Research Projects Directory

Loneliness and Older Adults

Social isolation and loneliness have had significant health consequences for older adults throughout the COVID-19 pandemic. However, little is known about the prevalence and correlates of loneliness among older adults, especially among those from underrepresented demographics..

Scientific Questions Being Studied

Social isolation and loneliness have had significant health consequences for older adults throughout the COVID-19 pandemic. However, little is known about the prevalence and correlates of loneliness among older adults, especially among those from underrepresented demographics..

Project Purpose(s)

Disease Focused Research (major depressive disorder)
Population Health
Social / Behavioral

Scientific Approaches

Descriptive statistics will be used to characterize the prevalence of loneliness (using the UCLA Loneliness Scale Short Form) among older adults overall and by each sociodemographic characteristic. Logistic regressions will be used to estimate the associations between loneliness and depression and suicidal ideation (using PHQ-9 data), adjusting for age, sex, race, ethnicity, and socioeconomic factors.

Anticipated Findings

We believe that a significant number of older adults will have high scores of loneliness throughout the COVID-19 pandemic, especially among socioeconomically disadvantaged groups. We also hypothesize that the odds of self-reporting high depression as well as suicidal ideation will be elevated among those reporting high levels of loneliness compared to those not reporting high levels of loneliness.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Kevin Yang - Research Fellow, University of California, San Diego

Collaborators:

Khusnnora Satybaldiyeva - Graduate Trainee, University of California, San Diego
Jaclyn Bergstrom - Project Personnel, University of California, San Diego

COVID-19, Sleep, PAL and Lung Function

COVID-19 presents with scarring of flung tissue often resulting in reduced lung compliance, which could compromise the ventilation and quality of life. This study aims to evaluate the association between the COVID-19 profile and selected clinical and functional parameters (such…

Scientific Questions Being Studied

COVID-19 presents with scarring of flung tissue often resulting in reduced lung compliance, which could compromise the ventilation and quality of life. This study aims to evaluate the association between the COVID-19 profile and selected clinical and functional parameters (such as sleep quality, fatigue, cardiorespiratory fitness, and physical activity level). It will also explore the distribution of these outcomes by race and gender.

Project Purpose(s)

Disease Focused Research (COVID-19)
Educational

Scientific Approaches

COVID-19 cohort using clinical and functional datasets.
The research method is a cross-sectional design using secondary data analysis of the All of Us dataset.

Research question: What is the association between COVID-19 Profile (previous infection, severity, vaccination status) and lung function, PAL, sleep, and quality of life; and if this association is different by gender or race?

Anticipated Findings

Hypothesis: Previous history of severe COVID-19 infection could be associated with poor quality of life, reduced PAL, and poor sleep quality; and this association could be different by gender and race.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Ayodele Akinremi - Early Career Tenure-track Researcher, Hampton University

Collaborators:

Tafadzwa Machipisa - Research Fellow, University of Pennsylvania
Joseph Aneke - Early Career Tenure-track Researcher, Hampton University
Graham Chakafana - Early Career Tenure-track Researcher, Hampton University

Risk of Life-Threatening Infections Secondary to COVID-19 Diagnosis

We will investigate the risk of life-threatening infections secondary to COVID-19 diagnosis. Such infections may include sepsis, endocarditis, meningitis, encephalitis, and other central nervous system infections in patients who experienced a wide range of COVID-19 severity, from mild symptoms to…

Scientific Questions Being Studied

We will investigate the risk of life-threatening infections secondary to COVID-19 diagnosis. Such infections may include sepsis, endocarditis, meningitis, encephalitis, and other central nervous system infections in patients who experienced a wide range of COVID-19 severity, from mild symptoms to life-threatening hospitalization. We will include participants in the database who contracted COVID-19 and evaluate their risk of secondary infection over a follow-up period of three months and beyond, a previously understudied temporal relationship. Additionally, we intend to explore participant and public perspectives on COVID-19 for potential associations with secondary infection risk.
This study will be the first investigation on this question using a nationally scaled cohort in the United States, the first to evaluate risk over a follow-up period longer than three months, and an opportunity to contribute to risk findings reported in other health databases.

Project Purpose(s)

Disease Focused Research (COVID-19, secondary infections to COVID-19)
Social / Behavioral

Scientific Approaches

We will use the All of Us database to build cohorts via its built-in software, identifying individuals with a positive COVID-19 test or COVID-related hospitalization and matching them to individuals without a COVID-19 diagnosis based on factors such as age, sex, and comorbidities. Risk will likely be assessed using hazard ratios through Cox regression models or similar methods, with significant attention given to accounting for confounding factors such as co-infections and other causes of life-threatening infections.

Anticipated Findings

We anticipate that individuals with a history of COVID-19 will have an increased risk of life-threatening secondary infections compared to those without prior COVID-19. We also hypothesize that greater COVID-19 severity correlates with a higher risk of secondary infection. Such findings would confirm previous investigations on this topic. This study will be the first to provide extensive follow-up on post-COVID-19 infection risk within a nationally representative U.S. cohort. We expect to detect potential risk factors influencing secondary infection susceptibility, such as participant demographics, disease severity, comorbidities, and social determinants of health. Our findings may support prior studies using other databases by investigating previously unstudied COVID-19 patients/cases or reveal novel trends in secondary infections, contributing to a broader understanding of post-COVID-19 complications.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Dylan Miller - Graduate Trainee, University of Washington

Collaborators:

Yesol Sapozhnikov - Research Fellow, University of Idaho
Jonathan Moore - Research Fellow, University of Idaho

COVID-19 vaccine and HIV

People living with HIV appears to have an elevated risk of the severe coronavirus 19 (COVID-19) outcomes, with a poorer prognosis. It is imperative to achieve high COVID-19 vaccination coverage rates in this group. This project aims to understand the…

Scientific Questions Being Studied

People living with HIV appears to have an elevated risk of the severe coronavirus 19 (COVID-19) outcomes, with a poorer prognosis. It is imperative to achieve high COVID-19 vaccination coverage rates in this group. This project aims to understand the COVID-19 vaccine hesitancy and uptake among people living with HIV comparing to people living without HIV. Understanding the determinants of vaccine hesitancy among people living with HIV and making tailored measures to alleviate hesitancy would help improve the coverage of COVID-19 vaccination in this population.

Project Purpose(s)

Disease Focused Research (Human immunodeficiency virus infectious disease, COVID-19, vaccine)
Population Health
Social / Behavioral

Scientific Approaches

We will build HIV and COVID-19 datasets using data all different domains of EHR and surveys. We will use R or Python to program and coding the datasets. The statistical methods involve descriptive statistics (e.g., chi-square, t-test), regression models (e.g., logistic regression, Cox proportional hazard modelling), advanced matching methods (e.g., propensity score matching) and other advanced statistical methods.

Anticipated Findings

Understanding whether and how HIV population have a different COVID-19 vaccine hesitancy and/or uptake will inform tailored messaging to build vaccine confidence, address questions about vaccine benefits, and support informed vaccination decision-making to promote COVID-19 vaccine uptake among this population, particularly underrepresented HIV population.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Xueying YANG - Research Fellow, University of South Carolina

Collaborators:

Ruilie Cai - Graduate Trainee, University of South Carolina
Jiajia Zhang - Late Career Tenured Researcher, University of South Carolina

COVID + Autoimmune Disease

We are hoping to see if there is any association between COVID infection and/or vaccination and autoimmune connective tissue diseases such as scleroderma and dermatomyositis.

Scientific Questions Being Studied

We are hoping to see if there is any association between COVID infection and/or vaccination and autoimmune connective tissue diseases such as scleroderma and dermatomyositis.

Project Purpose(s)

Disease Focused Research (Dermatomyositis, scleroderma)

Scientific Approaches

We plan to search the database for people with COVID infection and/or vaccination and evaluate if there are any associations with dermatomyositis and scleroderma.

Anticipated Findings

We think there may be a positive correlation. This would be helpful to know for patients with these diseases.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Sophia Manduca - Graduate Trainee, New York University, Grossman School of Medicine

Collaborators:

Soutrik Mandal - Other, New York University, Grossman School of Medicine
Jill Shah - Graduate Trainee, New York University, Grossman School of Medicine

tabpfn_controlled

We intend to investigate how genetic data would influence long-term COVID prediction. We hypothesise that genetic data might improve long-term COVID prediction performance.

Scientific Questions Being Studied

We intend to investigate how genetic data would influence long-term COVID prediction. We hypothesise that genetic data might improve long-term COVID prediction performance.

Project Purpose(s)

Disease Focused Research (Long COVID)

Scientific Approaches

We included all patients with EHR data and at least one of the mobile, survey, or genetic data. We will use machine learning models for such predictions.

Anticipated Findings

We hypothesize that genetic data would improve model performance. We will identify important features that are related to long COVID.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Xinmeng Zhang - Graduate Trainee, Vanderbilt University

Collaborators:

Srushti Gangireddy - Project Personnel, Vanderbilt University Medical Center
Christopher Guardo - Project Personnel, Vanderbilt University Medical Center

Spring 2025 of 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
Elizabeth Brenner - 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

Activity and sleep differences in wearable data

We wish to characterize sleep, activity, and other wearable data within the All of Us Research Program Cohort, to better understand population averages and differences across demographic groups. This may include time series analyses to determine how different life events…

Scientific Questions Being Studied

We wish to characterize sleep, activity, and other wearable data within the All of Us Research Program Cohort, to better understand population averages and differences across demographic groups. This may include time series analyses to determine how different life events (e.g. the COVID-19 pandemic, an individual diagnosis, seasonality) affect wearable data.

Project Purpose(s)

Population Health

Scientific Approaches

We will begin with basic characterization of the distributions of wearable data across different demographic groups and expand as time and billing credits allow.

Anticipated Findings

To our knowledge, the WEAR study includes the largest study-sponsored distribution of wearable devices to participants. This presents a unique opportunity to understand the data of people who would not otherwise purchase the devices for themselves.

Demographic Categories of Interest

Data Set Used

Registered Tier

Research Team

Owner:

Julia Vogel - Project Personnel, Scripps Research

Covid + DM + Scleroderma

We are hoping to see if there is any association between COVID infection and/or vaccination and autoimmune connective tissue diseases such as scleroderma and dermatomyositis.

Scientific Questions Being Studied

We are hoping to see if there is any association between COVID infection and/or vaccination and autoimmune connective tissue diseases such as scleroderma and dermatomyositis.

Project Purpose(s)

Disease Focused Research (Dermatomyositis, scleroderma)

Scientific Approaches

We plan to search the database for people with COVID infection and/or vaccination and evaluate if there are any associations with dermatomyositis and scleroderma.

Anticipated Findings

We think there may be a positive correlation. This would be helpful to know for patients with these diseases.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Sophia Manduca - Graduate Trainee, New York University, Grossman School of Medicine

Collaborators:

Kaitlin Martins - Research Assistant, New York University, Grossman School of Medicine

Impact of Hominin-specific variants

Modern humans differ significantly from our closest evolutionary relatives, such as Neandertals and Denisovans. Although genomic changes unique to modern or archaic humans have been identified, their functional implications remain largely unknown. We focus on changes that may affect neurodevelopment,…

Scientific Questions Being Studied

Modern humans differ significantly from our closest evolutionary relatives, such as Neandertals and Denisovans. Although genomic changes unique to modern or archaic humans have been identified, their functional implications remain largely unknown. We focus on changes that may affect neurodevelopment, metabolism, and behavior to identify changes that result in phenotypes that differ between modern and extinct humans. Moreover, studies have shown that in all non-African humans nowadays, around 2% of our genome is contributed by Neandertals, and many of these archaic variants are associated with disease-related phenotypes, such as pain sensation or severe COVID. Hence, studying these modern or archaic hominin-specific changes will help us to understand not only the biological roots of the difference between us and Neanderthals but also the underlying mechanism of disease-related phenotypes.

Project Purpose(s)

Ancestry

Scientific Approaches

We will use large-scale genomic datasets, such as All of Us, to investigate the persistence and effects of archaic variants - those inherited from Denisovans, Neandertals, or common ancestral populations. By integrating high-coverage archaic hominin genomes with present-day human genotype data, we will identify variants of archaic origin that are still segregating at low frequency in modern populations. Using statistical and population genetics methods, including allele frequency analysis and phenotype association testing, we aim to assess the functional impact of these variants. We will apply custom Python/R scripts for data processing and analysis. This approach allows us to test hypotheses about archaic introgression and its contribution to human phenotypic diversity and disease susceptibility in contemporary populations.

Anticipated Findings

This study will enhance our understanding of hominin-specific genetic variants and their physiological consequences. By investigating the modern and archaic human-specific changes, we aim to elucidate how these genetic modifications influence metabolic pathways, cellular function, and overall physiological traits. Our findings may reveal evolutionary trade-offs associated with these variants, shedding light on their potential roles in energy metabolism, adaptation to environmental pressures, and disease susceptibility. By integrating genomic, biochemical, and functional analyses, this research will provide new insights into the evolutionary forces shaping human physiology. More broadly, it will contribute to the growing knowledge of how archaic introgression and modern human-specific adaptations have shaped genetic diversity and trait variation in present-day populations.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Shin-Yu Lee - Research Fellow, Okinawa Institute of Science and Technology School Corporation

Duplicate of AOU_Recover_Long_Covid_v6-[uses_v8]

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:

Manavalan Gajapathy - Project Personnel, University of Alabama at Birmingham

CAHB

What long-term trends in physical activity have emerged in post-COVID health data, and how do these trends correlate with longevity?

Scientific Questions Being Studied

What long-term trends in physical activity have emerged in post-COVID health data, and how do these trends correlate with longevity?

Project Purpose(s)

Population Health
Social / Behavioral

Scientific Approaches

Longitudinal Analysis: Examine changes in physical activity patterns over time and their impact on longevity.
Statistical Software: R and Python for data analysis, leveraging libraries such as Pandas for data manipulation and Matplotlib/Seaborn for visualization.

Anticipated Findings

The analysis is likely to identify significant trends in physical activity post-COVID, detailing how these trends correlate with longevity and health outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Devyn Miller - Graduate Trainee, Chapman University

(CDRv8) Leveraging wearables for secondary prevention

We are interested in utilizing data from a wearable Fitbit device to develop a model to investigate 1) how daily step count, night-time sleep duration, and resting heart rate prior to and immediately after disease diagnosis are associated with disease…

Scientific Questions Being Studied

We are interested in utilizing data from a wearable Fitbit device to develop a model to investigate 1) how daily step count, night-time sleep duration, and resting heart rate prior to and immediately after disease diagnosis are associated with disease complications. We will look at multiple diseases, including Type 2 Diabetes and COVID-19.

Project Purpose(s)

Disease Focused Research (COVID-19, Type 2 Diabetes)

Scientific Approaches

We will use Fitbit, covariate (age, sex, SES, etc.), and outcome data.

We will use self-supervised learning to train a model to determine which features are most important for secondary prevention.

Anticipated Findings

We anticipate that this analysis will inform guidance for preventing disease progression and for identifying individuals at risk of disease complications.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Evelynne Fulda - Graduate Trainee, National Human Genome Research Institute (NIH - NHGRI)
Bennett Waxse - Research Fellow, National Institute of Allergy and Infectious Diseases (NIH - NIAID)

DB8 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

Collaborators:

Zain Mehdi - Graduate Trainee, Houston Methodist Research Institute
Wajih Hassan Raza - Graduate Trainee, University of Houston
Tania Banerjee - Early Career Tenure-track Researcher, 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
Meher Gajula - Graduate Trainee, University of Houston
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
Sicong Chang - Graduate Trainee, University of Houston
Aatin Dhanda - Graduate Trainee, Rutgers, The State University of New Jersey
Thamer Alnazzal - Graduate Trainee, University of Houston

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.

Using older v6 version as it was used in the original study I'm duplicating here.

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:

Manavalan Gajapathy - Project Personnel, University of Alabama at Birmingham

tabpfn

We aim to examine factors that are related to long COVID. Specifically, we will include survey data and genetic data.

Scientific Questions Being Studied

We aim to examine factors that are related to long COVID. Specifically, we will include survey data and genetic data.

Project Purpose(s)

Disease Focused Research (Long COVID)

Scientific Approaches

We will include survey data and genetic data to fit a prediction model. Then we will use SHAP value to identify important factors.

Anticipated Findings

There might be some socioeconomic factors, including insurance status, that are related to a higher likelihood of long COVID.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Xinmeng Zhang - Graduate Trainee, Vanderbilt University

Collaborators:

Christopher Guardo - Project Personnel, Vanderbilt University Medical Center

HAP464 Antidepressants Analysis

The specific scientific question I aim to study is how certain environmental factors influence the spread of respiratory infections in urban populations. This question is important because understanding how air pollution, temperature variations, and other environmental variables affect the transmission…

Scientific Questions Being Studied

The specific scientific question I aim to study is how certain environmental factors influence the spread of respiratory infections in urban populations. This question is important because understanding how air pollution, temperature variations, and other environmental variables affect the transmission of diseases like the flu or COVID-19 can help public health authorities create more effective prevention strategies. By studying these factors, we can gain insights into how to reduce the burden of infectious diseases, particularly in high-density areas. Additionally, this research will provide valuable data that could inform policies on improving urban environments for better public health outcomes. Through this study, I hope to identify patterns that can be used to predict and control outbreaks more efficiently.

Project Purpose(s)

Educational

Scientific Approaches

To answer my scientific question, I plan to use a combination of epidemiological analysis and environmental data modeling. Specifically, I will use publicly available datasets on air quality, weather patterns, and reported cases of respiratory infections, such as flu and COVID-19, from health organizations and government sources. I will apply statistical methods, including regression analysis, to examine the relationships between environmental factors (like air pollution levels, temperature, and humidity) and the spread of infections over time. Additionally, I will use geographic information system (GIS) tools to visualize the spatial distribution of infections and environmental conditions across urban areas. This will help identify patterns and high-risk zones for outbreaks.

Anticipated Findings

I anticipate that the study will reveal significant correlations between specific environmental factors—such as air pollution levels, temperature fluctuations, and humidity—and the spread of respiratory infections in urban populations. For example, I might find that higher levels of air pollution or certain temperature ranges are associated with an increased risk of infection transmission. These findings could also suggest that certain urban areas with more exposure to environmental stressors are more vulnerable to outbreaks.

The contribution to scientific knowledge will be twofold. First, this research could provide a clearer understanding of how environmental conditions directly influence the dynamics of disease transmission, which is still an area with many unknowns. Second, it could offer actionable insights for public health officials, helping them develop more effective preventative strategies tailored to specific environmental conditions.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Shahera Chowdhury - Undergraduate Student, George Mason University

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
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
Sicong Chang - Graduate Trainee, University of Houston
Aatin Dhanda - Graduate Trainee, Rutgers, The State University of New Jersey
Thamer Alnazzal - Graduate Trainee, University of Houston

Long COVID

We are interested in identifying effective treatments for Long COVID to develop a personalized recommendation algorithm that can be used as a Clinical Decision Support Tool in the future to improve patient outcome.

Scientific Questions Being Studied

We are interested in identifying effective treatments for Long COVID to develop a personalized recommendation algorithm that can be used as a Clinical Decision Support Tool in the future to improve patient outcome.

Project Purpose(s)

Disease Focused Research (Long COVID)
Methods Development
Commercial

Scientific Approaches

We will filter by patients diagnosed with Long COVID, identify trends in patient outcome with different treatments and other variables, and apply machine learning models to identify predictors of outcome using demographic and clinical data.

Anticipated Findings

We will identify important demographic and clinical factors that impact treatment efficacy for Long COVID, and use that information to optimize treatment plans for individual patients.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Lucy Li - Other, CompendiGO LLC

Duplicate of Longitudinal serum cytokines and health outcomes COVID-19

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

Final work

we wanted to study association between penumonia and hemolytic anemia and also COVID and other association since there has been differentassociation found so far and wanted to see if the gathered data aslo supportour hypotehsis

Scientific Questions Being Studied

we wanted to study association between penumonia and hemolytic anemia and also COVID and other association since there has been differentassociation found so far and wanted to see if the gathered data aslo supportour hypotehsis

Project Purpose(s)

Population Health
Educational

Scientific Approaches

we will use all of us research dataset, research method will be mostly cross sectional study, we will use the AI code generatior and our in house analyst to overview our statistical part

Anticipated Findings

We are working to see if our hypothesis is also applicable to all of us research dataset. If we see any asosication, given it will be cross sectional study, we can propose to do clinical trials and develop diagnostic or treatement guidelines

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Pooja Roy - Other, New York City Health & Hospitals

Housing Insecurity and Mental Wellbeing v6 CT

This exploratory analysis will examine the association between housing insecurity and the impact of COVID-19 on participant health and mental health as measured in the COPE surveys. The initial exploration will assess whether the sample sizes and cross-tabulations are sufficient…

Scientific Questions Being Studied

This exploratory analysis will examine the association between housing insecurity and the impact of COVID-19 on participant health and mental health as measured in the COPE surveys. The initial exploration will assess whether the sample sizes and cross-tabulations are sufficient to proceed with a research project examining the impact of the COVID-19 pandemic on housing insecure individuals, as compared to securely-housed individuals.

Project Purpose(s)

Social / Behavioral

Scientific Approaches

This analysis will pull data from the Basics survey and the COPE surveys to examine whether answers in the COPE surveys can be broken down by differential housing circumstances. This will include summary and bivariate analyses.

Anticipated Findings

We hypothesize that housing insecurity will be associated with enduring worse health and mental health outcomes as a result of the COVID-19 pandemic.
This research project seeks to reduce health disparities and improve health equity in underrepresented in biomedical research (UBR) populations.

Demographic Categories of Interest

Data Set Used

Controlled Tier

Research Team

Owner:

Catherine Xin - Graduate Trainee, New York University
Stephanie Cook - Early Career Tenure-track Researcher, New York University
Andrea Titus - Other, New York University, Grossman School of Medicine

Collaborators:

Giselle Routhier - Research Fellow, New York University, Grossman School of Medicine
Binyu Cui - Graduate Trainee, New York University
Chenziheng Weng - Graduate Trainee, New York University

COVID EHR Exploration

We intend to explore several aspects of the EHR to elucidate health patterns in hospital settings during COVID-19. Right now, we intend to build a set of COVID positive patients and determine burden of disease longitudinally. In the future, once…

Scientific Questions Being Studied

We intend to explore several aspects of the EHR to elucidate health patterns in hospital settings during COVID-19. Right now, we intend to build a set of COVID positive patients and determine burden of disease longitudinally. In the future, once genetic information is released, we hope to use genetic information to explain differences in COVID severity.

Project Purpose(s)

Disease Focused Research (Coronavirus)
Population Health
Ancestry

Scientific Approaches

We will use the EHR, COPE survey, and in the future genetic information to explore associations between genetics and COVID as well as burden of disease.

Anticipated Findings

Any discovered associations between genetics and COVID severity can help inform clinical practitioners about potential increased risk of severe illness and death that is attributable to genetic predisposition.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Tracey Ferrara - Project Personnel, National Human Genome Research Institute (NIH - NHGRI)
David Schlueter - Research Fellow, National Human Genome Research Institute (NIH - NHGRI)
David Schlueter - Early Career Tenure-track Researcher, University of Toronto

Metformin Association with PASC

The overall goal of this research is to evaluate the association between use of metformin prior to COVID-19 illness and subsequent incidence of PASC compared to patients who were prevalent users of other diabetes medications.

Scientific Questions Being Studied

The overall goal of this research is to evaluate the association between use of metformin prior to COVID-19 illness and subsequent incidence of PASC compared to patients who were prevalent users of other diabetes medications.

Project Purpose(s)

Disease Focused Research (Postacute sequelae of SARS-CoV-2 infection (PASC))

Scientific Approaches

Using condition and medication information in the Controlled Tier dataset, we will look for associations between patients who used different diabetes medications prior to a COVID-19 infection to quantify their risk of developing PASC. An analytic fact table will be developed and data will be analyzed using Python and SQL. The study design is a retrospective cohort analysis using trial emulation techniques in adults with documented SARS-CoV-2 infection. The index date will be the date of first documented SARS-CoV-2 infection, and the exposure of interest: existing metformin or other diabetes medication prescription. The outcome of interest is a subsequent diagnosis of PASC.

Anticipated Findings

In vitro data show metformin inhibits severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus and pathogenic inflammatory responses to the virus. Clinical trial data show metformin prevents severe Covid-19 and Long Covid. We anticipate seeing an association with metformin use and the risk of developing PASC.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

Steve Johnson - Early Career Tenure-track Researcher, University of Minnesota
Lisiane Pruinelli - Mid-career Tenured Researcher, University of Minnesota

Collaborators:

Tim Meyer - Project Personnel, University of Minnesota
Ragnhildur Bjarnadottir - Early Career Tenure-track Researcher, University of Florida
Marisa Sileo - Project Personnel, Georgia Institute of Technology

Impact of Covid19

My plan is to focus on understanding the impact of COVID-19 vaccination progress and its relationship with public health, economic recovery, and demographic factors. How did vaccination rates correlate with COVID-19 case and mortality trends over time? Were there disparities…

Scientific Questions Being Studied

My plan is to focus on understanding the impact of COVID-19 vaccination progress and its relationship with public health, economic recovery, and demographic factors.
How did vaccination rates correlate with COVID-19 case and mortality trends over time?
Were there disparities in vaccination distribution across different regions, age groups, or socioeconomic backgrounds?
Did higher vaccination rates lead to a significant reduction in hospitalizations and severe cases?
What factors contributed to vaccine hesitancy, and how did misinformation impact vaccination uptake?
Did social media influence public perception of vaccines, and what patterns can be identified from sentiment analysis?

Project Purpose(s)

Educational

Scientific Approaches

I plan to utilize available datasets related to COVID-19 vaccination, public health outcomes, and socioeconomic factors on All of Us platform.

Research Methods and Analytical Approaches:
(a) Exploratory Data Analysis (EDA)
Descriptive Statistics:
Compute means, medians, and standard deviations for vaccination rates, case fatality rates, and economic indicators.
Identify missing data and perform necessary cleaning (handling null values, duplicates).
Data Visualization:
Create time-series plots to observe vaccination progress vs. case and death rates.
Heatmaps to detect correlations between vaccination rates and socioeconomic factors.
(b) Statistical and Machine Learning Approaches
Correlation and Regression Analysis:
Pearson and Spearman correlation to assess relationships between vaccination rates, public health outcomes, and economic recovery.
Multiple Linear Regression to analyze the impact of vaccination rates on COVID-19 fatalities and economic growth.

Anticipated Findings

Policymakers can use findings to optimize vaccine distribution and public health campaigns for future pandemics.
Governments and businesses can develop economic recovery strategies informed by vaccination data.
Social media platforms can use insights to combat misinformation and promote credible health information more effectively.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Meenakshi Nerolu - Teacher/Instructor/Professor, Howard University

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