Epidemiology & Biostatistics
Permanent URI for this communityhttp://hdl.handle.net/1903/7125
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Item 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.Item 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.Item Examination of the Public’s Reaction on Twitter to the Over-Turning of Roe v Wade and Abortion Bans(MDPI, 2022-11-29) Mane, Heran; Yue, Xiaohe; Yu, Weijun; Doig, Amara Channell; Wei, Hanxue; Delcid, Nataly; Harris, Afia-Grace; Nguyen, Thu T.; Nguyen, Quynh C.The overturning of Roe v Wade reinvigorated the national debate on abortion. We used Twitter data to examine temporal, geographical and sentiment patterns in the public’s reaction. Using the Twitter API for Academic Research, a random sample of publicly available tweets was collected from 1 May–15 July in 2021 and 2022. Tweets were filtered based on keywords relating to Roe v Wade and abortion (227,161 tweets in 2021 and 504,803 tweets in 2022). These tweets were tagged for sentiment, tracked by state, and indexed over time. Time plots reveal low levels of conversations on these topics until the leaked Supreme Court opinion in early May 2022. Unlike pro-choice tweets which declined, pro-life conversations continued with renewed interest throughout May and increased again following the official overturning of Roe v Wade. Conversations were less prevalent in some these states had abortion trigger laws (Wyoming, North Dakota, South Dakota, Texas, Louisiana, and Mississippi). Collapsing across topic categories, 2022 tweets were more negative and less neutral and positive compared to 2021 tweets. In network analysis, tweets mentioning woman/women, supreme court, and abortion spread faster and reached to more Twitter users than those mentioning Roe Wade and Scotus. Twitter data can provide real-time insights into the experiences and perceptions of people across the United States, which can be used to inform healthcare policies and decision-making.Item UNCOVERING THE HIDDEN POPULATION IN THE TRANSMISSION OF COVID-19: ASYMPTOMATIC CASES(2023) Yu, Weijun; Nguyen, Quynh C.; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation investigates the impact of international and local mobility of asymptomatic versus symptomatic COVID-19 cases on the pandemic in Hong Kong.The first manuscript analyzed empirical data from 11,775 confirmed COVID-19 cases in Hong Kong from January 2020 to April 2021, building a retrospective cohort. The results indicated that COVID-19 asymptomatic airport or flight crew were ten times more likely to have inbound air travel history than symptomatic airport or flight crew (adjusted RR=10.00, 95% CI: 4.00–25.00), and the median flight duration of asymptomatic cases was 4.6 person-hours shorter than that of symptomatic cases (p<0.01). The second manuscript presented a social network analysis study that build networks for the three peaks of COVID-19 diagnosis in Hong Kong. The results showed that asymptomatic cases were 1.33 times more likely to be presented in the inbound flight cabin or airport with other COVID-19 cases simultaneously than symptomatic cases (95%CI: 1.21-1.45) at the early stage of the pandemic. Additionally, the study found that network percolation simulation targeted attacks were more efficient than random failures in dismantling networks with a low level of connectedness. The third manuscript used geocoded COVID-19 cases’ travel records in Hong Kong to conduct a spatial analysis study. The findings indicated that asymptomatic cases visited locations mostly clustered in the southern part of Hong Kong, while symptomatic cases visited locations mostly clustered in the middle and southern parts of Hong Kong. This study also found that Geographically Weighted Regression models performed better among symptomatic cases than asymptomatic cases, and median local travel time was higher (p<0.01) among asymptomatic (68.09 person-minutes) than symptomatic cases (59.46 person-minutes) based on 19,568 Origin-Destination Cost Matrix least-cost paths. Overall, this dissertation highlights the importance of promoting public health prevention strategies to contain future infectious disease pandemics at the early stage, regardless of the presence of symptoms. Moreover, it suggests that travel restriction may not be effective in dismantling networks with a low-level of connectedness. Local health authorities and policymakers should tailor detection and containment strategies based on spatial variability in different areas.