University of Southern California Dissertations and Theses

Disparities in exposure to traffic-related pollution sources by self-identified and ancestral Hispanic descent in participants of the USC Children’s Health Study

(USC Thesis Other)

Disparities in exposure to traffic-related pollution sources by self-identified and ancestral Hispanic descent in participants of the USC Children’s Health Study

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content DISPARITIES IN EXPOSURE TO TRAFFIC-RELATED POLLUTION SOURCES BY SELF-
IDENTIFIED AND ANCESTRAL HISPANIC DESCENT IN PARTICIPANTS OF THE USC
CHILDREN’S HEALTH STUDY

by

Garrett Weaver

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)

June 2014

Copyright 2014 Garrett Weaver

Table of Contents
List of Tables ............................................................................................................................. iii
List of Figures ............................................................................................................................ iv
Abbreviations .............................................................................................................................. v
Abstract...................................................................................................................................... vi
Chapter 1: Introduction ............................................................................................................... 1
Chapter 2: Methods ................................................................................................................... 4
Chapter 3: Results ....................................................................................................................11
3.1 Effect of self-identified Hispanic descent on distance to major roadways: ............................................ 11
3.2 Effect of self-identified Hispanic descent on Caline4 NOx exposure estimates: .................................. 19
3.3 Effect of Native American ancestry on distance to major roadways: ..................................................... 27
3.4 Effect of Native American ancestry on Caline4 NOx exposure estimates: ........................................... 32
Chapter 4: Discussion ...............................................................................................................39
Chapter 5: Conclusion ...............................................................................................................43
References ...............................................................................................................................44

ii

List of Tables

Table 1. Sample Size of subjects with self-identified Hispanic descent data stratified by Hispanic
descent ...................................................................................................................................... 5

Table 2. Sample size of subjects with available genetic data stratified by Hispanic descent ....... 5

Table 3. Coding of socioeconomic status based on education and income level ........................ 6

Table 4. Relationship of Hispanic-descent to continuous variable demographics ......................10

Table 5. Demographics of CHS Participants stratified by self-identified Hispanic-descent ........11

Table 6. Regression of distance to nearest freeway (m) on Hispanic-descent by cohort and
community ................................................................................................................................15

Table 7. Regression of distance to nearest major non-freeway road (m) on Hispanic-descent by
community and cohort ...............................................................................................................16

Table 8. Regression of Caline Non-Freeway NOx (ppb) on Hispanic-descent by community and
cohort ........................................................................................................................................20

Table 9. Regression of Caline Non-Freeway NOx (ppb) on Hispanic-descent by community and
cohort ........................................................................................................................................21

Table 10. Pearson Chi-Square test of proportion of subjects within 500m of a freeway by
Hispanic-descent in overall sample and by community .............................................................23

Table 11. Pearson Chi-Square test of proportion of subjects within 75m of a major non-freeway
road by Hispanic–descent in overall sample and by community ................................................24

Table 12. Effect of Native American ancestry on distance to nearest freeway (m) by community
.................................................................................................................................................27

Table 13. Effect of Native American ancestry on distance to nearest major roadway (m) by
community ................................................................................................................................29

Table 14. Effect of Native American ancestry on freeway CALINE4 (ppb) by community ..........32

Table 15. Effect of Native American ancestry on major non-freeway road CALINE4 (ppb) by
community ................................................................................................................................34

Table 16. Pearson Chi-Square test of proportion of subjects within 500m of a freeway by Native
American ancestry (HW Children Only) in overall sample and by community ............................35

Table 17. Pearson Chi-Square test of proportion of subjects within 75m of a major non-freeway
road by Native American (HW Children Only) ancestry in overall sample and by community ....36

iii

List of Figures

Figure 1. Children's Health Study Communities ......................................................................... 5

Figure 2. Effect of Hispanic-descent on distance to nearest freeway (m) by community ............12

Figure 3. Effect of Hispanic-descent on distance to nearest major non-freeway road (m) by
community ............................................................................................................................... .13

Figure 4. Effect of Hispanic-descent on freeway Caline4 NOx exposure (ppb) by community ...18

Figure 5. Effect of Hispanic-descent on non-freeway major road Caline4 NOx exposure (ppb) by
community ................................................................................................................................19

Figure 6. Effect of Native American ancestry on distance to nearest freeway (m) by community
.................................................................................................................................................26

Figure 7. Effect of Native American ancestry on distance to nearest major non-freeway road (m)
by community ............................................................................................................................28

Figure 8. Effect of Native American ancestry on freeway Caline4 NOx exposure (ppb) by
community ................................................................................................................................31

Figure 9. Effect of Native American ancestry on non-freeway major road Caline4 NOx exposure
(ppb) by community ..................................................................................................................33

iv

Abbreviations

CHS: Children’s Health Study

TRP: Traffic-related Pollution

HW: Hispanic White

NHW: Non-Hispanic White

NA: Native American

More-NA HW: Hispanic White with 50% or more Native American ancestry

Less-NA HW: Hispanic white with less than 50% Native American ancestry

SES: Socioeconomic Status

FRC1: Freeway

FRC3/4: Major non-freeway road

v

Abstract

Significant evidence has accrued that air pollution negatively impacts respiratory health
and other health outcomes. The USC Children’s Health Study has also demonstrated a link
between increasing levels of air pollution, at both regional and local levels, and reductions in
lung development in children. Within Los Angeles and other large urban cities, studies have
investigated whether air pollution exposure differs by ethnicity. Preliminary results from these
studies suggest that certain ethnic groups, such as Hispanics, may be exposed to higher levels
of air pollution compared to non-Hispanics. We aim to further determine whether a disparity
exists in local air pollution exposure between Hispanic white (HW) and non-Hispanic white
(NHW) children of Southern California. Among Hispanic white children, we also intend to test
whether children with a higher percentage of Native American are more exposed to local air
pollution sources as well.
From the Children’s Health Study cohort, we identified 4,279 NHW and 4,138 HW
children with sufficient data to calculate local pollution exposure levels. Among this sample,
2,571 NHW and 2,767 HW children had genetic ancestry data available as well. Multiple linear
regression and logistic regression were used to test the association between Hispanic-descent
and two measures of local air pollution, distance from freeways/major roadways (m) and
Caline4 predicted estimates of NOx levels (ppb). Differences between the groups were
determined both in the overall sample and within each of the 14 communities at baseline entry
into their respective cohorts.
HW children were found to live, on average, 139 m closer to freeways and 55 m closer
to major non-freeway roads in the overall sample compared to NHW children (p < 0.0001).
Additionally, HW children with 50% or more Native American ancestry (More-NA HW) lived 244
m closer to freeways compared to NHW children (p < 0.0001) and 143 m closer to freeways
than Hispanic White children with less than 50% Native American ancestry (Less-NA HW) (p =
0.003). For major non-freeway roads, More-NA HW live 89 m and 44 m closer to a major road
vi

compared to HW children and Less-NA HW, respectively. Based on the secondary outcome
measure, HW children had an estimated freeway Caline4 estimates that are 13% (95% CI: 8%
- 17%) higher than NHW children. The major non-freeway road NOx exposure is also estimated
to be 16% (95% CI: 12% - 19%) higher in HW children compared to NHW children. Among
children with ancestry data, freeway Caline4 estimates were 20% and 14% higher for More-NA
HW compared to NHW children (p < 0.0001) and Less-NA HW (p < 0.0001), respectively.
Additionally, major non-freeway road Caline4 estimates were 27% and 14% higher for More-NA
HW compared to NHW children (p < 0.0001) and Less-NA HW (p = 0.0001), respectively.
Lastly, the odds of HW children being within 500 m of a freeway or 75 m of major non-freeway
road is 1.34 (95% CI: 1.18 – 1.51) and 1.39 (95% CI: 1.21 – 1.58) times that NHW children,
respectively. Within HW, the odds of those with more than 50% NA ancestry being within 500 m
of a freeway or 75 m of a major non-freeway road is 1.41 (95% CI: 1.12 – 1.78) and 1.45 (95 CI:
1.10 – 1.90) times that of those with less than 50% NA ancestry.
These results provide strong evidence that a differences exist in local air pollution
exposure between HW and NHW children. With knowledge of this potential difference in
exposure levels, we can further investigate whether these discrepancies lead to significant
differences in respiratory and other health-related outcomes between the two groups.

vii

Chapter 1: Introduction

Over the past 40 years, significant changes have occurred in the regulation of air pollution
throughout the United States. One reason for introducing air quality standards is the accrued
evidence from prospective, cross-sectional, and air quality studies on the negative impact of air
pollution on respiratory health
1 - 5, 12, 15 - 16
. A recent study suggests that even with increased
regulation and improved technologies to reduce regional and local pollution sources, many
areas in the US still suffer from high levels of pollutants
1
. Within this particular study, pollution
monitoring sites in the 20
th
percentile or below in terms of air quality were highlighted across the
US. A number of these high pollution monitoring sites were found to be clustered in Los Angeles
County and surrounding communities. The states of New York, New Jersey, and Maryland
combine to form another region with similar levels of high pollution clustering.
Additional studies have also demonstrated the increased burden of air pollution on children
and adolescents in metropolitan areas
3 - 6, 12, 15, 16
. The USC Children’s Health Study (CHS)
continues to address this issue in the greater Los Angeles area. Previous results from the CHS
have demonstrated that children within 75m of a major roadway, a proxy for traffic-related
pollution (TRP) exposure, have higher odds of ever having medically diagnosed asthma
4
. Other
studies in past years successfully developed longitudinal models from annual lung function tests
and pollution measures. By tracking lung function growth over the formative childhood years
and into adolescence, the CHS was able to capture long-term effects of air pollution. Even after
controlling for other known exposures, the models consistently found an adverse effect of air
pollution on lung function and respiratory health outcomes
4 - 6
. Specifically, they found
significant reductions in lung function corresponded with increasing local and regional pollution
exposure.
Some studies have investigated whether exposure to, and effects from, air pollution differed
between certain ethnic groups
8 - 16
. In many cases, investigators focused on impacts for
1

Hispanics and African Americans, with the belief that these racial groups may be at greater risk
of exposure and negative health effects from pollution. In a study conducted within Long Beach,
CA and nearby communities, differences in exposure to traffic sources were determined by
measuring total nearby vehicle miles traveled (VMT) and particulate matter (PM) levels
8
.
Hispanics were found, on average, to have 0.76 higher Vehicle PM Parcel Levels compared to
non-Hispanic white. Another study in the Los Angeles area focused on racial disparities within
urban parks
11
. NO2 concentrations were found to be significantly associated with
neighborhoods and parks that had larger proportions of Hispanic residents. Racial disparities in
air pollution effects are noted in a number of other studies conducted throughout the United
States
8 – 9, 13 - 16
. The methods used to measure pollution exposure vary in technique and
duration of measurement. The prominent pollutants measured in past studies include ozone,
PM 10, PM 2.5, NO, and NO2. Indirect measures of local air pollution have focused on TRP
exposures through methods that quantify traffic exposure based on distance from roadways and
other variables, such as traffic volume.
A comparison of the methods used by previous studies to investigate racial differences in
pollution exposure highlights the advantages of exposure data available within the Children’s
Health Study. Specifically, the available data provides geocoded distances of each participant’s
residence from various road types, model-based estimates of TRP exposure at children’s
homes, and regional pollution exposures over a significant time period (1992 – 2013). Many
other studies were only able to analyze distance from local pollution sources at a nominal level,
whether a participant is within a particular distance of a particular road type. Another advantage
is the method of site selection. Within CHS the goal was to collect samples from recruitment
sites that cover a large spectrum of pollution levels present within and around the Los Angeles
area. Additionally, previous studies with CHS data have demonstrated that distance to freeways
2

and major roadways are correlated with true measures of local air pollution
25
, making them a
suitable choice as an outcome measure for comparative studies of TRP exposure
We intend to further investigate whether a disparity in exposure to local pollution sources
exists based on ethnicity. Specifically, we will determine whether Hispanic white children are
more exposed to TRP compared to NHW children. Within HW children, we will also test whether
those of greater Native American ancestry (determined based on the use of genetic information)
tend to live closer to local pollution sources and have higher residential exposures to TRP.

3

Chapter 2: Methods

Study Design

The Children’s Health Study (CHS) is a prospective study containing 5 cohorts of
children, aged 5 to 18 years, from 16 communities throughout the southern California region
(Figure 1). The earliest cohorts (A, B, and C) were recruited in 1993 from children in 4
th
, 7
th
, and
10
th
grade, respectively. The fourth cohort, D, was recruited in 1996 from children in 4
th
grade,
and the most recent cohort, E, was recruited from children in Kindergarten/1
st
grade in 2002. All
participants in cohorts A through D were followed up to 12
th
grade, while cohort E has been
followed up to 11
th
grade with planned follow up through 12
th
grade. Participant recruitment was
completed through public schools local to each community of interest in order to obtain a
sample that was representative of the population with respect to various factors that include
socioeconomic status and racial distribution.
A parent or legal guardian completed a self-administered baseline and yearly
questionnaire that captured demographic data and risk factors associated with respiratory-
related health outcomes. Informed consent was received from the parent or legal guardian of
each participant and the study protocol was approved by the institutional review board of the
University of Southern California.
Study Subjects

Of the nearly 12,000 participants recruited to the CHS, 5,609 and 4,819 were self-
identified as non-Hispanic white and Hispanic white at baseline, respectively. Due to the large
distance of residences in Lake Gregory (n=914) and Lompoc (n=460) from major roadways, all
subjects in these locations were excluded from analysis. Additionally, 112 non-Hispanic white
and 195 Hispanic white participants did not have valid geocoded data available for their primary
residences. Thus, a total of 4,279 non-Hispanic white and 4,138 Hispanic white participants
from 14 CHS communities were utilized for analysis by self-identified Hispanic descent (Table
1). Genetic data were also collected from participants, which allowed for determination of
4

ancestral origin. Among those participants, 125 did not have geocoded data available for their
primary residence, leaving a total of 2,767 Hispanic white and 2,571 non-Hispanic white
participants for more detailed analysis by ancestry (Table 2).

Figure 1. Children's Health Study Communities

5

Table 1. Sample Size of subjects with self-identified Hispanic descent data stratified by
Hispanic descent

Sample Size
Cohort Year of Enrollment Grade at Entry Non-Hispanic White Hispanic White
A 1993 10 421 180
B 1993 7 438 191
C 1993 4 885 421
D 1996 4 919 555
E 2002 K/1 1,615 2,791
Total: 4,278 4,138

Table 2. Sample size of subjects with available genetic data stratified by Hispanic
descent
Sample Size
Cohort Year of Enrollment Grade at Entry Non-Hispanic White Hispanic White
A 1993 10 174 79
B 1993 7 210 86
C 1993 4 514 228
D 1996 4 595 376
E 2002 K/1 1,077 1,998
Total: 2,570 2,767

Exposure Assessment:
Participant demographics were collected on the baseline questionnaire, including one of
the primary exposures of interest, whether or not the participant is identified as Hispanic white.
Two distinct questions were used to determine each participant’s race and whether their parent
or legal guardian identified them as being of Hispanic origin. The legal guardian selected their
child’s race from a pre-specified list of races. A choice of ‘Other’ was available in the event that
the list did not adequately describe their child’s racial background. Hispanic white was defined
as answering ‘Yes’ for being of Hispanic descent and included all race choices except for those
who answered ‘Asian’ or ‘African American’ for race. Non-Hispanic white is defined as any
participant who was identified as ‘White’ for their race and as not being of Hispanic descent by
their parent or legal guardian at baseline. If Hispanic descent or race was missing for a
participant, they were excluded from analysis. Additional covariates collected via the
questionnaire include household income, education level of the child’s parents, community,
6

gender, and known risk factors for respiratory health outcomes. Socioeconomic status is based
on the total household income and/or education reported by the parent or legal guardian of the
child on the questionnaire. The effect of total household income and education were analyzed
separately and as a combined 3-category variable where the highest socioeconomic status is
defined as having a total household income of more than $100000 USD per year or an income
of more than $15000 USD per year and at least 4 years of college. The second level of
socioeconomic status is defined as a total household income between $15000 and $100000
USD per year and some college or technical school. All other participants with available income
and education level data were categorized in the lowest socioeconomic status category. Use of
this categorical variable was added to match previous analyses completed in the CHS.

Table 3. Coding of socioeconomic status based on education and income level
Education Level

< 12th Grade Grade 12
Some post-high
school
4 years of
college
Some
postgraduate
Income
< $7,500 1 1 1 1 1
$7,500 - 29,999 1 1 1 1 1
$15,000 - 29,999 1 2 3 4 5
$30,000 - $49,999 1 2 3 4 5
$50,000 - 74,999 1 2 3 4 5
> $74,999 5 5 5 5 5
*SES is an ordinal variable from 1 - 5, with 5 being the highest

In a previous candidate gene study, the ancestral origins were determined in a subset of
Hispanic White and non-Hispanic white participants. Through the use of ancestral informative
markers and the program STRUCTURE (Pritchard et al., 2000), the percentage of Caucasian
and Native American ancestry for each participant in the subset was determined. For analysis
purposes, the proportions of Caucasian and Native American ancestry were categorized into
three categories. The first category is made up of only non-Hispanic whites. The other two
groups are made up Hispanic participants created by dividing the Hispanic-white participants
7

based on percentage Native American. One group is composed of Hispanic-white children with
less than 50% Native American ancestry and the other is made of Hispanic-white children with
50% or more Native American ancestry.
Outcome Assessment:
Local exposure to traffic-related pollution (TRP), the primary outcome of interest, was
determined by measuring the distance of each participant’s primary residence from roadways by
use of ESRI ArcGIS Version 9.2 and the TeleAtlas database. ArcGIS was used to geocode
residences and the TeleAtlas database allowed for the standardization of distance measures
across all participants. The roadways are categorized by size, with the largest being freeways,
followed by highways, and the smallest made up of major arterial and collector roadways. Each
variable is determined based on the distance from the residence to the center lane of the
nearest particular road type. The analysis focused on two specific measures, the minimum
distance of each participant’s residence from the nearest freeway (FRC1), and the minimum
distance of each participant’s residence from the nearest major non-freeway road
(FRC3/FRC4). Distance measures were considered as both continuous and binary outcomes.
For the latter, children within 500m of a major freeway (Gauderman et al., 2007) or 75m of a
major non-freeway road (McConnell et al., 2006) were considered as “more exposed” in their
respective analyses.
Model-based estimates of TRP were also determined and used as a measure of local
TRP exposure at each participant’s residence. A previously developed dispersion-based
regression model (CALINE4) was used to determine intra-community air pollution
concentrations of nitrogen oxides (NO
2, NO, NOx) near participants in each community. The
model takes into consideration the distance from roadways, vehicle counts on each roadway,
emission rates, and other conditions that affect pollution levels (P Bensen et al., 1989). The two
outcomes of interest are exposure levels to NO x due to major freeways and exposure levels to
NOx due to major non-freeway roads at each participant’s residence based on CALINE-4
8

estimates. Estimated exposures to NO2 and NO were collinear with estimates of NOx and NO,
and precluding the need to consider each of these pollutants separately in the models. Thus,
reference to “NO x exposures” below should be viewed more generally as an indicator of TRP
exposure that encompasses all of these pollutants.
Statistical Analysis:
Descriptive statistics were calculated to summarize the study sample and to determine
the association of air pollution measures and other covariates with TRP exposure. Within cohort
E, data on primary residence address and move history was used to impute missing baseline
TRP exposure data, which led to an increased sample size.
For primary analyses, multiple linear regression was used to assess whether Hispanic
white children were more exposed to local pollution sources than non-Hispanic white children at
baseline in the overall sample and within communities. Residual plots were used to assess the
linearity, normality, and homoscedasticity assumptions of our linear model. For the distance
measures to nearest freeway and major roadway, a cube root transformation was used to meet
both the linearity and homoscedasticity assumptions. A Box-Cox transformation and residual
analysis were used to determine the cube root transformation. A log transformation was used
for the Caline4 NOx exposure measures to obtain normality following proper diagnostics via
assessment of residuals. Because, untransformed, the residual plots for Caline4 NOx
demonstrated a positive skew. After appropriate transformations, all linear regression
assumptions were met. Community-specific as well as cohort- and community-specific
estimates of the difference in TRP exposure were determined for each outcome measure
through the addition of appropriate interaction terms to the regression models. All regression
analyses of the overall sample were adjusted for community as a fixed effect to account for
community clustering, and cohort was controlled for in any measure of freeway pollution
exposure. Similar regression analyses were completed for Hispanic descent by ancestry as a
categorical variable.
9

Forest plots were developed for comparison of effects across communities, cohorts, and
in the overall sample. Adjustments for income, education, and the 3-category socioeconomic
status were added to the regression models in order to understand factors that may have led to
the spatial distribution of the participants within each community.
Unconditional logistic regression was used to determine whether the odds of living within
500m of major freeway or 75m of a major non-freeway road is greater in Hispanic white children
compared to non-Hispanic white children. Tests of statistical significance are based on a 0.05
significance level. All were analyses completed using SAS (Version 9.3; SAS Institute Inc,
Cary, NC).

10

Chapter 3: Results

Among the participants eligible for analysis based on self-identified Hispanic origin, there
are significant differences in the demographics between Hispanic white (HW) and non-Hispanic
white (NHW) children (Table 5). The distribution of HW and NHW participants varies
significantly among communities (p < 0.0001). Compared to cohorts A-D, cohort E has a
significantly greater number of HW children relative to NHW children (p < 0.0001). There is also
a significant association of Hispanic descent with both parental income level and education level
(p < 0.0001). NHW participants tend to live in families with a higher total household income and
higher parental education level in comparison to HW participants. HW participants are shorter in
height and also weigh less compared to NHW participants (p < 0.001, Table 4). On average,
HW and NHW children live 1,464 m (± 1,261 m) and 1,975 m (± 1.651 m), respectively, away
from a freeway. HW and NHW children were also found to live 399 m (± 487 m) and 652 m (±
813 m), respectively, from a major non-freeway road.
3.1 Effect of self-identified Hispanic descent on distance to major roadways:

In the overall sample, HW children live an average of 139 m closer to a freeway than
NHW children (p<0.0001, Figure 2), after adjustment for cohort and community. In all
communities, except San Bernardino and Santa Maria, we estimate that HW children live closer
to a major freeway than NHW children. However, only in Riverside, Santa Barbara, and Upland
is the difference statistically significant (p < 0.05). The largest difference occurs is in the
community of Santa Barbara, where HW children live 996 m closer to a major freeway
compared to NHW children (p < 0.0001). A significant inverse association in Santa Maria finds
that NHW children live an average of 247 m closer to a major freeway than HW children.
The results are similar for major non-freeway roads, where HW children are estimated to
live an average of 55 m closer to a major non-freeway road compared to NHW children
(p<0.0001, Figure 3). Community-specific estimates show that HW children in Alpine, Lake
11

Elsinore, and Riverside live significantly closer to a major non-freeway road compared to NHW
children (p < 0.05). We again find an inverse, but non-significant, result for San Bernardino and
Santa Maria, where NHW children live closer to major non-freeway roads compared to HW.

Table 4. Relationship of Hispanic-descent to continuous variable demographics
A

N Mean SD
Distance to major freeway (m)
Hispanic White 4138 1,459 1,259
Non-Hispanic White 4278 1,975 1,650
Distance to major non-freeway (m)
Hispanic White 4138 397 484
Non-Hispanic White 4278 652 813
Caline-4 Freeway NOx (ppb)
Hispanic White 4390 6.10 3.80
Non-Hispanic White 4278 3.55 3.91
Caline-4 Non-Freeway NOx (ppb)
Hispanic White 4390 5.44 2.27
Non-Hispanic White 4278 3.45 2.55
Height (cm)+

Hispanic White 3810 127.84 15.04
Non-Hispanic White 3994 137.20 17.32
Weight(lbs)
+

Hispanic White 3810 67.67 29.50
Non-Hispanic White 3993 78.15 33.65
BMI (kg/m
2
)
+

Hispanic White 3808 18.06 3.83
Non-Hispanic White 3993 18.03 3.62
+
Complete data not available for all selected subjects from the CHS Cohort, which led to
inconsistent totals by Hispanic-descent and Overall.
A
Mean value at baseline

12

Table 5. Demographics of CHS Participants stratified by self-identified Hispanic-descent
Variable Frequency p-value
Non-Hispanic White Hispanic White Overall
Community < 0.0001
Alpine 645 (15.07%) 162 (3.91%) 807 (9.59%)
Anaheim
21 (0.49%)
318 (7.68%) 339 (4.03%)

Atascadero 327 (7.64%) 56 (1.35%) 383 (4.55%)
Glendora 280 (6.54%) 139 (3.36%) 419 (4.98%)
Lake Elsinore
498 (11.64%)
294 (7.10%) 792 (9.41%)

Lancaster 278 (6.50%) 125 (3.02%) 403 (4.79%)
Long Beach 245 (5.73%) 301 (7.27%) 546 (6.49%)
Mira Loma 345 (8.06%) 580 (14.02%) 925 (10.99%)
Riverside 330 (7.71%) 441 (10.66%) 771 (9.16%)
San Bernardino 62 (1.45%) 224 (5.41%) 286 (3.40%)
San Dimas 420 (9.82%) 323 (7.81%) 743 (8.83%)
Santa Barbara 113 (2.64%) 307 (7.42%) 420 (4.99%)
Santa Maria 202 (4.72%) 636 (15.37%) 838 (9.96%)
Upland 513 (11.99%) 232 (5.61%) 745 (8.85%)
Overall 4279 (100%) 4138 (100%) 8417 (100%)
Cohort < 0.0001
A 421 (9.84%) 180 (4.35%) 601 (7.14%)

B 438 (10.24%) 191 (4.62%) 629 (7.47%)

C 885 (20.68%) 421 (10.17%) 1306 (15.52%)

D 920 (21.50%) 555 (13.41%) 1475 (17.52%)

E 1615 (37.74%) 2791 (67.45%) 4406 (52.35%)

Overall 4279 (100%) 4138 (100%) 8417 (100%)
Gender

0.33
Male 2152 (50.29%) 2061 (49.81%) 4213 (50.05%)
Female 2127 (49.71%) 2075 (50.14%) 4202 (49.92%)
Missing 0 (0.00%) 2 (0.05%) 2 (0.02%)
Overall 4279 (100%) 4138 (100%) 8417 (100%)
Income Level < 0.0001
< $7,500 132 (3.08%) 339 (8.19%) 471 (5.60%)
$7,500 - 14,999 248 (5.80%) 553 (13.36%) 801 (9.52%)
$15,000 - 29,999 420 (9.82%) 787 (19.02%) 1207 (14.34%)
$30,000 - 49,999 774 (18.09%) 727 (17.57%) 1501 (17.83%)
$50,000 - 74,999 1546 (36.13%) 716 (17.30%) 2262 (26.87%)
>= $75,000 598 (13.98%) 212 (5.12%) 810 (9.62%)
Missing 561 (13.11%) 804 (19.43%) 1365 (16.22%)
Overall 4279 (100%) 4138 (100%) 8417 (100%)
Education Level < 0.0001
< 12th Grade 288 (6.73%) 1343 (32.46%) 1631 (19.38%)
Grade 12 762 (17.81%) 870 (21.02%) 1632 (19.39%)
Some post-high school 1956 (45.71%) 1233 (29.80%) 3189 (37.89%)
4 years of college 585 (13.67%) 221 (5.34%) 806 (9.58%)
Some postgraduate 607 (14.19%) 175 (4.23%) 782 (9.29%)
Missing 81 (1.89%) 296 (7.15%) 377 (4.48%)
Overall 4279 (100%) 4138 (100%) 8417 (100%)
13

14

15

The effect of Hispanic descent on distance to nearest major freeway differs by cohort
within certain communities (p < 0.05, Table 6). HW participants tend to live closer to the nearest
major freeway in all cohorts, but there is a greater difference in the mean distance between HW
and NHW in cohorts A-D compared to E. Within communities, the most noticeable difference
between cohorts occurs in Lake Elsinore. In cohorts A-D, HW children in Lake Elsinore live
significantly closer to a freeway, but in cohort E there is a non-significant inversion of the
association, with NHW children living 121 m closer to a major freeway. Another significant
interaction occurs within Mira Loma, where non-Hispanic white children in cohorts A-D live
closer to a freeway, but in cohort E, Hispanic white children are significantly closer to a freeway.
Overall, after stratifying by cohort, HW children still live significantly closer to major freeways
than NHW children within each cohort stratum (p < 0.01).
In contrast, we did not find significant effect modification due to cohort when comparing
cohorts A-D and cohort E on distance to nearest major non-freeway road (Table 7). In general,
we find that HW participants live significantly closer to major non-freeway roads compared to
NHW within each cohort stratum (p < 0.05). Within Alpine, we do note a large change in the
effect measure, with NHW participants living 50m closer to major non-freeway road in cohorts
A-D, but in cohort E HW participants living 190m closer to a major non-freeway road. Although,
this change seems fairly large, a test of interaction does not find the difference to be significant.

16

Table 6. Regression of distance to nearest freeway (m) on Hispanic-descent by cohort and community
Community All Cohorts Cohort A - D Cohort E
Interaction p-value
β
A
p-value β
B
p-value β
B
p-value
(Cohort*Hispanic-
descent)
C
Alpine -77 0.35 -15 0.90 -98 0.29 0.61
Anaheim 59 0.65 --- --- 51 0.63 ---
Atascadero -128 0.30 -134 0.28 --- --- ---
Glendora -339 0.009* --- --- -305 0.0064* ---
Lake Elsinore 118 0.37 -426 0.0071* 121 0.54 0.026*
Lancaster -99 0.53 -96 0.54 --- --- ---
Long Beach 234 0.012* -10 0.93 -253 0.15 0.27
Mira Loma 71 0.43 300 0.018* -119 0.38 0.028*
Riverside -598 < 0.0001* -533 < 0.0001* -950 <.0001* 0.054
San Bernardino 431 0.0004* --- --- 379 0.0002* ---
San Dimas 5 0.92 -5 0.95 -15 0.84 0.89
Santa Barbara -996 < 0.0001* --- --- -876 <.0001* ---
Santa Maria 247 0.0005* 282 0.0025* 261 0.0085* 0.72
Upland -738 < 0.0001* -315 0.28 110 0.24 0.078
Overall
+
-139 <0.0001* -155 < 0.0001* -111 0.006* 0.61
A
Mean difference in distance to nearest freeway (meters) for Hispanic white as compared to non-Hispanic white,
stratified by community, adjusted for cohort

B
Mean difference in distance to nearest freeway (meters) for Hispanic white as compared to non-Hispanic
white, stratified by community and cohort
C
Test for interaction of cohort and Hispanic descent, stratified by community
+
Overall analysis adjusted for community and adjusted for cohorts in ‘All Cohorts’ estimate
* Significant at 0.05 level

17

Table 7. Regression of distance to nearest major non-freeway road (m) on Hispanic-descent by community and cohort
Community
All Cohorts Cohort A - D Cohort E Interaction p-value

β
A
p-value β
B
p-value β
B
p-value
(Cohort*Hispanic-
descent)
C
Alpine -137 0.0078* 50 0.57 -190 0.0014* 0.11
Anaheim -44 0.41 -- -- -43 0.41 ---
Atascadero -130 0.27 -127 0.27 -- -- ---
Glendora -71 0.053 -- -- -69 0.052 ---
Lake Elsinore -137 0.0004* -229 <0.0001* -100 0.10 0.087
Lancaster -40 0.21 -39 0.21 -- -- ---
Long Beach -48 0.02* -18 0.58 -48 0.15 0.26
Mira Loma -37 0.17 5.6 0.89 -27 0.53 0.52
Riverside -104 <0.0001* -86 0.0058* -138 0.0003* 0.30
San Bernardino 27 0.48 -- -- 26 0.48 ---
San Dimas -49 0.033* -26 0.026* -72 0.0035* 0.23
Santa Barbara -85 0.0036* -- -- -83 0.084 ---
Santa Maria 42 0.11 6.5 0.83 48 0.31 0.35
Upland -5.7 0.79 3,9 0.91 -21 0.45 0.49
Overall
+
-55 < 0.0001* -53 < 0.0001* -54 <0.0001* 0.87
A
Mean difference in distance to nearest major non-freeway (meters) for Hispanic white as compared to non-Hispanic
white, stratified by community

B
Mean difference in distance to nearest major non-freeway (meters) for Hispanic white as compared to non-Hispanic
white, stratified by community and cohort
C
Test for interaction of cohort and Hispanic descent, stratified by community
+
Overall analysis adjusted for community
* Significant at 0.05 level
18

3.2 Effect of self-identified Hispanic descent on Caline4 NOx exposure estimates:

In the overall sample, Caline4 estimates of major freeway NOx concentration (ppb) are
13% (95% CI: 8.6% – 17.5%) higher in HW children compared to NHW children (Figure 4). In 8
of the 14 communities, community-specific estimates of Caline4 NOx major freeway exposure
are higher in HW children than NHW children. Significantly higher estimates of Caline4 NOx
major freeway exposure are present in Riverside, Santa Barbara, and Upland (p < 0.05). The
largest difference occurs in Santa Barbara, where HW children have a 132% (95% CI: 95% –
176%) higher Caline4 estimate of major freeway NOx concentration (ppb).
Similarly, Caline4 estimates of major non-freeway road NOx concentration (ppb) are
16% (95% CI: 13% – 20%) higher in HW children compared to NHW children (Figure 5). In all
communities, except Long Beach, estimates of Caline4 major non-freeway NOx exposure are
higher in HW children than NHW children. Significantly higher estimates are present in the
communities of Lake Elsinore, Lancaster, Mira Loma, Riverside, San Bernardino, and Santa
Barbara, and Upland (p < 0.05). Within San Bernardino, the highest exposure difference exists,
where the population-average Caline4 estimate of major non-freeway NOx exposure is 51%
(95% CI: 27% – 79%) higher for HW children compared to NHW children.
The effect of Hispanic descent on Caline4 estimates of major freeway NOx exposure
(ppb) does not differ significantly by cohort (p=0.17, Table 8), but does differ within particular
communities. HW children have significantly higher Caline4 estimates of major freeway NOx
exposure than NHW within each cohort stratum (p < 0.05). The effect of Hispanic descent
significantly differs by cohort within the communities of Lake Elsinore, Mira Loma, and Riverside
(p < 0.05). Caline4 estimates of major non-freeway NOx exposure do differ across cohorts (p <
0.001, Table 9). Population-average estimates of major non-freeway NO
X concentration (ppb)
were higher in cohort E compared to the earlier cohorts, A-D.

19

+
20

+
21

Table 8. Regression of Caline4 Freeway NOx concentration (ppb) on Hispanic-descent by community and cohort
Community All Cohorts Cohorts A - D Cohort E Interaction p-value
β
A
p-value β
B
p-value β
B
p-value (Cohort*Hispanic-descent)
C
Alpine 6.4% 0.25 -9.4% 0.29 13.1% 0.16 0.17
Anaheim 16.7% 0.26 -- -- -19.4% 0.18 --
Atascadero 9.5% 0.30 17.6% 0.12 -- -- --
Glendora 11.6% 0.08 -- -- 12.7% 0.11 --
Lake Elsinore 25.4% < 0.0001* 25.5% 0.0029* -5.3% 0.48 0.019*
Lancaster 15.6% 0.03* 10.3% 0.21 -- -- --
Long Beach -3.4% 0.51 -7.2% 0.40 12.2% 0.30 0.13
Mira Loma 22.8% < 0.0001* -3.3% 0.63 15.3% 0.10 0.029*
Riverside 31.5% < 0.0001* 39.6% < 0.0001* 95.4% < 0.0001* 0.005*
San Bernardino 50.7% < 0.0001* -- -- 23.4% 0.04* --
San Dimas 5.8% 0.21 -0.4% 0.96 9.0% 0.27 0.28
Santa Barbara 43.8% < 0.0001* -- -- 131.9% <0.0001* --
Santa Maria 6.9% 0.18 -15.4% 0.02* -19.6% 0.03* 0.59
Upland 10.7% 0.03* 18.1% 0.09 1.3% 0.86 0.33
Overall
+
13% < 0.0001* 9.8% 0.001* 15.7% < 0.0001* 0.17
A
Mean percent difference in Caline4 Freeway NOx concentration (ppb) for Hispanic white as compared to non-
Hispanic white, stratified by community
B
Mean percent difference in Caline4 Freeway NOx concentration (ppb) for Hispanic white as compared to non-
Hispanic white, stratified by community and cohort
C
Test for interaction of cohort and Hispanic descent, stratified by community
+
Overall analysis adjusted for community
* Significant at 0.05 level
22

Table 9. Regression of Caline Non-Freeway NOx (ppb) on Hispanic-descent by community and cohort
Community All Cohorts Cohorts A - D Cohort E Interaction p-value
β
A
p-value β
B
p-value β
B
p-value (Cohort*Hispanic-descent)
C
Alpine 6.4% 0.25 -13.8% 0.06 35.2% < 0.0001* 0.007*
Anaheim 16.7% 0.26 -- -- 16.7% 0.26 --
Atascadero 9.5% 0.30 9.4% 0.30 -- -- --
Glendora 11.6% 0.08 -- -- 11.6% 0.08 --
Lake Elsinore 25.4% < 0.0001* 39.1% < 0.0001* 11.3% 0.10 0.026*
Lancaster 15.6% 0.03* 14.5% 0.04* -- -- --
Long Beach -3.4% 0.51 6.1% 0.43 0.002% 0.99 0.29
Mira Loma 22.8% < 0.0001* 12.9% 0.04* 13.8% 0.07 0.89
Riverside 31.5% < 0.0001* 17.2% 0.01* 39.6% < 0.0001* 0.035*
San Bernardino 50.7% < 0.0001* -- -- 50.7% < 0.0001* --
San Dimas 5.8% 0.21 8.3% 0.21 8.8% 0.19 0.95
Santa Barbara 43.8% < 0.0001* -- -- 43.8% < 0.0001* --
Santa Maria 6.9% 0.18 1.3% 0.84 11.6% 0.20 0.28
Upland 10.7% 0.03* 2.2% 0.79 15.1% 0.03* 0.41
Overall
+
16.3% < 0.0001* 8.5% 0.0001* 23.9% <0. 0001* < 0.0001*
A
Mean difference in Caline non-freeway NOx (ppb) for Hispanic white as compared to non-Hispanic white, stratified by
community
B
Mean difference in Caline non-freeway NOx (ppb) for Hispanic white as compared to non-Hispanic white, stratified by
community and cohort
C
Test for interaction of cohort and Hispanic descent, stratified by community
+
Overall analysis adjusted for community
*

Significant at 0.05 level
23

Based on previous studies, living within 500m of a major freeway was associated with
significantly lower lung function growth (Gauderman et al., 2007) and within 75m of a major non-
freeway road was associated with significantly increased risk of asthma development
(McConnell et al., 2006). We found that Hispanic white children were 1.34 (95% CI: 1.18 –
1.51) times more likely to live within 500m of a major freeway compared to non-Hispanic white
(Table 10). The highest community-specific odds ratio was observed in Santa Barbara, where
Hispanic white children are 5.61 (95% CI: 3.44 – 9.17) times as likely to live within 500m of a
major freeway compared to non-Hispanic white. Of the 16 communities, Hispanic white children
are significantly more likely to live within 500m of a major freeway in 5 of them (p < 0.05). In San
Bernardino, there is a significant inversion of the association, with a greater proportion of non-
Hispanic white children within 500m of a major freeway.
Hispanic white children are also 1.39 (95% CI: 1.21 – 1.58) times as likely to live within
75m of a major non-freeway road (Table 11). Within Mira Loma, Riverside, and Santa Barbara,
Hispanic white children are significantly more likely to live within 75m of a major non-freeway
road (p < 0.05). In all communities, except Atascadero and San Bernardino, we find a greater
proportion of Hispanic white children living within 75m of a major non-freeway road.

24

Table 10. Pearson Chi-Square test of proportion of subjects within 500m of a freeway by Hispanic-descent in
overall sample and by community
Community
A
Proportion within 500m of major freeway Odds Ratio
B
95% CI p-value
Hispanic White Non-Hispanic White
Alpine 34.12% 32.50%
1.01 (0.71, 1.45)
0.95
Anaheim 62.14% 71.43%
0.66 (0.25, 1.73)
0.40
Atascadero 32.20% 32.76%
0.98 (0.54, 1.77)
0.95
Glendora 18.18% 11.03%
1.79 (1.02, 3.16)
0.043*

Lake Elsinore 10.65% 9.36%
1.04 (0.65, 1.67)
0.86
Lancaster 3.94% 6.03%
0.65 (0.23, 1.79)
0.40
Long Beach 15.58% 19.76%
0.62 (0.4, 0.96)
0.033*
Mira Loma 16.56% 6.92%
2.20 (1.37, 3.53)
0.001*
Riverside 37.14% 19.10%
2.30 (1.65, 3.22)
< 0.0001*
San Bernardino 26.45% 47.69%
0.39 (0.22, 0.69)
0.0012*
San Dimas 29.13% 22.92%
1.29 (0.93, 1.79)
0.13
Santa Barbara 62.12% 22.61%
5.61 (3.44, 9.17)
< 0.0001*
Santa Maria 22.19% 22.82%
0.86 (0.59, 1.25)
0.42
Upland 17.15% 10.96%
1.50 (0.97, 2.32)
0.071
Overall
+
28.34% 19.18% 1.34 (1.18, 1.51) < 0.0001*
A
Community analyses adjusted for cohort

B
Odds of living within 500m of a freeway for Hispanic white compared to non-Hispanic white (reference)
+
Overall analysis adjusted for community and cohort
* Significant at 0.05 level

25

Table 11. Pearson Chi-Square test of proportion of subjects within 75m of a major non-freeway road by
Hispanic–descent in overall sample and by community
Community
A
Proportion within 75m of major non-freeway Odds Ratio
B
95% CI p-value
Hispanic White Non-Hispanic White
Alpine
17.65% 15.81%
1.12 (0.72, 1.75)
0.61
Anaheim
23.99% 4.76%
6.31 (0.83, 47.75)
0.07
Atascadero
8.47% 10.92%
0.75 (0.28, 2.00)
0.57
Glendora
15.38% 12.46%
1.28 (0.72, 2.27)
0.40

Lake Elsinore
11.61% 8.97%
1.31 (0.82, 2.08)
0.26
Lancaster
18.90% 12.41%
1.63 (0.93, 2.89)
0.09
Long Beach
20.78% 15.73%
1.34 (0.86, 2.09)
0.19
Mira Loma
12.91% 5.48%
2.45 (1.45, 4.15)
0.0008*
Riverside
20.44% 10.45%
2.16 (1.42, 3.28)
0.0003*
San Bernardino
18.18% 23.08%
0.74 (0.38, 1.44)
0.38
San Dimas
17.72% 15.97%
1.11 (0.76, 1.63)
0.58
Santa Barbara
28.48% 13.91%
2.47 (1.38, 4.4)
0.0023*
Santa Maria
12.29% 10.68%
1.14 (0.69, 1.87)
0.62
Upland
19.67% 19.04%
1.01 (0.69, 1.49)
0.95
Overall
+
17.56% 13.12% 1.39 (1.21, 1.58) < 0.0001*
A
Community analyses adjusted for cohort

B
Odds of living within 75m of a major non-freeway road for Hispanic white compared to non-Hispanic
white (reference)

+
Overall analysis adjusted for community and cohort
* Significant at 0.05 level

26

3.3 Effect of Native American ancestry on distance to major roadways:

As described previously, HW children were categorized into those with less than 50%
NA ancestry (Less-NA HW) and those with 50% or more NA ancestry (More-NA HW). A third
category of participants with genetic data identified as NHW were used as the reference group
in these analyses. Upon dividing HW participants into two groups based on percentage of
Native American (NA) ancestry, we found that distance to the nearest freeway had a tendency
to decrease as percentage of NA ancestry increased in the overall sample (Figure 6). We found
that More-NA HW children and Less-NA HW children live 244m and 101m closer to major
freeways compared to NHW children, respectively. Significant variation in the effect of Native
American ancestry on distance to major freeways exists between communities (p < 0.0001).
Interestingly, we find the effect of Native American ancestry (More- vs. Less-NA within HW) on
distance to major freeways aligns with self-identified Hispanic descent results (HW vs. NHW) in
the communities of Glendora, Riverside, Santa Barbara, and Upland (p < 0.05). In addition, we
find a non-significant inverse association comparing More- to Less-NA HW in the communities
of San Bernardino and Santa Maria that are in agreement with the results based on self-
identified Hispanic descent. Distance to nearest freeway also varies by cohort due to the
increased number of Hispanic white participants in cohort E compared to earlier cohorts (p <
0.0001). In several communities (Glendora, Riverside, Santa Barbara), More-NA HW children
live significantly closer to a freeway compared to Less-NA HW children (p < 0.05). Within
Riverside, Santa Barbara, and Upland, Less-NA HW children live significantly closer to a
freeway than NHW children as well (p < 0.05).
More-NA HW and Less-NA HW also live 44m and 90m closer to a major non-freeway
road than NHW children, respectively (Figure 7). There is again significant variation in the effect
of Native American ancestry on distance to major non-freeway roads by community and cohort
(p < 0.0001). More-NA HW children in half of the 14 communities live significantly closer to a
major roadway compared to Caucasians (p < 0.05). Additionally, within the communities of
27

Riverside and Upland, we find that More-NA HW children live significantly closer to major
roadways in comparison to Less-NA HW children (p < 0.05). Less-NA HW children live
significantly closer to a major roadway than NHW children within the communities of Alpine and
San Dimas (p < 0.05). Overall, HW participants with a high percentage of Native American
ancestry (> 50%) live significantly closer to traffic-related pollution sources based on distance
from freeways and major roadways.

28

Table 12. Effect of Native American ancestry on distance to nearest freeway (m) by community
Community
A
Less-NA HW vs. NHW More-NA HW vs. NHW More-NA HW vs. Less-NA HW

Difference
B
p-value Difference
C
p-value Difference
D
p-value
Alpine -168.33 0.16 98.65 0.58 266.97 0.18
Anaheim 68.14 0.81 -43.82 0.84 -111.96 0.53
Atascadero -154.45 0.32 -515.27 0.21 -360.82 0.36
Glendora -289.94 0.1 -777.86 0.0002* -487.92 0.028*
Lake Elsinore 349.47 0.15 250.52 0.22 -98.95 0.73
Lancaster 230.07 0.4 -144.29 0.57 -374.36 0.27
Long Beach 195.64 0.28 267.52 0.0502 71.89 0.71
Mira Loma 125.72 0.47 61.85 0.62 -63.87 0.7
Riverside -306.95 0.045* -903.64 < 0.0001* -596.70 < 0.0001*
San Bernardino 188.59 0.33 655.53 < 0.0001* 466.94 0.018*
San Dimas 72.85 0.42 -34.77 0.69 -107.62 0.31
Santa Barbara -671.35 0.003* -1295.26 < 0.0001* -623.91 < 0.0001*
Santa Maria 161.47 0.2 302.91 0.0028* 141.45 0.22
Upland -583.63 0.0007* -963.06 < 0.0001* -379.42 0.06
Overall
+
-101.43 0.0033* -244.69 < 0.0001* -143.26 0.0029*
A
Community analyses adjusted for cohort

B
Mean difference in distance to nearest freeway (m) for Less-NA HW as compared to NHW (reference)
C
Mean difference in distance to nearest freeway (m) for More-NA HW as compared to NHW (reference)
D
Mean difference in distance to nearest freeway (m) for More-NA HW as compared to Less-NA HW (reference)
+ Overall analysis adjusted for community and cohort
* Significant at 0.05 level
29

30

Table 13. Effect of Native American ancestry on distance to nearest major roadway (m) by community
Community
A

Less-NA HW vs. NHW More-NA HW vs. NHW More-NA HW vs. Less-NA HW
Difference
B
p-value Difference
C
p-value Difference
B
p-value
Alpine
-226.41 0.02* -181.76 0.032* -44.64 0.69
Anaheim
-89.56 0.28 -111.33 0.29 21.77 0.7
Atascadero
668.04 0.45 -117.70 0.25 785.74 0.18
Glendora
-158.01 0.36 -45.66 0.0061* -112.36 0.07
Lake Elsinore
-143.83 0.18 -97.87 0.016* -45.96 0.55
Lancaster
-33.97 0.80 -14.06 0.53 -19.90 0.77
Long Beach
-87.71 0.14 -63.22 0.0039* -24.48 0.5
Mira Loma
-54.74 0.57 30.79 0.13 -85.53 0.077
Riverside
-153.66 0.98 -1.47 < 0.0001* -152.19 0.0005*
San Bernardino
13.96 0.37 -61.46 0.81 75.41 0.18
San Dimas
-84.99 0.024* -81.88 0.022* -3.12 0.94
Santa Barbara
-117.50 0.57 -35.26 0.0006* -82.25 0.08
Santa Maria
41.49 0.93 4.11 0.28 37.38 0.37
Upland
-61.36 0.33 32.91 0.07 -94.28 0.025*
Overall
+
-44.83 0.0033* -89.66 < 0.0001* -44.83 0.0029*
A
Community analyses adjusted for cohort
B
Mean difference in distance to nearest major non-freeway road (m) for Less-NA HW as compared to NHW (reference)
C
Mean difference in distance to nearest major non-freeway road (m) for More-NA HW as compared to NHW (reference)
D
Mean difference in distance to nearest major non-freeway road (m) for More-NA HW as compared to Less-NA HW (reference)
+ Overall analysis adjusted for community and cohort
* Significant at 0.05 level

31

3.4 Effect of Native American ancestry on Caline4 NOx exposure estimates:

In addition to living closer to major freeways and major non-freeway roads, CALINE4
estimates of major freeway NOx exposure increase with increasing Native American ancestry.
Less-NA HW and More-NA HW have 5.6% (95% CI: -1% – 12.6%) and 20.8% (95% CI: 13.9%
– 28%), respectively, higher CALINE4 NOx (ppb) concentration levels compared to Caucasian
participants (Figure 8). As with the distance measures, exposure estimates vary significantly by
community (p < 0.0001). Significantly higher freeway NOx estimates exist at the community-
level in Glendora, Upland, Santa Barbara, and Riverside for More-NA HW and in Upland and
Santa Barbara for Less-NA HW compared to NHW participants (p < 0.05). A significant inverse
association occurs in Long Beach, where NHW have significantly higher freeway CALINE4
estimate compared to More-NA HW (p < 0.0001). Among Hispanic participants, More-NA HW
have significantly higher freeway CALINE4 compared to Less-NA HW in the overall sample (p <
0.0001) and in the communities of Riverside, Santa Barbara, and Upland (p < 0.05). We also
note that CALINE4 freeway exposures are significantly higher within cohort E for all subgroups
by NA ancestry compared to subjects in cohorts A-D.
CALINE4 estimates of major non-freeway NOx exposure increase with Native American
ancestry in the overall sample (Figure 9). Less-NA HW and More-NA HW have 7% (95% CI: 2%
– 12.2%) and 27% (95% CI: 21.6% – 32.5%) higher CALINE4 NOx (ppb) exposure levels
compared to Caucasian participants (p < 0.05). Although variation occurs across communities,
More-NA HW have significantly higher CALINE4 NOx exposure estimates than NHW children in
7 of the 14 communities (p < 0.05). Additionally, in San Bernardino, Less-NA HW have
significantly higher CALINE4 exposure estimates than NHW children (p=0.02). Upland is the
only community that demonstrates a significant inverse association of Native ancestry with
CALINE4 estimates of both major freeway and major non-freeway NOx exposure estimates.
Higher estimates of non-freeway NOx estimates for More-NA HW compared to Less-NA HW
occur in 4 of the communities (Lake Elsinore, Riverside, Santa Barbara, and Upland) (p < 0.05).
32

+
+
33

Table 14. Effect of Native American ancestry on freeway CALINE4 (ppb) by community
Community
A
Less-NA HW vs. NHW More-NA HW vs. NHW More-NA HW vs. Less-NA HW

Difference
B
p-value Difference
C
p-value Difference
D
p-value
Alpine 0.1% 0.99 -16.8% 0.19 -16.9% 0.26
Anaheim -19.6% 0.52 -9.6% 0.71 12.5% 0.59
Atascadero 16.4% 0.31 63.8% 0.29 40.8% 0.48
Glendora 8.0% 0.46 36.8% 0.02* 26.6% 0.13
Lake Elsinore -17.2% 0.07 -10.9% 0.19 7.6% 0.54
Lancaster -3.2% 0.83 11.5% 0.46 15.2% 0.46
Long Beach -20.8% 0.08 -43.3% <.0001* -28.4% 0.01*
Mira Loma -7.0% 0.50 -3.8% 0.62 3.5% 0.73
Riverside 15.6% 0.18 83.6% <.0001* 58.7% <.0001*
San Bernardino -0.1% 1.00 18.5% 0.32 18.6% 0.35
San Dimas -7.5% 0.39 -1.6% 0.87 6.4% 0.57
Santa Barbara 42.1% 0.03* 174.7% <.0001* 93.4% <.0001*
Santa Maria 2.1% 0.86 -4.7% 0.60 -6.7% 0.49
Upland 45.7% <.0001* 161.6% <.0001* 79.5% <.0001*
Overall
+
5.6% 0.10 20.8% < 0.0001* 14.4% 0.0001*
A
Community analyses adjusted for cohort
B
Mean difference in Caline4 non-freeway NOx concentration (ppb) for Less-NA HW as compared to NHW
(reference)
C
Mean difference in Caline4 non-freeway NOx concentration (ppb) for More-NA HW as compared to NHW
(reference)
D
Mean difference in Caline4 non-freeway NOx concentration (ppb) for More-NA HW as compared to Less-NA
HW (reference)
+ Overall analysis adjusted for community and cohort
* Significant at 0.05 level

34

+
+
35

In the overall sample, approximately 19% of Less-NA HW and 29% of More-NA HW
children live within 500m of a freeway. More-NA HW children are 1.41 (95% CI 1.12 – 1.78)
times as likely to live within 500m of a freeway, after adjusting for both community and cohort
(Table 16). The largest difference occurs in Upland, where More-NA HW children are three
times as likely to live within 500m of a freeway compared to Less-NA HW children (p = 0.0036).
More-NA HW children are also 1.45 (95% CI: 1.1 – 1.9) times as likely to live within 75m
of a major roadway compared to Less-NA HW children, after adjustment for community and
cohort (Table 17). In eleven of the communities we see an increased odds of within 75m of
major roadway in More-NA HW children, but only in Mira Loma and Riverside is the increase
statistically significant (p < 0.05). In Mira Loma, More-NA HW children are over three times as
likely to live within a 75m of a major roadway compared to Less-NA HW.
Table 15. Effect of Native American ancestry on major non-freeway road CALINE4 (ppb) by community
Community
A
Less-NA HW vs. NHW More-NA HW vs. NHW More-NA HW vs. Less-NA HW

Difference
B
p-value Difference
C
p-value Difference
B
p-value
Alpine 1.8% 0.82 12.5% 0.27 10.5% 0.42
Anaheim 36.4% 0.23 26.4% 0.25 -7.3% 0.65
Atascadero 14.3% 0.23 -2.3% 0.95 -14.5% 0.66
Glendora 11.1% 0.18 13.3% 0.23 1.9% 0.87
Lake Elsinore -1.1% 0.89 41.6% <.0001* 43.1% <.0001*
Lancaster 0.4% 0.97 24.5% 0.05* 24.1% 0.13
Long Beach 6.2% 0.54 -5.4% 0.45 -11.0% 0.24
Mira Loma 11.7% 0.17 28.4% <.0001* 14.9% 0.06
Riverside 16.0% 0.06 53.0% <.0001* 31.9% 0.0005*
San Bernardino 50.9% 0.02* 67.1% <.0001* 10.7% 0.45
San Dimas 13.1% 0.07 13.9% 0.06 0.7% 0.93
Santa Barbara 21.4% 0.12 57.3% <.0001* 29.5% 0.02*
Santa Maria 1.1% 0.90 9.1% 0.22 7.9% 0.31
Upland -12.9% 0.04* 44.3% <.0001* 65.6% <.0001*
Overall
+
7% 0.0052* 27.0% < 0.0001* 14.4% 0.0001*
A
Community analyses adjusted for cohort
B
Mean difference in Caline4 Freeway NOx concentration (ppb) for Less-NA HW as compared to NHW
(reference)
C
Mean difference in Caline4 Freeway NOx concentration (ppb) for More-NA HW as compared to NHW
(reference)
D
Mean difference in Caline4 Freeway NOx concentration (ppb) for More-NA HW as compared to Less-NA HW
(reference)
+ Overall analysis adjusted for community and cohort
* Significant at 0.05 level
36

Table 16. Pearson Chi-Square test of proportion of subjects within 500m of a freeway by Native American
ancestry (HW Children Only) in overall sample and by community
Community
A
Proportion within 500m of freeway Odds Ratio
B
95% CI p-value
< 50% NA >= 50% NA
Alpine 33.3% 28.6% 0.83 (0.34, 2.02) 0.68
Anaheim 57.1% 63.5% 1.30 (0.44, 3.9) 0.64
Atascadero 27.3% 33.3% 1.74 (0.14, 22.02) 0.67
Glendora 12.5% 30.0% 3.00 (1.16, 7.73) 0.023*

Lake Elsinore 6.7% 4.3% 0.63 (0.18, 2.25) 0.48
Lancaster 0.0% 5.6% -- -- --
Long Beach 10.0% 11.9% 1.03 (0.36, 2.98) 0.95
Mira Loma 11.0% 14.1% 1.26 (0.58, 2.74) 0.55
Riverside 24.7% 35.7% 1.80 (0.99, 3.25) 0.053
San Bernardino 39.1% 20.6% 0.40 (0.16, 1.03) 0.058
San Dimas 23.9% 32.7% 1.59 (0.87, 2.9) 0.13
Santa Barbara 33.3% 60.6% 3.07 (1.42, 6.66) 0.0045*
Santa Maria 25.3% 17.9% 0.64 (0.36, 1.16) 0.14
Upland 9.6% 26.7% 3.47 (1.51, 7.98) 0.0034*
Overall
+
19.11% 29.16% 1.41 (1.12, 1.78) 0.0036*
A
Community analyses adjusted for cohort

B
Odds of living within 500m of a freeway for More-NA HW compared to HW less NA (reference)
+
Overall analysis adjusted for community and cohort
* Significant at 0.05 level

37

Table 17. Pearson Chi-Square test of proportion of subjects within 75m of a major non-freeway road by
Native American (HW Children Only) ancestry in overall sample and by community
Community
A
Proportion within 75m of major non-freeway Odds Ratio
B
95% CI p-value
< 50% NA >= 50% NA
Alpine 9.7% 20.0% 2.26 (0.72, 7.06) 0.16
Anaheim 28.6% 20.2% 0.63 (0.19, 2.12) 0.46
Atascadero 3.0% 0.0% -- -- --
Glendora 10.0% 12.5% 1.29 (0.39, 4.22) 0.68

Lake Elsinore 5.3% 8.5% 1.68 (0.51, 5.58) 0.40
Lancaster 17.1% 16.7% 1.02 (0.29, 3.54) 0.98
Long Beach 18.0% 21.7% 1.24 (0.54, 2.85) 0.62
Mira Loma 3.7% 11.3% 3.43 (1.02, 11.52) 0.047*
Riverside 6.2% 21.6% 4.22 (1.6, 11.16) 0.0037*
San Bernardino 34.8% 10.7% 0.22 (0.08, 0.62) 0.0041*
San Dimas 11.5% 14.4% 1.28 (0.58, 2.85) 0.54
Santa Barbara 15.2% 25.4% 1.90 (0.7, 5.17) 0.21
Santa Maria 7.6% 7.8% 1.04 (0.41, 2.64) 0.93
Upland 15.4% 20.0% 1.38 (0.63, 3) 0.42
Overall
+
10.9% 15.6% 1.45 (1.10, 1.90) 0.0079*
A
Community analyses adjusted for cohort

B
Odds of living within 500m of a major freeway for More-NA HW compared to Less-NA HW (reference)

+
Overall analysis adjusted for community and cohort
* Significant at 0.05 level

38

Chapter 4: Discussion

We have demonstrated significant evidence that both Hispanic white children (HW) and
children with a higher percentage of Native American ancestry (More-NA HW) within HW
children are exposed to higher levels of local air pollution compared to NHW children. Within the
overall sample and in particular communities, HW children and More-NA HW live significantly
closer to a freeway or major non-freeway road in comparison to NHW children. Additionally,
CALINE4 estimates of NOx concentration (ppb) were significantly higher for HW and More-NA
HW children compared to NHW. As mentioned previously, previous CHS studies demonstrated
the negative effects of TRP exposure on lung function and respiratory health outcomes
4 - 6
. By
showing that Hispanic descent and Native American ancestry are associated with TRP
exposure, there is a potential for the association between TRP and lung function or respiratory
outcomes to be confounded by either Hispanic descent or NA ancestry.
The distance to roadways and CALINE4 estimates vary significantly across communities
and cohorts. The differences are most likely due to the natural variation and concentration of
roadways within and around each community. This variation also affects our ability to identify
significant differences between HW and NHW children with respect to TRP exposure. We
believe this is due to the fact that the maximum distance a family can live from a freeway or
major non-freeway road differs between communities. An example of this discrepancy is found
by comparing Long Beach and Santa Barbara. With a smaller maximum distance from the
nearest freeway or major roadway in Long Beach compared to Santa Barbara, the ability to
detect differences between subgroups, would most likely require a larger sample for Long
Beach compared to Santa Barbara. Based on the results, we tended to find significant exposure
differences within communities where the maximum potential distance from a freeway or major
non-freeway road was larger. The sampling distribution is also influenced by the methodology
for site selection. We found that HW and NHW children in cohort E lived significantly closer to
39

major freeways compared to HW and NHW in cohorts A – D. A difference explained by changes
in sampling sites, including the addition and removal of particular communities in cohort E. The
ratio of HW and NHW also differs between cohorts A – D and cohort E, with a larger proportion
of HW in cohort E compared to cohorts A – D. Due to an unequal proportion of HW subjects
across cohorts and significant differences in TRP exposure, cohort has the potential to bias the
results. By controlling for cohort, we were able to account for changes in study design with
respect to site selection. These significant differences between cohorts highlight the importance
of sampling methodology when conducting spatial analysis, especially within longitudinal studies
with shifting sampling sites and research questions.
Comparison of the effect estimates obtained for Hispanic descent by self-identification and
by ancestry yield consistent patterns in most cases. Thus, self-identification of ethnicity is a valid
indicator of ancestral origins to a certain degree. However, differences did occur between Less-
NA HW (< 50% Native American) and More-NA HW (>=50% Native American). In a subset of
communities (Riverside, Santa Barbara, and Upland), we even discovered that More-NA HW
have higher TRP exposure compared to Less-NA HW. We were able to better refine TRP
exposure differences within HW and have also open up the potential to investigate GxE
interactions in the sample and subpopulations that are at greater health risks.
Beyond investigating the effect of ethnicity and genetic ancestry on pollution exposure, there
may also be interest in understanding how this distribution of HW and NHW participants
occurred within the communities. Demographics of the two groups show that HW tended to
have lower family income and/or lower education levels in comparison NHW participants. Lower
income and education levels were also found to be associated with living closer to a
freeway/major roadway and higher CALINE4 estimates. Addition of income, education, or the
derived SES variable to the regression models leads to a reduced, but still significant, effect of
Hispanic descent and/or Native American ancestry on each TRP exposure. One possible
explanation that has yet to be verified is that the cost of living increases as distance from all
40

major roadways increases. Thus, HW participants of lower income/SES are more likely to live
closer to TRP pollution sources for economic reasons. At this point, the data are not available to
fully determine what led to spatial distributions currently observed within the CHS sample,
although this is certainly a question of future interest.
The results of the study align with previous results completed within several other studies,
specifically, that HW participants have higher levels of TRP exposure compared to NHW. Other
studies have also made similar adjustment for socioeconomic status and found SES to be a
potential explanation for the spatial distribution of sample participants. There are several
differences in the study approach to others
8 – 9, 13 - 16
. Unlike several of the previous studies, the
focus within the CHS is on children, which have been shown to be at risk of higher exposure to
pollution due to a number of factors
4 - 6
. Additionally, previous studies relied solely on self-
identification of ethnic background. With the ability to categorize subjects through genetic data,
we found significant variation within HW participants as well. Although, this particular analysis
focused on exposure to local pollution sources, there is also the potential to analyze regional
exposure to pollutants such as ozone. In many cases, other studies were constricted to a subset
of pollutant measures and did not also have access to complex predictive models, such as
CALINE4 in this study. Additionally, a comparison of the outcome measures for TRP exposure
shows the effectiveness of distance to freeways and roadways as markers for local pollution
exposure. Significant differences between HW and NHW are fairly consistent between both
distance to roadway measures and CALINE4 estimates. These results align with previous
studies of CHS exposure data on predictors of intra-community air quality
25
.
These differences in study design highlight the strengths of the methods used to investigate
pollution exposure. The large sample size of over 8000 participants coupled with exposure
assessment by both proximity and dispersion models are significant strengths of the estimates
produced. The longitudinal design and method of site selection capture a diverse population and
41

increase the generalizability of the results. Even with the detailed level of exposure data
collected, we may still not fully capture actual individual exposure levels among participants. We
assume that residential location alone will not capture all variations in exposure levels due to
activities away from home. We are also unable to fully explain what brought about this
distribution of HW and NHW participants, but this not of ultimate concern. What is of concern is
that the distribution does exist and provides a natural experiment from which health outcomes
can be compared between HW and NHW participants. Distance to freeway or major non-
freeway road also required a fairly significant transformation in order to meet the assumptions
for linear regression, specifically homoscedasticity. Fortunately, a Box-Cox transformation was
an effective method to determine a sufficient power transformation to reduce visible
heteroscedasticity and allowed us to develop interaction models and evaluate the effect of
community and cohort on our estimates through regression. Use of the Box-Cox method also
allowed for us to back transform estimates into interpretable measures. Given more complicated
models that require a larger number of exposure variables, this method may not be suitable,
due to the inability to select a transformation that is efficient for all model variations. In the event
that a suitable transformation could not be determined, non-parametric or other methods that do
not require homoscedasticity may need to be considered.

42

Chapter 5: Conclusion

The results of this study have provided significant evidence that a disparity exists in
exposure to local pollution sources between HW and NHW participants of the Children’s Health
Study. Hispanic white children are not only more likely to live closer to either a freeway or major
roadway, but they also have higher predicted estimates of NOx estimates near their home
residences based on CALINE4 dispersion models. Within Hispanic white children, those with
greater than 50% Native American ancestry (More-NA HW) consistently live closer to a freeway
or major roadway and also have higher NOx prediction estimates in comparison to NHW
participants. Variation occurs within HW children as well, where More-NA HW participants have
higher pollution exposure levels and tend to live closer to busy roads than HW Less NA
participants. Through these results, we have found a large sample in which we can further
analyze how Hispanic descent affects the relationship between air pollution and respiratory
health. Due to the spatial distribution of the CHS participants and availability of genetic data, it is
also possible to investigate genetic and environment interactions as well. Future studies are
needed to better understand differences in pollution exposure at the regional level between HW
and NHW and how these differences resulted over time within the population. With an increased
understanding of the exposure differences in racial subgroups, we might be able to recognize
those populations that are at greatest risk of health effects due to air pollution.

43

References
24. Benson, P. E, & Pinkerman, K. O. (1984). CALINE4, a dispersion model for predicting air
pollution concentration near roadways. [Sacramento, Calif.]: State of California, Dept. of
Transportation, Division of Engineering Services, Office of Transportation Laboratory.
20. Brauer, M., Hoek, G., van Vliet, P., Meliefste, K., Fischer, P., Gehring, U., et al. (2003).
Estimating long-term average particulate air pollution concentrations: Application of traffic
indicators and geographic information systems. Epidemiology (Cambridge, Mass.), 14(2),
228-239.
12. Carter-Pokras, O., Zambrana, R. E., Poppell, C. F., Logie, L. A., & Guerrero-Preston, R.
(2007). The environmental health of latino children. Journal of Pediatric Health Care:
Official Publication of National Association of Pediatric Nurse Associates & Practitioners,
21(5), 307-314.
10. Chakraborty, J., & Zandbergen, P. A. (2007). Children at risk: Measuring racial/ethnic
disparities in potential exposure to air pollution at school and home. Journal of
Epidemiology and Community Health, 61(12), 1074-1079.
17. Choudhry, S., Seibold, M. A., Borrell, L. N., Tang, H., Serebrisky, D., Chapela, R., et al.
(2007). Dissecting complex diseases in complex populations: Asthma in latino americans.
Proceedings of the American Thoracic Society, 4(3), 226-233.
25. Franklin, M., Vora, H., Avol, E., McConnell, R., Lurmann, F., Liu, F., et al. (2012).
Predictors of intra-community variation in air quality. Journal of Exposure Science &
Environmental Epidemiology, 22(2), 135-147.
6. Gauderman, W. J., McConnell, R., Gilliland, F., London, S., Thomas, D., Avol, E., et al.
(2000). Association between air pollution and lung function growth in southern california
children. American Journal of Respiratory and Critical Care Medicine, 162(4 Pt 1), 1383-
1390.
5. Gauderman, W. J., Vora, H., McConnell, R., Berhane, K., Gilliland, F., Thomas, D., et al.
(2007). Effect of exposure to traffic on lung development from 10 to 18 years of age: A
cohort study. Lancet, 369(9561), 571-577.
15. Grineski, S. E., Staniswalis, J. G., Peng, Y., & Atkinson-Palombo, C. (2010). Children's
asthma hospitalizations and relative risk due to nitrogen dioxide (NO2): Effect modification
by race, ethnicity, and insurance status. Environmental Research, 110(2), 178-188.
13. Gwynn, R. C., & Thurston, G. D. (2001). The burden of air pollution: Impacts among racial
minorities. Environmental Health Perspectives, 109 Suppl 4, 501-506
23. Henderson, S. B., Beckerman, B., Jerrett, M., & Brauer, M. (2007). Application of land use
regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine
particulate matter. Environmental Science & Technology, 41(7), 2422-2428.
44

8. Houston, D., Li, W., & Wu, J. (2014). Disparities in exposure to automobile and truck traffic
and vehicle emissions near the los angeles-long beach port complex. American Journal of
Public Health, 104(1), 156-164.
16. Huynh, P., Salam, M. T., Morphew, T., Kwong, K. Y., & Scott, L. (2010). Residential
proximity to freeways is associated with uncontrolled asthma in inner-city hispanic children
and adolescents. Journal of Allergy, 2010, 10.1155/2010/157249. Epub 2010 Jun 13.
4. McConnell, R., Berhane, K., Yao, L., Jerrett, M., Lurmann, F., Gilliland, F., et al. (2006).
Traffic, susceptibility, and childhood asthma. Environmental Health Perspectives, 114(5),
766-772.
1. Miranda, M. L., Edwards, S. E., Keating, M. H., & Paul, C. J. (2011). Making the
environmental justice grade: The relative burden of air pollution exposure in the United
States. International Journal of Environmental Research and Public Health, 8(6), 1755-
1771.
22. Molitor, J., Marjoram, P., & Thomas, D. (2003). Application of bayesian spatial statistical
methods to analysis of haplotypes effects and gene mapping. Genetic Epidemiology, 25(2),
95-105.
14. National Coalition of Hispanic Health and Human Services Organizations (COSSMHO).
(1996). Hispanic environmental health: Ambient and indoor air pollution. Otolaryngology--
Head and Neck Surgery: Official Journal of American Academy of Otolaryngology-Head
and Neck Surgery, 114(2), 256-264.
2. Patel, M. M., & Miller, R. L. (2009). Air pollution and childhood asthma: Recent advances
and future directions. Current Opinion in Pediatrics, 21(2), 235-242.
19. Pritchard, J. K., Stephens, M., & Donnelly, P. (2000). Inference of population structure
using multilocus genotype data. Genetics, 155(2), 945-959.
9. Stuart, A. L., & Zeager, M. (2011). An inequality study of ambient nitrogen dioxide and
traffic levels near elementary schools in the tampa area. Journal of Environmental
Management, 92(8), 1923-1930.
11. Su, J. G., Jerrett, M., de Nazelle, A., & Wolch, J. (2011). Does exposure to air pollution in
urban parks have socioeconomic, racial or ethnic gradients? Environmental Research,
111(3), 319-328.
21. Su, J. G., Larson, T., Baribeau, A. M., Brauer, M., Rensing, M., & Buzzelli, M. (2007).
Spatial modeling for air pollution monitoring network design: Example of residential
woodsmoke. Journal of the Air & Waste Management Association (1995), 57(8), 893-900.
18. Torgerson, D. G., Ampleford, E. J., Chiu, G. Y., Gauderman, W. J., Gignoux, C. R., Graves,
P. E., et al. (2011). Meta-analysis of genome-wide association studies of asthma in
ethnically diverse North American populations. Nature Genetics, 43(9), 887-892.
45

3. Trasande, L., & Thurston, G. D. (2005). The role of air pollution in asthma and other
pediatric morbidities. The Journal of Allergy and Clinical Immunology, 115(4), 689-699.
7. Winer, R. A., Qin, X., Harrington, T., Moorman, J., & Zahran, H. (2012). Asthma incidence
among children and adults: Findings from the behavioral risk factor surveillance system
asthma call-back survey--United States, 2006-2008. The Journal of Asthma: Official
Journal of the Association for the Care of Asthma, 49(1), 16-22.

46

Abstract (if available)

Abstract Significant evidence has accrued that air pollution negatively impacts respiratory health and other health outcomes. The USC Children’s Health Study has also demonstrated a link between increasing levels of air pollution, at both regional and local levels, and reductions in lung development in children. Within Los Angeles and other large urban cities, studies have investigated whether air pollution exposure differs by ethnicity. Preliminary results from these studies suggest that certain ethnic groups, such as Hispanics, may be exposed to higher levels of air pollution compared to non‐Hispanics. We aim to further determine whether a disparity exists in local air pollution exposure between Hispanic white (HW) and non‐Hispanic white (NHW) children of Southern California. Among Hispanic white children, we also intend to test whether children with a higher percentage of Native American are more exposed to local air pollution sources as well. ❧ From the Children’s Health Study cohort, we identified 4,279 NHW and 4,138 HW children with sufficient data to calculate local pollution exposure levels. Among this sample, 2,571 NHW and 2,767 HW children had genetic ancestry data available as well. Multiple linear regression and logistic regression were used to test the association between Hispanic‐descent and two measures of local air pollution, distance from freeways/major roadways (m) and Caline4 predicted estimates of NOx levels (ppb). Differences between the groups were determined both in the overall sample and within each of the 14 communities at baseline entry into their respective cohorts. ❧ HW children were found to live, on average, 139 m closer to freeways and 55 m closer to major non‐freeway roads in the overall sample compared to NHW children (p < 0.0001). Additionally, HW children with 50% or more Native American ancestry (More-NA HW) lived 244 m closer to freeways compared to NHW children (p < 0.0001) and 143 m closer to freeways than Hispanic White children with less than 50% Native American ancestry (Less-NA HW) (p = 0.003). For major non‐freeway roads, More‐NA HW live 89 m and 44 m closer to a major road compared to HW children and Less‐NA HW, respectively. Based on the secondary outcome measure, HW children had an estimated freeway Caline4 estimates that are 13% (95% CI: 8%-17%) higher than NHW children. The major non‐freeway road NOx exposure is also estimated to be 16% (95% CI: 12%-19%) higher in HW children compared to NHW children. Among children with ancestry data, freeway Caline4 estimates were 20% and 14% higher for More‐NA HW compared to NHW children (p < 0.0001) and Less‐NA HW (p < 0.0001), respectively. Additionally, major non‐freeway road Caline4 estimates were 27% and 14% higher for More‐NA HW compared to NHW children (p < 0.0001) and Less‐NA HW (p = 0.0001), respectively. Lastly, the odds of HW children being within 500 m of a freeway or 75 m of major non‐freeway road is 1.34 (95% CI: 1.18–1.51) and 1.39 (95% CI: 1.21–1.58) times that NHW children, respectively. Within HW, the odds of those with more than 50% NA ancestry being within 500 m of a freeway or 75 m of a major non-freeway road is 1.41 (95% CI: 1.12–1.78) and 1.45 (95 CI: 1.10–1.90) times that of those with less than 50% NA ancestry. ❧ These results provide strong evidence that a differences exist in local air pollution exposure between HW and NHW children. With knowledge of this potential difference in exposure levels, we can further investigate whether these discrepancies lead to significant differences in respiratory and other health‐related outcomes between the two groups.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Native American ancestry among Hispanic Whites is associated with higher risk of childhood obesity: a longitudinal analysis of Children’s Health Study data

PDF

Examining exposure to extreme heat and air pollution and its effects on all-cause, cardiovascular, and respiratory mortality in California: effect modification by the social deprivation index

PDF

Assessment of the mortality burden associated with ambient air pollution in rural and urban areas of India

PDF

Evaluation of new methods for estimating exposure to traffic-related pollution and early health effects for large population epidemiological studies

PDF

Ambient air pollution and lung function in children

PDF

Street connectivity and childhood obesity: a longitudinal, multilevel analysis

PDF

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

PDF

Association of traffic-related air pollution and lens opacities in the Los Angeles Latino Eye Study

PDF

Airway inflammation and respiratory health in the Southern California children's health study

PDF

Spatial modeling of non-tailpipe emissions and its association with children's lung function

PDF

A pilot study of a global approach to assessing air pollution exposure in port communities: passive air monitoring of nitrogen dioxide concentrations

PDF

Air pollution and breast cancer survival in California teachers: using address histories and individual-level data

PDF

Spatial analysis of PM₂.₅ air pollution in association with hospital admissions in California

PDF

A cohort study of air-pollution and childhood obesity incidence

PDF

Effectiveness of individual- and household-level protective actions in reducing symptoms associated with hydrogen sulfide chronic low-level exposure

PDF

Association of maternal and environmental factors with infant feeding behaviors in a birth cohort study

PDF

Red and processed meat consumption and colorectal cancer risk: meta-analysis of case-control studies

PDF

Origins of the gender disparity in bladder cancer risk: a SEER analysis

PDF

Associations between ambient air pollution and hypertensive disorders of pregnancy

PDF

Ancestral/Ethnic variation in the epidemiology and genetic predisposition of early-onset hematologic cancers

Asset Metadata

Creator Weaver, Garrett M. (author)

Core Title Disparities in exposure to traffic-related pollution sources by self-identified and ancestral Hispanic descent in participants of the USC Children’s Health Study

School Keck School of Medicine

Degree Master of Science

Degree Program Applied Biostatistics and Epidemiology

Publication Date 07/15/2014

Defense Date 07/14/2014

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

Tag ancestry,Freeway,genetic,Hispanic,Latino,Native American,OAI-PMH Harvest,Pollution,Traffic

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Gauderman, William James (committee chair), Berhane, Kiros T. (committee member), McConnell, Rob (committee member)

Creator Email gmweaver@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-439655

Unique identifier UC11287886

Identifier etd-WeaverGarr-2683.pdf (filename),usctheses-c3-439655 (legacy record id)

Legacy Identifier etd-WeaverGarr-2683.pdf

Dmrecord 439655

Document Type Thesis

Format application/pdf (imt)

Rights Weaver, Garrett M.

Type texts

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

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

Repository Name University of Southern California Digital Library

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