Research Projects Directory

Research Projects Directory

638 active projects

This information was updated 7/27/2021

Information about each project within the Researcher Workbench is available in the Research Projects Directory below. Approved researchers provide their project’s research purpose, description, populations of interest, and more. This information helps All of Us ensure transparency on the type of research being conducted.

At this time, all listed projects are using data in the Registered Tier. The Registered Tier contains individual-level data from electronic health records, surveys, physical measurements, and wearables. Personal identifiers have been removed from these data to protect participant privacy.

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.

TOS Abstract

We are interested in assessing disease risk scores in the AOU dataset using survey, physical measures, EHR and Fitbit data. Using unsupervised and supervised ML to see if clusters have potential to improve current standards such as ACSVD, DRF and…

Scientific Questions Being Studied

We are interested in assessing disease risk scores in the AOU dataset using survey, physical measures, EHR and Fitbit data. Using unsupervised and supervised ML to see if clusters have potential to improve current standards such as ACSVD, DRF and others

Project Purpose(s)

  • Methods Development

Scientific Approaches

Datasets similar to current local NYC population from 2019 census data, using unsupervised (kmeans, knn) and supervised (regression) machine learning to determine if AOU data in framework of ACSVD and DRF risk factor questionnaires perform better with different variables of interest.

Anticipated Findings

We anticipate that we will be able to replicate or improve risk factor assessments; risk factors may change with subgroups.

Demographic Categories of Interest

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

Research Team

Owner:

Collaborators:

  • Francis Ratsimbazafy - Other, All of Us Program Operational Use

Diabetes Comparison Workspace

What are the differences in A1c levels between diabetic and control populations and how do these comparisons vary when controlling for other covariates (age, gender, race, demographic information).

Scientific Questions Being Studied

What are the differences in A1c levels between diabetic and control populations and how do these comparisons vary when controlling for other covariates (age, gender, race, demographic information).

Project Purpose(s)

  • Other Purpose (This workspace's main purpose will be to provide a place to learn first hand how to create and analyze data from All of Us. The "research aim" of this project will be to compare diabetes patients and control patients, however this is only meant as a directive for the ultimate purpose of better understanding workspace creation and analysis in AoU. )

Scientific Approaches

We plan to use simple comparative statistical analyses such as t-tests and Bayesian analyses to explore group differences. Linear regression and more advanced modeling techniques (regularized regression, tree based methods) may be used to further define differences between the groups. Most of the analysis will be conducted in R.

Anticipated Findings

Anticipated findings are that A1c levels are higher among diabetes and prediabetes patients than controls.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Collaborators:

  • Josh Denny - Other, All of Us Program Operational Use

Rheumatoid Arthritis Analysis 2

Prevalence of Rheumatoid Arthritis in AoU and the phenotypes associated after conditioning for different co-morbidities, medications, gender, race, etc.

Scientific Questions Being Studied

Prevalence of Rheumatoid Arthritis in AoU and the phenotypes associated after conditioning for different co-morbidities, medications, gender, race, etc.

Project Purpose(s)

  • Disease Focused Research (rheumatoid arthritis)

Scientific Approaches

Mainly statistical and machine learning modeling and PheWAS software for determining RA diagnosis and associated diagnoses as well as outcomes. The goal of this research is to compare findings across other datasets and especially the differences in phenotypes across different sites, as explained by the models.

Anticipated Findings

We expect that on average our models will find consistent markers for RA diagnoses across sites, however there will likely be large outliers. RA phenotypes will not likely be a surprise, since this is a commonly researched disease area.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

COPE survey analysis

We are interested in using the COPE survey questions and answers from respondents to see how differences may occur by geographic location and demographic information. Specifically, we are interested in social distancing and mental health questions.

Scientific Questions Being Studied

We are interested in using the COPE survey questions and answers from respondents to see how differences may occur by geographic location and demographic information. Specifically, we are interested in social distancing and mental health questions.

Project Purpose(s)

  • Disease Focused Research (COVID-19)

Scientific Approaches

We plan to use the COPE survey data, linked patient information, geographic information for each site, (possibly) medication data, and condition occurrence data. We plan to use phenome wide association studies (PheWAS) in order to determine likely phenotypes associated with outcomes of interest, such as social distancing measures.

Anticipated Findings

We anticipate to find those with debilitating diseases to be more concerned with social distancing, however we are unsure which diseases will have greater association. We also suspect that measures of depression and loss due to covid to be highly associated with more social distancing. We propose that these PheWAS analyses and investigations into differences of social distancing and mental health will heavily contribute to the field of study on COVID-19 and sociological and behavioral health effects.

Demographic Categories of Interest

  • Race / Ethnicity

Research Team

Owner:

N3C Comparison

We plan to provide phecode counts and percentages across COVID-19 diagnosis, both of enrolled participants and EHR observations for AoU and N3C. We plan to stratify by demographics of interest and age and compare measures of relative risk and odds…

Scientific Questions Being Studied

We plan to provide phecode counts and percentages across COVID-19 diagnosis, both of enrolled participants and EHR observations for AoU and N3C. We plan to stratify by demographics of interest and age and compare measures of relative risk and odds ratios between AoU and N3C COVID-19 positive and negative participants. This analysis is at a high level, but provides some valuable measures to compare common diagnoses and comorbidities associated with COVID-19. It also provides a good measure for differences in conditions between AoU and N3C participants.

Project Purpose(s)

  • Disease Focused Research (COVID-19)

Scientific Approaches

We plan to use N3C OMOP compliant datasets in order to compare diagnostic level counts with AoU. Mainly will be working with SQL, R, and Python programming languages to merge this information and conduct the broad analysis.

Anticipated Findings

We anticipate that some common respiratory, disease, and heart complications will be more common in the COVID-19 positive patients from N3C when compared to phenotypes from our AoU cohort. We do not have great expectations when comparing the patients not exhibiting COVID-19 in N3C with the AoU population.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Test - Work With Wearable Device Data

Testing and operational use

Scientific Questions Being Studied

Testing and operational use

Project Purpose(s)

  • Other Purpose (Testing and operational use)

Scientific Approaches

This Tutorial Workspace contains one Jupyter Notebook written in Python. The notebook contains information on how to extract and work with the current set of All of Us Fitbit data. What are the anticipated findings from the study? How would your findings contribute to the body of scientific knowledge in the field? By reading and running the notebook in this Tutorial Workspace, researchers will learn how to query information about steps, heart rate, and daily activity summary.

Anticipated Findings

By reading and running the notebook in this Tutorial Workspace, researchers will understand how to work with Fitbit CDR data from the workbench. They will learn how to query information about steps, heart rate, and daily activity summary.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Hiral Master - Project Personnel, All of Us Program Operational Use

Test: Work with All of Us Physical Measurements Data

How to navigate around physical measurements?

Scientific Questions Being Studied

How to navigate around physical measurements?

Project Purpose(s)

  • Other Purpose (Testing and operations purposes)

Scientific Approaches

N/A

Anticipated Findings

N/A

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Hiral Master - Project Personnel, All of Us Program Operational Use

MR

We are studying the phenotypic burdens of diseases among patients with diagnoses of mendelian diseases across multiple racial groups.

Scientific Questions Being Studied

We are studying the phenotypic burdens of diseases among patients with diagnoses of mendelian diseases across multiple racial groups.

Project Purpose(s)

  • Disease Focused Research (Mendelian diseases)
  • Methods Development

Scientific Approaches

We will include all participants. We will compare the diseases profiles of participants with ICD codes or other concepts of genetic diagnosis of mendelian diseases.

Anticipated Findings

We hope to understand the burden of diseases in these patients, particularly among non-white ancestral groups.

Demographic Categories of Interest

  • Race / Ethnicity

Research Team

Owner:

Health literacy

The Health Resources & Services Administration (HRSA) defines health literacy as "the degree to which individuals have the capacity to obtain, process, and understand basic health information needed to make appropriate health decisions." Health literacy is an important determinant of…

Scientific Questions Being Studied

The Health Resources & Services Administration (HRSA) defines health literacy as "the degree to which individuals have the capacity to obtain, process, and understand basic health information needed to make appropriate health decisions." Health literacy is an important determinant of health and well-being. People with low health literacy may have challenges in accessing healthcare services; understanding health information and risk probability; completing health-related forms and assessments; and managing health conditions.

We are interested in exploring health literacy among Latinos. We want to answer the following questions:
1. How does health literacy among Latinos in the United States differ by personal factors (i.e., age, nativity, education, primary language), geographic, and social factors?
2. How is health literacy associated with self-rated health, quality of life, and healthcare experiences among Latinos?

Project Purpose(s)

  • Population Health
  • Social / Behavioral

Scientific Approaches

We plan to use data from the All of Us surveys to explore the variables of interest and use bivariate and multivariate analysis techniques.

Anticipated Findings

We hope that by answering our reserach questions, we will be to better understand health literacy differs among Latino subgroups and how it is associated with perceptions of health and well-being. We anticipate that Latinos who are older, report lower formal education, and have limited English proficiency will have lower health literacy. Further, we anticipate that those with lower health literacy will report poorer experiences with the healthcare system, but they may continue to rate their health and quality of life high. This type of information may help public health practitioners to develop, promote, and disseminate health literacy interventions. It may also help healthcare providers, healthcare institutions, and public health authorities to reflect on their practices and policies related to health literacy, patient experiences, and community engagement.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Geography
  • Education Level

Research Team

Owner:

  • Athena Ramos - Senior Researcher, University of Nebraska Medical Center

Collaborators:

  • Rishad Ahmed - Research Assistant, University of Nebraska Medical Center
  • Natalia Trinidad - Project Personnel, University of Nebraska Medical Center
  • Harlan Sayles - Project Personnel, University of Nebraska Medical Center

15month Version Two ophthalmology epidemiology(DV3)

We would like to evaluate the epidemiology, treatments, and health outcomes of eye diseases using the diverse population in the All Of Us project. Over 12 million people in the United States over the age of 40 have visual impairment,…

Scientific Questions Being Studied

We would like to evaluate the epidemiology, treatments, and health outcomes of eye diseases using the diverse population in the All Of Us project. Over 12 million people in the United States over the age of 40 have visual impairment, and over 3 million have visual impairment despite glasses, contacts, or other treatments. Visual impairment has severe impacts on patients' quality of life and mortality. There are many common causes of visual impairment, including some reversible (such as cataract) and others that are treatable but can still cause irreversible vision loss (macular degeneration, glaucoma, diabetic retinopathy). Some of these diseases disproportionately impact minority populations (e.g. glaucoma in African Americans and Hispanics).
We hope to broadly characterize the prevalence of eye diseases in this cohort, as well as associated medical and surgical treatments. We hope to be able to investigate risk factors, patterns and outcomes of treatment of different eye diseases.

Project Purpose(s)

  • Disease Focused Research (eye diseases)
  • Population Health

Scientific Approaches

We plan to primarily use the EHR, survey, and physical measurements dataset to describe the epidemiology of eye diseases, using encounter-level billing codes to determine their presence or absence. We plan to investigate risk factors for these eye diseases, including demographics, medications, physical measurements (to the extent available), survey data, and other associated diagnoses. We will begin with simple descriptive statistics. In diagnoses with sufficiently sized cohort, we will also build logistic regressions to evaluate risk factors for diagnosis.
We will also evaluate treatment patterns (medical and surgical) for different eye diseases, using EHR data of medications and surgeries undergone. We will characterize demographic and patterns in patterns of medications and surgeries.

Anticipated Findings

We anticipate that our findings will contribute broadly to the knowledge of epidemiology of eye diseases in the US, as well as improve our understanding of patterns of treatments and outcomes of eye diseases in the US. In this diverse population, we will also be able to see if there are disparities in eye diseases and their treatment patterns and outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Wendeng Hu - Project Personnel, Stanford University

COPC

The project aims to study the comorbidities of chronic pains. Chronic pains usually co-occur together: on chronic pain may progress to another, yielding to chronic overlapping pain conditions (COPC). The project will investigate risk factors yielding to the progression of…

Scientific Questions Being Studied

The project aims to study the comorbidities of chronic pains. Chronic pains usually co-occur together: on chronic pain may progress to another, yielding to chronic overlapping pain conditions (COPC). The project will investigate risk factors yielding to the progression of other chronic pain conditions, including genetic factors, once the data is available. It will also study the subpopulation and disparity of COPC.

Project Purpose(s)

  • Disease Focused Research (Chronic pains)
  • Population Health
  • Ancestry

Scientific Approaches

We will use database query to collect relevant data of 10 frequent chronic pain conditions, then use logistic regression model to study the comorbidities. We may use data mining to identify the trajectories of COPC progress as well, followed by genetic and biological mechanism identification of subpopulation of COPCs.

Anticipated Findings

We will identify risk factors for progression of COPC, genetic and biological factors (genes, pathways) for the progression and subpopulation of COPCs.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Income Level

Research Team

Owner:

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

Collaborators:

  • Wenting luo - Graduate Trainee, University of Arizona
  • Edwin Baldwin - Graduate Trainee, University of Arizona

HPIMS

We are investigating risk factors for coronary artery disease. These include genetic, wearables, clinical, and demographics factors.

Scientific Questions Being Studied

We are investigating risk factors for coronary artery disease. These include genetic, wearables, clinical, and demographics factors.

Project Purpose(s)

  • Disease Focused Research (CAD)

Scientific Approaches

We will apply linear regression models to investigate each risk factor's association with CAD. We will perform univariate and multivariate analyses.

Anticipated Findings

We expect to identify known risk factors for CAD( ex hyperlipidemia, obesity, hypertension) as well as potential novel ones.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Ishan Paranjpe - Graduate Trainee, Icahn School of Medicine at Mount Sinai

Determinants of cardiovascular disease across minority populations V4

Cardiovascular disease (CVD) are responsible for a substantial proportion of the morbidity and mortality observed in the general population. Mounting evidence indicates that this impact disproportionately affects minority populations. This disproportionate effect is not only present in minorities defined by…

Scientific Questions Being Studied

Cardiovascular disease (CVD) are responsible for a substantial proportion of the morbidity and mortality observed in the general population. Mounting evidence indicates that this impact disproportionately affects minority populations. This disproportionate effect is not only present in minorities defined by race/ethnicity, but also in those defined by age, sexual orientation, and other characteristics. The main questions of this study are: (1) can we use All of US to identify novel risk factors for cardiovascular disease that are specific to a given minority group? (2) Are existing risk factors for CVD shared across all minority groups? (3) How do the effects of these risk factors vary when considering more than one minority group? These questions are important to (1) identify groups of persons at particularly high risk of sustaining these conditions that may benefit from tailored diagnostic and therapeutic interventions; and (2) identify new treatments for these conditions.

Project Purpose(s)

  • Disease Focused Research (cardiovascular system disease)
  • Population Health
  • Social / Behavioral
  • Ancestry

Scientific Approaches

We will use the All of US dataset V4. We will identify variables that represent (1) cardiovascular disease (myocardial infarction, coronary artery disease, stroke); (2) all the known risk factors for each of these conditions; (3) physiological variables that either define a risk factor or are associated with risk of cardiovascular disease (blood pressure, cholesterol levels, hemoglobin A1C); and (4) identify the minority groups of interest. We will use linear and logistic regression to test for association between risk factors and the conditions of interest.

Anticipated Findings

We expect to find that: (1) a substantial number of the known vascular risk factors increase risk of cardiovascular disease in across all evaluated groups; (2) known risk factors for cardiovascular disease disproportionately affect some minority groups; and (3) the effect of these risk factors will be stronger in some minority groups. These findings will helps us to (1) identify groups of persons at particularly high risk of sustaining these conditions that may benefit from tailored diagnostic and therapeutic interventions; and (2) identify new treatments for these conditions.

Demographic Categories of Interest

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

Research Team

Owner:

Collaborators:

  • Zachariah Demarais - Graduate Trainee, Yale University
  • Daniela Renedo - Research Fellow, Yale University
  • Cyprien Rivier - Research Fellow, Yale University
  • Carolyn Conlon - Graduate Trainee, Yale University

Phenome-wide associations of metabolic disorder measurements_v4

THe aims of this project are to identify known and novel disease associations with cardiometabolic traits, utilizing the All of Us (AoU) dataset. Evaluate if known racial/ethnic, education, and socioeconomic differences in cardiometabolic disorder can be replicated utilizing the AoU…

Scientific Questions Being Studied

THe aims of this project are to identify known and novel disease associations with cardiometabolic traits, utilizing the All of Us (AoU) dataset. Evaluate if known racial/ethnic, education, and socioeconomic differences in cardiometabolic disorder can be replicated utilizing the AoU dataset. We hope to expand the scope to include all relevant measures related to cardiometabolic disorders and assess the possibility for selection bias and issues of generalizability in cohort participant selection. There are well established disparities in rates of metabolic disorders related to race/ethnicity, gender, and socioeconomic status. There is also a general lack of diversity and the potential for healthy-patient bias in large epidemiological datasets. For these reasons we seek to use All of Us data to forerun projects that are more inclusive and facilitate a change in traditionally underrepresented research.

Project Purpose(s)

  • Population Health
  • Social / Behavioral

Scientific Approaches

Utilizing the CDC National Health and Nutrition Examination Survey(NHANES), a nationally representative sample, we will compare prevalence rates and racial/ethnic and gender group distributions of key metabolic disorder parameters. To quantitatively investigate the generalizability of the AoU data we will assess differences in the demographic and healthy-lifestyle characteristics between the AofU data and the NHANES data. We will use linear, logistic, and Poisson regression where appropriate to compare differences between groups.

Anticipated Findings

This project will serve as a springboard for future collaborations and grant applications utilizing AoU data and will generate information that will help future researchers better understand both the internal and external validity of the AofU dataset. We will build a foundation for understanding both the internal and external validity of this novel data source having this formative work influence the scientific communities’ understanding of the All of Us data source. We anticipate that this work will be highly cited and useful for future generations of researchers.

Demographic Categories of Interest

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

Research Team

Owner:

Collaborators:

  • Victoria Bland - Graduate Trainee, University of Arizona

Training_v4

I would like to use this workspace purely for educational purposes only. It will be used to demonstrate to students various data analysis approaches using large datasets and to familiarize them with All of Us cloud storage workflow.

Scientific Questions Being Studied

I would like to use this workspace purely for educational purposes only. It will be used to demonstrate to students various data analysis approaches using large datasets and to familiarize them with All of Us cloud storage workflow.

Project Purpose(s)

  • Educational

Scientific Approaches

To produce aggregate summary statistics and regression models for various measurement variables available in All of Us data.

Anticipated Findings

This exploratory analysis will enable us to explore heterogeneity in anthropometric measures among various racial-ethnic groups

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Collaborators:

  • Heidi Steiner - Graduate Trainee, University of Arizona

AYA Cancer Survivors

I am examining the All of Us data to understand patient-provider communication patterns among adolescent/young adult (AYA) cancer survivors. Prior research shows AYA survivors are at risk of disengaging in their care as they transition from pediatric to adult care…

Scientific Questions Being Studied

I am examining the All of Us data to understand patient-provider communication patterns among adolescent/young adult (AYA) cancer survivors. Prior research shows AYA survivors are at risk of disengaging in their care as they transition from pediatric to adult care settings. Evidence suggests high-quality communication with providers is protective against this disengagement. The questions I hope to answer include: (1) Are there patterns in patient-provider communication by patient age? and (2) What additional factors may be related to patient-provider communication? With the answers to these questions, there may be opportunities for improving healthcare engagement among AYA cancer survivors.

Project Purpose(s)

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

Scientific Approaches

First, I will identify a cohort of AYA cancer survivors within the All of Us data. I will explore options for dividing the cohort by age group. It is likely that cell sizes will be too small for reporting aggregated data. As such, I will also explore dividing the cohort by age at diagnosis. If data permits, I will examine differences in access and healthcare utilization.

Anticipated Findings

Taking into account guidelines for transitioning into adult care, I anticipate patient-provider communication to be stronger among older cohorts of AYA survivors compared to younger cohorts. If the contrary is observed, further investigation into communication patterns is needed and may better inform transition practices for AYA survivors.

Demographic Categories of Interest

  • Age
  • Access to Care

Research Team

Owner:

  • Karen Llave - Graduate Trainee, University of California, Irvine

Research Program for Vision Surveillance: Diabetes and Diabetic Retinopathy

How do data from the All of Us database compare against known data sources that are considered to be representative of the general population and have been traditionally used in vision health surveillance activities (such as NHANES, NHIS, etc.)? How…

Scientific Questions Being Studied

How do data from the All of Us database compare against known data sources that are considered to be representative of the general population and have been traditionally used in vision health surveillance activities (such as NHANES, NHIS, etc.)? How does All of Us compare to existing big-data sources such as IQVIA?

There is increasing interest in understanding how social factors impact health and vision outcomes. Social determinants of health are important considerations for disease management and prognosis, and our representative use case (diabetes and diabetic retinopathy) has huge implications for our health system as the leading cause of blindness and visual impairment among working-age adults in the United States. By answering the above questions, we can determine whether the All of Us database is representative and may be broadly generalizable for future studies.

Project Purpose(s)

  • Control Set

Scientific Approaches

- Develop standard cohort definition for diabetes
- Develop standard cohort definition for diabetic retinopathy
- Determine prevalence of diabetes and compare across different data sources – All of Us, NHANES, NHIS, IQVIA
o Numerator: Number of adults with diabetes
o Denominator: Total number of adults available in data source
- Determine prevalence of diabetic retinopathy and compare across different data sources – All of Us, NHANES, NHIS, IQVIA
o Numerator: Number of adults with diabetic retinopathy
o Denominator: Total number of adults available in data source vs. total number of adults with diabetes
- For prevalence calculations, will need to establish defined study periods and ensure consistency across data sources
- Potential analyses:
o Look at state/regional variations
o Examine demographics (age, gender, race, ethnicity) of cohorts across data sources
- Identify areas of similarity/alignment vs. differences

Anticipated Findings

If we are able to demonstrate that the All of Us database is representative and aligns with existing nationwide data sources, then findings regarding links between social determinants and vision health outcomes using All of Us would be felt to be more broadly generalizable. On the other hand, if there are major discrepancies between All of Us and previously established data sources, this would be important information for the vision research community to be aware of, and this could even inform future efforts to make the database more representative.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Geography
  • Access to Care

Research Team

Owner:

Self-reported knowledge of family health history

Family history is used as a screening tool to refer patients for predictive and diagnostic genetic testing; reimbursement of said testing is often also based upon a family history of disease. Therefore, individuals who are less knowledgeable about their family…

Scientific Questions Being Studied

Family history is used as a screening tool to refer patients for predictive and diagnostic genetic testing; reimbursement of said testing is often also based upon a family history of disease. Therefore, individuals who are less knowledgeable about their family history of disease are less likely to qualify for genetic counseling and/or testing.

I will seek to fulfill the following research aims:
1. Explore patterns of missingness in survey data in order to better understand populations that may or may not be represented among AoU survey respondents.
2. Characterize survey respondents with different levels of self-reported knowledge of a family history of disease, as indicated by the survey question "How much do you know about illnesses or health problems for your parents, grandparents, brothers, sisters, and/or children?"
3. Test whether self-reported family history of disease knowledge is associated with uptake of preventive screenings and/or genetic counseling.

Project Purpose(s)

  • Population Health

Scientific Approaches

Inclusion criteria:
- Adult All of Us survey participants
Exclusion criteria:
- None

Methods:
I will compare the characteristics of survey respondents who completed "The Basics" survey with those who completed other AoU surveys, those who consented to linkage of their EHR data, and those who completed other AoU Research Program activities.

I will then characterize study participants who completed the family health questionnaire. Survey and EHR data will be linked. Those with different levels of self-rated family history knowledge will be compared by their sociodemographic characteristics, overall health, and access to healthcare using descriptive statistics and/or regression analyses. I will focus on testing the hypothesis that traditionally underrepresented groups in biomedical research (UBR) are less likely to report about their family health history than non-UBR groups.

Anticipated Findings

I anticipate that individuals who completed only The Basics survey will differ from the populations that have high survey completion, consent to EHR linkage, and participate in other aspects of the AoU Research Program.

Use of family history as a primary screening tool to determine who receives genetic testing may be inherently flawed if knowledge of one's family history of disease differs amongst different populations. This study will provide new insights into whether family history risks assessments may miss groups of individuals who might benefit from genetic testing because of poor family history knowledge.

Demographic Categories of Interest

  • Race / Ethnicity
  • Sex at Birth
  • Gender Identity
  • Sexual Orientation
  • Education Level
  • Income Level

Research Team

Owner:

  • Leland Hull - Early Career Tenure-track Researcher, The Broad Institute

Collaborators:

  • Romit Bhattacharya - Research Fellow, The Broad Institute
  • Mark Trinder - Graduate Trainee, The Broad Institute

Duplicate of Mendelian diseases DX

We are studying the phenotypic burdens of diseases among patients with diagnoses of mendelian diseases across multiple racial groups.

Scientific Questions Being Studied

We are studying the phenotypic burdens of diseases among patients with diagnoses of mendelian diseases across multiple racial groups.

Project Purpose(s)

  • Disease Focused Research (Mendelian diseases)
  • Methods Development

Scientific Approaches

We will include all participants. We will compare the diseases profiles of participants with ICD codes or other concepts of genetic diagnosis of mendelian diseases.

Anticipated Findings

We hope to understand the burden of diseases in these patients, particularly among non-white ancestral groups.

Demographic Categories of Interest

  • Race / Ethnicity

Research Team

Owner:

5% trial

Numerous studies have reported that losing as little as 5% of one’s total body weight (TBW) can improve health. We have previously used UW electronic health record (EHR) data to examine long-term changes in weight and found that adults with…

Scientific Questions Being Studied

Numerous studies have reported that losing as little as 5% of one’s total body weight (TBW) can improve health. We have previously used UW electronic health record (EHR) data to examine long-term changes in weight and found that adults with severe obesity were more likely to lose at least 5% TBW compared to overweight patients and patients with class 1 obesity. However, sufficient weight loss to obtain a non-obese weight class was rare. The median weight change for the population was a net gain of 2.5% TBW.

Objective: To measure long-term weight changes and examine their predictors for adults in a large academic healthcare system

Project Purpose(s)

  • Methods Development

Scientific Approaches

Measures: Over a 5-year period: 1)  5% total body weight (TBW) loss; 2) weight loss into a non-obese BMI category (BMI < 30 kg/m2); 3) ≥10% TBW gain; and 4) predictors of %TBW change via quantile regression.

Anticipated Findings

We anticipate that the All of Us data will help us validate the findings from our prior research using the UW EHR data.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Manasa Venkatesh - Project Personnel, University of Wisconsin, Madison
  • Luke Funk - Mid-career Tenured Researcher, University of Wisconsin, Madison
  • Jacqueline Murtha - Research Fellow, University of Wisconsin, Madison

Mental Health and Substance Use Demo Projects

What are the prevalences of mental health conditions in the AoURP?

Scientific Questions Being Studied

What are the prevalences of mental health conditions in the AoURP?

Project Purpose(s)

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

Scientific Approaches

Not available.

Anticipated Findings

AoURP data can be used to assess mental health conditions in previously under-represented populations.

Demographic Categories of Interest

  • Sex at Birth
  • Education Level
  • Income Level

Research Team

Owner:

  • Kai Yin Ho - Project Personnel, Northwestern University
  • Chen Yeh - Project Personnel, Northwestern University
  • Joyce Rubinstein - Mid-career Tenured Researcher, Northwestern University

Duplicate of Mental Health Demonstration Project V4

As a demonstration project, this study aimed to explore the usability of the All of Us dataset and examined the prevalence of mental health conditions in the All of Us Research Program cohort. Specifically, we explored the lifetime prevalence of…

Scientific Questions Being Studied

As a demonstration project, this study aimed to explore the usability of the All of Us dataset and examined the prevalence of mental health conditions in the All of Us Research Program cohort. Specifically, we explored the lifetime prevalence of depressive disorder, bipolar disorder, and generalized anxiety disorder.

Our study looked prevalence rates for the above conditions in the following ways:
1. Prevalence in EHR data available by various demographic factors
2. Cohort characteristics
3. Congruency for diagnoses in EHR and self-report questionnaire
4. Among individuals who self-report as having been diagnosed with a mental health condition listed above, the percentage of individuals in treatment and associations between treatment and various demographic factors

Project Purpose(s)

  • Disease Focused Research (generalized anxiety disorder, depressive disorder, bipolar disorder)
  • Other Purpose (“This work is a result of an All of Us Research Program Demonstration Project. The projects are efforts by the Program designed to meet the program's goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. This work was reviewed and overseen by the All of Us Research Program Science Committee and the Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use”.)

Scientific Approaches

In this analysis, we calculated prevalence of mental health conditions by leveraging demographic information, questionnaire responses, and EHR data Specifically, we utilized the following surveys: Basics, Overall Health, Personal Medical History, and Healthcare Access PPIs. We utilized EHR data by creating a cohort of individuals with specific diagnoses code in their EHR. We referenced all relevant parent and child SNOMED codes for each mental health condition of the investigation (documented in Concept Set). Associations were calculated using Chi Square.

Anticipated Findings

We anticipated that the prevalence rates found in All of Us will be consistent with previous large scale studies, such as the National Comorbidity Survey. We found that the All of Us dataset is sensitive to detecting mood disorders and is usable for examining mental health conditions.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Kai Yin Ho - Project Personnel, Northwestern University
  • Joyce Rubinstein - Mid-career Tenured Researcher, Northwestern University

Mental Health Demonstration Project

As a demonstration project, this study aimed to explore the usability of the All of Us dataset and examined the prevalence of mental health conditions in the All of Us Research Program cohort. Specifically, we explored the lifetime prevalence of…

Scientific Questions Being Studied

As a demonstration project, this study aimed to explore the usability of the All of Us dataset and examined the prevalence of mental health conditions in the All of Us Research Program cohort. Specifically, we explored the lifetime prevalence of depressive disorder, bipolar disorder, and generalized anxiety disorder.

Our study looked prevalence rates for the above conditions in the following ways:
1. Prevalence in EHR data available by various demographic factors
2. Cohort characteristics
3. Congruency for diagnoses in EHR and self-report questionnaire
4. Among individuals who self-report as having been diagnosed with a mental health condition listed above, the percentage of individuals in treatment and associations between treatment and various demographic factors

Project Purpose(s)

  • Disease Focused Research (generalized anxiety disorder, depressive disorder, bipolar disorder)
  • Other Purpose (“This work is a result of an All of Us Research Program Demonstration Project. The projects are efforts by the Program designed to meet the program's goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. This work was reviewed and overseen by the All of Us Research Program Science Committee and the Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use”.)

Scientific Approaches

In this analysis, we calculated prevalence of mental health conditions by leveraging demographic information, questionnaire responses, and EHR data Specifically, we utilized the following surveys: Basics, Overall Health, Personal Medical History, and Healthcare Access PPIs. We utilized EHR data by creating a cohort of individuals with specific diagnoses code in their EHR. We referenced all relevant parent and child SNOMED codes for each mental health condition of the investigation (documented in Concept Set). Associations were calculated using Chi Square.

Anticipated Findings

We anticipated that the prevalence rates found in All of Us will be consistent with previous large scale studies, such as the National Comorbidity Survey. We found that the All of Us dataset is sensitive to detecting mood disorders and is usable for examining mental health conditions.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Kai Yin Ho - Project Personnel, Northwestern University
  • Chen Yeh - Project Personnel, Northwestern University
  • Joyce Rubinstein - Mid-career Tenured Researcher, Northwestern University

Predicting Major Adverse Cardiac Events in Heart Failure Patients with COVID-19

Aim 1: Determine the predictors of mortality and hospitalization for patients with acute or chronic heart failure (A/CHF) that had a diagnosis of COVID-19. Rapid onset of new or worsening heart failure symptoms are characteristic of AHF. Concomitance of COVID-19…

Scientific Questions Being Studied

Aim 1: Determine the predictors of mortality and hospitalization for patients with acute or chronic heart failure (A/CHF) that had a diagnosis of COVID-19. Rapid onset of new or worsening heart failure symptoms are characteristic of AHF. Concomitance of COVID-19 presents additional challenges towards treating A/CHF patients. Studies provide several candidate clinical and laboratory measures associated with worse clinical outcomes for patients with A/CHF and COVID-19. Identifying COVID-19 specific predictors of mortality and hospitalization for A/CHF patients would help explain the pathophysiology behind the progression of COVID-19 in A/CHF patients.

Aim 2: Stratify the risk for suboptimal guideline-directed medical therapy (GDMT) for A/CHF patients with COVID-19. COVID-19 obstructs A/CHF patients from reaching their optimal target doses. Assigning patients into different strata at risk of not achieving optimal GDMT targets may provide clinicians with more impactful treatment options.

Project Purpose(s)

  • Disease Focused Research (severe acute respiratory syndrome, acute on chronic heart failure)

Scientific Approaches

This retrospective study will include demographic characteristics and clinical features from the All of Us A/CHF and COVID-19 combined cohorts. Missing values will be imputed by multiple imputation. Dimensionality of the data will be reduced by supervised selection. Associations between demographic and clinical features will be made with the outcome of 1-year re-hospitalization with A/CHF as the primary diagnosis. Models generated will utilize standard regression, random forests, and gradient boosting, and will be evaluated by their predictive values, sensitivity, specificity, and c-statistics.

Combined clinical features at baseline will undergo k-means cluster analysis to subset groups. Features will undergo processing as described above. A predictive model will be developed, and a Cox proportional hazards regression analysis for re-hospitalization will be performed for each subgroup. All analyses are to be conducted on the All of Us workbench in the latest version of R and Python.

Anticipated Findings

We may expect to find clinical features and laboratory parameters associated with elevated systemic inflammation, endothelial dysfunction, and hypercoagulation to be strong predictors of adverse outcomes for A/CHF patients who has contracted COVID-19. Clinical features like carbon dioxide and oxygen partial pressures in arterial blood may serve as correlates of worse outcomes. Predictive laboratory features may include high-sensitivity C-reactive protein (hs-CRP), brain and atrial natriuretic peptides (BNP/ANP), ferritin, interleukins, neutrophils, complete blood count and d-dimer quantities among others.

In stratifying patients at-risk of not adhering to GDMT, stratification we expect that data pertaining to a patient’s health care access and utilization, as well as the severity of their COVID-19 infection, may put them at greater risk of non-adherence. Severity of COVID-19 infection may be understood as a profile of high inflammation like elevated levels of hs-CRP or interleukins.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

Collaborators:

  • Saud Alshammari - Graduate Trainee, Virginia Commonwealth University
  • Silas Contaifer - Graduate Trainee, Virginia Commonwealth University
  • Daniel Contaifer Junior - Project Personnel, Virginia Commonwealth University
  • VIRGINIA UNIVERSITY - Graduate Trainee, Virginia Commonwealth University

SPADE

The overarching goal of this study is to improve the prediction of clinically significant adverse drug events (ADEs) by harnessing the data that is made available through the All of Us program. Although a number of ADE risk prediction tools…

Scientific Questions Being Studied

The overarching goal of this study is to improve the prediction of clinically significant adverse drug events (ADEs) by harnessing the data that is made available through the All of Us program. Although a number of ADE risk prediction tools are published in the literature, none are universally accepted and used routinely in clinical practice. Robust ADE risk prediction tools are lacking because most datasets utilized to derive ADEs are largely not generalizable. Furthermore, interindividual susceptibility to ADEs might be explainable by genetic variations, and such information is not often available in prediction models. Our specific aims are:
1. Determine the prevalence, specific types and characteristics of ADEs among participants who are receiving chronic disease medications.
2. Derive and validate a prediction model to identify characteristics that are associated with ADEs related to selected chronic disease medications.

Project Purpose(s)

  • Disease Focused Research (Definitions, analyses and prediction of adverse drug reactions/events)
  • Population Health
  • Social / Behavioral

Scientific Approaches

Descriptive statistics will be utilized to characterize the prevalence, specific types and characteristics of ADEs for each drug. Univariate analysis with chi-square tests will be conducted to calculate odds ratios (together with 95% confidence interval) for ADEs associated with each potential risk factor, followed by multivariate logistic regression analysis using backwards selection to identify statistically significant factors and ultimately derive an ADE prediction model for each selected drug.

Anticipated Findings

Our study also intends to fill a current research gap and present findings to contribute an indispensable part of a future larger study that examines ADEs for association with both patient characteristics and pharmacogenetic information, when available, which will contribute an additional layer of information critical to the preventability of ADEs. There will be added focus on specific ethnic groups previously under-described in literature, including Hispanics and African Americans. While limitations such as inconsistency in recording of ADEs and risk factors are anticipated, an advantage of this study is that cases and controls will be selected from a large participant pool (All of Us) rather than a specific site, which will allow us to evaluate the impact of ADEs in a group that better mirrors the general patient population in the clinical setting.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Stanley Jia - Undergraduate Student, University of California, Irvine
  • Lu He - Graduate Trainee, University of California, Irvine
  • Kai Zheng - Mid-career Tenured Researcher, University of California, Irvine
  • Kevin Zhang - Undergraduate Student, University of California, Irvine
  • Ding Quan Ng - Graduate Trainee, University of California, Irvine
  • Alexandre Chan - Late Career Tenured Researcher, University of California, Irvine

Collaborators:

  • Jahnavi Maddhuri - Undergraduate Student, University of California, Irvine
  • Jatin Goyal - Undergraduate Student, University of California, Irvine
  • Arvind Kumar - Undergraduate Student, University of California, Irvine
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