National Institutes of Health

U.S. Department of Health and Human Services

ENVIRONMENTAL

HEALTH

PERSPECTIVES

ehponline.org

Chronic Traffic-Related Air Pollution and Stress

Interact to Predict Biological 

and Clinical Outcomes in Asthma

Edith Chen, Hannah M. C. Schreier,

Robert C. Strunk, and Michael Brauer

doi:10.1289/ehp.11076 (available at http://dx.doi.org/)

Online 27 February 2008
1

Chronic Traffic-Related Air Pollution and Stress Interact to Predict Biological and Clinical 

Outcomes in Asthma 

Edith Chen1

, Hannah M. C. Schreier1, Robert C. Strunk2, and Michael Brauer3 

1

Department of Psychology, University of British Columbia, Vancouver, BC, Canada; 2

Department of Pediatrics, Division of Allergy and Pulmonary Medicine, St. Louis 

Children’s Hospital, Washington University in St. Louis School of Medicine, St. Louis, 

MO, USA.; 3School of Environmental Health, University of British Columbia, Vancouver, 

BC, Canada 

Corresponding author: Edith Chen, University of British Columbia, Department of 

Psychology, 2136 West Mall, Vancouver, BC V6T 1Z4 Canada. (604) 822-2549 phone. 

(604) 822-6923 fax. echen@psych.ubc.ca 
2

Running title: Stress and Air Pollution in Asthma 

Keywords: air pollution, asthma, immune, psychosocial, stress, traffic 

Abbreviations: 

NO2 nitrogen dioxide 

IL interleukin 

IgE immunoglobulin E 

Acknowledgements: This study was supported by funding from the National Institutes 

of Health grant HL073975 and the Canadian Institutes of Health Research. The authors 

declare no competing financial interests. 
3

Outline 

Abstract 

Introduction 

Methods 

Patients 

Traffic-related Air Pollution Estimates 

Psychosocial Measure 

Immune Measures 

Clinical Measures 

Potential Confounders 

Statistical Analyses 

Results 

Cross-Sectional Associations with Biological Markers 

Longitudinal Associations with Clinical Outcomes 

Discussion 

References 

Table 1 

Table 2 

Figure Legends 

Figure 1 

Figure 2 
4

ABSTRACT 

Background: Previous research has documented effects of both physical and social 

environment exposures on childhood asthma. However, few studies have considered 

how these two environments might interact to affect asthma. 

Objective: To test interactions between chronic exposure to traffic-related air pollution 

and chronic family stress in predicting biological and clinical outcomes in children with 

asthma. 

Method: Children with asthma (N=73, ages 9-18) were interviewed about life stress, 

and asthma-relevant inflammatory markers (cytokine production, IgE, eosinophil 

counts) were measured. Parents reported on children’s symptoms. Children completed 

daily diaries of symptoms and peak expiratory flow rate (PEFR) measures at baseline 

and six months later. Exposure to traffic-related air pollution was assessed using a land 

use regression model for nitrogen dioxide (NO2) concentrations. 

Results: NO2 by stress interactions were found for IL-5 (β for interaction term=-.31, 

p=.02), IgE (interaction β=-.29, p=.02), and eosinophil counts (interaction β=-.24, 

p=.04). These interactions showed that higher chronic stress was associated with 

heightened inflammatory profiles as pollution levels decreased. Longitudinally, NO2 by 

stress interactions emerged for daily diary symptoms (interaction β=-.28, p=.02), 

parent-reported symptoms (interaction β=-.25, p=.07), and PEFR (interaction β=.30, 

p=.03). These interactions indicated that higher chronic stress was associated with 

increases over time in symptoms and decreases over time in PEFR as pollution levels 

decreased. 
5

Conclusions: The physical and social environment interacted in predicting both 

biological and clinical outcomes in children with asthma, suggesting that when pollution 

exposure is more modest, vulnerability to asthma exacerbations may be heightened in 

children with higher chronic stress. 
6

INTRODUCTION 

 Both the physical and the social environment have long been hypothesized to be 

important contributors to childhood asthma. Although research identifying the physical 

and social environment factors that play a role in asthma has each made great strides 

(Crain et al. 2002; Wright 2005), much of this research exists independently of one 

another. In contrast, few studies have examined how the physical and social 

environments might interact to affect asthma outcomes. For example, certain 

combinations of physical and social environment exposures might produce unique 

effects beyond exposure to one of these factors alone. Hence the goal of the present 

study was to empirically test interactive effects of the physical and social environment 

in order to gain a more comprehensive picture of how the environment, broadly 

defined, contributes to childhood asthma. 

Within the physical environment literature, exposures such as traffic-related air 

pollution have been repeatedly linked to increased respiratory symptoms, increased 

asthma-related hospitalizations, and the diagnosis of asthma (Brauer et al. 2007; 

Edwards et al. 1994; Studnicka et al. 1997). In addition, experimental exposure to 

markers of traffic-related air pollution such as diesel exhaust particles or nitrogen 

dioxide increases levels of inflammatory markers relevant to asthma (Barck et al. 2002; 

DiazSanchez et al. 1997; Pourazar et al. 2004), suggesting that greater inflammation 

may underlie the effects found in clinical studies. 

Within the social environment literature, factors such as psychological stress 

have been linked to wheezing, asthma exacerbations, and the diagnosis of asthma in 
7

children (Klinnert et al. 2001; Sandberg et al. 2000; Wright et al. 2002). In addition, 

high levels of stress have been associated with detrimental biological profiles, such as 

greater inflammatory responses following antigen challenge or in vitro stimulation of 

immune cells, among children with or at risk for asthma (Chen et al. 2006; Liu et al. 

2002; Wright et al. 2004). 

Much of this research, although important in its own right, has proceeded largely 

along separate lines with little overlap. Few studies have brought these two areas 

together to examine how physical and social environments might interact to affect 

asthma outcomes. While it is possible that social factors alone could affect asthma 

outcomes, or alternatively, that physical factors would override any effect of social 

factors, a number of researchers have argued that social and physical factors might 

instead combine to modify health outcomes such as asthma (Weiss and Bellinger 2006). 

This interaction between the physical and social environments could mean that 

negative social factors intensify the effect of physical environment exposures (Gee and 

Payne-Sturges 2004; Morello-Frosch and Shenassa 2006). For example, the 

combination of certain physical and social environment risk factors might create a 

‘double exposure,’ whereby the combination of factors such as high pollution exposure 

and high stress is most detrimental to asthma. One recent study has empirically tested 

the notion of physical by social environment interactions in asthma. This study involved 

a longitudinal investigation of a birth cohort of children, in which traffic-related air 

pollution (indicated by nitrogen dioxide, NO2) as well as chronic stress (indicated by 

exposure to violence) were measured. The risk of asthma incidence was elevated as 
8

residential NO2 exposure increased, but only among children who had high levels of 

exposure to violence (Clougherty et al. 2007). In the present study, we sought to 

determine whether a social by physical exposure interaction would predict outcomes in 

children with existing asthma. More specifically, we tested whether the physical and 

social environment would interact to predict biological markers implicated in asthma, 

and subsequently, whether their interaction would predict changes in clinical outcomes 

over time in a sample of children with asthma. While previous research provides some 

evidence for asthma onset suggesting that such interactions might fit a “double 

jeopardy” hypothesis, there is so little previous data addressing this question in children 

with asthma, that the present study aimed to describe the nature of any interaction 

patterns, rather than testing specific a priori hypotheses. 

METHODS

Patients 

73 children were recruited from Vancouver, B.C. through advertisements in 

physicians’ offices, local media, and community settings for an observational study of 

childhood asthma. Advertisements were placed throughout the Greater Vancouver 

area, and interested families contacted the lab for a screening to determine eligibility. 

Eligible families were then scheduled for a laboratory visit. Visits occurred throughout 

the year. Participants were required to be physician-diagnosed with asthma (82% with 

allergic asthma). Children were 9-18 years, fluent in English, free of acute respiratory 

illness at the time of their visit (by parent and child report), and had no chronic illnesses 

other than asthma. Children gave written assent, and parents provided written consent. 
9

The protocol was approved by the University of British Columbia Research Ethics Board. 

Traffic-related Air Pollution Estimates 

A land use regression model developed for the study region (Henderson et al. 

2007) provided high resolution spatial estimates of NO2 concentrations as an indicator 

of chronic exposure to traffic-related air pollution. Briefly, 116 passive samplers to 

collect NO2 were deployed for two-14 day periods at 116 sites in the study area. Mean 

concentrations during these two periods were highly correlated with, and closely 

approximated annual averages from regulatory monitoring network data. 

For each of the 116 measurement sites, 55 variables were generated in a 

Geographic Information System (GIS) and a linear regression model for NO2 was built 

with the most predictive covariates. For NO2, the model (R2 = 0.56) included the 

number of major roads within 100 and 1000 meter radius circular buffers of the 

measurement sites, the number of secondary roads within a 100 meter buffer, the 

population density within a 2500 meter radius the amount of commercial land use 

within 750 meters and elevation. Comparison of model predictions to measured annual 

average concentrations at 16 government regulatory air monitoring stations had an R2

of 0.69. The predictive error for the model was estimated with leave-one-out crossvalidation where each model is repeatedly parametrized on N - 1 data points and then 

used to predict the excluded measurement. The mean difference between predicted 

and measured values, an estimate of the model error, was 0.0 ppb with a standard 

deviation of 2.75 ppb (∼15% of the sample mean). The resulting surface was 

smoothed (Focal Statistics, ArcGis Spatial Analyst) to remove abrupt changes and edge 
10

effects so as to more accurately reflect the measured effect of proximity to roadways 

(Gilbert et al. 2003). Using the model, a smooth spatial surface of predicted (annual 

average) concentrations was generated for the entire study area at a resolution of 10 

meters. To adjust for temporal variability, the corresponding ambient monitoring 

network data were fit with a monthly dummy variables and a covariate for linear trend 

using the Times Series Forecasting System from SAS v 9.1. These month-year 

adjustment factors were then applied to the surface to estimate monthly average 

concentrations. Using these we then computed individual subject average exposures 

for the full period during which pollution estimates were taken (1998-2003) as an 

indicator of chronic traffic-related air pollution. We included the full period because the 

land use regression model is best suited for long-term exposures (Marshall et al. 9998; 

Nethery et al. 9998), as it is based upon spatial differences in land use that do not vary 

over time. Although other approaches can be used to assess short-term exposures, 

these suffer from less spatial resolution. This specific air pollution exposure indicator 

has been associated with bronchiolitis and asthma incidence in other studies in 

Vancouver (Clark et al. 2007; Demers et al. 2006), and as well, the same exposure 

assessment approach has been used in other cities to predict health outcomes, such as 

asthma incidence, allergies, and wheezing (Brauer et al. 2002; Brauer et al., 2007; 

Ryan et al. 2007). 

Psychosocial Measure 

Chronic stress was assessed in children with the UCLA Life Stress Interview 

(Hammen 1991). This interview was conducted at baseline and assessed chronic stress 
11

over the past six months, in domains such as family relationships, friendships, and 

school. This is a semi-structured interview, in which a trained interviewer asks a series 

of open-ended questions in each life domain, and uses the information gathered to rate 

the level of chronic, ongoing stress. Interviewers rate the extent of chronic stress on a 

1-5 scale, using 0.5 increments, with higher numbers reflecting more severe, persistent 

difficulties. This interview has been used successfully in children as young as 8, and 

has demonstrated reliability and validity (Adrian and Hammen 1993; Rudolph and 

Hammen 1999). In this study we focused on chronic family stress, given that, of all the 

life stress domains, family stress has been found to be the one most strongly related to 

asthma outcomes (Chen et al., 2006; Miller and Chen 2006). 

Immune Measures 

At the baseline visit, peripheral blood was drawn from children into BD 

Vacutainer Cell Preparation Tubes containing Sodium Heparin, and 3 x 106

 peripheral 

blood mononuclear cells (PBMC’s) were isolated through density-gradient centrifugation. 

PBMC’s were resuspended in culture medium consisting of RPMI plus 10% fetal calf 

serum, and incubated with phorbol myristate acetate (25ng/ml) and ionomycin (1 

ug/ml) for a period of 48 hours at 37°C in 5% CO2. Supernatants were frozen until the 

end of the study, and then assayed to determine levels of IL-4, IL-5, and IL-13 using 

enzyme-linked immunosorbent assays. Intra-assay CVs ranged from 3.68 – 4.76%. 

A complete blood count with five-part differential (Bayer ADVIA 70 hematology 

system, Holiston, Massachusetts) was performed to obtain eosinophil counts. Total 

serum IgE was measured using an automated fluorescence immunoassay (Pharmacia 
12

CAP system, Portage, Michigan), and was log-transformed because of its non-normal 

distribution. 

Clinical Measures

To obtain multiple perspectives on asthma symptoms, both parents and children 

were probed about symptoms. Parents were interviewed during the laboratory visit 

about their child’s symptoms over the previous two weeks as part of a health interview 

that gathered information about the child’s asthma (e.g., prescribed medications). The 

frequency of daytime symptoms, nighttime symptoms, and exertional symptoms were 

probed as per the NAEPP/EPR2 Guidelines (National Asthma Education and Prevention 

Program 1997). Daytime symptoms were assessed as the number of days over the 

past two weeks children had cough, wheeze, shortness or breath, or chest tightness. 

Nighttime symptoms were assessed as the number of days children awakened from 

sleep because of coughing, wheezing, shortness of breath, or chest tightness. 

Exertional symptoms were assessed as the number of days children had cough, 

wheeze, shortness of breath, or chest tightness while exercising or playing. 

Children were asked to rate their symptoms on a daily basis for two weeks 

following their lab visit. Children were asked to keep a diary of their symptoms, and 

every morning and evening, they rated the extent of coughing, wheezing, chest 

tightness, and shortness of breath, each on a 0 (none) to 4 (really bad) scale. Scores 

for each question were summed for each day, and then averaged across the 14 day 

diary period. 

Over the same period, children also monitored peak expiratory flow rates (PEFR) 
13

at home using an electronic monitor (Quadromed, Hoechberg, Germany). Three peak 

flow readings were taken upon awakening and before bedtime each day for two weeks, 

and the highest value at each time point was retained. Daily PEFR% was calculated as a 

percent of each youth’s laboratory best, and readings across the 2 weeks were 

averaged. 

All clinical measures were repeated 6 months after the baseline visit. 

Potential Confounders 

Variables that could provide alternative explanations for the above relationships 

were included as covariates in statistical analyses. This included asthma severity, 

determined from the NAEPP/EPR2 Guidelines based on the higher of symptom 

frequency and medication use, paralleling the approach of previous researchers 

(Bacharier et al. 2004). Families also brought children’s asthma medications to the 

research center, and inhaled corticosteroid use was coded (# days taken during the 

past 2 weeks), as was beta agonist use (# days taken during the past 2 weeks). 

In addition, we assessed whether demographic characteristics or study visit 

variables, such as child age, gender, ethnicity, or time of year of study visit were 

associated with study variables. Demographic or study variables that were significantly 

associated with any immune or clinical measures were included as covariates in 

analyses with that outcome. 

Statistical Analyses 

Statistical analyses were conducted to test the hypothesis that psychological 

stress would interact with air pollution exposure to predict both biological and clinical 
14

asthma outcomes. Biological variables were examined cross-sectionally, and clinical 

outcomes longitudinally, using a series of hierarchical multiple regression analyses. 

Biological variables were predicted from variables entered in three steps: 1) potential 

confounders including medical variables (asthma severity classification, use of inhaled 

corticosteroids, use of beta agonists) and any demographic variable associated with 

study outcomes; 2) main effects of chronic stress and air pollution; and 3) the 

interaction term for chronic stress by air pollution. These analyses were conducted 

according to the recommendations by Aiken and West (Aiken and West 1991). Note 

that because both chronic stress and pollution exposure are continuous variables, these 

involve statistical procedures to examine the significance of an interaction between two 

continuous variables. These analyses comprised the primary test of study hypotheses. 

The nature of an interaction between two continuous variables, however, can be 

difficult to visualize. To aid in interpretation, we provided two additional pieces of 

information. Significant interactions were plotted by graphing the relationship between 

stress and asthma outcomes at low (-1 SD) and high (+1 SD) levels of air pollution. 

This creates an artificial distinction within one of the continuous variables, but allows 

one to more easily see how the relationship between stress and asthma varies at 

different levels of pollution exposure. We also conducted secondary analyses to test 

whether the regression coefficients at these specific values of air pollution were 

significant. Again, this creates an artificial distinction, but allows the reader to gain 

some sense of where the differences are most pronounced. These procedures follow 

the statistical recommendations of Aiken and West (1991). However, because these 
15

analyses do not fully represent the nature of the study variables (as continuous), they 

are considered secondary, with the test of the interaction between the two continuous 

variables as the primary analyses. 

Clinical variables included two time points, spaced 6 months apart. In these 

analyses the difference score (Time 2 – Time 1) was predicted from the same set of 

variables described above except that Time 1 values were included as a control variable 

in step 1. 

RESULTS 

 Table 1 presents descriptive information about the sample. There were a few 

associations of demographic and study variables with biological or clinical outcomes. 

Child age was inversely correlated with eosinophil count (r=-.23, p=.04), positively 

correlated with daily diary symptoms (r=.25, p=.04), and positively correlated with 

PEFR% (r=.24, p=.04). Parents reported girls to have more symptoms than boys 

(t=3.36, p=.001). Children belonging to minority groups had higher production of IL-4 

compared to Caucasian children (t=3.40, p=.001). Time of year was correlated with IL13 production (r=-.22, p=.05), such that higher levels were found for those that were 

seen during the winter months. Variables that were associated with outcomes were 

included as covariates in the relevant equations below. 

Cross-Sectional Associations with Biological Markers

 Our first set of analyses tested whether stress and air pollution were associated 

with biological markers implicated in asthma. Regression coefficients are reported 

below, with interactions graphed to illustrate the direction of effects. 
16

With respect to IL-5 production, there was no main effect of stress (β=.16, 

p=.22) or air pollution (β=.15, p=.30), but there was a significant stress by air pollution 

interaction (interaction term β=-.31, p=.02). The negative coefficient indicates that as 

pollution decreases, higher levels of chronic family stress become associated with 

greater production of IL-5. This is illustrated in Figure 1A by plotting the relationship 

between chronic stress and IL-5 production at low and high levels of air pollution. The 

relationship between stress and IL-5 production was positive and statistically significant 

at 1 SD below the mean of air pollution (β=.39, p=.03), whereas the relationship 

between stress and IL-5 production at 1 SD above the mean of air pollution was not 

significant (β=-.06, p=.71), suggesting that the relationship between stress and IL-5 

production is stronger in lower pollution areas. 

No significant associations emerged for IL-4 or IL-13 (p’s > .2). 

 With respect to total IgE, there was a significant main effect of stress such that 

children with higher chronic stress had higher IgE levels (β=.32, p=.01), but no main 

effect of air pollution (β=.08, p=.52). In addition, there was a significant stress by air 

pollution interaction (interaction term β=-.29, p=.02). The negative coefficient 

indicates that as pollution decreases, higher levels of chronic family stress become 

associated with greater IgE levels. This is illustrated in Figure 1B by plotting the 

relationship between chronic stress and IgE at +1 SD and -1 SD of air pollution. The 

relationship between stress and IgE was positive and statistically significant at 1 SD 

below the mean of air pollution (β=.54, p=.002), whereas the relationship between 

stress and IgE at 1 SD above the mean of air pollution was not significant (β=.11, 
17

p=.46), suggesting that the relationship between stress and IgE is stronger in lower 

pollution areas. 

 With respect to eosinophil counts, there was no main effect of stress (β=.05, 

p=.67), and a weak effect of air pollution (β=.21, p=.10). There was a significant 

stress by air pollution interaction (interaction term β=-.24, p=.04). The negative 

coefficient indicates that as pollution decreases, higher levels of chronic family stress 

become associated with greater eosinophil counts. This is illustrated in Figure 1C by 

plotting the relationship between chronic stress and eosinophil counts at +1 SD and -1 

SD of air pollution. The relationship between stress and eosinophil counts was positive 

at 1 SD below the mean of air pollution (β=.23, p=.15), whereas the relationship 

between stress and eosinophil counts at 1 SD above the mean of air pollution was 

negative (β=-.13, p=.35). 

Longitudinal Associations with Clinical Outcomes

 Table 2 presents descriptive information on clinical variables at baseline and 

follow-up on the sample. This information is separated into a low and high pollution 

group by median split so readers can see how clinical variables vary by pollution. 

Average pollution exposure for those below the median was 14.1 ppb (SD=1.6). 

Average exposure for those above the median was 18.9 ppb (SD=3.5). Low and high 

pollution groups were similar on demographic variables such as age (average age for 

those below the median on pollution exposure=12.2 years; average age for those 

above the median=13.5 years), ethnicity (66% Caucasian among those below the 

median on pollution exposure; 61% Caucasian among those above the median), and 
18

parent education (average years of education for those below the median on pollution 

exposure=15.4; average years of education for those above the median=15.6). The 

table reveals that children in high pollution areas had higher child-reported daily 

symptoms and parent reported symptoms at both time points than those in low 

pollution areas. In the analyses below, we focused on change over time in clinical 

variables in order to assess whether the cross sectional associations with biological 

markers have implications clinically for asthma over time. To do this, we tested 

whether the interaction between stress and air pollution predicted changes in clinical 

variables over a six month period, controlling for baseline levels. 

 With respect to the daily diaries that children kept of symptoms, there was no 

main effect of stress (β=.06, p=.63) or air pollution (β=-.12, p=.38), but there was a 

significant stress by air pollution interaction (interaction term β=-.28, p=.02). The 

negative coefficient indicates that as pollution decreases, higher levels of chronic family 

stress become associated with increasing symptoms over time. This is illustrated in 

Figure 2A by plotting the relationship between chronic stress and change in symptoms 

at +1 SD and -1 SD of air pollution. The relationship between stress and symptom 

change was positive at 1 SD below the mean of air pollution (β=.25, p=.098), whereas 

the relationship between stress and symptom change at 1 SD above the mean of air 

pollution was negative (β=-.13, p=.35). 

 With respect to parent report of child symptoms, there was no main effect of 

stress (β=.08, p=.60) or air pollution (β=-.03, p=.85), but there was a marginal stress 

by air pollution interaction (β=-.25, p=.07). The negative coefficient indicates that as 
19

pollution decreases, higher levels of chronic family stress become associated with 

increasing parent-reported symptoms over time. This is illustrated in Figure 2B by 

plotting the relationship between chronic stress and change in symptoms at +1 SD and 

-1 SD of air pollution. The relationship between stress and symptom change was 

positive at 1 SD below the mean of air pollution (β=.36, p=.08), whereas the 

relationship between stress and symptom change at 1 SD above the mean of air 

pollution was negative (β=-.21, p=.36). 

 With respect to daily PEFR measures, there was no main effect of stress (β=.05, 

p=.68) or air pollution (β=.06, p=.70), but there was a significant stress by air pollution 

interaction (β=.30, p=.03). The positive coefficient indicates that as pollution 

decreases, higher levels of chronic family stress also becomes associated with 

decreasing PEFR over time. This is illustrated in Figure 2C by plotting the relationship 

between chronic stress and change in PEFR at +1 SD and -1 SD of air pollution. The 

relationship between stress and PEFR change was negative at 1 SD below the mean of 

air pollution (β=-.14, p=.40), whereas the relationship between stress and PEFR change 

at 1 SD above the mean of air pollution was positive (β=.24, p=.11). 

 Note that although the direction of change differed by pollution levels, this does 

not mean that symptoms are actually higher in lower pollution areas. As shown in 

Table 2, children above the median in pollution exposure had greater symptoms by 

daily diary report and parent report relative to children below the median in pollution 

exposure, and had comparable PEFR. Hence, children in higher pollution areas have 
20

greater symptoms, but these symptoms do not appear to worsen over time the way 

they do for children in lower pollution areas with chronic stress. 

DISCUSSION

This article is the first that we are aware of to document that the physical 

environment (chronic traffic-related air pollution) and social environment (chronic 

stress) interact to predict both biological and longitudinal clinical outcomes in children 

with asthma. The findings from this study demonstrated that the interactive effects 

between air pollution and stress are stronger than either factor alone, suggesting that 

the physical and social environments are in fact intertwined and critical to understand in 

concert, rather than independently. 

The nature of this interaction was such that the detrimental effects of chronic 

psychosocial stress were more evident among children living in lower pollution areas. 

That is, as pollution levels declined, higher levels of stress were associated with 

heightened inflammatory profiles cross-sectionally and worsening clinical profiles over a 

6-month period. In contrast, chronic stress had modest effects on biological and clinical 

measures as pollution exposure increased, and any suggestions of effects were in an 

opposite direction. 

The direction of the interaction effects in this study was different from what has 

been found in the small number of previous studies on this topic. For example, one 

recent study found that traffic-related air pollution (NO2) interacted with exposure to 

violence to predict the diagnosis of asthma in a birth cohort of children, such that 

children with both high pollution and violence exposures were at greatest risk of having 
21

asthma (Clougherty et al., 2007). Another study found that high levels of traffic-related 

air pollution (e.g., NO2) combined with low socioeconomic status predicted the greatest 

risk of asthma hospitalizations in children (Lin et al. 2004). These previous studies fit a 

“double jeopardy” hypothesis, suggesting that the combination of physical and social 

exposures synergistically affect asthma outcomes. 

In contrast, our data suggest that chronic stress may have the ability to 

accentuate the effects of environmental pollutants when chronic exposure levels are 

more modest. Previous research has suggested that social factors do require the 

presence of some dose of physical exposure to have effects on biological processes 

(Chen and Miller 2007). The findings from the present study fit the notion of a 

threshold model- that is, that there is a threshold at which chronic physical exposures 

begin to have effects on health outcomes, and that one role of chronic stress may be to 

lower the threshold at which physical exposures affect biological and clinical outcomes. 

One reason why this may occur is that when chronic exposure to traffic-related air 

pollutants is more modest, there may be greater room for social factors to increase or 

decrease vulnerability biologically. This is consistent with the notion that stress may be 

able to shift physiological response systems in a direction such that adverse outcomes 

occur in response to lower doses of physical exposures (Morello-Frosch et al., 2006; 

Paarlberg et al. 1995). Consistent with this notion, chronic stress under certain 

conditions has been found to heighten biological responses to negative social exposures 

(Gump and Matthews 1999); we speculate that similar processes may occur with 

responses to physical exposures. This type of response pattern is thought to occur 
22

because prolonged stress can sensitize and prevent adaptation of biological systems 

(McEwen 1998), potentially leading to lower doses of physical environment pollutants 

having detrimental effects on biological and clinical asthma measures. 

Reasons for the differences between our study and the two studies cited above 

are unclear, but we speculate that they may be due to distinctions between the 

diagnosis versus progression of a disease, or to different conceptualizations of social 

exposures. Our study focused on children with pre-existing asthma and predicted 

biological outcomes as well as changes in clinical outcomes over time; in contrast, the 

Clougherty (2007) study predicted the risk of being diagnosed with asthma. The way in 

which physical and social exposures affect the onset versus progression of disease 

could be different, resulting in the distinct patterns found in the two studies. In 

addition, different types of social exposures may have different effects on asthma. 

Although they all are forms of stress, exposure to violence, chronic family stress, and 

low socioeconomic status each represents different types of life stressors, and it is 

possible that air pollution interacts differently with different types of stressors. In the 

present study, we focused on family stress because this type of stress has been shown 

to have the most robust associations with asthma outcomes (Chen et al., 2006; Miller et 

al., 2006); nonetheless, it is possible that other unmeasured stressors, such as living in 

impoverished neighborhoods, also contribute to asthma biological and clinical outcomes 

and overlap with air pollution indicators. Future research should test these possibilities 

further. 
23

The effects that we found cross sectionally of interactions between chronic 

traffic-related air pollution and chronic stress on IL-5 production, total IgE levels, and 

eosinophil counts represent biological pathways that have implications for clinical 

asthma outcomes. Immune pathways in asthma include the secretion of cytokines that 

activate B cells to produce IgE. IgE in turn initiates an inflammatory cascade leading to 

airway constriction and mucus production. A second pathway involves the recruitment 

of eosinophils to the airways, which also promotes airway inflammation and obstruction. 

Secretion of the cytokine IL-5 is known to increase eosinophil production. Thus 

heightened production of IL-5 along with elevated IgE and eosinophil counts suggests a 

biological profile that is potentially detrimental for children with asthma in terms of 

vulnerability to symptoms. 

We considered this possibility by testing whether chronic traffic-related air 

pollution by chronic stress interactions could also predict changes in clinical outcomes 

over a six month period. Consistent with the implications of the cross-sectional 

biological data, we found that in lower pollution areas, higher levels of chronic stress at 

baseline predicted increases in asthma symptoms and decreases in daily PEFR over time. 

Hence chronic stress appears to exacerbate the effects of more modest exposures to 

chronic air pollutants on longer-term clinical asthma outcomes, in addition to biological 

markers. 

Interestingly, as pollution levels increased, an opposite pattern emerged whereby 

higher levels of chronic stress were associated with declines in asthma symptoms and 

increases in PEFR over time. This was an unexpected trend, and it is unclear what the 
24

implications are. Note, however, that because longitudinal analyses focus on change 

over time, this does not mean that children in high pollution areas have absolute levels 

of asthma morbidity that are low. Rather, children in higher pollution areas have more 

symptoms than children in lower pollution areas at both time points, but their clinical 

profiles (as assessed by PEFR and symptom reporting) do not appear to worsen over 

time. In contrast, children in lower pollution areas show stronger relationships of 

chronic stress with worsening clinical profiles over time. 

Strengths of the present study include the collection of asthma-relevant 

biomarkers; the tracking of longitudinal clinical outcomes; the use of a land use 

regression model to assess individual exposures to air pollution; and the use of an indepth interview for measuring chronic stress. In addition, the design of the study 

meant that directionality could be more clearly inferred. For example, although it would 

be reasonable to hypothesize that more severe asthma increases family stress levels, 

the fact that stress was assessed prior to clinical measures for longitudinal analyses 

meant that worsening asthma was not driving stress experiences. 

Limitations to the present study include the small sample size. Both the 

comprehensive stress interview and the collection and processing of biological samples 

limited the size of the present sample, and this raises the possibility that findings were 

due to chance and hence need to be replicated. We note, however, that our sample 

size is comparable to numerous other studies of acute stress and asthma cytokine 

production (Kang et al. 1997; Kang and Fox 2001; Marshall et al. 1998). A second 

limitation is the varying time frame for measures in this study. Time frames were set 
25

based on optimal periods for gathering information for different study constructs. For 

example, symptom reports are best assessed over shorter time intervals (weeks), given 

difficulties in accurately recalling symptoms over longer time periods (National Asthma 

Education and Prevention Program, 1997). Chronic stress is best assessed over a period 

of months to accurately capture persistent stressful influences in different life domains 

(Hammen, 1991). Finally, pollution estimates using land use regression models are 

best suited for long-term exposures, given that it is based on spatial differences in land 

use that do not vary over time. Because pollution data were available for a 6-year 

period (1998-2003), but this period did not overlap with the time frames of the other 

study constructs, we utilized the entire period as a more reliable indicator of long-term 

exposures. One limitation of this approach is that if families moved, the estimated 

exposures would be misclassified; however, this increase in measurement error would 

be expected to decrease the likelihood of observing associations. Nonetheless, future 

studies that are able to more precisely coordinate the periods of assessment for air 

pollution, stress, and clinical outcomes, and that could repeatedly assess families to 

track changes in pollution exposures based on moves, as well as changes in chronic 

stress experiences over time, would be useful for more clearly delineating the time 

frame of effects of physical and social exposures. A third limitation is the lack of health 

records to ascertain objective asthma-relevant outcomes such as hospitalizations and 

physician visits; future studies that have access to such databases would allow 

researchers to explore additional clinical indicators that may be influenced by both the 

physical and social environments. Finally, we note that we used NO2 as an indicator of 
26

traffic-related air pollution, and effects may or may not be attributable specifically to 

NO2. Further, while pollution exposure was assessed with a high resolution spatial 

model estimating air pollution concentrations at individual-subjects’ home address, we 

did not consider short-term temporal variability in exposures during the study period, 

nor did we consider other environments (e.g., schools) where participants spend a good 

deal of time. Because it was not feasible to measure the actual level of pollution 

exposure each individual child experienced throughout their day, this raises the 

possibility that some of the patterns could have been affected by unmeasured 

exposures, particularly in a small sample such as this one. 

In sum, the present study found that the physical and social environment 

interact to affect asthma outcomes in children. As pollution levels declined, higher 

levels of chronic family stress were associated with heightened inflammatory profiles 

cross-sectionally, and with increases in asthma symptoms and decreases in peak 

expiratory flow over a 6-month period. Conversely, as pollution levels increased, 

chronic stress either had no effect on outcomes (inflammatory measures) or in some 

cases showed an opposite effect (clinical measures). These findings suggest that 

vulnerability factors such as psychosocial stress most clearly modify the effects of 

traffic-related air pollution when exposure is present but not high. This highlights the 

need for increasing awareness among families who have a child with asthma of how the 

physical and social environments are intertwined, and how even in areas with lower 

levels of pollution, vulnerability to asthma exacerbations may persist in children who are 

experiencing higher levels of chronic stress. 
27

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33

TABLE 1. Descriptive information on study participants 

 

 % M SD 

 

Age 12.82 2.75 

Gender 

 Males 68 

 Females 32 

Ethnicity 

 Caucasian 63 

 Asian 26 

 Other 11 

Severity 

 Mild intermittent 16 

 Mild persistent 38 

 Moderate persistent 32 

 Severe persistent 14 

Inhaled corticosteroids 4.35 5.91 

Beta-agonists 3.93 5.55 

Chronic stress 2.14 0.74 

NO2 (ppb) 16.5 3.7 

IL-4 (pg/ml) 12.47 10.85 
34

IL-5 (pg/ml) 110.46 91.00 

IL-13 (pg/ml) 323.42 237.22 

IgE (log-transformed kU/L) 2.18 0.80 

Eosinophil count (x109

cells/L) 0.36 0.28 

Parent-reported symptoms 

 Baseline 1.96 2.79 

 6-month follow-up 1.23 2.08 

Child-report daily diary symptoms 

 Baseline 3.42 3.85 

 6-month follow-up 2.61 3.23 

Daily peak expiratory flow rate % 

 Baseline 99.83 15.62 

 6-month follow-up 95.08 16.64 

 

Note: Medication values are for number of days taken in the previous two weeks. 

Chronic stress is on a 1-5 scale. Parent reported symptoms=average number of days of 

symptoms in past 2 weeks reported during the lab visit. Child-reported daily diary 

symptoms=average daily symptom score from the 2 week home monitoring following 

the lab visit. 
35

TABLE 2. Descriptive information on clinical measures by pollution group 

 

 Low pollution High pollution 

 M SD M SD 

 

Child daily diary symptoms, baseline 2.79 3.42 4.08 4.21 

Child daily diary symptoms, follow-up 2.09 2.35 3.16 3.90 

Parent-reported symptoms, baseline 3.47 4.44 8.47 10.67 

Parent-reported symptoms, follow-up 2.91 6.22 4.50 6.26 

PEFR%, baseline 100.02 16.07 99.62 15.35 

PEFR%, follow-up 94.15 12.93 96.07 20.04 

 

Note. Low pollution group consists of those below the median on NO2 scores. High 

pollution group consists of those above the median on NO2 scores.
36

FIGURE LEGENDS 

Figure 1. Figure 1A (top) depicts the interaction between chronic stress and air 

pollution predicting IL-5 production. The graph displays the estimated regression line 

for the relationship between chronic stress and IL-5 production at low (-1 SD) and high 

(+1 SD) levels of air pollution. Figure 1B (middle) depicts the interaction between 

chronic stress and air pollution for total IgE. Figure 1C (bottom) depicts the interaction 

between chronic stress and air pollution for eosinophil counts. 

Figure 2. Figure 2A (top) depicts the interaction between chronic stress and air 

pollution predicting change in child-reported daily diaries of asthma symptoms over a 6 

month period. The graph displays the estimated regression line for the relationship 

between chronic stress and changes in asthma symptoms over time at low (-1 SD) and 

high (+1 SD) levels of air pollution. Figure 2B (middle) depicts the interaction between 

chronic stress and air pollution for changes in parent reported symptoms over a six 

month period. Figure 2C (bottom) depicts the interaction between chronic stress and 

air pollution for changes in daily peak expiratory flow rate percent over a 6 month 

period. 
37

0

30

60

90

120

150

LowHigh

Chronic family stress

IL-5 (pg/ml)

Low pollution

High pollution

1

1.3

1.6

1.9

2.2

2.5

LowHigh

Chronic family stress

Total IgE (lg10)

Low pollution

High pollution

0.2

0.3

0.4

0.5

0.6

0.7

LowHigh

Chronic family stress

Eosinophil count

Low pollution

High pollution
38

-20246

LowHigh

Chronic family stress

Change in child-reported daily diary 

symptoms over 6 months

Low pollution

High pollution

-20246

LowHigh

Chronic family stress

Change in parent-reported symptoms over 6 

months

Low pollution

High pollution

-12

-606

12

18

LowHigh

Chronic familystress

Change in Daily PEF% over 6 

months

Low pollution

High pollution