Environmental Health Perspectives • volume 118 | number 7 | July 2010 

### 1021 

# Research 

# | Children’s Health

The role of air pollution in the development of

new-onset asthma remains controversial, and the contribution of this environmental risk fac

tor to the pandemic remains unclear (Eder et al. 2006; Sarnat and Holguin 2007). Although increasing evidence indicates that living near heavy traffic is associated with increased rates of asthma, some well- designed studies have found only weak or no associations (Heinrich and Wichmann 2004 ; Oftedal et al. 2009). These inconsistencies may reflect incom

plete assessment of exposures to traffic in the microenvironments in which children spend most of their time. Exposure at locations other than home, especially at school where children spend a large proportion of waking hours and may engage in physical activity that would increase the ventilation rate and dose of inhaled pollutants, may have strong influences on occurrence of asthma. However, few stud

ies have examined the effect of traffic-related pollution at schools on asthma rates among children. Two cross-sectional Dutch studies that examined this question reported higher rates of respiratory symptoms among children in schools near roadways with heavy traffic, especially truck traffic (Janssen et al. 2003; van Vliet et al. 1997 ). A northern California study found that schools downwind from busy freeways had higher concentrations of oxides of nitrogen (NO
x ) and higher asthma prevalence rates (Kim et al. 2004). However, in a subse

quent analysis of these same data, the effect of school exposure was attenuated and no longer significant after adjusting for modeled residen

tial traffic-related exposure (Kim et al. 2008). Nitrogen dioxide (NO 2 ) is routinely measured at regulatory monitoring stations. However, these measurements reflect both background regional concentrations and local sources near the stations. Some studies have assessed local exposure to traffic-related pollu

tion using measured NO 2 (or NO
x ) as surro

gate for the complex mixture of traffic-related pollutants that occurs in close proximity to roadways (Brauer et al. 2007; Gauderman

et al. 2005; Jerrett et al. 2008; Kim et al. 2004; Kramer et al. 2000; Nordling et al. 2008). NO 2 can feasibly be measured at a large num

ber of locations, and it has been widely used as a proxy for the mixture of traffic- related pol

lutants that vary markedly depending on dis

tance from roadways, season, wind speed

, and wind direction. However, the mixture of pol

lutants in close proximity to roadways includes transition metals and organic aerosols, which are more plausible causes of asthma than NO 2 (Li et al. 2003). The Southern California Children’s Health Study (CHS) was designed to investigate the chronic effects of air pollution on respiratory health. Previous analyses have found associa

tions of asthma with residential distance to major roads and modeled and measured pol

lutant markers for intracommunity variation in exposure to traffic (Gauderman et al. 2005; Jerrett et al. 2008; McConnell et al. 2006; Salam et al. 2007b) and with regional pollut

ants in susceptible children (Islam et al. 2008; McConnell et al. 2002). In the current analy

sis
, a prospective, longitudinal evaluation of new cases of asthma has allowed assessment of exposure before the development of asthma in an ongoing cohort of school children recruited in kindergarten and first grade, an age at which physician- diagnosed asthma becomes reliable and valid (Martinez et al. 1995). Exposure to traffic-related pollutants has been estimated at participating schools and at residences. In addition, NO 2 , ozone (O 3 ), and particulate

matter < 10 μm (PM 10 ) and < 2.5 μm in aero

dynamic diameter (PM 2.5 ) have been measured continuously during the lifetime of most study participants at a location in each community representative of exposure in the neighbor

hoods where children live (Kunzli et al. 2003; McConnell et al. 2006). This design allowed examination of the joint effects of traffic-related pollution exposure at school and home and of regional measured pollution at community

Address correspondence to R. McConnell, Department of Preventive Medicine, USC Keck School of Medicine, 1540 Alcazar St., CHP 236, Los Angeles, CA 90033 USA. Telephone: (323) 442-1096. Fax: (323) 442-3272. E-mail: rmcconne@usc.edu

Supplemental Material is available online (doi:10.1289/ehp.0901232 via http://dx.doi.org/).

E. Rappaport and J. Manila managed data, and B. Penfold assigned exposures estimated from resi

dential traffic. R. Ma, R.T. Burnett, and E. Hughes assisted with the development and implementation of the random effects program.

This study was supported by National Institutes of Health grants 5P30ES007048, 5P01ES009581, 5 P01ES011627, 1R01ES016535, 5R01ES014447, 5R03ES014046, and 5R01HL61768; U.S. Environmental Protection Agency grants R826708, RD831861, R831845, and R82735201; the South Coast Air Quality Management District, and the Hastings Foundation.

F.L. is employed by Sonoma Technology, Inc, (Petaluma, CA). The other authors declare they have no actual or potential competing interests.

Received 22 July 2009; accepted 22 March 2010. 

## Childhood Incident Asthma and Traffic-Related Air Pollution at Home and School

Rob McConnell, 1 Talat Islam, 1 Ketan Shankardass, 2 Michael Jerrett, 3 Fred Lurmann, 4 Frank Gilliland, 1 Jim Gauderman, 1 Ed Avol, 1 Nino Künzli, 5 Ling Yao, 6 John Peters, 1 and Kiros Berhane 1

1 University of Southern California, Los Angeles, California, USA; 2 St. Michael’s Hospital, Toronto, Ontario, Canada; 3 University of California, Berkeley, California, USA; 4 Sonoma Technology, Inc, Petaluma, California, USA; 5 Swiss Tropical and Public Health Institute, Basel and University of Basel, Switzerland; 6 United Health Group, City of Hope Hospital Medical Center, Los Angeles, California, USA

B ackground : Traffic-related air pollution has been associated with adverse cardiorespiratory effects, including increased asthma prevalence. However, there has been little study of effects of traf

fic exposure at school on new- onset asthma.

O b jectives : We evaluated the relationship of new-onset asthma with traffic-related pollution near homes and schools.

M ethods : Parent-reported physician diagnosis of new-onset asthma ( n = 120) was identified during 3 years of follow-up of a cohort of 2

, 497 kindergarten and first-grade children who were asthma- and wheezing-free at study entry into the Southern California Children’s Health Study. We assessed traffic-related pollution exposure based on a line source dispersion model of traffic volume, distance from home and school, and local meteorology. Regional ambient ozone, nitrogen dioxide (NO 2 ), and particulate matter were measured continuously at one central site monitor in each of 13 study communities. Hazard ratios (HRs) for new-onset asthma were scaled to the range of ambient central site pollutants and to the residential interquartile range for each traffic exposure metric.

R

esults : Asthma risk increased with modeled traffic-related pollution exposure from roadways near homes [ HR 1.51; 95% confidence interval (CI), 1.25–1.82 ] and near schools (HR 1.45; 95% CI, 1.06–1.98). Ambient NO 2 measured at a central site in each community was also associated with increased risk (HR 2.18; 95% CI, 1.18–4.01). In models with both NO 2 and modeled traffic expo

sures, there were independent associations of asthma with traffic-related pollution at school and home, whereas the estimate for NO 2 was attenuated (HR 1.37; 95% CI, 0.69–2.71).

C onclusions : Traffic-related pollution exposure at school and homes may both contribute to the development of asthma.

K

ey words : air pollution, asthma, child, epidemiology, vehicular traffic. Environ Health Perspect 118:1021–1026 (2010). doi:10.1289/ehp.0901232 [Online 6 April 2010]McConnell et al.



### 1022 volume 118 | number 7 | July 2010 • Environmental Health Perspectives monitors. We hypothesized that exposure at school and home both contribute to the risk of new-onset asthma.


### Methods Study population. Characteristics of this cohort have been described previously (McConnell et al. 2006). Briefly, 5

, 349 children attend

ing kindergarten and first grade were enrolled into a new CHS cohort during the 2002 –2003 school year from 45 schools in 13 communities. Communities were selected to represent the range and mixture of regional particulate pol

lutants, NO 2 , and O 3 in southern California. Parents provided informed consent and com

pleted a baseline and yearly questionnaire with information about demographic character

istics, respiratory illness

, and risk factors for asthma at study entry. Children with a history of physician-diagnosed asthma at study entry ( n = 715, 13.4%) were excluded from this follow-up, because the focus was on risks for incident asthma. To ensure that the study pop

ulation was free of any previously undiagnosed asthma, we then also excluded children with a history of wheeze ( n = 868, 16.2%) and addi

tional children with missing information about wheeze, or missing or a “don’t know” answer about asthma ( n = 394, 7.4%). These exclu

sions resulted in a total of 3,372 children clas

sified as disease free at baseline. We excluded another 340 children with no residential traffic information because home address could not be geocoded [disproportionately from a single community, as described in the Supplement al Material (doi:10.1289/ehp.0901232 )] and another 535 children who did not participate in any of the annual follow-up questionnaires. The final sample included 2,497 children. New-onset asthma and covariates. Children with physician-diagnosed asthma reported on a yearly questionnaire during 3 years of follow-up were defined to have new-onset asthma. Children missing questionnaires in any year continued to contribute person-time to the models until they answered yes (at which time they were censored) or were lost to fol

low-up. The date of new onset of asthma could not be precisely defined. Therefore, the date of onset was assigned to the midpoint of the interval between the date of the questionnaire when asthma diagnosis was first reported and the date of the previous questionnaire reporting asthma status, and this date was used for esti

mating follow-up time in all statistical analyses. Sociodemographic characteristics, exposure to cigarette and wildfire smoke, health insurance, housing characteristics, history of allergy, and parental asthma were assessed by questionnaire [see Supplement al Material (doi:10.1289/ ehp.0901232) for details]. Community ambient air pollution mea

surements. Ambient levels of O 3 , NO 2 , PM 10 , and PM 2.5 were measured continuously dur

ing follow-up at a central site monitor in each community, as described in the Supplement al Material (doi:10.1289/ehp.0901232) and in previous reports (Peters et al. 1999). Because there is marked diurnal variation in O 3 and exposure occurs largely between 1000 and 1800 hours, this average was used in all analy

ses. Average temperature and relative humid

ity were also obtained during follow-up from measurements at the monitoring stations. Local traffic- related pollutant exposure. Exposure to traffic-related pollution was esti

mated using methods described previously (McConnell et al. 2006)

, and several metrics **Table 1.** Characteristics of participants at study entry and incidence of asthma on follow-up.

Characteristic n a (%) Cases IR b (95% CI) Age at entry (years)

< 6 496 (20) 22 17.0 (11.2–25.9) 6 1,178 (47) 58 19.0 (14.7–24.6)

> 6 823 (33) 40 18.9 (13.9–25.8) Race/ethnicity

Hispanic white 1,380 (55) 59 16.8 (13.0–21.7) Non-Hispanic white 905 (36) 45 18.9 (14.0–25.2) African American 77 (3.1) 6 33.9 (15.2–75.4) Asian 97 (3.9) 8 30.8 (15.4–61.5) Other/unknown 38 (1.5) 2 22.7 (5.7–90.8) Sex

Female 1,307 (52) 62 18.2 (14.2–23.3) Male 1,190 (48) 58 19.2 (14.8–24.8) History of allergy Yes 764 (34) 56 28.7 (22.1–37.4) No 1,508 (66) 50 12.8 (6.7–24.4) Play team sport Yes 918 (39) 47 19.7 (14.8–26.2) No 1,463 (61) 67 17.9 (9.2–34.5) Parental history of asthma Yes 389 (17) 31 31.3 (22.0–44.6) No 1,899 (83) 76 15.4 (7.2–33.3) Maternal smoking during pregnancy Yes 156 (6.3) 11 27.9 (15.5–50.5) No 2,308 (93.7) 107 18.0 (5.4–60.3) Secondhand smoke Yes 164 (6.7) 7 17.0 (8.1–35.6) No 2,281 (93.3) 110 18.7 (4.1–84.0) Mildew Yes 532 (22) 29 21.1 (14.7–30.4) No 1,911 (78) 85 17.3 (7.9–37.9) Pests in home Yes 1,603 (67) 75 18.1 (14.4–22.7) No 777 (33) 36 18.2 (9.7–33.9) Dogs in home Yes 730 (30) 30 15.7 (11.0–22.4) No 1,706 (70) 86 19.7 (9.1–42.8) Cats in home Yes 462 (19) 17 14.0 (8.7–22.5) No 1,972 (81) 98 19.4 (7.2–52.2) Indoor NO 2 source Yes 1,799 (73) 88 18.8 (15.3–23.2) No 675 (27) 31 18.3 (9.9–33.9) Wildfire exposure c

Yes 251 (12) 16 24.6 (15.1–40.1) No 1,764 (88) 79 17.3 (6.2–48.2) Health insurance Yes 2,135 (88) 106 19.2 (15.9–23.3) No 300 (12) 10 13.1 (5.7–30.3) Household income (US$)

≤ 14,999 329 (14) 16 19.5 (11.9–31.8) 15,000–49,999 714 (31) 38 21.1 (15.4–29.0)

> 50,000 1,292 (55) 57 16.6 (12.8–21.6) Parental education

Less than high school 508 (21) 23 17.9 (11.9–26.9) At least high school 452 (19) 20 17.5 (11.3–27.1) Some college 850 (36) 47 20.0 (14.9–26.9) College and above 560 (24) 24 13.9 (9.1–21.3) Total 2,497 120 18.7 (15.6–22.3)

a Numbers may not add up to 2,497 in some subgroups because of missing data. b Crude incidence (IR) rate per 1,000
person-years. c Occurred in 2003 during first year of follow-up.Childhood incident asthma and traffic-related air pollution

Environmental Health Perspectives • volume 118 | number 7 | July 2010 

### 1023 of local traffic related exposure were compiled. Participant residence and school addresses were geocoded, and we estimated distances to the nearest freeway or other highways or arterial roads and traffic density within 150 m of each child’s residence and school. Concentrations of pollutants from local vehicle emissions at homes and schools were estimated separately from freeway and nonfreeway sources, using a line source dispersion model of the incremental contribution by these nearby sources to oxides of nitrogen above the regional background levels (Benson 1989)

. The modeled annual concentration estimates ( parts per billion ) were based on distance to roadways, vehicle counts, vehicle NO

x emission rates, wind speed and direction, and height of the mixing layer in each community. However, these modeled expo

sures reflect the mixture of multiple pollutants from nearby traffic

, and the high correlation of pollutants in the mixture precludes identifying the effect of any specific pollutant in the mix

ture, as described in the Supplement al Material (doi:10.1289/ehp.0901232) . For brevity

, thesemodeled exposures will be referred to as trafficrelated pollutants (TRP ). An estimate of com

bined school and home exposure to traffic was made by weighting the estimates by approxi

mate time at school (35 hr weekly, or 21%) and assigning the remaining 79% of time to the home. Health effect estimates based on models in which the contribution of school to the com

bined exposure was reduced to 16% to account for a lack of school exposure during summer recess were very similar (data not shown). Statistical methods. We fitted a multilevel Cox proportional hazards model that allows for assessment of residual variation in time to asthma onset and also for clustering of chil

dren around schools and communities (Ma

et al. 2003). Letting u i and u ij denote com

munity

- and school (within community)-level random effects, with u ij assumed to be positive and independent conditional on u i , we fit the following model:

h ijl ( t ) = h 0s ( t ) u ij exp ( β X

ijl

+ δ T Z

ijl

), [1]

where, for the l th subject in the i th com

munity, and j th school, h ijl ( t ) is the hazard function at age t ; h 0s ( t ) is the baseline hazard function in stratum s (defined by age at study entry and sex), X ijl

is a traffic pollution expo

sure metric, and Z ijl

are other individual cova

riates, such as secondhand smoke exposure, pets in the home, and other possible con

founders. All models included race/ethnicity. The model allowed for joint evaluation of the effects of exposure to traffic-related pollutants at homes and at schools and to ambient pollutants measured at commu

nity central sites, with effects scaled to the interquartile range (IQR) for each metric of residential exposure (e.g., for TRP from the line source dispersion model) and to the total range across the 13 communities, respectively. Traffic exposure at homes and school were correlated. Therefore, in models including both exposures, home traffic estimates were deviated from the corresponding school met

ric to minimize the chance of collinearity and allow for valid independent effect estimates (for example, r -square for school with deviated home TRP were < 0.38 for freeway, nonfree

way, and total TRP). Although the schoolspecific estimates of exposure were of primary interest, models including the average resi

dential exposure corresponding to each school (and residential exposures deviated from this average) were also fitted [see Supplement al Material (doi:10.1289/ehp.0901232) for details]. Potential confounders were evalu

ated one at a time, based on whether the esti

mates for the pollutant associations changed by > 10%. We assessed heterogeneity of traf

fic pollution effects by level of community central site regional pollutant measurements by comparing nested models using a partial likelihood ratio test with and without inter

action terms. We examined any potential nonlinearity in the exposure

– response rela

tionship using cubic spline terms, piecewise polynomials joined smoothly at a number of break points (Hastie and Tibshirani 1990), for the exposure terms and comparing the nested models using a partial likelihood ratio test. All analyses were conducted using soft

ware designed to run within R software (R Development Core Team, Vienna, Austria) for implementing random effects Cox propor

tional hazards models (Krewski et al. 2009; Ma et al. 2003). All hypotheses were tested assuming a 0.05 significance level and a twosided alternative hypothesis.


### Results

Most of the 2

, 497 disease-free children included in this analysis were ≤ 6 years of age (range

, 4.8

– 9.0 years ) at study entry (Table 1). There were 120 new cases of asthma

, resulting in an incidence rate of approximately 18.7 cases per 1

, 000 person-years (based on 6

, 434 personyears of follow-up). Rates did not differ by age at study entry or sex. About half the children were Hispanic whites, and they had the low

est rates of asthma (16.8/1,000 person years), whereas African Americans had the highest rate (33.9/1,000 person-years) , but this was based on only six cases. Substantially higher rates were also observed for children with history of allergy, parental history of asthma, and mater

nal smoking during pregnancy. Both residential and school TRP met

rics had a wide range with skewed distribu

tions (Table 2). For example, the mean and median for nonfreeway residential TRP were 7.3 and 6.1 ppb, respectively, and the IQR was 8.0 ppb, with a total range from 0.08 to a maximum of 55.1 ppb. A few homes and schools had very low freeway TRP (largely in a single community that has no freeways). There

was ≥ 3 -fold variation in the multiyear average NO 2 , PM 10 , and PM 2.5 from the lowest to highest pollution community for measure

ments at the central site monitors. O 3 levels varied by 2 -fold. The associations of new-onset asthma were strongest with nonfreeway TRP. The haz

ard ratio (HR) at homes was 1.51; 95% con

fidence interval (CI), 1.25–1.81; p < 0.001

**Table 2.** Distribution of annual average residential traffic-modeled pollution and of community central site
measurements.
Mean Median IQR Minimum Maximum Range Residential traffic (ppb)

Nonfreeway TRP 7.3 6.1 8 0.08 55.1 55 Freeway TRP 11.1 7.3 13 < 0.0001 134.5 134.5 Total TRP 18.4 14.6 20.9 0.23 144.1 143.9 School traffic (ppb)

Nonfreeway TRP 6.1 5.9 5.9 0.3 19.7 19.4 Freeway TRP 10.9 8.2 15.2 < 0.0001 39.9 39.9 Total TRP 17 11.9 18.5 0.7 51.4 50.7 Central-site measurements

NO 2 (ppb) 20.4 21.8 12.8 8.7 32.3 23.6 PM 10 (μg/m 3 ) 35.5 34 11.7 17.6 61.5 43.9 PM 2.5 (μg/m 3 ) 13.9 15.1 9.7 6.3 23.7 17.4 1000

– 1800 hours O 3 (ppb) 44.6 43.6 11.1 29.5 59.8 30.3

**Table 3.** Association between new-onset asthma and modeled exposure at home and school. Traffic-related exposure a Home HR b (95% CI) School HR b (95% CI) Combined c HR b (95% CI) Nonfreeway TRP 1.51 (1.25–1.81)* 1.45 (1.06–1.98) 1.61 (1.29–2.00)* Freeway TRP 1.12 (0.95–1.31) 1.08 (0.86–1.34) 1.12 (0.94–1.35) Total TRP 1.32 (1.08–1.61)** 1.20 (0.91–1.58) 1.34 (1.07–1.68)

a Scaled to the IQR at homes for each metric (from Table 2). b HR (95% CI, adjusted for race/ethnicity and for baseline hazards strata of age at study entry and sex) with random effects for community and school. c Combined weighted for time at home and school. * p < 0.001. ** p < 0.01.McConnell et al.



### 1024 volume 118 | number 7 | July 2010 • Environmental Health Perspectives (Table 3). The HR for exposure at schools was almost as large as for residential exposure (HR = 1.45; 95% CI, 1.06–1.98), and the combined average exposure weighted for time at school and home had a slightly stronger association (HR = 1.61; 95% CI, 1.29–2.00). These associations were not confounded by any of the covariates in Table 1 or by com

munity relative humidity, which we have previously found to confound TRP exposure (Jerrett et al. 2008). There also were no sig

nificant interactions of either residential or school nonfreeway TRP with sex, allergy, or parental history of asthma, which we have previously shown modified effects of traffic exposure (McConnell et al. 2006). Similar effect sizes were observed in sensitivity analy

ses restricted to lifetime residents at the same address, analyses excluding children diagnosed in the first year of follow-up and excluding children who changed schools during the follow-up period. There was little evidence of nonlinearity in the exposure– response rela

tionship based on sensitivity analyses compar

ing the fit of a smoothed cubic spline model of asthma with a linear model ( p -value > 0.80) for the partial likelihood ratio test for models with 3 and 5 knots compared with the linear model. Weaker associations were observed with total TRP at homes (HR = 1.32; 95% CI, 1.08–1.61) and school (HR = 1.20; 95% CI, 0.91–1.58). There were weaker and non

significant associations with TRP modeled from freeways. Nonfreeway TRP was mod

erately to strongly correlated with freeway TRP at homes ( R = 0.64) and at schools [ R

= 0.70; Supplemental Material, Table E-1

, (doi:10.1289/ehp.0901232) ]. However, in models co-adjusted for freeway and nonfree

way TRP, the nonfreeway effect estimates for school and home were similar to the unadjusted estimates. Of the regional community central-site pollutants, NO 2 was associated with more than double the risk of new-onset asthma (HR = 2.17; 95% CI, 1.18–4.00; p = 0.01) over the range of exposure (23.6 ppb) across the 13 study communities (Table 4). In a model with NO 2 , school and residential nonfreeway TRP exposure, the estimate for NO 2 was attenuated (HR = 1.37; 95% CI, 0.69–2.71; Table 5). However, the adjusted associations of asthma with nonfreeway TRP at homes (HR = 1.46) and schools (HR = 1.45) in Table 5 were very similar to the unadjusted effect estimates (HR = 1.51 and 1.45, respectively, shown in Table 3). In sensitivity analyses, a similar pat

tern of attenuation of regional NO 2 effect esti

mates was observed in models weighted for time at school and home (results not shown), and in models adjusted for total (freeway plus nonfreeway) TRP exposure at school and home [Supplemental Material, Table E-2 (doi:10.1289/ehp.0901232) ]. There were nei

ther statistically significant interactions of cen

tral site pollutants with residential TRP nor any consistent patterns suggestive of different effects in high and low regional pollutant com

munities (results not shown). We have previ

ously shown that O 3 modified the association of team sports participation with new-onset asthma in an older cohort (McConnell et al. 2002), but there was neither a main effect of sports nor confounding or effect modification of effects of residential TRP or ambient central monitor pollutant exposure. Distance to freeways and other major roads and traffic volume on those roads have been used in other studies as independent predictors of traffic exposure, so we examined the dis

tribution [ Supplemental Material, Table E-3 (doi:10.1289/ehp.0901232 )] and correla

tions of these metrics ( Supplemental Material, Table E-1) and the association at school and home with asthma ( Supplemental Material, Tables E-4 and E-5 ). In general, these exposure metrics were weakly to moderately correlated with the TRP metrics but were not consistently associated with asthma in our data set.



### Discussion The study is unique in its prospective assess

ment of the relationship of new-onset child

hood asthma to community regional air pollution and near-source traffic-related exposure at home and in a large number of schools. The results indicate that associations of asthma with traffic-related pollution from nearby sources at schools were independent of estimated effects of exposures at homes, and these effects were of similar size over a range of exposure common in Southern California. Evidence of a school effect comparable with that associated with the much larger amount of time spent at home could potentially be explained by physical education and other exercise at school that may increase ventila

tion rate and dose of pollutants to the lungs and thereby increase the risk associated with exposure. Traffic-related pollutant levels may also be considerably higher during the morn

ing hours

, when children are arriving at school, especially during temperature inversions that occur largely in the winter months when chil

dren are attending school (Kim et al. 2002; Ning et al. 2007). It is possible that school exposure may have reflected time spent in other locations in the child’s neighborhood rather than school-specific exposure, and this possibil

ity merits further investigation. However, time at school during the school year accounts for more than one-third of all waking hours, and in this age group it is likely that most of the child’s remaining nonschool time was spent at home (Xue et al. 2004). The increased risk associated with TRP could not be explained by confounders commonly associated with asthma or by ambient NO 2 or other currently regulated regional pollutants measured at the central-site monitoring stations. These results strengthen an emerging body of evidence from both toxicologic and epidemiologic studies that air pollutants from nearby traffic contribute to the development of asthma (Salam et al. 2008 ). NO 2 measured at community monitor

ing stations has been associated with wheeze prevalence in an older CHS cohort (Peters

et al. 1999) and with increased risk of asthma incidence in a Japanese cohort (Shima et al. 2002). We have recently reported associations of both incident and prevalent asthma with ambient residential NO 2 measured outside homes of a relatively small sample of children from another CHS cohort in many of the same communities, but NO 2 in that study was a marker for intracommunity variation in the mixture of TRPs (Gauderman et al. 2005; Jerrett et al. 2008). Other studies also indicate that associations of respiratory dis

ease with intracommunity variation in NO 2 reflect the effects of other TRP rather than NO 2 (Kramer et al. 2000). A study that meas

ured NO 2 and NO

x in central neighborhood

**Table 4.** Association of new-onset asthma with community central site pollutant measurements.

Pollutant HR a (95% CI)
NO 2 2.17 (1.18–4.00) PM 10 1.35 (0.64–2.85) PM 2.5 1.66 (0.91–3.05) O 3 0.76 (0.38–1.54)

a HR (95% CI) across the range of exposure in the 13 com

munities (23.6 ppb for NO 2 , 43.9 μg/m 3 PM 10 , 17.4 μg/m 3 PM 2.5 , and 30.3 ppb for 1000–1800 hours O 3 ), adjusted for race/ethnicity and for baseline hazards strata of age at study entry and sex with random effects for community and school. **Table 5.** Mutually adjusted associations of new-onset asthma with community central site pollutant

meas urements and nonfreeway TRP at home and school. a

Central site pollutant HR b (95% CI) for ambient pollutant, adjusted for TRP at home and school HR b (95% CI) for home TRP, adjusted for school TRP and ambient pollutant HR b (95% CI) for school TRP, adjusted for home TRP

and ambient pollutant
NO 2 1.37 (0.69–2.71) 1.46 (1.16–1.84)* 1.45 (1.03–2.06) PM 10 1.40 (0.62–3.17) 1.46 (1.16–1.85)* 1.53 (1.10–2.12)* PM 2.5 1.30 (0.66–2.56) 1.48 (1.19–1.85)* 1.49 (1.07–2.08) O 3 1.01 (0.49–2.11) 1.50 (1.20–1.86)* 1.54 (1.10–2.14)*

a Mutually adjusted across each row (i.e., effect of each community pollutant was examined separately in a model including both home and school TRP). b HR (95% CI) for central-site pollutants scaled across the range of exposure in the 13 com munities (23.6 ppb for NO 2 , 43.9 μg/m 3 PM 10 , 17.4 μg/m 3 PM 2.5 , and 30.3 ppb for 1000–1800 hours O 3 ); household nonfreeway TRP was deviated from school, scaled to the IQR for home exposure (8 ppb from Table 2). * p < 0.01.Childhood incident asthma and traffic-related air pollution

Environmental Health Perspectives • volume 118 | number 7 | July 2010 

### 1025 locations found that associations with asthma were attenuated by adjustment for modeled residential traffic exposure (Kim et al. 2004, 2008 ). In the current study, the attenuated association between asthma and NO 2 con

tinuously measured at the community moni

tor in models with adjustment for TRP also suggests that NO 2 was not causally related to asthma. However, interpretation of this result is not clear because exposure measured at the community monitor may have misclassified exposures of children in parts of the com

munity with significant local sources of NO 2 . We did not find evidence for main effects of regional O 3 and PM with asthma, consistent with results from previous analyses in older CHS cohorts, although we have previously shown that O 3 modified the effect of outdoor exercise in a genetically susceptible subgroup (Islam et al. 2009; McConnell et al. 2002). Associations with asthma were significant for TRP exposure estimates modeled from local nonfreeway roadway proximity, traffic volume

, and meteorology. There was little evi

dence for an effect of major roadway proxim

ity alone, for traffic density

, or for pollution from freeways. The absence of a freeway TRP effect suggests that causal pollutants may be highly reactive, resulting in steep spatial gradi

ents from freeway sources and little exposure at the longer distances of homes and schools to freeways compared with distance to other major roads [ Supplemental Material, Table E-3 (doi:10.1289/ehp.0901232 )]. Several previ

ous studies have found that the largest gradi

ents in traffic-related pollutants occur within 150 m from roadways (Gilbert et al. 2003; Zhu et al. 2002), and variability has often been best explained by traffic volume within 300 m (Briggs et al. 2000; Gilbert et al. 2003; Ross

et al. 2006). High-volume traffic on secondary roads may also produce pollutants resulting from frequent stops and accelerations

, which have been found to be associated with asthma symptoms (Ryan et al. 2005). However, in an older cohort from some of these same commu

nities, we observed associations of freeway-mod

eled exposures and residential freeway proximity with respiratory outcomes (Gauderman et al. 2005, 2007). Different traffic metric associa

tions with respiratory health may have been due to variation in the distribution of freeway and nonfreeway traffic around homes in the dif

ferent cohorts and the relatively small number of children living near freeways (< 7% within 150 m in the current cohort). It is also possible that the accuracy of exposure models for the freeway and nonfreeway sources varied in the two cohorts or that the greater mobility of older children resulted in health effects associated with spending time in areas such as parks in close proximity to freeways near homes. An important distinction between the TRP and simpler traffic metrics is the inclusion of meteorology (average annual wind speed and direction and height of the mixing layer) in addition to proximity and volume. Traffic density alone [ Supplemental Material, Table E-4 (doi:10.1289/ ehp.0901232) ] and proximity to a major roadway ( Supplemental Material, Tables E-4 and E-5) had weaker positive associations with incident asthma that were not statisti

cally significant. Therefore, inconsistent find

ings in some of the (largely cross-sectional ) studies that have evaluated associations with asthma (Heinrich and Wichmann 2004) may reflect the failure to account for the impact of meteorology on exposure. We have recently measured NO

x at > 900 locations in these communities. Approximately two thirds of the within-community variability in NO

x was explained by TRP, a substantially higher amount of variation than was explained by any other traffic metric (unpublished data). We have also previously observed that both TRP and residential traffic proximity were associated with increased asthma prevalence in this cohort (McConnell et al. 2006). The prospective design is a strength of this study. In other prospective studies of birth cohorts in Sweden and the Netherlands, mod

eled traffic-related pollutants, including NO

x and/or PM 2.5 , were associated with incidence of wheeze and atopy (Nordling et al. 2008) and doctor-diagnosed asthma (Brauer et al. 2007) in children < 5 years of age, when the diagnosis of asthma may be difficult to distin

guish from transient wheeze not predictive of asthma. As these cohorts mature

, the relation

ship to asthma is likely to become more clear. For example, associations between PM 2.5 light absorption, roadway proximity

, and asthmatic bronchitis and atopy were found in a study of two German birth cohorts that followed some children to 6 years of age (Morgenstern et al. 2008). Associations with allergic symptoms in these cohorts were less robust at an earlier age (Gehring et al. 2002). Other recent results from prospective studies of birth cohorts (Gehring et al. 2009) and of children (Shima

et al. 2002) followed to older ages have gener

ally shown positive associations between mark

ers of residential TRP and asthma, although a recent Norwegian study found no association with modeled exposure at the birth address of a large cohort of children (Oftedal et al. 2009). Genetic studies examining pathways likely to mediate effects of air pollution also strengthen the causal inference that traffic-related pollut

ants may cause asthma. We have shown else

where that the risk of asthma associated with traffic exposure and with traffic-related ambi

ent PM was modified in a predictable way by functional gene variants in pathways associ

ated with asthma, including inflammation and airway remodeling (Salam et al. 2007a) and oxidative stress (Islam et al. 2008; Salam et al. 2007b). In another analysis of the data from this cohort

, we have shown that the estimated effect of TRP was modified by socioeconomic status and psychosocial stress (Shankardass

et al. 2009). There are some limitations to these data. Our conservative estimates of TRP for the center of school buildings are likely to under

estimate the exposure of students in build

ings closer to the roadways, especially during dropoff and pickup of students at the begin

ning and end of the school day. The impact of this underestimate of exposure on the assess

ment of health effects is not clear, although in general it might be expected that this exposure misclassification would be nondifferential with respect to asthma and would result in underes

timation of the effect of traffic exposure. Other limitations include the relatively short ( 3 -year) period of follow-up and the retrospective ques

tionnaire assessment of early- life risk factors, as this was not a pregnancy cohort. Our cohort excluded children who left the community before school entry and who had asthma or wheeze at study entry. However, our cohort allowed us to examine risk factors in school children, an age during which there has been little study of new-onset asthma. Asthma is a clinical syndrome with no sensitive and specific diagnostic test avail

able to confirm clinical assessments. Because of the clinical nature of the assessment, the reported physician diagnosis of asthma we used has been recommended and widely used as a method to classify asthma status in epide

miologic studies, although this approach has limitations (Asher et al. 1995; Burr 1992). If misclassification of true asthma based on this approach were random with respect to exposure to air pollution, then the observed associations would be likely to have under

estimated the effect of exposure. Bias could explain our results if asthma misclassification were related also to exposure. This could occur if exposure were associated with access to care and differences in practice among physicians that have the potential to influence asthma diagnosis (Samet 1987). However, adjustment for factors that mediate access to care including family income, parental education, and having medical insurance did not alter our results, indicating that confounding due to differen

tial access to care was unlikely to explain these findings. The validity of questionnaire- reported physician diagnosis has also been established in a subset of CHS participants (Salam et al. 2007a). In addition, any child with a history of wheezing at study entry was excluded from the analysis, so it is unlikely that the exacerba

tion of undiagnosed prevalent asthma by traffic explained these associations. Loss to follow-up could also potentially result in bias because of

selection. Sociodemographic characteristics varied between the analysis cohort and childrenMcConnell et al.



### 1026 volume 118 | number 7 | July 2010 • Environmental Health Perspectives not contributing to the analysis because of loss to follow-up or missing or poorly matched geocodes for addresses precluding estimation of residential exposure [ Supplemental Material, Table E-6 (doi:10.1289/ehp.0901232 )].Hispanic children were less likely than nonHispanic white children to be included in the follow-up analysis, as were children without insurance and with lower parental education and income. However, adjusting for these fac

tors had little impact on the pattern of observed effects. In addition, there was little difference between exposure to nonfreeway TRP in the study sample (7.3 ppb) and in the group not included because of loss to follow-up (7.6 ppb). Therefore, it is unlikely that selection bias due to loss to follow-up explained our results.


### Conclusions
An estimated 6.2 million children have asthma in the United States, making it the most com

mon chronic disease in childhood (Moorman

et al. 2007), and rates have increased mark

edly in developed countries over the past several decades (Braman 2006). Morbidity results in impaired quality of life for the affected child and other family members, in increased use of health services, and in school absences that have large social and economic costs (Wang

et al. 2005). Our results indicate that children exposed to higher levels of traffic-related air pol

lution at school and home are at increased risk of developing asthma. Almost 10% of public schools in California are located within 150 m of roadways with > 25,000 vehicles daily (Green

et al. 2004). Students in urban areas in eastern U.S. cities are even more likely than children in Los Angeles to attend schools near major high

ways (Appatova et al. 2008). Therefore, expo

sure to TRP is potentially an important public health problem affecting large populations of children. Planning transportation and other urban development to limit population expo

sure to traffic exhaust

, as well as more effective control of vehicular emissions, may result in substantial long-term public health benefits.

**R efe**  **r ences** 

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