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Behavioral, physiological and psychological stress among legal and unauthorized Brazilian immigrants: the moderating influence of neighborhood environments

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Behavioral, physiological and psychological stress among legal and unauthorized Brazilian immigrants: the moderating influence of neighborhood environments

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Content i

Behavioral, Physiological and
Psychological Stress among Legal
and Unauthorized Brazilian
Immigrants
The Moderating Influence of Neighborhood
Environments

Louisa M. Holmes
University of Southern California

ii
ABSTRACT
I use the 2007 Harvard-UMASS Boston Metropolitan Immigrant Health & Legal Status Survey
(BM-IHLSS) data to estimate cross-sectional associations of neighborhood-level disorder and
social capital with measures of physiological (systemic inflammation), psychological (serious
psychological distress) and behavioral (current smoking) stress among adult Brazilian migrants,
controlling for sociogeographic and individual characteristics. I further investigate the effect of
unauthorized legal status on these stress measures and whether physiological health markers
may partially explain associations between distress, smoking and neighborhood environment.
Employing logistic regression analysis I find that neighborhood social cohesion is significant and
negatively associated with inflammation, serious psychological distress and current smoking
while neighborhood disorder is significant and positively associated with inflammation only.
Neighborhood-level socioeconomic status, the topic covered most frequently in research on
neighborhoods and health, does not appear to be important for predicting any of these three
outcomes when social cohesion and disorder are controlled. I additionally find unauthorized
legal status to be significant and positively associated with both inflammation and psychological
distress. My findings suggest that neighborhood-level social cohesion and unauthorized legal
status are important factors in predicting stress levels among Brazilian migrants while
neighborhood disorder, measured here primarily as criminal activity and victimization, has a
more complicated relationship with stress measures.

iii
ACKNOWLEDGEMENTS
I have been fortunate in my graduate career to benefit from the support and scholarship
of many individuals to whom I am exceedingly grateful and without whom I would not have
completed my doctoral studies. First, I would like to thank Enrico Marcelli without whom I
would not have had the opportunity to collect the data used in this dissertation, and who has
been an invaluable mentor, supporter and friend throughout my graduate school experience. I
have learned a great many things from Enrico along the way, and without his guidance I would
not be the researcher I am today. I would also like to thank Manuel Pastor, my advisor, who has
generously shared the benefits of his experience as a researcher and a public scholar, and in
whose research center I gained considerable skills that will serve me as I move forward in my
career. Andrew Curtis has also been a great mentor to me while at USC and a champion of my
abilities. I have learned much from him about what it means to be a geographer and how I can
best apply my knowledge and skills, and he has provided me several opportunities to engage in
scholarship and research that I might not have otherwise pursued but which has been very
enriching. I would also like to thank Eileen Crimmins, from whom I have learned so much about
population health and biodemography, knowledge that has helped me secure the next phase of
my career, and who has always been generous with her time and advice. I have been fortunate
to know many other faculty and staff members and students at USC who have helped me along
the way. In particular I have learned a great deal from Caleb Finch who has also been an
interested supporter since I first took a class with him and from Dowell Myers and Michael
Dear.

iv
I also appreciate the assistance I have gained, the lessons I have learned and the fun I
have had with other students and staff members in the Center for the Study of Immigrant
Integration, the Spatial Science Institute and the Department of American Studies and Ethnicity
at USC as well as the Center for Spatially Integrated Social Science at UCSB, the Center on Social
Disparities and Health at Northwestern University and the Departments of Sociology and Public
Health at San Diego State University. Additionally, I began my graduate study at University of
Massachusetts Boston, and I am grateful for the support I received there from Christian Weller
and the support and friendship I have continued to receive from Nicole Agusti and Phil
Granberry. There are countless other people I have met along the way who have played a role
in shaping my career path and enhanced my professional and personal experience, and I am
grateful to them though I cannot name them all here.
Finally I am grateful to my parents, Dick and Nancy Holmes, educators themselves who
taught me from an early age the inestimable value of education and curiosity and who have
always been proud of me and my accomplishments. And to my siblings, Megan, Michael and
Patrick, who never allow me to take myself too seriously and who humble me with their
intellect and engagement with life.

v
TABLE OF CONTENTS

ABSTRACT ii
ACKNOWLEDGEMENTS iii
LIST OF FIGURES AND TABLES vii

CHAPTER 1 INTRODUCTION 1
Section 1.1 Introduction 1
Section 1.2 The Stress Response 3
Section 1.3 Neighborhoods, Stress and Health 6
Section 1.4 Immigrant Health and Stress 14
Section 1.5 A Sociogeographic Model of Health 19
Section 1.6 Data and Methods 22
Section 1.6.1 Sampling Methodology and Response Rates 23
Section 1.6.2 Survey Instrument and Data Characteristics 25
Section 1.6.3 Measuring Neighborhoods and Outcomes 26
Section 1.7 Dissertation Outline 27
CHAPTER 2 NEIGHBORHOOD SOCIAL COHESION, DISORDER & SYSTEMIC INFLAMMATION 30
Section 2.1 Introduction 30
Section 2.2 Data and Methods 35
Section 2.21 Measures 36
Section 2.3 Results 42
Section 2.4 Discussion 52
CHAPTER 3 NEIGHBORHOOD SOCIAL COHESION & PSYCHOLOGICAL DISTRESS 59
Section 3.1 Introduction 59
Section 3.2 Neighborhoods and Psychological Distress 62
Section 3.3 Past Research and A Sociogeographic Model of 65
Psychological Distress
Section 3.31 Physiological Dysregulation 67
Section 3.32 Other Sociogeographic Domains 68
Section 3.33 Individual Behavior and Characteristics 69
Section 3.4 Data and Methods 71
Section 3.41 Measures 71
Section 3.42 Statistical Analyses 75
Section 3.5 Results 76
Section 3.51 Descriptive Statistics 76
Section 3.52 Logistic Regression Results 79
Section 3.6 Discussion 86
CHAPTER 4 EFFECTS OF NEIGHBORHOOD SOCIAL COHESION & DISORDER ON
CURRENT SMOKING 91
Section 4.1 Introduction 91
Section 4.11 Neighborhoods and Smoking 96
Section 4.12 Smoking, Health Behavior and Health Status 101
Section 4.2 Data and Methods 103
Section 4.21 Measures 104
Section 4.22 Statistical Analyses 106
vi
Section 4.3 Results 107
Section 4.31 Descriptive Statistics 107
Section 4.32 Logistic Regression Results 110
Section 4.4 Discussion 119
CHAPTER 5 CONCLUDING REMARKS 126
Section 5.1 Concluding Remarks 126
Section 5.2 Primary Contributions to the Literature 129
Section 5.3 Policy Considerations 130
Section 5.4 Future Research 136

REFERENCES 139
APPENDIX 161
vii

LIST OF FIGURES AND TABLES

Figure 1.1 Concentration of Foreign-born Brazilians by County in the USA, ACS 2007-2011 16
Figure 1.2 Sociogeographic Model of Health 21
Figure 1.3 Boston-Cambridge-Quincy Metropolitan Statistical Area & BM-IHLSS Sample Areas 23
Figure 2.1 A Sociogeographic Model of Systemic Inflammation 35
Table 2.1 Weighted Descriptive Statistics 43
Table 2.2 Logistic Regression of High CRP on Neighborhood and Other Behaviors/Characteristics 47
Figure 2.2 Change in the probability of having high CRP 48
Figure 3.1 A Sociogeographic Model of Serious Psychological Distress 66
Table 3.1 Weighted Descriptive Statistics 78
Table 3.2 Logistic regression of psychological distress on neighborhood and other characteristics, 81
full sample (n=307)
Table 3.3 Logistic regression of psychological distress on neighborhood and other characteristics, 84
biological population (n=142)
Table 3.4 Logistic regression of psychological distress on neighborhood characteristics, 85
controlling for biological health markers (n=142)
Figure 4.1 A Sociogeographic Model of Current Smoking 103
Table 4.1 Weighted Descriptive Statistics 109
Table 4.2 Logistic regression of current smoking on neighborhood characteristics, 113
full population (n=307)
Table 4.3 Logistic regression of current smoking on neighborhood characteristics, 117
biological subpopulation (n=156)
Figure 4.2 Change in the probability of being a current smoker, biological sample (n=156) 119
Figure 5.1 Cambridge Street, Cambridge, Massachusetts. Image taken from Google Street View. 127
Figure A1 Violent/Property Crime Incidence overlaid on High CRP Hot Spots, 161
Framingham & Marlborough, MA
Figure A2 Violent/Property Crime Incidence overlaid on High CRP Hot Spots, 161
Everett & Somerville, MA
Figure A3 Violent/Property Crime Incidence overlaid on Serious Psychological Distress Hot Spots, 162
Framingham & Marlborough, MA
Figure A4 Violent/Property Crime Incidence overlaid on Serious Psychological Distress Hot Spots, 162
Everett & Somerville, MA
Figure A5 Violent/Property Crime Incidence overlaid on Current Smoking Hot Spots, 163
Framingham & Marlborough, MA
Figure A6 Violent/Property Crime Incidence overlaid on Current Smoking Hot Spots, 163
Everett & Somerville, MA

0

Chapter 1
Introduction

1
1.1 Introduction
In the past 200 years, life expectancy in high income countries like the United States has
effectively doubled, a pattern that has repeated itself at a rapid rate in most of the developing
world since 1950 (Finch, 2007). There are several theories to explain these unprecedented
changes in human longevity but a massive reduction in rates of infection and inflammation
resulting primarily from improvements in nutrition, hygiene and public health infrastructure is a
primary contributor (McKinlay and McKinlay, 1977; Crimmins and Finch, 2006). As infection
becomes less of a factor in mortality, chronic diseases of aging have become increasingly
prevalent in the USA and other high income countries, but the USA exhibits worse health
outcomes and earlier mortality than most other high income countries despite spending
considerably more on health care (Woolf and Aron, 2013).
Today's leading causes of death are cardiovascular and cerebrovascular diseases and
cancer (Heron et al., 2006), conditions that may plague people for years prior to death,
increasing older age morbidity and impacting quality of life. The probability of developing such
disease, the extent to which this is disabling and the age at onset may all be linked in part to
environmental, or what I refer to as sociogeographic, exposures early in life and throughout the
life course (Crimmins and Finch, 2006; Barker, 2007; Vlahov et al., 2007). As individuals age, the
likelihood of being diagnosed with chronic illness rises substantially. For example, by the age of
70 nearly 30 percent of men and 25 percent of women in the USA have been diagnosed with
heart disease (National Center for Health Statistics, 1999), which is the number one leading
cause of death in the USA (Heron et al., 2006). Because chronic disease prevalence rates are so
much higher in older populations, much of the research on these conditions has been
2
conducted with older subjects (i.e. 50 years of age and above). At the point of diagnosis,
however, it is too late for prevention. Any subsequent conclusions drawn about earlier life
circumstances that may have contributed to the onset of disease can only be speculative.
It is therefore crucial to engage younger populations in studies focusing on the
intervening biological, psychological, behavioral and sociogeographic factors that may
accumulate to explain disease etiologies. The recent emergence of biodemography and
development of new tools and protocols promoting safe and efficient biological data collection
in population studies now offer opportunities for measuring markers of later life chronic illness
in relatively young, healthy populations (Crimmins and Seeman, 2000). Furthermore, a
concordant and increasing tendency toward transdisciplinary research in health-related fields
(Finch et al., 2000b) has prompted movement beyond the constricting biomedical approach to
disease to a less compartmentalized, more holistic view of health and wellness (Engel, 1977). In
this more holistic view, brain and body are not separate entities working independently of each
other and individuals are seen to exist within a multi-faceted context in which social, physical
and structural environments influence health and behavior.
It is in this mode that I proceed in the following chapters to investigate stress in the
context of neighborhoods in a relatively young and healthy population of adult Brazilian
migrants residing in metropolitan Boston. Specifically, I employ data from the Boston
Metropolitan Immigrant Health and Legal Status Survey (BM-IHLSS) to examine three distinct
measures of stress – systemic inflammation, serious psychological distress and current smoking
behavior – and determine whether neighborhood-level social cohesion and disorder are likely
to moderate the experience of stress and its contribution to chronic disease.
3
1.2 The Stress Response
Stress is a particularly interesting topic to study from a more holistic viewpoint because
it has had a multidisciplinary evolution. Beginning with Walter Cannon's (1915) introduction of
the term to discuss both emotional and physical responses to fear, physical strain and other
triggers, stress has been considered a broad bio-psychological response to what Hans Selye
(1936) later termed "noxious stimuli," and what today we often refer to as "stressors"
(Dohrenwend, 1973). Selye's groundbreaking research on "general adaption syndrome" (1946)
raised the profile of stress as a topic ripe for study in the academic literature and launched
many subsequent inquiries into the stress response and its physiological ramifications in
particular. Most recently this research has culminated in the generation of theories designed to
explain the ways in which a stress response gone awry may lead to the onset of chronic disease.
In particular, the terms allostatic load (Sterling and Eyer, 1988; Seeman et al., 1997;
McEwen and Lasley, 2002), biological risk (Seeman et al., 2004; Crimmins et al., 2007) or
physiological dysregulation (Kiecolt-Glaser et al., 2002; Goldman et al., 2005; Glei et al., 2007)
are used to convey the idea that abnormal stress reactivity may be directly related to the
development of cardiometabolic and neurodegenerative diseases and cancer. When reacting to
threats, whether from infection, injury or psychosocial hazards, the body naturally enters a
heightened state of arousal (often referred to as the fight or flight response (Cannon, 1915)) in
which various biological functions, such as the hypothalamic-pituitary-adrenal (HPA) axis, acute
phase proteins (e.g. C-reactive protein) and constriction of blood vessels are activated to
contain the threat. Typically, these biological systems are able to adapt to the threat and return
to normal functioning. However, physiological dysregulation occurs when the body does not or
4
cannot adapt and these systems remain in a heightened state of arousal indefinitely, often as a
result of ongoing exposure to various stressors, such as economic strain, residing in a toxic
environment or suffering psychological illness. When this type of dysregulation occurs, chronic
disease may ensue. Over-activation of the HPA axis, for example, is implicated in brain cell
degeneration, increased adiposity, insulin resistance and depression (McEwen, 2002; Goldman
et al., 2005; Cowen, 2009) while systemic inflammation is linked to cardiovascular, metabolic
and neurodegenerative diseases and premature mortality (Black and Garbutt, 2002; Esposito
and Giugliano, 2004; Crimmins and Finch, 2006; Whitton, 2007).
The causes of this type of physiological strain are currently being explored across
various fields. At the same time that theories of allostatic load began developing in fields such
as neuroscience, gerontology and epidemiology, researchers in fields like sociology, public
health and psychology were generating evidence about sociogeographic factors that may cause
and perpetuate stress, and the methods people use to cope. There are many strands of this
social stress literature, but more prominent avenues of inquiry include studies of life events
stressors (Wolff, 1953; Holmes and Rahe, 1967; Dohrenwend et al., 1978), coping mechanisms
and resources (Rotter, 1954; Brown and Harris, 1978; Pearlin and Schooler, 1978; Thoits, 1995),
social support (Gottlieb, 1985; Brown et al., 1986; Kawachi and Berkman, 2001), stress
exposure and the distribution of stressors in the population, i.e. health disparities (Adler and
Newman, 2002; Lantz et al., 2005; Hatch and Dohrenwend, 2007), the accumulation of stress
across the life course (Finch and Crimmins, 2004; Pearlin et al., 2005; Nielsen et al., 2008) and
the contribution of sociogeographic environments to stress (Berkman and Kawachi, 2000;
Kawachi and Berkman, 2003; Mirowsky and Ross, 2003b; Boardman, 2004; Marmot, 2004;
5
Augustin et al., 2008). What these studies overwhelmingly demonstrate is that the stress
process is exceedingly complex, with a multitude of sociogeographic, physiological and
psychological contributors. However, what does seem clear from this research is that social
disadvantage substantially increases stress exposure, however measured, while social support
may act as a protective buffer for most populations. Despite these decades of research on the
stress process, there remain some gaps in the literature.
First, because of the complexity of the stressor-stress response-chronic disease
dynamic, most studies in this area focus primarily on one segment of the stress pathway or
response. Ice and James (2007b) have recently attempted to address this issue by creating an
overarching framework for measuring stress, which they describe as “a process by which a
stimulus elicits an emotional, behavioral and/or physiological response, which is conditioned by
an individual’s personal, biological and cultural context.” With this definition in mind I examine
three measures of stress, corresponding to each of the responses, in the context of
neighborhood environments in the following chapters. It is important to note that this is a
relatively broad definition allowing for various stimuli to be considered stress indicators; in this
dissertation I employ systemic inflammation (high C-reactive protein), serious psychological
distress and current smoking as my three measures of stress for their clear links to chronic
disease as well as their promise for elucidating pathways between sociogeographic
environments and disease.
However, each of these can also be considered distinct health markers that indicate
variations in behavioral, physiological and psychological health processes. Systemic
inflammation, for example, may be partially a function of obesity – adipose tissue secretes the
6
cytokine interleukin-6 (IL-6), which itself regulates the distribution of C-reactive protein (CRP),
and together increased levels of IL-6 and CRP may contribute to insulin resistance (Hak et al.,
1999; Yudkin et al., 1999). Nevertheless, the sociogeographic mechanisms contributing to
obesity, metabolic dysfunction and systemic inflammation, as well as the ways in which
immune and inflammatory responses interact physiologically in the face of external threats
have yet to be fully determined. What is clear is that health behavior, physiology and
psychology are inextricably intertwined and influenced by sociogeographic stressors; I therefore
contend that investigating these outcomes in a stress framework drawing from Ice and James'
comprehensive definition above is useful for identifying particular environmental
characteristics that may have broad impacts on health.
1.3 Neighborhoods, Stress and Health
Second, it is only very recently that researchers have begun to draw connections
between sociogeographic environments, physiological dysregulation and chronic disease, and it
is an issue that remains controversial. In particular, interest in neighborhood effects and health
has grown apace over the past two decades, yet even some of the earliest proponents of
research in this area have recently suggested that it may be a dead end, or that it is impossible
to parse individual from area-level effects to establish causal inference using observational data
(Mayer and Jencks, 1989; Oakes, 2004). However, this critique seems to be more about
methods, often erroneously employed, in neighborhood effects studies than about the validity
of the pursuit. Oakes (2004), for example, in his criticism of neighborhood effects estimation
acknowledges the topic as a "raison d'etre" for the field of social epidemiology; his primary
concern is "that many social epidemiologists are not clear on exactly what multilevel models
7
are or how they may be used..." in neighborhoods research. Furthermore, there is a persistent
debate in the neighborhoods and health literature about the relative importance of context
versus composition, which echoes the concerns regarding individual as opposed to area-level
effects. The debate essentially centers on the question of which is more important for
understanding area-level interactions with health – people or places (Duncan et al., 1998; Do
and Finch, 2008; Ross and Mirowsky, 2008). I contend that this is a reductive approach and that
the neighborhoods literature is only now evolving in a manner to allow empirical
demonstration of the axiom, "neighborhoods have consequences" (Fischer, 2013).
Specifically, neighborhoods and health research has largely failed to account for place in
its investigations (Macintyre et al., 2002; Sampson, 2013), despite the inherently place-bound
nature of neighborhoods. Attention has instead focused on individuals residing in places – what
Fischer (1995, 2013) refers to as "the ecological reality," as opposed to the ecological fallacy. In
other words, the neighborhood as a social unit is worthy of study in its own right and should
not be considered simply a backdrop for individual behavior. Just as stress research has become
increasingly comprehensive and interdisciplinary, so too must local area investigation draw
from broader theoretical traditions, such as those in health geography and biological
anthropology (e.g., Dressler, 1991; Parr, 1999; McDade and Worthman, 2004; Smyth, 2005;
Dressler et al., 2007), to interrogate not only individual but structural, cultural and place-
specific characteristics that may determine how "neighborhoods choose people" (Sampson,
2012) to influence stress and health.

8
Like stress research, studies of neighborhoods and health cover broad multidisciplinary
territory. Five areas in particular have been of interest to researchers: 1) methodological
concerns; 2) built environment; 3) socioeconomic environment; 4) neighborhood disorder; and
5) social capital. Three methodological concerns especially have captivated neighborhood
researchers – how to define "neighborhoods," how to devise appropriate scales for various
neighborhood measures and the previously mentioned debate between contextual and
compositional neighborhood effects. The issue of neighborhood definition has been a
preoccupation of urban sociologists since the early days of social scientific inquiry. Urban
neighborhoods were the social units in which the earliest demographers situated their surveys
(Booth, 1889; Hull House Association, 1895; Du Bois, 1899), it was at the neighborhood level
that John Snow conducted his landmark epidemiological investigation of cholera (Johnson,
2006) and the efforts of Chicago School researchers to create a typology of neighborhood
dynamics centered on an urban core have become the reference point for subsequent work in
urban social science research (Park and Burgess, 1967 (1925)). Since that time neighborhoods
research has become increasingly specific. Broader theoretical treatments of the ways in which
urban neighborhoods are imagined or experienced (Debord, 1956; Lynch, 1960; Fischer, 1982)
gave way to more technical analyses of neighborhood function and change (Olson, 1982;
Schwirian, 1983; Aitken, 1990). One exception to this trend was the emergence in the 1980s of
the Los Angeles School of Urbanism whose researchers reinvigorated the discussion around
urban dynamism and community change, positing that substantial geographic and technological
changes had rendered the Chicago School model outdated, that the metropolitan "hinterland"
9
had become the organizational nucleus driving neighborhood organization rather than the
urban center (Dear and Flusty, 2002).
However, more recent trends in neighborhoods research have centered on what I also
focus on in this dissertation – the potential for neighborhood, or local area, characteristics to
influence health and well-being. It is in this literature in particular that the latter two
methodological concerns become central. As neighborhoods and health research is in some
ways still in its early stages, studies in this area often rely on convenient metrics of both
neighborhood environment and the neighborhood spatial unit. Census tracts, for example, are
regularly used to proxy neighborhoods as they are convenient units of measurement, and
socioeconomic status is often the focal explanatory measure as census and administrative data
containing information about income, education, occupation and other indicators of SES are
readily available (Krieger et al., 2002). The likely problem with relying on existing data is that
census tracts are also artificially delineated boundaries that may have little relationship to the
boundaries neighborhood residents themselves would draw, and SES is simply one of several
possible overlapping explanations for why neighborhoods may matter for health.
Yet, this also signals an opportunity for researchers from various disciplines to apply
new modes of thinking to measurement of neighborhoods and their characteristics, as I
attempt to do in the following chapters. Increasingly researchers are devising interesting new
ways to demarcate neighborhoods, such as involving neighborhood residents in identifying
important area landmarks (Burke et al., 2005; Curtis et al., 2007b) or employing geospatial tools
to track individual activity through local spaces (Jacquez et al., 2005; Rainham et al., 2008).
Similar innovation is also taking place with respect to developing measures of neighborhood
10
characteristics. Beginning with Sampson and Raudenbush's (1999) implementation of
systematic social observation of Chicago neighborhoods, researchers are increasingly seeking to
combine contextual and compositional elements into more comprehensive treatments of
neighborhood effects on health, an approach that seems more authentic in terms of
understanding neighborhoods as a feedback loop between individual, collective and structural
considerations. Dietz (2002) argues that neighborhoods research has so far undergone two
phases – the first consisted of generating conceptual models and typologies of neighborhood
leading to initial empirical study, and the second has been a period of evaluating these theories
using empirical and econometric techniques. It is the third phase, emphasizing spatial
technique and place-based theoretical models, to which neighborhood researchers should now
turn.
As to a second main thrust of the neighborhoods and health literature, studies of the
built environment have largely concerned health behaviors, such as physical activity, and
related health issues, like obesity. For example, researchers have investigated the impacts of
neighborhood walkability (e.g. aesthetics, safety and maintenance of surrounding streets) on
the propensity to engage in physical activity (Leslie et al., 2007; Lovasi et al., 2008; Brown et al.,
2009), the concentration of fast food and alcohol outlets in relation to nutrition and collective
efficacy (Cohen et al., 2008; Kwate et al., 2009; Moore et al., 2009), the scarcity of nearby
nutritious food vendors (i.e. food deserts) (Walker et al., 2010) and residential proximity to
toxic facilities (Pastor et al., 2005; Peek et al., 2009). Built environment research has also looked
more broadly at issues such as urban planning and architecture to evaluate possibilities for
policy modification designed to enhance public use or reduce stress exposure (Dannenberg et
11
al., 2003; Frumkin et al., 2004). It is in this area of the literature primarily that geographers and
other spatially-oriented researchers have focused, showing interest especially in devising more
accurate or innovative metrics of built environment attributes (e.g., Pearce et al., 2006;
Apparicio et al., 2007; Curtis et al., 2010).
The balance of neighborhood and health studies have focused on area-level
socioeconomic characteristics in an effort to explain health disparities, and it is studies
measuring SES in particular that have led to the aforementioned critique about the paucity of
satisfying results neighborhood effects research has so far yielded. Yet, lower socioeconomic
status neighborhoods are almost always found to be worse off where health is concerned.
Living in low SES areas has been associated with poor health outcomes from cardiovascular
disease to depression to higher allostatic load to low birth weight in communities across the
USA and internationally (Caspi et al., 2000; Ross, 2000a; Pearl et al., 2001; Sundquist et al.,
2004; Cubbin et al., 2006; Bird et al., 2010). Moreover, neighborhood socioeconomic
disadvantage usually goes hand in hand with other negative health influences like high levels of
residential segregation and neighborhood disorder, low neighborhood social capital, less access
to healthy food outlets and outdoor recreation and closer proximity, or greater vulnerability, to
environmental hazards (Ross and Mirowsky, 2001; Steptoe and Feldman, 2001; Morello-Frosch
et al., 2002; Acevedo-Garcia and Lochner, 2003; Curtis et al., 2007a; Cutchin, 2007; Lovasi et al.,
2009b) . Even when considering these results with a critical eye for the potential confounding of
individual and area-level effects, it is nevertheless clear when the neighborhood is viewed as
more than the sum of its parts that extra-individual factors, such as property tax distribution or
structural drivers of residential segregation, play an important role in determining the health of
12
neighborhoods and their residents (Massey and Denton, 1998; Bayer et al., 2012; Coffee et al.,
2013).
In 1979, Cohen and Felson (1979) published a paper in the American Sociological Review
presenting what they called a "routine activity approach" to crime incidence and trends. In this
paper they emphasize the time-space dimension of criminal activity and view crime as given,
regardless of structural or sociogeographic factors (e.g. poverty, unemployment, residential
segregation). Indeed they argue that crime, like many of the other activities in which people
engage on a daily basis, is a routine activity, and it is local circumstance that determines
whether a certain area is prone to experiencing predatory behavior and at what level during a
given time period. They outline three circumstances in particular that they suggest will
determine criminal activity – (1) motivated predators; (2) appropriate targets; and (3) an
absence of "guardians" with the capacity to thwart predation. Three years later, Wilson and
Kelling (1982) authored an essay in The Atlantic Monthly, in which they outline the "broken
windows" theory of urban decay. This theory suggests that neighborhood visual cues, such as
graffiti, public intoxication or accumulations of litter, are likely to attract a criminal element
that views these cues as indication of residents' indifference to the state of their community.
Thus the cycle of decay begins – any previous maintenance no longer occurs due to a decline in
neighborhood safety, the visual cues proliferate and crime rates rise. Subsequent research on
crime, disorder and neighborhoods has attempted to integrate these theories with the idea
that criminal activity is more likely to be routine in areas rife with disorder, i.e. disordered
neighborhoods provide the appropriate targets for crime and the coincident absence of
guardians, and criminal activity once routine, spurs on the cycle of disorder (Maxfield, 1987;
13
Smith et al., 2000; Morenoff et al., 2001). These concepts continues to influence neighborhood
research with various studies, including this thesis, hypothesizing physical disorder and crime as
correlates of poor health (Sampson and Raudenbush, 1999; Doran and Lees, 2005; Miles, 2006).
Figures A1-A6 in the Appendix show the BM-IHLSS sample blocks overlaid on major crime
activity in metropolitan Boston neighborhoods using Massachusetts State Police data for the
years 2000-2003 for illustration.
On the other hand, neighborhood social capital is often thought to act as a buffer
against stress, and this as well I hypothesize in the three chapters to follow. There are three
primary constructs of social capital usually employed in studies of neighborhoods and health –
trust, social cohesion and collective efficacy. Trust is often measured by simply asking
neighborhood residents whether their neighbors can be trusted. This approach, most often
employed in public health studies, has been associated with various health indicators, including
lower mortality and better self-rated health (Lochner et al., 2003; Franzini et al., 2005). Social
cohesion and collective efficacy are measures initially employed by Sampson and Raudenbush
(1999) in their seminal study of Chicago neighborhoods. The former term is based on a scale
including four items designed to assess whether residents know, share values with, get along
with and help each other. The latter is a combination of social cohesion with a scale designed to
evaluate neighborhood norms, or the extent to which residents would take action in various
situations, such as breaking up a fight they observe on their street, or contributing financial
support to a struggling neighborhood firehouse (Sampson et al., 1997). These two measures,
independently and together, have been shown to mediate a variety of health outcomes,
including smoking, mental health, obesity and self-rated health (Browning and Cagney, 2002;
14
Patterson et al., 2004; Cohen et al., 2006; Fone et al., 2007; Ahern et al., 2009; Rios et al.,
2011).
1.4 Immigrant Health and Stress
Social cohesion and disorder are the measures of neighborhood on which I focus in the
following chapters, two measures that may have particular salience for newly arrived
immigrants seeking a connection to home or seeking to leave behind economically and
physically challenged environments. In the proceeding chapters, I evaluate distress,
inflammation and smoking among adult Brazilian migrants in metropolitan Boston, a population
for which no previous statistically representative health data exist. Brazilian migrants are
particularly interesting in the ways they differ from more commonly studied Latino migrant
groups. Most research on migrant health has focused on Mexicans and, to a lesser extent,
Cubans. This is logical as Mexicans are the most populous migrant group in the USA and it is
often possible to separately identify Mexican and Cuban populations in national-level health
surveys, while this is not the case for most other Latin American migrant groups. For this
reason, health attributes measured in Mexican populations especially are often applied
(inappropriately) to Latin American migrants as a whole. This is problematic as there are vast
differences in the migration experiences, legal status profiles, health outcomes and even
individual characteristics of various nativity groups. Brazilians are one of the most recent
migrant groups to settle in the USA, with the largest flows coming in the 2000s. According to
the BM-IHLSS data, the average Brazilian migrant adult in New England had resided in the USA
for six years in 2007. By comparison, Mexican migrant adults in Los Angeles County – where
the largest concentration is found – had resided in the USA for 24 years as of 2012. Brazilians
15
are also younger (34 years compared to 46 years), more educated (12 percent with a four-year
college degree compared to seven) and lighter-skinned (2.2 versus 3.9 on the NIS skin color
scale) than Mexican migrants in L.A. on average. Brazilian migrants also tend to come from
better socioeconomic circumstances in Brazil than Mexican and Central American migrants as
the cost of obtaining a visa and traveling to/settling in the USA is cost-prohibitive for poorer
groups. Brazilians are more likely to overstay their visas than to cross the border without
documentation as is more common among unauthorized Mexican and Central American
migrants (Marcelli et al., 2009b; Marcelli and Holmes, 2013). Once in the United States
Brazilians have largely settled in New England, New York-New Jersey and Florida. Unlike earlier
migrant groups, Brazilians have not exclusively followed a central city pattern of initial
settlement but appear in larger concentrations on the edges of Boston, Miami and New York as
well as in more suburban counties as Figure 1.1 illustrates. This is in line with recent scholarship
on Latin American migration patterns that demonstrates changing settlement patterns over the
past two decades in which migrants often skip central cities in favor of finding employment in
more suburban and rural areas (Passel et al., 2002; Kochhar et al., 2005; Liaw and Frey, 2007).

16
Figure 1.1 Concentration of Foreign-born Brazilians by County in the USA, ACS 2007-2011

Brazilian migrants also tend to be healthier on average than other Latino groups, such as
foreign-born Dominicans, who comprise the largest proportion of Latin American migrants in
metropolitan Boston, and Mexican-born adults in L.A. For example, 64 percent of Brazilians
report being in very good or excellent health compared to 60 percent of Dominicans and only
28 percent of Mexicans. This is despite only 40 percent of Brazilians having access to health
insurance, compared with 80 percent of Dominicans and 84 percent of Mexicans. Additionally,
we estimate that 71 percent of adult Brazilian migrants in New England are unauthorized to
reside in the USA, compared with eight percent of Dominicans and 44 percent of Mexicans in
17
Los Angeles. This is perhaps the most striking difference between Brazilians and other migrant
populations as it is the highest proportion of unauthorized migrants within a particular nativity
group that has yet been estimated. I address this characteristic in particular in the following
chapters as a potentially salient contributor to overall stress in this population.
Studying stress, health and neighborhoods in this population has numerous additional
advantages as well. First, population-based studies of stress have focused largely on older
adults or non-Hispanic white populations. Fewer investigations have evaluated differential
exposure to stress in relatively young or ethnic minority populations despite evidence (detailed
above) suggesting that stress accumulates across the life course, and nonwhite populations
may be especially prone to chronic stress. Studying stress in younger populations offers the
opportunity to identify etiological factors in chronic disease development as well as possibilities
for prevention or early intervention. It is equally essential to include nonwhite populations in
stress investigations to better understand potential sources of persistent health disparities and
the extent to which socioeconomic gradients may not fully explain such disparities.
Second, Latin American groups in the USA are especially understudied with respect to
health despite their rapid population growth – by 2030 one-fifth of the U.S. population is
expected to be Latino (U.S. Census Bureau, 2004). Currently 13 percent of the population is
foreign-born, 53 percent of whom are from Latin America (U.S. Census Bureau, 2013a). This
population is likely to be especially vulnerable to stress and its consequences because of the
substantial proportion that is unauthorized to reside in the USA (71 percent of Brazilians in the
BM-IHLSS sample) and simply the adjustment that attends any movement to a different country
than the one in which a person was born. Furthermore, federal policy prevents recent
18
immigrants from obtaining benefits, such as subsidized medical care, and unauthorized
immigrants are indefinitely barred from government assistance (Espenshade et al., 1997). Even
those young people participating in the recently implemented Deferred Action for Childhood
Arrivals program, which provides access to legal work permits for two years to immigrants
meeting certain requirements, will not be granted access to subsidized health care. Such
potential for stress may therefore amplify a desire on the part of immigrants to seek solace in
their new communities. Alternatively, any disorder or dissatisfaction experienced in their
neighborhoods may exacerbate existing stress.
Despite the potential stressors facing many Latin American immigrants, research on
health and migration in this population has focused largely on their purported health
advantages. The "healthy migrant" hypothesis and the Latino health paradox have received
disproportionate attention in this literature. The former theory posits that migrants tend to
select into the USA on good health and select out on poor health (Abraido-Lanza et al., 1999)
while the latter refers to the tendency for Latino immigrants to exhibit health outcomes similar
to those in the non-Hispanic white population despite their relative socioeconomic
disadvantage (Markides and Coreil, 1986). While these theories make for compelling stories,
they are supported by little evidence after accounting for differences by nativity and health
outcome (Abraido-Lanza et al., 1999; Hajat et al., 2000; Singh and Siahpush, 2002; Crimmins et
al., 2007; Cunningham et al., 2008; Rubalcava et al., 2008). They are further confounded by
methodological issues, such as the regular inclusion of U.S.-born populations of Mexican
ancestry or Puerto Ricans (who are not immigrants) in related studies (Landale et al., 2000;
Franzini et al., 2001). There does, however, seem to be some evidence to support the idea that
19
the health behaviors of Latino immigrants increasingly mirror those of the rest of the U.S.
population the longer they reside in the USA and the more integrated they become (Jasso et al.,
2004; Abraido-Lanza et al., 2005). It therefore seems less important for researchers to concern
themselves with demonstrating a paradox in Latino immigrant health profiles than to
understand how and why immigrant health trajectories may change across the life course and
what we can learn about disease etiology and onset from different nativity groups. It is
precisely this that I begin to investigate in the following chapters.
1.5 A Sociogeographic Model of Health
Neighborhoods are spaces within which people live, learn, socialize and sometimes
work, and it is these activities and the manner in which people imbue these experiences with
meaning that begin to define the ways in which neighborhoods may promote or detract from
long-term health. As Hunter (1979) argues, “the neighborhood is a uniquely linked unit of
social/spatial organization between the forces and institutions of the larger society and the
localized routines of individuals in their everyday lives." It is these forces of larger society to
which Sampson (2012) also alludes when postulating that neighborhoods have just as large role
to play in choosing their residents as individuals have in choosing their neighborhoods. Other
domains of daily life, such as the workplace, the home and social or civic organizations in which
people participate are additionally likely to impact both neighborhood selection and peoples'
perceptions of the neighborhoods in which they live.

20
With these ideas in mind, I employ a sociogeographic model of health, shown in Figure
1.2 below, to capture the ways in which neighborhoods and other environments, social
interactions and individual characteristics coalesce to determine health outcomes. In this model
neighborhoods are embedded in metropolitan areas that are also home to sociogeographic
domains, such as the workplace or school and civic groups. Homes are embedded within
neighborhoods, and there is likely to be overlap between each domain. Individuals are the
threads connecting these domains, and it is the iterative exchange between people and
domains, including social, structural, environmental and individual considerations, that
effectively produce health behaviors and outcomes. Specifically, the level to which a
neighborhood is disordered, or the cohesion experienced among neighbors is determined by a
variety of factors. Neighborhoods in which property values are high, residents are employed
and earning higher incomes and the streets and housing stock are well kept are likely to see
these material advantages reflected in many forms, such as additional attention and
investment from local and regional governments, low residential vacancy, less opportunity for
criminal activity and more leisure time and interest on the part of residents to actively engage
with the neighborhood physical environment and surrounding neighbors. For migrants, forming
place attachments by reproducing experiences of home, such as shopping at Brazilian grocery
stores, participating in a Brazilian futebol club or simply sharing traditional meals with
neighbors may be protective for health. In lower income or residentially segregated
neighborhoods social cohesion may be more difficult to forge, but it may also be more
important for residents to feel that their neighbors share similar values. Generally, social
interactions and processes that play out locally, combined with structural considerations, such
21
as residential segregation, help determine stress exposure and individual responses to health
threats that further predict long-term health. In the following chapters I focus on the
neighborhood domain to evaluate the influence of social cohesion and disorder on three
measures of stress in a population of Brazilian migrants residing in metropolitan Boston. An
overview of the data and methods employed in this dissertation and a chapter outline follow.

Figure 1.2 Sociogeographic Model of Health
22
1.6 Data and Methods
The 2007 Harvard-UMASS Boston Metropolitan Immigrant Health and Legal Status
Survey (BM-IHLSS) is a community-based participatory research project involving a randomized
survey of more than 600 households in which at least one adult was born in Brazil or the
Dominican Republic. Face-to-face interviews were conducted in respondent households
between June and September of 2007. The BM-IHLSS includes data on 307 Brazilian-born adults
and 120 of their U.S.- and Brazilian-born children, as well as 299 Dominican-born adults and 74
of their U.S.- and Dominican-born children who were residing in the Boston-Cambridge-Quincy
Metropolitan Statistical Area (BCQ-MSA) at the time of the survey. The BCQ-MSA is comprised
of five counties in Massachusetts (Essex, Middlesex, Norfolk, Plymouth and Suffolk) and two
counties in New Hampshire (Rockingham and Strafford) as Figure 1.3 illustrates (OMB, 2007).
Building on the survey-based legal status estimation methodology developed by Marcelli and
Heer (1997) in the mid-1990s, the BM-IHLSS was designed to collect the first individual-level
representative data from Brazilian and Dominican migrants living in the BCQ-MSA in order to
study how migration and legal status, socioeconomic status and sociogeographic context
impact behavioral, physiological and psychological health processes and outcomes. Enrico
Marcelli, Associate Professor at San Diego State University, is the principal investigator of this
study, which was funded by the National Cancer Institute, University of Massachusetts Boston
and Blue Cross Blue Shield of Massachusetts Foundation, and conducted in partnership with the
Brazilian Immigrant Center and the Dominican Development Center.

23

1.6.1 Sampling Methodology and Response Rates
For the purposes of this study, I employ the Brazilian sample of the BM-IHLSS. Using the
Census 2000 Summary File 3 (SF3) data to identify foreign-born residents by place of birth for
each census tract in the BCQ-MSA, 12 census tracts were identified that had at least 50
residents and in which at least seven percent of the residents were foreign-born Brazilians. PI
Marcelli chose a threshold of seven percent in order to contain the costs of surveying and to
have a sampling frame that was likely to include a large majority of foreign-born Brazilians;
attempting to interview Brazilian migrants at a lower threshold would have required many
more hours in the field to obtain a similar result. Ten tracts were selected from the original 12,
using a random number generator, in an effort to maintain efficiency by limiting the number of
tracts while also ensuring a representative sample of Brazilian migrants. Random selection of
Figure 1.3 Boston-Cambridge-Quincy Metropolitan Statistical Area & BM-IHLSS Sample Areas
24
these tracts was essential to maintain a representative sampling procedure. The concentration
of foreign-born Brazilians in these tracts ranged from seven to 21 percent, with a mean of 11.5
percent. According to 2000 census data, approximately 7,700 Brazilians resided in these 10
tracts, representing one in every four Brazilians in the BCQ-MSA. An additional 800 Brazilians
were estimated to reside in the two tracts we did not select. From the 10 selected tracts, we
then randomly selected 100 census blocks from a total of 580 (in 43 block groups) in which to
conduct our survey of foreign-born Brazilians.
We did not have an estimate of the number of Brazilians living in each block as census
data do not provide information on place of birth below the tract level, so the next step
involved project staff canvassing each of the 100 selected blocks in order to generate housing
unit lists. At times this process was straightforward and required only the documentation of
house numbers easily observed from the street, whereas in other cases project staff had to ask
residents of buildings how many units, zoned or otherwise, were in the building. There has
been a presumed undercount of minority populations in past censuses, including Brazilians and
especially unauthorized migrants, and non-enumerated housing units may be part of the
discrepancy (Margolis, 1995a; Massey and Capoferro, 2004). For example, we counted 45 more
housing units (total n = 8,247) in the 100 sample blocks than had been included in the 2000
census count; this may be partly due to development as we canvassed the blocks six years after
the decennial census, but missed housing units may also be a contributor. In addition to our
100 sample blocks, we selected three additional blocks from the 10 tracts and created housing
lists in order to test our methodology in blocks similar to those in which we planned to survey.
Once we had address lists for each of the 100 selected blocks, we randomly selected every
25
other housing unit to survey and ultimately knocked on 4,363 doors in order to obtain a sample
of 307 adults and 120 children, i.e. we obtained one completed survey for every 14 houses we
approached.
Our BM-IHLSS estimates suggest that 71 percent of the Brazilians in our sample are
unauthorized residents of the USA, potentially adding to the difficulty of collecting household-
level data. Although we successfully screened nearly 90 percent of households that had at least
one foreign-born Brazilian in residence in the selected blocks, the overall response rate was 44
percent. This rate is consistent with other surveys containing large proportions of unauthorized
and other minority residents, e.g. non-response rates for the annual American Community
Survey, which collects data by mail, telephone and face-to-face personal interview varies
between 30 and 60 percent depending on survey area (Diffendal, 2001).
1.6.2 Survey Instrument and Data Characteristics
We drafted our questions drawing from past experience with the Los Angeles County
Mexican Immigrant Legal Status Survey (LAC-MILSS) (Marcelli and Heer, 1997) and also drew
from a variety of existing surveys and research studies to develop our survey instrument (e.g.
American Community Survey, National Health Interview Survey, National Health and Nutrition
Examination Survey, Behavioral Risk Factor Surveillance System, New Immigrant Survey). We
additionally relied on the input of our community partners and focus groups of Brazilian and
Dominican migrants to ensure that our instrument was culturally appropriate. The BM-IHLSS
data collection process thus had two primary components: 1) a questionnaire translated into
Portuguese and Spanish and consisting of (a) a household screening form; (b) a household
roster; followed by sections on self-reported (c) migration and legal status; (d) socioeconomic
26
status; (e) social capital and neighborhood characteristics; (f) behavioral, physical and
psychological health; and (g) sociopolitical identity; and 2) the extraction of biological data in
the forms of (a) blood samples; (b) saliva samples; (c) measured height; (d) measured weight;
and (e) blood pressure readings. We supplemented the BM-IHLSS data by using Census 2000
Summary File 1 (SF1) block-level data to obtain additional measures of neighborhood context.
1.6.3 Measuring Neighborhoods and Outcomes
In the following chapters I evaluate three measures of stress – 1) systemic inflammation
(high CRP); 2) serious psychological distress; and 3) current smoking. Systemic inflammation is
measured using results from high-sensitivity C-reactive protein (hsCRP) assays, which were
performed on the blood samples we collected from survey participants. Thomas McDade at the
Center on Social Disparities and Health at Northwestern University conducted the assays
(McDade et al., 2004). High-sensitivity C-reactive protein levels are generally considered high
above a threshold of 3 mg/L, and it is this threshold that I employ to measure "high CRP" in
Chapter 2.
To measure serious psychological distress I utilize the six-item Kessler (K6) scale, which
has been validated in a variety of populations as a measure of nonspecific psychological
distress, i.e. mental illness that does not fall into a particular clinical category of disorder
(Kessler et al., 2002). The K6 scale asks a series of six questions: during the past 30 days how
often did you feel…1) so sad that nothing could cheer you up; 2) nervous; 3) restless or fidgety;
4) hopeless; 5) that everything was an effort; and 6) worthless. The responses range from 0-
none of the time to 4-all of the time for a total score of 24; a score of 13 or higher is considered
to indicate serious psychological distress.
27
The measure of current smoking is a composite of two variables indicating responses to
questions regarding whether the subject had 1) smoked at least 100 cigarettes in his/her entire
life; and 2) now smokes cigarettes…every day, some days, or not at all. Respondents are
considered current smokers if they responded positively to the first question and further
indicated that they now smoke on some days or every day.
In the BM-IHLSS data neighborhoods are defined in two ways: first, we depend on our
respondents to characterize "your neighborhood" based on their own conceptions and report
their assessments accordingly, and second, we employ data from the 2000 decennial census to
gain more objective measures of neighborhood environment, including population density,
homeownership rate and minority composition. The census data are measured at the block
level as this provides the most immediate neighborhood geography and encompasses features
that neighborhood residents will encounter as part of their daily activities.
1.7 Dissertation Outline
The dissertation proceeds as follows:
Chapter 2 tests three specific hypotheses: 1) neighborhood social cohesion is negatively
associated with the likelihood of having high CRP among adult Brazilian migrants; 2)
neighborhood disorder is positively associated with the likelihood of having high CRP; and 3)
unauthorized legal status is positively associated with high CRP.
In Chapter 3 I address three additional hypotheses: 1) neighborhood social cohesion is
negatively associated with serious psychological distress in adult Brazilian migrants; 2)
neighborhood disorder is positively associated with distress; and 3) physiological stress
28
(hypertension and CRP) moderates any demonstrated relationships between neighborhood
characteristics and distress.
Chapter 4 investigates two hypotheses: 1) neighborhood social cohesion reduces the
likelihood of being a current smoker for adult Brazilian migrants; and 2) neighborhood disorder
increases the likelihood of being a current smoker.
Finally, Chapter 5 offers concluding remarks, policy considerations and potential
avenues for future research.

29

Chapter 2
Neighborhood Social Cohesion, Disorder and
Systemic Inflammation

30
2.1 Introduction
The observation that newcomers to a socioeconomically disadvantaged urban area may
be at greater risk of various diseases and all-cause mortality was first recorded by John Graunt
in the 17
th
century, and since then many physical and social risk factors have been implicated in
this process (Macintyre and Ellaway, 2003). Indeed, the origins of American public health began
by acknowledging the need to protect the working class, many of whom were immigrants
residing in impoverished neighborhoods, from both environmental and social toxins associated
with early and rapid industrialization and urbanization (Duffy, 1992 [1990]; Melosi, 2000).
Residents of lower income areas have been shown to be more susceptible to conditions such as
psychological distress and depression (Ross, 2000a; Hill et al., 2005) obesity (Mujahid et al.,
2008) and chronic disease (Cubbin et al., 2001; Murray et al., 2010). The mechanisms linking
disadvantage to disease are varied and not always well-understood; however, lower income
groups that are also disproportionately composed of ethno-racial minorities, including
immigrants, are more likely to live in residentially segregated neighborhoods (Massey and
Denton, 1998; Acevedo-Garcia and Lochner, 2003), areas of higher crime and with greater
alcohol and fast food outlet density (Block et al., 2004; Cohen et al., 2008; Kwate et al., 2009),
as well as areas with less access to municipal services like recreational facilities or well-kept
sidewalks that may promote health (Cubbin et al., 2001; Lovasi et al., 2009a; Holmes and
Marcelli, 2011). Most studies demonstrating these links rely on measures of individual income,
often aggregated to a “neighborhood” or local area boundary, to define associations between
illness and disadvantage; however, those that have instead constructed measures of
31
neighborhood-level SES have similarly found material deprivation to be associated with poor
health outcomes (Merkin et al., 2009; Bird et al., 2010).
More recently and increasingly, neighborhood socioeconomic disadvantage has been
linked to cardiometabolic disease (Cubbin et al., 2001; Diez Roux et al., 2001; Cubbin et al.,
2006; Augustin et al., 2008; Murray et al., 2010) and cumulative biological risk for disease
(Merkin et al., 2009; Bird et al., 2010; Finch et al., 2010). However, much less research exists on
more specific links between neighborhood sociogeographic factors and physiological
mechanisms that may be important for predicting disease onset (Buxton and Marcelli, 2010).
Chronic systemic (as opposed to acute) inflammation has emerged as a potentially decisive risk
factor for the development of cardiometabolic disease, and validation of high-sensitivity assays
for inflammatory markers like C-reactive protein (CRP) have made it possible to measure
inflammation in population-based studies (McDade et al., 2004). CRP – an acute phase protein
produced in the liver in response to pro-inflammatory cytokines – has become a particularly
important marker of inflammation as well as an accurate predictor of cardiovascular disease
and mortality, even in individuals who otherwise have no identifiable clinical risk symptoms
(Ridker, 2001; Rifai and Ridker, 2001; McDade et al., 2006). Furthermore, recent research has
identified psychosocial factors that may be associated with inflammatory processes, such as
chronic and acute stressors, social support and poor psychological health (McDade et al., 2006;
Uchino, 2006; Ranjit et al., 2007). Yet only a handful of studies have investigated relationships
between neighborhood sociogeographic environments, in which stressors may be generated
and social support forged, and inflammation (Liang et al., 2008; Petersen et al., 2008; Nazmi et
al., 2010a; Schootman et al., 2010). In order to develop targeted policy interventions at
32
neighborhood- and otherwise collective levels it is essential to more clearly identify specific
pathways between sociogeographic environments and health outcomes; inflammation is a
promising avenue for such investigation for its demonstrated relationship to psychosocial
stressors as well as its clear involvement in cardiovascular events, and CRP is the most well-
validated marker of inflammation currently available.
Most population-based research investigating associations between cardiometabolic
risk factors (including systemic inflammation) and neighborhood characteristics has included
study populations with mean ages over 50. These populations exhibit a greater variety of risk
factors and higher disease prevalence than populations with wider age distributions, and such
data limit researchers’ ability to study the etiology of chronic disease onset. In order to better
understand how risk factors for cardiometabolic disease develop it is essential to study
relatively young, healthy populations when researching sociogeographic environments and
physiological health. By doing so it is possible to gain greater insight into: (1) how risk factors
accumulate across the life course and lead to disease onset in older populations, and (2)
opportunities for prevention earlier in life or later intervention. In this paper we employ data
from the Boston Metropolitan Immigrant Health & Legal Status Survey (BM-IHLSS) – a random
household sample of adult Brazilian migrants who resided in the Boston metropolitan area in
2007 – to estimate linkages between systemic inflammation as measured by CRP, and its
relationship to economic, physical and social aspects of the local environment.
While the experience of systemic inflammation among Brazilian migrants in the
Northeastern United States may not be fully generalizeable to the entire U.S. population, it is
particularly compelling to study inflammation in this group for several reasons: first, as is the
33
case with Latin American migrant populations more generally, Brazilian migrants are younger
and healthier on average than the U.S. population (Marcelli et al., 2009b) therefore providing
an opportunity to potentially discern processes by which risk factors for cardiometabolic
disease develop into chronic illness. Second, whereas most research on migrant health focuses
on migrants with relatively low socioeconomic status with low proportions of unauthorized
residents, Brazilian migrants have notably high levels of educational attainment, earnings and
unauthorized residents. Thus, although Brazilians are similar to many other U.S.-migrants in
terms of being recent arrivals and can be expected to face many of the same barriers of
hurdles, this is uncertain due to their unique SES and legal status profile (Margolis, 1998;
Marcelli et al., 2009b).
Brazilian migrants have also lived in their neighborhoods for shorter amounts of time
than their neighbors in many cases, and may have a different experience of these
neighborhoods depending on migration-related stressors. For example, some of the
neighborhoods included in the BM-IHLSS contained a multitude of Brazilian-owned businesses,
conceivably easing settlement for new residents, while others were situated near an
Immigration and Customs Enforcement (ICE) office, a potentially threatening environment. This
suggests another reason for studying Brazilian migrants – as the migrant population with the
largest proportion of unauthorized residents in the United States (71 percent in the BM-IHLSS),
Brazilians may have particular exposure to migration-related stressors, such as deportation
fears or cultural differences with neighbors, and this stress may in turn have an effect on
physiological regulation, such as inflammatory processes (Marcelli et al., 2009b). Finally, the
34
BM-IHLSS is the only area-level probabilistic U.S. household survey to date that includes data on
legal status, various sociogeographic life domains and bio-indicators of health.
In this chapter I thus hypothesize that migrants residing in neighborhoods with higher
levels of disorder (i.e., violence, theft, property damage) and lower levels of social capital (i.e.,
neighbors getting along, helping one another, sharing the same values, knowing one another
and not being afraid to go out at night) will be more likely to experience systemic chronic
inflammation in the form of high C-reactive protein (CRP). I propose to test this hypothesis
according to a sociogeographic model of systemic inflammation, shown in Figure 2.1 below. In
this model neighborhood is one important domain in the larger social environment in which
people are engaged, and neighborhood characteristics interact with individual behavior and
characteristics, such as legal status, to influence systemic inflammation. As there is no
commonly agreed upon definition of “neighborhood” in either the health literature or more
generally, the BM-IHLSS relied on subject perceptions of what constituted their neighborhoods
for measures of disorder and social capital – specifically, survey respondents were asked to
consider "your neighborhood" when answering questions about their experiences of violence,
property theft or damage and safety (Sawicki and Flynn, 1996; Coulton et al., 2001; Weiss et al.,
2007). Similarly, respondents were asked to consider "your neighbors" when answering
questions about shared values, familiarity among residents and reciprocity. Sampson and
colleagues (1999) validated these questions in a seminal study of Chicago neighborhoods as
being measures of the local area, and the BM-IHLSS neighborhood items were administered in
similar fashion. I therefore believe these measures to be representative of neighborhood-level
35
characteristics rather than personal resident experiences. We have further linked these
neighborhood data to 2000 Census block-level data as I describe further in the next section.
Figure 2.1 A Sociogeographic Model of Systemic Inflammation

2.2 Data and Methods
The Boston Metropolitan Immigrant Health & Legal Status Survey (BM-IHLSS) is a
community-based biodemographic research (CBBR) project designed and carried out in the
Boston-Cambridge-Quincy, MA-NH Metropolitan Statistical Area (BCQ-MSA) in 2007 (Marcelli
and Heer, 1997; Minkler and Wallerstein, 2003; Marcelli et al., 2009b; Marcelli et al., 2009a;
Marcelli and Buxton, 2010). Participants were randomly selected from 10 BCQ-MSA census
tracts in which at least seven percent of the population was born in Brazil. According to 2009-
2011 American Community Survey data, the BCQ-MSA is home to the largest population of
foreign-born Brazilians in the United States and this population has grown by 87 percent
36
between 2000 and 2009 (U.S. Census Bureau, 2001, 2012). Data regarding migration and legal
status, socioeconomic status, social capital, neighborhood characteristics and self-reported
health behavior and conditions were collected from 307 foreign-born Brazilian adults and 120
of their U.S.- and foreign-born children. In addition, 249 of the Brazilian adult subjects (81
percent) provided biological data in the forms of height, weight and blood pressure
measurements, and 176 subjects (57 percent) consented to providing blood droplets from
which CRP, glycated hemoglobin (HbA1c) and Epstein-Barr virus (EBV) measurements were
obtained.
2.21 Measures
High-sensitivity C-reactive protein (hsCRP). Blood samples were collected by trained
interviewers in the homes of randomly selected subjects using disposable lancets and
standardized filter paper. These were stored as dried blood spots (DBS) at -80° C in Brigham and
Women’s Hospital Biomarker and Actigraphy Data Coordinating Center. After completion of
BM-IHLSS fieldwork, the DBS were sent to the Laboratory for Human Biology Research at
Northwestern University where high-sensitivity enzyme immunoassays for CRP were
conducted. This method has been validated; the hsCRP concentrations obtained from DBS were
found to be very similar to those obtained from blood serum, and DBS are an effective means
of storing blood samples in population-based surveys for which laboratory examinations are
not feasible (Finch et al., 2000b; McDade et al., 2004). A regression equation is employed
(plasma = 2.3372xDBS + 0.0778) to convert the DBS values of hsCRP, which are lower than
serum values as a result of lysed erythrocytes in the sample, to serum values (McDade, 2010).
This is done in order to utilize recommended clinical risk tertiles for hsCRP, based on serum
37
measures, in order to delineate high CRP (≥3 mg/L) from moderate (1 to 3 mg/L) and low (≤1
mg/L) values (Pearson et al., 2003). Some clinicians have suggested that values of hsCRP
exceeding 10 mg/L may indicate acute inflammation, thereby making higher levels less useful
for predicting cardiovascular disease; however, Ridker and Cook (2004) have found that even
very high levels of hsCRP (>10 mg/L) provide important diagnostic information. Therefore, the
outcome variable I employ (High CRP) is a dichotomous variable indicating whether a subject
had high CRP (hsCRP levels ≥ 3 mg/L and ≤20 mg/L). I use a cutoff of 20 mg/L in order to exclude
active infection and retain as many subjects as possible who may be at cardiovascular risk
(Ledue and Rifai, 2001). Because no studies or data of which I am aware include information on
the distribution of CRP levels among foreign-born Brazilian migrant adults, in separate analyses
I compared BM-IHLSS CRP data to CRP levels reported in the 2005-2008 National Health and
Nutrition Examination Survey (NHANES) data for the total U.S. adult population and foreign-
born Latin American adults. NHANES utilizes a measure of low sensitivity CRP rather than high-
sensitivity CRP, and CRP levels>1 mg/dL are generally considered to be high (Visser et al., 1999).
According to the NHANES data, the frequency of high CRP among U.S. adults ages 18-64 is
approximately 18 percent (Centers for Disease Control and Prevention (CDC), 2010), which is
similar to the 21 percent found in the BM-IHLSS Brazilian adult sample.
Neighborhood environment. Neighborhood factors that may be independently
associated with high CRP are measured using self-reported responses to questions about
various neighborhood characteristics, and relying on subjects’ own definitions of
“neighborhood.” The BM-IHLSS data are further linked to 2000 Census Summary File 1 (SF1)
data to obtain measures of socioeconomic status and population composition. The census data
38
are estimated at the block level as this provides the most proximate local neighborhood
geography and encompasses features that residents may encounter as part of their daily
activities. The first three neighborhood measures, population density per square mile (Pop.
density), percent minority residents (Minority) and percent homeownership (Ownership), are
continuous and measured at the block level using the SF1 data. Each of these variables is often
used to assess neighborhood socioeconomic characteristics and stability: population density
and the concentration of nonwhite populations have commonly been used as proxies for the
neighborhood socioeconomic environment under the assumption that areas higher in density
and with larger minority populations are more likely to be lower income, although the opposite
relationship has been found in some studies of health behavior and the built environment
(Rohe and Stewart, 1996; Gordon-Larsen et al., 2006; Lovasi et al., 2009b). Similarly for
homeownership, the greater the rate of homeownership the wealthier and more stable the
neighborhood is generally expected to be (Ross and Mirowsky, 2001). Neighborhood disorder
(Disorder) is measured using a dummy variable indicating whether respondents or their
neighbors had personally been victims of crime in their neighborhood. Neighborhood “social
capital” (Social capital) is an index (0-5) based on whether subjects agreed that their
neighborhood is safe at night, and that their neighbors know each other, get along with one
other, share similar values, and are willing to help each other . Three of the five components
constituting my social capital index are clearly suggestive of positive social relations (i.e., safety,
getting along, willingness to help), and thus I assume that higher scores indicate higher subject-
assessed social capital. In other words, I acknowledge that the remaining two component
variables − sharing values and knowing one another − may represent healthful or harmful social
39
interactions depending on the characteristics of network members (Portes, 1998; Sampson and
Raudenbush, 1999). Finally, I control for length of residence in a subject’s current
neighborhood.
Individual socioeconomic characteristics include dichotomous variables indicating
whether a subject was married, completed high school (HS graduate), was currently employed,
reported speaking English “very well,” (English) and was unauthorized to reside in the USA. A
continuous measure of total individual earnings for 2006 (the year prior to the survey) is also
included. Individual socioeconomic status, most often measured by years of education, income
and occupation, has repeatedly been linked to health outcomes. Education in particular has
shown protective effects against inflammation and chronic illness, including in one study of
Brazilians (residing in Brazil) and CRP (Crimmins et al., 2004; Koster et al., 2006; Nazmi et al.,
2010b). Associations between marital status and health outcomes indicate that marriage may
also have a protective effect, at least in terms of health behavior (Trivedi et al., 2008; Krueger
and Friedman, 2009). For migrants, English-speaking ability is a measure of integration, and
poor English skills may be a source of psychosocial stress. Furthermore, unauthorized legal
status may be an important stressor that is unique to foreign-born populations (Marcelli, 2004),
and according to estimates from the BM-IHLSS, 71 percent of Brazilian migrant adults in New
England were unauthorized in 2007 (Marcelli et al., 2009b), a figure nearly 20 percent higher
than most estimates of the unauthorized Mexican migrant population in the United States
(Passel, 2006). Having children may also be a potent stressor, or alternatively a protective
factor in some instances; however, including a measure of dependent children in earlier
analyses demonstrated no effect and the variable was left out of the final model.
40
Health status and behaviors. Markers of health status and health behaviors that may be
independently associated with inflammation are measured using self-reported responses to
questions about psychological health and behavior as well as height, weight and blood pressure
measurements and two additional biomarkers obtained from DBS assays (HbA1c and EBV).
Serious psychological distress (Distress) is defined as a value of at least 13 using a well-known
distress index which ranges from 0-24, is based on six questions with a 5-point Likert response,
and gauges clinical and subclinical psychological illness in population-based studies (Kessler et
al., 2002). Psychological distress has been cited as a risk factor for cardiovascular disease and
linked to increases in inflammation (Black, 2002; Stansfeld et al., 2002; Hamer et al., 2008).
Body mass index (BMI) is a continuous variable calculated from height and weight
measurements taken at the time of interview. HbA1c and EBV are continuous measures of
glycated hemoglobin and EBV antibodies obtained from DBS assays. High blood pressure
(Hypertension) is a dichotomous variable indicating whether subjects had systolic blood
pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg or had ever been told by a doctor
that they had high blood pressure (Marcelli and Holmes, 2012).
With respect to health behavior, nutrition was evaluated using a dichotomous variable
indicating whether subjects consumed five or more servings of fruits and vegetables each day
on average (Nutrition), physical activity was assessed according to whether subjects engaged in
moderate (20 minutes) or vigorous (30 minutes) “physical activity” during the preceding week
(Physical activity), and smoking was measured according to whether subjects had smoked at
least 100 cigarettes in their lifetime and currently smoked every day. Finally, sleep debt is a
continuous measure indicating the difference between usual hours of sleep on non-work days
41
and work days. Sleep deficiency, nutrition, smoking, obesity, high levels of glycated hemoglobin
(HbA1c) and high blood pressure are all demonstrated risk factors for inflammation and
cardiovascular disease (Buxton and Marcelli, 2010). Furthermore, obesity, high blood sugar and
high blood pressure together are major components of metabolic syndrome, and CRP in
particular has been linked to inflammatory processes that exacerbate metabolic syndrome by
increasing insulin resistance (Fried et al., 1998; Winkleby et al., 1998; Esposito and Giugliano,
2004; Meier-Ewert et al., 2004; McDade et al., 2005; Ndumele et al., 2006; Hamer et al., 2008;
Buxton and Marcelli, 2010; Pirkola et al., 2010). Physical activity, alternatively, appears to have
preventive effects with respect to systemic inflammation (Ford, 2002). Additionally, EBV
antibody level has been validated as a marker of cell-mediated immune function, which may be
influenced by a variety of acute or chronic psychosocial and environmental stressors (Glaser et
al., 1991; McDade et al., 2000).
Individual exogenous characteristics are captured by measuring age in years since birth,
skin color – self-reported according to a scale numbered 1-10 where 1 corresponds with the
lightest pigmentation and 10 corresponds with the darkest (Massey and Martin, 2003) – and
dichotomous variables for sex (Male) and whether the subject was born in an urban area in
Brazil (Urban born). Age is the most important risk factor for cardiovascular and other chronic
diseases, and women, blacks and Latinos have been shown to be more likely to have high CRP
(Winkleby et al., 1998; Albert et al., 2004; Araújo et al., 2004; Woloshin and Schwartz, 2005;
Petersen et al., 2008). Brazilian migrants in the USA, however, are younger than the U.S.
population on average, and my sample reflects this with an age distribution from 18-59 years of
age. I also control for urbanicity in subject place of birth because more urbanized areas typically
42
include residents who are of a higher socioeconomic status, are less likely to expose children to
infection or other pro-inflammatory conditions, and offer access to established health care
infrastructures (Vlahov et al., 2005; Crimmins and Finch, 2006).
Descriptive and multivariate regression results for cross-sectional analyses are reported
below. STATA 10’s “logit” command was used to perform all logistic regressions. Bias that may
occur as a result of multiple respondents living in the same census blocks was controlled using
STATA’s “cluster” function (Huber, 1967). Three models were fitted: Model 1 controls for
neighborhood characteristics and length of neighborhood residence along with individual
exogenous characteristics, Model 2 adjusts for individual socioeconomic characteristics and
Model 3 controls for all listed variables in addition to health conditions and behaviors.
2.3 Results
Of the 176 DBS samples collected from adult Brazilian BM-IHLSS subjects, CRP data are
available for 157 (89 percent). The analyses are further restricted to the 151 subjects who had
CRP readings less than 20 mg/L to exclude those who may be experiencing active infection.
Subjects resided in 52 census blocks across the BCQ-MSA, with a mean of three per block. Sixty-
eight percent of these participants were estimated to have been unauthorized migrants, which
is very similar to the estimated 71 percent of unauthorized migrants found in the entire
Brazilian adult sample. Descriptive statistics are detailed in Table 2.1 below.

43
Table 2.1 Weighted Descriptive Statistics

All Adults CRP≤3mg/L
CRP>3mg/L &
≤20mg/L
μ
(S.D.)
μ
(S.D.)
μ
(S.D.)
OUTCOME VARIABLE
High CRP High CRP=1 if hsCRP>3.0mg/L & hsCRP<=20mg/L (converted from DBS to serum values) 0.22 0.00 1.00
- - -
hsCRP (mg/L) Levels of hsCRP (converted from DBS to serum values) 2.46 1.11 7.39
3.38 0.66 4.55
NEIGHBORHOOD CHARACTERISTICS
Length of residence (-) Number of years and months subject has resided in neighborhood 2.46 2.26 3.18
2.49 2.07 3.60
Population density (+/-) Number of residents per square mile by census block (mean in 10,000s) 23287.70 24235.28 19833.97
14843.61 14181.56 16834.99
% Minority (+) Percent of non-white residents by census block 0.40 0.41 0.36
0.20 0.20 0.20
% Ownership (-) Percent of residents who own their homes by census block 0.35 0.32 0.43
0.20 0.18 0.24
Disorder (+)
Disorder=1 if subject or neighbors experienced personal violence, had their homes broken
into, had anything stolen from their property or experienced damage to their personal
property in the neighborhood 0.29 0.26 0.41
- - -
Social capital (-)
Index from 0-5 indicating to what extent the subject agrees or strongly agrees that the
neighborhood is 1) safe at night, and neighbors 2) know each other, 3) get along, 4) share
values and 5) help each other 3.00 2.96 3.15
1.49 1.49 1.50
INDIVIDUAL EXOGENOUS CHARACTERISTICS
Age (+) Subject age in years 33.55 33.03 35.48
9.56 9.21 10.70
Male (-) Sex=1 if subject reported sex as male 0.59 0.65 0.35
- - -
Skin color (+) Self-reported subject skin color, measured from lightest (1) to darkest (10) 2.18 2.29 1.81
1.36 1.44 0.92
Urban born (+/-) Urban born=1 if subject was born in an urban area in Brazil 0.68 0.71 0.55
- - -
INDIVIDUAL SOCIOECONOMIC CHARACTERISTICS
Married (-) Married=1 if subject was married at time of survey 0.54 0.51 0.65
- - -
HS graduate (-) HS graduate=1 if subject graduated high school 0.80 0.85 0.64
- - -
Employed (-) Employed=1 if subject worked last week 0.91 0.91 0.88
- - -
Earnings (-) Subject earnings from all jobs in 2006 (Thousands of dollars) 33.83 35.40 28.08
24.41 25.40 19.66
English (-) English=1 if subject speaks English "very well" 0.30 0.32 0.23
- - -
Unauthorized (+) Unauthorized=1 if subject is unauthorized to reside in the USA 0.73 0.75 0.66
- - -
INDIVIDUAL HEALTH STATUS & BEHAVIOR
Distress (+) Distress=1 if subject's K6 score>12, indicating serious psychological distress 0.08 0.05 0.18
- - -
BMI (+) Body mass index 25.82 25.53 26.86
3.72 3.41 4.61
HbA1c (+) Glycated hemoglobin level (continuous) 4.80 4.76 4.94
0.63 0.53 0.90
Hypertension (+/-)
Hypertension=1 if subject has measured hypertension or has ever been diagnosed with
hypertension 0.07 0.08 0.03
- - -
EBV (+) Epstein-Barr Virus antibody level (continuous) 90.50 88.80 96.71
66.11 65.14 70.23
Nutrition (-) Nutrition=1 if subject consumes an average of five or more fruits/vegetables per day 0.20 0.19 0.27
- - -
Physical activity (-)
Physical activity=1 if subject engaged in moderate (20 min) or vigorous (30 min) exercise
at least one day during the previous week 0.27 0.30 0.16
- - -
Smoking (+) Smoking=1 if subject smokes every day 0.17 0.14 0.27
- - -
Sleep debt (+) Difference between self-reported non-workday and workday sleep hours 1.23 1.17 1.44
1.69 1.62 1.92
N (Weighted) 29,708 23,312 6,396
N (Unweighted) 151 119 32
*Difference in means is statistically significant, p<0.05
44

As the continuous measure of hsCRP in Table 2.1 illustrates, the mean level of hsCRP
among Brazilian migrant adults residing in the BCQ-MSA is 2.4 mg/L – within the moderate risk
range. However, when distinguishing between those with high CRP and those without, the
former group has a mean level of 7.2 mg/L, well above the high CRP cutoff of 3 mg/L, and the
latter group has a mean falling in the low end of the moderate risk range at 1.1 mg/L.
Altogether, approximately one in five (22 percent) Brazilian migrant adults residing in the BCQ-
MSA are estimated to have high CRP. Consistent with past research, those with high CRP are
slightly older than those with lower CRP levels (35 versus 34 years of age) and a higher
proportion are women (approximately two-thirds versus 40 percent). Surprisingly, Brazilians
with high CRP have lighter skin color on average (1.8 versus 2.2), but this may simply be an
artifact of Brazilian migrants residing in the BCQ-MSA having relatively light skin pigmentation
(2.2 on a scale of 1 to 10). In terms of individual socioeconomic characteristics, a higher
proportion of migrants among those with high CRP are married; and lower proportions are
employed, completed high school, report speaking English “very well” − and surprisingly −
unauthorized to reside in the USA. Higher proportions of Brazilian migrants with high CRP also
smoke, appear to be distressed, experienced sleep debt, and have higher BMI, HbA1c and EBV
levels.
Conversely, a lower proportion of those with high CRP engage in regular physical activity
and there does not appear to be a notable difference in terms of fruit and vegetable
consumption. Migrants with high CRP were also less likely to have high blood pressure, which
may seem antithetical as both CRP and hypertension are risk factors for cardiovascular disease.
Research has demonstrated, however, that blood pressure and CRP are independent markers
45

of disease (Blake et al., 2003) and do not necessarily have a causal relationship (Smith et al.,
2005). All of the variables in Table 2.1 discussed thus far are controls in my model − factors that
may be independently associated with high CRP and confound any estimated relationship
between neighborhood environment and high CRP if ignored. Among my main variables of
interest we see that those who had high CRP had resided in their neighborhoods a slightly
longer period of time on average, and reside in less densely populated neighborhoods with
smaller proportions of ethno-racial minorities but higher proportions of homeowners. Lastly,
although migrants with high CRP also rank their apparently higher socioeconomic status
neighborhoods as having more social capital on average, they were also more likely to report
neighborhood disorder.
Table 2.2 shows the parameter coefficients estimated from logistically regressing high
CRP on all the variables listed in Table 2.1. Hypothesized directional associations between high
CRP and each explanatory variable are indicated by a plus (+) or minus (-) sign following each
variable name. A two-tailed hypothesis − employed when the relationship between an
explanatory variable and high CRP is theoretically ambiguous (e.g., population density, being
born in an urban area) − is demarcated by a both positive and a negative sign (+/-). I report
parameter coefficients and convert these into changes in the probability that a migrant had
high CRP as a result of a one-unit (for dichotomous explanatory variables) or one standard
deviation (for continuous explanatory variables) change for two straightforward reasons. First,
as is customary in economics, I prefer to show estimated associations explicitly rather asking
readers to infer them from odds ratios. And second, regardless of how estimated relationships
46

are reported, most researches eventually employ the language of probability when discussing
regression results (Studenmund, 2001; Buxton and Marcelli, 2010).
47

Table 2.2 Logistic Regression of High CRP on Neighborhood and Other Behaviors/Characteristics

ß S.E. Prob. ß S.E. Prob. ß S.E. Prob. O.R.
NEIGHBORHOOD CHARACTERISTICS
Length of residence (-) 0.023 (0.089) 0.97% 0.066 (0.097) 2.77% 0.076 (0.103) 3.20% 1.079
Pop. density (+/-) -0.00002 (0.000) -4.21% -0.000004 (0.000) -1.08% -0.00003 (0.000) -7.80% 1.000
% Minority (+) -0.504 (1.459) -1.69% -0.672 (1.719) -2.25% -0.645 (1.606) -2.16% 0.525
% Ownership (-) 2.268 (1.381) 7.52% 3.097 (1.564) 10.27%
**
1.775 (1.614) 5.89% 5.901
Disorder (+) 0.666 (0.544) 11.26% 0.735 (0.559) 12.41%
*
1.122 (0.532) 18.96% 3.072
**
Social capital (-) -0.037 (0.185) -0.93% -0.133 (0.205) -3.34% -0.292 (0.221) -7.35% 0.746
*
INDIVIDUAL CHARACTERISTICS
Age (+) 0.013 (0.024) 2.05% 0.026 (0.025) 4.12% -0.013 (0.032) -2.10% 0.987
Male (-) -1.284 (0.563) -21.69%
**
-1.173 (0.710) -19.82%
**
-1.839 (0.801) -31.07% 0.159
**
Skin color (+) -0.294 (0.220) -6.74% -0.409 (0.231) -9.38% -0.549 (0.239) -12.60% 0.577
Urban born (+/-) -1.015 (0.454) -17.15%
**
-1.248 (0.570) -21.08%
**
-1.691 (0.572) -28.56% 0.184
***
INDIVIDUAL SOCIOECONOMIC CHARACTERISTICS
Married (-) 0.103 (0.543) 1.74% 0.351 (0.562) 5.93% 1.421
HS graduate (-) -1.709 (0.555) -28.88%
***
-2.313 (0.678) -39.07% 0.099
***
Employed (-) -0.448 (0.819) -7.57% -1.392 (1.078) -23.52% 0.249
*
Earnings (-) -0.033 (0.018) -13.51%
**
-0.017 (0.019) -7.07% 0.983
English (-) -0.917 (0.557) -15.49%
**
-1.316 (0.531) -22.23% 0.268
***
Unauthorized (+) 1.044 (0.543) 17.64%
**
1.265 (0.595) 21.38% 3.544
***
HEALTH STATUS & BEHAVIOR
Distress (+) 2.145 (1.016) 36.24% 8.544
**
BMI (+) 0.161 (0.077) 10.16% 1.175
**
HbA1c (+) 0.225 (0.416) 2.39% 1.253
Hypertension (+/-) -0.757 (1.449) -12.79% 0.469
Epstein-Barr Virus (+) -0.003 (0.006) -2.90% 0.997
Nutrition (-) -0.022 (0.723) -0.38% 0.978
Physical activity (-) 0.357 (0.758) 6.04% 1.429
Smoking (+) 1.570 (0.626) 26.53% 4.809
***
Sleep debt (+) 0.030 (0.175) 0.85% 1.030
Constant term (+/-) -0.330 (1.719) 1.294 (2.047) -0.172 (2.556)
Concordant Pairs
Prob > chi2
Pseudo R2
* p≤.10 ** p≤.05 *** p≤.01
0.813
0.000
0.362
Model 1 Model 2 Model 3
0.794
0.000
0.255
0.804
0.012
0.170
48

In my final model, which controls for both individual socioeconomic status and health, I
estimate that two neighborhood-level factors are significantly associated with high CRP among
adult Brazilian migrants. First, those residing in a neighborhood characterized as disordered are
19 percent more likely to have high CRP than those who do not. Second, those who report
higher neighborhood social capital are almost seven percent less likely to have high CRP. Three
more conventional explanatory variables for area-level socioeconomic status – minority
composition, population density and the rate of homeownership – do not appear to be
significantly related to high CRP. Statistically significant changes in the probability of having high
CRP are illustrated in Figure 2.2 by the filled bars, and the empty bars represent variables that
were included in my final model but are not significant.
Figure 2.2 Change in the probability of having high CRP

49

I should like to note from Table 2.2 (which also shows the changes in the probability of
high CRP associated with a one-unit or one standard deviation change in each explanatory
variable) that higher neighborhood disorder and social capital become significant only after
controlling for individual-level socioeconomic status and health-related factors. One might
expect, for instance, that individual SES and health behaviors would mediate any observed
associations between neighborhood factors and inflammation, but they instead appear to
clarify these relationships. In other words, Model 1 likely suffers from omitted variable
(specification) bias as systemic inflammation is not an isolated biological process. Incorporating
individual socioeconomic and health-related variables into my model not only provides an
estimate of how these are directly associated with high CRP, but also elucidates how
neighborhood environment and inflammation are related by placing the latter in context of
individual risk factors. For example, obese individuals have been found to exhibit chronic
systemic inflammation; thus, controlling for BMI should allow for a clearer picture of whether
neighborhood environment influences CRP directly or through pathways not proxied in my
model (Visser et al., 1999). Likewise, inflammation is a process tied to infection; thus,
accounting for EBV levels as an indicator of generalized infection likely crystallizes any
relationships between CRP and other explanatory variables.
Consistent with the literature, Brazilian migrant men are significantly and substantially
(28 percent) less likely to have high CRP than Brazilian migrant women. Age and skin color, on
the other hand, do not appear to be independently associated with high CRP. This is not
surprising, however, given that this population is relatively young and light-skinned.
Additionally, in separate analyses employing categorical measures of race akin to those used by
50

the U.S. Census Bureau, no significant associations were found. Alternatively, subjects who
were born in an urban area of Brazil, what I consider a crude proxy for having grown up in an
environment with relatively more resources and less inflammatory exposures, were almost 20
percent less likely to have high CRP.
I also find that those who graduated from high school are 22 percent less likely to have
had high CRP and those residing in the United States illegally are 29 percent more likely. Both of
these relationships remain statistically significant in my third model, which controls for health
behaviors and status, and only one of my remaining four individual-level socioeconomic status
variables is not signed as expected (being married). However, little is yet known about the
effects of spousal relationships on inflammation specifically, and the quality of marital
relationships is likely as important for predicting outcomes such as high CRP as the state of
being married itself (Kiecolt-Glaser et al., 2010; Donoho et al., 2013). The finding regarding
migrant legal status is provocative and is the only representative empirical evidence to date of
which I am aware that hints at the possible adverse effect of unauthorized legal status on
physiological health. If systemic inflammation is sensitive to psychosocial stressors, as previous
research has demonstrated, a precarious legal status may be an important risk factor for
chronic disease onset among foreign-born populations residing in the USA and elsewhere.
Finally, two of my nine health behavior and health status variables are estimated to be
positively and independently associated with high CRP (a higher height-adjusted body weight
and smoking), and only two are not signed as anticipated (physical activity and Epstein-Barr
virus). Brazilian migrant adults whose BMI was measured to be about four points higher had
about a 12 percent higher probability of having had high CRP, and those who smoked cigarettes
51

daily were 18 percent more likely. Existing evidence intimates that health behaviors including
sleep and physical activity influence inflammation, and CRP specifically (Ford, 2002; Meier-
Ewert et al., 2004; Frey et al., 2007). That these variables are not found to be significant as
reported in Table 2.2 and Figure 2.2, nor in separate analyses controlling simply for exogenous
characteristics and these variables, only makes my findings regarding BMI and smoking more
compelling among Brazilian migrants. More generally, as the BM-IHLSS data represent a young
and generally healthy population, it is possible that smoking and BMI are indicative of
pathology earlier in life whereas the effects of the other behaviors have more cumulative or
palliative effects as disease sets in later in life. It is also worth noting that high blood pressure
may not have been estimated to be statistically associated with high CRP because the BM-IHLSS
instrument did not include questions about anti-inflammatory medication.
That neighborhood-level environmental factors remain significantly associated with high
CRP in my model even after controlling for individual health and SES variables may also provide
further evidence that psychosocial and environmental stressors have a particular impact on
inflammation. As noted above, neighborhood disorder has repeatedly been found in public
health literature to be associated with increased psychological distress, and distress in turn −
although not significant in my analyses − has been linked to high CRP and inflammation more
generally (Hill et al., 2005; McDade et al., 2006; Ross and Mirowsky, 2009). Lack of social
support has additionally been linked to compromised physiological function, and my finding
regarding the relationship between low neighborhood social capital and high CRP may offer
more evidence of this phenomenon (Uchino, 2006; Kiecolt-Glaser et al., 2010). As these are
52

cross-sectional data; however, the true relationships between these measures and chronic
inflammation may be obscured.
2.4 Discussion
Only a few existing studies have examined relationships between inflammation and
neighborhood factors (Petersen et al., 2008; Schootman et al., 2010), and only one of which I
am aware has gone beyond conventional neighborhood socioeconomic (SES) characteristics to
investigate other sociogeographic aspects of local environments that may be linked to high CRP
(Nazmi et al., 2010a). I report new evidence from 2007 Boston Metropolitan Immigrant Health
& Legal Status (BM-IHLSS) data that neighborhood-level disorder and social capital are
associated with high CRP among Brazilian migrant adults residing in the Boston-Cambridge-
Quincy metropolitan statistical area. These two factors, furthermore, are estimated to be
important even though more conventional area-level SES indicators such as home ownership
and ethnoracial diversity are included in my models. And they are estimated to be increasingly
important once individual-level SES and health variables are included in my model.
The negative association between being born in an urban area in Brazil (and although
insignificant, block-level population density) and high CRP is contrary to past findings indicating
negative effects of high residential density and urban living on health; however, more recent
evidence has suggested that higher levels of urbanization may be an advantage for health as
more densely-populated urban areas are home to greater social and material resources, such as
health care services and opportunities for civic participation (Vlahov et al., 2005; Kim et al.,
2006). Furthermore, those residing in more urban areas may be less likely to face a variety of
infectious or other inflammatory exposures in childhood, thereby potentially lowering their risk
53

of systemic inflammation over the life course (Finch and Crimmins, 2004; Gurven et al., 2008). It
would be ideal to examine the effects of early living conditions and ongoing exposure to urban
environments using panel data to better understand the effects of these environments on
inflammation, immune function and overall health. Nevertheless, little is known about the
pathways from neighborhoods or other urban areas to physiological health in general, and the
current study, though employing cross-sectional data, is only the second of which I am aware
that specifically evaluates both socioeconomic and other neighborhood characteristics in
relation to inflammation.
It is also worth commenting on the estimated positive relationship between being
female or unauthorized to reside in the USA and high CRP among adult Brazilian migrants. The
2005-2008 NHANES data indicate that women aged 18-64 in the United States are
approximately 1.5 times as likely as men to have high CRP, and among foreign-born Mexicans –
the only foreign-born group that may be identified – this gap is greater (Centers for Disease
Control and Prevention, 2010). I also find that Brazilian men are 28 percent less likely to exhibit
high CRP than women, and at least two studies conducted among Brazilians in Brazil similarly
identified this gender gap in young and otherwise healthy populations (Ribeiro, 1997; Araújo et
al., 2004). It will be worthwhile in future studies to clearly articulate hypotheses regarding the
environmental and psychosocial factors that may help explain this disparity. Additionally, no
previous study has analyzed the relationship between legal status and physiological function
among any foreign-born population residing in the United States. I estimate unauthorized adult
Brazilian migrants – the majority in the BM-IHLSS data – are 22 percent more likely to have had
high CRP than were their legal compatriots. This is a striking finding given the relatively young
54

mean age (35 years among those with high CRP) and the near absence of diagnosed
cardiometabolic disease among adult Brazilian migrants residing in metropolitan Boston. Some
research in economics and sociology suggests that a legal status penalty may exist for earnings
(Marcelli, 2004) and social capital formation (Granberry and Marcelli, 2007) among Mexican
migrants residing in the USA, and findings reported in this article imply this penalty may extend
to physiological regulation. This, of course, will require future study.
There are several potential policy implications of these findings. The few studies
reporting that sociogeographic environment and physiological dysregulation are related have
focused largely on social support exchanged between individuals with close personal ties
(Uchino, 2006). My results complement these findings by suggesting that broader social
processes − such as less intimate interactions occurring within neighborhoods or other
relatively small local areas − are also important for influencing biological and thus long-term
health. In particular, collective efficacy and other social capital theorists argue that civic group
participation among neighborhood residents (e.g. sports clubs, neighborhood associations) may
increase residents’ attachment to their neighborhoods and thus their stake in maintaining
neighborhood well-being (Putnam, 2000; Browning and Cagney, 2002). Furthermore, a
neighborhood’s capacity to organize around common goals plays a role in determining how
effective residents are in negotiating with public institutions (e.g. police, city council) to obtain
services (Sampson, 2003). This capacity in turn may influence neighborhood disorder; for
example, previous research indicates that neighborhoods higher in collective efficacy tend to
have lower crime rates (Sampson et al., 1997). The social and material resources thus afforded
to neighborhoods exhibiting greater collective efficacy may influence health, though it remains
55

to be seen what kinds of interventions may be most effective in supporting such civic
organization (Cohen et al., 2006), especially among socioeconomically vulnerable populations.
Perhaps a first step toward fostering health among U.S. migrants would be to promote
awareness of local community-based organizations (CBOs) dedicated to assisting them. Fewer
than 50 percent of all foreign-born Brazilian adults residing in metropolitan Boston, for
instance, were familiar with community organizations committed to providing employment,
legal and other services according to the 2007 BM-IHLSS data. Furthermore, given that a
plurality of civically engaged Brazilians − regardless of gender or legal status − appear to have
been active in a church or to have participated in an internet-based group, social capital
accumulation among Brazilian migrants may be fostered by CBOs by reaching out in these
domains. Neither churches nor the internet necessarily include one’s neighbors, of course, but
they might, and even if they do not they may help connect individuals who might benefit from
knowing one another. Such efforts may be especially important for Brazilian migrants residing
in metropolitan Boston because more than two-thirds are estimated to be residing in the USA
illegally and unauthorized Brazilians were found to be 22 percent more likely to have had high
CRP compared to their legal compatriots. Fear of exposure or unwanted attention has the
potential to prevent unauthorized as well as legal migrants from fully engaging in existing civic
organizations or even reporting incidents of crime to the authorities. Although reducing such
disincentives to civic engagement will require national-level immigrant policy reforms that help
migrants integrate more quickly and thoroughly, local entities such as the cities of Cambridge
and Somerville in the BCQ-MSA have adopted sanctuary policies preventing city employees
from inquiring about legal status when providing services. It remains to be seen, of course,
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whether such progressive policies (Sullivan, 2009) or increased CBO outreach efforts will result
in more active civic engagement among migrants, and whether this would improve migrant
health.
The foregoing analyses have several limitations. First, the BM-IHLSS data are cross-
sectional and representative of a group of foreign-born migrants from Brazil who were residing
in New England in 2007. It is difficult to conclude whether my findings can be generalized to
geographically and ethnoracially diverse populations, and it is not possible to infer causality
regardless of how robust the estimated parameters. Second, the CRP concentrations are
derived from DBS samples rather than blood serum and converted to serum-equivalent values
using a regression equation, which although a validated technique, may slightly skew the CRP
results. Third, the BM-IHLSS survey did not include questions regarding medication currently
being taken for management of cardiovascular risk factors; however, no one in the sample
reported having been diagnosed with cardiovascular disease. Lastly, of necessity due to the
concentration of foreign-born Brazilians, the local area analyzed in this study consisted of a
relatively small number of census blocks (52) in a circumscribed area (Eastern Massachusetts).
This gives rise to the modifiable areal unit problem (Openshaw, 1984), or the propensity for
substantial variation to occur in evaluating an outcome depending on the geography in which it
is studied. However, recent neighborhood-health studies have suggested that this variation
may be minimal and small areal units may be best for capturing relationships between
neighborhood characteristics and health outcomes (Weiss et al., 2007; Tarkiainen et al., 2010).
Despite these limitations, the BM-IHLSS data are the only data available with
representative information concerning individual- and neighborhood-level characteristics
57

among foreign-born Brazilians in the United States. They are also the only data of which I am
aware that have information on both legal status and biological markers of health. Thus, my
findings emanate from analysis of the first community-based biodemographic research (CBBR)
data available and suggest that additional research is required to fully understand the specific
pathways between neighborhood characteristics and inflammation. More generally, it may
prove useful to investigate the influence of stressors in other sociogeographic domains, such as
the home and workplace, to gain a more comprehensive picture of the mechanisms that cut
across these domains to influence physiological regulation and disparities in systemic
inflammation.
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Chapter 3
Neighborhood Social Cohesion and
Psychological Distress

59

3.1 Introduction
Mental disorders are widespread among both foreign- and native-born residents of the
United States and around the world (Gaines, 1998; Vega and Alegría, 2001; Whitaker, 2002;
Carlat, 2010; Watters, 2010; Becker, 2013), and it has been suggested since the late 19
th

century that residential mobility (e.g., international migration, urbanization) is an important
source of stress (Rosenberg, 1961; Beard, 1972 [1881]; Marmot and Syme, 1976; Gatrell, 2002).
In high income countries mental illness is the leading cause of disability, outstripping even
cardiovascular disease and cancer (Reeves et al., 2011), and when combined with chronic
disease, mental illness has been shown to accelerate morbidity and mortality (Moussavi et al.,
2007). U.S. national-level surveys indicate that one in four adults suffer from a mental disorder
each year and the lifetime prevalence of having had any disorder outlined in the Diagnostic and
Statistical Manual of Disorders (DSM-IV) is estimated to be 46 percent (Reeves et al., 2011). The
estimated lifetime prevalence for having had some type of mood disorder (e.g., depressive or
bipolar disorder) is 21 percent (Kessler, 2010). Unfortunately, despite the troublesome picture
these figures paint, they likely understate the proportion of the population that experiences
acute or chronic mental distress. The DSM-IV is designed to capture clinical cases of discrete
mental disorders, such as major depression, but psychologists have long emphasized that the
DSM's symptom categories often fail to identify subclinical levels of distress (Dohrenwend et
al., 1980) from which most distress-related illnesses that will require medical treatment
emanate (Rose, 1992). Measures of nonspecific psychological distress, such as Kessler's K6 scale
of serious psychological distress (Kessler et al., 2002), therefore, have been developed to
identify segments of the population exhibiting potentially unhealthy levels of stress.
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Employing the K6 scale for assessing serious psychological distress (Kessler et al., 2002)
the National Center for Health Statistics (NCHS) consistently reports that approximately three
percent of the U.S. population (currently about 10 million Americans) is distressed each year
(Dey and Lucas, 2006; National Center for Health Statistics, 2007). My analysis of the most
recently available National Health Interview Survey (NHIS) data from NCHS suggests a
somewhat higher prevalence of distress among all foreign-born residents of the United States
(≈ four percent), but as will be discussed in greater detail below, foreign-born Brazilian adults
residing in metropolitan Boston have recently been estimated to be twice as likely as the
average American to be experiencing distress. In addition to generating social, emotional and
economic disruptions in peoples’ lives (Mirowsky and Ross, 2003b) psychological distress has
been associated with numerous disease processes and outcomes, such as depression (Cairney
et al., 2007), obesity (Zhao et al., 2009), cancer (Zabora et al., 2001), diabetes (Dharmalingam,
2005), sleep quality (Hill et al., 2009), smoking (Cosci et al., 2009), cardiovascular disease, (May
et al., 2002; Stansfeld et al., 2002; Hamer et al., 2008; Nabi et al., 2008) systemic inflammation
(Goldman-Mellor et al., 2010; Puustinen et al., 2011; Holmes, 2013) and multimorbidity (Fortin
et al., 2006). The relatively high prevalence of distress observed among foreign-born Brazilian
migrants provides an opportunity to investigate whether the neighborhood in which one
resides is associated with serious psychological distress. With the alleged decline in importance
of neighborhood-based community and social capital in the United States relative to other life
domains such as home, work, and various civic groups (Putnam, 2000), and immigrants
sometimes viewed as a main culprit (Huntington, 2005 [2004]; Putnam, 2007), this is not an
uncontested hypothesis (Abbott, 2012; Sampson, 2012). Nor is it a new one. A century ago
61

(1912), the Mental Hygiene Movement suggested that preventing mental distress in
communities may be more effective than attempting to cure those who suffered from it by
isolating them in asylums (Burnham, 1988). Since that time, and accelerated with the
implementation of the U.S. Community Mental Health Act in 1963, community-based mental
health promotion efforts have become standard practice.
In this chapter I use the 2007 Boston Metropolitan Immigrant Health and Legal Status
Survey (BM-IHLSS) data to investigate how neighborhood social cohesion and disorder may
influence psychological distress among legal and unauthorized adult Brazilian migrants residing
in the Boston-Cambridge-Quincy Metropolitan Statistical Area (MSA). I further investigate
whether two biological markers of stress – high-sensitivity C-reactive protein (CRP) and
hypertension – moderate any observed associations between neighborhood factors and
psychological distress. I do so in an effort to understand better whether any such observed
associations are partly or fully explained by biological processes. Specifically I hypothesize that
(1) neighborhood disorder is positively associated with distress; (2) social cohesion is negatively
associated with distress; and (3) biologically measured stress (i.e., CRP and hypertension)
moderates any effect neighborhood environment may have on psychological distress. Chapter
2 of this dissertation suggests that neighborhood disorder is positively, and neighborhood
cohesion is negatively, associated with one measure of physiological stress among adult
foreign-born Brazilians residing in metropolitan Boston (Holmes, 2013). This chapter estimates
potential sociogeographic sources of stress measured a second way (psychologically) as
articulated by Ice and James (2007a).

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3.2 Neighborhoods and Psychological Distress
Since at least as early as 1939 when Faris and Dunham (1939) hypothesized that
peoples' residential environments may influence their mental health, urban researchers have
been interested in exploring the physical and social elements of neighborhood settings for their
influence on individual well-being. Neighborhoods are environments in which a substantial
proportion of the factors that may influence distress merge, including material circumstances,
social interaction, home and family dynamics, health behaviors, and residents' individual
exogenous characteristics. However, there has been some disagreement in the literature as to
whether neighborhood context really matters after accounting for these individual
characteristics (Mayer and Jencks, 1989; Ellen et al., 2001). I contend that by addressing three
gaps in the current neighborhoods and health literature we can begin to demonstrate that
neighborhood context does exert an independent influence on long-term health. Specifically,
studies in this area have tended to: (1) overlook the importance of potential relationships
between neighborhood social capital or cohesion and psychological distress among
neighborhood residents; (2) neglect physiological health when considering pathways between
neighborhoods, distress and disease; and (3) view neighborhoods as the collection of individual
behavioral outcomes and decision-making on the part of neighborhood residents rather than
conceptualizing neighborhoods as distinct places with contextual histories, identities and
attachments.

63

In the tradition of therapeutic landscapes research in urban geography, the ways in
which people interact with physical and social landscapes in their residential environments and
further form attachments to those environments or, alternatively, draw fearful or stressful
associations with those environments may have substantial impact on psychological well-being
(Smyth, 2005; Sampson and Gifford, 2010). Furthermore, Sampson (2012) has recently argued
that rather than people choosing their neighborhoods, "neighborhoods choose people..."; in
other words, there are existing structural concerns that help define neighborhood contexts
over time, such as zoning laws or discriminatory housing practices, as well as constraints on the
capacities of underserved populations to afford housing in certain neighborhoods. Additionally,
it is not merely the choice of people selecting into neighborhoods that may influence
psychological health but the choices of existing residents to respond in various ways to new
neighbors. The literature on residential segregation has long demonstrated a tendency on the
part of some ethno-racial groups to relocate rather than remain in neighborhoods with
changing population compositions (Denton and Massey, 1991; Sampson, 2013). All of these
factors are likely to have an impact on the psychological health of neighborhood residents; and
this in turn may exacerbate or even instigate the occurrence of chronic disease.
Thus, whether neighborhood residents know one another and feel as though they share
a set of common values may substantially influence the likelihood of experiencing distress or
the ability to cope with stressors. This may be especially true for immigrant populations who
face a unique set of stressors, such as non-native language acquisition, unauthorized legal
status or those related to cultural or socioeconomic integration. Immigrants to the USA have
had a long history of settling in ethnic enclaves or in close proximity not only to their
64

countrymen, but to other immigrants from the same towns in which they originated or co-
ethnics, presumably because this helps ease the transition to a new residential location and
perpetuates a feeling of connection to home (Massey, 1990; Logan et al., 2002). This is equally
the case for my study population – metropolitan Boston is home to the largest concentration of
foreign-born Brazilians in the USA (U.S. Census Bureau, 2000, 2011), 57 percent of whom were
born in the Brazilian state of Minas Gerais (Marcelli et al., 2009b). These Brazilians are also
relatively concentrated in a handful of towns near the city of Boston in which a variety of
Brazilian storefronts and churches can be seen along main roads. In such context, social
cohesion may be particularly important for understanding psychological health outcomes.
Yet there remain few studies examining potential relationships between neighborhood
social capital or cohesion and psychological distress among neighborhood residents. The
studies that have addressed this utilize a variety of different social capital and psychological
health measurements. Nevertheless, there appears to be clear evidence from existing research
that social ties with neighbors and neighborhood social cohesion – e.g. trusting neighbors,
sharing values and engaging in reciprocal exchange with neighbors – impact the mental health
outcomes of neighborhood residents. Residents in neighborhoods with lower levels of social
cohesion across a variety of metropolitan areas have been found to exhibit higher rates of
depression (Echeverría et al., 2008) and mental disorder generally (Stockdale et al., 2007), and
those with higher levels of cohesion have lower levels of distress (Cutrona et al., 2000; Ross and
Jang, 2000; Kruger et al., 2007). However, the interaction between social cohesion and distress
may not always be positive or straightforward. For example, at least one study demonstrates
that knowing fewer neighbors in high socioeconomic status neighborhoods has a detrimental
65

effect on mental health, but so does knowing more neighbors and being more integrated into a
low SES neighborhood (Caughy et al., 2007). Additionally, the manner in which such
associations may impact mental health over the longer term are less clear.
Other studies investigating psychological health in relation to neighborhood
characteristics have demonstrated that low neighborhood-level socioeconomic status (SES) and
other forms of material deprivation (Driessen et al., 1998; Caspi et al., 2000; Boardman et al.,
2001; Steptoe and Feldman, 2001; Leventhal and Brooks-Gunn, 2003), and low accessibility to
recreational or health facilities (Weich et al., 2002; van den Berg et al., 2010; Fan et al., 2011)
are associated with worse psychological outcomes among adults and children residing in those
areas. The relationship between neighborhood disorder and mental distress has also been well-
documented; specifically, neighborhoods that are objectively or perceptively noisier, more
physically rundown, more violent, less stable and less walkable exhibit greater associations with
distress and mental illness (Silver et al., 2002; Evans, 2003; Hill et al., 2005; Matheson et al.,
2006; Hill et al., 2009; Ross and Mirowsky, 2009).
3.3 Past Research and a Sociogeographic Model of Psychological Distress
Figure 3.1 outlines my theoretical framework for approaching potential relationships
between neighborhoods and psychological distress. I employ the sociogeographic model first
introduced in Chapter 1 to illustrate potential pathways to serious psychological distress.
Interactions across various life domains (e.g. neighborhood, home, workplace, civic groups)
coalesce with individual characteristics to help determine psychological health outcomes and
long-term health. As indicated in Figure 3.1 by the bi-directional black arrow, it remains unclear
whether psychological distress associated with environmental factors leads to physiological
66

dysregulation or the other way around, but I theorize that there is an iterative relationship
between psychological and physiological health with each influencing the other, and both being
influenced by individual behaviors and characteristics as well as sociogeographic environments.
While it is theoretically possible to focus on any of the life domains shown in Figure 3.1, in this
paper I focus specifically on the neighborhood; hypothesizing that the particularities and social
processes of disorder and cohesion that occur at this level are important enough to consider
separately. Of course, I also include controls for individual-level factors and the other
sociogeographic domains in my final regression models.
Figure 3.1 A Sociogeographic Model of Serious Psychological Distress

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3.31 Physiological Dysregulation
Psychological distress may also play an essential role in mediating any demonstrated
associations between chronic disease and neighborhood characteristics; however, there is a
lack of research demonstrating this despite growing evidence of a connection between
sociogeographic context and physiological markers of health (Merkin et al., 2009; Bird et al.,
2010; Nazmi et al., 2010a; Holmes, 2013). This lack of evidence is especially apparent with
respect to younger populations in which it may be important to study pathways between
environments, psychological well-being and physiological health in order to better understand
disease etiology and onset in later life. Increasingly, studies are showing that poor psychological
health may have a detrimental impact on physiological regulation. Perceived stress ,
psychological distress and depression have been linked to systemic inflammation and immune
dysregulation (Kiecolt-Glaser and Glaser, 2002; Miller et al., 2002; McDade et al., 2006;
Goldman-Mellor et al., 2010), hypertension and the onset of cardiovascular disease (Rugulies,
2002; Marzari et al., 2005; Hamer et al., 2008; Nabi et al., 2008), metabolic dysfunction (Eaton
et al., 1996; Dharmalingam, 2005), cancer (Zabora et al., 2001; Reiche et al., 2004) and
neurodegenerative disease (Black, 2002; Catena et al., 2011).
These findings suggest a rich area of potential study for better clarifying the pathways
by which environments get under the skin. Yet there are only a handful of studies that evaluate
psychological and physiological health together with neighborhood characteristics (Steptoe et
al., 2003; Hill et al., 2005; Holmes, 2013). Although I focus here on the relationship between
neighborhood characteristics and psychological distress, I also account for two measures of
physiological health – hypertension and inflammation (C-reactive protein) – in order to more
68

comprehensively investigate pathways between the neighborhood environment, psychological
distress and long-term health. High CRP and hypertension have both been linked to
psychological distress, though the mechanisms are not fully resolved (Hamer et al., 2008;
Goldman-Mellor et al., 2010; Puustinen et al., 2011; Holmes, 2013). While psychological health
is an oft-visited topic with respect to neighborhoods and health, and the evidence strongly
suggests a role for local area characteristics in determining psychological health outcomes, it
remains unclear to what extent physiological health may be implicated in perpetuating or
moderating distress experienced as a function of sociogeographic stressors. It is possible that
physiological dysregulation exacerbates or contributes to distress, or that distress may cause
physiological dysregulation; in either case it is important to untangle these effects to gauge
whether there is a direct association between neighborhood characteristics and distress or if
physiological markers of health partially explain any demonstrated relationships. As
intermediate markers of long-term physiological health and predictors of cardiovascular disease
that have also been linked to distress in previous studies, CRP and hypertension can help to
clarify the influence of neighborhood-level stressors on serious psychological distress.
3.32 Other Sociogeographic Domains
Just as physiological measures of health may interact with distress to influence disease
outcomes, sociogeographic domains of life other than the neighborhood may modify
associations between neighborhood characteristics and psychological distress. I therefore
include measures related to the home environment and civic participation in my logistic
regression models. Family social support has been shown to be protective against psychological
distress, although parental relationships are more complicated. Having children has sometimes
69

been found to mitigate distress while a number of studies also demonstrate positive
associations between distress and children in the home, especially for women and where
economic stress is a factor (Mirowsky and Ross, 2003a). Civic participation has a similarly
complex interaction with psychological distress, with findings across the spectrum depending
on age group, sex and the measurement of civic engagement. For older adults who may be
isolated, for example, civic participation appears to moderate distress (Lum and Lightfoot,
2005). Studies among teens and young adults have found no such evidence (De Silva et al.,
2005); and at least one study of neighborhood civic participation suggests that a history of
mental distress predicts later civic engagement (Greenberg, 2001). With respect to other health
outcomes, the relationship between civic participation and health is similarly murky – while a
number of studies have found it to be positively associated with positive health outcomes,
several have also demonstrated no association between the two and at least a handful of
studies find negative effects to health. Importantly, social resources have been found to temper
the likelihood of distress in immigrant populations and may be useful for securing employment
and accelerating social integration in these populations (Ritsner et al., 2000; Whitley and
McKenzie, 2005).
3.33 Individual Behaviors & Characteristics
Although ethno-racial group and socioeconomic status appear to have clear
relationships with psychological distress, the associations of age and sex are less
straightforward. While women are consistently found to have higher levels of distress than men
(Mirowsky and Ross, 2003a), this association appears to depend on age; the gap appears to be
most distinct in young adulthood and to dissipate across the life course, even among immigrant
70

populations (Ritsner et al., 1999; Jorm et al., 2005). However, other studies focusing on age
without respect to sex have found a U-shaped pattern of distress and depression in which
middle-age adults are the least likely to experience distress compared with younger and older
populations (Mirowsky and Ross, 2003a). Low socioeconomic status is more clearly associated
with a likelihood of experiencing distress: less education, lower occupational status and lower
income are predictors of distress - but there are diminishing returns to income (Mirowsky and
Ross, 2003a). Nonwhite populations have also been shown to be more likely to experience
distress, even when accounting for socioeconomic status, and nonwhite populations with
relatively low social status are at even greater risk for distress (Kessler and Neighbors, 1986;
Bratter and Eschbach, 2005; Marcelli et al., 2009b).
These characteristics combine with the potential for discrimination and acculturation
stress in migrant populations, which may exacerbate any tendency to psychological distress.
Marcelli and colleagues (2007; 2009b; 2009a) have found a higher prevalence of psychological
distress in Brazilian, Dominican and Mexican migrants than in the U.S. population on average,
as well as an even higher prevalence among unauthorized immigrants in each nativity group.
Additional studies have found that a climate of fear or discrimination in immigrant communities
related to restrictionist immigration policy and attitudes not only increases the likelihood of
distress among unauthorized migrants, but also among legal and naturalized residents
(Rodriguez and DeWolfe, 1990; Finch et al., 2000a). Seventy-one percent of the Brazilian
migrants in my study population are unauthorized residents and have a lower socioeconomic
status than the population of metropolitan Boston on average; and it may be that the unique
71

immigration-related stressors they face compound any widespread potential for psychological
distress.
Finally, individual health behaviors likely moderate any connections between
neighborhood characteristics and psychological distress. In this study I control for regular
physical activity, smoking and sleeping fewer than seven hours a night during an average week.
Physical activity may buffer the effects of distress, while smoking and unhealthy sleep durations
have been shown to be positively associated with distress (Cosci et al., 2009; Hamer et al.,
2009; Hill et al., 2009; Buxton and Marcelli, 2010; Hamer et al., 2010; Puustinen et al., 2011).
3.4 Data and Methods
As in Chapter 2, I use data from the Boston Metropolitan Immigrant Health & Legal
Status Survey (BM-IHLSS) to investigate psychological distress among Brazilian adult migrants.
3.41 Measures
Serious psychological distress is measured using the K6 scale (Kessler et al., 2002), which
includes six questions asking, “during the past 30 days, how often did you feel…” (1) sad; (2)
nervous; (3) restless; (4) hopeless; (5) everything was an effort; and (6) worthless. The scale
employs Likert-style responses ranging from (0) none of the time to (4) all of the time, for a
possible total cumulative value of 24, where a score of 13 or higher indicates serious
psychological distress. My outcome variable (Distress) is a dichotomous variable where “0”
corresponds to a score of 12 or less on the K6 scale, and “1” corresponds to a score of 13 or
higher, indicating that the respondent is experiencing serious psychological distress.
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Neighborhood environment. The BM-IHLSS data employ two definitions of
neighborhood – first, survey respondents were asked to evaluate the characteristics of “your
neighborhood,” i.e. the boundaries were subjectively defined by the participants; second, we
linked the BM-IHLSS data to block-level Summary File 1 (SF1) data from the 2000 census in
order to evaluate population density, minority composition and homeownership rates. These
census variables were tested in earlier analyses but are not included in my final results as they
proved neither significant nor essential for determining psychological distress in my models. In
light of my hypotheses, three neighborhood-level variables are employed – neighborhood
disorder, neighborhood values, and familiarity with neighbors. Neighborhood disorder is
measured using a dichotomous variable indicating whether respondents or their neighbors had
experienced personal violence, had their homes broken into, had experienced property damage
or had property stolen from them in their current neighborhood. As indicated in Chapter 2, the
context in which these questions were asked of survey respondents was designed to elicit an
assessment of neighborhood-level disorder rather than personal experiences on the part of the
migrants. The latter two variables are taken from an index of social cohesion which asks
neighborhood residents to report whether they (0) strongly disagree, (1) disagree, (2) agree or
(3) strongly agree with four questions about the neighborhood environment, i.e. whether
neighbors : (1) get along with each other; (2) are willing to help each other; (3) share the same
values; and (4) know each other (Sampson and Raudenbush, 1999). Neighborhood values is a
dichotomous variable indicating whether respondents agree or strongly agree that neighbors
share the same values, and neighborly is a dichotomous variable indicating whether
respondents agree or strongly agree that their neighbors know each other. As opposed to
73

chapters 2 and 4 in which I measure social cohesion as one composite indicator incorporating
each of the four measures in the social cohesion scale, here I separate the four items for two
reasons – first, when looking at the variance inflation factors for the explanatory variables, the
composite measure of cohesion appeared to be multicollinear with other variables in the
model; second, more has been written on psychological health and neighborhood social capital
than for the other measures I investigate in this dissertation, but there is little evidence to
determine which properties of social capital may be most important for buffering stress. I
therefore measured each component of social cohesion in earlier analyses and found
neighborhood values and neighborly to be most important for protecting against distress.
Respondents’ length of residence, or the number of years and months a subject had resided in
the current neighborhood, is also included in my model to account for any effect that time in
the neighborhood environment may have on psychological distress.
Demographic characteristics. Three individual exogenous characteristics are included in
the final model – age, sex and skin color. Age is a continuous variable indicating respondent
years of life, and sex is a dichotomous variable representing females (0) and males (1). Subject
skin color was measured using the New Immigrant Survey Skin Color Scale (Massey and Martin,
2003); respondents were shown a picture of ten human hands numbered 1-10 with the skin
pigmentation growing increasingly darker from left to right along the scale, and asked to point
to the hand that most resembled their own pigmentation.
Home, social capital and individual socioeconomic characteristics. Five additional
variables are included to control for the influence of socioeconomic characteristics and other
life domains on psychological distress. First, the proportion of dependent children (Dependent
74

child) in the home is measured by dividing the number of children by total household members.
Second, civic participation is a dichotomous measure of social capital indicating whether a
subject participated in one or more civic groups over the previous 12 months. Next, educational
attainment (College) is measured using a dichotomous variable indicating whether or not the
subject received his or her Bachelor’s degree, and earnings is a continuous measure tallying a
subject’s earnings (in thousands of dollars) from all jobs in the previous year. Finally,
unauthorized legal status (Unauthorized) is included as a dichotomous variable indicating
whether or not the subject was legally authorized to reside in the United States.
Health status and behavior. As I have hypothesized, physiological health is likely to
modify any identified relationships between neighborhood environment and psychological
distress. In my initial set of models, which include all 307 adult Brazilian migrant subjects
surveyed in the BM-IHLSS, I employ one measure of physiological health – obesity (Obese) – a
dichotomous variable signifying whether a subject’s body mass index was greater than or equal
to 30. In my third set of models, which is limited to the population of Brazilian migrant adults
for which I have appropriate biological data (n=142), two additional biological markers are
added – high-sensitivity C-reactive protein (High CRP) and hypertension. The former is a
continuous measure representing a subject’s hsCRP level and the latter is a dichotomous
variable indicating whether a subject had systolic blood pressure greater than 139mmHg,
diastolic blood pressure greater than 89mmHg, or had ever been told by a doctor or health
professional that they had high blood pressure. I additionally include three behaviors that may
have particular salience for psychological and physiological health in my final model – physical
activity, smoking and sleep behavior (Short sleep). Physical activity is a dichotomous variable
75

indicating whether a subject engaged in at least 30 minutes of moderate exercise or 20 minutes
of vigorous exercise in the previous week; smoking is a dichotomous variable indicating
whether a subject currently smokes every day; and short sleep is measured with a dichotomous
variable indicating whether a subject “usually” slept less than seven hours on workdays or non-
workdays.
3.42 Statistical Analyses
Descriptive and multivariate (weighted) regression results for cross-sectional analyses
are shown below. Logistic regressions were performed using Stata 10. Stata’s “cluster” function
was employed to control for bias that may occur as a result of multiple respondents residing in
the same census block (Huber, 1967). The variance inflation factor (VIF) command was used
after each regression to measure multicollinearity among the independent variables. Three sets
of models were fitted for these analyses: first, I investigate psychological distress in the entire
Brazilian migrant adult sample (n=307) without controlling for biological stress markers; second,
I investigate distress in the population of Brazilian adults from which we obtained dried blood
spots and blood pressure readings (n=142), still without including the biological markers in the
model; and third, I measure distress in the latter population, including C-reactive protein and
hypertension as controls. Within each of these sets of models, I further test three different
logistic regression models – Model 1 controls for neighborhood environment and length of
neighborhood residence along with individual exogenous characteristics, Model 2 adjusts for
home and individual socioeconomic characteristics as well as social capital, and Model 3
controls for all listed variables in addition to health conditions and behaviors. The hypothesized
relationship between each explanatory variable and psychological distress is indicated by a (+)
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for a positive association, (-) for a negative association or (±) for a bi-directional association.
These symbols also indicate whether a one- or two-tailed test was performed in each case.
3.5 Results
3.51 Descriptive Statistics
Table 3.1 below shows individual characteristics and behaviors as well as neighborhood
characteristics for all foreign-born Brazilian residents of the BCQ MSA, and also for the
subpopulation representing those who provided biological data. Significant differences in
variable means for the populations with and without distress are indicated with an asterisk in
the rightmost column. Each population is estimated to have a higher prevalence of distress than
the U.S. population: specifically seven percent of all Brazilian migrants exhibit serious
psychological distress and eight percent of the smaller population did, compared with three
percent of all Americans (Dey and Lucas, 2006). In terms of exogenous characteristics, the mean
age for both groups is 33 years and both report relatively light skin pigmentation (≈2.2 out of
10). In the full adult Brazilian migrant population, slightly over half (54 percent) are male. And
sixty percent of the subpopulation is male. Both populations have lived in their neighborhoods
a similar number of years (≈2.5). Adults in the subpopulation appear to live in neighborhoods
with slightly higher levels of disorder (28 versus 26 percent) and are less likely to report having
neighbors who share their values (35 versus 49 percent). On the other hand, the subpopulation
is slightly more likely to agree that their neighbors know each other (57 versus 55 percent).

77

With respect to domains of life other than the neighborhood, those in the biological
population had a slightly smaller proportion of children in the household (0.11 versus 0.12) but
a larger proportion were involved with at least one civic organization (59 versus 56 percent).
Socioeconomically, eight percent of adults in the biological population versus 12 in the total
population were graduated from college, average annual earnings in the biological population
were $34,000 compared with $33,000 in the total population and 75 percent in the biological
population were unauthorized to reside in the USA compared with 71 percent of all Brazilian
adults. Finally, six percent of adults in the biological population had either diagnosed or
measured hypertension and the mean level of hsCRP in this group is 2.32 mg/L, within the
normal risk range for CRP levels (Pearson et al., 2003). For both populations, 27 percent of
adults reported engaging in moderate or vigorous physical activity in the past week, and
approximately 15 percent reported smoking every day. Slightly greater proportions of the
biological population were obese (15 versus 11 percent) and reported sleeping less than seven
hours on average (30 versus 23 percent). Bivariate differences of means tests comparing the full
population to the biological population show significant differences in the means of three
variables at the 99 percent confidence level – neighborhood values, college and obese – and at
the 90 percent level for short sleep.
78

Table 3.1 Weighted Descriptive Statistics

μ σ Min. Max. μ σ Min. Max.
OUTCOME VARIABLE
Distress
Distress=1 if subject's score on the Kessler 6 (K6) scale>12 (range=0-24), indicating serious
psychological distress 0.07 - 0.00 1.00 0.08 - 0.00 1.00
Distres Score from 0-24 on K6 scale (continuous) 4.03 5.40 0.00 24.00 4.20 5.85 0.00 24.00
NEIGHBORHOOD CHARACTERISTICS
Length of residence (-) Number of years and months subject has resided in neighborhood 2.42 2.25 0.00 20.00 2.38 2.26 0.08 20.00
Disorder (+)
Disorder=1 if subject or neighbors experienced personal violence, had their homes broken into,
had anything stolen from their property or experienced damage to their personal property in
the neighborhood 0.26 - 0.00 1.00 0.28 - 0.00 1.00
Neighborhood values (-) Neighborly=1 if subject agrees or strongly agrees that neighbors share the same values 0.49 - 0.00 1.00 0.35 - 0.00 1.00
Neighborly (-) Neighborhood values=1 if subject agrees or strongly agrees that neighbors know each other 0.55 - 0.00 1.00 0.57 - 0.00 1.00
INDIVIDUAL EXOGENOUS CHARACTERISTICS
Age (+) Subject age in years 33.66 9.88 19.00 69.00 33.37 9.74 20.00 58.00
Male (-) Sex=1 if subject reported sex as male 0.54 - 0.00 1.00 0.60 0.00 1.00
Skin color (+) Self-reported subject skin color, measured from lightest (1) to darkest (10) 2.17 1.35 1.00 7.00 2.20 1.38 1.00 7.00
HOME, SOCIAL CAPITAL & INDIVIDUAL SOCIOECONOMIC CHARACTERISTICS
Dependent children (±) Ratio of children to total persons in household 0.12 0.18 0.00 0.75 0.11 0.19 0.00 0.75
Civic participation (±)
Civic participation=1 if subject participated in one or more civic organizations in the previous 12
months 0.56 - 0.00 1.00 0.59 - 0.00 1.00
College (-) College=1 if subject has a Bachelors degree or higher 0.12 - 0.00 1.00 0.08 - 0.00 1.00 *
Earnings (-) Subject earnings from all jobs in 2006 (Thousands of dollars) 33.21 22.96 0.00 150.00 34.18 24.76 0.00 150.00 *
Unauthorized (+) Unauthorized=1 if subject is unauthorized to reside in the USA 0.71 - 0.00 1.00 0.75 - 0.00 1.00
INDIVIDUAL HEALTH STATUS & BEHAVIOR
hsCRP (+) Level of high-sensitivity C-reactive protein - - - - 2.32 2.98 0.18 18.75 *
Hypertension (+)
Hypertension=1 if subject's systolic blood pressure>139 mmHg, diastolic blood pressure>89
mmHg or subject has ever been diagnosed with hypertension by a health professional - - - - 0.06 - 0.00 1.00
Obese (+) Obese=1 if subject's body mass index (BMI)≥30 0.11 - 0.00 1.00 0.15 - 0.00 1.00 *
Physical activity (-)
Physical activity=1 if subject engaged in moderate (30 min) or vigorous (20 min) exercise at
least one day during the previous week 0.27 - 0.00 1.00 0.27 - 0.00 1.00
Smoking (±) Smoking=1 if subject smokes every day 0.15 - 0.00 1.00 0.16 - 0.00 1.00
Short sleep (+) Short sleep=1 if subject sleeps<7 hours per night on average 0.23 - 0.00 1.00 0.30 - 0.00 1.00 *
N (Weighted)
N (Unweighted)
*Difference in means is statistically significant, p<0.05
27,877
142
All Brazilian Migrant Adults
61,335
307
Brazilian Migrant Adults with
Biological Data
79

3.52 Logistic Regression Results
Table 3.2 shows the results of my first set of logistic regression models evaluating the
relationships between neighborhood environment and psychological distress for adult Brazilian
migrants. In line with my first hypothesis, I find that my two measures of neighborhood social
cohesion – sharing neighborhood values (VALUES) and knowing one’s neighbors (NEIGHBORLY)
– are negatively and significantly associated with psychological distress. Specifically, each of
these factors is negatively related to a seven percent lower probability of having been seriously
psychologically distressed among foreign-born Brazilian adults. Interestingly, knowing one’s
neighbors only becomes significant after including health controls in my model, suggesting that
rather than wiping out any effects of neighborhood on distress, health status and behaviors
instead clarify the relationship. Physical activity, for example, is often studied in neighborhood
contexts as the neighborhood environment may partially determine an individual’s propensity
to exercise; but one may engage in physical activities outside the neighborhood. In either case,
it has also been shown to have a protective effect for distress (Hamer et al., 2009; Puustinen et
al., 2011), and controlling for this elucidates the association between being “neighborly” and
the probability of experiencing distress here. Although there is a positive relationship between
distress and neighborhood disorder – my second hypothesis – this association is not significant.
Length of neighborhood residence, however, demonstrates a significant negative relationship
with distress, indicating that for every 2.25 years of additional residence the probability of
experiencing psychological distress decreases by about four percent.

80

Regarding other individual and sociogeographic level factors, age (specifically 10
additional years) is significantly and positively associated with an approximately two percent
greater probability of serious psychological distress. Being involved with at least one civic
organization in the previous year and having higher earnings were both negatively and
significantly associated with distress. Alternatively, unauthorized migrants had a five percent
greater probability of experiencing serious psychological distress. This finding is especially
compelling as there is a 12 percent greater probability (O.R.: 6.03, p-value<.10) that
unauthorized migrants are more likely to experience distress when controlling only for
exogenous characteristics, suggesting that the neighborhood social environment may diminish
the effect of legal status on distress for this population. In terms of health status and behavior,
obesity and smoking are both positively associated with distress, but these relationships are not
statistically significant at a 90 percent confidence level. However, sleeping less than seven
hours on average during work days or non-work days is significantly associated with a seven
percent increase in the probability of having been distressed. Physical activity on the other
hand is negatively associated, showing a 12 percent lower probability of having been seriously
psychologically distressed.
81

Table 3.2 Logistic regression of psychological distress on neighborhood and other characteristics, full sample (n=307)
ß R.S.E. Prob. ß R.S.E. Prob. ß R.S.E. Prob. O.R.
NEIGHBORHOOD CHARACTERISTICS
Length of residence (-) -0.439 (0.214) -6.67%
**
-0.331 (0.202) -5.02%
*
-0.313 (0.175) -4.75% 0.732
**
Disorder (+) 0.544 (0.529) 3.68% 0.333 (0.505) 2.25% 0.519 (0.486) 3.51% 1.681
Neighborhood values (-) -0.988 (0.577) -6.69%
**
-1.203 (0.604) -8.14%
**
-1.072 (0.569) -7.25% 0.342
**
Neighborly (-) -0.400 (0.502) -2.71% -0.535 (0.460) -3.62% -1.054 (0.504) -7.13% 0.349
**
INDIVIDUAL CHARACTERISTICS
Age (+) 0.046 (0.024) 3.10%
**
0.048 (0.022) 3.23%
**
0.036 (0.023) 2.43% 1.037
*
Male (-) -0.100 (0.516) -0.68% 0.181 (0.540) 1.22% 0.036 (0.623) 0.24% 1.036
Skin color (+) -0.101 (0.192) -0.92% -0.223 (0.194) -2.03% -0.193 (0.204) -1.76% 0.825
HOME, SOCIAL CAPITAL & INDIVIDUAL SOCIOECONOMIC CHARACTERISTICS
Dependent child (±) 1.004 (1.140) 1.25% 1.595 (1.118) 1.99% 4.926
Civic participation (±) -1.202 (0.646) -8.13%
**
-1.214 (0.736) -8.21% 0.297
*
College (-) -1.244 (0.976) -8.42% -1.304 (1.350) -8.82% 0.272
Earnings (-) -0.054 (0.015) -8.36%
***
-0.051 (0.019) -7.97% 0.950
***
Unauthorized (+) 0.654 (0.515) 4.42% 0.793 (0.600) 5.36% 2.209
*
HEALTH STATUS & BEHAVIOR
Obese (+) 0.865 (0.713) 5.85% 2.376
Physical activity (-) -1.785 (0.833) -12.08% 0.168
**
Smoking (±) 0.688 (0.626) 4.65% 1.989
Short sleep (+) 1.105 (0.697) 7.47% 3.018
*
Constant term (±) -2.628 (0.795) -1.158 (0.963) -1.202 (0.975)
Concordant Pairs
Prob > chi2
Pseudo R2
* p≤.10 ** p≤.05 *** p≤.01
Model 1 Model 2 Model 3
0.926 0.930 0.935
0.108 0.200 0.271
0.007 0.000 0.000
82

Table 3.3 exhibits the same set of logistic regression models, but restricting the sample
to those adult Brazilian migrant subjects who provided blood and blood pressure data in the
2007 BM-IHLSS survey. In this population (weighted sample) shared neighborhood values is the
only indicator of neighborhood environment that has a significant relationship with
psychological distress. Additionally, age, civic participation and unauthorized legal status are no
longer significant even though this population has a slightly larger proportion of unauthorized
migrants (74 percent) compared to the total population (71 percent). Physical activity remains
significant, but short sleep is no longer significant; instead it trades places with smoking, which
is associated with a four percent greater likelihood of experiencing distress.
Finally, Table 3.4 shows results of my final set of logistic regression models, also
restricted to the biological population but controlling for levels of high-sensitivity C-reactive
protein and hypertension. The same four relationships remain significant as in Table 3.3 –
neighborhood values are negatively and significantly related to experiencing distress, as are
earnings and physical activity, while smoking is positively associated. Returning to my third
hypothesis, there does not appear to be a substantial mediating role of CRP or hypertension
with respect to serious psychological distress. While the effect sizes for neighborhood values
and disorder do shrink slightly after controlling for biological markers of stress, the relationships
do not change substantially. Furthermore, though CRP and hypertension are positively
associated with distress as I would expect, neither relationship is significant. Instead, the main
differences in association seem to occur in the health behaviors exhibited by this population.
Nevertheless, corresponding to my first hypothesis, neighborhood values (one marker of social
cohesion) demonstrates a significant inverse relationship with distress across all three sets of
83

models, strongly suggesting that how individuals view and relate to the people with whom they
share residential space may impact their mental health outcomes.
84

Table 3.3 Logistic regression of psychological distress on neighborhood and other characteristics, biological population (n=142)

ß R.S.E. Prob. ß R.S.E. Prob. ß R.S.E. Prob. O.R.
NEIGHBORHOOD CHARACTERISTICS
Length of residence (-) -0.549 (0.340) -9.19%
*
-0.451 (0.399) -7.56% -0.374 (0.391) -6.25% 0.688
Disorder (+) 0.153 (1.055) 1.13% 0.462 (1.194) 3.42% 0.606 (1.259) 4.48% 1.833
Neighborhood values (-) -0.697 (0.770) -5.16% -0.689 (0.728) -5.10% -1.253 (0.686) -9.28% 0.286
**
Neighborly (-) -0.013 (0.867) -0.10% -0.167 (0.693) -1.24% -0.334 (0.607) -2.47% 0.716
INDIVIDUAL CHARACTERISTICS
Age (+) 0.030 (0.043) 2.16% 0.025 (0.035) 1.77% -0.053 (0.042) -3.80% 0.949
Male (-) -0.120 (0.677) -0.89% 0.503 (0.755) 3.72% 0.441 (1.133) 3.26% 1.554
Skin color (+) 0.168 (0.203) 1.71% 0.016 (0.221) 0.16% 0.268 (0.240) 2.74% 1.308
HOME, SOCIAL CAPITAL & INDIVIDUAL SOCIOECONOMIC CHARACTERISTICS
Dependent child (±) -1.204 (1.868) -1.65% 0.917 (1.974) 1.26% 2.501
Civic participation (±) -1.477 (1.125) -10.93%
*
-0.977 (1.129) -7.23% 0.376
College (-) 1.058 (1.901) 7.83% -0.013 (3.250) -0.10% 0.987
Earnings (-) -0.070 (0.035) -12.91%
**
-0.051 (0.020) -9.31% 0.950
***
Unauthorized (+) 1.987 (1.311) 14.71%
*
1.780 (1.853) 13.18% 5.931
HEALTH STATUS & BEHAVIOR
Obese (+) 1.032 (0.822) 7.64% 2.807
Physical activity (-) -1.989 (1.177) -14.72% 0.137
**
Smoking (±) 2.708 (1.287) 20.04% 14.996
**
Short sleep (+) 0.650 (1.499) 4.81% 1.916
Constant term (±) -2.645 (1.391) -1.725 (1.868) -1.232 (2.963)
Concordant Pairs
Prob > chi2
Pseudo R2
* p≤.10 ** p≤.05 *** p≤.01
Model 1 Model 2 Model 3
0.920 0.918 0.959
0.099 0.222 0.364
0.457 0.491 0.000
85

Table 3.4 Logistic regression of psychological distress on neighborhood characteristics, controlling for biological health markers (n=142)

ß R.S.E. Prob. ß R.S.E. Prob. ß R.S.E. Prob. O.R.
NEIGHBORHOOD CHARACTERISTICS
Length of residence (-) -0.549 (0.340) -9.19%
*
-0.451 (0.399) -7.56% -0.370 (0.394) -6.20% 0.691
Disorder (+) 0.153 (1.055) 1.13% 0.462 (1.194) 3.42% 0.524 (1.163) 3.88% 1.688
Neighborhood values (-) -0.697 (0.770) -5.16% -0.689 (0.728) -5.10% -1.229 (0.607) -9.10% 0.293
**
Neighborly (-) -0.013 (0.867) -0.10% -0.167 (0.693) -1.24% -0.260 (0.635) -1.92% 0.771
INDIVIDUAL CHARACTERISTICS
Age (+) 0.030 (0.043) 2.16% 0.025 (0.035) 1.77% -0.052 (0.041) -3.76% 0.949
Male (-) -0.120 (0.677) -0.89% 0.503 (0.755) 3.72% 0.483 (1.092) 3.58% 1.621
Skin color (+) 0.168 (0.203) 1.71% 0.016 (0.221) 0.16% 0.260 (0.222) 2.65% 1.297
HOME, SOCIAL CAPITAL & INDIVIDUAL SOCIOECONOMIC CHARACTERISTICS
Dependent child (±) -1.204 (1.868) -1.65% 1.006 (2.175) 1.38% 2.734
Civic participation (±) -1.477 (1.125) -10.93%
*
-0.975 (1.133) -7.22% 0.377
College (-) 1.058 (1.901) 7.83% -0.080 (3.251) -0.59% 0.923
Earnings (-) -0.070 (0.035) -12.91%
**
-0.050 (0.019) -9.18% 0.951
***
Unauthorized (+) 1.987 (1.311) 14.71%
*
1.720 (1.740) 12.73% 5.584
HEALTH STATUS & BEHAVIOR
hsCRP (+) 0.029 (0.191) 0.26% 1.030
Hypertension (+) 0.903 (0.841) 6.68% 1.433
Obese (+) 0.360 (1.295) 2.66% 2.467
Physical activity (-) -1.951 (1.245) -14.44% 0.142
*
Smoking (±) 2.711 (1.327) 20.07% 15.051
**
Short sleep (+) 0.618 (1.472) 4.57% 1.855
Constant term (±) -2.645 (1.391) -1.725 (1.868) -1.293 (2.944)
Concordant Pairs
Prob > chi2
Pseudo R2
* p≤.10 ** p≤.05 *** p≤.01
Model 1 Model 2 Model 3
0.920 0.918 0.959
0.099 0.222 0.364
0.457 0.491 0.000
86

3.6 Discussion
In this paper I utilize data from the 2007 Boston Metropolitan Immigrant Health and
Legal Status Survey (BM-IHLSS), a community-based migrant household probability sample of
Brazilian migrants in the Boston-Cambridge-Quincy metropolitan statistical area, to investigate
the relationships between neighborhood environment and psychological distress. Specifically, I
hypothesized that neighborhood social cohesion is likely to protect against serious
psychological distress, and conversely, neighborhood disorder may help induce it. I further
hypothesized that two biological markers of stress – CRP and hypertension – would moderate
any observed relationships between the neighborhood environment and distress. My results
demonstrate a strong negative association between elements of neighborhood social cohesion
and the experience of serious psychological distress in the full weighted sample and in the
weighted subsample of study participants who provided biological data. These relationships
persist even after controlling for a variety of behaviors, including health behaviors, such as
smoking and engaging in physical activity. This suggests that certain attributes of peoples’
neighborhoods, specifically those that foster a sense of community or place attachment, may
indeed be protective of psychological health.
On the other hand, I did not find neighborhood disorder to have a significant association
with distress as expected. Many previous studies have demonstrated a positive relationship
between distress and disorder, but few of these have accounted for social relations between
neighbors in their analyses. It may be the case that place-based social cohesion mitigates the
threat of disorder, or that social cohesion is less likely to exist in neighborhoods with higher
levels of disorder. Although I included measures of the neighborhood socioeconomic
87

environment in my initial models, the BM-IHLSS data do not permit extensive analysis of
physical attributes of the neighborhood that may shed additional light on various types of
disorder following the broken windows hypothesis (Wilson and Kelling, 1982).
Finally, it is difficult to draw conclusions about my third hypothesis, that biological
markers of stress (CRP and hypertension) moderate the relationships between distress and
neighborhood characteristics, because there appear to be some differences between the full
Brazilian adult population and the population that provided blood samples that may alter the
interpretation of results. For example, seven percent of those who provided blood have a
bachelor's degree compared to 17 percent of those who did not, a statistically significant
difference. Similarly, 17 percent of the biological population is obese compared to four percent
of the full adult population. When comparing exogenous characteristics and legal status the
total adult population is nearly identical to the subpopulation; however, differences on
measures like those mentioned above are important to explore further. It may be that non-
sampling errors, such as interviewer effects, influenced the likelihood of obtaining biological
data from some respondents (Groves et al., 2004). Even so, two things seem clear from the
results: (1) shared neighborhood values remain significantly and negatively associated with
psychological distress in this population, even after controlling for biological markers; and (2)
health behaviors, in particular smoking and physical activity, which I would expect to be
influential with respect to inflammation, play an important role in predicting psychological
distress among those in the biological population.

88

This study is subject to several limitations. First, the data are cross-sectional, making it
impossible to firmly establish the causal direction of the observed associations. Although I
conclude that neighborhood social cohesion protects against psychological distress, poor
psychological health may also lead individuals to assess their neighbors in a less positive light.
Second, my study population is unique, and although representative of Brazilian migrants in
New England, it is not representative of the population as a whole. I therefore cannot argue
with any certainty that the same factors that predict serious psychological distress for Brazilian
migrants will do so for the entire population. Additionally, I have biological data for only half of
the original sample, making it difficult to draw perfect comparisons between the two. Finally, I
acknowledge that the theoretical approach I take in this chapter is broad, encompassing a
variety of ideas that I cannot adequately test in my empirical model. For example, the BM-IHLSS
data do not include detailed information about places in the neighborhood that participants
may frequent, which may contribute to their perceptions of the neighborhood environment,
nor do the data include tangible information on various physical aspects of the environment,
e.g. building facades, graffiti, etc. However, these limitations leave the way open for a wealth of
future research on particular spatial, cultural and longitudinal associations between
neighborhoods and psychological health among migrants and other populations.
Despite my study limitations, I am confident that my results underline an aspect of
neighborhood environments that may be especially important for promoting mental health in
vulnerable populations. In particular, my study highlights the important contribution of social
engagement in elaborating the manner in which neighborhoods, and the broader civic arena,
influence psychological health. My results also demonstrate an avenue by which studies of the
89

neighborhood built environment may be enhanced, i.e. health behaviors are clearly important
to explaining the relationships between biological measures of stress, neighborhoods and
overall health; studies of health behavior and the built environment would do well to include
biological markers as potential mediating factors. Furthermore, these findings suggests that
viewing the neighborhood as a potentially therapeutic landscape, predicated on ascribed
meanings of place and social interactions in space, offers a compelling way forward for research
on neighborhoods and health. Data collection programs that combine indicators of built and
social environments along with objective measures of health, and that include younger,
healthier populations to elucidate chronic disease etiologies, have the potential to yield useful
information for health policy formation and place-based interventions.
90

Chapter 4
Effects of Neighborhood Social Cohesion and
Disorder on Current Smoking

91

4.1 Introduction
Smoking tobacco is one of the deadliest health behaviors in which people can engage.
Smoking accounts for more than 25 percent of deaths in men and more than 15 percent of
deaths in women over the age of 35 in the United States. Cigarettes kill more than half of
lifetime smokers and account for billions in health care costs and billions more in lost
productivity (Centers for Disease Control and Prevention (CDC), 2002). Smoking is also a
primary risk factor for the top four leading causes of death – cardiovascular, cerebrovascular
and lower respiratory diseases and cancer (Centers for Disease Control and Prevention (CDC),
1984; Bergen and Caporaso, 1999; Heron et al., 2006). Although smoking rates in the USA have
been on the decline for several decades from peaks of more than 60 percent among men in the
1940s and 40 percent among women in the 1960s, smoking behavior persists among a segment
of the population (Giovino, 2002). According to the latest National Health Interview Survey
data, 18 percent of the adult population in the USA currently smokes. Women remain less likely
to smoke than men – 15.9 versus 20.3 percent – but this gap has closed over the years,
especially as women have taken up smoking at younger ages (National Center for Health
Statistics, 2013).
Smoking is therefore important to study for its direct negative impact on health, but
also because it is one of the most preventable risk factors for disease at individual and
population levels (Bergen and Caporaso, 1999; Ebbert and Hays, 2008). Anti-smoking policies
implemented in recent years at local, state and federal levels seem to bear this out. To date 30
states have passed smoking bans in enclosed public places like bars, restaurants and other
workplaces, and thousands of municipalities have enacted their own anti-smoking legislation
92

(American Nonsmokers Rights Foundation, 2012). These types of policies, though still relatively
new in many places, appear to have had substantial impacts on smoking behavior. Farrelly et al.
(2008), for example, find that smoking cessation policies are associated with reductions in
smoking among adults over the age of 25, while increasing cigarette prices is associated with
reductions in smoking among people ages 18-24. Juster et al. (2007) further find an eight
percent decrease in hospital visits for myocardial infarction (i.e. heart attack) in New York state
as a result of that state’s smoking ban. For those individuals who manage to quit, smoking does
not have to be a death sentence. Cardiovascular and cerebrovascular disease risk decrease
dramatically following smoking cessation; after approximately a decade former smokers may
have levels of cardiovascular risk identical to people who never smoked, while cerebrovascular
disease risk decreases even more quickly (Centers for Disease Control and Prevention (CDC),
1984).
However, success in quitting smoking and the propensity to pick up smoking are not
evenly distributed throughout the population. In contradiction to the health advantage often
observed among non-Hispanic white individuals in the USA, this population is more likely to
smoke than any other, with the possible exception of American Indians (Siahpush et al., 2010;
National Center for Health Statistics, 2013). On the other hand, low socioeconomic status (SES),
whether measured by income, education or occupation, is a salient predictor of current
smoking, consistent with many other health outcomes and behaviors. Lower SES populations
are more likely to smoke, more likely to continue smoking for longer durations and less likely to
find success in completing cessation programs (Barbeau et al., 2004a; Kendzor et al., 2010;
Siahpush et al., 2010; Hiscock et al., 2012). Furthermore, individuals employed in lower wage
93

occupations more often work in environments lacking sufficient anti-smoking policies and may
experience greater exposure to second-hand smoke (Gerlach et al., 1997; Barbeau et al.,
2004b); and lower wage workers who themselves smoke appear to do so for longer durations
than higher wage workers (Siahpush et al., 2005). The reasons for this persistent disparity are
not entirely clear, though greater exposure to sociogeographic stressors, less access to health-
related and financial resources, targeted advertising and promotion by tobacco companies and
the greater likelihood of belonging to a social network that includes other smokers are all
considered important contributing factors (Perkins and Grobe, 1992; Barbeau et al., 2005;
Feldner et al., 2007; Christakis and Fowler, 2008; Pearce et al., 2012).
Latin American immigrants, who tend to be lower wage workers with lower
socioeconomic position than the U.S. population on average, and who come from regions of the
world that often exhibit higher smoking rates than those in the USA, are an understudied
segment of the U.S. population with respect to smoking behavior. Foreign-born Latin Americans
are more often employed in workplaces without anti-smoking policies, largely because these
migrants are disproportionately employed in service, construction and trade occupations
(Gerlach et al., 1997; Osypuk et al., 2009). The limited research that addresses smoking with
Latino immigrants has found that women are less likely to smoke than men (Perez-Stable et al.,
2001) and that Latina immigrants are less likely to smoke than the U.S. population on average
(Perez-Stable et al., 1994), but that Latino immigrants are also more likely to under-report their
smoking habits (Perez-Stable et al., 1990) and that more acculturated women but less
acculturated men were more likely to smoke (Marin et al., 1989). The tobacco industry has also
developed strategies to market directly to foreign-born Latin American populations in the USA,
94

using such tactics as identifying immigrant enclaves to focus advertisements in those
communities and conducting a series of studies designed to track differences in tobacco use
and brand preference among “assimilated” and “non-assimilated” migrants in order to exploit
cultural differences in behavior (Acevedo-Garcia et al., 2004). In this chapter I once again
employ data from the 2007 Boston Metropolitan Immigrant Health and Legal Status Survey
(BM-IHLSS) to investigate smoking habits among foreign-born Brazilian adults, including
differences by legal status.
Tobacco smoking in Latin America has recently been called "pandemic" (Müller and
Wehbe, 2008), and appears to be a growing trend among young women in particular in several
Latin American countries. Furthermore, Latin America has become a prime tobacco-producing
area – Brazil being the second largest producer in the world – which has allowed the black
market in tobacco to thrive. Nevertheless, the smoking prevalence rate in Brazil is estimated at
17 percent, just below that in the USA (18 percent), with women smoking at lower rates than
men, 13 versus 22 percent. Additionally, Brazil instituted a comprehensive smoking ban in most
public places as early as 1996 and currently bans the advertisement of tobacco products in
most media, which may partially account for the relatively low smoking rates in the Brazilian
population compared with other South American countries (e.g. 34 percent in Chile, 27 percent
in Argentina) (Müller and Wehbe, 2008; World Health Organization (WHO), 2011). In my sample
of Brazilian adults, the smoking prevalence rate falls between the Brazil and U.S. rates at 17.7
percent. The gap between current smoking among men and women in the Brazilian migrant
population is slightly greater than that in the U.S. population, however, and nearly identical to
the rates in Brazil with 21.2 (20.3) percent of Brazilian (U.S.) men reporting that they currently
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smoke, compared with 13.8 (15.9) percent of Brazilian (U.S.) women (Marcelli et al., 2009b;
National Center for Health Statistics, 2013). Legal Brazilian migrant men were most likely to
report being current smokers at 24 percent, which contradicts earlier research that found more
acculturated male Latino immigrants to be less likely to smoke, if legal status can be used as a
proxy for acculturation. However, most of these earlier studies were conducted with Mexican
and Caribbean nativity groups. Although the difference in overall smoking prevalence is small,
the slightly higher rate of smoking among Brazilians in the USA (17.7 percent) as compared to
Brazil (17.0 percent) suggests that smoking may be one behavior that does not adhere to
depictions of the "healthy migrant," or the idea that immigrants to the USA tend to be healthier
than the population in their home countries, on average (Jasso et al., 2004). One weakness of
the healthy migrant hypothesis though is that studies invoking this idea tend to focus on one or
two nativity groups, usually Mexicans as in the smoking studies referenced above, and
generalize findings to Latino immigrants as a whole.
Latin American migrants in the USA may share some common experiences, but the
substantial differences in smoking rates in their countries of origin provide at least one reason
for delineating nativity groups. Furthermore, the reception, integration and community-forming
experiences of different nativity groups depends on the geographies in which they settle,
foreign policy histories between the USA and their home countries, the influence of cumulative
causation in altering or reinforcing migrant streams and settlement patterns, current attitudes
toward immigrants and a variety of other sociogeographic factors (Massey, 1990; Margolis,
1995b; Singer, 2004; Golash-Boza, 2012a). These variant experiences are reproduced in the
form of "ethnic enclaves" (Wilson and Portes, 1980) in cities across the USA, or in more
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practical terms, at the level of the urban neighborhood. In Boston, for example, home to the
largest population of Brazilian migrants in the USA (U.S. Census Bureau, 2013b), there are
clearly identifiable Brazilian neighborhoods concentrated in certain parts of the metropolitan
area. These neighborhoods are home to Brazilian migrant residents as well as Brazilian
storefronts, social clubs and churches.
This type of community formation, or reproduction, is likely to have substantial impacts
on health behaviors and outcomes. Neighborhood-level social capital, perceptions of safety,
and the concentration of relative disadvantage in particular may be factors that influence
whether Brazilian migrants take up smoking. In this chapter I investigate whether neighborhood
social cohesion and disorder influence the probability of being a current smoker among legal
and unauthorized Brazilian migrants. I control for other sociogeographic factors at the home
and neighborhood levels, socioeconomic characteristics, other health behaviors and health
status, including biologically-measured stress in the form of C-reactive protein (CRP).
Specifically, I hypothesize that neighborhood-level: (1) social cohesion will be negatively
associated with the likelihood of being a current smoker; and (2) disorder will be positively
associated with the likelihood of being a current smoker.
4.11 Neighborhoods and Smoking
The literature on neighborhood characteristics and smoking behavior remains sparse as
yet with the bulk of research focusing on limited measures of neighborhood socioeconomic
status or deprivation. However, Pearce et al. (2012) have recently emphasized place as
determinant of smoking behavior, specifically along two primary pathways – practices and
regulation. In this framework, place-based practices encompass social capital, social norms,
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contagion and neighborhoods disorder, while place-based regulation refers to smoking
cessation policies, access to and availability of tobacco products, targeted tobacco advertising
and urban regeneration, or efforts to make environmental modifications to lower SES urban
areas designed to promote healthy behavior. Other theoretical models of smoking behavior
emphasize similar themes, though models of neighborhoods and smoking behavior have
tended to focus more on social and structural inequality, including issues of relative deprivation
and residential segregation, than on urban renewal per se (Lopez et al., 1994; Miles, 2006).
In this chapter I focus primarily on the "practices pathway," evaluating relationships
between neighborhood-level social cohesion, disorder and smoking. I have less opportunity to
directly measure the aforementioned regulation pathway between place and smoking using the
BM-IHLSS data. However, Massachusetts implemented a statewide smoking ban in enclosed
public places in 2004 and has the 9
th
highest excise tax on cigarettes in the country ($2.51), an
amount Governor Deval Patrick is proposing to raise by 20 percent in 2014 (WPRI, 2012;
Centers for Disease Control and Prevention (CDC), 2013). On the other hand, the Supreme
Court struck down a 2001 attempt to ban tobacco advertisements within 1,000 feet of public
schools, playgrounds and parks, and studies of tobacco marketing conducted in the Boston area
have demonstrated that storefront and other tobacco advertisements are disproportionately
denser in lower income and minority neighborhoods (Laws et al., 2002; Barbeau et al., 2005;
Smith, 2010). Although I do not directly control for regulatory influences in this study, I do
approximate neighborhood socioeconomic status by including population density and
homeownership measures using block-level decennial census data.
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The first three constructs included as part of the place-based practices pathway – social
capital, social norms and contagion – overlap to some degree. Social capital here refers largely
to social cohesion and trust, while social norms are the collective practices employed, whether
formally or informally, by neighborhood residents, and contagion indicates the tendency for
individuals to model behavior after members of their families or social networks. As to the
latter, Christakis and Fowler (2008) have repeatedly found social network behavior to influence
a variety of health behaviors including smoking – individuals who have a friend, sibling or
spouse quit smoking are much more likely to do so themselves, while those whose networks
continue smoking are more likely to continue smoking themselves. This tendency to model
behavior may be more diffuse at the neighborhood level, but there is evidence to suggest that
collective behavioral norms are associated with the probability of smoking. For example, Ahern
et al. (2009) found higher odds of smoking in neighborhoods with permissive smoking norms
where collective efficacy was high, while neighborhoods that had strongly anti-smoking norms
and high collective efficacy had much lower odds of smoking. Another study with particular
relevance for immigrant populations also found that living in ethnic enclaves decreased the
likelihood of smoking for Asians in California, which the authors surmise may be related to
cultural norms and social cohesion that buffer stress and reduce the propensity to use smoking
as a coping behavior (Kandula et al., 2009). Additional research has found neighborhood social
cohesion and civic participation to reduce smoking risk (Patterson et al., 2004; Xue et al., 2007)
while low levels of cohesion and trust are associated with higher smoking rates (Poortinga,
2006; Siahpush et al., 2006; Karvonen et al., 2008).
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More of the research on smoking and neighborhoods has focused on the neighborhood
disorder facet of Pearce and colleagues' framework along with social inequality and material
deprivation. Disorder can be characterized in a number of ways; the neighborhoods and health
literature has focused mostly on three issues – perceived safety, crime incidence and physical
disorder theorized along the lines of Wilson and Kelling's "broken windows" hypothesis (1982),
i.e. the idea that well-kept neighborhoods may help deter crime. With respect to smoking,
perceived neighborhood safety appears to be protective against smoking behavior (Patterson et
al., 2004; Miles, 2006) while perceived neighborhood disorder predicts smoking (Wilson et al.,
2005). The limited studies that have examined neighborhood crime and smoking found both
that higher crime rates were positively associated with smoking duration (Tseng et al., 2001)
and with the likelihood of smoking currently (Virtanen et al., 2007).
Lower neighborhood-level socioeconomic status and greater social disadvantage appear
to have clear relationships with smoking behavior. Perceived neighborhood disadvantage is
shown to be positively associated with smoking (Ennett et al., 1997) and a number of studies
find positive relationships between smoking and low SES or high material deprivation
(Kleinschmidt et al., 1995; Reijneveld, 1998; Duncan et al., 1999; Ross, 2000b; Reijneveld, 2002;
Xue et al., 2007). Additionally, Miles (2006) finds neighborhood population density, often used
as a proxy for area socioeconomic status, to be positively associated with smoking and Chuang
et al. (2007) find in Taiwan neighborhoods that neighborhood-level education was positively
associated with smoking for low income women, while it was negatively associated with
smoking for higher income women, lending support to the idea that relative deprivation may
have a powerful influence on health behaviors (Mayer and Jencks, 1989).
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While these findings establish an important foundation for investigating the complex
interactions between neighborhoods and smoking, much work remains to gain a more
comprehensive understanding of the ways in which neighborhood characteristics may influence
smoking. In particular, area-level social measures may be crucial to understanding when and
whether individuals begin smoking, how much they smoke and whether they find success in
attempts to quit. Furthermore, insofar as smoking may be an expression of stress, individual
propensity to smoke will be partially contingent on environmentally-induced stressors, ranging
from crime to noise to disputes with neighbors. For the Brazilian migrant population, these
place-based stressors may be exacerbated by fears related to legal status or discrimination that
may be encountered locally. The BM-IHLSS data were collected in the months following a highly
traumatic immigrant raid in New Bedford, MA (Adams, 2013) and at approximately the same
time an Immigration and Customs Enforcement (ICE) outpost opened in one of our sample
areas. In this case, living in close proximity with other Brazilian migrants may be a source of
comfort as well as a source of enhanced fear due to the heightened visibility of "enclave"-type
communities.
In addition, features of neighborhoods such as income gradients, crime rates and
physical disorder likely influence not only the propensity to smoke but also the extent to which
residents engage in other health behaviors, such as physical activity or alcohol consumption
that may buffer or exacerbate the effects of smoking. Moreover, just as studies of
neighborhood SES and health often control for individual-level SES in order to clarify any area-
level effects, it seems important in studies of smoking behavior and neighborhood
characteristics to control not only for other health behaviors, but also for psychological and
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physiological stress that may be caused or exacerbated by smoking, and may in turn influence
other health outcomes. In this chapter, I therefore account for other health behaviors as well as
psychological distress, cardiovascular disease and systemic inflammation.
4.12 Smoking, Health Behavior and Health Status
Smoking can be considered a behavioral measure of stress for its use as a coping
mechanism, its frequent co-morbid association with psychological disorders and the actual
physiological stress it places on the body (Shadel and Mermelstein, 1993; Bergen and Caporaso,
1999; Price et al., 1999; Hamer et al., 2008; Bowie et al., 2009). Nicotine dependence is one of
the most common psychiatric diagnoses in the USA, and it is also commonly diagnosed in
concert with major depression, substance abuse and anxiety disorders (Bergen and Caporaso,
1999). People experiencing psychological distress specifically are more likely to be current
smokers (Lawrence et al., 2011) and less likely to quit smoking once they have started (Cosci et
al., 2009). Current smokers are also more likely to exhibit substance use disorders (Kalman et
al., 2005), and for Latino immigrants, acculturation stress appears to be positively associated
with smoking and alcohol use (Caetano and Clark, 2003; Bethel and Schenker, 2005). With
respect to other health behaviors, current smokers are shown to have less healthy diets than
nonsmokers as do the nonsmoking wives of men who currently smoke (Koo et al., 1997).
Smoking is also negatively associated with physical activity (Paavola et al., 2004), although
among older adults, smokers who engage in regular physical activity appear to live longer and
with less morbidity than smokers who do not exercise regularly (Ferrucci et al., 1999). Current
smoking has also been associated with sleep disturbance generally (Riedel et al., 2004) as well
as longer time to fall asleep, less slow wave sleep and less total sleep time (Zhang et al., 2006).
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Smoking is also a well-documented contributor to cancer and cardiovascular disease
(Doll and Hill, 1952; U.S. Surgeon General's Advisory Committee on Smoking, 1964; Lakier,
1992; Denissenko et al., 1996; Ockene et al., 1997), and has been increasingly associated with
particular physiological predictors of cardiovascular and other diseases, such as C-reactive
protein (CRP), a measure of inflammation that I include in my regression models. Smoking is
associated with higher levels of CRP, resulting in systemic inflammation if not resolved (Bazzano
et al., 2003; McDade et al., 2006). This inflammation has in turn been implicated in the
development of atherosclerosis (Black and Garbutt, 2002), metabolic dysregulation (Esposito
and Giugliano, 2004), cancer (dos Santos Silva et al., 2010) and neurodegenerative disease
(Whitton, 2007) as well as being associated with poor psychological health as I have shown
previously (Holmes, 2013). It is additionally important to control for physiological measures of
health when studying smoking for two main reasons – first, smoking contributes to several
common chronic illnesses (e.g. cardiovascular disease, cancer, metabolic disorder) and having
been diagnosed with such illnesses, or risk factors for them, may prompt smokers to change
their behavior. I control for cardiovascular disease in the following analyses, but this measure is
based largely on a subject reporting that s/he had received a disease diagnosis. So, second,
including an objectively measured biological marker for inflammation, which is influenced by
smoking, allows me to capture those subjects who may be at risk for disease but have not been
diagnosed yet. As in chapter 3, including these measures clarifies any identified association
between neighborhood characteristics and smoking behavior. In this study I investigate the
influence of neighborhood-level social cohesion and disorder on the likelihood of current
smoking in a population of Brazilian migrants, controlling for other health behaviors and
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characteristics as well as demographic and socioeconomic factors. I again employ a
sociogeographic model of smoking (Figure 4.1), introduced in the foregoing chapters, to
evaluate the interactions between neighborhood and other sociogeographic factors, individual
characteristics and smoking.
Figure 4.1 A Sociogeographic Model of Current Smoking

4.2 Data and Methods
Once again, I employ data from the Brazilian sample of the 2007 Boston Metropolitan
Immigrant Health and Legal Status Survey (BM-IHLSS) to measure current smoking among
Brazilian migrants.

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4.21 Measures
Outcome. Currently smokes is measured dichotomously where a value of "1" indicates
that the subject reported smoking at least 100 cigarettes in their lifetime and currently smokes
"some days" or "every day."
Neighborhood environment. Respondents' length of residence is measured continuously,
indicating how many years and months they have resided in their current neighborhood.
Population density measures the number of residents per square mile by census block while
homeownership measures the proportion of owner-occupied housing units by block. With
respect to my hypotheses, neighborhood disorder is measured using a dichotomous variable
indicating whether subjects or their neighbors had experienced personal violence, had their
homes broken into, had experienced property damage or had property stolen from them in
their current neighborhood. Social cohesion is a continuous measure ranging from 0-12, based
on responses to four questions indicating whether subjects (0) strongly disagree, (1) disagree,
(2) agree or (3) strongly agree with four questions about the neighborhood environment –
whether neighbors: 1) get along with each other; 2) are willing to help each other; 3) share the
same values; and 4) know each other (Sampson and Raudenbush, 1999).
Demographic characteristics. Four individual exogenous characteristics are included in
the regression models – age, sex, skin color and parental health. Age is a continuous measure
indicating subject years of life. Sex is a dichotomous variable equal to "1" for males and "0" for
females. Skin color corresponds to the New Immigrant Survey Skin Color Scale (Massey and
Martin, 2003); subjects viewed a picture of ten human hands numbered 1-10 with the skin
pigmentation increasingly growing darker from left to right along the scale; they were then
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asked to point to the hand that they felt most resembled their own pigmentation. Parental
health is a measure ranging from 0-2 indicating whether none, one or both of the subject's
biological parents were in very good or excellent health at the age of 35.
Household and individual socioeconomic characteristics. Six variables are used in the
models to control for household and individual-level socioeconomic characteristics. Married is a
dichotomous measure equal to "1" if the subject is currently married and "0" if not. Household
smoker measures whether any member of the subject's household, other than the subject,
currently smokes (1) or not (0). College is a dichotomous measure equal to "1" if the subject
graduated from a four-year college. Earnings continuously measures subject income from all
jobs for the year prior to the survey, while insured is a dichotomous variable equal to "1" if the
subject has some form of health insurance coverage. Finally, unauthorized is dichotomous and
set equal to "1" if the subject is unauthorized to reside in the USA.
Health status and behavior. Lastly I control for one measure of psychological health, two
measures of physiological health and three health behaviors. First, psychological distress is
measured using the K6 scale (Kessler et al., 2002) which ranges from 0-24 with a cutoff score of
13. The scale includes six questions asking how often in the last 30 days the subject felt: 1) sad;
2) nervous; 3) restless; 4) hopeless; 5) everything was an effort; and 6) worthless, with
responses ranging from (0) none of the time to (4) all of the time for a possible total of 24. High
CRP is a dichotomous variable equal to "1" if the subject has high-sensitivity CRP (hsCRP) levels
greater than 3 mg/L, i.e. above the normal range, and less than or equal to 20 mg/L. The upper
bound is added in order to exclude any observations that may be indicative of active infection
rather than systemic inflammation. High CRP is included as a control only in the final set of
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regression models. Cardiovascular disease is a dichotomous measure set equal to "1" if the
subject has ever been diagnosed with a heart attack, heart disease, stroke, angina, high
cholesterol or high blood pressure. As to health behavior, nutrition is a dichotomous variable
equal to "1" if the subject reported eating five servings of fruits or vegetables each day on
average. Alcohol consumption is a continuous variable indicating how many days the subject
reported drinking alcohol in the past year, and short sleep is a dichotomous variable equal to
"1" if the subject sleeps less than seven hours per night on average.
4.22 Statistical Analyses
Descriptive and cross-sectional multivariate regression results are reported below. Stata
10 was employed for performing logistic regressions, and Stata's cluster function was used to
control for potential bias, which may occur as a result of multiple respondents living in the
same census blocks (Huber, 1967). As in Chapter 3, I fitted three sets of models – the first set of
models includes the entire sample of adult Brazilian migrants in the BM-IHLSS (n=307); the
second set of models is restricted to the subpopulation of adult subjects who provided blood
samples and for whom CRP results are available (biological population: n=156), but using the
same set of independent variables as included in the first set of models; while the third set of
models remains restricted to the biological population and adds high CRP as a control variable.
Within each set of models I perform three regressions – first, I measure smoking controlling for
neighborhood and individual exogenous characteristics; in the second model I add home and
socioeconomic characteristics; and in the third model I further control for health behavior and
status. When reporting both the descriptive and regression results, I include the following
symbols next to the variable names to indicate the direction of the hypothesized association
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with smoking: (+) for a positive association, (-) for a negative association or (±) for a bi-
directional association. These symbols also denote whether a one- or two-tailed significance
test was performed.
4.3 Results
4.31 Descriptive Statistics
Table 4.1 below shows descriptive statistics for all Brazilian migrant adults, then
separately for current smokers and nonsmokers. High CRP is included in the table, but the
statistics reported for this variable are only for the 156 Brazilian migrants for whom CRP is
measured. Approximately 18 percent of Brazilian adults are current smokers, very similar to the
prevalence rate in the U.S. adult population as a whole. Those who smoke had lived in their
neighborhoods for slightly shorter durations than nonsmokers (2.3 versus 2.5 years), reside in
neighborhoods with greater population density (≈30,000 versus ≈22,000 people per square
mile) and report lower social cohesion among neighbors (4.6/12 versus 5.2). Homeownership
rates are nearly equivalent at 36 percent, but surprisingly smokers are less likely to report
neighborhood disorder (21 percent) than their nonsmoking counterparts (27 percent). In terms
of individual exogenous characteristics, smokers are slightly older (35 versus 33 years), more
likely to be male (64 versus 52 percent) and reported very slightly darker skin pigmentation
(2.24 versus 2.16). Smokers also report that fewer than one (0.8) of their parents were in good
health at the age of 35 compared to nonsmokers who had at least one parent in good health
(1.0).

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Substantially fewer smokers are married (40 versus 59 percent of nonsmokers), and
they are much more likely to live with someone else who smokes (58 versus 21 percent).
Smokers have lower earnings than nonsmokers (≈$27,000/year versus ≈$35,000) and are less
likely to have health insurance (30 versus 43 percent), but smokers and nonsmokers share
identical educational profiles – 12 percent of both groups have a four-year college degree. Both
groups have similar legal status profiles as well, with 72 percent of smokers being unauthorized
compared with 71 percent of nonsmokers. With respect to health, smokers are more likely to
be experiencing serious psychological distress (12 versus 6 percent of nonsmokers) and more
likely to have high CRP (34 versus 21 percent), but less likely to have been diagnosed with
cardiovascular disease or its attendant risk factors (2 versus 10 percent). Smokers are actually
more likely to report consuming the USDA recommended servings of fruits and vegetables each
day on average (28 versus 24 percent) but also consumed alcohol on more days in the previous
year (18 versus 14) and were more likely to report sleeping fewer than seven hours a night on
average (29 versus 22 percent).

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Table 4.1 Weighted Descriptive Statistics

All Adults Non-smoker Smoker
μ
(S.D.)
μ
(S.D.)
μ
(S.D.) Min. Max.
OUTCOME VARIABLE
Smokes Smokes=1 if subject smokes some days or every day 0.177 0.00 1.00 0.00 1.00
- - -
NEIGHBORHOOD CHARACTERISTICS
Length of residence (+/-) Number of years and months subject has resided in neighborhood 2.420 2.451 2.276 0.00 20.00
(2.245) (2.207) (2.430)
Population density (+) Number of residents per square mile by census block (mean in 10,000s) 23,228.910 21,735.930 30,150.180 1,087.35 81,182.64 *
(15628.590) (14253.690) (19568.610)
% Ownership (-) Percent of residents who own their homes by census block 0.360 0.361 0.356 0.01 0.98
(0.211) (0.211) (0.213)
Disorder (+)
Disorder=1 if subject or neighbors experienced personal violence, had their
homes broken into, had anything stolen from their property or experienced
damage to their personal property in the neighborhood
0.259 0.270 0.206 0.00 1.00
- - -
Social cohesion (-)
Index ranging from (0) - subject strongly disagrees to (12) - subject strongly
agrees on all of the following measures: neighbors 1) know each other, 2) get
along, 3) share values and 4) help each other
5.052 5.152 4.590 0.00 12.00
(2.280) (2.287) (2.212)
INDIVIDUAL EXOGENOUS CHARACTERISTICS
Age (+/-) Subject age in years 33.661 33.347 35.122 19.00 69.00
(9.876) (9.788) (10.243)
Male (+) Male=1 if subject reported sex as male 0.538 0.516 0.642 0.00 1.00
- - -
Skin color (+) Self-reported subject skin color, measured from lightest (1) to darkest (10) 2.173 2.159 2.236 1.00 7.00
(1.347) (1.375) (1.218)
Parental health (-)
Range from 0-2 indicating whether 0, 1 or both of subject's parents were
reported to be in very good or excellent health at the age of 35 1.005 1.045 0.823 0.00 2.00
(0.836) (0.844) (0.780)
HOUSEHOLD & SOCIOECONOMIC CHARACTERISTICS
Married (+/-) Married=1 if subject was married at time of survey 0.554 0.587 0.397 0.00 1.00 *
- - -
Household smoker (+) Household smoker=1 if any other member of subject's household smokes 0.278 0.214 0.575 0.00 1.00 *
- - -
College graduate (-) College graduate=1 if subject received Bachelor's degree 0.123 0.123 0.123 0.00 1.00
- - -
Earnings (-) Subject earnings from all jobs in 2006 (thousands of dollars) 33.211 34.530 27.096 0.00 150.00
(22.965) (23.461) (19.560)
Insured (-) Insured=1 if subject had health insurance 0.404 0.427 0.297 0.00 1.00
- - -
Unauthorized (-) Unauthorized=1 if subject is unauthorized to reside in the USA 0.713 0.712 0.720 0.00 1.00
- - -
INDIVIDUAL HEALTH STATUS & BEHAVIOR
Distress (+) Distress=1 if subject's K6 score>12, indicating serious psychological distress 0.073 0.063 0.119 0.00 1.00
- - -
High CRP
○
(+)
High CRP=1 if hsCRP>3.0mg/L & hsCRP<=20mg/L (converted from DBS to
serum values) 0.236 0.213 0.335 0.00 1.00
(0.426) (0.411) (0.481)
CVD (+/-)
CVD=1 if subject had been diagnosed with cardiovascular disease,
hypertension or high cholesterol 0.082 0.095 0.022 0.00 1.00
- - -
Nutrition (-)
Nutrition=1 if subject consumes an average of five or more fruits/vegetables
per day 0.249 0.243 0.276 0.00 1.00
- - -
Alcohol consumption (+) Number of days subject reported drinking alcohol in past year 14.580 13.824 18.086 0.00 365.00
(59.243) (57.348) (67.837)
Short sleep (+)
Short sleep=1 if subject reported sleeping less than 7 hours per night on
average 0.231 0.220 0.285 0.00 1.00
- - -
N (Weighted) 61,335 50,452 10,883
N (Unweighted) 307 254 53
*Difference in means is statistically significant, p<0.05
○
High CRP is reported only for the 156 (30,631) subjects who provided blood samples
110

4.32 Logistic Regression Results
Table 4.2 below shows logistic regression results for current smoking in the full adult
population. Concordant with my first hypothesis, social cohesion is significantly associated with
a six percent reduction in the likelihood of smoking. However, contrary to expectation,
neighborhood disorder is associated with a 14 percent lower likelihood of smoking, and this
effect grows larger across the three regression models. The reason for this association is
unclear; however it is possible that this particular measure of neighborhood disorder, which
focuses more on crime and personal violence, may be less predictive of smoking behavior than
more chronic measures of disturbance, such as noise pollution, physical disorder or crowding.
This latter issue seems to be a possibility as population density is also positively and
significantly associated with smoking in my model, though the effect size is quite small.
Population density is often used as a proxy for neighborhood-level socioeconomic status as
well, and in this case, the positive association with smoking aligns with expectations and the
literature, though the probability of being a current smoker increases less than one percent
with an increase in population. None of the remaining neighborhood variables are significantly
associated with smoking, and homeownership is positively rather than negatively associated as
I initially hypothesized. Homeownership is included as a measure of wealth and stability at the
neighborhood level, but these characteristics may not be essential for understanding smoking
behavior in a population that has only resided in the sampled neighborhoods for an average of
2.5 years.

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As to individual exogenous characteristics, age and male sex are positively and
significantly associated with current smoking, and parental health is negative and significant.
Skin color is also positive but not significant. Specifically, for every nine additional years of life
there is a seven percent greater probability of being a current smoker, men are approximately
nine percent more likely to smoke and having one less parent who was healthy at the age of 35
increases the likelihood of smoking by four percent. Regarding home and individual
socioeconomic characteristics, three relationships prove significant – being married makes it 10
percent less likely that someone is a current smoker, while sharing a household with at least
one other person who smokes increases the likelihood of smoking by 25 percent. Additionally,
for every additional $23,000 in annual earnings there is a six percent reduction in the
probability of being a current smoker. Having health insurance and being unauthorized are also
negative, as expected, but not significant. Interestingly, being a college graduate is positively
associated with smoking, though not significant. Descriptively, identical proportions of the
smoking and nonsmoking populations in our Brazilian sample were graduated from a four-year
college, so education appears to be less of a factor for smoking than in other populations.
However, the positive nature of the association could possibly suggest a role for the relative
deprivation hypothesis as well – the Brazilian migrants in our sample were often
underemployed for their skill levels and education (Marcelli et al., 2009b), which may be a
source of stress with implications for health.

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Moving to health, none of the controls are significantly associated with smoking, but the
changes in the effect sizes of most of the other variables in the model after adding the health
variables suggest that the latter play an important role in clarifying the relationships between
sociogeographic environment and current smoking. Psychological distress, alcohol consumption
and short sleep are all positively associated with smoking as expected, while cardiovascular
disease/risk is associated with a 27 percent decrease in the likelihood of being a current
smoker. This seems logical as smoking cessation is one of the first behavioral modifications
recommended for those at risk of heart disease. Nutrition is positive, contrary to expectation,
but it lacks significance and the effect is quite small.
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Table 4.2 Logistic regression of current smoking on neighborhood characteristics, full population (n=307)

ß S.E. Prob. ß S.E. Prob. ß S.E. Prob. O.R.
NEIGHBORHOOD CHARACTERISTICS
Length of residence (+/-) -0.046 (0.077) -1.50% 0.010 (0.067) 0.31% 0.021 (0.063) 0.67% 1.021
Population density (+) 0.00004 (0.000) 9.53%
***
0.00004 (0.000) 9.01%
***
0.00004 (0.000) 8.44% 1.000
***
% Ownership (-) 1.350 (0.917) 4.16% 0.955 (0.996) 2.94% 0.613 (0.969) 1.89% 1.846
Disorder (+) -0.699 (0.378) -10.20% -1.011 (0.412) -14.76% -0.973 (0.409) -14.21% 0.378
Social cohesion (-) -0.131 (0.079) -4.35%
**
-0.185 (0.088) -6.14%
**
-0.182 (0.087) -6.06% 0.834
**
INDIVIDUAL CHARACTERISTICS
Age (+/-) 0.022 (0.019) 3.11% 0.044 (0.018) 6.41%
**
0.049 (0.018) 7.00% 1.050
***
Male (+) 0.622 (0.334) 9.08%
**
0.719 (0.392) 10.50%
**
0.612 (0.460) 8.93% 1.843
*
Skin color (+) 0.046 (0.151) 0.91% 0.000 (0.152) -0.01% 0.060 (0.162) 1.18% 1.062
Parental health (-) -0.325 (0.183) -3.97%
**
-0.365 (0.204) -4.46%
**
-0.344 (0.202) -4.20% 0.709
**
HOUSEHOLD & SOCIOECONOMIC CHARACTERISTICS
Married (+/-) -0.718 (0.313) -10.48%
**
-0.684 (0.332) -9.98% 0.505
**
Household smoker (+) 1.609 (0.388) 23.48%
***
1.738 (0.451) 25.36% 5.684
***
College graduate (-) 0.330 (0.532) 4.81% 0.435 (0.543) 6.35% 1.545
Earnings (-) -0.019 (0.011) -6.25%
*
-0.017 (0.011) -5.84% 0.983
**
Insured (-) -0.513 (0.460) -7.49% -0.460 (0.470) -6.71% 0.632
Unauthorized (-) -0.090 (0.351) -1.32% -0.026 (0.398) -0.37% 0.975
HEALTH STATUS & BEHAVIOR
Distress (+) 0.013 (0.593) 0.19% 1.013
CVD (+/-) -1.881 (1.297) -27.46% 0.152
Nutrition (-) 0.094 (0.556) 1.38% 1.099
Alcohol consumption (+) 0.001 (0.003) 1.10% 1.001
Short sleep (+) 0.505 (0.466) 7.36% 1.656
Constant term (+/-) -3.084 (0.918) -2.883 (0.916) -3.229 (1.085)
Concordant Pairs
Prob > chi2
Pseudo R2
* p≤.10 ** p≤.05 *** p≤.01
0.867
0.000
0.251
Model 1 Model 2 Model 3
0.868
0.000
0.229
0.824
0.000
0.099
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Table 4.3 shows the same three regression models, but now restricted to the Brazilian
adult migrant population for which CRP is measured. For this population my first hypothesis
that social cohesion is positively related to smoking bears out, as with the total adult
population. Here the effect is slightly larger though, with two additional points on the social
cohesion scale corresponding to a 10 percent decrease in the likelihood of current smoking
(compared to six percent in the full population). Also in these models, population density
remains positively associated with smoking but is no longer significant, whereas 2.5 additional
years of residence in an individual's neighborhood is significantly associated with a seven
percent increase in the likelihood of smoking. Again, in contradiction to my second hypothesis,
disorder is negatively related to smoking but not significant. Homeownership remains positively
associated but is also non-significant.
Age (positive) and parental health (negative) continue to be significantly related to
current smoking, but the effect sizes for these variables have increased in this subpopulation.
Male sex is no longer significant, which is notable because a slightly larger proportion of men
provided blood samples compared with women (60 versus 49 percent). Skin color remains
insignificant, but it now shows a negative association with smoking in this population. As with
education, the average reported skin color in both the full population and the biological
subpopulation is nearly identical, and at the very light end of the scale (2.16 and 2.24/10), so
this parameter may not be as important for predicting smoking behavior as some of the more
disparate individual characteristics included in the models. Being married (negative) and living
in a household with at least one other smoker (positive) are again significantly associated with
smoking, and the effect sizes here are much larger than in the initial model. Marriage is
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associated with a 21 percent decrease and having a smoker in the household a 41 percent
increase in smoking probability. In this subpopulation, earnings are no longer a significant
indicator of smoking, but having health insurance is significantly associated with a 22 percent
reduction in the likelihood of smoking. Unauthorized legal status remains insignificant in this
model, but the relationship to smoking becomes positive. This is unlikely to be a data artifact as
equal proportions of unauthorized and legal migrants provided blood.
In this subpopulation, the relationship between cardiovascular disease is significant with
a diagnosis being associated with a 21 percent decrease in the likelihood of being a current
smoker. None of the other health variables demonstrate a significant relationship with
smoking, but the effect size of psychological distress increases substantially and the signs
change for each of the health behaviors, with nutrition now being negatively related as initially
expected and alcohol consumption and short sleep showing positive relationships. These
changes may be indicative of the presence of unobserved differences between the population
that proved willing to provide blood samples and the full population. What these differences
may be is difficult to identify given the similarities across populations in individual exogenous
characteristics and legal status. One possibility is that interviewer effects impacted the
willingness of participants to provide biological data – survey research literature suggests that
the more experienced and professional interviewers are, the more likely people are to offer
sensitive information. Interviewer characteristics, such as sex and race/ethnicity are also likely
to influence survey response (Reese et al., 1986; Huddy et al., 1997; Davis et al., 2010; Durrant
et al., 2010). This explanation requires further investigation, but it is interesting to note that 61
percent of those subjects who provided blood samples were visited by BM-IHLSS interviewers
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who reported their skin pigmentation as 1-2 on the skin color scale (similar to the average skin
color among respondents), while those interviewers who reported their skin pigmentation as 3
or higher were able to collect blood samples only 50 percent of the time.
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Table 4.3 Logistic regression of current smoking on neighborhood characteristics, biological subpopulation (n=156)

ß S.E. Prob. ß S.E. Prob. ß S.E. Prob. O.R.
NEIGHBORHOOD CHARACTERISTICS
Length of residence (+/-) 0.063 (0.067) 2.35% 0.169 (0.077) 6.32%
**
0.190 (0.080) 7.13% 1.209
**
Population density (+) 0.00005 (0.000) 10.71%
***
0.00003 (0.000) 7.51%
*
0.00003 (0.000) 5.93% 1.000
% Ownership (-) 1.261 (1.246) 3.81% 1.288 (1.707) 3.89% 1.038 (1.785) 3.13% 2.823
Disorder (+) -0.565 (0.495) -8.57% -0.799 (0.674) -12.12% -0.733 (0.764) -11.12% 0.480
Social cohesion (-) -0.186 (0.101) -6.70%
**
-0.241 (0.125) -8.67%
**
-0.266 (0.125) -9.58% 0.766
**
INDIVIDUAL CHARACTERISTICS
Age (+/-) 0.026 (0.025) 3.77% 0.076 (0.029) 11.25%
***
0.081 (0.031) 11.91% 1.084
***
Male (+) -0.139 (0.427) -2.10% 0.116 (0.537) 1.75% 0.197 (0.507) 2.99% 1.218
Skin color (+) -0.110 (0.125) -2.25% -0.455 (0.188) -9.26% -0.491 (0.210) -9.99% 0.612
Parental health (-) -0.233 (0.248) -2.95% -0.449 (0.321) -5.67%
*
-0.440 (0.330) -5.56% 0.644
*
HOUSEHOLD & SOCIOECONOMIC CHARACTERISTICS
Married (+/-) -1.322 (0.467) -20.04%
***
-1.373 (0.526) -20.81% 0.253
***
Household smoker (+) 2.603 (0.720) 39.46%
***
2.713 (0.837) 41.13% 15.079
***
College graduate (-) 2.337 (0.882) 35.42% 2.281 (0.998) 34.57% 9.783
Earnings (-) -0.013 (0.015) -4.89% -0.013 (0.015) -4.59% 0.987
Insured (-) -1.503 (0.465) -22.78%
***
-1.437 (0.450) -21.77% 0.238
***
Unauthorized (-) 0.189 (0.619) 2.86% 0.153 (0.756) 2.31% 1.165
HEALTH STATUS & BEHAVIOR
Distress (+) 0.724 (0.901) 10.98% 2.063
CVD (+/-) -1.370 (1.023) -20.77% 0.254
*
Nutrition (-) -0.469 (0.629) -7.12% 0.625
Alcohol consumption (+) -0.002 (0.006) -1.04% 0.998
Short sleep (+) -0.220 (0.682) -3.33% 0.803
Constant term (+/-) -2.539 (1.241) -3.141 (1.661) -2.785 (1.789)
Concordant Pairs
Prob > chi2
Pseudo R2
* p≤.10 ** p≤.05 *** p≤.01
0.000 0.001 0.000
0.132 0.352 0.372
Model 1 Model 2 Model 3
0.820 0.894 0.889
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Figure 4.2 shows regression results for the biological subpopulation again, now
controlling for high CRP. Here all of the same relationships remain significant with the exception
of cardiovascular disease. Hollow bars represent non-significant relationships. High CRP
attenuates the effect of cardiovascular disease slightly as well as that of psychological distress.
The associations between neighborhood characteristics and stress change very little – social
cohesion remains positively related to current smoking while disorder remains negative and still
insignificant. Length of residence also remains significant and the associated probability
changes little. The same is true for age, parental health, being married, living with a smoker and
having health insurance. High CRP itself is positively related to smoking as expected but the
relationship is not significant. What this suggests with respect to my hypotheses is that low
neighborhood social cohesion robustly predicts current smoking, even when adjusting for other
sociogeographic characteristics and psychological, physiological and other behavioral health
factors. The persistent negative relationship between neighborhood disorder and smoking,
though not significant, requires further investigation to determine whether there is something
specific about the neighborhoods in which these Brazilians live or their experiences of these
neighborhoods, or whether a different measure of disorder may be more appropriate for
evaluating smoking behavior.

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Figure 4.2 Change in the probability of being a current smoker, biological sample (n=156)

4.4 Discussion
Researchers have long studied smoking behavior for its clear influence on the
development of cancer and cardiovascular disease, but little of this research to date has
focused on the varied sociogeographic or place-based factors that may contribute to or protect
against the propensity to smoke. Additionally, smoking has been understudied in vulnerable
populations, such as immigrants, in which particular stressors may be present that enhance the
probability of using smoking as a coping tool, or interfere with efforts to quit smoking. In this
chapter I use representative data from a population of Brazilian migrant adults residing in
metropolitan Boston to investigate neighborhood effects on smoking behavior. In line with
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Pearce and colleagues' (2012) framework for place and smoking behavior and previous studies
of neighborhood social capital and smoking in other populations, I find compelling evidence
that neighborhood-level social cohesion may reduce the likelihood of smoking. I do not find
support for my second hypothesis that neighborhood disorder predicts smoking, but the
negative association between disorder and smoking in this chapter does offer an intriguing
entree for further investigation into how various measures of disorder may interact with
smoking and other health behaviors.
Recommending practical policy interventions that may capitalize on the relationship
between neighborhood social cohesion and smoking remains a challenge. I suggest three
promising possibilities for making the best use of these and similar findings. First, there has
been much attention recently to the ways in which neighborhood built environments influence
health behaviors, but much less attention has been paid to the built environment's influence on
the propensity for social interaction in local areas. This is a potentially rich area of study given
what I and others have found regarding the protective nature of neighborhood-level social
capital. It may be that small changes to neighborhood environments, such as carving out areas
for green space or limiting the concentration of alcohol and tobacco outlets (Cohen et al.,
2008), could prompt further engagement between neighbors. Second, community-based
interventions in Latino communities that involve promotoras have shown promise with respect
to various health behaviors, including smoking cessation (Conway et al., 2004; Green et al.,
2012). Increasing these efforts and rigorously evaluating their effectiveness may prove useful,
especially among immigrant populations lacking health insurance and a usual source of medical
care. Third, and perhaps most difficult, focusing on the urban renewal piece of Pearce et al.'s
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(2012) framework could alter perceptions and structures of troubled neighborhoods in which
social interaction is stifled. The challenge in attempting to revitalize communities is to balance
respect for the existing resident population with alterations designed to increase neighborhood
safety, walkability and aesthetic appearance. There are a few novel experiments that have tried
to achieve this balance (e.g. Uitermark and Loopmans, 2013), but it is a formula in need of more
tinkering.
My study is novel for investigating current smoking in an understudied immigrant
population as well as its inclusion of legal status and psychological and physiological stress
markers. Many studies of neighborhood environments and health behavior suffer for their lack
of attention to biological or psychological pathways that intervene between any demonstrated
neighborhood effects and the health behavior in question. Here I find that although
psychological distress, inflammation and cardiovascular health may not share significant
associations with smoking, they do clarify and solidify the associations found between
neighborhood social cohesion and smoking behavior. It is worth noting that Brazilian migrants
are a relatively young and healthy population on average – the median age is 32 compared to
37 in the U.S. population – and disease incidence is very low. I do not include a restriction for
cancer in my analyses, for example, because only three people in the sample report having
been diagnosed with cancer, none of whom smoke. Therefore, the relationship I find between
cohesion and smoking may be even more substantial in less healthy or lower income
populations. In my biological analyses I also find that length of neighborhood residence is
positively associated with smoking behavior, which may align with previous findings regarding
the effect of acculturation on the likelihood of being a smoker – immigrants residing in the USA
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for longer periods reportedly behave increasingly like the U.S.-born population (Jasso et al.,
2004), and more acculturated Latina immigrants have been found to smoke more (Perez-Stable
et al., 1994).
I also find evidence to support the purported influence of social networks on smoking
behavior as individuals living with other smokers in their households were substantially more
likely to be current smokers themselves. On the other hand, having a spouse decreases the
likelihood of being a smoker quite noticeably. It is beyond the scope of this study to determine
how these two variables interact, e.g. what proportion of married individuals had spouses who
smoked, but living with someone who presumably looks out for one's health seems to be
protective. I further find what other researchers have repeatedly demonstrated – lacking health
insurance is bad for one's health, and unauthorized immigrants are less likely to have access to
health care than most (Ku and Matani, 2001; Derose et al., 2007; Rodríguez et al., 2009;
Finkelstein et al., 2011). The mechanisms by which insurance coverage interacts with smoking
behavior are less clear, but according to the BM-IHLSS data Brazilian adults are significantly
more likely to report that they had not received needed medical care in the past year if they
were also uninsured, and 73 percent of those without health insurance indicated that it had
been a year or more since they had received a routine medical checkup compared to 34
percent of those with insurance. If medical professionals and care are a key link in promoting
smoking cessation, then this gap is especially problematic for immigrants. Yet this is an area
that can be addressed with policy changes – any legalization effort that may proceed from the
passage of an immigration reform bill, or repealing the rule barring those eligible for the
Deferred Action for Childhood Arrivals policy from the benefits of the Affordable Care Act may
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have profound influence on not only smoking behavior among immigrants but health in general
(Capps and Fix, 2013).
This study is limited by the cross-sectional nature of the BM-IHLSS data. It is not possible
to determine whether neighborhood characteristics have a causal effect on current smoking or
vice versa. Furthermore, these data represent the population of Brazilian migrants residing in
New England, and the extent to which the results can be generalized to the larger population is
unclear. Nevertheless, the robust nature of the analyses leads me to suggest that social
cohesion in particular may be protective against smoking, at least for Brazilian migrants. The
study is further limited by a lack of measures designed to capture physical aspects of the
neighborhood built and tobacco regulatory environments. However, I am able to proxy these to
some extent by including census block population measures and a measure of disorder that
addresses issues of neighborhood safety and crime. There also remain questions about whether
unobserved characteristics distinguish the full adult population of Brazilians from the
subpopulation of participants who provided blood samples. While the two populations share
very similar and sometimes identical demographic characteristics, there may be other factors,
such as interviewer effects, that altered the probability of obtaining blood samples from certain
individuals. This offers an opportunity for additional investigation using the BM-IHLSS data.
In spite of these limitations, the BM-IHLSS is the first representative study of Brazilian
migrants in the USA, and the BM-IHLSS data are the only data that include comprehensive
measures of neighborhood characteristics along with biological and legal status information.
My findings suggest that neighborhood-level social cohesion may be an important buffer
against smoking behavior and offer further evidence for the idea that social networks have a
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probable influence on the smoking habits of their members. These analyses also provide
another example to suggest that lacking health insurance coverage is detrimental to health
behavior. Finally, by including measures of physiological, psychological and behavioral stress in
my models I am able to rigorously demonstrate the persistent associations of neighborhood
and other sociogeographic characteristics with a behavioral stress measure – current smoking.
Future work would do well to evaluate different measures of neighborhood disorder for their
relationship to smoking, including more readily observable traits, such as physical signs of
disorder and tobacco advertising density, as well as investigating sociogeographic influences on
smoking in other immigrant nativity groups and low income or minority populations.
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Chapter 5
Concluding Remarks

126

5.1 Concluding Remarks
In this study I investigated physiological, psychological and behavioral measures of
stress – systemic inflammation, serious psychological distress and current smoking – for their
relationships with neighborhood-level social cohesion and disorder in a population of adult
Brazilian migrants residing in metropolitan Boston. I further evaluated the effect of
unauthorized legal status on these three stress measures and the extent to which physiological
dysregulation may mediate or moderate demonstrated associations between sociogeographic
characteristics and distress and smoking. The main findings from these analyses are that: (1)
neighborhood-level social cohesion appears to mitigate stress among Brazilian migrants, and (2)
neighborhood disorder may be deleterious to physiological health. More traditionally measured
neighborhood-level socioeconomic characteristics, on the other hand, do not appear to predict
stress along these three outcomes, at least for Brazilian migrants. I additionally find that
unauthorized legal status is positively associated with all three measures of stress, though the
relationship to current smoking is not significant, and rather than diluting the associations
between social cohesion and distress and smoking, accounting for biological markers of health
in the analyses appears to solidify these interactions.
As indicated in Section 1.6.1, we randomly selected census blocks and households for
the BM-IHLSS based on an initial selection of 10 census tracts with concentrations of foreign-
born Brazilians ranging from seven to 21 percent. Compared, for example, to concentrations of
foreign-born Mexicans in Los Angeles tracts with a high of 55 percent or Chinese in San
Francisco tracts with a high of 78 percent the Brazilian concentrations appear relatively low
(U.S. Census Bureau, 2013a). This begs the question of how Brazilian these neighborhoods
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really are, or whether Brazilians constitute enough of a community in these neighborhoods to
be influenced by and influence collective social capital. The sampling, however, is misleading in
the sense that although the tracts themselves may have relatively low concentrations of
Brazilians, respondents were asked to consider "your neighborhood," which is a more diffuse
concept that does not adhere to census-derived boundaries. For the Brazilian respondents, the
areas they considered to be their neighborhoods may comprise large pockets of Brazilian
residents that do not necessarily live next door to one another but instead frequent the same
businesses, engage with the same civic organizations and share social network members in
proximate areas of town. Although a bit difficult to see, Figure 5.1 illustrates these kinds of
affiliations – this is a view of Cambridge Street in East Cambridge, Massachusetts, a
neighborhood noted for its concentration of Portuguese-speakers (from both Brazil and
Portugal). In a one-block radius can be seen a Brazilian bakery, churrascaria, hair salon and
insurance company in addition to the Massachusetts Alliance for Portuguese Speakers (MAPS)
office and a social club flying a Portuguese flag out front.
Figure 5.1 Cambridge Street, Cambridge, Massachusetts. Image taken from Google Street View.

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Social cohesion for the Brazilians in this study may therefore be less a measure of how
Brazilian residents feel about the person living next door to them, and more a measure of
whether they feel a sense of belonging or trust within their local communities. Despite decades
of literature on immigrant ethnic enclaves, a term that tends to evoke pictures of Chinatown in
San Francisco or Little Havana in Miami where there are high concentrations of foreign-born
residents and businesses and signs tend to be less in English than in other languages, it is
actually more commonly the case that foreign- and U.S.-born Latinos settle in areas with
substantial ethno-racial diversity. Suro and Tafoya (2004), for example, report that 57 percent
of Latinos in the USA were living in neighborhoods in which less than half of the other residents
were Latino as of 2000. While foreign-born Latinos were more likely to live in Latino-majority
neighborhoods than U.S.-born Latinos, the larger proportion of Latino immigrants nevertheless
lived in ethnically diverse neighborhoods. Furthermore, the dispersal of Latino immigrant
communities to every state in the nation as well as to more suburban and exurban areas has
been an established trend since the 1990s (Passel et al., 2002; Kochhar et al., 2005; Liaw and
Frey, 2007). But this has not diminished the extent to which migrants rely on community
bridging and bonding social capital to establish themselves in their places of residence, find
employment and otherwise engage in integration strategies, and cultivating both types of social
network ties is likely to be productive for health (Margolis, 1998; Kim et al., 2006). Hence, it will
be important and useful in future studies of this and other more diffusely settled immigrant
populations to explore more explicitly what "neighborhood" means to individual residents in
terms of both physically identified boundaries as well as more conceptual understandings in
order to identify whether social cohesion is a discrete and ultra-local property of
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neighborhoods or whether it is more broadly applied to proximate communities of people with
similar characteristics.
5.2 Primary Contributions to the Literature
I believe this dissertation makes several contributions to current literatures in social
epidemiology and health geography in particular. First, the study findings offer some of the only
empirical information available about a large and growing group of Brazilian migrants in the
USA, the largest proportion of whom reside in the Boston metropolitan area (U.S. Census
Bureau, 2013b). This population is younger and healthier than the U.S. adult population on
average, and therefore offers an opportunity to study stress and specific stressors as potential
risks for diseases that have yet to develop, an opportunity that is essential for better
understanding disease etiologies and target areas for early intervention. This population also
represents one distinct "Latino" immigrant subgroup with a different socioeconomic, health
and even linguistic profile than other foreign-born Latino immigrant groups on whom most of
the immigrant health literature has focused (e.g. Mexicans, Cubans). Though it may be
convenient to treat Latino immigrants or even Latinos generally as a homogeneous population
in health studies, Brazilians in New England offer a compelling illustration of why this approach
is problematic. The proportion of Brazilians that are unauthorized (71 percent) is reason alone
to imagine that stress may be experienced differently in this population than in others, or that
the calculus for health and well-being may be singular. Identifying differences in stress profiles
by legal status too is a contribution to the literature – very few data sets are available that
make it possible to estimate legal status in combination with individual characteristics; even
fewer include biological markers of health. And, as mentioned above, unauthorized legal status
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appears to be an important predictor of psychological distress and inflammation for Brazilian
migrants.
This study is also one of few that has evaluated neighborhood-level social factors and
other sociogeographic characteristics in relation to physiological as well as other markers of
health. With the tools now available to collect biological data in representative studies and new
biomarker assays being developed regularly, it is increasingly possible to investigate pathways
between sociogeographic environments and health outcomes using approaches that account
for not only self-reported and observable health conditions and behaviors, but physiological
processes as well. This study is an early entry in that pursuit. To examine the sociogeographic
side of the equation accurately and comprehensively it will also be necessary to perfect
existing, and devise new, measures and theoretical models of human domains of life. This will
be an ongoing process, but one to which I believe this study adds with its foci on emphasizing
neighborhoods as important units of investigation beyond the sum of their individual residents
and uncovering the potential importance of social cohesion for promoting health.
5.3 Policy Considerations
The findings in this dissertation call to mind a variety of policy considerations related
particularly to urban planning and immigration policy. This study is especially timely with
respect to the latter issue – a groundswell for comprehensive immigration reform has been
building for many years. President George W. Bush expressed his willingness to sign a bipartisan
reform bill in his second term of office, though attempts to pass such a bill in Congress
ultimately failed. In the 2008 election, President Obama supported a path to legalization for
unauthorized immigrants and since his re-election in 2012 has encouraged Congress to take up
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an immigration reform bill, which Congress is currently attempting to hammer out. In June of
2012, President Obama announced the Deferred Action for Childhood Arrivals (DACA) program,
which allows unauthorized immigrants who were 31 years of age or younger on the date of the
announcement, who came to the USA before they were 16 and who graduated or currently
attend high school or served in the military, to obtain a two-year work permit. While this was a
blessing for those immigrants who qualified, it is a temporary solution for only a segment of the
approximately 11 million unauthorized immigrants in the country (Passel and Cohn, 2012).
Furthermore, the Centers for Medicare and Medicaid Services of the U.S. Department of Health
and Human Services issued a rule in August 2012 effectively excluding any DACA-eligible
immigrants from obtaining access to federally-subsidized health insurance (National
Immigration Law Center, 2012). At this moment, therefore, legal status remains elusive for
many immigrants, and even those who have been granted some temporary relief will not have
access to the benefits that often accompany work authorization.
In this study I find unauthorized legal status to be positively associated with stress, and I
further find a lack of health insurance to be positively associated with smoking behavior. These
results are likely just the tip of a rather large iceberg with respect to the kinds of health risks
that attend unauthorized legal status, health risks that are difficult to rectify without access to
health insurance. There are countless stories in popular and academic literature of migrants
whose families have been split apart by deportation, migrants who live in fear of driving or
leaving their house, even legal migrants who suffer mental illness because of the tenor of fear
and discrimination that may surround their communities (Hagan et al., 2008; Finch et al., 2010;
Golash-Boza, 2012b; Adams, 2013). Added to the stresses of daily life and any other
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acculturative stress experienced, legal status stress may have lifelong effects for the migrants as
well as their families. This too is only one of many arguments for reforming an immigration
system rife with arbitrary visa restrictions, policy contradictions and inhumane practices but a
compelling one as it relates to the health of communities and the potential for unnecessary
health costs over the long term as uninsured migrants seek care in emergency rooms (DuBard,
2007) or when expensive medical procedures are needed for conditions that may have been
prevented if earlier intervention were available. I therefore suggest based on my findings that
legislation designed to legalize those unauthorized migrants currently residing in the USA and
thereby provide them full access to the benefits available through the Affordable Care Act may
have positive impacts on the health of immigrants with additional spillover effects (e.g.
reducing or maintaining health care costs overall) for the communities in which they reside.
A second set of policy considerations concern neighborhoods and urban planning for
health. There are a variety of avenues of intervention in this area that seem promising for
promoting healthy engagement with neighbors and neighborhood environments, three of
which I will briefly discuss – community-based behavioral health programs, urban greening
efforts and structural equity initiatives combined with innovative urban planning strategies. The
first two have significant overlap as at least a secondary goal of many urban greening efforts is
to support healthy behavior, but community-based health interventions fall more specifically
under the purview of the public health approach to advancing individual well-being. For
example, the promotora model – in which community members are trained to provide
specialized health education – is one potentially promising type of health intervention that has
been employed to improve health care access, reduce stress and smoking behavior and
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encourage healthy eating and physical activity in Latino communities in Latin America and the
USA (Hunter et al., 2004; Pazoki et al., 2007; Pérez-Escamilla et al., 2008; Green et al., 2012).
Expanding such efforts to a larger disease prevention framework may not only be effective for
general health promotion in vulnerable communities, but by providing economic opportunities
to community members and specific motives for interpersonal engagement neighborhood
collective efficacy is likely to increase as well. Community clinics can also be potent drivers of
health promotion, especially in immigrant populations that may rely on such facilities for most
of their medical care. Several community clinics in Boston, for example, conduct regular
cooking classes for residents in the surrounding neighborhoods. These classes are designed to
be enjoyable and generally informative, but a primary goal is to teach and encourage nutritious
food preparation for lower income residents at risk of obesity and diabetes. These are just a
few of the types of programs that may serve the dual goals of supporting healthy behaviors and
outcomes and connecting neighbors; finding new ways to enlist community members in the
pursuit of their own health seems like an area ripe for development.
Urban greening efforts have gained many proponents in recent years and have
advanced in formal and informal ways in areas across the country. Community gardening,
bicycle-sharing and land repurposing ventures are three examples of these efforts that have
received substantial attention. Community gardening has been both lauded as a harbinger of
community revitalization and health promotion in troubled metropolitan areas such as Detroit
or lower Manhattan as well as vilified for its association with gentrification (Schmelzkopf, 1995;
Kurtz, 2001; Wakefield et al., 2007). Encouraging community gardening initiatives is therefore
not a straightforward goal for health policy, but where such initiatives have been implemented
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in facilities such as schools and senior centers or with broad community input they seem to
offer promising opportunities for building social cohesion as well as providing neighborhood
residents with active control over an important aspect of their well-being (Saldivar-Tanaka and
Krasny, 2004; Graham and Zidenberg-Cherr, 2005; Austin et al., 2006). Certainly taking lessons
from existing community gardening efforts to tailor appropriate community-level greening
activities may be a worthy pursuit for neighborhood health proponents.
Metropolitan-level bicycle sharing programs and broader initiatives to support collective
physical activity and reduce toxic emissions are another potentially exciting area to explore for
influencing social cohesion and health. Several large cities – Minneapolis, Portland, Washington
D.C., New York – have implemented bike-sharing programs in which people can pick up a bike
at one of many docking stations around the city and leave it at another station near their
destination. These programs are cost-effective ways for metro areas to encourage public
transportation use without creating further traffic congestion as well as increasing individual
physical activity. These programs are relatively new in the USA, and there is little research
available to document the potential collective effects of such efforts, but it is compelling to
imagine that even the sight of more bike stations and people riding bikes in a neighborhood
may be enough to prompt changes in neighborhood perception in more disordered areas or to
bring more residents outside to engage with their physical environments.

135

The collective benefits are more apparent where movements such as CicLAvia or Sunday
Streets in San Francisco are concerned. CicLAvia is an organization in Los Angeles that works
with the city and county governments to arrange regular events in different parts of the county
in which major stretches of road are closed to traffic for a weekend day and people are invited
to bike, walk, skate or run through these areas in community with Angelenos from all over the
county. The latest occurrence opened 15 miles of streets between downtown L.A. and Venice
to foot and bike traffic and brought out thousands of participants, including Mayor Villaraigosa
who opened the event. Again, the extent to which such events result in long-term health
benefits has to be fully evaluated, but bringing community members together to engage in
physical activity and reduce carbon dioxide emissions for even a day in a city as congested as
L.A. is a laudable goal.
A final area of promise for promoting community health and cohesion regards structural
equity initiatives and urban planning for health, or what may be referred to as Smart Growth.
Most of the topics mentioned above may fall under this heading as well, but much of the
application of smart growth theory – an appeal to regional policies promoting the growth of
public transportation, mixed housing and land use and active, higher density population centers
in opposition to sprawl – has been to housing policy and regional efforts at equity and
environmental justice (e.g., Danielsen et al., 1999; Bullard, 2007; Pastor Jr. et al., 2009). Given
the authority and capacity of metropolitan governments to effect change in these areas, this
level of focus certainly makes sense. However, I suggest that attention to similar types of issues
at a more local level may promote health and neighborhood well-being. In particular
movements to ensure the development of mixed-income housing and mixed land use zoning in
136

emerging or changing urban neighborhoods seem to demonstrate promise in reducing poverty
and its attendant risks as well as promoting collective efficacy (Dannenberg et al., 2003;
Sampson, 2013). Local initiatives to repurpose blighted land, such as alleyways or vacant lots,
that otherwise bring down property values and contribute to physical disorder have also shown
promise (Sommer et al., 1994; Seymour et al., 2010; Branas et al., 2011). Certainly broader
regional equity initiatives will also impact local community health and have shown promise in
uniting seemingly disparate segments of society in collective efforts to advance their own well-
being (Pastor Jr. et al., 2009; Pastor et al., 2011), but such efforts have been little evaluated
with respect to their local health impacts. Thus in the final section of this thesis I briefly outline
potential areas for future research in the areas of stress, immigrant health and neighborhoods.
5.4 Future Research
Although researchers have been studying stress for a century and neighborhoods for
longer than that, it is only recently that demographic tools have emerged to the point of
allowing population-based study of stress in all its forms and that neighborhoods are being
investigated as independent spatial units with potential implications for health. Furthermore,
health disparities research, a priority for federal health agencies, has proceeded at a relatively
macro level, focusing largely on black-white differences in health and especially among older
populations. Given the quickly growing immigrant, Asian and Latino populations in the USA and
the importance of discovering the ways in which chronic disease evolves, this scope seems
limited. Looking forward I believe there are a number of areas of investigation that would
contribute substantially to the existing research in these areas.
137

First, it is important to involve younger populations and specific ethno-racial nativity
groups in analyses of stress, neighborhoods and health. Ideally such studies would incorporate
longitudinal designs in order to begin painting a picture of disease etiology and the causal links
between sociogeographic environments and health.
Second, given the findings in this dissertation and concordant findings in the literature,
neighborhood studies should pay additional attention to the ways in which social cohesion and
collective efficacy are likely to develop among neighbors and how these can be promoted and
leveraged for health promotion.
Third, neighborhood studies have relied heavily on linking area-level socioeconomic
factors to individual health outcomes, which has been a partial contributor to a debate in the
literature as to whether neighborhoods matter at all for health. I contend that neighborhoods
do indeed matter but are often studied in ways that do not necessarily elicit the most useful
information possible about how neighborhood environments influence health, or vice versa.
Three areas that deserve additional attention, therefore, are: (1) the development of
comprehensive place-based theoretical models of neighborhoods and health that treat
neighborhoods as entities in their own right; (2) testing and development of novel or
understudied indicators of neighborhood environment as well as new formulations of
neighborhood space (e.g. cognitive mapping exercises); and (3) data collection efforts that
emphasize systematic social observation of neighborhoods, including indicators of physical and
social disorder, geographic coordinate information, concentration of "toxic" exposures (i.e.
traditional toxic facilities as well as concentrations of fast food, alcohol outlets and tobacco
advertising) and other built environment features. Again, implementing such studies with a
138

longitudinal design is so far rare and would be ideal for better understanding neighborhood
effects over the long-term and observing neighborhood change.
There are many other avenues for future research hinted at in this study as well – for
example exploring gender disparities in inflammation, evaluating how a path to legalization and
access to subsidized health insurance may improve migrant health or examining how
interviewer effects may influence the provision of sensitive information – but above I focus on
those most immediate to this study's findings. This thesis is unique in its evaluation of stress,
measured biologically, psychologically and physiologically, in relation to neighborhood social
cohesion and disorder among an understudied population of legal and unauthorized Brazilian
migrants. In it I offer a potential starting point for devising future studies of health disparities in
young, distinct populations and comprehensive investigations of sociogeographic environments
for their effects on health.

139

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Appendix

Figures A1, A2: Violent/Property Crime Incidence overlaid on High CRP Hot Spots in Brazilian Neighborhoods

162

Figures A3, A4: Violent/Property Crime Incidence overlaid on Serious Psychological Distress Hot Spots in Brazilian Neighborhoods

163

Figures A5, A6: Violent/Property Crime Incidence overlaid on Smoking Hot Spots in Brazilian Neighborhoods

Abstract (if available)

Abstract I use the 2007 Harvard-UMASS Boston Metropolitan Immigrant Health & Legal Status Survey (BM-IHLSS) data to estimate cross-sectional associations of neighborhood-level disorder and social capital with measures of physiological (systemic inflammation), psychological (serious psychological distress) and behavioral (current smoking) stress among adult Brazilian migrants, controlling for sociogeographic and individual characteristics. I further investigate the effect of unauthorized legal status on these stress measures and whether physiological health markers may partially explain associations between distress, smoking and neighborhood environment. Employing logistic regression analysis I find that neighborhood social cohesion is significant and negatively associated with inflammation, serious psychological distress and current smoking while neighborhood disorder is significant and positively associated with inflammation only. Neighborhood-level socioeconomic status, the topic covered most frequently in research on neighborhoods and health, does not appear to be important for predicting any of these three outcomes when social cohesion and disorder are controlled. I additionally find unauthorized legal status to be significant and positively associated with both inflammation and psychological distress. My findings suggest that neighborhood-level social cohesion and unauthorized legal status are important factors in predicting stress levels among Brazilian migrants while neighborhood disorder, measured here primarily as criminal activity and victimization, has a more complicated relationship with stress measures.

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Holmes, Louisa M. (author)

Core Title Behavioral, physiological and psychological stress among legal and unauthorized Brazilian immigrants: the moderating influence of neighborhood environments

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Geography

Publication Date 08/22/2013

Defense Date 05/02/2013

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag biomarkers,Brazilian,C-reactive protein,immigrant,legal status,Neighborhoods,OAI-PMH Harvest,psychological distress,Smoking,Stress

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Marcelli, Enrico A. (committee chair), Pastor, Manuel, Jr. (committee chair), Crimmins, Eileen M. (committee member), Curtis, Andrew J. (committee member)

Creator Email holmes.louisa@gmail.com,louisa.holmes@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-322130

Unique identifier UC11287925

Identifier etd-HolmesLoui-2016.pdf (filename),usctheses-c3-322130 (legacy record id)

Legacy Identifier etd-HolmesLoui-2016.pdf

Dmrecord 322130

Document Type Dissertation

Rights Holmes, Louisa M.

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA