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# 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 Chen 1 , Hannah M. C. Schreier 1 , Robert C. Strunk 2 , and Michael Brauer 3

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 Sch ool of Medicine, St. Louis, MO, USA.; 3 School of Environmental Health, University of British Columbia, Vancouver, BC, Canada

Corresponding author: Edith Chen, Universi ty 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.ca2

Running title: Stress and Air Pollution in Asthma

Keywords: air pollution, asthma, i mmune, psychosocial, stress, traffic

Abbreviations: NO 2 nitrogen dioxide IL interleukin IgE immunoglobulin E

Acknowledgements: This stud y was supported by funding from the National Institutes of Health grant HL073975 and th e Canadian Institutes of He alth 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 24

ABSTRACT Background: Previous research has document ed effects of both physical and social environment exposures on childhood asthma. However, few studies have considered how these two environments migh t interact to affect asthma. Objective: To test interactio ns between chronic exposure to traffic-related air pollution and chronic family stress in pred icting 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 inflamma tory markers (cytokine prod uction, IgE, eosinophil counts) were measured. Pare nts reported on children’s symptoms. Children completed daily diaries of symptoms and peak expirato ry flow rate (PEFR) measures at baseline and six months later. Exposure to traffic-related air pollut ion was assessed using a land use regression model for nitrogen dioxide (NO 2 ) concentrations. Results: NO 2 by stress interactions were found for IL-5 ( β for interaction term=-.31, p=.02), IgE (interaction β =-.29, p=.02), and eosinoph il counts (interaction β =-.24, p=.04). These interactions showed that higher chronic stress was associated with heightened inflammatory prof iles as pollution levels decreased. Longitudinally, NO 2 by stress interactions emerged for da ily 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 sympto ms and decreases over time in PEFR as pollution levels decreased.5

Conclusions: The physical and social environmen t interacted in predicting both biological and clinical outcomes in children with asthma, suggesting that when pollution exposure is more modest, vulnerability to as thma 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 iden tifying 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 ex ists independently of one another. In contrast, few studies have examined how the ph ysical and social environments might interact to affect asthma outcomes. Fo r example, certain combinations of physical and social en vironment exposures might produce unique effects beyond exposure to on e of these factors al one. Hence the go al of the present study was to empirically test interactive effe cts of the physical an d social environment in order to gain a more comprehensive picture of how the en vironment, broadly defined, contributes to childhood asthma. Within the physical environment literature, exposure s such as traffic-related air pollution have been repeated ly linked to increased resp iratory symptoms, increased asthma-related hospitalizations, and the di agnosis 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 ex haust particles or nitrogen dioxide increases levels of in flammatory markers relevant to asthma (Barck et al. 2002; DiazSanchez et al. 1997; Pour azar et al. 2004), suggesting that greater inflammation may underlie the effects foun d in clinical studies. Within the social environm ent literature, factors such as psychological stress have been linked to wheezing, asthma exacerbations, and the diagnosis of asthma in7 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 response s following antigen challenge or in vitro stimulation of immune cells, among children with or at ri sk for asthma (Chen et al. 2006; Liu et al. 2002; Wright et al. 2004). Much of this research, although importan t in its own right, has proceeded largely along separate lines with little overlap. Few studies have brought these two areas together to examine how phys ical 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 argu ed that social and ph ysical factors might instead combine to modify heal th outcomes such as asthma (Weiss and Bellinger 2006). This interaction between the physical an d social environments could mean that negative social factors intens ify 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 fa ctors might create a ‘double exposure,’ whereby the combination of factors such as high pollution exposure and high stress is most detrimental to asthma. One r ecent study has empi rically tested the notion of physical by soci al 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, NO 2 ) as well as chronic stress (indicated by exposure to violence) were measured. The risk of asthma incidence was elevated as8

residential NO 2 exposure increased, but only among children who had high levels of exposure to violence (Clougherty et al. 2007) . In the present st udy, we sought to determine whether a social by physical exposure interactio n would predict outcomes in children with existing asthma. More specifi cally, we tested whether the physical and social environment would interact to predic t biological markers im plicated in asthma, and subsequently, whether thei r interaction would predict ch anges in clinical outcomes over time in a sample of children with asth ma. While previous research provides some evidence for asthma onset su ggesting that such intera ctions might fit a “double jeopardy” hypothesis, there is so little previous data addressing this question in children with asthma, that th e present study aim ed to describe the natu re of any interaction patterns, rather than testing specific a priori hypotheses. METHODS Patients 73 children were recruited from Vancouve r, B.C. through ad vertisements in physicians’ offices, local media, and communi ty settings for an ob servational study of childhood asthma. Advertis ements were placed throug hout the Greater Vancouver area, and interested families contacted the la b for a screening to determine eligibility. Eligible families were then scheduled for a la boratory visit. Visi ts occurred throughout the year. Participants were required to be physician-diagnosed with asthma (82% with allergic asthma). Children we re 9-18 years, fluent in Engl ish, free of acute respiratory illness at the time of their visit (by parent an d child report), and had no chronic illnesses other than asthma. Children gave written as sent, 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 fo r the study region (Henderson et al. 2007) provided high resoluti on spatial estimates of NO 2 concentrations as an indicator of chronic exposure to traffi c-related air pollution. Brie fly, 116 passive samplers to collect NO 2 were deployed for two-14 day periods at 116 sites in the study area. Mean concentrations during thes e two periods were highly correlated with, and closely approximated annual averages from regu latory 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 NO 2 was built with the most predictive covariates. For NO 2 , the model (R 2 = 0.56) included the number of major roads within 100 and 1000 meter radius circular buffers of the measurement sites, the numb er of secondary roads with in a 100 meter buffer, the population density within a 2500 meter radi us the amount of commercial land use within 750 meters and elevatio n. Comparison of model pred ictions to measured annual average concentrations at 16 government regu latory air monitoring stations had an R 2 of 0.69. The predictive error for the mode l 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 measuremen t. The mean differen ce 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 An alyst) to remove abrupt changes and edge10 effects so as to more accurately reflect th e measured effect of proximity to roadways (Gilbert et al. 2003). Using th e model, a smooth spatial su rface of predicted (annual average) concentrations was generated for th e entire study area at a resolution of 10 meters. To adjust for tempor al variability, the corresp onding 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 estima te monthly average concentrations. Using these we then computed individual subject average exposures for the full period during which pollution estimates we re taken (1998-2003) as an indicator of chronic tra ffic-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 spatia l 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 re solution. This specific air pollution exposure indicator has been associated with br onchiolitis and asthma inci dence in other studies in Vancouver (Clark et al. 2007; Demers et al. 2006), and as well, the same exposure assessment approach has been us ed in other cities to predic t 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 conducte d at baseline and asse ssed chronic stress11 over the past six months, in domains such as family rela tionships, friendships, and school. This is a semi-structu red interview, in which a trai ned interviewer asks a series of open-ended question s in each life domain, and uses th e information gathered to rate the level of chronic, ongoing st ress. Interviewers rate the extent of chronic stress on a 1-5 scale, using 0.5 in crements, with higher numbers re flecting more severe, persistent difficulties. This interview has been used successfully in children as young as 8, and has demonstrated reliabilit y 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 foun d 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 cont aining Sodium Heparin, and 3 x 10 6 peripheral blood mononuclear cells (PBMC’s) were isolat ed through density-gradient centrifugation. PBMC’s were resuspended in culture medium consisting of RPMI plus 10% fetal calf serum, and incubated with phorbol myrist ate acetate (25ng/ml) and ionomycin (1 ug/ml) for a period of 48 hours at 37 ° C in 5% CO 2 . 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. Intr a-assay CVs ranged from 3.68 – 4.76%. A complete blood count with five-part differential (B ayer ADVIA 70 hematology system, Holiston, Massachusetts) was performe d to obtain eosinoph il counts. Total serum IgE was measured using an automa ted fluorescence immunoassay (Pharmacia12 CAP system, Portage, Michigan ), and was log-transformed because of its non-normal distribution. Clinical Measures To obtain multiple perspect ives on asthma symptoms, both parents and children were probed about symptoms. Parents were interviewed during the laboratory visit about their child’s symptoms ove r the previous two weeks as part of a health interview that gathered information abou t the child’s asthma (e.g., pr escribed 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 numbe r of days over the past two weeks children had cough, wheeze, sh ortness or breath, or chest tightness. Nighttime symptoms were assessed as the number of days chil dren awakened from sleep because of coughing, wheezing, shor tness 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 symp toms on a daily basis for two weeks following their lab visit. Ch ildren 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 brea th, each on a 0 (none) to 4 (really bad) scale. Scores for each question were summed for each day, and then averag ed across the 14 day diary period. Over the same period, childre n also monitored peak expi ratory flow rates (PEFR)13 at home using an electronic monitor (Quadr omed, Hoechberg, Germany). Three peak flow readings were taken upon awakening an d 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 la boratory 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 Guidel ines based on the higher of symptom frequency and medication us e, paralleling the approach of previous researchers (Bacharier et al. 2004). Families also brou ght 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, ethnic ity, or time of year of study visit were associated with study variable s. Demographic or study vari ables 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 clinical14 asthma outcomes. Biological variables were examined cross-sectionally, and clinical outcomes longitudinally, using a series of hierarchical mult iple regression analyses. Biological variables were predicted from vari ables entered in three steps: 1) potential confounders including medical variables (asthma severity cl assification, use of inhaled corticosteroids, use of beta agonists) and any demographic variable associated with study outcomes; 2) main ef fects of chronic stress and air pollution; and 3) the interaction term for chronic stress by air po llution. These analyses were conducted according to the recommendations by Aike n and West (Aiken and West 1991). Note that because both chronic stre ss and pollution exposure are continuous variables, these involve statistical procedures to examine th e significance of an interaction between two continuous variables. These analyses comprised the primary test of study hypotheses. The nature of an interactio n between two continuous va riables, however, can be difficult to visualize. To aid in interpretation, we prov ided two additional pieces of information. Significant interactions were plotted by graphing th e relationship between stress and asthma outcomes at low (-1 SD) an d 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 st ress and asthma varies at different levels of po llution exposure. We also conduc ted secondary analyses to test whether the regression coefficients at th ese specific values of air pollution were significant. Again, this crea tes an artificial distinction, but allows the reader to gain some sense of where the differences are mo st pronounced. Thes e procedures follow the statistical recommendation s of Aiken and West (1991) . However, because these15 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 poin ts, 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 incl uded as a control variable in step 1. RESULTS Table 1 presents descriptive informatio n about the sample. There were a few associations of demographic an d study variables with biologic al or clinical outcomes. Child age was inversely correlated with eosi nophil 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 gi rls to have more symptoms than boys (t=3.36, p=.001). Children be longing to minority groups had higher pr oduction 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 th e relevant equations below. Cross-Sectional Associations with Biological Markers Our first set of analyses tested whethe r stress and air pollut ion were associated with biological markers implicated in asth ma. Regression coefficients are reported below, with interactions graphed to illustrate the direction of effects.16 With respect to IL-5 production, th ere was no main effect of stress ( β =.16, p=.22) or air pollution ( β =.15, p=.30), but there was a sign ificant stress by air pollution interaction (interaction term β =-.31, p=.02). The negative coefficient indicates that as pollution decreases, higher levels of chronic family st ress become asso ciated 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), wherea s 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 emerge d for IL-4 or IL-13 (p’s > .2). With respect to total IgE, there was a si gnificant main effect of stress such that children with higher chronic st ress had higher IgE levels ( β =.32, p=.01), but no main effect of air pollution ( β =.08, p=.52). In addi tion, there was a significant stress by air pollution interaction (interaction term β =-.29, p=.02). The negative coefficient indicates that as pollution decreases, high er 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 positi ve and statistically significant at 1 SD below the mean of air pollution ( β =.54, p=.002), whereas th e relationship between stress and IgE at 1 SD abov e the mean of air polluti on was not significant ( β =.11,17 p=.46), suggesting that the relationship betw een stress and IgE is stronger in lower pollution areas. With respect to eosinophil counts, th ere 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 inte raction (interaction term β =-.24, p=.04). The negative coefficient indicates that as pollution decreases, higher le vels of chronic family stress become associated with greater eosinophil counts. This is illustrated in Figure 1C by plotting the relationship betw een chronic stress an d eosinophil counts at +1 SD and -1 SD of air pollution. The rela tionship between stress and eosi nophil counts was positive at 1 SD below the mean of air pollution ( β =.23, p=.15), wherea s 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 informatio n on clinical variab les at baseline and follow-up on the sample . This information is separate d into a low and high pollution group by median split so readers can see ho w clinical variables vary by pollution. Average pollution exposure for those be low the median was 14.1 ppb (SD=1.6). Average exposure for those above the median was 18.9 pp b (SD=3.5). Low and high pollution groups were similar on demographi c variables such as age (average age for those below the median on pollution expo sure=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% Caucas ian among those above the median), and18 parent education (average years of educatio n for those below the median on pollution exposure=15.4; average years of educatio n 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 a ssociations with biological markers have implications clinically for as thma over time. To do this, we tested whether the interaction between stress and ai r pollution predicted changes in clinical variables over a six month period, controlling for ba seline 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 ai r pollution interaction (interaction term β =-.28, p=.02). The negative coefficient indicates that as pollutio n decreases, higher leve ls of chronic family stress become associated with increasing symp toms over time. This is illustrated in Figure 2A by plotting the re lationship between chronic st ress 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 be low the mean of air pollution ( β =.25, p=.098), whereas the relationship between stre ss 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 ther e was a marginal stress by air pollutio n interaction ( β =-.25, p=.07). The negative coefficient indicates that as19 pollution decreases, higher levels of chronic family st ress become asso ciated with increasing parent-reported symptoms over time . This is illustrated in Figure 2B by plotting the relationship betw een chronic stress and change in symptoms at +1 SD and -1 SD of air pollution. The relationship between st ress 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 ch ange 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 sign ificant stress by air pollution interaction ( β =.30, p=.03). The posi tive coefficient indicates that as pollution decreases, higher levels of chronic fami ly 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 relationsh ip between stress and PEFR change at 1 SD above the mean of air pollution was positive ( β =.24, p=.11). Note that although the dir ection 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 me dian 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 have20 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 th at we are aware of to do cument that the physical environment (chronic traffic-related air po llution) and social en vironment (chronic stress) interact to predict both biological and longitudinal cl inical outcomes in children with asthma. The findings fr om this study demonstrated that the interactive effects between air pollution and stress are stronger than either fact or alone, suggesting that the physical and social environm ents are in fact intertwined an d 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, high er levels of stress were associated with heightened inflammatory profiles cross-sectio nally and worsening clinical profiles over a 6-month period. In contrast, chronic stress ha d modest effects on biological and clinical measures as pollution exposu re increased, and any suggestio ns of effects were in an opposite direction. The direction of the interaction effects in this study was diffe rent from what has been found in the small number of previous studies on this topic. For example, one recent study found that traffi c-related air pollution (NO 2 ) interacted with exposure to violence to predict the diagnosi s of asthma in a birth cohort of children, such that children with both high pollution and violence exposures were at greatest risk of having21 asthma (Clougherty et al., 2007) . Another study foun d that high levels of traffic-related air pollution (e.g., NO 2 ) combined with low so cioeconomic status predicted the greatest risk of asthma hospitalizations in children (L in 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 envi ronmental pollutants when ch ronic exposure levels are more modest. Previous research has sugg ested that social fa ctors do require the presence of some dose of phys ical exposure to have eff ects on biological processes (Chen and Miller 2007). The fi ndings from the present st udy fit the notion of a threshold model- that is, that there is a threshold at whic h chronic physical exposures begin to have effects on heal th outcomes, and that one role of chronic stress may be to lower the threshold at which physical exposure s affect biological and clinical outcomes. One reason why this may occu r is that when chronic expo sure to traffic-related air pollutants is more modest, there may be grea ter room for social factors to increase or decrease vulnerability biologically. This is co nsistent with the notion that stress may be able to shift physiological re sponse systems in a direction such that adverse outcomes occur in response to lower doses of physic al exposures (Morello -Frosch et al., 2006; Paarlberg et al. 1995). Consistent with this notion, chronic stress under certain conditions has been found to heighten biolog ical responses to nega tive social exposures (Gump and Matthews 1999); we speculate th at similar processe s may occur with responses to physical exposure s. This type of response pattern is thought to occur22 because prolonged stress can sensitize and pr event adaptation of biological systems (McEwen 1998), potentially leading to lower doses of physical environment pollutants having detrimental effects on biolog ical 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 stud y 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 predicte d the risk of being diagnose d 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 th e two studies. In addition, different types of social exposure s 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 di fferent types of life stressors, and it is possible that air pollution interacts differentl y 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 associ ations 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 sectio nally of interactio ns between chronic traffic-related air pollution an d chronic stress on IL-5 prod uction, total Ig E levels, and eosinophil counts represent bi ological pathways that have implications for clinical asthma outcomes. Immune pathways in asth ma include the secretio n of cytokines that activate B cells to produce IgE. IgE in turn initiates an inflammatory cascade leading to airway constriction and mucu s production. A second path way involves the recruitment of eosinophils to the airways, which also promotes airway in flammation and obstruction. Secretion of the cytokine IL -5 is known to increase eo sinophil production. Thus heightened production of IL-5 along with el evated IgE and eosinoph il counts suggests a biological profile that is po tentially detrimental for children with asthma in terms of vulnerability to symptoms. We considered this possib ility by testing whether ch ronic traffic-related air pollution by chronic stress inte ractions could also predict ch anges 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 opposi te pattern emerged whereby higher levels of chronic stress were associat ed with declines in asthma symptoms and increases in PEFR over time. This was an un expected trend, and it is unclear what the24 implications are. Note, however, that beca use longitudinal analys es focus on change over time, this does not mean th at 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 lo wer pollution areas at both ti me points, but their clinical profiles (as assessed by PEFR and symptom reporting) do not appear to worsen over time. In contrast, children in lower poll ution areas show strong er relationships of chronic stress with worsening cl inical profiles over time. Strengths of the present study includ e the collection of asthma-relevant biomarkers; the tracking of longitudinal clinical outcomes; th e use of a land use regression model to assess individual exposure s 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 clearl y inferred. For exampl e, although it would be reasonable to hypothesize that more se vere asthma increases family stress levels, the fact that stress was assessed prior to c linical measures for longitudinal analyses meant that worsening asthma was not driving stress experiences. Limitations to the presen t study include the small sample size. Both the comprehensive stress interview and the collection and processi ng of biological samples limited the size of the presen t sample, and this raises the possibility that findings were due to chance and hence need to be replicated. We no te, however, that our sample size is comparable to numer ous other studies of acute stress and asthma cytokine production (Kang et al. 1997; Kang and Fox 2001; Marsha ll et al. 1998). A second limitation is the varying time frame for measur es in this study. Time frames were set25 based on optimal periods for gathering inform ation for different study constructs. For example, symptom reports are best assessed over shorter ti me intervals (weeks), given difficulties in accurately r ecalling symptoms over longer ti me periods (National Asthma Education and Prevention Prog ram, 1997). Chronic stress is be st assessed over a period of months to accurately capture persistent st ressful influences in different life domains (Hammen, 1991). Finally, pollution estimate s using land use regr ession models are best suited for long-term exposures, given that it is based on spatial differences in land use that do not vary over ti me. Because pollution data were available for a 6-year period (1998-2003), but this pe riod did not overlap with th e time frames of the other study constructs, we utilized the entire period as a more reliable in dicator of long-term exposures. One limitation of this approach is that if families moved, the estimated exposures would be misclassifi ed; however, this increase in measurement error would be expected to decrease the likelihood of observing associ ations. Nonetheless, future studies that are able to more precisely c oordinate the periods of assessment for air pollution, stress, and clinical outcomes, an d that could repeated ly 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 de lineating the time frame of effects of physical and social exposures. A third limi tation is the lack of health records to ascertain objectiv e asthma-relevant outcomes su ch as hospitalizations and physician visits; future studies that ha ve access to such da tabases would allow researchers to explore additional clinical indicators that ma y be influenced by both the physical and social envi ronments. Finally, we note that we used NO 2 as an indicator of26 traffic-related air pollution, and effects may or may not be attribut able specifically to NO 2 . Further, while pollution exposure was assessed with a high resolution spatial model estimating air pollution concentrations at individual-s ubjects’ home address, we did not consider short-term temporal variab ility in exposures duri ng the study period, nor did we consider other envi ronments (e.g., schools) wher e participants spend a good deal of time. Because it was not feasible to measure the actual level of pollution exposure each individual ch ild experienced throughout th eir day, this raises the possibility that some of the patterns coul d have been affected by unmeasured exposures, particularly in a sm all sample such as this one. In sum, the present stud y found that the physical and social environment interact to affect asthma ou tcomes in children. As pollution levels declined, higher levels of chronic family stress were associat ed with heightened in flammatory profiles cross-sectionally, and with increases in asthma sympto ms and decreases in peak expiratory flow over a 6-month period. Conversely, as pollution levels increased, chronic stress either had no effect on outcomes (inflamma tory measures) or in some cases showed an opposite e ffect (clinical measures). These findings suggest that vulnerability factors such as psychosocial stress most clearly modify the effects of traffic-related air pollution wh en exposure is present but no t high. This highlights the need for increasing awareness among families who have a ch ild with asthma of how the physical and social environm ents are intertwined, and how even in area s with lower levels of pollution, vu lnerability to asthma exacerbation s may persist in children who are experiencing higher levels of chronic stress.27

References Adrian C , Hammen C. 1993. Stress Exposure and Stress Generation in Children of Depressed Mothers. J Consult Clin Psychol 61: 354-359. Aiken LS , West SG. (1991). Mu ltiple regression: Testing an d interpreting interactions. London: Sage Publications. Bacharier LB, Strunk RC, Mauger D, Wh ite D, Lemanske RF, Sorkness CA. 2004. Classifying asthma severity in chil dren: Mismatch between symptoms, medication use, and lung functi on. Am J Respir Crit Care Med 170: 426-432. Barck C, Sandstrom T, Lundahl J, Hallden G, Svartengren M, Strand V et al. 2002. Ambient level of NO2 augments the inflammatory response to inhaled allergen in asthmatics. Respir Med 96: 907-917. Brauer M, Hoek G, Smit HA , de Jongste JC, Gerritsen J, Postma DS et al. 2007. Air pollution and development of asthma, allerg y and infections in a birth cohort. Eur Respir J 29: 879-888. Brauer M, Hoek G, Van Vliet P, Meliefste K, Fischer PH, Wi jga A et al. 2002. Air pollution from traffic and the development of re spiratory infections and asthmatic and allergic symptoms in children. Am J Respir Crit Care Med 166: 1092-1098. Chen E, Hanson MD, Paterson LQ, Gr iffin MJ, Walker HA, Miller GE. 2006. Socioeconomic status and inflammatory pr ocesses in childhoo d asthma: The role of psychological stress. J Allergy Clin Immunol 117: 1014-1020. Chen E , Miller GE. 2007. Stress and inflamma tion in exacerbations of asthma. Brain Behavior and Immunity 21: 993-999.28 Clark N, Demers P, Karr C, Koehoom M, Tamb uric L, Brauer M. 2007. Effect of ambient air pollution exposure on developmen t of childhood asthma. Epidemiology 18: S180-S181. Clougherty JE, Levy JI, Kubzansky LD, Ryan PB, Suglia SF, Jacobson Canner M et al. 2007. Synergistic effects of traffic-related air pollution and expo sure to violence on urban asthma etiology. Environ Health Perspect 115: 1140-1146. Crain EF, Walter M, O'Connor GT, Mitchell H, Gruchalla RS, Kattan M et al. 2002. Home and allergic characteristics of childr en with asthma in seven US urban communities and design of an enviro nmental intervention: The Inner-City Asthma Study. Environ Health Perspect 110: 939-945. Demers P, Karr C, Koehoom M, Lencar C, Tamburic L, Brauer M. 2006. The impact of air pollution on bronchiolitis. Epidemiology 17: S147. DiazSanchez D, Tsien A, Flem ing J, Saxon A. 1997. Combined diesel exhaust particulate and ragweed allergen ch allenge markedly enhances human in vivo nasal ragweed-specific IgE and skew s cytokine production to a T helper cell 2-type pattern. J Immunol 158: 2406-2413. Edwards J, Walters S, Griffi ths RK. 1994. Hospital admission s for asthma in preschool children: Relationship to major roads in Birmingham, United Kingdom. Archives of Environmental Health 49: 223-227. Gee GC , Payne-Sturges DC. 2004. Environm ental health disparities: A framework integrating psychosocial and environmen tal concepts. Enviro n Health Perspect 112: 1645-1653.29 Gilbert NL, Woodhouse S, St ieb DM, Brook JR. 2003. Ambi ent nitrogen dioxide and distance from a major high way. Sci Total Environ 312: 43-46. Gump BB , Matthews KA. 1999. Do background stressors in fluence reactivity to and recovery from acute stressors? J Applied Soc Psychol 29: 469-494. Hammen C. 1991. Generation of stress in the course of unipolar depression. J Abnorm Psychol 100: 555-561. Henderson SB, Beckerman B, Jerrett M, Br auer M. 2007. Application of land use regression to estimate long -term concentrations of traf fic-related nitrogen oxides and fine particulate matte r. Environ Sci Technol 41: 2422-2428. Kang D, Coe C, McCarthy DO, Jarjour NN, Kelly EA, Rodriguez RR et al. 1997. Cytokine profiles of stimulated bl ood lymphocytes in asthmati c and healthy adolescents across the school year. Journal of Interferon and Cytokine Research 17: 481-487. Kang DH , Fox C. 2001. Th1 and Th2 cytokine responses to academic stress. Research in Nursing & Health 24: 245-257. Klinnert MD, Nelson HS, Pr ice MR, Adinoff AD, Leung DY , Mrazek DA. 2001. Onset and persistence of childhood asthma: Pr edictors from infancy. Pediatrics 108: e69. Lin M, Chen Y, Villeneuve PJ, Burnett RT, Lemyre L, Hertzman C et al. 2004. Gaseous air pollutants and asthma hospitalization of children with lo w household income in Vancouver, British Columb ia, Canada. Am J Epidemiol 159: 294-303. Liu LY, Coe CL, Swenson CA, Kelly EA, Kita H, Busse WW. 2002. Sc hool examinations enhance airway inflammation to antigen challenge. Am J Respir Crit Care Med 165: 1062-1067.30 Marshall GD, Agarwal SK, Lloy d C, Cohen L, Henni nger EM, Morris GJ. 1998. Cytokine dysregulation associated with exam st ress in healthy medical students. Brain Behav Immun 12: 297-307. Marshall,JD, Nethery,E, Brauer,M Within-urb an variability in am bient air pollution: Comparison of estimation methods. Atmospheric Envi onment, (in press). r McEwen BS. 1998. Protective an d damaging effects of stre ss mediators. N Engl J Med 338: 171-179. Miller GE , Chen E. 2006. Life stress and di minished expression of genes encoding glucocorticoid receptor and beta(2)-adrenergic receptor in children with asthma. Proceedings of the Nation al Academy of Sciences 103: 5496-5501. Morello-Frosch R , Shenassa ED. 2006. The environmental "riskscape" and social inequality: Implications for explaining maternal and child health disparities. Environ Health Perspect 114: 1150-1153. National Asthma Education and Prevention Program. (1997). Expert Panel Report 2: Guidelines for the di agnosis and management of as thma. Bethesda, MD: National Institutes of Health Publication 97-4051. Nethery,E, Leckie,S, Teschke,K, Brauer,M From measures to models: An evaluation of air pollution exposure assessment for ep idemiologic studies of pregnant women. Occupational and Envi ronmental Medicine, (in press). Paarlberg KM, Vingerhoets AJJM, Passchi er J, Dekker GA, Vangeijn HP. 1995. Psychosocial factors and pregnancy outc ome: A review with emphasis on methodological issues. J Psychosom Res 39: 563-595.31 Pourazar J, Frew AJ, Blomberg A, Helleday R, Kelly FJ, Wilson S et al. 2004. Diesel exhaust exposure enhances the expression of IL-13 in the bronchial epithelium of healthy subjects. Respir Med 98: 821-825. Rudolph KD , Hammen C. 1999. Age and gender as determin ants of stress exposure, generation, and reactions in youngsters: A transactional perspective. Child Dev 70: 660-677. Ryan PH, LeMasters GK, Biswas P, Levin L, Hu SH, Lindsey M et al. 2007. A comparison of proximity and land use regression traffic exposure models and wheezing in infants. Environ Health Perspect 115: 278-284. Sandberg S, Paton JY, Ahola S, McCann DC , McGuinness D, Hillary CR. 2000. The role of acute and chronic stress in as thma attacks in children. Lancet 356: 982-987. Studnicka M, Hackl E, Pisching er J, Fangmeyer C, Haschke N, Kuhr J et al. 1997. Trafficrelated NO2 and the prevalence of asthma and respiratory sy mptoms in seven year olds. Eur Respir J 10: 2275-2278. Weiss B , Bellinger DC. 2006. Social ecology of children's vulnerability to environmental pollutants. Environ Health Perspect 114: 1479-1485. Wright RJ. 2005. Stress and atopic disorders. J Allergy Clin Immunol 116: 1301-1306. Wright RJ, Cohen S, Carey V, Weiss ST, Gold DR. 2002. Parental stre ss as a predictor of wheezing in infancy: A pros pective birth-cohort study. Am J Respir Crit Care Med 165: 358-365. Wright RJ, Finn P, Contreras JP, Cohen S, Wright RO, Staudenmayer J et al. 2004. Chronic caregiver stress and IgE expression , allergen-induced proliferation, and32 cytokine profiles in a birth cohort predisposed to atop y. J Allergy Clin Immunol 113: 1051-1057.33 TABLE 1. Descriptive inform ation 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 NO 2 (ppb) 16.5 3.7 IL-4 (pg/ml) 12.47 10.8534 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 (x10 9 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 re ported symptoms=average number of days of symptoms in past 2 weeks re ported during the lab visit. Child-reporte d daily diary symptoms=average daily symptom score from the 2 week home mo nitoring 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, fo llow-up 2.09 2.35 3.16 3.90 Parent-reported symptoms, baseline 3.47 4.44 8.47 10.67 Parent-reported symptoms, foll ow-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 NO 2 scores. High pollution group consists of those above the median on NO 2 scores.36

FIGURE LEGENDS Figure 1. Figure 1A (top) depicts the interaction between ch ronic stress and air pollution predicting IL-5 production. The gr aph displays the estima ted regression line for the relationship between ch ronic stress and IL-5 producti on at low (-1 SD) and high (+1 SD) levels of air pollutio n. Figure 1B (middle) de picts the interaction between chronic stress and air pollution for total IgE. Figure 1C (b ottom) depicts the interaction between chronic stress and air po llution for eosinophil counts.

Figure 2. Figure 2A (top) depicts the interaction between ch ronic stress and air pollution predicting change in child-reported daily diaries of asthma symptoms over a 6 month period. The graph displays the estima ted regression line for the relationship between chronic stress and chan ges in asthma symptoms over time at low (-1 SD) and high (+1 SD) levels of air poll ution. Figure 2B (middle) depicts the interaction between chronic stress and air pollutio n for changes in parent re ported symptoms over a six month period. Figure 2C (bottom) depict s the interaction betw een chronic stress and air pollution for changes in daily peak expiratory flow rate percent over a 6 month period.37

0 30 60 90 12 0 15 0

Low H i gh C h r onic f a m ily s t r e s sIL-5 (pg/ml) Low po l l u t i o n H i gh po l l u t i o n

1 1. 3 1. 6 1. 9 2. 2 2. 5

Low H i gh C h r o n i c f a m i l y s t r essTotal IgE (lg10) Lo w po l l u t i o n H i gh pol l u t i on

0. 2 0. 3 0. 4 0. 5 0. 6 0. 7

Low H i gh C h r o n i c f a m i l y st r essEosinophil count Low po l l u t i on H i g h pol l u ti on38

-2 0 2 4 6

Lo w H i g h C h r o n i c f a m i l y st r e s sChange in child-reported daily diarysymptoms over 6 months L o w po l l uti o n H i g h pol l u ti on

-2 0 2 4 6

Lo w H i g h C h r o n i c f a m i l y st r e s sChange in parent-reported symptoms over 6months Low pol l u ti o n H i gh po l l u t i o n

-1 2 -6 0 6 12 18

Lo w H i g h C h r o n i c f a m ily s t r e s sChange in Daily PEF% over 6months L o w po l l uti o n H i g h po l l ut i o n