The public health impact of outdoor and traffic-related air pollution may be large.1 The health effects of short-term exposure to outdoor air pollution are well known, while long-term effects are less well documented.2 A major reason for this lack of information has been the quality of exposure assessment, which usually has been estimated on the aggregate level by using average concentrations of a limited number of monitoring stations within each city or community. This approach may lead to measurement error that likely underestimates the effects of air pollution. Still, most of the studies on effects of long-term air pollution on children's lung function that have used this approach have found associations,3–8 with one exception.9
Several epidemiologic studies have used geographical information systems (GIS) to assess exposure to air pollutants on a more individual level.10–16 To illustrate the potential of improved exposure assessment, the recent mortality study16 by the American Cancer Society showed a 2 to 3 times larger effect compared with their earlier study.17 In our study, we used a GIS approach that included a dispersion model to calculate residential outdoor air pollution concentrations from 1992 to 2002 for children living in Oslo, Norway. Thus, we were able to include exposure to outdoor air pollution on several time scales—in early lifetime, in total lifetime, and just before the lung function test. To our knowledge, no study has investigated the effects of early exposure on lung function in schoolchildren; yet, as the lung system is not fully developed in the first years of life, this may be a critical period.18,19 Furthermore, the effect of cumulative exposure during lifetime is seldom included,20 but it is an important aspect of the relation between environment and respiratory diseases.21 Even more important is the potential to investigate the simultaneous effects of long- and short-term exposures on lung function, which rarely has been studied.8,22 We investigated the associations of these exposures to air pollution with lung function in 9- to 10-year-old children living in Oslo.
MATERIALS AND METHODS
A follow up of the Oslo Birth Cohort was carried out in 2001–2002.23 A cross-sectional study of all children born in 1992 and living in Oslo was carried out simultaneously. Of these children, 5279 living in Oslo in 2001 were invited to participate, and 3533 families accepted and completed the self-administered questionnaire (response rate 67%). The participants were also offered a clinical examination including spirometry. This examination was performed during school hours by a trained staff of research nurses. Results of lung function tests were available for 2803 children, collected in the period between November 2001 and December 2002. The current study includes the children living in Oslo since birth (n = 2307), defined as having nonmissing exposure in early lifetime, total lifetime, last year, and days before the lung function test. The study was approved by the Regional Ethics Committee and the Norwegian Data Inspectorate. Written informed consent was obtained from the parents.
Spirometry was performed according to European Respiratory Society guidelines,24 using Jaeger spirometers (Hochberg, Germany). Calibration was done with a 1-L calibration syringe at least twice a day, according to the guidelines of the manufacturer. Children with any respiratory infection during 3 weeks before the lung function test were excluded. Peak expiratory flow (PEF), forced vital capacity (FVC), forced expiratory volume during 1 second (FEV1), and forced expiratory flow at 25% and 50% of FVC (FEF25% and FEF50%, respectively) were recorded. For children unable to give at least 3 technically satisfactory and reproducible curves according to European Respiratory Society criteria,24 up to 8 maneuvers were performed. Later, all the curves were manually reviewed for technical acceptability. Flat, rounded, or right shifted curves and curves with more than 1 distinct peak were considered unacceptable, as were common faults in performing forced expiratory maneuvers.24 The largest value of each variable was used in the analyses, including only children with at least 3 acceptable maneuvers (n = 2170) (Table 1).
Estimation of Exposure
Outdoor air pollution was modeled by EPISODE, a dispersion model based on emissions, meteorology, topography, and background air pollution concentrations measured at regional background stations in the southern part of Norway.25 The model calculated hourly concentrations of nitrogen dioxide (NO2), particulate matter (PM) with aerodynamic diameter less than 10 μm (PM10) and less than 2.5 μm (PM2.5), for each square kilometer and at receptor points (varying from n = 3813 in 1992 to n = 8009 in 2002) with busy traffic. Modeling of long-term averages was recently evaluated by comparing modeled concentrations versus measurements from monitoring stations in Oslo; the EPISODE model was shown to represent long-term levels of local outdoor air pollution reasonably well.26
By using the national identification number, the Norwegian Population Register provided a complete residential history for each child during the period 1992–2002. The municipality of Oslo gave the accompanying geographical coordinates, and thereby we linked air pollution concentrations to each child's current home address. We included several time-scales of exposure—early exposure in first and second year of life, total lifetime exposure, and several short-term exposures before the lung function test. We calculated the average of the last 3 days (lags 1–3), the last week and the last 30 days before the test, and of the first years of life if at least 70% of the daily averages were available. Lifetime exposure was calculated by monthly values starting the month after birth to the month before the test. In addition to modeled concentrations, we used NO2 measurements from a centrally placed monitoring station in Oslo to represent variations in short-term exposure, by calculating individual average of lags 1 to 3 days before the lung function test.
Potential confounders were extracted mainly from the questionnaire. We performed regression analysis of all outcomes and single covariates commonly used as confounders of lung function, including exposure to pets. In the final model, we included those covariates which were statistically significant on 5% level; sex, height, age, body mass index, current asthma, maternal smoking in early lifetime and parental ethnicity, education, smoking, and atopy. As parental atopy was highly correlated with the child's current asthma, we only included current asthma defined as parental reported diagnosis, confirmed medically and including symptoms in last 12 months. Parental smoking was defined as current daily smoking indoors, whereas maternal smoking in early lifetime was maternal smoking in pregnancy or in the child's first year of life. To control for season, we adjusted for the average of daily temperature lags 1 to 3 days before the lung function test. Furthermore, we linked the study population to the Medical Birth Registry in Norway to adjust for birth weight. The covariates of the study population are presented in Table 1. In addition to individual covariates, we controlled for contextual neighborhood socioeconomic factors at birth address and at the address at clinical examination. These factors were percentage of unmarried residents, households with income below the median, residents with primary education only, residents of manual class only, nondwelling-owners, flat dwellers, and dwellers with less than 1 room per capita. The first 3 factors were obtained from the population census in 1990 and the last 4 in 1980.27
To investigate associations between exposure and lung function, we applied multiple linear regression stratified by sex. The continuous covariates were tested for deviation from linearity by including cubic spline smoothers and square and cubic terms of the covariates; optimally, the cubic polynomial of body mass index should be included. However, as the aim was not to obtain the best model, we only controlled for the covariates stated in the previous section, including an indicator variable for participation in the Oslo Birth Cohort. Homoscedastic (constant) variance was checked by residual plots, and normality was assessed by normal probability plots of the residuals. We log-transformed the outcomes and/or the confounders, but neither the normal probability plots nor the residual plots indicated any distributional improvement. Thus, we conducted all analyses on untransformed data. Cook's distance28 was used to assess the impact of each observation on the estimated effects. The air pollutants were highly correlated (r = 0.83–0.95), and we therefore assessed only one-pollutant models. We also included short- and long-term exposures to NO2 in the same model although the correlation was moderate to high (r = 0.46–0.52 between short-term and early exposure and r = 0.70–0.77 between short-term and lifetime exposure). Early and lifetime exposure were highly correlated (r = 0.80–0.88) and were not simultaneously included. We assessed effect modification by including interaction terms with gender, current asthma, and parental ethnicity. Furthermore, we added standardized percentage of the socioeconomic factors on neighborhood level, 1 factor at a time. Because of unchanged results including neighborhood as random variable, with 327 neighborhoods, we retained the model without random neighborhood. Results are expressed as change in lung function by one interquartile increase of each pollutant. The analyses were conducted using SPSS for Windows release 12.0.1 (SPSS Inc., Chicago, IL), Stata for Windows release 9.2 (Stata Corporation, TX), and S-Plus for Windows release 6.1 (Insightful Corporation, Seattle). We assessed spatial autocorrelation by constructing Thiessen polygons to assign first-order nearest neighbor contiguity between the residential addresses, and calculated Moran's I, using GeoDa 0.95i (Spatial Analysis Laboratory, University of Illinois).
Description of the Study Population and Exposure
Table 1 shows that sex, current asthma, and parental smoking were equally distributed among all children with questionnaire information and among the subset who were lifetime Oslo residents and had a valid lung function test. The entire group had less western parents than the subset (Table 1). Height, weight, and age from the questionnaire were equally distributed in the 2 populations (data not presented). The sex differences were small for the lung function variables, and all covariates had similar distributions except current asthma (7.5% in boys and 3.3% in girls). The subset with lifetime Oslo residence and valid lung function test (n = 2170) was similar to the analysis population without any missing covariates (n = 1847), except for a higher percentage (86.2%) of western parents in the analysis population. Parents of children from the Oslo Birth Cohort were more western, better educated, and fewer were smokers than parents of the other children.
The Oslo addresses during 1992–2002 were randomly scattered over the region, with highest levels of exposure close to roads with busy traffic or other point sources. Ten percent of the addresses were located within 30 m of at least 1 receptor point and were assigned the concentration of the nearest point. Most addresses (89%) were assigned the concentration of the square kilometer where the address was located, whereas 1% were located outside the modeling area and were assigned the background concentration used in EPISODE. The proportions were similar in the questionnaire population and for both sexes. Distributions of long-term residential outdoor air pollution levels are presented in Figure 1. The levels were highest and had the widest range in the first year of life. The distributions were similar in the 2 populations and for both sexes. The mean levels of NO2 were 39.1 μg/m3 in the first year and 29.0 μg/m3 for lifetime exposure; for PM10, these figures were 16.4 and 14.5 μg/m3, respectively, and for PM2.5 the figures were 12.7 and 12.3 μg/m3. The distribution of NO2 lags 1 to 3 days before the lung function test had a wider range (0.2–111.3 μg/m3) than annual levels, and a mean of 27.1 μg/m3.
Associations Between Exposure to Air Pollution and Lung Function
The estimated lung function effects of residential outdoor air pollutants in the first year of life are presented in Table 2. We found negative associations between all pollutants and the expiratory flow variables (PEF, FEF25% and FEF50%), especially in girls. We found no effect on the forced volumes (FEV1 and FVC). The effects of exposure in the second year of life were lower than in the first year of life (data not shown).
Table 2 also presents the results of total lifetime exposure. We found negative effects on the expiratory flow variables in all children and in girls, but the effects in boys were notable only for PEF. The interaction between lifetime exposure and sex was not statistically significant, and we found no effect on the forced volumes. The associations for lifetime exposure were slightly weaker than for exposure in first year of life, especially for PM2.5. For all long-term exposures, the associations were slightly stronger for NO2 than PM.
Figure 2 presents associations of short-term exposure to residential outdoor NO2 with the expiratory flow variables. We found associations in all children and in girls, with the weakest associations for FEF50%. The effects increased with increasing time lags, except in boys. For PM, we found no short-term effect, only effects of long-term exposure. Including short- and long-term (early or lifetime) NO2 exposures simultaneously, the short-term effect vanished whereas the long-term effect remained. Furthermore, using NO2 lags 1 to 3 days from a monitoring station as exposure gave no effect.
By including interactions with ethnicity, we found somewhat stronger effects of lifetime exposure in children with at least 1 nonwestern parent than in western children. These findings were consistent for both sexes. Adding interactions with current asthma the effects of lifetime exposure did not change in nonasthmatic children, whereas in the asthmatics the NO2 effect was weaker and the PM effects were stronger. Analyzing subpopulations showed somewhat weaker effects in children from the Oslo Birth Cohort than in the other children.
Additional adjustment for 7 contextual socioeconomic factors produced little change, except mainly for one variable, percentage of unmarried residents. Adjusting for unmarried residents at birth address, all associations were attenuated. One interquartile increase of lifetime NO2 exposure was associated with a change of −56.8 mL/s (CI = −107.1 to −6.5) in adjusted PEF, a change of −52.7 mL/s (−109.5 to 4.0) in adjusted FEF25%, and a change of −24.8 mL/s (−74.1 to 24.5) in FEF50%. Corresponding adjustment at clinical examination gave minor changes: −69.8 mL/s (−120.2 to −19.5), −65.0 mL/s (−121.7 to −8.4), and −39.1 mL/s (−88.4 to 10.1), respectively.
The spatial autocorrelation analyses found that the Moran's I values were low (varying from 0.010 to 0.025 (for FVC) for lifetime exposure using birth address). Including contextual socioeconomic variables in the model did not modify these values.
We found that both short- and long-term exposures to residential outdoor air pollution were associated with reduced expiratory flow variables, especially in girls. The short-term effects of NO2 became stronger with increasing time lags. Including short- and long-term NO2 exposures simultaneously, only the long-term effect remained. We found no effect on the forced volumes. Additional adjustment for a contextual socioeconomic factor diminished the associations.
We used the EPISODE dispersion model25 to calculate residential air pollution levels in the period 1992–2002. Recently, we evaluated modeling of long-term averages by comparing modeled concentrations versus measurements from fixed monitoring stations in Oslo.26 We found the lowest bias and scatter and the strongest correlation for NO2 and PM2.5; the correlation seemed to increase with length of averaging period (wintertime r = 0.61 for NO2, 0.64 for PM10, and 0.72 for PM2.5). Furthermore, the results were better at urban background stations than at traffic stations, indicating that EPISODE can represent long-term levels of local outdoor air pollution in Oslo.
Other approaches to assessment of long-term exposure have been to use data from one or a few monitoring stations in the study area,3,6,7,9 including interpolation of measurements,10,16 or to combine measurements with regression modeling using GIS, called land-use modeling.12,13,15 In contrast to the ecologic designs, land-use modeling can assign exposure on more or less individual level as in our study, but it uses pollution levels measured in a few short time periods to represent long-term levels backwards in time; thus, extrapolation to other periods or regions could be problematic.29 In contrast, our model is based on prospectively collected data for several time periods, including time variations in emissions and detailed measurements of meteorologic data and background air pollution, presenting a longitudinal design.26 A study from Munich showed that the 2 modeling approaches yielded similar classifications of cohort addresses regarding exposure to NO2 and PM.30 Furthermore, dispersion modeling addresses both the large seasonal differences in air pollution and the special topographic conditions in Oslo.14,26
Residential outdoor air pollution concentrations do not necessarily represent individual exposure because children do not stay at home all day and they often stay indoors when they are at home. During the first year of life, most children stay at home, due to a nearly fully paid 1-year maternity leave, and children's kindergarten and particularly school are often located in their neighborhood. We therefore believe that home address is a representative location for air pollution exposure in Oslo children. The next question is whether residential outdoor modeled concentrations can represent residential outdoor measurements. We have found acceptable correlations for long-term averages26 and short-term NO2 concentrations have been found satisfactory,26,31,32 but short-term PM levels seem more uncertain.26,31 The third consideration is whether residential outdoor measurements can represent personal measurements. Magnus et al33 found a relatively low correlation (r = 0.36) between outdoor and personal measurements obtained during a 2-week period, and we believe that a longer time span would have increased the correlation. Furthermore, there have been few indoor sources of NO2 in Norway, and studies have found moderate to high correlation between indoor and outdoor levels in Scandinavia.33,34 Regarding PM, studies in the Netherlands have found reasonably high correlations between ambient and personal concentrations in schoolchildren.35,36 An additional PM source in Norway is wood burning during wintertime, which accounts for about half of the PM2.5 levels. However, as fine particles penetrate easily from outdoor to indoor environments,2 and a study from Helsinki in Finland showed reasonably high correlations between residential indoor and outdoor PM2.5 in adults,37 long-term outdoor and personal PM levels should also correlate well.38 In summary, the modeled outdoor NO2 and PM levels at residential addresses seem to represent these children's long-term exposure and short-term exposure to NO2 reasonably well.
We found effects on lung function for all the correlated pollutants, probably because the sources are largely the same. The primary source is traffic emissions—through direct tailpipe emissions, atmospheric reactions of emissions, or resuspension of road dust. Another PM source is wood burning, as stated above. Because of their strong correlation, the independent effects of each pollutant could not be discerned, as has been noted in other studies of air pollutant mixtures and lung function.6,9,22
The modeled concentrations are probably more uncertain further back in time because data on the historical emissions may be less reliable. The concentrations in the first year of life are also the highest. Comparisons of monthly modeled concentrations versus measurements from one monitoring station indicated that NO2 was overestimated in 1992, less so in 1993, but not overestimated later on. However, PM showed regular seasonal trends as in the evaluation study,26 and the negative associations for early exposure were consistent for all pollutants. Furthermore, the spatial pattern of air pollution in Oslo is mostly controlled by the meteorologic and topographic conditions14,26; thus, although the pollution levels within the city may rise and fall in absolute terms, major changes in the spatial patterns within some years seem unlikely. Therefore, the ordering among assigned geographic exposures should be maintained, which supports our confidence in the associations of early exposure.
The greatest strength of our exposure assessment is its individual aspect. In addition, early exposure was included because the lung system is not fully developed in early lifetime and infants may thus be more susceptible to environmental agents.18,19 To our knowledge, no study has investigated the lung function effects of early air pollution exposure in schoolchildren. Lifetime exposure is another important aspect of the relation between environment and respiratory diseases,21 but has seldom been included for such a long time span.20 Including short- and long-term exposures in the same study has also rarely been done,8,22 and provides insight into whether associations with lung function are primarily related to short-term variations which would suggest reversibility, or to long-term concentrations, which would suggest permanent impairment. In addition, the study population lives in one city, and the age variation is small, which reduces heterogeneity.
We have no reason to believe that the Oslo children not willing to participate would be affected differently by air pollution exposure. The study population's parents were more western than those responding to the questionnaire only, but the difference was small and had probably not a major influence on the associations.
Air pollution exposure was calculated independently from information on potential confounders and outcome, but is by itself subject to uncertainty.26 However, we have no reason to assume that this uncertainty should not be random, and random error would probably dilute the association between exposure and outcome.
Effect Modification and Confounding
Air pollution was correlated with parental ethnicity and smoking, but adjusting for these conditions gave only minor changes in the estimated associations. Including interactions by ethnicity indicated stronger effects in “nonwestern” than in western children, and the exposure levels were also higher in “nonwestern” children. We found similar findings for other children compared with children from the Oslo Birth Cohort, which includes fewer “nonwestern” children. These children, especially girls, may also have lower test performance than Norwegian children who probably are more involved in physical training and sports, which may indicate that cultural differences are not fully controlled for. These aspects could explain this finding, but it needs further investigation in larger populations of nonwestern children. Furthermore, including interaction terms with current asthma indicated stronger PM and weaker NO2 effects in asthmatic children compared with nonasthmatics. In view of the wide confidence intervals, we do not attach any special meaning to these differences.
We found stronger effects on the expiratory flow variables in girls than in boys, although the interaction with sex was not statistically significant. Some other studies have also shown sex differences in lung function effects,4,5,7 while another found no significant difference.6 Why girls seem to be more susceptible to air pollution than boys is unknown. In contrast to boys, girls may have started their growth spurt at 10 years of age, but this is impossible to assess without longitudinal lung function measurements. Whether hormonal changes could affect girls’ susceptibility to air pollution is not known. Thus, this issue needs further investigation.39
We cannot exclude the possibility of unmeasured confounders, especially of social class.40 We considered 7 contextual socioeconomic factors, and percentage of unmarried residents was the only factor that substantially affected the associations, especially at birth. We speculate that children living in areas with mainly married parents are stimulated to participate in sports, whereas children living in neighborhoods with many unmarried parents may be less physically active, which could affect lung function. Whether contextual socioeconomic factors really are confounders is not obvious.27 Although the American Cancer Society study considered them as such,16 they might instead be in the pathway between pollution and lung function. Therefore, this issue should be further investigated. Regarding autocorrelation, the low values of Moran's I indicated that residual spatial autocorrelation would not be expected to influence the estimated associations.
Comparison With Other Studies
As other studies have not adjusted for contextual socioeconomic variables, we focus here on our results adjusted only for individual factors. These other studies include lung function measured once in children exposed to long-term outdoor air pollution. Compared with the Children's Health Effects Study in California,7 we found comparable effects on PEF and considerably weaker effects on the forced volumes, at comparable pollution levels. Forced volumes provide information primarily on central airways41 whereas expiratory flow variables represent peripheral airways,24 and traffic-related air pollution contains large numbers of ultrafine particles which can penetrate deep into the peripheral airways and alveoli.42 This theory suggests stronger associations for expiratory flow variables, as was found in our study, the California study,7 and the NHANES II study.3 Furthermore, a pooled analysis of 21 surveys of schoolchildren presented stronger effects of parental smoking for expiratory flow variables than for forced volumes.43 However, the Six City Study9 did not find any association between air pollution and lung function with similar PM and lower NO2 levels as compared with Oslo. The 24 Cities Study from the United States and Canada,6 estimated comparable PM effects on PEF as ours and found effects on the forced volumes.6 A study from East Germany, including 3 cross-sectional surveys and 3 communities, also found effects of total suspended particulates on the forced volumes, but no notable effects on FEV1 with total suspended particulates levels comparable to our PM10 levels.5 In summary, compared with other studies that have used a single exposure for each city or community, our findings for expiratory flow variables are comparable, whereas our volume effects are weaker.
Associations of Short- and Long-Term Exposure
By simultaneously including short- and long-term NO2 exposures, only the long-term effect remained. This finding is probably due to the moderate-to-high correlation between these exposures, and to less measurement error for long- than short-term levels,26 which probably also explains the stronger NO2 effects with increasing time lags. Thus, this study design makes it difficult to distinguish between short- and long-term associations.
Using updated addresses for each child during their lifetimes, our study showed that short- and long-term exposures to traffic-related pollutants in Oslo were associated with reduced expiratory flow in 9- to 10-year-old children, especially in girls. However, the current design makes it difficult to distinguish the relative contributions of the several exposures. The associations became weaker after controlling for a contextual socioeconomic factor.
The Oslo Birth Cohort was initiated in 1992 by the Norwegian Institute of Public Health in cooperation with Aker and Ullevål University Hospitals. We thank head physician Per G. Lund-Larsen and his staff at the Norwegian Institute of Public Health for conducting excellent data collection at the 10-year follow-up and thank Ingunn Brandt at the same institute for excellent data handling of the air pollution files.
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