Obesity in America Regression Project

Obesity in America: Causes of an Epidemic

1. Introduction

        The United States is currently facing an obesity epidemic.  For the past thirty years obesity rates have been rising steadily in the fifty states.  Obesity can lead to numerous health problems including, but not limited to, cardiovascular disease, certain types of cancer, and diabetes.  As obesity rates have increased so too have healthcare costs in America.  By pinpointing relationships between different factors and the increasing obesity rates, policies and programs can be implemented to better serve the U.S. population and help end the obesity epidemic. (Finkelstein et al. 247)

        Body Mass Index is a number calculated based on an individual’s height and weight.  According to the Center for Disease Control (CDC), the ranges for BMI measurements are as follows: below 18.5 indicates that an individual is underweight, between 18.5 and 24.9 indicates that an individuals is at a healthy weight, between 25 and 29.9 indicates that an individual is overweight, and a BMI measurement of 30 or above indicates that an individuals is obese.  Despite whatever claim a fad diet may make, weight loss and weight gain are simple.  Weight loss occurs when one consumes fewer calories than he expends.  Weight gain occurs when one consumes more calories than he expends.  But what is causing more than one third of American adults to over consume and become obese? (Center for Disease Control)

        Opinions differ greatly on what the cause of obesity is.  There are arguments about possible genetic causes of obesity, nature vs. nurture theories, and just about anything else one can think of as tying into obesity.  For my study I chose to look at five individual factors that I felt could have a relationship with the obesity rate in each of the fifty states (OB): age (AGE), median household income (INC), college level education attainment (EDU), access to healthy food (FOOD), and access to recreational facilities (REC).  I used data at the state level for the year 2010.

1. Theory

        An individual’s metabolism is one of the factors that determines how many calories are used for normal everyday activities.  It is common for metabolism rates to decrease as one ages.  This is one reason why weight gain occurs later in life for some individuals and could be related to the high rates of adult obesity within the U.S.  (Weight Watchers)  I expect that age will have a positive correlation with the obesity rate within a state.  This data is from the U.S. Census Bureau.

        Also from the U.S. Census bureau is data on the median household income of each state.  A recent study by researchers at the University of Washington found that the cost of eating a diet consistent with national nutritional guidelines is considerably more expensive than eating a diet of lower nutritional value.  This would lead to the belief that those with lower incomes are more likely to become obese.  A negative correlation between median household income and obesity is expected and would further confirm the findings of the study.  This would also indicate that a new direction must be taken in the fight against obesity.  While it will still be important to promote exercise programs and nutritional education, if it is difficult for individuals to actually afford living a healthy lifestyle, combating the obesity rate will remain a daunting task. (Monsivais, et al. 1475)

Ignorance may be bliss but if a lack of knowledge is resulting in higher obesity rates, something must be changed within schools and universities.  Courses on nutrition and health provide vital information about how to lead healthy lifestyles and may decrease the likelihood of becoming obese.  A report from the College Board Advocacy and Policy Center found that obesity rates within different age groups are lower for individuals with some level of college education.  This leads to the prediction that the college education attainment level of a state’s population will be negatively correlated with the obesity rate. (Baum et al. 29)  The source for this is the online database of County Health Rankings and it is the percentage of individuals ages 25-44 within a state with some post-secondary education.

Access to healthy food is also from the County Health Rankings database.  It is the percentage of counties within a state that have a grocery store or farmers’ market.  The report You are Where You Shop provides evidence suggesting a correlation between a decreased availability of grocery stores for neighborhoods and elevated BMIs within those neighborhoods.  If individuals are not able to shop for healthy foods, their diets will be less nutritious and could result to obesity.  A negative correlation between access to healthy food and the obesity rate of a state is predicted.  (Inagrami et al. 16)

Access to recreational facilities is a measurement of the number of facilities available per 100,000 individuals within each state.  The study presented in Inequality in the Built Environment Underlies Key Health Disparities in Physical Activity and Obesity found a negative correlation between the number of recreational facilities available within neighborhoods and the prevalence of obesity.  While the study drew further conclusions on income disparity and its influence on access to recreational facilities, the findings about facilities and overweight individuals is most relevant to this paper.  (Gordon-Larsen et al. 421)  If individuals do not have access to parks, gyms, and other places where they can perform physical activity, they are more likely to become obese.  A negative correlation between access to recreational facilities and obesity is expected.

I employed Ordinary Least Squares estimation.  My study is based on the theory of supply and demand where obesity is the dependent factor determined by a number of independent factors.  I used the following linear equation:

OB = B0 + B1*AGE + B2*INC + B3*EDU + B4*FOOD + B5*REC

1. Empirical Work