Research Projects Directory

Research Projects Directory

3,684 active projects

This information was updated 2/5/2023

The Research Projects Directory includes information about all projects that currently exist in the Researcher Workbench to help provide transparency about how the Workbench is being used. Each project specifies whether Registered Tier or Controlled Tier data are used.

Note: Researcher Workbench users provide information about their research projects independently. Views expressed in the Research Projects Directory belong to the relevant users and do not necessarily represent those of the All of Us Research Program. Information in the Research Projects Directory is also cross-posted on AllofUs.nih.gov in compliance with the 21st Century Cures Act.

217 projects have 'COVID' in the scientific questions being studied description
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COVID racial health disparities

I am interested in exploring whether the racial health disparity patterns we saw for COVID in the SF Bay Area are similar to those in the All of Us Data. Our study, "Racial/Ethnic, Biomedical, and Sociodemographic Risk Factors for COVID‑19…

Scientific Questions Being Studied

I am interested in exploring whether the racial health disparity patterns we saw for COVID in the SF Bay Area are similar to those in the All of Us Data. Our study, "Racial/Ethnic, Biomedical, and Sociodemographic Risk Factors for COVID‑19 Positivity and Hospitalization in the San Francisco Bay Area," was published in the Journal of Racial and Ethnic Health Disparities.

Project Purpose(s)

  • Population Health

Scientific Approaches

I plan to use a logistic regression. The dataset will largely mirror the one we used from the UC San Francisco EHR.

Anticipated Findings

I am hoping to further understand why racial health disparities manifest across various data sets.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

  • Wendy Cho - Mid-career Tenured Researcher, University of Illinois at Urbana Champaign

COVID-19 infection and healthcare access

The research question is: What is the impact of healthcare access on the severity of COVID-19 infection? Poor health status is associated with poor health outcomes from COVID-19 infections. The question is important to explore as limited access to healthcare…

Scientific Questions Being Studied

The research question is:
What is the impact of healthcare access on the severity of COVID-19 infection?
Poor health status is associated with poor health outcomes from COVID-19 infections. The question is important to explore as limited access to healthcare can restrict individuals from receiving preventive and therapeutic care, subsequently impacting their overall health. Investigating the impact of access to healthcare and COVID-19 infection might bring insights into policies affecting the health of different communities.

Project Purpose(s)

  • Disease Focused Research (severe acute respiratory syndrome)
  • Population Health

Scientific Approaches

The proposed study design is a linear regression model to investigate the impact of healthcare access on the severity of COVID-19 infection. If available, we will choose individuals with documented COVID-19 infection, using the Conditions, Labs and Measurements of the EHR domains, and classify them into three groups based on the infection severity (mild, moderate, and severe). We will use the World Health Organization (WHO) clinical progression scale. Then, we will reassess their healthcare access one year, if available, before they get infected.

Anticipated Findings

We anticipate that those with regular healthcare access are more likely to have mild or moderate COVID-19 infection. In contrast, those with irregular healthcare access are more likely to have severe COVID-19 disease (hospitalized).
Social determinants of health are the conditions in which people are born, grow, live, work, and age. Social determinants of health include socioeconomic status, education, environment, employment, social support, and healthcare access; research has documented that these factors impact individuals' health and wellbeing. Many studies explored the impact of COVID-19 on access to healthcare. To the research team's best knowledge, no paper has investigated the role of limited healthcare access to the severity of COVID-19 infection. Our research would contribute to the existing literature about the impact of social determinants of health (access to healthcare) on COVID-19 health outcomes.

Demographic Categories of Interest

  • Access to Care

Data Set Used

Registered Tier

Research Team

Owner:

Covid

This workspace is designed to investigate covid patients in All of Us and compare them to the covid positive patients in National Covid Cohort Collaborative. I want to learn the differences in demographic, disease risk and mortality.

Scientific Questions Being Studied

This workspace is designed to investigate covid patients in All of Us and compare them to the covid positive patients in National Covid Cohort Collaborative. I want to learn the differences in demographic, disease risk and mortality.

Project Purpose(s)

  • Disease Focused Research (Covid)

Scientific Approaches

I plan to run a logistic regression to uncover phenotypes associated with covid. I will be using the PheWAS tool for comparison.

Anticipated Findings

I anticipate the N3C will have more phenotypes associated with covid than All of Us. N3C is a databased designed to study Covid and All of Us is a disease neutral database. I except to see a wider range of phenotypes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Ariel Williams - Research Fellow, National Institutes of Health (NIH)

Collaborators:

  • Tam Tran - Other, National Institutes of Health (NIH)

Pregnancy & COVID-19

Exploring data to determine any associations between COVID-19 vaccine hesitancy and pregnancy in Black birthing people and subsequent reasons for that hesitancy.

Scientific Questions Being Studied

Exploring data to determine any associations between COVID-19 vaccine hesitancy and pregnancy in Black birthing people and subsequent reasons for that hesitancy.

Project Purpose(s)

  • Social / Behavioral

Scientific Approaches

Will conduct correlation analyses and utilize the COPE (COVID-19 Participant Experience) Survey and the Winter Minute Survey on COVID-19 Vaccines

Anticipated Findings

Information of the leading reasons for vaccine hesitancy among Black pregnant people across the nation.

Demographic Categories of Interest

  • Race / Ethnicity
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

Long-COVID AoU Project

1. Develop novel software tools to identify long COVID patients from EHR and integrate EHR, survey, and wearable sensor data for these patients. 2. Study the relationships between digital biomarkers from wearable sensor related to long COVID and the rate…

Scientific Questions Being Studied

1. Develop novel software tools to identify long COVID patients from EHR and integrate EHR, survey, and wearable sensor data for these patients.
2. Study the relationships between digital biomarkers from wearable sensor related to long COVID and the rate of long COVID complications in the EHR

Project Purpose(s)

  • Disease Focused Research (long COVID)
  • Methods Development

Scientific Approaches

Data integration, Result interpretation and statistical analysis and correlation between long COVID biomarkers and complications in EHR will be used. EHR data, wearable sensor data, and survey data from AoU will be used for this study

Anticipated Findings

We anticipate to provide a set of tools for future EHR data analysis in the AoU workbench and our findings can contribute to assessing the risk of long COVID.

Demographic Categories of Interest

  • Race / Ethnicity
  • Geography
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Kalyani Kottilil - Project Personnel, Scripps Research

COVID_PRS_PHEWAS

We intend to study the following scientific questions: • What are the genetic, behavioral, and comorbidity factors that impact the risk for COVID-19 infection and severity in the All of Us cohort? • Which one of these factors impacts the…

Scientific Questions Being Studied

We intend to study the following scientific questions:

• What are the genetic, behavioral, and comorbidity factors that impact the risk for COVID-19 infection and severity in the All of Us cohort?
• Which one of these factors impacts the risk for Long COVID in the All of Us cohort?
• Are these factors associated with pre-existing comorbidities in the medical phenome of All of Us?
• How do the findings from the All of Us cohort compare to findings from additional biobanks such as the UK Biobank and the Michigan Genomics Initiative?

Understanding the factors that contribute to COVID-19 infection, severity and PASC can help identify individuals at high risk and inform targeted prevention strategies. Additionally, comparing findings from different biobanks can provide a more comprehensive understanding of the factors that impact COVID-19 risk.

Project Purpose(s)

  • Disease Focused Research (COVID-19, LongCOVID)
  • Population Health
  • Methods Development
  • Ancestry

Scientific Approaches

We plan to:
• Develop polygenic risk scores using the results of external GWAS on COVID-19 outcomes to predict infection risk and severity.
• Create a medical phenome of the All of US cohort to identify relationships with COVID-19 / PASC outcome predictors.
• Use the comprehensive information of the All of Us cohort to extract relevant covariates to adjust for time-varying trends, confounders, health disparities, and pre-existing conditions.

We will use the following datasets:
• External GWAS summary statistics from the COVID-19 Host Genetics Initiative.
• The All of Us cohort dataset, i.e. its genetic data, demographic information, survey responses, and EHR records.
• The UK Biobank and the Michigan Genomics Initiative, to meta-analyze findings.

We will use the following methods/tools:
• Develop polygenic risk scores using state-of-the-art methods and external GWAS summary statistics.
• Create a medical phenomes using the PheWAS R package.
• The analysis will be performed using R.

Anticipated Findings

We anticipate the following findings:
• Development of polygenic risk scores that predict COVID-19 infection risk and severity. These genetic predictors could help identify individuals at high risk and help eliminate testing bias in hospital data.
• Identification of patterns and relationships in the data through phenome-wide screens, which could provide insights into underlying biological mechanisms.
• Identification of time-varying trends, confounders, health disparities, and pre-existing conditions that, when adjusted for, could provide a more accurate understanding of the factors that impact COVID-19 risk and outcomes.

Taken together, the anticipated findings could inform targeted prevention strategies and inform public health policy.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Lars Fritsche - Mid-career Tenured Researcher, University of Michigan

Practice Notebook to Explore AoU dataset

This project will explore the scope of patients with COVID-19 and the characteristics of patients with PASC.

Scientific Questions Being Studied

This project will explore the scope of patients with COVID-19 and the characteristics of patients with PASC.

Project Purpose(s)

  • Educational
  • Other Purpose (practice notebook to familiarize with RW)

Scientific Approaches

We will apply algorithms developed by the RECOVER PCORnet Adult Cohort and compare the overlap in cohorts with the set derived though the N3C algorithm

Anticipated Findings

We expect to find a high degree of concordance between the RECOVER Adult Cohort algorithm and the N3C algorithm, even though the approaches were developed through different machine learning methods on different source patient data sets

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
  • Mark Weiner - Mid-career Tenured Researcher, Cornell University
  • Hiral Master - Project Personnel, All of Us Program Operational Use

Collaborators:

  • Aashri Aggarwal - Undergraduate Student, Cornell University

Duplicate of COVID_SUD_MH

Our scientific question is about the health disparity in the impact of COVID pandemics on substance use disorder (SUD) and mental health. COVID pandemic has been bringing financial, social, and psychological burdens, which are known risk factors for SUD and…

Scientific Questions Being Studied

Our scientific question is about the health disparity in the impact of COVID pandemics on substance use disorder (SUD) and mental health. 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. The adolescent 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 (Substance use disorder and mental health )
  • Population Health
  • Social / Behavioral
  • Educational
  • Methods Development

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 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 SUD and for mental health issues. Such knowledge can help better understanding the impact of COVID to public health. A prediction model will also be developed to identify high-risk populations.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Gender Identity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

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

Collaborators:

  • Nathan Peters - Undergraduate Student, Indiana University
  • Megan Whitmore - Undergraduate Student, Indiana University
  • Carter Parrish - Undergraduate Student, Indiana University

Surgical Risk Stratification Following Recovery from COVID-19

Early pandemic data suggested an elevated risk of adverse surgical outcomes in patients following a recovery from SARS-CoV-2. However, there currently is a gap in understanding several key aspects of personal history of COVID-19 on surgical care including influence of…

Scientific Questions Being Studied

Early pandemic data suggested an elevated risk of adverse surgical outcomes in patients following a recovery from SARS-CoV-2. However, there currently is a gap in understanding several key aspects of personal history of COVID-19 on surgical care including influence of viral variants, vaccination status, and impact of care delays. This includes clarifying the impact of “long-COVID” on perioperative morbidity and mortality.

The overarching objective of this study is to understand the impact of a personal history of SARS-CoV-2 infection on perioperative care. This will be achieve through the following aims:

1- Measure the association between prior SARS-CoV-2 infection, including “long-COVID”, and adverse postoperative events.
2- Utilize machine learning to develop risk-stratification tools to aid in the decision-making for surgical risk estimation for patients with a personal history of SARS-CoV-2 infection.

Project Purpose(s)

  • Disease Focused Research (COVID-19)

Scientific Approaches

Our study will identify a patient cohort who undergo major elective surgery (defined prior literature) by developing an concept set using pre-specific concept IDs. Prior SARS-CoV-2 will be defining using standard OMOP vocabulary and be the exposure variable. Additional covariates will be derived from electronic health data, surveys, mobile health data, and physical measurements. Outcomes will include a standard set of surgical quality measures. This will include measurement of COVID-19 severity and acquisition of long-COVID (post-acute sequelae SARS-CoV-2 infection). Novel measures of surgical recovery will also be developed using mobile health data, surveys, and physical measurement data.

Anticipated Findings

This study will contribute to the growing literature characterizing the broad impact of the COVID-19 pandemic. Specifically, we will examine and provide insight into the interaction between a personal history of SARS-CoV-2 infection + surgical risk. The findings of this study will facilitate and inform patients, families, and healthcare professionals on how to optimimally navigate the next phase of the COVID-19 landscape.

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
  • Tahseen Shaik - Project Personnel, Medical College of Wisconsin
  • Salma Sheriff - Project Personnel, Medical College of Wisconsin
  • Nathaniel Verhagen - Graduate Trainee, Medical College of Wisconsin
  • Carson Gehl - Graduate Trainee, Medical College of Wisconsin
  • Anai Kothari - Early Career Tenure-track Researcher, Medical College of Wisconsin

FirstSmallStep

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
  • Ying Lin - Early Career Tenure-track Researcher, University of Houston

HIV and COVID-19

The COVID-19 pandemic has disrupted global HIV care engagement services and impede the progress in ending the HIV epidemic. There is a growing evidence that HIV patients might have an elevated risk of adverse COVID-19 outcomes. However, the impact of…

Scientific Questions Being Studied

The COVID-19 pandemic has disrupted global HIV care engagement services and impede the progress in ending the HIV epidemic. There is a growing evidence that HIV patients might have an elevated risk of adverse COVID-19 outcomes. However, the impact of the pandemic and/or SARS-CoV-2 infection on the clinical outcomes of HIV individuals is rarely investigated. This study aims to compare the differences of HIV clinical outcomes (CD4 count, viral suppression, disease progression) among HIV individuals with and without SARS-CoV-2 infection and whether such relationships would be modified by the demographics (age, sex, race, rurality), antiretroviral therapy (e.g., ART regimens), preexisting conditions (comorbidities), psychological wellbeing (e.g., depression, anxiety, resilience), healthcare utilization, and social environmental factors.

Project Purpose(s)

  • Disease Focused Research (Human immunodeficiency virus infectious disease, COVID-19)
  • Population Health
  • Social / Behavioral

Scientific Approaches

We will build HIV and COVID-19 datasets using cohort builder. We will use R or Python to program and coding the datasets. The statistical methods involve descriptive statistics (e.g., chi-square, t-test), regression models (e.g., logistic regression, Cox proportional hazard modelling), and other advanced statistical methods.

Anticipated Findings

We anticipate that HIV patients with COVID-19 infection might have worse HIV clinical outcomes and such association might be attenuated (e.g., antiretroviral therapy) or aggravated (e.g., preexisting conditions, social vulnerability) by different factors. The findings could help to reduce the health disparities, contribute to a better understanding of the interaction of the two virus-borne disease and inform the future research efforts to improve the health outcomes of HIV patients.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

  • Xueying YANG - Research Fellow, University of South Carolina

Collaborators:

  • Yunqing Ma - Graduate Trainee, University of South Carolina
  • xiaowen sun - Graduate Trainee, University of South Carolina
  • Ruilie Cai - Graduate Trainee, University of South Carolina
  • Jiajia Zhang - Late Career Tenured Researcher, University of South Carolina
  • Haoyuan Gao - Graduate Trainee, University of South Carolina
  • Shan Qiao - Mid-career Tenured Researcher, University of South Carolina

SDoH Analysis Jan 2023

Health disparities are critical factors that negatively impact the health of Hispanics in the United States. Health Disparities have been defined as inequities that exist between various groups that pertain to health outcomes or access to healthcare when compared to…

Scientific Questions Being Studied

Health disparities are critical factors that negatively impact the health of Hispanics in the United States. Health Disparities have been defined as inequities that exist between various groups that pertain to health outcomes or access to healthcare when compared to the general population (Healthy People 2020). According to the Institute of Medicine (2003), disparities exist among Hispanics in preventive healthcare for chronic conditions such as cardiovascular disease, cancer, diabetes, and mental health, and Hispanics lack access to healthcare (Quinn et al., 2011). This study will document differences in COVID-19 infection among Hispanic and non-Hispanic participants and investigating the mediating effect of infection on income, job, and health access measures. The results will demonstrate whether COVID-19 infection increased economic, job, and health access disparities among Hispanic participants.

Project Purpose(s)

  • Population Health
  • Social / Behavioral

Scientific Approaches

We will define a cohort on All of Us that includes Hispanic and non-Hispanic participants who self-report COVID-19 infection, have record of a positive PCR test for COVID-19, or have COVID-19 antigens. We will produce data visualization of key results and build logistic regression models predicting ethnicity build using the selected variables of COVID-19 infection and related social determinants of health. Appropriate interactions will be included to produce a model with explanatory power and parsimonious fit.

Anticipated Findings

1) The Hispanic group lacks access to health care resources
2) There weren’t as many job losses among the Hispanic group, and this could be a result of many working essential jobs where work-from-home was not possible
3) Low income among Hispanic group, in comparison to other race & ethnic groups. As a result, access to health care is low, but exposure to the COVID-19 virus is high due to essential jobs.
4) The following variables are associated with ethnicity: access to a doctor, having a positive COVID test, having a lower annual income, not having access to health insurance, is less likely to access a COVID vaccination, is less likely to have a person to take them to the doctor, having reduced pay, and are less likely to own a home.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Registered Tier

Research Team

Owner:

  • Amy Wagler - Mid-career Tenured Researcher, University of Texas at El Paso

Collaborators:

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

COVID19

Examining social determinants related to taking COVID-19 vaccine. Looking specifically at correlated with (1) race/ethnicity and (2) religion. Previously published results for an analysis of Pew data and concluded a need to look at Blacks and Hispanics more because results…

Scientific Questions Being Studied

Examining social determinants related to taking COVID-19 vaccine. Looking specifically at correlated with (1) race/ethnicity and (2) religion. Previously published results for an analysis of Pew data and concluded a need to look at Blacks and Hispanics more because results were inconsistent with expectations. We have hypothesized that this inconsistency may be related to growing connectivity of low-income, minority populations to the internet. Religion is due to ongoing interest in working with faith-based groups and anything that might shed light on correlates of religiously based vaccine hesistancy. All of Us allows us to look at interaction of race/ethnic and church attendance.

Project Purpose(s)

  • Population Health
  • Social / Behavioral

Scientific Approaches

Data sets: COVID19, basic, social determinants of health.
Research method: Probably would be a binary logistic regression (got or willing to get COVID vaccine)

Anticipated Findings

This analysis should be labeled at this point exploratory. I have lots of material from social media promoting antivaxxing but are questioning how this plays out in the general hesistancy.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Geography

Data Set Used

Registered Tier

Research Team

Owner:

  • Richard Rogers - Late Career Tenured Researcher, Youngstown State University

covid 19 in seniors

The scientific question is if there are genetic factors that influence the health conditions or symptoms or survival in seniors when they contract covid 19.

Scientific Questions Being Studied

The scientific question is if there are genetic factors that influence the health conditions or symptoms or survival in seniors when they contract covid 19.

Project Purpose(s)

  • Disease Focused Research (covid 19)
  • Ancestry

Scientific Approaches

We will construct datasets of seniors (age 70 +) and infected with covid 19, and study their clinical symptoms, ability to survive the infection; and further associate that with their genetic variations.
The data will also be compared with young covid patients (age <70), for the symptoms, clinical features, and genetic composition.
The rest of the data that did not contract covid will also be used as control.

Anticipated Findings

We expect to uncover some clinical features over-represented in the seniors; and genetic variants associated with these clinical features.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • CHUNYU LIU - Late Career Tenured Researcher, SUNY Upstate Medical University

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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

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

COVID-19 and Wearables CTDv5

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

  • Race / Ethnicity
  • Geography
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

v6_Disparities in Cervical Cancer Screening Among Hispanic Women

We propose using the All of Us Research Database to answer two primary scientific questions: 1. What was the impact of the COVID-19 pandemic on the cervical cancer screening behaviors of Hispanic women, compared to non-Hispanic White women, in the…

Scientific Questions Being Studied

We propose using the All of Us Research Database to answer two primary scientific questions:
1. What was the impact of the COVID-19 pandemic on the cervical cancer screening behaviors of Hispanic women, compared to non-Hispanic White women, in the United States?
a. Is this impact different between subgroups? We will specifically examine geographic regions of the country, immigration status, socioeconomic variables such as home community area deprivation index, and the impact of COVID-19 on both mental and physical health.
2. Have Hispanic women re-emerged into preventative screenings as the COVID-19 pandemic has continued?
a. Comparing screening rates for 2019, 2020 and 2021, we plan to examine if there is a responsive surge in catch-up screenings among Hispanic women, compared to non-Hispanic White women.

Project Purpose(s)

  • Population Health

Scientific Approaches

Our study will include Hispanic and non-Hispanic White women ages 18+ who are eligible for cervical cancer screening during 2019, 2020, and 2021. Cervical cancer screening will be identified based on the procedure of cervical cancer screening. The existence of COVID-19 will be classified firstly based on the onset of the COVID-19 in the U.S. Specific COVID-19 impact will be defined based on COPE survey questions. Descriptive analyses will be conducted to assess the screening rate over the study period and across US regions. The Cochrane-Armitage test will be performed to detect a linear trend of the screening rate. Multivariable logistic regression will be conducted to assess the association of COVID-19 impact and the likelihood of screening between Hispanic and non-Hispanic White women, adjusting for sociodemographic and socioeconomic factors. Maps showing the screening rates during 2019-2021 across US regions will be created using visualization software, if possible.

Anticipated Findings

Studies have shown that the COVID-19 pandemic has led to a significant decrease in cervical cancer screenings across the country. However, few studies have examined the impact of COVID-19 on the Hispanic population, which even without the pandemic, bears a disproportionate cervical cancer disease burden. It is anticipated that cervical cancer incidence will sharply increase in the future without a responsive surge in screening as catch-up. Furthermore, the direct impact of COVID-19 has likely exacerbated the existing disparities in the cervical cancer burden due to compounding socioeconomic factors which were affected by the pandemic, such as insurance status, employment, and income. Our project is unique in that it addresses a vulnerable population that was made more vulnerable during the COVID-19 pandemic. It is also distinctive in that we aim to use these results to inform our own outreach efforts to expand cervical cancer screening among Hispanic women in our community.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

  • Linh Nguyen - Project Personnel, University of Texas Health Science Center, Houston

Collaborators:

  • Yen-Chi Le - Other, University of Texas Health Science Center, Houston
  • Xochitl Olguin - Research Fellow, University of Texas Health Science Center, Houston
  • Tong Han Chung - Project Personnel, University of Texas Health Science Center, Houston
  • Abigail Zamorano - Early Career Tenure-track Researcher, University of Texas Health Science Center, Houston

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

  • Race / Ethnicity
  • Age
  • Gender Identity
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

  • Shani Evans - Project Personnel, Yale University
  • Adeel Zubair - Early Career Tenure-track Researcher, Yale University

Correlation between depression during COVID-19 lockdown on different communities

I intend to study the correlation of depression during the COVID-19 lockdown on different UBR categories, so that there is a better understanding of health disparities.

Scientific Questions Being Studied

I intend to study the correlation of depression during the COVID-19 lockdown on different UBR categories, so that there is a better understanding of health disparities.

Project Purpose(s)

  • Population Health

Scientific Approaches

I plant to use the COPE Survey and Jupyter Notebook/Python to find any correlations between depression and UBR categories.

Anticipated Findings

If any correlations are found, there will be an evidence based understanding/awareness of these correlations, and scientists can work to ensure these correlations don't continue in the future.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Gender Identity
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

ABO PheWAS

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
  • Jun Qian - Other, All of Us Program Operational Use

Collaborators:

  • Juvief Farol - Graduate Trainee, University of Arizona
  • Sadaf Raoufi - Graduate Trainee, University of Arizona

uci_yongh7

I am exploring the data to get insights of how social demographics status may impact the outcome of Covid-19 patients.

Scientific Questions Being Studied

I am exploring the data to get insights of how social demographics status may impact the outcome of Covid-19 patients.

Project Purpose(s)

  • Population Health
  • Social / Behavioral
  • Methods Development

Scientific Approaches

I am planning to apply causal inference tools to study the connection between social demographics status and COVID outcome using EHR data including labs, vitals and other measurements along with social status related tabular information such as demographics, insurance and if possible, wearable measurements as well.

Anticipated Findings

The anticipated findings including validating the casual connections of social status and covid outcome, and hopefully this study will contribute to help understand how might we deliver healthcare better to the underrepresented groups.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Geography
  • Disability Status
  • Access to Care
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

  • Yong Huang - Graduate Trainee, University of California, Irvine

COVID-19 Vaccine in North Carolina

This study seeks to compare the prevalence of COVID-19 vaccine hesitancy to another survey conducted in 9 counties in North Carolina. The Advanced Center for COVID-19 Related Disparities (ACCORD) at North Carolina Central University (an HBCU) hosted COVID-19 testing events…

Scientific Questions Being Studied

This study seeks to compare the prevalence of COVID-19 vaccine hesitancy to another survey conducted in 9 counties in North Carolina. The Advanced Center for COVID-19 Related Disparities (ACCORD) at North Carolina Central University (an HBCU) hosted COVID-19 testing events in underserved communities and distributed an in-person self-administered survey. (Doherty, I.A., Pilkington, W., et. al. 2021. COVID-19 vaccine hesitancy in underserved communities of North Carolina. PloS one, 16(11), p.e0248542 .https://doi.org/10.1371/journal.pone.0248542) Vaccine hesitancy was much higher than online national polls , and particularly among Blacks. Although it declined over time, it remained elevated relative national estimates. Because the All of Us study also purposively recruits and enrolls racial/ethnic minorities, this analysis will estimate COVID-19 vaccine hesitancy and uptake in the All of Us study among participants residing within the same counties as was done in ACCORD.

Project Purpose(s)

  • Population Health
  • Social / Behavioral
  • Control Set

Scientific Approaches

This study will draw from the COVID-19 Participant Experience (COPE) dataset that includes questions about COVID-19 vaccines. To emulate the ACCORD study analysis, this analysis will also select other survey variables including race/ethnicity, education, time of data collection, income, and gender. Because All of Us has more information than ACCORD was able to collect for logistical reasons, the analysis will likely expand to include other variables in the social determinants of health domains.

Anticipated Findings

We hypothesize that COVID-19 vaccine hesitancy will decline over time and the prevalence will be higher among Blacks. The COVID-19 pandemic exposed and exacerbated health disparities among minorities in the US. Use of online surveys to a nationally representative population to estimate vaccine hesitancy did not represent or describe vaccine hesitancy in the populations where it was needed most. Sampling from populations at greatest risk and greatest need for vaccines, health education, and resources informs where to direct resources more than national polls. All of Us and ACCORD both target and oversample minorities but may produce different estimates and different needs because of divergent recruitment strategies. thus, in addition to describing vaccine hesitancy, this study addresses methodologic conundrums of reaching the populations at greatest risk.

Demographic Categories of Interest

  • Race / Ethnicity
  • Geography
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

Social Determinants and Mental Health

We will explore the social determinants of health (e.g. social support, neighborhood cohesion, loneliness, housing security, etc.) and their impact on mental disorders such as depression and anxiety by utilizing the survey and EHR data within the All of Us…

Scientific Questions Being Studied

We will explore the social determinants of health (e.g. social support, neighborhood cohesion, loneliness, housing security, etc.) and their impact on mental disorders such as depression and anxiety by utilizing the survey and EHR data within the All of Us cohort.

Some questions of interest are:

1) Are the determinants associated with risk or protection for mental health disorders such as depression and anxiety?
2) How do the associations look like for different demographics including:
Age, sex assigned at birth, race and ethnicity, residence (urban, suburban, rural), sexual orientation, income, and education.

In the midst of a mental health crisis, accentuated by the COVID-19 pandemic, it is important to find risk and protective factors for mental illnesses in diverse populations. We hope this study will help elucidate this much-needed topic.

Project Purpose(s)

  • Population Health
  • Social / Behavioral

Scientific Approaches

We will use the EHR data and self-reported survey data on basic demographics and social determinants of health in the All of Us dataset. We will use epidemiological methods to account for possible biases (selection bias, missing data, etc.) in the dataset. We will use R to conduct logistic regression analyses for depression and anxiety separately adjusting for the covariates mentioned above. A Possible limitation is that the reliance on EHR diagnosis of mental disorders may leave room for misclassification.

Anticipated Findings

For this study, we anticipate that depression or anxiety status may be associated with varying levels of social determinants. We expect that this relationship may look different depending on the social demographic group. We believe these findings will be important for developing future targeted interventions.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Soomin Kim - Graduate Trainee, Harvard T. H. Chan School of Public Health

Collaborators:

  • Karmel Choi - Early Career Tenure-track Researcher, Mass General Brigham
  • Younga Lee - Research Fellow, Mass General Brigham

Veteran COVID 19 Assessment

I am interested to get any data from any research as far as covid 19. As far as detection, Veterans who were tested and were positive and negative Early treatments? Preventive measures? how did our treatment protocols do? - The…

Scientific Questions Being Studied

I am interested to get any data from any research as far as covid 19.
As far as detection,
Veterans who were tested and were positive and negative
Early treatments?
Preventive measures?
how did our treatment protocols do?
- The VA has some of the best patients as well as the best practices and I would like to see data to support that.
Even as far as vaccinated vs unvaccinated, who were more covid positive?
Immune response to the vaccines vs natural immunity.

Project Purpose(s)

  • Disease Focused Research (COVID-19)
  • Educational

Scientific Approaches

Mixed methods research approach that looks at the data collected by AOU and interpreting and how participant responses changed due to COVID-19.

Anticipated Findings

I hope to find evidence that support the effectiveness of the early COVID 19 vaccine, how participants were affected socially and psychologically, and help develop better public health procedures for future reference.

Demographic Categories of Interest

  • Others

Data Set Used

Registered Tier

Research Team

Owner:

  • Corey Tracey - Project Personnel, Department of Veteran's Affairs

COVID-19 Project

This study will examine disparities in the impact of COVID-19 and related outcomes in terms of age, gender, race/ethnicity and other characteristics among diverse adults in the United States.

Scientific Questions Being Studied

This study will examine disparities in the impact of COVID-19 and related outcomes in terms of age, gender, race/ethnicity and other characteristics among diverse adults in the United States.

Project Purpose(s)

  • Population Health
  • Social / Behavioral

Scientific Approaches

This study will utilize data from the COVID-19 Participant Experience (COPE) survey, as well as data from the basics, lifestyle, overall health, personal medical history and health care access utilization surveys.

Anticipated Findings

It is expected that there will be disparities in the impact of COVID-19 consistent with many other documented disparities in the U.S.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Felicia Wheaton - Early Career Tenure-track Researcher, Xavier University of Louisiana

Collaborators:

  • Farhana Islam - Undergraduate Student, Xavier University of Louisiana
  • Colin Cernik - Senior Researcher, Dana-Farber Cancer Institute
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