenarios where vertices were exposed to SARS-CoV-2. Results: The probability of being exposed to a COVID-19 case in Hong Kong among the three vulnerable population subgroups increased from 3.38% in early 2020 to 5.78% in early 2022. While domestic workers were less mobile intercontinentally compared to flight crews and sailors, domestic workers were 1.81-times in general more likely to be exposed to SARS-CoV-2. Conclusions: Vulnerable populations with similar ages and occupations, especially younger domestic workers and flight crew members, were more likely to be exposed to SARS-CoV-2. Social network analysis can be used to provide critical information on the health risks of infectious diseases to vulnerable populations.
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    Food insecurity among African Americans in the United States: A scoping review
    (2022-09-12) Dennard, Elizabeth; Kristjansson, Elizabeth; Tchangalova, Nedelina; Totton, Sarah; Winham, Donna; O’Connor, Annette
    In 2019, the estimated prevalence of food insecurity for Black non-Hispanic households was higher than the national average due to health disparities exacerbated by forms of racial discrimination. During the COVID-19 pandemic, Black households have experienced higher rates of food insecurity when compared to other populations in the United States. The primary objectives of this review were to identify which risk factors have been investigated for an association with food insecurity, describe how food insecurity is measured across studies that have evaluated this outcome among African Americans, and determine which dimensions of food security (food accessibility, availability, and utilization) are captured by risk factors studied by authors. Food insecurity related studies were identified through a search of Google Scholar, PubMed, CINAHL Plus, MEDLINE®, PsycINFO, Health Source: Nursing/Academic Edition, and Web of Science™ (Clarivate), on May 20, 2021. Eligible studies were primary research studies, with a concurrent comparison group, published in English between 1995 and 2021. Ninety-eight relevant studies were included for data charting with 37 unique measurement tools, 115 risk factors, and 93 possible consequences of food insecurity identified. Few studies examined factors linked to racial discrimination, behaviour, or risk factors that mapped to the food availability dimension of food security. Infrequently studied factors, such as lifetime racial discrimination, socioeconomic status (SES), and income insecurity need further investigation while frequently studied factors such as age, education, race/ethnicity, and gender need to be summarized using a systematic review approach so that risk factor impact can be better assessed. Risk factors linked to racial discrimination and food insecurity need to be better understood in order to minimize health disparities among African American adults during the COVID-19 pandemic and beyond.