Encouraging Anti-Diabetic Lifestyles in New Mexico Communities

Team: 17

School: Grady High

Area of Science: Biomedical

Interim: Problem Definition:

We aim to make people and communities more aware of the causes and effects of diabetes through a model showing variables which each correlate with diabetes rates. Diabetes is a major problem across New Mexico, 14.1% of New Mexicans have diabetes, and 39.7% of people have prediabetes. Diabetes rates are a result of many different variables. These variables include fast-food availability, traffic counts, poverty rates, healthy recreation, education levels, bike lanes, healthy restaurants, commute methods, ethnicity, and more. At this point, we have obtained fast-food availability, traffic counts, poverty rates, education levels, commute methods, and ethnicity for several New Mexico counties. As well as the ratio of daily traffic to number of fast-food restaurants, and ratio of TPR to number of fast-food restaurants.

Computational Plan:

To appropriately compare how many people are in each community at a given time, we have created the Total Population Rating(TPR). The value of the TPR is the population of the community plus the Average Annual Daily Traffic(AADT). It is not an accurate count of the total number of travelers plus the population because not every vehicle is holding only one passenger. So far, we have followed our original plan of sliders controlling several variables, each variable acting correspondingly to an appropriate outcome. We have yet to implement our research into our code. This is due to our data management not being complete. We plan to use Python to run a Chi-Square Test, which should assist us in determining if the variation in diabetes rates is due to chance(heretical or ethnical regional differences) or due to the variables we are testing. Certain ethnicities are more subject to diabetes than others.

Current Progress:

The mass amount of variables corresponding with diabetes rates makes it a very complicated topic to study. We have chosen the variables to study based on various factors. Our research shows that fast-food availability has an indirect correlation with diabetes rates(in the sense that fast-food is more affordable and corresponds with poverty levels). However, we have found that poverty rates have a direct correlation with diabetes rates. This is likely due to fast-food tending to be significantly less-expensive than other options. For instance, Cibola County, which has the highest diabetes rates in New Mexico, also has the highest fast-food density to fresh-food density ratio. Los Alamos County, which has the lowest diabetes rate in New Mexico, is also the only New Mexican county in which the fresh-food density is higher than the fast-food density. In-order to appropriately define which restaurants are “fast-food”and “fresh-food”, we used the google algorithm. We entered into Google Maps ex. “Fast-food restaurants in Tucumcari”.

Expected Results:

We are expecting people to be more aware of the causes and effects of diabetes. We are planning to represent different variables within our model. With our model we are going to show factors that contribute to diabetes and factors that prevent diabetes. We envision our model to have sliders with different variables that each control a certain element appropriately so that it corresponds with the data we have collected. We predict that if there are more bike lanes available, people are more likely to bike, which is a healthier choice over other transport options such as driving. However, we have yet to confirm a correlation between bike lanes and lower diabetes rates in our testing.


1.The Burden of Diabetes in New Mexico . (n.d.). Retrieved from http://main.diabetes.org/dorg/PDFs/Advocacy/burden-of-diabetes/new-mexico.pdf.
2.Haas , T. P. (Ed.). (2017, October 10). Traffic Counts New Mexico Interstates . Retrieved from https://www.nmlegis.gov/handouts/TIRS 101017 Item 1 B - Interstate Traffic data-map.pdf.
3. Average Travel Time to Work in the United States by Metro Area . (2019, October 16). Retrieved November 29, 2019, from https://www.census.gov/library/visualizations/interactive/work-travel-time.html#.
4. Statistics About Diabetes. (n.d.). Retrieved from https://www.diabetes.org/resources/statistics/statistics-about-diabetes.
5. U.S. Census Bureau QuickFacts: United States. (n.d.). Retrieved from https://www.census.gov/quickfacts/fact/table/US/PST045218.
6. Khatri, M. (2019, November 6). Type 2 Diabetes: Symptoms, Causes, Diagnosis, and Treatment. Retrieved from https://www.webmd.com/diabetes/type-2-diabetes#2.

Team Members:

  Tristen Pool
  Alexis Brandsma
  Erynn Vetterly

Sponsoring Teacher: NA

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