Tonya Lee
Graduate Trainee, University of California, San Diego
6 active projects
Effect of Thyroid Eye Disease on Mental Health
Scientific Questions Being Studied
Thyroid eye disease (TED) causes proptosis, diplopia, changes in vision, eyelid retraction with subsequent dry eye, and disfigurement that can have a negative effect on patients' quality of life and self-esteem. We hope to explore the effect of TED on mental health and quality of life, and to determine if treatments for TED change these parameters. We would also like to understand if there are socioeconomic factors that influence outcome, and therefore quality of life and mental health, in patients with TED.
Project Purpose(s)
- Disease Focused Research (Thyroid eye disease)
- Population Health
- Social / Behavioral
Scientific Approaches
Datasets: patients with TED and their history of treatment, socioeconomic classification (race, ethnicity, income, etc), coexisting mental health diagnoses (depression, anxiety)
Research methods: statistical analyses to evaluate correlation, independent risk factors
Tools: All of Us database and notebooks
Anticipated Findings
We anticipate finding greater prevalence of mental health diagnoses (ie, depression, anxiety) among patients with TED. Certain socioeconomic factors may increase risk of mood and anxiety disorders among these patients. History of TED treatment may decrease risk of these disorders persisting.
Demographic Categories of Interest
- Geography
- Access to Care
- Education Level
- Income Level
Data Set Used
Registered TierResearch Team
Owner:
- Tonya Lee - Graduate Trainee, University of California, San Diego
Collaborators:
- Sally Baxter - Research Fellow, University of California, San Diego
- Gordon Ye - Undergraduate Student, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
- Arash Delavar - Research Fellow, Baylor College of Medicine
- William Halfpenny - Research Fellow, University of California, San Diego
- Leo Li Tang Meller - Graduate Trainee, University of California, San Diego
- Jimmy Chen - Research Fellow, University of California, San Diego
TED Genomics v7
Scientific Questions Being Studied
We are exploring the genomics involved in thyroid eye disease to investigate differences in patients who have Graves' disease and go on to develop thyroid eye disease compared to those who do not develop thyroid eye disease.
Project Purpose(s)
- Disease Focused Research (Thyroid eye disease)
- Ancestry
Scientific Approaches
The plan for data analysis is pending; this is the initial exploration of data. We may use statistical tests such as t-tests and regression.
Anticipated Findings
This is the initial exploration of data, but we anticipate that there may be some genomic differences in patients with Graves' disease who develop TED vs those who do not. Findings may potentially contribute to a better understanding of the pathophysiology of thyroid eye disease.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierResearch Team
Owner:
- Tonya Lee - Graduate Trainee, University of California, San Diego
- Chanond Nasamran - Project Personnel, University of California, San Diego
Collaborators:
- Leo Li Tang Meller - Graduate Trainee, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Jimmy Chen - Research Fellow, University of California, San Diego
- Bonnie Huang - Graduate Trainee, Northwestern University
TED Genomics - v6 DO NOT USE
Scientific Questions Being Studied
We are exploring the genomics involved in thyroid eye disease to investigate differences in patients who have Graves' disease and go on to develop thyroid eye disease compared to those who do not develop thyroid eye disease.
Project Purpose(s)
- Disease Focused Research (Thyroid eye disease)
- Ancestry
Scientific Approaches
The plan for data analysis is pending; this is the initial exploration of data. We may use statistical tests such as t-tests and regression.
Anticipated Findings
This is the initial exploration of data, but we anticipate that there may be some genomic differences in patients with Graves' disease who develop TED vs those who do not. Findings may potentially contribute to a better understanding of the pathophysiology of thyroid eye disease.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Controlled TierSDHA in Eye Conditions - v6 Dataset
Scientific Questions Being Studied
We are planning to explore disparities in healthcare access and utilization for patients with eye conditions across different demographic groups. We would like to evaluate risk of developing advanced/severe disease in different eye conditions, and understand how social determinants contribute to this risk while adjusting for other known risk factors. We are also interested in understanding the availability of social determinants of health data in this data repository compared to EHR clinical data warehouses alone.
Project Purpose(s)
- Population Health
Scientific Approaches
We will build cohorts of patients with various eye diseases (i.e. diabetic retinopathy, retinal vein occlusions, glaucoma, etc.). Then we will develop concept sets and extract data on outcomes (i.e. development of complications), as well as predictors including clinical data and social data. We will draw on survey data and EHR data within All of Us. When genomic data and wearable data become available, we are interested in evaluating those data sources as well. We will use statistical modeling and machine learning to generate predictive models.
Anticipated Findings
We anticipate that there may be differential risk for developing complications based on disparities in healthcare access and utilization for patients with eye conditions.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Geography
- Disability Status
- Access to Care
- Education Level
- Income Level
Data Set Used
Registered TierResearch Team
Owner:
- Varsha Varkhedi - Undergraduate Student, University of California, San Diego
- Tonya Lee - Graduate Trainee, University of California, San Diego
- Sophia Sidhu - Graduate Trainee, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Kaela Acuff - Graduate Trainee, University of California, San Diego
- Gordon Ye - Undergraduate Student, University of California, San Diego
- Catherine Sheils - Research Fellow, University of California, San Diego
- Bita Shahrvini - Graduate Trainee, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
- Arash Delavar - Research Fellow, Baylor College of Medicine
Collaborators:
- Joy Guo - Graduate Trainee, University of California, San Diego
- Bonnie Huang - Graduate Trainee, Northwestern University
- Kiana Tavakoli - Research Fellow, University of California, San Diego
SDHA in Eye Conditions - v5 Dataset
Scientific Questions Being Studied
We are planning to explore disparities in healthcare access and utilization for patients with eye conditions across different demographic groups. We would like to evaluate risk of developing advanced/severe disease in different eye conditions, and understand how social determinants contribute to this risk while adjusting for other known risk factors. We are also interested in understanding the availability of social determinants of health data in this data repository compared to EHR clinical data warehouses alone.
Project Purpose(s)
- Population Health
Scientific Approaches
We will build cohorts of patients with various eye diseases (i.e. diabetic retinopathy, retinal vein occlusions, glaucoma, etc.). Then we will develop concept sets and extract data on outcomes (i.e. development of complications), as well as predictors including clinical data and social data. We will draw on survey data and EHR data within All of Us. When genomic data and wearable data become available, we are interested in evaluating those data sources as well. We will use statistical modeling and machine learning to generate predictive models.
Anticipated Findings
We anticipate that there may be differential risk for developing complications based on disparities in healthcare access and utilization for patients with eye conditions.
Demographic Categories of Interest
- Race / Ethnicity
- Age
- Geography
- Disability Status
- Access to Care
- Education Level
- Income Level
Data Set Used
Registered TierResearch Team
Owner:
- Terrence Lee - Graduate Trainee, University of California, San Diego
- Tonya Lee - Graduate Trainee, University of California, San Diego
- Sophia Sidhu - Graduate Trainee, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Kaela Acuff - Graduate Trainee, University of California, San Diego
- John McDermott - Graduate Trainee, University of California, San Diego
- Grace Ahn - Graduate Trainee, University of California, San Diego
- Gordon Ye - Undergraduate Student, University of California, San Diego
- Alison Chan - Graduate Trainee, University of California, San Diego
- Bita Shahrvini - Graduate Trainee, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
- Arash Delavar - Research Fellow, Baylor College of Medicine
Collaborators:
- Francis Ratsimbazafy - Other, All of Us Program Operational Use
- Jun Qian - Other, All of Us Program Operational Use
- Joy Guo - Graduate Trainee, University of California, San Diego
- Bonnie Huang - Graduate Trainee, Northwestern University
Socioeconomic factors associated with orbital fracture
Scientific Questions Being Studied
Studies have suggested that facial fractures occurring with trauma occur with relation to socioeconomic context. We would like to examine at a national level what types of social and economic risk factors are associated with orbital fractures in particular.
Project Purpose(s)
- Population Health
Scientific Approaches
Datasets: patients with diagnosis of orbital fracture, with socioeconomic/demographics data
Research methods: correlation tests, regression
Anticipated Findings
We anticipate that certain socioeconomic risk factors will have higher odds ratio of orbital fracture and shed light on the context of trauma in which these fractures often occur.
Demographic Categories of Interest
- Race / Ethnicity
- Gender Identity
- Sexual Orientation
- Geography
- Access to Care
- Education Level
- Income Level
Data Set Used
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