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Effects of stress and the social environment on childhood asthma in the children' s health study

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Effects of stress and the social environment on childhood asthma in the children' s health study

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Content EFFECTS OF STRESS AND THE SOCIAL ENVIRONMENT ON CHILDHOOD
ASTHMA IN THE CHILDREN’S HEALTH STUDY

by

Ketan Shankardass

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(EPIDEMIOLOGY)

May 2008

Copyright 2008 Ketan Shankardass

ii
ACKNOWLEDGEMENTS

I would like to thank my co-supervisors (Drs. Michael Jerrett and Rob McConnell), other members of
my committee (Drs. Kiros Berhane, Jean Richardson and Jennifer Wolch), and fellow co-authors
(Drs. Joel Milam, John Wilson and Zaria Tatalovich), for their thoughtful guidance during my
doctoral program. Their willingness to share their time and advice was invaluable to my training and
enabled me to complete this degree skillfully but swiftly. I would also like to recognize Drs. Rick
Burnett and Ed Hughes for their assistance with the multilevel random effects models in this
dissertation.

I am grateful to Anjali and the rest of my family for their unconditional love and support over the last
4 years.

iii
TABLE OF CONTENTS

Acknowledgements ii
List of Tables v
List of Figures vi
Abstract vii
Preface viii
Chapter One 1
Introduction 1
Specific Aims and Hypotheses 3
Background 4
Research Outline 21
Chapter One References 25
Chapter Two 31
Background 31
Methods 32
Results 36
Discussion 42
Chapter Two References 46
Chapter Three 50
Background 50
Methods 52
Results 58
Discussion 64
Chapter Three References 71
Chapter Four 77
Background 77
Methods 79
Results 84
Discussion 91
Chapter Four References 95
Chapter Five 100
Introduction 100
Summary of Findings 100
Contradictions and Additional Analyses 102
Contributions to Knowledge 112
Limitations 115
Future Directions 116
Chapter Five References 120
Bibliography 124

iv
Appendix 138
Appendix References 141

v
LIST OF TABLES

Table 2.1 Subject characteristics and associations with new onset asthma 37
Table 2.2 Associations of traffic-related pollution with incident asthma, by
parental education and parental stress 39
Table 2.3 Associations of in utero tobacco smoke with incident asthma, by
parental education and parental stress 41
Table 3.1 Individual-level associations for lifetime asthma (model 6) 59
Table 3.2 Characteristics of CHS communities 61
Table 3.3 Odds ratio for community-level socioeconomic characteristics and
unexplained lifetime asthma across the inter-quartile range (models 1-6) 62
Table 4.1 Characteristics of CHS neighborhoods, schools and communities, with
bivariate and co-adjusted associations with incident asthma 87
Table 4.2 Associations between incident asthma and characteristics of the
community and school adjusted for individual-level covariates 90
Table 5.1 Associations between new onset asthma and community
socioeconomic parameters for CHS children in the first CHS cohort,
with adjustment for individual covariates 106
Table 5.2 Associations between lifetime asthma and community
socioeconomic parameters for children in cohort E, with
adjustment for individual covariates 108

vi
LIST OF FIGURES

Figure 1.1 Pathways between areas of high deprivation and asthma 8
Figure 1.2 Pathways between areas of high income inequality and asthma 9
Figure 1.3 Pathways between areas of high segregation and asthma 11
Figure 1.4 Pathways between areas of low social capital and asthma 12
Figure 1.5 Analytic diagram for Chapter 2 22
Figure 1.6 Diagram of data hierarchy and covariates in Chapter 3 23
Figure 1.7 Diagram of data hierarchy and covariates in Chapter 4 24
Figure 2.1 Effect of traffic related pollution on incident asthma across parental
stress quartiles 40
Figure 3.1 CHS communities and distribution of participants 53
Figure 3.2 Scatter plot of unexplained lifetime asthma and percent male
unemployment across study communities (n=12); Atascadero and
Upland are influential communities 63
Figure 4.1 Map of study communities 79
Figure 4.2 Scatterplot of community larceny crime rate by crude community
asthma rate 90

vii
ABSTRACT

Childhood asthma prevalence is often higher in areas of low socioeconomic status (SES), but such
disparities are not explained by individual-level risk factors. Unexplained geographic variation in
asthma may reflect an incomplete understanding of asthma etiology. In particular, there is growing
interest in the role of chronic stress in the development of asthma. A series of analyses examined
associations of asthma with areas of low SES and an indicator of stress using data from two cohorts of
the Children’s Health Study based in Southern California. In the first analysis, children with higher
parental stress were significantly more susceptible to the effects of two sources of reactive oxygen
species, traffic-related pollution and in utero tobacco smoke exposure, on incident asthma, which
suggests that oxidative stress pathways may be important for the development of asthma. In the
second analysis, children from communities with high male unemployment were 12% less likely to
report lifetime asthma after adjusting for individual-level risk factors. However, additional analyses in
this same study population suggested that children from communities with high male unemployment
and poverty may be at increased risk for incident asthma, indicating that results from cross-sectional
versus prospective studies may be less accurate and/or negatively biased. In the third analysis,
increased risk for incident asthma was observed in subjects attending schools receiving Title I funds
compared to those from schools without funding (HR 1.71, 95% CI 1.14-2.58), and residing in
communities with higher rates of larceny crime (HR 1.31, 95% CI 1.08-1.60 across interquartile range
of 570 incidents per 100,000 population). These results were not explained by individual-level risk
factors for asthma, although the effect of the larceny crime rate was partially positively confounded
by residential exposure to traffic-related pollution. Increased risk for asthma in areas of low SES may
be related to unmeasured factors of the physical environment found in areas of high deprivation, or
they may be a marker for stressful environments. These results indicate that further investigation of
the social environment could both elucidate the role of risk factors such as stress in mediating
contextual effects and identify new avenues for disease prevention

viii
PREFACE

This dissertation includes five chapters that consider the relationship between the social environment
and the onset of asthma in the Southern California Children’s Health Study. The first chapter
introduces this topic and presents the specific aims and hypotheses of the enclosed research. Several
sections of background information are then presented, along with an outline of the research presented
in subsequent chapters. The second, third and fourth chapters comprise three standalone analyses that
represent the original research of this dissertation. Finally, the fifth chapter includes a synthesis of this
research, including several follow-up analyses, and a discussion of the contributions and limitations of
the results. This chapter also suggests directions for future research that can build on the results of this
dissertation.

1
CHAPTER ONE

1.1 BACKGROUND

Asthma is the most common chronic disease of childhood (Centers for Disease Control and
Prevention 2003). While there has been a growth in the epidemic globally, the distribution of
childhood asthma varies greatly between and within countries (Eder et al. 2006; GINA 2007; ISAAC
Steering Committee 1998; Mannino et al. 2002; Peters et al. 1999). The prevalence of childhood
asthma in the United States is among the highest in the world and has grown steadily since the 1960s.
Within the U.S., prevalence is generally higher in urban settings compared to rural settings, as well as
between and within large cities (GINA 2007; Mannino et al. 2002; Wright and Fischer 2003). In
particular, asthma rates are higher among non-white children living in urban areas and children living
in impoverished neighborhoods (Wright and Fischer 2003). The increase and geographic variation in
asthma has not been completely explained by risk factors at the individual level, including
socioeconomic status (SES) (Gold et al. 1993; Wright and Fischer 2003).

In recent years there has been some interest in examining how characteristics of the social
environment may account for population disparities in asthma (Gold and Wright 2005). Growing
interest in population health has been coupled with a call to go beyond merely documenting
geographic effects to articulate the relationships that facilitate such effects on asthma (Timmermans
2007). Traditionally, epidemiologists examine disease pathways, which describe the biological
mechanisms that connect a risk factor to illness within an individual. Social epidemiologists extend
this paradigm by considering ways in which the social environment can affect individual health. In
this respect, we may describe health effects of the social environment as composed of upstream
pathways that focus on connections between the distal social context (including economic and
structural circumstances) and proximate risk factors for health, and more traditional downstream
pathways concerned with the biological mechanisms that lead to disease. Therefore, in addition to an

2
understanding of the social patterning of risk that may inform population-level interventions to
prevent new onset of asthma in susceptible populations, research about the effects of the social
environment may ultimately lead to a greater understanding of downstream pathways, including
complex interactions between environmental factors and biological mechanisms. Furthermore, the
omission of such effects has the potential to confound the evaluation of other known risk factors for
asthma, where these factors are shown to follow some pattern within the social environment.

The development of multilevel models has allowed us to examine geographic effects concurrently
with individual-level risk factors, while accounting for similarity by proximity, or spatial
autocorrelation, in individual subjects nested within spatial units by employing random effects (Chaix
et al. 2005; Diez-Roux 2000). Multilevel models also facilitate the empirical tests required in order to
identify linkages between upstream and downstream pathways related to the onset of asthma.
Although few studies have utilized multilevel models to examine how the social environment affects
asthma (see page 27 for a review), this approach has been used successfully to explain the variation in
risk for other illnesses. For example, geographic variation in cardiovascular disease can be partly
explained by area deprivation through its relationship with diet, smoking, overweight and physical
activity (Riva et al. 2007).

The Children’s Health Study (CHS) is a longitudinal study of air pollution and other determinants of
childhood asthma and respiratory illness in two cohorts including over 11,000 school children in
Southern California (see Peters 1999 and McConnell 2006, among others). The first cohort recruited
subjects at four points during 1993 (groups A-C) and 1996 (group D), while the second cohort
recruited all subjects during a single period (2002-2003). This study includes an abundance of
information about risk factors for asthma, including measures of regional and residential air pollution,
and modeled estimates of residential traffic-related pollution and distance to roadways. Although the
focus has traditionally been on the effects of air pollution and other individual-level exposures,
significant community- and school-level variation remains unexplained by these risk factors. For

3
example, among the baseline sample of the initial cohort, lifetime prevalence rates of asthma across
communities varied by two-fold (from 11% to 22%) and incidence varied by almost 3-fold
(unpublished data), for reasons unexplained by known risk factors. The present collection of analyses
aims to explore associations between the social environment and asthma in the CHS by empirically
testing the upstream and downstream pathways that might explain such effects. Given the longitudinal
design, the large sample size, the recruitment of subjects from schools that nest within communities,
and the wealth of data pertaining to individual-level risk factors for asthma and air pollution exposure,
the CHS offers a rare opportunity to comprehensively examine the effect of the social environment on
the induction of asthma.

1.2 SPECIFIC AIMS AND HYPOTHESES

1. To examine the association between characteristics of the social environment with prevalent
and incident asthma. It is hypothesized that subjects residing and attending school in areas
described by low socioeconomic status (SES) will experience higher rates of asthma.

2. To examine the role of psychological stress in the onset of childhood asthma. It is expected
that increased exposure to stress in the family will increase the risk for asthma indirectly, by
increasing susceptibility to environmental exposures such as air pollution.

3. To examine the relationship between population- and individual-level factors in predicting
prevalent and incident asthma by assessing the extent to which risk factors describing
individuals and households confound effects at other levels. Observed relationships between
these factors should agree with plausible biological mechanisms for the induction of asthma.
For example, if a positive association were found between high rates of crime in an area and
incidence of asthma, chronic stress in subjects residing in these areas may increase
susceptibility to air pollution due to a shift toward a Th2 immunologic phenotype. Therefore,

4
we would expect adjustment for variables describing stress-related susceptibility to positively
confound the association between area crime and asthma incidence.

1.3 BACKGROUND

1.3.1 SES, stress and asthma

In general, individual SES associates with health in a dose-dependent manner, which implicates
factors other than material conditions as causal agents for socioeconomic inequalities in health (Kelly
et al. 1997). Other characteristics of low SES that may affect health include in utero and early
childhood experiences, access to health care, relative position in the social hierarchy and associated
exposures to physical conditions (e.g. air pollution), and psychosocial processes (e.g. stress) (Adler
and Ostrove 1999). The biologic mechanism for a dose-response relationship between SES and stress
remains unclear. Psychosocial stress may occur through social comparison by persons of lower status
with people in relatively high positions, which is supported by evidence that regional income
inequality is associated with poorer population health independent of poverty (Kawachi et al. 2002).

Although there is much evidence about risk factors related to biology and the physical environment,
the pathogenesis of asthma remains poorly understood (Eder et al. 2006). Low SES is often associated
with higher prevalence of asthma and may be important because of its association with other risk
factors for asthma, including exposure to environmental tobacco smoke, indoor allergens, indoor and
outdoor air pollution, diet, household size and daycare attendance, as well as early childhood
conditions including respiratory infections, and low birth weight and prematurity (Gold and Wright
2005). Also, effects of low SES on asthma may also be mediated by biological pathways related to the
experience of psychosocial stress (Chen et al. 2006; Gold and Wright 2005).

5
The extent to which the psychosocial stress associated with relative deprivation relates to asthma
remains unclear, although growing evidence implicates stress as a mechanism for triggering
symptoms and potentially inducing asthma. Stress has pro-oxidant effects that can increase airway
inflammation (Wright et al. 2005), and stress in both children and parents predict onset of wheeze and
asthma morbidity (e.g. severity, subsequent attacks) in children (Kilpelainen et al. 2002; McConnell et
al. 2005; Mrazek et al. 1999; Randolph and Fraser 1999; Sandberg et al. 2000; Sawyer et al. 2001;
Shalowitz et al. 2001; Weil et al. 1999; Wright et al. 2002; Wright et al. 1998). This stress may be
acute (e.g. personal confrontations at school) or chronic (e.g. dangerous living conditions) or some
combination of both (e.g. death of a parent). Effects of air pollution on respiratory health have been
found to be greater among individuals of lower SES (Wheeler and Ben-Shlomo 2005). A possible
mechanism by which SES may modify the effects of air pollution is psychological stress (Chen et al.
2006; Gold and Wright 2005). Chronic stress may increase vulnerability to antigens through direct
effects on the endocrine system, autonomic control of airways, and immune function (Marshall and
Agarwal 2000; Wright et al. 1998) (e.g. via the development of hypothalamic-pituitary-adrenal (HPA)
axis hyporesponsiveness resulting in a shift toward a pro-inflammatory Th2 phenotype, a hallmark of
allergic asthma) (McEwen and Seeman 1999; Umetsu and Dekruyff 2006; Wright et al. 2005; Wright
et al. 1998). Stress may thus increase vulnerability to environmental factors associated with asthma.

1.3.2 The social environment and health

(Macintyre and Ellaway 2003) suggest five key mechanisms through which a person’s health may be
impacted by their local area. First, low SES areas may affect health through features of the physical
environment that are shared by all residents. For example, neighborhoods with high rates of minorities
and poverty in Southern California may be located in areas with higher levels of harmful traffic-
related and industrial air pollution (Houston et al. 2004; Morello-Frosch et al. 2002). Second, areas of
low SES may be unhealthy due to the unavailability of healthy environments related to home, work,
school and play. For instance, poor neighborhoods may feature buildings with older housing stock,

6
which not only collect more indoor allergens, but also feature less air-tight construction, thus
increasing the potential for outdoor pollution to penetrate indoors (Forastiere et al. 2007; Jones 1998;
Liu and Nazaroff 2001). Also, employment at workplaces that feature stressful job tasks may be more
plentiful in areas of low SES (Siegrist and Marmot 2004). Third, access to public and private services
supporting health and welfare may be inadequate in areas of low SES, such as health care and
policing (Macintyre and Ellaway 2003). Fourth, socio-cultural features of an area may affect health in
a number of ways. For example, the political, economic and ethnic history of a neighborhood may
determine social norms that influence health behaviors (Macintyre and Ellaway 2003). Finally, poor
neighborhood reputation can produce stigma that negatively affects morale and self-esteem and can
contribute to chronic stress in individuals.

Because the individual mechanisms described above may overlap, there are likely to be complex
effects of place on health. As a result, measurement of the social environment for epidemiologic
studies is not straightforward. Much of the literature about health effects of the social environment has
focused on four concepts that may pertain to some or all of the above mechanisms. These are briefly
defined below:

1. Absolute Deprivation: The lack of economic wealth in an area. In addition to the median
income of residents, absolute deprivation is often described by the percent of residents living
below the poverty line, the percent of residents with low education, and the percent of
residents who are unemployed.

2. Income Inequality: The degree of unevenness in the distribution of wealth within a
population. Inequality is commonly defined by the Gini coefficient (Yitzhaki 1979).

3. Social Capital: The structure and quality of social networks within an area resulting mutual
benefits (Kawachi 1999; Sampson et al. 1999). Two types of social capital are often

7
discussed: bonding and bridging (Altschuler et al. 2004). Bonding refers to benefits derived
from internal cohesion, (e.g. availability of social networks to cope with stress related to
exposure to violence); while bridging pertains to benefits achieved in the context of wider
societal structures (e.g. reductions in crime resulting from changes to social welfare
programs that were motivated by grassroots community action). Social capital is measured in
a number of different ways both quantitatively (e.g. crime rates) and qualitatively (e.g.
perceptions of neighborhood safety or trust).

4. Segregation: The degree of racial or ethnic separation in daily life. Segregation is usually
measured by the index of dissimilarity, which measures the evenness of residential
distribution of one group compared to another (e.g. African-Americans versus all other
race/ethnicities) (White 1986).

1.3.3 Proposed pathways for effects of the social environment on childhood asthma

A growing literature suggests several ways that areas of low SES may plausibly be related to the onset
of childhood asthma (Cagney and Browning 2004; Gold and Wright 2005; Shapiro and Stout 2002;
Timmermans 2007; Wright 2006; Wright and Fischer 2003; Wright and Subramanian 2007).
Delineating these relationships requires a multi-disciplinary approach in order to understand how
broad social, economic and structural process may shape population disparities in asthma. The
following diagrams summarize plausible links between a series of upstream and downstream
pathways for the four above-mentioned characteristics of the social environment. These diagrams
represent a priori hypotheses for associations between areas of low SES and asthma in the multilevel
analyses presented in Chapters 3 and 4 of this dissertation.

Areas characterized by high absolute deprivation may be associated with higher rates of childhood
asthma due to older housing that contains more indoor environmental toxins, including allergens in

8
settled dust and mildew (Corburn et al. 2006; Evans and Kantrowitz 2002; Wright and Fischer 2003)
(Figure 1.1). Such housing may also allow greater penetration of outdoor air pollution, which may
also be in higher concentration in these areas (Forastiere et al. 2007; Houston et al. 2004; Jones 1998;
Liu and Nazaroff 2001; Morello-Frosch et al. 2002). Greater exposure to indoor environmental toxins
could also occur in areas of high deprivation due to higher rates of local crime (Hsieh and Pugh 1993),
which may result in unsafe parks and recreation spaces and cause parents to keep their children
indoors more often (Sampson 2003; Wright and Fischer 2003). In this way, high crime may also
reduce the potential for physical activity in children (Babey et al. 2005; Babey et al. 2007; Molnar et
al. 2004), which may lead to overweight. Finally, children in areas of high deprivation may be more
likely to experience chronic stress related to multiple factors, including high ambient noise resulting
from older housing with poorer construction (Evans and Kantrowitz 2002), and exposure to violence
(i.e. post-traumatic stress) attributable to higher rates of crime (Wright 2006).

Figure 1.1 Pathways between areas of high deprivation and asthma
Chronic
Stress
Indoor
Environmental
Toxins
Older
Housing
Air
Pollution
ASTHMAGENIC SOCIAL ENVIRONMENT
High
Deprivation
High
Segregation
High
Income
Inequality
Low
Social
Capital
ASTHMA
UPSTREAM DOWNSTREAM
Overweight
High
Crime
Low
Physical
Activity
Unsafe
Parks

9

Societies with high levels of income equality may feature higher rates of crime attributable to
frustration, stress and family disruption in individuals who are relatively deprived (Blau and Blau
1982; Kawachi et al. 1999; Kennedy et al. 1998), which can lead to overweight, increased exposure to
indoor environmental toxins, and chronic stress (Figure 1.2), as mentioned above. Also, children of
lower socioeconomic position in areas of high income inequality may experience chronic stress due to
the quality of their relationships and social exclusion related to their relative social position (Putnam
2007; Wilkinson 1999).

Figure 1.2 Pathways between areas of high income inequality and asthma
Overweight
High
Crime
ASTHMAGENIC SOCIAL ENVIRONMENT
High
Deprivation
High
Segregation
High
Income
Inequality
Low
Social
Capital
ASTHMA
UPSTREAM DOWNSTREAM
Low
Physical
Activity
Unsafe
Parks
Chronic
Stress
Social
Comparison
Indoor
Environmental
Toxins

Areas characterized by racial segregation may have poorer access to fresh and healthy foods (e.g.
fewer supermarkets, more fast food restaurants), which may encourage poorer diet and, thus,

10
overweight (Moore and Diez Roux 2006; Morland et al. 2002) (Figure 1.3). Segregated communities
may also have less power to resist the local siting of major roadways and industrial complexes, which
may lead to higher levels of air pollution in these areas (Morello-Frosch and Lopez 2006). Also, areas
of segregation may feature higher rates of crime, possibly due to increased isolation resulting in lower
social and economic mobility (Massey et al. 1987; Shihadeh and Flynn 1996), which may increase the
risk for overweight and exposure to indoor environmental toxins, as previously mentioned. Increased
crime and social disorder in areas of segregation may also lead to higher risk for chronic stress
(Charles et al. 2004; Massey 2004). Also, in immigrant communities that are segregated from the rest
of society, chronic stress may be higher in children as they struggle through the process of
acculturation (e.g. learning a second language while trying to maintain academic achievement)
(Thomas 1995). While the impacts of race and racialization on health (e.g. stress related to racial
discrimination) should be considered separately from the effects of low SES, areas of low SES are
likely to be disproportionately African American or Hispanic in Southern California. As a result,
pathways related to the social environment may plausibly contribute to racial disparities in asthma in
this region, and vice versa, so the underlying causes of effects related to segregation should be
carefully considered.

11
Figure 1.3 Pathways between areas of high segregation and asthma
Overweight
High
Crime
Poor Access
To Healthy
Food
ASTHMAGENIC SOCIAL ENVIRONMENT
High
Deprivation
High
Segregation
High
Income
Inequality
Low
Social
Capital
ASTHMA
UPSTREAM DOWNSTREAM
Poor
Diet
Low
Physical
Activity
Unsafe
Parks
Chronic
Stress
Acculturation Indoor
Environmental
Toxins Air
Pollution

Areas with low bridging social capital may experience higher levels of air pollution due to ineffective
resistance against the local siting of major roadways and industry (Morello-Frosch et al. 2002) (Figure
1.4). Higher rates of crime may occur in areas with low bonding social capital since there may be less
respect for fellow residents (Kawachi et al. 2004; Putnam 2001), which may increase risk for
overweight, exposure to indoor environmental toxins and chronic stress, as previously described. In
areas with less “bonding” there may also be a greater likelihood for unhealthy behaviours due to the
ineffectiveness of health promotion, which may lead to overweight, smoking, in utero tobacco smoke
exposure and low birth weight (Kawachi et al. 2004; Wakefield and Poland 2005; Weitzman and
Kawachi 2000). Finally, children in areas with low social capital may be less able to cope with
stressors due to a lack of social support, increasing the risk for chronic stress (Altschuler et al. 2004;
Siegrist 2000; Taylor et al. 1997).

12

Figure 1.4 Pathways between areas of low social capital and asthma
Low
Birth Weight
Overweight
ETS
Smoking
Unhealthy
Behaviors
High
Crime
ASTHMAGENIC SOCIAL ENVIRONMENT
High
Deprivation
High
Segregation
High
Income
Inequality
Low
Social
Capital
ASTHMA
UPSTREAM DOWNSTREAM
Poor
Diet
Low
Physical
Activity
Poor
“Bonding”
Unsafe
Parks
Chronic
Stress
Air
Pollution
Poor
“Bridging”
Indoor
Environmental
Toxins
Low
Social
Support

One caveat for the diagrams presented above is that the relationship between specific characteristics
of the social environment and pathways occurring further downstream will be conditional on the
wider social, economic and structural context. This is indicated by the repetition of certain pathways
across multiple characteristics of the social environment (e.g. all characteristics have been associated
with crime, which may lead to overweight, exposure to indoor environmental toxins and chronic
stress). For example, while Figure 1.3 suggests that children of recent immigrants in areas of
segregation may experience chronic stress due to problems with acculturation, recent evidence
suggests that Hispanics immigrants who reside in neighborhoods with a high density of fellow
immigrants may actually have lower risk for asthma than non-immigrants in these neighborhoods, and
compared with Hispanics immigrants in neighborhoods with low density of immigrants (Cagney et al.

13
2005). It is suggested that this protective effect may occur due to increased social capital formed
among Hispanic immigrants, and this may explain the so-called “Latino Paradox”. In this way, areas
of high segregation may be healthy if they are also areas with high social capital.

1.3.4 The school environment and asthma

Just as adults spend a significant amount of their time in the workplace, the school is a focal point of
children’s lives. School attendance is mandatory for all children between the ages of 6 and 18 in
California and every other state in the U.S. has a similar compulsory attendance law. As a result,
children spend around one third of their waking life in school. Yet, while occupational health research
has developed into an important discipline, the school environment is rarely characterized as a source
of risk for ill health, including asthma. Multilevel random effects models unadjusted for any
covariates indicate that variation in incident asthma is greater at the school level than at the
community level in the CHS (unpublished data), suggesting that important effects of the school level
may exist for asthma. Therefore, exclusion of the school level may result in confounding of
associations with asthma for risk factors at various other spatial levels with asthma, and may also
mask key interactions with these variables. Furthermore, the discovery of associations related to
schools could be translated into interventions that may efficiently prevent asthma in children (Persky
et al. 2007).

There is a growing literature that describes how the physical environment of schools may affect
asthma (Frumkin et al. 2006; Tranter 2005), but the school social environment has been granted far
less attention. Application of Macintyre and Ellaway’s aforementioned mechanisms to the school
setting provides plausibility for a school effect on asthma. For example, chronic stress in students may
be related to violence or conflict at schools (Graham-Bermann and Seng 2004), or academic
underachievement (Gibby and Gibby 1967; Gillock and Reyes 2004; Repetti 1996). Also, the
construction of low SES schools may feature older building materials that allow greater penetration of

14
outdoor air pollution, or that collect higher levels of indoor allergens, such as mold (Frumkin et al.
2006; Tranter 2005).

In addition to the school itself, the social environment in areas included in a child’s commute to
school may be associated with increased risk for asthma. For example, children who commute to
school in low SES or high minority areas may be regularly exposed to greater levels of pollution
compared with children in other schools (Pastor et al. 2002). Also, children living in high-density
urban areas may be more likely to face stressful commutes to schools due to the density of traffic and
related hazards than children who commute in lower density areas (Frumkin et al. 2006).

1.3.5 The association between exposure to air pollution and low SES areas

In Southern California, minority and high poverty neighborhoods bear over twice the level of traffic
density compared with the rest of the region, and a disproportionate level of stationary sources of air
pollution, including hazardous waste storage and disposal (TSDFs) and Toxic Release Inventory
(TRI) facilities (Houston et al. 2004; Morello-Frosch et al. 2002). These neighborhoods are also more
likely to have older multifamily housing, which is associated with higher indoor penetration of
outdoor pollution (Houston et al. 2004). Finally, schools in the lowest quintile of exposure to air
pollution in Southern California were found to be comprised of 30% Anglo students (when only 15%
of the school district was Anglo) whereas the most polluted quintile contained schools containing
92% minority students (Pastor et al. 2002), which suggests that air pollution exposure is higher for
low SES schools.

Given this association between high exposure to air pollution and low SES areas, previous estimates
of the effect of air pollution in the CHS unadjusted for characteristics of the social environment may
be confounded positively, away from the null. Another cohort study with a focus on the effects of air
pollution on mortality from the same region as the CHS recently reported similar confounding (Jerrett

15
et al. 2005). In turn, care should be taken to adjust observed effects of the social environment for
exposure to air pollution.

1.3.6 Issues concerning multilevel models 1: Causal inference

Recent commentary has discussed the limitations of using multilevel models to make causal inference
(Diez Roux 2004; Oakes 2004; Riva et al. 2007; Subramanian 2004). What follows is a brief review
of two of these methodological concerns and their relevance for the current investigation of childhood
asthma.

Context versus composition

In theory, population disparities in asthma can be broken down into pathways operating at the
individual level (i.e. “compositional” effects) and at the area level (i.e. “contextual” effects) (Diez
Roux 2002). Although compositional effects can be adequately controlled with individual-level
variables, contextual effects imply independent or interactive contributions to health from the area in
which an individual lives or works, or as (Schnittker and McLeod 2005) put it, “social processes that
are not reducible to any particular proximate risk factor”. For example, children who reside in old
inner-city housing projects and are regularly exposed to high rates of crime and violence may be
expected to have higher levels of chronic stress compared with children from suburban gated
communities since inner-city children may be regularly concerned about their safety and the well-
being of their family and friends. In this case, chronic stress is not derived from an individual-level
exposure; instead, it is created by the general environment that is shared by all children.

The separation of contextual and compositional pathways has been criticized as unrealistic given that
area- and individual-level pathways are often linked in complex and dynamic relationships that
sometimes work bi-directionally (Oakes 2004). However, some value in distinguishing between these

16
pathways lies in their interpretation. Apart from cases where there are direct effects on biology, e.g.
residence in a high crime environment causing chronic stress, contextual effects can help to identify
upstream conditions and forces that may be ideally targeted for intervention in order to reduce
downstream risk factors and disease onset, e.g. neighborhoods featuring older construction that collect
more indoor allergens. Furthermore, where contextual effects are not confounded by compositional
factors or found to operate through established biological pathways, their identification can contribute
to the identification of previously unknown risk factors and mechanisms for asthma that operate
further downstream.

Multiple spatial levels of risk and MAUP

Population disparities in childhood asthma may be explained by risk factors that are distributed across
multiple spatial scales. For example, local traffic density in the neighborhood may generate noise
pollution and increase chronic stress, while funding allocation for policing may increase crime at the
wider community level and cause chronic stress in children who learn about such events affecting
their family and friends throughout the community. Therefore, a statistical approach that includes
multiple levels of data, while accounting for clustering of people within different spatial groups, is
required in order to correctly adjust effects at any one level for effects at other levels of importance
(Diez-Roux 2000; Merlo et al. 2005). In the past, multilevel studies of asthma have often utilized two-
level models that only account for the effect of a single spatial level adjusted for individual level
confounders. Failure to capture the variance in all relevant variables at the salient levels may transfer
random variance to the incorrect spatial level. Furthermore, if variables describing regional variation
are used to model effects that are inherently local, important local variation in data may be merged,
hiding important contrasts between local areas and causing non-differential misclassification of
exposures. Such spatial scale dependencies relate to scale effects described by the “modifiable areal
unit problem” (MAUP) whereby the size of the unit of analysis may influence subsequent analytical
results (Steel and Holt 1996). The scale effect also suggests that movement to larger units tends to

17
diminish statistical power because the available number of units in the analysis is reduced (Amrhein
and Reynolds 1997), highlighting the benefit to modeling effects at the largest scale that is
appropriate. Another component of this problem, the zoning effect, refers to how the spatial contours
of ecological units can change the meaning and nature of geographic effects through the inclusion or
exclusion of key data. The use of administrative or statistical units (e.g. census data) is common but
there are questions about their intrinsic meaning with respect to health, for example, how these units
correspond to actual environmental features and ecological exposures that are relevant for health.
There has been a suggestion that units should be consistent with how residents define and experience
their residential area (Riva et al. 2007). For example, the enclosed analyses utilize data describing the
schools where subjects visit regularly, as well as community-level variables defined by novel GIS
methods that utilize data relevant to the majority of subjects, while excluding data pertaining to
subjects living at spatial boundaries where there may be potential outliers.

1.3.7 Issues concerning multilevel models 2: Why not Structural Equation Modeling?

In Chapters 3 and 4 of this dissertation, multilevel models are used to measure associations between
the social environment and asthma, and then examine the impact of adjusting for a range of potential
intermediate pathways. Structural equation modeling (SEM) is another approach that is commonly
used to examine such mediating relationships between social concepts and disease. However, there
are two main reasons why multilevel models are more appropriate than SEM for the analyses in this
dissertation.

First, one of the strengths of SEM is the ability to test directional hypotheses (i.e. A B C) using
a measurement model, which examines the covariance of factors in the chain of causation, and a
structural model, which examines the structural relationship between these factors. As a result, this
approach may be most appropriate for confirmatory analyses that assess the validity of hypothesized
causal relationships. In contrast, the research in Chapter 3 and 4 is relatively exploratory since

18
mediation of contextual effects of the social environment on asthma (e.g. by chronic stress) hasn’t
been examined in depth by prior studies. Given the range of compositional factors and potential
mediators that are explored in these analyses, the flexibility of the traditional regression approach (e.g.
simpler model specification) is appreciated. Second, chapters 2 and 4 constitute survival analysis, but
software to carry out SEM for survival analysis are not commonly available at present.

1.3.8 Review of multilevel studies about asthma incidence and prevalence

Research on the impact of the social environment on asthma has traditionally focused on individual-
level factors, such as socioeconomic position of the family (King et al. 2004; Mielck et al. 1996;
Ortega et al. 2001; Pearce et al. 1998; Wright et al. 2004); however, research about such effects does
not completely explain population disparities in asthma. Potential contextual effects on asthma have
been proposed (Gold and Wright 2005), indicating the need for a multilevel approach to studies of
childhood asthma epidemiology (Merlo et al. 2005). Multilevel models have only rarely been used to
explore how the socioeconomic environment can impact asthma (Basagana et al. 2004; Cagney and
Browning 2004; Salmond et al. 1999), and few studies have focused specifically on childhood asthma
(Juhn et al. 2005; Litonjua et al. 1999; Nepomnyaschy and Reichman 2006). In general, these studies
suggest that areas of low SES are associated with higher rates of asthma; however, there is
inconsistency in the characteristics associated with asthma, and some studies suggest a negative or
null relationship.

Cagney and Browning (2004) examined whether 4 measures of the neighborhood social environment
(i.e. deprivation, residential stability, disorder and collective efficacy) predicted prevalence of self-
reported doctor-diagnosed adult respiratory disease in inner-city Chicago neighborhoods, with
comprehensive control for individual demographic and SES variables. Neighborhoods were defined
by clusters of 2-3 census tracts that were homogeneous with respect to racial/ethnic mix, SES,
housing density and family structure. This was accomplished though consideration of major

19
geographic boundaries, local knowledge of neighborhoods and cluster analyses. The measure for
deprivation was the result of a factor analysis using census data describing percent below the poverty
line, receiving public assistance, unemployed, in female-headed households, under age 18
(concentration of children), and African American. The measure of residential stability was also
derived from a factor analysis, this time using data describing percentage living in the same house
since 1985 and the percentage of owner-occupied dwellings. Scale variables describing collective
efficacy and disorder were created by aggregating individual-level responses from related questions in
the Project on Human Development in Chicago Neighborhoods Community Survey. The authors cite
an interesting multilevel reliability measure describing each of these neighborhood measures, which is
function of the sample size of each neighborhood and the proportion of total variance between groups
relative to the amount within groups (see: Raudenbush and Bryk, 2002). This study found no
association between deprivation and disorder with respiratory disease, but they did observe that
greater collective efficacy within communities was protective and that residential stability was a mild
risk factor when in the presence of collective efficacy, which is counterintuitive. The authors suggest
that stability may be a reflection of older housing stock or neighborhoods characterized by social
isolation. Collective efficacy can be described as shared expectations and effort that lead to beneficial
community resource. The authors suggest that collective efficacy may improve asthma-inducing
conditions in inner-city neighborhoods. For example, low levels of trust may relate to a tendency to
remain indoors and keep windows and doors closed, increasing exposure to household allergens. High
collective efficacy may spur health promotion and trust of health care providers.

Juhn et al. (2005) examined the association between data describing the maternal residential census
tract at delivery and the incidence of physician-diagnosed definite or probable asthma (via medical
records linkage) in a retrospective cohort study based in Rochester, Minnesota from 1976 through
1983, with minimal adjustment for individual-level demographics and SES. The independent
variables describing neighborhoods included the status of whether a census tract faces intersections
with major roadways or railways (a proxy for traffic volume) and census variables for mean family

20
income, mean number of years of maternal education and proportion of people with managerial and
professional occupations. The authors found a borderline significant protective effect of low mean
family income (assigned to the subject’s census tract at birth) and incident asthma. Interestingly, the
variance of the random effects component actually increased when the individual-level variables were
adjusted for both neighborhood SES and neighborhood traffic.

Nepomnyaschy and Reichman (2006) indirectly examined the association between housing
characteristics and poverty rates of maternal residential census tract at delivery and parental report of
doctor-diagnosed lifetime asthma at age 3 in a birth cohort from 75 hospitals in 20 large U.S. cities.
There was comprehensive adjustment for individual-level demographic and SES variables, as well as
low birth weight and maternal medical and behavioral characteristics. The authors observed a positive
association between asthma at a young age and residence in census tracts with higher rates of renter-
occupied housing, higher rates of vacancy and high poverty rates.

Basagana et al. (2004) examined the association between self-reported prevalent asthma (determined
by the presence of one out of three factors) and area-level SES in a cohort of adults located in 32
centers throughout Europe, Australia, New Zealand and the U.S.. They observed a positive
association between low SES at the center level (% with low or medium education) and asthma
prevalence, and this effect confounded a previously significant effect for individual level SES.

Litonjua et al. (1999) studied how SES at the individual and zip code level confounded increased risks
for prevalent asthma associated with being African American and Hispanic in a cross-sectional study
of adults and children. This study included a birth cohort of parents with allergies or asthma and their
children. Asthma was determined at 2-3 months of age via parental report of doctor-diagnosis in
children. Area poverty was calculated as a three-level variable distinguishing <10%, 10-20% and
>20% poverty in the zip code of residence. In an unadjusted model, there was a positive, significant
odds ratio for Hispanic ethnicity, and a positive, non-significant odds ratio for the effect of African

21
American race. Adjustment for individual and area level SES individually reduced those effects
slightly and rendered the effect for Hispanicity non-significant in some cases. Joint adjustment further
reduced both racial/ethnic effects (non-significant) and, of the three SES variables (i.e. 2 individual
level and 1 area level) only the area level effect was robust. However, there was no account for
collinearity between the three variables and no account for clustering of subjects within zip codes.
There was a similar pattern of confounding for adult prevalent asthma but here the individual level
SES effects were robust and not the area level SES one.

Finally, Salmond et al. (1999) performed a cross sectional analysis of small area deprivation
(NZDep91 measure) and asthma prevalence in a large population-based cohort of New Zealand
adults. With minimal adjustment of individual-level variables (age, gender, ethnicity), prevalence of
asthma in the 3 (of 10) most deprived areas was higher than prevalence in the lowest deprivation area.

1.4 RESEARCH OUTLINE

The specific aims of this dissertation are addressed in three analyses based on subjects in both cohorts
of the CHS (Chapters 2-4). This outline contains a qualitative description of these analyses.

The analysis presented in Chapter 2 focuses on the influence of the social environment at a much
larger spatial scale: the household. Specifically, while effects of traffic-related pollution and in utero
tobacco smoke exposure on asthma have been previously reported in the CHS (Gilliland et al. 2006;
Gilliland et al. 2002; Gilliland et al. 2001; McConnell et al. 2006; McConnell et al. 2007), this
analysis examines whether subjects from low SES households, or with higher levels of parental stress,
are more susceptible to the effects of these oxidant pollutants on new onset asthma (Figure 1.5).
Effect modification of risk for asthma by household SES and stress is examined among all non-
wheezers at baseline of the second CHS cohort (i.e. cohort group E) using Cox proportional hazards

22
random effects models. Confounding of moderation by SES and stress is examined by other
individual and household risk factors for asthma.

Figure 1.5 Analytic diagram for Chapter 2
Traffic-related
pollution (NOx)
or
In utero tobacco
smoke exposure
New onset
asthma
Low SES /
High Stress
in Household

Chapter 3 is a preliminary attempt at examining whether areas of low SES are associated with higher
rates of asthma in the CHS. Subjects are from the first CHS cohort (i.e. groups A through D) and
reside in one of 12 communities. The dependent variable is lifetime doctor-diagnosed asthma and the
predictors are four measures of community deprivation. Confounding of contextual effects for
community deprivation is controlled by adjusting for individual and household risk factors for asthma.
All models are run using a multilevel logistic regression model. Therefore, this model contains data at
two spatial levels: individual subjects (I) within communities (Figure 1.6). Because there are likely to
be similarities in subjects between communities, community random effects are included to account
for unexplained variation in lifetime asthma not accounted for by community- or individual-level
measures.

23
Figure 1.6 Diagram of data hierarchy and covariates in Chapter 3
Individuals
n=5762
1
Communities
n=12
1 I
1
1 I
1
12
1 I
12
1 I
12
Cohort group; Age, Gender,
Race/ethnicity; Body Mass Index; Height
Parental education; Health insurance; Hay
fever or early life infections in child;
Parental history of asthma; Number of
siblings; Smoking in the home or in utero;
Air conditioning, gas stove, plants, pets,
pests, water damage, mildew or smoking
in the home; Carpet in child’s bedroom;
Regular vitamin use; Residential distance
to major roadway.
Deprivation, e.g. Percent male
unemployment, percent poverty, percent
low education, median household income
Hierarchical structure Spatial Level Covariates

Finally, Chapter 4 presents a comprehensive attempt at examining population disparities in asthma in
the second cohort of the CHS (i.e. cohort group E). In contrast to the analysis in Chapter 2,
associations with incident asthma are examined for multiple measures of the community, school and
neighborhood social environment, including characteristics describing deprivation, income inequality,
racial segregation and social capital. Multilevel Cox proportional hazards models are used to control
for a range of confounders at multiple spatial levels, and a 2-level random effects structure accounts
for unexplained variation at community and school or neighborhood levels. Therefore, this analysis
includes data at four spatial levels (Figure 1.7): individual subjects (I) reside in 274 neighborhoods
(N) and attended kindergarten or first grade in one of 45 schools (S) distributed in 13 communities.

24
Figure 1.7 Diagram of data hierarchy and covariates in Chapter 4
Individuals
n=2456
Neighborhoods
n=274
Schools
n=45
1 I
1,1,N1
1 I
1,1,N1
1 N
1,1
1 N
1,1
1 I
1,S1,1
1 I
1,S1,1
1 I
1,S1,N1
1 I
1,S1,N1
1 N
1,S1
1 N
1,S1
1
Communities
n=13
1 I
1,1,1
1 I
1,1,1
1 S
1
1 S
1
1 N
13,1
1 N
13,S13
13
1 S
13
1 S
13
1 I
13,1,1
1 I
13,1,1
1 I
13,1,N13
1 I
13,1,N13
1 I
13,S13,1
1 I
13,S13,1
1 I
13,S13,N13
1 I
13,S13,N13
Deprivation; Racial composition;
Population density; Income
inequality
Deprivation; Racial composition;
Academic performance and funding;
Administrative characteristics
Deprivation; Racial composition
and segregation; Population size
and density; Crime
Race/ethnicity;
Underweight/overweight; Health
insurance; Parental education;
Parental stress; Parental history of
asthma; Smoking in the home and in
utero; Carpet in the child’s bedroom;
Mildew, cockroaches, water damage,
musty odor, pets, and gas stove in the
home; Traffic-related air pollution;
Exposure to wildfires.
Hierarchical structure Spatial Level Covariates

25
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31
CHAPTER TWO

Parental stress increases the effect of air pollution on childhood asthma incidence
Shankardass K, McConnell R, Jerrett M, Milam J, Berhane K, Richardson J

2.1 BACKGROUND

Asthma is the most common chronic childhood illness in developed countries and a growing concern
worldwide (Centers for Disease Control and Prevention 2003). It is considered to be a complex
disease with a multifactorial etiology as established risk factors have failed to explain trends in the
global epidemiology of asthma (Pearce and Douwes 2006). The incidence of asthma has been
associated with environmental factors, including combustion products in air pollution and second
hand smoke, especially in utero exposure (Gilliland et al. 2006; King et al. 2004; McConnell et al.
2007). Several studies indicate that increased severity of asthma among low socioeconomic status
(SES) children and adults may be explained by stress (Chen et al. 2003; Chen et al. 2006), yet few
studies have examined whether these factors modify the risk for asthma onset attributable to
environmental pollution.

It is generally recognized that air pollution exacerbates asthma in children (Schildcrout et al. 2006),
and some studies suggest an effect on induction of asthma (Brauer et al. 2007; McConnell et al. 2002;
Sarnat and Holguin 2007; Shima et al. 2002). We have recently reported associations of residential
traffic-related pollution with prevalent asthma and new onset asthma during follow-up in the southern
California Children’s Health Study (McConnell et al. 2006; McConnell et al. 2007). Effects of
pollution are biologically plausible given emerging evidence from human experimental, animal and in
vitro studies suggesting that ambient particulate matter and gaseous co-pollutants cause oxidative
stress and inflammation, which are important features of asthma pathogenesis (Li et al. 2003). We

32
have also shown asthma to be associated with another oxidant pollutant, in utero tobacco smoke
(Gilliland et al. 2006; Gilliland et al. 2001; Li et al. 2005; Salam et al. 2004), results which are
consistent with other studies of in utero and other second hand smoke exposure (Boulet et al. 2006;
Pattenden et al. 2006).

Effects of air pollution on respiratory health have been found to be greater among individuals of lower
SES (Wheeler and Ben-Shlomo 2005). A possible mechanism by which SES may modify the effects
of air pollution is psychological stress (Chen et al. 2006; Gold and Wright 2005). Stress has pro-
oxidant effects that can increase airway inflammation (Wright et al. 2005), and high levels of stress in
both children and parents predict onset of wheeze and asthma morbidity (e.g. severity, subsequent
attacks) in children (Kilpelainen et al. 2002; McConnell et al. 2005; Sandberg et al. 2000; Shalowitz
et al. 2001; Weil et al. 1999; Wright et al. 2002; Wright et al. 1998). Stress may also increase
vulnerability to antigens through direct effects on the endocrine system, autonomic control of airways,
and immune function (Marshall and Agarwal 2000; Wright et al. 1998). Stress may thus increase
vulnerability to environmental factors associated with asthma, and may explain the observed
susceptibility to asthma attributed to SES. A recent cross-sectional study showing that effects of
traffic-related pollution on asthma were larger in children who reported exposure to violence provides
epidemiological support for this hypothesis (Clougherty et al. 2007).

We hypothesized that low SES and high parental stress increase childhood susceptibility for new
onset asthma from two sources of oxidant pollution, residential traffic and maternal smoking during
pregnancy.

33
2.2 METHODS

2.2.1 Study population

The Children’s Health Study cohort enrolled students in kindergarten and first grade (ages 5-9) from
participating schools in 13 southern California communities in 2002 and 2003. This analysis includes
2497 children with no history of asthma or wheeze at study entry. All students in kindergarten and
first grade at selected schools in the 13 study communities were invited to participate, and 5349 (65%)
returned valid questionnaires. Children were excluded if they had a history of physician diagnosed
asthma at study entry (715), a history of wheezing episodes (1505), and missing or “don’t know”
responses about history of asthma (397) or wheeze (261) to remove subjects with previously
undiagnosed asthma from follow-up. Out of the 3,372 children classified as ‘disease free’ at baseline,
340 children had no information about residential traffic-related pollution because their home address
could not be geo-coded, and another 535 children were lost prior to one year of follow-up. Therefore,
the study population for this analysis included 2497 children. Informed consent was obtained from
parents, and the study was approved by the University of Southern California Institutional Review
Board.

2.2.2 Assessment of new onset asthma and covariates

Children with new onset asthma were identified by parental report of physician-diagnosed asthma on
annual questionnaires during three years of follow-up. Variables describing potential effect modifiers
and confounders were measured from responses given by parents on the baseline questionnaire.
Educational attainment in parents was used as a measure of SES. Information was collected about
exposure to in utero and second hand smoke at study entry. Parental stress was assessed using the
Perceived Stress Scale (PSS), a widely used measure of the degree to which respondents believed
their lives were unpredictable, uncontrollable, or overwhelming (Cohen and Williamson 1988).

34
Parental stress was assessed using the Perceived Stress Scale (PSS), a widely used measure of the
degree to which respondents believed their lives were unpredictable, uncontrollable, or
overwhelming.(Cohen and Williamson 1988) The PSS has been validated as a measure of negative
affective states and physical symptoms of stress.(Hewitt et al. 1992; Pbert et al. 1992) We employed a
4-item version of the scale that has been previously used to predict incidence of wheeze in
children.(McConnell et al. 2005; Wright et al. 2002) Items included: “In the last month, how often
have you felt”: (1) “that you were unable to control the important things in your life”, (2) “confident
about your ability to handle your personal problems”, (3) “that things were going your way”, and (4)
“your difficulties were piling up so high that you could not overcome them”. Each item is scored on a
scale of 0-4 and the PSS gives equal weight to each item, resulting in scores ranging from 0 to 16. A
representative U.S. sample found an overall mean and standard deviation of 4.49 and 2.96,
respectively.(Cohen and Williamson 1988)

Covariates considered as potential confounders in this study were measured from responses given by
parents on the baseline questionnaire at study entry. In addition to race and ethnicity, English- or
Spanish-language questionnaire response was recorded for each subject. Characteristics of the child’s
current residence included mold or mildew on household surfaces, history of water damage or
flooding, presence of a musty odor, history of cockroaches and other pests, use of a gas stove, air
conditioner, humidifier or vaporizer, carpet in the child’s bedroom, type of dwelling, and whether the
child lived at another dwelling for more than 50 days per year. Type of medical insurance coverage,
history of chest-related illness and allergies, and family history of asthma was reported, and body
mass index was calculated based on measurements of height and weight at study baseline using the
Centers

for Disease Control and Prevention gender-specific

body mass index-for-age reference values
for the year 2000. Accordingly, subjects with a body mass index below the 5
th
percentile of the
reference values were classified as underweight, while those between 85
th
and 95
th
percentile were at
risk for overweight, and those above the 95
th
percentile were overweight.

35
2.2.3 Air pollution exposure assessment

Methods to estimate exposure to local traffic-related pollution in this cohort have been described
elsewhere (McConnell et al. 2006). Briefly, household exposure to total nitrogen oxide (NO
X
) from
traffic on local roads was estimated as a marker for pollutants from traffic exhaust using the
CALINE4 dispersion model (Benson 1989). Estimates of NOx represented annual average
incremental increases due to primary emissions from local vehicular traffic independent of
background ambient levels (McConnell et al. 2006).

Since there was a high correlation between measures of NOx and other pollutants generated using the
same model (e.g. carbon monoxide, nitrogen dioxide, elemental and organic carbon and particulate
matter with aerodynamic diameter less than 10 and less than 2.5 ug/m
3
) (R>0.90), measures of NOx
represented not only primary local NOx from vehicular traffic, but a mixture of other pollutants
related to near-source traffic exposure (McConnell et al. 2006).

2.2.4 Statistical methods

Risk factors for asthma onset were assessed using multilevel Cox proportional hazards models (Ma et
al. 2003). All models contained age and gender stratifications of the baseline hazard, adjustment for
race and ethnicity, and random effects for community of residence, which allowed for clustering and
assessment of residual community variation in time to asthma onset. Analyses were conducted using
R software (R Development Core Team 2005) and software designed to run within R for
implementing random effects Cox proportional hazards models (Jerrett et al. 2005; Ma et al. 2003).
The multilevel Cox proportional hazards model took the following form:

h
ij
(t)=h
os
(t) η
j
exp(βX
ij
+ δ
T
Z
ij
);
h
ij
(t): hazard function for the ith subject in jth community;

36
h
0s
(t) : the baseline hazard function for stratum s (i.e., age at study entry and gender);
η
j
: positive random effects for community j with expectation 1 and variance σ
2
;
Z
ij
: risk factors (e.g., race and ethnicity) for individual i in community j; and
X
ij
: traffic-related pollution or maternal smoking in utero for individual i in community j.

Modification of the effect of NO
X
and maternal smoking during pregnancy by SES and parental stress
was assessed by modeling multiplicative interaction terms along with base terms. We evaluated
confounding of pollutant interactions with parental stress using a two-step process. For example, for
traffic-related pollution, all relevant covariates were first screened for two-way interactions with NO
X

on asthma onset using an alpha level of 0.20. Second, the two-way interaction models for parental
stress with NO
X
were co-adjusted for relevant interactions from the first step (i.e. p<0.20) and
confounding was identified where the coefficient for the stress-specific interaction term was reduced
by more than 10%.

2.3 RESULTS

The study population ranged from 5 to 9 years of age at baseline and 80% of subjects were at least 6
years old (Table 2.1); there were slightly more girls (52%) than boys. The majority of subjects were of
Hispanic ethnicity (55%) and the plurality of the remainder was non-Hispanic white (36%); there
were few African Americans (3%) and these subjects were more likely to become asthmatic during
follow-up than the Hispanic comparison group (HR 2.44, 95% CI 1.05-5.67). The mean score for
parental stress using the PSS was 3.85 (standard deviation, 2.79), with a median value of 4. In
ascending order, the four quartiles of the PSS distribution included values of 0 to 1, 2 to 3, 4 to 5 and
6 to 15. For some analyses, stress was divided into quartiles or treated as a continuous variable.
Approximately 21% of subjects had parents who had not finished high school (“low SES”), while
almost 79% had parents with a high school diploma or greater (“high SES”). Additional cohort

37
characteristics can be found in Table 2.1. Significant risk factors for new onset asthma included
underweight, history of chest illness or allergy, parental asthma and musty odor in the home.

Table 2.1 Subject characteristics and associations with new onset asthma
Risk factor N (%)
a

Hazard ratio
(95% confidence
interval)
b

Subject Characteristics
Age at baseline
b

5 years 496 (19.9) 1.00
c

6 years 1178 (47.2) 1.12 (0.69-2.83)
7-9 years 823 (33.0) 1.11 (0.66-1.87)
Male gender
b
1190 (47.7) 1.07 (0.75-1.53)
Race/Ethnicity
b

Hispanic ethnicity 1380 (55.3) 1.00
c

African-American race 77 (3.1) 2.44 (1.05-5.67)
d

White Non-Hispanic race 905 (36.2) 1.08 (0.73-1.59)
Other race 135 (5.4) 1.67 (0.86-3.27)
Spanish language questionnaire 610 (24.4) 0.81 (0.49-1.35)
Body mass index
Underweight 92 (4.0) 2.50 (1.28-4.89)
e

Healthy weight 1587 (68.3) 1.00
c

At risk of overweight 337 (14.5) 1.36 (0.79-2.34)
Overweight 308 (13.3) 1.45 (0.85-2.46)
Chest-related illness
None 1932 (85.0) 1.00
c

Before age 2 129 (5.7) 1.93 (0.96-3.86)
After age 2 126 (5.6) 2.76 (1.58-4.84)
d

Before and after age 2 85 (3.7) 2.98 (1.48-5.98)
d

Allergies 764 (33.6) 2.27 (1.54-3.33)
d

Child resides in more than one home 178 (7.3) 0.60 (0.24-1.46 )
Parental Characteristics
Parental history of asthma 389 (17.0) 2.05 (1.35-3.12)
d

Parental stress (PSS)
Quartile 1 563 (23.8) 1.00
c

Quartile 2 605 (25.6) 1.00 (0.59-1.70)
Quartile 3 531 (22.5) 1.21 (0.72-2.04)
Quartile 4 664 (28.1) 0.97 (0.57-1.64)
a
Numbers may not total to 2497 due to missing values.
Adjusted for race/ethnicity, with baseline strata for age and gender and community
random effects, except
b
.
c
Denotes reference group.
d
Denotes statistically significant finding, i.e. p ≤ 0.05.

38
Table 2.1 Continued. Subject characteristics and associations with new onset asthma
Risk factor N (%)
a

Hazard ratio
(95% confidence
interval)
b

Medical Care & SES
Medical insurance coverage 2135 (87.7) 1.36 (0.70-2.63)
Type of medical insurance coverage
HMO/PPO 1591 (65.3) 1.00
c

Social assistance 547 (22.4) 1.28 (0.82-2.01)
No insurance coverage 300 (12.3) 0.79 (0.82-2.01)
Parental education
Did not finish high school 508 (21.2) 1.00 (0.61-1.64)
High school diploma or some college 1318 (55.1) 1.00
c

College diploma or greater 567 (23.7) 0.74 (0.45-1.19)
Home Characteristics
Mildew in home 532 (21.8) 1.22 (0.80-1.86)
Cockroaches in home 257 (10.8) 1.53 (0.90-2.63)
Water damage/flooding in home 321 (13.1) 1.42 (0.88-2.30)
Musty odor in home 84 (3.5) 2.15 (1.05-4.42)
d

Humidifier/vaporizer in home 484 (20.1) 1.06 (0.67-1.67)
Carpet in child's bedroom 2090 (85.8) 1.09 (0.63-1.88)
Any pets in home 1313 (53.9) 0.84 (0.57-1.23)
Dogs in home 730 (30.0) 0.79 (0.52-1.21)
Cats in home 462 (19.0) 0.73 (0.43-1.24)
Gas stove in home 2042 (84.5) 1.18 (0.70-2.02)
Air conditioning in home 1448 (60.0) 1.16 (0.79-1.72)
Type of home
Single-family house 1938 (79.5) 1.00
c

Apartment (2-10 units) 328 (13.5) 1.30 (0.76-2.23)
Apartment (>10 units) 102 (4.2) 1.71 (0.73-3.96)
Mobile home/trailer/other 69 (2.8) 1.52 (0.55-4.15)
a
Numbers may not total to 2497 due to missing values.
Adjusted for race/ethnicity, with baseline strata for age and gender and community
random effects, except
b
.
c
Denotes reference group.
d
Denotes statistically significant finding, i.e. p ≤ 0.05.

The distribution of traffic-related exposure in this population has been previously described
(McConnell et al. 2006; McConnell et al. 2007). As shown in Table 2.2, the risk of asthma onset
increased with exposure to traffic (HR 1.31, 95% CI 1.07-1.61). The risk associated with traffic was
higher in low SES subjects (HR 1.55, 95% CI 1.09-2.19) than in those with of high SES (HR 1.20,
95% CI 0.93-1.55; p-value for interaction = 0.25). Traffic-related risk of asthma was also increased

39
in subjects with parental stress above the median (HR 1.51, 95% CI 1.16-.1.96), but there was little
risk associated with NO
X
among subjects with parental stress below the median (HR 1.05, 95% CI
0.74-1.49, p-value for interaction = 0.05). The risk of asthma associated with NO
X
increased
monotonically across quartiles of parental stress (Figure 2.1). When the interaction between parental
stress and NO
X
was adjusted for the interaction between SES and NO
X
, the coefficient for the stress-
related interaction was unchanged; on the other hand, the coefficient related to the interaction with
SES was reduced by 27% and became more non-significant (p=0.48). The overall pattern of
susceptibility to traffic pollutant exposure based on parental stress was not changed by adjusting for
other potential confounding susceptibility factors shown in Table 2.1, and the interaction p value
(from Table 2.2) remained significant in adjusted models.

Table 2.2 Associations of traffic-related pollution with incident asthma, by parental education and
parental stress
Risk factor Stratum N (%)
a

Mean
(Standard
deviation)
b

25
th
-75
th

percentile
b

Hazard ratio
(95% confidence
interval)
c

Interaction
p-value
d

NOx (Total) All subjects 2456 (100) 18.41 (16.04) 6.19-27.11 1.31 (1.07, 1.61)
e

Low parental
education
1845 (78.4) 20.36 (17.15) 7.53-29.24 1.55 (1.09, 2.19)
e

High parental
education
507 (21.6) 17.79 (15.58) 5.71-26.48 1.20 (0.93, 1.55)
0.25

High parental
stress
1179 (50.7) 18.99 (16.42) 6.85-27.61 1.51 (1.16, 1.96)
e

Low parental
stress
1145 (49.3) 17.81 (15.79) 5.76-26.90 1.05 (0.74, 1.49)
0.05
a
Denominator varies due to missing data about NOx (Total), parental education and parental stress.
b
NOx in parts per billion.
c
All models are adjusted for race/ethnicity of subjects. Hazard ratios and 95% confidence intervals are scaled
across the interquartile range of exposure to NOx in all subjects (21 ppb).
d
p-value based on the chi-square statistic using the likelihood ratio test to compare a model with base terms
only to a model also containing the multiplicative interaction term. Interactions involving parental stress are
based on a continuous variable describing the PSS.
e
Indicates P-value <0.05.

In utero tobacco smoke exposure was associated with a positive hazard ratio which did not achieve
statistical significance (HR 1.49, 95% CI 0.79-2.80, Table 2.3). However, maternal smoking during
pregnancy increased the risk of asthma among subjects with low SES (HR 5.69, 95% CI 1.88-17.26),
while there was little indication of an effect among high SES subjects (HR 1.10, 95% CI 0.51-2.41, p-
value for interaction = 0.03). Subjects with high parental stress had a significantly increased risk of

40
asthma associated with in utero exposure (HR 2.66, 95% CI 1.33-5.33). The hazard ratio was less than
unity with wide confidence intervals among subjects with low parental stress (HR 0.30, 95% CI 0.04-
2.18), but the effect was significantly different than that observed in children with more parental stress
(p-value for interaction = 0.03). Because there were relatively few participants who reported maternal
smoking in utero (156, 6.3%) and small numbers in subgroups based on other potentially relevant
confounders (from Table 2.1), we were not able to comprehensively test for confounding of
interactions related to SES and parental stress.

Figure 2.1 Effect of traffic related pollution on incident asthma across parental stress quartiles
0.00
0.50
1.00
1.50
2.00
2.50
1 2 3 4
Parental Stress Quartile
Hazard Ratio
(95% Confidence Interval) .

41
Table 2.3 Associations of in utero tobacco smoke with incident asthma, by parental education and
parental stress
Risk factor Stratum N (%)
a

Hazard ratio
(95% confidence
interval)
b

Interaction
p-value
c

Maternal smoking in utero All subjects 156 (6.3) 1.49 (0.79, 2.80)

Low parental
education
16 (3.2) 5.69 (1.88, 17.26)
d

High parental
education
137 (7.3) 1.10 (0.51, 2.41)
0.03

High parental
stress
89 (7.5) 2.66 (1.33, 5.33)d

Low parental
stress
62 (5.4) 0.30 (0.04, 2.18)
0.03
a
Denominator varies due to missing data about maternal smoking in utero, parental education
and parental stress.
b
All models are adjusted for race/ethnicity of subjects.
c
p-value based on the chi-square statistic using the likelihood ratio test to compare a model with
base terms only to a model also containing the multiplicative interaction term. Interactions
involving parental stress are based on a continuous variable describing the PSS.
d
Indicates P-value <0.05

An earlier analysis based on the same study population found that the effect of parental stress on
wheeze onset was modified by family history of asthma and gender, where the effect of stress was
limited to males with no family history of asthma (Milam et al. 2007). Others found that the effect of
regular smoking on new onset asthma was modified by personal history of allergy (Gilliland et al.
2006). Therefore, we examined the interaction involving parental stress by strata of gender, family
history of asthma and personal history of allergy. Effect modification by stress appeared to be
stronger for male subjects. In a single model, the hazard ratio for total NOx among males with high
parental stress was 1.60 (95% CI 1.16-2.22) compared to 0.98 (95% CI 0.61-1.59) among males with
low parental stress (p-value for interaction among males = 0.02). Within the same model, a hazard
ratio of 1.36 (95% CI .87-2.11) was observed for females with high parental stress compared with
1.14 (95% CI 0.68-1.90) for females with low parental stress (p-value for interaction among males =
0.84). The p-value for a three-way “gender-parental stress-NOx” interaction term was 0.10. When
parental education was substituted for stress, the hazard ratio for the three-way interaction was close
to 1 and non-significant. The interaction between stress and NOx was similar within strata of family
history of asthma and personal history of allergy.

42
2.4 DISCUSSION

Susceptibility to traffic-related pollution and maternal smoking during pregnancy for new onset
asthma increased among children with lower SES and higher parental stress. Modification of the
effect on asthma from pollution was only significant for parental stress and susceptibility attributable
to SES was markedly attenuated after accounting for the effect of stress; since parental stress and
parental education were weakly correlated (Spearman rank correlation coefficient -0.21), the pattern
of effects by SES may be explained by parental stress. Susceptibility to traffic-related pollution
attributable to parental stress was not explained by a range of biological, behavioral, socioeconomic
and environmental risk factors for asthma, including second hand smoke, allergies, early childhood
chest illness, racial-ethnic group, and characteristics of the home, including presence of cockroaches
and other pests, flood damage, mildew, pets, gas stove use and air conditioning use. Although there
were relatively few children with a history of in utero tobacco smoke exposure, significantly larger
effects were observed both among children with low parental education and with high parental stress.
These results suggest that children whose parents perceive their lives as unpredictable, uncontrollable,
or overwhelming are more susceptible to the effects that common products of combustion have on
asthma pathogenesis. Effects of stress on lifetime asthma (Clougherty et al. 2007), asthma severity
(Liu et al. 2002) and incident wheeze (McConnell et al. 2005; Wright et al. 2002) have been reported,
but this is the first study to report effects related to incident asthma.

Particulate and gaseous air pollutants can initiate and exacerbate cellular inflammation in the airways,
which is a central feature of asthma (Bernstein et al. 2004; McConnell et al. 2005; Nel et al. 2001;
Tatum and Shapiro 2005). The mechanisms linking exposure to inflammation have been closely
examined in recent years. Exposure to ambient particulate matter has been associated with the
generation of reactive oxygen species, which are mediators of inflammation (Blomberg et al. 1998;
Kumagai et al. 1997; Li et al. 1996; Pourazar et al. 2005). Air pollution may also have an adjuvant
effect with common allergens that favors the development of a T helper cell 2 response, a hallmark of

43
allergic asthma (Diaz-Sanchez et al. 1996; Diaz-Sanchez et al. 1997; Fujieda et al. 1998). Finally,
these pollutants may also directly increase inflammation by enhancing mast cell degranulation and
cytokine release (Diaz-Sanchez et al. 2000; Nel 2005).

Control of inflammation following exposure to reactive oxygen species appears to depend on an
individual’s ability to detoxify these products. Evidence indicates that chronic stress can hinder the
ability to deal with such oxidative burden, possibly due to the development of hypothalamic-pituitary-
adrenal axis hyporesponsiveness resulting in a shift toward a pro-inflammatory T helper cell 2
phenotype (Umetsu and Dekruyff 2006; Wright et al. 2005; Wright et al. 1998). Therefore, the effects
of air pollution and tobacco smoke on asthma may be augmented in individuals with chronic stress
due to increased toxicity of oxidative stress relative to individuals without chronic stress, and this may
explain patterns of susceptibility to air pollution by SES.

The difference in susceptibility by gender may reflect differences in the development of asthma since
boys tend to experience asthma onset earlier in childhood than girls (e.g. De Marco et al., 2002).
During early and middle childhood, boys are thought to be more sensitive to dysphoria in parents than
girls (Cummings and Davies 1994), so boys in our study may have been more likely to be negatively
affected by parental stress than girls. Gender differences in susceptibility may also reflect differences
in biobehavioral responses between males and females; for example, it has been suggested that girls
tend to seek social support when responding to stress, while the reflexive “fight-or-flight” response
may be more predominant among boys (Taylor et al. 2000).

Case ascertainment was done by parental report of physician-diagnosed asthma without clinical
examination, which is widely used in epidemiological studies (Burr 1992), is reproducible (Ehrlich et
al. 1995; Peat et al. 1992), and is a valid measure of what physicians actually report to patients
(Burney et al. 1989; Greer et al. 1993). Examinations of stress and asthma using cross-sectional
measurement are subject to criticism because sick children may cause stress in parents. However, the

44
prospective study design and the restriction to baseline non-wheezers make it unlikely that parental
stress at study baseline resulted from earlier undiagnosed asthma. Finally, in a sensitivity analysis, we
excluded cases occurring during the first year of follow-up and the coefficient for the interaction
between parental stress and NO
X
was not substantially changed.

Mean levels of NO
X
were slightly higher among subjects with higher parental stress and lower
parental education, although the ranges of exposure in these strata overlapped. If the main effect of
NO
X
on asthma onset was non-linear, e.g. quadratic, then larger effects of NO
X
among subjects with
higher parental stress may reflect exposure to higher levels of traffic-related pollution. However, a
previous analysis found no deviation from linearity for the main effect of NO
X
on asthma onset
(McConnell et al. 2007), which suggests that effect modification was not simply a reflection of
differential exposure to NO
X
.

Parental stress has been measured with the PSS in other studies to predict asthma-related outcomes in
children prospectively. For example, Wright, et al. (2002 & 2004) found that high parental stress
measured in the months immediately following birth predicted increased severity of asthma and onset
of wheeze among children (Wright et al. 2002; Wright et al. 2004). In this work, parental stress could
have been a proxy for maternal stress in utero, rather than stress in the infant. In contrast, parental
stress was measured when children were older in our study (range 4-8 years), which increases the
likelihood that parental stress is a proxy for stress in children.

In utero exposure to tobacco smoke has been found to increase the risk of asthma in several cross
sectional studies, and in a limited number of prospective studies (Gilliland et al. 2006; Pattenden et al.
2006; Strachan and Cook 1998). Although the effect of this exposure was not statistically significant
by itself, our results suggest that exposure to tobacco combustion products increased the susceptibility
to effects of later co-exposure to stress and to factors associated with low parental education. Other
recent studies found that early life tobacco smoke exposure increased the risk of incident asthma both

45
among children with co-exposure to ambient air pollution (Miller et al. 2004) and among children
who smoked in later childhood (Gilliland et al. 2006). Although the rates of maternal smoking during
pregnancy in our study sample (6.3%) were somewhat lower than in the greater California population
(9.5%) (Schumacher 2006), this was largely explained by the exclusion by design of participants with
wheeze at study entry, who had a higher rate of maternal smoking in utero (11.3%) than our
longitudinal study sample. Lower rates of maternal smoking during pregnancy were observed in low
(3.2%) than in high SES subjects (7.3%; see Table 2.3), which is opposite to the relationship reported
elsewhere (Lu et al. 2001). This is explained by the large proportion of Hispanics in our population
who were of low SES (35.0%) compared with non-Hispanic subjects (4.0%), and the low rates of
smoking during pregnancy among Hispanic (2.9%) compared with non-Hispanic mothers (10.8%).
However, we found no evidence that Hispanic ethnicity explained the increased risk of asthma
associated with the joint exposure to stress (or SES) and in utero tobacco smoke exposure. Although
these results are consistent with the robust joint effects of traffic-related pollution exposure and stress,
the effects of in utero tobacco smoke exposure should be interpreted with caution due to small sample
size in some strata of exposure.

This study provides the first evidence that parental stress increases susceptibility in children to air
pollution for incident asthma, highlighting the complex relationship between SES, air pollution and
asthma. The similarity in the pattern of susceptibility to maternal smoking in utero suggests that
oxidative stress may be a common biological pathway of this type of vulnerability to asthma. Future
studies could clarify the role of stress as the agent of susceptibility by using biological measures of
stress response in parents and children (e.g. salivary cortisol) across the lifespan (e.g. in utero, early
life, later childhood).

46
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50
CHAPTER THREE

The association between contextual socioeconomic factors and prevalent asthma in a
cohort of Southern California school children

Shankardass K, McConnell RS, Milam J, Berhane K, Tatalovich Z, Wilson JP,
Jerrett M

3.1 BACKGROUND

Childhood asthma is now the leading chronic disease of children amongst industrialized countries
(Centers for Disease Control and Prevention, 2003). In the United States, prevalent asthma rose from
3.4% to 5.5% between 1980 to 1996 in children 0-14 years of age, and in some communities the
prevalence exceeds 20% (Mannino, Homa, Akinbami, Moorman, Gwynn, & Redd, 2002). The
increase in prevalence, as well as large variation in prevalence between settings, within the U.S. and
globally, has yet to be fully explained (Basagana, Sunyer, Kogevinas, Zock, Duran-Tauleria, Jarvis et
al., 2004; Beasley, Ellwood, & Asher, 2003; Cagney & Browning, 2004; Federico & Liu, 2003; Juhn,
Sauver, Katusic, Vargas, Weaver, & Yunginger, 2005; Pearce, Douwes, & Beasley, 2000; Pearce,
Pekkanen, & Beasley, 1999; Redd, 2002). Similarly, the etiology of childhood asthma remains
unclear. Current research is focused on both genetic and environmental factors. While genetic factors
may predispose a child to asthma, geographical heterogeneity along with the rise in prevalence
indicate that environmental factors probably contribute to the pathogenesis of asthma. There is a large
literature examining associations of asthma with an individual’s housing characteristics, including
home dampness, indoor allergens, environmental tobacco smoke, dietary factors and social position
(Billings & Howard, 1998; Gold & Wright, 2005; Mielck, Reitmeir, & Wjst, 1996). Although

51
findings remain mixed, collectively the studies suggest an effect of the built and social environment
on asthma onset and severity.

Lower socioeconomic position (SEP) has been associated with severity of asthma in children and is
linked with racial/ethnic disparities in the U.S. (Gold & Wright, 2005; Mielck, Reitmeir, & Wjst,
1996), although reasons for this relationship are not well understood. Lower SEP may act as a proxy
for causal environmental effects, such as elevated air pollution exposure (O'Neill, Jerrett, Kawachi,
Levy, Cohen, Gouveia et al., 2003) and poor housing conditions (Shapiro & Stout, 2002).
Theoretically, geographic variation in asthma may be broken down into pathways operating at the
individual level (i.e. “compositional” effects) and at the community level (i.e. “contextual" effects)
(Diez Roux, 2002). Although compositional effects can be adequately controlled with individual-level
variables, contextual effects imply independent or interactive contributions to health from the social
and environmental place in which an individual lives or works (e.g. neighborhood crime that might
result in increased stress and other behaviors not well measured by readily available individual-level
variables). Research on the impact of the social environment on childhood asthma has traditionally
focused on individual-level exposures, for example socioeconomic status of the family (King,
Mannino, & Holguin, 2004; Mielck, Reitmeir, & Wjst, 1996; Ortega, Belanger, Paltiel, Horwitz,
Bracken, & Leaderer, 2001; Pearce, Beasley, Burgess, & Crane, 1998; Wright, 2004); however,
research about these compositional effects has not completely explained differences in asthma
between communities. Potential contextual effects on asthma have been proposed (Gold & Wright,
2005), indicating the need for a multilevel approach to studies of childhood asthma epidemiology
(Merlo, Chaix, Yang, Lynch, & Rastam, 2005). Multilevel models have only rarely been used to
explore how the socioeconomic environment can impact asthma (Basagana, Sunyer, Kogevinas et al.,
2004; Cagney & Browning, 2004), and few studies have focused specifically on childhood asthma
(Juhn, Sauver, Katusic et al., 2005; Nepomnyaschy & Reichman, 2006). The purpose of this paper is
to analyze the relationship between community socioeconomic characteristics and asthma among
school children in southern California using a multilevel model.

52

3.2 METHODS

3.2.1 Study Design and Subjects

The Children’s Health Study (CHS) is a study of the long-term effects of air pollution and other risk
factors on the respiratory health of children. The study originally comprised 12 southern California
communities with 5762 participants (79% participation rate) (McConnell, Berhane, Gilliland, London,
Islam, Gauderman et al., 2002b) (See Figure 3.1 for map of community and respondent locations).
Children in the 4
th
, 7
th
and 10
th
grades were recruited in 1993 and 1996 and followed through high
school graduation. Associations of asthma with housing characteristics, obesity, air pollution, family
history and exercise have been previously described (Gauderman, Avol, Lurmann, Kuenzli, Gilliland,
Peters et al., 2005; Gilliland, Berhane, Islam, McConnell, Gauderman, Gilliland et al., 2003; Kunzli,
McConnell, Bates, Bastain, Hricko, Lurmann et al., 2003; London, James Gauderman, Avol,
Rappaport, & Peters, 2001; McConnell, Berhane, Gilliland, Islam, Gauderman, London et al., 2002a;
McConnell, Berhane, Gilliland et al., 2002b; McConnell, Berhane, Yao, Jerrett, Lurmann, Gilliland et
al., 2006). A detailed description of the selection of communities, subject recruitment, and survey
methods to assess demographic, household, activity, and baseline medical characteristics has also
been reported (Peters, Avol, Navidi, London, Gauderman, Lurmann et al., 1999). The current analysis
is the first examination of the contextual effects of SEP on asthma in the CHS.

Written informed consent was obtained from a parent or legal guardian for all study subjects. The
University of Southern California’s institutional review board approved the study protocol.

53
Figure 3.1 CHS communities and distribution of participants

3.2.2 Dependent Variables

A questionnaire was completed at study entry by parents or legal guardians of subjects to collect
information about children’s baseline characteristics. The main outcome of interest was lifetime
doctor-diagnosed asthma and valid data was obtained for 96.8% of participants (N=5581). By using
lifetime asthma as a dependent variable, our results might be biased if participants of lower SEP did
not have access to health care comparable with subjects of higher SEP. Thus, we performed a
sensitivity analysis that substituted severe wheeze (i.e. ever wheezed for 3+ days/week for more than
1 month, or shortness of breath with wheezing, or awakened by wheezing, or emergency
room/overnight hospital visit for wheezing) as the dependent variable. Wheeze is a common symptom
of asthma that is more easily recognized by parents without a doctor diagnosis and is more specific
than cough (Warren, 1999).

54
3.2.3 Individual Variables

Socioeconomic position and other demographic, medical and home environmental exposures have
been examined in the CHS with respect to childhood asthma (Gauderman, Avol, Lurmann et al.,
2005; Gilliland, Berhane, Islam et al., 2003; Peters, Avol, Navidi et al., 1999). Variables of interest
here include age of the child (centered on 10 years of age), race/ethnicity, gender, height, age- and
sex-adjusted body mass index below the 10
th
percentile or in the 90
th
percentile, cohort group (defined
by year and subject’s grade at enrollment: 1993 in 4
th
, 7
th
or 10
th
grade or 1996 in 4
th
grade), current
vitamin use, hay fever symptoms in the last 12 months, current medical insurance coverage and
parental history of asthma. Parental education (12 grades or less, some college, and college and/or
graduate education) was used as an indicator of individual-level SEP. Housing characteristics
included air conditioning, gas stove, carpet in subject’s bedroom, any current daily smokers, pests,
plants, pets, water damage or mildew in the home. Residential distance to the nearest freeway or
major road (i.e. indicators of traffic-related pollution) and in utero tobacco smoke exposure have been
previously associated with increased risk for prevalent asthma in previous CHS studies (Gauderman,
Avol, Lurmann et al., 2005; Gilliland, Li, & Peters, 2001; McConnell, Berhane, Yao et al., 2006). In
sensitivity analyses, we examined these measures as potential confounders but neither significantly
changed the contextual effects of primary interest to this analysis. We also evaluated the effect of
number of siblings as an indicator of exposure to early life infections or related exposures (Ramsey &
Celedon, 2005).

3.2.4 Community Socioeconomic Variables

Estimates of community-level socioeconomic characteristics were calculated by aggregating data
associated with census block groups where study subjects lived. Data from the U.S. 1990 census was
used to estimate community conditions at baseline since study enrollment began in 1993. Because
subjects were recruited through schools, residential locations cluster together; therefore, to

55
compensate for clustering of study subjects, a Geographic Information System was used to derive
community-level estimates weighted by the proportion of the census block groups included in a
community-specific bounding rectangle that contained 95% of local study subject residences
(Tatalovich, Wilson, Milam, Jerrett, & McConnell, 2006).

Characteristics of interest at the community level have previously been suggested as measures of SEP
that were associated with health effects (Haan, Kaplan, & Camacho, 1987; Jin, Shah, & Svaboda,
1994; Juhn, Sauver, Katusic et al., 2005; Wissow, Gittelsohn, Szklo, Starfield, & Mussman, 1988).
They included community measures of median household income, proportion of respondents with low
education (i.e. no high school diploma), the unemployment-to-population ratio (UPR) for males (i.e.
number employed divided by total population of working age), and percent living in poverty (U.S.
Census Bureau, 1992). Male unemployment (as opposed to general or female unemployment) was
considered in this study since the relationship between employment and health is more apparent for
males than for females during the period prior to study baseline (Jin, Shah, & Svaboda, 1994;
Sorensen & Verbrugge, 1987). In a sensitivity analysis, female and total unemployment were
substituted for male unemployment.

Hispanic ethnicity was of interest as a potential effect modifier of the association between community
SEP and asthma. In the U.S., Mexican Americans, particularly those born outside of the U.S., have
been shown to have the lowest low rates of asthma compared with other ethnic groups, (Arif, Delclos,
Lee, Tortolero, & Whitehead, 2003; Holguin, Mannino, Anto, Mott, Ford, Teague et al., 2005;
National Center for Health Statistics, 2002). Hispanic Americans also have less access to health care,
lower median family income and more children living in poverty than other ethnicities in the U.S.
(Dougherty, Meikle, Owens, Kelley, & Moy, 2005; Public Policy Institute of California, 2001). The
contradiction that some foreign-born Hispanic populations have lower SEP but better health outcomes
than other ethnic populations in the United States, including asthma, is known as the Latino Paradox.
For example, in a multi-ethnic cross-sectional study of adults in Chicago, foreign-born Latinos were

56
shown to have less prevalent asthma than subjects of other ethnicities, including U.S.-born Latinos,
when living within immigrant neighborhoods (Cagney, Browning, & Wallace, 2005).

3.2.5 Statistical Analysis

A multilevel random effects model was designed to relate lifetime asthma at baseline to community-
level socioeconomic characteristics adjusting for individual-level covariates. Previous statistical
analyses of CHS data have utilized similar models to examine community-level effects (Berhane,
Gauderman, Stram, & Thomas, in press; Gilliland, Berhane, Rappaport, Thomas, Avol, Gauderman et
al., 2001; Hoffman, Gilliland, Eley, Harlan, Stephenson, Stanford et al., 2001; McConnell, Berhane,
Gilliland, Molitor, Thomas, Lurmann et al., 2003).

A hierarchical logistic regression model with random effects for the 12 CHS communities was fitted
in SAS using the GLIMMIX procedure with a binomial distribution assumption and a logit link (SAS
Institute Inc., 2002). Random effects accounted for community lifetime asthma unexplained by
individual- or community-level risk factors. To ascertain the impact of adjusting for specific
individual-level covariates on community-level effects, six models featuring different levels of
adjustment were tested (refer to Table 3.2 footnotes). All models included key demographic variables
(age, race and ethnicity, gender, and cohort group), and model 1 was adjusted only for these variables.
Model 2 added parental education, which was included in subsequent models so that these results
would be adjusted for individual SEP. Model 3 added medical insurance to account for possible
diagnostic bias resulting from differential access to care. Model 4 adjusted for a total of 21 covariates
that follow the adjustment used by Peters et al. in their original CHS study (Peters, Avol, Navidi et al.,
1999). Model 5 added census block group population density. Finally, model 6 derived a
parsimonious model by entering all 22 covariates from model 5 into a backwards manual stepwise
regression using a cutoff value of p=0.10. We then attempted to re-enter variables excluded during

57
this process using the same cutoff value. The resultant model contained covariates for cohort group,
African-American race, gender, age, age- and sex-specific body mass index below the 10
th
percentile
or in the 90th percentile, parental education, medical insurance coverage, parental history of asthma,
hay fever, plants in the home, pets in the home, gas stove in the home and mildew in the home.
Estimates derived for individual-level covariates represented the adjusted log odds ratio for lifetime
asthma at baseline.

Because the SAS procedure GLIMMIX uses pseudo-likelihood estimations which can underestimate
fixed effects and standard errors, the HLM software (Scientific Software International Inc., 2005) was
used in a sensitivity analysis to replicate all of the main analyses using single-stage mixed effects
models capable of producing exact solutions. The patterns of effects were similar for models in HLM,
so we only report results from the SAS procedure GLIMMIX.

When assigning contextual exposures to study subjects, the spatial scale and geographic zone used to
configure the unit of analysis can influence results generated from analysis due to changes in
population characteristics; this is known as the modifiable areal unit problem (MAUP) (Jerrett &
Finkelstein, 2005). To confirm that our results were not subject to such bias due to our community-
level apportionment of socioeconomic characteristics, all iterations of modeling with the GLIMMIX
procedure were rerun using a single-stage design which replaced community-specific measures of
SEP with census block group measures specific for individual subjects. Individual-level covariates
were modeled simultaneously with estimators of smaller neighborhood socioeconomic characteristics,
with clustering on the census block group to adjust the estimate of variance for clustering of study
subjects within communities (Williams, 2000). Models were run with STATA software using the
cluster function (Stata Corporation, 2003). Because the pattern of effects was similar using both
approaches, we only present results derived from the SAS procedure GLIMMIX.

58
Our method for measuring community-level socioeconomic characteristics (i.e. 95% minimum
bounding rectangle) could have introduced bias if contextual effects were not clustered by 95% of
population density; thus, the second-stage analysis was repeated using two alternative methods of
measurement: (1) estimates from specific census block groups containing subjects were weighted by
the proportion of the community sample living within those census block groups, and (2) data
pertaining to all census block groups that contained study subjects were summarized, unweighted
(Tatalovich, Wilson, Jerrett, Milam, & McConnell, 2006). Our results were not affected by the
configuration of this measurement so only results utilizing the 95% minimum bounding rectangle are
presented.

3.3 RESULTS

Individual-level covariates in the parsimonious model revealed a number of significant associations
with lifetime asthma (Table 3.1). African-American race, age- and sex-adjusted body mass index and
male gender were positively associated with lifetime asthma. Mildew and pets in the home were
positively associated with asthma, while having plants in the home was inversely associated. Having a
parental history of asthma and symptoms of hay fever in the last year both increased the odds for
asthma. Subjects with current medical insurance coverage were more likely to be asthmatic at study
baseline. Finally, a measure of family SEP, parental education, suggested a positive association
between asthma and low SEP, consistent across two categories indicating education below college
graduation compared to college graduates (non-significant).

Characteristics of the 12 study communities are shown in Table 3.2. Across the 12 communities,
median income ranged from $31,938 in Long Beach to $57,815 in Upland (mean: $39,498; standard
deviation: $7,003). Percent poverty ranged from 14.2% in Santa Maria to 3.8% in Upland (mean:
9.2%; standard deviation: 3.2). Percent male unemployment ranged from 5.5% in Santa Maria to
2.2% in Atascadero (mean: 4.5%; standard deviation: 1.1). Finally, percent low education ranged

59
from 29.5% in Santa Maria to 9.9% in Lake Arrowhead (mean: 20.1%; standard deviation: 5.9). There
was medium-to-high correlation between these four variables, in general; male unemployment was
most highly correlated with percent poverty (r
2
=0.54) and least correlated with median income
(r
2
=0.26). White Non-Hispanic was the majority race/ethnicity in all communities except Santa Maria,
where Hispanic race/ethnicity was most prevalent. Long Beach was the most ethnically diverse
community, while Lake Arrowhead was the least diverse (88.4% White). Prevalence of asthma was
highest in Atascadero (21.5%) and lowest in Mira Loma (11.5%).

Table 3.1 Individual-level associations for lifetime asthma (model 6)
Risk factor N (%)
a

Odds ratio
(95% confidence
interval) p-value
Subject Characteristics
BMI
b

Below 10
th
percentile 551 (10.69) 0.75 (0.54-1.03) 0.08
10
th
-90
th
percentile 4129 (80.13) 1.00
f
-
90
th
percentile 473 (9.18) 1.43 (1.06, 1.93) 0.02
African-American race 256 (4.59) 1.87 (1.23, 2.86) <0.01
Male gender 2712 (48.59) 1.95 (1.34, 1.95) <0.01
Age (years)
c
- 0.87 (0.70, 1.07) 0.18
Hay fever
d
909 (17.64) 2.37 (1.92, 2.91) <0.01
Medical Care & SEP
Medical insurance coverage 4603 (84.33) 1.59 (1.17, 2.18) <0.01
Parental education
High school or less 1894 (35.02) 1.27 (0.97, 1.65) 0.08
Some college 2351 (43.47) 1.22 (0.96, 1.56) 0.10
College or greater 1163 (21.51) 1.00
f
-
Co-exposures
Mildew in home 1514 (28.10) 1.33 (1.09, 1.62) <0.01
Plants in home 2005 (36.49) 0.79 (0.65, 0.96) 0.02
Pets at home 4356 (78.05) 1.32 (1.02, 1.70) 0.04
Gas stove in home 4323 (79.03) 1.22 (0.97, 1.53) 0.09
Family History
Parental history of asthma
e
1007 (19.56) 3.67 (1.11, 4.45) <0.01
a
Subject number varied due to missing values

b
Age- and sex-adjusted body mass index

c
Age was centered on 10 years of age

d
Hay fever symptoms in the last 12 months

e
Medical diagnosis of asthma in either biological parent

f
Denotes reference group.

60
Across all six models, there was an inverse association between male unemployment and lifetime
asthma (Table 3.3). This association was statistically significant at the 0.05 level for all models except
for the highly adjusted model 4, where missing observations reduced the sample size by 40%. Results
from the parsimonious model showed an odds ratio for lifetime asthma of 0.88 (95% CI 0.78, 0.99)
across the inter-quartile range (IQR) of communities and 0.71 (95% CI 0.52, 0.97) across the full
range (data not shown). When stratified by Hispanic ethnicity, the inverse effect for % male
unemployment was slightly stronger amongst Hispanics (OR IQR 0.85, 95% CI 0.67, 1.08), while it
remained stable amongst non-Hispanics (OR IQR 0.88, 95% CI 0.75, 1.03), but this difference was
not statistically significant. An inverse association was also found between % male unemployment
and severe wheeze in the parsimonious model (OR IQR 0.93, 95% CI 0.83, 1.04). In general, the
pattern of effects for all community socioeconomic characteristics from models examining severe
wheeze was similar to the main results (data not shown). There was no effect for female
unemployment (OR IQR 0.99, 95% CI 0.82, 1.20), while the effect for total unemployment was very
similar to that for male unemployment, but with wider confidence intervals (OR IQR 0.89, 95% CI
0.76, 1.05). This suggests that it is the variation in male unemployment that is responsible for the
results we have observed.

A scatter plot of community unexplained lifetime asthma versus percent male unemployment revealed
two outliers, Atascadero and Upland (Figure 3.2). Sensitivity analyses were performed to ascertain
how estimates might change if one or the other community was excluded. When Atascadero was
removed from the model, the association between community male unemployment and prevalent
asthma was diminished and rendered non-significant (OR IQR 0.94, 95% CI 0.80, 1.10). When
Upland was removed, the original association became more negative and remained statistically
significant (OR IQR 0.84, 95% CI 0.74, 0.96). Both communities are points of influence; however,
since each community in the survey represents approximately 500 individuals, we cannot justify
excluding these communities in the analysis.

61
Table 3.2 Characteristics of CHS communities
Race/Ethnicity (%)
a

Community N
Crude
asthma
prevalence
(%)
Male
unemployment
(%)
a

Median
family
income
($)
a

Low
education
(%)
a

Below
poverty
(%)
a
White Hispanic
African
American Asian Other
CHS
subjects of
Hispanic
ethnicity
(%)
CHS
subjects
with
medical
insurance
coverage
(%)
Alpine 478 13.39 4.38 39,295 18.11 8.51 82.19 9.99 1.31 1.59 4.93 16.08 88.06
Atascadero 404 21.53 2.18 34,739 16.09 7.85 84.47 8.32 2.13 1.13 3.96 14.22 87.98
Lake
Elsinore 483 14.49 4.51 37,283 23.41 8.79 70.44 16.27 2.56 1.73 8.99 27.42 82.33
Lake
Arrowhead 516 13.95 4.61 42,095 9.87 7.42 88.39 8.01 0.65 0.86 2.09 18.32 84.18
Lancaster 450 15.78 4.74 41,089 18.67 9.19 65.58 14.99 6.91 3.58 8.94 28.29 88.31
Lompoc 472 12.50 5.07 35,024 18.60 12.19 52.31 21.74 6.51 5.05 14.39 26.56 88.09
Long Beach 469 13.22 5.46 31,938 25.87 13.11 33.30 24.08 13.35 14.31 14.96 22.88 85.56
Mira Loma 460 11.52 4.99 41,521 26.96 6.87 57.70 23.70 2.68 2.24 13.66 41.09 78.34
Riverside 518 13.71 5.42 34,984 22.51 12.35 40.72 26.80 9.10 5.11 18.27 40.58 80.16
San Dimas 459 18.08 3.54 44,799 19.43 5.54 59.57 20.80 5.46 5.90 8.27 31.47 87.86
Santa Maria 451 13.53 5.53 33,390 29.50 14.21 27.73 38.95 1.86 5.21 26.25 62.72 68.10
Upland 421 14.01 2.92 57,815 11.85 3.81 68.97 12.70 3.81 8.42 6.11 16.51 92.33
All
communities
5581 14.55 4.45 39,498 20.07 9.15 60.95 18.86 4.69 4.59 10.90 28.33 84.28
a
Data obtained from U.S. Census 1990.

62
Table 3.3 Odds ratio for community-level socioeconomic characteristics and unexplained lifetime asthma across the inter-quartile range (models 1-6)
Model 1
a
(n=5364) Model 2
b
(n=5364) Model 3
c
(n=5263) Model 4
d
(n=3687) Model 5
e
(n=3481) Model 6
f
(n=4092) Community
socioeconomic
parameter
OR
(95% CI)
g

p
OR
(95% CI)
g

p
OR
(95% CI)
g

p
OR
(95% CI)
g

p
OR
(95% CI)
g

p
OR
(95% CI)
g

p
Median income
(per $10,000)
1.01
(0.88, 1.15)
0.93
0.99
(0.87, 1.14)
0.92
0.98
(0.86, 1.13)
0.8
0.94
(0.83, 1.06)
0.32
0.93
(0.82, 1.07)
0.3
0.98
(0.89, 1.09)
0.76
Below poverty (%)
0.89
(0.72, 1.09)
0.26
0.92
(0.74, 1.14)
0.42
0.94
(0.75, 1.17)
0.55
1.04
(0.84, 1.28)
0.75
1.03
(0.81, 1.31)
0.83
0.97
(0.82, 1.16)
0.75
Male
unemployment (%)
0.81
(0.73, 0.90)
<0.01
0.83
(0.73, 0.93)
<0.01
0.83
(0.74, 0.95)
<0.01
0.88
(0.77, 1.01)
0.06
0.86
(0.75, 0.99)
0.03
0.88
(0.78, 0.99)
0.03
Low education (%)
0.92
(0.78, 1.09)
0.33
0.93
(0.79, 1.11)
0.44
0.95
(0.80, 1.14)
0.6
0.96
(0.82, 1.13)
0.64
0.95
(0.75, 1.15)
0.61
0.96
(0.84, 1.10)
0.53
a
Model 1 adjusted for community, age, race, gender and cohort group.
b
Model 2 adjusted as in model 1 plus parental education.
c
Model 3 adjusted as in model 2 plus medical insurance coverage.
d
Model 4 adjusted as in model 3 plus air conditioning in home, gas stove in home, carpet in child’s room, parental history of asthma, any smokers in the home, hay
fever in child, pest in home, plants in home, pets in home, regular vitamin use, body mass index, height, water damage in home and mildew in home.
e
Model 5 adjusted as in model 4 plus census block group population density.
f
Model 6 adjusted for community, age, African-American race, gender, cohort group, parental education, medical insurance coverage, gas stove in home, parental
history of asthma, hay fever in child, plants in home, pets in home, body mass index, and mildew in home.
g
Odds ratio and 95% confidence interval. Estimates are scaled across the interquartile range of exposure: Median income, $6947; Below poverty, 5.13%; Male
unemployment, 1.29%; Low education 7.54%

63
Figure 3.2 Scatter plot of unexplained lifetime asthma and percent male unemployment across study
communities (n=12); Atascadero and Upland are influential communities
1
1.5
2
2.5
3
3.5
4
2 2.5 3 3.5 4 4.5 5 5.5 6
% Male Unemployment
% Unexplained Asthma .
R = -0.63
p = 0.03

Missing values in our dataset reduced our sample by 26.7% in the parsimonious model. Missing cases
were generally from households with significantly less education, lower socioeconomic status, fewer
air conditioners and pets, and less mildew, and were more likely to be Hispanic. Missing subjects also
tended to be from communities with greater average population density (data not shown). This
suggests that we were missing subjects from the lower end of the SEP distribution in our analysis. In a
sensitivity analysis, we included subjects with missing data in the parsimonious model using an
indicator variable for individuals with missing values for each covariate (White & Thompson, 2005).
When covariates with missing data are correlated to the outcome of interest, this method can produce
biased estimators; in our case, relevant covariates were generally weakly correlated to lifetime asthma
(data not shown). After this adjustment, the OR IQR for community percent male unemployment was
similar to the original estimate (OR IQR 0.87, 95% CI 0.78, 0.98), suggesting that missing values did
not bias our results.

64
3.4 DISCUSSION

We evaluated the association between community-level SEP and asthma, after controlling for
individual-level effects. There was lower prevalence of asthma in communities with contextual
markers of low SEP, which was significant for community average male unemployment. Percent
poverty and low education were less strongly protective. The results were robust to methods for
summarizing census block group data to the community level (i.e. 95% bounding rectangle vs.
weighted or unweighted methods), the scale of analysis (i.e. community-level vs. census block group-
level), confounding by compositional factors, and the modeling algorithm.

Medical insurance coverage was strongly associated with asthma at the individual level. However,
access to medical care and disparities in quality of care may vary both within and between
communities (Gold & Wright, 2005), for instance, depending on the relative location of medical
facilities (Garrett, 1997; McLafferty & Grady, 2004; McLafferty, 2003). Therefore, communities and
neighborhoods with relatively lower access to care may have more undiagnosed cases of asthma than
places with easy access to care. The inverse effect for low community SEP could be explained if
lower rates of asthma resulted from reduced access to health care in low SEP populations or from less
accurate self-reporting of doctor-diagnosed asthma by low SEP parents. With respect to the former,
by adjusting for individual-level medical insurance coverage we would expect to confirm that the
observed association is not due to access to health services (Callahan & Cooper, 2005; Kempe, Beaty,
Crane, Stokstad, Barrow, Belman et al., 2005). While insurance coverage was a predictor of prevalent
asthma in models 3 through 6, adjustment for insurance coverage in model 3 did not confound the
association observed between community male unemployment and asthma in model 2 (see Table 3.3).
Due to the cross-sectional design of this study, this may indicate that children diagnosed with asthma
prior to study baseline were more likely to obtain medical insurance subsequently, but it is also
possible that the variable about insurance coverage did not capture complex differences in access to
and the quality of care. In our study, parents were only asked “does this child have any health plan or

65
health insurance?”, at study entry; thus, in communities with higher unemployment, such an indicator
may not measure increased interruptions in continuity of care that may be brought on by periods of
unemployment within families, and it will not account for under-diagnosis that may occur among
children with less comprehensive health plans (e.g. Medicaid) in such families (Vivier, 2005).
Therefore, reduced access to diagnostic care in communities with high unemployment could explain
the lower rates of asthma. While we did not have an independent measure of insurance coverage
within communities, we averaged subject-level data on insurance coverage by community and
examined its relationship with male unemployment. There was a significant inverse association,
meaning places with higher unemployment had lower insurance coverage (r
2
=0.60; p=0.04). Male
unemployment might therefore be a proxy for entire communities with reduced access to care (e.g.
there may be fewer specialists, who may be more likely to diagnose health problems). That the
association between male unemployment and severe wheeze was slightly less negative than the
association for lifetime asthma also supports the idea that differential access to medical insurance may
partially explain our results.

Another possible explanation for the observed inverse effect for low SEP is the hygiene hypothesis
(Gold & Wright, 2005). This hypothesis suggests that reduced infectious challenge in early childhood
may contribute to the development and severity of asthma as a result of failure to develop
immunological tolerance (Ramsey & Celedon, 2005). While the biological plausibility of this
hypothesis remains under study (Borchers, Keen, & Gershwin, 2005; Bufford & Gern, 2005), it would
predict that lower rates of asthma might be expected in communities with higher unemployment, if
children in these communities were more likely to be exposed to infectious challenges. Recent
evidence from the International Study of Asthma and Allergies in Childhood (ISAAC) has shown a
general trend of highly developed countries having relatively more prevalent asthma than less
developed countries (Beasley, Ellwood, & Asher, 2003), which generally have greater childhood
communicable disease burden (Lopez, 2005; Murray & Lopez, 1997). Within countries, lower rates of
asthma have been observed among children growing up in close proximity to farm animals,

66
suggesting a protective effect of early childhood exposure to endotoxin and other bacterial compounds
(Von Ehrenstein, Von Mutius, Illi, Baumann, Bohm, & von Kries, 2000). Strachan first suggested this
hypothesis after observing that having a greater number of older siblings was associated with reduced
prevalence of atopy in children (Strachan, 1989), and this initial observation has been followed-up by
other studies which show exposure to siblings leads to increased exposure to allergens and viral
infection for children (Karmaus & Botezan, 2002; Randi, Altieri, Chatenoud, Chiaffarino, & La
Vecchia, 2004). As a sensitivity analysis, we further adjusted our parsimonious model for number of
siblings in the first stage of model 6. At the individual-level, each additional sibling conferred a
reduction in risk for asthma (OR 0.88; 95% CI 0.81, 0.95), causing a slight weakening in the
community-level effect for male unemployment (OR IQR 0.89, 95% CI 0.79, 1.00). This attenuation
of the effect of community SEP is what would be expected if the hygiene effect were partially
responsible for our community-level results.

Cagney et al. (2004) found no association between a measure of community socioeconomic
deprivation and asthma, but they did observe that greater collective efficacy within communities was
protective. Collective efficacy is defined as shared expectations and effort that lead to beneficial
community resource (Cagney & Browning, 2004). Our study focused on using measures of
community deprivation, yet an emerging literature on measures of social capital, including collective
efficacy, indicates that deprivation measures may not completely explain the mechanisms that
potentially exist where contextual effects of SEP occur (Sampson, Morenoff, & Earls, 1999). The
inverse effect for male unemployment was stronger amongst Hispanics than non-Hispanics
(difference not statistically significant), and communities with a greater proportion of Hispanic
subjects tended to be communities with higher male unemployment (r
2
= 0.35; p=0.04), results which
are suggestive of the Latino Paradox. While the main effect for Hispanic ethnicity was not statistically
significant in our analysis (results not shown), the slightly more protective effect of high
unemployment amongst the Hispanic strata suggests that communities of Hispanics have a collective
advantage against asthma compared to communities of other racial-ethnic groups, in the context of

67
low SEP. A protective effect of social capital on markers of health in Hispanic-American
communities has been suggested as an explanation for this trend (Cagney, Browning, & Wallace,
2005; Denner, Kirby, Coyle, & Brindis, 2001).

The environmental justice literature has highlighted a spatial relationship between communities of
color and exposure to harmful air pollutants in southern California (Morello-Frosch, Pastor, Porras, &
Sadd, 2002), which suggests that associations between community SEP and asthma may be driven by
measures of residential segregation rather than deprivation (Morello-Frosch & Lopez, 2006). In a
sensitivity analysis, we tested measures of community racial composition and neighborhood stability
derived from the U.S. 1990 census as measures of SEP which may relate to segregation. We found no
main effects of these variables on asthma prevalence, nor did they confound the association between
community male unemployment and asthma.

In this study, we used the UPR to represent unemployment, as opposed to the unemployment rate
(UR). The UPR is thought to better capture unemployment in populations featuring discouraged
workers, who are not considered a part of the labor force and are thus excluded from the UR (Jones &
Riddell, 1999). Given the economic recession in the United States in the late 1980s and early 1990s,
which is the period during which lifetime asthma would have occurred in our cohort, the UPR may be
a more appropriate measure than the UR in our study. Furthermore, southern California has a
disproportionately high number of undocumented immigrants who make up the actual labor force
(U.S. Census Bureau, 1994). Undocumented immigrants may be less likely to report that they are
actively seeking work on the U.S. Census, thus excluding them from Census estimates of the labor
force which are used as the denominator for the UR. In contrast, the UPR includes all individuals 16
years of age or older in the denominator and may be more sensitive to communities in our study
which have a smaller or larger presence of undocumented immigrants. In a sensitivity analysis, we
substituted the UR (U.S. Census Bureau, 1992) into model 6 (in Table 3.3) and observed a similar but
a slightly diminished relative risk for the effect of community unemployment on prevalent asthma

68
(RR IQR 0.91, 95% CI 0.83-1.00). The stronger protective effect that we observe when the UPR was
used may reflect lower rates of asthma in communities with undocumented immigrants from Mexico,
a population with especially low rates of asthma, in spite of their SEP (i.e. the Latino Paradox)
(Holguin, Mannino, Anto et al., 2005). Alternatively, it may reflect under-diagnosis of asthma in
communities hit hardest by economic recession and subsequent joblessness (i.e. discouraged
workers).

Although there is considerable interest in the role of SEP in geographic differences in rates of asthma
(Federico & Liu, 2003; Gold & Wright, 2005), there has been relatively little systematic study of this
issue compared to investigation of other risk factors. Few studies have explored the effects of SEP on
childhood asthma using a multilevel model. Juhn et al. (2005) found a borderline significant positive
association between mean family income (assigned to the subject’s census tract at birth) and incident
asthma (Juhn, Sauver, Katusic et al., 2005). Their results are not completely comparable to ours
because of differences in assignment of community of residence (i.e. at birth vs. at study entry) and
individual-level adjustment (i.e. very reduced number of risk factors vs. comprehensive in ours).
While results for mean family income were inconclusive in our analysis, the similarity in the
relationship between SEP and asthma supports the findings here. In contrast, Nepomnyaschy and
Reichman (2006) observed a positive association between asthma at a young age and residence in
census tracts with higher rates of renter-occupied housing and vacancies, a marker for low SEP
neighborhoods (Nepomnyaschy & Reichman, 2006). There are many differences in study design and
sample composition, but their results with prevalent asthma suggest the opposite relationship between
SEP and asthma than we observed. Basagana et al. (2004) also found a positive association between
dichotomous measures of low SEP and adult asthma in community settings across Europe, Australia,
New Zealand and the US (Basagana, Sunyer, Kogevinas et al., 2004). Results presented in this paper
contribute to the growing body of evidence suggesting a role for SEP in explaining geographic
patterns of asthma. The direction of this SEP-asthma relationship, however, still seems unclear, with

69
divergent findings emerging from different scales of analysis, underlying populations and study
designs.

We found a contrast between the individual- and community-level effects of SEP on asthma. While
we observed a protective relationship between high unemployment and asthma at the community
level, asthma rates were higher in families with lower parental education. Other studies have generally
found increased rates of asthma associated with individual-level effects of low SEP (Gold & Wright,
2005; Mielck, Reitmeir, & Wjst, 1996), results which are consistent with the observed increased rates
of asthma associated with individual-level insurance coverage and lower parental education in our
study. In contrast to our observation of protective community-level effects of lower SEP, increased
rates of asthma have been observed in impoverished inner cities in the U.S. (Federico & Liu, 2003).
Contextual factors have been proposed to explain the increased rates of asthma observed in inner
cities, including the absence of community facilities (e.g. park space, fast food restaurants) that could
affect health behaviors such as exercising and consuming a high fat diet, resulting in elevated risks for
asthma (Gold & Wright, 2005). Stress arising from conditions in low SEP communities (e.g. poor
housing, violence, unemployment) also might have a role in asthma exacerbation and increased
asthma rates (Chen, Hanson, Paterson, Griffin, Walker, & Miller, 2006; Liu, Coe, Swenson, Kelly,
Kita, & Busse, 2002; Marmot, 2001; Wright, Mitchell, Visness, Cohen, Stout, Evans et al., 2004;
Wright, Rodriguez, & Cohen, 1998). The range of contextual SEP in our communities did not
represent the extremes found in inner cities, so associations with SEP in inner cities are not directly
analogous for our study, nor could our results be expected to be generalizable to the inner city setting.

Estimates of contextual effects may be underestimated in our analysis. We used 1990 Census data to
derive community-level socioeconomic characteristics, and this may have resulted in non-differential
misclassification bias in estimating the true community-level characteristics for the time our study
was conducted in 1993. Furthermore, the measurement of contextual effects can be biased if there is
collinearity with compositional effects that are included in the same model (Oakes, 2004). In our

70
analysis, we adjusted for some covariates at the individual-level (e.g. hay fever, medical insurance
coverage) that may also be on the causal pathway for community-level SEP effects; in effect, we may
have over-adjusted the between-community differences in asthma, overstating the reduction in
between-community variability in unexplained asthma. We evaluated the effect of removal of medical
insurance coverage and hay fever from the first stage of analysis and estimates of contextual effects
grew stronger for all four community socioeconomic characteristics, but the changes were small. For
example, the odds ratio across the IQR for community male unemployment in model 6 after excluding
hay fever decreased from 0.88 to 0.85 (95% CI 0.76, 0.95). Thus, the true effect for male
unemployment may be stronger than we measured, and observed effects for other community-level
characteristics, and their statistical significance, may also be attenuated by our adjustment for
covariates potentially on the causal pathway.

We sought to explain residual differences in between-community asthma prevalence with community
indicators of SEP in a population-based cohort. In doing so, we used a multilevel modeling
framework to estimate contextual effects which account for random effects at the community-level, as
well as for potential confounders at the individual level. While many studies about contextual effects
use jurisdictional boundaries to define community characteristics, we used a novel method of spatial
and statistical analysis to derive characteristics of community SEP which better reflect the residential
locations which subjects inhabit. Our results reveal a consistent protective effect for community
unemployment, one that persisted even with the inclusion of many individual risk factors and with
smaller units of analysis (i.e., census block groups). We have hypothesized some mechanisms that
may explain this association. In general, unemployment has been associated with risky health
behaviors and worse health outcomes (Catalano, 1991; Ferrie, Martikainen, Shipley, Marmot,
Stansfeld, & Smith, 2001; Jerrett, Eyles, & Cole, 1998). Although our results do not fall into this
general pattern, other research has indicated a protective effect of lower SEP at the community scale.
Future studies may examine the impact that type of medical insurance coverage has on contextual
effects of SEP. Further investigation into the links between contextual SEP and asthma is warranted.

71
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CHAPTER FOUR

Effects of the social environment on incident asthma in childhood: Associations with
school Title I funding and crime in the community

Shankardass K, Jerrett M, Milam J, Richardson J, Berhane K, McConnell R

4.1 BACKGROUND

Higher rates of childhood asthma are often reported in areas of low socioeconomic status (SES) (Carr
et al. 1992; Getahun et al. 2005; Gupta et al. 2008; Weinmayr et al. 2007; Weiss and Wagener 1990).
For example, in the United States, prevalence rates are higher in urban settings, especially among
non-white children and children living in impoverished neighborhoods (Mannino et al. 2002; Wright
and Fischer 2003). Socioeconomic disparities in asthma are not fully explained by the SES of
individuals (Basagana et al. 2004; Cagney and Browning 2004; Juhn et al. 2005; Litonjua et al. 1999;
Nepomnyaschy and Reichman 2006; Shankardass et al. 2007), and individual-level determinants of
asthma may be regulated through complex relationships with social, economic and other conditions in
the wider environment (Gehlert et al. 2008; Wright 2006). As a result, there is growing interest in the
relationship between the social environment and asthma (Federico and Liu 2003; Gold and Wright
2005; Timmermans 2007; Wright and Subramanian 2007).

The social environment may plausibly affect risk for asthma due to behaviors that may be risk factors
for asthma (e.g. smoking), access to medical care, by association with exposure to hazards of the
physical environment (e.g. air pollution or indoor allergens), or by it’s effect on psychosocial stress,
(Ford and McCaffrey 2006; Gold and Wright 2005; Shapiro and Stout 2002; Wright 2006; Wright and
Fischer 2003). Areas characterized by low SES may engender more stressful environments (Catalano

78
1991; Dearing 2007; Siegrist 2000; Taylor et al. 1997), and growing evidence suggests that stress
triggers asthma symptoms and may play a role in the development of disease (Kilpelainen et al. 2002;
Milam et al. 2008; Mrazek et al. 1999; Sandberg et al. 2000; Shalowitz et al. 2001; Weil et al. 1999;
Wright et al. 2005; Wright et al. 2002; Wright et al. 1998). Recent studies suggest that traffic-related
air pollution also plays a role in the development of asthma (Brauer et al. 2007; McConnell et al.
2002; McConnell et al. 2006; McConnell et al. 2007; Sarnat and Holguin 2007; Shima et al. 2002),
and exposure to such pollution may be higher in minority and low SES areas due to their proximity to
major roadways and industrial sources (Evans and Kantrowitz 2002; Morello-Frosch et al. 2002;
Pastor et al. 2002).

Because risk factors for asthma onset may be determined by multiple aspects of the social
environment surrounding individuals, including the household and local neighborhood, and places of
work and study throughout the wider community (Wright and Subramanian 2007), a multilevel
perspective is needed to help explain social disparities in asthma (Basagana et al. 2004; Cagney and
Browning 2004; Juhn et al. 2005; Nepomnyaschy and Reichman 2006; Salmond et al. 1999;
Shankardass et al. 2007). Multilevel models are useful for studying associations with the social
environment because they can simultaneously adjust for predictors across multiple spatial scales,
while accounting for “similarity by proximity” of spatially clustered study populations with random
effects (Chaix et al. 2005; Diez-Roux 2000). Theoretically, measured associations between the social
environment and asthma onset may reflect causal pathways involving a complex mixture of
contextual effects that are directly related to asthma through biological mechanisms (e.g. chronic
stress) and compositional effects that are mediated by individual-level risk factors in the study
population (e.g. smoking in the home) (Diez Roux 2002).

In this paper, we examine contextual effects of the neighborhood, school and community social
environment for new onset asthma, after accounting for a range of individual and household risk
factors, in a prospective cohort of school children in Southern California.

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4.2 METHODS

4.2.1 Study Population

We selected 2497 subjects with no wheeze or asthma at baseline enrollment (2002-2003) from the
Children’s Health Study (CHS) cohort, which has been previously described (McConnell et al. 2006;
Milam et al. 2008). Subjects resided in 274 census tracts and attended kindergarten or first grade in
one of 45 schools distributed in 13 communities throughout Southern California (Figure 4.1). Forty-
one subjects (1.6%) were excluded from this analysis due to missing data about exposure to traffic-
related pollution, which was previously identified as an important risk factor for new onset asthma in
this cohort (McConnell et al. 2007). Therefore, the study population for this analysis included 2456
subjects. Informed consent was obtained from parents, and the study was approved by the University
of Southern California Institutional Review Board.

Figure 4.1 Map of study communities

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4.2.2 Assessment of new onset asthma and covariates

Subjects with new onset asthma were identified by self-reported physician-diagnosed asthma on
annual questionnaires during three years of follow-up. Covariates were measured from responses
given on the study baseline questionnaire by one of the child’s parents or guardians, and were all
potential risk factors for asthma that may explain observed associations with measures of the social
environment. Figure 1.7 contains a full list of covariates considered at the individual level. The
race/ethnicity of subjects was classified as either non-Hispanic white, Hispanic, African American or
other. Body mass index (BMI) was calculated based on measurements of height and weight at study
baseline. Overweight subjects were those in the 95
th
percentile of the Centers

for Disease Control and
Prevention (CDC) 2000 gender-specific

BMI-for-age reference values, while underweight subjects
were those below the 5
th
percentile. Educational attainment by the parent filling out the questionnaire
was used to indicate family SES of subjects. Subjects whose parent had not completed high school
were classified with low parental education, while those with a parent who attained a high school
diploma or had some college experience were of medium parental education, and those whose parent
had a college degree or higher were of high parental education. Parental stress was determined using a
4-item version of the Perceived Stress Scale (PSS), which measures the extent that individuals believe
their lives are unpredictable, uncontrollable, or overwhelming (Cronbach coefficient alpha, 0.66 for 4
items of PSS) (Cohen and Williamson 1988). Finally, a recent paper highlighted effects of the 2003
Southern California wildfires on the respiratory health of subjects in this cohort (Kunzli et al. 2006),
so we also tested smoke exposure related to fires and community-level estimates of particulate matter
(PM10) concentrations during the 5 days with the highest fire activity as potential confounders.

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4.2.3 Measurement of the social environment

Figure 1.7 contains an overview of the hierarchical relationship between individual subjects and the
three spatial levels of interest: neighborhoods, schools and communities. Data describing a range of
social characteristics was available at each spatial level.

Neighborhood characteristics were defined by matching the census tract of residence to data from the
U.S. 2000 Census. Characteristics included measures of median household income, proportion of
respondents over age 25 with low and high education (e.g. percent with no high school diploma),
percent unemployed, percent living in poverty, and measures of community racial composition (e.g.
percent African American). We also used the Gini coefficient to describe economic inequality within
neighborhoods (Yitzhaki 1979). This commonly used measure of inequality can range from 0 to 1.0,
with higher values indicating greater income inequality among neighborhood households.

The school social environment was characterized by data reported to the California Department of
Education for the 2002-2003 school year (Education Data Partnership 2007). Variables included
percent minority ethnicity, percent students receiving free meals, percent students who were English-
language learners, mean class size, pupil-to-teacher ratio, percent full credential teachers, enrollment
size, variables describing academic performance, and indicators for whether or not the majority of
students were of Hispanic ethnicity, and for whether or not the school followed a year-round
curriculum. Another variable, called the Ethnic Diversity Index, reflected how evenly students were
distributed among seven ethnic categories reported to the Department. A higher value indicated a
more even distribution of students. Finally, a variable described whether or not schools received
funding related to Title I (‘Improving the Academic Achievement of the Disadvantaged’) of the No
Child Left Behind Act of 2001. The purpose of this funding is to ensure that all children have access
to high-quality education and achieve proficiency on State academic standards. Two types of funding
are available to support academic improvement: Schools with >40% of student living in poverty are

82
eligible to combine funding from Title I with other federal, state and local funds to implement school-
wide programs aiming to improve conditions for all students, while schools with lower levels of
poverty can apply for funding to support specific students of interest. We also obtained data related to
the Federal Gun Free Schools Act of 1994 describing student expulsions related to firearms; however,
there were no such incidents reported for any of our schools, so these data were not used in our
analyses.

The community social environment was estimated by the same variables used to describe
neighborhoods (except for the Gini coefficient), based on data describing census block groups which
were aggregated to the community level. Subjects were recruited through schools; therefore,
residential locations clustered together within the administrative boundaries of communities. To
compensate for clustering of study subjects, community-level variables were weighted by the
proportion of the census block groups included in a community-specific bounding rectangle that
contained 95% of local study subjects (Tatalovich et al. 2006). Also, we calculated two measures of
the dissimilarity index to describe residential segregation of African Americans and Hispanics,
respectively, compared with all other races (White 1986). The dissimilarity index ranges from 0 to
100 and can be interpreted as the percent of minority residents within the community who would need
to move from their census block group to another in order to achieve even distribution across census
block groups within the community. Finally, data from Federal Bureau of Investigation Uniform
Crime Reports were used to characterize crime in the community (U.S. Department of Justice 2007).
These data are reported annually by city, county and state law enforcement agencies, and describe
rates of various types of violent (e.g. murder, rape, robbery and aggravated assault) and property (e.g.
burglary, larceny, motor vehicle theft) crime. Because the recruitment of study subjects was not
completed until partway through 2003, data for 2004 were used in this analysis to reflect crime
occurring near study baseline that may have affected all subjects in the cohort.

83
4.2.4 Exposure to traffic-related pollution

Recent evidence from this cohort suggests that exposure to traffic-related pollution at the household
may cause asthma (McConnell et al. 2006). Methods to estimate exposure to traffic-related pollution
at households and schools have been described previously (McConnell et al. 2006). In summary,
addresses of subjects were geo-coded, and exposure to total oxides of nitrogen (NO
x
) from traffic on
local roads near residences and schools was estimated as a marker for pollutants from traffic exhaust,
accounting for traffic volume and local meteorology, using the CALINE4 dispersion model (Benson
1989).

4.2.5 Statistical methods

Contextual effects for asthma onset were assessed using multilevel Cox proportional hazards models
(Ma et al. 2003). All models contained age and gender stratification of the baseline hazard, adjustment
for race and ethnicity, and a 2-level independent random effects structure, which allowed for
clustering around schools and communities, and assessment of residual variation in time to asthma
onset. Neighborhood, rather than school, random effects were included in a sensitivity analysis, which
resulted in a similar pattern of effects; therefore, only models featuring school and community random
effects are presented. Letting u
i
and

u
ij
denote community and school (within community) level
random effects, the model takes the following general form:

h
ijl
(t)= h
0s
(t) u
i
u
ij
exp(βX
ijl
+ δ
T
Z
ijl
)
h
ijl
(t): hazard function for the lth subject in the ith community, and jth school;
h
0s
(t) : the baseline hazard function for stratum s (i.e., age at study entry and gender);
X
ijl
: characteristics of the social environment for the lth subject in the ith community, an jth school;
and

84
Z
ijl
: covariates (e.g., race and ethnicity, traffic-related pollution) for the lth subject in the ith
community, and jth school.

In this model, the school level random effects are assumed to be positive and independent conditional
on the community level random effects (see Hughes (2007) for details). Analogous models could be
developed for neighborhood level random effects. All analyses were conducted using R software (R
Development Core Team 2005) and software designed to run within R for implementing random
effects Cox proportional hazards models (Jerrett et al. 2005; Ma et al. 2003).

Variables describing the neighborhood, school and community social environment were initially
tested for main effects on incident asthma. All variables which were statistically significant at an
alpha level of 0.10 were then systematically co-adjusted to determine whether these variables had
independent effects on incident asthma. To determine the extent to which effects of the social
environment may be attributable to compositional rather than contextual pathways, the model
containing all statistically significant, independent effects of the social environment was adjusted for
hypothesized individual-level risk factors for asthma (Figure 1.7).

Because high minority and low SES environments have been associated with higher exposure to air
pollution at homes and schools in Southern California (Morello-Frosch and Lopez 2006; Morello-
Frosch et al. 2002), we further adjusted the final contextual effects model for residential and school
traffic-related pollution to examine potential confounding. Confounding of contextual effects was also
examined by performing a principal components analysis using Varimax rotation with all non-
significant characteristics of the social environment. We adjusted the final contextual effects model
both independently and simultaneously for all component factors with an Eigenvalue close to or
above 1.0.

85
Spatial autocorrelation in asthma incidence rates across spatial levels was measured in order to
determine whether there was any clustering in asthma incidence, since this may indicate that there
were factors operating at broader spatial scales broader driving population disparities in asthma, such
as regional weather patterns or county-level policies (see Appendix for details).

4.3 RESULTS

The characteristics of subjects in this cohort have been described previously (McConnell et al. 2007).
There were 118 cases of new onset asthma resulting in a crude incidence rate of 18.6 per 1000 person-
years. Subjects ranged from 5-9 years of age at baseline and 47.7% were male. The majority of
subjects were of either Hispanic ethnicity (55.3%) or white (non-Hispanic) race (36.2%), and the
remainder were either African-American (3.1%) or of other (5.4%) race/ethnicity. Low parental
education was reported for 21.2% of subjects, while the majority of subjects (55.1%) were of medium
parental education, and 23.7% were of high parental education. Individual level risk factors for
asthma have been previously examined (McConnell et al. 2007; Shankardass et al. 2008), and effects
mostly took the expected sign. Significant positive associations were found for African-American
race, underweight, parental history of asthma, musty odor in the home and residential traffic-related
pollution.

Wide variation existed in the distribution of characteristics describing neighborhood, school and
community social environments (Table 4.1). Students attending schools that received any Title I
funding were 1.66 times more likely to be diagnosed with asthma during follow-up (95% CI 1.09-
2.52); the crude incidence rate in schools with Title I funding was 21.1 per 1000 person-years, and
15.1 per 1000 person-years in schools without funding. Analysis of type of Title I funding suggested
that the effect for schools receiving targeted assistance funds (HR 1.77, 95% CI 1.12-2.80) was
stronger than for those with school-wide funding (HR 1.49, 95% CI 0.89-2.52); however, a likelihood
ratio test comparing a model which distinguished type of Title I funding to one which did not was

86
non-significant (p=0.59). The effect of the total crime rate in communities on asthma (HR 1.20, 95%
CI 0.98-1.49) was borderline significant, but analyses examining specific types of crime revealed that
this effect was primarily driven by the larceny crime rate (HR 1.23, 95% CI 1.02-1.48), which was by
far the most prevalent type of crime. The mean larceny crime rate (1908 per 100,000) was similar to
the mean rate for all other types of crime combined (1904 per 100,000). The larceny crime rate was
also associated with the crude incidence rate in a clear dose-response relationship across the 13 study
communities (Figure 4.2). Therefore, we substituted the larceny crime rate for the total crime rate in
subsequent models.

The school Ethnic Diversity Index and neighborhood population density initially had borderline
significant positive associations with asthma (HR for Ethnic Diversity Index 1.31, 95% CI 1.01-1.70;
HR for population density 1.19, 95% CI 1.00-1.43); however, when these variables were
simultaneously co-adjusted for Title I funding and the larceny crime rate, their effects were greatly
reduced and became non-significant (see Co-adjusted HR in Table 4.1; HR for Ethnic Diversity Index
reduced by 26%, p=0.14; HR for population density reduced by 35%, p=0.26). On the other hand,
effects for Title I funding and the larceny crime rate remained largely unchanged in the co-adjusted
model. The Pearson correlation coefficients between these variables were generally low (i.e. below
0.3).

When co-adjusted in a single model, effects for the larceny rate and Title I funding remained
significant and increased in size (see Model 1 in Table 4.2). Effects for the larceny crime rate and
Title I funding were robust to adjustment for hypothesized risk factors for asthma describing
individual characteristics and the indoor household environment. This was true when we ran a series
of tests adjusting individually for each potential confounder, i.e. where we allowed missing data in
individual-level variables to dictate the sample size of the adjusted model (data not shown), and when
all of these variables were entered simultaneously using indicators of missing data in order to allow
all 2456 subjects into the model (see Model 2a in Table 4.2) (White and Thompson 2005).

87
Table 4.1 Characteristics of CHS neighborhoods, schools and communities, with bivariate and co-adjusted associations with incident asthma
Level n Variable N (%)
Mean
(Standard
deviation)
Range
(Interquartile range)
Bivariate
HR (95% CI)
a

Co-adjusted
HR (95% CI)
a

Neighborhood 274 Median income ($) 48,287 (17,286) 13,506-106,596 (20,968) 0.84 (0.68-1.05)
Education below high school diploma (%) 25.7 (16.1) 3.7-68.6 (23.1) 1.08 (0.81-1.42)
Education at or above graduate degree (%) 6.9 (5.8) 0-27.6 (7.0) 0.96 (0.78-1.18)
Below poverty (%) 14.6 (10.8) 0.9-56.7 (12.5) 1.18 (0.96-1.46)
Total unemployment (%) 7.4 (4.4) 0.9-23.4 (5.1) 1.16 (0.93-1.44)
White residents (%) 48.9 (23.9) 9.0-91.4 (39.6) 0.82 (0.58-1.15)
Hispanic residents (%) 36.4 (20.0) 5.9-89.8 (30.7) 1.13 (0.83-1.52)
African American residents (%) 6.6 (7.4) 1.9-54.5 (7.5) 1.17 (0.92-1.49)
Asian residents (%) 6.2 (5.8) 0.1-44.4 (5.5) 1.12 (0.89-1.40)
Other race/ethnicity residents (%) 1.9 (1.4) 5.4-17.3 (0.7) 0.96 (0.80-1.15)
Population density (per sq km) 2550 (2780) 5-19121 (2340) 1.19 (1.00-1.43)
c
1.12 (0.92-1.37)
Gini coefficient 0.40 (0.05) 0.27-0.52 (0.06) 1.12 (0.85-1.47)
a
Hazard ratio and 95% confidence interval. All models have baseline hazards stratified for age and gender of subjects with adjustment for race/ethnicity. Models for
school and community variables included random effects for the school and community levels, while models for neighborhood variables included random effects for
neighborhood and community levels. The co-adjusted model included all variables that had p<0.10 in bivariate models, and included random effects for unique
combinations of neighborhoods and schools, and communities. Hazard ratios are scaled across the interquartile range of exposure.
b
Sub-categories of Title I funding were modeled together; therefore hazard ratios and 95% confidence intervals are mutually co-adjusted.
c
p<0.10
d
p<0.05

88
Table 4.1 Continued. Characteristics of CHS neighborhoods, schools and communities, with bivariate and co-adjusted associations with incident asthma
Level n Variable N (%)
Mean
(Standard
deviation)
Range
(Interquartile
range)
Bivariate
HR (95% CI)
a

Co-adjusted
HR (95% CI)
a

School 45 Enrollment size (# of students) 683.7 (227.2) 216-1260 (318) 1.14 (0.87-1.51)
Average class size? (# of students) 22.3 (2.2) 13.3-26.7 (1.1) 1.03 (0.92-1.15)
Year-round calendar? (Yes/no) 14 (31.1) 0.94 (0.62-1.42)
Pupil-to-teacher ratio 21.0 (1.7) 16.0-23.5 (2.0) 0.95 (0.75-1.19)
Full teaching credentials (% of teachers) 94.2 (6.2) 73.2-100 (8.7) 0.93 (0.72-1.21)
Title I funding? (Yes/no) 27 (60.0) 1.66 (1.09-2.52)
c
1.62 (1.07-2.47)
d

Targeted assistance program? (Yes/no)
b
14 (31.1) 1.77 (1.12-2.80)
c

School-wide program? (Yes/no)
b
13 (28.9) 1.49 (0.89-2.52)
Free meals (% of students) 45.3 (28.8) 3.7-98.3 (48.0) 1.25 (0.89-1.75)
English learners (% of students) 20.7 (20.6) 1.3-78.9 (25.9) 1.03 (0.80-1.31)
Ethnic diversity index 34.4 (12.6) 4-58 (17.0) 1.31 (1.01-1.70)
c
1.22 (0.94-1.59)
Minority ethnicity (% of students) 54.0 (26.2) 19.7-98.4 (54.0) 1.43 (0.92-2.21)
Majority of students are Hispanic ethnicity? (Yes/no) 20 (44.4) 1.35 (0.90-2.02)
Academic performance index, base score 756.5 (73.9) 582-875 (100) 0.89 (0.69-1.15)
Academic performance index, state rank 6.3 (2.4) 1-10 (3) 0.88 (0.69-1.12)
School met "Adequate Yearly Progress" criteria? (Yes/no) 36 (81.8) 0.91 (0.57-1.46)
a
Hazard ratio and 95% confidence interval. All models have baseline hazards stratified for age and gender of subjects with adjustment for race/ethnicity. Models for
school and community variables included random effects for the school and community levels, while models for neighborhood variables included random effects for
neighborhood and community levels. The co-adjusted model included all variables that had p<0.10 in bivariate models, and included random effects for unique
combinations of neighborhoods and schools, and communities. Hazard ratios are scaled across the interquartile range of exposure.
b
Sub-categories of Title I funding were modeled together; therefore hazard ratios and 95% confidence intervals are mutually co-adjusted.
c
p<0.10
d
p<0.05

89
Table 4.1 Continued. Characteristics of CHS neighborhoods, schools and communities, with bivariate and co-adjusted associations with incident asthma
Level n Variable N (%)
Mean (Standard
deviation)
Range
(Interquartile range)
Bivariate
HR (95% CI)
a

Co-adjusted
HR (95% CI)
a

Community 13 Median income ($) 46,127 (8,729) 29,891-56,690 (12,678) 1.05 (0.78-1.42)
Low education, below high school diploma (%) 23.4 (9.6) 9.0-37.4 (15.9) 1.00 (0.91-1.10)
High education, at or above graduate degree (%) 5.7 (3.4) 2.1-13.7 (4.2) 1.06 (0.85-1.32)
Below poverty (%) 15.6 (6.8) 8.2-31.2 (6.8) 1.04 (0.84-1.29)
Total unemployment (%) 7.7 (2.3) 5.4-13.3 (7.4) 1.05 (0.85-1.29)
White residents (%) 49.6 (17.9) 23.5-83.5 (22.2) 0.85 (0.64-1.13)
Hispanic residents (%) 37.8 (14.3) 12.4-61.5 (14.2) 1.08 (0.87-1.34)
African American residents (%) 5.7 (5.3) 0.9-18.3 (4.7) 1.13 (0.94-1.35)
Asian residents (%) 5.2 (3.3) 1.0-13.2 (5.2) 1.17 (0.86-1.60)
Other race/ethnicity residents (%) 1.7 (0.5) 1.2-2.9 (0.6) 0.91 (0.72-1.15)
Population size 124,712 (142,053) 452,588 (156,473) 1.00 (1.00-1.00)
Population density (per sq km) 1756 (1310) 261-4305 (1360) 1.10 (0.89-1.36)
Dissimilarity index (African Americans) 27.2 (6.0) 19.6-41.6 (5.6) 0.89 (0.74-1.09)
Dissimilarity index (Hispanics) 30.9 (8.3) 18-8.47.0 (9.2) 1.04 (0.84-1.29)
Total crime rate (per 100,000) 3757 (1252) 2283-7051 (1369) 1.20 (0.98-1.49)
c

Larceny crime rate (per 100,000) 1908 (580) 1005-2832 (570) 1.23 (1.02-1.48)
c
1.26 (1.03-1.54)
d

Non-larceny crime rate (per 100,000) 1904 (829) 914-4264 (583) 1.07 (0.93-1.23)
a
Hazard ratio and 95% confidence interval. All models have baseline hazards stratified for age and gender of subjects with adjustment for race/ethnicity. Models for school
and community variables included random effects for the school and community levels, while models for neighborhood variables included random effects for neighborhood
and community levels. The co-adjusted model included all variables that had p<0.10 in bivariate models, and included random effects for unique combinations of
neighborhoods and schools, and communities. Hazard ratios are scaled across the interquartile range of exposure.
b
Sub-categories of Title I funding were modeled together; therefore hazard ratios and 95% confidence intervals are mutually co-adjusted.
c
p<0.10
d
p<0.05

90
Figure 4.2 Scatterplot of community larceny crime rate by crude community asthma rate
0
5
10
15
20
25
30
750 1250 1750 2250 2750 3250
Larcey Crime Rate, 2004 (per 100,000)
Asthma Incidence Rate (per 1,000)
R
2
= 0.46
p = 0.005

Table 4.2 Associations between incident asthma and characteristics of the community and school
adjusted for individual-level covariates
Model 1 Model 2a Model 2b
Level Effect HR (95% CI)
a
HR (95% CI)
a
HR (95% CI)
a

Community Larceny crime rate (per 100,000)
b
1.31 (1.08-1.60) 1.29 (1.06-1.57) 1.25 (1.02-1.52)
School Title I funding 1.71 (1.14-2.58) 1.68 (1.10-2.56) 1.64 (1.08-2.47)
Model 2a is adjusted for individual and household characteristics, including race/ethnicity, parental
history of asthma, underweight/overweight, medical insurance coverage, parental education,
parental stress, second hand smoke in utero and in the home, musty odor, mildew, cockroaches,
water damage, pets and gas stove in the home, and carpet in child's bedroom.
Model 2b is adjusted for race/ethnicity and modeled non-freeway traffic-related air pollution (NOx).
a
Hazard ratio and 95% confidence interval. All models have baseline hazards stratified for age and
gender of subjects and random effects for school and community. In order to include all 2456
subjects in these models, indicators for missing data were included for all individual and household
indoor variables. All missing data indicators were non-significant, except for mildew.
b
Hazard ratios and 95% confidence intervals are scaled across the interquartile range of the larceny
crime rate in all communities (570 per 100,000).

91
Residential traffic-related pollution had a weak positive correlation with the larceny crime rate
(R=0.24), and a weaker positive correlation with Title I funding (R=0.09). When the model containing
the larceny crime rate and Title I funding was adjusted for traffic-related pollution, the effect of the
larceny rate was reduced by 20% but it remained significant (HR 1.25, 95% CI 1.02-1.52; Model 2b
in Table 4.2). There was a 25% increase in risk for incident asthma across the IQR (570 per 100,000)
and a 202% increase in risk across the full range (1827 per 100,000; data not shown). In this model,
effects for school Title I funding (HR 1.64, 95% CI 1.08-2.47) and traffic-related pollution (HR 1.44,
95% CI 1.19-1.76) were relatively stable compared with their unadjusted values. Adjustment for
exposure to traffic-related pollution at school, rather than the household, resulted in a similar but
weaker pattern of confounding (data not shown).

There may be other unmeasured characteristics to consider, so we tested for confounding by factor
components derived from a principal components analysis that included all characteristics of the
social environment other than the larceny crime rate and Title I funding; however, contextual effects
were not changed by adjustment for these factor components (data not shown).

4.4 DISCUSSION

We used a multilevel model to evaluate effects of the social environment on asthma incidence in a
cohort of Southern California school children. Subjects living in communities with high larceny crime
rates, or attending schools receiving Title I funds, had increased risk for new onset asthma compared
with subjects living in communities with lower larceny crime rates, or attending schools without Title
I funding, respectively. These contextual effects were independent of one another and not explained
by a wide range of individual and household characteristics, including SES (i.e. highest educational
attainment in parent), race and ethnicity, indicators of indoor allergens, exposure to second hand and
in utero tobacco smoke, BMI, medical insurance coverage, parental stress and estimates of traffic-

92
related pollution (see Appendix for further discussion about the relative magnitude of contextual
effects).

Community crime rates and school Title I funding may both be markers of deprivation, but SES did
not explain increased risk for asthma at either the individual (i.e. parental education) or population
levels (e.g. percent poverty in communities, percent free meals in schools). However, there may be
physical characteristics correlated with these variables that are poorly measured in households and
schools, and that may be risk factors for asthma. For example, Title I schools may be older or more
poorly maintained and thus harbor higher concentrations of indoor allergens (e.g. mold secondary to
water damage) (Evans and Kantrowitz 2002; Frumkin et al. 2006), or allow greater indoor penetration
of outdoor air pollution(Jones 1998; Liu and Nazaroff 2001). Although we examined some of these
characteristics at the household level, we did not have school-level assessments of the indoor physical
environment.

Communities with higher rates of larceny crime, and schools with Title I funding, may be more
stressful for children. Exposure to violence has been associated with increased asthma morbidity and
higher risk for lifetime asthma (Clougherty et al. 2007; Graham-Bermann and Seng 2004; Wright et
al. 2004), and crime in the community can lead to post-traumatic stress in residents (Christopher
2004; Wright 2006; Wright and Fischer 2003). High crime may also be a proxy for areas with low
social capital, where children are less able to rely on social support to cope with stressors(Siegrist
2000; Taylor et al. 1997). Finally, because schools apply for Title I funds to support academic
improvement, students attending these schools may be more likely to experience stress related to
academic difficulties (Alva and Reyes 1999; Gibby and Gibby 1967; Gillock and Reyes 2004; Repetti
1996; Simpson et al. 2005). In addition to possible direct effects on the development of asthma,
accumulating evidence suggests that chronic stress may increase vulnerability to exposure in the
physical environment associated with asthma (Clougherty et al. 2007; Marshall and Agarwal 2000;
Wright et al. 2005). For example, increased inflammation of the airways in college students with

93
asthma exposed experimentally to allergens has been associated with stressful examination periods
(Liu et al. 2002). Also, we previously showed that children in this cohort with higher levels of
parental stress were more susceptible to the effects of traffic-related pollution on new onset asthma
(Shankardass et al. 2008). We adjusted contextual effects in this analysis for parental stress, traffic-
related pollution and for interactions between these two variables, and saw no evidence that stress was
mediating contextual effects on asthma (data not shown). However, while parental stress may be a
good proxy for stress experienced by subjects in the household, it is unlikely to capture variation in
stress among children attributable to, for example, the school environment or exposure to crime in the
community. Further research is needed to more directly examine the relationship of childhood stress
with asthma, including stress associated with academic failure, exposure to crime and social
relationships.

These results suggest that environments that are low SES but also stressful may increase the risk for
new onset asthma. This is supported by some multilevel studies (Basagana et al. 2004; Cagney and
Browning 2004; Nepomnyaschy and Reichman 2006; Salmond et al. 1999), while others suggest the
opposite relationship (Juhn et al. 2005; Shankardass et al. 2007). Contradictory results may partly
reflect differences in methodological approach across studies (Diez Roux 2004; Oakes 2004;
Subramanian 2004). Previous analyses have been primarily cross-sectional, while our study uses a
prospective design. Also, whereas past studies have examined effects at one spatial level, we
simultaneously examined characteristics across three spatial levels and show that multiple levels of
influence may affect risk for asthma. For example, because effects for both Title I funding and the
larceny crime rate appeared to be biased towards the null in bivariate models compared with co-
adjusted effects (see change in estimates from Table 4.1 to Model 1 in Table 4.2), previously reported
contextual effects may be confounded by effects at other spatial levels (Diez-Roux 2000; Merlo et al.
2005).

94
Many studies in the past have noted population disparities in asthma using census-based measures to
characterize residential areas (Cagney and Browning 2004; Juhn et al. 2005; Nepomnyaschy and
Reichman 2006; Saha et al. 2005), but some have argued that such arbitrary boundaries may not
capture meaningful variation in health outcomes (Tatalovich et al. 2006). While associations between
census-based measures of neighborhoods and asthma generally took the expected direction in our
study, we found significant effects using variables not based on census data. In particular, children
spend around a third of their waking hours at school, and the quality of the school social environment
may be related to the residential areas that they serve; therefore, further examination of the role of the
school social environment in childhood asthma, and as a potential confounder of effects related to
residential and individual characteristics, is warranted. Finally, we observed partial confounding of
contextual effects by a measure of traffic-related pollution; more so than by individual or household
characteristics. In study areas where exposure to traffic-related pollution is higher in low SES
neighborhoods, previously reported contextual effects for residence in a low SES area may be
positively biased, and future studies should attempt to control for exposure to such pollution.

This is the first multilevel study to examine the relationship between the social environment and
asthma using a prospective design. Increased risk for incident asthma was associated with schools
characterized by academic failure and poverty, and communities with high rates of larceny crime, and
these effects were not explained by a wide range of individual and household characteristics. These
results indicate that further investigation of the social environment could both elucidate the role of
risk factors such as stress in mediating contextual effects and identify new avenues for disease
prevention (Eder et al. 2006; Persky et al. 2007; Wright and Subramanian 2007).

95
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CHAPTER FIVE

5.1 INTRODUCTION

The goal of this dissertation is to examine interrelationships of the social environment and
psychological stress with asthma in the Children’s Health Study (CHS). The analyses presented in the
preceding chapters produced novel and sometimes unexpected results that provide food for thought,
but also beg some immediate questions. In this final chapter, after summarizing the main results of the
preceding three chapters, several additional analyses are presented that seek to resolve apparent
contradictions in the relationship between areas of low socioeconomic status (SES) and asthma from
Chapters 3 and 4. Following this, the key contributions and limitations of this dissertation are
discussed. Finally, suggestions are made for directions in future research that can build on the results
of this dissertation.

5.2 SUMMARY OF FINDINGS

In Chapter 2, the risk for incident asthma attributable to residential traffic-related pollution was
significantly greater among children with higher parental stress in the second CHS cohort. Effect
modification was not attributable to other individual and household risk factors for asthma, including
parental education, which had a similar but weaker pattern of effect modification for traffic-related
pollution that was confounded by control for the stress interaction. Also, effects on asthma from
another source of reactive oxygen species (ROS), in utero tobacco smoke, were also higher among
children with greater parental stress and lower parental education, although we were not able to
comprehensively test for confounding of these interactions due to small sample sizes. These results
suggest that children with stressful home environments are more susceptible to the effects of certain
environmental toxins on asthma, and that such synergistic effects may be mediated by pathways
related to oxidative stress (Li et al. 2003; Wright et al. 2005).

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In Chapter 3, children from the first CHS cohort living in communities with higher percent male
unemployment had lower risk for lifetime asthma than children living in communities with less
unemployment. This association was not explained by numerous individual and household risk factors
for asthma, or by exposure to air pollution at the home or in the community. The inverse relationship
between community male unemployment and asthma is unexpected and may reflect a negative bias.
In particular, we could not account for lifetime access to care comprehensively in this analysis since
we only adjusted for a dichotomous measure of current medical insurance coverage. Since access to
care may be poorer in communities with high unemployment (Garrett 1997; Gold and Wright 2005),
children with asthma in these areas may be differentially misclassified as non-cases at study baseline.
On the other hand, if children in high unemployment communities were more likely to be exposed to
allergens during early childhood than children in low unemployment communities (e.g. due to
crowding), then they may have developed immunological tolerance that subsequently protected them
from asthma later in life (i.e. the Hygiene Hypothesis) (Ramsey and Celedon 2005; Strachan 1989).
Finally, the protective effect for high percent male unemployment may be driven by Hispanic children
living in communities rich in Hispanic immigrants, since these populations may be less likely to
become asthmatic (i.e. the Latino Paradox) (Cagney et al. 2005; Denner et al. 2001). On a related
note, children from communities with high percent male unemployment who were also Hispanic
immigrants may have had greater exposure to infections during early childhood (i.e. before they
immigrated to Southern California), which suggests that both the Hygiene Hypothesis and the Latino
Paradox could be related to our results.

Finally, in Chapter 4, children from the second CHS cohort residing in communities with higher rates
of larceny crime, or attending schools receiving Title I funding, had increased risk for incident asthma
compared with children in lower crime communities and schools without Title I funding, respectively.
While no individual or household risk factors explained these associations, there was partial positive
confounding of the effect of larceny crime by residential traffic-related pollution, which suggests that

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part of this association is attributable to higher levels of air pollution in communities with high rates
of larceny crime. Since high rates of crime and Title I funding are likely to be present in
disadvantaged populations, these effects may be attributable to unmeasured risk factors for asthma at
the individual-level related to high deprivation that pattern across communities and schools (e.g.
indoor allergens at Title I schools). Alternatively, contextual effects for asthma may be related to
increased chronic stress resulting from exposure to violence in communities with high rates of larceny
crime, or academic difficulties/failure in schools receiving Title I funding.

5.3 CONTRADICTIONS AND ADDITIONAL ANALYSES

Results from Chapters 3 and 4 suggest an inconsistent relationship between areas of low SES and
asthma. Children in the first CHS cohort from communities with high percent male unemployment
were less likely to have lifetime asthma at baseline, and there was suggestion of a protective
association for other measures of community deprivation (i.e. percent poverty and percent low
education). On the other hand, children in the second CHS cohort residing in communities with high
rates of larceny crime were at increased risk for new onset asthma. There was also increased risk for
asthma at schools receiving Title I funding, which is related to reducing academic achievement gaps
for disadvantaged students, and a pattern of positive associations with asthma for two measures of
high community deprivation (i.e. percent poverty and percent male unemployment), although these
effects were small and non-significant.

Several differences between these two analyses may explain conflicting results:

1. Three of the communities from the first CHS cohort were absent from the second cohort,
while four new communities were added to the latter cohort; therefore, factors associated
with unique communities may have driven contradictory results between cohorts.

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2. Children in the first CHS cohort were older at study baseline compared to children in the
second cohort (11.3 versus 6.7 years, respectively). If effects of the social environment on
childhood asthma are dependent on the age of the child, then conflicting associations
between cohorts may be plausible.

3. Study baseline for the first CHS cohort was much earlier than for the second cohort
(1993/1996 versus 2002-2003). Effects of the social environment on asthma may be complex
due to the potential for multiple, overlapping pathways. Therefore, if the relationship
between social characteristics and risk factors for asthma changed in the study areas between
baseline of the two studies, then a period effect may explain conflicting results.

4. The potential for negative bias in Chapter 3 related to community-wide under-diagnosis of
lifetime asthma in low SES communities was previously discussed, and such bias may be
less likely to have occurred in Chapter 4 due to the examination of incident asthma.

A series of additional analyses were conducted in order to examine the possibility that these
differences could explain the inconsistent relationship between areas of low SES and asthma.

5.3.1 Exclusion of unique communities

In order to determine whether communities that were unique to each analysis influenced the direction
of the association between areas of low SES and asthma, the final models from both analyses were
replicated in a sample limited to children residing in communities common to both CHS cohorts (i.e.
Alpine, Lake Elsinore, Lake Arrowhead, Long Beach, Mira Loma, Riverside, San Dimas, Santa Maria
and Upland). As a result, subjects from Lancaster, Lompoc and Atascadero were excluded from the
first cohort, while subjects from Santa Barbara, Glendora, Anaheim and San Bernardino were
excluded from the second CHS cohort.

104

In the first CHS cohort, the effect of percent male unemployment on lifetime asthma in children
residing in communities common to both cohorts remained negative but was reduced towards the null
and became non-significant compared with the original effect (OR 0.96, 95%CI 0.81-1.14); effects for
other measures of deprivation centered around 1.0 and were highly non-significant (p > 0.85; data not
shown). This is not unexpected since Atascadero, which was previously identified as a point of
influence in the association between percent male unemployment and lifetime asthma (see Figure
2.2), was excluded from this analysis. In fact, if only children from Lancaster and Lompoc were
excluded, the original effect of percent male unemployment was largely unchanged (OR 0.88, 95% CI
0.78-0.99).

Limiting the analysis of incident asthma in the second CHS cohort to the sample of common
communities produced mixed results, although effects were relatively robust compared with the
results from the first cohort. The association with asthma for the larceny crime rate remained similar
to the original effect (HR 1.22, 95% CI 1.00-1.49), while the effect for Title I funding was reduced by
about 24% and became non-significant (HR 1.47, 95% CI 0.89-2.41). Hazard ratios for measures of
deprivation on incident asthma remained slightly positive and non-significant (data not shown).

These results suggest that factors associated with Atascadero, a community that was unique to the first
CHS cohort, were partly responsible for the conflicting association observed between areas of low
SES and asthma.

5.3.2 Comparison of associations with lifetime and incident asthma in both CHS cohorts

Additional analyses were undertaken in order to directly compare the relationship between areas of
low SES and asthma across both age groups and study periods using the same study design. First, the
effect of community deprivation measures (i.e. percent low education, percent poverty, percent male

105
unemployment and median household income) on incident asthma in the first CHS cohort was
examined. Second, the effects of community deprivation and larceny crime, as well as Title I funding
at schools, on lifetime asthma was examined in the second CHS cohort.

Associations between measures of community deprivation and incident asthma in the first CHS cohort
were measured in 2966 children with no history of asthma or wheeze and valid baseline
questionnaires with non-missing data about age, gender and race. New onset asthma was measured by
self-report of doctor-diagnosed asthma on an annual questionnaire over 11 years of longitudinal data.
Measures of deprivation in the 12 study communities were obtained from the U.S. 1990 Census and
calculated using the 95% minimum bounding rectangle method, as described in Chapter 3.

Analytic methods were similar to those used in Chapter 4. In brief, contextual effects for asthma onset
were assessed using multilevel Cox proportional hazards models. All models contained age and
gender stratification of the baseline hazard, adjustment for race and ethnicity, and random effects for
community. Analyses were conducted using software designed to run within R for implementing
random effects Cox proportional hazards models (Jerrett et al. 2005; Ma et al. 2003). Effects for
community deprivation were adjusted for potential confounders in three models. Model 1 was
adjusted simply for the race and ethnicity of children. Model 2 was further adjusted for 17 potential
risk factors for asthma as in (Peters et al. 1999) (see footnote in Table 5.1 for a complete list). Finally,
a parsimonious model (Model 3) was derived by including only variables that changed the main effect
for community percent poverty by at least 10%. Percent poverty was used to develop the
parsimonious model since it had a borderline significant association with asthma in Model 1.

Across all four models, there was a consistently positive association between high community
poverty, and high percent male unemployment, with incident asthma, and these associations were
borderline significant in Models 1 and 3 (Table 5.1). Associations with median income and percent
low education in the community changed direction across models and never approached statistical

106
significance. These effects were robust to adjustment for measures of residential distance to major
roadway.

Table 5.1 Associations between new onset asthma and community socioeconomic parameters for CHS
children in the first CHS cohort, with adjustment for individual covariates
Model 1
a
(n=2966) Model 2
b
(n=2144) Model 3
c
(n=2907) Community socioeconomic
parameter
e
HR (95% CI)
f
HR (95% CI)
f
HR (95% CI)
f

Median income (per $10,000) 0.91 (0.76-1.10) 1.07 (0.89-1.28) 0.91 (0.76-1.09)
Below poverty (%) 1.30 (0.99-1.71) 1.10 (0.79-1.51) 1.31 (1.00-1.71)
Male unemployment (%) 1.19 (0.94-1.50) 1.09 (0.86-1.38) 1.20 (0.96-1.52)
Low education (%) 1.00 (0.77-1.29) 0.93 (0.73-1.18) 1.01 (0.78-1.30)
a
Model 1 adjusted for race/ethnicity, with baseline hazards stratified by age and gender, and random
effects for community.
b
Model 2 adjusted as in model 1 plus parental education, medical insurance coverage, any smokers in
the home, maternal smoking during pregnancy, pest in home, pets in home, air conditioning in home,
gas stove in home, water damage in home, mildew in home, plants in home, carpet in child’s room, hay
fever in child, parental history of asthma, body mass index, height, and regular vitamin use.
c
Model 3 adjusted for race/ethnicity and medical insurance coverage, with baseline hazards stratified
by age and gender, and random effects for community.
e
Community socioeconomic parameters are based on data from the U.S. 1990 Census. Low education
is defined as no high school diploma among the population over 25 years of age.
f
Hazard ratio and 95% confidence interval. Estimates are scaled across the interquartile range (IQR)
of parameters. Median income IQR = $6,947; Below poverty IQR = 5.13%; Male unemployment IQR
= 1.29%; and Low education IQR = 7.54%.

Associations with lifetime asthma at study baseline of the second CHS cohort were measured in 4922
children with valid baseline questionnaires and information about lifetime asthma, age, gender and
race. Lifetime asthma at study baseline was assessed by parental report of doctor-diagnosed lifetime
asthma, while the measurement of the community and school social environment followed the same
methods as described in Chapter 4.

In measuring associations between the social environment and lifetime asthma, attempts were made to
replicate the analytic methods used in Chapter 3 as closely as possible. Confounding of contextual
effects by individual-level covariates was examined in three models featuring different levels of
adjustment. In Model 1, the main effects of community and school characteristics were examined in a
model featuring adjustment for only key demographic variables, age, race/ethnicity and gender.
Model 2 featuring adjustment for all covariates originally examined by (Peters et al. 1999) that

107
were available for children in cohort E was tested. This included variables describing parental
education, medical insurance coverage, air conditioning in home, gas stove in home, carpet in child’s
room, parental history of asthma, any smokers in the home, pest in home, pets in home, body mass
index, height, water damage in home and mildew in home. Variables describing vitamin use and
plants in the home were not available for this cohort, while a variable describing the subject’s history
of allergies was substituted for one describing a history of hay fever. Finally, a parsimonious model
(Model 3) was derived with a backwards stepwise regression using a cut off of p=0.10 with attempts
to re-enter variables excluded during the process. All covariates from the Model 2 were included and
key demographic variables, as well as parental education, were forced to remain in the final model.
The parsimonious model included the aforementioned forced variables along with adjustment for
family history of asthma, allergies in the child and body mass index. All associations were examined
using a hierarchical logistic regression model with community random effects fitted in SAS using the
GLIMMIX procedure (SAS Institute Inc. 2002) with a binomial distribution assumption and a logit
link.

Table 5.2 presents associations between these community- and school-level characteristics with
lifetime asthma across the three levels of adjustment discussed above. The association with the
larceny crime rate was slightly positive but non-significant across all levels of adjustment (Model 1
HR 1.04, 95% CI 0.93-1.17), while the effect for Title I funding was slightly negative or at 1.0 and
also non-significant across all models (Model 1 HR 0.95, 95% CI 0.80-1.12). All measures of
deprivation, except for percent male unemployment, had a negative association with lifetime asthma.
While none of these associations were statistically significant, there was a borderline significant effect
for median income in model 1 (p=0.10), which was reduced in size and significance across subsequent
models. Associations with asthma for percent male unemployment fluctuated around 1.0 with
different levels of adjustment; replacing female and total unemployment for male unemployment did
not change this pattern.

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Table 5.2 Associations between lifetime asthma and community socioeconomic parameters for
children in cohort E, with adjustment for individual covariates
Model 1
a

(n=4922)
Model 2
b

(n=3252)
Model 3
c

(n=3323)
Level Socioeconomic parameter OR (95% CI)
d
OR (95% CI)
d
OR (95% CI)
d

Community Median income (per $10,000) 1.05 (0.88-1.26) 1.09 (0.92-1.31) 1.08 (0.90-1.29)
Below poverty (%) 0.99 (0.87-1.12) 0.98 (0.86-1.11) 0.99 (0.87-1.13)
Male unemployment (%) 1.02 (0.91-1.14) 0.99 (0.88-1.10) 1.00 (0.89-1.12)
Low education (%) 0.85 (0.70-1.03) 0.90 (0.72-1.11) 0.92 (0.75-1.15)
Larceny crime rate (per 100,000) 1.04 (0.93-1.17) 1.04 (0.93-1.17) 1.05 (0.93-1.18)
School Title I funding 0.95 (0.80-1.12) 1.00 (0.79-1.26) 0.98 (0.79-1.22)
a
Model 1 adjusted for community, age, race and gender.
b
Model 2 adjusted as in model 1 plus parental education, medical insurance coverage, air conditioning in
home, gas stove in home, carpet in child’s room, parental history of asthma, any smokers in the home,
allergies in child, pest in home, pets in home, body mass index, height, water damage in home and mildew
in home.
c
Model 3 adjusted for community, age, African American race, gender, parental education, family history of
asthma, allergies in child and body mass index.
d
Odds ratio and 95% confidence interval. Estimates are across the interquartile range (IQR) of parameters.
Median income IQR = $12,678; Below poverty IQR = 6.78%; Male unemployment IQR = 1.96%; Low
education IQR = 17.83%; and Larceny crime rate IQR = 570 per 100,000.

A third of the children in Model 1 were excluded from the parsimonious model due to missing data. In
order to determine whether the reduction in the protective effect of percent low education was
attributable to the exclusion of these children, a sensitivity analysis was performed where missing
children were included into the parsimonious model using the missing indicator method (White and
Thompson 2005); however, the association with percent low education remained similar (OR 0.98,
95%CI 0.84-1.16)

In summary, in the first CHS cohort we observed a protective association with lifetime asthma for
high percent male unemployment in the community (Chapter 3), but there was a suggestion that
communities with high percent male unemployment and high percent poverty were positively
associated with incident asthma. Similarly, while there were strong positive hazard ratios for effects
of the community larceny rate and school Title I funding with incident asthma in the second CHS
cohort, these effects were centered around zero and non-significant when examined with lifetime
asthma. Also, effects for deprivation measures were highly non-significant and close to zero for

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incident asthma in this cohort, although there was a suggestion that percent low education in the
community was protective for lifetime asthma. These results do not suggest that the negative
association between high percent male unemployment and lifetime asthma reported in Chapter 3 was
driven by an older age at baseline or earlier period of cohorts A-D. Instead, these results suggest that
there is a positive association between areas of low SES and incident asthma that is negatively biased
in the context of cross-sectional comparisons. Therefore, the difference in study design may have
contributed to the conflicting results in Chapters 3 and 4.

5.3.3 Conclusions

In total, these additional analyses suggest that, while Atascadero was certainly influential in creating
the significant negative association between high community unemployment and asthma in Chapter 3,
there is also evidence of a general pattern of negative bias in cross-sectional versus prospective
analyses. Such negative bias could be attributable to the examination of prevalent rather than incident
asthma, for example, if there was under-diagnosis of asthma prior to study baseline in low SES
communities. In order to be applicable to results from both study cohorts, such community under-
diagnosis would have to be persistent across both periods, and affect children from both age groups.

The strong influence of Atascadero could be explained by a separate negative bias that is also related
to differences in access to care, and may have occurred at the same time as under-diagnosis of asthma
in communities of higher unemployment. Evidence suggests that children referred to tertiary care
clinics with a diagnosis of asthma on the basis of cough or wheeze are likely to be over-diagnosed
(Thomson et al. 2002). Therefore, if children in Atascadero had more comprehensive access to
medical care, they may actually have been over-diagnosed for asthma at some point in their lifetime.
For example, Atascadero is a small community (population 26,411, according to 2000 U.S. Census)
where the largest employer is the Atascadero State Hospital, who offer comprehensive health care
benefits to all employees and their families (California Department of Mental Health 2008).

110
Therefore, subjects from this community may have been more likely to have comprehensive access to
care than subjects from other communities. Alternatively, there may be other unmeasured factors
unique to this community that increased risk for lifetime asthma at baseline of the first CHS cohort.

In general, these results suggest that the limitations of using cross-sectional study designs may
obscure the real association between characteristics of the social environment and asthma. Although it
offers a convenient approach, future studies in this field should use this design cautiously, and
carefully consider the possibility of spurious associations.

5.3.4 Epilogue 1: Undetected asthma in low SES communities of the second CHS cohort

Negative bias due to lower access to medical care and subsequent under-diagnosis of lifetime asthma
in low SES communities has already been discussed as a reason for the protective effect of low
community SES in Chapter 3. If such under-diagnosis is, in fact, occurring across both study periods,
as follow-up analyses in this chapter suggest, then we must question whether the positive association
between the larceny crime rate and incident asthma in Chapter 4 has been positively biased by
subjects in low SES communities with undetected asthma at baseline. This concern has already been
addressed, to some extent, by the exclusion of wheezers, who may be more likely to have
undiagnosed asthma, at study baseline from the analysis in Chapter 4. However, in a sensitivity
analysis, the final contextual effects model was re-run excluding 26 cases of asthma occurring within
the first 18 months of baseline, since these cases would be the most likely to be undetected prior to
baseline. In this analysis, the first 18 months of follow-up were excluded for all remaining subjects. In
the restricted sample, effects for the community larceny crime rate (HR 1.34, 95%CI 1.10-1.64) and
Title I funding at school (HR 1.57, 95%CI 1.04-2.39) remained strong predictors of new onset
asthma. Therefore, undetected asthma among subjects from low SES communities does not appear to
have biased the association between the larceny crime rate and incident asthma in Chapter 4.
5.3.5 Epilogue 2: Deprivation versus social capital

111

While follow-up analyses suggest that there may actually be a consistently positive relationship
between low area SES and new onset asthma across both CHS cohorts, it is interesting that effects are
related to different characteristics of the community social environment in each cohort. In particular,
in the first CHS cohort, there was a suggestion of a positive association with incident asthma for two
measures of community deprivation: percent male unemployment and percent poverty. On the other
hand, children living in communities with higher larceny crime rates, which are normally used to
proxy for social capital, were at higher risk for incident asthma in the second CHS cohort. Further,
effects for measures of community deprivation were only slightly positive and highly non-significant
in the second CHS cohort, and further analyses suggests that there is a protective, although non-
significant, hazard ratio for the community larceny crime rate in the first cohort (HR 0.88 across an
IQR of 2199 per 100,000; 95% CI 0.65-1.21).

As outlined in Figures 1.1 and 1.4, there is a great deal of overlap in the downstream factors that may
be influenced by deprivation and social capital in the community. Therefore, while it is possible that
effects of the social environment on the incidence of asthma may be mediated by different biological
pathways in the two CHS cohorts (e.g. deprivation may be a marker for increased exposure to
allergens, while social capital may be a proxy for increased chronic stress), it is also plausible that
both of these effects are related to the same compositional or mediating factors, such as increased
chronic stress in children, but that the definition of a stressful environment changed due to differences
in the social, economic and structural context of the period. Greater field knowledge of the
communities involved in the CHS may be required in order to understand why different
characteristics of the social environment have similar positive associations with asthma across the two
cohorts. For example, there may be community differences in the prevalence of street gangs in
Southern California, and interactions with gangs may be a source of stress for children.

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5.4 CONTRIBUTIONS TO KNOWLEDGE

The positive interaction between parental stress and exposure to traffic-related air pollution for
incident asthma (Chapter 2) is consistent with growing evidence that chronic stress increases
susceptibility to the effects of air pollution on asthma (Clougherty et al. 2007; Marshall and Agarwal
2000; Wright et al. 1998). As the first paper to report this interaction for incident asthma, it adds
plausibility for biological mechanisms related to synergistic effects of chronic stress and pollution,
which are poorly understood at present. Further, the finding of an interaction between parental stress
with another source of reactive oxygen species (ROS), in utero tobacco smoke, suggests that it may be
particularly important to examine the role of oxidative stress pathways more closely (Bowler and
Crapo 2002; Dworski 2000; Henricks and Nijkamp 2001; Li et al. 2003; Nel 2005; Nel et al. 2001).
Finally, modification of the effects of traffic-related pollution on asthma by parental stress appeared to
explain a similar non-significant pattern of modification parental education, which suggests that
socioeconomic disparities in asthma, at the individual- and population-level, may be partly related to
the experience of stress.

While areas of low SES are often reported to have higher prevalence of asthma (Gupta et al. 2008;
Pearlman et al. 2006; Weinmayr et al. 2007), such disparities may reflect a coincident pattern in
compositional factors. In Chapter 4, elevated risk for incident asthma in communities with higher
rates of larceny crime and schools receiving Title I funding was found after controlling for a wide
range of individual and household risk factors, as well as school and community random effects.
Similarly, examination of associations with new onset asthma in another CHS cohort indicated
positive contextual effects for two measures of community deprivation (i.e. percent male
unemployment and percent poverty) (Chapter 5). These findings are the first to demonstrate a direct
relationship between areas of low SES and asthma using a prospective design. Because areas
characterized by high deprivation and low social capital may plausibly introduce stressors to a child’s
life (Altschuler et al. 2004; Evans and Kantrowitz 2002; Siegrist 2000; Taylor et al. 1997; Wright

113
2006), these results support theories that contextual effects of areas of low SES may be mediated by
chronic stress (Chen et al. 2003; Chen et al. 2006; Ford and McCaffrey 2006; Wright 2006; Wright
and Subramanian 2007).

The negative association between communities of high male unemployment and lifetime asthma in
Chapter 3 provide support to interesting but controversial theories about the etiology of asthma,
including the Hygiene Hypothesis (Borchers et al. 2005; Ramsey and Celedon 2005; Strachan 1989),
and the Latino Paradox (Cagney et al. 2005). However, further examination of the effect of areas of
low SES on lifetime and new onset asthma in both CHS cohorts suggests that there may have been a
negative bias in cross-sectional versus prospective analyses, and that this bias may be strong enough
to produce null, or even protective associations. Since the bulk of existing studies that examine this
relationship have been carried out using a cross-sectional study design (Basagana et al. 2004; Cagney
and Browning 2004; Litonjua et al. 1999; Nepomnyaschy and Reichman 2006; Saha et al. 2005;
Salmond et al. 1999), evidence of such a bias may help explain inconsistencies in the literature about
the direction of the association between area SES and asthma. In our case, negative bias may be
related to under-diagnosis of asthma in areas of low SES resulting from poorer access to health care
over the lifetime. Given the heterogeneity in quality and continuity of care in the United States
(Chevarley et al. 2006), such bias may affect other similar studies from this setting.

The size of the effect for rates of larceny crime in the community on incident asthma was partly
positively confounded by residential exposure to traffic-related pollution. Such confounding was not
unexpected since growing evidence suggests that traffic-related pollution causes asthma (Brauer et al.
2007; McConnell et al. 2002; McConnell et al. 2006; McConnell et al. 2007; Sarnat and Holguin
2007; Shima et al. 2002), and previous research highlights that areas of low SES may experience
higher rates of pollution related to traffic than other areas in Southern California (Houston et al. 2004;
Morello-Frosch and Lopez 2006). A similar relationship between areas of low SES and exposure to
pollution is found elsewhere (Evans and Kantrowitz 2002), including places typically considered to be

114
egalitarian (Chaix et al. 2006); our results suggest that it is important to control for exposure to traffic-
related pollution when examining effects of the social environment in order to increase the accuracy
of these effects and prevent false positive associations.

We found increased risk for asthma in schools receiving Title I funding, which may be a proxy for
schools with high levels of deprivation or stress related to academic failure. This was the first
multilevel study to examine the association between the school social environment and asthma.
Because children spend a significant portion of their lives at schools, effects related to Title I funding
represent an important contribution to the literature about socioeconomic disparities in childhood
asthma.

Finally, the results of this dissertation can be applied to the design of public health interventions in
communities and schools that aim to reduce social disparities in childhood asthma (Gehlert et al.
2008). Traffic-related air pollution has been previously shown to increase risk for new onset asthma
(McConnell et al. 2007), and Chapter 4 suggests that higher exposure to these toxins explains part of
the increased risk attributable to areas of high larceny crime. Also, the effect of traffic-related
pollution were increased in subjects likely to have chronic stress (Chapter 2), and low SES
populations may have more stressful lifestyles or reside in more stressful environments. Therefore, by
reducing the amount of traffic-related pollutants that are released, we may reduce social disparities in
asthma. While the mediators of effects related to Title I funding at schools may be unclear at present,
children spend a large proportion of their lives in schools, and may be especially vulnerable to hazards
in this environment since they are away from the supervision of their parents. Thus, schools may
represent an important and convenient level of intervention for reducing asthma disparities in
children.

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5.5 LIMITATIONS

Moderation of the effect of traffic-related pollution on asthma by parental stress was borderline
significant in Chapter 2. Power to detect this interaction may have been reduced by non-differential
measurement error attributable to unmeasured in differences in the impact of household stress on
children. In general, exposure to a stressful home environment does not mean that a child will
certainly experience stress. Differences in how children cope with stressors, and biologically buffer
the effects of acute and chronic stress (Taylor et al. 1997), may have resulted in non-differential
measurement error of stress, so children with higher levels of household stress may actually be more
susceptible to the effects of traffic-related pollution on asthma than our results suggest.

Evidence that higher levels of parental stress, or lower levels of parental education, may increase
susceptibility to the effects of in utero tobacco smoke on asthma was based on small cell sizes, so
effects related to these interactions may be unreliable. On a related note, we were not able to properly
test for confounding by other risk factors for asthma, so increased susceptibility to the effect of in
utero tobacco smoke exposure on asthma may be related to other factors associated with high parental
stress and low parental education (e.g. cockroaches in the home).

Increased risk for asthma in communities with high rates of larceny crime and in schools receiving
Title I funding (Chapter 4) was not explained by a wide range of compositional factors. Pathways
related to chronic stress could plausibly mediate such effects on asthma, yet we did not observe a
reduction in the size of contextual effects with adjustment for parental stress or the synergistic effects
of traffic-related pollution and parental stress. However, the ability to properly examine such
mediation was limited since the measure of parental stress tested is not necessarily an accurate proxy
for the stress experienced by children in schools and in the wider community.

116
Measures of deprivation in the neighborhood and community were based on definitions from the U.S.
Census Bureau, which may be arbitrary with respect to effects on health (Tatalovich et al. 2006).
Further, because the Southern California region is generally characterized by a higher standard of
living than other parts of the United States (Median household income in 2000, $49,078; U.S.
average, $43,396) (Posey et al. 2003), census definitions of deprivation, such as the percent of
residents living below the poverty lines, may not effectively differentiate underprivileged areas from
areas of higher SES. In general, effects related to incident asthma for measures of deprivation in the
two CHS cohorts (i.e. Chapters 4 and 5) may be biased negatively, towards the null, due to non-
differential measurement error. In particular, since there has been an increasing trend of income
inequality in recent decades (Nielsen et al. 2005), measures of deprivation may be particularly
inadequate for the period covering the second CHS cohort since it was based on data from the 2000
U.S. Census. The relative inadequacy of deprivation measures may be an alternative explanation for
why deprivation was not as important a risk factor for incident asthma in second CHS cohort
compared with the first cohort.

5.6 FUTURE DIRECTIONS

Evidence of a synergistic relationship between parental stress and traffic-related pollution (Chapter 2)
suggests that stress plays a role in the development of asthma in children. Further, given contextual
effects for potentially stressful environments, such as community with high crime and schools
attempting to improve academic achievement (Chapter 4), this synergy may help explain global
socioeconomic disparities in asthma. These results have important implications for future research
about the relationship between the social environment, stress and asthma; however, limitations
identified in the previous section also highlight the need for additional analyses to confirm certain
aspects of these results.

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Studies are needed to verify that the positive interaction between parental stress and traffic-related
pollution is related to increased stress in children. Using a survey based measure of perceived stress
alone may not account for differences in coping (Taylor et al. 2007), or in biological responses to
stress (e.g. resulting from epigenetic programming) (Weaver 2007), therefore a biological measure of
stress, such as salivary cortisol or corticotrophin-releasing hormone, should also be collected to
validate this finding (Kelly et al. 1997). The interaction found between parental stress and in utero
tobacco smoke is also important because it indicates that immunological mechanisms related to the
detoxification of products of oxidative stress may underlie stress-related susceptibility (Mak and
Chan-Yeung 2006; Wright et al. 2005). Because this interaction was observed in a sample with a low
prevalence of in utero tobacco smoke exposure, comprehensive examination of potential confounding
by factors related to stress was not possible. Therefore, future studies should also re-examine
interactions between stress and in utero tobacco smoke exposure in a population with higher
prevalence of in utero exposure, but also examine synergistic effects of stress with other sources of
reactive oxygen species (ROS) that may be risk factors for asthma, such as personal smoking in
teenagers (Floreani and Rennard 1999).

The contextual effects found in Chapter 4 were not mediated by the synergistic effects of parental
stress and traffic-related pollution. This may indicate that parental stress is a poor measure of the
stress that children experience related to academic failure and exposure to violence. Following up
these results in a new study may be costly and time consuming considering the types of data that
would be required, including biological measures of stress, finely resolved estimates of exposure to
traffic-related pollution and measures of the social environment. Instead, this analysis could be
conveniently replicated in the current CHS cohort. For example, survey-based and biological
measures of stress could be collected in a sample of children without asthma or wheeze. Risk for new
onset asthma attributable to traffic-related pollution, modification of the effect of traffic and the
presence of contextual effects could then be re-examined. Assuming that these effects were
confirmed, mediation of contextual effects by stress could then be tested properly. On the other hand,

118
case-control and other cross-sectional study designs may not be as well suited for this type of analysis
since asthma may increase stress in children (Lewis and Vitulano 2003; Mrazek 2003).

Even within the dataset that was used for the analysis in Chapter 4, there may be ways to further
examine whether childhood stress plays a role in mediating contextual effects on asthma. One
approach would be to adopt a sociological perspective in classifying children with respect to their
likelihood for chronic stress. Because children of low SES may face more stressors, and have fewer
options for coping with those stressors (Wright and Subramanian 2007) than children of high SES, an
index of stress could be created using data about parental stress, parental education and neighborhood
deprivation. For example, we may expect children with high parental stress, low parental education,
and who live in neighborhoods with high deprivation (e.g. percent poverty) to be more likely to
experience stressors, and less likely to effectively cope with these events, than children with low
parental stress, high SES and from neighborhoods with low deprivation. Alternatively, theories of
social comparison imply that measures of individual SES may reflect chronic stress if they are
centered on the wider socioeconomic context (Singh-Manoux et al. 2003; Taylor et al. 1990;
Wilkinson 1999). For example, children of low SES within neighborhoods or schools of high SES
may be more likely to experience stress related to their social dissimilarity and disadvantage than
children of low SES who are from low SES neighborhoods or schools. Similarly, children of high
SES who live in low SES neighborhoods or attend low SES schools may be expected to derive some
stress form their dissimilarity, although they may also be expected to have more resources to cope
with such stress than children of low SES.

Future studies about the relationship between the social environment, stress and asthma could also
benefit from improved measures of areas that may be stressful. The ecological nature of contextual
effects means that not all individuals may be affected by their environment in the same way. Further,
the harmful or protective nature of certain measures of the social environment can depend on the
wider social, economic and structural context (Cagney et al. 2005). Therefore, by improving our

119
measurement of stressful environments, we may reduce the potential for measurement error related to
the ecological fallacy, thereby increasing the power to detect mediation by individual-level stress.
Several authors have already suggested theories for how the environment can create stress for
populations (Siegrist 2000; Taylor et al. 1997; Wright 2006; Wright and Fischer 2003). Future studies
may also examine indicators of the built environment, which may be related to the social environment
and lead to stress, but are missing from this dissertation (e.g. green cover, infrastructural maintenance,
condition of public facilities) (Evans 2003; Galea et al. 2005). Contextual effects for Title I funding
also suggest a need to better measure stress in schools, possibly related to academic difficulties or
delinquency. We may also learn more about stressful environments using an empirical approach. For
example, we could measure stress in children across different neighborhoods, and then examine
correlations of stress levels in these areas (e.g. by averaging individual-level measures) with a series
of measures of the social environment. Geographic information systems could also then be used to
detect clustering in neighborhood stress in order to explore the potential scale of stressful
environments.

Finally, the failure of the interaction between parental stress and traffic-related pollution to mediate
contextual effects in Chapter 4 may also indicate that unmeasured risk factors for asthma, possibly
related to areas of high deprivation (e.g. indoor allergens) (Gold and Wright 2005), or contextual
effects unrelated to stress (e.g. crime as a measure of low social capital and poor health promotion)
(Berkman 1995; Kawachi 1999), are important to examine in future studies. In particular, we were
able to adjust for a range of important risk factors for asthma from the indoor household environment,
but had no measures of the physical environment inside of schools. The role of schools in explaining
socioeconomic disparities in childhood asthma is understudied, although the presence of allergens that
are risk factors for asthma has been identified in some schools and may be related to the age and
construction of these buildings (Frumkin et al. 2006; Tranter 2005). Given that schools are a focal
point of recruitment for the CHS, further examination of disparities in asthma across schools may be
well suited to this ongoing study.

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APPENDIX

GEOGRAPHIC VARIATION IN ASTHMA INCIDENCE ACROSS COMMUNITIES AND
SCHOOLS IN THE SECOND CHS COHORT

A.1 Spatial autocorrelation in asthma incidence rates

Spatial clustering in asthma incidence rates across communities and schools may indicate that there
were factors operating at broader spatial scales broader driving population disparities in asthma, such
as regional weather patterns or county-level policies. Therefore, tests for spatial autocorrelation were
performed in order to determine whether there was any clustering in community or school crude
asthma incidence rates, or in rates adjusted for age, gender, race and ethnicity that was subsequently
explained by adjustment for contextual effects. The location of schools was geocoded using GPS
measurements, and the geographic location of communities was determined based on the geometric
center of the 95% minimum bounding rectangle used to calculate community-level variables. Spatial
autocorrelation of community and school asthma rates was examined using Moran’s I statistic, which
estimated the strength of correlation between these observations as a function of the distance
separating them (Anselin 1995), using ArcGIS 9.2 software (ESRI Corp., Redlands, California, USA).
There was no indication of spatial autocorrelation in incidence rates across communities or schools,
regardless of adjustment for covariates (data not shown).

A.2 Relative magnitude of contextual effects for asthma

Chapter four highlights positive associations between community larceny crime rates and Title I
funding at schools with incident asthma in the second CHS cohort. After stratifying the baseline
hazards by age and gender, adjusting for race and ethnicity, and accounting for community and school
random effects, there was a 31% increase in risk for asthma across the interquartile range of 570

139
incidents of larceny crime per 100,000 population, and a 71% increase in risk in subjects attending
schools receiving Title I funding compared to those at school without funding. In order to understand
the importance of these effects, we can estimate the amount of asthma incidence explained by these
effects, and compare them to incidence explained by traditional risk factors for asthma.

After accounting for the age, gender, race and ethnicity of subjects, the asthma incidence rate ranged
from 5.8 to 20.1 per 1000 person-years across the 13 study communities; assuming that the
relationship between the larceny crime rate and incident asthma is linear, this suggests that across the
interquartile range of the larceny crime rate there was a 4.4 per 1000 person-years increase in the
incidence rate of asthma. There was greater variation in asthma incidence across the 45 schools in the
analysis; adjusted incidence rates ranged from 0 (due to no cases in three schools) to 68.1 per 1000
person-years. Among schools receiving Title I funding, the adjusted asthma incidence rate was 15.6
per 1000 person-years, while the adjusted incidence rate was 10.8 per 1000 person-years in schools
without such funding, which suggests that Title I funding explained an increase of 4.8 per 1000
person-years in the incidence rate. In comparison, exposure across the interquartile range of traffic-
related pollution exposure at the home (i.e. 8 parts per billion) was related to an increase in the
adjusted asthma incidence rate of about 27.5 per 1000 person-years, while African-American race was
associated with a 18.1 per 1000 person-years increase in this rate. Therefore, the contextual effects in
this analysis explained a small amount of variation in asthma compared with traditional risk factors
for the disease.

Although the contextual effects in this analysis were statistically significant, there were large,
overlapping confidence intervals for crude and adjusted incidence rates across communities and
schools, and tests for heterogeneity using the chi-square statistic did not find significant variation
across both spatial levels (data not shown). Although there appears to be wide variation in incidence
rates across communities and schools, small sample sizes within these spatial units may have limited
the power to differentiate these rates. This suggests that larger study populations may be required to

140
adequately examine such contextual effects, and effects related to the larceny crime rate and Title I
funding in this analysis should be interpreted with care.

141
Appendix References

Anselin, L. 1995. Local indicators of spatial association. Geog Anal 27:93-115.

Abstract (if available)

Abstract Childhood asthma prevalence is often higher in areas of low socioeconomic status (SES), but such disparities are not explained by individual-level risk factors. Unexplained geographic variation in asthma may reflect an incomplete understanding of asthma etiology. In particular, there is growing interest in the role of chronic stress in the development of asthma. A series of analyses examined associations of asthma with areas of low SES and an indicator of stress using data from two cohorts of the Children 's Health Study based in Southern California. In the first analysis, children with higher parental stress were significantly more susceptible to the effects of two sources of reactive oxygen species, traffic-related pollution and in utero tobacco smoke exposure, on incident asthma, which suggests that oxidative stress pathways may be important for the development of asthma. In the second analysis, children from communities with high male unemployment were 12% less likely to report lifetime asthma after adjusting for individual-level risk factors. However, additional analyses in this same study population suggested that children from communities with high male unemployment and poverty may be at increased risk for incident asthma, indicating that results from cross-sectional versus prospective studies may be less accurate and/or negatively biased.

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Creator Shankardass, Ketan (author)

Core Title Effects of stress and the social environment on childhood asthma in the children' s health study

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Epidemiology

Publication Date 05/01/2008

Defense Date 03/14/2008

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

Tag Air pollution,childhood asthma,chronic stress,contextual effects,multilevel models,OAI-PMH Harvest,socioeconomic status

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