Research Projects Directory

Research Projects Directory

3,097 active projects

This information was updated 11/27/2022

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.

Exploring Hypertension data types part 2

There are many types of medical records available for patients with hypertension. We want to explore variation in the count and demographics of cohorts of patients with hypertension depending on which type of data is selected to retrieve a given…

Scientific Questions Being Studied

There are many types of medical records available for patients with hypertension. We want to explore variation in the count and demographics of cohorts of patients with hypertension depending on which type of data is selected to retrieve a given cohort of patients with hypertension. Such data types include surveys, procedures, conditions, and drugs.

Project Purpose(s)

  • Educational

Scientific Approaches

We will create at least one cohort of patients with hypertension based on a data type indicative of hypertension. We will then compare the count and demographics of the patients in this cohort with other cohorts of patients with hypertension created with different data types indicative of hypertension than the one we used.

Anticipated Findings

The anticipated findings are that there will be much variation in count and demographics depending on which data type indicative of hypertension one uses to create a cohort for hypertension. We will then identify the best data types to use in the future to create a cohort for hypertension. We will reflect on mistakes that can be made in creating a cohort that is not optimized for our research topic of interest because we selected data types that do not fully and accurately create a cohort for the phenotype of interest for our research topic.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Step Count and Chronic Disease

I want to study an association between step count and chronic disease. This is important so that we are able to understand an ideal step count to avoid chronic disease among all people.

Scientific Questions Being Studied

I want to study an association between step count and chronic disease. This is important so that we are able to understand an ideal step count to avoid chronic disease among all people.

Project Purpose(s)

  • Educational

Scientific Approaches

I plan to use survival analysis for this study. Additionally, we want to study how to use data over time to look at chronic disease.

Anticipated Findings

The anticipated findings are a number of steps that people should take everyday to avoid chronic disease.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Venous thromboembolism

We are developing a machine learning model to predict the occurrence of venous thromboembolism (including pulmonary embolism and deep vein thrombosis), which is an underdiagnosed yet highly prevalent condition that represents the most common preventable cause of death among hospitalized…

Scientific Questions Being Studied

We are developing a machine learning model to predict the occurrence of venous thromboembolism (including pulmonary embolism and deep vein thrombosis), which is an underdiagnosed yet highly prevalent condition that represents the most common preventable cause of death among hospitalized patients in the United States. A model using already available electronic health record data that does not require imaging could alert healthcare providers regarding elevated risk for venous thromboembolism and help prevent mortality.

Project Purpose(s)

  • Disease Focused Research (pulmonary embolism)

Scientific Approaches

We will primarily use All of Us as a validation dataset for machine learning models trained using data from (1) a large New York hospital system and (2) the UK Biobank. Our models are trained using gradient boosting methods and include demographics, common laboratory measurements, vitals, past diagnoses, and medication usage. Model performance is evaluated using both sensitivity-specificity and precision-recall metrics. We will also specifically examine disparities in performance between races and other demographic subsets.

Anticipated Findings

We have already developed models that can predict venous thromboembolism using electronic health record data with high sensitivity and specificity. Validation on the All of Us dataset would help us ensure the validity of our model on a large, diverse, and more representative population. Besides possible implementation of our model in the healthcare setting, we anticipate the large size of our study population will also help identify previously unknown risk factors for venous thromboembolism.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Robert Chen - Graduate Trainee, Icahn School of Medicine at Mount Sinai

Vanderbilt Survival Analysis Project 2022 Elisa Yazdani

Our research question is whether blood pressure after the development of diabetic retinopathy (DR) impacts the risk for diabetic macular edema (DME). DME is a particularly severe clinical outcome among individuals with DR. We would additionally like to see if this…

Scientific Questions Being Studied

Our research question is whether blood pressure after the development of diabetic retinopathy (DR) impacts the risk for diabetic macular edema (DME). DME is a particularly severe clinical outcome among individuals with DR. We would additionally like to see if this effect varies by time, because this may inform a potential clinical intervention.

Project Purpose(s)

  • Disease Focused Research (diabetic macular edema)
  • Educational

Scientific Approaches

We plan on using traditional Cox regression and adding time-dependent covariates as becomes appropriate. We have conducted a preliminary analysis using local EHR data and are hoping to replicate that study in AOU.

Anticipated Findings

We hypothesize that hypertension is associated with an increased risk of developing DME among patients with a diabetic retinopathy. Our study will contribute to the ongoing discussions about whether hypertension is a risk factor for DME.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Megan Jones - Graduate Trainee, Vanderbilt University
  • Lydia Yao - Graduate Trainee, Vanderbilt University
  • Lan Shi - Graduate Trainee, Vanderbilt University
  • Yeji Ko - Graduate Trainee, Vanderbilt University
  • Jared Strauch - Graduate Trainee, Vanderbilt University
  • Zhuohui Liang - Graduate Trainee, Vanderbilt University
  • Bailu Yan - Graduate Trainee, Vanderbilt University

HealthyElders

We want to query a relatively healthy elder cohort in All of Us participants and analyze behavioral patterns that healthy elders have. We hypothesize that social determinants of health highly relevant to elder adults' life quality.

Scientific Questions Being Studied

We want to query a relatively healthy elder cohort in All of Us participants and analyze behavioral patterns that healthy elders have. We hypothesize that social determinants of health highly relevant to elder adults' life quality.

Project Purpose(s)

  • Social / Behavioral

Scientific Approaches

This is a retrospective observational research
1. Cohorts: 1) relatively healthy elders, check with age groups [ 70-80)[80,90),[90+) 2) elders with serious disease, as cancer, sever chronic diseases
2. Extract Social Determinants of Health elements for these two cohorts
3. Using statistics description, statistical inference, and Machine learning algorithms to identify the elements that will influence elders health.
4. Analytic tool: AoU workbench, jupyter notebook-python environment.

Anticipated Findings

We aim to identify main SDoH that will influence elder's health. The findings will contribute to SDoH research, and health people 2030 research.

Demographic Categories of Interest

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

Data Set Used

Registered Tier

Research Team

Owner:

  • Chenyu Li - Graduate Trainee, University of Pittsburgh

PTC and BMI in Ages 18-27 years

The PTC receptor on taste buds allows individuals to taste certain bitter foods. However, some people have mutations in the gene that PTC is produced from that affects the ability to taste bitter foods. We predict that an individual's PTC…

Scientific Questions Being Studied

The PTC receptor on taste buds allows individuals to taste certain bitter foods. However, some people have mutations in the gene that PTC is produced from that affects the ability to taste bitter foods. We predict that an individual's PTC receptor status has an effect on their diet because bitter yet healthy foods will be less appealing to those with functional PTC receptors. Because PTC receptor haplotype may affect diet, we also hypothesize that individuals with a sensitivity to tasting PTC are at a higher risk of obesity compared to those who cannot taste PTC. For this study, we will focus our cohort on individuals ages 18-27.

Project Purpose(s)

  • Ancestry

Scientific Approaches

We plan to use the All of Us database to examine the correlation between a participant's PTC haplotype and their BMI. In order to do so, we will use whole genome sequencing information to look for specific mutations in the PTC gene that affect tasting ability. We will use an R notebook and Python to extract and analyze the genetic and medical information of the cohort. Our cohort will only consist of individuals ages 18-27 with no further restrictions placed on the composition of the cohort.

Anticipated Findings

We anticipate that there will be a positive correlation between a taster or "supertaster" PTC haplotype and a higher BMI. We believe that this finding will contribute to our current understanding of obesity and the risk factors associated with it.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Samantha Ronalds - Undergraduate Student, Arizona State University

Chronic disease prevention and Wearables

The goals is to study wearable heath data activity and and its effect on risk for chronic disease like cardiovascular disease. .

Scientific Questions Being Studied

The goals is to study wearable heath data activity and and its effect on risk for chronic disease like cardiovascular disease.

.

Project Purpose(s)

  • Drug Development
  • Control Set
  • Ancestry

Scientific Approaches

Initial focus Project focus area: The activity monitoring, Heart rate (HR) measurements during rest and exercise using Fitbit data and its relation with EHR.

Research focus :
Activity monitoring
Cardiovascular disease.
Obesity and medical condition
Population Health
Social / Behavioral

Anticipated Findings

The initial objective of this work is Education Level

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:

PTC vs. BMI (28-37)

The scientific question that will be studied is how does BMI and PTC correlate with each other? How does this taste blindness to the phenylthiocarbamide influence our nutrition which delegates our body weight? These questions are important because they can…

Scientific Questions Being Studied

The scientific question that will be studied is how does BMI and PTC correlate with each other? How does this taste blindness to the phenylthiocarbamide influence our nutrition which delegates our body weight? These questions are important because they can help provide sufficient data on how this taste compound can affect the nutrition of individuals.

Project Purpose(s)

  • Ancestry

Scientific Approaches

The scientific approaches used for this study is to look at certain datasets ranging from ages 28-37 in order to investigate to see if certain individuals are either super-tasters, tasters, or non-tasters. Also to see how BMI is affected if an individual is a super-taster, taster, or non-taster. The R-program and Python will be used to generate graphs.

Anticipated Findings

The anticipated findings of this study is to see how BMI is affected by taste blindness. These findings will contribute how the human taste response to PTC influences dietary preference which can affect an individuals BMI. This project is being conducted to promote healthy living in all populations.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Leah Wild - Undergraduate Student, Arizona State University

PTC Vs. BMI in Age Group 38-47

PTC tasters can taste the bitter properties in many healthy vegetables like Brussel sprouts and asparagus. As a result of this bitter taste, PTC tasters could be more inclined to avoid consumption of these foods, leading to a less healthy…

Scientific Questions Being Studied

PTC tasters can taste the bitter properties in many healthy vegetables like Brussel sprouts and asparagus. As a result of this bitter taste, PTC tasters could be more inclined to avoid consumption of these foods, leading to a less healthy diet. In this project, we are seeking to determine if there is a correlation between PTC tasting ability and Body Mass Index (BMI), a measure of body fat based on height and weight.

Project Purpose(s)

  • Ancestry

Scientific Approaches

We are a subgroup of a much larger team working on this project. Specifically, we are tasked with researching individuals aged 38-47. We plan to utilize and credit the All of Us Research Database's file on individuals aged 38-47 on whether or not they are PTC tasters and their BMI. Additionally, in our class we extracted DNA samples, ran PCR, and gel electrophoresis to determine whether we were tasters/non-tasters,

Anticipated Findings

We anticipate that PTC tasters aged 38-47 will have a higher average BMI compared to PTC non-tasters.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Collin Gordon - Undergraduate Student, Arizona State University

Collaborators:

  • Ryan Keating - Undergraduate Student, Arizona State University

PICAR - CT

Current literature calls attention to new and alarming records in drug-related deaths during the COVID-19 pandemic. According to the Centers for Disease Control and Prevention, 13% of Americans reported starting or increasing substance use as a coping mechanism for COVID-19…

Scientific Questions Being Studied

Current literature calls attention to new and alarming records in drug-related deaths during the COVID-19 pandemic. According to the Centers for Disease Control and Prevention, 13% of Americans reported starting or increasing substance use as a coping mechanism for COVID-19 related stress. (1) In addition, reports of anxiety and depressive disorders were also increased during the earlier months of the COVID-19 pandemic as compared to the same months in 2019. To date, the effects of COVID-19 related psychological distress and its associated outcomes remain largely un-investigated in communities of color partly due to stigmas surrounding mental health, medical distrust, and decreased behavioral health resource accessibility within these communities. (2-7) Results from this study are intended to highlight a need for continued expansion of equitable access to behavioral health resources and services among racial- ethnic minority communities.

Project Purpose(s)

  • Population Health

Scientific Approaches

Data on a cohort of racial-ethnic minority patients will be collected from the All of Us COPE survey database and reviewed for baseline and sociodemographic information. Participants eligible for inclusion in this study are those who self-report their race or ethnicity as Hispanic and/or nonwhite. The period of time from which data will be collected will be May 2020 through July 2020. Medication misuse will be defined as affirmative responses to the query “did you use prescription opioids/stimulants/sedatives in any way a doctor did not direct you to use it?”. Psychological distress will be classified using questions assessing mood and anxiety based on the Patient Health Questionnaire (PHQ-9) depression module and the Generalized anxiety disorder (GAD-7) mental health rating scales. Both screening tools are based on the DSM-IV criteria for major depression and generalized anxiety disorder and have well-documented reliability and validity in literature. (8,9)

Anticipated Findings

This study intends to provide an analysis of the COPE survey data to investigate the relationship between substance misuse and COVID-19 induced psychological distress among racial-ethnic minorities. As the immensity of both direct and indirect effects of COVID-19 remains under explored, investigating this topic in our study is paramount to further understanding substance misuse and behavioral health within these vulnerable populations. The overarching goal of this study is to utilize the granularity of ethnicities and races in the All of Us program to supplement recently described increases in rates of substance misuse amongst various racial and ethnic groups during the COVID-19 pandemic. We are hopeful that this study will inform on an area of focus which could promote health equity and overall improved health outcomes for minority communities. Results of this study have great potential to inform health services and policies.

Demographic Categories of Interest

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

Data Set Used

Controlled Tier

Research Team

Owner:

  • Nana Entsuah - Project Personnel, University of California, Irvine
  • Ding Quan Ng - Graduate Trainee, University of California, Irvine
  • Aryana Sepassi - Early Career Tenure-track Researcher, University of California, Irvine

Predictors of Unrelieved Symptoms

For adults with chronic conditions, what co-occurring symptoms can be identified? Can symptom clusters be identified?

Scientific Questions Being Studied

For adults with chronic conditions, what co-occurring symptoms can be identified? Can symptom clusters be identified?

Project Purpose(s)

  • Educational

Scientific Approaches

Utilize survey data from adults with chronic conditions to determine symptom clusters. Use Cohort Builder to identify eligible adults diagnosed with certain conditions. Use participant responses to survey questions to generate symptom phenotypes and analyze potential predictors of symptomatic phenotypes.

Anticipated Findings

Find meaningful relationships between demographic, healthcare access, and health-related and symptom phenotypes. Want to discover what factors that lead to unrelieved symptoms.

Demographic Categories of Interest

  • Age
  • Disability Status
  • Access to Care

Data Set Used

Controlled Tier

Research Team

Owner:

  • Julia Burek - Graduate Trainee, University of Virginia

Rheumatoid Arthritis (RA) phenotype algorithm

The purpose of this homework is for students to familiarize ourself with the All of US database, to see how those with different skill sets navigate the site, and to learn how to implement algorithms.

Scientific Questions Being Studied

The purpose of this homework is for students to familiarize ourself with the All of US database, to see how those with different skill sets navigate the site, and to learn how to implement algorithms.

Project Purpose(s)

  • Educational

Scientific Approaches

Algorithm 1:
https://phekb.org/phenotype/rheumatoid-arthritis-ra
This algorithm uses ICD9, ICD10, and laboratory. Use the code that the authors provided to extract the data

Algorithm 2:
https://phekb.org/phenotype/rheumatoid-arthritis-demonstration-project
This algorithm uses ICD9, medications and natural language processing. You just need to use the ICD codes and medications only.

Anticipated Findings

The intention of the findings is for our professor who is a part of the Department of BioMedical Informatics to understand how different backgrounds utilize the website.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

AntibioticResistanceUTI

Whether using deep reinforcement learning can help make better antibiotic choices in patients hospitalized with UTI.

Scientific Questions Being Studied

Whether using deep reinforcement learning can help make better antibiotic choices in patients hospitalized with UTI.

Project Purpose(s)

  • Disease Focused Research (UTI)

Scientific Approaches

We will use Deep Deep Reinforcement Learning with subjects in the AllOfUs dataset who have urine culture reports while being hospitalized. We will test a locally developed algorithm with AllOfUs data.

Anticipated Findings

We will confirm whether a model which makes better choices of antibiotics can be externally validated.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Duplicate of How to Work with All of Us Genomic Data (Hail - Plink)(v6)

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Scientific Questions Being Studied

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Project Purpose(s)

  • Other Purpose (Demonstrate to the All of Us Researcher Workbench users how to get started with the All of Us genomic data and tools. It includes an overview of all the All of Us genomic data and shows some simple examples on how to use these data.)

Scientific Approaches

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Anticipated Findings

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Henry Condon - Project Personnel, All of Us Program Operational Use

Duplicate of Duplicate of Data Wrangling in All of Us Program (v6)

For Educational purpose to show best practices when using jupyter notebooks for data access, storage, data manipulations - transformations, conversions, cleaning, optimization and other research support related issues that is useful for multiple AoU researchers.

Scientific Questions Being Studied

For Educational purpose to show best practices when using jupyter notebooks for data access, storage, data manipulations - transformations, conversions, cleaning, optimization and other research support related issues that is useful for multiple AoU researchers.

Project Purpose(s)

  • Educational
  • Other Purpose (For use with Office hours. notebooks for adding code snippets useful for researchers. This is a placeholder for creating notebooks for best practices among other things)

Scientific Approaches

For Educational purpose to show best practices when using jupyter notebooks for data access, storage, data manipulations - transformations, conversions, cleaning, optimization and other research support related issues that is useful for multiple AoU researchers.

Anticipated Findings

For Educational purpose to show best practices when using jupyter notebooks for data access, storage, data manipulations - transformations, conversions, cleaning, optimization and other research support related issues that is useful for multiple AoU researchers.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Christopher Lord - Project Personnel, All of Us Program Operational Use

Duplicate of How to Work with All of Us Genomic Data (Hail - Plink)(v6)

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Scientific Questions Being Studied

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Project Purpose(s)

  • Other Purpose (Demonstrate to the All of Us Researcher Workbench users how to get started with the All of Us genomic data and tools. It includes an overview of all the All of Us genomic data and shows some simple examples on how to use these data.)

Scientific Approaches

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Anticipated Findings

Not applicable - these notebooks demonstrate example analysis how to use Hail and PLINK to perform genome-wide association studies using the All of Us genomic data and phenotypic data.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Lan Jiang - Other, Vanderbilt University Medical Center
  • Jun Qian - Other, All of Us Program Operational Use
  • Jennifer Zhang - Project Personnel, All of Us Program Operational Use
  • Tabitha Harrison - Graduate Trainee, University of Washington
  • Will Dolbeer - Other, All of Us Program Operational Use

Collaborators:

  • Henry Condon - Project Personnel, All of Us Program Operational Use

PREVENTv6

Our goal is to develop polygenic risk scores (PRSs) for diverse ancestry groups to ensure equitable implementation of genomic medicine and reduce the potential worsening of health disparities in the context of genomic medicine. Our focus is on atherosclerotic vascular…

Scientific Questions Being Studied

Our goal is to develop polygenic risk scores (PRSs) for diverse ancestry groups to ensure equitable implementation of genomic medicine and reduce the potential worsening of health disparities in the context of genomic medicine. Our focus is on atherosclerotic vascular disease (ASCVD) including coronary heart disease (CHD), peripheral artery disease (PAD), abdominal aortic aneurysm (AAA), and the related risk factors: hypertension, diabetes, obesity, and hypercholesterolemia. We hypothesize that we can reduce the gap in the performance of PRSs between diverse populations by developing methods to generate PRSs for populations of diverse ancestry. All of Us will be a critical resource in this context, given that diversity is a priority in this program. We will meta-analyze the available genotype data along with similar data from dbGaP and additional datasets to improve performance of PRSs in African American, Latino, and Asian populations.

Project Purpose(s)

  • Disease Focused Research (Coronary Heart Disease)
  • Population Health
  • Methods Development
  • Control Set
  • Ancestry

Scientific Approaches

To generate PRSs for diverse ancestries, we will use data from the eMERGE consortium, Million Veteran’s Program (MVP), the All of Us (AoU) program, dbGaP, PRIMED consortium sites, the UK Biobank, and collaborations with several international groups representing Middle Eastern, South Asian, and East Asian cohorts. Our specific aims are: Aim 1. Integrate and harmonize data from heterogeneous sources to enable cross platform phenotyping and generation of PRSs for common diseases in diverse ancestry groups. Aim 2. Develop PRSs for CHD and its major risk factors (hypertension, diabetes, obesity, hypercholesterolemia) in populations of diverse ancestry. Aim 3. Develop novel statistical and computational methods to account for diverse genetic ancestry and admixture in models of polygenic risk. Aim 4. Develop ‘clinic ready’ PRSs for diverse ancestry groups by creating reference distributions of a CHD PRS and integrate it with clinical information to compute absolute risk estimates.

Anticipated Findings

We anticipate that increasing representation of diverse populations in genotyped datasets will enable the generation of more robust PRSs in these populations. We expect to advance PRS methodology for diverse populations and use novel population genetics approaches. Additionally, we will develop ‘clinic ready’ PRSs for diverse ancestry groups by creating reference distributions of a CHD PRS and integrate it with clinical information to compute absolute risk estimates.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Yue Yu - Project Personnel, Mayo Clinic
  • jie na - Project Personnel, Mayo Clinic
  • Marwan Hamed - Research Fellow, Mayo Clinic
  • Matt Kosel - Project Personnel, Mayo Clinic
  • Ozan Dikilitas - Research Fellow, Mayo Clinic
  • Angad Johar - Research Fellow, Mayo Clinic
  • Jacob Petrzelka - Project Personnel, Mayo Clinic

Tuberculosis risk factors

Tuberculosis (TB) has been decade after decade the greatest infectious disease killer globally. 1.6 million died in 2021. Diagnosis, lack of access to healthcare, difficulty adhering to and completing regimens, and other comorbidities (HIV, diabetes, cancer, immunosuppression such as for…

Scientific Questions Being Studied

Tuberculosis (TB) has been decade after decade the greatest infectious disease killer globally. 1.6 million died in 2021.
Diagnosis, lack of access to healthcare, difficulty adhering to and completing regimens, and other comorbidities (HIV, diabetes, cancer, immunosuppression such as for autoimmune disorders) worsen TB outcomes. TB in the US is often in those who are foreign born, those who are older, associated with lack of housing or incarceration, and those with other comorbidities.
We will track the demographics of those diagnosed with TB, while also identifying modifiable risk factors - such as untreated HIV, untreated latent TB, uncontrolled diabetes, and immunosuppression.

Project Purpose(s)

  • Disease Focused Research (tuberculosis)
  • Population Health
  • Educational

Scientific Approaches

Using the ALL of US full cohort, we can help determine the associations of some of these factors which would have been modifiable. Such modifiable risk factors would include: HIV without any or consistent HIV (ART) treatment, use of prednisone or common immunosuppressive drugs before the onset of TB (note TB treatment can include prednisone which should not be considered a risk), poorly controlled diabetes (stratified by hemoglobin a1c or in those insulin dependent without Hga1c recorded), known latent TB (quantiferon, T-spot, or Mantoux positivity) without latent TB treatment. We will also assess the timeframe of who has been assessed for latent TB before or with TB diagnosis and how many are not treated for latent TB, especially in those with another risk factor.

Anticipated Findings

We hope to identify any gaps in latent TB screening and treatment, while also highlighting any gaps in HIV and diabetes care, as well as care for those who are immunocompromised. We hope to better understand who is at risk for TB, including basic demographics, but also modifiable medical risk factors which may track with social determinants of health.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Oge Marques - Late Career Tenured Researcher, Florida Atlantic University

Collaborators:

  • Mindy Knowles - Graduate Trainee, Florida Atlantic University

AL x Breast Cancer

We will analyze various biomarkers of inflammation to better understand their role in breast cancer in the All of Us study.

Scientific Questions Being Studied

We will analyze various biomarkers of inflammation to better understand their role in breast cancer in the All of Us study.

Project Purpose(s)

  • Disease Focused Research (breast cancer)

Scientific Approaches

Dataset: All of Us dataset
Research methods: General Linear Model (GLM) statistical test to assess the data. Additional statistical tests will be used based on the GLM. We will conduct our analysis in R coding language.

Anticipated Findings

We hypothesize that inflammation will be different in breast cancer patients from different race/ethnicities. This will help us to build new methods for estimating breast cancer risk to be used in clinical settings.

Demographic Categories of Interest

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

Data Set Used

Registered Tier

Research Team

Owner:

  • Celina Valencia - Early Career Tenure-track Researcher, University of Arizona

Heart Failure Quality of Care and Relation to Social Determinant of Health

We will look at the optimization of guideline directed medical therapy (GDMT) for chronic heart failure (diastolic vs. systolic) in participants across different demographic backgrounds by looking at data available on the database of All of Us. Multiple studies have…

Scientific Questions Being Studied

We will look at the optimization of guideline directed medical therapy (GDMT) for chronic heart failure (diastolic vs. systolic) in participants across different demographic backgrounds by looking at data available on the database of All of Us. Multiple studies have shown that patients with heart failure (HF) who experience adverse downstream effects of social determinants of health (SDOH) and healthcare disparities are less able to access care and achieve the GDMT for heart failure. Our main goals for the project are:
1. We will observe the optimization of GDMT in adults with HF while taking into account their race/ethnicity (African-Americans, Hispanics, Asians and Whites) and their socio-economic status (income level and education status).
2. In addition, we will use survey data such as health insurance status, usual point of care, and how often a participant sees his or her provider for his/her condition to further explain the observation of medication uses.

Project Purpose(s)

  • Disease Focused Research (Heart Failure)

Scientific Approaches

We will use the cohort builder to find US participants aged 18 years and older who have been diagnosed with chronic diastolic or systolic heart failure. We will use R to retrieve heart failure medications, antihypertensive medications, and diabetes medications. For each type of heart failure, we will categorize the sample into 4 groups: optimized group (those who are on all four recommended medications), 3 reference groups (those who are on 3 medications vs. 2 medications vs. 1 medication). We will use the Chi-Square test to compare the extent of adherence to recommended medications to demographic characteristics. Multiple logistic regression will be used to examine the association of optimization of guideline directed therapy for diastolic and systolic heart failure and the SDOH mentioned above. One limitation is that we can only include a specific group of participants with diagnoses that belong to either chronic diastolic or systolic heart failure, so our population is limited.

Anticipated Findings

With our cross-sectional study, we anticipate observing significant differences in GDMT adherence among different races/ethnicities. Also, certain non-white ethnic groups and those of lower socioeconomic status or without health insurance will be less likely to be on GDMT. Our findings hope to make physicians more aware of socio-economic barriers to care that may undermine the ability to achieve GDMT, thus achieving a better quality of life for patients.

Demographic Categories of Interest

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

Data Set Used

Registered Tier

Research Team

Owner:

  • Trinh Do - Graduate Trainee, University of California, Irvine

Collaborators:

  • Yufan Gong - Graduate Trainee, University of California, Los Angeles

WKS Data Analysis

The primary guiding aim underpinning this small-scale, student-led research project is to better inform Wernicke-Korsakoff Syndrome (WKS) prevention strategies using a precision-based framework and approach that considers relevant genetic, lifestyle, and social/environmental determinants shaping disease susceptibility among high-risk, vulnerable groups…

Scientific Questions Being Studied

The primary guiding aim underpinning this small-scale, student-led research project is to better inform Wernicke-Korsakoff Syndrome (WKS) prevention strategies using a precision-based framework and approach that considers relevant genetic, lifestyle, and social/environmental determinants shaping disease susceptibility among high-risk, vulnerable groups found in the All of Us Database. The goal is to advance and translate precision medicine research by advising on what constitutes an effective, concrete, and comprehensive intervention tailored specifically to high-risk WKS communities to help close the translational bench-to-bedside gap as it relates to this disease.

Project Purpose(s)

  • Disease Focused Research (Wernicke-Korsakoff syndrome)
  • Population Health
  • Social / Behavioral
  • Ancestry

Scientific Approaches

Our methodology consists of compiling and analyzing the relevant data on the potential genetic, lifestyle, and social/environmental risk factors associated with WKS. We hope to pull raw genomic and environmental data from All of Us participants with WKS (or perhaps, a high risk of developing the disease based on family history or known health behaviors) to ascertain any outstanding genetic markers and measure the prevalence of certain social and behavioral determinants (including low income/socioeconomic status, employment, frequent alcohol use, poor diet/nutrition, etc.). Our project is purely correlative and will provide insight into how WKS may present as a complex disease, which is crucial to recognize to effectively devise WKS prevention programs.

Anticipated Findings

We expect our findings to overwhelmingly support and substantiate the existing literature surrounding WKS, including how behavioral determinants such as alcohol use/consumption may contribute to WKS onset and how a poorer nutrient and thiamine-deficient diet could play a role in disease susceptibility. We also hope to confirm how the ApoE epsilon 4 allele may behave as a prognostic marker for those with WKS, particularly as it relates to determining and assessing the degree of cognitive impairment which is a notable symptom. Our project aims to extend the available science by focusing on how some external social determinants could be implicated in WKS risk, including income level and nature of employment. This aspect has not yet been extensively studied.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Mantej Singh - Undergraduate Student, Arizona State University
  • Arjun Nair - Undergraduate Student, Arizona State University

Exploring Engagement Among Persons of African Descent in All of Us

This study will explore the demographic profiles of populations of African ancestry or descent engaged in the All of Us Research Program. This study is conducted under NIH project number 1OT2OD031925-01: https://reporter.nih.gov/search/GObFuyvhLUiUo4ouO3CeZA/project-details/10307280

Scientific Questions Being Studied

This study will explore the demographic profiles of populations of African ancestry or descent engaged in the All of Us Research Program. This study is conducted under NIH project number 1OT2OD031925-01: https://reporter.nih.gov/search/GObFuyvhLUiUo4ouO3CeZA/project-details/10307280

Project Purpose(s)

  • Ethical, Legal, and Social Implications (ELSI)

Scientific Approaches

For this study, we will descriptive and inferential statistical methods to explore potential patterns around engagement in the All of Us Research program among the presently diverse cohort of participants of African ancestry or descent.

Anticipated Findings

We anticipate garnering a deeper understanding of the demographic profiles of participants of African ancestry or descent to determine areas of potential need for engagement with and/or retention among more diverse populations of African ancestry or descent. Insights will be used to support All of Us Research Program engagement efforts and inform scientific literature.

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:

Patient-clinician identity discordance among cancer survivors

Some people think it is helpful if their providers are from the same background that they are – in terms of race or religion or native language –because they think their doctors will better understand what they’re experiencing or going…

Scientific Questions Being Studied

Some people think it is helpful if their providers are from the same background that they are – in terms of race or religion or native language –because they think their doctors will better understand what they’re experiencing or going through. Further, for some, lack of identity concordance with the clinician may lead to delayed medical care. We evaluated the prevalence of delayed or forgone care among cancer survivors in the All of Us database and identify factors associated with delays in care due to this barrier.

Project Purpose(s)

  • Social / Behavioral

Scientific Approaches

We will study adults with a history of cancer who answered the health care access and utilization survey. We will describe the cohort's demographic characteristics and use propensity matching to identify similar adults without a prior history of cancer. We will compare the prevalence of delayed/forgone care among these groups. We will apply generalized linear models to identify risk factors associated with delays in care due to patient-clinician identity discordance.

Anticipated Findings

We anticipate that racial, ethnic, and gender minorities, as well as those with low income, and with a high burden of disease are at risk of delays in care due to patient-clinician identity discordance, and that some of these groups will be identified as such in this study. Our findings may support ongoing efforts to increase the diversity of physician workforce and promote the adoption of regular cultural competency leadership training.

Demographic Categories of Interest

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

Data Set Used

Controlled Tier

Research Team

Owner:

  • Vishal Patel - Graduate Trainee, University of Texas at Austin

Healthcare Disparities in Dermatologic Disorders

To investigate prevalence, barriers to care among patients with dermatologic diseases that predominately impacts minority individuals.

Scientific Questions Being Studied

To investigate prevalence, barriers to care among patients with dermatologic diseases that predominately impacts minority individuals.

Project Purpose(s)

  • Population Health
  • Social / Behavioral
  • Other Purpose (The purpose of this workbench is to investigate the data available on a list of dermatologic diseases that predominately affects individuals from underrepresented backgrounds.)

Scientific Approaches

We hope to collect survey data from participants with specific dermatologic diseases. Multivariable regression will be used to analyze the relationship between race, ethnicity, and barriers to care.

Anticipated Findings

We hope to better understand if certain dermatologic diseases that predominately impact minorities are also associated with delay in diagnosis, higher cost, and structural barriers.

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:

  • Yacine Sow - Research Fellow, University of Pennsylvania

Explore healthcare access and SDH

In the proposed project, we will be exploring the Control Tier dataset to determine how best to establish a cohort for examining the relationship between healthcare access and social determinants of health.

Scientific Questions Being Studied

In the proposed project, we will be exploring the Control Tier dataset to determine how best to establish a cohort for examining the relationship between healthcare access and social determinants of health.

Project Purpose(s)

  • Population Health

Scientific Approaches

In this workspace, we are seeking to understand how the data are structured, approaches to setting up cohorts, concepts, and datasets for analysis. The objective of this workspace is to become more familiar with the All of Us dataset so we can conduct a more in-depth analysis examining the association between access to healthcare services and social determinants of health.

Anticipated Findings

We anticipate gaining a better understanding of how to set up a workspace and utilize the All of Us notebook to conduct new research that will deepen our understanding of ways SDH impact healthcare access.

Demographic Categories of Interest

  • Geography
  • Access to Care

Data Set Used

Controlled Tier

Research Team

Owner:

  • Janessa Graves - Mid-career Tenured Researcher, Washington State University

Collaborators:

  • Shawna Beese - Graduate Trainee, Washington State University
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