Exploring Health and Income Disparities: A Comparative Analysis of Utah and West Virginia
The quest to understand and address health disparities across different regions of the United States remains a pertinent endeavor. In this study, the focus is on investigating whether Utah, with its reputation for outdoor activities and generally perceived healthier populace, stands out as a healthier state compared to West Virginia, which grapples with issues of negative health habits and drug addiction among its population. Previous research was conducted on this topic by myself several months ago, and it felt necessary to return and attempt to find more reasoning and evidence. Although evidence was along the lines of conclusive, it felt pertinent to include another factor that might explain why the data showed how it did. Therefore, the study additionally seeks to explore the role of income as a potential contributing factor to health outcomes in these two states. It is known that economic factors tend to influence the health of the populace, as wealthier people have more opportunities and liberty to buy what they want. Based on anecdotal evidence and the perceived lifestyle differences between the two states, it is hypothesized that Utah would exhibit better health outcomes compared to West Virginia. This hypothesis is grounded in the notion that Utah’s active outdoor lifestyle, coupled with possibly higher average incomes, may contribute to overall better health outcomes.
Methods
To conduct this research, data from reputable sources such as a CSV file from The Centers for Disease Control and Prevention (CDC) and an API from the U.S. The Census Bureau were utilized. Several comprehensive datasets were compiled, focusing on variables such as diabetes mellitus, diseases of the heart, and income levels in the capitals of both Utah and West Virginia. The first examination included utilizing matplotlib to create histograms examining the frequency of different weekly death tolls related to Diabetes and Heart Disease. Although the initial study that occurred included 4 total histograms(additionally studying Natural Causes of Death and All Deaths in each state) it felt unnecessary to include each of these as it tended to crowd the data. These histograms were terrific at looking at a compilation of data over time, but it was difficult to see the shape and magnitude of each state, as well as comprehend the comparison, so Altair was also employed with the same data to create side by side comparisons of the same variable data over the course of a year. When it came to comparing census data with income, Bar charts seemed to be the most straightforward way of identifying trends. Census data was collected from the counties of each states capital, Charleston and Salt Lake City, and it was then split into income brackets, showing the count for each yearly income in each section. The 2 state bar charts are put next to each other to show comparison
Visualizations
Histograms and Stacked Arrays:
Weekly numbers of deaths attributed to diabetes mellitus and diseases of the heart were depicted using histograms. These visualizations aimed to highlight the frequency of deaths per week over a period from 2014 to 2023, providing insights into the prevalence and trends of these health conditions in each state. Stacked arrays were employed to juxtapose the data from Utah and West Virginia side by side, allowing for direct comparison of health outcomes. The use of contrasting colors, such as red and blue, further facilitated visual differentiation between the two states. We can see that Utah is lower in each area, having higher frequencies of lower deaths. For heart disease, both shaped in this overlap are parabolas showing normal distribution but as we can see West Virginia’s mean number is around 20 deaths higher per week than Utah. This suggests a higher burden of cardiovascular disease within the populace. This could be due to a number of reasons such as smoking prevalence, drug consumption, or obesity, all of which exude more pressure onto the heart. The distribution for Diabetes is strange as well with West Virginia on the right side of the graph with much of its data in the high teens to low 20s, whereas Utah’s data is more left skewed. This indicates heightened burden of diabetes-related morbidity and mortality within the state.
Scatter Plots
Four scatter plots were created, with two plots dedicated to each state. These scatter plots aimed to illustrate the relationship between health outcomes (diabetes mellitus and diseases of the heart) in each state. By plotting each variable separately, the magnitude of differences between Utah and West Virginia could be visually discerned. For diseases of the heart we can see a very similar shape between the 2 states throughout the year. One factor to keep in mind however, is that the magnitude of deaths is different. Utah has its highest number of deaths below 120 , whereas West Virginia skyrockets all the way into the 150s. If you mentally picture the Utah Deaths cutting off below 120 on West Virginia’s graph it is quite shocking to think of the difference. When examining these comparisons on the diabetes graphs the same trend can be seen, where West Virginia tends to high many more higher numbers and Utah has less of a spread.
Bar Charts
Income data for the capitals of both states were represented using bar charts. The use of a logarithmic scale enabled clearer visualization of income disparities, thus providing insights into the socio-economic factors that may influence health outcomes. When examining these graphs side by side, we first notice different shapes. Utah is at an angle indicating how there are far more people in higher paying jobs. Each price group additionally is larger than the one below it in almost every case. This is different from the graph for Charleston. It seems that most income groups have around the same number of people. This means that income is spread more evenly. There is also a much larger quantity of high paying jobs in Utah leading to more opportunity.
Analysis
The analysis of health and income disparities between Utah and West Virginia reveals significant differences in health outcomes and income distribution, shedding light on potential factors contributing to these disparities.
Health Disparities
The histograms and stacked arrays depicting weekly numbers of deaths attributed to diabetes mellitus and diseases of the heart highlight distinct patterns between the two states. Utah consistently exhibits lower frequencies of deaths in both categories compared to West Virginia, indicating a potentially healthier populace in Utah. The higher burden of cardiovascular disease and diabetes-related morbidity and mortality in West Virginia suggests underlying health challenges within the state, which may be influenced by factors such as the citizen’s physical habits as well as their income.
The scatter plots further illustrate the disparities in health outcomes, with Utah generally showing lower magnitudes of deaths compared to West Virginia. Particularly noteworthy is the stark contrast in the number of deaths related to diseases of the heart, where West Virginia experiences significantly higher mortality rates compared to Utah. These findings underscore the need for targeted interventions to address the underlying health issues prevalent in West Virginia and promote better health outcomes.
Income Disparities
The bar charts representing income data for the capitals of both states reveal disparities in income distribution, with Utah exhibiting a more pronounced skew towards higher-paying jobs compared to West Virginia. The logarithmic scale used in the visualization enables clearer visualization of income disparities, highlighting the socio-economic factors that may influence health outcomes.
In Utah, the distribution of income is characterized by a higher proportion of individuals in higher-paying jobs, indicating greater economic opportunities and potentially better access to healthcare and other resources. In contrast, West Virginia demonstrates a more even distribution of income across different income brackets, suggesting a lack of significant disparity in income levels but potentially limited opportunities for higher-paying jobs.
Conclusion
The comparative analysis of health and income disparities between Utah and West Virginia underscores the complex interplay between socio-economic factors and health outcomes. While Utah exhibits better health outcomes and a more pronounced skew towards higher-paying jobs, West Virginia faces challenges associated with higher mortality rates and a more even distribution of income. More data would be required in both fields to determine how much these factors are correlation vs causation, but at the same time it is a well known facto that income affects people’s ability to live healthy. We live in a country with lots of opportunities for fast food meals and frozen nourishment that are proven to not be good for us. In addition, they are much cheaper and more accessible to the general populace. These health factors could be related to the citizens habits, but more likely the issue goes much further. Addressing these disparities requires multifaceted interventions that consider not only healthcare access but also socio-economic determinants such as income inequality and employment opportunities. By understanding the underlying factors contributing to health and income disparities, policymakers and public health officials can develop targeted strategies to promote equitable access to healthcare and improve health outcomes for all residents, regardless of geographic location or socio-economic status.
