Exploring Disparities in Population Health: A Comparative Analysis of Utah and West Virginia
In the realm of public health research, the effort to understand disparities in health quality within the borders of our country is an ongoing endeavor. Within the United States, there are significant contrasts in health practices within the populace, which prompts interest into the factors and conditions that affect the different regions.
In this study, I embark on an exploration of health conditions and outcomes of two very different states, Utah and West Virginia. The main hypothesis studied in this inferential analysis posits that Utah, with a reputation for an active outdoor lifestyle including hiking, biking, skiing, and fishing, would show superior health outcomes when compared statistically against West Virginia, a state that has been ranked among some of the most unhealthy states. Through analysis of CDC-provided data that spans from 2014 all the way to 2023, nearly a decade later, I aim to shed light on these mortality rates on chronic conditions such as Diabetes and Heart Disease. These conditions are both associated with poor health and seem to be sufficient factors to study in relation to poor health conditions as they are often associated with such.
My research methodology is grounded in the analysis of this dataset sourced from the Center for Disease Control covering deaths in every state in the United States. There was a very large amount of data, as there were 15 different columns of information on illnesses as well as 50 states in 52 weeks in a year for nearly a decade. The first step was to narrow down the data to make it less broad and to yield better findings. I created 2 dataframes (one for each state) to make it easier to write code down the line.
Given the myriad of variables available, I decided to select Diabetes Mellitus, Diseases of the Heart, and Natural Causes. These variables were chosen based on their relevance in reflecting prevalent health conditions and behavioral patterns. The thought was that Diabetes and heart conditions are two very big and present issues in America, and that Natural Cause would almost act as a bit of a control, to see how often people were dying in the state otherwise. The data was formatted in a way that every week there was a data point for the number of deaths that occurred in that week.
Due to the large amount of data it seemed fitting to utilize histograms. By graphing the weekly numbers of deaths across the provided years (2014–2023), the aim was to discern patterns and trends in mortality rates with the selected variables. Histograms work by graphing the frequency of an occurrence so this could include how many weeks between 2014 and 2023 that there were 300 people that died of diabetes. The last step in this analysis was to compare the two states against each other. The Utilization of stacked arrays was a great way to facilitate a direct, side-by-side comparison between Utah and West Virginia. Additionally the use of contrasting colors in a strategic way enhanced the visual distinction between the datasets.
Upon examination of the histograms and the statistical analysis, the findings illuminate disparities in the health outcomes between Utah and West Virginia. While there was overlap in the distribution in each graphs, the discrepancies in death counts and frequencies that were present in the chronic conditions were a bit more striking.
Firstly in our graph of natural causes, we can see that Utah has a similar shape in its frequency to West Virginia, but over several places to the left. This means that it has a similar distribution but for less deaths. It should be noted that even with natural causes when there isn’t any chronic condition at play, West Virginia is showing more deaths.
Next is the graph showing the distribution 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.
Lastly is the graph showing the distribution for diabetes. This one has the strangest distribution of the three 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.
This comparative analysis provides evidence in support of the hypothesis that Utah displays superior health outcomes when it is compared to a state like West Virginia. The aforementioned disparities in the mortality rates that are tied to diabetes and heart disease are cause for concern and underscore the need for potential policy initiative aimed at mitigating some of the inequalities across parts of the country, at least in health related fields.
