Research Projects Directory

Longitudinal serum cytokines and health outcomes in COVID-19 patients

How longitudinal serum cytokines (IL-6, IL-10, CRP etc.) is associated with intubation among COVID-19 patients? Understanding how longitudinal serum cytokines like IL-6, IL-10, and CRP correlate with severe outcomes in COVID-19 patients is critical. These cytokines are pivotal in immune…

Scientific Questions Being Studied

How longitudinal serum cytokines (IL-6, IL-10, CRP etc.) is associated with intubation among COVID-19 patients?
Understanding how longitudinal serum cytokines like IL-6, IL-10, and CRP correlate with severe outcomes in COVID-19 patients is critical. These cytokines are pivotal in immune response and their levels can indicate cytokine storm, which worsens inflammation and tissue damage. Tracking these markers over time helps predict disease severity and outcomes such as respiratory failure or death. This knowledge aids in timely intervention and personalized treatment, potentially improving patient outcomes amid the pandemic.

Project Purpose(s)

Disease Focused Research (COVID-19)
Population Health

Scientific Approaches

We are using a retrospective cohort study design to examine factors associated with intubation in COVID-19 patients.
Dependent Variable: Binary variable indicating whether intubation occurred (1) or did not occur (0).
Independent Variables: Demographics: Age, sex, race, and ethnicity.
Physical Measurements: BMI and pregnancy status.
Biomarkers: D-dimer, Interleukin-6 (IL-6), IL-10, CRP. Collect biomarker data at specific times relative to the diagnosis of COVID-19.
COVID Vaccine Status: Document vaccination status and dates.
Drug: Record medications administered and their timing relative to COVID-19 diagnosis.

Repeated measures logistic regression will be performed to assess the relationship between the biomarkers and intubation status. Adjusted odds ratios (aOR) will be reported with their 95% confidence intervals. We will also examine potential interaction effects potential interactions between independent variables (e.g., biomarkers and demographics).

Anticipated Findings

We anticipate to reveal that elevated levels of biomarkers such as D-dimer, IL-6, IL-10, and CRP are significantly associated with increased odds of intubation. This could underscore the role of systemic inflammation and coagulopathy in disease severity. In the meantime, the we also would like to know the impact of demographic factors that older age, male sex, and specific racial or ethnic groups are more prone to requiring intubation, highlighting demographic disparities in COVID-19 outcomes.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Weize Wang - Project Personnel, Florida International University

Latina Mental Health, SAV, COVID-19

Specific Scientific Questions 1. Did Latina women experience increased rates of substance abuse, as measured by prescription drug use and emergency department visits, during the COVID-19 pandemic? 2. Did electronic health record data indicate increases in intimate partner violence-related injuries…

Scientific Questions Being Studied

Specific Scientific Questions
1. Did Latina women experience increased rates of substance abuse, as measured by prescription drug use and emergency department visits, during the COVID-19 pandemic?
2. Did electronic health record data indicate increases in intimate partner violence-related injuries or mental health diagnoses among Latina women during the pandemic?
3. How did changes in financial stress, as measured by income and employment status, impact the mental health of Latina women during the COVID-19 pandemic?

Project Purpose(s)

Disease Focused Research (COVID-19)
Population Health
Social / Behavioral

Scientific Approaches

We will utilize the All of Us Research Program dataset to conduct a retrospective cohort study. We will identify a cohort of Latina women and compare their healthcare utilization, prescription drug use, and diagnostic codes before and after the pandemic. We will use statistical analysis techniques, such as logistic regression and linear regression, to examine the association between exposure to COVID-19-related stressors (e.g., financial stress, violence, substance abuse) and mental health outcomes.

Anticipated Findings

We anticipate finding that Latina women experienced significant increases in substance abuse, violence, and financial stress during the COVID-19 pandemic, leading to negative impacts on their mental health. Our findings will contribute to the body of scientific knowledge by:

Identifying the specific mental health challenges faced by Latina women during the pandemic
Highlighting the importance of addressing social determinants of health, such as financial stress and violence, to improve mental health outcomes
Informing the development of culturally competent interventions to support the mental health of Latina women
By understanding the impact of the pandemic on the mental health of Latina women, we can develop targeted interventions to promote their well-being.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Gira Ravelo - Research Associate, Florida International University

Disparities :Depression, Stress and Rates of Hospitalization- Heart Failure

Despite the high prevalence of depression and stress in patients with congestive heart failure (CHF), especially during the COVID-19 pandemic, this has not been well characterized in patients who have been historically underrepresented in research. This study aims to determine…

Scientific Questions Being Studied

Despite the high prevalence of depression and stress in patients with congestive heart failure (CHF), especially during the COVID-19 pandemic, this has not been well characterized in patients who have been historically underrepresented in research.

This study aims to determine the prevalence of depression, stress, and rehospitalization rates in patients with CHF, using the AOU database.

Project Purpose(s)

Educational

Scientific Approaches

We will identify a cohort of patients with CHF (any type) of all ages who answered the COVID-19 Participant Experience (COPE) survey of the NIH All of Us Research Program.

We will analyze several measures of depression, stress, and social support by gender and race and evaluate hospitalization rates.

Regression analyses will be used to assess the relationship between reported stress related to social distancing, depression (measured by Patient Health Questionnaire-9 [PHQ-9] scores), and levels of social support, categorized by self-reported gender and race, with men and white race serving as the reference groups.

The multivariable models will be adjusted for by age, race and ethnicity, health insurance status, education, and income.

Anticipated Findings

We anticipate that we will be able to identify groups of patients with CHF who are more likely to experience depression and stress, may tend to have less social support, and that these factors will be related to higher rates of hospitalization.
We expect that our findings will highlight the need for mental health screening in patients with CHF, especially in under-represented groups. The use of the AOU database will allow for a more comprehensive investigation of the rates of depression and stress in patients with CHF within the U.S. population.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Webster Donaldy - Research Fellow, New York City Health & Hospitals

COVID19_PRS_PHEWAS2

We intend to study the following scientific questions: • What are the genetic, behavioral, and comorbidity factors that impact the risk for COVID-19 infection and severity in the All of Us cohort? • Which one of these factors impacts the…

Scientific Questions Being Studied

We intend to study the following scientific questions:

• What are the genetic, behavioral, and comorbidity factors that impact the risk for COVID-19 infection and severity in the All of Us cohort?
• Which one of these factors impacts the risk for Long COVID in the All of Us cohort?
• Are these factors associated with pre-existing comorbidities in the medical phenome of All of Us?
• How do the findings from the All of Us cohort compare to findings from additional biobanks such as the UK Biobank and the Michigan Genomics Initiative?

Understanding the factors that contribute to COVID-19 infection, severity and PASC can help identify individuals at high risk and inform targeted prevention strategies. Additionally, comparing findings from different biobanks can provide a more comprehensive understanding of the factors that impact COVID-19 risk.

Note: workspace is a copy of a project/workspace that was set up with Controlled Tier v6 but experienced severe issues in the GC environment.

Project Purpose(s)

Disease Focused Research (COVID-19, LongCOVID)
Population Health
Methods Development
Ancestry

Scientific Approaches

We plan to:
• Develop polygenic risk scores using the results of external GWAS on COVID-19 outcomes to predict infection risk and severity.
• Create a medical phenome of the All of US cohort to identify relationships with COVID-19 / PASC outcome predictors.
• Use the comprehensive information of the All of Us cohort to extract relevant covariates to adjust for time-varying trends, confounders, health disparities, and pre-existing conditions.

We will use the following datasets:
• External GWAS summary statistics from the COVID-19 Host Genetics Initiative.
• The All of Us cohort dataset, i.e. its genetic data, demographic information, survey responses, and EHR records.
• The UK Biobank and the Michigan Genomics Initiative, to meta-analyze findings.

We will use the following methods/tools:
• Develop polygenic risk scores using state-of-the-art methods and external GWAS summary statistics.
• Create a medical phenomes using the PheWAS R package.
• The analysis will be performed using R.

Anticipated Findings

We anticipate the following findings:
• Development of polygenic risk scores that predict COVID-19 infection risk and severity. These genetic predictors could help identify individuals at high risk and help eliminate testing bias in hospital data.
• Identification of patterns and relationships in the data through phenome-wide screens, which could provide insights into underlying biological mechanisms.
• Identification of time-varying trends, confounders, health disparities, and pre-existing conditions that, when adjusted for, could provide a more accurate understanding of the factors that impact COVID-19 risk and outcomes.

Taken together, the anticipated findings could inform targeted prevention strategies and inform public health policy.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Lars Fritsche - Mid-career Tenured Researcher, University of Michigan

Replication and validation of combinatorial genetic risk factors for long COVID

Long COVID is a debilitating chronic condition that has affected over 100 million people globally. Despite considerable global research, traditional genetic studies have identified a single gene linked to long COVID, with little insight into the mechanisms underlying this complex…

Scientific Questions Being Studied

Long COVID is a debilitating chronic condition that has affected over 100 million people globally. Despite considerable global research, traditional genetic studies have identified a single gene linked to long COVID, with little insight into the mechanisms underlying this complex heterogeneous disease. Using PrecisionLife’s unique combinatorial approach to analyzing complex, chronic diseases, Taylor et al. (2023) identified 73 genetic associations with long COVID, including mechanistic differences between different patient subgroups. These genetic associations are reflected in combinatorial disease signatures, i.e., combinations of SNP genotypes that are significantly over- or under-enriched in long COVID patients. This study aims to replicate and validate those signatures in a diverse patient population. Validated signatures will then be used as the basis for a clinical decision support tool that can be used to stratify patients based on genetic risk and mechanistic subcategorization.

Project Purpose(s)

Disease Focused Research (Long COVID)
Methods Development
Ancestry

Scientific Approaches

For each Long COVID disease signature from Taylor et al. (2023), we will generate summary statistics (e.g., # cases & controls, odds ratio, p-value) to evaluate the overall degree of replication in a patient cohort comprised of long COVID patients and healthy controls. Signatures with odds ratio <1 will be flagged as non-replicating. We will also test whether the count of disease signatures possessed by each patient is significantly associated with case-control status. This test will be repeated in ancestry-specific cohorts to identify potential challenges for health equity.
For each signature, we will evaluate the contribution of each component SNP to disease risk by comparing the odds ratio for patients with the full signature to the odds ratio for patients with the broader signature excluding the focal SNP. SNPs will be removed from the signature when the odds ratio of the latter exceeds the former. This refinement process will be repeated using a 5-fold cross validation approach.

Anticipated Findings

The main output of this study will be a set of combinatorial disease signatures that are associated with elevated risk of Long COVID in multiple datasets. Each signature will be paired with summary statistics (e.g., odds ratio, p-value), allowing us to assess the identify and annotate signatures that are individually significant. We expect to further demonstrate that a risk score based on the cumulative effects of refined signatures is significantly correlated with prevalence of long COVID and that this correlation is significant in all broad ancestry groups and not just patients with European ancestry.

Validated signatures will be further clustered based on shared mechanistic hypotheses as identified in the Taylor et al. (2023) manuscript. We expect to demonstrate that these signatures can be used to stratify the population, opening potential for precision medicine-based treatment of long COVID.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Matthew Pearson - Other, Metrodora Foundation
Karolina Collins - Other, Metrodora Foundation

WS1

Inequities in cancers and covid-19, exploratory analysis to learn more about all of us data.

Scientific Questions Being Studied

Inequities in cancers and covid-19, exploratory analysis to learn more about all of us data.

Project Purpose(s)

Disease Focused Research (COVID-19 and cancers)
Population Health
Educational

Scientific Approaches

Analysis using SAS and R.

Anticipated Findings

It may be too early to say, but disease and inequity etiology.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Louis Weissert - Graduate Trainee, University of Wisconsin, Milwaukee

FC Gamma Receptor (IIA) Mutations and HIV

The intent of this project is to study how mutations of the Fc Gamma Receptor (IIA) affect an individual's susceptibility to HIV. Since Fc Gamma Receptors have a hand in the humeral and innate immune response, polymorphisms have been associated…

Scientific Questions Being Studied

The intent of this project is to study how mutations of the Fc Gamma Receptor (IIA) affect an individual's susceptibility to HIV. Since Fc Gamma Receptors have a hand in the humeral and innate immune response, polymorphisms have been associated with susceptibility to certain conditions and illnesses, such as Covid-19 and specific cancers. In this case, we wish to determine if there is a relationship between polymorphisms and susceptibility HIV, a global health issue that has claimed the lives of millions.

Project Purpose(s)

Educational

Scientific Approaches

We plan to use data sets from the human genome database to analyze the prevalence of HIV in individuals with FcγRIIA polymorphism. The tools we plan to use are simple data analysis strategies and R programming to conduct this study.

Anticipated Findings

We are anticipating to find that there is a possible relationship between FcγRIIA polymorphisms and susceptibility to HIV, given that it has links to other chronic illnesses and increased susceptibility to infection. Our findings could potentially alter the way we think about conditions such as HIV, as well as disease prevention itself.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Madeline Husser - Undergraduate Student, Arizona State University

Collaborators:

Yanett Alegria - Undergraduate Student, Arizona State University
Mohga Talib - Undergraduate Student, Arizona State University
Gerardo Rodriguez - Undergraduate Student, Arizona State University
Elizabeth Brenner - Undergraduate Student, Arizona State University

2024-11-08_Duplicate of AOU_Recover_Long_Covid_v6

The purpose of this workspace was to implement the published XGBoost machine learning (ML) model, which was developed using the National COVID Cohort Collaborative’s (N3C) EHR repository to identify potential patients with PASC/Long COVID in All of Us Research Program.

Scientific Questions Being Studied

The purpose of this workspace was to implement the published XGBoost machine learning (ML) model, which was developed using the National COVID Cohort Collaborative’s (N3C) EHR repository to identify potential patients with PASC/Long COVID in All of Us Research Program.

Project Purpose(s)

Disease Focused Research (Long COVID)

Scientific Approaches

To achieve this objective, data science workflows were used to apply ML algorithms on the Researcher Workbench. This effort allowed an expansion in the number of participants used to evaluate the ML models used to identify risk of PASC/Long COVID and also serve to validate the efforts of one team and providing insight to other teams. These models were implemented within the All of Us Controlled Tier data (C2022Q2R2), which was last refreshed on June 22, 2022. We intend to provide a step-by-step guide for the implementation of N3C's ML Model for identification of PASC/Long COVID Phenotype in the All of Us dataset.

Anticipated Findings

We intend to provide a step-by-step guide for the implementation of N3C's ML Model for identification of PASC/Long COVID Phenotype in the All of Us dataset.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Sidney Bolden - Early Career Tenure-track Researcher, Bethune Cookman University

Duplicate of Angela Long COVID Fall 2024

We will see if we can identify a cohort of AoU participants who meet the case definition of Long COVID as defined by Thaweethai et al, JAMA 2023. Long COVID is a currently public health concern and may continue to…

Scientific Questions Being Studied

We will see if we can identify a cohort of AoU participants who meet the case definition of Long COVID as defined by Thaweethai et al, JAMA 2023. Long COVID is a currently public health concern and may continue to be significant in the future. Identifying those who may have Long COVID in the AoU dataset will be an important first step toward follow up on their long-term health outcomes.

Project Purpose(s)

Disease Focused Research (Long COVID)

Scientific Approaches

Our work will be mainly descriptive. We will create a case definition using standard data builder queries to understand the N of those who may have long COVID, their comorbidities, and their sociodemographic characteristics.

Anticipated Findings

We anticipate that we will not be able to find the exact criteria outlined in Thaweethai et al, but that we will find significant overlap. We anticipate that using an amended case definition, we will be able to to find a cohort of participants with possible Long COVID.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Angela Choi - Undergraduate Student, University of Chicago

GenoMLizer_test

We plan to use the All of Us research dataset to investigate the genetics involved with developing loss of smell with COVID-19. The loss of smell developed with COVID-19 is similar to other complex genetic disorders in the way that…

Scientific Questions Being Studied

We plan to use the All of Us research dataset to investigate the genetics involved with developing loss of smell with COVID-19. The loss of smell developed with COVID-19 is similar to other complex genetic disorders in the way that the development of these conditions potentially involves several genetic variations. On a local COVID-19 dataset, we have constructed a novel analysis for investigating and prioritizing candidate genetic variants for the loss of smell. The All of Us data will serve as further validation of our methods. Yielding important biological results for the understanding of symptoms developed with COVID-19 as well as the development of a computational tool for investigating complex genetic traits.

Project Purpose(s)

Disease Focused Research (COVID-19 symptoms)
Methods Development
Ancestry

Scientific Approaches

We will use the All of Us whole genome sequencing data from individuals that had COVID-19 and compare those that developed loss of smell with those that did not. We will utilize algorithms developed at the University of Iowa for prioritizing variants with machine learning techniques. This analysis will be done in R and Python programming languages.

Anticipated Findings

We expect that the prioritized candidate variants found in our local discovery dataset analysis will have the same predictive power and results in the All of Us dataset. These results will further validate our methods and the development of a analytical tool that can be used to investigate the genetics of other similar disorders. These results will also provide valuable insight into the development of symptoms with COVID-19.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Lucas Pietan - Graduate Trainee, University of Iowa

irAE in cancer with vaccination

The rapid development of effective vaccines against SARS-CoV-2 greatly relieved the global COVID-19 pandemic. Since boosters or higher doses of the COVID-19 vaccine are employed to enhance immunogenicity, vaccinated cancer patients undergoing treatment are expected to present more active immune…

Scientific Questions Being Studied

The rapid development of effective vaccines against SARS-CoV-2 greatly relieved the global COVID-19 pandemic. Since boosters or higher doses of the COVID-19 vaccine are employed to enhance immunogenicity, vaccinated cancer patients undergoing treatment are expected to present more active immune system activities against tumors. Since COVID-19 vaccination stimulates the exuberant immune response, there are existing concerns and controversial perspectives on whether COVID-19 vaccination could increase immune-related adverse effects (irAE) in cancer patients, especially in those actively treated with immune checkpoint inhibitors. Therefore, whether COVID-19 vaccinations bring in effectiveness and potential risks warrants close study using real-world evidence data. The comprehensive interrogation of COVID-19 vaccination effects will contribute to the precise care of cancer patients in clinical settings. Herein, to elucidate the effects of COVID-19 vaccines on cancer patient outcomes.

Project Purpose(s)

Disease Focused Research (cancer and irAE)
Population Health
Methods Development

Scientific Approaches

Our multidisciplinary team proposes the following approaches in this project.
(1) Develop computable phenotypes of severe outcomes in vaccinated and non-vaccinated COVID-19 cancer patients.
(2) Develop a computational pipeline to extract and unify important patient characteristics and outcomes from both structured and unstructured EHR data.
(3) Harmonization, standardization and evaluation.

Anticipated Findings

This project will address the unique challenges in longitudinal EHR data by using the graph-based AI model for the representation learning of temporal patterns of clinical encounters, and the reversed graph embedding for trajectory reconstruction. From the learned trajectories, we will identify cancer patient subgroups that present improved clinical outcomes after COVID-19 vaccination. The identified longitudinal trajectories will present a comprehensive characterization of COVID-19 vaccination effects, provide real-world evidence for clinical decision support, and improve current treatment strategies for vaccinated patients with cancer.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Rui Yin - Early Career Tenure-track Researcher, University of Florida

Collaborators:

Qiang Yang - Research Fellow, University of Florida

Angela Long COVID Fall 2024

We will see if we can identify a cohort of AoU participants who meet the case definition of Long COVID as defined by Thaweethai et al, JAMA 2023. Long COVID is a currently public health concern and may continue to…

Scientific Questions Being Studied

We will see if we can identify a cohort of AoU participants who meet the case definition of Long COVID as defined by Thaweethai et al, JAMA 2023. Long COVID is a currently public health concern and may continue to be significant in the future. Identifying those who may have Long COVID in the AoU dataset will be an important first step toward follow up on their long-term health outcomes.

Project Purpose(s)

Disease Focused Research (Long COVID)

Scientific Approaches

Our work will be mainly descriptive. We will create a case definition using standard data builder queries to understand the N of those who may have long COVID, their comorbidities, and their sociodemographic characteristics.

Anticipated Findings

We anticipate that we will not be able to find the exact criteria outlined in Thaweethai et al, but that we will find significant overlap. We anticipate that using an amended case definition, we will be able to to find a cohort of participants with possible Long COVID.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Angela Choi - Undergraduate Student, University of Chicago
Andrew Craver - Project Personnel, University of Chicago

DB7 of CRS study

What are some of the significant characteristics of Covid 19 patients who lost sense of smell. Why important: to understand the potential cause of the loss of smell for Covid 19 Patients.

Scientific Questions Being Studied

What are some of the significant characteristics of Covid 19 patients who lost sense of smell.
Why important: to understand the potential cause of the loss of smell for Covid 19 Patients.

Project Purpose(s)

Disease Focused Research (covid 19)
Methods Development

Scientific Approaches

Build ML models to discover the potentail patterns for the Covid 19 patients who had smell lose

Anticipated Findings

Find significant features that can predict the smell lose for Covid 19 patients and potentially guide the recovery process of the patients

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Renjie Hu - Early Career Tenure-track Researcher, University of Houston
Meher Gajula - Graduate Trainee, University of Houston

Collaborators:

Zain Mehdi - Graduate Trainee, Houston Methodist Research Institute
Tania Banerjee - Early Career Tenure-track Researcher, University of Houston
sarvenaz khatami - Graduate Trainee, University of Houston
Roshan Dongre - Graduate Trainee, Houston Methodist Research Institute
Khoa Nguyen - Student, University of Houston
Natalia Freire - Undergraduate Student, University of Houston
Najm Khan - Graduate Trainee, Rutgers, The State University of New Jersey
Likhitha Reddy Kesara - Graduate Trainee, University of Houston
Koyal Ansingkar - Graduate Trainee, Houston Methodist Research Institute
Jagan Mohan Reddy Dwarampudi - Graduate Trainee, University of Houston
Faizaan Khan - Graduate Trainee, Houston Methodist Research Institute
Ethan Hoang - Undergraduate Student, University of Houston
Ying Lin - Early Career Tenure-track Researcher, University of Houston
Aatin Dhanda - Graduate Trainee, Rutgers, The State University of New Jersey
Thamer Alnazzal - Graduate Trainee, University of Houston
Boaz Adikaibe - Undergraduate Student, University of Houston

RF Duplicate of COVID-19 and Wearables CTDv6

Our primary goal is to understand the interaction between activity levels and the development, progression, and societal effects of COVID-19. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and the development, progression, and societal effects of COVID-19. 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)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence of COVID-19. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We may find substantial variation in activity and disease prevalence/severity by socioeconomic status and/or location which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Collaborators:

Runqi Yuan - Graduate Trainee, Vanderbilt University

COVID-19 and Wearables CTDv6

Our primary goal is to understand the interaction between activity levels and the development, progression, and societal effects of COVID-19. These analyses will generate hypotheses guiding clinical and research interventions focused on activity and sleep to reduce morbidity and mortality…

Scientific Questions Being Studied

Our primary goal is to understand the interaction between activity levels and the development, progression, and societal effects of COVID-19. 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)

Population Health
Social / Behavioral

Scientific Approaches

We will examine the relationship between daily activity (steps, activity intensity) over time and the prevalence of COVID-19. We will use the Fitbit data, EHR-curated diagnoses, laboratory values, quality of life survey results, and clinical outcomes (hospitalizations/mortality).

Anticipated Findings

We may find substantial variation in activity and disease prevalence/severity by socioeconomic status and/or location which would motivate studies/interventions to reduce these health disparities.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Jeffrey Annis - Other, Vanderbilt University Medical Center

Long COVID's effect on performing complex daily routines

About 30% of adults who have survived COVID-19 experience new symptoms that make it harder for them to fulfill their normal roles and routines. This collection of symptoms is sometimes called "post-acute sequelae of COVID-19 (PASC)" by researchers, or "Long…

Scientific Questions Being Studied

About 30% of adults who have survived COVID-19 experience new symptoms that make it harder for them to fulfill their normal roles and routines. This collection of symptoms is sometimes called "post-acute sequelae of COVID-19 (PASC)" by researchers, or "Long COVID" by patients. People with long COVID may benefit from rehabilitation, but because it is a new disease we don't yet know enough about how to rehabilitate people safely and effectively. We want to know whether All of Us participants with symptoms of long COVID experience daily activity restrictions, are getting rehabilitation therapies like occupational, physical, and speech/language therapy, and whether there are things that increase the risk of impairment.

Project Purpose(s)

Disease Focused Research (Long COVID-19 (or Post-acute sequelae of COVID-19, "PASC"))

Scientific Approaches

We will analyze v.7 data on participants who have had a COVID-19 infection, and use methods that tell us whether they are likely to have long COVID, whether the tools (e.g. tests and surveys) used to identify their impairments are working, and whether they've been referred for therapy. We will select participants who, since their COVID-19 illness, have been diagnosed with long COVID specifically or with "clusters" or groups of symptoms/disorders that have been found in previous population-based studies of long COVID. Then we will explore whether these groups are similar or different from those in the previous studies, and see how likely they are to get a referral for therapies that might help them get back to their daily routines. We might also compare them to people without COVID-19 to determine whether the group with long COVID is very different in other ways that matter to health, like socioeconomic situation, insurance, or social connectedness.

Anticipated Findings

From this study, we aim to show how likely All of Us participants with COVID-19 are to experience long COVID symptoms that restrict what they can do, whether rehabilitation recommendations are followed, and whether there are factors that increase or decrease the likelihood of having trouble in daily routines and/or getting rehabilitation. We hope that this will help focus future research on the way long COVID affects participation in meaningful aspects of life, and how rehabilitation can help. Ultimately, this knowledge can help us develop rehabilitation programs for people with long COVID so that they can get back to the things that matter to them.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Kristen Kehl-Floberg - Graduate Trainee, University of Wisconsin, Madison

Collaborators:

Emma Freisberg - Undergraduate Student, University of Wisconsin, Madison
Maria Rosario Marin Marmol kilrain - Project Personnel, All of Us Program Operational Use

Duplicate of AOU_Recover_Long_Covid_v6

The purpose of this workspace was to implement the published XGBoost machine learning (ML) model, which was developed using the National COVID Cohort Collaborative’s (N3C) EHR repository to identify potential patients with PASC/Long COVID in All of Us Research Program.

Scientific Questions Being Studied

The purpose of this workspace was to implement the published XGBoost machine learning (ML) model, which was developed using the National COVID Cohort Collaborative’s (N3C) EHR repository to identify potential patients with PASC/Long COVID in All of Us Research Program.

Project Purpose(s)

Disease Focused Research (Long COVID)

Scientific Approaches

To achieve this objective, data science workflows were used to apply ML algorithms on the Researcher Workbench. This effort allowed an expansion in the number of participants used to evaluate the ML models used to identify risk of PASC/Long COVID and also serve to validate the efforts of one team and providing insight to other teams. These models were implemented within the All of Us Controlled Tier data (C2022Q2R2), which was last refreshed on June 22, 2022. We intend to provide a step-by-step guide for the implementation of N3C's ML Model for identification of PASC/Long COVID Phenotype in the All of Us dataset.

Anticipated Findings

We intend to provide a step-by-step guide for the implementation of N3C's ML Model for identification of PASC/Long COVID Phenotype in the All of Us dataset.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

QIANXIN LIANG - Graduate Trainee, Florida Atlantic University

Duplicate of Duplicate of Covid-19 vaccine uptake among cancer survivors V

The purpose of the study is to evaluate the modifiable, multilevel factors associated with COVID-19 vaccine uptake among cancer survivors from the All of Us dataset.

Scientific Questions Being Studied

The purpose of the study is to evaluate the modifiable, multilevel factors associated with COVID-19 vaccine uptake among cancer survivors from the All of Us dataset.

Project Purpose(s)

Disease Focused Research (cancer)
Population Health
Social / Behavioral

Scientific Approaches

A cohort of cancer survivors will be from using the database. Various survey questions will aid in answering our research aims. In addition, the covid-19 survey questionnaires will also be used to determine our outcome of interest.

Anticipated Findings

Multilevel factors are anticipated to be associated with vaccine uptake and hesitance. These results can help to identify specific characteristics of cancer survivors that make them more or less likely to experience vaccine hesitancy and inform efforts to target, adapt and tailor interventions to their needs.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Angel Arizpe - Graduate Trainee, University of Southern California

Collaborators:

Katelyn Queen - Graduate Trainee, University of Southern California
Alberto Carvajal Jr - Graduate Trainee, University of Southern California
Albert Farias - Early Career Tenure-track Researcher, University of Southern California

Duplicate of Covid-19 vaccine uptake among cancer survivors V

The purpose of the study is to evaluate the modifiable, multilevel factors associated with COVID-19 vaccine uptake among cancer survivors from the All of Us dataset.

Scientific Questions Being Studied

The purpose of the study is to evaluate the modifiable, multilevel factors associated with COVID-19 vaccine uptake among cancer survivors from the All of Us dataset.

Project Purpose(s)

Disease Focused Research (cancer)
Population Health
Social / Behavioral

Scientific Approaches

A cohort of cancer survivors will be from using the database. Various survey questions will aid in answering our research aims. In addition, the covid-19 survey questionnaires will also be used to determine our outcome of interest.

Anticipated Findings

Multilevel factors are anticipated to be associated with vaccine uptake and hesitance. These results can help to identify specific characteristics of cancer survivors that make them more or less likely to experience vaccine hesitancy and inform efforts to target, adapt and tailor interventions to their needs.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Angel Arizpe - Graduate Trainee, University of Southern California

Collaborators:

Katelyn Queen - Graduate Trainee, University of Southern California
Alberto Carvajal Jr - Graduate Trainee, University of Southern California
Albert Farias - Early Career Tenure-track Researcher, University of Southern California

covid_autoimmunedisease_relation

I intend to see if there is long term effect or any association to covid-19 with any autoimmune disease. Also, I intend to develop a machine learning model that would be able to identify these associations

Scientific Questions Being Studied

I intend to see if there is long term effect or any association to covid-19 with any autoimmune disease. Also, I intend to develop a machine learning model that would be able to identify these associations

Project Purpose(s)

Disease Focused Research (covid-19, autoimmune diseases)
Population Health
Drug Development
Methods Development
Ancestry
Other Purpose (research for Florida Atlantic University hackathon 2024 on precision medicine)

Scientific Approaches

I plan to use a dataset of patients with covid 19 and autoimmune diseases co-morbidity. I also want to take their survey data to see if they are vaccinated and which covid vaccine they have received. I want to see whether their is any correlation between autoimmune diseases and covid 19. Then I plan to create a machine learning model to asses the risk of onsetting autoimmune diseases for patients with covid 19 diagnosis.

Anticipated Findings

I am anticipating to find some associations between covid 19 and autoimmune diseases which would validate the already studied scientific papers on this topic.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Antara Anika Piya - Graduate Trainee, Florida Atlantic University

Collaborators:

Ksenia Macias Calix - Graduate Trainee, Florida Atlantic University

Aleena COVID

This workspace will investigate the relationship between COVID-19 infection and the new onset of mental health conditions including anxiety and depression after infection. We hope to determine whether there is a meaningful association between COVID-19 infection and the incidence of…

Scientific Questions Being Studied

This workspace will investigate the relationship between COVID-19 infection and the new onset of mental health conditions including anxiety and depression after infection. We hope to determine whether there is a meaningful association between COVID-19 infection and the incidence of these conditions in participants with no EHR-documented history before infection.

Project Purpose(s)

Disease Focused Research (COVID-19, Depression, Anxiety)
Educational

Scientific Approaches

We will ascertain diagnoses and time deltas from participant EHR. We will incorporate covariates from PPI (sex at birth, education, race, income, etc...) and potentially from COPE and other follow-up surveys. We will generate descriptive statistics and conduct logistic regression and Cox Proportional Hazards regression using R.

Anticipated Findings

Based on our review of the literature, we think it is possible that we could find a positive association between COVID-19 infection and new onset of anxiety and/or depression. However, we would not be able to discern whether this is due to some real biological mechanism or if the relationship is mediated by personal/socioeconomic variables beyond the scope of this dataset. In other words, we won't be able to tell whether COVID-19 infection somehow predisposes a person to a new mental health disorder, or if the experience of life in a pandemic-era context (which entailed loss of livelihood, alienation, grief, and uncertainty for many) is a greater contributor to the condition. This investigation will be a good start, however, and it is an interesting exercise for this student.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Andrew Craver - Project Personnel, University of Chicago

Collaborators:

Yuqing Yang - Project Personnel, University of Chicago

Long COVID

I will be looking into Long COVID data to see if there are links to any treatments as I am a patient researcher.

Scientific Questions Being Studied

I will be looking into Long COVID data to see if there are links to any treatments as I am a patient researcher.

Project Purpose(s)

Educational

Scientific Approaches

I will be looking into Long COVID data to see if there are links to any treatments as I am a patient researcher.

Anticipated Findings

I will be looking into Long COVID data to see if there are links to any treatments as I am a patient researcher.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Jon Douglas - Graduate Trainee, University of Texas at Austin

Duplicate of AOU_Recover_Long_Covid_v6

The purpose of this workspace was to implement the published XGBoost machine learning (ML) model, which was developed using the National COVID Cohort Collaborative’s (N3C) EHR repository to identify potential patients with PASC/Long COVID in All of Us Research Program.

Scientific Questions Being Studied

The purpose of this workspace was to implement the published XGBoost machine learning (ML) model, which was developed using the National COVID Cohort Collaborative’s (N3C) EHR repository to identify potential patients with PASC/Long COVID in All of Us Research Program.

Project Purpose(s)

Disease Focused Research (Long COVID)

Scientific Approaches

To achieve this objective, data science workflows were used to apply ML algorithms on the Researcher Workbench. This effort allowed an expansion in the number of participants used to evaluate the ML models used to identify risk of PASC/Long COVID and also serve to validate the efforts of one team and providing insight to other teams. These models were implemented within the All of Us Controlled Tier data (C2022Q2R2), which was last refreshed on June 22, 2022. We intend to provide a step-by-step guide for the implementation of N3C's ML Model for identification of PASC/Long COVID Phenotype in the All of Us dataset.

Anticipated Findings

We intend to provide a step-by-step guide for the implementation of N3C's ML Model for identification of PASC/Long COVID Phenotype in the All of Us dataset.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Jon Douglas - Graduate Trainee, University of Texas at Austin

Long COVID

I will be experimenting with the Long COVID datasets, EHR data, etc to try to understand various questions on symptoms, treatments, and potentially novel therapeutic usage.

Scientific Questions Being Studied

I will be experimenting with the Long COVID datasets, EHR data, etc to try to understand various questions on symptoms, treatments, and potentially novel therapeutic usage.

Project Purpose(s)

Disease Focused Research (Long COVID)
Population Health
Social / Behavioral
Educational
Drug Development
Ancestry

Scientific Approaches

I will use various techniques I am learning in my AI for Healthcare class at UT Austin. Mostly NLP, SQL, and other visualization techniques to interpret EHR data.

Anticipated Findings

I'm not sure yet. I have to look at the data. I would like to better understand common symptoms, treatments, and any novel usage of covid-19 therapeutics and outcomes.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Jon Douglas - Graduate Trainee, University of Texas at Austin

Aleena Summer 2024 Project Working Copy

This workspace will investigate the relationship between COVID-19 infection and the new onset of mental health conditions including anxiety and depression after infection. We hope to determine whether there is a meaningful association between COVID-19 infection and the incidence of…

Scientific Questions Being Studied

This workspace will investigate the relationship between COVID-19 infection and the new onset of mental health conditions including anxiety and depression after infection. We hope to determine whether there is a meaningful association between COVID-19 infection and the incidence of these conditions in participants with no EHR-documented history before infection.

Project Purpose(s)

Disease Focused Research (COVID-19, Depression, Anxiety)
Educational

Scientific Approaches

We will ascertain diagnoses and time deltas from participant EHR. We will incorporate covariates from PPI (sex at birth, education, race, income, etc...) and potentially from COPE and other follow-up surveys. We will generate descriptive statistics and conduct logistic regression and Cox Proportional Hazards regression using R.

Anticipated Findings

Based on our review of the literature, we think it is possible that we could find a positive association between COVID-19 infection and new onset of anxiety and/or depression. However, we would not be able to discern whether this is due to some real biological mechanism or if the relationship is mediated by personal/socioeconomic variables beyond the scope of this dataset. In other words, we won't be able to tell whether COVID-19 infection somehow predisposes a person to a new mental health disorder, or if the experience of life in a pandemic-era context (which entailed loss of livelihood, alienation, grief, and uncertainty for many) is a greater contributor to the condition. This investigation will be a good start, however, and it is an interesting exercise for this student.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Yuqing Yang - Project Personnel, University of Chicago
Aleena Syed - Undergraduate Student, University of Chicago
Andrew Craver - Project Personnel, University of Chicago

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