Carol Li
Undergraduate Student, University of Rochester
2 active projects
Duplicate of Demo - Medication Sequencing
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.
Data Set Used
Registered Tier
Contraception
Scientific Questions Being Studied
Previous studies have been able to map variations in medication administration and treatment decisions in conditions like Type 2 Diabetes and substance abuse disorder. Similar to contraception, there are many options for the treatment of high blood sugar and substance use and may be a complex decision-making process between personal and provider preferences. No currently published work has mapped the contraception decisions of an individual over time. My goal is to quantify the variation in contraception prescriptions for individuals in the All of Us Research Program dataset.
The overall objective of this project is to understand contraception prescription patterns in individuals who identify as female within the All of Us dataset.
(1) What are the most prevalent types of contraception? (2) Has there been a change in the percentages of participants who were prescribed a particular contraceptive? (3) What are the most common sequences of contraceptive prescriptions?
Project Purpose(s)
- Educational
- Methods Development
Scientific Approaches
A retrospective, longitudinal analysis will be conducted using the All of Us Research Program dataset to identify contraceptive prescribing patterns for individuals who identify as female in the United States. First, we will use the Cohort Builder within the workbench to identify all eligible adult participants (≥18 years of age) who identify as female. We will then limit the cohort to just individuals who have a medication record of at least one contraceptive including, birth control pills (e.g., Microgestin), IUDs (e.g., Levonorgestrel [Mirena]), implants (e.g., Etonogestrel [Nexplanon]), and injections (e.g., Depo-Provera). I will use R within the All of Us Researcher Workbench to complete the study objectives.
Anticipated Findings
I anticipate that I will be able to complete the study objectives within the timeframe of the summer. Overall, the anticipated results of the project will have a significant positive impact on the field of contraception research and ultimately improve the quality of care for individuals seeking contraception. In the long term, I expect to develop predictive models that can help healthcare providers identify the most effective contraceptive methods for individual patients based on their characteristics and preferences. More excitingly, I am looking forward to the project that will conduct and provide some educational resources which help individuals and healthcare providers make decisions.
Demographic Categories of Interest
This study will not center on underrepresented populations.
Data Set Used
Registered TierResearch Team
Owner:
- Caitlin Dreisbach - Early Career Tenure-track Researcher, University of Rochester
- Carol Li - Undergraduate Student, University of Rochester
Request a Review of this Research Project
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