Introduction
Heart disease is a major public health concern and is a leading cause of death worldwide. The risk factors of heart disease are obesity, diabetes, high cholesterol, smoking, and physical inactivity. Studying the dataset of heart disease and the risk factors can allow public health officials to identify patterns and trends in heart disease in order to come up with prevention and strategies to treat. Not only studying the dataset and trends of heart disease is important, but analyzing how accessible healthcare and hospitals are is also as important. Studying the datasets of heart disease, its risk factors, and healthcare accessibility can help public health officials to come up with the best approach for treating patients.
In this report, we will be focusing on heart disease and its risk factors in the US, focusing on New York state, along with the accessibility of healthcare and hospitals.
Dataset and Tools
I got the datasets of heart disease, its risk factors, and healthcare accessibility from cdc.gov, selecting the most useful datasets and information in order to analyze them and create the visualization maps on QGIS. I decided to collect the dataset of heart disease hospitalization rate per 1,000 medicare beneficiaries by state and also in New York. I then collected the dataset of risk factors in New York — obesity, diabetes, high cholesterol, smoking, and physical inactivity in 2019. Finally, I collected the dataset of hospitals accessibility in New York in 2019 and the cost of heart disease medicare in New York in 2020.
I used QGIS to create maps in order to show the correlation between heart disease, its risk factors, and healthcare accessibility.
Visualizations and Findings
The first visualization I created was a heart disease hospitalization rate by State (2018 – 2020). The visualization shows that the states with the highest hospitalization rate for heart disease are Illinois, Indiana, Kentucky, Ohio, New Jersey, and more. The states with the lowest rate of hospitalization for heart disease are Montana, Idaho, Utah, Colorado, and New Mexico.
Moving on, the second visualization I created was a heart disease hospitalization rate in New York (2018 – 2020). What I did different from the visualization by state is labeling the colors as how high or low the rates are. As you can see right away, the city with the highest heart disease hospitalization rate are Clinton and St. Lawrence.
Here is the map visualization of heart disease hospitalization rate and hospitals in Bronx, New York, Queens, Richmond, and Kings.
For the risk factors, I created map visualizations of obesity, diabetes, high cholesterol, smoking, and physical inactivity, which are the main 5 most common cause of heart disease. I want to be able to see the correlation between the heart disease hospitalization rate in New York and the risk factors. As you can tell from comparing all the risk factors of heart disease — obesity, diabetes, high cholesterol, smoking, and physical inactivity in Clinton, Franklin, and St. Lawrence are high, which correlates with the high rate of heart disease hospitalization. These visualizations proved that these risk factors do impact the probability of a person developing heart disease.
Finally, I created a map visualization for cost of medicare in New York for heart disease in 2020. The map shows that the counties with the highest cost of medicare for heart disease are New York and Bronx. On the other hand, the counties with the lowest cost of medicare are St. Lawrence and Franklin.
This map visualization shows that New York, Bronx, and St. Lawrence have one of the highest hospital accessibility. On the other hand, Clinton which has one of the highest rate for heart disease hospitalization has the lowest accessibility. From comparing the rate of heart disease hospitalization with the accessibility, Clinton does not have the best medicare for the patients.
Peer Critique and Reflection
This was an interesting topic to analyze. What I wish I could do better is to be able to overlay the hospitals in the entire New York State into the map of heart disease hospitalization rate as I believe it could be more effective in showing viewers the problem of hospital accessibility correlated to the rate of heart disease hospitalization. The most difficult part about this project was to find the dataset with elements that match each other. Also, I noticed that there are some insufficient data; for that reason, I cannot be sure how accurate the visualizations are.
My peer suggested that the scale box should be labeled clearly which I will make sure they are in the next project and future visualizations.
Conclusion
I believe that analyzing the datasets of heart disease rate and its cause factors by comparing them with each other can show viewers what are the risks of heart disease. The visualizations of medicare and hospital accessibility can also show how the healthcare availability and preparedness should improve, especially in places with a higher rate of heart disease.