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## WHAT IS IT?

This model is showing the incidence of diabetes in the United States as set within particular clinical parameters for fasting glucose levels. Each year, approximately 1 in 10 adult Americans are diagnosed with diabetes, and currently, roughly 10% of the U.S. population is living with the disease. This model hopes to act as a visual aid to show the impact of this disease on the population as so many people are affected, or rather, are at risk and may go undiagnosed for years until they find out too late.

## HOW IT WORKS

The model begins by setting up the amount of people globally in a population. At the start, 300 people are able to start off in the global environment. Depending on what the user of this model would like to specifically model will determine how many people start off either healthy (absence of diabetes), at risk, or has diabetes. Use the sliders to determine those amounts of people. Whether a person falls under these aforementioned categories depends on particular fasting glucose amounts as practiced clinically. Diabetes (>120mg/dL), at risk (105-120mg/dL), and healthy (<105 mg/dL). These were the rules presented in the code to define these populations. The plots to the right of the interface show the rise and fall of people with diabetes to place a numeric value on the amount of people in each category. The patches represent eating a healthy diet versus eating a diet which is shown to be a risk factor for diabetes incidence (high fat/sugar). Pink represents eating a healthy diet, black patches represent eating an unhealthy diet. The red people in the interface represent those with diabetes; yellow indicates at risk, and green indicates healthy. There are "unhealthy" and "healthy" buttons which are turtle-only commands which allow the user of the model to manually adjust the parameters for eating a healthy diet versus an unhealthy diet.

## HOW TO USE IT

The buttons "setup", "go once" and "go" will act as the model starters. There are three sliders, "initial-diabetes", "initial-healthy" and "initial-risk" which shows the amount of people who are starting out as either diagnosed with diabetes, healthy, or at risk for developing diabetes. Click on the "go once" button to see slowly the progession of people either getting diabetes, becomnig at risk, or regressing from diabetes and becoming healthy. Click "go" to watch in real-time the overlap of peolpe eating healthy, unhealthy, getting diabetes, becoming at risk, or getting healthier. Watch the plots as they spike up and down or grow steady as time goes on.

## THINGS TO NOTICE
Notice as glucose levels rise, the incidence of diabetes is more prevalent, and as glucose levels fall (eat-healthy) the incidence of diabetes is not as prevalent, but either remains steady or even decreases. The more people with diabetes will mean the more red and yellow people, and less green people, and vice versa. Notice the plots changing, increasing up and down as they correlate with these fluctuations.

## THINGS TO TRY

Try clicking on the "healthy" and "unhealthy" buttons to manually force the amount of uneahlthy eating habits or healthy eating habits and notice the changes in diabetes prevalence and incidence in the global environment. Try scaling the amount of people who intially have diabetes, who are at risk, or who are healthy. This will adjust the amount of time it takes for everyone to be at risk for diabetes or who have diabetes.

## EXTENDING THE MODEL

Try extending the model to adjust the patches to be more fluid, or make a segmented version of the global environment to portray possibly different demographic or geographic locations of food deserts or those who are of low SES. This could help model health disparities and differences amongst populations. Another extension could be to include more parameters that act as either risk factors or risk reducers, such as physical activity.

## NETLOGO FEATURES

Special NetLogo features include the use of setting the turtle parametes and patches colors. The language in of itself is unique to this model, but it made it simple to set the parameters and rules that I needed in order to help this model come to fruition.

## RELATED MODELS

There is a model which shows blood glucose levels and insulin resistance, titled "Blood Sguar Regulation". I did not use much, if at all, of this code, but it did help me create this idea to make it more applicable to real life situations, such as incidence of disease. This model is related to blood glucose levels, however does not show prevalence or incidence of diabetes specifically.

## CREDITS AND REFERENCES

Blood Sugar Regulation model that gave me this idea: http://ccl.northwestern.edu/netlogo/models/BloodSugarRegulation

Credit to Dr. Garcia of Arizona State University who introduced NetLogo to our class and allowed us the opportunity to learn a new skill that can be applied in scientific research, especially in systems thinking.

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