Aymone Kouame

All of Us Program Operational Use

8 active projects

Workshop Attendees: Intro to All of Us Electronic Health Records Data

The hands-on workshop covers the following topics: an overview of electronic health records (EHR) data, how EHR data are structured, stored, and standardized on the All of Us Researcher Workbench, how to build cohorts and datasets with EHR data on…

Scientific Questions Being Studied

The hands-on workshop covers the following topics: an overview of electronic health records (EHR) data, how EHR data are structured, stored, and standardized on the All of Us Researcher Workbench, how to build cohorts and datasets with EHR data on the Researcher Workbench, and how to analyze EHR data on the Researcher Workbench. By working through the exercises in this workspace, users will become more familiar with All of Us EHR data and learn how to perform EHR data analysis on the Workbench.

Project Purpose(s)

  • Other Purpose (This workspace is intended to provide an introduction to working with electronic health records data on the All of Us Researcher Workbench.)

Scientific Approaches

We will use the Cohort/Dataset builder and Jupyter notebook to create a cohort and analyze EHR data. Specifically, we will investigate whether there is a temporal trend in A1C values leading up to the time of an electrocardiogram among participants with type 2 diabetes.

Anticipated Findings

We anticipate that workshop attendees will understand how EHR data are stored and standardized on the Researcher Workbench. In addition, they will learn how to build cohorts and analyze longitudinal EHR data.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Zhixing Song - Research Fellow, University of Alabama at Birmingham
  • Yuhan Xie - Research Fellow, Yale University
  • Yang Sui - Research Fellow, Broad Institute
  • Yvonne Ford - Early Career Tenure-track Researcher, North Carolina A&T State University
  • Youjeong Kang - Early Career Tenure-track Researcher, Emory University
  • Yifan Lou - Research Fellow, Yale University
  • xuewei cao - Research Fellow, Memorial Sloan Kettering Cancer Center
  • Ximena Oyarzún González - Research Fellow, Ohio State University
  • Weize Wang - Project Personnel, Florida International University
  • Vinicius Borges - Other, Marshall University
  • Curisa Tucker - Early Career Tenure-track Researcher, University of South Carolina
  • Megana Thamilselvan - Graduate Trainee, University of Toronto
  • Tadesse Abegaz - Graduate Trainee, Florida A&M University
  • Soha Shahidi - Graduate Trainee, University of California, San Francisco
  • Spencer Harpe - Mid-career Tenured Researcher, Midwestern University
  • Sean McClellan - Early Career Tenure-track Researcher, University of Illinois at Chicago
  • Byram Ozer - Other, National Cancer Institute (NIH - NCI)
  • Nicole Freund - Teacher/Instructor/Professor, University of Kansas Medical Center
  • Nathan Bowen - Mid-career Tenured Researcher, Clark Atlanta University
  • Nicholette Allred - Mid-career Tenured Researcher, Wake Forest Baptist Health
  • Meng Wang - Early Career Tenure-track Researcher, University of Michigan
  • Min Ji Kim - Research Fellow, New York Genome Center
  • Ming Lim - Early Career Tenure-track Researcher, University of Utah
  • Lisa Connor - Early Career Tenure-track Researcher, Sam Houston State University
  • Katherine Forthman - Project Personnel, Laureate Institute for Brain Research
  • Keer Zhang - Graduate Trainee, University of California, Los Angeles
  • Nabil Kahouadji - Mid-career Tenured Researcher, Northeastern Illinois University
  • JIng Zhang - Research Fellow, University of Kentucky
  • Jiaxi Tang - Graduate Trainee, Arkansas State University
  • Jason Carbone - Early Career Tenure-track Researcher, Wayne State University
  • Md.Mohaimenul Islam - Research Fellow, Ohio State University
  • Irene Kan - Mid-career Tenured Researcher, Villanova University
  • Hubert Chua - Teacher/Instructor/Professor, Long Island University
  • John ("Jack") Hettema - Late Career Tenured Researcher, Texas A&M University
  • Osaro Mgbere - Teacher/Instructor/Professor, University of Houston
  • Nifang Niu - Other, University of Iowa
  • Paola Giusti-Rodriguez - Other, University of Florida
  • Fanghui Shi - Graduate Trainee, University of South Carolina
  • Erica Abushouk - Undergraduate Student, Arizona State University
  • Heather Harvey - Late Career Tenured Researcher, Touro University
  • Daniel Chen - Graduate Trainee, Institute for Systems Biology
  • Claire Niedzwiedz - Research Fellow, University of Glasgow
  • Young Chandler - Research Associate, Ohio State University
  • Brian Anderson - Other, Palmer College of Chiropractic
  • Brandy Mapes - Other, All of Us Program Operational Use
  • Balu Bhasuran - Research Fellow, Florida State University
  • Amy Sitapati - Other, University of California, San Diego
  • Angel Arizpe - Graduate Trainee, University of Southern California
  • Ainslie Tisdale - Project Personnel, National Center for Advancing Translational Sciences (NIH - NCATS)
  • Andrea Gillis - Early Career Tenure-track Researcher, University of Alabama at Birmingham
  • Arthur Ko - Other, Children's Research Institute

Type 2 DM and Wearables Data RTDv6

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease with a primary focus on type 2 diabetes mellitus. Higher physical activity is associated with lower prevalence and…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and sleep quality with the development and progression of human disease with a primary focus on type 2 diabetes mellitus. Higher physical activity is associated with lower prevalence and better outcomes in virtually every human disease. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality in patients seeking care.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Population Health
  • Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence and progression of coded human diseases with a primary focus on Type 2 DM. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We expect to find that lower levels of activity are associated with a higher prevalence and more rapid progression of Type 2 DM and other diseases. These data will provide the rationale to link wearables data with electronic health records nationwide as a window into behavioral activity choice as a modifiable risk factor for chronic diseases. We may find substantial variation in activity and disease prevalence/severity by socioeconomic status, which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

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

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Omar Costilla Reyes - Research Fellow, Massachusetts Institute of Technology

Duplicate of Phenotype - Type 2 Diabetes (v7)

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research, using the Controlled Tier Curated Data Repository (CDR).

Scientific Questions Being Studied

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research, using the Controlled Tier Curated Data Repository (CDR).

Project Purpose(s)

  • Educational
  • Methods Development
  • Other Purpose (This is an All of Us Phenotype Library Workspace created by the Researcher Workbench Support team. It is meant to demonstrate the implementation of key phenotype algorithms within the All of Us Research Program cohort, using the Controlled Tier Curated Data Repository (CDR).)

Scientific Approaches

Controlled-tier All of Us cohort data; Jupyter Notebooks, Cohort Builder, Concept Set Selector, Dataset Selector

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms: Jennifer Pacheco and Will Thompson. Northwestern University. Type 2 Diabetes Mellitus. PheKB; 2012 Available from: https://phekb.org/phenotype/18

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Duplicate of Phenotype - Type 2 Diabetes (v7)

This pilot project seeks to determine if there is an association between sleep disturbance and type 2 diabetes mellitus in African Americans.

Scientific Questions Being Studied

This pilot project seeks to determine if there is an association between sleep disturbance and type 2 diabetes mellitus in African Americans.

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes mellitus)
  • Population Health

Scientific Approaches

Controlled-tier All of Us cohort data; Jupyter Notebooks, Cohort Builder, Concept Set Selector, Dataset Selector

Anticipated Findings

This pilot study will support previous findings of an association between sleep disturbance and development of type 2 diabetes mellitus in African American individuals. These findings have the capacity to support existing hypotheses using a new database.

Demographic Categories of Interest

  • Race / Ethnicity

Data Set Used

Controlled Tier

Research Team

Owner:

Duplicate of How to Get Started with Registered Tier 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? 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:

Demo Project: State-level Activity Inequality [Published Work]

How is physical activity distributed within states in the US? Analysis of such activity distributions and inequality can reveal important relationships between physical activity disparities, health outcomes, and modifiable factors, as Althoff et al. studied in their paper, "Large-scale physical…

Scientific Questions Being Studied

How is physical activity distributed within states in the US? Analysis of such activity distributions and inequality can reveal important relationships between physical activity disparities, health outcomes, and modifiable factors, as Althoff et al. studied in their paper, "Large-scale physical activity data reveal worldwide activity inequality" (2017).

Project Purpose(s)

  • Educational

Scientific Approaches

The cohort will consist of Fitbit users in the US, with analysis being subdivided to the state level. Various graphs will be utilized to help visualize the low- and high-activity trends across states. Well-defined measures such as the Gini coefficient will be used to aid in the analysis of activity inequality.

Anticipated Findings

The study aims to find relationships between activity inequality and health outcomes, such as obesity levels. With the growing accessibility of fitness trackers and activity sensors built into personal devices, this study hopes to leverage the volume of available data and potentially inform measures to improve population activity and health.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Duplicate of Skills Assessment Training Notebooks For Users

This workspace contains multiple notebooks that assess users' understanding of the workbench and OMOP. These notebooks are meant to help users check their knowledge not only on Python, R, and SQL, but also on the general data structure and data…

Scientific Questions Being Studied

This workspace contains multiple notebooks that assess users' understanding of the workbench and OMOP. These notebooks are meant to help users check their knowledge not only on Python, R, and SQL, but also on the general data structure and data model used by the All of Us program.

Project Purpose(s)

  • Educational

Scientific Approaches

There are no scientific approach used in this workspace because it is meant for educational purposes only. We will cover all aspects of OMOP, and hence will use most datasets available in the workbench.

Anticipated Findings

We do not anticipate to have any findings. Instead, we are educating people on the use of the workbench and the common data model OMOP used by the program.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Collaborators:

  • Michael Lyons - Project Personnel, All of Us Program Operational Use
  • Hunter Hollis - Project Personnel, All of Us Program Operational Use
  • Christopher Lord - Project Personnel, All of Us Program Operational Use

Work with All of Us Physical Measurements Data - Class Teaching

How to navigate around physical measurements?

Scientific Questions Being Studied

How to navigate around physical measurements?

Project Purpose(s)

  • Other Purpose (Testing and operations purposes)

Scientific Approaches

N/A

Anticipated Findings

N/A

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

  • Hunter Hollis - Project Personnel, All of Us Program Operational Use
  • Hiral Master - Project Personnel, All of Us Program Operational Use
  • Aymone Kouame - Other, All of Us Program Operational Use
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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.