This data helps policymakers and constituents alike understand the sustainability of Medicare as a social security program. Medicare is a federally funded health insurance program funded by taxpayers for those over 65 or those who are disabled. In recent years, the issue of whether or not the state can continue to fund the program in light of reduced population sizes has become a hot political issue. Reformation of these programs has become politically contentious as it may result in lower re-election chances and issues with raising taxes to continue these programs are highly unfavorable. Therefore, these visualizations set out to better understand the costs imposed by specific drugs and states alike as an exploration into a relevant policy issue.

Based on the visualizations, there are several ways in which the data could be utilized more meaningfully for future cases. By comparing the population size of Medicare beneficiaries to total drug cost, policymakers can better understand the costs imposed per person in a given state, region or city. This would help policymakers focus in on efficient reform for the program through a public health lens. Furthermore, claim count compared to rates of diseases could reflect which states are most unhealthy or which states prescribe drugs more liberally. Applying the Medicare data in cross-comparative analyses with relevant population, health, age, gender, racial or even socio-economic data would help policymakers and manufacturers better understand and appeal to their audiences/constituents."),

This data was sourced from Center for Medicare and Medicaid Services for the year 2015.

Because of file sizes, we haven't included the original data set that we used in this github repository. If you wish to explore the original data or replicate our set up, please follow these steps: (Be warned, the dataset is 2.8 gb with 24.5 million rows and 21 columns)

1. Go to https://data.cms.gov/Medicare-Part-D/Medicare-Provider-Utilization-and-Payment-Data-201/3z4d-vmhm
2. Download it in the Data folder, as medicaredata.csv
3. run splitWorkingSet.R
4. You're now set to replicate or change up our work