Research Projects Directory

Research Projects Directory

At this time, all listed projects are using data in the registered tier. The registered tier contains individual-level data from electronic health records, survey answers, and physical measurements. These data have been altered to protect participant privacy.

Note: Researcher Workbench users provide information about their research projects independently. Any 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.

There are currently 73 active workspaces. This information was updated on 7/2/2020.

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AD prediction from polygenetic and lifestyle data

Project Purpose(s)

  • Disease Focused Research (Alzheimer's disease)
  • Social / Behavioral ...
  • Methods Development
  • Ancestry

Scientific Questions Being Studied

Within the genetics study of Alzheimer's disease (AD), previous studies have identified multiple genetic risks for the disease, including APOE, CD33, and more. Many rare variants have also been implicated, such as in PLCG2 and ABI3. However, these identifications are essentially from univariate analyses (excluding pleiotropic effects), ex. GWAS, and hence they remain only risk factors but do not have sufficient predictive power for diagnosis. Recently, studies have shown the usefulness of using multivariate machine learning (ML) models to identify new rare variants that can also predict abdominal aortic aneurysm (AAA) with good accuracy.
Here, we hypothesize that there exist novel rare variants in whole genome sequence (WGS) that are identifiable through complex ML and capable of AD. diagnosis. Additionally, we also aim to incorporate lifestyle data to further increase the predictive power.

Scientific Approaches

The dataset:
We aim to use WGS data, with available EHS or lifestyle data, of participants with AD phenotype, together with sex- and age-matched healthy controls. We would also include disease controls such as Lewy body or Parkinson's disease.

Research method:
As an exploration, we first aim to follow the previous work for AAA(Li et al., 2018, Cell 174), which includes, identifying rare variants from the WGS, quantifying gene mutation burden, and feed these features, along with lifestyle information, into ML models to get prediction and to identify important variants through the model's agnostic interpretation. The details of these steps are flexible to get optimal results. We could implement feature selection before inputting into ML or even use deep learning models depending on the amount of data.

Tools:
The computational resources required for this work is publicly available, such as ML packages, Tensorflow, GATK for variant calling, ANNOVAR (for mutational burden calculation).

Anticipated Findings

We expect to identify new variants and pathways that would be predictive of AD and potentially lead to the etiology of the disease (using network analysis after the identification of the variants from ML models). If successful, apart from better AD diagnosis (against other types of dementia) and prognosis, the finding from this study could provide new therapeutic targets for gene therapy.

Demographic Categories of Interest

  • Age

Research Team

Owner:

  • Thanaphong Phongpreecha - Research Fellow, Stanford University

Asian Americans and Type 2 Diabetes

Project Purpose(s)

  • Disease Focused Research (diabetes mellitus) ...

Scientific Questions Being Studied

Despite having a lower body mass index (BMI) than other racial/ethnic groups, Asian Americans are more likely to develop Type 2 diabetes. In fact, Asian Americans have a 60% higher risk of Type 2 diabetes than non-Hispanic whites. Given the growing public health attention on the elevated risk of type 2 diabetes among Asian Americans, clinicians are expected to intensify efforts to screen this population. In 2015, the American Diabetes Association (ADA) changed the BMI cut point for screening Asian Americans for prediabetes and type 2 diabetes to 23 kg/m2 (vs. 25 kg/m2) based on the evidence that this population is at an increased risk for diabetes at lower BMI levels relative to the general population. In this study, we aim to exam the prevalence of type 2 diabetes at a BMI of ≥ 23 kg/ m2 in the Asian American cohort of the All of Us Research Program. Are Asian Americans more likely to develop Type 2 diabetes at a lower BMI?

Scientific Approaches

First, we will select the Asian American cohort from the demographic dataset. We will then select a subset of participants who had a medical condition of Type 2 diabetes, or diabetes, or glycated hemoglobin greater than or equal to 6.5%, or serum glucose greater than or equal to 126 mg/dL. We will use their height and weight under the physical measurements to calculate their BMI. We will exam the prevalence of Type 2 diabetes for BMI < 23, BMI 23-25, and BMI >25, as well as by gender and age group (18-44 years old, 45 -64 years old, and > 65 years old). Furthermore, we will assess their employment status and health insurance status.

Anticipated Findings

We expect to find a higher number of Asian Americans diagnosed with Type 2 diabetes at a lower BMI, below the recommended screening cutpoint BMI >25. We also expect to find a higher number of Asian Americans diagnosed with Type 2 diabetes at a younger age, before the recommended screening age 45 years old.

Demographic Categories of Interest

  • Race / Ethnicity

Research Team

Owner:

  • Fornessa Randal - Senior Researcher, Asian Health Coalition

Collaborators:

  • Helen Lam

Asian Represnetation

Project Purpose(s)

  • Other Purpose (Describe the demographic characteristics of the Asian American cohort in the All of Us Research Program.) ...

Scientific Questions Being Studied

The ability of the ALL of Us Research Program to collect a wide range of patient information is critical, so is to increase the number and the portion of racial/ethnic minority participants. Without representation from diverse racial/ethnic minority populations to ensure the generalization of findings, the advance of precision medicine will only magnify health disparities. Asian Americans are one of the fastest-growing racial/ethnic groups. Unlike other racial/ethnic minority groups, the diversity and complexity of the Asian Americans are remarkable and represent more than 30 different. Thus, the specific aim of this study to find out whether the Asian American cohort in the All of Us Research Program reflects the general Asian population in the U.S.

Scientific Approaches

First, we will select the Asian American cohort from the demographic dataset. We will then summarize the demographic characteristics (age, gender, educational attainment, and annual household income) of the Asian American cohort using the self-reported data from the Participant Provided Information dataset. Finally, we will compare the finding to the 2013-2017 American Community Survey 5-year estimates.

Anticipated Findings

We expect that the Asian American cohort in the All of Us Research Program will be younger and have higher educational attainment than the general Asian population in the U.S.

Demographic Categories of Interest

  • Race / Ethnicity

Research Team

Owner:

  • Fornessa Randal - Senior Researcher, Asian Health Coalition

Collaborators:

  • Helen Lam

Asthma

Project Purpose(s)

  • Disease Focused Research (Asthma)
  • Methods Development ...
  • Ancestry

Scientific Questions Being Studied

Exploring diversity of childhood onset asthma. Would like to see the prevalence of the condition in the current dataset.

Scientific Approaches

Would like to explore genetic associations with childhood onset asthma.

Anticipated Findings

Identifying population specific risk factors for asthma.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Research Team

Owner:

  • Keoki Williams - Late Career Tenured Researcher, Henry Ford Health System

Asthma and COPD Demonstration Project

Project Purpose(s)

  • Disease Focused Research (Asthma, COPD)
  • 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 Questions Being Studied

Asthma and Chronic Obstructive Pulmonary Disease (COPD) are the two most common respiratory diseases. Therefore, understanding the characteristics and frequency of participants with either of these diseases in the US by analyzing the All of Us database has public health implications. We will analyze the relationship of diagnosis for each disease to known risk factors and covariates including gender, age, race, BMI, smoking status, level of education and common comorbidities.

Scientific Approaches

Standard statistical analysis will be used to determine the frequency of asthma or COPD diagnoses in the EHR and surveys in relationship to gender, age, BMI, race, educational status as a reflection of socioeconomic status (SES), regions of the country, co-morbidities and smoking status. Results will be compared to published data including the CDC. Since COPD is under diagnosed in the US, we will compare the demographics and health characteristics of participants with a significant history of smoking (but no diagnosis of COPD) to participants with a COPD diagnosis. In addition, we will examine the medication data in the EHR for comparison with the published guidelines for treatment of both of these diseases.

Anticipated Findings

We anticipate a higher frequency of asthma in women compared men but that men are more likely to have reported that asthma started in childhood. In addition, we anticipate a higher frequency of asthma and less medication use in African-Americans compared to non-Hispanic whites. Since COPD is mainly due to cigarette smoking, we anticipate that participants with a diagnosis of COPD are more likely to be men of middle to older age with a significant history of tobacco exposure. In addition, we expect that a proportion of participants with a significant history of smoking will not have a diagnosis of COPD either by EHR or survey but may report symptoms of airways disease. Also, the degree of misclassification of the two diseases will be determined; for example, a young adult with a limited degree of tobacco exposure diagnosed as COPD is more likely to have asthma. In both diseases, we expect an increase frequency of common comorbidities including increased BMI.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • LISA WHITE - Project Personnel, University of Arizona

Collaborators:

  • Deborah Meyers

Autoimmune Prevalence Project

Project Purpose(s)

  • Disease Focused Research (Autoimmune disease) ...

Scientific Questions Being Studied

We want to establish the prevalence of each of the approximately 150 autoimmune diseases individually, and to establish the prevalence of autoimmune disease as a class of disease. The NIH has a published value of 23.5 million that was an estimate, and there no scientific study to support the number.

The reasons for this research are as follows:

1. Autoimmune diseases are viewed individually as rare disease, so the impact of the class of disease is underappreciated as a cause of comorbidity
2. Belief that these diseases are rare leads to underdiagnosis. One study showed it takes 4 to 5 years to be properly diagnosed with an autoimmune disease, during which time patients are improperly subjected to medications that are ineffective and, in some cases, detrimental to their health
3. Understanding the aggregate impact on the US population generally, and underserved populations specifically, could improve funding for this class of disease.

Scientific Approaches

We have identified over 100 diseases that have some evidence of being autoimmune. We will use ICD Diagnosis codes and other data to create computable phenotypes for each disease. We will then create a patient-disease table (all deidentified) that can be used to generate prevalence for each disease individually, and the prevalence of any autoimmune disease. Then we will identify comorbidities among the autoimmune diseases, and look at correlations between these diseases and other data such as age, sex, race, ethnic background, location, etc.

Computing a denominator that will allow us to compute prevalence will be a key goal - we plan to use ICD data for well-characterized diseases to help us estimate the denominator and identify any bias that could affect the generalizability of our study.

Anticipated Findings

The prevalence of autoimmune disease in the US is not known.

Dr. Noel Rose, who wrote the book "The Autoimmune Diseases" and is arguably one of the leading authorities on autoimmune disease, is supportive of this research and will review the paper. He would like to know if the estimate of 23.5 million, which he gave to the NIH, is actually correct.

Knowing prevalence and comorbidity among these diseases will help patients understand their own health better by supporting awareness of the entire class of disease. We believe this work is of particular interest to women, since many of these diseases affect women at a rate twice that of men.

Demographic Categories of Interest

  • Others

Research Team

Owner:

  • Aaron Abend - Senior Researcher, Autoimmune Registry, Inc.

Collaborators:

  • Eric Chen - Project Personnel, Autoimmune Registry, Inc.

Body Temperature 121719

Project Purpose(s)

  • Educational
  • Other Purpose (This work is the 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 Research Center to ensure compliance with program policy, including policies for acceptable data access and use.) ...

Scientific Questions Being Studied

As a demonstration project, this study will will examine the distribution of normal body temperature by sex, age, and race from data available in the All of Us research dataset. The results will be compared to those reported in several other large epidemiologic studies to see if the same established patterns of these distributions are demonstrable in the All of Us dataset. Specific questions include:

1. What are the distributions of normal body temperature demonstrated in the All of Us research dataset by sex, age and race?
2. How do the distributions of normal body temperature demonstrated in the All of Us research dataset compare with those reported in other large epidemiologic studies?

Scientific Approaches

To examine the distributions of normal body temperature in different age, sex, and race groups represented in the All of Us research dataset, we will identify normal body temperatures as the lowest oral temperature recorded for each individual with a body temperature value in the dataset, stratify the cohort by sex, race, and age at time of temperature recording (in 10 year intervals). The mean, standard deviation, median, and interquartile ranges of temperatures will be determined for each strata.
Sources for the data required for the analysis include:
EHR Oral Body Temperature values and date of measurement
Participant Provided Information (PPI) for date of birth
Participant Provided Information (PPI) for demographics

Anticipated Findings

For this study, we anticipate that we will be able to replicate the previously established patterns in distributions of body temperature in adults by age, sex, and race. This will serve to demonstrate that physiologic measures derived from EHR data within the All of Us dataset are valid for epidemiologic study. It will also provide a basis for further study of body temperature trends within individuals to include investigations into possible individualized definitions of normal and febrile temperature ranges.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Jill Waalen - Mid-career Tenured Researcher, Scripps Research

Breast Cancer

Project Purpose(s)

  • Educational ...

Scientific Questions Being Studied

How breast cancer phenotype can be implemented in AoU.

Scientific Approaches

Not available.

Anticipated Findings

That AoU has great data.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Tsung-Ting Kuo - Early Career Tenure-track Researcher, University of California, San Diego

Breast Cancer Demo

Project Purpose(s)

  • Educational ...

Scientific Questions Being Studied

Learning

Scientific Approaches

Not available.

Anticipated Findings

Learning

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • King Jordan - Mid-career Tenured Researcher, Georgia Tech

Breast Cancer Demo

Project Purpose(s)

  • Educational ...

Scientific Questions Being Studied

How can breast cancer phenotypes be studied in AoU

Scientific Approaches

Not available.

Anticipated Findings

Learning about the platform

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Shashwat Deepali Nagar - Graduate Trainee, Georgia Tech

breast cancer demo

Project Purpose(s)

  • Educational ...

Scientific Questions Being Studied

How breast cancer phenotypes can implemented in AoU

Scientific Approaches

Not available.

Anticipated Findings

breast cancer phenotypes

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Jihoon Kim - Other, UCSD

Breast Cancer Demo

Project Purpose(s)

  • Educational ...

Scientific Questions Being Studied

How breast cancer is diagnosed in different ancestral populations

Scientific Approaches

Not available.

Anticipated Findings

Diagnoses are detected at later sages in URM populations

Demographic Categories of Interest

Not available.

Research Team

Owner:

  • Cenai Zhang - Project Personnel, Cornell University

Collaborators:

  • Margaret Ross - Late Career Tenured Researcher, Cornell University

Breast Cancer Phenotype Tutorial (mkn)

Project Purpose(s)

  • Educational ...

Scientific Questions Being Studied

following guided breast cancer tutorial

Scientific Approaches

Not available.

Anticipated Findings

following guided breast cancer tutorial

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Mary Nielsen - Project Personnel, University of Arizona

Breast Cancer prevalence

Project Purpose(s)

  • Disease Focused Research (breast cancer) ...

Scientific Questions Being Studied

Breast Cancer prevalence

Scientific Approaches

Not available.

Anticipated Findings

Breast Cancer prevalence

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Jihoon Kim - Other, UCSD

Burden of mental health among US adults with and without hypertension

Project Purpose(s)

  • Disease Focused Research (hypertension)
  • Population Health ...

Scientific Questions Being Studied

Mental health diseases contribute to a significant proportion of disease burden and are a leading cause of years lived with disability globally. Mental disorders are risk factors for a number of chronic diseases. In addition, poor mental health status is more prevalent among women than men. We aim to examine the prevalence of mental problems among US adults with and without hypertension disorders. We will also stratify by gender and other demographic factors. including age, race/ethnicity, income.

Scientific Approaches

We will first identify participants who answered the following questions from the overall health questionnaire.
-PPI1585729. In general, how would you rate your mental health, including your mood and your ability to think?
-PPI1585760. In the past 7 days, how often have you been bothered by emotional problems such as feeling anxious, depressed or irritable?

Then we will extract the conditions of hypertension disorders from the EHR condition domain. We will examine the prevalence of mental disorders among participants with and without hypertension disorders. We will also stratify the population by gender, age.

Anticipated Findings

We expect to find a higher prevalence of mental disorders among participants with hypertension, especially among women with hypertension.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth

Research Team

Owner:

  • Xiang Li - Research Fellow, Tulane University

Collaborators:

  • Christopher Pottle - Project Personnel, Tulane University

Burden of mental heatlh US adults with and without hypertension

Project Purpose(s)

  • Disease Focused Research (hypertension)
  • Population Health ...

Scientific Questions Being Studied

Mental health diseases contribute to a significant proportion of disease burden and are a leading cause of years lived with disability globally. Mental disorders are risk factors for a number of chronic diseases. In addition, poor mental health status is more prevalent among women than men. We aim to examine the prevalence of mental problems among US adults with and without hypertension disorders. We will also stratify by gender and other demographic factors. including age, race/ethnicity, income.

Scientific Approaches

We will first identify participants who answered the following questions from the overall health questionnaire.
-PPI1585729. In general, how would you rate your mental health, including your mood and your ability to think?
-PPI1585760. In the past 7 days, how often have you been bothered by emotional problems such as feeling anxious, depressed or irritable?

Then we will extract the conditions of hypertension disorders from the EHR condition domain. We will examine the prevalence of mental disorders among participants with and without hypertension disorders. We will also stratify the population by gender, age.

Anticipated Findings

We expect to find a higher prevalence of mental disorders among participants with hypertension, especially among women with hypertension.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth

Research Team

Owner:

  • Xiang Li - Research Fellow, Tulane University

Cancer

Project Purpose(s)

  • Disease Focused Research (Cancer) ...

Scientific Questions Being Studied

By enrolling one million or more participants, the All of Us Research Program will have the scale and scope to enable research on a wide range of diseases, especially cancer. This is especially relevant for African Americans, as less than 2% of cancer studies have been powered to consider race/ethnicity, and Black Americans continue to have higher cancer mortality rates and shorter survival times.

Accordingly, we are conducting a preliminary investigation of the distribution and characterization of cancer in the All of Us Research Program, including a comparison to expected national rates reported by the Surveillance, Epidemiology, and End Results (SEER). We believe this could offer important insight into opportunities for cancer research, as well as highlight the capacity for precision cancer research that All of Us will offer in the coming years.

Scientific Approaches

In addition to accessing self-reported cancer and cancer in EHR, we compare our results to SEER. The SEER registries collect data on patient demographics, primary tumor site, tumor morphology, stage at diagnosis, the first course of treatment, and follow-up for vital status. The SEER Program is the only comprehensive source of population-based information in the United States that includes the stage of cancer at the time of diagnosis and patient survival data.

For cancer overall and for leading sites of new cancers (prostate, breast, lung/bronchus, colon/rectum, urinary bladder, melanoma of the skin, kidney/renal, Non-Hodgkin lymphoma, oral cavity/pharynx, leukemia, pancreas, thyroid, and uterine, we reported the frequency of diagnosis in 2016 to determine the relative frequency and percent contribution of each cancer type to overall cancer in the population. The most recent year of diagnosis was selected given the time period for which All of Us has been conducting enrollment.

Anticipated Findings

We expect the EHR will report a lower prevalence of cancer than self-report.

We expect the self-reported cancer prevalence will broadly match the prevalence data from the CDC.

This project will provide insight into the data collection approach being used by AOU for cancer research and the distribution of cancer and cancer-related variables in the AoU study population.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Paul Zakin - Project Personnel, University of Chicago

Collaborators:

  • David Schlueter - Other, All of Us Program Operational Use
  • Francis Ratsimbazafy - Other, All of Us Program Operational Use
  • Andrew Craver
  • Brisa Aschebrook-Kilfoy - Mid-career Tenured Researcher, University of Chicago

Cancer Prevalence and Family History

Project Purpose(s)

  • Disease Focused Research (cancer)
  • 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 Questions Being Studied

As a demonstration project, we seek to understand the regional, demographic, and family history characteristics in prevalence and incidence of both solid and hematologic (blood) cancers. Our questions are:
1. How do rates of cancer differ based on the self-report (participant provided information, or “PPI”) and electronic health records.
2. Are characteristics related to cancer similar or different among the people represented in All of Us compared to other national cohorts.

Scientific Approaches

We will analyze all types of cancer in adults to compare incidence (new cases) and prevalence (current levels in the population). We will compare our results with the published information from SEER national cancer registry and national surveys (e.g., National Health Information Survey). We will analyze socio-demographics and geographic differences.

We will identify cancer cases based on self-report from PPI individual medical history survey and from diagnosis codes plus lab results from E.H.R.
We will identify family history of cancer form the PPI family medical history survey.
We will map the cancer categories by physiologic site used for the SEER registry to the SNOMED and ICD codes used in the EHR and to the cancer conditions in the PPI. We use Jupyter notebooks to generate reusable code for the mapping.

Anticipated Findings

For this study, we anticipate we will be able to replicate the relative prevalence and incidence rates of cancer and family history of cancer. This will serve to demonstrate the quality and utility of All of Us data and tools for conducting epidemiologic analyses.

Demographic Categories of Interest

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

Research Team

Owner:

  • Jihoon Kim - Other, UCSD

Collaborators:

  • Paulina Paul
  • Katherine Kim - Early Career Tenure-track Researcher, University of California, Davis

Cancer Rates

Project Purpose(s)

  • Disease Focused Research (cancer)
  • Population Health ...
  • Methods Development
  • Ancestry

Scientific Questions Being Studied

Distribution, comparison to SEER, and consideration of the feasibility of ascertainment from EHR.

Scientific Approaches

Not available.

Anticipated Findings

Understanding of demographic differences in case ascertainment and differences by PPI and EHR.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Brisa Aschebrook-Kilfoy - Mid-career Tenured Researcher, University of Chicago

Cardiovascular Disease Study

Project Purpose(s)

  • Disease Focused Research (cardiovascular system disease)
  • Population Health ...

Scientific Questions Being Studied

This is an example walkthrough/tutorial on forming a cohort and dataset for analysis. This is a new platform the research group has not worked with, so this is simply to gain familiarity with the platform.

Scientific Approaches

This is an example walkthrough/tutorial on forming a cohort and dataset for analysis. We will use the workspace tools provided by All of Us to identify a specific cohort to examine, then narrow down variables of interest before exporting for analysis.

Anticipated Findings

This is an example walkthrough/tutorial on forming a cohort and dataset for analysis. We hope to be able to generate an example dataset focusing on a particular cardiovascular disease that we can analyze.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Christopher Pottle - Project Personnel, Tulane University

Cardiovascular Disease Study

Project Purpose(s)

  • Disease Focused Research (cardiovascular system disease)
  • Population Health ...

Scientific Questions Being Studied

This is an example walkthrough/tutorial on forming a cohort and dataset for analysis. This is a new platform the research group has not worked with, so this is simply to gain familiarity with the platform.

Scientific Approaches

This is an example walkthrough/tutorial on forming a cohort and dataset for analysis. We will use the workspace tools provided by All of Us to identify a specific cohort to examine, then narrow down variables of interest before exporting for analysis.

Anticipated Findings

This is an example walkthrough/tutorial on forming a cohort and dataset for analysis. We hope to be able to generate an example dataset focusing on a particular cardiovascular disease that we can analyze.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Christopher Pottle - Project Personnel, Tulane University

Chronic sinusitis

Project Purpose(s)

  • Disease Focused Research (Chronic sinusitis) ...

Scientific Questions Being Studied

Looking for evidence regarding the epidemiology of chronic sinusitis, include associated comorbid conditions and need for procedures.

Scientific Approaches

Will use association statistics (correlation, t-tests) to look at the prevalence of comorbid disease with chronic sinusitis.

Anticipated Findings

Anticipate that asthma, bronchitis, allergic rhinitis, and other airway diseases will be over-represented in the CRS population.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Naweed Chowdhury - Early Career Tenure-track Researcher, Vanderbilt University Medical Center

CKD

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 Questions Being Studied

Chronic kidney disease (CKD) is a growing public health problem that affects over 27 million adults in the US and 11% of the population worldwide. It is well established that CKD disproportionately affects racial/ethnic minorities, as well as women. Furthermore, in addition to an increased risk for kidney failure, persons with CKD experience poor quality of life and physical function, and a very high risk of morbidity and mortality. In this demonstration project, we focus on the prevalence of CKD and its awareness, treatment, and control in a large and diverse participant sample of the All of Us Research Program. Specific questions include:
1) What is the prevalence of CKD among participants in the All of Us Research Program?
2) Among CKD participants, what is the prevalence of awareness, treatment and control?
3) How do these estimates compare to the general US population assessed in the National Health and Nutrition Examination Survey (NHANES), 2015-2016?

Scientific Approaches

This descriptive analysis is based on eGFR and blood pressure measurements from the participants’ physical measurement evaluations, and data derived from participant provided information (PPI) and electronic health records (EHR).
1) Demographic factors such as age, sex, race/ethnicity, educational attainment, income and health insurance were assessed in PPI questionnaire.
2) PPI questionnaire data was also used to define self-reported doctor diagnosis of CKD, self-reported medication use.
3) EHR evidence of CKD diagnosis was defined as the presence of ICD9/ICD10 codes corresponding to CKD any time before baseline.
4) EHR evidence of CKD medication use was defined as at least 1 drug exposure to CKD medications any time before baseline.

Anticipated Findings

For this study, we anticipate that the prevalence, awareness, treatment and control of CKD will be different across demographic strata. This will help to identify health disparities and improve health equity in vulnerable populations. We also anticipate that estimates will be different between the All of Us Research program and the general US population assessed in NHANES 2015-2016. Understanding these differences will help to characterize potential selection bias and demonstrate the quality and utility of the All of Us data and tools.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Madhawa Saranadasa - Graduate Trainee, University of Illinois at Chicago