Earlier this year, as a part of undergraduate courses available through Drexel’s Dornsife School of Public Health (Dornsife), students used data developed by the Urban Health Collaborative’s (UHC) Research and Data Core to take part in an annual Data Challenge.
This Data Challenge was the culminating project for the class . They’re short (3-week), team-based sprints “designed to help the students apply what they learned in the class about thinking with data in public health,” said Yvonne Michael, Professor of Epidemiology at Dornsife and Core Faculty with the UHC.
It was carried out with the teams working together to identify a meaningful research question, conduct an analysis to answer their question using UHC data (and additional datasets they identified if needed), and create/share a presentation to tell the story about what they discovered. 2 teams took part, and they topics they chose were:
- The association between neighborhood violent crime and all-cause mortality, controlling for poverty.
- Heat Vulnerability, PM 2.5 (fine inhalable particles less than 2.5 micrometers in diameter) exposure, and their relationship to health outcomes.
We spoke with two of the students who took part to learn more about the experience, using UHC data, and their goals for careers in public health in the future:
Sumyaha Aispy ‘27, Team 1
For the Data Challenge, my team and I attempted to investigate the association between the number of reported violent crimes and all-cause mortality rates after controlling for poverty as a confounder and whether this association varies by poverty as an effect modifier. We used the UHC data to identify our key variables for our analysis, deciding on which of the variables would be meaningful to select which in this case was our primary exposure (violent crime counts), outcome (all-cause mortality), and confounder (poverty).
I think this was a great opportunity in navigating through the research process involving crafting an answerable research question, cleaning the data in order to make it ready to perform analysis, understanding the relevance the insights have towards public health, and presenting our final results through a series of visualizations for easier comprehension. What we have achieved is surely not as complex as research done in the real world. However, I believe this project has provided me with helpful lessons to keep in mind, such as acknowledging that research is an iterative and dynamic process.
In addition, it truly is collaborative in nature, and this was an insightful opportunity that has given me exposure to the behind-the-scenes of research in reality.
I hope to utilize what I have learned through this experience in the future, where I intend on being involved in research within public health to further explore my interests and strengthen my skills in data analysis.
Brooke Schaeffer ‘26, Team 2
The final data challenge we completed for our course built off of work we'd been doing throughout the quarter on a different research question. We used the UHC data to calculate average annual PM2.5 levels per Philly census tracts and explore its associations with age and sex adjusted mortality and poverty.
For my team's research question, we explored how heat vulnerability is associated with age and sex adjusted mortality and if that relationship varies by PM2.5 level among Philly census tracts. We found the data to be informative for its scale and were able to merge it easily with other data (from Open Data Philly) that were also measured by census tract.
We were able to gain substantial insight into our research question and identify areas for further research and improvement. Overall, we learned a lot about how to assess data, understand its background and connections to public health, and use it to identify areas of need that could be improved through changes within the community and policy.
My main goal is to continue pursuing research in Autism and work with local organizations to increase community integration for adults with Autism and other developmental disabilities.