| Name: | Fiona Shafer |
|---|---|
| Email: | fiona.shafer@rutgers.edu |
| Home Institution: | Rutgers University - New Brunswick |
| Project: | Health Disparities & COVID-19 |
| Mentor: | Dr. Christie Nelson |
About My Project
This project will focus on disparities related to COVID in three main areas: long term care, education settings, and mobility trends. Health disparities are preventable burdens of disease that exist in disadvantaged populations. Through this project, I plan to identify exisiting disparities to promote health equity domestically and internationally. After compiling datasets and sources, I will visualize the results to answer research questions in all three areas.
Previous Work
Throughout the past two semesters in the Rutgers Masters of Business and Science Externship Exchange program, I completed work on a similar COVID related project. First, the team administered an IRB approved survey on COVID related factors (i.e. vaccination opinions) to three populations: healthcare workers, the general public, and current Rutgers students. Upon completion, we published brief analyses, conducted interviews with multiple industry professionals, and compiled the results into an online dashboard.
Research Log
Week 1: May 24 - May 28
Alongside orientations and tutorials, I heavily researched existing health disparities to establish background information. After gaining a strong understanding, I began a literature review on COVID-19 and established my areas of focus. I decided to focus my research in three areas: long term care (nursing homes and assisted living facilties), educational settings (primary, secondary, and higher education), and mobility trends (relocation and travel data). I identified appropriate datasets available for further analysis and prepared my introductory presentation for the meeting on June 1st. I am looking forward to expanding my project throughout the summer.
Week 2: May 31 - June 4
This week I completed in depth analyses on the aformentioned sources. Such close reading afforded me a comprehensive understanding of the available COVID datasets which allowed me to expand upon my literature review. Additionally, I reviewed my previous externship work to see which areas required further attention. I enjoyed hearing the other presentations on Tuesday and look forward to everyone's final results.
Week 3: June 7 - June 11
I attended the TRIPODS Data Institute everyday this week which covered experiment design and model validation in python. All of the speakers shared insightful concepts which will guide me through my data visualizations this summer. Moreover, I began to select datasets to clean and identify key attributes. I also trained in Tableau which I will use heavily in my analysis.
Week 4: June 14 - June 18
Throughout the week I gained knowledge in Tableau by completing an online course. I documented and synthesized this knowledge in a manual format for future reference by other students. Following the course, I began visualizing the 2019 Long Term Care data in order to select the most valuable attributes. Specifically, I discovered a wide range of average ADL (Average Daily Life) scores across states and plan to do continued analysis to examine the spread. Additionally, I attended our seminars on online educational data and research ethics. I made progress in my data exploration this week with preliminary visualizations which inspired research into other areas like LDA (latent dirichlet analysis).
Week 5: June 21 - June 25
This week I spent time studying a print source entitled 'Just Medicine, A Cure for Racial Inequality in American Healthcare' by Dayna Bowen Matthew. The reading lead me to numerous new sources and methods in health disparities research that I will implement in my project. Moreover, I continued my long term care data analysis including several regression analyses and visualizations. After making formal conlcusions, I plan to move into the second section of my research next week.
Week 6: June 28 - July 2
Throughout week 6 I completed my comprehensive notes on the previously mentioned print source. Additionally, I began cleaning attendance related COVID data which is separated by racial and ethnic groups. Next I began complining chronic absenteeism data for correlation related analyses with 2020 attendance data. The analysis of the education related data has proved to be more challenging than long term care data, as less is publically available.