Epidemiology & Biostatistics
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Item CLIMATE CHANGE RELATED EXTREME EVENTS AND ADVERSE HEALTH OUTCOMES AMONG HEMODIALYSIS PATIENTS(2024) Song, Hyeonjin; Sapkota, Amir AS; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The increased frequency and intensity of extreme heat events (EHEs) and wildfires due to climate change are posing significant threats to vulnerable communicates including end-stage kidney disease (ESKD) patients. The specific aims of this dissertation are to Aim 1) Examine the association between EHEs exposure and serum concentrations of sodium and potassium among hemodialysis patients in the Western U.S. (2008-2018), Aim 2) Quantify the mortality and hospitalization risk associated with exposure to 2023 Canadian wildfire-related air pollution in the Eastern U.S., and Aim 3) Investigate how EHEs modify the association between wildfire-related air pollution exposure and the risk of mortality and hospitalization among hemodialysis patients in the Western U.S. (2010-2018). We analyzed health records of patients who receiving hemodialysis treatment at Fresenius Kidney Care clinics. We used the 10°C increase in daily average temperature and daily extreme heat events (EHEs) of each county as the primary exposures. The presence of wildfire smoke plume and wildfire fine particulate matter (PM2.5) concentrations for each clinic were measured using satellite-derived smoke polygons (Hazard Mapping System) and ground-based PM2.5 monitors (Air Quality System). We estimated mean serum sodium and potassium change per 10 °C increase in daily average ambient temperature using random intercepts linear mixed-effects models. We employed a time-stratified case-crossover analysis with conditional quasi-Poisson model to investigate the risks of mortality and hospitalization associated with exposure to wildfire-related air pollution and EHEs. In the first study, a 10°C increase in daily average temperature was associated with 0.43 mEq/L (95% Confidence Interval [CI]: 0.47, 0.59) increase in serum sodium during July-August. The serum sodium was 0.15 mEq/L (95% CI: 0.10, 0.20) higher during EHE days compared to non-EHE days. The serum potassium level did not show a significant change. In the second study, during June-July 2023, the presence of wildfire smoke plume was associated with an 18% increase in all-cause mortality risk (Rate Ratio [RR]:1.18; 95% CI: 1.13, 1.24) and a 3% increase in all-cause hospitalization risk (RR:1.03; 95% CI: 1.00, 1.07). A 10-μg/m3 increase in wildfire-related PM2.5 was associated with a 139% increase in all-cause mortality (RR: 2.39; 95% CI: 1.79, 3.18) and a 33% increase in all-cause hospitalization (RR:1.33; 95% CI: 1.10, 1.62). In the third study, we observed significant interactions between EHEs and wildfire smoke plume for mortality RRs among the hemodialysis patients in the Western U.S. Mortality risk was considerably higher when hemodialysis patients were simultaneously exposed to wildfire smoke plume and EHE compared to wildfire smoke plume alone (RR: 1.52; 95% CI: 1.25, 1.86 vs. RR: 1.15; 95% CI: 1.08, 1.23). We did not observe a significant interaction for all-cause hospitalization. Our findings underscore the need to revise operational and care protocols to prepare for such potential join exposures to extreme events that are exacerbated by ongoing climate change. Future work should focus on developing early warning systems to enhance resilience against such threats.Item COVID-19 Vaccine Hesitancy and Uptake in the United States Considered Through the Lens of Health Behavior Theory(2024) Kauffman, Lauren Emily; Nguyen, Quynh; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Given the low COVID-19 vaccine uptake rates in many areas of the United States despite their demonstrated safety and effectiveness, COVID-19 vaccine hesitancy and vaccination barriers continue to be critical areas of research in epidemiology and behavioral health science. This series of studies focuses on COVID-19 vaccine hesitancy and vaccination barriers, as they relate to vaccination intention and vaccine uptake, considered in the context of established health behavior theories. The first study is a systematic review of existing research on COVID-19 vaccine hesitancy using one or more health behavior theories as key components of the design or analysis. This study examined the types of theories that are most often used, how they are used, and where research gaps exist. The remaining two studies use data from the U.S. COVID-19 Trends and Impact Survey, a national cross-sectional survey. The second study investigates the association between recent feelings of anxiety or depression and vaccination intention, as well as between these feelings and identifying with specific vaccine hesitancy reasons. The third study examines vaccine hesitancy and barriers among those with chronic illness or disease, a particularly vulnerable population. Factor analysis was conducted using constructs from the Theory of Planned Behavior as a framework, and the results were used in a regression model to investigate the association between these underlying factors and vaccination intention. This research demonstrated the usefulness of the Theory of Planned Behavior, the Health Belief Model, and the 3 Cs Model in existing and future COVID-19 vaccine hesitancy research, as well as identified Protection Motivation Theory as a promising area for future research. Additionally, psychological states were demonstrated to be significantly associated with vaccine hesitancy, adjusting for demographic, socioeconomic, and time factors. Lastly, the Theory of Planned Behavior was found to be applicable to those unvaccinated and with chronic illness, as the construct factor scores developed were significantly associated with vaccine hesitancy (adjusting for the presence of specific chronic conditions and demographic, socioeconomic, and time factors). These associations were also consistently demonstrated in subgroup analyses of participants with specific chronic conditions.Item Social Determinants of Cardiovascular Disease Across the Life Course(2023) Ng, Amanda Erin; Dyer, Typhanye; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)According to data from the National Center for Health Statistics, cardiovascular disease is one of the leading causes of death in the United States, contributing to about 697,000 (or 1 in 5) deaths in 2020 alone. Given the public health burden of this disease, it is imperative that research identifies and continues to investigate population factors that may contribute to or alleviate this burden in the United States. The proposed study aimed to analyze such factors across the life course. Study 1 examined associations between an expanded set of Adverse Childhood Experiences (ACEs) and childhood obesity among 10-17 year olds using the National Survey of Children’s Health, as well as sex and age differences within these associations. Study 2 investigated high optimism as a modifier and mediator of the association between childhood socioeconomic disadvantage and CVD in midlife, using the Midlife in the United States Study, a U.S. prospective cohort. Study 3 examined temporal trends in the associations between adult socioeconomic status and CVD mortality using nationally-representative data from the 1997-2018 National Health Interview Survey.Item EXHALED BREATH AEROSOL TRANSMISSION OF ACUTE RESPIRATORY INFECTIONS(2023) Lai, Jianyu; Milton, Donald K; Epidemiology and Biostatistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Acute respiratory infections (ARIs), which usually appear in the form of common colds and influenza, as well as outbreak brought on by emerging viruses such as SARS-CoV-2, result in millions of deaths and hospitalizations each year. Aerosols being exhaled by infected population and inhaled by susceptible population has been identified as an important transmission route for ARIs; yet few studies have compared the viral load in exhaled breath aerosol (EBA) in naturally and experimentally infected cases, as well as among various infections. The specific aims of this dissertation were: 1) examine the comparability of EBA viral shedding between experimentally and a full range of natural ambulatory influenza cases; 2) compare seasonal coronavirus, influenza, SARS-CoV-2 Omicron, and other SARS-CoV-2 variants in terms of viral loads in exhaled breath aerosols; and 3) examine the relative efficacy of face masks, KN95, and N95 respirators as source control for SARS-CoV-2. We analyzed data from four studies that collected 30-minute fine (≤5 μm) and coarse (>5 μm) EBA samples using a Gesundheit-II sampler. Viral RNA load in EBA was quantified using real-time RT-PCR. Nasal inoculation of influenza virus A/Wisconsin/67/2005 showed lower EBA viral shedding compared to the natural influenza A H3 infections. Among the viruses studied, SARS-CoV-2 Omicron variants demonstrated the highest viral RNA loads in both EBA size fractions, emphasizing its superior spread capability via inhalation. Furthermore, while all masks and respirators showed significant reductions in viral RNA load in exhaled aerosols, the duckbill N95 respirators stood out, providing reductions of up to 99% and outperforming both surgical and cloth masks, and KN95 respirators. Given the evident transmission risk via inhalation for the studied viruses, measures such as masking and indoor air hygiene are crucial. The pronounced efficacy of N95 respirators highlights their importance in healthcare settings and places with vulnerable populations, especially during periods of heightened respiratory viral infections.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 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 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, TolgaThe 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.Item Racial and Sex Differences between Urinary Phthalates and Metabolic Syndrome among U.S. Adults: NHANES 2005–2014(MDPI, 2021-06-26) Ghosh, Rajrupa; Haque, Mefruz; Turner, Paul C.; Cruz-Cano, Raul; Dallal, Cher M.Phthalates, plasticizers ubiquitous in household and personal care products, have been associated with metabolic disturbances. Despite the noted racial differences in phthalate exposure and the prevalence of metabolic syndrome (MetS), it remains unclear whether associations between phthalate metabolites and MetS vary by race and sex. A cross-sectional analysis was conducted among 10,017 adults from the National Health and Nutritional Examination Survey (2005–2014). Prevalence odds ratios (POR) and 95% confidence intervals (CIs) were estimated for the association between 11 urinary phthalate metabolites and MetS using weighted sex and race stratified multivariable logistic regression. Higher MCOP levels were significantly associated with increased odds of MetS among women but not men, and only remained significant among White women (POR Q4 vs. Q1 = 1.68, 95% CI: 1.24, 2.29; p-trend = 0.001). Similarly, the inverse association observed with MEHP among women, persisted among White women only (POR Q4 vs. Q1 = 0.53, 95% CI: 0.35, 0.80; p-trend = 0.003). However, ΣDEHP metabolites were associated with increased odds of MetS only among men, and this finding was limited to White men (POR Q4 vs. Q1 = 1.54, 95% CI: 1.01, 2.35; p-trend = 0.06). Among Black men, an inverse association was observed with higher MEP levels (POR Q4 vs. Q1 = 0.43, 95% CI: 0.24, 0.77; p-trend = 0.01). The findings suggest differential associations between phthalate metabolites and MetS by sex and race/ethnicity.Item 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, KirstenCharacteristics 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.Item Meta-Analysis of Transcriptome-Wide Association Studies across 13 Brain Tissues Identified Novel Clusters of Genes Associated with Nicotine Addiction(MDPI, 2021-12-23) Ye, Zhenyao; Mo, Chen; Ke, Hongjie; Yan, Qi; Chen, Chixiang; Kochunov, Peter; Hong, L. Elliot; Mitchell, Braxton D.; Chen, Shuo; Ma, TianzhouGenome-wide association studies (GWAS) have identified and reproduced thousands of diseases associated loci, but many of them are not directly interpretable due to the strong linkage disequilibrium among variants. Transcriptome-wide association studies (TWAS) incorporated expression quantitative trait loci (eQTL) cohorts as a reference panel to detect associations with the phenotype at the gene level and have been gaining popularity in recent years. For nicotine addiction, several important susceptible genetic variants were identified by GWAS, but TWAS that detected genes associated with nicotine addiction and unveiled the underlying molecular mechanism were still lacking. In this study, we used eQTL data from the Genotype-Tissue Expression (GTEx) consortium as a reference panel to conduct tissue-specific TWAS on cigarettes per day (CPD) over thirteen brain tissues in two large cohorts: UK Biobank (UKBB; number of participants (N) = 142,202) and the GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN; N = 143,210), then meta-analyzing the results across tissues while considering the heterogeneity across tissues. We identified three major clusters of genes with different meta-patterns across tissues consistent in both cohorts, including homogenous genes associated with CPD in all brain tissues; partially homogeneous genes associated with CPD in cortex, cerebellum, and hippocampus tissues; and, lastly, the tissue-specific genes associated with CPD in only a few specific brain tissues. Downstream enrichment analyses on each gene cluster identified unique biological pathways associated with CPD and provided important biological insights into the regulatory mechanism of nicotine dependence in the brain.