1140 VOLUME 115 | NUMBER 8 | August 2007 • Environmental Health Perspectives

Research

The gradient of socioeconomic position (SEP)

on health may be explained partly by a combination of increased contaminant exposures

and greater susceptibility to their effects. Air

pollution, for instance, may be higher near

major roadways, power plants, and industrial

sites, where property values are lower and

lower-income populations reside (Graves

1988). Increased life stress among lower-SEP

populations has also been proposed as a primary pathway through which SEP affects

health (Gee and Payne-Sturges 2004;

Morello-Frosch and Shenassa 2006). 

Because of this potential spatial covariance

across exposures, and because stress and pollution may influence common physiologic pathways (i.e., oxidative stress) and health outcomes

(i.e., respiratory disease), stronger methods are

needed to disentangle their effects and investigate synergies (Gee and Payne-Sturges 2004;

O’Neill et al. 2003; Weiss and Bellinger 2006).

The environmental justice literature has documented significant disproportionate contaminant exposures in minority and lower-SEP

communities (Brulle and Pellow 2006), and

the resultant influence on asthma exacerbation

patterns (Maantay 2007). However, fewer

studies have considered disproportionate

susceptibility among lower-SEP populations. 

Exposure to violence (ETV) has been

conceptualized as a chronic urban stressor,

potentially elevated in communities where

pollution is higher. Chronic stress effects of

episodic violence are grounded in trauma theory (e.g., post-traumatic stress), detailed elsewhere (Wright 2006). Episodic violence,

post-traumatic stress (Augustyn et al. 2002;

Overstreet and Braun 2000), and hypervigilance (Gordon and Riger 1991)—more

prevalent in lower-SEP urban communities

(Sampson et al. 1997)—may negatively influence health though physiologic alterations,

including immune dysregulation, and behavioral pathways. Many urban caregivers, for

example, restrict children’s behavior, keeping

them indoors due to fear of violence (Levy

et al. 2004; Wright et al. 2004b), making children more sedentary, increasing indoor exposures, and decreasing spatial autonomy that is

important to development (Katz 1991). 

Chronic stress has been linked to asthma

exacerbations in cross-sectional (Oh et al.

2004) and prospective (Sandberg et al. 2004)

population studies. Other evidence suggests a

role for stress in the onset of asthma (Wright

et al. 2002, 2004a). Chronic stress may influence hypothalamic–pituitary–adrenal (HPA)

axis and cortisol dysregulation (Hellhammer

et al. 1997; Ockenfels et al. 1995), glucocorticoid resistance (Miller et al. 2002), sympathetic–adrenal–medullary (SAM) activation,

catecholamine production (Glaser and

Kiecolt-Glaser 2005), immune mediator

function, inflammation (Umetsu et al. 2002),

and cytokine production (Chen et al. 2003;

Wright et al. 2004a). Stress and pollution

affect some common physiologic systems,

facilitating synergistic effects; for example,

psychological stress (Epel et al. 2004) and

ozone (Fugisawa 2005) both affect oxidative

stress pathways. 

Few studies have examined the influence

of stress on pollution susceptibility, though

some findings suggest differential susceptibility by SEP, possibly mediated by life stress

(Morello-Frosch and Shenassa 2006). Timeseries studies indicate effect modification of

short-term pollution exposures by SEP

(Jerrett et al. 2004; Lin et al. 2004; Martins

et al. 2004), though others found no significant modification (Zanobetti and Schwartz

2000). Fewer studies have considered longterm exposures, though some indicate greater

associations between long-term air pollution

Address correspondence to J.E. Clougherty, Harvard

School of Public Health; Department of

Environmental Health, Landmark Center Room

404, P.O. Box 15677, Boston, MA 02215 USA.

Telephone: (617) 384-8811. Fax: (617) 384-8859.

E-mail: jcloughe@hsph.harvard.edu 

Analysis was supported by American Lung

Association Lung Health Dissertation grant (J.E.C.).

Data collection and analysis for the nitrogen dioxide

passive network and violence exposure assessment

were supported by the Massachusetts Port Authority

(P.B.R.) and K08 HL 04187 (R.J.W.), respectively.

J.I.L. was supported by Health Effects Institute grant

4727-RFA04-5/05-1 and National Institutes of

Health grant R03 ES013988. S.F.S. was supported

by National Research Service Award F31 HD049317

and T32ES007142. 

The authors declare they have no competing

financial interests.

Received 27 October 2006; accepted 22 March

2007.

Synergistic Effects of Traffic-Related Air Pollution and Exposure to Violence

on Urban Asthma Etiology

Jane E. Clougherty,1 Jonathan I. Levy,1 Laura D. Kubzansky,2 P. Barry Ryan,3 Shakira Franco Suglia,1,4

Marina Jacobson Canner,5 and Rosalind J. Wright2,5

1Department of Environmental Health, and 2Department of Society, Human Development, and Health, Harvard School of Public Health,

Boston, Massachusetts, USA; 3Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia,

USA; 4Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA; 5Channing Laboratory, Department

of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA

BACKGROUND: Disproportionate life stress and consequent physiologic alteration (i.e., immune

dysregulation) has been proposed as a major pathway linking socioeconomic position, environmental exposures, and health disparities. Asthma, for example, disproportionately affects lowerincome urban communities, where air pollution and social stressors may be elevated. 

OBJECTIVES: We aimed to examine the role of exposure to violence (ETV), as a chronic stressor, in

altering susceptibility to traffic-related air pollution in asthma etiology.

METHODS: We developed geographic information systems (GIS)–based models to retrospectively

estimate residential exposures to traffic-related pollution for 413 children in a community-based

pregnancy cohort, recruited in East Boston, Massachusetts, between 1987 and 1993, using monthly

nitrogen dioxide measurements for 13 sites over 18 years. We merged pollution estimates with

questionnaire data on lifetime ETV and examined the effects of both on childhood asthma etiology. 

RESULTS: Correcting for potential confounders, we found an elevated risk of asthma with a 1-SD

(4.3 ppb) increase in NO2 exposure solely among children with above-median ETV [odds ratio

(OR) = 1.63; 95% confidence interval (CI), 1.14–2.33)]. Among children always living in the same

community, with lesser exposure measurement error, this association was magnified (OR = 2.40;

95% CI, 1.48–3.88). Of multiple exposure periods, year-of-diagnosis NO2 was most predictive of

asthma outcomes.

CONCLUSIONS: We found an association between traffic-related air pollution and asthma solely

among urban children exposed to violence. Future studies should consider socially patterned susceptibility, common spatial distributions of social and physical environmental factors, and potential

synergies among these. Prospective assessment of physical and social exposures may help determine

causal pathways and critical exposure periods. 

KEY WORDS: childhood asthma, exposure to violence (ETV), geographic information systems (GIS),

intraurban variability, nitrogen dioxide (NO2), social–environmental synergy, stress. Environ

Health Perspect 115:1140–1146 (2007). doi:10.1289/ehp.9863 available via http://dx.doi.org/

[Online 22 March 2007]
and mortality among less-educated adults

(Hoek et al. 2002; Krewski et al. 2000). 

In urban settings, traffic-related air pollution may be elevated along with ETV, and previous studies have linked traffic-related air

pollution to asthma exacerbation and respiratory outcomes. In the United States and

Europe, children living or attending school

near truck routes and highways show increased

asthma and allergy symptoms (Brauer et al.

2002), hospitalizations (Edwards et al. 1994;

Lin et al. 2002), allergic rhinitis (Duhme et al.

1996), and reduced lung function (Brunekreef

et al. 1997). Traffic-related pollutants have also

been associated with asthma development

(Gordian et al. 2006; Zmirou et al. 2004). 

Incorporating traffic-related air pollution

into large-scale epidemiologic studies requires

models linking traffic and ambient concentrations. Relationships between traffic and

health have been examined using several different traffic indicators, with no consensus on

which indictors best capture variability in

traffic-related pollution or health outcomes in

different settings. Prior studies have successfully extrapolated traffic exposures from sampling homes to larger cohorts using predictive

land-use regression (LUR) models (Brauer

et al. 2002; Brunekreef et al. 1997). LUR

shows strong predictive power for intraurban

nitrogen dioxide variability (Hochadel et al.

2006; Sahsuvaroglu and Jerrett 2004), using

traffic and land use characteristics (i.e., population density, major sources). 

In this study, we explore the hypothesis

that a chronic stressor (here, lifetime ETV)

predicts stronger associations between trafficrelated air pollution exposure and childhood

asthma development. We employ data from

the Maternal-Infant Smoking Study of East

Boston (MISSEB), a community-based

prospective pregnancy cohort examining

asthma, respiratory, and cognitive development. Questionnaires were administered

detailing violence exposures (both witnessing

and victimization) and avoidance behaviors

(staying in at night, avoiding certain areas,

keeping children indoors). Because the MISSEB did not examine air pollution, we used

geographic information systems (GIS) and

LUR at the neighborhood scale to retrospectively estimate pollution exposures, using

monthly NO2 data collected over 18 years in

the surrounding neighborhoods. 

Materials and Methods

Pregnant women were recruited from

East Boston Neighborhood Health Center

(EBNHC), Boston, Massachusetts, between

1987 and 1993, as described elsewhere

(Hanrahan et al. 1992). East Boston is a

working-class urban neighborhood bisected

by major highways and access roads to Logan

International Airport (Figure 1). Following

888 live births originally enrolled, caregivers

of 417 children completed questionnaires in

1997 detailing the child’s lifetime violence

exposure. Loss to follow-up was attributed

largely to families moving out of the neighborhood; those who moved but continued to

participate are included. Written informed

consent was obtained from participants

(mothers) before study initiation, in accordance with both Brigham and Women’s

Hospital and Beth Israel Deaconess Medical

Center Human Subjects Committees.

Measures. Traffic-related pollution exposures. NO2 has been shown to be a reliable

indicator for traffic-related primary air pollution (Hochadel et al. 2006; Nieuwenhuijsen

2000). We used a long-term spatially resolved

NO2 data set, explored temporal trends in

Air pollution and violence in asthma etiology

Environmental Health Perspectives • VOLUME 115 | NUMBER 8 | August 2007 1141

Figure 1. Distribution of East Boston cohort and NO2 sampling sites. (A) Residential addresses at enrollment (about 1990). (B) Residential addresses at violence

questionnaire date (1997). 

21.2–25.0

25.0–30.0

30.0–33.0

33.0–36.0

36.0–49.0

NO2 sampling sites

Cohort addresses 1997

NO2 estimate (ppb)

22.5–26.0

26.0–28.0

28.0–31.0

31.0–35.0

35.0–43.7

NO2 sampling sites

Cohort addresses intake

NO2 estimate (ppb)

A B

0 445 890 1,780 2,670 3,560Meters

N N

0 445 890 1,780 2,670 3,560Meters
pollution concentrations, compared multiple

traffic indicators as predictors of concentrations, and developed retrospective exposure

indices. 

Passive NO2 samples were collected contemporaneously 1 week each month from

January 1987 through December 2004, using

Palmes tubes and analyzed by spectrophotometry. Samples were collected at 28 unobstructed locations, 1 m above ground, across

the Logan Airport grounds and surrounding

communities (Ayres 2006). We used monthly

averages for 13 sites within community

spaces, geocoded by hand using aerial photography. Geocoding (identifying residential

locations on an active map embedded in a

GIS) allowed for the analysis of spatial characteristics of each location. Missing concentrations were imputed using weighted average

concentrations for surrounding months at the

same site.

To explain variability in NO2, we considered 25 traffic indicators derived from

Massachusetts Highway Department (MHD)

1990 data (Table 1) and site characteristics

(land use, elevation, proximity to industrial

areas, population density) derived from U.S.

Census 2000 data and aerial photography

(U.S. Census Bureau 2001). 

[NO2]ij = β1j × Yearj + β2 × (traffici) 

+ β3 × (land usei) + eij , [1]

where Yearj is a categorical indicator for each

sampling year j and capturing secular pollution trends; traffici is a suite of traffic characteristics for site i (candidate variables listed in

Table 1); and land usei is a suite of candidate

site characteristics (listed above).

Candidate variables were selected with

p < 0.05 in univariate nonparametric associations with NO2. Backward elimination produced a parsimonious model with p < 0.1 for

all terms. Because the assumption of normal

distribution in NO2 could not be rejected

(Shapiro-Wilk p < 0.0001), concentrations

were not transformed. Models were run

in PROC GLM, corroborated in PROC

MIXED (SAS Institute Inc., Cary, NC) with

random effects by site, to account for withinsite autocorrelation.

Residential retrospective exposure estimates. Using the predictive NO2 model, we

created residential exposure indices. Each

child’s address at enrollment (during pregnancy) and follow-up were geocoded using the

ESRI StreetMap Address Locator (ESRI,

Redlands, CA), and spatial variables derived in

GIS, to apply Equation 1 to each address.

NO2 exposure estimates were defined for each

year of follow-up, for both reported residences. NO2 exposures for years following

1997 were derived using the questionnaire

address; between 1990 and 1997, lacking residential information, we created time-weighted

annual exposures using the two known

addresses. For example, estimated exposure in

1991 for a child enrolled in 1990 is: [(6/7) ×

(estimated NO2 in 1991 at residence reported

at enrollment) + (1/7) × (estimated NO2 in

1991 for residence reported in 1997)]. 

We created this lifetime NO2 exposure

trajectory for each child, and calculated exposures during seven intervals potentially influencing asthma etiology: a) life course through

diagnosis (or end of follow-up); b) year of

birth; c) before 5 years of age (median age of

diagnosis); d) year of ETV questionnaire

(known residential address); e) years between

first violent event and diagnosis; f ) year of

diagnosis; and g) 1 year before diagnosis. 

For intervals ending at diagnosis, median

diagnosis age was used for nonasthmatics.

Some exposures after diagnosis (i.e., at ETV

questionnaire) are considered to be attributed

to the measure’s relative stability. We used

univariate logistic regression for each NO2

interval against asthma diagnosis to select an

optimal exposure interval, which is applied

going forward. Sensitivity analyses on the

final epidemiologic models assessed whether

interval selection affected results. 

Violence exposure assessment. Exposure

to violence was assessed using the My Child’s

ETV scale (Selner-O’Hagan et al. 1998). At

interview, children ranged from 4 to 11.5 years

of age; those > 8 years of age also answered the

questionnaire themselves. The questionnaire

includes items on witnessing events: a) hitting,

slapping, punching, b) a shooting, c) a stabbing, or d) hearing gunshots. We added one

question on witnessing domestic verbal abuse.

Respondents indicate the event frequency,

over their lifetime and previous year, on a scale

of 1 (0–1 time) to 4 (more than 10 times).

Caregivers reported child’s approximate age at

first witnessing. 

Rasch modeling summarized responses

into a continuous score, modeling the conditional probabilities for each “yes” response,

given the presumed event severity and each

child’s true-but-unobserved exposure (a latent

construct) (Kindlon et al. 1996). Expanding

on this approach, the model was generalized

to account for features theoretically influencing severity—frequency, whether events

occurred at home, whether the child knew the

victim(s) or perpetrator(s)—and included

parent and child’s report, wherever available,

as detailed elsewhere (Franco Suglia 2006). 

As a validity check, we assessed the

relationship between the Rasch ETV and

Checklist of Children’s Distress Symptoms

(CCDS) (Richters and Martinez 1990),

administered contemporaneously. CCDS is a

parental report of 24 post-traumatic stress

symptoms over the prior 6 months (e.g., irritability, inability to fall asleep, nightmares,

fear of attending school). The lifetime Rasch

ETV and 6-month CCDS were significantly

correlated (Spearman r = 0.21, p < 0.0001),

corroborating an association between violence

and distress symptoms reported elsewhere

(Martinez and Richters 1993). 

Child’s asthma diagnosis. During MISSEB

follow-up, parental reports of child’s asthma

diagnosis were ascertained through bimonthly telephone or face-to-face interviews

over the child’s first 2 years, every 6 months

through 4 years of age, and annually thereafter. Parents were asked “Since we last spoke

to you, have you been told by a doctor or

nurse that your child has asthma?” and likewise for asthmatic bronchitis. The same questions were asked on ETV questionnaire

administration. Children were considered to

have asthma if the parent reported any diagnosis of asthma or asthmatic bronchitis.

Potential confounders. We also ascertained demographic characteristics, smoking,

and medical history through standardized

questionnaires administered during MISSEB

Clougherty et al.

1142 VOLUME 115 | NUMBER 8 | August 2007 • Environmental Health Perspectives

Table 1. Traffic indicators explored as predictors of monthly NO2 concentrations at sampling sites.

Indicator type Unit

Cumulative density scores

Unweighted traffic density within 50-, 100-, 200-, 300-, 500-m buffers Vehicle-meters per day/m2

Kernel (inverse-distance)-weighted density: 50-, 100-, 200-, 300-, 500-m buffers Vehicle-meters per day/m2

Density of urban roads (> 8,500 cars/day) within 200 m Vehicle-meters per day/m2

Summary measures

Total road length within 50, 100, 200, 300, 500 m Meters

Total average daily traffic × length within 200 m Vehicle-meters per day/m2

Distance-based measures

Distance to nearest urban road (> 8,500 cars/day) Meters

To nearest major road (> 13,000 cars/day) Meters

To nearest highway (> 19,000 cars/day) Meters

To nearest MHD-designated truck route Meters

Characteristics of nearest major road

Average daily traffic Vehicles/day

Average daily traffic/distance to major road (Vehicles/day)/m

Diesel fraction Percent

Trucks per day Vehicles/day

Trucks/distance to major road (Vehicles/day)/m
follow-up visits. Maternal education was categorized as less than high school, high school

or technical school graduate, or some college.

At each clinic visit during pregnancy, mothers

were asked about smoking status. A urine

specimen was obtained for determination of a

creatinine-corrected cotinine level as detail,

and mothers were classified as never-smokers

during pregnancy if they always reported

never smoking and their urine cotinine levels

were consistently < 200 ng cotinine/mg creatinine (Hanrahan et al. 1992). Maternal

asthma status was based on report of physician-diagnosed asthma using the standardized

American Thoracic Society respiratory questionnaire (Ferris 1978). 

Data analysis. We examined independent

associations of air pollution and ETV on

asthma diagnosis using univariate odds ratios

(ORs). To examine the modifying effect of

violence on the NO2–asthma relationship, we

constructed two interaction models, of the

forms used elsewhere (Tsaih et al. 2004): 

logit(asthma diagnosis) = 

intercept + β1 × (ILowETV × NO2) 

+ β2 × (IHighETV) + β3 × (IHighETV × NO2) 

+ (potential confounders), [2]

where ILowETV = 1 if below-median ETV (reference group), 0 otherwise. IHighETV = 1 if

above-median ETV, 0 otherwise. NO2 is a

centered continuous variable with SD = 1.0;

ORs thus refer to a 1-SD increase above the

mean. Potential confounders included maternal asthma, education, smoking before and

after pregnancy, child’s sex and age. Equation

2 produces the slopes by ETV group and

their significance. 

A second regression model contained

main effects for ETV and NO2, and their

interaction: 

logit(asthma diagnosis) = 

intercept + β1 × (IHighETV) 

+ β2 × (NO2) + β3 × (IHighETV × NO2) 

+ (potential confounders). [3]

Equation 3 produces the statistical test of the

interaction; if β3 differs significantly from

zero, ETV significantly modifies the association of NO2 on asthma.

Because NO2 data were collected only in

East Boston and adjoining Winthrop, we

expect NO2 estimates to be more accurate for

children always living in these neighborhoods.

Likewise, the Rasch ETV likely better reflects

chronic stress among children always living in

the same community where exposure occurred.

Therefore, all statistical analyses are performed

for the entire cohort, and repeated using only

lifetime residents of East Boston and Winthrop.

Results

We were able to geocode home addresses for

409 children at follow-up, and 369 at enrollment. Demographics of those children and

their caregivers are presented in Table 2.

Caregivers of 100 children (24%) reported

asthma diagnoses during follow-up; caregivers

of an additional seven children (2%) retrospectively reported asthmatic bronchitis. Only

8% of mothers reported ever having asthma.

ETV varied significantly; at least one violent

event was reported for 46% of children, at

least two for 18%, and Rasch scores ranged

from –1.00 to 2.93. Lifetime residents did

not significantly differ from the full cohort in

ETV, asthma rates, or other demographic

factors or confounders.

Traffic-related pollution and LUR models.

Over 18 years, 2,291 monthly NO2 samples

were collected and analyzed, averaging 24.0

ppb (0.78–69.4 ppb). Some overall NO2

decline occurred over time, with some reordering of sites (Figure 2). LUR models

explained significant variability as a function

of secular trends, traffic density within 500 m,

distance from major roads, and block group

population density (R2 = 0.83; Table 3). More

variability in NO2 was explained by spatial

(R2 = 0.53) than temporal terms (R2 = 0.29).

Residential estimates averaged 27.5 ppb

(18.7–42.6 ppb) overall, higher than measured concentrations, because several NO2

samplers were located in open spaces (i.e.,

parks, backyards), whereas cohort homes

clustered near major roads (Figure 1).

Effects of NO2 and ETV on asthma.

Univariate ORs for the seven candidate NO2

exposure periods indicated that a 1-SD increase

only in year-of-diagnosis NO2 (4.3 ppb)

showed near-significant associations with

asthma for the full cohort [OR = 1.17; 95%

confidence interval (CI), 0.94–1.46], and was

used going forward as our key exposure metric. 

For the full cohort, we found no independent effect of ETV on asthma (OR = 0.98;

95% CI, 0.78–1.22). Stratified analyses, however, indicated that year-of-diagnosis NO2

was significantly associated with asthma solely

among children with above-median ETV

(OR = 1.65; 95% CI, 1.16–2.34). For children with below-median ETV, there was no

association between NO2 and asthma (OR =

0.94; 95% CI, 0.70–1.26).

Among lifetime resident children, with

less expected exposure measurement error, we

Air pollution and violence in asthma etiology

Environmental Health Perspectives • VOLUME 115 | NUMBER 8 | August 2007 1143

Table 2. Characteristics of cohort participants.

Lifetime 

Full cohort residents 

Characteristic (n = 413) (n = 255)

Child

Sex [female (%)] 50 53

Race (%)

Hispanic 52 52 

Caucasian 44 44

Asthma diagnosis (%)

Overall 26 24

Low ETV–Low NO2 24 22

Low ETV–High NO2 26 21

High ETV–Low NO2 16 11

High ETV–High NO2 36 43

Age in 1997 6.8 ± 1.6 6.6 ± 1.6

Rasch ETV 0.60 ± 0.97 0.60 ± 0.96

NO2 year of diagnosis 27.5 ± 4.3 27.6 ± 4.3

Mother (%)

Asthma diagnosis 7.7 7.0

Less than high school education 41 43

Smoker 25 22

Values are percent or mean ± SD.

Figure 2. Annual average NO2 (ppb) across 13 neighborhood sampling sites. For legibility, average annual

values at three clustered sites on Bayswater Street and in adjacent Winthrop have been combined.

Numbers in the key correspond to sampling sites in Figure 1.

1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

50

40

30

20

10

0

NO2 (ppb)

Webster St. (11)

East Boston Health Center (12)

Day Square (13)

Coleridge and Short Sts. (14)

Bayswater St. (15, 16, 17)

Winthrop (18, 19, 20)

Marginal St. (26)

Playing fields (29)

Lamson and Sumner Sts. (30)

Year
found similar effects with greater magnitude.

There was a near-significant effect of NO2 on

asthma (OR = 1.28; 95% CI, 0.97–1.69),

with no independent effect of ETV (OR =

1.12; 95% CI, 0.84–1.48). Stratified analyses

indicated that NO2 was significantly associated with asthma solely among children with

above-median ETV (OR = 2.33; 95% CI,

1.47–3.71, vs. OR = 0.87; 95% CI, 0.59–1.28

with below-median ETV).

We used multivariate logistic regression to

formally test the observed interaction, adjusting for potential confounders. In stratified

analysis across the full cohort, we found elevated odds of asthma with increased NO2

solely among children with above-median

ETV (Table 4), and the magnitude was unaffected by addition of potential confounders

(adjusted OR = 1.63; 95% CI, 1.14–2.33).

The interaction between NO2 and ETV was

statistically significant (p = 0.03), and robust

to inclusion of both main effects.

Among the lifetime residents, in multivariate logistic regression, we found similar associations with greater magnitude. Stratified

analyses indicated elevated odds of asthma with

increased NO2 solely among children with

above-median ETV (adjusted OR = 2.40; 95%

CI, 1.48–3.88). The NO2–ETV interaction

term was statistically significant (p = 0.0009),

and robust to inclusion of main effects.

Sensitivity analyses indicated that only

average NO2 for the years between first violence exposure and asthma diagnosis could be

substituted for year-of-diagnosis NO2 with

significance (adjusted OR = 1.52; 95% CI,

1.05–2.19) for children with above-median

ETV, in the full cohort multivariate model.

We repeated analyses removing 10 children

whose diagnosis occurred before first violent

event, with little bearing on results. Last, sensitivity analyses indicated that mediandichotomized My ETV, My Child’s ETV,

and CCDS scores could not be substituted

for the Rasch indicator with significance.

Discussion

We found an association between traffic-related

pollution (NO2) and asthma diagnosis only

among children with elevated ETV, after controlling for potential confounders. Of multiple

exposure intervals, year-of-diagnosis NO2 best

predicted asthma (with some evidence for NO2

after first violence exposure), supporting a theoretical model wherein individuals may become

susceptible or “primed” through social pathways to some environmental triggers, including

traffic-related pollutants. Despite limitations of

our data sets, these results agree with evidence

elsewhere that chronic stress may shape biologic

response in early life (Wright et al. 2004a) and

potentiate effects of air pollution through

common physiologic systems.

Our findings contribute to the existing

environmental justice literature by identifying

potential changes in pollution susceptibility

within communities affected by violence and

other stressors. We also indicate ancillary

effects of violence on children in urban communities, in addition to direct injury and

post-traumatic stress. Sampson et al. (1997)

identified associations among neighborhood

deterioration, violence, and low collective efficacy (social control of neighborhoods through

residents’ collective effort); we observed that a

heightened susceptibility to pollution, associated with violence exposures or fear thereof,

may lead to synergistic health effects of social

and physical environmental conditions.

Although our findings are biologically

plausible and highly suggestive, there are

potential limitations in interpreting our models. A clear limitation is our small sample size

for investigating multiplicative effects; despite

the relatively high asthma prevalence (26%), a

larger cohort would be required to consider

interactions across numerous potential risk

factors. The pollution model relies on NO2

data, commonly used as a marker of primary

vehicular emissions, but not necessarily representative of all pollutants of interest. Road

construction and airplane technology changes

made it unclear a priori whether NO2 could

be predicted by spatial indicators in this neighborhood, particularly using 1990 traffic data,

though our model proved robust. We lacked

complete residential history, likely creating

some misclassification in a cohort with significant residential instability. However, we found

no difference in asthma prevalence or exposures between movers and nonmovers; thus we

expect misclassification to be nondifferential,

biasing results toward the null and making our

significant results more noteworthy. 

The interpretation of our interaction

model is complicated by the fact that behaviors may differ in violent neighborhoods,

where parents often keep children indoors

due to fear of violence. Therefore, children

may be more exposed to indoor NO2, which

is generally higher than outdoor NO2, and is

influenced both by infiltration from outdoors

and by smoking, gas stoves, and other sources

which may be more prevalent in lowerincome communities (Baxter et al. 2006;

Spengler et al. 1983). Future studies should

examine differential susceptibility to a wider

range of pollution exposures, including

indoor allergens and environmental tobacco

smoke; although we accounted for maternal

smoking, we did not examine the effect of

other smokers in the home. Likewise, ETV

may proxy for other social exposures responsible for the susceptibility effect we found; children witnessing violence may have greater

family instability (Overstreet and Braun

2000) or may be directly victimized, potentially leading to injury-related susceptibilities. 

We aimed to investigate whether a psychosocial stressor modifies pollution effects on

asthma, and would prefer a long-term stress

measure to corroborate the ETV scale. The

CCDS elicits distress using a 6-month symptoms recall, inappropriate to our goal of capturing life-course stress. The correlation

between CCDS and violence does, however,

corroborate an association, supporting the

Clougherty et al.

1144 VOLUME 115 | NUMBER 8 | August 2007 • Environmental Health Perspectives

Table 3. Land use regression modeling results for

annual average NO2 at 13 sampling sites (R2 = 0.83). 

Overall 

estimate (p-value)

Year (categorical)

1987 19.84

1988 23.06

1989 24.12

1990 21.97

1991 20.62

1992 19.02

1993 19.90

1994 21.29

1995 18.20

1996 17.98

1997 22.65

1998 24.14

1999 23.55

2000 21.45

2001 21.22

2002 17.35

2003 11.85

2004 10.64

Distance to major road –1.27 × 10–3 (< 0.0001)

(> 13,000 cars/day) (m)

Kernel traffic density within 0.0775 (< 0.0001)

500 m (VMT/day)

Population density 1.086 × 10–4 (< 0.0001)

(persons/km2) 

VMT, vehicle miles traveled. The year values are coefficients for yearly categorical variables.

Table 4. Multivariate model for asthma diagnosis [OR (95% CI)]. 

Full cohort Lifetime residents 

Maternal asthma (ever diagnosed) 1.31 (0.58–2.96) 0.89 (0.29–2.74)

In utero tobacco smoke exposure 1.07 (0.44–2.58) 1.87 (0.53–6.57)

Maternal smoking since birth 1.10 (0.70–1.72) 0.85 (0.45–1.63)

Less than high school education 1.14 (0.71–1.81) 1.12 (0.60–2.07)

Child’s sex (female) 0.85 (0.54–1.34) 0.62 (0.34–1.14)

Child’s age (≥ 7 years) 1.44 (0.90–2.33) 1.06 (0.56–2.00)

High ETV 0.89 (0.56–1.43) 1.10 (0.59–2.04) 

NO2 year of diagnosis: low ETV 0.99 (0.73–1.34) 0.85 (0.56–1.27) 

NO2 year of diagnosis: high ETV 1.63 (1.14–2.33) 2.40 (1.48–3.88)

ORs for NO2 are associated with a 1-SD (4.3 ppb) increase. 
plausibility of stress pathways to pollution susceptibility, and should be investigated further.

Reporting bias—generally underreporting

by survivors, perpetrators, and witnesses—hampers quantitative violence research (Gordon and

Riger 1991), particularly for domestic and intimate violence. Our questionnaire focused

largely on within-community events, potentially more accurately reported. A prior analysis

indicated that parents underestimate older children’s exposures, but are better corroborated

for near-home events, potentially due to shared

experience (Thomson et al. 2002). We were

unable to examine direct victimization because

very little was reported, owing either to low

prevalence or underreporting, and thus we

likely have some misclassification of true ETV.

Most asthma cases were reported during longitudinal follow-up, limiting recall bias in diagnoses, but our retrospective violence report is

subject to recall bias. To assess recall bias in

violence reports, we asked the caregiver to

report on asthma episodes triggered by violence; because very few parents associated violent events with asthma symptoms, recall bias

in ETV by asthma status is unlikely.

Despite these limitations, our study provides evidence of a synergistic effect between

social and physical factors in asthma etiology.

The study also provides a model for retrospectively estimating traffic-related exposures,

accounting for intracommunity heterogeneity

and temporal trends using GIS. We were able

to create temporally calibrated estimates using

a spatially dense and temporally rich pollution data set of NO2 measurements collected

throughout our epidemiologic study period.

Though few studies will have access to such

data, existing models provide insight about

site characteristics influencing exposure variability, and spatially resolved satellite pollution data may prove useful in some settings

(Liu et al. 2005). 

In future studies, a wider and more frequently assessed suite of social stressors and perceived stress measures should be employed to

examine stress trajectories over time and temporality in susceptibility to environmental triggers.

The roles of social support and health beliefs in

mediating the effects of perceived stress on

health outcomes should be considered as

buffers in the pathway from stressor to stress

response, the latter of which may influence contaminant susceptibility and health (Chen et al.

2003). We observed no correlation between

ETV and pollution levels, but suggest that longitudinal investigation of multiple exposures,

and multiple pathways, are needed to further

elucidate these effects. The spatial correlation

across multiple exposures deserves greater

investigation, and should be addressed in larger

studies investigating spatial autocorrelations

across, and potential synergies among, social

and physical environmental factors.

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