1. 1
Table of Contents
I. Abstract 2
II. Introduction 3
III. Literature Review 5
a. Influences to Obesity 5
b. Obesity in relation to healthcare cost: 9
c. Conclusion 11
IV. Research and Methods 12
V. Results 18
VI. Conclusion 25
VII. Works Cited 27
VIII. Appendix 29
2. 2
Abstract
The obesity epidemic within the United States has reached new heights. The amount
of individuals who are overweight or obese has reached 69% in the year 2011 over two-
thirds of our population. Obesity can lead to various health problems including, but not
limited to heart disease, type 2 diabetes, cancer, depression, and physical impairments.
Approximately 300,000 deaths each year in the United States are associated with obesity or
being overweight (Miljkovic, Nganje, 2007). These effects are seen daily, whether through a
wage gap, physical job demand decrease and prominently through the cost of healthcare.
This study ran a two-part regression model, one specifically the correlation between
healthcare and obesity, and the other for the determinants of obesity. The data was
collected for a single year, 2015. Obesity in particular was contributable to a .64% increase
in the cost of healthcare per a 1% increase in obesity. These results imply the continual
growth of health spending is due to a controllable, and frightening factor.
3. 3
Introduction:
The United States of America has been known as one of the most prosperous
countries in the world. It is known for its opportunities, wealth to be offered and freedom
to name a few. However, over the past decade the U.S. has taken a leading role on a new
scale as one of the fattest countries in the world. The adult obesity rate has increased
drastically over the past twenty years. In 1990 the obesity rate was estimated to be 12%
(Clinical Journal of Oncology Nursing, September/October 2002). Flash forward to a study
conducted by Cynthia L. Ogden, PhD, for the National Center for Health Statistics and
Centers for Disease Control and Prevention, observed the years between 2003 and 2012,
which found that the adult obesity rate (ages 20 and older) was a staggering 34.9%. This is a
third of our population, roughly 78.6 million people.
Obesity can lead to various health problems including, but not limited to heart
disease, type 2 diabetes, cancer, depression, and physical impairments. Approximately
300,000 deaths each year in the United States are associated with obesity (Miljkovic,
Nganje, 2007). With this, the associated health care cost has increased. Through 2006 the
increased prevalence of obesity is responsible for almost $40 billion of increased medical
spending, including $7 billion in Medicare prescription drug costs (Eric A. Finkelstein et al.,
2009). With the obesity rate drastically increasing, this can suggest the increase in the cost
of healthcare as well. These increased costs could potentially be pushed onto tax payers,
obese and non-obese individuals. With a higher premium for healthcare, the money and
resources for that purpose means that it is being taken away from others such as quality of
food, standard of living or other potential insurances and costs.
4. 4
The rise in obesity does not only effect healthcare cost. In a daily routine the effects
can be noticed. In a work place, subjects who are overweight will have a harder time
completing tasks. This leads to a decrease in productivity. This decrease in productivity will
ultimately have an effect on the wage given to employees. Though this may seem as a
discriminatory factor, the potential employee is seen as less valuable because of the lower
productivity that is associated with them. Also, in a study conducted by Jay Bhattacharya
and M. Kate Bundorf (2009) using data from the National Longitudinal Survey of Youth and
the Medical Expenditure Panel Survey, they found that in many cases, the cost of employer
sponsored health care was offset by the wage given to the employee. By this example, the
study conveys that employers potentially factor in the cost of sponsored healthcare when
deciding what wage to give an employee. Thus showing how obese individuals will lack a
certain percentage of wage.
Due to this effect on the economy this study will look at factors effecting the cost of
health care, specifically the obesity rate. Also, this study will control for factors including
income, region, education, age, race, a person’s access to physical activity, food insecurity,
and the uninsured adults in the United States.
5. 5
Literature review:
Influences to obesity:
Obesity-related illnesses can lead to a staggering $209.7 billion industry (Cawley et
al., 2011). To grasp this, it is important to first have an understanding of what it means to be
obese or overweight. The World Health Organization (WHO) bases the definition of
underweight, overweight, obese, and healthy on BMI. BMI is the person’s weight in
kilograms divided by the square of the height in meters (kg/m2) (WHO, 1998). Table 1 is
used to more accurately describe how BMI is broken down.
CLASSIFICATION BMI
UNDERWEIGHT < 18.50
NORMAL RANGE 18.50-24.99
OVERWEIGHT ≥25.00
PREOBESE 25.00-29.99
OBESE CLASS 1 30.00-34.99
OBESE CLASS 2 35.00-39.99
OBESE CLASS 3 ≥40.00
The specific literature for this study will mainly focus on adults aged 18 and older as it is
difficult to accurately represent childhood obesity levels. This will be the main reference
point for classifying an individual. However, this is not a direct comparison to body fat
percentage. Therefore, there is the possibility of misclassification for athletes and muscular
persons (Rosin, 2008).
6. 6
The consequences of becoming overweight or obese are overwhelming. Large
portions of body fat stored can lead to illnesses such as coronary heart disease, stroke, high
blood pressure, Type 2 diabetes, cancers such as endometrial, breast, and colon, high total
cholesterol or high levels of triglycerides, liver and gallbladder disease, sleep apnea and
respiratory problems, degeneration of cartilage and underlying bone within a joint
(osteoarthritis), reproductive health complications such as infertility, and mental health
conditions (CDC). Obese individuals have a 50-100% increased risk of death when compared
to their normal weight counterparts (Mokdad et al.,2004; Flegal et al., 2005). These risks
greatly exceed the health problems associated with smoking and problem-drinking (Sturm,
2002).
With such great health risks the question is raised what would lead someone to
becoming overweight or obese. Rosin (2008) explains that much of the reason is due to
genetic, behavioral and environmental factors that affect a person’s energy intake and
energy expenditure. These main factors include schooling and education of obesity, price
levels when compared to other affordable options and income compared to the poverty
levels. These will be addressed.
In a study conducted by Kenkel (1991) he suggests that the amount of schooling one
possesses will contribute to that person’s weight, specifically the chance that they will
become overweight or obese. The argument is that the more someone has access to health
knowledge the less likely they will be to spend money or more cautious they will be on food
choices. However, a counter point to this is that the more education one possesses the
7. 7
more money they will make, therefore they will be able to purchase higher quality food. In
this study, Kenkel (1991) controls for income to limit the bias for this.
The main focus of this paper is to look closely at the factors that affect the cost of
healthcare in relation to obesity. Because the years of education is valued for decisions in
this study, this may play a role when focusing on price level. The more years of education
the greater the possibility of a higher wage. This gives the consumer a choice to make on
food and lifestyle adoptions. Lakdawalla and Philipson (2002) and Cutler et al. (2003)
suggest that calories have become cheaper to produce and consume while the cost of
exercising has increased. In simple economics the individual wants to maximize their utility
in their personal budget constraint. The individual has a variety of choices such as less
eating and more exercise, or counterintuitively, the person may eat more and exercise less.
This may lead to the increase in their BMI and the obesity rate. This can then lead to the
previous diseases which in time could increase healthcare cost.
Prices have a tendency to fluctuate due to the region. This can come in many
fashions based off of the cost living for that area. The cost of living is much higher in dense
metropolitan areas as opposed to more rural areas. A strong example of this are cities such
as New York City or San Francisco. However, there are many determinants to this. This is
where state policies may effect purchases of a consumer. Certain states may impose taxes
on items that result in the cost becoming largely higher when compared to other purchases.
On a smaller scale, French et al. (1997, 2001) found evidence within a study using vending
machines that price differentials between high-fat and low-fat snack substitutes would
cause people to change their consumption behavior. This would have a direct relation to
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obesity as the higher fat foods consumed might also lead to weight gain. Another study
examines more closely the price sensitivity in relation to the per capita number of
restaurants, specifically meals in fast food, full-service, food consumed at home, and
cigarettes and alcohol. This study by Chou et al. (2004) however, used a different measure
for obesity specifying that it depended on working hours, family income, relative prices,
schooling, and marital status, his results were still conclusive that the increase in weight
rose relative to prices of food at home declining. In other words, money spent on food at
home was less than that being spent on food consumed at convenience stores and fast food
restaurants (Chou et al., 2004). This can vary in magnitude depending on the urbanization of
the community. A more populated city will contain more restaurants, more convenience
stores, and more fast food options as opposed to rural areas.
More specifically, the built environment of a city can impact how healthy individuals
can be. A built environment consists of the neighborhoods, roads, buildings, food sources,
and recreational facilities in which people live, work, are educated, eat and play (Sallis,
Glanz, 2006). Communities that spend more money on areas for individuals to become
active will likely have a lower BMI rating and body fat. Sallis and Glanz (2006) state “People
who have access to safe places to be active, neighborhoods that are walkable, and local
markets that offer healthful food are likely to be more active and to eat more healthful
food.” This study being conducted will control for income. Therefore, the income and
poverty level in a area will contribute to consumption choices made. As with the built
environment, a lower income area will have less access to proper walkways and physical
activity stations. Also, there will be a reduction in the quality of food consumption.
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Drewnowski and Specter (2004) discuss four points that explain how income impacts the
choices of food consumption made. First, the highest rates of obesity are located within
areas of the lowest income and least education. Second, the inverse relationship between
energy density and energy cost. Higher energy dense foods come at the lowest cost to the
consumer. Third, sweets and fats are associated with higher energy intakes. Lastly, poverty
and low incomes are associated with lower food expenditures, thus diets based on refined
grains, added sugars and fats are more affordable than diets containing lean meats, fish,
fresh fruits and vegetables. Other theories such as a phenomenon referred to as
carbohydrate addiction support the urge for consumption of these types of foods. Other
explanations are the myopic addiction theory and rational addictions theory. The myopic
addiction theory implies that only the current and past, but not future, price and
consumption changes of the addictive goods have an impact on the current consumption
(Miljkovic, Nganje, 2008). The rational addiction theory implies that future price and
consumption changes of the addictive good have a significant impact on the current
consumption (Miljkovic, Nganje, 2008). Miljkovic and Nganje (2008) use myopic addiction as
their explanation of obesity.
Obesity in relation to healthcare cost:
Overall, in 1998 the medical cost of obesity was responsible for as much as $78.5
billion (Finkelstein et al., 2009). In 2008 that number climbed to $147 billion (Finkelstein et
al., 2009). Thorpe et al. (2004) found that obesity was responsible for 27% of the rise of
inflation-adjusted health spending between 1987 and 2001. Finkelstein et al. (2003) found
that the average annual medical expenditures are $732 higher for those who are obese as
10. 10
compared to normal weight individuals. This cost could be seen in a variety of aspects. One
view is the effect obesity has on employee-sponsored health care. Many companies will pay
a premium varying across individuals based on observable factors (Bhattacharya, Bundorf,
2009). The study conducted by Bhattacharya and Bundorf (2009) had two key findings. One,
it is employees who ultimately bear the cost of employee-sponsored healthcare. The wages
of obese workers varied across one another. The second finding was that obese workers
had lower wages than those of normal weight. They suggest that much of this may be due
to the higher cost to insure these workers. The study used a dependent variable as the
workers hourly wage. This would have helped to declare wage differences across various
types of workers, men, women, race, and obese or normal weight.
There has been a concern with technology advancements as a possible explanation
for the increasing cost of healthcare. The theory is that there needs to be funding for these
advancements and increasing healthcare would counteract this rapid increase. Thorpe et al.
(2004) notes that “the introduction of new medical technology is thought to account for
most of the growth in health care spending, while aging and population growth account for
smaller portions of the rise”. Thorpe argues this point in his study, stating that “studies have
not addressed the relationship between the increase in obesity prevalence and the growth
in costs over time”. In Thorpe et al. (2004) fourteen year study, the observations were that
the proportion of the population with normal weight had decreased by thirteen percent. At
this same time the proportion of adults categorized as obese increased by 10.3 percent.
These results have also been noted in the National Health and Nutrition Examination Survey
11. 11
(NHANES). These findings have shown a stronger correlation and account for more of the
reasoning for the increase in healthcare cost.
Presumably, if you are overweight or obese you are spending more for health care
as the possibility for complications due to obesity related diseases are much greater than
that of a normal weight individual. There is statically significant difference in these groups.
The estimated per capita spending on health care in 1987 was $2,188 (in 2001 dollars)
(Thorpe et al., 2004). In that year there was a 15 percent difference between normal weight
and obese individuals. Transfer to 2001 and the spending among obese has risen to 37
percent higher than the normal weight group (Thorpe et al., 2004). Inflation-adjusted
spending per capita increased by $1,110 between the years 1987 and 2001. However, if the
obesity rate had stayed at 1987 levels, per capita spending would have increased only by
$809 (Thorpe et al., 2004). Spending for obesity related conditions is attributed to the extra
$301. Therefore, obesity is attributable to 27 percent of the $1,110 spending growth. When
obesity prevalence is isolated, it is found to contain a 12 percent increase for the real per
capita spending growth (Thorpe et al., 2004).
Conclusion:
Obesity is one of the major epidemics to take over the United States in recent years.
It accounts for nearly a $40 billion increase in medical spending including $7 billion in
Medicare prescription drug costs (Eric A. Finkelstein,et al., 2009). To understand this impact
of obesity, it is first important to understand how, where, and why it can occur. The
previous factors discussed play a role in determining obesity, but also how obesity will
12. 12
ultimately determine the cost of health care. The discussion of obesity can relate widely to
the economy as it has a significant impact on work performance and employers decision.
Prices, region, access to physical activities, government policies, education and level of
income will ultimately play a role in obesity, but also how these obesity rates will impact the
economy and specially, health care cost.
Research and Methods:
My research will explore the relationship between the obesity rate and the
economic effects that follow this increasing statistic, specifically the cost of healthcare. The
data used for this research was taken from the County Health Rankings (CHR). The CHR
ranks each state and county from a sophisticated set of variables. The data the CHR uses is
from various sources from separate years. The years may be found on the CHR website. I
hypothesis that the increasing rate of obesity is largely positively correlated with the cost of
healthcare overall. I have developed a model to explain the variation in healthcare cost:
𝑦ℎ𝑙𝑡ℎ𝑐𝑎𝑟𝑒𝑐𝑜𝑖 = 𝛽0 + 𝛽1 𝑜𝑏𝑠𝑖 + 𝛽2 𝑀𝐻𝐻𝑖𝑛𝑐𝑖 + 𝛽3 𝑒𝑑𝑢 𝑖 + 𝛽4 𝑚𝑎𝑙𝑒𝑖 + 𝛽5 𝑀𝐴𝑖 + 𝛽6 𝑠𝑜𝑢𝑡ℎ𝑖
+ 𝛽7 𝑚𝑖𝑑𝑤𝑒𝑠𝑡𝑖 + 𝛽8 𝑛𝑜𝑟𝑡ℎ𝑒𝑎𝑠𝑡𝑖 + 𝛽9 𝑏𝑙𝑎𝑐𝑘𝑖 + 𝛽10 𝑤ℎ𝑖𝑡𝑒𝑖 + 𝛽11ℎ𝑖𝑠𝑝𝑎𝑛𝑖𝑐𝑖
+ 𝛽12 𝑎𝑐𝑐𝑒𝑠𝑠𝑝ℎ𝑦𝑠𝑎𝑐𝑡𝑖 + 𝛽13 𝑓𝑑𝑖𝑛𝑠𝑒𝑐𝑢𝑟𝑒𝑖 + 𝛽14 𝑈𝑛𝑖𝑛𝑠𝑢𝑟𝑒𝑑𝑖 + 𝑒
My dependent variable is represented by yhlthcareco. The constant in the equation is 𝛽0.
The independent variables are represented as 𝛽1 through 𝛽14 with the regional variables as
dummies. The data collected for this study was on the county level for the entire United
States. This study was focused on one year, 2015 thus accounting for 3140 observations.
This amount of observations will give a significant insight upon the range for all variables.
13. 13
Because the cost of healthcare is so large this study will take the natural log on healthcare,
meaning that a one unit increase in an independent variable with all others held constant
will lead to a 𝛽 ∗ 100 percent increase or decrease in the cost of healthcare. Specifically,
this is measured from the Dartmouth Atlas of Health Care which has documented glaring
variations in how medical resources are distributed and used in the United States
(Dartmouth, 2015). The project uses Medicare data to provide information and analysis
about national, regional, and local markets, as well as hospitals and their affiliated
physicians (Dartmouth, 2015).
Dependent Variable: Cost of healthcare (Yhlthcareco)
Healthcare has been a presidential debate over many decades. In recent events, the
cost of health care has risen drastically and many Americans are concerned about this
trend. It is important to look at the various reason as why healthcare cost has risen. Also,
these results may be significant for future expectations of increasing costs. I believe that
obesity and the health consequences derived from it will be a leading cause. I expect the
correlation between healthcare cost and obesity to be positively correlated.
Variable of Interest: % that report BMI > 30 (𝛽1Obs)
The World Health Organization (WHO) bases the definition of obese on BMI. BMI is
the person’s weight in kilograms divided by the square of the height in meters (kg/m2)
(WHO, 1998). Specifically, to be obese ones BMI must be greater than 30. As the BMI
increases the more obese you become the greater of the chances one has of developing
14. 14
health problems. The increase in health problems could lead to a greater need for health
coverage and the use of it more frequently. Thus, raising the cost of health coverage.
Independent Variables:
Median household income (𝛽2MHHinc)
The ability for an individual to pay for healthcare can be dependent upon the
amount of income that person creates per year. Higher incomes would be more likely to
purchase higher, more costly health coverages. In contrast, the lower income an individual
holds will lead them to purchase lower healthcare, if any at all. Since many individuals
reside under their parents or guardians healthcare, this study has controlled for the median
household income. The hypothesis of this is that the more income within a household the
higher the health coverage will be making this positively correlated.
% attaining higher education (𝛽3Edu)
Higher education levels allows one to be prosperous in many areas of life. For
example, the higher level of education a person has the better a job one should be able to
attain, therefore leading to higher wage. This higher wage would allow this person to buy
better qualities of food, improve living situations, and allocate more money into the
economy. However, this higher level of knowledge would in theory, allow someone to
obtain more knowledge of living a healthier lifestyle and avoiding the chance to become
obese. This study focuses on those adults aged 25-44 who have obtained some post-
secondary education, meaning there has been some college or more of experience. This
factor is hypothesized to be negatively correlated to healthcare.
15. 15
Gender (𝛽4Male)
The importance of gender can be found in a plethora of areas. The gender gap for
wage is one example. On average, men earn more per dollar than women. This may
influence the type of health coverage that a person may purchase. The propensity to
consume more food than the opposing female is also another potential explanation. In
general, men engage in work and home environments that are more dangerous. This
exposes men to more injuries and would in turn need better, more expensive, healthcare.
The percent of male population per county may have a significantly positive correlation to
the cost of healthcare.
Median age in the county (𝛽5MA)
The median age of the county will provide insight on how they rank compared to
one another. BMI is a function from height and weight but, looking at the age will help
signify this even more. The younger population may not spend as much on healthcare as
their budget will be consumed by other factors. This younger population also tends to be
healthier when compared to older generations. The older the population the more likely the
cost of health care will increase as health problems ascend. A higher median age will likely
be significantly, positively correlated to healthcare.
Regions (Dummy variables) (𝛽6South) (𝛽7Midwest) (𝛽8Northeast)
A regional dummy variable will help account for why different counties may
experience higher levels of health care cost. For example, those areas that experience
16. 16
hotter weather will be able to exercise more therefore lower the cost of health care. In
contrast, the colder climates will have trouble getting outside and becoming active. This
dummy variable will also account for unobservable factors.
Race (𝛽9Black) (𝛽10White) (𝛽11Hispanic)
The race of an individual has a significant impact on their ability to pay for health
coverage. Traditionally, minority groups such as those who identify as black/ African
American or Hispanic, obtain lower incomes making it harder to pay for premium health
coverage. Those with lower incomes are more likely to purchase lower quality health
coverages.
% with access to physical activity (𝛽12Accessphysact)
The percent of the population that has access to physical activity accounts for the
opportunity that people within the county will take advantage of working out. The more
access someone has to physical activity the healthier they should become. The percent of
the population that has access to physical activity is likely to be significantly, negatively
correlated to the cost of healthcare.
% Food Insecure (𝛽13fdinsecure)
Food insecurity is the most broadly used measure of food deprivation in the United
States. Food insecurity refers to USDA’s measure of lack of access, at times, to enough food
for an active, healthy life for all household members and limited or uncertain availability of
nutritionally adequate foods (Feeding America). This measure allows the quality of food
that a household is consuming to be examined. Assuming that the price of food impacts
17. 17
buying decisions, the family may buy more amounts of lower quality food. The lower the
quality the higher the percentage of gaining weight. In contrast, the family may purchase
no food leading to malnutrition and other diseases. This does not mean, however, that
these families are food insecure all of the time. Food insecurity may reflect a household’s
need to make trade-offs between important basic needs, such as housing or medical bills,
and purchasing nutritionally adequate foods (Feeding America). These buying adaptations is
the reason for controlling this variable.
% Uninsured adults (𝛽14Uninsured)
Some individuals choose to remain uninsured during a period of their life. This
involves taking a large risk. Tragedies may occur at any time. The data used for this variable
is collected for the Small Area Health Insurance Estimates (SAHIE) within the United States
Census Bureau. This accounts for individuals under the age of 65. The basis for these
estimates comes from the American Community Survey (ACS). The ACS specifically asks if
this person is currently covered as opposed to being covered only some time during the
year. The estimates for county levels come from a cross-classification defined by the same
age, sex, and income groups. We assume these survey estimates are unbiased and follow
known distributions. This will help explain the variation in average cost of healthcare. Due
to those that do not purchase health coverage the hypothesis is that this variable will be
negativity correlated, thus as the percent of uninsured adults increases it will lower the
cost.
18. 18
Results:
The hypothesis of this study is to understand the correlation of obesity with the cost
of healthcare. Specifically, that the increase in obesity would cause a rise in healthcare. The
other controlled factors that could impact healthcare costs were the median household
income, education, being male, median age, region, race, access to physical activity, food
insecurity, and the percent that were uninsured within the county. Obesity itself
contributed a .64% positive correlation and statistically significant change in the cost of
healthcare. This measure is important because of the growing contributions to obesity.
More and more individuals are subjecting themselves to an increased financial hazard that
can be avoided. To continue the analysis, it is noted that the following values are in terms of
the robust, after performing a Breusch-Pagan test for heteroskedasticity. To further
understand the results from the study, the full results may be seen in table 2 of the
appendix.
The region dummy variables proved to have a significant impact upon the cost of
healthcare. The region of a consumer is large contributor due to many factors such as state
law, activities, weather and the cost of living. The regions tested were the south, midwest
and northeast all compared to the omitted, the west. Each was proven to be statistically
significant with being located in the south having the largest impact of a 22% increase to
healthcare. Second was the northeast with a 19.4% increase and lastly the midwest at
13.9%. This may make sense as those more towards the west have more sunny days which
result in more potential for active days outside thus producing a healthier body and lower
costs towards healthcare. However, this contrasts the results from the percent that have
19. 19
access to physical activity. This variable had a very low relationship impact on healthcare
and have rejected the null hypothesis. There may be a possibility that even though
someone has the access to do the physical activity they may choose not to partake within
the activities present.
The demographic variables such as being African American, Caucasian or Hispanic
compared to the omitted group, Asian, performed poorly as well. The Caucasians and
Hispanics proved to be statistically significant and increased healthcare by .27% and .24%
respectively. In contrast, being African American did not affect healthcare nearly as much at
a .009% level increase and was also found to be statistically insignificant. Another
demographic was the difference between being male or female. The odds of being male led
to a .16% increase in healthcare. This may be due to that males tend to consume more and
are also traditionally in more physically demanding and dangerous jobs when compared to
women. However, this was also found to be insignificant at the 5% significance level thus
we reject the null hypothesis.
Certain variables add insight and explanation to the economy and eventually to the
study at hand. These variables were median household income, education, and the median
age. Median household income was found to be statistically significant, however, impact of
the coefficient was incredibly small, accounting for a 1.08e-04 percent affect upon
healthcare for every one percent increase in median income. This finding is shocking as the
amount of income being obtained has a significant power upon what type of coverage
someone can purchase. Education was found to be negatively correlated as predicted. For
every year of college experience obtained by someone, this would lower the cost of
20. 20
healthcare by .059 percent. Lastly, median age proved to have one of the largest impacts.
For each additional year of aging the cost of health care would increase by .71 percent. This
was expected, as one ages the more possibility of health problems can occur.
Two unique variables used in this study were the food insecurity percent and the
percent that were uninsured. Food insecurity had the highest impact between these two
variables with a .92% increase to healthcare. The rationale behind this is could be that those
who are food insecure tend to choose foods that are cheaper, calorie dense foods like those
found in McDonalds, Burger King, and other fast food restaurants instead of higher quality
foods or the consumer may choose to skip meals at a time. In either case, this can lead to
weight gain and other contracted diseases froma weakened immune system. The second
unique variable is the percent that are uninsured. This statistic led to a .28% increase in
cost. Due to those not paying into healthcare, it could raise it for others since there is not
enough going into the funding, therefore, demanding more from others. Since the
description for this variable states that at the present moment if someone is currently
covered, this would ultimately raise the people’s rate in the future if they were not. Also,
due to inflation purposes this could also explain the increase for the future buying of
coverage.
To follow up my observations I performed another regression to assess the
relationship of my variables to obesity. This regression used the same previously stated
formula. The only differences were that the cost of health care was substituted out and
replaced with the percent that were obese within the counties. This equation was not
logged and contained the same sourcing of data as the previous regression, therefore, it
21. 21
contained 3140 observations as well. The results for this regression found a significant
explanation for the variation in obesity with an R-squared value of .604, or 60.4% variation
in obesity. All explanations of values will be in terms after conducting a Breusch-Pagan test
for heteroskedasticity. The full results of this regression may be seen in table 3 of the
appendix.
The demographic variables, the south, Midwest and northeast accounted for a 3.9,
4.1 and 2.3 percent increases respectively towards obesity. These drastic numbers are in
line with the previous significant impact they played when regressing healthcare cost. This
makes sense as those that live in better weather may take full advantage of being more
active and healthier. The racial demographic variables showed that being white or Hispanic
would lower the obesity while African Americans increased the obesity percent.
The economic variables such as income, median age and education had strong
impacts upon obesity. Education and income were negatively correlated, reducing obesity
by -.07 percent for each additional experience of education and -.0001 percent for each
increase of income. Median age had one of largest accounts for increases in obesity. The
coefficient value of .36 would suggest a .36 percent increase in weight, or obesity, due to a
one year increase in age. This, again, makes sense as older people will hold more weight for
various reasons.
Two shocking statistics were the results for food insecurity and the percent that
were uninsured. Being food insecure led to a .013 percent increase in obesity. This can be
expected because of unhealthier food choices. However, the statistic was found to be
22. 22
insignificant. Due to unforeseen factors this comes as a shock to the model. The percent of
uninsured adults, those with no coverage, was found to be negatively correlated to obesity
with a -.07 percent change.
Lastly, the variable with the most shock came from the percent that have access to
physical activity. This was negatively correlated with obesity, as one would expect, however,
at a very small rate of -.013. As noted, this may be due to those that have this opportunity
but choose to forgo the actively to perform some other action.
A last check in analyzing the results for this study was to look at the correlation
between the variables. This is important to account for because a correlation between the
variables would indicate a bias within our study, thus leaving the results almost useless.
Based on this concern, a correlation matrix was constructed and it depicts that a high
degree of correlation between the independent variables was not found. The results of this
matrix can be found in table one of the appendix.
To further confirm the results from this study, they will be compared to previous
studies that have tried to accomplish the same goal. Comparing the results to other studies
will signify the importance of the results. The studies that will be compared are Finkelstein
(2009), Thorpe (2004), Bhattacharya and Bundorf (2009) and Cawley and Meyerhoefer
(2011). Each of these studies specifically looked at the effect the increased obesity rate has
had upon health and medical coverage, each using its own unique variable approach. It is
also important to note that these studies were conducted over multiple years, in which
23. 23
time, were able to run multiple regressions and variable approaches to additionally explain
their hypotheses.
Finkelstein (2009) discovered during a two year study between 1998 and 2006 that
overall, obesity had increased the cost of medical spending by 37%. When comparing
normal-weight individuals to obese individuals the cost that the obese exceeded $1429
more than normal-weight persons. The regression model used for this was a four part study
finding specific estimates for inpatient, non-inpatient or drug prescription services. While
Finkelstein used a deeper value as calculating the money spent and a four part regression
model, this study further exhibits the value of obesity increasing the cost of healthcare
spending. His findings of a positive correlation when controlling for obesity back up the
results that were found in the current study.
Thorpe (2004) like other studies used a two part model. Within these models he
discovered that in 1987 the difference of pay that normal and obese individuals pay is
15.2% less, and 37% by 2001. In a per capita income model, Thorpe explains that the
increase in spending growth was contributable to obese by up to 12%. He associates this
increase in spending to obesity by stating that with obesity comes greater risk for
contracting disease and the spending to cure or treat those diseases.
Bhattacharya and Bundorf (2009) collected data from the National Longitudinal
Survey of Youth (NLSY) and from the Medical Expenditure Panel Survey (MEPS). With this
data they were able to estimate the difference-in-difference of the effect of obesity on
hourly wage. With employee sponsored healthcare, obese workers earned approximately
24. 24
$1.42 per hour less on average than their counterparts. In contrast, the difference-in-
difference estimate for employees that did not get healthcare from their employer was
$.25. This statically shows that obesity is effecting the cost of healthcare. In this form, it
offsets wages as employers fear that this individual is costing more than others.
Cawley and Meyerhoefer (2011) used a unique instrumental variable approach to
reduce the bias from reporting errors in weight. This study also used data from MEPS for
the years 2000-2005. The importance of this study is to show that many of the previous
studies have underestimated the contributable obesity factor to healthcare. Within their
study they concluded that the additional unit added of BMI will increase medical spending
by $49 and a $59 increase if you happen to be male. For total expenditures the estimated
marginal effect of obesity proved to bring an estimated $2741 of spending. However, two
key facts should be kept in mind when comparing his study. This study was comparing
obese to non-obese (this is included healthy weight and overweight). Secondly, this study
used adults with at least one biological child. This may bring about bias as this leans to
individuals that may be healthier.
Overall, the current study being performed was able to explained roughly 38% of the
variation in the cost of healthcare. Obesity specifically accounted for a higher portion of
change than most other variables with a 64.4 unit change. This is important to understand
because of the ability for individuals to change this. Most factors that apply to healthcare
cannot be easily changed. This study varies different from others as they did not correlate
their variables to explain obesity. Explaining obesity allows for a more in-depth look at how
obesity would increase, in turn increasing the cost of health care. Some changes that could
25. 25
be altered for future studies would be a more specific allocation of money within a
household to gage what the household is paying for per month. Another factor would be to
take into consideration the type of laws that effect each state for healthcare. Many times
people are covered under very specific situations, which could also result in lower or higher
cost of coverage. Accounting for the population density would allow for more accurate
mapping of health cost. This would allow for notice of trends within the country of lower
costs. A final change to be done to this study be to find a more accurate measure of some
the statistics because most were self-reported. This may leave room for error on both sides,
those that report and those that choose not to report. Due to time factors, to better
understand these numbers, the optimal study would be to compare it to other years. This
would allow better understanding of exactly how big of an issue obesity has caused to
healthcare and our society.
Conclusion:
These results reveal continues economic burden upon health coverage, specifically,
the relevance of obesity. The connections between rising healthcare costs and obesity rates
is undeniable. More accurately, the results speak to say that obesity is a large contributor to
the consistent increase in costs. Unlike other studies, this one in particular accounted for
the ability to exercise and food insecurity. The ability to exercise was found to be
insignificant this can be due to foregoing the opportunity to partake in a physical activity to
perform a task deemed more important. However, it is very relative because of the chances
that the more exercise opportunities available to more likely it is that someone would lose
weight and prevent obesity. Food insecurity is extremely important because of the
26. 26
situations where families must decide how much money to spend on what type of food or if
food is even purchased at all. In theory this impacts the amount of money put towards the
purchase of health coverage, but does not impact the cost that it would come to. The statics
show that food insecurity is positively correlated to the cost of health care and actually
raises the total amount to buy. These were the types of differences within this study
compared to others that should be noted.
27. 27
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