Analyzing Regional Health Disparities in the U.S

Introduction

The “Epidemiological Transition” is a term in public health to describe the changing patterns we are observing in mortality rates and life expectancy globally over the past century. Specifically, it highlights that the leading causes of disability and death are no longer infectious diseases, but chronic and noncommunicable diseases. As Global Public Health major, these types of large-scale shifts in population health fascinate me. I wanted to center my project on the epidemiologic transition, particularly studying how it has manifested in the U.S.

The reduction of deaths from infectious diseases can be attributed to major developments in public health, such as vaccine development, improved sanitation infrastructure, antibiotic development, and disease surveillance, which have drastically reduced global prevalence of diseases like the flu, malaria, HIV/AIDS, and tuberculosis. As a result, we are living longer and life expectancy is going up. While this shift is a major accomplishment in public health, it opens the door to new health challenges. The longer people live, the more time they have to age and develop chronic diseases that lead to disability and death.

While infectious diseases in the past could wipe out entire communities indiscriminately, the impact of chronic diseases across populations is controlled far more by environmental factors and social determinants of health (SDOH) such as poverty, education, income, pollution, and built environment. While the U.S has made incredible strides in improving the health of Americans, we still have a long way to go to reduce health inequality and combat the rise of debilitating chronic diseases.

For my project, I was curious to focus on regional variation of health in the U.S, specifically focusing on life expectancy, cardiovascular disease rates, and their relationships to social determinants of health across states. I also wanted to study obesity, in particular, as both an indicator of health and a byproduct of things like social inequality, economic inequality, geographic barriers. With the rise of GLP-1 weight loss medications and toxic body image culture, there is a common misconception that obesity is solely the responsibility or “fault” of the individual. In reality, there are complex societal factors beyond people’s control that can significantly increase or decrease the liklihood that they face obesity in their lifetime. Exploring these factors is critical to understand health crises as structural failures, rather than individual ones, and work to improve the health and well-being of all Americans.

Guiding Questions:

What are factors impacting variation in life expectancy and deaths from heart disease across the U.S? Do these factors intersect to augment the burden of disease?

How does poverty and obesity interact with social determinants of health and health outcomes in the U.S? Why does poverty lead to poorer health outcomes?

In what ways to we see the emidemiological transition manifesting in the U.S? How is it impacting the health of Americans?

To begin my project, I wanted to establish a baseline to show how far we have come in terms of life expectancy. The CDC has published and analyzed life expectancy data dating back to 1900, recognizing it as an important indicator of population health. Over the past 125 years (from 1900-2025), life expectancy in the U.S has risen from 46.3 for men and 48.3 for women, to 75.8 for men and 81.1 for women. This is an incredible feat of science, medicine, and public health, granting the average American about 30 more years of life. That being said, I’ve learned repeatedly in my public health classes that the “average American” does not actually exist. In reality, there are significant variations in the health of Americans when you consider where in the U.S they live. The second visual (below) shows the average life expectancy of each U.S. state. Although I was expecting regional differences, I was shocked by how drastic they were. While certain states including Massachusetts, California and New York have life expectancies between 79-80, other states such as Oklahoma, Mississippi, and West Virginia have ones around 72-73. This is a staggering difference, emphasizing the need for researching the factors at play and working to reduce this major health disparity.

From the map above, you can also start to see regional differences, such as lower numbers in the South and Midwest, and higher numbers in the Northeast. This implies that there are certain characteristics on both a state and regional level that impact life expectancy. We we explore these more in-depth later in the project.

In plot 3, I wanted to highlight the epidemiological transition by comparing the top causes of death in the U.S in 1900 versus 2024. The graphs show both the number of deaths per 100,000 people for each cause of death, and the percentage of total annual deaths that each one accounted for. In 1900, the leading 3 causes of death were all infectious diseases: flu, TB, and gastrointestinal infections (which are spread through parasites and viruses). Diphtheria was the 10th leading cause, which is also a highly contagious infectious disease. In 2024, however, we see that the leading causes of death are heart disease and cancer, two chronic diseases. In total, 7 out of 10 of the leading causes are chronic diseases, accounting for 51.7% of deaths in 2024. Thus, an important focus for understanding regional differences in life expectancy is to study the rates and risk factors of chronic disease, particularly heart disease, across different regions.

In plot 4, I created a scatterplot, with one point per state, to explore cardiovascular disease and life expectancy in relation to important social determinants and risk factors for CVD: poverty, food insecurity, and obesity. Plot 5 provides information about these variables in a different format to better depict regional and state differences. The dots are color coded by the U.S region to better understand regional trends of these variables, as well. The plots reveal that these variables are not isolated risk factors, but complex intertwined determinants that collectively increase CVD rates and lower life expectancy.

There are several critical relationships and trends to note here.

1. There is a strong negative correlation between CVD rates and life expectancy, meaning as CVD rates decrease in a state, life expectancy increases. This makes logical sense: CVD is the leading cause of death, so fewer CVD deaths will have a significant impact on life expectancy. It confirms my point above that studying CVD is critical to understanding life expectancy variation in the U.S. In plot 5, we also see the massive variation of CVD rates among states, ranging from 105 cases per 100,000 people in Minnesota to 250 cases in Oklahoma. The top 7 states with the highest CVD rates are all in the South, while many Western states have some of the lowest rates.

2. There are strong positive correlations between obesity, CVD deaths and life expectancy. Obesity places excess strain on the heart, which can lead to structural heart changes, chronic inflammation, and metabolic dysfunction. While obesity is in part caused by individual lifestyles changes, it is also a structural issue. This is exemplified by the strong positive correlation between obesity and poverty, and obesity and food insecurity. People living in poverty often live in food deserts and don’t have access to healthy options. Healthier foods are often more expensive, as well. As a result, people below the poverty line are far more likely to develop CVD and die earlier due to these factors.

3. Generally, the states that have the worst outcomes and rates of all five variables are located in the South and Midwest. Nearly all Southern and Midwest states are above the national obesity rate. Out of the 15 states with the highest poverty rates, 13 of them are Southern states. Also, 9 out of 10 of states with the lowest life expectancies are in the South. While this project does not dig into the root historical, political and cultural causes of why the South faces these problems, this graph illuminates that regional health disparities are strongly rooted in economic disparities.One of the strongest correlations depicted is between life expectancy and poverty rate. This speaks to the broader importance of this project: to highlight the troubling health inequality that exists in the U.S. People’s health and length of life should not be predetermined by their families’ income or where they live. These findings support that improved welfare programs and more equitable wealth distribution are public health services as much as they are finacial ones.

After exploring current obesity rates, I wanted to explore trends of obesity prevalence over time to observe how they have changed. The animated choropleth graphs highlight state obesity rates 1990, 2000, 2010, 2015, and 2022. The results are troubling, as they show obesity rates increasing drastically across that period of time. To name a few, California’s went from 10.5% to 28.5%, and Mississippi from 14% to 40.8%. While there are state-to-state differences, the general upward trends emphasizes that this is a nation-wide issue that is actively harming the health of Americans. While life expectancy has gone up, rising obesity rates continue to put Americans’ health at risk by increasing risk of leading causes of death like CVD, diabetes, and cancer.

Plot 7 dives deeper into the relationship between obesity and income, but also into income-adjusted regional differences in obesity. While plots 4 and 5 studied poverty, plot 7 observes how obesity differs across all income brackets, again color-coded by the U.S region. While I anticipated to see similar trends, I was surprised by the consistency of the regional patters: obesity rates ordered high-to-low across all income brackets are (1)South, (2)Midwest, (3)West, (4) Northeast. This highlights something that the plots 5 and 6 didn’t: that within any income bracket, Southern states still have the highest rates of obesity, and Northeastern states have the lowest. On average, those living under the poverty line of $15k in the Northeast have a similar obesity rate as someone in the South making $35-75k. This reveals that aside from income and poverty rates, there are other regional-specific factors that are impacting obesity, and thus impacting CVD rates and life expectancy. While this project’s focus is not on these other regional factors, it provides grounds for further research into the role of culture, race, and built environment on regional differences in health.

In my last graph explores, I wanted to dive deeper into the reasons why poverty is correlated to lower life expectancy, specifically by comparing uninsured rates and rates of Medicaid coverage to preventable deaths. Preventable deaths can be defined as a death that could have been avoided through timely and effective public health interventions and/or quality medical care. This is an especially important variable in regard to chronic diseases, since consistent primary care measure are essential to managing a chronic disease and preventing it from worsening. In the U.S, healthcare is unfortunately considered a privilege, not a right. With no system of universal healthcare, millions rely on Medicaid coverage while millions more have no insurance at all. The graph shows that preventable deaths increase as rates of uninsured people increase. This supports that when people cannot afford health insurance, they do not have access to quality, life-saving preventative care and curative treatments that could save their lives. When people have to pay for health services out of pocket, they are more likely to become poorer and consequentially sicker, as the data reveals.

Medicaid coverage - offered to low-income households and individuals with certain disabilities - appeared to have a more nuanced relationship to preventable deaths. States like New York and California have relatively high Medicaid coverage rates but low preventable death rates. Contrarily, states like West Virginia have similar Medicaid and uninsured rates, but far more preventable deaths. This implies there are other important regional differences like variation in the quality of healthcare services provided. The plot also shows similar regional differences as depicted in the other visualizations, with Southern states having higher uninsured and preventable death rates compared to Northeast states. Healthcare coverage (or lack thereof) is thus an important regional factor that contributes significantly to higher rates of preventable death and lower life expectancy.

Conclusion:

Through the exploration of CVD, life expectancy, social determinants of health, these visualizations provide strong evidence that health outcomes in the U.S. are deeply tied to socioeconomic factors that are often beyond individual control. States with higher rates of poverty, food insecurity, and uninsurance consistently face higher rates of obesity, cardiovascular disease, preventable deaths, and shorter life expectancy. This burden falls disproportionately on Southern states, who have some of the worse rates of these variables.

As life expectancy continues to increase, chronic diseases will continue to persist and change. Unlike infectious disease, there is no vaccine-like treatment to chronic diseases, or “one-size-fits-all” solution to these complex health issues. Instead, we must develop strategies and campaigns that support health education, expand access to healthcare and nutritious foods, improve built environments to encourage individual healthy habits, and bolster financial support systems to mitigate the impact of chronic diseases in communities and nations. To do so requires an in-depth understanding of social determinants of health and the ways they impact chronic disease rates and health outcomes.