Research Projects Directory

Duplicate of sociodemographic and genetic determinants of infectious diseases

For most infectious diseases, there are considerable disparities in outcomes among diverse racial and ethnic communities. The COVID-19 pandemic brought the issue of racial/ethnic disparities in infectious disease outcomes to the forefront of science and medicine with several studies showing…

Scientific Questions Being Studied

For most infectious diseases, there are considerable disparities in outcomes among diverse racial and ethnic communities. The COVID-19 pandemic brought the issue of racial/ethnic disparities in infectious disease outcomes to the forefront of science and medicine with several studies showing that infection incidence was significantly higher, and the outcomes were more severe among African Americans and Hispanics compared to matched white Americans. We showed a similar pattern for other infectious diseases within our health system, where most infectious diseases are significantly more prevalent in Filipino, African American, and Puerto Rican communities compared to white Americans in New York City. Identifying the factors contributing to disparities in infectious disease outcomes and understanding their relative role in each community is essential for reducing the burden of infectious diseases, improving health equity, and reducing the high costs associated with health inequity.

Project Purpose(s)

Disease Focused Research (disease by infectious agent)

Scientific Approaches

we will:
define fine-scale communities by combining self-reported race/ethnicity with genetically inferred ancestry data, compare the prevalence of infectious diseases in sub-groups and identify infectious diseases that are differentially prevalent among sub-groups adjusted for compare demographic, behavioral, and social determinants of health, perform global ancestry inference, test the association of global ancestry proportions with infection outcome accounting for demographic, behavioral, and social determinants of health, for infectious diseases that are significantly associated with genetic ancestry, we will perform admixture mapping

Anticipated Findings

Our results will be valuable in understanding inequities in environmental and sociodemographic contributors of health and the role of these factors in disparities in infectious disease outcomes. Our findings will broaden our understanding of both human evolutionary history and human genetic factors contributing to differential susceptibility to present-day pathogens. Together these results can contribute to developing more effective therapies and public health policies to reduce the burden of infectious disease in an equitable manner. Beyond this project, our fine-scale population structure results, global and local ancestry inferences, and data about the prevalence of infectious diseases and demographic, behavioral, and social determinants of health in communities will be available to the scientific community and All of US users as a resource for future similar studies on other diseases.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Samira Asgari - Early Career Tenure-track Researcher, Icahn School of Medicine at Mount Sinai

Collaborators:

Abhijith Biji - Graduate Trainee, Icahn School of Medicine at Mount Sinai

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

Collaborators:

Yichen Li - Research Fellow, University of South Carolina
Ruilie Cai - Graduate Trainee, University of South Carolina
Fanghui Shi - Graduate Trainee, University of South Carolina
Yuanhao Cai - Graduate Trainee, University of Minnesota
Buwei He - Graduate Trainee, University of South Carolina
Jiajia Zhang - Late Career Tenured Researcher, University of South Carolina

Common Addictions vs COVID-19

We hope to answer the question of if alcohol and tobacco addictions can affect the likelihood of taking the COVID-19 vaccine due to changes in the brain chemistry, as well as if there are potential health risks of taking the…

Scientific Questions Being Studied

We hope to answer the question of if alcohol and tobacco addictions can affect the likelihood of taking the COVID-19 vaccine due to changes in the brain chemistry, as well as if there are potential health risks of taking the vaccine and booster shots when taking large amounts of alcohol and tobacco. The reason we are exploring this data is to find some correlative data between addictions and potential negative effects that are not common knowledge among the public. If strong correlational data along with scientific evidence to explain the correlations are released, it could help dissuade some people from either using addictive substances, or from furthering their addictions. By using a recent topic which people are afraid of (SARS-CoV-2/COVID-19) the dangers of taking alcohol or tobacco become more tangible to many than hearing about the potential of developing (for example) lung cancer.

Project Purpose(s)

Educational

Scientific Approaches

We will be pulling data from "Drug Exposures"-- specifically tobacco and alcohol --; a Lifestyle survey question asking whether participants drank alcohol/took tobacco before or not (this data includes frequency of consumption); a dataset from COPE where participants were asked if they drank alcohol during a period between May-July 2020 and their frequency during each month; COPE Minute Survey which asks participants whether or not they took the COVID-19 vaccine, as well participants judgement of their likelihood of ever taking the vaccine, and their reasons for their judgement; and COPE datasets about tobacco usage. For tools and methods, we will collect the data into the workspace, then analyse the data using python in order to create graphs showing the correlation between two data groups. If a strong correlation is found, we will look for scientific justifications and reasoning as to how the two data sets might be related (causation).

Anticipated Findings

Alcohol and tobacco consumption will have a negative affect on the likelihood of taking the COVID-19 vaccine (less likely). Effects on the brain due to addiction, such as sedative effects, is the main reason we believe that alcohol may have an effect on hesitancy.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Zheyang Wu - Late Career Tenured Researcher, Worcester Polytechnic Institute
Aaron Mathieu - Teacher/Instructor/Professor, Acton-Boxborough Regional School District
Ryan Lee - Student, Acton-Boxborough Regional School District
Aryan Berube - Student, Acton-Boxborough Regional School District

covid_vaccine_and_heart_rate

Heart rate, as one of the physiological indicator of myocarditis, is investigated before and after COVID-19 vaccination.

Scientific Questions Being Studied

Heart rate, as one of the physiological indicator of myocarditis, is investigated before and after COVID-19 vaccination.

Project Purpose(s)

Educational

Scientific Approaches

We aim to target at all populations regardless of age, sex and race. However, given that there are studies suggesting that males in their 20s are more suspectible, we may restrain ourselves to study only male or female individuals, with a smaller range of age (e.g., 18-39 or 18 to 65). Speciﬁcally, we will look at individuals with or without related health conditions (e.g., contracted with COVID-19, has other types of chronical cardio related diseases).

Anticipated Findings

There is or isn't a significant correlation between vaccination and heart rate change. As a teaching tool, this research will be the first step of studying the safety of mRNA vaccines

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Zheyang Wu - Late Career Tenured Researcher, Worcester Polytechnic Institute
Yue Bao - Graduate Trainee, Worcester Polytechnic Institute

Metformin Association with PASC

The overall goal of this research is to evaluate the association between use of metformin prior to COVID-19 illness and subsequent incidence of PASC compared to patients who were prevalent users of other diabetes medications.

Scientific Questions Being Studied

The overall goal of this research is to evaluate the association between use of metformin prior to COVID-19 illness and subsequent incidence of PASC compared to patients who were prevalent users of other diabetes medications.

Project Purpose(s)

Disease Focused Research (Postacute sequelae of SARS-CoV-2 infection (PASC))

Scientific Approaches

Using condition and medication information in the Controlled Tier dataset, we will look for associations between patients who used different diabetes medications prior to a COVID-19 infection to quantify their risk of developing PASC. An analytic fact table will be developed and data will be analyzed using Python and SQL. The study design is a retrospective cohort analysis using trial emulation techniques in adults with documented SARS-CoV-2 infection. The index date will be the date of first documented SARS-CoV-2 infection, and the exposure of interest: existing metformin or other diabetes medication prescription. The outcome of interest is a subsequent diagnosis of PASC.

Anticipated Findings

In vitro data show metformin inhibits severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus and pathogenic inflammatory responses to the virus. Clinical trial data show metformin prevents severe Covid-19 and Long Covid. We anticipate seeing an association with metformin use and the risk of developing PASC.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Steve Johnson - Early Career Tenure-track Researcher, University of Minnesota
Lisiane Pruinelli - Mid-career Tenured Researcher, University of Minnesota

Collaborators:

Tim Meyer - Project Personnel, University of Minnesota
Ragnhildur Bjarnadottir - Early Career Tenure-track Researcher, University of Florida
Marisa Sileo - Project Personnel, Boston Children's Hospital

COVID-19 Puerto Rican Resilience Comparison

The COVID-19 pandemic has had a negative impact on the mental health of individuals worldwide. The social isolation, financial hardships, healthcare crisis and overall uncertainty about the future has increased the probability of anxiety and depression. These trends are highly…

Scientific Questions Being Studied

The COVID-19 pandemic has had a negative impact on the mental health of individuals worldwide. The social isolation, financial hardships, healthcare crisis and overall uncertainty about the future has increased the probability of anxiety and depression. These trends are highly visible in Puerto Rico, where the usage of mental hotlines have doubled during the pandemic. However, a preliminary analysis of assessments and surveys by the Montalvo-Ortiz lab have suggested a high degree of resilience to depression in the Puerto Rican population (N=200). In this project I will expand on this proposal by comparing responses from the Montalvo-Ortiz questionnaires with responses from individuals within the United States to identify any differences in healthcare outcomes from the COVID-19 pandemic, controlled for across differing sociodemographic factors.

Project Purpose(s)

Control Set

Scientific Approaches

The Montalvo-Ortiz and All of Us questionnaires include sociodemographic information pertaining to the participant’s gender, age, ethnic group, financial situation, educational background and region of origin. Moreso, they include information about health including diagnoses, stress levels, depression, suicidality, mental health treatment history, sleep quality, smoking use, alcohol use, post-traumatic stress, and self-reported resilience. The datasets will be stratified across the differing sociodemographic statuses using R, to compare the different health outcomes after the COVID-19 pandemic. This analysis is integral in understanding whether there may be a genetic underpinning that provides certain groups with a higher capacity to endure adversity.

Anticipated Findings

The anticipated findings are that we will find a difference between the two populations. This analysis is integral in understanding whether there may be a genetic underpinning that provides certain groups with a higher capacity to endure adversity.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Rachel Eloy - Undergraduate Student, University of Florida

Collaborators:

Paola Giusti-Rodriguez - Other, University of Florida

Anxiety

We intend to study the genetic variation and factors that contribute to the presentation of Anxiety in individuals. Anxiety is one of the most common mental illnesses in the US, affecting 19.1% of the adult population. Additionally, the COVID-19 pandemic…

Scientific Questions Being Studied

We intend to study the genetic variation and factors that contribute to the presentation of Anxiety in individuals. Anxiety is one of the most common mental illnesses in the US, affecting 19.1% of the adult population. Additionally, 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

Registered Tier

Research Team

Owner:

Maya Sharma - Undergraduate Student, Loyola University Chicago

AUD_MH_Genomics_v7_v2

Our scientific question is about the health disparity in alcohol use disorder (AUD), substance use disorder (SUD), and mental health, as well as the impact of the COVID pandemic on such health disparity. The COVID pandemic has been bringing financial,…

Scientific Questions Being Studied

Our scientific question is about the health disparity in alcohol use disorder (AUD), substance use disorder (SUD), and mental health, as well as the impact of the COVID pandemic on such health disparity. The COVID pandemic has been bringing financial, social, and psychological burdens, which are known risk factors for SUD and mental problems. Populations from minority groups, being socioeconomically disadvantaged, of younger ages, or with limited access to corresponding health care are at particularly higher risk of developing SUD or mental problems. Adolescents and young adults are also at higher risk. The understanding of how social determinants of health (SDoHs) are associated with the risk of new SUD and mental health problems will help better support the high-risk populations during and after the COVID pandemic.

Project Purpose(s)

Disease Focused Research (Alcohol use disorder, substance use disorder, and mental health )
Population Health
Social / Behavioral
Drug Development
Methods Development
Ancestry
Ethical, Legal, and Social Implications (ELSI)

Scientific Approaches

We plan to use the survey data, including the COVID-19 Participant Experience (COPE), the Basics, the Personal Medical History, the Family Heath History as well as the Conditions in EHR Domain data set and the genetics data to identify newly developed SUD and mental health issues occurred during the COVID-19 pandemics as well as SDoHs and other major risk factors. Logistic regression models will be used to identify the major risk factors. We will also explore whether graph artificial intelligence models can be used to disentangle the effects of SDoHs from other risk factors.

Anticipated Findings

We expect to quantitatively identify major risk factors, especially SDoHs, for AUD/SUD and for mental health issues. Such knowledge can help better understand the health disparity as well as impact of COVID on public health. A prediction model will also be developed to identify high-risk populations.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jing Su - Early Career Tenure-track Researcher, Indiana University

Collaborators:

Yao Chen - Graduate Trainee, Indiana University
Chenxi Xiong - Graduate Trainee, Indiana University
Tae-Hwi An - Early Career Tenure-track Researcher, Indiana University
Shihui Jiang - Project Personnel, Indiana University
Netsanet Gebregziabher - Project Personnel, Indiana University
Baifang Zhang - Project Personnel, Indiana University
Dongbing Lai - Project Personnel, Indiana University
Colin Hoffman - Project Personnel, Indiana University
Allison Gatz - Graduate Trainee, Indiana University
Chi Nguyen - Project Personnel, Indiana University

SUD

Evaluating Substance Use Disorder as it relates to Covid-19 Outcomes

Scientific Questions Being Studied

Evaluating Substance Use Disorder as it relates to Covid-19 Outcomes

Project Purpose(s)

Disease Focused Research (substance-related disorder)
Educational
Methods Development
Control Set

Scientific Approaches

Fill in later

Anticipated Findings

Fill in later

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Clarence White - Graduate Trainee, Meharry Medical College

Collaborators:

Wajehah Sanders - Graduate Trainee, Meharry Medical College
Aize Cao - Early Career Tenure-track Researcher, Meharry Medical College
Jeffrey Goodwin - Early Career Tenure-track Researcher, Meharry Medical College

Mental health impact of COVID-related policies (v7)

Previous studies have reported substantial variations in the prevalence of psychiatric disorders and the distributions of social determinants of mental health outcomes. However, it remains unknown if we would observe similar patterns during the COVID-19 pandemic. We are interested in…

Scientific Questions Being Studied

Previous studies have reported substantial variations in the prevalence of psychiatric disorders and the distributions of social determinants of mental health outcomes. However, it remains unknown if we would observe similar patterns during the COVID-19 pandemic. We are interested in exploring social determinants of health that are known to vary substantially across the geographic regions in the United States and examining their relationship with mental health outcomes during the pandemic.

Project Purpose(s)

Disease Focused Research (psychiatric disorders)
Population Health
Social / Behavioral

Scientific Approaches

We plan to use demographic and socioeconomic characteristics measured using the baseline survey, COVID-related measures from both the COPE surveys, and electronic health records in terms of datasets. We will analyze these data using statistical methods ranging from mixed-effects regression models (when modeling repeated COPE survey measurements) to causal inference methods to estimate the potential causal effects of COVID-related policies on mental health outcomes during the pandemic.

Anticipated Findings

Taking into account the regional variations in both the COVID-related policies and baseline prevalence of mental health outcomes would help us minimize confounding by geographic regions and thus allow us to produce potential causal evidence that could inform policies and interventions.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Younga Lee - Research Fellow, Mass General Brigham

Collaborators:

Yingzhe Zhang - Graduate Trainee, Mass General Brigham
Yu Zhou - Project Personnel, Mass General Brigham
Mihael Cudic - Research Fellow, Mass General Brigham
Justin Tubbs - Research Fellow, Mass General Brigham

Comparing self-reported and Fitbit data in All of Us program participants

Research Question (RQ): How are sociodemographic characteristics correlated with self-reported physical activity levels and Fitbit data among individuals who have participated in the COVID-19 participant experience survey within the All of Us dataset? Hypotheses: Ho: There is no statistically significant…

Scientific Questions Being Studied

Research Question (RQ): How are sociodemographic characteristics correlated with self-reported
physical activity levels and Fitbit data among individuals who have participated in the COVID-19
participant experience survey within the All of Us dataset?

Hypotheses:
Ho: There is no statistically significant relationship between sociodemographic characteristics and
self-reported physical activity levels and Fitbit data among individuals who have taken part in the
COVID-19 participant experience survey within the All of Us dataset.

H1: There is a statistically significant relationship between sociodemographic characteristics and the
accuracy of self-reported physical activity levels and Fitbit data among individuals who have
participated in the COVID-19 participant experience survey within the All of Us dataset.

Project Purpose(s)

Methods Development

Scientific Approaches

Various sociodemographic characteristics including age, sex, income, education, and race/ethnicity, will be summarized using frequencies, percentages, means, and standard deviations, as appropriate. The self-reported physical activity measures will derive scales and subscales by analyzing the participants' responses. This encompasses the computation of the cumulative score for high intensity and moderate-intensity physical exertion, the duration of walking, and the amount of time spent in a sedentary position.
Pearson correlation coefficients between each sociodemographic variable and the accuracy measures of self-reported physical activity levels and Fitbit data will be calculated to determine the association between sociodemographic characteristics and the precision of self-reported physical activity levels and Fitbit data as objective measures.

Anticipated Findings

The study's results will offer insights into the accuracy and consistency of self-reported
physical activity data, with implications for both the research initiatives of the All of Us program
and public health interventions. This knowledge has the potential to contribute to the
formulation of precise and efficient strategies aimed at promoting physical activity and
enhancing overall health outcomes within the diverse participant population encompassed by
the All of Us dataset. Also, understanding how sociodemographic characteristics can impact the
precision of self-reported and objective physical activity data can shape the program's future
approaches to gathering and analyzing data, enabling more thorough evaluations of participants'
physical activity patterns.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

SUBIN PARK - Graduate Trainee, A.T. Still University of Health Sciences

Duplicate of 2023 Capstone Project 2

Social determinants of health (SDoH) are the non-medical factors that influence health outcomes. The US Department of Health and Human Services categorizes SDoH into five main groups: economic stability, education access and quality, health care access and quality, neighborhood and…

Scientific Questions Being Studied

Social determinants of health (SDoH) are the non-medical factors that influence health outcomes. The US Department of Health and Human Services categorizes SDoH into five main groups: economic stability, education access and quality, health care access and quality, neighborhood and built environment, and social and community context. In this project, we will explore how and to what extent SDoH impact the outcomes of patients across long COVID patients. SDoH strongly influence health inequities. Within and between countries, lower socioeconomic status translates to lower health status.
The goal of this project is  to track health care resource utilization and well-being outcomes for patients who did and did not contract COVID and stratify them into demographic profiles to understand who is at greater risk of these long COVID symptoms.

Project Purpose(s)

Disease Focused Research (Post-acute COVID-19)
Educational

Scientific Approaches

Our work will largely rely upon the survey (Basics, SDoH, COPE, HAU) and electronic health record data in the All Of Us database. We will use participants' responses to questions about their medical spending habits, health coverage, or neighborhood as indicators of one of the five categories of SDoH. After preliminary exploratory data analysis and data visualization, we will experiment with designing a variety of statistical analysis and machine learning models. Methods may include techniques such as Markov chain Monte Carlo simulations or multivariate logistic regression. Our team will use R and Python programming languages on the notebooks provided by the site.

Anticipated Findings

Overall, we expect that patients from a lower socioeconomic class will have less favorable outcomes. Those with less access to care, a lower income level, and a lower education level likely experience greater obstacles to receiving healthcare. However, we are not sure of the specifics. Other demographic information may come into play and different SDoH may have different impacts across the major diseases that we will examine.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Zhengbang Yang - Graduate Trainee, University of California, Irvine
Manchen Wang - Graduate Trainee, University of California, Irvine
Haoyuan Tu - Graduate Trainee, University of California, Irvine
Shawn DeLuz - Graduate Trainee, University of California, Irvine
Meixuan Liu - Graduate Trainee, University of California, Irvine

ABO PheWAS - v7

Research questions: 1) Can our novel ABO blood typing algorithm using genetic data be used effectively to extensively type ABO subtypes from whole genome sequencing and array data in a diverse cohort? 2) Will a SNP approach for ABO blood…

Scientific Questions Being Studied

Research questions:

1) Can our novel ABO blood typing algorithm using genetic data be used effectively to extensively type ABO subtypes from whole genome sequencing and array data in a diverse cohort?
2) Will a SNP approach for ABO blood typing be concordant with available serotype?
3) What disease association ABO blood types can be replicated using the AllofUs dataset?
4) What novel disease associations, if any, with ABO blood types can be identified in a diverse cohort?

Relevance: Genomic variation in RBC and antigens is associated with a myriad of conditions. The ABO locus alone is associated with many conditions including venous thromboembolism (VTE), pancreatic cancer, malaria, and COVID-19. Furthermore, it is not common practice to extensively type beyond the traditional ABO blood groups, and the studies that do so are primarily done in individuals of European ancestry. Thus, we seek to do the first PheWAS on extensively typed RBC antigens and to do so in a diverse cohort.

Project Purpose(s)

Disease Focused Research (red blood cell (RBC) antigen-associated diseases)

Scientific Approaches

We plan to employ a blood typing algorithm to extensively type RBC antigens from 1) whole genome sequencing and 2) array data in the AllofUs cohort, and compare the two outcomes. Then, we plan to employ the phenome-wide association study (PheWAS) approach to identify associations between RBC antigen types and other clinical phenotypes. PheWAS will be carried out using multivariable linear regression and logistic regressions with ABO blood groups with our novel ABO blood type. For example, in the case of the ABO blood group, ABO blood subtypes (A101, A102, Aw01, B101, etc.) will act as the independent variable and phenotypes, derived from participant provided information (PPI) electronic health records (EHR), as the dependent variable. Initial models will include adjustments for age, gender, and race/ethnicity. Differential associations by race/ethnicity, gender, and sex will also be evaluated.

Anticipated Findings

This proposed project aims to test our novel ABO blood typing algorithm on WGS and array data in the diverse AllofUs cohort. We also aim to replicate known RBC-disease associations as well as identify any novels ones that may be identified within a diverse cohort.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Kiana Martinez - Research Fellow, University of Arizona
Jason Karnes - Early Career Tenure-track Researcher, University of Arizona

Collaborators:

Anthony Vicenti - Project Personnel, University of Arizona
Sadaf Raoufi - Graduate Trainee, University of Arizona
Rudramani Pokhrel - Other, University of Arizona
Jason Giles - Research Fellow, University of Arizona
Andrew Klein - Graduate Trainee, University of Arizona

"Official State" Language Equity

With a growing diverse, immigrant-based population in the US we need to ensure that everyone has the healthy literacy and knowledge to protect us all. In the US with many speaking another primary language or language other than English at…

Scientific Questions Being Studied

With a growing diverse, immigrant-based population in the US we need to ensure that everyone has the healthy literacy and knowledge to protect us all. In the US with many speaking another primary language or language other than English at home, states that have put forward "English-only" legislation require government information (health, voting information, personal rights) are limiting the scope and reach of this information that these institutions themselves consider vital.

My current project looks to see how the communities in these different states are impacted in their education, workplace safety, COVID understanding, and neighborhood based on "English-only" verse other language requirements or no requirements.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

1) Three-digit zipcode related data to show comparisons between Illinois and Conneticut which have a similar Limited English Proficiency population and Immigrant population, but differ for state language (Illinois is English Only)
2) Consider socio-demographic variables, but also adjust the analysis for health and healthcare access variables including insurance, chronic conditions, primary care. etc.
3) Outcomes of COVID-19 understanding, protection access, and self-reported health

Anticipated Findings

Current research highlights how trust in the source of information influences following guidance and was especially the case related to the government during the COVID-19 pandemic and initial rollout of protective behavior advice and vaccination. Studies have also highlighted how Limited English Proficiency is an additional barriers to immigrants and those who speak another language than English at home. The combination of the government only being required to share information in English may further have immigrants feel isolated and unable to receive necessary healthcare.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Katherine Goscilo - Early Career Tenure-track Researcher, University of Chicago

Hepatitis C Positivity During the COVID-19 Pandemic Era

How has HCV positivity changed before and after the COVID-19 pandemic? Hepatitis C is the most common bloodborne infection in the United States. It is possible that HCV positivity increased over the pandemic creating a higher public health burden.

Scientific Questions Being Studied

How has HCV positivity changed before and after the COVID-19 pandemic? Hepatitis C is the most common bloodborne infection in the United States. It is possible that HCV positivity increased over the pandemic creating a higher public health burden.

Project Purpose(s)

Disease Focused Research (hepatitis C)

Scientific Approaches

This project will most probably utilize an interrupted time series analysis (dependent on the available data).

Anticipated Findings

We anticipate finding differences in HCV infection before and after the pandemic. These findings may help to better understand the impact of global health crises on the health of the U.S population.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jonathan Moore - Research Fellow, University of Idaho

Covid-19 Exploration RSP

Identifying associations between socioeconomic and environmental factors with covid outcomes and survival.

Scientific Questions Being Studied

Identifying associations between socioeconomic and environmental factors with covid outcomes and survival.

Project Purpose(s)

Disease Focused Research (COVID-19)
Population Health
Social / Behavioral

Scientific Approaches

Longitudinal study of COVID outcomes using survival analysis in R; Association studies with linear regression.

Anticipated Findings

We hope to find significant factors associated with COVID-19 outcomes and survival using the self-reported and linked EHR data from All of Us. This will help us better understand COVID-19.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Natalia Morado-Mata - Undergraduate Student, University of Texas, San Antonio

Covid/ depression

The impacts of Covid on the increasing prevalence rate of depression during the lockdown

Scientific Questions Being Studied

The impacts of Covid on the increasing prevalence rate of depression during the lockdown

Project Purpose(s)

Other Purpose (Supportive research )

Scientific Approaches

I would like to do temporal analysis covid and depression over the year 2018- to 2022

Anticipated Findings

That covid 19 may have some impact on the prevalence rate of depression

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Hanna Wessenu - Graduate Trainee, George Mason University

2023 Capstone Project

Social determinants of health (SDoH) are the non-medical factors that influence health outcomes. The US Department of Health and Human Services categorizes SDoH into five main groups: economic stability, education access and quality, health care access and quality, neighborhood and…

Scientific Questions Being Studied

Social determinants of health (SDoH) are the non-medical factors that influence health outcomes. The US Department of Health and Human Services categorizes SDoH into five main groups: economic stability, education access and quality, health care access and quality, neighborhood and built environment, and social and community context. In this project, we will explore how and to what extent SDoH impact the outcomes of patients across long COVID patients. SDoH strongly influence health inequities. Within and between countries, lower socioeconomic status translates to lower health status.
The goal of this project is  to track health care resource utilization and well-being outcomes for patients who did and did not contract COVID and stratify them into demographic profiles to understand who is at greater risk of these long COVID symptoms.

Project Purpose(s)

Disease Focused Research (Post-acute COVID-19)
Educational

Scientific Approaches

Our work will largely rely upon the survey (Basics, SDoH, COPE, HAU) and electronic health record data in the All Of Us database. We will use participants' responses to questions about their medical spending habits, health coverage, or neighborhood as indicators of one of the five categories of SDoH. After preliminary exploratory data analysis and data visualization, we will experiment with designing a variety of statistical analysis and machine learning models. Methods may include techniques such as Markov chain Monte Carlo simulations or multivariate logistic regression. Our team will use R and Python programming languages on the notebooks provided by the site.

Anticipated Findings

Overall, we expect that patients from a lower socioeconomic class will have less favorable outcomes. Those with less access to care, a lower income level, and a lower education level likely experience greater obstacles to receiving healthcare. However, we are not sure of the specifics. Other demographic information may come into play and different SDoH may have different impacts across the major diseases that we will examine.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Zhengbang Yang - Graduate Trainee, University of California, Irvine
Manchen Wang - Graduate Trainee, University of California, Irvine
Haoyuan Tu - Graduate Trainee, University of California, Irvine
Shawn DeLuz - Graduate Trainee, University of California, Irvine
Meixuan Liu - Graduate Trainee, University of California, Irvine

Covid-19 Vaccine Hesitancy

What populations have increased vaccine hesitancy to the Covid 19 vaccine? The pandemic provided many firsts in the world of medicine, including the development of the mRNA vaccine against Covid-19. The US population was very polarized in their decision to…

Scientific Questions Being Studied

What populations have increased vaccine hesitancy to the Covid 19 vaccine? The pandemic provided many firsts in the world of medicine, including the development of the mRNA vaccine against Covid-19. The US population was very polarized in their decision to get vaccinated or not. This research project will investigate whether there were certain groups that were more or less hesitant to get this particular vaccine.

Project Purpose(s)

Educational

Scientific Approaches

We will use the COPE and Minute Survey for Covid-19 Vaccines to analyze different factors such as race, income, gender, geographic area, education, etc.

Anticipated Findings

We anticipate hesitancy to be highest in populations that have low education and low socioeconomic status.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Morgan Decker - Graduate Trainee, Philadelphia College of Osteopathic Medicine

Lung Function and COVID

Does previous COVID-19 infection impair lung function? The aim of this study is to evaluate the impact of previous COVID-19 infections on lung function and to examine the impact of other covariates (age, gender, smoking status, race, physical activity, sleep,…

Scientific Questions Being Studied

Does previous COVID-19 infection impair lung function? The aim of this study is to evaluate the impact of previous COVID-19 infections on lung function and to examine the impact of other covariates (age, gender, smoking status, race, physical activity, sleep, severity and frequency of COVID infection, vaccination history, and comorbidity) on this association.

Project Purpose(s)

Disease Focused Research (Long-term effect of COVID on lung function)

Scientific Approaches

Approach: Cross-sectional design
Data set: COVID-19 Survey data, electronic health records, and fit-bit data.

Anticipated Findings

To see how the severity and frequency of previous COVID-19 infections affect lung function decline and to classify the type of impairment common among COVID-19 survivors.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Ayodele Akinremi - Early Career Tenure-track Researcher, Hampton University

Neuromuscular disorders

We propose a study looking at the incidence and prevalence of ALS, myasthenia gravis, inclusion body myositis, and CIDP in the “All of Us” dataset. We would plan on using the All of Us research program and extract baseline data…

Scientific Questions Being Studied

We propose a study looking at the incidence and prevalence of ALS, myasthenia gravis, inclusion body myositis, and CIDP in the “All of Us” dataset. We would plan on using the All of Us research program and extract baseline data from patients age 18 and older who had diagnosis of the diseases listed above. We would plan on calculating the age standardized disease prevalence according to the age distribution. Cases of these disease processes would be identified using ICD-9 and ICD-10 codes, as well as SNOMED codes. We would calculate the prevalence of these disease processes and 95% confidence intervals among participants across the age and self-identified racial and ethnic groups using the Wald method. Additionally, we will plan on doing a case control study in the all of us research program to evaluate the association of myasthenia gravis, CIDP, and inclusion body myositis with covid-19 infection. Risk factors would be compared between cases and controls using multivariable analyzes.

Project Purpose(s)

Disease Focused Research (inclusion body myositis)

Scientific Approaches

The dataset we will plan on using is the All of Us Dataset. We will do statistical analysis to help calculate the differences between groups and outcomes.

Anticipated Findings

This study would be important in two ways. One would be to help understand the racial and ethnic differences amongst groups with common neuromuscular disorders; the second would be to understand the association between these disorders in COVID-19. The goal would be to better the outcomes of these patients by leading to future research.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Shani Evans - Project Personnel, Yale University
Reza Peyravi - Graduate Trainee, Yale University
Adeel Zubair - Early Career Tenure-track Researcher, Yale University

Disparities in COVID-Related Stress Exposure and Impact on Health Outcomes

During the COVID-19 pandemic, individuals may encounter changes in their work environment, financial situation, living conditions, and may have directly or indirectly been impacted by COVID-19 infection. These types of changes and experiences have been conceptualized as COVID-related stressors. The…

Scientific Questions Being Studied

During the COVID-19 pandemic, individuals may encounter changes in their work environment, financial situation, living conditions, and may have directly or indirectly been impacted by COVID-19 infection. These types of changes and experiences have been conceptualized as COVID-related stressors. The overarching goal of this study is to model these COVID-related stressors using a person-centered approach and identify latent classes of individuals who demonstrate different patterns of exposure to COVID-related stressors. After the identification of latent classes, we plan to identify demographic groups that might be at risk for greater COVID-related stress exposure as captured by the latent classes. Furthermore, we plan to examine latent class differences in a range of mental, behavioral, and physical health outcomes.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We plan to analyze data from the All of Us COPE Survey. The list of COVID-related stressors will be derived from questions inquiring ways through which the COVID-19 pandemic has affected the study participants (e.g., worked reduced hours, became unemployed, had serious financial problems). Latent class analysis will be conducted to categorize individuals into latent classes based on COVID-related stress exposure patterns. We plan to include the following demographic characteristics: age, biological sex, gender identity and sexual orientation, race/ethnicity, education level, household income, marital status, birth country, and military background. For mental health outcomes, we plan to include perceived stress measured by the Perceived Stress Scale, anxiety symptoms measured by the GAD-7, depressive symptoms measured by the PHQ-9, and loneliness measured by the UCLA Loneliness Scale. Additional measures of substance use and physical health will also be included.

Anticipated Findings

We anticipate that the latent class analysis approach will identify multiple latent classes that are differentiated in terms of COVID-19 stress exposure patterns. For demographic differences, we hypothesized that older individuals, female participants, racial/ethnic minorities, individuals who self-identified as LGBTQ+, those with less education, lower income, were not married, born outside of the United States, and had a military background would be at higher risk for exposure to COVID-related stressors. In terms of associations between COVID-19 stress exposure and health outcomes, we hypothesized that compared to the latent class with minimal COVID-related stress exposure, latent classes characterized higher probabilities of selected or multiple COVID-related stress exposures would report worse mental, behavioral, and physical health outcomes. The study findings will inform targeted prevention efforts to reduce the burden attributable to mental and physical health related issues.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Jeremy Luk - Other, National Institute on Alcohol Abuse and Alcoholism (NIH - NIAAA)

Collaborators:

Tommy Gunawan - Research Fellow, National Institute on Alcohol Abuse and Alcoholism (NIH - NIAAA)
Daniel Geda - Research Assistant, National Institute on Alcohol Abuse and Alcoholism (NIH - NIAAA)

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

Collaborators:

Sai Phani Ram Popuri - Graduate Trainee, University of Houston
Najm Khan - Graduate Trainee, Rutgers, The State University of New Jersey
Meher Gajula - Graduate Trainee, University of Houston
Muyun Lu - Graduate Trainee, University of Houston
Ying Lin - Early Career Tenure-track Researcher, University of Houston
Aatin Dhanda - Graduate Trainee, Rutgers, The State University of New Jersey

Food Security and Health

I will be exploring food insecurity as it impacts stress and health conditions. This is important to look at food security in populations after COVID-19 and I am in interested in exploring how that might impact future health outcomes.

Scientific Questions Being Studied

I will be exploring food insecurity as it impacts stress and health conditions. This is important to look at food security in populations after COVID-19 and I am in interested in exploring how that might impact future health outcomes.

Project Purpose(s)

Other Purpose (Honors Thesis )

Scientific Approaches

I plan on using Social Determinants of Health survey and other health measurements to examine my research question.

Anticipated Findings

I plan on presenting my findings at WIN, a nursing conference next year. I also plan on using the work in my Honors College thesis.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Amelia Kohut - Undergraduate Student, Washington State University

Collaborators:

Shawna Beese - Graduate Trainee, Washington State University

COVID-19 and Developmental Screenings

Investigate the effects of COVID-19 on developmental screenings to determine if there is a significant delay in pre versus post-covid developmental screenings. Race, SES, gender will also be assessed to determine if there are any effects on increasing gaps between…

Scientific Questions Being Studied

Investigate the effects of COVID-19 on developmental screenings to determine if there is a significant delay in pre versus post-covid developmental screenings. Race, SES, gender will also be assessed to determine if there are any effects on increasing gaps between demographic groups as a result of COVID-19.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

This plan is still being fully developed, although we will most likely compare developmental screening scores post-COVID to those pre-COVID. Additionally, demographic variables will be factored out to determine if there is a specific impact on those variables.

Anticipated Findings

I anticipate that there will be significant delays in developmental milestones in post-covid individuals compared to pre-covid individuals. Additionally, I anticipate that the gaps between race and SES achievement of developmental milestones will likely have widened as well demonstrating an affect of lack of childcare and early healthcare on developmental achievement.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Kendal Smith - Early Career Tenure-track Researcher, Jackson State University

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