American Journal of Respiratory and Critical Care Medicine

Despite the important contribution of traffic sources to urban air quality, relatively few studies have evaluated the effects of traffic-related air pollution on health, such as its influence on the development of asthma and other childhood respiratory diseases. We examined the relationship between traffic-related air pollution and the development of asthmatic/allergic symptoms and respiratory infections in a birth cohort (n ∼ 4,000) study in The Netherlands. A validated model was used to assign outdoor concentrations of traffic-related air pollutants (nitrogen dioxide, particulate matter less than 2.5 μm in aerodynamic diameter, and “soot”) at the home of each subject of the cohort. Questionnaire-derived data on wheezing, dry nighttime cough, ear, nose, and throat infections, skin rash, and physician-diagnosed asthma, bronchitis, influenza, and eczema at 2 years of age were analyzed in relation to air pollutants. Adjusted odds ratios for wheezing, physician-diagnosed asthma, ear/nose/throat infections, and flu/serious colds indicated positive associations with air pollutants, some of which reached borderline statistical significance. No associations were observed for the other health outcomes analyzed. Sensitivity analyses generally supported these results and suggested somewhat stronger associations with traffic, for asthma that was diagnosed before 1 year of age. These findings are subject to confirmation at older ages, when asthma can be more readily diagnosed.

There is growing evidence that the incidence of asthma and inhalant allergies in childhood is increasing in developed countries (1). Although genetic factors are certainly important determinants of the prevalence and severity of asthma, they cannot explain observed increases in prevalence. Among the environmental risk factors that have been identified as causative agents of asthma and inhalant allergy in children are sensitization to allergens such as house dust mite and cockroach (26) and maternal smoking (7, 8). The effect of outdoor air pollution has been less clear. Whereas there is ample evidence that outdoor air pollution exacerbates pre-existing asthma (9, 10), there is little evidence to suggest that outdoor air pollutants increase the incidence of asthma or allergic diseases in children. One argument is that other types of ambient air pollutants that are not routinely monitored and are specifically associated with traffic exposure may be of greater importance to the development of asthma and allergic diseases.

Recent studies indicate that traffic is a major source of air pollutants in urban areas. Despite the important contribution of traffic sources to reduced urban air quality, relatively few studies have evaluated the specific effects of traffic-related air pollution on health. Several cross-sectional studies have found the prevalence of respiratory disease symptoms to be elevated in individuals living close to high-traffic roads (1117). In a previous study, an inverse relationship between lung function in children and the intensity of traffic on the main road within the school district was found (11). Two case–control studies found associations between measures of traffic intensity and hospitalization for wheezing, bronchitis (4- to 48-month-old children), and asthma (children under 5 years of age) (18, 19). A recent case–control study reported a proximity-related increased risk of wheezing in children living within 90 m of major roadways (20). A major limitation in all of these studies was the lack of measured exposure of subjects to air pollution. Several studies have relied on the distance to major roads as a proxy for exposure (1520), whereas others used self-reported (14, 15) or measured traffic intensity (11, 16). Despite their limitations, these studies suggest that living near busy roads leads to adverse respiratory health effects. In a cross-sectional study conducted in Germany, in which air pollutant concentrations were estimated at the homes and schools of children, an association between air pollution and prevalence of cough and bronchitis but not of atopy was found (17). A recent series of studies completed in The Netherlands indicated that children living near roads with high intensity of truck traffic have lower lung function and more chronic respiratory symptoms compared with children living on roads with less truck traffic (21). A similar relationship was found with Black Smoke, but not with nitrogen dioxide (NO2) concentrations measured in schools, and respiratory health (22). Because Black Smoke is a marker for diesel exhaust, this finding suggests that diesel exhaust was primarily responsible for the effects on respiratory health. Several studies have also suggested that traffic-related air pollution may be associated with increased respiratory symptoms in young children (23, 24), although data regarding associations with respiratory infections, especially from cohort studies, are lacking.

In this article we describe a study of the relationship between traffic-related air pollution and development of asthmatic symptoms, allergic diseases, and respiratory infections in a birth cohort study in The Netherlands. A unique aspect of the study was the use of individual exposure estimates for each cohort member. These estimated exposures to traffic-related air pollution were computed using a validated model based on geographic data, measurements of air pollutants, such as of NO2, particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5), and filter reflectance, a measure of particulate elemental carbon.

Study Population

The Prevention and Incidence of Asthma and Mite Allergy study is a prospective birth cohort study with an initial enrollment of 4,146 children (25, 26). The cohort was recruited during the second trimester of pregnancy from a series of communities, varying from rural areas to large cities, living in the northern, western, and central parts of The Netherlands (Figure 1)

. The study protocol was approved by the institutional review boards of each participating institute, and written informed consent was obtained from all participants.

Exposure Assessment

Air pollutant concentrations at the home of each member of the cohort were calculated by a modeling approach combining air pollution measurements with a geographic information system (GIS) (2730). Briefly, air pollutants were measured at 40 individual sites designed to capture the maximum variability in pollution from traffic sources. PM2.5 were collected with Harvard Impactors (Air Diagnostics and Engineering Inc., Harrison, ME), NO2 by Palmes tubes, and “soot” was measured as the reflectance of the PM2.5 filters, a method shown to be highly correlated with elemental carbon measurement (31). On the basis of colocated measurements, a soot measurement of 2 × 10−5/m corresponds to an elemental carbon concentration of 2.9 μg/m3. At each location, measurements were conducted for four 2-week periods dispersed throughout 1 year and then adjusted for temporal trends to calculate long-term average concentrations of PM2.5, soot, and NO2 in the 40 locations.

GIS data were also collected regarding traffic in the vicinity of each monitoring location. Regression models were developed to relate the annual average concentrations measured at the 40 monitoring sites with the GIS variables. These models explained 73, 81, and 85% of the variability in the annual average concentrations of PM2.5, soot, and NO2, respectively (27, 28). Additional information regarding the GIS models is presented in the online data supplement. These models were then applied to the same GIS variables measured at the home of each individual in the cohort to obtain unique long-term ambient air pollutant concentrations for each member of the cohort.

Questionnaire Data

A limited number of health outcomes (Table 1)

TABLE 1. Prevalence of selected health outcomes in the cohort of 2-YEAR old patients in the netherlands


Variable

n*

% Prevalence
Wheeze697 (3,707)19
Doctor-diagnosed asthma176 (3,704)5
Dry cough at night with or without cold581 (3,685)16
Doctor-diagnosed bronchitis418 (3,701)11
Itchy rash919 (3,715)25
Doctor-diagnosed eczema867 (3,685)23
E, N, T infections540 (3,689)15
Doctor-diagnosed flu, serious cold
1,582 (3,697)
43

*Number (total valid in parentheses) of subjects with the symptom/condition. The specific questions pertaining to each outcome measure are discussed in the online data supplement.

Definition of abbreviations: E, N, T = ear, nose, throat.

All outcomes refer to incidence or diagnoses within the past 12 months, in patients between 12 and 24 months of age.

were selected a priori to describe symptom frequencies and physician diagnoses for indicators of asthma, allergic disease, and infections. The relevant questions were identical to those of the International Study of Asthma and Allergies in Childhood. All data were obtained through questionnaires that were completed by the parents at home. Questionnaires were completed during pregnancy, at the time the child was 3 months old, and at the time of the child's first and second birthdays. Beginning with the 3-month questionnaire, each serial questionnaire contained a core set of questions that have been supplemented with additional questions as the cohort ages. A series of potential confounding variables (listed in Table 2

TABLE 2. Prevalence of selected confounders in the cohort


Variable

n*

% Prevalence
Mother smoking during pregnancy583 (4,079)14
Smoking in home1,121 (3,903)29
Intervention study855 (4,146)21
Natural history study3,291 (4,146)79
Mattress cover (allergen-free)416 (4,146)10
§ Education of mother: ⩽ Medium level professional1,743 (3,709)47
§ Education of mother: ⩾ High school1,966 (3,709)53
§ Education of father: ⩽ Medium level professional1,816 (3,546)51
§ Education of father: ⩾ High school1,730 (3,546)49
Boys2,035 (3,934)52
Girls1,899 (3,934)48
Gas stove3,236 (3,911)83
Gas water heater, unvented192 (3,760)5
Any other siblings1,986 (3,919)51
Netherlands ethnicity3,478 (3,690)94
Other ethnicity212 (3,690)6
Any breastfeeding at 3 months1,827 (3,883)47
§ Any mold at home1,215 (3,704)33
Any pets at home2,011 (3,905)51
Allergies in mother1,281 (4,114)31
Allergies in father1,172 (4,051)29
Residence in northern region of country1,275 (4,146)31
Residence in middle region of country1,647 (4,146)40
Residence in western region of country
1,224 (4,146)
29

*Number/prevalence of subjects.

Data collected from questionnaires at the time of pregnancy.

Data collected from questionnaires at the time the child was 3 months old.

§Data collected from questionnaires at the time the child was 1 year old.

Data collected from questionnaires at the time the child was 2 years old.

Definition of the variables are given in the online data supplement.

, together with the age of the mother at the time of birth) were selected if exploratory data analysis suggested substantial variability within the cohort or if variables were suspected of being risk factors for symptom development. Confounding data were selected from the earliest questionnaire that was available to coincide with the exposure data, which were estimated for members of the cohort at birth.

Statistical Analysis

The association between exposure and health outcomes was analyzed by multiple logistic regression, adjusting for confounding factors. Because exposures were calculated based on the birth address, and outcomes were assessed at 2 years of age, we conducted sensitivity analyses to assess the impact of retaining only those children who had moved between birth and 2 years of age. Additional sensitivity analyses were conducted to evaluate the effect of maternal and paternal allergy status, day care attendance, region of the country, contact with farm animals, and time of asthma diagnosis on the associations between air pollution and health outcomes. All odds ratios are presented for an interquartile range increase in air pollutant concentrations, equivalent to 3.2 μg/m3 for PM2.5, 0.54 × 10−5/m for soot (corresponding to 0.8 μg/m3 elemental carbon), and 10.3 μg/m3 for NO2.

Exposures were successfully calculated at the homes of 4,135 of the 4,146 children (99.7%). For 11 subjects, exposures were not calculated due to an inability to geocode their homes (n = 7) or an error in the GIS calculation of one variable (n = 4). Table 3

TABLE 3. Summary statistics of estimated annual average air pollution concentrations at home of each member (birth) of the cohort




PM2.5
 (μg/m3)

Soot, filter
 absorbance
 (10−5/m)

NO2
 (μg/m3)
Minimum13.50.7712.6
10th percentile14.01.1614.8
25th percentile15.01.3818.9
50th percentile17.31.7826.1
Mean16.91.7225.6
75th percentile18.21.9229.2
90th percentile19.12.1935.3
Maximum
25.2
3.68
58.4

Definition of abbreviations: NO2 = nitrogen dioxide; PM2.5 = particulate matter less than 2.5 μm in aerodynamic diameter.

shows the summary statistics of the exposure estimates. The exposure estimates for the different pollutants were very highly correlated. The correlation of PM2.5 with soot and with NO2 was 0.99 and 0.97, respectively; the correlation of soot with NO2 was 0.96.

At 3 months of age, the time of the first questionnaire, 212 children had dropped out of the study (n = 3,934). The cohort size was 3,745 (401 missing) and 3,730 (416 missing) when children were aged 1 and 2 years, respectively. The main reasons for dropout were subjects' loss of interest or lack of time and subjects moving and therefore becoming untraceable. The mean age of the mother at time of birth was 30.3 years (SD = 3.9; range: 17–42 years).

The association between exposure to air pollutants and development of respiratory symptoms/disease is shown in Table 4

TABLE 4. Association between long-term exposure to air pollution and infections, asthmatic and allergic symptoms at 2 years of age



Unadjusted

Adjusted*

OR
95% CI
n
OR
95% CI
n
Wheeze
PM2.51.140.99–1.303,6991.140.98–1.342,991
soot1.110.99–1.243,6991.110.97–1.262,991
NO21.121.00–1.253,6991.130.99–1.292,991
Doctor-diagnosed asthma
PM2.51.080.84–1.373,6961.120.84–1.502,989
soot1.070.87–1.313,6961.120.88–1.432,989
NO21.110.91–1.363,6961.180.93–1.512,989
Dry cough at night
PM2.51.100.95–1.273,6771.040.88–1.232,969
soot1.080.95–1.213,6771.020.88–1.172,969
NO21.070.95–1.203,6771.020.89–1.182,969
Doctor-diagnosed bronchitis
PM2.51.000.85–1.183,6931.040.85–1.262,986
soot0.980.85–1.123,6930.990.84–1.172,986
NO20.950.82–1.093,6930.990.84–1.172,986
E,N,T infections
PM2.51.140.99–1.333,6811.201.01–1.422,969
soot1.120.99–1.273,6811.151.00–1.332,969
NO21.090.99–1.233,6811.161.00–1.342,969
Doctor-diagnosed flu/serious colds
PM2.51.151.03–1.283,6891.121.00–1.272,981
soot1.131.03–1.233,6891.090.98–1.212,981
NO21.141.04–1.243,6891.111.00–1.232,981
Itchy rash
PM2.51.070.95–1.203,7071.010.88–1.162,995
soot1.070.97–1.193,7071.020.91–1.152,995
NO21.060.96–1.173,7071.020.91–1.152,995
Doctor-diagnosed eczema
PM2.51.020.90–1.163,6770.950.83–1.102,970
soot1.010.91–1.133,6770.960.85–1.082,970
NO2
1.00
0.90–1.11
3,677
0.96
0.85–1.08
2,970

*OR and 95% CI adjusted for confounding factors in Table 3 and mothers' age but not for region. ORs are calculated for an interquartile range change in concentration.

Definition of abbreviations: CI = confidence interval; E, N, T = ear, nose, throat; NO2 = nitrogen dioxide; OR = odds ratio; PM2.5 = particulate matter less than 2.5 μm in aerodynamic diameter.

Crude and adjusted OR and 95% CI.

. Before adjustment for confounding variables, air pollution was associated with increased incidence of wheezing, physician-diagnosed asthma, ear/nose/throat infections, flu/serious colds, and to some extent, with dry cough at night and itchy rash, although only the associations with flu/serious colds were statistically significant (Table 4). After adjustment for the full set of confounding variables (except for region of the country), the associations between air pollution and increased risk of wheezing, asthma, ear/nose/throat infections, and flu/serious colds remained elevated, but the odds ratios for dry cough at night and itchy rash decreased substantially. Sample sizes for the adjusted odds ratios were reduced due to incomplete questionnaires for some of the confounding variables (Table 2).

Sensitivity Analyses
Parental allergy status.

In addition to the analyses presented in Table 4, analyses were conducted with the full set of confounding variables listed in Table 2, with the exception of parental allergy status. Previous work with this cohort has suggested that parents with allergies consider home environment risk factors when purchasing and furnishing their homes more than do parents without allergies (25). Therefore, parental allergy status itself may be associated with differences in lifestyle and home environment, which, in turn, may influence the prevalence of environmental risk factors in the homes of the children. Accordingly, analyses were also conducted without adjustment for parental allergy status. In these analyses there were no major changes in odds ratios, with the exception of the itchy rash outcome variable that was slightly increased, relative to analyses that included parental allergy status. In analyses in which parental allergy status was not included, odds ratios (95% confidence intervals) for itchy rash were 1.04 (0.91–1.20), 1.05 (0.93–1.18), and 1.04 (0.93–1.17) for PM2.5, soot, and NO2, respectively.

Region.

After additional adjustment for region, the odds ratios for wheezing were reduced to unity, whereas the odds ratios for asthma were stable and remained elevated. Region is, however, an important determinant of exposure in the stochastic models used for the estimation of the cohort exposures; hence, this may be an overadjustment. The prevalence of wheezing differed significantly across regions in the same pattern as did air pollution. Sensitivity analyses were conducted for a subset (n = 2,029) of subjects living in the middle and western parts of the country, after the exclusion of subjects living in the northern part where air pollution levels were lower and prevalence of wheezing lowest. In this analysis, odds ratios (95% confidence intervals) for wheezing were still reduced to unity (1.00 [0.74–1.36], 0.99 [0.79–1.25], 1.08 [0.86–1.34] for PM2.5, soot, and NO2, respectively), whereas those for asthma were increased (1.35 [0.77–2.37], 1.29 [0.88–1.96], 1.48 [1.00–2.19] for PM2.5, soot, and NO2, respectively).

Farm Environment.

Because living in a farming environment has been associated with a decreased risk of asthma incidence (10), we also examined the possibility that the sensitivity of our observations to control for the region of the country could be explained by differences in the prevalence of subjects living on farms. Although subjects were not specifically queried regarding living in a farm environment, the Year 3 questionnaire included questions regarding the frequency of contact with horses, cows, or pigs. As a substitute for living in a farm environment, we selected all subjects who reported one or more contacts per week with horses, cows, or pigs and who did not move between the Year 2 and Year 3 questionnaires. Of the 288 (9.5% of total) subjects who met this criteria, 124 (4.1%) lived in the northern region, 110 (3.6%) in the middle region, and 54 (1.8%) in the western region. Including “farming environment” in the logistic regression analysis (n = 2,428) resulted in somewhat reduced odds ratios (relative to those presented in Table 4) for wheezing (1.12 [0.94–1.34], 1.08 [0.93–1.25], 1.11 [0.96–1.29] for PM2.5, soot, and NO2, respectively) and asthma (1.09 [0.78–1.52], 1.04 [0.78–1.38], 1.08 [0.81–1.44] for PM2.5, soot, and NO2, respectively).

Moving.

Because exposures were estimated on the basis of the location of the child at birth, whereas outcomes were based on data collected from questionnaires administered when the child was 2 years old, we also analyzed the effect of address change on the associations between air pollution and outcomes. Of the 3,745 children who continued in the study until 1 year after birth, 343 had moved in the period between 3 months and 1 year of age. A total of 514 of the 3,730 children who remained in the study at 2 years of age had moved between 1 and 2 years of age. Analyses were conducted by excluding those children who had moved by 1 year of age and in addition by excluding those children who had moved by 2 years of age. First, we conducted an analysis in which children who had moved between 3 months and 1 year of age were excluded. In this case (n = 2,668), the (adjusted) odds ratios for asthma were slightly increased (1.19 [0.88–1.60], 1.17 [0.91–1.51], 1.25 [0.97–1.60] for PM2.5, soot, and NO2, respectively) compared with the total cohort, whereas those for wheezing were slightly decreased (1.13 [0.96–1.33], 1.09 [0.95–1.26], 1.12 [0.98–1.29] for PM2.5, soot, and NO2, respectively). An additional analysis was conducted after excluding those children who had moved at any point within the period between 3 months and 2 years of age. In this case (n = 2,239), odds ratios for wheezing were slightly decreased relative to the entire cohort (1.19 [0.99–1.42], 1.14 [0.98–1.33], 1.17 [1.01–1.36] for PM2.5, soot, and NO2, respectively). In contrast, the odds ratios for asthma were markedly decreased and tended toward unity, except for NO2 (1.02 [0.73–1.41], 1.01 [0.76–1.33], 1.09 [0.82–1.45] for PM2.5, soot, and NO2, respectively). It should be noted, however, that this analysis included a substantially reduced sample size.

Daycare.

Because air pollutant concentrations were estimated only at the homes of the cohort members, we were concerned about potential exposure misclassification for those cohort members who spent significant amounts of time at daycare centers. Although we did not have geographic coordinates available for daycare centers, we did conduct sensitivity analyses excluding those individuals who attended daycare centers for more than 10 hours per week, according to the Year 1 questionnaire. Of the 981 children who reported attending daycare, the median number of hours of daycare attendance per week was 18, and 735 of these children attended daycare centers for more than 10 hours per week. After excluding these children from the analysis, the (adjusted) odds ratios for asthma were essentially unchanged (1.13 [0.81–1.56], 1.11 [0.84–1.47], 1.15 [0.87–1.52] for PM2.5, soot, and NO2, respectively) compared with the total cohort, whereas those for wheezing were somewhat increased (1.22 [1.02–1.46], 1.16 [1.00–1.35], 1.17 [1.00–1.36] for PM2.5, soot, and NO2, respectively). Because daycare attendance has also been independently associated with respiratory tract infections in this cohort (26), we conducted additional sensitivity analyses of influenza and ear, nose, and throat infections after including daycare attendance (more than 10 hours per week) as a confounding variable. Odds ratios for influenza were essentially unchanged, whereas those for ear, nose, and throat infections were slightly reduced (1.17 [0.98–1.39], 1.13 [0.97–1.30], 1.12 [0.97–1.30] for PM2.5, soot, and NO2, respectively).

Asthma.

We analyzed the asthma outcome in more detail because the questionnaire referred only to diagnoses in the previous 12 months. The analyses described previously therefore refer to incidence of symptoms in the period between 12 and 24 months. Because diagnosis of asthma is not considered an acute or transient outcome, we also evaluated the association between traffic-related air pollution and asthma diagnosis in the first 12 months (6% of subjects [223/3,693]) and in the 0- to 24-month period (9% [317/3,583]). Adjusted odds ratios for diagnosis during the first 2 years were slightly higher compared with those for diagnosis during the 12- to 24-month period (Figure 2)

. We further analyzed only those diagnoses in the first 12 months of life and found substantially higher, and statistically significant, odds ratios compared with diagnosis during the 12- to 24-month period (Figure 2).

In this prospective cohort study we have observed several positive associations between traffic-related air pollutants and wheezing, asthma, and respiratory infections. These findings should be interpreted with caution because the observed associations were mostly nonsignificant. Although nonsignificant, these observed associations were generally robust because odds ratios were not altered to any great extent by the inclusion of potential confounding variables in the regression models or in the sensitivity analyses that evaluated parental allergy, change of home, and daycare attendance. Odds ratios were somewhat sensitive to adjustment for region and contact with farm animals. Our indications of associations between exposure to traffic-related air pollutants and respiratory symptoms in children are consistent with results reported elsewhere, although no cohort studies to date have evaluated effects in infants. In other studies, exposure to traffic-related air pollution has been associated with increased reporting of cough (17, 23, 32), wheezing (11, 12, 22, 33), bronchitis (17, 23, 33), asthma (22, 32, 34, 35), runny nose (22), allergic rhinitis (15), and decreased lung function (11, 21).

Although our findings of an increased association between traffic-related air pollution and asthma diagnosis in the first year of life, relative to diagnosis during the second year, needs to be confirmed by further follow-up of the cohort, it does suggest that the timing of exposure may be important. This finding is in overall agreement with studies of indoor allergen exposure that suggest that the timing of exposure, and specifically exposure within the earlier stages of childhood, is most important for the development of allergic disease (36). It should be noted that asthma prevalence in the cohort (after accounting for dropout from the cohort) does increase as expected from 6% in the first 12 months to 9% at 24 months.

This analysis used a unique approach to calculate individual air pollutant concentrations at the home of each cohort member and therefore has distinct advantages over ecologic analyses that assign single air pollutant concentrations to all individuals living in certain areas (2730). In particular, the exposure model was developed to incorporate the effect of traffic sources on air pollutant concentrations and therefore to capture the variability in exposure to traffic-related air pollution. This represents a major difference from the more typical use of government monitoring network data to estimate exposures in epidemiologic studies. Most network monitors are located to measure urban or regional background air pollutant concentrations and therefore to specifically not reflect the influence of local traffic. Because the cohort was drawn from throughout The Netherlands and included individuals living in urban and rural areas, there was significant variability in exposure to traffic-related pollutants.

Although we specifically measured PM2.5 filter absorbance (soot) as a marker for diesel exhaust particles, due to a high correlation between the three pollutants that were measured, we were unable to differentiate between heavy- and light-duty vehicles in this study. Heavy-duty vehicles, typically fueled with diesel, but not light-duty vehicles, have been shown in a previous study to be associated with reduced lung function and increased prevalence of chronic respiratory symptoms in children (21, 22). To specifically differentiate between light- and heavy-duty vehicles would require a study population where exposures differed on the basis of the proximity to diesel traffic sources (22). Despite our ability to explain a high degree of the variability in measured long-term average air pollutant concentrations using GIS variables describing traffic sources (see online data supplement), we were unable to effectively describe differences in pollutant concentrations on the basis of very local scale proximity to traffic sources, for example, the distance from the home to the nearest road.

Limitations

Although future analyses of subsets of this cohort will include objective measurements of allergic sensitization and lung function, no objectives measures were included in this analysis of the entire cohort. Measurements of indoor allergens (which may have an interactive effect with air pollutants on the development of allergic disease) have been obtained for all the “high-risk” children and for a random sample of the “natural history” cohort at 3 months of age [see online data supplement]. The potential interaction between outdoor air pollution and indoor allergen exposure will be explored in more detail once data from later follow-up becomes available.

A further limitation relates to the validity of the specific health outcome variables for disease diagnosis at very young ages. Unfortunately, no generally accepted criteria for asthma are available for infants and very young children. Difficulties inherent in distinguishing asthma from other respiratory diseases at young ages suggest that considerable misclassification can be expected. In addition to reporting physician-diagnosed asthma, we also evaluated associations with wheezing, a major symptom of asthma. However, wheezing in infants does not appear to be predictive of asthma development (37) because wheezing in infants is highly associated with viral infections (38). Balanced against this are our findings of an association between air pollution and asthma, as well as wheezing, and the lack of an association with bronchitis. Taken together, these findings are suggestive of an association between traffic-related air pollution and early asthma, and not infection-associated lower respiratory tract illness. Analysis of the cohort at later ages will be required to confirm this association.

The lack of indoor measurements of traffic-related air pollutants is likely to result in some nondifferential misclassification of exposure to traffic-related air pollution as a result of differences in the amount of outdoor air pollutants that penetrate indoors in the different homes of the cohort. Among other factors, these indoor–outdoor relationships will be affected by building height, presence of open windows, and heating systems. Previous studies have indicated that in homes without indoor combustion sources, indoor and outdoor concentrations of NO2 are highly correlated, suggesting that indoor exposure to NO2, which originates from outdoor sources such as traffic, is also correlated with outdoor levels of NO2 (39). Recent work has shown that personal exposure to NO2 is related to the degree of urbanization, the traffic density, and the distance from a nearby major road, supporting the use of our ambient concentration estimates as indicators of personal exposure (40). Similar studies have indicated that indoor concentrations and exposure to particulate matter from traffic sources, such as soot, is highly correlated with ambient levels (31, 41). Therefore, the outdoor concentrations estimated at the homes of members of the cohort should be good alternatives for studying at-home exposure to air pollutants originating from traffic.

The high correlations among the different pollutants that were measured precluded the analysis of the effect of specific pollutants or indicators of specific components (for example, heavy-duty vehicles) of traffic-related air pollution. As described in detail elsewhere, the geographic variables that were used to estimate exposure were selected as substitutes for exposure to traffic sources. Results of the regression models indicate that a greater proportion of the variability in NO2 and soot concentrations was explained by traffic substitutes than by PM2.5 (27) (see online data supplement). Accordingly, the NO2 and soot exposure and effect estimates for the cohort members are likely to be more specific to traffic-related pollution than to PM2.5.

A further argument for the specificity of the exposure estimates relates to the production and fate of the individual pollutants themselves. PM2.5 is a measure of a mixture of particles originating from different sources, including those resulting from natural sources, contributions of long-range transported air pollution, industrial sources, and motor vehicles. NO2 is a secondary pollutant produced by atmospheric transformation of nitric oxide. Major sources of nitric oxide emissions are motor vehicles and power production. Furthermore, due to the time required for atmospheric transformation to occur, NO2 concentrations will also be more spatially diffused than their original sources. In contrast, fine particle soot (measured in this study as PM2.5 filter reflectance) is a measure of particulate elemental carbon, which has been shown to be an indicator of diesel exhaust particles and therefore quite specific to traffic sources.

Conclusions

This study has suggested an association between self-reported prevalence of respiratory illness, specifically wheezing, ear/nose/throat infections, and reporting of physician-diagnosed asthma and flu or serious cold, and traffic-related air pollution. These associations were based on outcome data collected in a birth cohort at 2 years of age. Although the early age at which outcomes were measured precludes definitive assessment of a link between asthma and exposure, this is the first indication in a prospective cohort study of a link between air pollution exposure and development of asthma in children. Future analyses that evaluate the relationship between traffic-related air pollution and development of asthma at more advanced ages are needed to confirm or refute this association.

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Correspondence and requests for reprints should be addressed to Dr. Michael Brauer, School of Occupational and Environmental Hygiene, University of British Columbia, 2206 East Mall, Vancouver, BC, V6T 1Z3 Canada. E-mail:

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