The US Census’ American Community Survey has data that can answer an unlimited amount of questions about our population. When interpreted correctly, it can reveal detailed statistics that lay the foundation for decisions on policies and planning. I created data visualizations based on health insurance in Oregon, organized by age, and found the relationship in the data concerning. Due to the costs of health insurance, many adults are electing to forego health insurance and self pay for necessary medical visits. This data visualization shows that 12% of adults aged 20-39 do not have health insurance.

The first step to evaluating a data set is to formulate a question. In this case, I asked, “What is the distribution of health insurance in Oregon by age?” I created a pivot chart in Excel and applied the count of SERIALNO as the value field, HICOV (health insurance coverage) under the row field, and AGEP under the column field. I then grouped the ages by 20. From there, I created a second table and converted the numbers into percentages, and used that table to create a clustered column bar chart. To create the second data visualization in Tableau, I uploaded my Excel chart and toggled the age and health insurance coverage into their respective columns and rows.  

I used the Oregon PUMS data from the Census Bureau and created a data visualization for the distribution of health insurance by age. We hear a lot of coverage about the issues of health insurance in the US. It is significantly more expensive than in other countries, and even with “good” insurance, medical bills can become so high that they are the number one cause for bankruptcy. Just looking at births shows the huge variation in costs, and the fact that consumers have no tools to find out where they call in the pricing variation.  

Written by Kimberly O’Hanlon
Essay for BA375: Applied Quantitative Methods
Instructor: Kylene Tyler
February 2nd, 2020