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Over the course of this class, I have been analyzing data on obesity trends in the United States. Nutrition and health is very personal to me as someone who has struggled with eating healthy, non-processed foods due to unhealthier processed foods being the more convenient and cheaper option during my childhood. According to the CDC, there are several factors that are associated with a higher risk of obesity. Those factors include lack of physical activity, too many highly processed foods or added sugars (especially sugar sweetened beverages), stress, bad health, lack of access to affordable and healthy foods, nutrition education, and more. My argument for my final project is that socioeconomic status and education play a major role in obesity rates in the United States.
Below I present data that I have cleaned using Python or Tableau, and visualized using Tableau or Data Wrapper.
Data Source: https://data.cdc.gov/500-Cities-Places/500-Cities-Obesity-among-adults-aged-18-years/bjvu-3y7d/about_data (quantitative data, 29008 rows)
Part of the resulting Pandas DataFrame after grouping by State (29008 rows total):
Data Wrapper map visualization of the data above:
Here is a view of the average obesity by state. Does it seem to match up with the city data above?
Association: Some association but not super strong association.
According to data published by BMC Public Health (biology, health, and science journal) all categories across three socioeconomic statuses (lower class, middle class, and upper class), the one category that differs the most is the purchasing of sugar sweetened beverages (ranges 0.56 to 0.40 to 0.22 for lower, middle, upper classes respectively). Sugar sweetened beverages is extremely worrying because unlike regular food that you can chew, highly sugar-sweetened beverages are almost always empty calories.
Stronger assocation above with obesity rates.
According to research published by the Human Kinetics journal, across all racial categories analyzed, those with higher income tend to exercise the most while those with lower income tend to exercise the least. As stated by the CDC, low physical activity is associated with higher obesity rates.
Similarly, I decided to map the average walkability index score by county in the United States. Do you see the similarity with the map below compared with the map above? Please zoom out to see Alaska and Hawaii as well.
Data Source (below):
Do you see the trend with percentage of bachelor's degree with per capita income below?
Among the least obese countries in the world include Ethiopia, Vietnam, Japan, Uganda, Bangladesh, India, Laos, and more.
Data below (charts from the WHO) depicts physical activity by region
Conclusion: Socioeconomic status and education do play a role in obesity rates in the United States, but not directly. It is the result of side effects that come with being higher income or lower income that contributes to obesity. For example, we saw above that people with lower income are more likely to splurge on sugar sweetened drinks (and as such, intake higher calories). In addition, those with higher income are more likely to engage in more physical exercise. Suggestions taken from countries that have a relatively low obesity rate (such as Japan, Vietnam, Laos, etc.) are to increase physical activity, have governments introduce physical activity campaigns and guidelines, and to reduce the amount of sugar-sweetened drinks.
I did not expect sugar sweetened beverages (such as sodas) to have such a significant role in obesity but it makes sense considering it has no nutritional value compared to traditional foods that we need to process. So next time you reach for a soda, maybe think twice before drinking (in moderation).
Changes implemented from presentation: Added Walkability by Country Index Score as a means of viewing association with increased physical activity. In states with higher percentage of people who are physically active, there are indeed more counties with higher walkability score (west coast and east coast states particularly). Additionally, higher socioeconomic status also correlates with higher walkability scores, which could be indicative of more opportunities to exercise.
Challenges: Similar to the past projects, finding and cleaning up data (done in Python and Tableau) was by far the most time-consuming and difficult part of the process. With the complexity of obesity, I wanted to explore the topic from as many angles as I could so that required working with many datasets. Overall, while this project was very challenging and pushed me out of my comfort zone, I found it very rewarding.
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