SOCIOECONOMIC STATUS, SOCIAL RELATIONSHIPS,
AND HIGHER WEIGHT STATUS
by
ANTHONY DAVID CAMPBELL
ELIZABETH H. BAKER, COMMITTEE CO-CHAIR
PATRICIA DRENTEA, COMMITTEE CO-CHAIR
BELINDA L. NEEDHAM
A THESIS
Submitted to the graduate faculty of The University of Alabama at Birmingham,
in partial fulfillment of the requirements for the degree of
Master of Arts
BIRMINGHAM, ALABAMA
2013
Copyright by
Anthony David Campbell
2013
SOCIOECONOMIC STATUS, SOCIAL RELATIONSHIPS,
AND HIGHER WEIGHT STATUS
ANTHONY DAVID CAMPBELL
SOCIOLOGY
ABSTRACT
This thesis serves two main research objectives. The first objective is to examine
the extent to which higher weight status is patterned by indicators of socioeconomic
status. The second objective is to explore the involvement of social relationships in the
association between socioeconomic status and weight status. I use a theoretical
framework incorporating aspects of fundamental cause theory and insights from the
literature on social relationships and health.
Data for this study were obtained from the 2003-2008 National Health and
Nutrition Examination Survey (n = 3,957). The analytic sample focused on Black and
White respondents over the age of 40. Higher weight status, was operationalized in two
ways: a continuous measurement of body mass index and a dichotomous obesity
indicator. SES measurements included educational attainment, income to poverty ratio,
and home ownership. Social relationships were measured by emotional support, financial
support, and number of close friends. Ordinary least squares regression models were
conducted for the continuous measure of BMI and logistic regression models for the
dichotomous obesity indicator.
Findings suggest that associations of social factors with higher weight status are
complex, especially in terms of gender. Education has the most consistent inverse
associations with higher weight status for both genders but income and home ownership
do not follow the same pattern. Income appears to be a protective factor for women but
iii
not for men while home ownership is positively associated with men’s higher weight
status. The association between social relationships and higher weight status is
inconsistent. Financial support is the only social relationship variable that is significantly
associated with weight status for both genders. However, the direction of results for
emotional support and number of close friends suggests that women and men may seek
out different resources to compensate for disadvantages in socioeconomic status.
In conclusion, results show that indicators of socioeconomic status and social
relationships are associated with higher weight status. However, both sets of social
factors have varied associations with higher weight status by gender. The use of multiple
theoretical perspectives in this study offers a more integrated framework for examining
the complexities of higher weight status.
iv
DEDICATION
I dedicate this thesis to my incredible family who has stood by me no matter what.
I would not be where I am today without their encouragement and support. As I reflect
upon my accomplishments, I must recognize the profound influence of several wonderful
women in my life including my mother, grandmother, aunts, and cousins. This is
especially dedicated to Ashlee and Lily Cate, two beautiful girls who inspire me in
everything I do.
v
ACKNOWLEDGEMENTS
I would like to thank the members of my thesis committee, Dr. Elizabeth Baker,
Dr. Patricia Drentea, and Dr. Belinda Needham for their expertise, guidance, and patience
as I have completed this project. I have learned so much from you and I am very
fortunate to have had the three of you serve as my chair throughout this process.
vi
TABLE OF CONTENTS
Page
ABSTRACT...................................................................................................................... iii
DEDICATION ....................................................................................................................v
ACKNOWLEDGEMENTS .............................................................................................. vi
LIST OF TABLES ............................................................................................................ ix
LIST OF FIGURES ............................................................................................................x
LIST OF ABBREVIATIONS ........................................................................................... xi
CHAPTER
1 INTRODUCTION ........................................................................................................1
2 LITERATURE REVIEW .............................................................................................4
Fundamental Cause Theory ....................................................................................4
Multiple Outcomes ..........................................................................................6
Multiple Risk Factors ......................................................................................6
Replacement of Intervening Mechanisms .......................................................7
Access to Resources ........................................................................................8
Socioeconomic Status, Health, and Higher Weight Status ................................... 10
Social Support and Social Integration Theory ...................................................... 12
Hypotheses ............................................................................................................ 18
3 METHODS ................................................................................................................. 21
Data ....................................................................................................................... 21
Sample Weights ............................................................................................. 21
Analytic Sample............................................................................................. 22
Measures ............................................................................................................... 23
Dependent Variable ....................................................................................... 23
Independent Variables ................................................................................... 24
Control Variables ........................................................................................... 27
vii
Analytic Strategy
Multivariate Regressions ............................................................................... 28
Diagnostic Tests............................................................................................. 30
4 RESULTS ................................................................................................................... 32
Descriptive Statistics............................................................................................. 32
Bivariate Statistics ................................................................................................ 35
Linear Regression Results..................................................................................... 39
Logistic Regression Results .................................................................................. 44
5 DISCUSSION AND CONCLUSIONS ...................................................................... 49
SES and Higher Weight Status ............................................................................. 49
Social Relationships and Higher Weight Status ................................................... 52
Role of Social Relationships in the Association between SES and Higher Weight
Status ..................................................................................................................... 54
Strengths of the Study ........................................................................................... 56
Limitations ............................................................................................................ 58
Future Research .................................................................................................... 59
Conclusion ............................................................................................................ 60
LIST OF REFERENCES .................................................................................................. 62
APPENDIX: IRB APPROVAL FORM........................................................................... 72
viii
LIST OF TABLES
Table
Page
1 Weighted Means, Standard Errors (SE) and Percentages (%) for Study Variables
by Gender, National Health and Nutrition Examination Survey 2003-2008 ............... 33
2 Weighted Mean Body Mass Index for Independent Variables by Gender,
National Health and Nutrition Examination Survey 2003-2008 .................................. 36
3 Percent Obese for Independent Variables by Gender, National Health and
Nutrition Examination Survey 2003-2008 (n = 3,957) ................................................ 38
4 Weighted Ordinary Least Squares Regression of Body Mass Index (BMI)
in Female Sub-Sample, National Health and Nutrition Examination Survey
2003-2008 (n = 1,968) .................................................................................................. 41
5 Weighted Ordinary Least Squares Regression of Body Mass Index (BMI)
in Male Sub-Sample, National Health and Nutrition Examination Survey
2003-2008 (n = 1,989) .................................................................................................. 44
6 Weighted Odds Ratios from a Logistic Regression of Variables on Obesity
in Female Sub-Sample, National Health and Nutrition Examination Survey
2003-2008 (n = 1,968) .................................................................................................. 46
7 Weighted Odds Ratios from a Logistic Regression of Variables on Obesity
in Male Sub-Sample, National Health and Nutrition Examination Survey
2003-2008 (n = 1,968) .................................................................................................. 48
ix
LIST OF FIGURES
Table
Page
1 Conceptual Diagram ..................................................................................................... 20
x
LIST OF ABBREVIATIONS
BIC
Bayesian information coefficient
BMI
Body mass index
NCHS
National Center for Health Statistics
NHANES
National Health and Nutrition Examination Survey
NHLBI
National Heart, Lung, and Blood Institute
OLS
Ordinary least squares
PIR
Income to poverty ratio
SES
Socioeconomic status
VIF
Variance inflation factor
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CHAPTER 1
INTRODUCTION
Socioeconomic status (SES), measured by indicators such as education, income,
and wealth, is often cited as an important determinant of health. In addition, social
relationships, including social support and integration, have known influences on health.
However, it remains unclear how these separate social domains jointly impact a variety of
health outcomes. Clarification on this issue is needed in order to improve our
understanding of how different health outcomes may be produced for those with different
levels of socioeconomic status, social support, and social integration. This thesis
examines the associations between these social factors and the outcome of higher weight
status, an important health indicator that includes categories of overweight and obesity.
Overweight and obesity affect more than two-thirds of the U.S. adult population,
a statistic that includes a doubling of the obesity rate over the past three decades (Ogden
et al. 2012). As a result, higher weight status represents a national epidemic with serious
health, economic, and social consequences (Ljungvall and Zimmerman 2012). Obesity
increases risk for many major diseases including diabetes, heart disease, stroke, arthritis,
and certain cancers (National Heart, Lung, and Blood Institute (NHLBI) 1998). In 1998,
an estimated 78.5 million dollars were spent on obesity-related medical costs
(Finkelstein, Fiebelkorn, and Wang 2003); and by 2008, these costs were estimated at
over 147 billion dollars (Finkelstein et al. 2009). An additional economic burden results
from increased absenteeism and lost productivity due to obesity-related illness (Sullivan,
Ghushchyan, and Ben-Joseph 2008). Higher weight status is related to psychosocial costs
1
including the experience of stigma, discrimination, poor body image, and increased
psychological difficulty (Carr and Friedman 2005; NHLBI 1998). Prevalence rates of
overweight and obesity are expected to continue their upward trends for the foreseeable
future (Wang and Beydoun (2007). Extensive and expanding consequences of higher
weight status have prompted calls for more sociological attention to the issue (Lee 2011;
Peralta 2003).
At the individual level, higher weight status results from an energy imbalance that
involves excessive calorie consumption and inadequate physical activity (Himes 2011).
To explain how obesity has increased at the aggregate level, Candib (2007:547) argues
that obesity represents a ‘syndemic,’ a health phenomenon created by a confluence of
diverse factors including “genetic, physiological, psychological, familial, social,
economic, and political” conditions. While most researchers agree that there are multiple
pathways to higher weight status, there remains debate about which mechanisms are
more responsible for its increased prevalence. Given the dramatic increase in higher
weight status over a relatively short time and the unequal distribution of higher weight
status across certain groups in society, social factors offer keys to understanding these
developments (Cockerham 2007; McLaren 2007).
Empirical research confirms an enduring association between higher weight status
and lower SES, most often indicated by income and education (Ailshire and House 2011;
Schafer and Ferraro 2011; Wang and Beydoun 2007). This association is also found for
measures of wealth, a less commonly used indicator of SES (Fonda et al. 2004).
However, the associations of SES with higher weight status may not be as clear cut as
2
those observed for other health outcomes (Braveman 2009). Specifically, the associations
are complicated by differences in gender.
A proposed set of mechanisms linking SES (income, education, and wealth) to
health is social relationships, which are composed of social networks, social integration,
and social support (House, Umberson, and Landis 1988; Thoits 2011; Umberson and
Montez 2010). To date, this linkage is not adequately explained and has yet to be fully
explored with diverse health outcomes such as higher weight status. The current study
focuses on two aspects of social relationships—social support and social integration.
The first purpose of this study is to determine the extent to which higher weight
status is patterned by SES (education, income, and wealth). The second purpose is to
explore the involvement of social support and social integration in the association
between SES indicators and higher weight status. Given research suggesting that the
separate associations of SES and social relationships on weight status varies by gender,
differences in their combined associations by gender will be explored. This will be the
first study to address these specific inquiries by using nationally representative data from
the 2003-2008 National Health and Nutrition Examination Survey (NHANES).
3
CHAPTER 2
LITERATURE REVIEW
Several theoretical perspectives are relevant to the current study. I begin by
discussing the fundamental cause theory (Link and Phelan 1995; Phelan, Link, and
Tehranifar 2010) and its significance for research on SES and health. I will place
particular attention on the role of flexible resources, including social support and social
connections, as intervening variables. I will follow this by describing theories describing
how social support and integration are related to health. Although these theories are
typically applied separately, they share a focus on the relevance of intervening resources
for health. I will conclude this section by suggesting that these separate but
complementary theoretical streams may be combined into a more comprehensive
explanation of how social factors influence higher weight status and, by extension,
general health.
Fundamental Cause Theory
An ongoing debate in medical sociology centers on the role of SES in the
production of health and disease. The introduction of fundamental cause theory elevated
SES from an obscure control variable to a leading role as a causal variable for
understanding health disparities (Link and Phelan 1995; Phelan, Link, and Tehranifar
2010). The premise of fundamental causation is that social conditions, particularly SES,
are the root causes of health inequalities. Prior to this theoretical shift, health research
favored proximal risk factors and generally overlooked the causal importance of SES
4
(Link 2008; Phelan et al. 2004; Schnittker and McLeod 2005). Distal or upstream factors,
including factors such as income, education, and wealth, exert their influence farther
away from a particular outcome with their causal impacts not immediately evident. In
contrast, proximal or downstream factors, such as health behaviors, stress, coping, and
medical care, occur closer to a particular outcome, are more readily observable, and are
therefore often implicated as primary causal agents. However, targeting proximal factors
focuses only on immediate precedents while the underlying causes of a disease, the distal
social conditions, remain unchanged. Braveman (2009) relates this kind of misdirected
attention to the study of higher weight status. She points out to the tendency of some
researchers to “focus on the behaviors that immediately lead to obesity without
considering the factors that shape the behaviors” (3).
Although the theory of fundamental causes is widely used and accepted, relatively
few tests of the theory have been undertaken by researchers other than its original
theorists (Phelan, Link, and Tehranifar 2010). Even Phelan and colleagues acknowledge
that “empirically testing the importance of resources per se is difficult, because it requires
the identification of situations in which the ability to use socioeconomic resources can be
analytically separated from socioeconomic status itself” (Phelan et al. 2004:269). In
addition to this difficulty, researchers must demonstrate that the following requirements
of fundamental causation are satisfied: (1) an influence on multiple health outcomes, (2) a
production of health outcomes through multiple risk factors, (3) a persistent association
with health outcomes over time, and (4) a connection to health-promoting resources
(Link and Phelan 1995; Phelan, Link, and Tehranifar 2010). In response to these
challenges, a few creative studies isolate specific testable circumstances and find strong
5
empirical support for the theory. I group these findings by the aforementioned theoretical
requirements in the following paragraphs.
Multiple Outcomes
In order to be considered a fundamental cause of disease, a social condition must
have an impact on multiple health outcomes. Income and education, in particular, have
been demonstrated to possess this quality as evidenced by their influences on diverse
outcomes ranging from cigarette smoking and cessation (Link et al. 1998), cocaine use
(Miech 2008), diabetes (Lutfey and Freese 2005), multiple causes of mortality (Phelan et
al. 2004), and psychological distress (Song 2011). In addition, wealth is associated with a
variety of population health outcomes (Pollack et al. 2007), such as all-cause mortality
(Bond-Huie 2003) and self-rated health (Ostrove, Feldman, and Adler 1999). Obesity
may be added to the list of health outcomes that are thought to be affected by
socioeconomic status (Miech et al. 2011). Even if one does not consider obesity a health
condition, it is difficult to deny that obesity itself is associated with multiple health
outcomes.
Multiple Risk Factors
In an ethnographic study of patients of two urban primary care clinics (one high
SES and the other low SES), Lutfey and Freese (2005) identify a number of mechanisms
through which SES influences health outcomes. These multiple mechanisms, or
metamechanisms, demonstrate the complexity of resources that are related to SES. Lutfey
and Freese include social support as one of these mechanisms and that different levels of
6
social support produce varied consequences for health behaviors, such as compliance
with treatment, medication, and diet and ultimately may lead to differences in diabetes
outcomes. Obesity can be viewed as the result of exposure to multiple mechanisms,
including poor nutrition, over-nutrition, higher cost of nutritious foods, physically unsafe
neighborhoods with limited opportunities for physical activity, and economic
disadvantage.
Replacement of Intervening Mechanisms
Distal social conditions, such as income, education, and wealth, are considered
fundamental causes of disease because of their persistent association with health, despite
changing proximal risk factors (Link and Phelan 1995; Phelan, Link, and Tehranifar
2010). Furthermore, as proximal risk factors are identified and addressed, new risk
factors emerge to replace the original mechanisms, thus maintaining the link between
distal factors and health. A comparison of major health conditions from two different
historical periods illustrates this process. For example, acute diseases in the early 20th
century (e.g. cholera) were mainly attributed to environmental risk factors (Link et al.
1998). In this scenario, lower SES groups were at increased risk due to greater exposure
to poor sanitation, contaminated water, and overcrowded living conditions and higher
SES groups possessed resources to avoid such risks. In contrast, contemporary diseases
have become more chronic in nature (e.g. cardiovascular disease) and are increasingly
associated with behavioral risk factors such as diet, exercise, and smoking (Link et al.
1998). While the proximal risk factors have changed over time, the most common health
conditions remain fundamentally patterned by distal factors such as SES.
7
Once a cause of a particular disease is identified, outcomes become patterned by
socioeconomic status. Miech and colleagues (2011) discuss how multiple diseases
become associated with socioeconomic status and then producing widening disparities
between those with lower versus higher socioeconomic status. Obesity is a condition that
is strongly linked to numerous health issues and therefore can be considered an
intervening mechanism for the SES-health relationship. In the future, it is possible that
obesity may be replaced by an alternative set of intervening mechanisms. For example,
with the investigation of heritable causes of obesity, gene-environment interactions may
become a plausible intermediary.
Access to Resources
An essential feature of the fundamental cause theory is that social conditions
affect health through various flexible resources that are stratified by social position.
These flexible resources include “money, knowledge, power, prestige, and the kinds of
interpersonal resources embodied in the concepts of social support and social network”
(Link and Phelan 1995:87). Therefore, fundamental causes, through the vehicle of
flexible resources, are what place people at “risk of risks” for disease (Link and Phelan
1995:85). For example, through their greater accumulation of flexible resources, persons
with higher education, income, and wealth are then better able to access and utilize health
information and resources. Based on these advantages, persons with higher education or
income are thereby afforded reduced risk for disease because of their ability to more
effectively prevent and respond to potential health threats (Phelan, Link, and Tehranifar
2010). Material wealth is also associated with better health outcomes because of its
8
connection to resources. Home ownership is just one component of material wealth but
may provide important health-promoting advantages. For instance, owning a home may
provide residential stability for individuals and families, foster increased involvement in
community activities, and strengthen social networks and support systems (Ahmed et al.
2005).
Phelan and colleagues (2004) analyze data from the National Longitudinal
Mortality Study to determine whether educational attainment and family income are
related to causes of death that are deemed more versus less preventable. They discover
that causes of death considered more preventable, such as diabetes, atherosclerosis, and
chronic liver disease, are more likely to be associated with socioeconomic indicators. In
contrast, diseases identified as less preventable, such as leukemia, multiple sclerosis, and
pancreatic cancer, have reduced associations with income and education. Low
preventability causes of death present a situation in which the availability of resources do
not offer a health advantage because risk factors are not yet known and there is no
available cure for such diseases. However, when risks factors for a health condition are
identified and effective treatments or avoidance strategies subsequently become
available, the condition soon becomes connected to SES-related resources. This idea is
relevant to the current study since obesity is considered a highly preventable condition
for which those of higher socioeconomic status are better equipped to avoid.
Once risk factors for a health condition are identified, individuals and groups with
higher SES adopt new information and more readily adapt their behaviors. This process is
what Link (1998) refers to as the “social shaping of population health,” for which
knowledge about the risks of cigarette smoking is a prime example. Following the release
9
of new information warning against the health risks of smoking, rates of cigarette
smoking declined much more rapidly among higher SES groups, suggesting differences
in access to information or possibly other resources such as social support for healthy
behaviors. Other studies focus on unequal knowledge of and access to technological
innovations in screens for cancer (Link et al. 1998) and statin medications for reducing
cholesterol (Chang and Lauderdale 2009). In each of these studies, those with higher
socioeconomic status experience more rapid reductions in disease rates due to greater
knowledge of and access to new health information.
Link and Phelan criticize attempts to explain the effects of social factors on health
"with the inclusion of intervening variables in a path or regression model" (Link and
Phelan 1995:88; Phelan et al. 2004). Aneshensel (2009) argues that such models are
important because they "specify the mechanisms that generate health disparities, they do
indeed "explain" these disparities in a meaningful way" (386). Even Link and Phelan
show some ambivalence regarding whether social support and social connections are
fundamental causes themselves or intervening factors. For example, in their seminal
article (Link and Phelan 1995), they suggest that social support and social connections
are fundamental causes. In more recent work, this language is absent and is replaced by
language suggesting that social support has an intervening function in the association
between SES and health (Phelan et al. 2004).
Socioeconomic Status, Health, and Higher Weight Status
It is widely established that an inverse relationship exists between SES and health
(Adler and Ostrove 1999; House 2002; Ross and Wu 1995; Schnittker 2004; Williams
10
and Collins 1995). An inverse relationship between SES and higher weight status is also
well documented, especially for women (McLaren 2007; Sobal and Stunkard 1989; Wang
and Beydoun 2007; Zhang and Wang 2004). The relationship between SES and higher
weight status is complex and specific details of this relationship vary across studies that
use different measures, datasets, and populations.
Using data from the NHANES III (1988-1994), Zhang and Wang (2004) report
considerable inequalities in obesity by family income, with variations by age, gender and
race. Specifically, they find that this inequality is more prevalent among women and
middle-aged individuals (41-49). Chang and Lauderdale (2005), using data from
NHANES between 1971 and 2002, examine the effect of income to poverty ratio on BMI
trajectories for adults (18-64). They conclude that weight status widely varies by income
but that their findings depend heavily on sex and, to a lesser extent, by race. Using the
National Social Life, Health, and Aging Project (2005-2006), which focuses on older
adults (57-85), Schafer and Ferraro (2011) find a stronger inverse education-BMI
relationship among women. Another recent study by Ailshire and House (2011) uses
longitudinal data from the American’s Changing Lives (adults 25-64) and reports
significant inverse relationships between education and income with higher weight status
for men as well as women. Despite variation across datasets or measures of SES, research
consistently finds an inverse relationship between SES and higher weight status for
women and, to a lesser extent, for men as well.
Demonstrating that a relationship exists between distal social conditions, such as
income, education, and material wealth, and higher weight status is important for
confirming a fundamental cause argument. However, simply documenting such a
11
relationship does not explain how this essential relationship occurs (Phelan, Link, and
Tehranifar 2010). For example, if we simply observe that higher weight status is
associated with education, we are likely missing important information. While a direct
relationship between education and weight status is possible, there is also potential for
intervening processes. For example, increases in education are related to positive health
behaviors such as improved exercise and diet (Miech and Shanahan 2000) which in turn
are related to weight. It is also well known that the adoption of positive health behaviors
is associated with social networks (Christakis and Fowler 2007), but social support and
social integration “may shape the way that social networks affect health” (Umberson and
Montez 2010:S63). One way this might occur is through relationships with family and
friends and the degree that individuals are connected to these socializing agents. Home
ownership may similarly connect individuals to improved social support and social
integration. Therefore, factors such as social support and social integration may provide
insight into the mechanisms between SES and higher weight status.
Social Support and Social Integration Theory
Medical sociology has a rich tradition of research on social support and social
integration. Durkheim’s Suicide (1951) [1897] encouraged scientific research that
focused on social factors as causal forces in seemingly individual phenomena. The
concepts and methods employed to explain the phenomenon of suicide could have
appropriately been applied to any variety of health outcomes. Durkheim’s
groundbreaking research on social integration continues to serve as a foundation for
contemporary research on the influence of social relationships on health. More recently,
12
research in the second half of the 20th century focused heavily on the impact of social
stress on health, with psychosocial resources such as social support and social integration
serving as important explanatory variables. In recent years, there has been a resurgence of
research on social relationships and health, in part due to an increased interest in social
networks, in which social support is an important component.
Social support and social integration are defined in various ways in the literature
and the lack of a standard definition has limited research in the area of social
relationships and health (Berkman et al. 2000; Taylor and Seeman 1999). For the
purposes of this research, I use Thoits’ (2010:146) definitions of the related but distinct
concepts of social support and social integration. Social support refers to “the functions
performed for the individual by significant (i.e., primary) others” that may consist of
emotional, informational, or instrumental assistance. On the other hand, social integration
represents a more structural measure of social relationships consisting of the quantity of
“social ties” or “connections to and contacts with other people.”
Research connecting social support and social integration to general health is
plentiful (see reviews by Berkman et al. 2000; House, Umberson, and Landis 1988;
Thoits 1995, 2011; Uchino 2006; Umberson and Montez 2010). Additionally, studies link
social support and/or social integration to a wide variety of specific health outcomes
including hypertension and self-rated health (Gorman and Sivaganesan 2007; Klein et al.
2012), depressive symptoms (Lin, Ye, and Ensel 1999), and cardiovascular,
neuroendocrine, and immune function (Uchino 2006).
There is a positive association between social support and social integration with
socioeconomic status (Belle and Doucet 2003; Kawachi and Berkman 2001; Mechanic
13
and Tanner 2007; Taylor and Seeman 1999; Thoits 1995; Turner and Marino 1994;
Weyers et al. 2008). Therefore, individuals from lower socioeconomic circumstances and
socially disadvantaged groups may experience increased vulnerability due to lower levels
of social support and social integration. Women may be especially vulnerable to the
intersection of socioeconomic position and social relationships. Different levels of social
support and social interaction at various configurations of socioeconomic status may
contribute to variations in weight status outcomes. In a recent meta-analysis of the social
support and health literature, social support and integration are found to explain more of
the SES-health relationship than the popular stress explanation (Matthews and Gallo
2009).
It is impossible to examine the influence of SES and social relationships on
weight status without considering life course differences. In general, weight increases
throughout adulthood, peaking at 60-69, then decreasing somewhat (Himes 1999). House
and colleagues (1990) attest that health inequalities are at their peak in middle age and
early old age. They suggest that this is the time in the life course when environmental and
psychosocial risk factors have their greatest influence, such as health behaviors, stress,
social support, and work hazards. Also, resources used to respond to such risk factors, are
cumulative and allow for an advantage over time (Miech and Shanahan 2000). Antonucci
and colleagues (1999) conclude that supportive social relationships have a differential
influence on health based on age and educational level. For example, older adults with
lower education are more likely to be unhealthy especially if they experience social
support deficits, such as a lack of spousal or adult child caregivers.
14
Antonucci and Akiyama (1987) claim that older age has different associations
with social support by gender, reporting that older women tend to have larger social
networks and more varied sources of social support compared to men who tend to rely
almost exclusively on their spouses. Perhaps this is due to women being in more
caregiver relationships and men’s poorer health status compared to women. Due to these
interesting findings on the relationships between SES, social support, and health by age
and gender, this study focuses on individuals aged 40 and older and runs models
separately for women and men.
Findings that are consistent with the idea that SES produces poor health or higher
body weight may also be explained by the concept of resource substitution which
suggests that those with lower education and income may compensate with other
resources such as social support (Ross and Mirowsky 2006). In other words, social
support and social integration serve as important mediators in the association between
socioeconomic disadvantage and health (Matthews and Gallo 2009; Taylor and Seeman
1999; Thoits 1995; 2011). Social relationships may also mediate the association between
SES and higher weight status (Everson et al. 2002). Also, since gender differences are
found in studies on the independent relationships between SES and social relationships
with health, it is possible that a mediating role may be more pronounced in women than
in men, given women’s relatively lower SES, stronger relationship between SES and
weight status, and lower levels of social support and social integration.
Recent studies examine the existence of a mediating role of social support and
social integration within the SES-health relationship (Gorman and Sivaganesan 2007;
Klein et al. 2012). These studies are especially relevant to this research because of their
15
concurrent examination of the connection between socioeconomic conditions, social
relationships, and health outcomes. Gorman and Sivaganesan (2007) find that SES and
social integration, but not social support, have direct associations with hypertension and
self-rated health. Surprisingly, neither social support nor social integration are found to
mediate the SES-health relationship but social integration is found to substantially
moderate the relationship, such that low SES has the greatest health benefit of social
relationships. They concluded that social integration may buffer the harmful impacts of
certain socioeconomic deficits in specific circumstances such as unemployment and
financial barriers to medical care. However, the health outcomes they analyze are
perceived, not measured, possibly influencing the strength of their findings. More
significant results are found by a recent German study that tests a mediation hypothesis
for social relations on the SES-health relationship. This study reports that low income and
education are associated with lower levels of social relations, operationalized by a social
integration index. They found that part of the association between low income or
education and worse health is due to the greater likelihood for low SES individuals to
have lower levels of social support and social integration (Klein et al. 2012).
Social support and social integration are two related mechanisms by which
socioeconomic status may be linked to higher body weight. Despite extensive research on
the role of social support/integration in general health and specific health outcomes, a
surprising and substantial research gap exists for higher weight status. In general, weight
status, in its various measurements, is an underutilized outcome in the health literature
examining social support and integration. Perhaps this is the result of a preoccupation
with higher weight status as a proximal risk factor for various diseases instead of
16
considering what leads to higher weight status. A small number of studies generally finds
that deficits in social support and social integration lead to higher weight status
(Kouvonen 2011), while positive social support is protective against weight gain (Ball
and Crawford 2006).
It is well documented that social relationships influence health, but much less is
known about their association with higher weight status. Given the large number of
studies showing the influence of social support and integration on other health outcomes,
continued examination of social support and integration with higher weight status as an
outcome is needed. Social support and social integration may both contribute to the
development of higher weight status by communication of health norms such as diet,
exercise, and other health behaviors, provision of emotional, instrumental and financial
support, and offering other psychosocial benefits. For example, people with higher
education tend to have better social support and are more likely to exercise (Ross and Wu
1995). Some studies suggest a reverse causation hypothesis such that lower social support
is the result of higher weight status, such that obese people have fewer friends due to the
stigmatizing nature of obesity (Carr and Friedman 2006). While an interesting direction
for future research, this avenue is beyond the scope of this project. Despite this, I believe
that the positive influences of social support and social integration outweigh the potential
negative aspects as well as the possibility of selection.
The theoretical frameworks of fundamental causation and general social support
and social integration theories are typically applied separately to the relationship between
social factors and health. However, it appears that these related frameworks are highly
compatible and together provide a more integrated explanation for how social factors,
17
such as socioeconomic status and social relationships, are associated with higher weight
status. While numerous studies investigate direct relationships between socioeconomic
status and higher weight status, no known studies focus on the intervening nature of the
flexible resources, such as social support and social integration, in the context of higher
weight status. The current study aims to contribute information about how the resources
in social support and social integration serve as the more proximal vehicles through
which social conditions are associated with higher weight status. This study will also
attempt to provide a connection between the separate, but related theoretical frameworks
of fundamental cause theory and social support and integration theory. The findings of
this study may also yield macro- and micro-level practical solutions. For example,
individuals and families may be able to use various types of social support and
connections to compensate for disadvantaged socioeconomic status. Health professionals
may maximize the effectiveness of their service delivery by focusing on both the
socioeconomic status and availability of psychosocial resources for consumers. Finally,
policy makers may strategize ways to improve population health without creating further
socioeconomic status differences while incorporating the role of social relations in
planning public health interventions.
Hypotheses
This study has three hypotheses relating to expected associations of various
indicators of SES, social support and integration with higher weight status. These
hypotheses are illustrated in the conceptual diagram in Figure 1. Research suggests that
social conditions such as education, income, and wealth are independently associated
18
with higher weight status (Ailshire and House 2011; Schafer and Ferraro 2011; Wang and
Beydoun 2007). Higher levels of education and income may be connected to a variety of
resources that help individuals and families maintain healthy weight status and avoid
higher weight status. Therefore, Hypothesis 1 states that higher weight status will be
inversely associated with socioeconomic status (education, income, and home
ownership). These proposed associations are illustrated in the lower half of the diagram
in Figure 1. Additionally, I expect that these associations will be stronger for women
than men.
Social support and social integration are among the flexible resources that are
attached to education and income (Kawachi and Berkman 2001; Link and Phelan 1995).
These resources may also be related to single measures of material wealth such as home
ownership (Pollack et al. 2007). Social support and social integration are associated with
general health as well as specific health outcomes (Gorman and Sivaganesan 2007; Klein
et al. 2012). In terms of the relationship between social support and social integration
with higher weight status, existing findings are limited and inconclusive. Based on the
ideas that higher weight status has an inverse relationship with socioeconomic status and
that socioeconomic status is linked to differential social connections, I propose
hypothesis 2: Higher weight status will be inversely associated with social support and
social integration. This relationship is demonstrated in the top right portion of the
diagram in Figure 1. Gender differences in this association have not been thoroughly
explored by past research related to higher weight status. As such, I do not have clear
expectations concerning gender differences, but this will be examined.
19
Figure 1. Conceptual Diagram
Social Support and Social
Integration
+
Socioeconomic Status (SES)
Family Income to Poverty Ratio (PIR)
Adequate Emotional Support
Available Financial Support
Number of Close Friends
-
Highest Educational Attainment
Higher Weight Status
Continuous BMI
Obese/Non-Obese
Home Ownership
Based on an integrated framework of theories related to fundamental causes and
social support and social integration, I believe that investigating the relationships between
higher weight status and the separate influences of SES and social relationships is
necessary. This examination may provide a better picture of how various social factors
combine to influence weight status differences. Therefore, Hypothesis 3 states that social
support and social integration attenuate the association between all three indicators of
socioeconomic status (income, education, home ownership) and higher weight status. I
expect that this hypothesis will be supported for women and men and will operate
similarly for both continuous and categorical measures of higher weight status. This
association is demonstrated in the top portion of the diagram in Figure 1.
20
CHAPTER 3
METHODS
Data
Data were obtained from the National Health and Nutrition Examination Survey
(NHANES), a large nationally representative sample of children and adults in the United
States. The survey utilizes a stratified, multi-stage probability sample design. NHANES
is administered by the National Center for Health Statistics (NCHS) and consists of a
combination of in-home, computer-assisted personal interviews and standardized
physical examinations that are conducted in standardized mobile laboratories. A major
advantage of the dataset is its use of National Heart, Lung, and Blood Institute (NHLBI)
guidelines for the objective measurement of height and weight instead of less reliable
self-reported height and weight. Therefore, NHANES is considered the premier tool for
measuring prevalence rates and trends for weight status in the U.S. (Kelly 2011).
Sample Weights
Researchers using NHANES data are advised to combine multiple cycles “in
order to produce estimates with greater statistical reliability” (NCHS 2010). Therefore,
this study combines three 2-year cycles of the continuous NHANES (2003-2004, 20052006, 2007-2008) to create a 2003-2008 dataset. These survey cycles include
modifications designed to increase the representativeness of data for minority groups.
The oversampling of non-Hispanic Black respondents is of particular value to this study,
with this group comprising 22 percent of the total sample. Because of the use of complex
21
sampling methods, sample weights are required for any analysis of data. More
specifically, since both the questionnaire and examination portions of the dataset are used
in this study, the sample weights for the exam portion of the survey should be applied to
the data. Furthermore, the sampling weight for the combined waves making up the 20032008 survey was constructed by multiplying the single two-year exam sampling weight
by a factor of one-third.
Analytic Sample
The analytic sample was restricted to non-Hispanic Black and non-Hispanic
White respondents aged 40 years and older who were not pregnant and provided valid
responses on all study variables. I focused exclusively on Black and White respondents
because of their larger proportions in the sample. Also, health differences between Blacks
and Whites in the United States are extensive and have been most commonly studied
(Lynch 2008). Therefore, after creation of the analytic sample, Mexican Americans (n =
759), other Hispanics (n = 240), and respondents of other races (n = 155) were not
included. Minor exclusions from the analytic sample included five pregnant respondents
and one extreme outlier with a BMI of 131.2 kg/m2.
This study strategically focused on adults aged 40 and older for several reasons.
First, this allowed for indicators of socioeconomic status to have been well-established
for the vast majority of participants in the sample. Second, some researchers suggest that
inequalities by SES are at their highest levels in middle age (House et al. 1990; Zhang
and Wang 2004). Third, resources such as social support and social integration may work
differently depending on one’s temporal location within the life course. Finally,
22
NHANES provides data on social support and social integration for this targeted age
group but not for younger adult respondents.
Missing values were carefully analyzed for each variable in the study. The NCHS
states that less than ten percent of missing data for individual variables within an analytic
dataset is acceptable and suggests proceeding “without further evaluation or adjustment”
(NCHS 2010:8). In the current analytic sample, the highest levels of missingness were
found in PIR (5.23%), financial support (2.86%), and adequate emotional support with
(0.87%), well below the recommended level. Thus, the 419 cases with missing values
(9.58%) were addressed using listwise deletion. The combination of study exclusions and
deletion of missing cases produced a final analytic sample of 3,957 respondents with
roughly equal sub-samples of women (n = 1,968) and men (n = 1,989).
Measures
Dependent Variable
Higher Weight Status. Higher weight status is measured using two different
variables. The first operationalization of higher weight status is body mass index (BMI),
calculated as weight in kilograms divided by height in meters squared and then rounded
to the nearest tenth (i.e., BMI = kg/m2). The second operationalization of higher weight
status is a dichotomous measure of obesity (obese = 1; non-obese = 0). Non-obese, the
comparison category, is defined as BMI of 18.5 to 29.9 kg/m2 and obese corresponds to
BMI ≥ 30 kg/m2. Underweight respondents (n = 111), with BMI less than 18.5 kg/m2,
are excluded from analyses because of their small numbers and to avoid skewness in the
non-obese category.
23
Independent Variables
Socioeconomic Status. Socioeconomic status, or SES, is operationalized by three
separate indicators including education, income, and material wealth. These variables are
detailed in the following sections.
Education. Education is defined as the highest level of schooling completed
according to four categories; less than 12 years, high school diploma/GED or equivalent,
some college or AA degree, and college graduate or above. A continuous measurement of
education is preferable for this study but is not available in the public-use NHANES.
Education is a useful measure of SES because it is available for most respondents in
national surveys, is typically stable in adulthood, and is not influenced by health
problems that occur in adulthood (Elo 2009). Also, as stated earlier, education is related
to the development of a variety of resources that enhance health outcomes such as higher
weight status. These resources may include social support, mastery, and self-esteem
(Pearlin et al. 1981) and personal control and problem solving (Ross and Mirowsky
2003). However, there are limitations of using education as a measure of socioeconomic
status. First, different age cohorts may assign unique meanings and values to different
levels of educational attainment (Crimmins, Hayward, and Seeman 2004). Also, Shuey
and Willson (2008) find that similar educational attainment may not confer equal
advantage to Blacks compared to Whites.
24
Income. Income is measured by the variable family income to poverty ratio (PIR),
constructed by NHANES. This variable is created by dividing a respondent’s total annual
family income by the U.S. Census Bureau’s poverty threshold that corresponds to the
respondent’s family size and ages of family members in a particular year. For example, a
single mother of two minor children who earned $36,000 in 2011 with a corresponding
poverty threshold of $18,123 would have a family income to poverty ratio equal to 1.99.
This ratio can be interpreted as approximately twice the poverty line. The PIR is
continuous and ranges from 0 to 5 (top-coded at 5 by NHANES); with scores less than 1
indicating below poverty status and higher numbers representing incomes above the
poverty level. Non-linearity was tested by including a squared term for PIR but this was
non-significant and therefore a PIR squared term was not included in regression models.
This measure is chosen for its adjustments for variation in household composition and
size, inflation within the data collection period, and the skewed quality of continuous
income. In addition, PIR is recommended as a strategic measure of socioeconomic status
in studies of higher weight status because income relative to poverty status is associated
with varying levels of diet and physical activity level (Miech et al. 2006). The PIR may
also account for differential access to quality food, medical care, and neighborhood
conditions.
Material Wealth. Wealth is multifaceted and difficult to measure as an indicator
of SES (Pollack et al. 2007; Shavers 2007). However, studies suggest that home
ownership may be used as a valid single marker of material wealth (Bennett, Wolin, and
James 2012; Braveman et al. 2005). In addition, Ahmed and colleagues (2005) conclude
25
that home ownership can be considered a proxy for overall personal wealth and level of
investment. Some suggest that wealth may even be a more valid measure of SES than
family income, especially for disadvantaged groups (Shavers 2007; Williams and Collins
1995). Therefore, wealth is measured in this study by a single item that assesses the
respondent’s home ownership status at the time of survey. This variable is dichotomously
coded as 1 for current home owner and 0 for non-homeowner.
Social Relationships
Social Support. Social support is measured by two dichotomous variables. The
first measure assesses whether respondents felt they could have used more emotional
support in the previous twelve months (no = 1; yes = 0). This item is coded so that it may
be interpreted as a measure of perceived adequacy of emotional support. The second
measure assesses the availability of financial support, which is considered an indicator of
instrumental social support. The measure is based on the following question: “If you need
some extra help financially, could you count on anyone to help you, for example, by
paying any bills, housing costs, hospital visits, or providing you with food or clothes?”
Responses to this item were coded 0 for no available financial support and 1 for available
financial support.
Social Integration. Social integration is measured by a single item pertaining to
the reported number of close friends (relatives or non-relatives) a respondent feels at ease
with, can talk to about private matters, and can call on for help. This variable may be
considered a measure of social integration or the number of close social ties. The original
26
response range for this variable is 0 to 50 and the mean response is approximately eight
close friends. For descriptive purposes only, this variable is dichotomized at the mean
number of close friends (fewer than eight = 0; eight or more = 1). The continuous version
of this variable is used for all other analyses. Marital status, typically used as a measure
of social integration in health research, is not included here because of its reported
associations with weight status as well as gender differences in higher weight status
(Sobal, Rauschenbach, and Frongillo 2003; Umberson, Lui, and Powers 2009).
Control Variables
Control variables include gender, age, age squared, race/ethnicity, marital status,
and health insurance status. Weight status has a curvilinear relationship with age, such
that body weight is at its peak around the middle of the life course and proceeds to
decline with age (Umberson, Liu, and Powers 2009). To test for non-linearity in the data,
I included a squared term for age and found a significant non-linear association with
weight status and age across all models. Therefore, age is represented in original and
squared quadratic forms. Race/ethnicity is grouped into two dummy variables; nonHispanic White (reference category) and non-Hispanic Black. Marital status is measured
as a dichotomous variable (married = 1; not married = 0). Health insurance status is an
additional known factor that may confound the association between SES and health
(Hayward et al. 2000). The health insurance status variable is created from a single
indicator of whether respondents have any type of health insurance and is coded as 1 for
respondents with any health insurance and 0 for those with no health insurance.
27
Analytic Strategy
The first phase of analysis consists of generating univariate statistics on the final
non-missing sample of 3,957 respondents. Bivariate statistics were conducted to test
preliminary associations between independent variables and both operationalizations of
higher weight status. For the continuous measure (BMI), ANOVA was used for
categorical variables and t-tests were used for other variables. For the categorical
measure (obesity), Chi-square was used for categorical variables. Finally, to test my
hypotheses, I estimated separate multivariate regression models by gender for both
measures of higher weight status. All data analyses were conducted using Stata 11.0
(StataCorp 2009).
Multivariate Regressions
The first set of regression analyses used linear regression with ordinary least
squares estimators to examine the associations of study variables with BMI, the
continuous measure of higher weight status. Linear regression has several assumptions
that are required before its use is appropriate. These assumptions include a linear
association between the independent and dependent variables, normal distribution of
dependent variable, and homoscedasticity of standard errors (Allison 1999). Tests of
these assumptions were satisfied and therefore use of linear regression is justified.
The linear regression models for testing hypotheses are demonstrated by the
model equations below, where Z is a vector of control variables and ε represents
unmeasured error. Model 1 tests whether there is support for the first hypothesis that BMI
will be inversely associated with each indicator of SES. Model 2 tests the second
28
hypothesis that social support and integration variables will be inversely associated with
BMI. The third hypothesis, that social support and integration variables mediate the
association between the SES indicators and BMI, is tested by comparing coefficient
differences between models 1 and 3. Again, analyses will be conducted separately for
subsamples of women and men and differences between gender subsamples will be
explored.
Model 1: BMI = α + β1 Education + β2 PIR + β3 Homeowner + Z + ε
Model 2: BMI = α + β4 Emotional Support + β5 Financial Support + β6 Friends + Z + ε
Model 3: BMI = α + β1 Education + β2 PIR + β3 Homeowner + β4 Emotional Support +
β5 Financial Support + β6 Friends + Z + ε
The second set of regression analyses consisted of logistic regressions using
maximum likelihood estimators to determine the associations of study variables with the
dichotomous outcome of obesity. Logistic regression is preferred here because it does not
require the assumptions of normality and homoscedasticity that become problematic with
dichotomous outcomes (Pampel 2000). For the logistic regressions, Model 1 tests for the
inverse association between SES and the dichotomous measure of obesity as stated in
hypothesis 1. The second hypothesis, that social support and integration variables will be
inversely associated with the likelihood of obesity, is tested by Model 2. The third
hypothesis, that social support and social integration will attenuate the association
between SES and obesity, will be tested through an examination of differences in
coefficients for SES variables across models. Changes in magnitude or significance level
29
of SES variables from Model 1 to Model 3 will provide some evidence of the role of
social relationships variables in the association between SES and obesity. These logistic
regression models are identical in form as the linear model equations shown above except
for the substitution of the categorical measure of obesity as the outcome.
Diagnostic Tests
Multicollinearity. In regression models, it is crucial to test for multicollinearity,
the presence of independent variables that are too highly related (DeMaris 2004). A
variance inflation factor (VIF) was computed for each of the independent variables
entered together into a single regression model. Each independent variable had a VIF
below 2 and well under the recommended threshold of 10, thus ruling out the potential
for multicollinearity (DeMaris 2004). Analyses were conducted with the socioeconomic
status variables separately and together in a single block. The single block approach
appeared to be the most parsimonious method; therefore the SES variables are included
together in regression analyses.
Gender-Pooled versus Gender-Stratified Models. Given the literature suggesting
that higher weight status varies by gender, tests were conducted to determine the need for
models stratified by gender. A Chow test is used to assess whether gender-pooled or
gender-stratified models provide a better model fit for ordinary least squares regression
analyses (Demaris 2004). The Chow test comparing the subsamples of women versus
men yielded an F (17, 3923) = 6.79 with a significance level of less than .001. This result
confirms that gender-stratified models are a better fit for the linear regression analyses. In
30
addition, I conducted a goodness of fit test for logistic regression models with the lower
BICꞌ value representing the better model fit. Comparing the BICꞌ for the fully interacted
model (gender-pooled) (-2.918e+06) with the BICꞌ for the non-interacted (genderstratified) model (-3.401e+.06) provides evidence for the gender-stratified model as a
better fit. Also, the difference between the two models was 4.8e +05, further suggesting
strong support for the gender-stratified model (StataCorp 2009).
Difference of Coefficients Tests. In hypothesis three, I propose that social support
and social integration variables will attenuate the association between SES and higher
weight status. I assess this hypothesis by comparing observed coefficients and
significance levels for SES variables. Specifically, a Clogg test will be used to examine
coefficient differences for each of the SES variables across models 1 and 3 (the models
before and after the introduction of the social support and social integration variables. A
significant test statistic will provide evidence of the attenuating role of social relationship
variables (Clogg 1995).
31
CHAPTER 4
RESULTS
Descriptive Statistics
Table 1 presents descriptive characteristics, including weighted means and
standard errors or percentages for all study variables, displayed by gender sub-samples
and the full sample. Significance tests for differences within and between genders were
conducted using ANOVA for categorical variables and t-tests for other variables. Sample
weights were constructed according to NHANES instructions for analyzing both
questionnaire and laboratory data from multiple survey cycles. These weights are used to
produce estimates that are representative of the national population. The mean BMI for
the full sample is 29.1, near the upper limit of 29.9 for the overweight category. In this
sample, neither gender differs significantly from the mean BMI (women 29.2; men 29.0).
For the dichotomous measure of higher weight status (obese = 1; non-obese = 0), 37.1
percent of the full sample are considered obese with 38.2 percent of women and 35.9
percent of men falling into this category. Gender differences were non-significant for
both the continuous and categorical measures of higher weight status.
In the full sample, roughly 15 percent of both women and men respondents have
less than a high school education. Approximately 28 percent of sample respondents have
a high school or equivalent education, with women having a higher percentage in this
category (women 28.6; men 27.0). Some college education is the most frequently
reported educational category, representing 30.9 percent of the full sample. Thirty-two
percent of women compared with 30 percent of men have some college education.
32
Table 1. Weighted Means, Standard Errors (SE) and Percentages (%) for Study Variables by Gender, National
Health and Nutrition Examination Survey 2003-2008
Variable Name
Women
N = 1,968
Mean/% SE
Dependent variables
Body mass index (BMI) (18.5 to 62.5)
Obese (BMI 30.0 to 62.5)
29.2
38.2
Independent variables
Highest educational attainment
Less than high school
High school
Some college
College or higher (reference)
15.0
28.6
31.9
24.5*
Family income to poverty ratio (PIR) (0 to 5)
Home ownership
Adequate emotional support
Available financial support
Number of close friends and family (0 to 50)
Control variables
Gender
Women
Men
Age (40 to 85 years)
Race
Black
White (reference)
Married
Health insurance
0.19
-
Men
N = 1,989
Mean/%
SE
29.0
35.9
14.8
27.0
29.8
28.3
F = 4.44; p < .05
Total
N = 3,957
Mean/%
SE
0.14
-
29.1
37.1
0.12
-
-
14.9
27.9
30.9
26.3
-
3.19*
81.2*
74.2*
82.4*
7.29
0.04
0.19
3.43
84.2
80.9
77.7
7.60
0.04
0.21
3.31
82.6
77.4
80.1
7.44
0.03
0.14
57.8*
0.31
56.1
0.29
49.7
50.3
57.0
0.21
12.5*
87.5*
57.3*
90.3
-
11.0
89.0
72.3
90.0
-
11.8
88.2
64.5
90.0
-
Indicates significant gender difference at *p < 0.05; † p < 0.10
Respondents with four-year college degrees or higher account for 26.3 percent of
the full sample, with more college graduates among men (28.3%) than women (24.5%) (p
< .05). For educational attainment overall, there is a significant difference by gender (F =
4.44; p < .05). The mean for PIR in the full sample is 3.31 with a mean for women of
3.19, significantly lower than the mean for men of 3.43 (p < .001). In terms of home
ownership, 82.6 percent of all respondents own their own homes. Eighty-four percent of
33
men report being home owners compared with 81.2 percent of women home owners (p <
.05).
Adequate emotional support is reported by 77.4 percent of respondents in the full
sample. This perception of emotional support adequacy appears to be higher among men
at 80.9 percent compared with 74.2 percent of women (p < .001). Eighty percent of
respondents report having financial support available to them if needed. This perception
of available instrumental support appears to be more prevalent in women (82.4 percent)
than men (77.7 percent) (p < .001). The mean for close friends and family is 7.44 with
men reporting more close contacts (7.60) than women (7.29).
The full sample consists of 49.7 percent women and 50.3 percent men and all
analyses are performed with gender sub-samples (women = 1,968; men = 1,989). The
mean age of respondents overall is 57.0 years with a range of 40 to 85 years. On average,
women in the sample are 1.7 years older than men (women 57.8 years; men 56.1 years; p
< .001). Non-Hispanic Black respondents represented 11.8 percent of the weighted
sample with the other 88.2 percent for non-Hispanic White respondents. The unweighted
percentages included 26 percent black respondents and 74 percent white respondents.
Sixty-five percent of respondents in the full sample are married but large differences in
marital status are observed by gender (women 57.3%; men 72.3%; p < .001). Ninety
percent of respondents had some form of health insurance and the percentages by gender
were relatively uniform (women 90.3%; men 90.0%).
34
Bivariate Statistics
Table 2 displays the weighted means for BMI, the continuous measure of weight
status, for each of the independent variables by gender. As expected, those with a college
degree or higher have lower BMI than those with lower levels of education. One
exception is found in the similar mean BMI for men with less than high school as men
with college degrees or above (28.3 versus 28.6). For men, the distribution of BMI by
educational attainment appears as an inverted “U” shape and this is consistent with prior
research on education and weight status (Leigh, Fries, and Hubert 1992). The difference
of means for women and men is significant (F = 6.65; p < .001).
Mean BMI for those with PIR less than 1 compared to those with PIR of 1 or
greater are 29.4 and 29.1 respectively. The difference around the poverty threshold is in
the expected inverse direction but is not a substantial difference. However, when PIR is
compared by gender, women below the poverty threshold have higher BMI than those
who are at the level of poverty or higher (30.6 versus 29.0). In contrast, men at or above
the poverty threshold experience a higher mean BMI of 29.1 relative to a mean of 27.6
for men living in poverty. The differences of means for BMI at the two categorical PIR
levels for women and men are both significant at the .05 level. The mean BMI for all
home owners is 29.2 with only a small decrease to 29.1 for non-homeowners. When
analyzed by gender, women who are homeowners experience a significantly lower mean
BMI (28.9) than non-homeowners (30.3). On the other hand, men experience a higher
mean BMI if they are homeowners.
35
Table 2. Weighted Mean Body Mass Index for Independent Variables by Gender, National Health and Nutrition
Examination Survey 2003-2008
Women
N = 1,968
Men
N = 1,989
Total
N = 3,957
Mean
Mean
Mean
Less than high school
29.3†
28.3*
28.8
High school
29.4
29.5*
29.5
Some college
29.7*
29.3
29.5
College or higher (reference)
28.1*
28.6*
28.3
Variable Name
Socioeconomic Status
Highest educational attainment
F = 8.86; p < 0.001
Family income to poverty ratio (PIR)
PIR less than 1
30.6*†
27.6*
29.3
PIR 1 to 5
29.0
29.1
29.1
No
30.3*†
27.7*
29.2
Yes
28.9
29.3
29.1
No
29.8*†
28.6
29.3
Yes
28.9
29.1
29.0
No
30.1*
29.4
29.8
Yes
29.0
28.9
28.9
Less than or equal to mean of 8
29.4
29.0
29.2
More than mean of 8
28.7
29.0
28.9
Home ownership
Social support and social integration
Adequate emotional support
Available financial support
Number of close friends and family
1
Significance tests for gender differences included ANOVA for categorical variables and t-test for dichotomous
variables.
*
indicates significant within gender difference at p < 0.05
†
indicates significant between gender difference at p < 0.10
Adequate emotional support is associated with a mean BMI of 29.0 compared
with 29.8 among those who report inadequate emotional support. Having adequate
emotional support is associated with lower BMI for both women and men, but is
significant only in women (p < .05). Gender differences in the mean BMI for either
36
category of adequate emotional support are both significant. Those with financial support
have a mean BMI of 28.9 compared to a higher mean BMI of 29.8 for those without
financial help. A significant difference in mean BMI for the dichotomous financial
support categories are observed within women and differences between genders for both
financial support categories are significant as well. Interestingly, men without financial
support have higher BMI than men with financial support but men with lower incomes
and non-homeowners have higher BMIs than others. For descriptive purposes only, the
variable of number of close friends is dichotomized at the mean of 8 (rounded to nearest
digit). The group with fewer than eight friends than the mean had an average BMI of 29.2
compared to 28.9 in the group with eight or more close friends. Women with fewer
friends have a higher mean BMI (29.4) than those who have more close social
connections (28.7). There is no difference in mean BMI for men, at least in relation to the
mean for close friends.
Table 3 provides percentages for the dichotomous measure of obesity for the
independent variables, again with gender comparisons. Most of the general patterns
observed in the previous table are repeated here but often with more distinct gender
differences. The percentage of women categorized as obese decreases with each step up
in educational level, ranging from 31.8 percent of women with college degrees to 41.4
percent of women with less than a high school education. For men, again there is
evidence of the inverted “U” shape distribution with the lowest percentage of obesity
(30.5%) observed in men with college degrees and the next lowest percentage (33.8%)
found in men with less than a high school education. The highest obesity percentages are
in men with some college (39.8%) and high school or equivalent (38.6%).
37
Table 3. Percent Obese for Independent Variables by Gender, National Health and Nutrition Examination
Survey 2003-2008 (n = 3,957)
Variable Name
Socioeconomic status (SES)
Highest educational attainment
Less than high school
High school
Some college
College or higher
Family income to poverty ratio (PIR)
PIR less than 1
PIR 1 to 5
Home ownership
No
Yes
Social support and social integration
Adequate emotional support
No
Yes
Available financial support
No
Yes
Number of Close Friends and Family
Less than or equal to mean of 8
More than mean of 8
Women
N = 1,968
%
Men
N = 1,989
%
Total
N = 3,957
%
41.4
33.8
40.2
38.6
39.8
39.8
31.8*
30.5*
X2 = 13.44; p < 0.001
37.8
39.4
39.8
31.1
46.1*†
37.5
26.6*
36.6
37.6
37.1
45.0*†
36.6
28.4*
37.3
37.8
37.0
41.9
36.9
34.4
36.3
38.9
36.6
43.1
37.1
38.3
35.2
40.5
36.3
39.7
34.9
36.5
34.7
38.2
34.8
1
Significance tests for gender differences included Chi-square tests for categorical variables
and difference of proportion tests for dichotomous variables.
*
indicates significance within gender at p < 0.05
†
indicates significant between gender difference at p < 0.10
Bivariate analyses of the total sample show very little difference in BMI for all
independent variables. However, when the sample is stratified by gender, interesting
differences emerged. Gender appears to play an important role in BMI differences for
respondents depending on their socioeconomic status. For example, women with lower
educational status, lower PIR, and who are non-homeowners have significantly higher
BMI than men of the same status. Women of higher socioeconomic status do not
38
experience significant differences in BMI when compared to men. A similar trend occurs
for indicators of social support in which women reporting inadequate social support have
higher BMIs than men but this difference again dissipates with higher levels of social
support.
Linear Regression Results
The general patterns noted in the univariate and bivariate analyses are further
tested in a multivariate format. Tables 4 and 5 present results from the weighted ordinary
least squares regression models predicting body mass index for women and men
respectively. Beginning with Table 4, model 1 shows associations with BMI for the SES
variables entered together as a single block. Control variables including age, age-squared,
race, marital status, and health insurance status are included in all models. Significant
negative coefficients in this first model would provide evidence for Hypothesis 1 that
SES is inversely associated with BMI in women. Results reveal support for this
hypothesis for all three SES measures. In terms of education, women at each educational
category below college graduate had higher aggregate BMI ranging from 1.08 higher in
the less than high school category to 1.28 higher in the high school group (p < .05).
Women with some college education reported significantly higher BMI (1.37 higher BMI
on average) than women with college degrees (p = < .01). The regression coefficient for
PIR shows an inverse association with BMI, such that for a one-unit increase in PIR
(range 0-5), women have a decrease of 0.46 in BMI (-0.46; p < .001). For example,
compared with women at the poverty line (PIR = 1), women with family incomes four
times the poverty threshold (PIR = 4) have a 1.84 decrease in BMI. The final measure of
39
SES, home ownership, is inversely associated with BMI but this association is nonsignificant.
Model 2 shows the associations of social support and social integration variables
with BMI. Two of these variables, emotional support and financial support, are observed
in the expected negative direction with BMI. The perceived availability of financial
support is the only significant association with BMI (-1.00; p < .05). Women who report
having financial support available have a BMI one point lower than those with no
financial help. Thus, support is found for Hypothesis 2 only in the availability of financial
support. Model 3 shows regression coefficients for SES variables with the addition of the
social support and social integration variables. Education, which was significant in Model
1, is virtually unaffected by this addition but the coefficient for PIR decreased by 7
percent across models. Furthermore, after accounting for SES indicators, the coefficient
for financial support is reduced to marginal significance (p < .10). The coefficient
changes in PIR warrants further testing. A Clogg test, using the suest command in Stata,
was performed to test for a significant difference between the coefficients for PIR in
model 1 and model 3. However, the observed differences were not significant (z = -1.54;
p = .124).
As expected, age and age-squared are negatively associated with BMI and both
are highly significant throughout all models. In other words, there is a curvilinear
association between age and BMI. In the final model, Black women in this sample have
significantly higher BMI (2.43; p < .001) than White women. Married women have
significantly lower BMI than non-married women (-0.82; p < .10). Finally, health
insurance has a strong positive association with BMI; women with health insurance have
40
a 2.65 higher BMI than those without. The final model explains 7.1 percent of the
variance in women’s BMI.
Table 4. Weighted Ordinary Least Squares Regression of Body Mass Index (BMI) in Sub-Sample of Women, National
Health and Nutrition Examination Survey 2003-2008 (n = 1,968)
Models
Variable Name
1
2
3
Socioeconomic status (SES)
Highest educational attainment
(reference: college or higher)
Less than high school
High school
Some college
Income to poverty ratio (PIR)
Home ownership
Social support and social integration
Adequate emotional support
Available financial support
Number of close friends
Controls
Age
Age-squared
Black
Married
Health insurance
Constant
R2
1.08† (.583)
1.28* (.562)
1.37** (.532)
-0.46*** (.142)
-0.57 (.496)
-
1.12† (.583)
1.28* (.567)
1.37** (.533)
-0.43** (.143)
-0.53 (.498)
-
-0.41 (.440)
-1.00* (.481)
0.01 (.021)
-0.27 (.440)
-0.74† (.481)
0.01 (.022)
-0.08*** (.014)
-.01*** (.001)
2.38*** (.415)
-0.84† (.458)
2.68*** (.580)
-0.06*** (.014)
-0.00*** (.001)
2.76*** (.423)
-1.26** (.424)
1.84*** (564)
-0.08*** (.015)
-0.00*** (.001)
2.43*** (.421)
-0.82† (.460)
2.65*** (.986)
28.9 (.903)
29.8 (.727)
29.6 (.986)
.069
.052
.071
Unstandardized coefficients and robust standard errors are shown
† p < .10, *p < .05, ** p < .01, *** p < .001
In summary, Table 4 shows that women’s educational, income, and home
ownership statuses are all inversely associated with BMI, providing support for
Hypothesis 1. Financial support is the only significantly and negatively associated
predictor of BMI when accounting for differences in SES, giving partial support for
Hypothesis 2 for women. Finally, Hypothesis 3 is partially supported since the
41
availability of financial support for women appears to slightly reduce the association
between PIR and BMI.
Table 5 provides the weighted ordinary least squares regression coefficients of
BMI by study variables for men. Model 1 reveals an inverted “U” shaped association of
educational levels with BMI. Men with less than a high school education do not have
significantly different BMI than college educated men. At the high school level and
above, the expected inverse association with BMI is observed. Therefore, education
provides some support for Hypothesis 1. In contrast, PIR does not have a significant
association with BMI among men; thus no support is found for Hypothesis 1 in PIR.
Home ownership has an unexpectedly positive and significant association with BMI
(1.30; p < .001). To summarize, support for Hypothesis 1 is found in education but not in
PIR or home ownership.
Model 2 shows expected inverse associations with BMI for available financial
support (-0.63; p < .10) and the number of close friends (-0.02; n.s.). Adequate emotional
support is positively associated with BMI for men but is non-significant (a negative
association was found for women). Model 3 presents the combined associations of SES
and social relationships with BMI. The addition of social support and social integration
yields a slight attenuation of the coefficients for education but the significance remains at
the .01 level. These reductions do not appear to be sufficient to warrant further testing of
differences in coefficients. PIR remains essentially flat and non-significant and does not
appear to be an important predictor for BMI in men. Home ownership remains highly
significant at the .001 level and the coefficient actually increases in magnitude from
model 1 to model 3, suggesting that social relationships may suppress the association
42
between home ownership and BMI for men. Model 3 also shows the number of close
friends becoming a significant factor (-0.03; p < .10) and available financial support
maintaining marginal significance as well. No significant racial difference was found
among men for BMI. Married men have significantly (p < .05) higher BMI (the opposite
trend occurred for women). In addition, age and health insurance were both positively
associated with BMI. This final model explains 4.4 percent of the variance in men’s BMI,
compared with the higher 7.1 percent explained for women’s BMI.
In summary, education is the only SES indicator that remains significant in the
hypothesized inverse direction for men, providing only partial support for Hypothesis 1.
Financial support and the number of close friends are both factors that are significantly
and negatively associated with BMI when accounting for differences in SES, giving
partial support for Hypothesis 2 for men. Finally, there is no evidence to support
Hypothesis 3 since social relationships variables did not appear to substantially change
the association between SES and BMI in the male subsample.
43
Table 5. Weighted Ordinary Least Squares Regression of Body Mass Index (BMI) in Sub-Sample of Men, National
Health and Nutrition Examination Survey 2003-2008 (n = 1,989)
Variable Name
Socioeconomic status (SES)
Highest educational attainment
(reference: college or higher)
Less than high school
High school
Some college
Income to poverty ratio (PIR)
Home ownership
Social support and social integration
Adequate emotional support
Available financial support
Number of close friends
Controls
Age
Age-squared
Black
Married
Health insurance
Constant
R2
1
Models
2
3
0.60 (.449)
1.27** (.430)
0.98** (.375)
0.05 (.105)
1.22*** (.368)
-
0.55 (.447)
1.23** (.424)
0.94** (.372)
0.05 (.105)
1.30*** (.372)
-
0.47 (.370)
-0.63† (.372)
-0.02 (.015)
0.39 (.365)
-0.66† (.367)
-0.03† (.015)
-0.05*** (.010)
-0.00** (.001)
0.22 (.330)
0.71* (.322)
0.97* (.443)
-0.05*** (.015)
-0.00** (.001)
-0.03 (.329)
0.95** (.315)
1.11* (.445)
-0.05*** (.010)
-0.00** (.001)
0.19 (.333)
0.67* (.328)
0.99* (.449)
26.0 (.658)
27.8 (.562)
26.2 (.709)
.039
.030
.044
Unstandardized coefficients and robust standard errors are shown
† p < .10, *p < .05, ** p < .01, *** p < .001
Logistic Regression Results
Table 6 provides weighted odds ratios from logistic regression models predicting
obesity among women (obese = 1; non-obese = 0). As expected, compared with the
reference group of college educated women, respondents in the other three educational
categories had higher relative log odds of being classified as obese. Women with less
than high school educations have 51 percent greater log odds of being obese (p < .05)
than college educated women and the relative odds decrease with each higher level of
educational attainment. PIR is also inversely associated with the likelihood of being
44
categorized as obese. A one-unit increase in PIR is associated with a 12.0 percent
reduction in the relative log odds of being obese (p < .01). Home ownership shows a
similar inverse association with obesity but this association is not statistically significant.
Model 2 introduces social support and social integration variables with controls
and yields no significant associations with obesity. Emotional support and financial
support are both in the expected negative direction while the number of close friends
appears to have no association. With the reintroduction of SES indicators in Model 3, the
coefficients for the social relationships variables are even further reduced. Coefficients
for SES variables remain unchanged after controlling for differences in social
relationships variables. Thus, education and PIR remain significant factors associated
with the odds of obesity. However, social relationships appear to make no difference in
the association between SES and obesity. An examination of control variable coefficients
reveals that any associations of social relationships with obesity are explained away by
demographic factors. For example, with SES and social relationships held constant, Black
women appear to have 86 percent higher odds of being obese than White women. The
final model explains approximately seven percent of the variance in women being
classified as obese. Overall, these models show support only for Hypothesis 1, that there
is an inverse association between SES measures and the dichotomous measure of obesity
for women. The final model accounts for approximately four percent of the variance in
women’s likelihood of obesity.
45
Table 6. Weighted Odds Ratios from a Logistic Regression of Variables on Obesity in Sub-Sample of Women,
National Health and Nutrition Examination Survey 2003-2008 (n = 1,968)
Models
Variable Name
1
2
3
Socioeconomic status (SES)
Highest Educational Attainment
Less than high school
High school
Some college
(College or higher)
Income to poverty ratio (PIR)
Home ownership
1.50* (.301)
1.43* (.246)
1.31† (.214)
-
1.51* (.304)
1.43* (.248)
1.30† (.215)
0.88** (.039)
0.91 (.136)
-
0.88** (.040)
0.91 (.139)
Social support and social integration
Adequate emotional support
Available financial support
Number of close friends
-
0.91 (.119)
0.80 (.116)
1.00 (.007)
0.94 (.126)
0.86 (.127)
1.00 (.008)
0.97*** (.004)
1.00** (.000)
1.85*** (.222)
0.91 (.114)
1.71** (.340)
0.98*** (.004)
1.00* (.000)
2.04*** (.238)
0.81† (.096)
1.71** (.340)
0.97*** (.004)
1.00** (.000)
1.86*** (.226)
0.91 (.115)
1.71** (.338)
32879569
33236794
32852775
Wald Chi-Square
112.35
94.27
114.11
Pseudo R2
0.035
0.025
0.036
Controls
Age
Age-squared
Black
Married
Health insurance
BIC
† p < 0.10, *p < .05, ** p < .01, *** p < .001
Table 7 reports odds ratios from logistic regression models predicting obesity
among men. The results for men were quite different from those for women. First, the
associations of education with obesity in men follow the opposite pattern found in
women. The likelihood of obesity actually increases with each higher level of education
until college. Men with college degrees continue to benefit from lower odds of being
obese than those with lower educational levels. For example, men with college education
have 56 percent lower log odds of being obese than men with only some college
education. PIR does not appear to be associated with men’s likelihood of being obese,
46
whereas it was a significant factor for women. Home ownership also shows a different
pattern; men who are home owners have 48 percent higher log odds of being obese than
those who do not own their own homes.
The insertion of social relationships variables in Model 2 shows no significant
associations with obesity and only financial support is in the expected negative direction.
This pattern remained after introducing social support variables into the equation in
Model 3. Education continues to be the only significant and inverse association with
obesity and the positive association of home ownership and obesity remained. Compared
with the female subsample, demographic factors such as race are not significant factors in
obesity among men. The final model explains approximately two percent of the variance
for obesity among men.
In summary, the logistic regression analyses further suggest that individual
indicators of SES have different associations with SES for women and men. Education,
especially college education, is an important factor for both men and women. PIR is a
protective factor for women but not for men and home ownership is positively associated
with obesity in men but not women. In terms of social relationships, social support and
social integration are not significantly associated with the likelihood of obesity in women
or men and do not mediate the SES-obesity association.
47
Table 7. Weighted Odds Ratios from a Logistic Regression of Variables on Obesity in Sub-Sample of Men, National
Health and Nutrition Examination Survey 2003-2008 (n = 1,989)
Models
Variable Name
1
2
3
Socioeconomic status (SES)
Highest educational attainment
(reference: college or higher)
Less than high school
High school
Some college
Income to poverty ratio (PIR)
Home ownership
1.42† (.283)
1.52* (.261)
1.56** (.257)
0.98 (.044)
1.48* (.258)
-
1.41† (.281)
1.51* (.260)
1.55** (.255)
0.98 (.044)
1.50* (.264)
-
1.07 (.163)
0.87 (.118)
0.99 (.007)
1.06 (.161)
0.87 (.118)
0.99 (.007)
0.98*** (.004)
1.00* (.000)
1.01 (.128)
1.19 (.167)
1.28 (.269)
0.98*** (.004)
1.00* (.000)
0.98 (.120)
1.27† (1.70)
1.24 (.252)
0.98*** (.004)
1.00*(.000)
1.00 (.127)
1.19 (.169)
1.28 (.272)
30272840
30504949
30239036
Wald Chi-square
35.51
25.16
36.92
Pseudo R2
0.02
0.01
0.02
Social support and social integration
Adequate emotional support
Available financial support
Number of close friends
Controls
Age
Age-squared
Black
Married
Health insurance
BIC
† p < 0.10, *p < .05, ** p < .01, *** p < .001
48
CHAPTER 5
DISCUSSION AND CONCLUSIONS
In this study, I examined associations between SES, social relationships and
higher weight status. This study used education and income as traditional measures of
SES and added home ownership as an indirect indicator of material wealth. Social
relationships were measured by emotional support adequacy, financial support
availability, and number of close social connections. I used a combination of fundamental
cause theory and social support and integration theories to test hypotheses that SES (H1)
and social support and integration (H2) are each independently and inversely associated
with higher weight status. I tested an additional hypothesis that social support and
integration would mediate the association between SES and higher weight status (H3).
In this section, I outline major findings by hypotheses and connect these findings
to prior empirical research and theory. Throughout this discussion, I highlight the finding
that higher weight status is associated with SES and social relationships in a gendered
fashion. I also review strengths and limitations of this study as well as opportunities for
future research. I conclude with a brief discussion of the relevant contributions of this
study to existing literature on social factors and higher weight status.
SES and Higher Weight Status
I hypothesized that there would be inverse associations with SES indicators and
higher weight status. The results show that educational attainment, in particular, is
inversely associated with both operationalizations of higher weight status. However,
49
other SES indicators such as the family income to poverty ratio (PIR) and home
ownership do not follow predicted patterns. For example, PIR is a protective factor for
women but not for men and home ownership is positively associated with higher weight
status in men. In addition, I expected that there would be stronger associations of SES
variables with higher weight status for women. It appears that these associations are not
necessarily stronger for women but are certainly different from those for men.
Significant differences in BMI and obesity are noted within the female and male
sub-samples as well as significant differences by gender for a variety of different
variables. A simple examination of descriptive statistics shows that women with lower
levels of SES tend to have higher BMI as well as a greater likelihood of being obese.
However, higher levels of SES, especially education and income, seem to reduce gender
differences in weight status by bringing women more equal with men. Perhaps this
observation is related to women’s lower baseline SES and therefore women may have
more to gain (Mujahid et al. 2005).
For women, education and income appear to be the most beneficial SES factors
that are associated with weight status. Despite this, there are interesting variations within
the female and male subsamples. For instance, women with some college education have
significantly higher BMI than women with a high school degree or less. By comparison,
men are also highly influenced by education, with higher levels of education associated
with lower BMI, with the exception of less than high school. For both genders, it appears
that achieving a college education or higher is the benchmark for protection against
higher weight status.
50
Another striking finding is that the association of higher weight status with
income is positive for men and negative for women. This finding is consistent with metaanalyses showing that inverse associations are not consistently found for men (Sobal and
Stunkard 1989). A recent Canadian study confirms that high SES men are at higher risk
for higher weight status and high SES women are at a lower risk (Dumas 2011).
Home ownership is also an important predictor for men but it is unexpectedly and
strongly associated with BMI in a positive direction. This finding is similar to Fonda and
colleagues’ (2004) study in which another wealth measure, net worth, was positively
associated with BMI in men. They concluded that the positive association may be due to
different meanings of higher weight status for women and men such that higher weight in
men is seen as a positive attribute but as a negative quality in women. It is possible that
men’s success is simultaneously attached to wealth status (such as owning a home) and
higher weight status.
There are several possible explanations for these gender differences in weight
status and here I list only three. First, cultural ideals may be important factors in whether
higher body weight is acceptable in women and men. Phelan, Link, and Tehranifar
(2010:S36) suggest that these cultural norms are “likely to be embedded in strong social
norms and support.” In this scenario, higher relative weight is advantageous for men and
lower relative weight is beneficial for women. Kawachi, Adler, and Dow (2010:59) refer
to this cultural explanation as the “fat bias” in which women are judged more harshly for
being overweight. As a result, men who are heavier tend to earn higher incomes than
thinner men and women who are thinner enjoy higher income compared to women with
higher weight status. Although this implies causal ordering, it is an important
51
consideration when studying weight status. Second, some research suggests that men in
lower SES groups are more likely to engage in employment where physical activity is
more frequent. Higher SES men tend to be employed in positions which require less
physical activity. Future research could address this by controlling for level of physical
activity. Third, stress may play a role in the development of higher weight status (Lee
2011). A variety of stress-related factors such as gendered stress responses, differential
stress associated with disadvantage, persistent stressors such as racism and
discrimination, and differences in physiological responses to stress may impact
differences in weight status. However, the influence of stress may not be limited to the
disadvantaged; stressors related to higher status may have implications for weight status
as well (Schieman, Whitestone, and van Gundy 2006).
Data from the current study verify that higher weight status may not follow the
predictable step-wise gradient generally found in other health outcomes (Braveman 2009;
Marmot and Wilkinson 2006). To conclude, socioeconomic status measures appear to
have varied and complex associations with higher weight status and operate differently
for women and men.
Social Relationships and Higher Weight Status
The association between social relationships and higher weight status is
inconsistent. Findings suggest that distinct aspects of social relationships may serve
different roles depending on the context in which they are employed. This is particularly
evident for gender. A look at descriptive data indicates that women with higher levels of
social support and integration were less likely to have higher BMI or be obese. However,
52
when demographic controls are accounted for, financial support is the only significant
resource for women and men. Adequate emotional support is not significantly associated
with higher weight status in either gender but directional differences (negative for women
and positive for men) hint at important gender differences. The number of close social
connections is not a significant predictor for women but emerges as a factor for men’s
BMI only when socioeconomic indicators are simultaneously considered.
The notion of resource substitution (Ross and Mirowsky 2006) is applicable here
because groups that are disadvantaged in one area may purposively rely more heavily on
other resources. This also fits well with Link and Phelan’s concept of flexible resources.
For example, women of low income status may have to compensate by seeking
alternative sources of financial support or continuing their education in order to improve
greater capital. Resources included in social relationships may also be substituted so that
one area of deficiency is offset by strengths in another area. Data indicate that both
women and men seek out financial support but that men may also solicit other forms of
support from their typically larger social networks. Furthermore, because of their larger
social networks, men may experience an advantage in greater exposure to health
knowledge, resources, and opportunities. This idea is consistent with fundamental cause
theory with flexible resources that include knowledge and social support. The process by
which information and resources are transmitted through social networks is also a strong
theme in the literature on how social relationships influence health.
53
Role of Social Relationships in the Association between SES and Higher Weight Status
In general, the combination of SES and social relationships appears to be more
related to higher weight status than the association with either of these factors alone.
When compared separately, SES variables appears to have stronger connections to higher
weight status than social support and integration variables, providing support for the
primacy of distal factors over proximal factors. When these two sets of variables are
combined into a single model, the explanatory power appears to increase.
It is clear from the descriptive data that the gender gap in mean BMI is attenuated
or even significantly eliminated for women who have greater levels of SES and social
support. When women experience any of the advantages of socioeconomic status and
social support they have significant reductions in percentage of obesity. However,
relative to the smaller reductions for men with the same advantages, women continue to
have a higher percentage in the obesity category. However, when SES and other
demographic information are accounted for in regression analyses, the influence of social
relationships are not as clear or disappear altogether. These descriptive data belie the lack
of robust findings for social relationships variables in the regression analyses. The limited
findings of mediation or “association explained by” social relationships provides support
to the basic association between SES and higher weight status. Social support and
integration may represent resources that are so closely tied to SES that their associations
with weight status may be difficult to separate (Phelan et al. 2004).
The current results support fundamental cause theory yield in several ways. First,
strong associations between indicators of SES and higher weight status are found,
including education and PIR for women and education and home ownership for men.
54
Regardless of the direction of these associations, it appears that SES is a powerful force
underlying higher weight status. Also, despite the use of relevant controls and the
introduction of social relationships variables, the associations of SES with higher weight
status persist across models. In other words, these important associations cannot be
explained away by other factors that were included in this study.
Higher weight status may be a good outcome for testing fundamental cause theory
because it is strongly associated with SES as well as meta-mechanisms that include social
relationships. Another relevant aspect of fundamental cause theory is the replacement of
intervening mechanisms. Current data may still reflect a transition from higher weight
status as a sign of wealth to a sign of poverty. In fact, there may be lingering cultural
norms that endorse higher weight status in wealthy men (Dumas 2011).
Access to flexible resources is probably the most important aspect of fundamental
cause theory for higher weight status. According to Link and Phelan, resources of money,
power, prestige, and social connections are flexible and can be used interchangeably to
gain advantage. Financial support appears to be most consistently linked to women’s
socioeconomic status and weight status. This study provides evidence for this idea, given
that financial support appears to be most beneficial for those of low income but not
necessarily for those with low education. While one’s own financial resources are
important to obtaining better health and lower weight status, it appears that one’s
financial support network figures heavily as well, especially for those individuals of
lower socioeconomic standing. In other words, SES is not only connected to one’s
personal financial resources but to a network of financial assistance (whether it include
family, friends, private or governmental agencies). With respect to financial support from
55
community agencies, individuals who are eligible for this assistance may experience a
buffer against the adverse health impacts that they would otherwise suffer from without
the assistance.
Another flexible resource that is linked to both SES and social relationships is the
provision of knowledge. Link (1998) shows how knowledge of new risk factors for
disease tends to quickly become unequally distributed by SES. Since SES is associated
strongly with social relationships, this important knowledge may be transmitted through
social networks. However, Winkleby and colleagues (1992:819) point out that
“information alone appears to be a weak stimulus to human behavior change.” On this
point, I believe that health behavior is modeled differently by those with higher SES and
there may be more pressure to conform among higher SES groups. In addition to the
mere provision of information, higher education involves an array of other resources that
confer advantage. Paeratakul and colleagues (2002) argue that education is positively
associated with the knowledge that one is overweight and this knowledge may be helpful
in changing health behaviors.
Strengths of the Study
This study has several strengths including the use of measured height and weight,
two operationalizations of higher weight status, comparisons by gender and age, and the
use of multiple theoretical perspectives.
Use of NHANES data is an advantage of this study because of its measured
weight and height. Although NHANES provides alternative measurements of weight
status such as waist-hip ratio and waist circumference, I chose BMI because it is the most
56
common indicator of higher weight status and therefore it is most useful for comparing
results to prior studies. Also, the use of two operationalizations of higher weight status
was a beneficial analytic choice. Results provide a cautionary note to researchers who
focus on dichotomous measures of obesity that are not as sensitive as the continuous
measure of BMI. Also analyzing these associations using both continuous and categorical
indicators of higher weight status is advantageous for ruling out threshold effects of
measurement. For example, linear regressions show that financial support is significant
for both genders but non-significant in logistic regressions. In addition, I have focused
extensively on gender differences in higher weight status and this advantage is noted
throughout this discussion.
This study’s focus on middle-aged and older adults (40-85 years) provides
more nuanced information on the associations of higher weight status with SES and
social relationships. It is possible that middle-aged and older adults in this sample differ
significantly from adults between the ages of 18 and 40. Perhaps SES indicators operate
differently among those over 40 and may even differ as one progresses from middle age
to old age. While I do not have the longitudinal data to support this kind of investigation,
past research suggests that different types of social relationships matter more at different
points in the life course (Clarke and Wheaton 2005). For example, financial support may
matter most in middle age and social connections may be more valuable more in old age.
Based on a post hoc analysis of the data by age groups (40 to 65 and 65 and
older), associations are stronger in magnitude and significance for the younger cohort
than in the older cohort. Among the older group there were no significant associations in
any of the regression models. This is possibly due to the influence of safety nets for
57
economically disadvantaged groups, such as Social Security income, health insurance
provided for older adults (Medicare), and government-subsidized housing for the elderly.
Another explanation may be that there is a survivorship effect, that healthier older adults
are more likely to have lower BMI.
Perhaps one of the greatest strengths of this study is the use of multiple theoretical
perspectives to examine the intricacies of higher weight status. Researchers are calling
for more elaborate explanations that reflect the complexities of health outcomes (Ailshire
and House 2011; Diez-Roux 2012; Lee 2011). Link and Phelan recognize the benefit of
supplementing fundamental cause theory with other theories to “achieve a more complete
accounting of the full pattern of health outcomes” (2010:15).
Limitations
An important limitation of this study is its use of cross-sectional data which does
not allow for causal inferences. Therefore, there are many interesting questions that
cannot be answered with this data. One of these causal order questions is related to the
extent to which weight status affects socioeconomic status. A second question is whether
and to what extent does one’s social support and integration increase as a result of
declining health and the need for additional assistance.
SES and social relationships variables are not perfectly suited for this study but
are considered the best alternative. Education, of course, would have been a better
indicator if it was continuous. Ross and Mirowsky (1999) compare differences in health
effects by education measured as years of schooling and degree attainment and find that
the continuous measure of education has a much stronger relationship with health. In
58
addition, NHANES variables representing social support and social integration are not
ideal and “they limit the capacity to finely distinguish structural and functional aspects of
support” (White et al. 2009:1876). The availability of only one social integration measure
and the lack of a social conflict measure are additional limitations. Further study in this
area would ideally use datasets that possess an optimum combination of longitudinal
design, more sensitive measures of SES, and expanded and improved measures of social
support and social integration.
Future Research
It is important to build upon the preliminary findings of this study to delineate the
social determinants of higher weight status. A logical next step is to examine how race,
alongside SES, social relationships, and gender, is associated with higher weight status.
For instance, the data show that Black women have the highest weight status and are
significantly different from white women. In contrast, Black and White men tend to have
comparable weight status. Taking an intersectionality approach, as Ailshire and House
(2011) propose, would be helpful in determining how the observed associations by
gender in this study might be influenced by multiple vectors combining gender with age,
race, class, or other stratification levels.
This study provides some preliminary ideas for augmenting the socioeconomic
and interpersonal assets of individuals and groups to address growing concerns about
higher weight status. Future research would examine data to determine appropriate
interventions based on women’s and men’s tendencies to use social resources differently.
At the macro level, these results suggest that enhancing the perceptions of social support
59
and integration for women especially can offset the limitations of lower socioeconomic
status. It seems that this may be particularly helpful for Black women. At the community
level, health interventions aimed at higher weight status should recognize the interplay
between SES and social relationships and focus on enhancing social interaction and
perceptions of support available support. For instance, men are more likely to be married
and may rely more heavily on emotional support from their partners. Encouraging men to
strengthen their connections outside of the home may offset partner dependence. Also,
for both men and women, providing opportunities for greater social support for older
adults at risk for declining health, may provide protection against the rate of decline as
well as the risk for higher weight status.
Conclusion
This thesis adds to the large body of literature showing that different measures of
SES and social relationships are associated with higher weight status. The analysis of
these factors in tandem provides a richer sense of how higher weight status becomes
differentially distributed in a population. Perhaps the most important contribution to the
literature is a confirmation of a gendered pattern of associations for SES and social
relationships with higher weight status. Both sets of social factors have varied
associations with higher weight status by gender. This study lends support to Link and
Phelan’s (1995) suggestion that gender may be treated as a fundamental cause in itself
because of its connections to SES and flexible resources such as social support.
A more intricate explanation is needed to explore differences in higher weight
status by social factors. This study provides further evidence for the idea that multiple
60
theoretical frameworks should be used to understand how the complex intersections of
socioeconomic status and social relationships are associated with higher weight status
(Lee 2011). Future studies would benefit from the incorporation of additional
perspectives including intersectionality theory (Ailshire and House 2011; Hill Collins
2000), stress process theory (Pearlin et al. 1981; Thoits 2010), and the life course
perspective (Elder 1975; Shuey and Willson 2008).
61
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APPENDIX A
IRB APPROVAL FORM
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