Huiding Chen

Project Personnel, Vanderbilt University

3 active projects

Adjusting results using multiple crossproduct marginal distributions

Using external open-to-public data source (e.g. US Census, NHANES), we want to better represent the U.S. population using AoU source.

Scientific Questions Being Studied

Using external open-to-public data source (e.g. US Census, NHANES), we want to better represent the U.S. population using AoU source.

Project Purpose(s)

  • Methods Development
  • Control Set

Scientific Approaches

We intend to utilize marginal and individual-level datasets which are open-to-public.
In order to include the populational information, the demographics provided by U.S. Census website (https://data.census.gov/profile/United_States?g=0100000US) will be gathered. The marginal information will be used as the finite population adjustment in raking method.
For individual-level data, the NHANES survey data from 2017 to 2020 will be imported to workspace (https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/P_DEMO.htm). This individual-level data will be used to calibrate the sampling probability/propensity score for AoU data in U.S. population.
Using the calibrated weights from the external sources, we want to compare the prevalence for several diseases by implenting regression to the U.S. population. The demographics and original disease status in AoU will be used comprehensively.
Methods: Raking, Iterative Proportional Fitting, GREG, Propensity Score

Anticipated Findings

By observing that some indexes derving from AoU database are different from those among the general U.S. population, we want to better adjust these indexes using multiple crossproduct of some demographics provided by other sources. We may making these observations more convincing(e.g. some diease risks) with the applied methods.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Qingxia Chen - Other, All of Us Program Operational Use
  • Lina Sulieman - Other, All of Us Program Operational Use
  • Robert Cronin - Early Career Tenure-track Researcher, Vanderbilt University Medical Center

Duplicate of Demo - Hypertension Prevalence

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical…

Scientific Questions Being Studied

We are using the All of Us Researcher Workbench interface to answer the question, "Is hypertension prevalence in the All of Us Research Program similar to hypertension prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES) ?". Clinical approaches to understanding and treating hypertension may benefit from the integration of a precision medicine approach that integrates data on environments, social determinants of health, behaviors, and genomic factors that contribute to hypertension risk. Hypertension is a major public health concern and remains a leading risk factor for stroke and cardiovascular disease.

Project Purpose(s)

  • Other Purpose (This work is an AoU demo project. Demo projects are efforts by the AoU Research Program designed to meet the program goal of ensuring the quality and utility of the Research Hub as a resource for accelerating discovery in science and medicine. As an approved demo project, this work was reviewed and overseen by the AoU Research Program Science Committee and the AoU Data and Research Center to ensure compliance with program policy, including policies for acceptable data access and use. )

Scientific Approaches

In this cross-sectional, population-based study, we used All of Us baseline data from patient (age>18) provided information (PPI) surveys and electronic health record (EHR) blood pressure measurements and retrospectively examined the prevalence of hypertension in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED codes and blood pressure medications recorded in the EHR. We used the EHR data (SNOMED codes on 2 distinct dates and at least one hypertension medication) as the primary definition, and then add subjects with elevated systolic or elevated diastolic blood pressure on measurements 2 and 3 from PPI. We extracted each participant’s detailed dates of SNOMED code for essential hypertension from the Researcher Workbench table ‘cb_search_all_events’. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18-39, 40-59, ≥ 60).

Anticipated Findings

The prevalence of hypertension in the All of Us cohort is similar to that of published literature. All of Us age-adjusted HTN prevalence was 27.9% compared to 29.6% in National Health and Nutrition Examination Survey. The All of Us cohort is a growing source of diverse longitudinal data that can be utilized to study hypertension nationwide. The prevalence of hypertension varies in the United States (U.S.) by age, sex, and socioeconomic status. Hypertension can often be treated successfully with medication, and prevented or delayed with lifestyle modifications. Even with these established hypertension intervention and prevention strategies, the prevalence of hypertension continues to be at levels of public health concern. The diversity within All of Us may provide insight into factors relevant to hypertension prevention and treatments in a variety of social and geographic contexts and population strata in the U.S.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Registered Tier

Research Team

Owner:

Adjusting results using multiple crossproduct marginal distributions

Using external open-to-public data source (e.g. US Census, NHANES), we want to better represent the U.S. population using AoU source.

Scientific Questions Being Studied

Using external open-to-public data source (e.g. US Census, NHANES), we want to better represent the U.S. population using AoU source.

Project Purpose(s)

  • Methods Development
  • Control Set

Scientific Approaches

We intend to utilize marginal and individual-level datasets which are open-to-public.
In order to include the populational information, the demographics provided by U.S. Census website (https://data.census.gov/profile/United_States?g=0100000US) will be gathered. The marginal information will be used as the finite population adjustment in raking method.
For individual-level data, the NHANES survey data from 2017 to 2020 will be imported to workspace (https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/P_DEMO.htm). This individual-level data will be used to calibrate the sampling probability/propensity score for AoU data in U.S. population.
Using the calibrated weights from the external sources, we want to compare the prevalence for several diseases by implenting regression to the U.S. population. The demographics and original disease status in AoU will be used comprehensively.
Methods: Raking, Iterative Proportional Fitting, GREG, Propensity Score

Anticipated Findings

By observing that some indexes derving from AoU database are different from those among the general U.S. population, we want to better adjust these indexes using multiple crossproduct of some demographics provided by other sources. We may making these observations more convincing(e.g. some diease risks) with the applied methods.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Sex at Birth
  • Education Level
  • Income Level

Data Set Used

Controlled Tier

Research Team

Owner:

Collaborators:

  • Qingxia Chen - Other, All of Us Program Operational Use
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