Jie Chen

Late Career Tenured Researcher, Augusta University

11 active projects

Duplicate of Duplicate of Demo - PheWAS Smoking

As a demonstration project, this study will present the results of Phenome-Wide Association Studies (PheWAS) to show how the various sources of data contained within All of Us research dataset can be used to inform scientific discovery. We will perform…

Scientific Questions Being Studied

As a demonstration project, this study will present the results of Phenome-Wide Association Studies (PheWAS) to show how the various sources of data contained within All of Us research dataset can be used to inform scientific discovery. We will perform separate PheWAS studies with smoking status as the independent variable. Specific questions include:

1. How can one implement a PheWAS within the All of Us Researcher Workbench?
2. How can one use heterogeneous data sources within the All of Us dataset to explore disease associations using self-reported exposures (Participant Provided Information, or “PPI”) and exposures captured in the electronic medical record (EHR).

Project Purpose(s)

  • Methods Development
  • 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

As a method for assessing the health burden of smoking on potential observed phenotypes, we implement a Phenome-Wide Association study. A Phenome-wide association study consists of an array of association tests over an indexed representation of the human phenome. In this analysis, we will conduct PheWAS for EHR derived smoking and PPI derived smoking exposures included in the All of Us research dataset. We will be representing "Smoking Exposure” in three ways:
EHR Smoking ICD Billing Codes
Participant Provided Information (PPI) Smoking lifetime 100 cigarettes yes/no
Participant Provided Information (PPI) Smoking lifetime smoking everyday
To perform PheWAS, we will map ICD representations of disease to a common vocabulary of PheCodes. We then use Jupyter Notebooks to create reusable functions to perform PheWAS and generate Manhattan Plots to summarize associations.

Anticipated Findings

For this study, we anticipate that we will be able to replicate known disease associations with smoking exposure. This will serve to demonstrate the quality, utility, and diversity of the All of Us data and tools and the power of gathering multiple data sources for a single phenotype, providing researchers options for study design and validation. Importantly the entire pheWAS package is made available for reuse by researchers in the Workbench, for new hypothesis generation.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Jie Chen - Late Career Tenured Researcher, Augusta University

Duplicate of Phenotype - Type 2 Diabetes

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

Scientific Questions Being Studied

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

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.)

Scientific Approaches

Not Applicable

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.

Research Team

Owner:

  • Jie Chen - Late Career Tenured Researcher, Augusta University

Analysis of the COVID-19 data

To explore the data for formalizing the research questions such as whether the death rate is different during certain period of time, across states, etc.

Scientific Questions Being Studied

To explore the data for formalizing the research questions such as whether the death rate is different during certain period of time, across states, etc.

Project Purpose(s)

  • Population Health

Scientific Approaches

Depending on the final research questions formulated, we try to find if mitigation measures would help reduce the cases and death rate.

Anticipated Findings

The findings will raise public health awareness and understand the benefit of implementation of mitigation measures during a pandemic.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Jie Chen - Late Career Tenured Researcher, Augusta University

Autism_health_care

Survey data will be analyzed for adults with autism to explore their general health and health care utilization.

Scientific Questions Being Studied

Survey data will be analyzed for adults with autism to explore their general health and health care utilization.

Project Purpose(s)

  • Population Health

Scientific Approaches

Survey data including demographic factors and characteristics (age, sex, race, Hispanic ethnicity, educational attainment, marital status, health care insurance, employment status, annual household income, and number of people in household), personal medical history of autism, general health and health care utilization, will be analyzed using statistical models.

Anticipated Findings

We expect to find out the relationship between self-reported delayed receipt of healthcare according to demographic and personal characteristics. The results may leverage better health care utilization for autism patients.

Demographic Categories of Interest

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

Research Team

Owner:

  • Jie Chen - Late Career Tenured Researcher, Augusta University

SmokingCessation

Examine receipt of smoking cessation treatment among rural and non-rural individuals in the United States who are current or former smokers.

Scientific Questions Being Studied

Examine receipt of smoking cessation treatment among rural and non-rural individuals in the United States who are current or former smokers.

Project Purpose(s)

  • Population Health

Scientific Approaches

Data to be used:

(Survey data: Demographics/the Basics)
Limit cohort to participants age 18 years or older
Variables of interest:
Age
Sex
Race
Hispanic ethnicity
Health insurance

(Survey data: Health Care Access/Utilization)
Rural residence
There are many reasons people delay getting medical care. Have you delayed getting care for any of the following reasons in the PAST 12 MONTHS?
You live in a rural area where distance to the health care provider is too far.•Yes•No•Don’t know

(Data: Procedures)

Smoking Cessation
Smoking cessation education
Smoking and tobacco use cessation counseling visit

(Data: Medications)
Bupropion
Varenicline
nicotine

Anticipated Findings

The specific aim of the proposed research is to examine receipt of smoking cessation treatment among rural and non-rural individuals in the United States who are current or former smokers. We hypothesize that:
H1: Rural individuals will be less likely to have received smoking cessation treatment than urban individuals.
H1: Rural individuals who have a lower household income will be less likely to have received smoking cessation treatment than rural individuals who have a higher household income.

Demographic Categories of Interest

  • Geography
  • Access to Care

Research Team

Owner:

  • Jie Chen - Late Career Tenured Researcher, Augusta University

Duplicate of How to Backup Notebooks and Intermediate Results

Not applicable - these utility notebooks do not perform any analyses.

Scientific Questions Being Studied

Not applicable - these utility notebooks do not perform any analyses.

Project Purpose(s)

  • Other Purpose (Demonstrate to workbench users how to create snapshots of notebooks and backups of intermediate results stored in other files such as plot images and derived data.)

Scientific Approaches

Not available.

Anticipated Findings

Not applicable - these utility notebooks do not perform any analyses.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Jie Chen - Late Career Tenured Researcher, Augusta University

Cancer Survival

We try to study the quality of life of Adult Cancer Survivors by using All of US data.

Scientific Questions Being Studied

We try to study the quality of life of Adult Cancer Survivors by using All of US data.

Project Purpose(s)

  • Disease Focused Research (Cancer)
  • Methods Development

Scientific Approaches

We will use the survey and demographic data.

Anticipated Findings

We hope to find the factors that contribute to the quality of life of Adult Cancer Survivors. The findings will inform the plans for higher quality of life for cancer survivors.

Demographic Categories of Interest

  • Race / Ethnicity

Research Team

Owner:

  • Jie Chen - Late Career Tenured Researcher, Augusta University

Duplicate of How to Work with All of Us Survey Data

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

Not available.

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.

Research Team

Owner:

  • Jie Chen - Late Career Tenured Researcher, Augusta University

Duplicate of How to Work with All of Us Physical Measurements Data

How to navigate around physical measurements?

Scientific Questions Being Studied

How to navigate around physical measurements?

Project Purpose(s)

  • Educational
  • Methods Development

Scientific Approaches

N/A

Anticipated Findings

N/A

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Jie Chen - Late Career Tenured Researcher, Augusta University

Duplicate of Phenotype - Breast Cancer

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

Scientific Questions Being Studied

The Notebooks in this Workspace can be used to implement well-known phenotype algorithms in one’s own research.

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.)

Scientific Approaches

Not Applicable

Anticipated Findings

By reading and running the Notebooks in this Phenotype Library Workspace, researchers can implement the following phenotype algorithms:

Ning Shang, George Hripcsak, Chunhua Weng, Wendy K. Chung, & Katherine Crew. Breast Cancer. Retrieved from https://phekb.org/phenotype/breast-cancer.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Jie Chen - Late Career Tenured Researcher, Augusta University

Duplicate of Demo - PheWAS Smoking

As a demonstration project, this study will present the results of Phenome-Wide Association Studies (PheWAS) to show how the various sources of data contained within All of Us research dataset can be used to inform scientific discovery. We will perform…

Scientific Questions Being Studied

As a demonstration project, this study will present the results of Phenome-Wide Association Studies (PheWAS) to show how the various sources of data contained within All of Us research dataset can be used to inform scientific discovery. We will perform separate PheWAS studies with smoking status as the independent variable. Specific questions include:

1. How can one implement a PheWAS within the All of Us Researcher Workbench?
2. How can one use heterogeneous data sources within the All of Us dataset to explore disease associations using self-reported exposures (Participant Provided Information, or “PPI”) and exposures captured in the electronic medical record (EHR).

Project Purpose(s)

  • Methods Development
  • 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

As a method for assessing the health burden of smoking on potential observed phenotypes, we implement a Phenome-Wide Association study. A Phenome-wide association study consists of an array of association tests over an indexed representation of the human phenome. In this analysis, we will conduct PheWAS for EHR derived smoking and PPI derived smoking exposures included in the All of Us research dataset. We will be representing "Smoking Exposure” in three ways:
EHR Smoking ICD Billing Codes
Participant Provided Information (PPI) Smoking lifetime 100 cigarettes yes/no
Participant Provided Information (PPI) Smoking lifetime smoking everyday
To perform PheWAS, we will map ICD representations of disease to a common vocabulary of PheCodes. We then use Jupyter Notebooks to create reusable functions to perform PheWAS and generate Manhattan Plots to summarize associations.

Anticipated Findings

For this study, we anticipate that we will be able to replicate known disease associations with smoking exposure. This will serve to demonstrate the quality, utility, and diversity of the All of Us data and tools and the power of gathering multiple data sources for a single phenotype, providing researchers options for study design and validation. Importantly the entire pheWAS package is made available for reuse by researchers in the Workbench, for new hypothesis generation.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Jie Chen - Late Career Tenured Researcher, Augusta University
1 - 11 of 11
<
>
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.