Stanley Jia

Undergraduate Student, University of California, Irvine

3 active projects

SPADE

The overarching goal of this study is to improve the prediction of clinically significant adverse drug events (ADEs) by harnessing the data that is made available through the All of Us program. Although a number of ADE risk prediction tools…

Scientific Questions Being Studied

The overarching goal of this study is to improve the prediction of clinically significant adverse drug events (ADEs) by harnessing the data that is made available through the All of Us program. Although a number of ADE risk prediction tools are published in the literature, none are universally accepted and used routinely in clinical practice. Robust ADE risk prediction tools are lacking because most datasets utilized to derive ADEs are largely not generalizable. Furthermore, interindividual susceptibility to ADEs might be explainable by genetic variations, and such information is not often available in prediction models. Our specific aims are:
1. Determine the prevalence, specific types and characteristics of ADEs among participants who are receiving chronic disease medications.
2. Derive and validate a prediction model to identify characteristics that are associated with ADEs related to selected chronic disease medications.

Project Purpose(s)

  • Disease Focused Research (Definitions, analyses and prediction of adverse drug reactions/events)
  • Population Health
  • Social / Behavioral

Scientific Approaches

Descriptive statistics will be utilized to characterize the prevalence, specific types and characteristics of ADEs for each drug. Univariate analysis with chi-square tests will be conducted to calculate odds ratios (together with 95% confidence interval) for ADEs associated with each potential risk factor, followed by multivariate logistic regression analysis using backwards selection to identify statistically significant factors and ultimately derive an ADE prediction model for each selected drug.

Anticipated Findings

Our study also intends to fill a current research gap and present findings to contribute an indispensable part of a future larger study that examines ADEs for association with both patient characteristics and pharmacogenetic information, when available, which will contribute an additional layer of information critical to the preventability of ADEs. There will be added focus on specific ethnic groups previously under-described in literature, including Hispanics and African Americans. While limitations such as inconsistency in recording of ADEs and risk factors are anticipated, an advantage of this study is that cases and controls will be selected from a large participant pool (All of Us) rather than a specific site, which will allow us to evaluate the impact of ADEs in a group that better mirrors the general patient population in the clinical setting.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Stanley Jia - Undergraduate Student, University of California, Irvine
  • Lu He - Graduate Trainee, University of California, Irvine
  • Kai Zheng - Mid-career Tenured Researcher, University of California, Irvine
  • Kevin Zhang - Undergraduate Student, University of California, Irvine
  • Ding Quan Ng - Graduate Trainee, University of California, Irvine
  • Alexandre Chan - Late Career Tenured Researcher, University of California, Irvine

Collaborators:

  • Jahnavi Maddhuri - Undergraduate Student, University of California, Irvine
  • Jatin Goyal - Undergraduate Student, University of California, Irvine
  • Arvind Kumar - Undergraduate Student, University of California, Irvine

DIVERS

Immunizations are one of the most important and effective preventative health measures available, but relative to public health goals, are underutilized in adults. Developing a better understanding of how vaccines are used positions us to develop strategies to mitigate modifiable…

Scientific Questions Being Studied

Immunizations are one of the most important and effective preventative health measures available, but relative to public health goals, are underutilized in adults. Developing a better understanding of how vaccines are used positions us to develop strategies to mitigate modifiable risk factors and improve vaccination rates. Results from our study will address a knowledge gap in understanding data characteristics available in the All of Us dataset for patients who have received one or more vaccine(s). Results from this study is intended to provide us with stronger justification for access to medical and pharmacy claims data to develop prediction models on which variables have the highest amplitude of impact.

Project Purpose(s)

  • Disease Focused Research (Baseline analysis of the population with documented vaccination record.)
  • Population Health
  • Social / Behavioral

Scientific Approaches

The All of Us database will be used as a source population for a convenience sample in this cross-sectional study to characterize the sociodemographic, health-related and lifestyle characteristics of adults who receive single and/or multiple types of vaccines in addition to health and lifestyle choices. Data on a cohort of patients who received vaccine doses during a period of time that may vary depending on the indicated use of each vaccine will collected from the All of Us database and reviewed for trends. These categories of factors will also be used to compare groups of participants who completed the hepatitis B and HPV vaccine series with those who started, but did not complete these vaccine series. Descriptive statistics, correlations and cross-tabulation will be used to describe specific differences in racial/ethnic, socioeconomic, gender-based, and health- and lifestyle-related determinants of the use of vaccines in patients included in this unique database.

Anticipated Findings

This proposed study provides a cross-sectional evaluation of the All of Us program data to develop a baseline understanding of the relevant and available vaccination data. Most existing literature describing adult vaccination rates are based on self-reports and few focus on multiple vaccines with correlating health data. Comprehensive data regarding vaccination rates exist, but lack investigation to specific lifestyle, health, and sociodemographic characteristics. More importantly descriptions of vaccine studies have been mainly limited to individual vaccine types. Developing a baseline assessment of individuals who have vaccine data included in the All of Us program will provide insights into which characteristics are modifiable as well as a description of the data available. This is intended to serve as a starting point for future research endeavors.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Stanley Jia - Undergraduate Student, University of California, Irvine
  • Kevin Zhang - Undergraduate Student, University of California, Irvine
  • Ding Quan Ng - Graduate Trainee, University of California, Irvine
  • Alexandre Chan - Late Career Tenured Researcher, University of California, Irvine

Duplicate of Demo - Medication Sequencing

1- What are the main prescribed medication sequences that participants with type 2 diabetes and depression took over three years of treatment? In this questions, we are extracting the anti-diabetes and anti-depressant medications used to to treated participants who have…

Scientific Questions Being Studied

1- What are the main prescribed medication sequences that participants with type 2 diabetes and depression took over three years of treatment?
In this questions, we are extracting the anti-diabetes and anti-depressant medications used to to treated participants who have T2D and depression codes. We retrieved medications prescribed after the first diagnosis code for each disease. We represented the medications using their ATC 4th level.
2- What is the most common first anti-diabetic and anti-depressant that were prescribed for All of Us participants? We extracted the first medications prescribed to treat T2D and depression. We identified the most common first medication with the highest number of participants.
3- Is there a change in the percentages of participants who were prescribed first common medication, treated using one medication, treated only using one common medication between 2000-2018?

Project Purpose(s)

  • Disease Focused Research (type 2 diabetes, depression)
  • 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 Approaches

In this project, we plan on using the medication sequencing developed at Columbia University and the OHDSI network as a means to characterize treatment pathways at scale. Further, we want to demonstrate implementation of these medication sequencing algorithms in the All of Us research dataset to show how the various sources of data contained within the program can be used to characterize treatment pathways at scale. We will perform separate medication sequence analyses for three different common, complex diseases: type 2 diabetes, depression
1- Data manipulation
Using python and BigQuery to:
A- Retrieve medication and their classes
B-Create the medications sequences

2- Visualization:
A- Creating sunburst to visualize the sequences
B- Plotting the percentages of participants the first common medication and one medication during three years

Anticipated Findings

For this study, we anticipate demonstrating the validity of the data by showing expected treatment patterns despite gathering data from over 30 individual EHR sites. Specifically, we expect to find:
1- Variation in the medication sequences prescribed to treat All of Us participants who had type 2 diabetes and depression.
2- The most common medication used to treat participants as first line treatment with type 2 diabetes and depression diagnosis.
3- A trend or change over time of prescribing the first common medication over the study period
4- Trend overtime for the percentage of participants
Importantly, the detailed code developed herein is made available within the Researcher Workbench to researchers, so that they may more easily extract medication data and class information using a common medication ontology, an approach useful in many discovery studies.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Research Team

Owner:

  • Stanley Jia - Undergraduate Student, University of California, Irvine
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