John McDermott
Graduate Trainee, University of California, San Diego
6 active projects
Social Determinants and Healthcare Access 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:
- Varsha Varkhedi - Undergraduate Student, University of California, San Diego
- Terrence Lee - Graduate Trainee, University of California, San Diego
- Sally Baxter - Research Fellow, University of California, San Diego
- Niloofar Radgoudarzi - 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:
- Joy Guo - Graduate Trainee, University of California, San Diego
- Alireza Kamalipour - Research Fellow, University of California, San Diego
- Priyanka Soe - Project Personnel, University of California, San Diego
- Mahasweta Nayak - Undergraduate Student, University of California, San Diego
- Cecilia Vallejos - Undergraduate Student, University of California, San Diego
- Albert Sohn - Graduate Trainee, Washington State University
Original - Social Determinants and Healthcare Access in Eye Conditions
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
- Sally Baxter - Research Fellow, 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:
- Hua Ou - Mid-career Tenured Researcher, National Institute of Child Health and Human Development (NIH - NICHD)
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
SDHA in Eye Conditions - v4 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
- Sally Baxter - Research Fellow, 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
Social Determinants and Healthcare Access in Eye Conditions - v4 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
- Sally Baxter - Research Fellow, 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:
- Priyanka Soe - Project Personnel, University of California, San Diego
- Mahasweta Nayak - Undergraduate Student, University of California, San Diego
- Cecilia Vallejos - Undergraduate Student, University of California, San Diego
Retinal Vein Occlusion and associated risk factors
Scientific Questions Being Studied
To assess for risk factors for retinal vein occlusion (RVO) among participants in the NIH All of Us database, particularly social risk factors that have not been well-studied, including substance use.
Project Purpose(s)
- Disease Focused Research (retinal vein occlusion)
Scientific Approaches
Data will be extracted regarding demographics, co-morbidities, income, housing, insurance, and substance use. Opioid use will be defined by relevant diagnosis and prescription codes, with prescription use >30 days. Controls will be sampled at a 4:1 control to case ratio from a pool of individuals >18 years of age without a diagnosis of RVO and proportionally matched to the demographic distribution of the 2019 U.S. census. We will use multivariable logistic regression to identify medical and social determinants significantly associated with RVO. Statistical significance will be defined as p<0.05.
Anticipated Findings
Understanding RVO risk factors is important for primary prevention and improving visual outcomes. Several studies have demonstrated an increasing prevalence of RVO with age, but little consensus has been reached regarding associations with race and/or ethnicity. Other studies exploring medical risk factors have shown strong associations with hypertension, hyperlipidemia, diabetes mellitus, glaucoma and cigarette smoking. However, the majority of these studies were conducted on small populations limited to individuals identifying as Asian or white. Few studies have investigated associations with substance use outside of cigarettes and alcohol. The opioid epidemic began in the early 2000s, and as of 2019, more than 1.6 million Americans suffer from opioid use disorder. Given that long term opioid use increases risk of cardiovascular events such as myocardial infarction, an investigation into whether opioid use increases risk of retinal vascular disease, such as RVO, is warranted
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Sally Baxter - Research Fellow, University of California, San Diego
- John McDermott - Graduate Trainee, University of California, San Diego
- Bharanidharan Radha Saseendrakumar - Project Personnel, University of California, San Diego
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