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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Item Census Tract Food Tweets and Chronic Disease Outcomes in the U.S., 2015–2018(MDPI, 2019-03-18) Huang, Yuru; Huang, Dina; Nguyen, Quynh C.There is a growing recognition of social media data as being useful for understanding local area patterns. In this study, we sought to utilize geotagged tweets—specifically, the frequency and type of food mentions—to understand the neighborhood food environment and the social modeling of food behavior. Additionally, we examined associations between aggregated food-related tweet characteristics and prevalent chronic health outcomes at the census tract level. We used a Twitter streaming application programming interface (API) to continuously collect ~1% random sample of public tweets in the United States. A total of 4,785,104 geotagged food tweets from 71,844 census tracts were collected from April 2015 to May 2018. We obtained census tract chronic disease outcomes from the CDC 500 Cities Project. We investigated associations between Twitter-derived food variables and chronic outcomes (obesity, diabetes and high blood pressure) using the median regression. Census tracts with higher average calories per tweet, less frequent healthy food mentions, and a higher percentage of food tweets about fast food had higher obesity and hypertension prevalence. Twitter-derived food variables were not predictive of diabetes prevalence. Food-related tweets can be leveraged to help characterize the neighborhood social and food environment, which in turn are linked with community levels of obesity and hypertension.Item Census Tract Food Tweets and Chronic Disease Outcomes in the U.S., 2015–2018(MDPI, 2019-03-18) Huang, Yuru; Huang, Dina; Nguyen, Quynh C.There is a growing recognition of social media data as being useful for understanding local area patterns. In this study, we sought to utilize geotagged tweets—specifically, the frequency and type of food mentions—to understand the neighborhood food environment and the social modeling of food behavior. Additionally, we examined associations between aggregated food-related tweet characteristics and prevalent chronic health outcomes at the census tract level. We used a Twitter streaming application programming interface (API) to continuously collect ~1% random sample of public tweets in the United States. A total of 4,785,104 geotagged food tweets from 71,844 census tracts were collected from April 2015 to May 2018. We obtained census tract chronic disease outcomes from the CDC 500 Cities Project. We investigated associations between Twitter-derived food variables and chronic outcomes (obesity, diabetes and high blood pressure) using the median regression. Census tracts with higher average calories per tweet, less frequent healthy food mentions, and a higher percentage of food tweets about fast food had higher obesity and hypertension prevalence. Twitter-derived food variables were not predictive of diabetes prevalence. Food-related tweets can be leveraged to help characterize the neighborhood social and food environment, which in turn are linked with community levels of obesity and hypertension.Item Relationships of social and physical environmental factors with cardiometabolic outcomes(2019) Huang, Dina; Puett, Robin; Nguyen, Quynh C; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The social and physical environmental factors impact health in general and have been linked with increased risks of cardiometabolic outcomes including obesity, diabetes, hypertension and cardiometabolic biomarkers. The dissertation added to important knowledge on this topic in two ways: 1) by leveraging innovative Twitter-derived characteristics to study the potential influence of social environment on cardiometabolic outcomes, 2) investigating the effects of air pollution exposures on cardiometabolic outcomes in youth living with type I diabetes. The first study investigated the associations between Twitter-derived area-level predictors (happiness, diet, physical activity) with cardiometabolic outcomes (obesity, diabetes, hypertension) using a nationally representative sample from National Health and Nutrition Examination Survey (NHANES). People living in neighborhoods with higher happiness, healthier diet and more physical activity had lower prevalence of obesity and hypertension but not diabetes. Twitter-derived social neighborhood characteristics can be used to identify communities with higher risk of cardiometabolic outcomes. We obtained data from SEARCH for Diabetes in Youth (SEARCH) study for the second and the third study. The second study examined the associations between chronic exposure to air pollution and glucose hemostasis (HbA1c) in youth living with type I diabetes. Particulate matter with aerodynamic diameter <2.5 (PM2.5), proximity to heavily trafficked roads and annual average daily traffic count were associated with higher HbA1c in study site South Carolina, Colorado and Washington, but not in study site Ohio and California. Differences in particulate matter compositions may explain the inconsistent results. The third study assessed the effect of acute exposure to air pollution on subclinical CVD markers including pulse wave velocity (PWV), augmentation index (AIx) and brachial distensibility (BrachD) using a repeated measures design. Reduction in PM2.5 on the day prior to assessment was associated with lower AIx, but not associated with either PWV or BrachD. In summary, exposure to air pollution may be associated with cardiometabolic outcomes and reducing air pollution may have implications in early prevention of cardiovascular complications for youth living with type I diabetes. Overall, reducing social stressors and reducing hazardous physical environmental factors may decrease the risk of cardiometabolic outcomes, providing possible directions for CVD prevention for public health practitioners.