There is substantial evidence that patient outcomes are impacted by socio-economic factors. We aim to identify particularly significant disparities in readmission rates in California. We assess what factors are most important for improving this outcome and identify suggested areas of improvement and further study.
We obtained datasets detailing each factor, aggregated the data, and used a regression model to determine the significance. In particular, we studied data tracking the poverty levels, population density, hospital counts, ethnic diversity, and hospital bed counts.
Our project relied on both Python and R, where most data cleaning was performed in Python and analysis in R.
Finding reliable and complete sets of data was one of the most challenging parts of this project. We had to ensure our data was from a trustworthy source and included the information we needed.
Our report, including graphs of our findings, is submitted below.
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