Research Projects Directory

Research Projects Directory

At this time, all listed projects are using data in the registered tier. The registered tier contains individual-level data from electronic health records, survey answers, and physical measurements. These data have been altered to protect participant privacy.

Note: Researcher Workbench users provide information about their research projects independently. Any views expressed in the Research Projects Directory belong to the relevant users and do not necessarily represent those of the All of Us Research Program.

Information in the Research Projects Directory is also cross-posted on AllofUs.nih.gov in compliance with the 21st Century Cures Act.

There are currently 214 active workspaces. This information was updated on 9/23/2020.

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Health & Wealth

Project Purpose(s)

  • Disease Focused Research (Cardiovascular disease and respiratory illnesses)
  • Population Health ...
  • Ancestry

Scientific Questions Being Studied

Is there a correlation between levels of household income, and CVD, and respiratory illnesses? Are low income populations/ communities at a higher risk of developing preventable diseases? What can be done to decrease the risk of developing CVD and respiratory illness among these communities?
These questions as well as the solution establishes a greater understanding of health disparities and how to reduce them.

Scientific Approaches

I will be using R software and the Jupyter notebook offered through the All of Us workstation, to examine the correlation between income and CVD and how the association of risk factors with CVD differs by income level. I will be using logistic regression to test the main effects and effect modifications in income and other risk factors.

Anticipated Findings

I anticipate that there will be a correlation between all of the variables mentioned in the research questions. Based on these results, I hope to bring awareness to some of the possible causes for increased risk of preventable diseases in these communities. I anticipate that this research will allow people to gain a deeper understanding of the health disparities that affect certain populations and why these communities are more or less susceptible to increased preventable disease risk.

Demographic Categories of Interest

  • Income Level

Research Team

Owner:

  • Jo-el Banini - Undergraduate Student, University of Arizona

Collaborators:

  • Yann Klimentidis - Mid-career Tenured Researcher, University of Arizona

Gender Identity Algorithm

Project Purpose(s)

  • Population Health
  • Methods Development ...

Scientific Questions Being Studied

Understanding of the unique health needs of gender minorities, including transgender and gender non-binary individuals, is critical. Gender minorities face greater health disparities due in part to a lack of research into population-specific health concerns. Consequently, it is critical to understand health outcomes such as cancer risk s in this population. However, the primary challenge of studying cancer and other rare diseases among gender minorities is that they represent a hard-to-reach population and the collection of gender identity is often lacking in national surveys or healthcare databases. Previous investigations have used diagnosis codes to create algorithms to identify non-binary gender identity in large databases but lacked patient-reported gender identity. We seek to build an algorithm in All of Us, which uses self-reported gender identity (the gold standard), to identify and characterize the health of gender minorities more accurately.

Scientific Approaches

We propose to create an algorithm with diagnostic, procedure, and medication codes from electronic health records to identify transgender individuals using a gold standard for gender identity. We will use machine learning techniques to classify patients as transgender/non-binary or cisgender among the >100,000 individuals, including 400 transgender and 520 non-binary participants, in All of Us. Predictors of transgender status will be selected based on consultations with clinicians with expertise in transgender healthcare. These variables include sociodemographic (age, race/ethnicity), ICD-9/10 diagnosis codes (gender dysphoria), procedure codes for gender-affirming procedures (e.g. hysterectomy), and prescriptions for gender-affirming hormone therapy. We will use 10-fold cross-validation for the internal validation of the models. We will calculate sensitivity, specificity, positive predictive value, and negative predictive value to assess model performance.

Anticipated Findings

Previous investigations into the use of diagnosis codes for identifying non-binary gender identities did not have a gold standard for defining gender identity. We aim to create an algorithm that accurately defines transgender gender identity in large administrative databases to aid in future research efforts. The next step will be to apply this algorithm to other electronic health data such as Medicaid, Medicare, and private insurance databases. The ability to identify a population of transgender patients in large healthcare datasets will be a boon to health research on transgender and non-binary individuals.

Demographic Categories of Interest

  • Sex at Birth
  • Gender Identity

Research Team

Owner:

  • Sarah Jackson - Research Fellow, NIH

Genetic data

Project Purpose(s)

  • Educational ...

Scientific Questions Being Studied

We are studying how genetic data may be incorporated into electronic health records, exploring the data formats and interoperability issues as well as data presentation needs for clinicians and patients.

Scientific Approaches

We will use various methods of data visualization and thinkaloud usability protocols to test ability to visualize the data in meaningful ways with value for the clinician decision maker, and possibly use the FHIR standard for data interoperability.

Anticipated Findings

The findings will help us understand how to incorporate genetic data with other clinical data for decision making, and what are the most efficient and usable ways to visualize data for this purpose.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Yalini Senathirajah - Early Career Tenure-track Researcher, University of Pittsburgh

Genomic risk prediction of substance dependence in diverse populations

Project Purpose(s)

  • Disease Focused Research (substance dependence)
  • Population Health ...
  • Ancestry

Scientific Questions Being Studied

Genome-wide association studies of substance addiction and dependence have identified variants that contribute to a small portion of the total disorder variance. While these data have been useful in identifying novel genetic associations, in large part these data have not been used predict risk of disease. Recently, groups have begun leverage the contribution of variants that do not reach the level of genome-wide significance to improve the prediction of complex diseases. Unfortunately, these measures have not been developed or applied to non-European populations. Our hope is leverage the All of Us data to estimate disease effect estimates for substance use in diverse populations so as to be able to appropriately apply a ancestry-tailored risk component to polygenic risk scores involving admixed populations (i.e., populations admixed with multiple continental including European ancestry).

Scientific Approaches

We will cover both non-Hispanic white population and black populations in this study. Three types of phenotypes for substance use will be investigated: 1) alcohol use; 2) smoking; 3) Opioid use.
Aim 1: perform GWAS, create and validate PRS in All-of-US dataset
A standard GWAS will be conducted. PRS will be created by a few different variant sets and approaches. Approaches include BSLMM, LDPred, PRSice and Lasso penalized regression. These PRSs will be evaluated in the second half of All-of-Us cohort. The one with highest Area under ROC curve, or other discrimination metrics will be selected.
Aim 2: test and evaluate PRS in internal testing dataset from a health system
We will test PRS in our study populations consisting of European Americans (EA) and African Americans (AA) individuals. Testing risk scores in African Americans will involve applying the appropriate risk estimate for African and European ancestry to each individual based on their admixture and local ancestry.

Anticipated Findings

1. Ancestry-shared and ancestry-specific susceptibility loci for alcohol, smoking and opioid use and dependence.
2. Optimized PRS for each disease or phenotype in European population and African-descent populations.
3. Clinically actionable risk prediction model incorporating PRS, family history, and environmental factors will be built to help patient treatment and risk management.
4. This will greatly improve risk prediction in non-white, minority populations for substance use.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Research Team

Owner:

  • Hongsheng Gui - Early Career Tenure-track Researcher, Henry Ford Health System

Genomic risk prediction of substance dependence in diverse populations

Project Purpose(s)

  • Disease Focused Research (substance dependence)
  • Population Health ...
  • Ancestry

Scientific Questions Being Studied

Genome-wide association studies of substance addiction and dependence have identified variants that contribute to a small portion of the total disorder variance. While these data have been useful in identifying novel genetic associations, in large part these data have not been used predict risk of disease. Recently, groups have begun leverage the contribution of variants that do not reach the level of genome-wide significance to improve the prediction of complex diseases. Unfortunately, these measures have not been developed or applied to non-European populations. Our hope is leverage the All of Us data to estimate disease effect estimates for substance use in diverse populations so as to be able to appropriately apply a ancestry-tailored risk component to polygenic risk scores involving admixed populations (i.e., populations admixed with multiple continental including European ancestry).

Scientific Approaches

We will cover both non-Hispanic white population and black populations in this study. Three types of phenotypes for substance use will be investigated: 1) alcohol use; 2) smoking; 3) Opioid use.
Aim 1: perform GWAS, create and validate PRS in All-of-US dataset
A standard GWAS will be conducted. PRS will be created by a few different variant sets and approaches. Approaches include BSLMM, LDPred, PRSice and Lasso penalized regression. These PRSs will be evaluated in the second half of All-of-Us cohort. The one with highest Area under ROC curve, or other discrimination metrics will be selected.
Aim 2: test and evaluate PRS in internal testing dataset from a health system
We will test PRS in our study populations consisting of European Americans (EA) and African Americans (AA) individuals. Testing risk scores in African Americans will involve applying the appropriate risk estimate for African and European ancestry to each individual based on their admixture and local ancestry.

Anticipated Findings

1. Ancestry-shared and ancestry-specific susceptibility loci for alcohol, smoking and opioid use and dependence.
2. Optimized PRS for each disease or phenotype in European population and African-descent populations.
3. Clinically actionable risk prediction model incorporating PRS, family history, and environmental factors will be built to help patient treatment and risk management.
4. This will greatly improve risk prediction in non-white, minority populations for substance use.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age

Research Team

Owner:

  • Hongsheng Gui - Early Career Tenure-track Researcher, Henry Ford Health System

Genomics of psychiatric-related traits in the Latinx population

Project Purpose(s)

  • Disease Focused Research (Psychiatric disorders)
  • Population Health ...
  • Methods Development
  • Ancestry

Scientific Questions Being Studied

This study seeks to conduct a large-scale genome-wide association study (GWAS) of psychiatric disorders in Latinx populations. Latinx cohorts have been greatly understudied in psychiatric genomic studies. Further, they have complicated population histories, with multiple waves of admixture between several ancestral groups.Our aim is to identify genetic variants associated with psychiatric-related traits in Latinx populations and generate polygenic risk scores that are specific to this population. We expect to conduct the largest GWAS of different psychiatric traits in the Latinx population. This number could increase even further based on our current efforts in identifying additional Latinx cohorts by leading the newly-created Latin American Genomics Consortium, which includes the participation of over 50 investigators from 7 different Latin American countries and the US.

Scientific Approaches

We will apply a newly developed GWAS method, Tractor, to account for admixture in the All of Us Latinx cohort. The Tractor framework, in development by collaborators, shows promise in correcting for fine-scale population structure at the level of haplotypes, improving long-range phase in admixed cohorts, and boosting GWAS power in case-control settings of similar sample, prevalence, and effect sizes expected for our cohort aggregations and phenotypes. We will test and optimize Tractor here for best performance in multi-way admixed Latinx cohorts in preparation to run local ancestry aware association studies in the All of Us cohort. The All of Us cohort offers a unique opportunity to test the method in a large Latinx population (n=47,580). We will also meta-analyze the All of Us cohort with additional Latinx cohorts; we expect to include ~100K individuals.

Anticipated Findings

We will identify novel genetic variants associated with psychiatric-related traits in Latinx populations. Our findings will increase our understanding of the genetic risk factors of psychiatric disorders specific to Latinx, a greatly understudied population, thus decreasing health disparities in the psychiatric field.

Demographic Categories of Interest

  • Race / Ethnicity

Research Team

Owner:

  • Janitza Montalvo-Ortiz - Early Career Tenure-track Researcher, Yale University

Health Disparities

Project Purpose(s)

  • Population Health ...

Scientific Questions Being Studied

Are there any disparities in pregnancies in medicaid expansion vs. non-expansion space.

Scientific Approaches

Not available.

Anticipated Findings

Medicaid expansion states will have better outcomes.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Shaquille Peters - Project Personnel, Scripps Research

hiv all of us

Project Purpose(s)

  • Educational ...

Scientific Questions Being Studied

An initial testing workspace to get oriented and develop further thoughts on usage of the data.

I'm particularly interested in what lab data is available including T cell subsets

Scientific Approaches

An initial testing workspace to get oriented and develop further thoughts on usage of the data.

Anticipated Findings

I expect to become more familiar with the all of us cohort and determine if I can successfully mine data for a grant application about immune recovery, Currently it is unclear why some people living with HIV on therapy have better immune recovery than others

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Rob striker - Mid-career Tenured Researcher, University of Wisconsin, Madison

HK_workspace_train_v1

Project Purpose(s)

  • Educational ...

Scientific Questions Being Studied

How breast cancer is diagnosed in different ancestral populations.

Scientific Approaches

Not available.

Anticipated Findings

Diagnoses are detected at different stages in different ancestral populations.

Demographic Categories of Interest

Not available.

Research Team

Owner:

  • Hooman Kamel - Mid-career Tenured Researcher, Cornell University

hma4

Project Purpose(s)

  • Disease Focused Research (cardiovascualr diseases, diabetes and dementia)
  • Population Health ...
  • Social / Behavioral
  • Educational
  • Drug Development
  • Methods Development
  • Control Set
  • Ancestry
  • Commercial
  • Ethical, Legal, and Social Implications (ELSI)

Scientific Questions Being Studied

Diabetes, cardiovascualar diseases and dementia are the major challenges to human health, which are determined by genetic susceptibility, environmental risk factors, and their interactions. However, the evidence on the G×E (genetic facotrs* environmental factors) interaction and unconfounded estimates of a modifiable exposure is still laking. This study plan aim to investigate whether modifiable factors for such disease may interact with the genetic variations in relation to risks of diabetes, cardiovascualar diseases and dementia.

Scientific Approaches

Datasets: all the genotype and phenotype related datasets
Reseach method:I plan to use G×E interaction , COX model, Losigical model in my study.
Tools: R.
Scientific question: whether modifiable factors could modify the association between genetic risk and disease risks (Diabetes, cardiovascualar diseases and dementia)

Anticipated Findings

Some individual environmental fators or an overall modifiable-risk-factor profile may modify the association between genetic risk and disease risk.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Hao Ma - Research Fellow, Tulane University

How to Backup Notebooks and Intermediate Results

Project Purpose(s)

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

Scientific Questions Being Studied

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

Scientific Approaches

Not available.

Anticipated Findings

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

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Eric Song - Administrator, All of Us Program Operational Use

Collaborators:

  • Francis Ratsimbazafy - Other, All of Us Program Operational Use
  • Karthik Muthuraman - Other, All of Us Program Operational Use
  • Jennifer Ayala

HTN

Project Purpose(s)

  • Disease Focused Research (hypertension) ...

Scientific Questions Being Studied

What is the prevalence of hypertension (HTN) defined using an electronic health record definition from eMERGE among UBR groups defined by race/ethnicity, income and education?

Do treatment patterns for HTN (using medication sequencing analysis) vary by UBR groups defined by race/ethnicity, income and education, and in geographic regions based on grouping states?

Scientific Approaches

Not available.

Anticipated Findings

There may be disparities in HTN across racial and income groups of policy interest.

Demographic Categories of Interest

  • Income Level

Research Team

Owner:

  • Guohai Zhou - Early Career Tenure-track Researcher, Massachusetts General Hospital

Collaborators:

  • Elizabeth Karlson - Late Career Tenured Researcher, Massachusetts General Hospital
  • Francis Ratsimbazafy - Other, All of Us Program Operational Use
  • Andrea Ramirez - Other, All of Us Program Operational Use

HTN_stroke_race

Project Purpose(s)

  • Disease Focused Research (stroke)
  • Other Purpose (“This work is a result of an All of Us Research Program Demonstration Project. The projects are efforts by the Program designed to meet the program's goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. This work was reviewed and overseen by the All of Us Research Program Science Committee and the Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use”. ) ...

Scientific Questions Being Studied

Prior studies indicate that there are racial differences in the impact of elevated blood pressure on stroke risk. The exact blood pressure threshold at which to begin antihypertensive therapy remains controversial, as does the ideal blood pressure target. Regardless of the specific thresholds and targets chosen, current guidelines and clinical practice patterns do not account for race and ethnicity when managing blood pressure. However, prior studies indicate that there are racial differences in the impact of elevated blood pressure on stroke risk; in a large, longitudinal cohort study, every 10-mm Hg increase in systolic blood pressure was associated with an 8% increase in stroke risk among white individuals versus a 24% increase in black individuals. These findings suggest that blood pressure targets may need to be personalized, at least based on race/ethnicity and ideally based on genetics, vascular risk factors, and lifestyle factors.

Scientific Approaches

The study population comprised 108,322 participants with a SBP measurement, of whom 369 had stroke before/after the measurement. In an unadjusted logistic regression model, systolic blood pressure was significantly associated with stroke (OR per mm Hg, 1.01; 95% CI, 1.00-1.01; P < 0.001). This confirms a well-established finding from numerous prior studies. We then examined this association stratified by black versus non-black. Among black participants, SBP was significantly associated with stroke (OR per mm Hg, 1.01; 95% CI, 1.00-1.02; P = 0.002); in patients of other races, SBP was non-significantly associated with stroke (OR per mm Hg, 1.00; 95% CI, 1.00-1.01; P = 0.11). The lack of association in non-black participants is most likely due to insufficient power, but the different strength of association between black and non-black participants confirms prior findings in other cohorts such as REGARDS.

Anticipated Findings

That the association between hypertension and stroke is stronger among African Americans compared to patients of other races. These results suggest that ethnicity-specific blood pressure thresholds may be superior to a uniform population-wide threshold and promises to inform current uncertainties about blood pressure management.

Demographic Categories of Interest

  • Race / Ethnicity

Research Team

Owner:

  • Cenai Zhang - Project Personnel, Cornell University

Collaborators:

  • Margaret Ross - Late Career Tenured Researcher, Cornell University
  • Hooman Kamel - Mid-career Tenured Researcher, Cornell University

Hypertension

Project Purpose(s)

  • Population Health
  • Other Purpose (This work is a result of an All of Us Research Program Demonstration Project. The projects are efforts by the Program designed to meet the program's goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. This work was reviewed and overseen by the All of Us Research Program Science Committee and the Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use) ...

Scientific Questions Being Studied

Uncontrolled hypertension is a primary contributor to coronary heart disease, stroke, and heart failure. Hypertension can be treated successfully in many cases with medication and prevented or delayed with lifestyle modifications. Even with this success, the prevalence of hypertension continues to be at levels of public health concern, and its control in the United States is far below what is possible. In this demonstration project, we focus on the prevalence of hypertension and its awareness, treatment, and control in a large and diverse participant sample of the All of Us Research Program. Specific questions include:
1) What is the prevalence of hypertension among participants in the All of Us Research Program?
2) Among hypertensive participants, what is the prevalence of awareness, treatment, and control?
3) How do these estimates compare to the general US population assessed in the National Health and Nutrition Examination Survey (NHANES), 2015-2016?

Scientific Approaches

This descriptive analysis is based on blood pressure measurements from the participants’ physical measurement evaluations, and data derived from participant provided information (PPI) and electronic health records (EHR).
1) Demographic factors such as age, sex, race/ethnicity, educational attainment, income and health insurance were assessed in the PPI questionnaire.
2) PPI questionnaire data was also used to define self-reported doctor diagnosis of hypertension and self-reported hypertension medication use.
3) EHR evidence of hypertension diagnosis was defined as the presence of ICD9/ICD10 codes corresponding to hypertension any time before baseline.
4) EHR evidence of hypertension medication use was defined as at least one drug exposure to hypertension medications any time before baseline.

Anticipated Findings

For this study, we anticipate that the prevalence, awareness, treatment, and control of hypertension will be different across demographic strata. This will help to identify health disparities and improve health equity in vulnerable populations. We also anticipate that estimates will be different between the All of Us Research Program and the general US population assessed in NHANES 2015-2016. Understanding these differences will help to characterize potential selection bias and demonstrate the quality and utility of the All of Us data and tools.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Madhawa Saranadasa - Graduate Trainee, University of Illinois at Chicago

Collaborators:

  • Maria Argos - Mid-career Tenured Researcher, University of Illinois at Chicago

Hypertension Cost Effectiveness

Project Purpose(s)

  • Disease Focused Research (essential hypertension)
  • Population Health ...
  • Drug Development
  • Methods Development

Scientific Questions Being Studied

We want conduct an exhaustive cost-effectiveness analysis on all antihypertensive drugs across a variety of outcomes to be able to suggest modified prescription guidelines. Hypertension affects 30-45% of adults in the United States (depending on the definition of hypertension used) representing a significant economic burden on both individuals and society. However, there are many guidelines spanning different countries, associations, and organizations which often recommend multiple drug classes even in the case of another existing condition (e.g. type 2 diabetes). We want to use real world evidence to conduct cost effectiveness analysis to improve the existing guidelines.

Scientific Approaches

We plan to use the electronic health records (EHR) data as real world evidence to conduct a cost-effectiveness analysis. Specifically, we will get the EHR of all individuals who have been diagnosed with hypertension and are first time users of antihypertensive monotherapies. We will then match the cohorts with propensity score matching and conduct cost-effectiveness analysis on all pairwise combinations of drugs with respect to a number of outcomes including: QALY and ICER.

Anticipated Findings

We anticipate that for most disease/secondary outcomes there is no major difference between different classes or active ingredients of antihypertensives especially for the short term outcome of 5 years. With our findings, we will be able to suggest changes to the existing hypertension prescription guidelines for different races, sexes, ages, preexisting diseases (e.g. chronic kidney disease, type 2 diabetes, etc.) which we hope will have a positive impact on healthcare.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Douglas Arneson - Research Fellow, University of California, San Francisco

Hypertension Cost Effectiveness

Project Purpose(s)

  • Disease Focused Research (essential hypertension)
  • Population Health ...
  • Drug Development
  • Methods Development

Scientific Questions Being Studied

We want conduct an exhaustive cost-effectiveness analysis on all antihypertensive drugs across a variety of outcomes to be able to suggest modified prescription guidelines. Hypertension affects 30-45% of adults in the United States (depending on the definition of hypertension used) representing a significant economic burden on both individuals and society. However, there are many guidelines spanning different countries, associations, and organizations which often recommend multiple drug classes even in the case of another existing condition (e.g. type 2 diabetes). We want to use real world evidence to conduct cost effectiveness analysis to improve the existing guidelines.

Scientific Approaches

We plan to use the electronic health records (EHR) data as real world evidence to conduct a cost-effectiveness analysis. Specifically, we will get the EHR of all individuals who have been diagnosed with hypertension and are first time users of antihypertensive monotherapies. We will then match the cohorts with propensity score matching and conduct cost-effectiveness analysis on all pairwise combinations of drugs with respect to a number of outcomes including: QALY and ICER.

Anticipated Findings

We anticipate that for most disease/secondary outcomes there is no major difference between different classes or active ingredients of antihypertensives especially for the short term outcome of 5 years. With our findings, we will be able to suggest changes to the existing hypertension prescription guidelines for different races, sexes, ages, preexisting diseases (e.g. chronic kidney disease, type 2 diabetes, etc.) which we hope will have a positive impact on healthcare.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Douglas Arneson - Research Fellow, University of California, San Francisco

Hypertension Cost Effectiveness

Project Purpose(s)

  • Disease Focused Research (essential hypertension)
  • Population Health ...
  • Drug Development
  • Methods Development

Scientific Questions Being Studied

We want conduct an exhaustive cost-effectiveness analysis on all antihypertensive drugs across a variety of outcomes to be able to suggest modified prescription guidelines. Hypertension affects 30-45% of adults in the United States (depending on the definition of hypertension used) representing a significant economic burden on both individuals and society. However, there are many guidelines spanning different countries, associations, and organizations which often recommend multiple drug classes even in the case of another existing condition (e.g. type 2 diabetes). We want to use real world evidence to conduct cost effectiveness analysis to improve the existing guidelines.

Scientific Approaches

We plan to use the electronic health records (EHR) data as real world evidence to conduct a cost-effectiveness analysis. Specifically, we will get the EHR of all individuals who have been diagnosed with hypertension and are first time users of antihypertensive monotherapies. We will then match the cohorts with propensity score matching and conduct cost-effectiveness analysis on all pairwise combinations of drugs with respect to a number of outcomes including: QALY and ICER.

Anticipated Findings

We anticipate that for most disease/secondary outcomes there is no major difference between different classes or active ingredients of antihypertensives especially for the short term outcome of 5 years. With our findings, we will be able to suggest changes to the existing hypertension prescription guidelines for different races, sexes, ages, preexisting diseases (e.g. chronic kidney disease, type 2 diabetes, etc.) which we hope will have a positive impact on healthcare.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Douglas Arneson - Research Fellow, University of California, San Francisco

Hypertension Cost Effectiveness

Project Purpose(s)

  • Disease Focused Research (essential hypertension)
  • Population Health ...
  • Drug Development
  • Methods Development

Scientific Questions Being Studied

We want conduct an exhaustive cost-effectiveness analysis on all antihypertensive drugs across a variety of outcomes to be able to suggest modified prescription guidelines. Hypertension affects 30-45% of adults in the United States (depending on the definition of hypertension used) representing a significant economic burden on both individuals and society. However, there are many guidelines spanning different countries, associations, and organizations which often recommend multiple drug classes even in the case of another existing condition (e.g. type 2 diabetes). We want to use real world evidence to conduct cost effectiveness analysis to improve the existing guidelines.

Scientific Approaches

We plan to use the electronic health records (EHR) data as real world evidence to conduct a cost-effectiveness analysis. Specifically, we will get the EHR of all individuals who have been diagnosed with hypertension and are first time users of antihypertensive monotherapies. We will then match the cohorts with propensity score matching and conduct cost-effectiveness analysis on all pairwise combinations of drugs with respect to a number of outcomes including: QALY and ICER.

Anticipated Findings

We anticipate that for most disease/secondary outcomes there is no major difference between different classes or active ingredients of antihypertensives especially for the short term outcome of 5 years. With our findings, we will be able to suggest changes to the existing hypertension prescription guidelines for different races, sexes, ages, preexisting diseases (e.g. chronic kidney disease, type 2 diabetes, etc.) which we hope will have a positive impact on healthcare.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Douglas Arneson - Research Fellow, University of California, San Francisco

Hypothesis generation

Project Purpose(s)

  • Population Health
  • Methods Development ...

Scientific Questions Being Studied

Use observational study data and other real world evidence to help generate hypotheses for whole health research.
Study latent traits/constructs related to whole health using methods such as classification and clustering.

Scientific Approaches

Statistical analyses using big data, especially longitudinal data: classification, clustering, structural equation modelling (SEM) etc.

Anticipated Findings

Hypotheses of effectiveness of a combination of behavioral and other interventions on whole person health, which can be tested in future clinical trials.
Constructs that characterize underlying traits of whole health.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Geography
  • Access to Care
  • Income Level

Research Team

Owner:

  • Qilu Yu - Senior Researcher, NIH

Initial Testing Workspace

Project Purpose(s)

  • Other Purpose (An initial testing workspace to get oriented and develop further thoughts on usage of the data. ) ...

Scientific Questions Being Studied

An initial testing workspace to get oriented and develop further thoughts on usage of the data.

Scientific Approaches

An initial testing workspace to get oriented and develop further thoughts on usage of the data.

Anticipated Findings

An initial testing workspace to get oriented and develop further thoughts on usage of the data.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Allison Heath - Other, Children's Hospital of Philadelphia