Research Projects Directory

New - COPE-Anxiety

Our research aims to investigate the impact of the COVID-19 pandemic on individuals' mental health and to understand the coping mechanisms people have employed during this time. To achieve this, we will analyze various sources of data, including surveys and…

Scientific Questions Being Studied

Our research aims to investigate the impact of the COVID-19 pandemic on individuals' mental health and to understand the coping mechanisms people have employed during this time. To achieve this, we will analyze various sources of data, including surveys and electronic medical records, focusing on the following specific questions:
1. How has the prevalence of anxiety, depression, and other mental health issues changed since the onset of the COVID-19 pandemic?
2. What coping strategies have individuals commonly used to manage their mental health during the pandemic?
3. Are there particular demographic groups that have been more severely affected in terms of mental health, and what factors contribute to these disparities?

Project Purpose(s)

Population Health

Scientific Approaches

Plan to use COPE dataset, other survey data and EHR data for this study. The outcomes included six repeated surveys assessing depression and anxiety symptoms during the COVID-19 pandemic from May 2020 to Feb 2021 using the 9-item Patient Health Questionnaire (PHQ-9) and General Anxiety Disorder 7-item scale (GAD-7). This will be a retrospective study design by using Linear Mixed-Effect Models on the numeric responses of the screening scores for depression and anxiety symptoms. These models included fixed effects for sociodemographic factors, and repeated measurements as random effects.

Anticipated Findings

We hypothesize that there will be a significant increase in reported mental health issues, such as anxiety and depression, during the pandemic compared to pre-pandemic levels. Additionally, we anticipate that coping strategies will vary widely, with some individuals turning to social support networks and others to professional mental health services.

This study will provide a comprehensive understanding of how the COVID-19 pandemic has influenced mental health across different populations. The insights gained will be valuable for both the public and mental health professionals, helping them better understand the psychological impacts of the pandemic and improving strategies for mental health support and intervention during such crises.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Hsueh-Han Yeh - Research Associate, Henry Ford Health System

Collaborators:

Gage Rion - Project Personnel, All of Us Program Operational Use
Aymone Kouame - Other, All of Us Program Operational Use

COPE-Anxiety

Our research aims to investigate the impact of the COVID-19 pandemic on individuals' mental health and to understand the coping mechanisms people have employed during this time. To achieve this, we will analyze various sources of data, including surveys and…

Scientific Questions Being Studied

Our research aims to investigate the impact of the COVID-19 pandemic on individuals' mental health and to understand the coping mechanisms people have employed during this time. To achieve this, we will analyze various sources of data, including surveys and electronic medical records, focusing on the following specific questions:
1. How has the prevalence of anxiety, depression, and other mental health issues changed since the onset of the COVID-19 pandemic?
2. What coping strategies have individuals commonly used to manage their mental health during the pandemic?
3. Are there particular demographic groups that have been more severely affected in terms of mental health, and what factors contribute to these disparities?

Project Purpose(s)

Population Health

Scientific Approaches

Plan to use COPE dataset, other survey data and EHR data for this study. The outcomes included six repeated surveys assessing depression and anxiety symptoms during the COVID-19 pandemic from May 2020 to Feb 2021 using the 9-item Patient Health Questionnaire (PHQ-9) and General Anxiety Disorder 7-item scale (GAD-7). This will be a retrospective study design by using Linear Mixed-Effect Models on the numeric responses of the screening scores for depression and anxiety symptoms. These models included fixed effects for sociodemographic factors, and repeated measurements as random effects.

Anticipated Findings

We hypothesize that there will be a significant increase in reported mental health issues, such as anxiety and depression, during the pandemic compared to pre-pandemic levels. Additionally, we anticipate that coping strategies will vary widely, with some individuals turning to social support networks and others to professional mental health services.

This study will provide a comprehensive understanding of how the COVID-19 pandemic has influenced mental health across different populations. The insights gained will be valuable for both the public and mental health professionals, helping them better understand the psychological impacts of the pandemic and improving strategies for mental health support and intervention during such crises.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Hsueh-Han Yeh - Research Associate, Henry Ford Health System

WCUCOMAOUGroup5_2

This project aims to examine the heterogeneous health impacts of food insecurity in the diabetes mellitus type 2 (T2DM) population during the COVID-19 pandemic lockdown. Food insecurity is a public health concern for many reasons that are well documented. It…

Scientific Questions Being Studied

This project aims to examine the heterogeneous health impacts of food insecurity in the diabetes mellitus type 2 (T2DM) population during the COVID-19 pandemic lockdown. Food insecurity is a public health concern for many reasons that are well documented. It can be particularly burdensome for the T2DM population due to its need to access specific foods to adequately manage insulin levels. The lockdowns following the onset of the COVID-19 pandemic may have exacerbated pre-existing food insecurity or caused locations that previously had good food access to become “food deserts.” These factors will likely precipitate a change in dietary patterns, affecting glycemic control in T2DM patients. The paucity of such research focusing on US demographics, particularly in Mississippi, also underscores a need to investigate the link between food insecurity in COVID pandemic and diabetes management.

Project Purpose(s)

Disease Focused Research (type 2 diabetes mellitus)
Population Health

Scientific Approaches

We would like to investigate how food insecurity affected the Mississippi population during the peak COVID-19 social distancing period (Mar 2020 – Mar 2022). We will group diabetes patients into a food-secure group and a food-insecure group using answers from Social Determinants of Health survey. We will use HbA1c data from Mar 2019 to Mar 2020 for pre-COVID period as baseline and compare that with the data of the same patients from Mar 2020 to Mar 2021. We will also compare the BMI, HTN condition, eating behavior during COVID and medication adherence due to financial concerns between the two groups using additional survey questions. Demographic information such as race, sex, gender, education, employment, and income level from the Basics Survey will be used to control for confounding factors in the analysis and any statistically significant trends will be reported.

Anticipated Findings

We expect that patients with diabetes in the food insecure group experienced a greater increase in their HA1c and more diabetic complications than the those in the food secure group during COVID-19. The findings will aid in our understanding of rising HA1c levels in relation to food insecurity focusing on an underrepresented population in research and can shed light on disease prevention during a pandemic in such a vulnerable population. The outcome of our research may provide insights on reducing health disparity and improving health equity in an underrepresented population.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Yadanar Yukyi - Graduate Trainee, William Carey University
Thao Nguyen Van - Graduate Trainee, William Carey University
Zhitao Ming - Graduate Trainee, William Carey University
Fang Li - Graduate Trainee, William Carey University
Daniel Thompson - Graduate Trainee, William Carey University
Danielle Fastring - Other, William Carey University

COVID-19 Vaccination Acceptance

It has been evident that socioeconomic characteristics significantly impact healthcare, environmental exposures, and health behaviors for individuals (Adler & Newman, 2002; Miranda et al., 2009; Newacheck et al., 2003; Pampel et al., 2010). As the COVID-19 pandemic continues to spread,…

Scientific Questions Being Studied

It has been evident that socioeconomic characteristics significantly impact healthcare, environmental exposures, and health behaviors for individuals (Adler & Newman, 2002; Miranda et al., 2009; Newacheck et al., 2003; Pampel et al., 2010). As the COVID-19 pandemic continues to spread, and its long-term effects still remain uncertain, it is essential to explore the underlying economic and social determinants of the COVID-19 vaccination, with a specific focus on socioeconomic status, to better address the disparities in COVID-19 vaccination acceptance and hesitancy. To achieve this goal, this study will focus on 1) measuring the socioeconomic determinants of COVID-19 vaccination at the individual level and at the social level, and 2) examining the current mechanisms of COVID-19 vaccination administration at the individual and social levels.

Project Purpose(s)

Population Health

Scientific Approaches

Dataset: The basics" and "COPE."
Research methods: Multiple linear regression
According to the social cognitive theory (SCT), the cognitive process is a dynamic and reciprocal interaction between an individual, the environment, and the individual’s behavior within a social context (Bandura, 1989). It highlights the powerful internal and external social reinforcement for an individual’s cognition development. In the health and healthcare context, the SCT posits that health promotion scholars should identify socio-structural determinants of health and improve the social system, besides changing an individual’s habits, to navigate the individual's cognition. (Bandura, 1998). Therefore, based on the SCT, we will measure personal environmental and behavioral factors at the individual level and other factors such as social trust at social levels to explore the socioeconomic determinants of COVID-19 vaccination acceptance.

Anticipated Findings

We anticipate that the social factors are more influential for determining individual Covid-19 vaccination acceptance. Our findings could provide insights for policymakers to develop strategies and policies to increase individuals vaccination acceptance, thereby better addressing public health crisis.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Xiao Li - Graduate Trainee, University of Texas Health Science Center, Houston
Maria Rosario Marin Marmol kilrain - Project Personnel, All of Us Program Operational Use
Jishu Zheng - Graduate Trainee, University of Texas Health Science Center, Houston
Jae Man Park - Graduate Trainee, University of Texas Health Science Center, Houston

Biological and social determinants of well-being in Filipinx Americans

This project proposes to examine the interactive influence of biomarkers of allostatic load and social determinants on mental health and well-being in Filipinx Americans. Before formalizing hypotheses, we will explore the data to ascertain the number of Filipinx American participants…

Scientific Questions Being Studied

This project proposes to examine the interactive influence of biomarkers of allostatic load and social determinants on mental health and well-being in Filipinx Americans. Before formalizing hypotheses, we will explore the data to ascertain the number of Filipinx American participants represented in each dataset, estimate power to conduct tests, and evaluate assumptions of normality.

Questions of interest include:

Do biomarkers (e.g., inflammatory, metabolic, or cardiovascular markers) predict mental health and well-being as measured during the COVID-19 pandemic? Do such markers predict change or trajectory of mental health and well-being throughout the pandemic?
Do social health determinants measured at baseline moderate the influence of allostatic load on mental health and well-being during the COVID-19 pandemic?
Do changes in key social health determinants (e.g., social support, loneliness, discrimination) affect mental health and well-being during the COVID-19 pandemic?

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will explore the following datasets and measures:

Labs & Measurements: systolic blood pressure (SBP), diastolic blood pressure (DBP), high density lipoprotein (HDL), low density lipoprotein (LDL), glycosylated hemoglobin, albumin, BMI, and c-reactive protein

Social Determinants of Health: neighborhood deprivation, social support, loneliness, spirituality, discrimination, perceived stress, and generational status (U.S. born = 1, other = 0)

COVID-19 Participant Experience (COPE): social distancing behavior, COVID-19 related impact, anxiety and mood, general well-being, social support, general stress, loneliness, substance use, resilience, discrimination.

We propose to apply multivariate approaches (e.g., structural equation modeling, factor analysis) to prospectively examine the unique and interactive influence of biological and social factors on indices of mental health and well-being during COVID-19.

Anticipated Findings

We anticipate that spirituality will buffer the influence of allostatic load on poor mental health and well-being during the COVID-19 pandemic. We also anticipate that neighborhood deprivation, loneliness, and discrimination will exacerbate the influence of allostatic load on poor mental health and well-being during the COVID-19 pandemic. This finding will be among the few to examine both biological and social health factors in Filipinx American samples and results will be interpreted in co-partnership with Filipinx American community stakeholders.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Hadley Rahrig - Research Fellow, University of Wisconsin, Madison

Long COVID

We will see if we can identify a cohort of AoU participants who meet the case definition of Long COVID as defined by Thaweethai et al, JAMA 2023. Long COVID is a currently public health concern and may continue to…

Scientific Questions Being Studied

We will see if we can identify a cohort of AoU participants who meet the case definition of Long COVID as defined by Thaweethai et al, JAMA 2023. Long COVID is a currently public health concern and may continue to be significant in the future. Identifying those who may have Long COVID in the AoU dataset will be an important first step toward follow up on their long-term health outcomes.

Project Purpose(s)

Disease Focused Research (Long COVID)

Scientific Approaches

Our work will be mainly descriptive. We will create a case definition using standard data builder queries to understand the N of those who may have long COVID, their comorbidities, and their sociodemographic characteristics.

Anticipated Findings

We anticipate that we will not be able to find the exact criteria outlined in Thaweethai et al, but that we will find significant overlap. We anticipate that using an amended case definition, we will be able to to find a cohort of participants with possible Long COVID.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Andrew Craver - Project Personnel, University of Chicago

Collaborators:

Angela Choi - Undergraduate Student, University of Chicago

Mental health and well-being in Filipinx Americans

This project proposes to examine the interactive influence of biomarkers of allostatic load and social determinants on mental health and well-being in Filipinx Americans. Before formalizing hypotheses, we will explore the data to ascertain the number of Filipinx American participants…

Scientific Questions Being Studied

This project proposes to examine the interactive influence of biomarkers of allostatic load and social determinants on mental health and well-being in Filipinx Americans. Before formalizing hypotheses, we will explore the data to ascertain the number of Filipinx American participants represented in each dataset, estimate power to conduct tests, and evaluate assumptions of normality.

Questions of interest include:

Do biomarkers (e.g., inflammatory, metabolic, or cardiovascular markers) predict mental health and well-being as measured during the COVID-19 pandemic? Do such markers predict change or trajectory of mental health and well-being throughout the pandemic?

Do social health determinants measured at baseline moderate the influence of allostatic load on mental health and well-being during the COVID-19 pandemic?

Do changes in key social health determinants (e.g., social support, loneliness, discrimination) affect mental health and well-being during the COVID-19 pandemic?

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will explore the following datasets and measures:

Labs & Measurements: systolic blood pressure (SBP), diastolic blood pressure (DBP), high density lipoprotein (HDL), low density lipoprotein (LDL), glycosylated hemoglobin, albumin, BMI, and c-reactive protein

Social Determinants of Health: neighborhood deprivation, social support, loneliness, spirituality, discrimination, perceived stress, and generational status (U.S. born = 1, other = 0)

COVID-19 Participant Experience (COPE): social distancing behavior, COVID-19 related impact, anxiety and mood, general well-being, social support, general stress, loneliness, substance use, resilience, discrimination.

We propose to apply multivariate approaches (e.g., structural equation modeling, factor analysis) to prospectively examine the unique and interactive influence of biological and social factors on indices of mental health and well-being during COVID-19.

Anticipated Findings

We anticipate that spirituality will buffer the influence of allostatic load on poor mental health and well-being during the COVID-19 pandemic. We also anticipate that neighborhood deprivation, loneliness, and discrimination will exacerbate the influence of allostatic load on poor mental health and well-being during the COVID-19 pandemic. This finding will be among the few to examine both biological and social health factors in Filipinx American samples and results will be interpreted in co-partnership with Filipinx American community stakeholders.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Hadley Rahrig - Research Fellow, University of Wisconsin, Madison

Explorations in Women's Health

Broadly, this workspace will serve to explore a number of topics in the women's health sphere. Given the overlap of symptoms between long-COVID and Cushing's Disease, this study aims to understand if rates of hypercortisolism and Cushing's Disease are higher…

Scientific Questions Being Studied

Broadly, this workspace will serve to explore a number of topics in the women's health sphere.

Given the overlap of symptoms between long-COVID and Cushing's Disease, this study aims to understand if rates of hypercortisolism and Cushing's Disease are higher in patients who have been diagnosed with COVID, have symptoms of long-COVID, or received a formal diagnosis of long-COVID. Follow-up analyses will work to uncover related demographic, environmental, and biological factors.

Additionally, this project will follow up on a study performed in mice that suggested prophylactic antibiotics can lead to IgA deficiency and subsequently increase the risk of infection by Pseudomonas post-partum. This investigation aims to better understand the relationship between antibiotic administration leading up to delivery and immunoglobulin levels. Furthermore, we are interested in understanding if immunoglobulin levels are linked to any of the major complications of birth and antepartum.

Project Purpose(s)

Disease Focused Research (Pregnancy)
Population Health
Educational

Scientific Approaches

I will pull data for non-steroid and steroid-related hypercortisolism. I will then pull data associated with COVID status, long-COVID diagnoses, and steroid usage. Demographic data will also be incorporated. The groups will then be compared according to rates of hypercortisolism in non-COVID-infected, mild-COVID-infected, COVID-infected with moderate/severe symptoms, patients with symptoms of long COVID but no formal diagnosis, and diagnosed long-COVID patients.

For the second investigation, I will select all patients with reported pregnancy and immunoglobulin labs. I will, then, sort the patients according to antibiotic usage and type of antibiotics leading up to delivery and compare to a control group of a) no antibiotics or b) typical antibiotics. Birth complications will then be pulled and rates of major complications in the two groups will be compared. For those that are hits, will move forward and look into immunoglobulin levels in those individuals.

Anticipated Findings

I would like to know if hypercortisolism is something we should be screening for more regularly in the general population. Hypercortisolism and Cushing's Disease are serious illnesses that impart significant mortality on patients with these diagnoses. Furthermore, if this is a slowly growing, chronic condition related to COVID, it would be important for physicians and healthcare systems to prepare accordingly.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Rebecca Zack - Graduate Trainee, University of Pittsburgh

Collaborators:

Alexis Cenname - Graduate Trainee, University of Pittsburgh

Race And Class Impact On Social Support During COVID-19

In this study I am seeking to understand the relationship between race and class as it relates to social support access among American workers during COVID-19. My intended research question is "What is the impact of race and class on…

Scientific Questions Being Studied

In this study I am seeking to understand the relationship between race and class as it relates to social support access among American workers during COVID-19. My intended research question is "What is the impact of race and class on access to social support for American workers during COVID-19?". This study provides sociologists insight into support systems of marginalized populations. This study is supported by social stratification and inequality research where it will further substantiate the understanding that occupation, a key aspect of social class, has implications on resource access and life outcome particularly during public health crises. This is also supported by public health and society research where social status is a key component of the social determinants of health.

Project Purpose(s)

Educational

Scientific Approaches

Being an inferential study, I can conduct a linear regression analysis to test the significance of a selected IV on another DV. I can use linear regression analysis because it gives me the ability to quantify precisely the relative importance of any proposed factor or variable. Or I can test the significance of multiple IV’s on a single DV using multiple regression analysis. Regression analysis is used to answer questions concerning which factor is most closely associated with a particular outcome. I can use the T or F test or statistical significance of singular or multiple selected IV and the selected DV converted to a parameter to predict the relationship converted to a population level (based on the statistic).

Anticipated Findings

I anticipate that the analyses will reveal a strong correlation between IV's such as race, U.S. Born citizenry, education level, health insurance coverage, employment status, income, rent or owning, and concern about not having a place to live and a decreased access to the DV of social support during COVID-19. I anticipate that these findings will be statistically significant when converted to a parameter.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Shane Appiah - Graduate Trainee, Northwestern University

Collaborators:

Trey-Rashad Hawkins - Project Personnel, All of Us Researcher Academy/RTI International
Jorge Avila - Teacher/Instructor/Professor, University of California, Los Angeles

Contrastive Learning - Long COVID Recovery

The purpose of this workspace is to benchmark a novel robust contrastive learning algorithm using the established long covid dataset. We will pull additional control data and attempt to identify long covid cases using our contrastive learning methods.

Scientific Questions Being Studied

The purpose of this workspace is to benchmark a novel robust contrastive learning algorithm using the established long covid dataset. We will pull additional control data and attempt to identify long covid cases using our contrastive learning methods.

Project Purpose(s)

Disease Focused Research (Long COVID)
Methods Development

Scientific Approaches

To achieve this objective, data science workflows were used to apply ML algorithms on the Researcher Workbench. This effort allowed an expansion in the number of participants used to evaluate the ML models used to identify risk of PASC/Long COVID and also serve to validate the efforts of one team and providing insight to other teams. These models were implemented within the All of Us Controlled Tier data (C2022Q2R2), which was last refreshed on June 22, 2022. We intend to provide a step-by-step guide for the implementation of N3C's ML Model for identification of PASC/Long COVID Phenotype in the All of Us dataset.

Anticipated Findings

We intend to provide a step-by-step guide for the implementation of N3C's ML Model for identification of PASC/Long COVID Phenotype in the All of Us dataset. We will do the same with our novel contrastive learning methods.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Arush Ramteke - Undergraduate Student, University of California, Los Angeles

Perinatal Health Controlled

Do the Latino health patterns in All of Us data replicate well-established Latino paradox patterns in pregnancy outcomes? Has COVID-19 disrupted this pattern?

Scientific Questions Being Studied

Do the Latino health patterns in All of Us data replicate well-established Latino paradox patterns in pregnancy outcomes? Has COVID-19 disrupted this pattern?

Project Purpose(s)

Population Health

Scientific Approaches

All variables necessary for carrying out this research project are available in the All of Us data. Descriptive analyses will first focus on breaking down and stratifying pregnancy outcomes into cross-tabulations rates in the following ways:
• Hispanic/Latino versus non-Hispanic Latino White, non-Hispanic Latino Black, and non-Hispanic Latino Asian groups;
• Then, to explore whether these patterns vary by US-born versus foreign-born status (using country of birth or preferred language as a proxy, depending on what’s available in the dataset)

Anticipated Findings

We expect patterns between non-Hispanic Whites and Hispanic/Latinos to be similar, but when splitting by nativity status, for us to see differences, with US born Hispanic/Latinos having worse patterns compared to foreign born Hispanic/Latinos.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Vida Pourmand - Graduate Trainee, University of California, Irvine

Collaborators:

Miranda Chen - Project Personnel, University of California, Irvine

WCUCOMAOUGroup5

This project aims to examine the heterogeneous health impacts of food insecurity in the diabetes mellitus type 2 (T2DM) population during the COVID-19 pandemic lockdown. Food insecurity is a public health concern for many reasons that are well documented. It…

Scientific Questions Being Studied

This project aims to examine the heterogeneous health impacts of food insecurity in the diabetes mellitus type 2 (T2DM) population during the COVID-19 pandemic lockdown. Food insecurity is a public health concern for many reasons that are well documented. It can be particularly burdensome for the T2DM population due to its need to access specific foods to adequately manage insulin levels. The lockdowns following the onset of the COVID-19 pandemic may have exacerbated pre-existing food insecurity or caused locations that previously had good food access to become “food deserts.” These factors will likely precipitate a change in dietary patterns, affecting glycemic control in T2DM patients. The paucity of such research focusing on US demographics, particularly in Mississippi, also underscores a need to investigate the link between food insecurity in COVID pandemic and diabetes management.

Project Purpose(s)

Disease Focused Research (type 2 diabetes mellitus)
Population Health

Scientific Approaches

We would like to investigate how food insecurity affected the Mississippi population during the peak COVID-19 social distancing period (Mar 2020 – Mar 2022). We will group diabetes patients into a food-secure group and a food-insecure group using answers from Social Determinants of Health survey. We will use HbA1c data from Feb 2018 to Feb 2020 for pre-COVID period as baseline and compare that with the data of the same patients from Mar 2020 to Mar 2022. We will also compare the eating behavior during COVID and medication adherence due to financial concerns between the two groups using additional survey questions. Demographic information such as race, sex, gender, education, employment, and income level from the Basics Survey will be used to control for confounding factors in the analysis and any statistically significant trends will be reported. Additionally, we would like to conduct a specific analysis focusing on the African-American patient population in Mississippi.

Anticipated Findings

We expect that patients with diabetes in the food insecure group experienced a greater increase in their HA1c and more diabetic complications than the those in the food secure group during COVID-19. The findings will aid in our understanding of rising HA1c levels in relation to food insecurity focusing on an underrepresented population in research and can shed light on disease prevention during a pandemic in such a vulnerable population. The outcome of our research may provide insights on reducing health disparity and improving health equity in an underrepresented population.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Yadanar Yukyi - Graduate Trainee, William Carey University
Thao Nguyen Van - Graduate Trainee, William Carey University
Zhitao Ming - Graduate Trainee, William Carey University
Fang Li - Graduate Trainee, William Carey University
Daniel Thompson - Graduate Trainee, William Carey University
Danielle Fastring - Other, William Carey University

Aleena Summer 2024 Project Working Copy

This workspace will investigate the relationship between COVID-19 infection and the new onset of mental health conditions including anxiety and depression after infection. We hope to determine whether there is a meaningful association between COVID-19 infection and the incidence of…

Scientific Questions Being Studied

This workspace will investigate the relationship between COVID-19 infection and the new onset of mental health conditions including anxiety and depression after infection. We hope to determine whether there is a meaningful association between COVID-19 infection and the incidence of these conditions in participants with no EHR-documented history before infection.

Project Purpose(s)

Disease Focused Research (COVID-19, Depression, Anxiety)
Educational

Scientific Approaches

We will ascertain diagnoses and time deltas from participant EHR. We will incorporate covariates from PPI (sex at birth, education, race, income, etc...) and potentially from COPE and other follow-up surveys. We will generate descriptive statistics and conduct logistic regression and Cox Proportional Hazards regression using R.

Anticipated Findings

Based on our review of the literature, we think it is possible that we could find a positive association between COVID-19 infection and new onset of anxiety and/or depression. However, we would not be able to discern whether this is due to some real biological mechanism or if the relationship is mediated by personal/socioeconomic variables beyond the scope of this dataset. In other words, we won't be able to tell whether COVID-19 infection somehow predisposes a person to a new mental health disorder, or if the experience of life in a pandemic-era context (which entailed loss of livelihood, alienation, grief, and uncertainty for many) is a greater contributor to the condition. This investigation will be a good start, however, and it is an interesting exercise for this student.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Aleena Syed - Undergraduate Student, University of Chicago
Andrew Craver - Project Personnel, University of Chicago

Duplicate of HIV outcomes in the context of the COVID-19 pandemic

Underrepresented populations, like racial or ethnic minority populations, are disproportionately affected by HIV and subsequently experience more adverse HIV clinical outcomes. The COVID-19 pandemic might magnify the risk of compromised clinical outcomes among underrepresented Persons with HIV (PWH) due to…

Scientific Questions Being Studied

Underrepresented populations, like racial or ethnic minority populations, are disproportionately affected by HIV and subsequently experience more adverse HIV clinical outcomes. The COVID-19 pandemic might magnify the risk of compromised clinical outcomes among underrepresented Persons with HIV (PWH) due to interruptions in healthcare access and other worsened socio-economic and environmental conditions. This study aims to:
1. Examine the impact of the COVID-19 pandemic on the change of HIV care continuum outcomes among a broadly defined underrepresented HIV population by harnessing the All of Us big data resources.
2. Personalized viral suppression prediction using advanced statistical analysis (e.g., artificial intelligence) by incorporating COVID-19 interruption, antiretroviral therapy history, preexisting conditions, psychological well-being (e.g., depression, anxiety), healthcare utilization, and social environmental factors in All of Us.

Project Purpose(s)

Disease Focused Research (TREATY TRADER OR INVESTOR (E))
Population Health
Social / Behavioral
Methods Development

Scientific Approaches

We will build HIV and COVID-19 datasets using data from all different domains of EHR and surveys. We will use R or Python to program and code the datasets. The statistical methods involve descriptive statistics (e.g., chi-square, t-test), regression models (e.g., logistic regression, Cox proportional hazard modelling), advanced matching methods (e.g., propensity score matching), and other advanced statistical methods (e.g., machine learning).

Anticipated Findings

The results from this project will facilitate the clinical identification of people with HIV among underrepresented populations with poor HIV care continuum outcomes and inform tailored HIV care management among this vulnerable group, particularly in the context of the COVID-19 pandemic. The proposed personalized viral suppression prediction can provide data-driven evidence on tailored HIV treatment strategies to different underrepresented populations, particularly in the face of unexpected interruptions like the COVID-19 pandemic, and eventually, serve towards the goal of ending the HIV epidemic in the US.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Xueying YANG - Research Fellow, University of South Carolina

Genetic Predispositions and Post Acute Sequelae of COVID-19 (PASC)

Post-acute sequelae of COVID-19 (PASC), also commonly referred to as 'Long COVID', involves a range of symptoms that continue for weeks to months after the initial recovery from a COVID-19 infection. The risk factors for developing PASC include severe initial…

Scientific Questions Being Studied

Post-acute sequelae of COVID-19 (PASC), also commonly referred to as 'Long COVID', involves a range of symptoms that continue for weeks to months after the initial recovery from a COVID-19 infection. The risk factors for developing PASC include severe initial COVID-19 symptoms, age, and pre-existing health conditions such as obesity, diabetes, and heart disease. Furthermore, immune system responses and genetic factors might influence susceptibility. The purpose of this research project is to test for any relationship between genetic predisposition for diseases such as obesity, diabetes, and and heart disease and risk for PASC. If any associations are identified, we will try to identify immune system characteristics that may link the genetic predisposition with risk for PASC. Hypotheses generated through this research will be validated in the NIH-funded RECOVER cohort established for the study of PASC.

Project Purpose(s)

Disease Focused Research (Post-acute Sequelae of COVID-19 (PASC))
Ancestry

Scientific Approaches

We will identify polygenic scores (PGS) for a set of complex health conditions, such as obesity, diabetes, and heart disease that have been generated using modern methods and validated on diverse cohorts. These PGS will be computed on the All of Us population to generate distributions of genetic risk. Relevant disease diagnoses and other relevant covariates, such as social determinants of health (SDOH), will be identified and assembled alongside genetic predispositions. If possible, Individuals with PASC will be identified using surveys and electronic health care records or other available data. We will perform an association analysis between genetic predispositions for health conditions associated with risk for PASC and the identified PASC cohort, as well as other relevant immune phenotypes, in the All of Us population.

Anticipated Findings

We will identify statistically significant associations between genetic predisposition for certain health conditions such as obesity, diabetes, and heart disease and a cohort of individuals reporting PASC, as well as any immune phenotypes or other biological phenotypes that could be explanatory in nature. The findings will be used to generate hypotheses for follow up validation studies performed in the NIH-funded RECOVER cohort, which consists of individuals who volunteered to participate to further the study of PASC, it's biological underpinnings, and possible treatments.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Andrew Magis - Senior Researcher, Institute for Systems Biology

Anxiety_GWASsumstats

We intend to study the genetic variation and factors that contribute to the presentation of various Anxiety phenotypes in individuals. Anxiety is one of the most common mental illnesses in the US. The COVID-19 pandemic triggered a 25% increase in…

Scientific Questions Being Studied

We intend to study the genetic variation and factors that contribute to the presentation of various Anxiety phenotypes in individuals. Anxiety is one of the most common mental illnesses in the US. The COVID-19 pandemic triggered a 25% increase in the prevalence of anxiety and other mental illnesses such as depression worldwide. By investigating the genetic components that potentially predispose individuals to anxiety, we would be one step closer to developing therapeutics and medicines to aid individuals with anxiety.

Project Purpose(s)

Methods Development
Ancestry

Scientific Approaches

We will be focusing on historically underrepresented groups in our investigation. Our research methods consist of creating a dataset, running a Genome Wide Association Study (GWAS), and then furthering our analysis by running a Transcriptome Wide Association Study (TWAS) with SPrediXcan.

Anticipated Findings

We are hoping to replicate our findings from previous work we have done.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Maya Sharma - Undergraduate Student, Loyola University Chicago

Fanconi Anemia Exploration

This workspace is created as the initial data exploration of patients who may have Fanconi Anemia (FA). I would like to build a cohort of FA patients by their SNOMED code. Since FA does not have a standard ICD9/ICD10 clinical…

Scientific Questions Being Studied

This workspace is created as the initial data exploration of patients who may have Fanconi Anemia (FA). I would like to build a cohort of FA patients by their SNOMED code. Since FA does not have a standard ICD9/ICD10 clinical diagnostic code, it is difficult to find additional FA patients using EHR data. In the National COVID Cohort Collaborative (N3C) Enclave, a computational phenotyping approach using semantic similarity was used to identify FA patients. Genetic analysis of pathogenic variants in any of the 23 FA genes may indicate the presence of FA and help to identify additional FA patients. The phenotypes of the FA cohort in will be assessed according to the standard FA diagnostic criteria that commons utilizes VACTERL-H and PHENOS. The goal of this workspace is to explore genetic and phenotypic data to find FA patients in preparation of duplicating the computational phenotyping approach in AoU, that was previously done in N3C.

Project Purpose(s)

Disease Focused Research (Fanconi's anemia)
Population Health
Social / Behavioral
Methods Development
Ancestry

Scientific Approaches

I plan to use the various OMOP tables to explore the phenotypes. The condition_occurrence and person tables will be used to gather phenotypes and demographics. The phenotypes will be assessed using existing criteria including VACTERL-H & PHENOS associations. Various methods for genotyping will be used to explore the optimal tools, methods, and workflows for FA genotyping.

Anticipated Findings

The exploration of various tools, methods, and workflows for FA genotyping would provide insight on the optimal approach to genotyping FA patients, which will serve as a biomarker to validate potentially known FA patients for the upcoming studies.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Evan Connelly - Research Associate, University of North Carolina, Chapel Hill

Disparities in Surgical Care

During the COVID-19 recovery phase, did the demographics of patients presenting for and receiving surgical care shift disparately? The COVID-19 pandemic caused dramatic shifts in surgical and procedural care, with many hospitals acutely limiting elective surgeries/procedures to reduce exposures. However,…

Scientific Questions Being Studied

During the COVID-19 recovery phase, did the demographics of patients presenting for and receiving surgical care shift disparately? The COVID-19 pandemic caused dramatic shifts in surgical and procedural care, with many hospitals acutely limiting elective surgeries/procedures to reduce exposures. However, after the pandemic, hospitals rapidly restored surgical volume to meet the backlog of cases, entering recovery phases. The impact of SDOH on access to care is increasingly recognized across a range of surgeries and procedures, and the pandemic accentuated SDOH, with Black and Latinx Americans in particular suffering disproportionate infection/mortality rates. While surgical volume recovered during the COVID-19 recovery phase, the impact of pandemic-induced changes in access to surgical care during the recovery phase has not been well described. By understanding if any demographic changes exist, health systems can develop strategies to ensure equitable access to elective surgical care.

Project Purpose(s)

Population Health

Scientific Approaches

Our analysis will focus on the All of Us database and Social Determinants of Health surveys. We will use statistical software packages in R and Python, including libraries like Pandas, NumPy, and SciPy, or SAS for data manipulation, analysis, and visualization. We will also use machine learning libraries such as Scikit-learn, TensorFlow, or PyTorch for implementing machine learning algorithms to detect significant predictors. We will also aim to use geospatial analysis tools like ArcGIS or QGIS to investigate neighborhood-level factors. We will use the United States Census Bureau 2014–2018 American Community Survey 5–year Estimates were used to determine ZIP code median income. We may also incorporate area deprivation index information into our study.

Anticipated Findings

This study will aim to examine whether the demographics of patients presenting and receiving elective surgical care shifted during the pandemic acute and recovery phases. As operating room volume was restored in the COVID-19 recovery phase, we expect to see that the demographics of patients presenting for and receiving surgical care shifted disparately among different race/ethnicity and socioeconomic groups.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Laleh Jalilian - Mid-career Tenured Researcher, University of California, Los Angeles

Collaborators:

Sonali Tamhankar - Senior Researcher, Fred Hutchinson Cancer Research Center
Daniel Brannock - Senior Researcher, All of Us Researcher Academy/RTI International

Impact of COVID-19 on the Hispanic Community

This Workspace will be used for the “Impact of COVID-19 on the Hispanic Community” Driver Project, that will go beyond validation and result in novel findings. The study aims to replicate findings from the following articles: “COVID-19 Pandemic: Disparate Health…

Scientific Questions Being Studied

This Workspace will be used for the “Impact of COVID-19 on the Hispanic Community” Driver Project, that will go beyond validation and result in novel findings. The study aims to replicate findings from the following articles: “COVID-19 Pandemic: Disparate Health Impact on the Hispanic/Latinx Population in the United States” (Gil et al. 2020), “Life in the Time of COVID-19: a Case Study of Community Health” (Schelly 2021), “Racial/Ethnic Disparities In COVID-19 Exposure Risk, Testing, And Cases At The Subcounty Level In California” (Reitsma et al. 2021), that shed light on how COVID-19 has disproportionately affected the Hispanic community in the United States. We will focus on replicating the findings as they relate to the social determinants of health (SDOH) such as income, education, coexisting medical conditions such as obesity and diabetes, lack of access to health care, language barriers, working conditions, and living conditions.

Project Purpose(s)

Social / Behavioral

Scientific Approaches

1. Has the Hispanic community been disproportionately affected by COVID-19?
What is the impact COVID-19 has had on the U.S. Hispanic Community?

2. What role have social determinants of health, such as income, education, and access to health care, played in the disproportionate effect of COVID-19 on the Hispanic community?

Anticipated Findings

We expect that the Hispanic cohort in the All of Us Research Program reflects the disproportionate effect of COVID-19 on the Hispanic community found in the aforementioned articles. We expect to see that a large proportion of Hispanics will have had COVID-19, or known someone who did, or lack access to healthcare facilities in the event of a COVID-19 infection, based on COPE Survey findings. These findings would reinforce the importance of taking social determinants of health into account when creating policy relating to access to health care and safety net programs for the Hispanic community in the United States.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

William Agyapong - Project Personnel, University of Texas at El Paso

Impact of COVID-19 on the Hispanic Community - Dataset v7

This Workspace will be used for the “Impact of COVID-19 on the Hispanic Community” Driver Project, that will go beyond validation and result in novel findings. The study aims to replicate findings from the following articles: “COVID-19 Pandemic: Disparate Health…

Scientific Questions Being Studied

This Workspace will be used for the “Impact of COVID-19 on the Hispanic Community” Driver Project, that will go beyond validation and result in novel findings. The study aims to replicate findings from the following articles: “COVID-19 Pandemic: Disparate Health Impact on the Hispanic/Latinx Population in the United States” (Gil et al. 2020), “Life in the Time of COVID-19: a Case Study of Community Health” (Schelly 2021), “Racial/Ethnic Disparities In COVID-19 Exposure Risk, Testing, And Cases At The Subcounty Level In California” (Reitsma et al. 2021), that shed light on how COVID-19 has disproportionately affected the Hispanic community in the United States. We will focus on replicating the findings as they relate to the social determinants of health (SDOH) such as income, education, coexisting medical conditions such as obesity and diabetes, lack of access to health care, language barriers, working conditions, and living conditions.

Project Purpose(s)

Social / Behavioral

Scientific Approaches

1. Has the Hispanic community been disproportionately affected by COVID-19?
What is the impact COVID-19 has had on the U.S. Hispanic Community?

2. What role have social determinants of health, such as income, education, and access to health care, played in the disproportionate effect of COVID-19 on the Hispanic community?

Anticipated Findings

We expect that the Hispanic cohort in the All of Us Research Program reflects the disproportionate effect of COVID-19 on the Hispanic community found in the aforementioned articles. We expect to see that a large proportion of Hispanics will have had COVID-19, or known someone who did, or lack access to healthcare facilities in the event of a COVID-19 infection, based on COPE Survey findings. These findings would reinforce the importance of taking social determinants of health into account when creating policy relating to access to health care and safety net programs for the Hispanic community in the United States.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Kyle Melin - Mid-career Tenured Researcher, University of Puerto Rico Medical Sciences
William Agyapong - Project Personnel, University of Texas at El Paso

Myalgic Encephalopathy, fibromyalgic, long COVID19

CSF/ME can often be misdiagnosed for a condition with shared presentation (e.g., fibromyalgia, chronic pain syndrome, post-viral fatigue syndrome). We hypothesise the trio of conditions, ME/CSF, Fibromyalgia, and Long COVID-19, plus their comorbidities, share causal pathways (biological mechanisms) and that…

Scientific Questions Being Studied

CSF/ME can often be misdiagnosed for a condition with shared presentation (e.g., fibromyalgia, chronic pain syndrome, post-viral fatigue syndrome). We hypothesise the trio of conditions, ME/CSF, Fibromyalgia, and Long COVID-19, plus their comorbidities, share causal pathways (biological mechanisms) and that these can be discovered using genetic and novel causal inference methods. This raises the possibility that these may not represent distinct clinical entities but disparate phenotypical expressions of the same disease. We will investigate:
1) The patterns of co-occurrence in the US population,
2) The risk factors shared between the diseases,
3) The shared genetics between the diseases, highlighting potentially modifiable mechanisms associated with the onset of ME/CSF.

Project Purpose(s)

Disease Focused Research (chronic fatigue syndrome)
Population Health
Ancestry

Scientific Approaches

We will perform genome-wide analyses for ME/CSF, Fibromyalgia and post-viral fatigue syndrome and additional long-term conditions using defined clinical code lists and the REGENIE software. We will use LD-score regression to estimate pairwise genetic correlations between the trio of conditions and additional long-term conditions with significance set at a false discovery rate (FDR) of 5%. We will investigate the role of common risk factors (e.g. blood pressure and BMI) in explaining the shared genetics between disease pairs (using partial LDSC). We will use causal inference methods (Mendelian Randomization) to confirm which modifiable risk factors drive comorbidities. We will use fine mapping and colocalization methods to identify specific shared causal variants, and bioinformatics databases to gain insight into common disease biology.

Anticipated Findings

This study will help understand the specific and shared causes of ME/CSF, fibromyalgia and long COVID19, and their interplay with common comorbidities. This new knowledge addresses current gaps in evidence and will help formulate new strategies and interventions for preventing or delaying the onset and managing these chronic illnesses affecting the population. The project should provide new ideas for prevention and management, tailored for specific patient needs, by applying the now well-established tools of genetic association in large populations to reveal causation and identify drug targets.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Joao Delgado - Other, University of Exeter

DB7 of CRS study

What are some of the significant characteristics of Covid 19 patients who lost sense of smell. Why important: to understand the potential cause of the loss of smell for Covid 19 Patients.

Scientific Questions Being Studied

What are some of the significant characteristics of Covid 19 patients who lost sense of smell.
Why important: to understand the potential cause of the loss of smell for Covid 19 Patients.

Project Purpose(s)

Disease Focused Research (covid 19)
Methods Development

Scientific Approaches

Build ML models to discover the potentail patterns for the Covid 19 patients who had smell lose

Anticipated Findings

Find significant features that can predict the smell lose for Covid 19 patients and potentially guide the recovery process of the patients

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Renjie Hu - Early Career Tenure-track Researcher, University of Houston
Meher Gajula - Graduate Trainee, University of Houston

Collaborators:

Tania Banerjee - Early Career Tenure-track Researcher, University of Houston
Thamer Alnazzal - Graduate Trainee, University of Houston
Roshan Dongre - Graduate Trainee, Houston Methodist Research Institute
Khoa Nguyen - Student, University of Houston
Najm Khan - Graduate Trainee, Rutgers, The State University of New Jersey
Koyal Ansingkar - Graduate Trainee, Houston Methodist Research Institute
Jagan Mohan Reddy Dwarampudi - Graduate Trainee, University of Houston
Faizaan Khan - Graduate Trainee, Houston Methodist Research Institute
Ying Lin - Early Career Tenure-track Researcher, University of Houston
Aatin Dhanda - Graduate Trainee, Rutgers, The State University of New Jersey
lichang zhu - Graduate Trainee, University of Houston
Boaz Adikaibe - Undergraduate Student, University of Houston

RECOVER

We are trying to understand the gaps in current aspects of long COVID in recent research and development efforts. In the form of a meta-analysis, we aim to identify current gaps in this space to highlight a need for further…

Scientific Questions Being Studied

We are trying to understand the gaps in current aspects of long COVID in recent research and development efforts. In the form of a meta-analysis, we aim to identify current gaps in this space to highlight a need for further investment.

Project Purpose(s)

Population Health

Scientific Approaches

We aim to analyze EHR datasets to find similarities among specific sub-domains and identify gaps in specific research initiatives. To this end, we will use standard clustering and natural language processing (NLP) approaches to accomplish our research objectives.

Anticipated Findings

We anticipate finding under-researched domains in the healthcare, drug development, and infectious disease spaces that have yet to be exhaustively investigated. We hope to inform the scientific community of these fundings to encourage downstream investment and initiatives in these spaces.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Mark Laurie - Graduate Trainee, Stanford University

Longitudinal serum cytokines and health outcomes in COVID-19 patients

How longitudinal serum cytokines (IL-6, IL-10, CRP etc.) is associated with intubation among COVID-19 patients? Understanding how longitudinal serum cytokines like IL-6, IL-10, and CRP correlate with severe outcomes in COVID-19 patients is critical. These cytokines are pivotal in immune…

Scientific Questions Being Studied

How longitudinal serum cytokines (IL-6, IL-10, CRP etc.) is associated with intubation among COVID-19 patients?
Understanding how longitudinal serum cytokines like IL-6, IL-10, and CRP correlate with severe outcomes in COVID-19 patients is critical. These cytokines are pivotal in immune response and their levels can indicate cytokine storm, which worsens inflammation and tissue damage. Tracking these markers over time helps predict disease severity and outcomes such as respiratory failure or death. This knowledge aids in timely intervention and personalized treatment, potentially improving patient outcomes amid the pandemic.

Project Purpose(s)

Disease Focused Research (COVID-19)
Population Health

Scientific Approaches

We are using a retrospective cohort study design to examine factors associated with intubation in COVID-19 patients.
Dependent Variable: Binary variable indicating whether intubation occurred (1) or did not occur (0).
Independent Variables: Demographics: Age, sex, race, and ethnicity.
Physical Measurements: BMI and pregnancy status.
Biomarkers: D-dimer, Interleukin-6 (IL-6), IL-10, CRP. Collect biomarker data at specific times relative to the diagnosis of COVID-19.
COVID Vaccine Status: Document vaccination status and dates.
Drug: Record medications administered and their timing relative to COVID-19 diagnosis.

Repeated measures logistic regression will be performed to assess the relationship between the biomarkers and intubation status. Adjusted odds ratios (aOR) will be reported with their 95% confidence intervals. We will also examine potential interaction effects potential interactions between independent variables (e.g., biomarkers and demographics).

Anticipated Findings

We anticipate to reveal that elevated levels of biomarkers such as D-dimer, IL-6, IL-10, and CRP are significantly associated with increased odds of intubation. This could underscore the role of systemic inflammation and coagulopathy in disease severity. In the meantime, the we also would like to know the impact of demographic factors that older age, male sex, and specific racial or ethnic groups are more prone to requiring intubation, highlighting demographic disparities in COVID-19 outcomes.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Weize Wang - Project Personnel, Florida International University

Covid Vaccine Uptake CT

The primary purpose of using All of Us data is to investigate health behaviors, outcomes, access, and disparities in populations. Epidemiologic characterization of covid vaccination uptake among All of Us participants. We plan to explore demographic differences in covid vaccine…

Scientific Questions Being Studied

The primary purpose of using All of Us data is to investigate health behaviors, outcomes, access, and disparities in populations. Epidemiologic characterization of covid vaccination uptake among All of Us participants. We plan to explore demographic differences in covid vaccine uptake, the association between covid vaccination and usual source of care, and the association between the presence of existing morbidities and covid vaccine uptake.

Project Purpose(s)

Population Health

Scientific Approaches

The Covid Minute Survey, Healthcare Access & Utilization Survey, The Basics Survey, and the Personal and Family Health History Survey will be used in this project. Rate/odds ratios will be used to characterize differences between groups.

Anticipated Findings

Epidemiologic characterization of covid vaccination uptake among All of Us participants will assist in future analysis of covid vaccine uptake among participants and in an effort to reduce health disparities and improve health equity in underrepresented populations

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Randi Henderson-Mitchell - Early Career Tenure-track Researcher, University of Alabama
Hui Wang - Project Personnel, University of Alabama
Graham Jones - Graduate Trainee, University of Alabama

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