US Health Trends

Posted on May 25, 2023

Obesity is a growing concern, with rising rates causing significant health problems and contributing to high healthcare expenses. As a major public health issue in the United States, it requires effective prevention and intervention strategies to address the prevalence of obesity and alleviate its associated health and financial burdens. By implementing comprehensive approaches, we can make progress in reducing obesity rates and improving the overall well-being of individuals and the healthcare system.


In 2017, the prevalence of obesity in the United States was 41.9% and appears to be on an upward trend. This alarming rate of obesity poses significant health risks, including an increased likelihood of developing chronic conditions such as cardiovascular disease, type 2 diabetes, and certain types of cancer. Addressing obesity early on is crucial in preventing these diseases.

In addition to the health implications, obesity also carries a heavy financial burden. In 2019, the United States spent approximately $173 billion on the treatment of obesity. The medical costs for obese adults were found to be $1,861 higher than those with a normal body mass index (BMI).

When considering the prevalence of obesity across different ethnicities, certain groups stand out. Non-Hispanic Black adults had the highest age-adjusted prevalence of obesity at 49.9%, followed by Hispanic adults at 45.6%. Non-Hispanic White adults had a prevalence rate of 41.4%, while non-Hispanic Asian adults had the lowest rate at 16.1%.

Moreover, there are notable differences in obesity rates based on education levels.

Obesity rates appears inversely correlated with degree of education. Adults without a high school degree or equivalent had the highest self-reported obesity (37.8%), followed by adults with some college (35.6%) or high school graduates (35.5%), and then by college graduates (26.3%).

These statistics highlight the urgency of addressing obesity as a public health issue in the United States.


This project is an exploration of the growing rate of chronic diseases in America.

  1. Where are these comorbidities most prevalent and in what areas would intervention and targeted efforts be most impactful?
  2. How does obesity impact rates of diabetes and hypertension?

Analyzing the raw data

The data I utilized for my analysis was found in Kaggle, but it can also be accessed through Data Society. The dataset consisted of 11 different datasets, encompassing over 200 indicators. To focus specifically on health-related indicators related to obesity, heart disease, and cancer, I filtered through the data to extract relevant information from 3,141 counties spanning the years 1993 to 2003.

To effectively map this information, I needed to combine the gleaned dataset with the tigris and sf modules. These libraries provided crucial data such as 5-digit FIPS codes, longitude and latitude coordinates, which were necessary for rendering the map using ggplot. It is worth noting that although the data covered all 50 states, Alaska and Alabama had limited data available for mapping the desired indicators.

The health indicators were organized and grouped based on their respective 5-digit FIPS county codes. Once the geometry data was incorporated, I was able to interact with the data and visualize it using the leaflet map. This allowed for a dynamic exploration and analysis of the health indicators across different counties.

Key Findings

My app can be accessed here. The leaflet map features a convenient dropdown menu that enables users to select specific indicators of interest. Once an indicator is chosen, the map dynamically responds by displaying circles representing the selected data points. The size of these circles corresponds to the percentage associated with the chosen indicator, providing a visual representation of the magnitude.

For instance, when exploring ethnicity indicators, the map showcases distinct colored circles to represent different ethnic groups. Blue circles depict the distribution of Black Americans, primarily concentrated in the southern regions of the United States. Cyan circles signify the presence of Hispanic individuals, predominantly residing in the southwestern states. Red circles highlight Native American populations, mainly found in designated reservations. Meanwhile, orange circles indicate the concentration of Asian communities, primarily in major cities along the West Coast and East Coast. Although not illustrated in the map, yellow circles represent Caucasians and are dispersed across the entire country.

By leveraging these interactive features, users can gain valuable insights into the geographic distribution of various ethnicities, fostering a deeper understanding of the demographic landscape within the United States.

The leaflet map also offers the option to explore socioeconomic factors. One notable example is the visualization of the poverty rate, which indicates individuals or families living below the federal poverty level and may qualify for certain federal assistance programs. By selecting this indicator, you can observe the distribution of poverty rates across the United States.

The map reveals that poverty is prevalent throughout the country, but it appears to be more concentrated in the southern regions. This visual representation provides valuable insights into the geographic disparities and highlights areas where poverty rates are particularly pronounced.

When exploring the health indicators section, specifically when selecting both diabetes (represented by blue circles) and obesity (represented by green circles), the leaflet library generates a compelling map that visually depicts the widespread prevalence of these diseases. The map highlights the pervasive nature of these health conditions, providing valuable insights into the areas where intervention and targeted efforts may be necessary.

When examining the health indicators section, particularly when selecting both hypertension (represented by red circles) and obesity (represented by green circles), the leaflet library generates a powerful map that illustrates the heightened prevalence of these diseases, particularly in the south.

Here is a zoomed in depiction of diabetes prevalence in the five boroughs of New York City. If a county is selected, the map will display the percentage rate.

As shown above, the shiny app has a data tab that allows users to skim through the dataset by state.

The state comparison tab provides a map of disease prevalence by state, enabling a quick visual assessment of counties that require urgent interventions.

Boxplots offer an alternative way of visualizing these disease across the fifty states. Users can quickly compare the medians by state to assess rates of disease. For example, West Virginia appears to have one of the highest rates of diabetes, hypertension and obesity across the U.S. There appears to be some outlier counties that may skew the median, but the leaflet map also confirms that these chronic diseases seem most prevalent in this surrounding region.

The density plot provides a clear depiction of the significant overlap between diabetes and hypertension prevalence. It is interesting to note that diabetes appears to lag behind the other two conditions. This observation can be partially attributed to various factors. One such factor is that the dataset does not distinguish between different types of diabetes, such as Type 1 and Type 2. Type 1 diabetes is characterized by the autoimmune destruction of beta cell function over a short period of time, while Type 2 diabetes is strongly associated with insulin resistance and weight gain. Unlike obesity and hypertension, which can be diagnosed and monitored through simple tests at a doctor's office using a scale and blood pressure machine, diabetes diagnosis typically requires laboratory testing. In many cases, a repeat test on a different day is necessary to confirm the findings, especially if the patient does not exhibit clear symptoms of hyperglycemia like excessive urination, thirst, and hunger. Unfortunately, this distinction can result in the overlooked diagnosis of prediabetes, leading to the escalation of the condition to full-blown diabetes in some patients.


The app takes time to load. In particular, the third and fourth tabs require time for the graphs to populate. In addition, the circles are drawn to scale. In clustered counties, the circles overlap significantly and can be hard to distinguish the counties when the map is zoomed in.

Future Work

  1. How are these rates changing over time?
  2. How much money is spent treating these comorbid diseases at national and at state level?
  3. How does the US compare to other countries?


Joe Cheng and Garrett Grolemund. (2015). R Shiny Superzip Example. Retrieved from

Data Society. (2016). Health Status Indicators. Retrieved from

Centers for Disease Control and Prevention. (2023). Adult Obesity Prevalence Maps. Retrieved from

Mentors: David Corrigan, Vinod Chugani, Denis Nguyen

About Author

Chui Pereda

I am an experienced clinical dietitian and diabetes educator looking to expand into data science. I have a growing, working knowledge of Python and R. During my free time, I like to go for a run.
View all posts by Chui Pereda >

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