Research Projects Directory

Research Projects Directory

5,357 active projects

This information was updated 6/7/2023

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.

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264 projects have 'COVID' in the scientific questions being studied description
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Covid-19 vaccine uptake among cancer survivors V

The purpose of the study is to evaluate the modifiable, multilevel factors associated with COVID-19 vaccine uptake among cancer survivors from the All of Us dataset.

Scientific Questions Being Studied

The purpose of the study is to evaluate the modifiable, multilevel factors associated with COVID-19 vaccine uptake among cancer survivors from the All of Us dataset.

Project Purpose(s)

  • Disease Focused Research (cancer)
  • Population Health
  • Social / Behavioral

Scientific Approaches

A cohort of cancer survivors will be from using the database. Various survey questions will aid in answering our research aims. In addition, the covid-19 survey questionnaires will also be used to determine our outcome of interest.

Anticipated Findings

Multilevel factors are anticipated to be associated with vaccine uptake and hesitance. These results can help to identify specific characteristics of cancer survivors that make them more or less likely to experience vaccine hesitancy and inform efforts to target, adapt and tailor interventions to their needs.

Demographic Categories of Interest

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

Data Set Used

Controlled Tier

Research Team

Owner:

  • Angel Arizpe - Graduate Trainee, University of Southern California
  • Albert Farias - Early Career Tenure-track Researcher, University of Southern California
Request a Review of this Research Project

Social Determinants and Mental Health (v7)

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:

  • Younga Lee - Research Fellow, Mass General Brigham
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Duplicate of 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:

Request a Review of this Research Project

yong_longcovid

We would like to investigate risk factors for developing long covid, particularly via causal inference methods using only observational data.

Scientific Questions Being Studied

We would like to investigate risk factors for developing long covid, particularly via causal inference methods using only observational data.

Project Purpose(s)

  • Disease Focused Research (long covid)
  • Methods Development
  • Ancestry

Scientific Approaches

We plan to apply causal inference methods to study the causal effect of certain genes along with other variables such as age gender and lifestyle to developing long covid. The data we are interested in includes genetic data, demographics, lab tests, vital signs, drug exposure etc.

Anticipated Findings

We would like to identify predictors of long covid and propose appropriate intervention strategy for helping people who suffer from long covid

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Yong Huang - Graduate Trainee, University of California, Irvine
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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
  • Anthony Vicenti - Project Personnel, University of Arizona
  • Sadaf Raoufi - Graduate Trainee, University of Arizona
Request a Review of this Research Project

Built_environment_covid_V4

Study the COVID-19 spread and mental health associated with built environment using COPE COVID-19 survey data. COPE data provides unique opportunity to study the medical and social impacts of built environment, such as the household types. The study will conduct…

Scientific Questions Being Studied

Study the COVID-19 spread and mental health associated with built environment using COPE COVID-19 survey data. COPE data provides unique opportunity to study the medical and social impacts of built environment, such as the household types. The study will conduct secondary use of the survey to study the association, providing evidence for policy makers.

Project Purpose(s)

  • Disease Focused Research (COVID-19)
  • Social / Behavioral
  • Ethical, Legal, and Social Implications (ELSI)

Scientific Approaches

We will use the COPE survey data and conduct logistic regression analyses to study the associations.

Anticipated Findings

We expect built environment types will be associated with the spread of COVID-19 and potentially impose stress to the residents. We also expect indoor behaviors (e.g., shopping) will be related to COVID-19 spread.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Haiquan Li - Early Career Tenure-track Researcher, University of Arizona

Collaborators:

  • Wenting luo - Graduate Trainee, University of Arizona
  • Anna Jiang - Undergraduate Student, University of Arizona
  • Edwin Baldwin - Graduate Trainee, University of Arizona
Request a Review of this Research Project

long_covid_and_depression

We want to investigate the presence of 5HTTP genome and its relationship with the increased incidence of depression among long-covid patients. We are still exploring the data at this stage to see if we can find some sort of connection…

Scientific Questions Being Studied

We want to investigate the presence of 5HTTP genome and its relationship with the increased incidence of depression among long-covid patients. We are still exploring the data at this stage to see if we can find some sort of connection with this genome expression and biomarkers for long-covid such as lymphocyte to neutrophil ratio. Understanding long-covid from a more genetic level and how it affects mood disorders can help us influence the production of better medication to treat and alleviate the symptoms of long-covid patients.

Project Purpose(s)

  • Disease Focused Research (COVID-19)

Scientific Approaches

We would like to include some causal discovery mechanisms in a data-driven fashion to see the links between the 5HTTP genome sequence and other biomarkers. We would also like to use deep learning and statistical techniques to classify these people into long-haulers for COVID-19.

Anticipated Findings

We hope to show a link between long-covid and certain genome sequences like the 5HTTP sequence. This finding can help elucidate a course of action for medical professionals to diagnose a patient with long-covid.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Request a Review of this Research Project

Causality of Depression in Young Adults in Covid-19 Pandemic

The question we intend to answer is "What is the causality of depression among young adults?" We intend to study the causality of depression among young adults (ages 18-25) during the three-year period from March 11, 2020, the day that…

Scientific Questions Being Studied

The question we intend to answer is "What is the causality of depression among young adults?"

We intend to study the causality of depression among young adults (ages 18-25) during the three-year period from March 11, 2020, the day that the World Health Organization declared Covid-19 a world pandemic, and March 10, 2023. ( Cucinotta D, Vanelli M. WHO Declares COVID-19 a Pandemic. Acta Biomed. 2020 Mar 19;91(1):157-160. doi: 10.23750/abm.v91i1.9397. PMID: 32191675; PMCID: PMC7569573.)

Our assumption is that the EXTREME nature of the pandemic may have essentially "fast-forwarded" the time it might normally have taken for depression and anxiety to manifest physically and emotionally in young adults. Understanding these attributes, their pairings/triplets and the order in which they manifest, could help clinicians more quickly identify symptoms of depression in young adults, allowing them to diagnose and treat individuals earlier than in years past.

Project Purpose(s)

  • Disease Focused Research (Depression)
  • Population Health
  • Social / Behavioral

Scientific Approaches

We will create a cohort and a set of data from the All of Us data set conduct this research. We will:
-- Define our initial population broadly within the 18-25 year-old age range. We will include people with AND without depression, the condition we will predict.
-- Select as our response (outcome) variable Depression, which is a type of Depression included in the All of Us data taxonomy. We will define the date of occurrence of the response variable as the first time any variable/condition of any kind of Depression (any condition with the term "depression" in it).
We will also:
--Review how others have done similar research by examining PubMed publications that have defined the variable of interest. We will do this using EHR codes.
--In our cohort, we will select demographics, such as age, gender and race, and all conditions as independent variables of interest. We will not rely on survey responses. We will rely only on EHR data. We will include that critical date of occurrence.

Anticipated Findings

We expect to:
--Validate that depression and anxiety was prevalent among young adults during the Covid-19 pandemic. (We need to find and cite this research.)
--Identify the independent variables that contributed, directly and indirectly, to the onset of depression and anxiety in young adults during this time.
--Use LASSO to regress the response variable on all independent variables, and pairwise or triple cluster of independent variables that precede the response variable.
--Use LASSO to regress each variable that is a direct predictor of response/outcome variable on all preceding variables (demographics and conditions).
--Create a Causal Network for clusters of conditions in predicting the response variable.

These findings will contribute to the body of scientific knowledge in the field by identifying attributes that contribute directly and indirectly to depression and anxiety in young adults in EXTREME circumstances, such as a pandemic, and by default, in normal circumstances.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Jeanne Peck - Graduate Trainee, George Mason University

Collaborators:

  • Rediet Woldeselassie - Graduate Trainee, George Mason University
  • Muhanad Almoneef - Graduate Trainee, George Mason University
  • Adnan Esilan - Graduate Trainee, George Mason University
Request a Review of this Research Project

COVID vaccine update among chiropractic users

Patients and providers utilizing complementary and integrative health (CIH) approaches have complicated and conflicting perspectives on the science and public health benefit of immunization programs. This is particularly true among chiropractic users (the most widely used CIH approach), where multiple…

Scientific Questions Being Studied

Patients and providers utilizing complementary and integrative health (CIH) approaches have complicated and conflicting perspectives on the science and public health benefit of immunization programs. This is particularly true among chiropractic users (the most widely used CIH approach), where multiple studies with conflicting results have evaluated perspectives and uptake of influenza and pneumococcal vaccines. This population has yet to be studied regarding uptake of COVID vaccine.
Accordingly, our primary research question is as follows: How does the uptake of COVID-19 vaccine differ in chiropractic vs non-chiropractic users?

Project Purpose(s)

  • Population Health
  • Educational

Scientific Approaches

Cohort characteristics:
1- adults aged 18+
2- completed COPE survey

Concept sets:
3- Healthcare utilization survey question regarding chiropractic utilization in past 12 months + total number of visits
4- Healthcare utilization survey question regarding PCP utilization in past 12 months + total number of visits
5- Basic survey
6- Comorbid conditions (hypertension, diabetes, anxiety disorder, depressive disorder, obesity)

Analysis:
Descriptive statistics comparing COPE responses among chiropractic users vs non-users,
t-testing for continuous and chi-square for categorical variables
Regression analysis to predict COVID-19 vaccine uptake based on chiropractic utilization; same analysis for primary care utilization

Anticipated Findings

Our hypothesis is that chiropractic users will have a lower uptake of COVID-19 vaccine when compared to non-uses, and when compared to primary care users. These findings have important public health implications, as CIH utilization continues to increase.

Demographic Categories of Interest

  • Geography
  • Education Level
  • Income Level

Data Set Used

Registered Tier

Research Team

Owner:

Request a Review of this Research Project

Duplicate of All of Us chronic conditions Fitbit analysis

Objective: To access differences in Fitbit measures across various chronic conditions, such as diabetes, Covid and long Covid, hypertension, heart diseases, and others. Our hypothesis is that individuals with chronic conditions will have poorer Fitbit measure health outcomes than those…

Scientific Questions Being Studied

Objective: To access differences in Fitbit measures across various chronic conditions, such as diabetes, Covid and long Covid, hypertension, heart diseases, and others. Our hypothesis is that individuals with chronic conditions will have poorer Fitbit measure health outcomes than those without chronic conditions.

Project Purpose(s)

  • Disease Focused Research (chronic conditions)
  • Population Health
  • Social / Behavioral

Scientific Approaches

Dataset: develop a dataset of Fitbit users with and without certain chronic conditions.
We will describe the sample in terms of sociodemographics. We will use the combination of feature engineering and machine learning techniques to assess differences between groups.

Anticipated Findings

We expect to find differences in heart rate and activity levels, and sleep across different disease groups as well as heterogeneities across sociodemographic groups. The findings will help develop passive characterization and predictive models of chronic conditions.

Demographic Categories of Interest

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

Data Set Used

Controlled Tier

Research Team

Owner:

  • Citina Liang - Graduate Trainee, University of Southern California
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Sleep effects in long COVID-19

Many people experience long-term health effects from COVID, even after the acute disease has resolved; this condition is called post-acute sequelae of COVID (PASC), or long-COVID. Based on prior reports, fatigue and poor sleep quality are common symptoms of long…

Scientific Questions Being Studied

Many people experience long-term health effects from COVID, even after the acute disease has resolved; this condition is called post-acute sequelae of COVID (PASC), or long-COVID. Based on prior reports, fatigue and poor sleep quality are common symptoms of long COVID. We are exploring the All of Us dataset to obtain further information about how sleep is affected in people who had COVID-19.

Project Purpose(s)

  • Disease Focused Research (COVID-19, poor sleep, PASC, long COVID)

Scientific Approaches

We will use the All of Us dataset to identify a cohort of people who previously had COVID-19, and compare them to people who did not not specifically report an infection. We will analyze data for these individuals including actigraphy (fitbit data) as a measure of sleep. We will also analyze secondary data relevant to the condition of long COVID, pertaining to fatigue and mental and cognitive health. We will use the standard all of us tools to conduct this analysis.

Anticipated Findings

We anticipate that poor sleep is more common in people who experienced COVID-19 infection in the past, vs people who did not. We predict that poor sleep may also be associated with other health effects in this population characteristic of long COVID (such as fatigue and mental or cognitive health issues).

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Wendy Walker - Early Career Tenure-track Researcher, Texas Tech University Health Sciences Center at El Paso
Request a Review of this Research Project

AUD_MH_Genomics_v7

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

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

Data Set Used

Controlled Tier

Research Team

Owner:

Request a Review of this Research Project

long_covid_causal_discovery

I am exploring the dataset to understand what sort of questions we could potentially be asking about long covid. Especially causal questions that will help us understand how an intervention or treatment like the COVID-19 vaccine affects long covid outcomes.

Scientific Questions Being Studied

I am exploring the dataset to understand what sort of questions we could potentially be asking about long covid. Especially causal questions that will help us understand how an intervention or treatment like the COVID-19 vaccine affects long covid outcomes.

Project Purpose(s)

  • Population Health
  • Methods Development

Scientific Approaches

I plan to use causal inference and machine learning approaches to understand the effect of vaccination and other interventions on long covid outcomes. Specifically, with the vast amount of data present in this dataset, I can use causal discovery techniques to understand how the underlying causal mechanisms behind interventions.

Anticipated Findings

I hope to show a causal link between vaccination and long covid outcomes. My findings will hopefully open the door for further analysis and experiments on this topic.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Request a Review of this Research Project

V7 PASC Workspace

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
  • Ancestry
  • 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:

Request a Review of this Research Project

Covid Mental Health v6

Our team has been collaborating with ELSA UK and ELSA Brazil cohorts through the International Hundred K+ Cohort Consortium to examine the mental health consequences of the global COVID-19 pandemic in different countries. As the site based in the U.S.,…

Scientific Questions Being Studied

Our team has been collaborating with ELSA UK and ELSA Brazil cohorts through the International Hundred K+ Cohort Consortium to examine the mental health consequences of the global COVID-19 pandemic in different countries. As the site based in the U.S., we decided to use data from the All of Us Research Program as they reflect the remarkable diversity of the U.S. population and provide a unique opportunity to explore risk/protective factors that shape the mental health impact of the pandemic across various sociodemographic contexts.

Project Purpose(s)

  • Disease Focused Research (disease of mental health)
  • Population Health
  • Social / Behavioral

Scientific Approaches

We plan to use a mixed-modeling approach to analyze COPE surveys to estimate the time-varying effects of risk/protective factors on mental health outcomes during the COVID-19 pandemic.

Anticipated Findings

We expect that the findings from our study may help discover new insights into predictors of psychological vulnerability and resilience that may be unique to the period of the COVID-19 pandemic.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Younga Lee - Research Fellow, Mass General Brigham
Request a Review of this Research Project

HIV outcomes in the context of the COVID-19 pandemic

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

Scientific Questions Being Studied

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

Project Purpose(s)

  • Disease Focused Research (HIV and COVID-19)
  • Population Health
  • Social / Behavioral
  • Methods Development

Scientific Approaches

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

Anticipated Findings

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

Demographic Categories of Interest

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

Data Set Used

Controlled Tier

Research Team

Owner:

  • Xueying YANG - Research Fellow, University of South Carolina

Collaborators:

  • Ruilie Cai - Graduate Trainee, University of South Carolina
  • Fanghui Shi - Graduate Trainee, University of South Carolina
  • Buwei He - Graduate Trainee, University of South Carolina
  • Jiajia Zhang - Late Career Tenured Researcher, University of South Carolina
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Disparities in breast Cancer Screening

What are the racial disparities pre covid and during covid What is the prevalence of breast cancer screening

Scientific Questions Being Studied

What are the racial disparities pre covid and during covid
What is the prevalence of breast cancer screening

Project Purpose(s)

  • Population Health

Scientific Approaches

Explore the dataset.
The use of python and SQL to extract the dataset and for analysis.
Clean up the data using Python
Analysis of the data using R

Anticipated Findings

Difference in prevalence of screening before and during covid
racial disparities in screening before and during covid

Demographic Categories of Interest

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

Data Set Used

Controlled Tier

Research Team

Owner:

  • Chiamaka Diala - Graduate Trainee, National Human Genome Research Institute (NIH-NHGRI)

Collaborators:

  • Tam Tran - Other, National Human Genome Research Institute (NIH-NHGRI)
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ADRs of COVID-19 vaccines

The aim of this research project is to determine whether there is any increased risk of specific adverse events, such as myocarditis, thrombocytopenia, guillain-barre syndrome, or multisystem inflammatory syndrome, following  different doses of COVID-19 vaccines.

Scientific Questions Being Studied

The aim of this research project is to determine whether there is any increased risk of specific adverse events, such as myocarditis, thrombocytopenia, guillain-barre syndrome, or multisystem inflammatory syndrome, following  different doses of COVID-19 vaccines.

Project Purpose(s)

  • Drug Development

Scientific Approaches

This project will calculate the standardized morbidity ratio using an observed-to-expected analysis, which is a component of the quantitative pharmacovigilance toolkit for vaccines. Observed-to-expected studies can monitor and provide insight into adverse drug reactions by quantifying the unexpectedness of observing a particular number of cases. The reported adverse drug reactions will be compared to the expected cases. We will use the Minute Survey on COVID-19 vaccinations to do an observed-to-expected analysis. The analysis will be conducted in R.

Anticipated Findings

This study may help in the differentiation of the risks and benefits of COVID-19 vaccination. The detection of adverse drug reactions is essential for risk-benefit analyses and informing post-vaccination clinical practice.

Demographic Categories of Interest

  • Age

Data Set Used

Registered Tier

Research Team

Owner:

  • Joana Tome - Graduate Trainee, Georgia Southern University
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Duplicate of 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:

  • Younga Lee - Research Fellow, Mass General Brigham
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Long COVID's effect on performing complex daily routines

About 30% of adults who have survived COVID-19 experience new symptoms that make it harder for them to fulfill their normal roles and routines. This collection of symptoms is sometimes called "post-acute sequelae of COVID-19 (PASC)" by researchers, or "Long…

Scientific Questions Being Studied

About 30% of adults who have survived COVID-19 experience new symptoms that make it harder for them to fulfill their normal roles and routines. This collection of symptoms is sometimes called "post-acute sequelae of COVID-19 (PASC)" by researchers, or "Long COVID" by patients. People with long COVID may benefit from rehabilitation, but because it is a new disease we don't yet know enough about how to rehabilitate people safely and effectively. We want to know whether All of Us participants with symptoms of long COVID experience daily activity restrictions, are getting rehabilitation therapies like occupational, physical, and speech/language therapy, and whether there are things that increase the risk of impairment.

Project Purpose(s)

  • Disease Focused Research (Long COVID-19 (or Post-acute sequelae of COVID-19, "PASC"))

Scientific Approaches

We will analyze v.7 data on participants who have had a COVID-19 infection, and use methods that tell us whether they are likely to have long COVID, whether the tools (e.g. tests and surveys) used to identify their impairments are working, and whether they've been referred for therapy. We will select participants who, since their COVID-19 illness, have been diagnosed with long COVID specifically or with "clusters" or groups of symptoms/disorders that have been found in previous population-based studies of long COVID. Then we will explore whether these groups are similar or different from those in the previous studies, and see how likely they are to get a referral for therapies that might help them get back to their daily routines. We might also compare them to people without COVID-19 to determine whether the group with long COVID is very different in other ways that matter to health, like socioeconomic situation, insurance, or social connectedness.

Anticipated Findings

From this study, we aim to show how likely All of Us participants with COVID-19 are to experience long COVID symptoms that restrict what they can do, whether rehabilitation recommendations are followed, and whether there are factors that increase or decrease the likelihood of having trouble in daily routines and/or getting rehabilitation. We hope that this will help focus future research on the way long COVID affects participation in meaningful aspects of life, and how rehabilitation can help. Ultimately, this knowledge can help us develop rehabilitation programs for people with long COVID so that they can get back to the things that matter to them.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

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HIV and COVID-19 update

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:

  • Ruilie Cai - Graduate Trainee, University of South Carolina
  • Fanghui Shi - Graduate Trainee, University of South Carolina
  • Buwei He - Graduate Trainee, University of South Carolina
  • Jiajia Zhang - Late Career Tenured Researcher, University of South Carolina
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All of Us chronic conditions Fitbit analysis

Objective: To access differences in Fitbit measures across various chronic conditions, such as diabetes, Covid and long Covid, hypertension, heart diseases, and others. Our hypothesis is that individuals with chronic conditions will have poorer Fitbit measure health outcomes than those…

Scientific Questions Being Studied

Objective: To access differences in Fitbit measures across various chronic conditions, such as diabetes, Covid and long Covid, hypertension, heart diseases, and others. Our hypothesis is that individuals with chronic conditions will have poorer Fitbit measure health outcomes than those without chronic conditions.

Project Purpose(s)

  • Disease Focused Research (chronic conditions)
  • Population Health
  • Social / Behavioral

Scientific Approaches

Dataset: develop a dataset of Fitbit users with and without certain chronic conditions.
We will describe the sample in terms of sociodemographics. We will use the combination of feature engineering and machine learning techniques to assess differences between groups.

Anticipated Findings

We expect to find differences in heart rate and activity levels, and sleep across different disease groups as well as heterogeneities across sociodemographic groups. The findings will help develop passive characterization and predictive models of chronic conditions.

Demographic Categories of Interest

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

Data Set Used

Controlled Tier

Research Team

Owner:

  • Citina Liang - Graduate Trainee, University of Southern California
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Mental Wellbeing and Healthcare Access

I intend to study the effects of healthcare access (proxied by health insurance) on patients' mental health and well-being. I am particularly interested in looking at the effects of healthcare access on patients' mental health during the COVID-19 pandemic period.

Scientific Questions Being Studied

I intend to study the effects of healthcare access (proxied by health insurance) on patients' mental health and well-being. I am particularly interested in looking at the effects of healthcare access on patients' mental health during the COVID-19 pandemic period.

Project Purpose(s)

  • Population Health
  • Social / Behavioral
  • Educational

Scientific Approaches

I hope to use mental health data as reported in the All of Us dataset. I also hope to look at patient-level data for health insurance. I will perform linear regression OLS techniques to determine a correlation between the dependent and independent variables described above.

Anticipated Findings

I anticipate that there will be a positive correlation between healthcare access and mental health. Specifically, I expect patients to experience worse mental health as access to healthcare decreases. Most literature that discusses this topic focuses on the effects of depriving patients of mental health care on mental health. However, I hope to go a step further, defining the relationship between depriving patients of physical health care on mental health.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

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Covid-19 vaccine uptake among cancer survivors

The purpose of the study is to evaluate the modifiable, multilevel factors associated with COVID-19 vaccine uptake among cancer survivors from the All of Us dataset.

Scientific Questions Being Studied

The purpose of the study is to evaluate the modifiable, multilevel factors associated with COVID-19 vaccine uptake among cancer survivors from the All of Us dataset.

Project Purpose(s)

  • Population Health
  • Social / Behavioral

Scientific Approaches

A cohort of cancer survivors will be from using the database. Various survey questions will aid in answering our research aims. In addition, the covid-19 survey questionnaires will also be used to determine our outcome of interest.

Anticipated Findings

Multilevel factors are anticipated to be associated with vaccine uptake and hesitance. These results can help to identify specific characteristics of cancer survivors that make them more or less likely to experience vaccine hesitancy and inform efforts to target, adapt and tailor interventions to their needs.

Demographic Categories of Interest

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

Data Set Used

Registered Tier

Research Team

Owner:

  • Angel Arizpe - Graduate Trainee, University of Southern California
  • Albert Farias - Early Career Tenure-track Researcher, University of Southern California

Collaborators:

  • Katelyn Queen - Graduate Trainee, University of Southern California
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Metformin & the Incidence of Non-DM Diseases in Type 2 Diabetics

This is an exploratory workspace, the first I have attempted, to help me get familiar with All of Us Research methods. At the same time, it will investigate a real research question: "Is treatment of type 2 diabetes with Metformin…

Scientific Questions Being Studied

This is an exploratory workspace, the first I have attempted, to help me get familiar with All of Us Research methods. At the same time, it will investigate a real research question:
"Is treatment of type 2 diabetes with Metformin (as opposed to no treatment) associated with the incidence of infectious diseases such as COVID, flu, Lyme disease, or Zoster?"
My hypothesis is that Metformin is protective, and this effect may or may not be mediated by lowering HbA1c levels.
This question is important, because over 37 million Americans (not to mention other people around the world) have type 2 diabetes, and there is evidence that this puts them at risk for greater incidence of and/or more severe infectious disease. Does Metformin lower their risk?

Project Purpose(s)

  • Drug Development

Scientific Approaches

I will build a cohort of participants with type 2 diabetes, excluding pre-diabetics and type 1 diabetics. The exposure of interest will be Metformin treatment (yes/no) throughout the study period. Outcomes of interest will be time from baseline to incidence of COVID 19, influenza, Lyme Disease, and shingles. I will use a Cox Proportional Hazards model to test for difference between participants on Metformin and those not. Lab values for HbA1c levels (mean of all tests during period?) will also be tested for a mediation effect if the primary hypothesis proves true. I will gather demographic variables to be included in the models as covariates: age, sex, race/ethnicity, household income and education level, if available. Depending on results, I may test some general health indicators, like cardiovascular disease, as potential confounders or effect modifiers.

Anticipated Findings

My hypothesis is that Metformin is protective against infectious disease among people with T2DM, and this effect may or may not be mediated by lowering HbA1c levels.
This question is important, because over 37 million Americans (not to mention other people around the world) have T2DM, and there is evidence that this puts them at risk for greater incidence of and/or more severe infectious disease. Does Metformin lower their risk?

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

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