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Socioeconomic Disparities in Health Behaviors

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The inverse relationships between socioeconomic status (SES) and unhealthy behaviors such as tobacco use, physical inactivity, and poor nutrition have been well demonstrated empirically but encompass diverse underlying causal mechanisms. These mechanisms have special theoretical importance because disparities in health behaviors, unlike disparities in many other components of health, involve something more than the ability to use income to purchase good health. Based on a review of broad literatures in sociology, economics, and public health, we classify explanations of higher smoking, lower exercise, poorer diet, and excess weight among low-SES persons into nine broad groups that specify related but conceptually distinct mechanisms. The lack of clear support for any one explanation suggests that the literature on SES disparities in health and health behaviors can do more to design studies that better test for the importance of the varied mechanisms.
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Socioeconomic Disparities
in Health Behaviors
Fred C. Pampel,
Patrick M. Krueger,
and Justin T. Denney
Department of Sociology, University of Colorado, Boulder, Colorado 80309-0484;
Department of Sociology, University of Colorado, Denver, Colorado 80217;
Department of Sociology, Rice University, Houston, Texas 77005;
Annu. Rev. Sociol. 2010. 36:349–70
First published online as a Review in Advance on
April 20, 2010
The Annual Review of Sociology is online at
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2010 by Annual Reviews.
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Key Words
smoking, exercise, diet, obesity, education, socioeconomic status
The inverse relationships between socioeconomic status (SES) and un-
healthy behaviors such as tobacco use, physical inactivity, and poor nu-
trition have been well demonstrated empirically but encompass diverse
underlying causal mechanisms. These mechanisms have special theoret-
ical importance because disparities in health behaviors, unlike disparities
in many other components of health, involve something more than the
ability to use income to purchase good health. Based on a review of
broad literatures in sociology, economics, and public health, we classify
explanations of higher smoking, lower exercise, poorer diet, and excess
weight among low-SES persons into nine broad groups that specify re-
lated but conceptually distinct mechanisms. The lack of clear support
for any one explanation suggests that the literature on SES disparities
in health and health behaviors can do more to design studies that better
test for the importance of the varied mechanisms.
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Why do low–socioeconomic status (SES)
groups more often act in ways that harm their
health than high-SES groups? Health behaviors
such as use of tobacco, lack of exercise, and poor
diet contribute importantly to—though by no
means completely explain (Lantz et al. 1998)—
SES differences in health and mortality (Rogers
et al. 2000). In addition, these behaviors have
a characteristic that makes them of special in-
terest: They involve more than the inability
to purchase goods and services that promote
good health. Smoking involves expenditure of
money to purchase an unhealthy product, and
some forms of exercise such as walking cost lit-
tle. The tendency of low-SES groups to adopt
unhealthy behaviors despite the monetary and
health costs is a puzzle that many studies have
examined but, with one very recent exception
(Cutler & Lleras-Muney 2010), not addressed
in a comprehensive way.
We review explanations of the relationship
between low SES and unhealthy behaviors
and the empirical support they have received.
In focusing on the mechanisms that account
for the relationships between SES and health
behaviors, we review studies from sociology,
economics, and public health that go beyond
description and that offer insight into the
sources of health inequality. These studies rec-
ognize that SES disparities in health behavior
involve more than freely chosen lifestyles. To
the contrary, the explanations reviewed below
suggest that unhealthy behaviors result from
the vast differences in the social circumstances
of low- and high-SES groups. Our attention
to the social origins of health behavior runs
counter to perspectives that ignore how SES
structures social life.
For s everal reasons, we limit the health be-
haviors we examine. Avoidance of tobacco, par-
ticipation in physical activity, and maintenance
of proper weight and diet involve actions that,
certainly for smoking and by most accounts for
the others, promote health and extend longevity
(Rogers et al. 2000). These behaviors also dif-
fer from others that depend more directly on
having the financial resources to purchase
health. It makes sense that the less well-off
have fewer opportunities to undergo regular
preventive medical checkups and screenings,
to work at jobs with low physical danger or
contact with hazardous materials, to live in
well-built housing in safe neighborhoods with
low pollution, and to drive safe cars. Although
finances relate in some ways to tobacco cessa-
tion (paying for counseling), exercise (joining
gyms and clubs), and good diet (buying fresh
fruits and vegetables or lean meats), money
nonetheless is not a requirement as it is
for other health behaviors. Tobacco use, to
the contrary, involves significant monetary
costs—on average about $1638 per year for a
pack-a-day smoker (Smith 2008).
Other behaviors involving use of illegal
drugs and participation in criminal violence
likewise create risks to health but raise issues
different from legal behaviors. Reviews of these
behaviors and of research on crime and deviance
warrant separate study (although some theories
posit a link between participation in legal but
unhealthy behavior and participation in illegal
behavior). Alcohol consumption has similarities
to use of illegal substances (e.g., driving under
the influence) and, except at extremely high
levels that are relatively rare in the U.S. adult
population, has an ambiguous relationship
with health and mortality outcomes (Rogers
et al. 2000, Thun et al. 1997). Another health
behavior, sleep durations of shorter or longer
than seven hours per night, is associated with
increased mortality, but little research has sys-
tematically examined the relationship between
SES and sleep duration (Krueger & Friedman
2009, Moore et al. 2002). Still further, health
care consumption, adherence to treatment
regimens, interaction with physicians, and
acceptance of new medical technologies
greatly influence health and mortality and
are the subject of large literatures (Cutler &
Lleras-Muney 2010, Glied & Lleras-Muney
2008, Hadley 2003, Lutfey & Freese 2005,
Phelan et al. 2004, Ross & Mirowsky 2000).
Because of space constraints, we give only
minimal attention to these behaviors.
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SES (or sometimes socioeconomic position)
refers to standing in the stratification system
and is usually measured by education, occupa-
tion, employment, income, and wealth. These
components of SES are not interchangeable
and have different kinds of influences on health
behavior. SES can reflect diverse underlying
theoretical concerns such as material well-
being, human capital, prestige, and produc-
tive relations. Even so, it is convenient to refer
to SES as a summary term (without assuming
that it represents a unidimensional construct)
when the particular measure does not have key
The focus on SES disparities in health behav-
iors has generated some dispute. Lantz et al.
(1998) reject claims that elevated mortality
risks among disadvantaged SES groups come
primarily from the higher prevalence of risky
behaviors in these groups. In comparing
mortality over a 7.5-year period, Lantz and
colleagues find that the odds ratio for the
lowest to the highest education group falls by
only 14% with controls for smoking, drinking,
sedentary lifestyle, and relative body weight.
Similarly, a study of British civil servants
over a 25-year period shows that smoking
and other coronary risk factors account for
27% of the inverse social gradient in coronary
heart disease (Marmot 2006). Studies that use
different methods find stronger effects. Using
indirect estimation techniques to attribute
mortality to either smoking-related causes or
other causes among men ages 36–69 in four
nations, Jha et al. (2006) calculate that smoking
accounts for nearly half of the excess mortality
among the lowest stratum in each nation.
Regardless of the method used or conclu-
sion reached, SES disparities clearly involve
more than health behaviors such as smoking,
exercise, and eating. Some suggest that even if
differences in health behaviors across socioe-
conomic strata disappeared, the relationship
between SES and health would change little,
as other sources of disparities would grow in
importance (Link & Phelan 1995). At the same
time, health behaviors account for, on average,
roughly one-quarter of SES disparities in
health—an amount of some importance.
To present some numbers, Table 1 de-
scribes the disparities for several components
of SES and smoking, exercise, and body mass
using the 2006 National Health Interview Sur-
vey for persons ages 25–64 (National Center
for Health Statistics 2008). The dichotomous
outcomes assign values of one to survey partic-
ipants who currently smoke, do not participate
in vigorous or moderate physical activity,
or have a body mass index (BMI) of 30 or
more (obese). The SES predictors are treated
categorically, with the highest category serving
as the referent so that odds ratios compare
lower SES groups to higher SES groups. The
logistic regression estimates for SES control
for gender, race-ethnicity, age, age squared,
and foreign birth.
The results in Table 1 show large disparities
for smoking and no exercise and more modest
disparities for obesity. Of the SES variables, ed-
ucation generally has the strongest influence.
For example, without controls for other SES
variables, high school dropouts have odds of
smoking and not exercising that are, respec-
tively, 3.7 and 4.9 times larger than for college
graduates. The three columns on the right of
the table show that controls for the other SES
variables reduce the odds ratios to 2.9 and 2.8,
respectively, but they remain substantial. The
lowest occupation and income groups have net
odds ratios for smoking and not exercising rang-
ing from 1.2 to 1.9. Renting rather than owning
and unemployment increase the odds of smok-
ing but do not increase the odds of not exercis-
ing. Not surprisingly given evidence suggesting
that SES disparities in obesity have lessened
over the past three decades (Zhang & Wang
Although measures are seldom available and rarely studied,
subjective status may influence health independent of objec-
tive status (Schnittker & McLeod 2005).
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Table 1 Odds ratios from logistic regression of health behaviors on SES variables (Ns range from 14,129 to 14,608)
No controls for other SES variables Controls for other SES variables
SES Variables
No exercise BMI obese
No exercise BMI obese
Education (years)
0–11 3.7
12 2.7
13–15 2.3
16+ 1.0 1.0 1.0 1.0 1.0 1.0
Labor-Farm 2.2
1.2 1.7
Protect-Service 1.9
1.1 1.2 1.4
Admin-Sales 1.6
1.1 1.2 1.2
Prof-Manager 1.0 1.0 1.0 1.0 1.0 1.0
Low 2.6
Middle low 1.5
1.2 1.1 1.6
Middle high 1.4
1.0 1.2 1.1
High 1.0 1.0 1.0 1.0 1.0 1.0
Yes 1.6
No 1.0 1.0 1.0 1.0 1.0 1.0
Rent 1.9
1.1 1.5
1.1 0.9
Own 1.0 1.0 1.0 1.0 1.0 1.0
Controlling for age, age squared, gender, race, and foreign birth.
p < 0.001.
2004), the results for obesity show smaller SES
differences. The odds ratios reach 1.8 in the
gross models and 1.5 in the net models.
Socioeconomic conditions may produce an
underlying health lifestyle that similarly af-
fects smoking, exercise, and diet (Cockerham
2005). Vigorous exercise, for example, pro-
motes weight control and nonsmoking. This
clustering encourages prevention efforts on un-
healthy behaviors in general and warrants draw-
ing generalizations across multiple behaviors.
However, health behaviors differ in important
ways that may affectthe potential for disparities.
Smoking requires action to purchase cigarettes,
whereas lack of exercise involves inaction;
quitting smoking often produces unpleasant
symptoms of withdrawal and increases weight,
whereas starting to exercise often increases feel-
ings of well-being and reduces weight. Given
these differences, we treat literatures on smok-
ing, exercise, and diet as separate but also at-
tempt to draw some generalizations.
Link & Phelan (1995) point out that resources
favoring high-SES groups are so extensive and
wide-ranging as to make SES a fundamen-
tal cause of health. Because the underlying
relationship between SES and health persists
through historical changes in causes of death,
advances in medical treatments, and new pub-
lic health efforts, no single mechanism accounts
for the observed relationship (Lutfey & Freese
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2005). Rather, some diminish in importance in
particular situations while others increase. To
define the sources of enduring SES advantages
more clearly, we consider nine broad mecha-
nisms that underlie the relationship between
SES and health behavior.
Deprivation, Inequality, and Stress
In the stress paradigm, disadvantaged social
position is both a source of adversity and a
drain on the capacity to cope (e.g., Pearlin
1989). Given these circumstances, smoking,
overeating, and inactivity represent forms
of pleasure and relaxation that help regulate
mood among the disadvantaged (Lantz et al.
2005, Layte & Whelan 2009, Wilkinson 1996).
The coping or self-medicating functions of
these behaviors make the costs of giving them
up particularly salient and limit the ability
of low-SES individuals to adopt healthy but
challenging behaviors (Lutfey & Freese 2005).
Those deprived economically and living in
disadvantaged neighborhoods face a variety of
chronic stressors in daily living: They struggle
to make ends meet; have few opportunities to
achieve positive goals; experience more nega-
tive life events such as unemployment, mari-
tal disruption, and financial loss; and must deal
with discrimination, marginality, isolation, and
powerlessness (Baum et al. 1999, Lantz et al.
2005, McEwen 1998). These stresses trigger a
host of compulsive behaviors such as overeat-
ing, drinking, and smoking (Bj
orntorp 2001,
Marmot 2004). Studies give indirect support
to stress arguments by showing higher smok-
ing among persons in positions of high stress,
including unemployed workers (Fagan et al.
2007), poor single women with childrearing du-
ties (Graham 1995, Marsh & McKay 1994),
those from disadvantaged backgrounds (Lynch
et al. 1997), and residents of deprived neighbor-
hoods (Duncan et al. 1999). In terms of diet,
Miech et al. (2006) find that family poverty
status is associated with increasing overweight
prevalence for 15- to 17-year-olds.
Other studies do more than describe
the association between stress and unhealthy
behaviors. Johnson & Hoffman (2000) find that
smoking increases among poor performing stu-
dents in more competitive schools, a proxy for
pressures faced by students. Colby et al. (1994)
find that a stress index based on divorce, busi-
ness failures, and natural disasters for U.S. states
relates to state smoking prevalence. Several
studies measuring perceived stress or biologi-
cal stress markers find relationships with higher
fat consumption and lower levels of mild, mod-
erate, and strenuous physical activity (Burdette
& H ill 2008, Dallman et al. 2003, Ng & Jeffery
2003). Grunberg et al. (1999) find evidence that
workers who report higher job stress also report
problem drinking and heavy drinking more fre-
quently, but only if they also endorse the notion
that drinking is an effective strategy for coping
with stress.
The research on the relationship between
SES, stress, and health behaviors faces at least
two limitations, however. First, although low-
SES individuals may experience more stressors,
they also report lower levels of perceived stress
than their high-SES counterparts (Krueger &
Chang 2008, Schieman et al. 2006). Further,
low-SES individuals report fewer but more se-
vere daily stressors (Grzywacz et al. 2004). The
inconsistent relationship between SES and var-
ious measures of stress (e.g., perceived stress,
acute or chronic stressors, daily hassles) sug-
gests the need for research on whether some
dimensions of stress are especially important
mediators of the relationship between SES and
health behaviors.
Second, prior research often assumes that
stress precedes rather than follows unhealthy
behavior, although the evidence on smoking
and physical activity is less clear. Parrott (1999)
argues that smoking worsens stress by creating
nicotine dependency. Although cigarettes tem-
porarily relieve withdrawal symptoms, greater
exposure to nicotine withdrawal increases stress
among smokers. Lang et al. (2007) similarly find
that levels of pleasure differ little among low-
SES smokers and nonsmokers. Exercise and
stress may also have a bidirectional relation-
ship. Although most individuals may respond
to stress by becoming more sedentary, Salmon
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(2001) suggests that exercise may produce neu-
rochemical changes in the body that reduce de-
pression and anxiety and moderate sensitivity
to stress. Although smoking or inactivity may
alleviate stress in the short term, they might in-
crease stress levels in the long term.
A related version of the stress argument
focuses less on absolute than on relative de-
privation. Because the SES gradient in health
behavior is continuous, with each level show-
ing quantitatively higher prevalence of health
behaviors, something more than a threshold
of economic and social deprivation must be
involved (Adler et al. 1994, Marmot 2004).
Rather, disadvantageous social comparisons
among nearly all SES groups with those at
higher levels weaken social cohesion across so-
ciety in ways that motivate unhealthy behaviors
to deal with the stress (Marmot 2004). High
degrees of societal inequality thus worsen feel-
ings of relative deprivation and contribute to
disparities in health behavior (Wilkinson 1996).
Support for arguments about relative de-
privation comes from studies showing a posi-
tive relationship between income inequality and
mortality across nations, states, or metropoli-
tan areas. However, a huge literature that has
grown on the topic has led to negative evidence
(e.g., Beckfield 2004) or at least failed to reach
a consensus on whether the relationship is real
(Schnittker & McLeod 2005). Although health
behaviors have a prominent role in the relative
deprivation arguments, few studies examine be-
haviors directly. Using individual-level data on
smoking, Siahpush et al. (2006a) find that high
perceived income inequality is associated with
higher smoking, and Eibner & Evans (2005)
find that deprivation measured relative to those
in the same state, race, education, and age group
increases the probability of taking health risks.
In an aggregate-level study, however, Pampel
(2002) finds that relative deprivation does not
explain strong SES smoking disparities across
egalitarian member nations of the European
Union. Similarly, Chang & Christakis (2005)
find no positive association between income in-
equality and the odds of being overweight or
Fewer Benefits of Health Behaviors
for Longevity
Claims that low-SES groups have less to gain
from healthy behavior come from economics,
epidemiology, and sociology. Economists argue
that the lower lifetime earnings and wealth of
low-SES groups give them less reason to invest
in future longevity and more reason to focus on
the present in making decisions about health
behaviors (Cutler & Lleras-Muney 2008). In
the language of economics, their risk or time
preferences more heavily discount the future.
For example, the declining cost of food in re-
cent decades should increase the utility of all
individuals, but some economists suggest that a
subset of individuals have hyperbolic discount
functions and benefit intensely from eating im-
mediately, regardless of the medium- or long-
term health costs (Cutler et al. 2003).
With regard to smoking, the rational addic-
tion model of Gary Becker (Becker & Murphy
1988, Becker et al. 1994) predicts that, given
their future orientation, the highly educated
are little influenced by cigarette prices but that
less educated persons make smoking decisions
based more on current cigarette prices. Evi-
dence that cigarette prices reduce smoking is
strong—a 10% increase in cigarette prices leads
to a 4% decline in smoking (Gallet & List 2003).
Consistent with the model, some argue further
that prices more strongly reduce smoking
among low-SES groups and therefore reduce
disparities (Farrelly & Bray 1998, Thomas
et al. 2008). However, skeptics claim that youth
are more influenced by peers than by prices
(DeCicca et al. 2002) and note that SES
disparities have failed to decline as expected
in the United States with increasing prices
(Pampel 2009).
Physical activity, eating patterns, and
sleep, although different from smoking in
their requirement for substantial time com-
mitments, also reflect different incentives by
SES. Economists note that individuals with
higher incomes face greater opportunity costs
in exercising, preparing nutritious meals, or
sleeping because their time is valued more
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highly in the labor force. Simultaneously,
however, individuals with greater human
capital or higher earnings gain more financially
from exercise, nutrition, and sleep through
improved productivity and longer lives (Biddle
& Hamermesh 1990). High-SES groups
therefore exercise more on weekends and
holidays when the opportunity costs are lowest
(Mullahy & Robert 2008), report shorter sleep
hours on average (Biddle & Hamermesh 1990,
Mullahy & Robert 2008), and avoid very short
sleep hours (Krueger & Friedman 2009).
Technological advances in food process-
ing and preparation lower the price and time
costs of eating but may increase obesity, al-
though those changes likely affect all SES
groups equally (Cutler et al. 2003). Chou et al.
(2004) find that the growth in fast-food restau-
rants and the simultaneous decline in the rel-
ative cost of a meal over time in the United
States coincide with weight gain. The authors
suggest that this may result from the “increas-
ing scarcity and increasing value of household
or nonmarket time” (Chou et al. 2004, p. 584).
Because both high- and low-SES groups value
their increasingly limited time outside of work
and come to rely on fast food, such explanations
may partially account for the declining SES gra-
dient in obesity in the United States (Zhang &
Wang 2004).
Epidemiological and sociological arguments
also suggest that increased risks of premature
death brought on by worse social conditions
among low-SES persons make health behav-
iors less beneficial. Low-SES groups may be-
lieve they gain little in terms of longevity from
healthy behavior (Lawlor et al. 2003) and feel
fatalistic about their ability to act in ways that
extend their lives (Niederdeppe et al. 2008).
For example, smoking is more common among
blue-collar workers who are exposed at work
to dangerous dust, fumes, and toxic substances
(Sterling & Weinkam 1990). Adams & White
(2009) find that a strong concern with the future
consequences of health decisions partially me-
diates the relationship between SES and body
weight. Lynch et al. (1997) find that those hav-
ing experienced socioeconomic disadvantage
early in their lives feel a heightened sense of
hopelessness throughout the life course that
affects health behaviors, and Vangeli & West
(2008) find that high-SES groups attempt to
quit smoking because of future health concerns,
whereas low-SES groups are more often mo-
tivated by cost and current health problems.
Niederdeppe & Levy (2007) find that the less
educated are more likely to agree with fatalistic
statements about their ability to reduce their
risks of cancer. Those agreeing with fatalistic
statements are more likely to smoke and are less
likely to exercise or eat fruits and vegetables.
Indulging in enjoyable but unhealthy be-
haviors may make sense given a shorter life
expectancy and a limited payoff from health-
ier behavior. Interestingly, Blaxter (1990) offers
empirical support for these beliefs. She finds
that healthy lifestyles do less to lower mortal-
ity among low-SES groups than among high-
SES groups. But recent evidence contradicts
Blaxter’s hypothesis (Krueger & Chang 2008,
Pampel & Rogers 2004). Even so, belief in the
limited benefits of healthy behavior may ob-
struct action among low-SES groups.
Latent Traits
Some arguments suggest that latent traits
determine both SES and health behavior. If
traits determined early enough in life affect
educational and occupational attainment as
well as adult health behaviors, then SES has a
spurious relationship with health rather than
a direct or indirect causal effect (Fuchs 1982).
Studies generally find a causal impact of ed-
ucation on health more generally (de Walque
2007, Mirowsky & Ross 2003). However, some
arguments about self-control and intelligence
fit a latent trait perspective.
One stream of literature notes that both
crime and unhealthy behaviors such as
cigarette use come from the same family, peer,
and community influences ( Jacobson et al.
2001). According to Gottfredson & Hirschi
(1990), both crime and smoking involve
low self-control, attraction to risk, and the
propensity to choose short-term gain even in
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the face of long-term harm. The attraction to
short-term gain emerges early in life, in large
part through poor parental socialization, and
in turn leads to poor performance in school,
limited career options, and unhealthy behavior
(Hirschi & Gottfredson 1994). Gottfredson
(2004) suggests that general intelligence rather
than self-control and attraction to risk is crucial
to the relationship between SES and health.
This latent trait not only affects educational
and occupational attainment but also physical
fitness, nonsmoking, preference for low-sugar,
low-fat diets, and adherence to regimens to
change unhealthy behaviors.
Few direct tests of the latent trait arguments
appear in the literature, nearly all focused on
smoking. Farrell & Fuchs (1982) show that the
relationship between schooling and smoking at
age 24 is accounted for by smoking at age 17—
before education is completed. They do not
measure the factor underlying the early emer-
gence of the relationship between schooling
and smoking, but their results are consistent
with the latent trait argument. Other studies
demonstrate that health lifestyles are related to
crime and deviance among teens (Paternoster
& Brame 1998) and that college students who
smoke are involved in various risky activities
(Emmons et al. 1998). Again, the findings pro-
vide only indirect evidence. Gottfredson (2004)
cites much literature on the relationship be-
tween intelligence and health, but Link et al.
(2008) find that controls for intelligence do lit-
tle to change the relationship between SES and
health. Also rejecting the latent trait argument,
Cutler & Lleras-Muney (2008) cite findings of
little relationship between risk preferences and
Class Distinctions
High-SES groups may use the adoption
of healthy behaviors and lifestyles to set
themselves apart from other SES groups
(Cockerham 2005). Tobacco avoidance
(Pampel 2006), exercise (Stempel 2005),
and thinness (Hesse-Biber 2007, McLaren
2007) represent forms of SES-based social
distinction as well as means to a longer life.
Although applied to health behavior rather
than consumer goods, such arguments stem
from classical and modern theorists (Bourdieu
1984, Veblen 1992 [1899]) who emphasize
lifestyle as a source of social differentiation and
the adoption of innovative fashion as a way to
reinforce those differences.
Smoking represented an innovative behav-
ior early in the twentieth century that, despite
early worries about its harm to health, was first
adopted by high-SES groups, perhaps as a form
of distinction, and later diffused to lower SES
groups (Pampel 2005). In more recent decades,
class distinction shows in the early adoption
of the innovative idea that changing individual
behavior can extend life (Link 2008). Today,
smoking is stigmatized more among highly edu-
cated than among less educated groups (Stuber
et al. 2008). If class distinction gives motives for
high-SES groups to act in healthy ways, it may
also motivate lower SES groups to set them-
selves apart with behavior like smoking that,
in some contexts, symbolizes independence,
toughness, and freedom from convention.
High-status groups also distinguish them-
selves from low-status groups by participating
in activities such as strenuous aerobic sports,
moderate levels of weight training, compet-
itive sports that limit physical domination,
or activities that require extensive training
or other hidden entry requirements aside
from simple economic costs (Bourdieu 1984,
Stempel 2005). In contrast, low-status groups
participate in activities that emphasize strength
or the visible appearance of strength, physical
domination, and direct physical violence.
Wilson (2002) finds that higher levels of eco-
nomic capital (as indicated by income) and cul-
tural capital (as indicated by education) predict
greater participation in sports in general, but
attendance at automobile or motorcycle races
is most common among those with low levels
of cultural capital and is insensitive to levels of
economic capital. Scheerder et al. (2002) show
that high occupational status groups are more
likely to play golf, low-status groups are more
likely to participate in boxing or wrestling, and
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both high- and low-status groups participate in
The tastes for different sports across socioe-
conomic groups may result in SES differences
in lifelong activity levels. First, high-status
groups often embrace activities that can be
maintained throughout middle and older age
such as tennis or jogging. Second, Howell
& McKenzie (1987) find t hat high-status
individuals cultivate participation in any sport:
Compared to students in vocational tracks in
high school, students in college preparatory
tracks have higher levels of sports participation
in later life. Further, watching television is a
sedentary activity that often displaces time for
exercise and is stigmatized in higher SES house-
holds. College-educated parents are less likely
than others to permit their children to have
televisions in their bedrooms or to allow televi-
sion viewing during meals (Berry 2007). Finally,
high-status groups exhibit the characteristics of
cultural omnivores—they have wide-ranging
participation in both elite and middle-brow
sports but avoid the low-brow sports favored
by low-status individuals (Stempel 2005).
Participation in a wide variety of sports among
high-status groups facilitates continued activity
even if injury, time constraints, or other barriers
limit participating in any single activity.
Although the effects have possibly lessened
over time, there is also evidence of class distinc-
tions in diet and weight (McLaren 2007, Sobal
& Stunkard 1989). Research suggests a socioe-
conomic gradient in diet for more developed
countries that results in lower weights for the
most well-off (McLaren 2007). The gradient is
especially pronounced for women (McLaren &
Kuh 2004), where thinness is viewed as a sign
of beauty, fashion, and prestige. In less devel-
oped countries, a positive association between
SES and weight predominates, where excess
weight may signify success and well-being. Es-
pecially for men, larger body size is often viewed
as a sign of physical dominance and prowess
(McLaren 2007). Consistent with class-based
norms in higher-income nations, however, an
inverse gradient in obesity is emerging in lesser
developed countries (Monteiro et al. 2004).
Lack of Knowledge and Access to
Information about Health Risks
Less educated persons with jobs that offer few
opportunities for learning may have limited
knowledge of the harm of unhealthy behavior
and therefore less motivation to adopt healthy
behaviors. They are exposed less often to warn-
ings about smoking, poor diet, and lack of exer-
cise and may not grasp the potential long-term
harm of these activities (Siahpush et al. 2006b).
They instead may be exposed more to advertis-
ing t hat promotes the enjoyment of tobacco and
unhealthy food and associates smoking, drink-
ing, and eating with a glamorous lifestyle.
However, evidence suggests that knowledge
of the risks of smoking is widespread and does
little to account for SES disparities. Although
differences in knowledge of risks of smoking
played a more important role in the past (Link
2008), more recent antitobacco campaigns,
public education, the nonsmokers rights move-
ment, and comprehensive state programs to
raise prices, pass clean indoor air laws, and fund
media campaigns have successfully publicized
risks and reduced smoking (Warner 2005). In
1999, 92% of Americans linked smoking with
cancer, and in 2006 84% agreed that smoking is
very harmful for adults (Saad 2006). Although
they may rationalize their habit by minimiz-
ing the harm, smokers in all SES groups likely
know of the majority opinion about the harm
of smoking. In support of this claim, the desire
to quit differs little by SES (Barbeau et al. 2004,
Link 2008). In fact, Viscusi & Hakes (2008) find
that better educated persons perceive smok-
ing as less dangerous than less educated per-
sons. Layte & Whelan (2009) find that mea-
sures of knowledge about the risks do little to
explain SES differences in smoking. Interest-
ingly, many physicians in developing nations
smoke, despite medical education and knowl-
edge of the harm of smoking (Smith & Leggat
Knowledge about the importance of ade-
quate exercise and sleep for good health is also
widespread. In 2005, 86% of adults agreed with
the statement that a lack of sleep is bad for their
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health and 63% said that regular exercise is a
highly important activity for a healthy lifestyle
(Lyons 2005).
But knowledge about the risks of obesity is
less widespread and differs by SES. Only 36% of
U.S. adults rate obesity as a very serious health
problem—far behind AIDS, even though obe-
sity contributes to more deaths each year
(Bleich et al. 2007). Compared to those with less
than a high school degree, those with more than
a college degree are 3.6 times as likely to report
that they pay a lot or a fair amount of attention
to nutritional information from scientific ex-
perts. Similarly, higher educational attainment
is related to an awareness of whether one is
overweight (Paeratakul et al. 2002), and knowl-
edge about the risks of obesity can contribute to
attempts to control weight (Kan & Tsai 2004).
Indeed, the effectiveness of educational pro-
grams for promoting proper diet among low-
income adults (Howard-Pitney et al. 1997) sug-
gests that poor knowledge about nutrition may
partially account for SES differences in weight.
Efficacy and Agency
Schooling increases the efficacy, problem-
solving skills, ability to process information,
and locus of control needed to overcome ob-
stacles to good health such as nicotine addic-
tion, the inertia of inactivity, the discomfort
of exercise, and the desire for unhealthy foods
and excess calories. Mirowsky & Ross (2003,
p. 1) make the case for the causal benefits of
Education creates desirable outcomes because
it trains individuals to acquire, evaluate, and
use information. It teaches individuals to tap
the power of knowledge. Education devel-
ops learned effectiveness that enables self-
direction toward any and all values sought,
including health.
The increase in human capital, effective
agency, and a sense of personal control that
comes with greater education (Mirowsky &
Ross 2007) proves particularly important in
dealing with the difficulties of adopting and
maintaining healthy lifestyles.
In fact, highly
educated persons may induce short-term stress
as a means for long-term gain (Thoits 2006)—
a key to overcoming initial feelings of discom-
fort and deprivation that come from adopting
healthy behaviors. Conversely, less educated
persons in positions of powerlessness have more
trouble overcoming the obstacles to healthy be-
havior. Thus, the ability to act on health knowl-
edge rather than the knowledge itself affects
health behavior.
Efficacy and agency include the search for
innovative means to help change behavior and
relate to a long tradition of research on diffu-
sion of innovations that identifies education as a
key to early adoption (Rogers 2003). Consistent
with diffusion research, high-SES groups are
quickest to use new medical technologies, such
as Pap smears and mammography for screen-
ing or coronary stents and statins for treatment
(Glied & Lleras-Muney 2008, Link et al. 1998).
High-SES groups not only seek out new health-
promoting technologies, but they also are bet-
ter able to overcome obstacles to using those
new technologies effectively to promote health
behavior. In relation to health behaviors, then,
high-SES groups are open to new smoking ces-
sation methods, diets, and exercise regimens.
Consistent with these arguments, studies of
smoking find that education increases both the
use of aids to help quit (Honjo et al. 2006)
and the responsiveness to antismoking ad cam-
paigns (Niederdeppe et al. 2008). The higher
educated also learn more from negative health
Mirowsky & Ross (2003) in particular make causal argu-
ments. They reject claims that latent traits or advantaged
backgrounds produce a spurious relationship between edu-
cational attainment and health. Rather, those attaining ad-
vanced education learn new skills and gain new confidence for
problem solving that make it easier to adopt healthy, though
difficult, behaviors.
This form of means-based knowledge differs conceptually
from knowledge of the risks of unhealthy behavior discussed
above. Although knowledge of both risks and ways to re-
duce risks overlaps, the more widespread access to knowl-
edge of risk across SES groups makes it different from
the less widespread knowledge and skills to adopt healthy
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events. Wray et al. (1998) find that educated
persons are more likely to quit smoking af-
ter a heart attack. Droomers et al. (1998) find
that locus of control and active problem solving
explain about half the relationship between
education and physical activity. Cutler &
Lleras-Muney (2010) attribute roughly 30% of
the education gradient across a large number
of health behaviors to cognitive ability but note
that the influence of cognitive ability stems ulti-
mately from higher education rather than from
a latent trait determined earlier in life.
A weakness in the efficacy argument comes
from SES disparities in health behaviors other
than smoking, exercise, and weight control that
require little in the way of effort, problem solv-
ing, or efficacy (Freese & Lutfey 2010). For ex-
ample, seat belts require less than a second to
buckle, but despite decades of publicity about
the benefit, laws making seatbelts mandatory in
many states, and dashboard warning lights, a
SES disparity persists (Harper & Lynch 2007).
This persistence suggests that disparities in-
volve something more than problem solving or
cognitive ability.
Aids for Healthy Behavior
Adopting many healthy behaviors does not re-
quire money, but paying for tobacco cessation
aids, joining fitness clubs and weight loss pro-
grams, and buying more expensive fruits, veg-
etables, and lean meats can help realize desires
for healthy behavior. Income and the ability to
pay for these kinds of aids can help overcome
low education, efficacy, and agency and thus
represent an independent means to healthy be-
havior. Cutler & Lleras-Muney (2010) attribute
roughly 20% of the education gradient in health
behaviors to economic resources.
For smoking, individual counseling and
medications to ease withdrawal symptoms can
be costly, and low-SES groups tend to use low-
cost and often less effective methods (Lillard
et al. 2007). F or exercise, since the 1960s the
prices of sports equipment, bicycles, and sports
club memberships have increased more quickly
than the prices of televisions or movie tickets,
whereas the income per hour of leisure time
has fallen most among those with lower in-
comes (Berry 2007). Droomers et al. (1998) find
that income accounts for up to 40% of the in-
creased odds of physical inactivity among less
educated individuals, even after adjusting for
psychosocial factors. And for weight control,
some researchers contend that the obesity epi-
demic is a relatively simple matter of chang-
ing economics—the drastic increase in obese
persons in developed countries coincides with
dropping prices of refined grains and added
sugars and fats, making these cheap, high-
calorie foods accessible to low-SES groups
(Drewnowski 2004).
Occupational resources overlap with finan-
cial ones. Those with good jobs and benefit
packages gain access to aids for healthy behav-
ior without having to purchase them. In regard
to smoking, workers with better jobs may have
employer-provided health insurance and better
access to health care, which increasingly em-
phasizes the treatment of tobacco dependency
(Manley et al. 2003). In addition, the work-
sites of higher prestige professional, manage-
rial, and administrative employees more often
have clean indoor air rules that make smok-
ing more difficult, and they sometimes offer
smoking cessation programs that help smokers
quit (Bauer et al. 2005). Blue-collar and fac-
tory workers have less access to these benefits
(Sorensen et al. 2004).
Working in some occupations can directly
impact physical activity. Since the 1950s, many
workers have moved into occupations that
are traditionally sedentary, whereas strenuous
occupations have become less so due to tech-
nological changes, leading to lower levels of
physical activity and higher levels of obesity
(Brownson et al. 2005, Ladkawalla & Philipson
2007). And although some have found that
blue-collar workers who have more physically
demanding jobs are more likely to undertake
more vigorous activity in their spare time
(Wu & Porell 2000), others have found that
white-collar workers with the least strenuous
jobs are more likely to participate in vigorous
activity outside of the workplace and, as such,
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are more likely to retain their higher levels
of activity even after retirement (Berger et al.
2005). Consistent with that perspective, leisure
time has increased in recent decades, and
low-status groups allocate a greater share
of their time to sedentary activities than do
high-status groups (Berry 2007).
Community Opportunities
Communities shape opportunities to adopt
and maintain healthy behaviors. Low-income
neighborhoods have more than their share of
fast-food restaurants, liquor stores, and places
to buy cigarettes and have less than their share
of large grocery stores with a wide selection of
healthy fresh foods. Some research finds that
low-SES neighborhoods have greater or equal
access to gyms, parks, or recreation centers
than high-SES neighborhoods, although
others find that high-SES neighborhoods have
more attractive open spaces and free recreation
facilities, and greater access to beaches, rivers,
golf courses, tennis courts, and bike trails
(Giles-Corti & Donovan 2002, Powell et al.
2006). Even when residents in low-SES neigh-
borhoods have access to more recreational
resources than residents in high-SES neigh-
borhoods, they tend to report lower perceived
access to recreational facilities (Giles-Corti &
Donovan 2002). Poor communities and neigh-
borhoods are targeted by tobacco companies
for outdoor advertising (Barbeau et al. 2004)
and have weaker enforcement of restrictions
on sales of cigarettes to minors (Gemson et al.
1998). In contrast, affluent communities are
more likely to pass clean air laws that tend to
lower smoking among high-SES groups (Skeer
et al. 2004).
Research from the United States and
Canada (see Cummins & Macintyre 2006 for a
review) finds associations between obesity and
food quality, prices, and availability in a com-
munity. Persons in disadvantaged areas with
less access to healthier foods also consume
fewer fruits and vegetables and have higher
body weight (Cummins & Macintyre 2006).
Inagami et al. (2006) find a positive association
between distance traveled to nearest grocery
store and weight. Similarly, Morland et al.
(2006) report a lower prevalence of obesity and
overweight in neighborhoods with greater ac-
cess to supermarkets.
But the effects of supermarket availability
on SES differences in overweight and obesity
are inconsistent across nations. Studies from
the United Kingdom and Australia find no
socioeconomic differences in food and super-
market availability, nor in fruit and vegetable
consumption (Cummins & Macintyre 2002,
Pearson et al. 2005, Winkler et al. 2006). The
persistent relationship in the United States may
result from high residential segregation in cities
and neighborhoods (Cummins & Macintyre
2006) and more pronounced inequalities in the
availability of high-quality foods. Evidence for
fast-food outlet density and increased weight
is more consistent across countries. Studies
from the United States, United Kingdom,
and Australia show increased numbers of
fast-food outlets in poorer areas and propose
a link with the increased levels of obesity in
disadvantaged neighborhoods (Cummins et al.
2005, Maddock 2004, Reidpath et al. 2002).
Nevertheless, selection effects may operate.
To some extent, grocery stores and restaurants
sell the food in most demand, cigarette compa-
nies market in neighborhoods with high smok-
ing, and physically active people locate closer
to parks and amenities for exercise than less
active people. The literature has done more
to demonstrate an association than to establish
causal direction (Freese & Lutfey 2010).
Social Support, Social Cohesion,
and Peer Influence
Group membership—and the characteristics
of individuals within communities—can affect
health behavior through two forms of social
capital (Kawachi et al. 2008). First, networks
of health-oriented family members, relatives,
friends, and neighbors support healthy be-
havior, sanction unhealthy behavior, and ex-
change information on ways to change (Smith
& Christakis 2008). Given that high-SES
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persons adopt healthy behaviors and associate
with other high-SES persons, their networks
of social support, influence, and engagement
promote health and widen disparities. Freese &
Lutfey (2010) refer to spillover effects as the in-
fluence that high-SES persons who care much
about healthy behavior have on other high-
SES individuals who otherwise would care little
about healthy behavior.
The effects of network social support on
SES disparities in smoking emerge in several
studies. Cutler & Glaeser (2007) find a social
multiplier impact of workplace smoking bans
whereby workers who change their smoking be-
havior influence family and friends outside the
workplace to do the same. Such processes tend
to occur more strongly among the highly ed-
ucated. Christakis & Fowler (2008) find that
among friends who both had at least one year of
college, a decision by one friend to quit smoking
decreased the chance of the other smoking by
61%. Among friends with a high school educa-
tion or less, no such influence appeared. I nter-
estingly, highly educated smokers who continue
to smoke become less central to their network
than do less educated smokers.
Using national data from the United States,
Boardman et al. (2005) show that persons liv-
ing in economically depressed areas and neigh-
borhoods with high rates of obesity are more
likely to be obese, regardless of their individ-
ual characteristics. This suggests a type of so-
cial contagion. Indeed, Christakis & Fowler’s
(2007) analyses show that obesity can follow
social networking paths that influence persons
and cement inequalities in obesity by SES.
Although not couched in terms of social
capital, a huge literature on teen smoking high-
lights the importance of peer influences and
group membership. The smoking of friends
is perhaps the strongest predictor of smoking
among adolescents ( Jacobson et al. 2001,
p. 85). Similarly, support from peers, parents,
and siblings are important predictors of adoles-
cents’ physical activity and drinking behaviors,
as are opportunities and access to recreation fa-
cilities or places to acquire alcohol ( Jessor et al.
2006, Sallis et al. 2000). In terms of disparities,
parental SES affects smoking among teens
(Lindstrom 2008). However, teen disparities
based on parental SES are weaker than adult
disparities based on own SES ( Jacobson et al.
2001, p. 93), suggesting that SES disparities
in smoking crystallize and strengthen after
adolescence when disparities in social capital
widen (Glendinning et al. 1994).
Second, social capital based on social co-
hesion helps explain community differences in
health behavior. Kawachi et al. (1999) define
this form of social capital as encompassing trust
between citizens, norms of reciprocity, and
group membership that facilitate cooperation
for mutual benefit. Social cohesion tends to be
greater in high-SES neighborhoods and to pro-
mote healthy behavior (Lindstrom 2008).
Studies offer mixed evidence of neighbor-
hood and community effects on smoking. Ross
(2000) finds that neighborhood disadvantage
increases the s moking of men but not of women.
Miles (2006) finds in a study of seven European
cities that indicators of neighborhood disorder
such as litter, graffiti, and the lack of plants and
flowers increase smoking, again more for men
than for women. Brown et al. (2006) find that
community social capital from religious groups
reduces the number of cigarettes consumed, al-
though not the overall prevalence.
The importance of social capital for un-
derstanding the relationship between neigh-
borhood SES and exercise is ambiguous. Resi-
dents of disadvantaged neighborhoods are more
likely to walk to shops or work but are less
likely to walk, bicycle, or participate in other
sports for leisure, even after adjusting for indi-
vidual SES, in part because low-SES neighbor-
hoods are less safe and attractive (Giles-Corti &
Donovan 2002, Ross 2000). Wen et al. (2007a)
find that an index of social capital is associated
with regular exercise among neighborhood res-
idents in Chicago until adjusting for neighbor-
hood SES, which suggests that the relationship
between neighborhood social capital and phys-
ical activity is either spurious or mediated by
neighborhood SES. But Wen et al. (2007b) find
that neighborhood SES in California is not as-
sociated with physical activity, although higher
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levels of neighborhood social cohesion persis-
tently predict increased physical activity.
Social cohesion is also linked to neighbor-
hood obesity levels. For example, examining
adolescents in Los Angeles County, California,
Cohen et al. (2006) find that neighborhood SES
is not significant in models of body mass and
overweight after adjusting for neighborhood
collective efficacy. Burdette & Hill (2008) find
in Texas that the relationship between neigh-
borhood disorder and obesity is entirely me-
diated by the psychological distress associated
with living in a disadvantaged neighborhood.
Although arguments about social capital and
peer influence receive much attention, they are
incomplete. They do not explain the origins of
SES differences in norms that support healthy
behavior or why social capital supports healthy
behavior. If cohesive groups of high-SES fam-
ily members, friends, and neighbors who adopt
healthy behaviors help others do the same, then
additional arguments are needed to account
for the initial adoption of healthy behaviors
by high-SES groups. Other theories relating to
motives and problem solving thus remain cru-
cial to arguments about social influence.
The literature has done little to systematically
compare and contrast the mechanisms reviewed
here. This makes it difficult to offer an overar-
ching framework that integrates or adjudicates
between the various approaches. We instead
aimed more modestly to present interrelated ar-
guments in a way that helps organize previous
work on health behaviors and sets the stage for
future research.
The very recent study by Cutler & Lleras-
Muney (2010) represents a notable exception
to the lack of comparative research on mecha-
nisms and evaluates numerous explanations of
the relationship of education and a variety of
health behaviors using several data sets. Cutler
& Lleras-Muney (2010, p. 1) conclude that
income, health insurance, and family back-
ground can account for about 30 percent of
the gradient. Knowledge and measures of cog-
nitive ability explain an additional 30 percent.
Social networks account for another 10 per-
cent. Our proxies for discounting, risk aver-
sion, or the value of future do not account for
any of the education gradient, and neither do
personality factors such as a sense of control
of oneself or over one’s life.
Although their findings need replication with
other data sets and measures, these conclu-
sions help organize and evaluate the diversity
of causes and represent a valuable starting point
for future empirical research on SES disparities
in health behaviors.
In addition, we can offer some thoughts
on improvements in theory and method that
go beyond the current state of the field. In
terms of theory, it may help to organize
these mechanisms by distinguishing between
motives and means for health behavior (Freese
& Lutfey 2010). First, SES can affect the
incentives or motivations for healthy behav-
ior. Low-SES groups may have less reason
than high-SES groups to want to forego the
short-term pleasures of unhealthy behavior for
long-term gain in longevity. Arguments related
to stress, limited benefits, class distinctions, and
knowledge of risk each emphasize how SES
shapes motives for healthy behavior. High-SES
groups face less stress that might encourage
coping through unhealthy behavior; they gain
more longevity benefits from healthy behavior;
they accrue prestige by setting themselves
apart with healthy behavior; and, although less
clearly, they have greater knowledge of risks to
motivate healthy behavior.
Second, SES can affect the means to reach
health goals. All SES groups may have simi-
lar desires for healthy behavior, but low-SES
groups have more difficulty in realizing their
goals. Arguments about efficacy in reaching
goals for healthy behavior, access to aids for
healthy behavior, and community opportuni-
ties for healthy behavior focus more on SES dif-
ferences in resources for goal attainment. The
distinction between motives and means tends
to blur at the edges, as strong motives increase
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efforts to find effective means, and possessing
scant means tends to sap motivation to change.
Indeed, arguments about social support and so-
cial capital tend to mix motives rather than em-
phasize one over the other. However, motives
and means are analytically distinct, and distin-
guishing among them may aid in the study of
disparities in health behaviors.
In terms of methods, the literature has done
better in describing associations than in testing
specific causes. Designs that distinguish among
the various mechanisms are not straightfor-
ward. Creative efforts to measure stress, control
for latent traits, and examine the psychosocial
benefits of schooling move in this direction, but
studies can do more to explicitly match mech-
anisms with measures. Future research could
also do more to help isolate the causal in-
fluence of education and related components
of SES. Progress toward these goals requires
longitudinal studies with adequate controls for
underlying heterogeneity, well-measured me-
diators, and attention to the emergence of
health behaviors that begin early in life and
accumulate over the life course. For exam-
ple, with detailed data on genetic, family, and
school characteristics, the National Longitudi-
nal Study of Adolescent Health (http://www. offers op-
portunities to evaluate the mechanisms that link
education to health behaviors early in life. Al-
ternatively, qualitative designs such as used by
Lutfey & Freese (2005) in their study of dia-
betes clinics can help us understand how the
motives and means for healthy behavior differ
by education and SES.
Some insights may come from designs
that compare health behaviors as well as
the determinants. Our review emphasizes
the similarities among health behaviors, but
each behavior also has unique dimensions
in terms of the time and effort required, the
pleasure it provides, and its social meanings.
Whereas physical activity, sleep, and preparing
nutritious meals take a considerable amount of
time, smoking and drinking practices seem less
constrained by temporal concerns. Modeling
the different dimensions of health behaviors
has received the most sustained attention in the
literature on physical activity—SES appears to
have different relationships with walking out of
necessity (to work or to shops) than walking for
leisure. But the method of tobacco consump-
tion (e.g., cigarettes, pipes, cigars), the kinds of
alcohol consumed (e.g., wine, beer, or spirits),
the ingredients and food preparation methods
employed, or the contexts where food, tobacco,
or alcohol is consumed may also provide
important insight into how the behaviors are
linked to SES. Research that compares mea-
sures from multiple theoretical perspectives
across multiple health behaviors could clarify
when specific mechanisms are most important
for shaping SES disparities in health behaviors.
The approach of comparing health behav-
iors also applies to health care consumption.
High-SES groups may see adoption of healthy
behaviors and effective use of medical care as
closely linked in the pursuit of health (Ross &
Mirowsky 2000). Medical care, which depends
greatly on access to insurance and relates di-
rectly to income and affluence, has some simi-
larities to lifestyle behaviors. Much as they af-
fect smoking, exercise, and diet, SES differences
in knowledge, efficacy, and opportunities affect
the willingness to use new medical technolo-
gies (Glied & Lleras-Muney 2008) and follow
treatment regimens laid out by medical person-
nel (Lutfey & Freese 2005). Commitment to
a healthy lifestyle could encompass efforts to
take full advantage of medical care resources
and to avoid unhealthy behaviors, and compar-
isons of the determinants of medical care usage
and other behaviors may offer new insights.
Focusing on historical trends in health be-
haviors may also illuminate the mechanisms
that link SES to health behaviors. First, the
salience of mechanisms may change over time.
For example, high-SES individuals accumu-
late the most benefit from their knowledge,
efficacy, and resources in adopting innovative
health-related behaviors and using emerging
medical technologies (Glied & Lleras-Muney
2008, Link 2008). In contrast, more widespread
publicity about and agreement on established
risks (e.g., smoking) or common medical
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treatments (e.g., cholesterol tests) tend to re-
duce the importance of SES differences in
knowledge. Second, the direction of the re-
lationship between SES and health behaviors
can change over time. For example, high-
status groups once had higher rates of tobacco
use (Pampel 2005), cocaine use (Miech 2008),
and cholesterol (Chang & Lauderdale 2009).
Changes in the social meanings and circum-
stances affected the motives and means of high-
SES groups to reject these behaviors. However,
changes can occur in the opposite direction.
Goldman & Lakdawalla (2005) find that the in-
troduction of beta-blockers to treat hyperten-
sion led to reduced disparities in hypertensive
heart disease. In these cases, other influences
such as the ease of using new medicines and the
willingness of clinicians to prescribe them may
overwhelm forces of stratification in health be-
haviors. Historical trends can thus offer lever-
age in understanding changes in the mecha-
nisms that link SES to health behaviors and
reversals in the relationship between SES and
some health behaviors.
The authors are not aware of any affiliations, memberships, funding, or financial holdings that
might be perceived as affecting the objectivity of this review.
We received funding and administrative support for this work from the University of Colorado
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Brayden G King and Nicholas A. Pearce ppppppppppppppppppppppppppppppppppppppppppppppppppp249
Conservative and Right-Wing Movements
Kathleen M. Blee and Kimberly A. Creasap ppppppppppppppppppppppppppppppppppppppppppppppp269
The Political Consequences of Social Movements
Edwin Amenta, Neal Caren, Elizabeth Chiarello, and Yang Su pppppppppppppppppppppppppp287
Comparative Analyses of Public Attitudes Toward Immigrants
and Immigration Using Multinational Survey Data: A Review
of Theories and Research
Alin M. Ceobanu and Xavier Escandell pppppppppppppppppppppppppppppppppppppppppppppppppppp309
Differentiation and Stratification
Income Inequality: New Trends and Research Directions
Leslie McCall and Christine Percheski ppppppppppppppppppppppppppppppppppppppppppppppppppppp329
Socioeconomic Disparities in Health Behaviors
Fred C. Pampel, Patrick M. Krueger, and Justin T. Denney ppppppppppppppppppppppppppppp349
Gender and Health Inequality
Jen’nan Ghazal Read and Bridget K. Gorman ppppppppppppppppppppppppppppppppppppppppppp371
Incarceration and Stratification
Sara Wakefield and Christopher Uggen pppppppppppppppppppppppppppppppppppppppppppppppppppp387
Achievement Inequality and the Institutional Structure of Educational
Systems: A Comparative Perspective
Herman G. Van de Werfhorst and Jonathan J.B. Mijs ppppppppppppppppppppppppppppppppppp407
vi Contents
Annu. Rev. Sociol. 2010.36:349-370. Downloaded from
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SO36-FM ARI 2 June 2010 4:38
Historical Studies of Social Mobility and Stratification
Marco H.D. van Leeuwen and Ineke Maas pppppppppppppppppppppppppppppppppppppppppppppppp429
Individual and Society
Race and Trust
Sandra Susan Smith pppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp453
Three Faces of Identity
Timothy J. Owens, Dawn T. Robinson, and Lynn Smith-Lovin pppppppppppppppppppppppppp477
The New Homelessness Revisited
Barrett A. Lee, Kimberly A. Tyler, and James D. Wright pppppppppppppppppppppppppppppppp501
The Decline of Cash Welfare and Implications for Social Policy
and Poverty
Sandra K. Danziger ppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp523
Cumulative Index of Contributing Authors, Volumes 27–36 ppppppppppppppppppppppppppp547
Cumulative Index of Chapter Titles, Volumes 27–36 pppppppppppppppppppppppppppppppppppp551
An online log of corrections to Annual Review of Sociology articles may be found at
Contents vii
Annu. Rev. Sociol. 2010.36:349-370. Downloaded from
by University of Colorado - Denver on 07/13/10. For personal use only.
... Low patient awareness could be attributable to low patient education and prevailing social inequalities, which affect access to and use of health services and medications, as well as adherence to medical prescription [39]. In Brazil, nearly half of all CVD-related deaths in patients aged < 65 years are attributable to poverty, social inequalities, and low educational status [40][41][42]. Furthermore, awareness regarding the benefit of a healthy lifestyle is limited, and we observe the adoption of unhealthy diets, low physical activity, and high consumption of alcohol and tobacco especially in low-income communities [40]. ...
... In Brazil, nearly half of all CVD-related deaths in patients aged < 65 years are attributable to poverty, social inequalities, and low educational status [40][41][42]. Furthermore, awareness regarding the benefit of a healthy lifestyle is limited, and we observe the adoption of unhealthy diets, low physical activity, and high consumption of alcohol and tobacco especially in low-income communities [40]. It is important that both patients and health care professionals belonging to the public health system [43,44] receive continued education and updates from the latest available clinical practice guidelines from the Brazilian Society of Cardiology, and that they understand the importance of adopting the guidelines and achieving the goals in daily practice. ...
Full-text available
Background and objective Non-communicable diseases like systemic arterial hypertension (SAH) and dyslipidemia are poorly studied in terms of patient journey aspects. This semi-systematic review provides evidence synthesis for the management of SAH and dyslipidemia in Brazil and also discusses challenges faced by patients at the local level along with a suggested care approach by local experts. Methods A semi-systematic review using both structured literature databases (Embase and Medline) and unstructured scientific records (WHO, IPD, MOH and Google) on hypertension and dyslipidemia in the English language from 2010 to 2019 was performed by reviewers. After two-level screening based on pre-defined criteria, patient journey touchpoints and prevalence information were extracted from the included articles. Data gaps were bridged through the insights of local experts. Results Prevalence of hypertension and dyslipidemia in Brazil were 23% and 40.8%, respectively. Awareness of dyslipidemia was found in a larger proportion (58.1%) than in SAH (22.2%). Similarly, screening for hypertension (97%) and dyslipidemia (55.4%) were found to be effective, while treatment was (62.9%) and (30.0%) for hypertension and dyslipidemia, respectively. Conclusion There were important gaps on patient awareness and treatment of dyslipidemia and hypertension. Limited patient education, regional disease distribution, and treatment allocation, along with limited resources for diagnosis and treatment are the key challenges.
... For example, studies have shown that people who have lower income are more present-oriented, meaning that they prefer a smaller reward today to a larger reward in the future (Bulley & Pepper, 2017;Griskevicius et al., 2011;Sunde et al., 2018). Individuals with fewer resources also tend to consume more alcohol and drugs (Droomers et al., 1999;Hanson & Chen, 2007;Hiscock et al., 2012;Pampel et al., 2010), be more violent (McCullough et al., 2013), invest less in their children's education (Gibson & Lawson, 2011;Nettle, 2008Nettle, , 2010Quinlan, 2007), take less risk in terms of variance in returns (Boon-Falleur et al., 2021), invest less in cognitive exploration and information gathering (Jacquet et al., 2018), are less trusting of others (Petersen & Aarøe, 2015), are more likely to prefer authoritarian leaders (Safra et al., 2017), and have a tight culture with many strong norms and a low tolerance of deviant behavior (as opposed to a loose culture) (Gelfand et al., 2011). Personality traits also seem to vary with the amount of resources of individuals: individuals with more resources are more optimistic (Heinonen et al., 2006;Robb et al., 2009), have more 2 self-control (Carver et al., 2014;Duckworth et al., 2013), score higher on conscientiousness and agreeableness (Akee et al., 2018). ...
... There is a vast empirical literature demonstrating that individuals with fewer resources not only have poorer health outcomes, but also engage in riskier health behaviors (Brennan et al., 2009;Droomers et al., 1999;Everson et al., 2002;Hanson & Chen, 2007;Hersch & Viscusi, 1998;Hiscock et al., 2012;McLaren, 2007;Pampel et al., 2010;Pill et al., 1995). Such behaviors are often considered evidence for a scarcity mindset, or more broadly as evidence for impairment in decision making among individuals with fewer resources. ...
Life history theory is increasingly invoked in psychology as a framework for understanding differences in individual preferences. In particular, evolutionary human scientists tend to assume that, along with reproductive strategies, several behavioral traits such as cooperation and risk-taking and, in its broadest version, a range of psychological and personality traits also cluster into ‘fast’ and ‘slow’ life histories. However, the inclusion of such a wide range of traits in a life history strategy is founded on relatively little theoretical justification. Beyond environmental factors such as mortality risk, we argue that quantitative changes in resources - either embodied, physical or social - can lead to qualitative changes in people’s priorities and psychology. People use their resources to satisfy their different needs. People with access to fewer resources focus on their needs with the greatest marginal fitness benefit, while people with more resources can also afford to satisfy their needs with smaller fitness benefits. We show that, depending on the total amount of resources available, the optimal resource allocation of these resources differs, leading to the empirically observed ‘pyramid of needs’. In addition, the optimal allocation of resources shapes people’s risk and time preferences, which in turn affect a whole range of behaviors such as parenting style, cooperation, health, skill acquisition and exploration.
... Emerging evidence has suggested that accelerated biological aging may serve as the critical player linking neighborhood disadvantage and aging-related chronic diseases, which may also contribute to poorer outcomes among breast cancer patients. Living in a disadvantageous neighborhood may lead to poor health behaviors 6,7 , increased toxicant exposures 8 , and lack of access to health services 9 . In addition, living in a deprived neighborhood can also lead to increased discrimination and segregation 10 . ...
Full-text available
Living in a disadvantaged neighborhood is associated with adverse clinical outcomes among breast cancer patients, but the underlying pathway is still unclear. Limited evidence has suggested that accelerated biological aging may play an important role. In this study, using a sub-sample of 906 women with newly diagnosed breast cancer at M.D. Anderson, we examined whether levels of selected markers of biological aging (e.g., allostatic load, telomere length, and global DNA methylation) were affected by neighborhood disadvantage. The Area Deprivation Index was used to determine the neighborhood disadvantage. Based on the median ADI at the national level, the study population was divided into low and high ADI groups. Overall, breast cancer patients from the high ADI group were more likely to be younger and non-Hispanic Black than those from the low ADI group (P < 0.001, respectively). They were also more likely to have higher grade and poorly differentiated breast tumors (P = 0.029 and 0.019, respectively). For the relationship with markers, compared to the low ADI group, high ADI group had higher median levels of allostatic load (P = 0.046) and lower median levels of global DNA methylation (P < 0.001). Compared to their counterparts, those from the high ADI group were 20% more likely to have increased allostatic load and 51% less likely to have increased levels of global DNA methylation. In summary, we observed that levels of allostatic load and global DNA methylation are influenced by neighborhood disadvantage among breast cancer patients.
... Given socioeconomic differences in the distribution of health-relevant behaviors such as smoking and physical inactivity (Stringhini et al. 2017), some diseases are thought to be a consequence of scarce socioeconomic capital. Nonetheless, the biological basis that underlies SES-related behavioral dispositions remains poorly understood (Pampel et al. 2010). ...
Full-text available
Socioeconomic status (SES) anchors individuals in their social network layers. Our embedding in the societal fabric resonates with habitus, world view, opportunity, and health disparity. It remains obscure how distinct facets of SES are reflected in the architecture of the central nervous system. Here, we capitalized on multivariate multi-output learning algorithms to explore possible imprints of SES in gray and white matter structure in the wider population (n ≈ 10,000 UK Biobank participants). Individuals with higher SES, compared to those with lower SES, showed a pattern of increased region volumes in the left brain and decreased region volumes in the right brain. The analogous lateralization pattern emerged for the fiber structure of anatomical white matter tracts. Our multimodal findings suggest hemispheric asymmetry as an SES-related brain signature, which was consistent across six different indicators of SES: degree, education, income, job, neighborhood, and vehicle count. Hence, hemispheric specialization may have evolved in human primates in a way that reveals crucial links to socioeconomic status.
... Given socioeconomic differences in the distribution of health-relevant behav-iors such as smoking and physical inactivity (Stringhini et al. 2017), some diseases are thought to be a consequence of scarce socioeconomic capital. Nonetheless, the biological basis that underlies SES-related behavioral dispositions remains poorly understood (Pampel et al. 2010). ...
Full-text available
Socioeconomic status (SES) anchors individuals in their social network layers. Our embedding in the societal fabric resonates with habitus, world view, opportunity, and health disparity. It remains obscure how distinct facets of SES are reflected in the architecture of the central nervous system. Here, we capitalized on multivariate multi-output learning algorithms to explore possible imprints of SES in gray and white matter structure in the wider population (n ≈ 10,000 UK Biobank participants). Individuals with higher SES, compared to those with lower SES, showed a pattern of increased region volumes in the left brain and decreased region volumes in the right brain. The analogous lateralization pattern emerged for the fiber structure of anatomical white matter tracts. Our multimodal findings suggest hemispheric asymmetry as an SES-related brain signature, which was consistent across six different indicators of SES: degree, education, income, job, neighborhood, and vehicle count. Hence, hemispheric specialization may have evolved in human primates in a way that reveals crucial links to socioeconomic status.
Background Population-level estimates of hospitalisation risk in children are currently limited. The study aims to characterise morbidity patterns in all children, focusing on childhood cancer survivors versus children without cancer. Methods Employing hospital records of children aged <19 years between 1997 to 2018 in England, we characterised morbidity patterns in childhood cancer survivors compared with children without cancer. The follow-up began on the 5th anniversary of the index hospitalisation and the primary outcome was the incidence of comorbidities. Findings We identified 3,559,439 eligible participants having 12,740,666 hospital admissions, with a mean age at study entry of 11.2 years. We identified 32,221 patients who survived for at least 5 years since their initial cancer diagnosis. During the follow-up period and within the whole population of 3.6 million children, the leading conditions for admission were (i) metabolic, endocrine, digestive renal and genitourinary conditions (84,749, 2.5%), (ii) neurological (35,833, 1.0%) and (iii) musculoskeletal or skin conditions (23,574, 0.7%), fever, acute respiratory and sepsis (22,604, 0.7%). Stratified analyses revealed that females and children from socioeconomically deprived areas had a higher cumulative incidence for morbidities requiring hospitalisation (p < 0.001). At baseline (5 years after the initial cancer diagnosis or initial hospitalisation for survivors and population comparisons, respectively), cancer survivors experienced a higher prevalence of individual conditions and multimorbidity (≥ 2 morbidities) compared with children without cancer. Cox regression analyses showed that survivors had at least a 4-fold increase in the risk of hospitalisation for conditions such as chronic eye conditions (hazard ration (HR):4.0, 95% confidence interval (CI): 3.5-4.7), fever requiring hospitalisation (HR: 4.4, 95% CI: 3.8-5.0), subsequent neoplasms (HR: 5.7, 95% CI:5.0-6.5), immunological disorders (HR: 6.5, 95% CI:4.5-9.3) and metabolic conditions (HR: 7.1, 95% CI:5.9-8.5). Interpretation The overall morbidity burden among children was low in general; however, childhood cancer survivors experienced a higher prevalence and subsequent risk of hospitalisation for a range of morbidities. Targeted policies may be required to promote awareness on health vulnerabilities and gender disparity and to improve advocacy for healthcare in deprived communities. Funding Wellcome Trust, National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre, NIHR Great Ormond Street Hospital Biomedical Research Centre and Academy of Medical Sciences. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
According to the existing, extensive scientific evidence in the literature, men on average are living sicker and dying younger at a higher rate compared to women. The causes of men's infirmities are complex and multifaceted, contributing to increased morbidity and mortality rates and decreased life expectancy. Despite the statistics of the doomed health status among men, interests to eliminate their health disparities remain inconsistent and ill‐defined. Therefore, efforts to eradicate men's health disparities should be positioned in interdisciplinary health education, research, and policy using sex and gender‐based health frameworks. The purpose of this paper is to explore men's health generally by discussing common health disparities, the causes of the gender gap in men's health outcomes, proposing some strategies for advancing men's health, and finally providing nursing implications for education, practice, research, and health/public policy. Notably, interdisciplinary, gender‐based health education potentially has significant impacts on men's health. The promotion of such initiatives can consequently address the intricacies of men's health, which will provide future healthcare professionals with the knowledge, attitude, and skills necessary to improve men's health.
Injection drug use is a significant mode of HIV transmission. Social networks are potential avenues for behavior change among high-risk populations. Increasing knowledge should include a classification or taxonomy system of networks’ attributes, risks, and needs. The current study employed 232 networks comprising 232 indexes, with 464 network members enrolled in Philadelphia. LCA revealed a three-class solution, Low-Risk, Paraphernalia Risk, and High Sex/Moderate Paraphernalia Risk class, among participants. The analysis found receiving money or drugs for sex and employment status increased the odds of belonging to PR and PSR classes. Homelessness and incarceration increased the odds of belonging to the PR class when compared to the LR class. Our findings suggest that classes of risk among PWID comprise clusters of information concerning their members. These findings add depth to our understanding while extending our knowledge of the contextual environment that nurtures or exacerbates the problem.
Disparities in type 2 diabetes (T2D) care is a global problem across diverse cultures. The Dysglycemia-Based Chronic Disease (DBCD) model promotes early and sustainable interventions along the insulin resistance (stage 1), prediabetes (stage 2), T2D (stage 3), and complications (stage 4) spectrum. In this model, lifestyle medicine is the cornerstone of preventive care to reduce DBCD progression and the socioeconomic/biological burden of disease. A comprehensive literature review, spanning 2000 to 2021, was performed and 55 studies were included examining the effects of lifestyle medicine and their cultural adaptions with different prevention modalities. In stage 1, primordial prevention targets modifiable primary drivers (behavior and environment), unhealthy lifestyles, abnormal adiposity, and insulin resistance with educational and motivational health promotion activities at individual, group, community, and population-based scales. Primary, secondary, and tertiary prevention targets individuals with mild hyperglycemia, severe hyperglycemia, and complications, respectively, using programs that incorporate structured lifestyle interventions. Culturally adapted lifestyle change in primary and secondary prevention improved quality of life and biomarkers, but with a limited impact of tertiary prevention on cardiovascular events. In conclusion, lifestyle medicine with cultural adaptations is an integral part of preventive care in patients with T2D. However, considerable research gaps exist, especially for tertiary prevention.
Background Nearly 20% of children in the United States experience one or more chronic health conditions. Parents of a child with a special healthcare need (CSHCN) experience increased stress caring for a child with chronic illness. Purpose The purpose of this descriptive study is to describe stress in parents of a child with chronic illness during the COVID-19 pandemic. Methods Parents of CSHCN (n = 34) were asked to fill out the Pediatric Inventory for Parents (PIP) and answer two questions related to caring for their child during the COVID-19 pandemic. Conclusions The means of the PIP-F (M = 146.6, SD = 20.5) and PIP-D (M = 141.9, SD = 23.9) were significantly higher than in previous studies. There is statistically significant positive correlation between parent stress and variables of age of the child and the length of time since diagnosis. In response to the questions about the impact of COVID, nearly all parents reported COVID increased their stress and reported their stress was related to isolation, lack of resources, and concern for the mental health of other children in the household. Practice implications COVID-19 likely exacerbated feelings of stress for parents of children with chronic health conditions. Although unprecedented, COVID-19 shed light on the existing fragility and high stress of parents of CSHCN. Pediatric nurses not only care for children, but must be advocates for the mental health of their patient's parents.
Conference Paper
This article utilizes the agency-structure debate as a framework for constructing a health lifestyle theory. No such theory currently exists, yet the need for one is underscored by the fact that many daily lifestyle practices involve considerations of health outcomes. An individualist paradigm has influenced concepts of health lifestyles in several disciplines, but this approach neglects the structural dimensions of such lifestyles and has limited applicability to the empirical world. The direction of this article is to present a theory of health lifestyles that includes considerations of both agency and structure, with an emphasis upon restoring structure to its appropriate position. The article begins by defining agency and structure, followed by presentation of a health lifestyle model and the theoretical and empirical studies that support it.
Conference Paper
This article reveals race differentials in obesity as both an individual- and neighborhood-level phenomena. Using neighborhood-level data from the 1990-1994 National Health Interview Survey, we find that neighborhoods characterized by high proportions of black residents have a greater prevalence of obesity than areas in which the majority of the residents are white. Using individual-level data, we also find that residents of neighborhoods in which at least one-quarter of the residents are black face a 13 percent increase in the odds of being obese compared to residents of other communities. The association between neighborhood racial composition and obesity is completely attenuated after including statistical controls for the poverty rate and obesity prevalence of respondents' neighborhoods. These findings support the underlying assumptions of both institutional and social models of neighborhood effects.
On June 16, 2004, cigarette smoking killed some twelve hundred Americans. That shocking death toll warranted no headlines. Neither did the same outcome-some twelve hundred more deaths-the following day, nor the day after. Indeed, it is the rare headline that informs the public that smoking accounts for nearly one of every five deaths in the United States, one in three during middle age. Smoking is simply too commonplace, too mundane. Ye t it is far and away the nation's- and increasingly the world's-leading killer. In this chapter I examine the burden smoking has imposed on society and what we have learned in attempting to deal with that burden. I then consider lessons drawn from this experience for addressing the most rapidly growing behavioral cause of chronic disease: the epidemic of obesity, the only behavior that threatens to overtake smoking as a cause of death. Copyright
Tobacco use, particularly cigarette smoking, remains the leading cause of preventable illness and death in the United States. Studies have shown that increases in the price of cigarettes will decrease the prevalence of smoking and the number of cigarettes smoked both by youth and adults. However, the potential impact of price increases on minority and lower-income populations is an important consideration. This report summarizes the analysis of data for 14 years from the National Health Interview Survey (NHIS), which indicates that lower-income, minority, and younger populations would be more likely to reduce or quit smoking in response to a price increase in cigarettes.