Analyzed Covid-19 data from Washington State to understand the relationship between income, poverty, political affiliation, and age with three Covid-19 outcomes: cases, hospitalizations, deaths.
Extracted data from the CDC, Census, and usa.com to assemble a dataset of over 50,000 cases.
Linear regression, Chi-Square, outlier analysis, and visualizations conducted using Python, Pandas, Plotly, and Matplotlib.
The full presentation of the project can be found here: Covid-19 Presentation
Our team was interested in exploring some lesser studied predictor variables for Covid-19 outcomes. We decided to focus our research to Washington State to limit the scope of the project.
Disclaimer: we have no vested interest in either political party. The purpose of the project was to showcase our programming and analytic skills. Additionally, this is a cross-sectional analysis and we are observing associations and not causative relationships.
H1A: Washington counties grouped by political affiliation will show a significant difference in Covid-19 outcomes (cases, hospitalizations, and deaths).
H2A: Mean household income will negatively correlate to Covid-19 outcomes (cases, hospitalizations, and deaths).
H3A: Poverty rate will positively correlate to Covid-19 outcomes (cases, hospitalizations, and deaths).
Additionally, age and Covid-19 outcomes were examined visually.
After sourcing, cleaning, and merging the data, we reviewed the summary statistics and created several visuals to get a first look at the data. Some examples are presented below:
Washington State: Mean Income by County
Here we can see how mean household income is spread accross the state, with more urban areas having a higher mean income. Created using Plotly.
Washington State: Age Categories by Covid-19 Outcomes
Bar graphs for each of the three Covid-19 outcome variables: cases, hosptalizations and deaths. We can see here that while younger people make up a higher perecentage of cases, older people make up a higher pecentage of deaths. Created using Matplotlib.
In order to compare counties with heterogeneous popuations, we used transformation on our outcome variables, so that we looked at cases, hospitalizations, and deaths per 100 thousand residents.
Outlier Analysis
We conducted an outlier analysis and found four counties that met our criteria. The top three were all republican majority counties. We conducted a simple linear regression with and without the outliers and found that it did greatly impact the strength of the model in that there was a strong positive correlation between income and Covid-19 outcomes when the outliers were removed. Created using Matplotlib.
Linear Regression: Income, Poverty, and Population Density vs Covid-19 Outcomes
For democrat majority counties, we found a strong, positive correlation between mean income and all three Covid-19 outcomes. There was not a significant correlation for republican majorty counties. As the direction of the effect was in the opposite direction we investigated one potential confounding predictor varialbe: Population Density and found that it did positively correlate with the outome variable hospitalizations. Created using Matplotlib.
Chi Square: Democrat Majority Counties with Republican Majority Counties
We found a significant difference in Covid-19 cases per 100 thousand population when comparing democrat majority counties and republican majority counties. Created using Matplotlib.
Revisiting our hypotheses, we conclude that:
H1A: Washington counties grouped by political affiliation will show a significant difference in Covid-19 outcomes (cases, hospitalizations, and deaths).
- A significant difference was found between counties that voted majority republican vs majority democrat, in that republican majority counties has a signficantly higher rate of COVID-19 cases.
H2A: Mean household income will negatively correlate to Covid-19 outcomes (cases, hospitalizations, and deaths).
- For democrat leaning counties income was significantly and positively correllated to all three Covid-19 outcomes. This was the opposite direction of the effect that we predicted. We investigated one potential confounding predictor variable, population density, which did show as significant positive correlation with hospitalizations.
H3A: Poverty rate will positively correlate to Covid-19 outcomes (cases, hospitalizations, and deaths).
- For democrat leaning counties poverty rate was significantly and negatively correllated to hospitalizations only
In conclusion we found evidence that supports our hypothesis that political affiliation may impact Covid-19 outomes at the county level. We did not find evidence to support our hypotheses that lower income and higher poverty rates is correlated with higher Covid-19 outcomes. We discovered the effect worked in the opposite direction, and found evidence that population density is a potental confounding predictor variable.
Project Team Contacts:
Michael Occhicone: [email protected]
Drew Gilmore: [email protected]
Nghia Nguyen: [email protected]
James Park: [email protected]
Jessie King: [email protected]