Research Projects Directory

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:

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

Education exersice-Steven

How do SDOH influence individuals' mental well-being in the context of a public health crisis, such as the COVID-19 pandemic? Our primary objective is to accurately pinpoint individuals who are most susceptible to experiencing mental health issues based on specific…

Scientific Questions Being Studied

How do SDOH influence individuals' mental well-being in the context of a public health crisis, such as the COVID-19 pandemic?

Our primary objective is to accurately pinpoint individuals who are most susceptible to experiencing mental health issues based on specific social determinants of health. These determinants include but are not limited to, the safety of the neighborhood they live in, social support that they receive from their peers, discrimination that they may be subjected to, loneliness, and food insecurity. By identifying these factors, we can proactively anticipate and provide support to individuals at the highest risk of experiencing mental health challenges, especially during critical periods of vulnerability, such as those experienced during the COVID-19 pandemic. This project seeks to uplift and empower vulnerable communities, acknowledging the importance of a collective and compassionate response to mental health challenges in the face of adversity.

Project Purpose(s)

Disease Focused Research (Mental Health)
Population Health
Social / Behavioral

Scientific Approaches

Our approach involves a multi-step process to investigate how social determinants of health (SDoH) contribute to the worsening of mental health during a public health crisis. First, we will conduct a detailed descriptive data analysis to better understand the information and its characteristics. This will involve examining various factors that are available for participants, such as age, gender, socioeconomic status, and SDoH. After completing the descriptive analysis, we will implement linear mixed models to identify individuals at high risk of poor mental health outcomes overall and over time during the pandemic. Specifically, we will explore the relationship between these waves, SDoH, and mental health outcomes. This will allow us to better understand the impact of the pandemic on mental health and identify factors that may be contributing or aggravating to mental health decline.

Anticipated Findings

Our hypothesis is that social determinants of health (SDoH) have a significant impact on mental health, and that the effect is not limited to a single SDoH factor. Instead, we expect to see that a combination of these factors can contribute to the decline of participants' mental health. These factors may include socioeconomic status, access to healthcare, education, employment, housing, and social support. By analyzing the data collected, we hope to gain a better understanding of how multiple SDoH factors interact and affect mental health outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

yury garcia - Research Fellow, University of California, Davis
kuang li - Graduate Trainee, University of California, Davis

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
Haining Wang - Project Personnel, Indiana University
Huyunting Huang - Graduate Trainee, Indiana University
Jiangqiong Li - 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

STAT 4710 Final Project

The scientific question we plan to study is: How do COVID-19 outcomes vary by socio-demographic factors (location, education level, etc) and healthcare access/utilization in people who have or have had cancer. This is relevant because it helps us identify disparities…

Scientific Questions Being Studied

The scientific question we plan to study is: How do COVID-19 outcomes vary by socio-demographic factors (location, education level, etc) and healthcare access/utilization in people who have or have had cancer. This is relevant because it helps us identify disparities in COVID-19 outcomes.

Project Purpose(s)

Disease Focused Research (cancer)
Population Health
Educational

Scientific Approaches

We will use the R and Python programing languages to do exploratory data analysis, logistic regression, decision tree construction and neural network modeling for binary classification. We plan to use the Basics, Healthcare Access and Utilization, Personal Medical History and COPE portions of the All of Us dataset.

Anticipated Findings

The anticipated findings are to identify variables that are correlated with worse COVID-19 outcomes.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Irene Liang - Undergraduate Student, University of Pennsylvania

COVID-19, Sleep, PAL and Lung Function

COVID-19 presents with scarring of flung tissue often resulting in reduced lung compliance, which could compromise the ventilation and quality of life. This study aims to evaluate the association between the COVID-19 profile and selected clinical and functional parameters (such…

Scientific Questions Being Studied

COVID-19 presents with scarring of flung tissue often resulting in reduced lung compliance, which could compromise the ventilation and quality of life. This study aims to evaluate the association between the COVID-19 profile and selected clinical and functional parameters (such as sleep quality, fatigue, cardiorespiratory fitness, and physical activity level). It will also explore the distribution of these outcomes by race and gender.

Project Purpose(s)

Disease Focused Research (COVID-19)
Educational

Scientific Approaches

COVID-19 cohort using clinical and functional datasets.
The research method is a cross-sectional design using secondary data analysis of the All of Us dataset.

Research question: What is the association between COVID-19 Profile (previous infection, severity, vaccination status) and lung function, PAL, sleep, and quality of life; and if this association is different by gender or race?

Anticipated Findings

Hypothesis: Previous history of severe COVID-19 infection could be associated with poor quality of life, reduced PAL, and poor sleep quality; and this association could be different by gender and race.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

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

Collaborators:

Joseph Aneke - Early Career Tenure-track Researcher, Hampton University

COVID_HIV_dissertation_project

I wish to explore data as it pertains to short and long term cardiovascular (CV) sequelae relating to a past medical history of both COVID-19 among HIV+ patients vs. those other healthy patient who have only experienced COVID-19. The scientific…

Scientific Questions Being Studied

I wish to explore data as it pertains to short and long term cardiovascular (CV) sequelae relating to a past medical history of both COVID-19 among HIV+ patients vs. those other healthy patient who have only experienced COVID-19. The scientific question I am attempting to explore is the interplay between coronavirus disease (which appears to have immune hypersensitivity component to the illness) within individuals who have an existing compromised immune system(as is the case with HIV+ patients). Logic would dictate on the one hand, a preexisting comorbid condition would leave HIV+ patients highly predisposed to much worse CV outcomes, subsequent to infection with COVID-19. On the other hand, the interplay between an immunosuppressive virus vs. a virus that leads to hyperregulation of the immune system, might leave HIV+ patient potentially more protected than the everyday health individual.

Project Purpose(s)

Disease Focused Research (COVID-19)
Population Health

Scientific Approaches

I haven't decided yet if I wish to perform a case-control or cohort study (depends on how robust the data is). I would likely construct a cohort of HIV+ adults, ages 18-85 years old. My main outcome of interest would likely be a composite variable related to cardiovascular health (i.e. history of myocardial infarction, stroke, pulmonary embolism - as well as death as a direct result of any one of these 3 causes). My main exposure would likely be the past medical history of COVID-19. Time would play a significant factor of consideration (both time living with HIV+ and time subsequent to COVID-19 illness). However, other cofactors of consideration with be current medication history (after all, HAART therapy might have lead to a less severe form of COVID-19)

Anticipated Findings

I honestly am not certain what I anticipate my finds to be, given the number of covariates that could potentially play a role in this study. I do feel though that overall, investigating this will result in my illumination into the "auto-immune" role that COVID-19 seems to play, which is of general pertinence to both the HIV+ population, as well as the population in general.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Marco Stillo - Graduate Trainee, SUNY Downstate Health Sciences University

Cystic Fibrosis and COVID-19: US-based Outcomes and Vaccination Rates

The COVID vaccine was rolled out during the pandemic with the intention of creating an immune response without potentially severe consequences from acquiring the infection. With the COVID vaccine being provided free of charge, widely distributed, and early access for…

Scientific Questions Being Studied

The COVID vaccine was rolled out during the pandemic with the intention of creating an immune response without potentially severe consequences from acquiring the infection. With the COVID vaccine being provided free of charge, widely distributed, and early access for those with chronic illness, patients with cystic fibrosis (CF) represent a cohort that would benefit from preventative care. Investigation of vaccination status and hospital outcomes may demonstrate the value of preventative care for patients with CF and vaccine’s efficacy. Further stratification of this cohort by mutation type may reveal additional depth to these findings. Through this investigation, the following research questions will be addressed: How many patients with CF acquired COVID vaccines? What were hospital outcomes for patients with COVID? Of the infected population, which CF mutations had worse outcomes? Is COVID vaccination status correlated with better hospital outcomes in patients with CF?

Project Purpose(s)

Population Health
Ancestry

Scientific Approaches

The primary methodologies employed are literature review and statistical analysis of NIH data. Identification of cystic fibrosis patients and subsequent health data, such as mutation type, vaccination status, and hospitalizations, is the first step in creating a data set which the research will draw from. With this analysis, we will categorize patients and their outcomes as related to vaccination status. Literature review will set the background for how cystic fibrosis patients have been affected in the pandemic at large and inform discussion of our analysis.

Anticipated Findings

Cystic Fibrosis is a respiratory disease, therefore it is expected that CF patients who contract COVID would have worse outcomes. Initial reports from several countries outside the US, however, showed that most patients with CF did well and there were not many adverse outcomes. The fact that most patients with CF are younger and getting access to the vaccine earlier may be possible reasons for not seeing more severe disease in patients with CF. Our study aims to look at whether or not the trends seen in patients with CF in other countries holds true for the US population and we anticipate our findings to reflect this as such. In addition we will analyze the effects of the vaccines available in the US and the specific timeline of availability for patients with CF, and anticipate finding that preventative care through vaccination to correlate with better outcomes in all measures. This will provide analysis specifically relating to US patient populations.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Noah Woford - Graduate Trainee, Lincoln Memorial University-DeBusk College of Osteopathic Medicine
Nicholas Toma - Graduate Trainee, Lincoln Memorial University-DeBusk College of Osteopathic Medicine

COVID-19 Anosmia ML Analysis

We plan to use the All of Us research dataset to investigate the genetics involved with developing loss of smell with COVID-19. The loss of smell developed with COVID-19 is similar to other complex genetic disorders in the way that…

Scientific Questions Being Studied

We plan to use the All of Us research dataset to investigate the genetics involved with developing loss of smell with COVID-19. The loss of smell developed with COVID-19 is similar to other complex genetic disorders in the way that the development of these conditions potentially involves several genetic variations. On a local COVID-19 dataset, we have constructed a novel analysis for investigating and prioritizing candidate genetic variants for the loss of smell. The All of Us data will serve as further validation of our methods. Yielding important biological results for the understanding of symptoms developed with COVID-19 as well as the development of a computational tool for investigating complex genetic traits.

Project Purpose(s)

Disease Focused Research (COVID-19 symptoms)
Methods Development
Ancestry

Scientific Approaches

We will use the All of Us whole genome sequencing data from individuals that had COVID-19 and compare those that developed loss of smell with those that did not. We will utilize algorithms developed at the University of Iowa for prioritizing variants with machine learning techniques. This analysis will be done in R and Python programming languages.

Anticipated Findings

We expect that the prioritized candidate variants found in our local discovery dataset analysis will have the same predictive power and results in the All of Us dataset. These results will further validate our methods and the development of a analytical tool that can be used to investigate the genetics of other similar disorders. These results will also provide valuable insight into the development of symptoms with COVID-19.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Lucas Pietan - Graduate Trainee, University of Iowa

Duplicate of AOU_Recover_Long_Covid_v6

The purpose of creating this duplicate workspace is to learn the methodologies for implementing machine learning into research. While the he purpose of original workspace was to implement the published XGBoost machine learning (ML) model, which was developed using the…

Scientific Questions Being Studied

The purpose of creating this duplicate workspace is to learn the methodologies for implementing machine learning into research. While the he purpose of original workspace was to implement the published XGBoost machine learning (ML) model, which was developed using the National COVID Cohort Collaborative’s (N3C) EHR repository to identify potential patients with PASC/Long COVID in All of Us Research Program.

Project Purpose(s)

Educational

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.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Xin Yang - Project Personnel, Medical College of Wisconsin

HAP-835

To use the COVID-19 Participant Experience (COPE) survey to investigate possibility of predicting COVID-19 test results from especially from patients symptoms and their sequences over time, as well as other factors.

Scientific Questions Being Studied

To use the COVID-19 Participant Experience (COPE) survey to investigate possibility of predicting COVID-19 test results from especially from patients symptoms and their sequences over time, as well as other factors.

Project Purpose(s)

Educational

Scientific Approaches

- Quantitative
- Finding the most frequent sequence of symptoms among individuals with different test results.
- Training classic machine learning models in Python mostly using Scikit-learn algorithms with bootstrapping and picking up the best models using different metrics including ROC-AUC, PR-AUC, sensitivity, specificity.
- Comparing results with traditional statistical regression models using R

Anticipated Findings

Using smartphone-based anonymous national symptom tracker software applications, authors found relationship between symptoms and the sequence of them in Israel, the UK, the US, and Germany. However, to the best of my knowledge, no one investigated relevant questions of the COPE survey to find if the test results can be predicted or not.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Mohammad Qodrati - Graduate Trainee, George Mason University

Chronic respiratory diseases after TB and need for rehabilitation

The incidence of reported tuberculosis (TB) in the United States is about 2.7 cases per 100,000 persons every year except in 2020, a year that coincides with COVID-19. In 2022 8,331 TB cases were reported in the United States. Studies…

Scientific Questions Being Studied

The incidence of reported tuberculosis (TB) in the United States is about 2.7 cases per 100,000 persons every year except in 2020, a year that coincides with COVID-19. In 2022 8,331 TB cases were reported in the United States. Studies report higher chronic respiratory diseases among survivors of TB infection. A substantial proportion of tuberculosis patients remain with pulmonary symptoms and reduced physical capacity despite successful treatment. The objective of this research is to 1) assess the distribution of chronic respiratory disease development among diverse TB infection survivors in the United States, 2) evaluate disparity post-TB follow-up (and respiratory rehabilitation effort, if any) disparity by birthplace (foreign-born vs domestic-born), Race/ethnicity, and insurance status, and 3) develop a prediction model on who would develop chronic respiratory disease among TB-infection survivors.

Project Purpose(s)

Disease Focused Research (tuberculosis)

Scientific Approaches

We will follow TB diagnosed patient for the development of chronic respiratory development. environmental and socioeconomic, and genetic data will be incorporated in the analysis. Disparities will be analyzed using descriptive and other appropriate statistical approach.

Anticipated Findings

Characterization of TB diagnosed patient and development of chronic respiratory development.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Kedir Turi - Early Career Tenure-track Researcher, Indiana University

Disparities within Class I Obesity

We intend to study the following research question: How did community status (AKA rural-urban status) affect the differing rates of Class I Obesity between Black and White women right before COVID, according to the BMI scale? This question is important…

Scientific Questions Being Studied

We intend to study the following research question: How did community status (AKA rural-urban status) affect the differing rates of Class I Obesity between Black and White women right before COVID, according to the BMI scale? This question is important for addressing the differing rates of Obesity between Black and White adult women in order to inform targeted interventions and policies to address the inequities present. Understanding how community status affects adult women's Obesity rates is important in identifying the different risk factors, while also understanding health disparities. The goal of studying this health disparity is to bring awareness to the inequality of health and support surrounding Obesity in differing communities. This study shines a light on just one of many health disparities present, pushing others to bridge the gap between different groups and their health. Every person deserves an equal amount of treatment regardless of circumstances and/or ethnicity.

Project Purpose(s)

Population Health
Educational

Scientific Approaches

In this study we will collect nominal continuous data to conduct a cross-sectional study. Using electronic health records, physical measurements, surveys, and geographic information, we hypothesize, "As the mean frequency of Class I Obesity increases when migrating to more rural communities, Black women and White women are affected at differing rates" as an overall outcome in this cross-sectional study. It is expected that two-way ANOVA not assuming equal variances will reveal the frequencies of Obesity change at the same rate between Black and White women of differing communities. We expect Black and White women to have differing rates of change when measuring different communities.

Anticipated Findings

We anticipate that Black women and White women will not only have differing rates of Obesity in all three communities, but that as the community migrates from urban to rural, Black women will have a disproportionate increase in Obesity rates compared to White women.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Rance Greer - Undergraduate Student, Prairie View A&M University
Timesha Steele - Undergraduate Student, Prairie View A&M University
Kennedy Collins - Undergraduate Student, Prairie View A&M University
Joshua Murphy - Undergraduate Student, Prairie View A&M University
Charcacia Sanders - Other, Prairie View A&M University

Severe Mental Illness and COVID pos - SDOH

Patients with schizophrenia and bipolar (Severe Mental Illness, SMI) are considered at increased risk for acquiring COVID 19 positivity, as well as having more severe COVID-19. Such patients are frequently of lesser income and may have an unsecure living environment.…

Scientific Questions Being Studied

Patients with schizophrenia and bipolar (Severe Mental Illness, SMI) are considered at increased risk for acquiring COVID 19 positivity, as well as having more severe COVID-19. Such patients are frequently of lesser income and may have an unsecure living environment. Additionally, such illnesses are considered neuroimmune, and could be associated with decreased ability to fight or resist COVID infection. It is unknown if the vulnerability to COVID-19 is due to social determinants of health or biological aspects of the immune system in such patients, or both.

We aim to test the hypothesis that likelihood of developing COVID positive is same in patients with schizopshrenia or bipolar as without, when one controls for SDOH. SDOH to test, that are available in the database are: household income, concern about having a place to live, employed, have health insurance, education, bug/mold infestation, inadequate heat, food insecurity, lack of command over English language.

Project Purpose(s)

Disease Focused Research (psychiatric disorders (schizophrenia, bipolar, schizoaffective))
Population Health
Social / Behavioral

Scientific Approaches

We will compare covid rates and covid testing rates among groups, controlling for SDOH. Groups will be limited by SDOH (e.g., comparisons will be done within over 100K income, within less than 35K income, among those with food insecurity, etc.). ANOVA, MANOVA will be the main analysis methods. Principal component analysis may also be used.

Anticipated Findings

Our anticipated finding is whether SDOH are the primary factor in determining the difference in COVID positivity among those with Severe Mental Illness, or do non social determinants of health, such as changes in immunity, also play a role.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Sonya Dave - Research Associate, Washington University in St. Louis

Genomics of infectious disease susceptibility

Despite increasing evidence of the role of host genetics in infectious diseases, most genomics studies have been limited to a few pathogens (HIV infection, TB, malaria, viral hepatitis, and recently COVID-19) and populations of European ancestry. We aim to narrow…

Scientific Questions Being Studied

Despite increasing evidence of the role of host genetics in infectious diseases, most genomics studies have been limited to a few pathogens (HIV infection, TB, malaria, viral hepatitis, and recently COVID-19) and populations of European ancestry. We aim to narrow this gap by expanding the repertoire of studies to all infectious diseases for which we have at least 50 case numbers in All of Us and to diverse genetic ancestries.

Project Purpose(s)

Disease Focused Research (disease by infectious agent)
Population Health
Ancestry

Scientific Approaches

In brief, we will: Perform common and rare variant testing using All of Us genotyping and exome-sequencing data respectively. We aim to use SAIGE to conduct variant testing to understand genomic loci contributing to infectious disease susceptibility. Diseases will be defined on the basis of PhecodeX definitions, which are derived from ICD10 and ICD9 codes. For increased statistical power we will conduct a similar analysis on BioMe and Pan UK Biobank and subsequently meta-analyze the results with results from All of Us.

Anticipated Findings

Overall, leveraging the combined cohort from all three biobanks would enable us to discover novel associations in previously unexplored disease and understudied populations which paves the way to develop risk prediction scores and new therapies. Utilizing diverse biobanks will allow us to map ancestry-specific markers that influence infectious disease susceptibility. Our results will contribute not only to infectious disease research but also to the broader mission of supporting diversity in genomics. The completion of this project will open multiple routes for future exploration including scope for development of targeted preventive measures and personalized therapeutics and prophylactics.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

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

Collaborators:

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

Hampton Research hub_covid_study

Background: COVID-19 infection scars the lungs with the potential for lung tissue scarring. Scarring causes restrictive lung impairment. Long-term pulmonary sequelae of COVID-19 include: Research Question: What is the Association between COVID-19 infection and lung function impairment among apparently healthy…

Scientific Questions Being Studied

Background:
COVID-19 infection scars the lungs with the potential for lung tissue scarring. Scarring causes restrictive lung impairment.
Long-term pulmonary sequelae of COVID-19 include:

Research Question: What is the Association between COVID-19 infection and lung function impairment among apparently healthy adults?

Aims of the study:
1. To investigate the association between COVID-19 infection and lung function impairment among apparently healthy adults.
2. Estimate the magnitude of the association between COVID-19 infection and lung function impairment among apparently healthy adults.

Project Purpose(s)

Disease Focused Research (severe acute respiratory syndrome)
Population Health
Social / Behavioral
Educational

Scientific Approaches

Participants
Selection Criteria
Inclusion: Adult (above 18 years)

Exclusion: history of acute lung disease; history of chronic lung diseases;

Measurements
Outcome: Lung function impairment

Exposure: History of COVID-19

Covariates: Physical activity level, severity of COVID infection, frequency of infection, vaccination status, race, gender, smoking status, residential zip code, health insurance information, education, income bracket, blood pressure, previous hospitalization, comorbidity,

Anticipated Findings

Our findings will
1. Show the influence of previous covid infections on lung infection impairments.
2. Help to identify individuals at risk of chronic lung disease
3. Evaluate predictors of severe lung impairment among individuals with COVID-19 infection

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Joseph Aneke - Early Career Tenure-track Researcher, Hampton University

V7 ARI Workspace - 4-21-23

We now have 4 goals in our research. This workspace is for goals 1 through 3. We have created a new workspace for Goal #4. 1. Determine prevalence of autoimmune diseases, individually and as a class of disease, in the…

Scientific Questions Being Studied

We now have 4 goals in our research. This workspace is for goals 1 through 3. We have created a new workspace for Goal #4.

1. Determine prevalence of autoimmune diseases, individually and as a class of disease, in the US.

2. Determine comorbidity of autoimmune diseases, including statistics on comorbidity of other autoimmune diseases and non-autoimmune diseases for each autoimmune disease.

3. Determine the impact of COVID-19 on the autoimmune and autoinflammatory disease population. This work will be conducted in parallel with work we are doing at University of Southern California under an IRB there.

4. Explore the genomic component of autoimmune diseases, particularly among patients with more than one autoimmune disease, so that the underlying mechanisms of disease among these diseases can be better understood.

Project Purpose(s)

Disease Focused Research (Autoimmune diseases)
Population Health
Ancestry

Scientific Approaches

We will create three data sets for analysis:

1. A list of diseases rated in the following ways:

a. Evidence Class
i. Strong evidence it is autoimmune
ii. Moderate evidence it is autoimmune
iii. Weak evidence for autoimmunity
iv. A comorbidity of autoimmune disease
v. Symptom or symptom set with no known mechanism

b. Autoinflammatory versus autoimmune flag

c. “Not always autoimmune” flag – to indicate diseases that could have alternative mechanisms of cause

2. A list of patients, anonymized, with socioeconomic, geographic and other data that would be of interest to patients and public health officials to understand which communities are affected by these diseases
3. Outcomes data for patients over time assessing quality of life using PROMIS metrics

Anticipated Findings

The current NIH estimate of 23.5 million people with autoimmune disease was a guess by a knowledgable clinician, but has no scientific support. As a consequence, there are numerous figures in the public sphere and nobody knows which one is correct.

Many reports say autoimmune diseases are on the increase, but since the number is unknown, it is impossible to say whether this is a public health issue or not. Having a methodology that can be used to recompute the number of people with autoimmune disease will help us understand if these reports are true.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Aaron Abend - Senior Researcher, Autoimmune Registry

Collaborators:

Francis Ratsimbazafy - Other, All of Us Program Operational Use
Jun Qian - Other, All of Us Program Operational Use
Jeremy Harper - Senior Researcher, Autoimmune Registry
Jeffrey Green - Project Personnel, Autoimmune Registry
Ingrid He - Project Personnel, Autoimmune Registry
Emily Holladay - Project Personnel, Autoimmune Registry
Chenchal Subraveti - Project Personnel, All of Us Program Operational Use
Boyd Ingalls - Project Personnel, Autoimmune Registry
Adnaan Jhetam - Project Personnel, Autoimmune Registry
Alexander Burrows - Research Assistant, Autoimmune Registry
Jagannadha Avasarala - Other, University of Kentucky

Duplicate of Discrimination, Depression, Suicide

As part of a grad school course, we plan to look at the association of everyday discrimination during COVID with depressive and suicidal symptoms.

Scientific Questions Being Studied

As part of a grad school course, we plan to look at the association of everyday discrimination during COVID with depressive and suicidal symptoms.

Project Purpose(s)

Population Health
Social / Behavioral
Educational

Scientific Approaches

We will use the COVID-19 Participant Experience (COPE) survey and the Patient Health Questionnaire (PHQ-9). We will conduct mixed effects modeling and lagged analyses. We may also conduct mediation analyses.

Anticipated Findings

We anticipate that people who experience higher levels of discrimination will be more likely to have increased symptoms of depression and suicidality.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Sarah Lee - Graduate Trainee, University of Massachusetts Medical School
Inbar Plaut - Research Fellow, University of Massachusetts Medical School

Collaborators:

Alexander Wilkins - Other, University of Massachusetts Medical School

Discrimination, Depression, Suicide

As part of a grad school course, we plan to look at the association of everyday discrimination during COVID with depressive and suicidal symptoms.

Scientific Questions Being Studied

As part of a grad school course, we plan to look at the association of everyday discrimination during COVID with depressive and suicidal symptoms.

Project Purpose(s)

Population Health
Social / Behavioral
Educational

Scientific Approaches

We will use the COVID-19 Participant Experience (COPE) survey and the Patient Health Questionnaire (PHQ-9). We will conduct mixed effects modeling and lagged analyses. We may also conduct mediation analyses.

Anticipated Findings

We anticipate that people who experience higher levels of discrimination will be more likely to have increased symptoms of depression and suicidality.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Sarah Lee - Graduate Trainee, University of Massachusetts Medical School

Collaborators:

Inbar Plaut - Research Fellow, University of Massachusetts Medical School
Alexander Wilkins - Other, University of Massachusetts Medical School

Materials and Methods COVID-19

During our research, we plan to utilize Genome-wide Association studies (GWAS). This is a powerful approach to identify genetic variants associated with specific traits or diseases. Researchers can analyze large datasets comprising the genomes of individuals with severe COVID-19 symptoms…

Scientific Questions Being Studied

During our research, we plan to utilize Genome-wide Association studies (GWAS). This is a powerful approach to identify genetic variants associated with specific traits or diseases. Researchers can analyze large datasets comprising the genomes of individuals with severe COVID-19 symptoms compared to those with mild or asymptomatic cases to pinpoint genetic variations linked to susceptibility.

Project Purpose(s)

Educational

Scientific Approaches

During our research, we plan to utilize Genome-wide Association studies (GWAS). This is a powerful approach to identify genetic variants associated with specific traits or diseases. Researchers can analyze large datasets comprising the genomes of individuals with severe COVID-19 symptoms compared to those with mild or asymptomatic cases to pinpoint genetic variations linked to susceptibility.

Anticipated Findings

During our research, we plan to utilize Genome-wide Association studies (GWAS). This is a powerful approach to identify genetic variants associated with specific traits or diseases. Researchers can analyze large datasets comprising the genomes of individuals with severe COVID-19 symptoms compared to those with mild or asymptomatic cases to pinpoint genetic variations linked to susceptibility.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

David Santana - Undergraduate Student, Arizona State University

Bonus project

How does genetic variation among individuals influence susceptibility to severe covid-19 symptoms and long-term health outcomes?

Scientific Questions Being Studied

How does genetic variation among individuals influence susceptibility to severe covid-19 symptoms and long-term health outcomes?

Project Purpose(s)

Population Health
Educational
Ancestry

Scientific Approaches

Genome-wide Association Studies (GWAS): GWAS is a powerful approach to identify genetic variants associated with specific traits or diseases. Researchers can analyze large datasets comprising the genomes of individuals with severe COVID-19 symptoms compared to those with mild or asymptomatic cases to pinpoint genetic variations linked to susceptibility.

Anticipated Findings

identify specific genes responsible for its virulence, transmission, and interaction with the human immune system

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

madison maria - Undergraduate Student, Arizona State University

Social distancing and mental health

During the COVID-19 pandemic, social distancing emerged as a crucial public health measure. While effective in reducing transmissions, it inadvertently placed strains on mental health. This study examines how two coping mechanisms—increased media consumption and maintenance of social connections—moderate the…

Scientific Questions Being Studied

During the COVID-19 pandemic, social distancing emerged as a crucial public health measure. While effective in reducing transmissions, it inadvertently placed strains on mental health. This study examines how two coping mechanisms—increased media consumption and maintenance of social connections—moderate the adverse impacts of social distancing on mental health. We will conduct regression and moderation analyses using data from the All of Us research program, one of the most diverse health databases in history.
H1: Increased social distancing correlates with worse mental health.
RQ1: Does heightened media consumption moderate the relationship between social distancing and mental health?
RQ2: Does maintaining social connections moderate the relationship between social distancing and mental health?

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We plan to use the COVID-19 Participant Experience survey, part of the All of Us research program (NIH, 2024).
We plan to conduct regression and moderating analyses.

Measures
Demographic factors (e.g., age, gender, ethnicity, education) were included in the study.

Social distancing was assessed by asking participants’ social habits in the last five days, including frequency of staying at home, attending large social gatherings, wearing a facemask/covering, and social interactions with people outside their home.

Mental health was assessed by asking participants how often they felt nervous, anxious, on edge, down, depressed, or hopeless over the last two weeks.

Increased media consumption was measured by asking participants whether they were increasing watching, reading, or listening to news stories, including social media.

Maintaining social connections was measured by asking participants whether they were connecting with others, including talking with people they trust.

Anticipated Findings

This study will reveal the role of two communication-related coping behaviors in either amplifying or alleviating the adverse effects of social distancing on mental health. Findings will contribute to the health communication literature and provide valuable insights for developing communication strategies to address the crises posed by infectious disease outbreaks.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Cindy Chen - Teacher/Instructor/Professor, Sam Houston State University

Collaborators:

Promethi Das Deep - Graduate Trainee, Sam Houston State University
Alexandra Andrews - Undergraduate Student, Sam Houston State University

Heart Disease

We are intending to study the effects of COVID 19 on heart disease between male and female young adults (18-24).

Scientific Questions Being Studied

We are intending to study the effects of COVID 19 on heart disease between male and female young adults (18-24).

Project Purpose(s)

Disease Focused Research (heart disease)
Population Health
Educational
Methods Development
Control Set
Ancestry

Scientific Approaches

We are going to seek the trends on patients that contracted COVID19 that has also been diagnosed of heart disease and see if there are any significant risks that it imposed on these male and female young adults (18-24).

Anticipated Findings

We anticipate that COVID 19 has a significant effect on the male and female young adults (18-25) that have already been diagnosed with heart disease. We hope that our findings can identify what the extent of COVID 19 has affected the individual's immune system as well as the extent of how it worsens their current condition.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Francis Xavier Malinao - Undergraduate Student, Arizona State University

Collaborators:

nikka encinas - Undergraduate Student, Arizona State University

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:

Daniel Brannock - Senior Researcher, All of Us Researcher Academy/RTI International

V7 ARI Genomics Workspace - 4-21-23

We now have 4 goals in our research - this workspace has been created specifically for Goal #4. 1. Determine prevalence of autoimmune diseases, individually and as a class of disease, in the US. 2. Determine comorbidity of autoimmune diseases,…

Scientific Questions Being Studied

We now have 4 goals in our research - this workspace has been created specifically for Goal #4.

1. Determine prevalence of autoimmune diseases, individually and as a class of disease, in the US.

2. Determine comorbidity of autoimmune diseases, including statistics on comorbidity of other autoimmune diseases and non-autoimmune diseases for each autoimmune disease.

3. Determine the impact of COVID-19 on the autoimmune and autoinflammatory disease population. This work will be conducted in parallel with work we are doing at University of Southern California under an IRB there.

4. Explore the genomic component of autoimmune diseases, particularly among patients with more than one autoimmune disease, so that the underlying mechanisms of disease among these diseases can be better understood.

Project Purpose(s)

Disease Focused Research (Autoimmune diseases)
Ancestry

Scientific Approaches

We will create three data sets for analysis:

1. A list of diseases rated in the following ways:

a. Evidence Class
i. Strong evidence it is autoimmune
ii. Moderate evidence it is autoimmune
iii. Weak evidence for autoimmunity
iv. A comorbidity of autoimmune disease
v. Symptom or symptom set with no known mechanism

b. Autoinflammatory versus autoimmune flag

c. “Not always autoimmune” flag – to indicate diseases that could have alternative mechanisms of cause

2. A list of patients, anonymized, with socioeconomic, geographic and other data that would be of interest to patients and public health officials to understand which communities are affected by these diseases
3. Outcomes data for patients over time assessing quality of life using PROMIS metrics
4. We will develop statistics analyzing the association of variants known to affect autoimmune diseases for specific diseases to see if those variants corelate with other autoimmune diseases.

Anticipated Findings

There are recognized associations between specific gene variants and some autoimmune diseases. We are going to explore whether those associations can be found in other autoimmune and autoinflammatory diseases. We hope this work can uncover the common mechanisms that underlie autoimmune conditions that appear to be unconnected but which are comorbid.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Aaron Abend - Senior Researcher, Autoimmune Registry

COVID-19 and Wearables CTDv6

Our primary goal is to understand the interaction between activity levels and the development, progression, and societal effects of COVID-19. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and the development, progression, and societal effects of COVID-19. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence of COVID-19. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We may find substantial variation in activity and disease prevalence/severity by socioeconomic status and/or location which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Runqi Yuan - Graduate Trainee, Vanderbilt University
Jeffrey Annis - Other, Vanderbilt University Medical Center

Collaborators:

Laleh Jalilian - Mid-career Tenured Researcher, University of California, Los Angeles
STACY DESINE - Project Personnel, Vanderbilt University Medical Center
Hiral Master - Project Personnel, All of Us Program Operational Use
Aymone Kouame - Other, All of Us Program Operational Use

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