HHS Opioid Code-A-Thon
Last December, I along with my coworkers Aziz and Jason took a trip to DC for a code-a-thon event sponsored by the Department of Health and Human Services. HHS wanted to know what solutions for treatment, prevention, or communication of the opioid crisis could be built using federal and state-level datasets collected as part of the Department’s opioid initiative.
Based on my previous work with CDC wonder and SAMHSA data and my company’s focus on improving treatment accessibility, I thought that using datasets to visualize the treatment gap would be a good place to start. Something I thought could be improved from the way treatment gaps were typically visualized, including in my previous projects, was a greater level of interactivity to help inspire community-based thinking. It’s a common wisdom that stories with data are more powerful when your viewer can make a reference to something she already knows well, like commute times. I believe eliciting empathy is important when communicating data, and particularly for stigmatized conditions.
I led the project definition and worked with python to clean and prepare datasets for entry into the app; it was my first time using python for data science. I also worked with Aziz to mock up the future design in Illustrator, and helped Jason a bit with the mapbox platform.