Health & Place 22 (2013) 56–67
Contents lists available at SciVerse ScienceDirect
Health & Place
journal homepage: www.elsevier.com/locate/healthplace
Segregation and cardiovascular illness: The role of individual and
metropolitan socioeconomic status
Antwan Jones n
Department of Sociology, The George Washington University, 801 22nd Street NW, Suite 409C, Washington, DC 20052, USA
art ic l e i nf o
a b s t r a c t
Article history:
Received 17 May 2012
Received in revised form
29 January 2013
Accepted 11 February 2013
Available online 22 March 2013
Demographic and epidemiologic research suggest that cardiovascular illness is negatively linked to
socioeconomic status and positively related to racial residential segregation. Relying on 2005 data from
the Behavior Risk Factor Surveillance Survey and the American Community Survey, this study examines
how segregation and SES (individual and metropolitan) impact hypertension for a sample of 200,102
individuals. Multilevel analyses indicate that both segregation and hypersegregation are associated with
hypertension, net of individual and spatial SES. While individual and metropolitan SES have independent
effects on hypertension, these effects also differ across segregation type. In segregated and hypersegregated environments, highly educated and high-earning individuals seem to be protected against
hypertension. In extremely hypersegregated areas, areas where there is very little interaction with nonblack residents, SES does not have any protective benefit. These findings reveal that SES has differential
effects across segregation types and that hypertension in disadvantaged (extremely hypersegregated)
areas may be a function of structural constraints rather than socioeconomic position.
& 2013 Elsevier Ltd. All rights reserved.
Keywords:
Health Disparities
Hypersegregation
Hypertension
Segregation
Socioeconomic Status
1. Introduction
Sociological and social epidemiological research on spatial
inequality emphasizes how racial residential segregation and socioeconomic conditions influence health. Specifically, impoverished
individuals of low socioeconomic standing may live in residentially
segregated areas because of differential access to employment or
educational opportunities. For this socioeconomically disadvantaged
population, gaining employment or a degree could potentially
propel them into higher levels of socioeconomic status (SES), and
could provide opportunities to live in more racially and socioeconomically diverse areas (de Souza Briggs, 1997).
Moreover, residentially segregated areas and economically disinvested communities may be gatekeepers for health-promoting
resources because of a lack of social infrastructure. Research has
shown that segregated areas lack convenient access to stores (Zenk
et al., 2005), places to exercise (Kaczynski et al., 2010) and quality
health care facilities (Williams and Collins, 2001). In addition,
segregated areas have been associated with a higher risk of
exposure to crime and environmental hazards, which are sources
of stress on a daily basis (Acevedo-Garcia et al., 2003). Because
there are large variations in the degree of racial clustering in
metropolitan areas, racial differences in health also tend to be more
n
Tel.: þ1 202 994 0266; fax: þ1 202 994 3239.
E-mail address: antwan@gwu.edu
1353-8292/$ - see front matter & 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.healthplace.2013.02.009
pronounced in segregated cities (Subramanian et al., 2005). In this
manner, residential segregation not only exacerbates socioeconomic disadvantage but also geographically accumulates healthrelated risks for minority residents in these areas.
While the research on segregation and health is expansive,
there are noticeable gaps that this research addresses. First,
there is a paucity of research relating spatial inequality
(vis-à-vis residential segregation) across metropolitan areas to
socioeconomic inequality within those areas. At the individual
level, social class and health (Adler et al., 1994), race and health
(Williams and Collins, 2001) and race and social class (Oliver and
Shapiro, 2006) are all inextricably linked, but the interconnectivity
of these three key social indicators varies across space (AcevedoGarcia et al., 2003). As such, this research uses geographic
heterogeneity to explore how space plays a role in determining
health among people of various socioeconomic and racial
backgrounds.
Second, because poor socioeconomic conditions (e.g., supermarket access and quality health care facilities) and dangerous
environmental conditions (e.g., crime and environmental hazards)
are characteristic of segregated areas, it is unclear to what extent a
person's own socioeconomic status and the socioeconomic environment in which one lives influences an individual's risk of being
chronically ill. That is, could the potentially negative effects of
living in an impoverished area be ameliorated by elevating a
person's socioeconomic standing? The present study suggests that
in some areas where there is extreme segregation, certain
A. Jones / Health & Place 22 (2013) 56–67
socioeconomic indicators (such as education) may not be as
predictive of health as other indicators that are more proximately
related to health care (such as income). Also, while higher levels of
segregation are associated with poor health, it is uncertain if
extreme types of segregation (i.e., hypersegregation) have differential effects on health or if the effects of hypersegregation on
health are identical to those from living in segregated environments. Thus, it is necessary to determine whether there are
differences in individual and metropolitan-level health risks in
segregated versus hypersegregated areas (Massey and Denton,
1989; Wilkes and Iceland, 2004), where there is an almost
exclusive interaction with members of one's own race.
Third, while it is assumed that segregation is negatively
associated with all health outcomes, very few studies quantify
the effect that segregation has on hypertension. These studies
focus on racial differences in hypertension within segregated areas
and assume there is socioeconomic homogeneity within segregated areas. Research on this topic suggests that whites and blacks
have similar cardiovascular outcomes when they live in areas with
similar levels of segregation (Thorpe Jr. et al., 2006), and higher
rates of hypertension for both whites and blacks are associated
with higher levels of segregation (Kershaw et al., 2011). One study
finds that within the context of New York City, segregation is not
associated with racial differences in hypertension (White et al.,
2011), which suggests that not all segregated areas produce racial
differences in hypertension. Because of the insufficiency of
research on this topic, further evidence is needed to assess
whether race, socioeconomic status, or both are key in understanding the role segregation has on hypertension diagnoses.
Hypertension is an important health concern: it is a major risk
factor for heart disease, stroke, congestive heart failure, and kidney
disease (Kannel, 1996). Currently one-third of adults have been
diagnosed with hypertension (Rabe-Hesketh and Skrondal, 2012).
The current study uses data from a large, nationally representative sample to answer these three questions. Specifically, this
research explores whether living in segregated areas is predictive
of hypertension, how socioeconomic factors at the metropolitan
level are related to socioeconomic factors at the individual level in
predicting hypertension, and whether a person's SES buffers
hypertension differently depending on the type of segregated
environment in which he or she lives. In this manner, different
levels and kinds of segregation in metropolitan areas may be
shown to produce differential effects from individual SES on
whether a person will be diagnosed with having hypertension.
2. Background
2.1. Racial residential segregation and health
As a persistent feature in the US, residential segregation, or the
extent to which two or more groups are physically separated in
urban areas, is tied to poor health among African Americans
(Williams and Jackson, 2005) through institutional racism, which
is designed to protect whites from social interaction with minorities (Williams and Collins, 1995; Wilson, 1987). Further, the
degree of residential segregation is much greater for blacks than
for any other racial group (Massey and Denton, 1989). While
individual dimensions of segregation have been tied to health, it
is important to note that blacks also experience simultaneous high
segregation across multiple dimensions of segregation (Osypuk
and Acevedo-Garcia, 2008). The deleterious health effects of this
kind of segregation, called hypersegregation, are less established
in the literature. However, for both segregation and hypersegregation, related poor health outcomes range from disparities in
57
engaging in risky behaviors such as smoking or drinking alcohol
(Kramer and Hogue, 2009) to mortality (Eitle, 2009; Hearst et al.,
2008).
It is important to disentangle segregation from hypersegregation for several reasons. First, methodologically, the two are
composed of different measurements. Hypersegregation is defined
as jointly high values on five segregation indices (evenness,
exposure, centralization, clustering and concentration). Conversely, segregation is conventionally defined as lying on the numerical continuum of one or two segregation indices. Thus, areas can
be quantified as having low or high levels of segregation, and areas
can be typified as being hypersegregated.
Second, conceptually, segregation and hypersegregation present different social realities for individuals who reside in the
areas. By definition, segregation is the separation of one group
from another. In this research, segregation is further clarified by
suggesting that it occurs racially within the context of where
people live. Thus, racial residential segregation is the racial
separation of one group in a particular residential area from
another. The division between races is physical. In contrast,
hypersegregation is defined as a “multidimensional character
[istic]” (Massey and Denton, 1989: 389) whereby minorities are
economically, educationally, environmentally, politically, residentially, and socially isolated from whites. Accordingly, there is an
exclusive daily interaction with members of one's own race within
hypersegregated locales. Thus, the division between races is
extreme primarily because it is both physical and social.
Third, compositionally, black representation in segregated and
hypersegregated metropolitan areas is distinctively different.
Massey (2004) suggests that a majority of all blacks and a great
majority of urban blacks experience high levels of residential
segregation in metropolitan areas. Additionally, about half of all
urban blacks and about 40% of all blacks experience hypersegregation, which is the kind of separation that mirrored South African
apartheid (Massey, 2004). The compositional difference between
segregated and hypersegregated areas is an important distinction
because educational, health and social outcomes have been shown
to be more detrimental for blacks in hypersegregated metropolitan
areas than in segregated ones. Specifically, blacks in hypersegregated communities experience higher frequencies and more
negative ramifications of school dropout (Williams and Collins,
2001), low birth weight (Osypuk and Acevedo-Garcia, 2008) and
personal victimization (Eitle, 2009) than blacks in segregated
communities. Thus, it is critical to compare segregated and
hypersegregated metropolitan areas instead of treating them as
the same phenomenon.
While researchers have linked segregation to health outcomes,
the literature shows some inconsistency, as other research illustrates advantageous and race-based nuances to the relationship
between segregation and health. For example, Smaje (1995)
suggests that the concentration of minorities in areas may mean
that there is a greater level of political empowerment and
community integration, which are both associated with favorable
health. In addition, LeClere et al. (1997) notes that in the National
Health Interview Survey-National Death Index linked files, neighborhood characteristics such as ethnic concentration lowered the
risk of mortality but only for particular ethnic groups such as
Mexican Americans. As evident in these studies, the community's
social content (i.e., how individuals in a community are organized
and relate to one another) may be a protective factor for health.
Regardless, a plethora of research suggests that segregated
environments are associated with deleterious health outcomes.
These effects are concentrated in the spatial environment and are
not artifacts of general racial differences in health. To illustrate,
Fang et al. (1998) uncovered racial differences in health in racially
concentrated areas. Independent of socioeconomic status, whites
58
A. Jones / Health & Place 22 (2013) 56–67
living in majority black neighborhoods in New York City had
higher all-cause and coronary heart disease (CHD) mortality rates
than whites living in majority white neighborhoods. However, for
blacks, there was no difference in all-cause and coronary heart
disease mortality rates net of SES when they lived in majority
black or white neighborhoods. Moreover, recent research suggests
that segregation may be positively associated with black–white
differences in CHD mortality in some US counties and negatively
associated in others (Gebreab and Diez-Roux, 2012). Despite the
small number of studies that illustrate a nuanced relationship
between race, space and health, it is generally understood that
segregation restricts socioeconomic opportunities for minority
individuals, which consequently affect health (Acevedo-Garcia
et al., 2003). In this case, segregation can be conceptualized as
the accumulated effect of poverty among its residents.
There is a paucity of research that examines metropolitan
socioeconomic status in tandem with individual-level SES. Metropolitan SES goes beyond individual- and neighborhood-level
indicators in order to capture larger forces that help shape the
socioeconomic conditions of a metropolitan area. Metropolitan
SES is important to the study of health in three key ways. First,
areas are generally more than the sum of individuals, and thus,
metropolitan SES is more than the agglomeration of individuallevel SES. Metropolitan areas also encompass infrastructure, which
would influence resources available to residents in a given area.
Thus, incorporating socioeconomic indicators above and beyond
individual SES measures can tap into a structural aspect to
segregation.
Second, neighborhoods in metropolitan areas provide conditions for healthy (and conversely, unhealthy) behaviors. Research
on built environment and health suggests that segregated and
poor neighborhoods tend to have fewer healthy food options while
having greater access to corner stores which provide foods high in
calories, sugar, and trans fat (Kramer and Hogue, 2009). In
addition, neighborhoods that have parks, gyms, and bike/pedestrian trails are also found in less segregated metropolitan areas
with higher affluence. Indeed, these community perks are represented in white, upper middle-class neighborhoods, which may
indicate that businesses and urban planners take into account
individual SES when strategically building up metropolitan SES
(Morland et al., 2002). Thus, the conditions and the amenities
associated with living in a particular neighborhood may not be
accurately captured through the use of individual SES measures
alone.
Third, metropolitan areas also have histories tied to access to
advantage. There is a semi-stabilizing aspect to these areas, in that,
historically (and longitudinally), certain communities within
metropolitan areas have been racially and/or socioeconomically
exclusive. As a consequence, low-income, minority groups had
poor access to affluent residential areas and to the often highquality resources and services available in those areas, limiting
socioeconomic achievement. In turn, denial of homeownership
and other mechanisms of wealth accrual created barriers for racial
minorities to have the means to live in established areas of
affluence (Massey and Denton, 1993), creating de facto racially
segregated neighborhoods. The disproportionate amount of
wealth that whites have accrued, and continue to accrue (Taylor
et al., 2011) has translated into a health advantage. Findings from
recent research suggest that wealth is predictive of health, even
after adjusting for income (Pollack et al., 2007). In addition, the
black/white wealth gap is partially responsible for the black/white
health gap, independent of differences in income between blacks
and whites (Oliver and Shapiro, 2006). While the mechanisms
used to exclude certain groups out of neighborhoods are not as
visible as in the past (Massey and Denton, 1993), both historical
disadvantage and new methods of exclusion (e.g., Freeman (2005))
result in sustained uneven development. In turn, uneven development translates to areas having differing levels of SES (Ellen
et al., 2001). In all, segregation is uniquely tied to socioeconomic
conditions of metropolitan areas which could relate to racial
differences in health.
2.2. Theoretical perspectives to segregation and health
Current perspectives regarding segregation and health stress
the importance of the built environment in facilitating healthy
lifestyles. In racially-segregated residential areas, often characterized as being of low socioeconomic standing, there are limited
facilities that would serve to promote healthy living. For instance,
research has suggested that low-income and majority–minority
neighborhoods are associated with a lack of parks and open spaces
for leisure and exercise (Adler et al., 1994; Freeman, 2005), supermarkets and other healthy food-serving outlets (Morland et al.,
2002; Zenk et al., 2005), amenities associated with a middle-class
lifestyle such as fitness centers (de Souza Briggs, 1997), and
environmentally-sound neighborhoods without chemical hazards
or pollution that could lead to a decreased life expectancy
(Do et al., 2012; Oliver and Shapiro, 2006). Indeed, lower SES
individuals who tend to live in areas where there are limited
health facilities often have an increased likelihood of being in poor
health (Acevedo-Garcia et al., 2003), as they do not have the
resources (e.g., cars and public transportation) to compensate for
living in areas that lack these opportunities. This disadvantage
manifests as "double jeopardy" for low-income racial and ethnic
minorities, who are structurally disadvantaged by both their
residential location and their relative positions in the hierarchies
of race and class in US society (Taylor et al., 2011). As such,
research has suggested that low-income minorities fare worse in
segregated areas than minorities in the same area who have
greater social standing (Pollack et al., 2007).
Moreover, the built environment alone cannot fully explain
racial differences in health outcomes. Recent research suggests
that the health risks are similar for many outcomes for individuals
who live close to these amenities and those persons who live far
from them (Raudenbush and Byrk, 2002). Access and utilization
are often discussed in tandem when discussing the built environment. That is, places for exercise highly curb the risk of being
hypertensive (Messer et al., 2010), high-crime, fairly-dense metropolitan areas, then residents may not feel compelled to exercise
since their immediate and tangible risk of being a victim of crime
would outweigh the long-term risk of having unmanageable blood
pressure (Maldonado and Greenland, 2002).
Another mechanism which affects the health of individuals
living in segregated areas is environmental stress. The early work
of Harburg et al. (1973) and the subsequent work of James et al.
(1984) on the John Henryism hypothesis contains insight on the
relationship between stress and health in the urban environment.
They argued that persons, African American or white, living in
high “socioecologic stress” areas (characterized by low SES and
high rates of social instability, as measured by crime) had a high
risk for stressful experiences on a daily basis, increasing the
likelihood of high blood pressure (Thorpe Jr. et al., 2006). For
African Americans, and especially darker skinned African American men (Dressler, 1991; Dressler et al., 2003), there was the
added stressor of racist interactions with police or other white
persons in positions in authority. These racist interactions are
likely to provoke hostility on the part of the African American
participant in the interaction, who may then suppress that
hostility to avoid negative repercussions. Thus, John Henryism
hypothesis predicts that darker-skinned African American men
who lived in high stress areas and who have suppressed hostility
would have the highest blood pressure. Research results have been
A. Jones / Health & Place 22 (2013) 56–67
generally consistent with these predictions, although the strength
of anger expression and suppression effects have been found to be
modest (Schum et al., 2003).
While individual and societal level explanations remain prevalent in this literature, there is an emerging research on biosocial
mechanisms relating race to health. The need for a new model has
emerged because racial differences in health are resistant to full
explanation using the usual array of socioeconomic and demographic control variables. Massey (2004) argues that long-term
exposure to social disorder and violence in segregated environments produces a high allostatic load (Leal et al., 2011; Mair et al.,
2011) among African Americans, which leads to a variety of
deleterious health outcomes. He specifies a biosocial model of
racial stratification and uses well-documented literature to help
support his argument that health is indirectly associated with
segregation. This biosocial model takes advantage of the community context in which a person resides. However, lacking empirical
evidence, it is important not to assume that segregated and
hypersegregated environments explain health outcomes in the
same way.
Based on the work that has been reviewed, this research
attempts to provide clarity on whether individual characteristics
or the environment in which a person lives is more important to
health. Specifically, this project is concerned with whether the
potentially negative effects of living in poor or segregated environments can be potentially negated when SES is high. Past
research suggests a tenuous connection between individual and
area-level measures in predicting health, particularly with regards
to socioeconomic characteristics (Lee and Ferraro, 2007). However,
this connection has not been explored at a large scale with all
metropolitan areas. This work is also concerned with whether the
type of segregation has differential effects on cardiovascular risk.
Prior research assumes that increases in segregation levels would
produce linear increases in poor health (Acevedo-Garcia et al.,
2003). However, it is unclear whether segregated environments
are more detrimental to cardiovascular health than hypersegregated areas. As such, this research addresses whether living in
segregated areas is predictive of hypertension, how socioeconomic
factors at the metropolitan level are related to socioeconomic
factors at the individual level in predicting hypertension, and
whether a person's own SES buffers hypertension differently,
depending on the type of segregated environment in which he
or she lives.
3. Data and methods
This project relies on two complimentary data sources. Individuallevel information is derived from the 2005 Behavioral Risk Factor
Surveillance System (BRFSS) data. The BRFSS is a collaborative project
between the Centers for Disease Control and Prevention, and U. S.
states and territories that measures behavioral risk factors in the adult
(at least 18 years old) population living in households (Centers for
Disease Control and Prevention, 2006). Its objective is to collect
uniform, state-specific data on preventive health practices and risk
behaviors that are linked to chronic disease, injuries and preventable
infectious diseases. Respondents are identified through telephonebased methods. Although around 95% of all households have telephones, coverage ranges from 87 to 98% across states and varies for
subgroups (Mokdad et al., 2003). Post-stratification weights are used
to correct for any bias caused by non-telephone coverage. One of the
highlights for using this particular dataset is that the publicly available
dataset has geocoded information at the metropolitan level. That is,
information on the metropolitan area in which the respondent lives is
included in this dataset. A unique strength of this data is its large
population sample, where missing data accounts for only 7% of each
59
measure used in this analysis. Unless otherwise specified, mean
replacement is used to handle missing data. The analytic sample
contains 200,102 persons.
To supplement the respondent's information captured in the
BRFSS, metropolitan measures come from the 2005 American
Community Survey (ACS). The ACS is a monthly household survey
being developed by the US Census Bureau to provide data users
with annual estimates of household, social, and economic characteristics for geographies and populations of at least 65,000
people. In addition, the ACS annually updates their multi-year
demographic estimates for geographies down to the block group.
The ACS data will be used to supplement the BRFSS with area-level
indicators that affect health.
3.1. Outcome measure
The primary focus of the analysis is on how individual and
metropolitan measures differentially influence cardiovascular illness. The outcome of interest is a self-reported measure of
hypertension. Hypertension is measured by a question that asks if
respondents have ever been told by a health professional that they
have high blood pressure. Approximately 38% of the total sample
had been told that they have high blood pressure.
3.2. Individual-level independent measures
A key indicator to health is the socioeconomic standing of the
respondent. Three variables are used in order to operationalize
socioeconomic status. The respondent's highest level of completed
education is divided into four categories: less than high school (the
reference category), high school graduate, some college, and graduated college. Income is also assessed and includes the household's
total income, excluding income from interest, dividends, and other
investment, as well as other income for each person such as
disability assistance, social security, and public assistance, which
are contributed to the household but are not necessarily from
earnings. Poisson iterative regression replacement (Agresti, 2002;
Rencher, 2002) was used to substitute any missing data in the
original income variables and sample selection tests (Allison, 1995;
Miranda and Rabe-Hesketh, 2006) via maximum likelihood were
performed to assess whether there was a selection effect of
income in the sample. There was no support for a sample selection
bias with regards to income. Sensitivity analyses (not shown)
indicated that because of the far right skewness of income in this
data, categorical breakdowns of income best fit the data. As such,
income is represented by a series of dichotomous variables
represented by these categories: less than $15,000 (the reference
category), $15,000 to $20,000, $20,000 to $25,000, $25,000 to
$35,000, $35,000 to $50,000, $50,000 to $75,000, and more than
$75,000. Lastly, for all respondents, there is a variable that tracks
employment status. This is a binary variable indicating if the
respondents worked during the previous calendar year.
Other health-related variables at the individual level are used
as control measures that have been used in research because they
are related to hypertension (Dressler et al., 2003; Eitle, 2009). Body
Mass Index (BMI) is a continuous measure of body fat based on
height and weight. Smoker status indicates if respondents are
current smokers (the reference category), former smokers, or have
never smoked at survey date. Alcohol consumption is measured by
the number of days per month that the respondents had at least
one alcoholic drink. Physical activity is measured by the number of
days per week that respondents performed at least 10 minutes of
physical activity excluding work-related activities. Insured is a
binary variable indicating if the respondent has public or private
health insurance.
60
A. Jones / Health & Place 22 (2013) 56–67
Demographic controls are also included in the analysis. This
project controls for race, gender, age (in years), and marital status.
Race is defined by a series of categorical variables. Blacks serve as the
reference category in all analyses. There are additional variables for
white and other racial groups. The majority of individuals included in
the “other” category are of Asian/Pacific Islander ancestry. However,
due to sample size limitation, this particular racial group cannot be
separated into a different category. Also, a probable limitation of the
dataset is that the sample is overwhelmingly white, which may
create constraints in sample variability within the analyses (Agresti,
2002). Marital status is coded with several categorical variables
indicating if the respondent is married (the reference category),
separated, never married, or coupled with an unmarried partner.
3.3. Metropolitan-level independent measures
The focal metropolitan measure used in this research is segregation type. The BRFSS data includes metropolitan statistical area
(MSA) codes (also present in the ACS data) that correspond to a
particular metropolitan area. The work of Wilkes and Iceland (2004)
was used to operationalize the different types of black–white
segregation that exist across metropolitan areas. Wilkes and Iceland
identified 29 MSAs as hypersegregated areas in their research. Using
the assumption that hypersegregated communities are semi-stable
throughout time, this research assumes that the hypersegregated
areas in 2004 would be hypersegregated in 2005. Wilkes and
Iceland (2004) also found that five of the 29 hypersegregated MSAs
scored high (0.60 or greater) on five indices of segregation, indicating extreme hypersegregation. Those five indices are evenness,
exposure, centralization, clustering, and concentration, which are
detailed in Massey and Denton's (1989) work on segregation. Based
on the previous work on segregation and hypersegregation, this
research distinguishes four unique types of segregation: extreme
hypersegregation, hypersegregation, segregation, and nonsegregated.
Extremely hypersegregated MSAs correspond to the five MSAs that
score high (0.60 or greater) on all five indices of segregation
according to Wilkes and Iceland's research. Hypersegregated MSAs
correspond to the MSAs who scored high on four of the five indices
of segregation, as outlined by the researchers (i.e., the remaining 24
of the 29 originally mentioned in Wilkes and Iceland's research).
Segregated MSAs are thus, by definition, those remaining MSAs that
scored high on one, two, or three of the five segregation measures.
Lastly nonsegregated MSAs (the reference category) are those MSAs
that did not score high on any of the segregation indices.
Also important to account for is the socioeconomic standing in
these metropolitan areas, in order to fully exhaust the spatial
nature of hypersegregation. Four measures are used to account for
metropolitan SES: first is population size, which is logged since
there is univariate non-normality in the measure (i.e., the population sizes are not normally distributed across all metropolitan
areas). Second, the percentage of black residents in the MSA is used
to control for racial concentration. Third, the percentage of residents in poverty is also used as a control measure. Lastly, the
percentage of female-headed households, a measure of concentrated
poverty determined for each MSA, is employed. These covariates
are based on the 2005 ACS estimates and were merged onto the
BRFSS data file using the MSA codes.
3.4. Analytic strategy
Hierarchical linear modeling (HLM) was used to assess the
independent effects of individual and metropolitan measures on
an individual's hypertension diagnosis. Because of the binary
nature of the outcome measure, the dependent variable is modeled in a logistic regression run in HLM. Also, to evaluate the fixed
effects of the variables as well as the explained and unaccounted
variation in the measures, mixed effects HLM modeling was
employed. Analyses were performed in Stata 12.1 (StataCorp,
2011) using the XTMELOGIT command. To ensure the most
accurate results, ten adaptive Gaussian quadrature (AGQ) points
were used. The more points that are used, the more accurate the
approximation to the log likelihood (Rabe-Hesketh and Skrondal,
2012). The model development is as follows: Model 1 is the zeroorder model with individual SES measures, Model 2 adds the
individual-level controls, Model 3 adds segregation type, and
Model 4 adds metropolitan SES measures. It is important to note
that the first two models are first-order models, while the last two
models incorporate level-2 measures. Thus, for the last two
models, random effects will also be estimated as part of variance
analysis. Lastly, stratified models will be estimated to see the effect
of these measures in nonsegregated, segregated, hypersegregated,
and extremely hypersegregated metropolitan areas.
4. Results
4.1. Sample description
Table 1 presents an overview of the variables used for this
study. With a large, representative sample size of 200,102, the data
suggest that 37.8% of the respondents were told by a medical
professional that they have high blood pressure (i.e., hypertension). Approximately 8.7% of the sample is black and 7.2% are
classified as other. The sample is overwhelmingly white, representing about 84.1% of the entire sample.
Turning to the socioeconomic characteristics of the individuals in
the sample, over half of the respondents (60%) have either some
college training or have completed college. Conversely, less than 10%
never finished high school and approximately 30% graduated high
school. For income, the sample is somewhat skewed, in that
percentages increase as income categories increase. Over half of the
sample is at or below the $35,000–$50,000 range, indicating that a
sizable proportion of the sample makes above $50,000. In addition,
an overwhelming majority of the respondents are employed (88%).
The measures associated with health suggest a moderately
healthy sample of individuals in the data. The average BMI score
is 27.2, which indicates a slightly overweight sample. Also, over
half of the sample (52.3%) has never smoked, and 28.1% has quit
smoking at the time of the survey. On average, respondents drank
some alcohol about 10 days in the previous month, suggesting a
low to moderate amount of alcohol consumption. Individuals in
the survey engaged in physical activity at least 4 days of the week,
on average. A large majority of the respondents have some kind of
health insurance (88%).
Demographically, the sample is overwhelmingly female, with
about 38.2% of the sample comprised of men. The average age of the
respondent is around 51.2 years old, although there are sizable
representations of age groups in the sample. The predominant union
type in this sample is marriage, with about 55.7% of the respondents
being married and 2.6% cohabiting with their partner. About 28.1% of
the sample has been married, but are legally separated, divorced, or
widowed. Close to 14% of the sample has never been married.
Table 1 indicates that the respondents overwhelming live in
nonsegregated metropolitan areas, with only 30% living in areas with
some degree of racial segregation. Moreover, 22.2% of the sample lives
in segregated areas, while 6.4% of the sample lives in hypersegregated
areas. Comprising only a small number of areas, extremely hypersegregated areas are least represented in this data—less than 2% of the
sample resides in this type of segregated metropolitan area.
The socioeconomic characteristics of the areas suggest low
levels of spatial disadvantage. The mean population is around
848,000 residents. Areas in this sample have, on average, 10.5% of
A. Jones / Health & Place 22 (2013) 56–67
61
Table 1
Distribution of individual- and metropolitan-level characteristics in sample.
Source: 2005 BRFSS and 2005 ACS.
Outcome measure
Hypertension diagnosis
Individual (level 1) variables
Educational level
Less than high school
High school graduate
Some college
Graduated college
Household income
Less than $15,000
$15,000 to $20,000
$20,000 to $25,000
$25,000 to $35,000
$35,000 to $50,000
$50,000 to $75,000
More than $75,000
Employed
Body mass index
Smoker status
Current smoker
Former smoker
Never smoked
Alcohol consumption (days per month)
Physical activity (days per week)
Insured
Race
White
Black
Other
Male
Age
Marital status
Never married
Cohabiting
Married
Separated
Metropolitan (level 2) variables
Segregation type
Nonsegregated
Segregated
Hypersegregated
Extremely hypersegregated
Population size
Percentage of black residents
Percentage of residents in poverty
Percentage of female-headed households
N
Mean or %
Standard deviation
Range
37.8%
–
–
9.9%
30.1%
27.0%
33.0%
–
–
–
–
–
–
–
–
9.6%
7.9%
9.6%
12.7%
17.5%
18.8%
23.8%
88.0%
27.2
–
–
–
–
–
–
–
–
5.7
–
–
–
–
–
–
–
–
8.4–42.1
19.6%
28.1%
52.3%
10.4
4.4
88.0%
–
–
–
5.3
2.4
–
–
–
–
0–30
0–7
–
84.1%
8.7%
7.2%
38.2%
51.2
–
–
–
–
17.1
–
–
–
–
18–99
13.6%
2.6%
55.7%
28.1%
–
–
–
–
–
–
–
–
69.9%
22.2%
6.4%
1.5%
848,169.8
10.5%
9.7%
12.8%
200,102
–
–
–
–
941,084.2
–
–
–
–
–
–
–
65,076–5,193,448
-
Table 2
Number and percentage of hypertensive and non-hypertensive respondents by segregation type and race.
Source: 2005 BRFSS and 2005 ACS.
Nonsegregated MSA
Segregated MSA
Hypersegregated MSA
Extremely hypersegregated MSA
Total
N
%
N
%
N
%
N
%
N
%
White
Hypertensive
Non-Hypertensive
Total
47,906
70,794
118,700
40.4%
59.6%
11,587
26,405
37,992
30.5%
69.5%
2,924
6,506
9,430
31.0%
69.0%
656
1,561
2,217
29.6%
70.4%
63,073
105,266
168,339
37.2%
62.8%
Black
Hypertensive
Non-Hypertensive
Total
5,609
4,090
9,699
57.8%
42.2%
1,675
2,500
4,175
40.1%
59.9%
1,078
1,711
2,789
38.7%
61.3%
276
389
665
41.5%
58.5%
8,638
8,690
17,328
49.9%
50.1%
Other
Hypertensive
Non-Hypertensive
Total
3,262
8,240
11,502
28.4%
71.6%
455
1,751
2,206
20.6%
79.4%
120
445
565
21.2%
78.8%
42
120
162
25.9%
74.1%
3,879
10,556
14,435
26.9%
73.1%
its residents in poverty. Similarly, 10.5% of residents in the sample are
black. Lastly, the average percentage of female-headed households in
the metropolitan areas represented in this data is roughly 13%.
In order to see if race plays a role in the distribution of
hypertension across metropolitan areas, a separate cross-tab is
presented in Table 2 that includes a racial breakdown in hypertensive
62
A. Jones / Health & Place 22 (2013) 56–67
diagnoses within each segregation type. As a whole, 37.2% of whites,
compared to 49.9% of blacks and 26.9% of other races, have been
diagnosed with hypertension. The racial disparity in hypertension is
greatest within nonsegregated areas, where there is a 17.4
percentage-point difference between whites and blacks. The black–
white gap in hypertension is lowest in hypersegregated areas, where
38.7% of blacks and 31% of whites have been diagnosed with
hypertension.
4.2. Multivariate analyses
Table 3 presents the multilevel coefficients for predicting hypertension diagnosis for the entire sample. Model 1 includes all
measures of individual SES. In Model 1, education is statistically
associated with hypertension. Having higher levels of education is
protective against being diagnosed with hypertension. This association is also present in both income and employment status. However,
for income, a positive relationship between the two lowest levels of
income emerges. Compared to individuals who make less than
$15,000, individuals who earn between $15,000 and $20,000 have
significantly higher odds of being diagnosed with hypertension. This
result appears to illustrate that the “working poor,” i.e., individuals
who fall short of being below the poverty line and do not qualify for
Medicare benefits, are more likely to be diagnosed with hypertension
than persons who fall below the poverty line (less than $15,000).
Even though the threshold difference between working poor
and individuals below the poverty line is compelling, the effect
diminishes in Model 2. The remaining individual-level controls
related to the demographics of the respondents and their health
are added to this model. With the exception of one category of
income, the other SES coefficients remain significant, in the same
direction and in approximately the same magnitude as seen in the
previous model. Supplemental analyses suggest that the inclusion
of insurance status creates nonsignificance in the $15,000–$20,000
income category.
The health and demographic controls also independently affect
hypertension diagnosis net of the socioeconomic status of the
respondent. Increasing levels of BMI and alcohol consumption are
Table 3
Multilevel analyses of hypertension diagnosis, combined sample (N ¼200,102).
Source: 2005 BRFSS and 2005 ACS.
Intercept
Individual (level 1) variables
Educational level (less than high school)
High school graduate
Some college
Graduated college
Household income (less than $15,000)
$15,000 to $20,000
$20,000 to $25,000
$25,000 to $35,000
$35,000 to $50,000
$50,000 to $75,000
More than $75,000
Employed
Body mass index
Smoker status (current smoker)
Former smoker
Never smoked
Alcohol consumption (days per month)
Physical activity (days per week)
Insured
Race (black)
White
Other
Male
Age
Marital status (married)
Never married
Cohabiting
Separated
Model 1
Model 2
Model 3
Model 4
−0.03nnn
−5.60nnn
−5.59nnn
−5.67nnn
−0.08nnn
−0.21nnn
−0.36nnn
−0.04nnn
−0.11nnn
−0.25nnn
−0.05nn
−0.10nnn
0.25nnn
−0.03
−0.09nnn
−0.23nnn
0.05nnn
−0.04nnn
−0.05nnn
−0.12nnn
−0.22nnn
−0.32nnn
−0.88nnn
0.03
−0.06nnn
−0.07nnn
−0.07nnn
−0.10nnn
−0.14nnn
−0.19nnn
0.10nnn
0.03
−0.06nnn
−0.07nnn
−0.07nnn
−0.10nnn
−0.14nnn
−0.19nnn
0.10nnn
0.03
−0.05nnn
−0.07nnn
−0.06nnn
−0.09nnn
−0.13nnn
−0.19nnn
0.10nnn
−0.05n
−0.16nnn
0.00nnn
−0.03nnn
0.18nnn
−0.05n
−0.16nnn
0.00nnn
−0.03nnn
0.18nnn
−0.04
−0.16nnn
0.00nnn
−0.03nnn
0.19nnn
−0.57nnn
−0.56nnn
0.12nnn
0.05nnn
−0.57nnn
−0.57nnn
0.12nnn
0.05nnn
−0.57nnn
−0.50nnn
0.12nnn
0.05nnn
−0.02
−0.08
0.06nnn
−0.02
−0.08
0.06nnn
−0.01
−0.06
0.06nnn
0.01n
0.01nnn
0.02nnn
0.01
0.08nnn
0.00
−0.01
0.09
0.66nnn
1.32nnn
Metropolitan (level 2) variables
Segregation type (nonsegregated)
Segregated
Hypersegregated
Extremely hypersegregated
Log population size
Percentage of black residents
Percentage of residents in poverty
Percentage of female-headed households
AIC
Pseudo R2 (level 1)
2,135
0.06
Notes: Contrast categories are in parentheses. AIC ¼Akaike's information criterion.
n
p o 0.05.
p o0.01.
nnn
po 0.001.
nn
1,983
0.28
1,976
0.29
1,970
0.34
A. Jones / Health & Place 22 (2013) 56–67
positively associated with hypertension. Not smoking at the survey
date and engaging in physical activity are associated with not
being diagnosed with hypertension. Having insurance is associated
with being medically diagnosed with hypertension. Age, gender,
and marital status are all positively associated with hypertension
in the expected ways. That is, individuals who are older, male, or
separated are statistically likely to be diagnosed with hypertension. Also, net of socioeconomic status, whites and others are less
likely to be diagnosed with hypertension compared to blacks.
Model 3 introduces the higher-order (level 2) metropolitan
measures. This model assesses the effect of segregation on
hypertension diagnosis. An auxiliary zero-order model containing
only the four segregation measures revealed all measures to be
positive, significant, and increasing monotonically. This unshown
analysis suggests that there is an independent effect of segregation
on hypertension. Model 3, which includes the individual measures, shows a somewhat consistent pattern. After adjusting for
individual measures (which in this model changed slightly from
the previous model but retained their significance), residence in a
segregated, hypersegregated, or extremely hypersegregated areas
continue to indicate a higher probability of being diagnosed with
hypertension, relative to living in a nonsegregated area.
Model 4 adds measures relating to metropolitan-level SES to
explore how different kinds of socioeconomic status indicators
from various different levels affect hypertension. In a zero-order
model with only the four metropolitan SES measures, all measures
except the proportion of black residents were significant (results
not shown). In this model, which controls for individual variables
and segregation measures, only the proportion of MSA residents in
poverty and the proportion of female-headed households remain
significant. In both cases, these variables, net of all controls, are
positively related to hypertension diagnosis. Indeed, the more
people in poverty and the more female-headed households in an
area, the greater the chance of being diagnosed with hypertension.
Adding the metropolitan SES measures attenuates the effect that
living in an extremely hypersegregated area has on hypertension.
The proportion of female-headed households and the proportion in
poverty both contribute to explaining away the effect of living in
extreme hypersegregation. However, residence in a segregated or
hypersegregated area is still significant in predicting hypertension,
net of individual-level characteristics including socioeconomic
status.
Lastly, most of the individual effects of socioeconomic status
retain their significance from Models 3 to 4, which indicates that
both metropolitan and individual SES impact hypertension. The
only category to become statistically insignificant is high school
graduate. In the previous models, being a high school graduate is
associated with a lower likelihood of being diagnosed with
hypertension relative to having less than a high school education.
However, in the final model, the likelihood of having hypertension
is the same for high school graduates and those with less than a
high school education. Supplemental analyses indicate that the
proportion of poverty contributes to this change in statistical
significance across Models 3 and 4.
4.3. Variance components
Because both fixed and random effects are estimated in the
models, a discussion of the variance components is warranted.
Table 4 displays the random effects estimates for the higher order
(i.e., level 2) measures. Across the models, the intercept is
significant, indicating that there is some unexplained variance
among metropolitan areas when variables are entered into the
models. To apply, the intercept for Model 2 (which includes all of
the individual, or level 1, variables) suggests that 80% (0.04/0.05)
of the variance in hypertension diagnosis that is explainable by
63
Table 4
Variance components of multilevel analyses of hypertension.
Source: 2005 BRFSS and 2005 ACS.
Model 1 Model 2 Model 3 Model 4
Intercept
0.05n
0.04n
Segregation type (nonsegregated)
Segregated
Hypersegregated
Extremely hypersegregated
Log population size
Percentage of black residents
Percentage of residents in poverty
Percentage of female-headed households
Level 1 error
0.45nnn
0.41nnn
0.02n
0.00
0.01
0.01
0.00
0.00
0.00
0.00
0.01
0.07nnn
0.49nnn
0.01
0.41nnn
0.40nnn
Notes: Contrast categories are in parentheses.
n
p o 0.05.
nn
po 0.01.
p o 0.001.
nnn
individual-level factors is accounted for by including health and
demographic information. Likewise, Model 3 (which includes all of
the individual variables and segregation type) suggests that 50%
(0.02/0.04) of the variance in hypertension diagnosis that is
explainable by metropolitan-level factors is accounted for by
including information on segregation type. However, most notably, in Model 4, which includes all variables and metropolitanlevel SES controls, the intercept loses significance. This suggests
that the addition of the metropolitan-level SES measures explains
the variance in hypertension diagnosis that could have been
explained by incorporating individual-level measures and segregation type. As such, there is no statistical difference in hypertension diagnosis across MSAs. Statistically, a zero variance is not
uncommon for hierarchical linear models and indicates that the
MSA variance is not statistically different from zero (Lee and
Ferraro, 2007).
Two of the MSA-level variables have significant random effects
as well. Between MSAs, there is significant variation in the
proportion of black residents and the proportion of individuals
in poverty that affects being diagnosed with hypertension, which
is unaccounted for by including the individual and metropolitan
measures in the analyses. The variance components for the
segregation types are not statistically different from zero, suggesting that the variation in hypertension across all types of segregation is accounted for by including individual measures.
4.4. Stratified analyses
Table 5 is an abridged table that presents the multilevel
analyses of hypertension diagnosis for each area type. Each model
presented is a full model, with all measures controlled for
simultaneously. However, only the coefficients of the individual
SES measures appear in the table. Because the segregation variables were significant in the final model of Table 3, one would
expect to find area-specific differences in the effects that individual measures have on being diagnosed with hypertension. In
unshown analyses, there were significant cross-level interactions
between segregation type and individual SES measures, which
suggest that stratified analyses are methodologically appropriate
to explore the variable effects within each segregation context
(DeMaris, 2004; Raudenbush and Byrk, 2002).
In nonsegregated metropolitan areas, the individual-level SES
effects are similar to those of the combined sample. That is, being
highly educated (i.e., a college graduate), having more than
$15,000 in annual income, or being employed at least part-time
are all negatively related to hypertension diagnosis. Moreover, this
64
A. Jones / Health & Place 22 (2013) 56–67
Table 5
Selected coefficients for multilevel analysis of hypertension stratified by metropolitan segregation.
Source: 2005 BRFSS and 2005 ACS.
Individual SES
Educational level (less than high school)
High school graduate
Some college
Graduated college
Household income (less than $15,000)
$15,000 to $20,000
$20,000 to $25,000
$25,000 to $35,000
$35,000 to $50,000
$50,000 to $75,000
More than $75,000
Employed
Pseudo R2 (Level 1)
N
Nonsegregated MSA
Segregated MSA
Hypersegregated MSA
Extremely hypersegregated MSA
0.01
−0.03
−0.16nnn
0.05
0.03
−0.08
−0.07
−0.16
−0.27
0.00
−0.12
−0.19
−0.14nnn
−0.22nnn
−0.25nnn
−0.29nnn
−0.36nnn
−0.40nnn
−0.15nnn
−0.12nnn
−0.27nnn
−0.22nnn
−0.26nnn
−0.37nnn
−0.37nnn
−0.15nnn
0.01
−0.21
−0.37nn
−0.25nnn
−0.19nnn
−0.31nnn
−0.22nnn
0.05
−0.29
−0.51
−0.33
−0.51
−0.43nnn
−0.14
0.37
139,901
0.29
44,373
0.31
12,784
0.32
3,044
Notes: Contrast categories are in parentheses. All models control for all measures used in previous analyses.
n
po 0.05.
nn
p o0.01.
po 0.001.
nnn
model suggests that education is influential in predicting hypertension diagnosis in nonsegregated areas only if you are a college
graduate. Thus, the monotonic nature of education is not present
within nonsegregated metropolitan areas.
In segregated areas, an interesting pattern emerges—individual
SES loses some statistical strength in predicting hypertension.
While income and employment maintain their relationships as
discussed in the model for nonsegregated areas, education is not
significant in predicting hypertension diagnosis. Degree attainment (or levels of education), which previously has been protective against hypertension, appears not to have a significant
protective effect in areas that are racially segregated. Also, some
of the coefficients are smaller in magnitude, which provides some
evidence that the disparities among the income groupings are
smaller than in nonsegregated MSAs. However, because these
coefficients are not standardized, it is important to interpret them
with caution.
In hypersegregated areas, the pattern of declining SES significance and magnitude continues. In hypersegregated areas (i.e.,
areas where minority residents may not encounter members
outside of their own race) the effects of individual-level SES
become sparse. In this kind of environment, education and lower
levels of income (i.e., $15,000 to $25,000) are not significant in
predicting hypertension diagnosis. Thus, only through employment and earning higher levels of income are individuals in these
areas more likely to have lower odds of being diagnosed with
hypertension.
Lastly, in extremely hypersegregated areas, neither education
nor employment matter in predicting hypertension. In fact, only
high levels of income (i.e., more than $75,000) result in lower odds
of being diagnosed with hypertension.
5. Discussion
This research used nationally representative data to assess how
socioeconomic status and metropolitan area residential segregation jointly affect hypertension. It sought out to answer three
questions. First, was segregation associated with hypertension?
This research suggested that there is an association between
segregation and hypertension. Bivariate results indicated that
hypertension diagnoses were represented most in nonsegregated
metropolitan areas, where nearly 70% of people in the sample
reside. Turning to racial differences in hypertension across segregation types, the black-white gap in hypertension is greatest in
nonsegregated areas and lowest in hypersegregated areas. This
research provided some evidence that, although there is a clear
racial component to the distribution of hypertension in metropolitan areas, the gap is narrowest in areas that are characteristically
the poorest in infrastructure. Thus, extreme forms of segregation
may shape hypertension risk by affecting all people with a given
area similarly due to the lack of access to health-promoting
structures (such as safe, open spaces, and high-quality hospitals).
Multivariate results suggested that living in segregated and
hypersegregated metropolitan areas increased hypertension risk
compared to living in nonsegregated areas. However, when controlling for metropolitan-level socioeconomic status indicators, there
was no statistical difference in hypertension diagnosis for individuals
living in nonsegregated and extremely hypersegregated areas. This
finding could be due to the low sample size of individuals who live in
this extreme type of segregation. In either case, it is important to
note that the risk of being diagnosed with hypertension is enhanced
with more extreme forms of segregation. Prior work has indicated
that segregation is tied to various health outcomes (Gebreab and
Diez-Roux, 2012; Kannel, 1996; Mair et al., 2011). This research has
added strong support in this finding by suggesting that segregation is
also related to hypertension.
Second, how did metropolitan SES relate to individual SES in
predicting hypertension? The findings indicated that individual
SES measures (education, income, and employment) maintained
statistical significance even after controlling for metropolitan SES
measures. This result indicated that individual SES is very powerful in predicting hypertension. Specifically, college graduates,
individuals who are in higher income levels, and those who are
employed had low risks of being diagnosed with hypertension.
Also, metropolitan SES measures significantly and independently
predicted hypertension diagnosis. That is, increasing numbers of
blacks and individuals in poverty within a metropolitan area was
associated with an increased risk of an individual in a metropolitan area being diagnosed with hypertension. Thus, context
matters in predicting hypertension diagnosis. In addition, metropolitan SES uniquely explained variation in hypertension. The
variance in hypertension explainable by MSA factors was
accounted for by including metropolitan SES measures. So, while
A. Jones / Health & Place 22 (2013) 56–67
segregation was important in predicting hypertension, both the
material conditions and racial composition inequalities that exist
within segregated areas were more powerful in explaining hypertension. Recent work suggested various pathways through which
segregation affects health (Acevedo-Garcia et al., 2003; Gebreab
and Diez-Roux, 2012). This research has suggested two potential
mechanisms that future research can use to investigate a causal
link between segregation and hypertension.
Third, did the effect of an individual's SES differ across different
types of segregation? While the robustness of the data is compromised due to the low representation of people in the sample
residing in extremely hypersegregated areas (3,044 out of 200,102
individuals lived in this type of area), the research overwhelmingly
supports the notion that individual SES mattered less in more
segregated areas. Specifically, in segregated areas, education was
not protective against hypertension diagnosis. In hypersegregated
areas, education and lower-to-mid levels of income were not
protective. In areas where interactions are exclusively within one's
minority group (i.e., extremely hypersegregated areas), only at
high levels of income (4$75,000) were people protected against
hypertension diagnosis. However, these results should be interpreted with caution, since the low number in extremely hypersegregated areas is statistically problematic to generalize to all
individuals within this type of segregated context. This finding
adds to our understanding of how social and economic inequalities
in health are context-dependent. In extremely hypersegregated
areas, where there are poor living conditions, unreliable or poor
quality medical care, and a high concentration of poverty, only
those individuals who are affluent have the means to seek proper
medical/health care and engage in diversions from the stressors of
living in these areas, which would relatively improve their health.
Indeed, healthy lifestyles are most effective for people living in
positive social circumstances and least effective in the converse
(Blaxter, 1990). This research provided some support for that
argument.
It is important to recognize that individual and metropolitan
SES are interrelated but affect health in different ways. Individual
SES corresponds to a social raking based on the indictors used in
this project, while metropolitan SES relates to the objective social
organization of individuals within a space with various different
physical structures and living conditions (Diez-Roux et al., 1997).
In prior work, neighborhood disadvantages negatively affect the
health of its residents above the effects of personal disadvantages
(Ross and Mirowsky, 2001). Some research have suggested that
because of this interplay, more research needs to be done in order
to capture the intersectionality of individual and spatial SES
(Acevedo-Garcia et al., 2003), which was the main goal of this
research.
Prior research on segregation and hypertension has used only
one measure of segregation such as an index of isolation (Kershaw
et al., 2011; Thorpe Jr. et al., 2006) or composite proxies such as
economic deprivation (White et al., 2011), and these studies have
focused on racial differences in hypertension. Based on these
measures, they have suggested that whites and blacks have similar
cardiovascular outcomes when they live in areas with similar
levels of segregation (Thorpe Jr. et al., 2006), but higher rates of
hypertension for both whites and blacks are found in higher levels
of segregation (Kershaw et al., 2011). This research has used
multiple indicators of segregation to create typologies of segregated areas, and this study has included numerous economic
indicators at the individual and metropolitan level. This research
has some similarities and divergences from prior research. Consistent with past research, this research found that in all segregation types, blacks comprise the largest percentage of hypertensive
cases than whites, but the gap narrows in more extreme forms of
segregation. However, unlike prior research that suggests that race
65
differences are statistically nonsignificant in segregated areas, this
research found that race and SES were dually and differentially
important in predicting hypertension diagnosis depending on the
type of segregated area in which one resides. Thus, this research
contributes to the larger body of work on segregation and
hypertension by empirically demonstrating the independent and
joint roles that both race and socioeconomic standing have on
hypertension in various different kinds of segregated environments.
An interesting finding with direct policy implications deals with
insurance. Intuitively, insurance was associated with a lower risk of
being diagnosed with hypertension. Moreover, when insurance status
is controlled for in the model, individuals who made less than
$15,000 had the same risk of high blood pressure as those who
could be considered working poor (i.e., income between $15,000 and
$20,000), who had a higher risk of being diagnosed with hypertension in a previous model. Thus, having insurance seems to be a
normalizing force for individuals who do not get insurance from their
work, do not qualify for public insurance (e.g., Medicaid) because
they work, and who cannot afford private insurance. Expanding
health insurance coverage through universal health care could
combat health disadvantage by reducing hypertension risk for
individuals who are unable to afford insurance.
In addition, as many researchers suggest (Rosenbaum, 1995;
Stuart and Sorenson, 2003), the negative effects of segregation
could be combated if US policy is shifted from focusing on
desegregation to a pointed effort to provide equal opportunity to
reside in neighborhoods and communities. Racial integration is
not an appropriate goal for curbing health disparities. Rather,
giving people chances to live in different areas would correct SESrelated health disparities.
This study is not without its limitations. First, the sample size for
the BRFSS was large, indicating that perhaps the estimates were
capturing small differences in a large sample as being significant.
The reader is warned that the same results may not be guaranteed if
using a smaller sample size. In addition, the sample was collected
via telephone interviews, which indicates some class bias on who
can be selected to participate in the study. While no sample
selection effects were found with regards to income, it is important
to note that by virtue of the sampling method, severely impoverished persons may not be adequately represented in this sample.
However, coupled with a class bias, there is also a smaller
representation of blacks in this large sample. With less than 9% in
the analytic sample being African American, the sample may be
skewed to more affluent blacks, a demographic that is rather small
in representation in the US. Future studies may want to improve on
ways to estimate those who are least represented in the BRFSS.
Second, this research could not analyze racial groups other than
whites and blacks due to sample size limitations. Having the
ability to look at racial differences and effects of covariates would
have strengthened this research because it could have pointed to
mechanisms that affect cardiovascular disease across racial groups.
Future research should use this work to situate why racial and
ethnic differences in hypertension continue to persist. Recent
research by Do et al. (2012) used propensity score matching and
found that the racial differences in self-rated health could be
attenuated by the inclusion of family poverty levels (FPL) and
household wealth. They argued that prior researchers have not
completely measured or accounted for SES in their models.
Unfortunately, the 2005 BRFSS does not include wealth or FPL
data, so this project is unable to replicate Do's research.
Third, hypertension is self-reported and not based on medical
examinations or records. The reporting of health depends on
whether patients choose to consult their general practitioner and
is based on their own decisions. The self-reporting of health on
surveys is conceptually problematic for two reasons. Bias could be
66
A. Jones / Health & Place 22 (2013) 56–67
embedded in the study by the use of self-reported data. If this
research relied on the assumption that people accurately report their
morbidities based on a doctor's evaluation, then the assumption fails
to acknowledge those people who are unable to seek medical advice.
Also, self-reported health status could be self-diagnosed, which could
lead to error in misdiagnosis. Misdiagnosis through self-reports could
result in significant variability in self-reported health, which problematizes the validity of the measurement.
Fourth, with cross-sectional data, it is nearly impossible to disentangle causal ordering of events because all information is gathered at
one specific point in time. This research presented associations
between certain processes (e.g., SES) and health. However, it is
unclear, for instance, if SES directly impacts health, if SES is impacted
by health, or if SES and health are both impacted by each other. Thus,
a longitudinal study on hypertension and SES would illuminate how
direct associations (or causal ordering of events) impact health.
Finally, structural confounding is a concern in multilevel analysis
of segregation and health. Specifically, people who reside in vastly
different areas are not exchangeable because various factors have
allowed these individuals to select differentially into their specific
area (Maldonado and Greenland, 2002). Even if these individuals are
matched on numerous factors that are present in the data, there are
still unobserved characteristics (such as individual motivations for
moving into a particular area or experiences of housing discrimination) that would make these individuals incomparable and therefore
not exchangeable. This concern is relevant for this study, since
structural conditions have dually shaped minorities' access to certain
neighborhoods and access to opportunities tied to individual socioeconomic status, such as entrance into institutions of higher education or employment (Wilson, 1987). As such, the people who live in
extremely hypersegregated areas may not be exchangeable with
those living in less segregated areas because of unobserved factors
that influence and constrain their residential decisions. Methodologically, this kind of selection would mean that “certain covariate strata
will contain only subjects who could never be exposed, a violation of
the positivity or experimental treatment effect assumption” (Messer
et al., 2010: 664). Outside of a few studies that use propensity score
matching to address this problem (e.g., Leal et al., 2011; Messer et al.,
2010), structural confounding has largely been ignored in the
literature. Future research should be aware of this concern when
trying to make more causal inferences from observational data
(Messer, 2007).
Despite the aforementioned limitations, this research is not
without its strengths. With recent data, this study has expanded
our understanding of segregation and hypertension in two key
ways. This research conceptualized segregation as a multidimensional characteristic in metropolitan areas that relies on five
unique indicators to assess it. As such, this research was able to
focus on different types of segregation, namely hypersegregation.
As a conceptually different phenomenon from segregation, hypersegregation was found to be a unique predictor of hypertension
diagnosis. Lastly, this research also conceptualized socioeconomic
status as having different effects at different levels of analysis. That
is, an individual's SES and the corresponding socioeconomic
context in which that individual lives are both unique predictors
of hypertension. However, these two levels of SES matter less in
segregated areas. As such, the linkages between race, socioeconomic context, segregation and hypertension could prompt for a
new line of research that directly examines the specific ties
between the individual, community, and society on health.
References
Acevedo-Garcia, D., Lochner, K.A., Osypuk, T.L., Subramanian, S.V., 2003. Future
directions in residential segregation and health research: a multilevel
approach. American Journal of Public Health 93, 215–221.
Adler, N.E., Boyce, T., Chesney, M.A., Cohen, S., Folkman, S., Kahn, R.L., Syme, S.L.,
1994. Socioeconomic status and health. American Psychologist 49, 15–24.
Agresti, A., 2002. Categorical Data Analysis, second ed. John Wiley & Sons, Hoboken, NJ.
Allison, P.D., 1995. Survival Analysis Using the SAS System: A Practical Guide. SAS
Institute, Cary, NC.
Blaxter, M., 1990. Health and Lifestyles. Routledge, New York, NY.
Centers for Disease Control and Prevention, 2006. Behavioral Risk Factor Surveillance System Survey Data. U.S. Department of Health and Human Services,
Atlanta, Georgia.
de Souza Briggs, X., 1997. Moving up versus moving out: neighborhood effects in
housing mobility programs. Housing Policy Debate 8, 195–234.
DeMaris, A., 2004. Regression with Social Data: Modeling Continuous and Limited
Response Variables. Wiley, Hoboken, NJ.
Diez-Roux, A.V., Nieto, F.J., Muntaner, C., Tyroler, H.A., Comstock, G.W., Shahar, E.,
Cooper, L.S., Watson, R.L., Szklo, M., 1997. Neighborhood environments and coronary
heart disease: a multilevel analysis. American Journal of Epidemiology 146, 48–63.
Do, D.P., Frank, R., Finch, B.K., 2012. Does SES explain more of the black/white
health gap that we thought? Revisiting our approach toward understanding
racial disparities in health. Social Science & Medicine 74, 1385–1393.
Dressler, W., 1991. Social class, skin color and arterial blood pressure in two
societies. Ethnicity and Disease 1, 60–77.
Dressler, W., Oths, K.S., Gravlee, C.C., 2003. Race and ethnicity in health research:
models to explain disparities. Annual Review of Anthropology 34, 231–252.
Eitle, D., 2009. Dimensions of racial segregation, hypersegregation, and black
homicide rates. Journal of Criminal Justice 37, 28–36.
Ellen, I.G., Mijanovich, T., Dillman, K.N., 2001. Neighborhood effects on health: exploring
the links and assessing the evidence. Journal of Urban Affairs 23, 391–408.
Fang, J., Madhavan, S., Bosworth, W., Alderman, M.H., 1998. Residential segregation
and mortality in New York City. Social Science & Medicine 47, 469–476.
Freeman, L., 2005. Displacement or succession?: Residential mobility in gentrifying
neighborhoods. Urban Affairs Review 40, 463–491.
Gebreab, S.Y., Diez-Roux, A.V., 2012. Exploring racial disparities in CHD mortality
between blacks and whites across the United States: a geographically weighted
regression approach. Health & Place 18, 1006–1014.
Harburg, E., Erfurt, J.C., Hauenstein, L.S., Chape, C., Schull, W.J., Schork, M.A., 1973.
Socio-ecological stress, suppressed hostility, skin color, and African Americanwhite male blood pressure: Detroit. Psychosomatic Medicine 35, 276–296.
Hearst, M.O., Oakes, J.M., Johnson, P.J., 2008. The effect of racial residential segregation
on black infant mortality. American Journal of Epidemiology 168, 1247–1254.
James, S.A., Hartnett, S.A., Kalsbeek, W.D., 1984. John Henryism and blood pressure
differences among African American Men II: The role of occupational stressors.
Journal of Behavioral Medicine 7, 259–275.
Kaczynski, A.T., Johnson, A.J., Saelens, B.E., 2010. Neighborhood land use diversity
and physical activity in adjacent parks. Health & Place 16, 413–415.
Kannel, W.B., 1996. Blood pressure as a cardiovascular risk factor. Journal of the
American Medical Association 275, 1571–1576.
Kershaw, K.N., Roux, A.V.D., Burgard, S.A., Lisabeth, L.D., Mujahid, M.S., Schulz, A.J.,
2011. Metropolitan-level racial residential segregation and black–white disparities in hypertension. American Journal of Epidemiology 174, 537–545.
Kramer, M.R., Hogue, C.R., 2009. Is segregation bad for your health? Epidemiologic
reviews 31, 178–194.
Leal, C., Bean, K., Thomas, F., Chaix, B., 2011. Are associations between neighborhood socioeconomic characteristics and body mass index or waist circumference based on model extrapolations? Epidemiology 22, 694–703.
LeClere, F., Rogers, B., Peters, K.D., R.G., 1997. Ethnicity and mortality in the United
States: individual and community correlates. Social Forces 76, 169–198.
Lee, M.-A., Ferraro, K.F., 2007. Neighborhood residential segregation and physical
health among Hispanic Americans: good, bad, or benign? Journal of Health and
Social Behavior 48, 131–148.
Mair, C.A., Cutchin, M.P., Peek, M.K., 2011. Allostatic load in an environmental
riskscape: the role of stressors and gender. Health & Place 17, 978–987.
Maldonado, G., Greenland, S., 2002. Estimating causal effects. International Journal
of Epidemiology 31, 422–429.
Massey, D.S., 2004. Segregation and stratification: a biosocial perspective. Du Bois
Review 1, 7–25.
Massey, D.S., Denton, N.A., 1989. Hypersegregation in US metropolitan areas:
African American and Hispanic segregation along five dimensions. Demography
26, 373–391.
Massey, D.S., Denton, N.A., 1993. American Apartheid: Segregation and the Making
of the Underclass. Harvard University Press, Cambridge, MA.
Messer, L.C., 2007. Invited commentary: beyond the metrics for measuring
neighborhood effects. American Journal of Epidemiology 165, 868–871.
Messer, L.C., Oakes, J.M., Mason, S., 2010. Effects of socioeconomic and racial
residential segregation on preterm birth: a cautionary tale of structural
confounding. American Journal of Epidemiology 171, 664–673.
Miranda, A., Rabe-Hesketh, S., 2006. Maximum likelihood estimation of endogenous switching and sample selection models for binary, ordinal and count
variables. The Stata Journal 6, 285–308.
Mokdad, A.H., Stroup, D.F., Giles, W.H., 2003. Public health surveillance for
behavioral risk factors in a changing environment: recommendations from
the behavioral risk factor surveillance team. Morbidity and Mortality Weekly
Report 52, 1–12.
Morland, K., Wing, S., Diez-Roux, A., Poole, C., 2002. Neighborhood characteristics
associated with the location of food stores and food service places. American
Journal of Preventive Medicine 22, 23–29.
A. Jones / Health & Place 22 (2013) 56–67
Oliver, M.L., Shapiro, T.M., 2006. Black Wealth/White Wealth: A New Perspective on
Racial Inequality. Routledge, New York, NY.
Osypuk, T.L., Acevedo-Garcia, D., 2008. Are racial disparities in preterm birth larger
in hypersegregated areas? American Journal of Epidemiology 167, 1295–1304.
Pollack, C.E., Chideya, S., Cubbin, C., Williams, B., Dekker, M., Braveman, P., 2007.
Should health studies measure wealth?: a systematic review. American Journal
of Preventive Medicine 33, 250–264.
Rabe-Hesketh, S., Skrondal, A., 2012. Multilevel and Longitudinal Modeling Using
Stata, third ed. Stata Press, College Station, TX.
Raudenbush, S.W., Byrk, A.S., 2002. Hierarchical Linear Models: Applications and
Data Analysis Methods, second ed. Sage Publications, Thousand Oaks, CA.
Rencher, A.C., 2002. Methods of Multivariate Analysis, second ed. WileyInterscience, New York, NY.
Rosenbaum, J.E., 1995. Changing the geography of opportunity by expanding
residential choice: lessons from the Gautreaux program. Housing Policy Debate
6, 231–269.
Ross, C.E., Mirowsky, J., 2001. Neighborhood disadvantage, disorder, and health.
Journal of Health and Social Behavior 42, 258–276.
Schum, J.L., Jorgensen, R.S., Verhaeghen, P., Sauro, M., Thibodeau, R., 2003. Trait
anger, anger expression and ambulatory blood pressure: a meta-analytic
review. Journal of Behavioral Medicine 26, 395–415.
Smaje, C., 1995. Ethnic residential concentration and health: evidence for a positive
effect. Policy and Politics 23, 251–269.
StataCorp, 2011. Stata Statistical Software: Release 12. StataCorp LP, College Station,
TX.
Stuart, T., Sorenson, O., 2003. The geography of opportunity: spatial heterogeneity
in founding rates and the performance of biotechnology firms. Research Policy
32, 229–253.
67
Subramanian, S.V., Acevedo-Garcia, D., Osypuk, T.L., 2005. Racial residential
segregation and geographic heterogeneity in black/white disparity in poor
self-rated health in the US: a multilevel statistical analysis. Social Science &
Medicine 60, 1667–1679.
Taylor, P., Kochhar, R., Fry, R., Velasco, G., Motel, S., 2011. Twenty-to-One: Wealth
Gaps Rise to Record Highs between Whites, Blacks and Hispanics. Pew Research
Center Social & Demographic Trends, Washington, DC.
Thorpe Jr., R.J., Brandon, D.T., LaVeist, T.A., 2006. Social context as an explanation for
race disparities in hypertension: findings from the exploring health disparities
in integrated communities (EHDIC) study. Social Science & Medicine 67,
1604–1611.
White, K., Borrell, L.N., Wong, D.W., Galea, S., Ogedegbe, G., Glymour, M.M., 2011.
Racial/ethnic residential segregation and self-reported hypertension among
US- and foreign-born blacks in New York City. American Journal of Hypertension 24, 904–910.
Wilkes, R., Iceland, J., 2004. Hypersegregation in the twenty-first century. Demography 41, 23–36.
Williams, D., Collins, C., 2001. Racial residential segregation: a fundamental cause
of racial disparities in health. Public Health Reports 116, 404–416.
Williams, D.R., Collins, C., 1995. US socioeconomic and racial differences in health:
patterns and explanations. Annual Review of Sociology 21, 349–386.
Williams, D.R., Jackson, P.B., 2005. Social sources of racial disparities in health.
Health Affairs 24, 325–334.
Wilson, W.J., 1987. The Truly Disadvantaged: The Inner City, the Underclass and
Public Policy. University of Chicago Press, Chicago, Illinois.
Zenk, S.N., Schulz, A.J., Israel, B.A., James, S.A., Bao, S., Wilson, M., 2005. Neighborhood racial composition, neighborhood poverty, and the spatial accessibility of
supermarkets in metropolitan Detroit. American Journal of Public Health 95,
660–668.