For Spring '23 of DataBlog at DataRes UCLA, I, along with four other team members, analyzed influenza and cardiovascular disease mortality rates across the US relating to access to private versus public insurance and government funding.
For my component of the project, I downloaded data from the CDC about the mortality rate of cardiovascular diseases across the US from 2010 to 2020. Using Tableau, I cleaned and joined the data together, creating 10 different maps showing the mortality across 48 US states, excluding Hawai'i and Alaska. For the final article, I edited the 10 maps into a video animation and gif format to show the mortality rate distribution changing per year from 2010 to 2020.
Such visualization helps the reader of our blog better understand how a higher mortality rate relating to prevalent diseases in the US like cardiovascular diseases could be associated with the lack of government funding for medical institutions and resources alongside a patient's access to private or public medical insurance that can affect their quality of healthcare. Further analysis can be done on the poverty percentage of a state's population compared to mortality rates, percentage of cures per admittance, and much more.
Below is a gif format of the maps I created:
Crude Rate of Cardiovascular Diseases in the US 2010-2020, Source: CDC.gov
Here is the link to my Tableau Public Dashboard of the map visualizations I created: https://public.tableau.com/app/profile/janet.yu6646/viz/DataBlogS23/Dashboard1
For my team's entire article, please visit this link to read. (will update once our blog is published)
My team members analyzed influenza crude rates, characteristics relating to one's access to private or public insurance e.g. race, and a predictive forest model including factors like age and sex that affect how much an individual's medical insurance costs.