Jun Qian

All of Us Program Operational Use

12 active projects

Duplicate of Demo - Systemic Disease and Glaucoma

We have previously published a predictive model of glaucoma progression using electronic health record (EHR) data pertaining to systemic attributes from a single institution. We aim to use the All of Us dataset to 1) serve as external validation for…

Scientific Questions Being Studied

We have previously published a predictive model of glaucoma progression using electronic health record (EHR) data pertaining to systemic attributes from a single institution. We aim to use the All of Us dataset to 1) serve as external validation for this single-center model and 2) to train new models focused on predicting glaucoma progression using systemic predictors. This is important to understand whether the original findings are generalizable and provide additional knowledge about the utility of systemic predictors on a national-level dataset.

Project Purpose(s)

  • Disease Focused Research (Primary open angle glaucoma)
  • Other Purpose (This work is the result of an All of Us Research Program Demonstration Project. Demonstration Projects are efforts by the All of Us Research Program designed to meet the goal of ensuring the quality and utility of the Research Hub as a resource for accelerating precision medicine. This work has been approved, reviewed, and overseen by the All of Us Research Program Science Committee and Data and Research Center to ensure compliance with program policy. )

Scientific Approaches

We plan to primarily work with EHR data contained in All of Us for a cohort of adult participants diagnosed with primary open-angle glaucoma. We will extract data on systemic conditions and medications for this cohort, as well as physical measurements and vital signs. We will clean the data such that the format is consistent with the data from our previously published model. Then, we will use this data as an external validation of a logistic regression model derived from our prior study that was based at a single academic center. Next, we will use All of Us data to train a new set of models, using techniques such as logistic regression, random forests, and artificial neural networks. We will optimize these models using feature selection methods and class balancing procedures. By evaluating performance metrics such as area under the curve (AUC), precision, recall, and accuracy, we will assess whether we can achieve superior predictive performance when training models using All of Us.

Anticipated Findings

We anticipate that the All of Us data will validate the findings from the model, which demonstrated that blood pressure-related metrics and certain medication classes had predictive value for glaucoma progression. In addition, we anticipate that the models trained with All of Us data will outperform the model trained with single institution data due to larger sample size and greater diversity. These findings will support further investigation in understanding the relationship between systemic conditions like blood pressure with glaucoma progression.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Polygenic_Risk_Score_Genetic_Ancestry_Calibration

Polygenic risk scores (PRS) are available for a wide array of traits and conditions, offering many potential applications including preventative medicine. There is, however, a serious concern that clinical use of PRS could contribute to health disparities due to the…

Scientific Questions Being Studied

Polygenic risk scores (PRS) are available for a wide array of traits and conditions, offering many potential applications including preventative medicine. There is, however, a serious concern that clinical use of PRS could contribute to health disparities due to the poorer performance of PRS in non-European ancestry individuals.
We aim to improve our ability to correct the genetic ancestry-dependent bias in PRS for 10 conditions (Asthma, Atrial fibrillation, Breast Cancer, Chronic Kidney Disease, Coronary heart disease, Hypercholesterolemia, Obesity/BMI, Prostate cancer, Type 1 Diabetes, Type 2 Diabetes). We will use the AoU dataset to produce a resource that can be used to reduce the ancestry-dependent bias in these 10 PRS. This resource will initially be used by the eMERGE IV consortium, which is an NIH-funded consortium of clinical centers across the United States, with an aim to enroll a prospective cohort of 25,000 individuals.

Project Purpose(s)

  • Control Set

Scientific Approaches

Arrays will be imputed using the phasing and imputation tools Eagle2 and Minimac4. Polygenic risk score will then be calculated using the population genomics tool PLINK. A simple linear model will then be fit to the scores, which attempt to describe the macroscopic relationship between genetic ancestry and observed polygenic scores. The fitted parameters of this model can then be used to reduce genetic ancestry-dependent bias when calculating these scores in a clinical setting.

Anticipated Findings

We will produce a set of fitted parameters for a simple model which attempts to describe the macroscopic relationship between genetic ancestry and observed polygenic scores. The fitted parameters of this model can then be used as a resource to reduce genetic ancestry-dependent bias when calculating these scores in a clinical setting.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Michael Gatzen - Project Personnel, Broad Institute
  • Fabio Cunial - Project Personnel, Broad Institute

ABO PheWAS - v6

Research questions: 1) Can our novel ABO blood typing algorithm using genetic data be used effectively to extensively type ABO subtypes from whole genome sequencing and array data in a diverse cohort? 2) Will a SNP approach for ABO blood…

Scientific Questions Being Studied

Research questions:

1) Can our novel ABO blood typing algorithm using genetic data be used effectively to extensively type ABO subtypes from whole genome sequencing and array data in a diverse cohort?
2) Will a SNP approach for ABO blood typing be concordant with available serotype?
3) What disease association ABO blood types can be replicated using the AllofUs dataset?
4) What novel disease associations, if any, with ABO blood types can be identified in a diverse cohort?

Relevance: Genomic variation in RBC and antigens is associated with a myriad of conditions. The ABO locus alone is associated with many conditions including venous thromboembolism (VTE), pancreatic cancer, malaria, and COVID-19. Furthermore, it is not common practice to extensively type beyond the traditional ABO blood groups, and the studies that do so are primarily done in individuals of European ancestry. Thus, we seek to do the first PheWAS on extensively typed RBC antigens and to do so in a diverse cohort.

Project Purpose(s)

  • Disease Focused Research (red blood cell (RBC) antigen-associated diseases)

Scientific Approaches

We plan to employ a blood typing algorithm to extensively type RBC antigens from 1) whole genome sequencing and 2) array data in the AllofUs cohort, and compare the two outcomes. Then, we plan to employ the phenome-wide association study (PheWAS) approach to identify associations between RBC antigen types and other clinical phenotypes. PheWAS will be carried out using multivariable linear regression and logistic regressions with ABO blood groups with our novel ABO blood type. For example, in the case of the ABO blood group, ABO blood subtypes (A101, A102, Aw01, B101, etc.) will act as the independent variable and phenotypes, derived from participant provided information (PPI) electronic health records (EHR), as the dependent variable. Initial models will include adjustments for age, gender, and race/ethnicity. Differential associations by race/ethnicity, gender, and sex will also be evaluated.

Anticipated Findings

This proposed project aims to test our novel ABO blood typing algorithm on WGS and array data in the diverse AllofUs cohort. We also aim to replicate known RBC-disease associations as well as identify any novels ones that may be identified within a diverse cohort.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Kiana Martinez - Research Fellow, University of Arizona
  • Jason Karnes - Early Career Tenure-track Researcher, University of Arizona
  • Jun Qian - Other, All of Us Program Operational Use

Collaborators:

  • Anthony Vicenti - Project Personnel, University of Arizona
  • Sadaf Raoufi - Graduate Trainee, University of Arizona

Duplicate of How to Work with All of Us Survey Data (v7)

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data. What should you expect? By running the notebooks in this workspace, you should get familiar with how to query…

Scientific Questions Being Studied

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data.

What should you expect?
By running the notebooks in this workspace, you should get familiar with how to query PPI questions/surveys, what the frequencies of answers for each question in each PPI module are.

Project Purpose(s)

  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Tutorial Workspace created by the Researcher Workbench Support team. It is meant to provide instruction for key Researcher Workbench components and All of Us data representation.)

Scientific Approaches

By running the notebooks in this workspace, you should get familiar with how to query PPI questions/surveys, what the frequencies of answers for each question in each PPI module are.

Anticipated Findings

By reading and running the notebooks in this Tutorial Workspace, researchers will learn the following:
- how to query the survey data,
- how to summarize PPI modules, and questions.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Jun Qian - Other, All of Us Program Operational Use
  • Christopher Lord - Project Personnel, All of Us Program Operational Use
  • Chenyu Li - Graduate Trainee, University of Pittsburgh
  • Brandy Mapes - Other, All of Us Program Operational Use

Demo - Assessment of pathogenic variants across the All of Us Research Program

We will assess the relative frequency of positive findings across AoU samples and compare the aggregate findings with those from other cohorts - e.g. GnomAD. The frequencies of positive findings will be further broken down by the ancestry background of…

Scientific Questions Being Studied

We will assess the relative frequency of positive findings across AoU samples and compare the aggregate findings with those from other cohorts - e.g. GnomAD. The frequencies of positive findings will be further broken down by the ancestry background of the participants to understand the impact of diverse backgrounds has on the rate of pathogenic findings.

Project Purpose(s)

  • Other Purpose (Demonstrate the potential utility of Researcher Workbench data by describing the frequency of known pathogenic & pharmacogenomic variants in the current genomic dataset.)

Scientific Approaches

We will annotate genomic variants from AoU participants with variant curations that have been recorded by the HGSC-CL clinical annotation team in its ‘VIP’ database or in ClinVar. We will assess the frequency of these previously-known pathogenic mutations, and provide breakdowns by ancestry.

Anticipated Findings

These data will likely identify groups that are underrepresented and overrepresented by the current knowledge of pathogenic variants, and may provide important directions for prioritizing future research. Additionally, they may point to systematic differences between the AoU resource and other resources, such as GnomAD.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

  • Eric Venner - Early Career Tenure-track Researcher, Baylor College of Medicine
  • Marsha Wheeler - Project Personnel, University of Washington
  • Jun Qian - Other, All of Us Program Operational Use
  • Ashley Green - Project Personnel, All of Us Program Operational Use

Collaborators:

  • Huyen Nguyen - Project Personnel, Baylor College of Medicine
  • Philip Empey - Mid-career Tenured Researcher, University of Pittsburgh
  • Karynne Patterson - Project Personnel, University of Washington
  • Joshua Smith - Late Career Tenured Researcher, University of Washington
  • Divya Kalra - Project Personnel, Baylor College of Medicine
  • Andrew Haddad - Graduate Trainee, University of Pittsburgh
  • Aniko Sabo - Other, Baylor College of Medicine

GeneticAncestryDemoProject

As a demonstration project, this project will describe, characterize and, validate the extent of diversity in the All of Us cohort with respect to the participants' race & ethnicity (which are socially defined), and genetic ancestry (which can be objectively…

Scientific Questions Being Studied

As a demonstration project, this project will describe, characterize and, validate the extent of diversity in the All of Us cohort with respect to the participants' race & ethnicity (which are socially defined), and genetic ancestry (which can be objectively inferred from participants' genome). Socially defined race & ethnicity and genetically inferred ancestry are both relevant to health outcomes. Race & ethnicity shape individuals’ lived experience and social environment, eg structural inequities, environmental injustice, and barriers to healthcare access. Genetic ancestry can affect health outcomes via differences in the frequencies of variants associated with disease and drug response. Specifically, we will ask:

1. What is the extent of racial, ethnic, and genetic diversity in the All of Us cohort?

2. How do genetic ancestry and admixture change over geography and with age in the US?

3. Are there associations between genetic ancestry and health outcomes in the All of Us cohort?

Project Purpose(s)

  • Population Health
  • Methods Development
  • Ancestry
  • 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

To characterize the diversity of the All of Us cohort, we analyzed participant genetic, demographic, and geographic data.

Here is a brief list of methods used:

1. All of Us participant genome-wide genotype was merged and harmonized with global reference population data.

2. Unsupervised clustering analysis techniques - Hopkins statistic, visual assessment of clustering tendency, K-means clustering & UMAP - to assess the extent of genetic structure in the cohort.

3. Supervised genetic ancestry inference using global reference populations, principal components analysis, and the Rye (Rapid ancestrY Estimation) program.

4. Genetic ancestry was compared to participants' self-identified race & ethnicity.

5. Geocoded data and participant age were used to measure how genetic ancestry and admixture vary with respect to participant geography and age.

6. Admixture regression to associate participant health outcomes, gleaned from electronic health records, with their genetic ancestry.

Anticipated Findings

1. The All of Us participant cohort will be racially, ethnically, and genetically diverse, consistent with the project’s aim to recruit underrepresented biomedical research groups in support of health equity.

2. All of Us participant genetic variation will be highly structured and best modeled by clusters rather than a continuum of variation.

3. All of Us participants’ will show patterns of genetically inferred ancestry that are correlated with their socially defined ancestry (i.e. race and ethnicity).

4. All of Us participants’ genetic ancestry and admixture will change over geography and with age.

5. All of Us participants’ genetic ancestry will be associated with a variety of health outcomes.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

  • Shivam Sharma - Graduate Trainee, Georgia Institute of Technology
  • Jun Qian - Other, All of Us Program Operational Use
  • Christopher Lord - Project Personnel, All of Us Program Operational Use
  • Ashley Green - Project Personnel, All of Us Program Operational Use

Collaborators:

  • Jennifer Zhang - Project Personnel, All of Us Program Operational Use

Extraction of Stomach tumor Data (Hail - Plink)

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)

  • Ancestry
  • 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 How to Work with All of Us Physical Measurements Data (v6)

How to navigate around physical measurements?

Scientific Questions Being Studied

How to navigate around physical measurements?

Project Purpose(s)

  • Population Health
  • Social / Behavioral
  • Educational
  • Drug Development
  • Methods Development
  • Control Set
  • Ancestry
  • Ethical, Legal, and Social Implications (ELSI)

Scientific Approaches

N/A

Anticipated Findings

N/A

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:

  • Micaela Siraj - Research Fellow, Georgia Institute of Technology
  • Jun Qian - Other, All of Us Program Operational Use
  • Will Dolbeer - Other, All of Us Program Operational Use

Duplicate of How to Get Started with Registered Tier Data (tier 5)

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data. What should you expect? This notebook will give you an overview of what data is available in the current…

Scientific Questions Being Studied

We recommend that all researchers explore the notebooks in this workspace to learn the basics of All of Us Program Data.

What should you expect? This notebook will give you an overview of what data is available in the current Curated Data Repository (CDR). It will also teach you how to retrieve information about Electronic Health Record (EHR), Physical Measurements (PM), and Survey data.

Project Purpose(s)

  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Tutorial Workspace. It is meant to provide instruction for key Researcher Workbench components and All of Us data representation.)

Scientific Approaches

This Tutorial Workspace contains two Jupyter Notebooks (one written in Python, the other in R). Each notebook is divided into the following sections:

1. Setup: How to set up this notebook, install and import software packages, and select the correct version of the CDR.
2. Data Availability Part 1: How to summarize the number of unique participants with major data types: Physical Measurements, Survey, and EHR;
3. Data Availability Part 2: How to delve a little deeper into data availability within each major data type;
4. Data Organization: An explanation of how data is organized according to our common data model.
5. Example Queries: How to directly query the CDR, using two examples of SQL queries to extract demographic data.
6. Expert Tip: How to access the base version of the CDR, for users that want to do their own cleaning.

Anticipated Findings

By reading and running the notebooks in this Tutorial Workspace, you will understand the following:

All of Us data are made available in a Curated Data Repository. Participants may contribute any combination of survey, physical measurement, and electronic health record data. Not all participants contribute all possible data types. Each unique piece of health information is given a unique identifier called a concept_id and organized into specific tables according to our common data model. You can use these concept_ids to query the CDR and pull data on specific health information relevant to your analysis. See our support article Learning the Basics of the All of Us Dataset for more info.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Duplicate of AFib epidemiology

The overall goal of this study, as a Demonstration project, is to evaluate the ability of the All of Us Research Program data to replicate epidemiologic patterns of atrial fibrillation (AF), a common arrhythmia, previously described in other setting. We…

Scientific Questions Being Studied

The overall goal of this study, as a Demonstration project, is to evaluate the ability of the All of Us Research Program data to replicate epidemiologic patterns of atrial fibrillation (AF), a common arrhythmia, previously described in other setting. We will address this goal with these two aims:
• Specific Aim 1. To determine the association of race and ethnicity with the prevalence and incidence of atrial fibrillation (AF). We hypothesize than non-whites will have lower prevalence and incidence of AF than whites.
• Specific Aim 2. To estimate associations of established risk factors for AF with the prevalence and incidence of AF. We hypothesize that increased body mass index, higher blood pressure, diabetes, smoking and a prior history of cardiovascular diseases will be associated with increased prevalence and incidence of AF.

Project Purpose(s)

  • Population Health
  • 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

We will select all All of Us participants who self-reported sex at birth male or female, whose self-reported race was white, black or Asian, as well as those who self-reported being Hispanics.

Atrial fibrillation (AF) will be identified from self-reports in the medical survey or from electronic health records (EHR).

Clinical factors will be identified from EHR and study measurements (blood pressure, weight, height).

We will evaluate the association of demographic (age, sex, race/ethnicity) and clinical (body mass index, blood pressure, smoking, cardiovascular diseases) factors with prevalence of self-reported AF and prevalence of AF in the EHR, as well as incident AF ascertained from the EHR.

Anticipated Findings

The overall goal of this project is to evaluate the prevalence and incidence of atrial fibrillation (AF), overall and by race/ethnicity, as well as to confirm the association of established risk factors for AF in the All of Us Research participants. We expect to confirm associations between demographic and clinical variables previously reported in the literature, demonstrating the value of the All of Us Research Program data to address questions regarding this common cardiovascular disease.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Data Set Used

Registered Tier

Research Team

Owner:

  • Jun Qian - Other, All of Us Program Operational Use
  • Ashley Able - Other, All of Us Program Operational Use
  • Alvaro Alonso - Late Career Tenured Researcher, Emory University

Duplicate of D014 - Opioids

As a demonstration project, this study will present the results of prevalence of opioid use in the United States. Specific questions include: 1. What is the prevalence of prescription opioids received from healthcare systems? 2. What is the prevalence of…

Scientific Questions Being Studied

As a demonstration project, this study will present the results of prevalence of opioid use in the United States. Specific questions include:

1. What is the prevalence of prescription opioids received from healthcare systems?
2. What is the prevalence of opioids misuse including nonmedical prescription opioids use and street opioid use?
3. Data in both previous questions will also be stratified by geographic region

Project Purpose(s)

  • 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

We will identify prevalence of opioid use in two ways and stratified by state.
First, we use EHR Drug Exposures to capture use of prescription opioid.
Second, we use lifestyle survey questionnaire to capture substance use reported by patients themselves:
1. In your LIFETIME, which of the following substances have you ever used?
2. In the PAST THREE MONTHS, how often have you used this substance?
The prevalence will be stratified by state, therefore EHR Observation Table will be used to capture this information.

Anticipated Findings

For this study, we anticipate that we will be able to replicate previous national studies of estimating prevalence of opioids. All of Us workbench research data also provides an alternative tool for assessing prevalence rate of substance use and prescription opioids for US population.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Hsueh-Han Yeh - Research Associate, Henry Ford Health System
  • Jun Qian - Other, All of Us Program Operational Use
  • Ashley Able - Other, All of Us Program Operational Use

Demo - Hypertension Prevalence

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical…

Scientific Questions Being Studied

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical approaches to understanding and treating hypertension may benefit from the integration of a precision medicine approach that integrates data on environments, social determinants of health, behaviors, and genomic factors that contribute to hypertension risk. Hypertension is a major public health concern and remains a leading risk factor for stroke and cardiovascular disease.

Project Purpose(s)

  • Other Purpose (This work is an AoU demo project. Demo projects are efforts by the AoU Research Program designed to meet the program goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. As an approved demo project, this work was reviewed and overseen by the AoU Research Program Science Committee and the AoU Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use. )

Scientific Approaches

In this cross-sectional, population-based study, we used All of Us baseline data from patient (age>18) provided information (PPI) surveys and electronic health record (EHR) blood pressure measurements and retrospectively examined the prevalence of hypertension in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED codes and blood pressure medications recorded in the EHR. We used the EHR data (SNOMED codes on 2 distinct dates and at least one hypertension medication) as the primary definition, and then add subjects with elevated systolic or elevated diastolic blood pressure on measurements 2 and 3 from PPI. We extracted each participant’s detailed dates of SNOMED code for essential hypertension from the Researcher Workbench table ‘cb_search_all_events’. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18-39, 40-59, ≥ 60).

Anticipated Findings

The prevalence of hypertension in the All of Us cohort is similar to that of published literature. All of Us age-adjusted HTN prevalence was 27.9% compared to 29.6% in National Health and Nutrition Examination Survey. The All of Us cohort is a growing source of diverse longitudinal data that can be utilized to study hypertension nationwide. The prevalence of hypertension varies in the United States (U.S.) by age, sex, and socioeconomic status. Hypertension can often be treated successfully with medication, and prevented or delayed with lifestyle modifications. Even with these established hypertension intervention and prevention strategies, the prevalence of hypertension continues to be at levels of public health concern. The diversity within All of Us may provide insight into factors relevant to hypertension prevention and treatments in a variety of social and geographic contexts and population strata in the U.S.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

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Request a Review of this Research Project

You can request that the All of Us Resource Access Board (RAB) review a research purpose description if you have concerns that this research project may stigmatize All of Us participants or violate the Data User Code of Conduct in some other way. To request a review, you must fill in a form, which you can access by selecting ‘request a review’ below.