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Association of traffic-related pollution and stress on childhood lung function

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Association of traffic-related pollution and stress on childhood lung function

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ASSOCIATION OF TRAFFIC-RELATED POLLUTION AND STRESS ON
CHILDHOOD LUNG FUNCTION

by

Robert Urman

A Thesis Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(APPLIED BIOSTATISTICS AND EPIDEMIOLOGY)

December 2010

Copyright 2010 Robert Urman
ii
Table of Contents

List of Tables iii

Abstract iv

Chapter 1: Introduction 1

Chapter 2: Methods 5
Study Subjects 5
Questionnaire 6
Lung Function 7
Exposure Assessment 8
Statistical Methods 11

Chapter 3: Results 15
Main Effect of TRP 15
Influence of Parental Stress 23

Chapter 4: Discussion 31

Chapter 5: Conclusion 38

References 39

iii

List of Tables

Table 1: Sample size of total active participants in cohort 5

Table 2: Correlation of CALINE4 predicted TRP 10

Table 3: Parameter estimates of base models 13

Table 4: Subject characteristics during time of the test 16

Table 5: Gender specific lung function by town 18

Table 6: TRP characteristics 19

Table 7: Association between lung function and distance to various
road types 20

Table 8: Association between lung function and modeled-based TRP 22

Table 9: Differences in TRP effect on FEV
1
by gender 24

Table 10: Differences in TRP effect on FVC by gender 25

Table 11: Subject characteristics at baseline and their relationship to
Parental Stress Score 26

Table 12: Differences in TRP effect on FEV
1
by stress 28

Table 13: Differences in TRP effect on FVC by stress 30

iv

Abstract

Background: Studies have shown that decreased lung function is predictive of
various health outcomes including asthma, heart disease, and death. Although there are
some inconsistencies in the literature, many studies have shown that exposure to various
types of air pollutants negatively impact lung growth in children and adults. Furthermore,
there is some evidence that social stress may also be of importance in the understanding
of respiratory health; however, no studies have explored the joint relationship between
lung function, air pollution, and stress. The aims of this study are to further explore the
relationship between childhood lung function and air pollution, specifically traffic-related
pollutants (TRPs), and to assess the potential modifying effects of parental stress.
Methods: A total of 1,811 children from a new southern Californian Children’s
Health Study cohort participated in lung function testing during the sixth year of the
study. Forced vital capacity (FVC), forced expiratory volume during the first second
(FEV
1
), maximal mid-expiratory flow (MMEF) and peak expiratory flow rates (PEFR)
were measured at each of the 44 participating schools. Various metrics of TRP were used
including distance to road and two different types of model-based estimates of pollutant
concentration. The first set of modeled TRP estimates were based on the CALINE4 line-
source air-quality dispersion model, which took into account local traffic information and
meteorological conditions. The other set of TRP are predicted residential exposures of
NO
2
, NO, and NO
x
that incorporated population demographics and topography in
v
addition to local traffic information. Parental perceived stress and other covariates were
collected through questionnaire.
Results: Close proximity to a major road was found to be negatively associated
with both FEV
1
and FVC, while close proximity to a freeway was negatively associated
with only FVC. Among all children, no observed associations were found with CALINE4
predicted exposures. However with respect to the 10
th
-90
th
percent distribution, predicted
residential NO was associated with a 1.4% deficit in FEV
1
and a 2% deficit in FVC (per
7.6 ppb). Similarly, predicted residential NO
x
was associated with a 1.3% deficit in FEV
1

and a 1.9% deficit in FVC (per 21.9 ppb), while residential NO
2
was associated with a
1.7% deficit in FVC (per 14.5 ppb). The impact of various TRPs was more pronounced
among children whose parents were classified as being highly stressed. Over the same
distribution of predicted residential exposures to NO, NO
x
, and NO
2
, these children
experienced a 3.3%, 3.6%, and 3.6% deficit in FEV
1,
respectively, while for children in
lower stress homes, these pollutants had little impact. Similar findings were also observed
with respect to the non-freeway component of CALINE4-modeled TRP and distance to a
major road as well as when FVC was chosen as the outcome. Effect modification by
parental stress was statistically significant in all of these examples. No statistically
significant associations of TRP were found with MMEF and PEFR.
Discussion: These findings suggest that exposure to traffic-related pollutants
negatively impacts the development of children’s lungs. Furthermore, living in a high
stress household potentiates the adverse effects of these residential traffic-related
pollutants.
1
Chapter 1: Introduction

Understanding the effects of air pollution and its interaction with other factors has
been the focus of many studies because of the potential adverse effects on pulmonary
health. This topic is especially relevant to the Southern California region, from which
some of the highest levels of air pollution in the nation are regularly reported
1
.
Furthermore, air pollution exposure may be especially important to children, since they
are more active, spend a larger portion of their time outside, and often exercise at higher
ventilation rates compared to adults
2
.
Lung function has proven to be one of the more important health outcomes being
studied, as it has been found to be associated with morbidity and mortality. Schroeder et
al. found an association whereby men and women with lower forced expiratory volume in
1 second (FEV
1
) had an increase risk of developing coronary heart disease
3
. A lower
percent predicted FEV
1
has also been shown to be associated with an increased risk of
death from ischemic heart disease
4
. In addition to these findings with coronary heart
disease, decreased FEV
1
was also shown to be associated with an increased risk of
mortality due to stroke, cancer, and other respiratory diseases
5,6
.
Many epidemiological studies have investigated the association of regional air
pollution on lung function
7-17
, but a growing number of studies have also examined the
local effects of traffic
18-25
. Exposure to air pollution has been shown to lead to oxidative
stress in the lungs, which subsequently signals the arrival of inflammatory cells into the
lung
26
. While most studies have reported at least one association between air pollution
2
and lung function
7-20
, there are some that found no relationship
21-25
. Even among studies
that found some associations, there are some inconsistencies. Studies by Kunzli et al. and
Tager et al. showed an association between ozone and FEF
25-75%
12,13
, while studies by
Gauderman et al. found no associations with ozone but rather with other pollutants
9,18
. In
a study performed in the Netherlands, researchers, using distance to a road and traffic
density as surrogate measures for local traffic pollutant, found strong associations with
lung function
19
. When this study was later repeated by the same research team, they were
unable to confirm the previously-observed associations
21
.
Short-term and long-term exposures to regional air pollution concentrations and
traffic-related pollution (TRP) have been linked to many other types of health respiratory
morbidities. For example, higher levels of NO
2
, acid vapors, benzene, modeled traffic
exposures, traffic counts, and proximity to a major road have all been shown to be
associated with an increased risk of developing wheeze
22,27-33
. Furthermore, many of
these same exposures have also been found to be associated with asthma, cough, and
bronchitis
22,23,28,30-36
. It has also been reported that an increase in hospitalizations can
result due to air pollution's effects on respiratory health
37,38
.
Studies have revealed that reduced lung function may be predictive of asthma and
wheeze
39,40
. Recently, a study demonstrated that air pollution may modify the association
between lung function and asthma
41
. Specifically, this study showed that an increase in
lung function was associated with fewer cases of new-onset asthma, but exposure to high
levels of particulate matter reduced this protective effect.
3
It has been shown that about 70% of the inter-subject variation in lung capacity
can be explained by age, sex, height, weight, and race with the remaining variability
being attributed to environmental and personal exposures, personal characteristics such as
underlying genetic factors, and measurement error
42
. From a public health point of view,
it is important to understand the mechanism by which pollution acts upon lung function
and to identify sub-populations that may be more vulnerable to exposure. For example,
many studies have suggested that the impact of air pollutants is greater in girls
7,19,20,43
, yet
some studies found stronger effects among boys
14,18
. Genetic research has led to the
identification of genes that may play an important role in lung function development
44,45
.
In this thesis, we will investigate the possible modifying effects of stress on air pollution
associations with lung function.
Social stress has been shown to play an important role in various respiratory
diseases. Some studies have shown a direct relationship between parental and caregiver
reported stress and an increased risk of asthma and wheeze among children
46-48
. Similarly
to the effects of air pollution, psychological stress has been reported to increase oxidative
stress and cytokine levels, which in turn are related to the inflammatory response
mechanism
26,49
. It has also been suggested that stress may disrupt the body's immune
function
49
, thus increasing susceptibility to other exposures. A few epidemiological
studies have looked at this possible association whereby stress modifies the effect of air
pollution. Clougherty et al. found that air pollution was related to asthma risk only among
children exposed to higher levels of violence, a life stressor
50
. A previous study from the
Children’s Health Study (CHS) has found that the association between traffic-related
4
pollution, in-utero tobacco smoke, and asthma was stronger among children whose
mothers reported higher levels of stress
51
. To our knowledge, no epidemiological study
has thoroughly investigated the joint association between air pollution, stress, and lung
function. Given that most of the same pollutants play an important role in lung function
development and other respiratory morbidities and that stress confers susceptibility to
asthma and wheeze, we hypothesize that parental stress should play a similar role in lung
function development.
The goals of this study were to validate and advance the work previously done by
the CHS group through the use of a newly identified cohort. Specifically, we looked at
the intra-community effects of traffic-related pollutants on children’s lung function using
several sophisticated pollution metrics. These included a new model that has been
developed to predict residential ambient exposures based on local traffic information,
population demographics, and topography. We also investigated the effects of stress to
determine whether it modified the association between TRP and lung function.

5
Chapter 2: Methods

Study Subjects
In 2002 and 2003, a new CHS cohort of children was recruited from schools in 13
Southern California communities. All children in kindergarten or in the first grade in a
subset of available classrooms (n=8,193) were invited to join the study. Study consent
forms and medical history questionnaires were sent home for parents or legal guardians
to review, complete, and return to school. Of those sent home, 5,488 were returned
(67%). In subsequent follow-up years, participating families completed annual
questionnaires to update health and residential status information. In 2007, five
communities were excluded from active follow-up (on-site health testing) in order to
focus resources on intra-community exposure assessment. To partly offset this loss of
subjects, an additional 347 children in the fifth grade from the schools of the remaining 8
communities were added to the cohort. Table 1 documents the sample sizes of this cohort
from study inception to lung function testing in 2008. Informed consent was obtained

Table 1: Sample size of total active participants in cohort
Calendar
year Year of study
Active
children in cohort
Subset of 8
communities with
lung function data
2002-2003 1 5,488 3,455
2003-2004 2 4,678 2,937
2004-2005 3 3,981 2,559
2005-2006 4 3,635 2,324
2006-2007 5 3,276 2,121
2007-2008
†
6 3,513 2,433
†347 additional children were recruited in the 8 participating communities.

6
from all parents and legal guardians who wished to enroll their kids into the study. The
study was approved by the University of Southern California Institutional Review board.
Questionnaire
The self-administered questionnaire elicited information from the parents about
the child’s demographic nature (gender, race, Hispanic ethnic background, and outdoor
activity), as well as medical and residential history characteristics. Medical history
details included questions about doctor-confirmed diagnosis of asthma, history of
wheeze, cough, bronchitis, and medication usage for treatment of asthma or wheeze.
Other personal information collected included parental income and education, insurance
coverage, maternal smoking during pregnancy, environmental tobacco smoke, type of
dwelling, and presence of pets, mold, pests, gas stove, heating and air conditioning, and
carpeting.
Parental stress was assessed using the Perceived Stress Scale
52
. This was
calculated by assigning a value of 0 to 4 according to the parent’s written response to
each of the following questions: “in the last month, how often have you felt”: (i) “that
you were unable to control the important things in your life,” (ii) “confident about your
ability to handle your personal problems,” (iii) “that things were going your way,” and
that (iv) “your difficulties were piling up so high that you could not overcome them.”
The scores to each of these questions were summed to create a single stress score,
ranging from 0 to 16. This score was dichotomized at the median: parents with scores of
4 or higher were classified as having higher stress while those below 4 were considered
as having lower stress.
7
Lung Function
Lung function testing began in the Fall of 2007, and testing continued in
successive communities, until the Spring of 2008. Testing was performed on 1,811
children (74% of active cohort participants). Pressure-transducer-based spirometers
(Screenstar Spirometers, Morgan Scientific) were used to measure a variety of lung
function variables, including forced vital capacity (FVC), forced expiratory volume
during the first second (FEV
1
), maximal mid-expiratory flow (MMEF, also known as
FEF
25-75%
), and peak expiratory flow rate (PEFR). These maneuvers were carried out
during the morning hours between October and May in order to avoid the influence of
peak oxidant season pollution levels. Students were asked to perform three acceptable
maneuvers (as defined by the American Thoracic Society (ATS) recommendations) in no
more than seven tries
7
. The ATS criteria stipulates that the FEV
1
and FVC agree within
5%, the extrapolated FEV
1
volume be less than 100 ml or 5% of FVC, less than 50 ml of
air be expired in the last two seconds of forced exhalation, and the total forced expiratory
time exceed three seconds. These same criteria have been utilized in prior lung function
studies conducted by the CHS.
Health testing was conducted by trained lung function technicians. Project staff
demonstrated proper spirometric technique, and then actively coached subjects through
the spirometric procedures. Individual maneuvers were flagged as suboptimal if they
were considered suspect by the attending field technicians. All spirometric loops were
computer-recorded and forwarded to the home laboratory for review and acceptance. In
the field, spirometer calibrations were checked before and after each day’s testing.
8
Unannounced field-site inspections, by an external quality assurance officer with many
years of spirometry and field testing experience, were performed to ensure the overall
quality of the lung function testing.
Prior to data acceptance for actual analyses, pulmonary function data were
carefully examined. Measurements greater than three standard deviations from the mean,
field technicians’ memos of poor subject performance, and subject’s test results with
differences between best maneuver and second-best maneuver or second-best maneuver
and the third-best maneuver of greater than five percent, and the spirometry software’s
built-in selection algorithm for identifying apparent faults were key criteria used to
invalidate some of the spirometric data. Additional faulty maneuvers were identified by
assignment of various nominal acceptable reporting ranges: children having extreme
(>98th or <2nd percentile) sex-age-size-race-adjusted FVC and/or FEV
1
as determined
by initial statistical models of this study's data, extreme MMEF/FVC ratio, or FVC or
FEV
1
<70% or >150% of literature-based predicted values provided by spirometry
software were flagged, reviewed, and invalidated. Following this detailed flow-volume
review, raw data was accepted for subsequent data analyses.
Exposure Assessment
Air quality monitors were operated by regional air pollution control agencies in
pre-existing regional stations or in augmented locations established for the Children
Health Study. These stations recorded hourly regional concentration levels of ozone (O
3
),
nitrogen oxides (NO
x
), nitric oxide (NO), and the gravimetric mass of airborne particle
matter (PM) with diameters less than 10uM (PM
10
). In addition, a novel two-week
9
integrated sampler
53
was used to collect PM with diameter less than 2.5uM (PM
2.5
) as
well as for other gaseous acidic compounds. Two other metrics of regional pollution were
computed: nitrogen dioxide (NO
2
) was calculated by subtracting NO from NO
x
while
coarse particle mass was estimated by subtracting PM
2.5
from PM
10
.
Measurements to the nearest freeway, highway, and large surface streets, which
included arterial and major collector roads, were made using ERSI ArcGIS Version 9.2
(ESRI, Redland, CA, www.esri.com). These measurements were based on the shortest
distance from the home of residence to the center lane of each of the nearest road types.
The locations of the homes were geocoded and standardized using the TeleAtlas database
and software (Tele Atlas Inc., Menlo Park, CA, www.na.teleatlas.com). A “major road”
variable was created by taking the minimum distance to either the freeway or one of the
major road types. A categorical major road variable was created by grouping distances
into <75 m, 75-150 m, >150-300 m, and >300 m categories
32
. Similarly, a categorical
freeway variable was generated that had freeway grouped as <500m, 500-1,000m,
>1,000-1,500m, and >1,500m
18
.
Two different types of model-based estimates of TRP at the participants' homes
were generated. The first model has previously been used in related studies
18,28,51
and is
based on the CALINE4 line-source air-quality dispersion model
54
. The CALINE4
predictions make use of information about traffic volume, roadway link geometry,
vehicle emission rates, and meteorological conditions such as wind speed, wind direction,
atmospheric stability, and mixing heights. These predictions were separated by freeway
and non-freeway traffic contribution to residential exposure and were also combined
10
during the analysis to create a total exposure variable for the traffic-related pollutants.
Because various CALINE4 predicted TRP were highly correlated (R>0.9, see Table 2),
only NO
x
analyses were carried through the analyses, but essentially the same
conclusions about health effects can be drawn for CALINE4 predictions of CO, NO
2
, or
NO.
We also used a novel set of traffic-exposure measures that have not been featured
in any previous analyses. These were developed based on measurements of intra-urban
variations of NO
2
, NO, and NO
x
at 940 locations within the 12 Southern California
communities from the Children's Health Study
55
. A stepwise multiple regression
approach was used to develop the best predictive model where measured levels of
residential pollutants entered as dependent variables, while traffic-related metrics and
other pertinent variables were entered as independent variables. After running the
regression, the best model contained the following variables: distances to major roads,
traffic volumes on neighborhood streets, CALINE4 based TRP, population density, and
elevation. Individual level cross validations showed that this model was able to predict
70%, 64%, and 72% of the variation in 4-week average residential levels of NO
2
, NO,

Table 2: Correlation of CALINE4 predicted TRP
†

total NO
x
total CO total NO total NO
2

total NO
x
1

total CO 0.927 1

total NO 0.974 0.905 1

total NO
2
0.997 0.923 0.952 1
†Town adjusted correlations

11

and NO
x
, respectively. This model was used to predict concentrations of NO
2
, NO, and
NO
x
at the homes of the 1,811 study subjects who performed a lung function test. TRP
based on this model will be referred to as predicted residential exposures.
Statistical Methods
Standard multiple linear regression was used to assess the association between
pollutants and each lung function parameter. A base regression model was formed
consisting of logged height and its squared value, body mass index (BMI) and its squared
value, gender, age at time of pulmonary function testing, race, Hispanic ethnicity,
presence of a respiratory illness (cold or other chest illness) during testing, indicator
variables for which field technician administered the lung function test, indicators for
community, and an interaction term between age and gender. These variables were
selected for use since they were known predictors of lung function development or were
believed to be major determinants in the variability observed in our data. Residuals were
tested to determine whether model assumptions of homoscedasticity and normality held.
Scatter plots of residuals against the predicted lung function were generated for each PFT
outcome. These plots revealed that model residuals appeared to satisfy the
homoscedasticity assumption. The Kolmogorov D-statistic and observations of the
histograms of the residuals were used to test for normality. The residuals for each PFT
outcome followed a rough bell-shaped distribution; however, the null hypothesis of
normality using the Kolmogorov D-statistic was rejected at the p<0.05 level. Log
transformation of the PFT outcomes improved the shape of the bell-shaped curve, but the
12
null hypothesis of normality was still rejected at the p<0.05 level. Because of the
relatively large number of subjects in our study, any small deviations from normality will
cause a rejection of the null hypothesis due to the robust nature of this test. This problem
is frequently encountered with such studies; we therefore felt that a log transformation of
the outcome was sufficient in order to proceed with the analyses. The percent of total
variation in the natural log of FEV
1
, FVC, MMEF, and PEFR explained by the base
regression model containing all variables except for the exposure variable of interest was
59%, 62%, 24%, and 30%, respectively (Table 3).
In addition to linear regression, potential sources of stress were analyzed using
unconditional logistic regression to calculate the odds of having higher stress. This
analysis was performed to determine the factors that most strongly associate with having
higher levels of stress, and sources that were significantly associated with stress were
later used for adjustment purposes. Because stress was collected at baseline, these
potential sources represent baseline values. Among the kindergarten/first grade entrants,
these variables were taken from the first year of the cohort’s testing. Among the fifth
grade entrants that were later added to the cohort, these variables were selected from the
sixth year of study testing.
The deviation from the community mean for each pollutant measured on a
continuous scale was calculated to obtain an average within-community distribution
across all communities. Because our model has a fixed effect for town, using a deviation
rather than the predicted pollutant assigned to each resident will provide the same
estimate for any given pollutant. Deviations were scaled to the 10%-90% range in intra-
13
Table 3: Parameter estimates of base models
logFEV
1
logFVC logMMEF logPEFR
Variable Beta Beta Beta Beta
LogHeight (in logCM) 1.226 * 1.339 ** 1.249 1.546 *
LogHeight
2
1.091 1.091 0.467 0.055
Male 0.229 * 0.197 * 0.172 -0.037
Age at PFT 0.033 *** 0.023 *** 0.050 *** 0.026 **
Age*male -0.019 * -0.014 -0.021 0.004
BMI (in kg/m
2
) 0.024 *** 0.025 *** 0.034 *** 0.025 ***
BMI
2
-0.0004 *** -0.0004 *** -0.001 *** -0.0005 ***
Race (vs. White) Asian -0.039 *** -0.051 *** 0.018 0.013
Black -0.117 *** -0.120 *** -0.093 * -0.032
Mixed -0.008 -0.017 0.010 0.021
Other 0.004 -0.007 0.035 0.008
Don't Know 0.008 -0.008 0.041 0.026
Hisp (vs. Not Hisp) Yes 0.024 *** 0.030 *** 0.016 0.008
Don't Know 0.012 0.031 * 0.008 -0.016
Resp. Illness (vs. No) Yes -0.023 * -0.018 -0.041 * -0.043 ***
Don't Know -0.016 -0.018 -0.019 -0.011
Field Tech (vs. 4) 3 -0.037 * -0.016 -0.070 * -0.057 **
5 -0.036 -0.051 * 0.032 -0.055
Town code (vs. SB) LB -0.014 -0.021 -0.002 -0.048 **
ML -0.033 *** -0.031 *** -0.033 -0.059 ***
RV -0.033 *** -0.005 -0.104 *** -0.051 ***
SD -0.015 -0.005 -0.050 * -0.039 **
UP -0.015 -0.007 -0.046 * -0.014
GL -0.025 * -0.006 -0.078 *** -0.044 ***
AN -0.022 * -0.014 -0.042 -0.053 ***
Total R
2
0.59 0.62 0.24 0.30
Town code definitions: SB=Santa Barbara; LB=Long Beach; ML= Mira Loma; RV=Riverside; SD= San Dimas,
UP=Upland; GL=Glendora; AN=Anaheim
p-values: * <0.05, ** <0.01, *** <0.005

14
community exposure levels across all towns to improve interpretability. Pollutants were
added to the base regression model one at a time so that a regression estimate (beta) could
be calculated. A value of 1 was subtracted from the exponentiated betas, which were then
multiplied by 100 to yield a percent change in lung function for a 10
th
to 90
th
percentile
change in exposure. For categorical variables (e.g. categories of distance to roads), a
similar transformation of the betas was performed and can be interpreted as the percent
change in lung function compared to the baseline group. The baseline group for distance
to a major road was >300m, while the baseline for distance to freeway was >1,500m.
Potential confounders were assessed by checking to see whether the effect
estimate of a pollutant changed by more than 15% when the variable of interest was
added to the model. Tests of interaction were assessed by performing the likelihood ratio
test between a model containing the TRP and variable of interest along with covariates
and another model containing these same variables plus a product term between the
variable of interest and the TRP. Confounding of the stress and TRP interaction was
analyzed by including in the model an interaction between the baseline variable of
interest and TRP. Again, a change of 15% in beta of the interaction between stress and
TRP was established as the threshold for determination of confounding. All analyses
were performed using Statistical Analysis System (SAS version 9.2; SAS Institute Inc.,
Cary, NC). A two-tailed alternative hypothesis was assumed in all analyses with
significance being claimed at the 0.05 level.
15
Chapter 3: Results

Main Effect of TRP
Characteristics of participating children are summarized in Table 4. In short,
51.9% of the study subjects were female with the distribution of racial and ethnic
backgrounds being about the same in boys and girls. Male subjects were slightly older
than female subjects, but no significant differences in their height, weight, and BMI were
detected. In addition, boys had a higher prevalence of asthma and were more likely to
participate in outdoor organized teams sports at least twice per week compared to girls.
The average cross-sectional measurements of FEV
1
, FVC, MMEF, and PEFR
among boys were 2,473ml, 2,902ml, 2,786ml/s, and 5,207ml/s, respectively, while the
measurements among girls were 2,442ml, 2,783ml, 2,963ml/s, and 5,188ml/s,
respectively (Table 5). Across all communities, approximately 14% of children lived
within 75 meters of a major road and 27% of children lived within 500 meters of a
freeway (Table 6).
When treated as a continuous variable, neither proximity to a freeway, highway,
or large surface street were found to be associated with a change in FEV
1
(Table 7).
However, increased distance away from a major road was found to be associated with a
1.5% increase in FEV
1
(95% CI: 0.33, 2.65) over the 10
th
to 90
th
percentile change in the
deviation of the exposure (684 meters). A positive relationship was found between FVC
and proximity to a freeway (p=0.042) as well as to a large surface street (p=0.022), but

16
Table 4: Subject characteristics during time of the test
FEMALE MALE Interaction
N % N %
Hispanicity
Don't Know 43 (4.6) 49 (5.6) 0.589
Hispanic 538 (57.2) 490 (56.3)
Not Hispanic 359 (38.2) 332 (38.1)
Race
Asian 50 (5.3) 36 (4.1) 0.558
Black 23 (2.5) 16 (1.8)
Don't Know 120 (12.8) 119 (13.7)
Mixed 119 (12.7) 110 (12.6)
Other/Native American Indian 261 (27.8) 225 (25.8)
White 367 (39.0) 365 (41.9)
Ever doctor diagnosed asthma
†

No 741 (79.5) 648 (75.2) 0.028
Yes 191 (20.5) 214 (24.8)
Respiratory illness
‡

No 795 (87.8) 781 (93.7) <.0001
Yes 111 (12.3) 53 (6.4)
Roach*
No 776 (89.0) 714 (89.5) 0.751
Yes 96 (11.0) 84 (10.5)
Mold during past 12 months
No 744 (88.5) 724 (90.6) 0.156
Yes 97 (11.5) 75 (9.4)
Dog*
No 572 (65.1) 501 (63.2) 0.419
Yes 307 (34.9) 292 (36.8)
Cat*
No 714 (81.6) 638 (80.9) 0.700
Yes 161 (18.4) 151 (19.1)
Sports twice per wk in past year
No 476 (54.3) 320 (39.2) <.0001
Yes 401 (45.7) 497 (60.8)
In-utero tobacco smoke*
No 831 (93.7) 776 (94.8) 0.348
Yes 56 (6.3) 43 (5.3)
Envir. tobacco smoke (home)
No 884 (96.3) 818 (96.1) 0.848
Yes 34 (3.7) 33 (3.9)
† Among the kindergarten/first grade entrants, represents a "yes" response on any questionnaire to
doctor diagnosed asthma during the study (years 1-6).
‡Subject report of a cold or other chest illness at the time of the test.
*Among the kindergarten/first grade entrants, presence of roach and exposure to in-utero tobacco
smoke were assessed at baseline (year 1), while possession of dog and cat were assessed during
year 5.

17
Table 4 continued: Subject characteristics during time of the test
FEMALE MALE Interaction
Mean SD Mean SD

Height 147.0 (7.9) 146.7 (7.9) 0.420
Weight 97.8 (26.7) 97.7 (26.5) 0.941
BMI 20.4 (4.4) 20.4 (4.4) 0.763
Age 11.2 (0.6) 11.3 (0.7) 0.001

18

19
Table 6: TRP characteristics
10-90%
Traffic Measure Median IQR
†
Range
‡

Distances (in meters)
Major Road 236.0 326.2 684.1
Freeway 1,076.1 1,320.5 2,398.3
Highway 3,493.3 1,922.1 4,141.4
Large Surface 307.3 390.2 809.0
CALINE4 NO
x
(in ppb)
Freeway 12.5 8.8 21.0
Non-freeway 5.3 3.0 6.4
Total 19.3 11.1 24.9
ICV Predictions (in ppb)
NO
2
24.3 3.5 7.6
NO 19.0 6.8 14.5
NO
x
44.3 10.2 21.9
O
3
18.6 1.9 5.3

Major Road category n %
<75m 254 14.3
75-150m 309 17.4
150-300m 498 28.1
>300m* 712 40.2
Freeway category
<500m 478 27.0
500-1,000m 352 19.9
1,000-1,500m 272 15.3
>1,500m* 671 37.9
†Interquartile range of the deviation between home values and average
of the home values in a given town across all towns.
‡10
th
to 90
th
percentile change in the deviation between home values
and average of the home values in a give town across all towns.
*Reference group in all analyses.

20

21
not with respect to a highway. Similarly to FEV
1
, an increase of 684 meters in distance
away from a major road was associated with a 1.6% increase in FVC (95% CI: 0.45,
2.74).
Categorical distance to a freeway was statistically associated with FVC, but with
none of the other outcomes. Compared to children living at least 1,500 meters away from
a freeway, those that lived within 500 meters had an almost 2% decrease in FVC
(p=0.009). When the distance to a major road was examined by categories, there was a
1.6% deficit in FEV
1
(95% CI: -3.21, 0.09) among those living within 75 meters. The
trend of decreased FEV
1
across categories was not significant (p=0.09).
CALINE4 derived predictions of TRP were not statistically associated with FEV
1

or FVC, although a suggestion of a negative effect was present for both outcomes.
However, predicted residential NO and NO
x
were found to be strongly negatively
associated with FEV
1
(percent change: -1.4%, 95% CI: -2.66, -0.11 for NO; percent
change -1.3%, 95% CI: -2.66, -0.11 for NO
x
) and FVC (percent change: -2.0%, 95% CI:
-3.21, -0.71 for NO; percent change: -1.9%, 95% CI: -3.19, -0.60 for NO
x
), while
predicted residential NO
2
was found to be significant only with FVC (percent change:
-1.7, 95% CI: -2.94, -0.40, see Table 8). The above exposure metrics were found to be
unassociated with MMEF or PEFR. Separate adjustments for each of parental asthma,
education and income, home type, presence of mold, air conditioning, dogs, cats, mold,
and gas stove did not change any of the exposure effect estimates by more than 15%.
The association between freeway CALINE4 predicted NO
x
and FEV
1
was found
to vary by gender (p=0.033). A significant decrease in FEV
1
was found among girls
22

23
(percent change: -1.2%, 95% CI: -2.30, -0.02), but no association was detected among
boys (Table 9). The effect of CALINE4 derived predictions of TRP and predicted
residential exposures were generally stronger with respect to FVC among girls compared
to boys, but the differences were not statistically significant (Table 10).
Gender was found to modify to effect of distance to a major road type for both
FEV
1
(p=0.026) and FVC (p=0.017) when distance was treated as a categorical variable
(Table 9 and Table 10). Girls living within 75 meters of a major road had a 2.4% decline
in FEV
1
(95% CI: -4.64, -0.06) and a 2.2% decline in FVC (95% CI: -4.49, 0.08)
compared to girls living at least 300 meters from such roads. No observable differences
were found among boys.
In a separate analysis, the main effects of each of the exposure metrics were
analyzed in both non-asthmatic and asthmatic children. Although there was no significant
difference among non-asthmatics and asthmatics, larger effects of TRP were generally
observed among non-asthmatics with respect to FEV
1
and FVC, while TRP effects were
stronger among asthmatics with respect to MMEF (data not shown).
Influence of Parental Stress
Parents who self-reported as Asian had the highest levels of stress and those who
used Spanish questionnaires had higher stress than those that did not. Parental stress was
also associated with baseline levels of education, income, insurance, home type, presence
of mold (in the past 12 months), use of air conditioning, and presence of roaches, dogs,
and/or cats in the home (Table 11).

24

25

26
Table 11: Subject characteristics at baseline
†
and their relationship to Parental Stress Score
Parental Stress
Variable n (%)
‡
Mean
High Parental stress
OR (95%CI)
Gender
Female 855 (52.0) 4.06 1.00
Male 789 (48.0) 3.90 0.89 (0.73, 1.08)
Race
White 705 (42.9) 3.62 1.00
Asian 77 (4.7) 4.75 2.29 (1.40, 3.76)
Black 35 (2.1) 3.83 1.24 (0.63, 2.44)
Don't Know 186 (11.3) 4.55 2.13 (1.52, 2.97)
Mixed 219 (13.3) 3.76 0.95 (0.70, 1.28)
Other/Native American Indian 422 (25.7) 4.33 1.90 (1.48, 2.42)
Hispanicity
Not Hispanic 670 (40.8) 3.65 1.00
Hispanic 936 (56.9) 4.18 1.49 (1.22, 1.82)
Don't Know 38 (2.3) 5.00 2.78 (1.36, 5.7)
Parental Education
Did not finish high school 304 (19.0) 4.77 1.00
High school diploma/some college 827 (51.7) 4.05 0.53 (0.40, 0.70)
College diploma or greater 469 (29.3) 3.23 0.32 (0.23, 0.43)
Income
<$30,000 375 (26.3) 4.88 1.00
$30,000 or more 1053 (73.7) 3.59 0.42 (0.33, 0.54)
Insurance
No 158 (9.9) 4.41 1.00
Yes 1440 (90.1) 3.90 0.71 (0.51, 1.00)
Family history of asthma
No 1209 (78.2) 3.96 1.00
Yes 338 (21.9) 3.88 1.02 (0.80, 1.30)
Asthma
No 1347 (86.3) 3.92 1.00
Yes 214 (13.7) 3.99 0.93 (0.70, 1.24)
Home
Single-family house 1310 (81.3) 3.82 1.00
Apartment (2-10 units) 221 (13.7) 4.57 2.12 (1.57, 2.87)
Apartment (>10 units) 56 (3.5) 4.84 2.56 (1.42, 4.62)
Mobile home/trailer/other 25 (1.6) 4.28 1.54 (0.69, 3.45)
Mold
No 1116 (74.0) 3.88 1.00
Yes 393 (26.0) 4.09 1.24 (0.99, 1.57)
Air conditioning
No 543 (33.5) 4.38 1.00
Yes 1080 (66.5) 3.76 0.56 (0.46, 0.70)
†Among the originally selected children into the study, baseline characteristics were collected
during year 1 of the cohort. Among the children that were later added to the study, baseline
characteristics were collected during year 6 of the cohort.
27

Table 11 continued: Subject characteristics at baseline
†
and their relationship to Parental Stress Score
Parental Stress
Variable n (%)
‡
Mean
High Parental stress
OR (95%CI)
Roach
No 1423 (89.4) 3.90 1.00
Yes 168 (10.6) 4.61 1.74 (1.24, 2.43)
Dog
No 1121 (69.8) 4.02 1.00
Yes 486 (30.2) 3.86 0.92 (0.75, 1.14)
Cat
No 1318 (82.0) 4.03 1.00
Yes 289 (18.0) 3.72 0.75 (0.58, 0.97)
Envir. tobacco smoke (home)
No 1531 (94.3) 3.93 1.00
Yes 93 (5.7) 4.85 1.73 (1.11, 2.68)
In-utero tobacco smoke
No 1521 (94.3) 3.98 1.00
Yes 92 (5.7) 4.05 1.01 (0.66, 1.54)
†Among the originally selected children into the study, baseline characteristics were collected
during year 1 of the cohort. Among the children that were later added to the study, baseline
characteristics were collected during year 6 of the cohort.
‡Frequency counts of those that provided information about stress.

Parental stress was not found to have a significant direct effect on children's lung
function (data not shown). However, parental stress was found to modify the effect of air
pollution on FEV
1
and FVC. In particular, several pollution indicators had a larger,
negative impact on lung function among children whose parents were classified as being
highly stressed. For example, among children of high-stressed parents, non-freeway
CALINE4 predicted NO
x
was associated with about a 2.1% decrease in FEV
1
(p=0.004),
compared to a 1.1% increase (p=0.25) for children of lower-stressed parents (p=0.009 for
the interaction, see Table 12). The effect was even more pronounced for estimated
residential ambient pollution. Here, predicted residential NO
2
was associated with a
28

29
3.6% decrease (p<0.001) in FEV
1
in the high-stressed group compared to a 0.8% increase
(p=0.42) in the low-stressed group (p=0.003 for the interaction). Similar results were
identified for other pollutant indicators and when FVC was chosen as the outcome (Table
13). No significant negative associations between lung function and air pollutant
indicators were found among parents who were classified as having low stress.
Interactions between stress and pollutant indicators were not substantially altered when
adjustments were made for interactions between other factors collected at baseline (e.g.
education, income, insurance, home type, presence of mold, use of air conditioning, and
presence of roaches, dogs, and/or cats in the home) and pollutant indicators.
Because parental stress was assessed at different time points with relation to lung
function testing among the kindergarten/first grade entrants and fifth grade entrants, the
influences captured by this stress measurement may have differing properties. We
performed a sensitivity analysis by dropping the fifth grade entrants that had stress and
pulmonary function ascertained during the same time period. Results from this sensitivity
analysis were very similar to the original analysis (data not shown).

30
31
Chapter 4: Discussion

The results of this study suggest that an increase in TRP is correlated with
reduced lung function. This study focused primarily on three different types of estimates
for TRP. Similarly to previous studies by the CHS
18,28,32,51
, distance to the nearest major
road or freeway was used as a marker of TRP as well as modeled predicted estimates of
TRP based on collected traffic information (CALINE4). In addition to these two
estimates, a prediction model of estimated residential exposures was used that was based
on traffic information as well as population demographics and topography. After
adjustment for known predictors of lung function development, some measures of TRP
were associated with deficits in FEV
1
and FVC with the strongest associations being
detected with predicted residential exposures. No associations were detected between
TRP and indicators of smaller airways, as measured by MMEF and PEFR. Furthermore,
household stress appeared to modify the above associations with the negative impact of
TRP being larger among children whose parents reported higher levels of stress.
One of the present study's objectives was to replicate past findings between
traffic-related exposures and lung function. Similar to the findings by Gauderman et al.
18
,
we found no statistically significant association between CALINE4-modeled TRP and
respiratory outcomes. However, there was a discrepancy between these two studies with
respect to the effect of living near a freeway on lung development. The previously
published study
18
found that living closer to a freeway resulted in lower growth rates of
FEV
1
and MMEF, while this study found significant findings only with FVC. One
32
possible explanation for the difference in findings could be attributed to the fact that this
study is a cross-sectional analysis that compares differences in lung function level at
average age 11 years, while the previous study was a longitudinal analysis that examined
differences in rates of lung function development over the period from age 10 to age 18.
Mixed results have arisen from other studies that have used distance to a major
road or traffic density as surrogate measures of TRP. In studies of children, Wjst et al.
reported an association between traffic density and maximal expiratory flow at 50% of
FVC expired
33
, while Brunekreef found that increased truck traffic was associated with
decreased FEV
1
and PEFR among children living within 300km of a motorway
19
.
However, some studies were unable to detect any significant findings with lung
function
22,21,24,25,56
. Although associations with lung function were not detected, Nicolai
et al
22
reported traffic effects on other respiratory morbidities such as asthma, wheeze and
cough. Furthermore, Dales et al
25
reported traffic associations with exhaled nitric oxide, a
biomarker of airway inflammation, but was unable to detect a significant association with
any of the lung function metrics. Among adults, Schikowski et al. and Sekine et al. both
reported traffic effects on FVC and FEV
1

57,58
, while Kan et al. found an association only
between distance to a major road and FVC
43
.
Traffic characteristics are used in epidemiological studies because of their ability
to explain some of the pollutant concentration variability. For example, within 500m of a
freeway, concentrations of black carbon, NO
2
, and other pollutants typically decrease
exponentially as distance increases
59,60
. However, pollutant concentrations may vary
depending on wind direction and time of day
59-61
. Furthermore, elemental carbon, a
33
product of vehicle exhaust and a contributor to decreased lung function, is capable of
being transported great distances
8,62
. Thus, traffic characteristics alone such as distance to
roadways and traffic density may not most accurately capture pollutant variability, and
other methods of estimating pollutants have to be considered, such as pollutant modeling.
A land-use regression model, for example, has been shown to do a better job of capturing
pollutant variability than using concentration measurements from nearby central site
monitors
63
. While some studies using modeled exposures were unable to detect
associations
22,25
, Forbes et al. modeled background exposures of various pollutants
using an air dispersion model and detected an association with FEV
1
among adults
64
. Our
results demonstrated that CALINE4-modeled TRP was weakly associated with lung
function, which is in agreement with our previous finding
18
. On the other hand, our new
modeled estimates of predicted residential exposures were found to be significantly
associated with FEV
1
and FVC.
Our results also indicate that close proximity to a major road has a larger effect
on girls than it does on boys. This is in contrast to our previous findings that found
distance to a freeway to have a greater impact on adolescent lung function development
in boys than it does on girls
18
. In agreement with the current study, Brunekreef et al.
found that traffic associations were stronger among girls than boys
19
, and Peters et al. saw
that the effects of regional pollutants were larger among girls
7
. Furthermore, Kan et al.
demonstrated that traffic's effects had a larger impact on women than men
43
. Conversely,
Jedrychowski found that effects of pollutants were stronger among boys
14
. The biological
role of gender in the interaction between air pollution and lung function development is
34
unclear; however, it is possible that the difference we observed is an artifact of the "true"
exposure or other covariates being estimated or measured with error. For example, if
boys’ exposure to TRP were non-differentially misclassified because of their difference
in lifestyle compared to girls (boys were more likely to play sports outdoors twice per
week), this could possibly explain the attenuated effects that were observed in boys.
Furthermore, it is possible that other factors may play an important contribution in the
observed differences between boys and girls including hormonal differences as well as
possible X-linked genetics.
This study has demonstrated that parental stress modifies the impact of traffic-
related pollution on children's lung function. An increase in predicted residential
exposures to NO
2
, NO, and NO
x
and the non-freeway component of CALINE4-modeled
TRP were all strongly associated with decreases in children's FEV
1
and FVC among
children whose parents reported higher levels of stress compared to children whose
parents reported lower levels of stress. Similar associations were found for children who
lived between 75m and 150m of a major road compared to children living greater than
300m from a major road. The effect of traffic-related exposures among these two stress-
defined groups of children was significantly different. No main effect of stress was
detected on children's lung function, but positive associations between stress and other
childhood respiratory morbidities have previously been reported
46-48
. Additionally, it was
found that less optimistic elderly men had larger rates of decline of FEV
1
and FVC than
more optimistic men
65
. Our findings of parental stress modifying the effects of traffic-
35
related pollution on lung function is similar to that of studies that found stress modifying
the effect of pollution on asthma risk in children
50,51
.
The relationship between stress, pollution, and lung function can be difficult to
disentangle. Findings have suggested that increased stress due to traffic is related to
overall self-reported decline in general health and greater depressive symptoms
66
.
However, it should be noted that living near high traffic areas does not necessarily
translate to higher levels of family stress or violence
49
. Social economic status (SES) has
also been suggested to play an important role in this interaction. SES has been found to
be associated with chronic stress
67,68
, and people of lower SES in urban settings are often
situated in areas with poorer air quality
69
. Education, a proxy for SES, has been
demonstrated to modify the effect of air pollution and risk of mortality
70,71
. In our study,
adjustment for the interaction between traffic-related pollutants and stress by the
interaction between traffic-related pollutant and education did not markedly affect the
stress interaction, suggesting that SES does not explain the modifying effects of stress
that we observe. There are many sources of stress and no single variable can fully explain
our findings. A potential issue in this study is the fact that we are analyzing traffic-related
pollutants on children's lung function, but we assessed parental stress rather than
children's stress through study questionnaires. It has been shown that parental stress can
be used to measure physiological changes in children. For example, the stress hormone
cortisol, as measured in the saliva of children, and exaggerated responses to
adrenocorticotropic hormone were found to be significantly correlated with their mothers'
symptoms of depression
72,73
. It is possible that these hormonal changes in children of
36
highly stressed parents were somehow related to deficiencies in immune function which
can then confer excess susceptibility to air pollution.
A strength of this study is the detailed residential information that was obtained
from many of the study subjects. We were able to use this information to assess many
potential confounding effects as well as possible effect modification. It is still however
possible that confounding by other undetermined variables exists. As is the case of all
cross-sectional studies, biases may occur when outcome and exposure are collected at the
same time. Because exposure and outcome were determined at about the same time,
many of these same biases are applicable to this study. For example, bias can arise if
exposure assessment was influenced by the children’s lung function or if testing of
children’s lung function was influenced by knowledge of the children’s exposure
assignment. In this study, the main exposures and outcomes of interest were separately
collected in an objective manner by trained staff. Field technicians who measured
children’s lung function had no knowledge of children’s assigned exposure, while
exposure status was ascertained independently of measured outcome. Therefore, the way
we measured lung function and assessed exposure status would minimize these potential
biases and would thus be considered a strength of this study. Another problem in this
study is the fact that for a majority of the children, perceived parental stress was
determined at a single time point 5 years prior to the current study. Thus, it is not clear
whether the levels of stress experienced during the time period of the study were the
same as those levels when they were originally collected. Whether levels of stress
changed or not, this single marker of stress appeared to play a significant role as a
37
modifier of pollutant exposure effects. The role of this marker is unlikely to be biased as
it was obtained before pollutant exposures and outcome were determined.
38
Chapter 5: Conclusion

This study has reaffirmed previous findings that traffic-related pollutants may
impact the development of lung function in children. In this study’s findings, children
living closer to a major road were more likely to have lower FEV
1
and FVC, while close
proximity to a freeway was associated with lower FVC. Our use of estimated residential
ambient exposures revealed an inverse relationship between predicted residential NO
x
,
NO and NO
2
and the outcomes FEV
1
and FVC. This refined measurement of predicted
residential exposures, which takes into account traffic information, population
demography, and topography, was most strongly correlated with our outcomes and has
not been seen before in previous lung function studies performed by the CHS. While this
study did not find a main effect of parental stress, we did find that children whose parents
reported higher levels of stress were more sensitive to traffic-related pollutants’ effects on
lung function. Because of the limited assessment of stress in the context of this study,
further studies are warranted to explore the effect of stress, preferably measures of stress
in the children, and its relationship with traffic-related pollutants and respiratory health.
In light of these findings, improvement in childhood lung function is likely with the
reduction in exposure to traffic-related pollution. Additional analysis of longitudinal lung
function data currently being collected in these study subjects may help to further
elucidate the relationship of traffic-related pollutants to childhood lung function and the
potential modifying effects of stress.

39
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Abstract (if available)

Abstract Background: Studies have shown that decreased lung function is predictive of various health outcomes including asthma, heart disease, and death. Although there are some inconsistencies in the literature, many studies have shown that exposure to various types of air pollutants negatively impact lung growth in children and adults. Furthermore, there is some evidence that social stress may also be of importance in the understanding of respiratory health

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Asset Metadata

Creator Urman, Robert (author)

Core Title Association of traffic-related pollution and stress on childhood lung function

School Keck School of Medicine

Degree Master of Science

Degree Program Preventive Medicine (Health Behavior)

Publication Date 09/14/2010

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

Tag Air pollution,lung function,OAI-PMH Harvest,Stress,Traffic

Place Name California (states)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Gauderman, James W. (committee chair), Avol, Edward (committee member), McConnell, Robert (committee member)

Creator Email rurman@gmail.com,rurman@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m3443

Unique identifier UC1330135

Identifier etd-Urman-4121 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-387235 (legacy record id),usctheses-m3443 (legacy record id)

Legacy Identifier etd-Urman-4121.pdf

Dmrecord 387235

Document Type Thesis

Rights Urman, Robert

Type texts

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

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu