THE ROLE OF OBESITY ON MOOD AND ANXIETY DISORDERS
A Thesis
Presented to the faculty of the Department of Psychology
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
Psychology
by
Najia Nafiz
FALL
2012
THE ROLE OF OBESITY ON MOOD AND ANXIETY DISORDERS
A Thesis
by
Najia Nafiz
Approved by:
__________________________________, Committee Chair
Rebecca P. Cameron, Ph.D.
__________________________________, Second Reader
Lawrence S. Meyers, Ph.D.
__________________________________, Third Reader
Peter M. Yellowlees, M.D.
____________________________
Date
ii
Student: Najia Nafiz
I certify that this student has met the requirements for format contained in the University format
manual, and that this thesis is suitable for shelving in the Library and credit is to be awarded for
the thesis.
__________________________, Graduate Coordinator ___________________
Lisa Harrison, Ph.D.
Date
Department of Psychology
iii
Abstract
of
THE ROLE OF OBESITY ON MOOD AND ANXIETY DISORDERS
by
Najia Nafiz
The present study was designed to investigate the co-occurrence of obesity, mood
disorders, and anxiety disorders among a rural population. The sample (N = 117)
consisted of Hispanic Americans and non-Hispanic whites with an average body mass
index of 30.9, which is considered obese (range 15.7 - 56.5). Data on the mental health
status of the participants was collected using the SCID-I and MINI. Results indicated a
significant difference in the proportion of obese adults in the sample as compared to the
county, the state, and the national averages. Odds of non-Hispanic white females being
obese were nearly four times greater, and the odds of Hispanic males being obese were
five times greater, than the odds of non-Hispanic white males being obese. Odds of
Hispanic females being depressed were seven times greater than odds of Hispanic males
being depressed. Prevention and treatment plans need to be implemented in rural
communities.
_____________________, Committee Chair
Rebecca P. Cameron Ph.D.
_______________________
Date
iv
ACKNOWLEDGEMENTS
So many people have been instrumental in the completion of my education. I have to thank
Dr. Rebecca Cameron who from the beginning took me under her wing. Thank you for believing
in me, supporting all of my endeavors, and pushing me when I needed to be pushed. Thank you
also for encouraging me to pursue all the different routes I have taken and to support me no
matter which route I chose.
Dr. Lawrence Meyers, thank you for all of your countless hours of helping me understand
research methodology and statistics. Also, thank you for giving me my first teaching opportunity
that helped me realize how much I love teaching.
To Dr. Peter Yellowlees, you have been an incredible mentor and advisor. Thank you for
believing in me and giving me the opportunity to work with you. I always felt like your door was
open, as you always made time for me to discuss the many paths I’ve explored. I owe a large debt
of gratitude to you.
I also have to thank my parents who gave up everything in order to give their children a
chance to succeed. Thank you for allowing me to create my own paths and supporting me
regardless of the outcomes. To my siblings Sara, Najma, and Zubair, my wonderful friends, and
my ever so patient and loving fiancé Joseph, I love you all and thank you because without all of
your hours of bouncing ideas with me, sitting in coffee shops to write, and general love and
support this process would have been so much more difficult.
v
TABLE OF CONTENTS
Page
Acknowledgements ................................................................................................................... v
List of Tables ......................................................................................................................... vii
Chapter
1. INTRODUCTION ……………………………………………………………………….. 1
Obesity ................................................................................................................................ 3
Key Factors in the Rise of Obesity .............................................................................. 5
Trends in Obesity ........................................................................................................ 8
Psychiatric Disorders ........................................................................................................ 10
Mood Disorders ......................................................................................................... 10
Anxiety Disorders ...................................................................................................... 12
Trends in Psychiatric Disorders ................................................................................. 15
Rural Communities ........................................................................................................... 16
Current Study .................................................................................................................... 18
2. METHOD ......................................................................................................................... 20
Participants................................................................................................................. 20
Materials .................................................................................................................... 21
Procedures.................................................................................................................. 22
Data Analysis ............................................................................................................. 23
3. RESULTS ......................................................................................................................... 25
4. DISCUSSION ................................................................................................................... 31
Limitations ................................................................................................................. 33
Future Research ......................................................................................................... 35
Implications ............................................................................................................... 36
Appendix A. Psychiatric Disorders Prevalence for Sample ................................................. 37
Appendix B. Psychiatric Diagnoses for non-Obese and Obese Participants ....................... 38
Appendix C. Obesity Prevalence with Co-morbid Psychiatric Disorders ............................ 39
References ............................................................................................................................... 40
vi
LIST OF TABLES
Tables
Page
1. Demographic Characteristics of Participants .................................................................. 21
2. Comparison of the Demographics for the Sample, County, State, and Nation ............... 25
3. Obesity Comparisons across the County, State, and Nation ........................................... 26
4. Prevalence of Obesity (BMI > 30) across Gender for non-Hispanic whites................... 27
5. Prevalence of Obesity (BMI > 30) across Males and Ethnicity ...................................... 28
6. Mood Disorders and Gender ........................................................................................... 29
7. Mood Disorders across Gender for Hispanics ................................................................ 30
vii
1
Chapter 1
INTRODUCTION
The healthcare industry in the United States is faced with unprecedented increases
in treatment and management of chronic diseases. Two of the major contributing factors
to this public health dilemma are obesity and mental health disorders. Researchers have
observed a dramatic increase in rates of obesity over the last 20 years in the U.S. (Flegal,
Carroll, Ogden, & Curtin, 2010; Centers for Disease Control and Prevention [CDC],
2012a). According to the World Health Organization (WHO, 2012a) obesity has more
than doubled since 1980 worldwide and is now considered the second leading cause of
preventable death. Smoking remains the leading cause, but researchers are estimating that
if trends continue, obesity will surpass smoking to become the leading cause of
preventable death (Koh, 2010).
It is also estimated that in a given year approximately one in four American adults
will experience a mental health disorder, and 22% of those will have a serious mental
health disorder (defined as a 12-month suicide attempt with serious lethality intent; work
disability or substantial limitation due to mental or substance disorder) (Kessler, Chiu,
Demler, & Walters, 2005a). Depression is currently the leading cause of disability,
affecting approximately 350 million people worldwide (WHO, 2012b). Obesity and
mental health disorders can also become co-morbid with other chronic conditions,
potentially by precipitating disease or exacerbating pre-existing disease states. This cycle
2
of disease is contributing to the billions of dollars spent on treatment and management of
disease, as well as reduced quality of life and increased mortality (WHO, n.d.).
It is estimated that in 2010 national healthcare expenditures neared $2.6 trillion
compared to $256 billion three decades ago (Centers for Medicare & Medicaid Services,
Office of the Actuary, n.d.). Treatment of chronic diseases (such as heart disease, cancer,
stroke, and diabetes) is a major factor accounting for over 75% of healthcare costs (CDC,
2009). In 2006, the cost of mental health care in the U.S. was estimated to be nearly
$57.5 billion (National Institute of Mental Health [NIMH], n.d.). The current state of
obesity in the U.S. is exacerbating this problem by contributing not only to physical
chronic conditions such as diabetes and heart disease, but also potentially affecting
mental health conditions such as depression. Although there is a plethora of information
on obesity and chronic physical health conditions, research on links between mental
health conditions and obesity is lacking.
The obesity epidemic, albeit a major health and cost concern throughout the U.S.,
could be especially problematic for rural communities. Residents of rural communities
may be at higher risk for obesity, psychological distress, and co-morbid physical health
ailments due to their geographic location and socioeconomic status. These areas are often
lacking vital resources in terms of the availability of specialty healthcare practitioners,
nutritious food, health education, and fitness facilities as compared to their urban
counterparts (Gamm, Hutchinson, Bellamy, & Dabney, 2002; Gordon-Larsen, Nelson,
Page, & Popkin, 2006; Rose & Richards, 2004). Research and preventative solutions are
desperately needed in these hard-to-reach communities.
3
The present study was designed to investigate the co-occurrence of obesity and
several of the most common mental health disorders (i.e., mood and anxiety disorders)
among a rural population. Before presenting findings, I will first review the current state
of obesity, key factors contributing to the rise in obesity, and trends in obesity, with a
focus on socioeconomic status, gender, and ethnic differences. Second, I will discuss the
current state of psychiatric disorders, specifically focusing on anxiety and depression. I
will further discuss psychiatric trends in terms of socioeconomics, gender, and ethnicity.
Throughout the paper I will compare rural communities to their urban counterparts.
Obesity
According to WHO (2012a), overweight and obesity are defined as excessive fat
accumulation that may impair health. Obesity is defined by the Body Mass Index (BMI),
which is used as a standard to measure what constitutes a healthy, or unhealthy, weight.
BMI measures weight status based on an individual’s height and weight. The Centers for
Disease Control and Prevention offer the following breakdown of the four categories of
BMI: a score of 18.5 or below is underweight, 18.5 - 24.9 is normal, 25 - 29.9 is
overweight and a BMI greater than 30 is considered obese (CDC, 2011a).
In the U.S. obesity has become so prevalent that it is considered an epidemic by
researchers and healthcare providers. Researchers are estimating that if trends continue,
obesity will surpass smoking to become the leading cause of preventable death (Koh,
2010). Whereas two decades ago 22.9% of adults were obese, it is estimated that
currently one in three American adults are obese, or roughly one-third of the general
population (35.7%) and another 33% are overweight (Fryar, Carroll, & Ogden, 2012;
4
Ogden, Carroll, Kit, & Flegal, 2012). In 2010, 24% of Californians were considered
obese (In terms of obesity Mississippi ranked highest at 34% and Colorado lowest at
21%) (CDC, 2012a).
Obesity has been linked to chronic illnesses such as cardiovascular disease, Type
II diabetes and certain types of cancers (Barry, Pietrzak, & Petry, 2008; Dragan &
Akhtar-Danesh, 2007; WHO, 2012a). Raised BMI is a major risk factor for
cardiovascular disease, which was the leading cause of death in 2008 (WHO, 2012a).
Worldwide, diabetes affects 347 million, and Type II diabetes (which is predominantly
due to excess body weight and physical inactivity) accounts for nearly 90% of those cases
(WHO, 2012c). It is estimated that approximately 26 million Americans have diabetes
and another 79 million adults are pre-diabetic (CDC, 2011b). Risk factors for Type II
diabetes include obesity, age (with the highest rate of newly diagnosed cases occurring
among those between the ages of 45-64 years), and ethnicity (for example, Type II
diabetes affects 11.8% of Hispanic Americans versus 7.1% of non-Hispanic whites)
(CDC, 2011b).
The obesity epidemic cost Americans $147 billion dollars in 2008 compared to
$78.5 billion a decade prior (Finkelstein, Trogdon, Cohen, & Dietz, 2009). This cost
takes into account several factors, such as: the direct medical costs for diagnosis and
treatment of Type II diabetes; heart disease and hypertension; loss of productivity at work
due to absenteeism because of obesity-related health issues; decreased productivity while
at work; disability leave; and premature mortality due to obesity (Hammond & Levine,
2010).
5
Key Factors in the Rise of Obesity
There are many contributing factors to the stark increase in obesity rates; some of
the major factors are discussed in this section. These include portion size, fast food, high
fructose corn syrup, inactive lifestyles, and certain medical conditions.
Portion size. In the past several decades portion sizes have increased, and
continue to grow, in parallel with the rise in body mass (Young & Nestle, 2002). Studies
have demonstrated that the more food we are presented with, the more likely we are to
overeat (Wansink, Painter, & North, 2005; Rolls, Morris, & Roe, 2002; Rolls, Roe, Kral,
Meengs, & Wall, 2004). Increases in portion size influence our expectations of how much
to eat and lessen our reliance on self-monitoring (Wansink et al., 2005). This was
exemplified in a study of soup consumption in which one group received a normal bowl
of soup while another group received a self-refilling bowl. Results indicated that those in
the group with refilling bowls consumed 73% more soup than those in the group with
normal bowls, demonstrating the importance of visual cues in relation to portion size
(Wansink et al., 2005). Rolls et al. (2002) found that portion size significantly influenced
energy intake whereby subjects consumed 30% more food when offered the largest
portion size entrée than the smallest portion size entrée. In another study comparing
snack portion sizes, Rolls and colleagues again found a significant increase in
consumption of potato chips as they increased portion sizes of the potato chip bags
offered to participants (Rolls et al., 2004).
Fast food. Over the last 30 years, Americans’ spending on fast food increased
from $6 billion to $110 billion (Schlosser, 2011). Fast food chains are readily available
6
and offer high fat, high calorie, inexpensive food in a fraction of the time and cost it
would take to prepare a meal at home. Neighborhoods with predominantly minority and
low socioeconomic status (SES) residents have higher prevalence of fast food chains than
predominantly white or higher SES neighborhoods. Specifically, researchers found that
predominantly black neighborhoods have 2.4 fast food restaurants per square mile
compared to 1.5 restaurants in predominantly white neighborhoods (Block, Scribner, &
DeSalvo, 2004). Other studies have found similar results where poorer neighborhoods
have greater access to fast food establishments (Lee, 2012).
High fructose corn syrup. High fructose corn syrup (HFCS) is a sweetener
commonly added to processed foods such as certain beverages (i.e., sodas, fruit juices)
baked goods, certain cereals, breads, canned fruits, jams, jellies, desserts, and flavored
yogurts (Bray, Nielsen, & Popkin, 2004; Hanover & White, 1993). Researchers have
linked this widely-used sweetener to the rise in obesity. A recent study discovered that
rats with access to HFCS gained significantly more weight and augmented fat deposition
compared to the control/sucrose group, even though both groups consumed the same
amount of calories (Bocarsly, Powell, Avena, & Hoebel, 2010). Translated to humans,
this could mean that people who consume more HFCS will be more likely to be obese. In
the U.S. consumption of HFCS increased more than 1000% between 1970 and 1990
(Bray et al., 2004). Currently, HFCS accounts for 40% of all added caloric sweeteners,
and it is the sole caloric sweetener in most soft drinks in the U.S. (Bray et al., 2004). It
should be noted however, that the role of HFCS in obesity is controversial, and additional
research is necessary.
7
Inactive lifestyles. Physical activity plays a significant role in weight
management. Most Americans lead sedentary lives with numerous hours spent sitting in
front of a computer screen or the television. We tend to rely on cars for transportation
rather than walking or riding bicycles. Those living in rural communities, or areas with
lower SES, are at special risk of inactivity. In part, this may be due to limited access to
physical activity facilities since research demonstrates that public facilities, YMCAs, and
youth organizations are significantly more likely to be in higher SES, low-minority,
communities (Gordon-Larsen et al., 2006). Other studies have found that rural women are
more sedentary than urban women, with correlates of sedentary behavior including older
age, less education, lack of enjoyable scenery, not frequently seeing others exercise, and
lower levels of social support (Wilcox, Castro, King, Housemann, & Brownson, 2000).
Combined, the intake of high calorie-dense foods and lack of physical activity leads to an
energy imbalance, in which more energy is consumed than expended, leading to weight
gain and eventually to increased risk of obesity (National Institute of Health [NIH],
2012).
Medical conditions. It is important to note that not all obesity cases are due to
eating habits and physical inactivity. Physical health conditions such as hypothyroidism
(an under active thyroid, which decreases metabolism), Cushing syndrome (where the
body makes too much of the hormone cortisol) and polycystic ovarian syndrome (a
condition affecting 5-10% of women in which high levels of hormones called androgens
are present) can all cause weight gain. Emotional factors such as eating out of boredom,
8
anger, or stress can also lead to weight gain. Finally, weight gain is a side effect for
certain medicines (NIH, 2012).
Trends in Obesity
Although obesity is on the rise in general, its patterns are not uniform. Obesity
rates differ based on several characteristics of the population. These differences include
gender, ethnicity, socioeconomic status, and psychiatric status.
Gender differences. As of 2008, 1.5 billion adults worldwide were overweight,
and of this group over 200 million men and nearly 300 million women were obese
(WHO, 2012a). Although the gender difference in obesity is not significant, obesity rates
for women are slightly higher than for males (35.5% and 33.9%, respectively) while
males are more likely to be overweight than females (73.3% versus 63.9%) (CDC,
2011c).
Ethnicity. Hispanics are consistently more likely to be obese than non-Hispanic
whites (Petry, Barry, Pietrzak, & Wagner, 2008; Simon et al., 2006; CDC, 2012b).
According to the U.S. Department of Health and Human Services (2012), in 2010
Hispanics were 1.2 times more likely to be obese than non-Hispanic whites. With regard
to gender, Hispanic females were more likely to be obese than Hispanic males and
significantly more likely to be obese than non-Hispanic white females. Non-Hispanic
white males were slightly more likely to be obese than non-Hispanic white females
(CDC, 2011c; Ogden et al., 2006). Hispanic males were also more likely to be obese than
non-Hispanic whites (CDC, 2012b). Various factors may contribute to this difference in
9
obesity rates, including differing cultural norms with regard to body size and differences
in eating or exercise behavior.
Socioeconomic status. Several decades ago there was a clear inverse relationship
between socioeconomic status (SES; measured in terms of education, income, and
occupational status) and obesity; this was especially pronounced for women (Moore,
Stunkard, & Srole, 1962; Zhang & Wang, 2004). Although the link between SES and
obesity has abated slightly over the past three decades (Zhang & Wang, 2004) various
SES factors, such as level of education, poverty and geographic location, are still risk
factors for obesity (Drewnowski, 2004; Tanumihardjo et al., 2007; Scott, McGee, Wells,
& Oakley-Browne, 2008b). This can be clearly seen in the higher rates of obesity found
in rural communities (which are often characterized by higher rates of poverty and lower
levels of education) as compared to their urban counterparts (Probst et al., 2006). Higher
rates of obesity are also observed with rural women as compared to their urban peers
(Patterson et al., 2004).
Psychiatric status. The average person seeking psychiatric treatment has been
found to have a higher BMI when compared to those not seeking psychiatric treatment
(Dickerson et al., 2006). Several reasons for the high rates of obesity in psychiatric
populations have been posited; these range from insufficient access to primary and
preventive health care, poverty, and the side effects of psychotropic medication (Health
Resources and Services Administration [HRSA], 2006; Littrell, Hilligoss, Kirshner,
Petty, & Johnson, 2003; Monteleone, Martiadis, & Maj, 2009).
10
Several psychotropic medications are associated with clinically significant weight
gain. This is especially true for atypical antipsychotics (Littrell et al., 2003; Monteleone
et al., 2009). Most antidepressants, such as traditional tricyclics and monoamine oxidase
inhibitors (MAOI), are known to increase weight, as are the new selective serotonin
reuptake inhibitors (i.e., Prozac, Zoloft, and Paxil) (Harvey & Bouwer, 2000; Fava,
2000).
Psychiatric Disorders
It is postulated that nearly a quarter of the adult American population will
experience a psychiatric disorder in any given year (Kessler et al., 2005a). Psychiatric
disorders are typically co-morbid in nature, meaning it is not unusual for someone
diagnosed with depression to also suffer from an eating disorder, anxiety disorder,
substance abuse and/or dependence disorder, or some other psychiatric disorder. The
most prevalent categories of psychiatric disorders are typically mood and anxiety
disorders.
Mood Disorders
Depression is a common psychiatric disorder that is characterized by depressed
mood, loss of interest or pleasure, disturbed sleep, low energy, low self-esteem, feelings
of guilt, and poor concentration, among other symptoms. It is estimated that depression
affects about 9% of Americans and 350 million people worldwide (CDC, 2010; WHO,
2012b). Unipolar depressive disorders are the leading cause of disability in middle and
high income countries (WHO, 2004). In the U.S., Major Depressive Disorder is the
leading cause of disability (Kessler et al., 2005a).
11
Mood and obesity. Research shows a clear positive association between obesity
and mood disorders (Petry et al., 2008; Mather, Cox, Enns, & Sareen, 2009; Scott et al.,
2008a; Simon et al., 2006; Stunkard, Faith, & Allison, 2003). Specifically, obesity has
been linked to a significant increase in lifetime diagnosis of major depression and bipolar
disorder (Simon et al., 2006).
Whereas research shows a clear relationship between mood disorders and obesity,
researchers have yet to determine causality. In other words, does obesity cause the onset
of mood disorders, or do mood disorders contribute to the onset of obesity, or are they
linked because of their association with a third variable? Roberts and colleagues found
that obesity at baseline increased the risk of developing depression five years later but
depression did not increase the risk for developing obesity (Roberts, Deleger,
Strawbridge, & Kaplan, 2003). Other research shows that individuals who had higher
levels of depressive symptoms at baseline had faster rates of increases in BMI (Needham,
Epel, Adler, & Kiefe, 2010). In a meta-analysis of 17 community-based studies, obese
individuals were found to be 1.18 times more likely to have depressive symptoms than
non-obese individuals (de Wit et al., 2010).
Gender may modify the relationship between depression and obesity; for
example, research has shown that obese females have a higher prevalence of depression
than do obese males (Afari et al., 2010; de Wit et al., 2010; Mather et al., 2009; Scott et
al., 2008b). In fact, some research has even found an inverse relationship of depression
and obesity for males (Stunkard et al., 2003).
12
Anxiety Disorders
Anxiety disorders, as a cluster, are the most prevalent psychiatric disorder
experienced in developed countries (Lykouras & Michopoulos, 2011). It is estimated that
in a given year 18.1%, or about 40 million, U.S. adults have an anxiety disorder (Kessler
et al., 2005b).
Anxiety and obesity. Whereas the links between mood disorders and obesity
have begun to be explored, Scott and colleagues (2008b) argue that there is a dearth of
information on anxiety disorders. Furthermore, the literature that is available documents
conflicting results with findings on the association between obesity and anxiety disorders
ranging from a moderate relationship (Simon et al., 2006) to negative correlations
(Rivenes, Harvey, & Mykletun, 2009) and strong positive correlations (Simon et al.,
2006).
Anxiety disorders involve characteristic symptoms that seem to bear some
similarity to characteristics associated with obesity. For example, most anxiety disorders
share the elements of preoccupation and avoidance, and it is not illogical to compare
these behaviors to someone whose weight management difficulties include a
preoccupation with food and the use of food as an avoidance coping mechanism. There
may also be a shared genetic heritability and environmental factors that link obesity and
anxiety disorders (Lykouras & Michopoulos, 2011). As in mood disorders, it is unclear
what the causal nature of a possible link between obesity and anxiety disorders might be.
Obesity might bring about anxiety disorders for various reasons, such as stigma and
discrimination from a society obsessed with the thin ideal.
13
Gender again may modify any relationship between anxiety disorders and obesity;
in comparison to obese males, obese females having higher rates of anxiety disorders
(Mather et al., 2009; Scott et al., 2008a). Socioeconomics may also play a role since
research finds a relationship between anxiety disorders and obesity for those with less
education (Scott et al., 2008b).
Due to the lack of information on obesity and anxiety disorders, I have chosen
this topic as an area to explore more thoroughly. The current project will focus on six of
the anxiety disorders listed in the DSM-IV-TR (American Psychological Association
[APA], 2000) including: panic disorder, agoraphobia, social phobia, obsessivecompulsive disorder, posttraumatic stress disorder, and generalized anxiety disorder.
In discussing panic disorder first panic attacks need to be defined. A panic attack
is a discrete period of intense fear in the absence of a real threat. These symptoms have a
sudden onset and usually peak in 10 minutes or less (APA, 2000). Panic disorder is
characterized by the presence of recurrent, unexpected panic attacks followed by at least
one month of persistent worry about having another panic attack. Roughly 2.7% of the
population has a panic disorder and one in three people with panic disorder develop
agoraphobia (Kessler et al., 2005a). Panic disorder has been associated with obesity
(Mather et al., 2009).
Agoraphobia is anxiety related to having difficulty escaping (or embarrassing
oneself while trying to escape) a place or a situation, or not being able to find help if one
should have a panic attack or panic-like symptoms. Agoraphobia typically leads to
avoiding: crowds, being in an elevator or on a bridge, being home alone, or being alone
14
outside the home (APA, 2000). Less than 1 percent of the population has agoraphobia
(.8%) (Kessler et al., 2005a). Agoraphobia has been positively associated with obesity
both in lifetime and past year prevalence (Mather et al., 2009).
Social phobia is a marked and persistent fear of one or more performance or
social situations in which a person is concerned about embarrassment or being judged
negatively by others. Fear of the situation is so great that the person either avoids the
situation all together or endures it with intense anxiety that may take the form of a panic
attack (APA, 2000). It is estimated that 6.8% of the population suffers from social phobia
(Kessler et al., 2005a). Social phobia has been positively associated with obesity in both
lifetime and past year prevalence (Mather et al., 2009).
Obsessive-Compulsive Disorder (OCD) is characterized by recurrent obsessions
or compulsions that are severe enough to be time consuming or that cause significant
distress or impairment. Obsessions are persistent ideas, thoughts, images or impulses that
are experienced as intrusive and cause a high degree of distress or anxiety. Obsessions
have been referred to as ego-dystonic, meaning they are alien to the individual, are not
within her control, and are not the kind of thought she would expect to have. However,
someone with obsessive thinking also recognizes that the thoughts are a product of their
own mind and not thought insertions (APA, 2000).
Compulsions are repetitive behaviors (such as hand washing) or mental acts
(praying or counting) that serve to reduce or prevent anxiety. Normally a person is driven
to compulsions in order to reduce the distress from the obsession(s). OCD is equally
common in males and females and affects approximately one percent of the population
15
(APA, 2000; Kessler et al., 2005a). To my knowledge, research on OCD and obesity is
sparse, furthermore the one study that I did find by Scott et al. (2008b) found no
relationship between the two variables.
Posttraumatic Stress Disorder (PTSD) is developed following exposure to an
extremely traumatic stressor, specifically, “direct personal experience of an event that
involves actual or threatened death or serious injury, or other threat to one’s physical
integrity; or witnessing an event that involves death, injury, or a threat to the physical
integrity of another person; or learning about unexpected or violent death, serious harm,
or threat of death or injury experienced by a family member or other close associate”
(APA, 2000). It is estimated that PTSD affects 3.5% of the population (Kessler et al.,
2005a). PTSD has been positively associated with obesity (Scott et al., 2008b).
Generalized Anxiety Disorder (GAD) involves the experience of excessive worry
and anxiety about multiple events or activities. The symptoms must occur on more days
than not for a period of at least six months. Those diagnosed with GAD experience
constant worry about important areas of functioning (such as their jobs, family, finances,
or health) (APA, 2000). The prevalence for GAD is 3.1% (Kessler et al., 2005a). Scott et
al. (2008b) examined the relationship between GAD and obesity and failed to find a
significant relationship.
Trends in Psychiatric Disorders
The prevalence of psychiatric disorders differs based on many factors. Some of
the key demographic factors are discussed below, including gender, ethnicity, and
socioeconomic status.
16
Gender differences. Research shows that females are significantly more likely to
have a current and lifetime diagnosis of depression as compared to males (Strine et al.,
2008). Females are also more likely to report a lifetime diagnosis of anxiety disorders
(Strine et al., 2008b). A current study estimates that nearly one in three women meet
criteria for an anxiety disorder as compared to 22% of men (McLean, Asnaani, Litz, &
Hofmann, 2011).
Ethnicity. Research shows that Hispanics are less likely than non-Hispanic
whites to experience a mood disorder during their lifetime (Strine et al., 2008). Research
shows that non-Hispanic whites have the highest prevalence of depression while
Hispanics have the lowest prevalence (Probst et al., 2006). Hispanic Americans are also
less likely to report lifetime anxiety disorders when compared to non-Hispanic whites
(Strine et al., 2008).
Socioeconomics. Research has linked lower socioeconomic status with higher
likelihood of psychiatric disorders (Lorant et al., 2003; Hach et al., 2007). Specifically,
lower education (typically less than high school) and higher financial strain are associated
with higher likelihood of depression (Strine et al., 2008; Scott et al., 2008b). Lower
education levels have also been linked to a greater likelihood of having an anxiety
disorders (Chazelle et al., 2011; Bjelland et al., 2008), specifically generalized anxiety
disorder (Chazelle et al., 2011).
Rural Communities
It is important to understand the prevalence of obesity and mental health disorders
within rural communities since these communities have high prevalence of both
17
(Drewnowski, 2004; Tanumihardjo et al., 2007; Strine et al., 2008). Education levels in
rural communities are less than those of urban environments with fewer rural adults
having a college degree as compared to urban adults; and this gap is growing (U.S.
Department of Agriculture [USDA], 2012a). Individuals living in rural areas are more
likely to be unemployed, and twice as likely to earn minimum wage in the service sector
as their urban counterparts. It is estimated that 28% of people living in completely rural
counties live in persistent poverty (USDA, 2012b). In Tulare County, where data was
collected for the current study, 22.9% of the population is below the poverty line as
compared to 13.7% for the state of California (U.S. Census Bureau, 2012a).
These characteristics put rural communities at a disadvantage in terms of
resources such as access to fresh, quality food, and adequate healthcare options thereby
exacerbating the obesity and mental health issues (Gamm, et al., 2002; Rose & Richards,
2004). Residents of lower income areas have fewer supermarkets or chain stores per
capita than higher income neighborhoods (Beaulac, Kristgansson, & Cummins, 2009).
Therefore, these neighborhoods have limited selection and higher cost of fresh produce.
And due to distance and limited transportation options, shopping for healthier foods may
be difficult. Research has shown that distance between home and supermarket is
associated with fruit and vegetable use (Rose & Richards, 2004). There is also an issue
with a lack of education about the importance of nutrition (Rural Assistance Center,
2012).
According to the Rural Healthy People 2010 survey, psychiatric health ranked 4th
highest in rural health concerns, and 37% of the state and local rural health leaders listed
18
psychiatric health as one of their top rural health priorities after access to quality health
services, heart disease, and diabetes (Gamm et al., 2002). A study using data from the
1999 National Health Interview Survey estimated that 2.6 million adults living in rural
areas suffer from depression and that the difference between rural and urban depression
rates were slight but significant (Probst et al., 2006). There is contradictory research that
finds residents of rural areas to have fewer major depressive episodes than their urban
counterparts in Canada but this may be due to rural communities in Canada having a
greater sense of community and social support (Romans, Cohen, & Forte, 2011; Wang,
2004).
Current Study
This thesis will focus on the relationships between the diagnoses of mood
disorders, anxiety disorders, and body mass index for an unselected primary care sample
from a rural county in Northern California. Based on current literature I will explore the
following hypotheses:
H1) I expect the sample to have a higher prevalence of obesity than does the surrounding
county, the state of California, and the nation, due to their geographic location and the
sampling technique.
H2) I expect females to have a higher risk for obesity; this will be explored in terms of
ethnicity.
H3) I expect females to have higher prevalence of mood and anxiety disorders; this will
also be explored in terms of ethnicity.
19
H4) Participants classified as obese will be at greater risk of mood disorders; this will be
explored across gender and ethnicity. An exploratory approach will be taken with regard
to the rates of anxiety disorder among obese participants; no specific prediction is made.
20
Chapter 2
METHOD
Participants
The sample (N = 117) consisted of mostly females; the mean age of the
participants was 48 years (range 23 –73 years). The average body mass index was 30.9,
which is considered obese (range 15.7 - 56.5). Other demographic characters are listed in
Table 1.
21
Table 1
Demographic Characteristics of Participants
Gender
Male
Female
Ethnicity
Non-Hispanic white
Hispanic
Age
20-30
31-40
41-50
51-60
61+
Education Level1
Less than high school
High school
College
Employment
Currently employed2
Marital status
Single
Married
Body Mass Index
Underweight
Normal
Overweight
Obese
1
n
%
39
78
33.3
66.7
66
51
56.4
43.5
11
18
39
31
18
9.4
15.4
33.3
26.5
15.4
13
54
24
14.2
59.3
26.3
23
19.7
66
33
56.4
28.2
3
18
37
59
2.6
15.4
31.6
50.4
Missing n = 26 2 Missing n = 1. Note. N = 117.
Materials
BMI
Participants self-reported their height and weight and BMI was calculated by
using the standard formula (weight in pounds / (height in inches)2 * 703). The Center for
Disease Control and Preventions’ breakdown of BMI was used to create categories of
underweight, normal, overweight, and obese (CDC, 2011a).
22
SCID-I
Data on the mental health status of the participants was collected using the
Structured Clinical Interview for DSM-IV disorders. The Structured Clinical Interview
for DSM-IV Axis I Disorders (SCID-I) is a semi-structured interview used for making
major DSM-IV Axis I diagnoses. The SCID-I is widely considered to produce reliable
and valid psychiatric diagnoses for clinical, research and training purposes (Skre, Onstad,
Torgersen, & Kringlen, 1991; Ventura, Liberman, Green, Shaner, & Mintz, 1998).
MINI
The Mini International Neuropsychiatric Interview (MINI) is a short, structured
diagnostic interview that was developed in 1990 by psychiatrists and clinicians in the
United States and Europe for DSM-IV and ICD-10 psychiatric disorders. With an
administration time of approximately 15 minutes, the MINI is the most widely used
structured psychiatric interview for psychiatric evaluation and outcome tracking in
clinical psychopharmacology trials and epidemiological studies (Sheehan et al., 1998).
Procedures
Data for the present study were drawn from a project led by Dr. Peter Yellowlees
and funded by the Blue Shield of California Foundation grant. The project was reviewed
and approved by the University of California, Davis Institutional Review Board. Primary
care physicians (PCP) from two underserved clinics in Northern California were asked to
refer any patients who might need a non-urgent psychiatric evaluation to the study.
23
Interviews were conducted in the largest of four community health clinics in Tulare
County, California by a trained researcher using the SCID-I and MINI.
Patients gave informed consent prior to admission to the study and prior to the
interview. Interviews were written as well as video recorded and lasted one to two hours;
participants were paid $100 each to take part. All patients were informed that the video of
their interview would be viewed by a psychiatrist, and that a consultation opinion would
be written and provided to their PCP. Patients were told that they would be able to see
their consultation opinion if their PCP agreed.
Due to the sample overwhelmingly representing non-Hispanic white and Hispanic
Americans the other three ethnic groups (Asian Americans, Native Americans, and
African Americans) were excluded, leaving a final sample of 117 participants.
Results and demographics were exported from summary sheets. A coding system
was created in agreement with Dr. Yellowlees and the researcher who originally collected
the data. Any ambiguity on the data was discussed with the group and consensus was
obtained prior to coding.
Data Analysis
Chi square analysis will be used to determine relationships between BMI, gender,
and ethnicity, for the sample. Odds ratios will also be calculated for any significant
findings. The sample data will also be compared descriptively with Tulare County, the
state of California and national data on BMI to see if the sample is representative or
unique. The sample will also be compared to the state and nation on socioeconomic
24
status, such as employment rates and education to see how representative the sample is
using the U.S. Census Bureau reports.
25
Chapter 3
RESULTS
This rural sample was overwhelmingly unemployed as compared to the county,
state, and national averages. Majority of the sample held a high school degree. Rates of
college graduates were nearly half of the average for California (Table 2).
Table 2
Comparison of the Demographics for the Sample, County, State, and Nation
Unemployment
Education Level
High School
College or beyond
Ethnicity
Non-Hispanic white
Hispanic
1
Sample
80.3
%
Tulare County
14.71
California
11.42
National
7.82
59.3
26.3
67.33
13.03
80.73
30.13
85.04
27.94
52.0
40.2
32.03
61.33
39.73
38.13
63.44
16.74
U.S. Bureau of Labor Statistics (2012a) 2U.S. Bureau of Labor Statistics (2012b) 3 U.S. Census
Bureau (2012a) 4 U.S. Census Bureau (2012b)
To test my first hypothesis the chi square goodness of fit test indicated there was a
significant difference in the proportion of obese adults in the sample as compared to the
county, the state, and the national averages (Table 3).
26
Table 3
Obesity Comparisons across the County, State, and Nation
Sample
Tulare County
California
National
1
Obese (%)
50.4
31.01
24.02
35.73
2
2 (1, N = 117) = 20.6*
2 (1, N = 117) = 44.7*
2 (1, N = 117) = 12.2*
County Health Rankings and Roadmaps (2012) 2 CDC (2012a) 3 Ogden et al., (2012)
* p < .05
To examine my second hypotheses, chi square tests of independence were
performed to examine the relationship between gender and obesity separately for
Hispanics and non-Hispanic whites. A significant relationship was observed only
between non-Hispanic white females and males, 2 (1, n = 66) = 5.25, p = .02, 2 = .07,
with odds ratio indicating that odds for non-Hispanic white females being obese were
nearly four times greater than the odds of non-Hispanic white males being obese (Table
4). The relationship between gender and obesity was not significant for Hispanics.
27
Table 4
Prevalence of Obesity (BMI > 30) across Gender for non-Hispanic whites
Non-Hispanic white
male
Count
Expected count
% of row
Adjusted residual
Non-Hispanic white
female
Count
Expected count
% of row
Adjusted residual
Not
obese
Obese
Total
14
9.8
73.7
2.3
5
9.2
26.3
-2.3
19
19
100
20
24.2
42.6
-2.3
27
22.8
57.4
2.3
47
47
100
Another chi square test of independence was performed to examine the
relationship between ethnicity and obesity separately for gender. There was a significant
relationship between Hispanic and non-Hispanic white males and obesity, 2 (1, n = 39) =
5.86, p = .015, 2 = .15, with odds ratio indicating that odds for Hispanic males being
obese were five times greater than the odds for non-Hispanic white males being obese
(Table 5).
28
Table 5
Prevalence of Obesity (BMI > 30) across Males and Ethnicity
Non-Hispanic white
male
Count
Expected count
% of row
Adjusted residual
Hispanic male
Count
Expected count
% of row
Adjusted residual
Not
obese
Obese
Total
14
10
73
2.4
5
8
26
-2.4
19
19
100
7
10
35
-2.4
13
9
65
2.4
20
20
100
For the third hypotheses, two separate chi square tests of independence were
performed to examine the relationship between gender and mood disorders, and gender
and anxiety disorders. Results only indicated a significant relationship between gender
and mood, 2 (1, n = 94) = 4.57, p = .03, 2 = .03. Odds ratio indicated that the odds for
females being depressed were 2.7 times greater than the odds for males being depressed
(Table 6).
29
Table 6
Mood Disorders and Gender
Not
Depressed
depressed
Male
Count
Expected count
% of row
Adjusted residual
Female
Count
Expected count
% of row
Adjusted residual
Total
12
7.7
30.8
2.1
27
31.3
69.2
-2.1
39
39
100
11
15.3
14.1
-2.1
67
62.7
85.9
2.1
78
78
100
The significant relationship between mood and gender was driven by ethnicity;
chi squares test of independence showed a significant relationship between gender and
mood for Hispanics, 2 (1, n = 51) = 8.43, p < .001, 2 = .16. Even though absolute
frequencies were small, odds ratio indicate that the odds of Hispanic females being
depressed were seven times greater than the odds of Hispanic males being depressed
(Table 7). No significant difference was observed for non-Hispanic whites. Frequencies
on psychiatric disorders for the sample by ethnicity and gender can be found in Appendix
A.
30
Table 7
Mood Disorders across Gender for Hispanics
Not
Depressed
depressed
Hispanic male
Count
Expected count
% of row
Adjusted residual
Hispanic female
Count
Expected count
% of row
Adjusted residual
Total
9
4.7
45
2.9
11
15.3
55
-2.9
20
20
100
3
7.3
9.7
-2.9
28
23.7
90.3
2.9
31
31
100
To test the fourth hypothesis, chi squares tests of independence showed no
significant relationship between obesity and mood disorders, 2 (1, n = 94) = .03, p = .85,
2 = .00, or obesity and anxiety disorders, 2 (1, n = 59) = .21, p = .64, 2 = .00. The
breakdown of mood and specific anxiety disorders is included in Appendix B along with
frequencies of comorbid obesity and psychiatric disorders by ethnicity and gender in
Appendix C.
31
Chapter 4
DISCUSSION
My first hypothesis was supported, as the sample had a significantly higher
prevalence of obesity when compared to its surrounding county, state, and national
averages (50.4%, 31.4%, 24.0% and 33.8%, respectively).
Consistent with rural education level trends, the majority of the sample held a
high school degree and an astounding 80% of the sample was unemployed at the time
data was collected (Table 2). The low rates of attaining a college degree in this sample
may be a reflection of individuals moving from the area once they get a degree, or
moving out of the area to attend college. The high unemployment rate could be a
combination of several factors such as; lack of employment opportunities, disability due
to the high rates of depression and anxiety within this sample, disability due to the high
prevalence of obesity within this sample, or possibly a combination of these factors. As
stated elsewhere, going on disability leave is a key factor in the high cost of obesity and
mental health care on the healthcare system.
The sample had a higher prevalence of non-Hispanic white Americans than
Hispanic Americans even though the county has a much larger Hispanic community. This
may be due to the sampling technique used for the study in which PCPs referred clients
who may need a psychiatric evaluation. Non-Hispanic whites may have higher
prevalence of psychiatric disorders than Hispanics and/or there might be cultural barriers
to seeking psychiatric help for Hispanics. Sampling techniques could also account for the
32
large proportion of females to males in the sample since research shows that females are
more likely to seek help from and disclose psychiatric problems to their PCPs (WHO,
2012d).
My second hypothesis was partially supported in terms of the relationship of
gender, ethnicity, and obesity. Two significant relationships were observed for obesity
and gender in terms of ethnicity 1) the odds of non-Hispanic white females being obese
were nearly four times greater than the odds of non-Hispanic white males being obese, 2)
and the odds of Hispanic males being obese were five times greater than the odds of nonHispanic white males being obese. No significant relationship was observed for gender in
the Hispanic group. The significant relationship of gender and obesity is consistent with
national trends (CDC, 2011c).
My third hypothesis was partially supported in that a significant relationship was
observed between gender and mood, where the odds of females being depressed were
nearly three times greater than odds of males being depressed. The difference in gender
and mood was driven by ethnicity, in which the odds of Hispanic females being
depressed were seven times greater than odds of Hispanic males being depressed.
In terms of psychiatric disorders and gender females are known to have higher
rates of depression in literature (Strine et al., 2008) and a possible explanation for this
may be due to gender bias in treatment of psychiatric disorders. It has been noted that
even when males and females have identical symptoms, doctors are more likely to
diagnose females with depression. Female gender has also been shown to be a significant
predictor of being prescribed mood-altering medication (WHO, 2012d).
33
No significant relationship was observed between gender and/or ethnicity and
anxiety disorders, due to all groups having fairly similar prevalence (Appendix A). The
most frequently diagnosed anxiety disorders for the sample were; panic disorder with
agoraphobia, panic disorder without agoraphobia, and anxiety disorder not otherwise
specified (this encompasses disorders that have prominent anxiety or phobic avoidance
but that do not meet criteria for any specific anxiety disorder (APA, 2000) (Appendix B).
The high percentages of obesity and psychiatric disorders within this sample set
this sample aside from, not only the national averages, but even their own surrounding
county. In comparing percentages of mood and anxiety disorders among the non-obese
and obese sample, the percentages of mood disorders were similar (Appendix B). With
anxiety disorders there were some clear differences in diagnoses based on non-obese and
obese group membership. The obese group had higher percentages of GAD, social
phobia, and anxiety disorders not otherwise specified. The non-obese group had higher
percentages of OCD, PTSD, and panic with agoraphobia. Both groups had the same
percentage of panic without agoraphobia diagnoses (Appendix B). It should be noted
however that none of these relationships could be tested due to small sample sizes.
Limitations
This data set is unique compared to other studies in that 1) the sample is from a
rural community 2) the sample is unselected. This means there were no restrictions
placed on who could be in the study (other than being over the age of 18). This data set is
therefore representative of who primary care physicians typically treat. However there are
a few limitations that should be noted.
34
With a relatively small sample size it is difficult to observe the expected
relationships. For example, I was not able to assess whether there might have been
interactions of gender and ethnicity affecting any potential relationships between obesity
and mood or anxiety disorders because the sample was too small and therefore any
outcomes would have been unstable. A larger sample size would perhaps reflect the
anticipated relationships with these variables.
Under/overestimation is a practical concern when participants self-report their
height and weight (Johnson, Bouchard, Newton, Ryan, & Katzmarzyk, 2009). Validation
studies of self-report data show that overweight participants tend to underestimate their
weight and overestimate their height (Rowland, 1990; Spencer, Appleby, Davey, & Key,
2002). Future studies could involve the researcher weighing the participant and
measuring their height to avoid this potential self-report bias.
Medication for psychiatric disorders have been known to affect body mass and the
current study did not take into account the sample’s prescription use. Atypical
antipsychotics used to treat psychotic disorders are known for significant weight gain as a
side effect with estimated weight gain. However with a relatively small sample size of
participants with psychotic disorders in this data set (n = 7) this is unlikely to have
affected the results.
Anti-depressants such as traditional tricyclics and monoamine oxidase inhibitors
(MAOI) are known to increase weight more than the newer selective serotonin reuptake
inhibitors (SSRI) medications such as Prozac, Zoloft, and Paxil, which still have weight
gain as a potential side effect but not to as great a degree as the older categories of
35
antidpressants (Harvey & Bouwer, 2000; Fava, 2000).
Many antidepressants are also prescribed for anxiety disorders, such as SSRIs and
the drug Wellbutrin. Benzodiazapines, a class of drugs targeted for anxiety disorders, has
been shown to reduce weight due to their side effects of nausea and vomiting. Although
studies that have controlled for psychiatric medication still show higher BMIs for those
with mental disorders than those without, future studies should nevertheless take into
account the prescriptions participants are taking.
Future research
Although there is some information on mood disorders and obesity, research is
needed to determine if there is causality. The role of anxiety disorders in terms of any
effect on obesity also needs to be explored further, specifically, whether certain anxiety
disorders are more prone to be associated with obesity.
Future studies that want to assess obesity and psychiatric disorders in rural
communities should use a large enough sample to be able to examine possible
relationships between these variables. It would be ideal if the sample is collected through
random sampling rather than referred by primary care providers, as this will give a more
accurate picture of the prevalence of obesity and psychiatric disorders in the community.
Weight and height should be assessed objectively using appropriate tools. Current use of
medication and a type of dietary summary should be acquired along with specific
questions regarding physical activity.
36
Implications
In the battle against chronic disease, special attention needs to be paid to rural
communities. These communities have higher prevalence of both obesity and mental
health disorders with fewer healthcare options, than urban dwellers (Patterson, Moore,
Probst, & Shinogle, 2004; Strine et al., 2008). Preventative programs need to be
developed specifically to target rural areas, and more research needs to be conducted on
rural communities as these communities have fewer resources. Policy and planning
responses are needed to address the socioeconomic inequalities in the availability of
affordable, and accessible, healthier foods (Beaulac, Kristjansson, & Cummins, 2009).
Clinicians, primary care providers, and policy makers need to be aware that
psychiatric disorders are one of the top rated concerns for rural communities and the
factors contributing to these problems in rural communities have been listed as 1) limited
access to providers, 2) lack of sufficient training, 3) limited utilization of available
services due to stigma and decreased anonymity 4) and limited awareness and
acceptability of mental disorders (HRSA, 2006). The use of telemedicine, higher
incentives for specialists to practice in rural communities, and increasing education about
mental disorders might alleviate these concerns.
37
APPENDIX A
Psychiatric Disorders Prevalence for Sample
All
Hispanic
Non-Hispanic white
Female
Male
Hispanic Female
Hispanic Male
Non-Hispanic white Female
Non-Hispanic white Male
n
117
51
66
78
39
31
20
47
19
n (%)
Mood
94(80)
39(76)
55(83)
67(86)
27(69)
28(90)
11(55)
39(82)
16(84)
Anxiety
59(50)
25(49)
34(51)
39(50)
20(51)
14(45)
11(55)
25(53)
9(47)
38
APPENDIX B
Psychiatric Diagnoses for non-Obese and Obese Participants
%
Mood
Anxiety Disorders
OCD
GAD
PTSD
Panic with agoraphobia
Panic without agoraphobia
Social phobia
Anxiety NOS
Non-obese
(n = 58)
79.0
Obese
(n = 59)
84.7
5.1
0
5.1
29.3
6.8
3.4
13.7
0
6.7
3.3
27.0
6.8
6.7
15.2
39
APPENDIX C
Obesity Prevalence with Co-morbid Psychiatric Disorders
All
Hispanic
Non-Hispanic white
Female
Male
Hispanic Female
Hispanic Male
Non-Hispanic white Female
Non-Hispanic white Male
n
117
51
66
78
39
31
20
47
19
n (%)
Obese
Mood
59(50)
47(80)
27(53)
20(74)
32(49)
27(84)
41(52)
35(85)
18(46)
12(67)
14(45)
13(93)
13(65)
7(54)
27(57)
22(81)
5(26)
5(100)
Anxiety
31(52)
15(56)
16(50)
23(56)
8(44)
7(50)
8(62)
16(59)
0
40
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