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Ambient air pollution has well-established acute effects among asthmatics, leading to increased severity of symptoms, increased medication, doctor visits and hospitalizations, and even increased mortality.1–9 Although air pollution has also been associated with increased rates of asthma prevalence and incidence in children,10–14 few studies have investigated air pollution and new onset of asthma in adults. In the Adventist Health Study of Smog, conducted among a highly selected population in California, Abbey et al15 found a modest positive association between asthma incidence and particulate matter, whereas McDonnell et al16 reported an increased risk of asthma incidence for an increase in ozone only in men. These studies, however, were based on central measurements of air pollution with no characterization of exposure to local traffic-related pollution, which may play a role in the onset of childhood asthma.13 To our knowledge, a study by Modig et al,17 is the only one using a marker of local traffic-related pollution, namely NO2, to investigate the asthma hypothesis in adults. This small Swedish case-control study suggested that traffic-related pollution might be associated with asthma incidence but the study lacked power.
The European Community Respiratory Health Survey (ECRHS), a large cross-European cohort study in adults18,19 and the Air Pollution Modelling for Support to Policy on Health and Environmental Risk in Europe APMoSPHERE20 project team have now initiated a collaboration to assign modeled outdoor nitrogen dioxide (NO2) concentrations to the subjects of the survey. This approach has helped to resolve the inherent limitations of central monitoring data.21 The aim of this study was to assess the association between individually assigned exposures to home outdoor NO2 (used as a marker of traffic-related pollution) and onset of asthma in adults.
METHODS
Study Population
The European Community Respiratory Health Survey was conducted in 28 cities in 11 western European countries. It was first performed in 1991–1993 (ECRHS I) and repeated in 1999 to 2001 (ECRHS II). Centers were chosen based on preexisting administrative boundaries, the size of centers, and the availability of sampling frames. Subjects were randomly selected from the populations aged 20–44 in 1991–1993. Both surveys included a main questionnaire, skin prick test, immunoglobulin E (IgE) determination in blood samples, spirometry, and methacholine challenge test. Details of this project are described elsewhere.18,19 This analysis includes 4185 subjects with assigned home NO2 value (modeled) and no asthma at ECRHS I (see later). Of those, 2955 have full information on all the covariates used in this analysis. Ethical approval was obtained for each center from the appropriate institutional or regional ethics committee, and written consent was obtained from each participant.
Definition of Asthma and Population at Risk
New cases of asthma were those who developed asthma between ECRHS I and ECRHS II. They were defined as subjects answering positively to the question: “Have you ever had asthma?” in ECRHS II among those subjects who answered “no” to this question in ECRHS I (n = 186). We also performed a stricter analysis considering as new cases only subjects with new asthma at ECRHS II who reported age of onset of asthma after ECRHS I. A total of 95 subjects who answered affirmatively only on the second survey in fact gave an age at onset that was earlier than ECRHS I. These might be considered remissions of childhood asthma rather then adult-onset asthma.
Other Variables
Total serum IgE and specific IgE to cat (e1), house dust mites (Dermatophagoides pteronyssinus d1), Cladosporidium as indicator of mold (g6), and timothy grass were determined using the Pharmacia CAP system (Pharmacia, Uppsala, Sweden). Atopy was defined as a concentration >0.35 kUA/L−1 for any specific IgE.
The other variables used in this analysis were collected through a questionnaire. Sociodemographic factors were age, sex, and socioeconomic class (based on occupation). Occurrence of the following asthma symptoms was asked with reference to the last 12 months: wheezing or whistling, breathless when the wheezing noise was present, wheezing or whistling without a cold, awakened up with a feeling of tightness, attack of short breath while at rest, attack of short breath after activity, awakened by shortness of breath.
We also included smoking, categorized as current, former and never-smokers, and exposure to second-hand tobacco smoke. Other variables analyzed were family history of asthma or atopy (mother or father), cooking done mainly with gas, any acute job exposure to any gas, dust or fume, and season of the interview.
Modeled NO2 Concentrations With APMoSPHERE
NO2 has been widely used in epidemiologic studies as a marker for traffic-related air pollution.22–24 As part of the APMoSPHERE project 1-km-resolution emission maps were developed for the then 15 member states (EU15) by disaggregating national emissions estimates, categorized by sources of air pollution (Selected Nomenclature for Air Pollution categories), to the 1-km level on the basis of relevant proxies (eg, population density, road distribution, land cover).20 The NOx emission map was then used as the basis for modeling NO2 concentrations by using focal sum techniques, in a Global Information System. The model provides estimates of concentrations by calibrating the distance-weighted sum of the emissions (tons/km/y), in concentric rings (annuli) circles around each monitoring site, to the monitored concentrations (μg/m3).
Models are developed by setting the weight of the innermost annulus to 1, and each successively outer annuli (to a maximum of 11 km) to Wa−1/2 (where Wa−1 is the weight of the next, inner annulus). Weights for each of the annuli were then incrementally adjusted, from the second annulus outwards under the rule that Wa ≤ Wa−1, and the correlation with the monitored concentrations recomputed, until R2 was maximized. The resulting regression model was then used to convert the sum of the weighted emissions to a concentration (in μg/m3).
Models were developed using monitoring data from 714 background sites for the year 2001, drawn from the EU Airbase database. Validation was conducted by comparing predictions with observations for a separate set of 228 reserved background sites (r2 = 0.60). The resulting model was converted into a kernel file (with weights for each annulus), which was then moved across the EU emissions map to produce a 1-km gridded map of concentrations.
Finally, the NO2 at the place of residence of each subject at follow-up (ECRHS II) was obtained by intersecting the geographic coordinates of the address with the air pollution map. The eFigure (available with the online version of this article) shows the European map of NO2, modeled in APMoSPHERE.
Statistical Analysis
The association between onset of asthma and NO2 was expressed by odds ratios (ORs) and 95% confidence intervals (CIs) derived from logistic regression models. We first report the crude unadjusted associations followed by 2 levels of adjustment. In the first step, we adjusted for center effects. Second, a set of predefined covariates was included (sex, age, socioeconomic status, atopy, family history of asthma or atopy, and smoking). Age and smoking were forced into the model. The following variables were also tested but not retained in the final model: cooking done mainly with gas, any job exposure, exposure to second-hand tobacco smoke, and season of the interview. The multivariate analyses were stratified by sex, living in the same residence between ECRHS I and ECRHS II, and atopy, to explore patterns of different susceptibility. P values for interactions were calculated.
For the subjects who had a date of onset of asthma between both surveys, the association between NO2 and asthma incidence was also modeled using multivariate Cox regression models. The age at the first survey was considered as the beginning of the follow-up, whereas the age of first attack of asthma or age at the second survey was considered as the end of the follow-up. We obtained hazard ratios, which are expressed as relative risks (RR). Effect estimates were derived for each center, and heterogeneity across cities was examined by using standard methods for random-effects meta-analysis.25
Among subjects without asthma at baseline, we further explored the association between NO2 and symptoms during the last 12 months reported at ECRHS II. Each asthma symptom was tested separately, in subjects who had not reported the respective symptom at baseline. These analyses used logistic regression and adjusted for the same variables as for asthma incidence. We present all associations of asthma onset with assigned NO2 for a contrast of 10 μg/m3. This corresponds to the difference between the fifth and the 95th percentile in the city with the smallest contrast (Umeå). To evaluate selection bias, we compared the characteristics of subjects with (n = 4185) and without (n = 2478) NO2 measurements from the same centers. The analysis was conducted using STATA 8.2 (StataCorp, College Station, TX).
RESULTS
NO2 values could be assigned to 70% of participants. The main reason for not assigning modeled NO2 was the inability to geocode the address of the subjects. Participants for whom NO2 could be assigned were older, more likely to be women and informally employed (as housewives or students), and less likely to be current smokers. The proportion of participants with new-onset asthma was lower in those with NO2 measures than those without (4.4% vs. 5.8%; P value 0.07).
Table 1 shows the distribution of NO2 levels by center. Medians of NO2 levels per center varied from 12 μg/m3 in Umeå to 57 μg/m3 in Barcelona, with a gradual increase from north to south.
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TABLE 1: Outdoor Modeled NO2 (μg/m3) and Asthma Incidence, By City (Ordered North to South)
Between 2% and 9% of participants in each center reported asthma at follow-up without having reported asthma at baseline (Table 1). Among the 186 subjects reporting “ever-asthma” in ECRHS II but not in ECRHS I, 175 provided information on age of asthma onset. Among those, 79 reported age of onset after ECRHS I (the baseline assessment) whereas the others gave age of onset, suggesting their symptoms had begun prior to ECRHS I. Only 54 of the 79 cases with consistent data on asthma onset also had information on all the covariates used in the adjusted model; most of the missing values were for serum-specific IgE.
Table 2 describes the general characteristics of the study population by asthma status. Table 3 shows the multivariate association between new asthma and the individual characteristics. Women were more likely to report new asthma, as were people who themselves had atopy or who had a family history of asthma and/or atopy.
TABLE 2: Characteristics of Subjects Without Asthma and With Incident Asthma at ECRHS II
TABLE 3: Multivariate Association Between Individual Characteristics and Incident Asthma
When assessing the risk of developing asthma using the 186 new cases, the adjusted OR for asthma onset was 1.43 (95% CI = 1.02–2.01) for a 10-μg/m3 contrast in NO2. All the stratified associations between asthma incidence and NO2 were higher than 1 (Table 4).
TABLE 4: Association Between Each Increase of 10 μg/m3 of Modeled NO2 and Incident Asthma
Adding duration of follow-up to the model (thus including only the subjects with a known age of onset of asthma) the OR for asthma incidence was 1.72 (0.99–3.00). The RR for developing asthma was 1.68 (0.98–2.89) using Cox modeling. The limited number of new cases precluded further analyses in subgroups. As shown in Table 5, all the associations between NO2 and asthma symptoms at ECHRS II were positive; the strongest was for waking “with a feeling of tightness in the last 12 months.” Results were homogeneous among the centers in both the crude and the adjusted analyses (Figure). We attempted to identify more susceptible subpopulations based on sex, body mass index, socioeconomic status, (data not shown) and atopy, but no interaction was significant.
TABLE 5: Multivariate Association Between Each Increase of 10 μg/m3 of Modeled NO2 Symptoms Reported at ECRHS II Among Those Without Asthma nor Reporting the Respective Symptom at ECRHS I
FIGURE 1.:
Adjusted ratios of new asthma in ECRHS II for every 10 μg/m3 NO2 increase by center in subjects with no asthma in ECRHS I. P value for heterogeneity 0.594. Erfut, Pavia and Torino automatically dropped from the analysis due to empty cells. Boxes represent the OR per center where the size of the box is proportional of the sample size of such center. Lines represent the 95% CI of the respective OR. Diamond represents the combined OR.
DISCUSSION
This study suggests a positive association between NO2 and asthma incidence in adulthood, with similar findings across Europe. The exposure assessment had strengths and limitations. By geocoding home addresses of European Community Respiratory Health Survey participants, we were able to assign an ambient NO2 concentration derived from the APMoPSPHERE map to each subject. The APMoSPHERE map, however, has a spatial resolution of 1 km2, and was modeled on the basis of annual mean concentrations measured only at background sites (traffic sites were not included). It therefore does not necessarily capture the spatial and temporal contrasts in exposure due to very local emissions or dispersion patterns, such as those occurring in street canyons. Asthma incidence or prevalence studies in children have typically used more local markers of exposure, such as living within 50 or 100 m of busy roads. Direct quantitative comparison of our results with those studies may thus not be valid given the different spatial scales. Nevertheless, the modeled NO2 does capture spatial contrasts of traffic-related pollution that are not identified in studies using single-monitor data alone. In line with the studies of children, our data suggest a potential role of traffic-related pollution in the onset of asthma among adults. The cities in the European Community Respiratory Health Survey have different characteristics that could affect the precision of the modeled NO2. For example population density varied considerably among the cities, from 47 inhabitants/km2 in the Umeå study area to 24,783 inhabitants/km2 in Paris.26 This means that in Umeå 47 inhabitants have the same assigned value of NO2 whereas in Paris almost 25,000 inhabitants would be assumed to share the same level of NO2. Thus, one may argue that nondifferential errors in assigned exposure affect densely populated centers more. Although we have no indication of heterogeneity of effects across these cities, we are also aware of the rather limited power to detect such heterogeneity.
A further weakness of this study is that the subsample with assigned NO2 values was somewhat different from those with missing NO2 estimates. The reasons for these discrepancies are not clear because a high proportion (70%) of participants in the random sample of ECRHS II had NO2 values. Availability of assigned NO2 values was essentially independent of personal characteristics. In Umeå and Goteborg, geocodes were accessible only for those living in the city center but not in the outskirts, which partly explains the selection phenomena.
Also, NO2 is considered a marker of traffic-related air pollution, and not necessarily the agent that causes health effects. Nonetheless, NO2 may play an interacting role in combination with other pollutants prevalent in the urban air.23 Finally, a limitation of our study was the definition of a new case of asthma. There are various problems, including a poor repeatability of the question, which could lead to nondifferential errors. There could also be genuine changes in perception of disease due to other factors. In this study, the incidence of asthma using only the ever-asthma question was higher than the expected rates (4.9 actual vs. 1 to 2 expected per 1000 subjects per year27). This is probably due in part to the inconsistencies in the reported age of onset of asthma. The reported number of “reliable” cases was within the expected range when taking into account only the subjects who reported the first attack of asthma between both surveys. Interestingly, almost half of the “new cases” (45%) who reported an age of onset of asthma at ECRHS II before their age at ECRHS I, reported their age of onset as before the age of 20. Perhaps they were asymptomatic at ECRHS I and then at ECRHS II they recalled they had had asthma in childhood or adolescence—recall perhaps prompted by recurrence of asthma or the diagnosis of asthma in an offspring. Nevertheless, it is important to note 2 points. Both estimates (using the “strict” definition of new cases with a concordant age of onset, and the “loose” definition of all the cases disregarding the reported age of onset), were positive and in the same range. This decreases the probability that the findings are due to misclassification. Also, even if it is difficult to disentangle “real” new cases from “false” new cases (which may be reactivation of childhood asthma) the effect of air pollution on both processes of the disease is still relevant.
Symptoms in the last 12 months at ECRHS II among people without asthma at baseline were also associated with NO2. These observations are, on the one hand, complementary to and in strong support of our main findings. On the other hand, due to the study design, the symptom results also call for a partly different interpretation. The standard questionnaire asks about symptoms during the last 12 months only; thus people with new asthma onset because ECRHS I who did not suffer symptoms during the last 12 months (eg, due to treatment) would not be captured. Instead, a subject without asthma who reported symptoms during the last 12 months (eg, due to some infection) would be identified as an incident case. Moreover, air pollution is a known trigger of several asthma-related symptoms. Thus, reporting of symptoms at ECRHS II (but not at ECHRS I) may not necessarily reflect the onset of asthma due to air pollution, but summarize the acute effects of air pollution exposure during the past 12 months. Our main approach using asthma incidence is less affected by these methodologic issues. Cases diagnosed during the entire follow-up period contributed to these findings, independent of symptom status during the last 12 months. The investigation of acute effects of pollution or the integration of symptoms to assess incidence and remission of asthma is beyond the scope of this work. It will be promising, though, to use the recently propagated symptom scores and modified approaches to define asthma phenotypes to further enhance our understanding of the role of ambient air pollution in the onset and course of adult asthma.28,29
Our study adds to a very small and inconclusive literature about the role of air pollution in adult-onset asthma. Previously, within the Adventist study, several publications have reported an association of ozone and particulate matter with asthma incidence. The subjects came from a highly selected adult population of residentially stable, nonsmoking, non-Hispanic whites. All were Seventh-day Adventists from California who were recruited in 1977 and followed-up in 1982 and 1992. To assess air pollution exposure, the researchers assigned an average of interpolated values based on the subject's zip codes at home and work, using measurements from fixed-site monitors. The 8-hour average ozone (from 9 am to 5 pm) was found in men to be associated with a risk of developing asthma, after 10 years (RR = 3.1 [95% CI = 1.6–5.8] for an interquantile range increase)30 or 20 years (2.1 [1.0–4.2]).16 A separate analysis reported a weaker association with particulate matter (1.3 [1.0–1.7]).15
In a matched-incidence case-control study performed in a single city (Luleå, Northern Sweden), NO2 was measured at subjects’ homes, and traffic flow was determined using land road maps. A positive association between asthma incidence and high traffic flow was found (OR = 2.4 [95% CI = 0.9–6.2]); with NO2 an association was observed only in subjects with a positive skin-prick test (1.2 [1.0–1.3] per each μg/m3 increase).17 The Swedish OR would be (1.2)10 or 6.2 per 10 μg/m3 increase of NO2 compared with 1.4 in our study. This large difference could be due to the different design between the 2 studies. For example, people with asthma identified in the general population may have milder symptoms than those recruited in the Swedish study by doctors. It could be assumed that their cases were more severe and better defined than in our study, which identified cases in the general population using a questionnaire. Another reason could be that they used home-based measurements of NO2, which led to a bigger exposure range. The strong discrepancy in these 2 effect sizes also highlight the inherent challenges in interpreting results based on very different spatial scales of exposure assignment. The difficulties of integrating the various approaches to characterize “traffic-related” exposure are inherent to this field.31,32
Most of the experimental studies looking at the mechanisms of lung damage from air pollution have looked at acute effects of diesel exhaust or ozone exposure. The mechanisms by which air pollution could cause asthma exacerbations have been explained mainly by oxidative stress and the inflammation in the upper and lower respiratory tract.33–39 It has also been proposed that diesel exhaust particles interacts with allergens, thus increasing the allergic response.40 The way in which air pollution could cause asthma incidence is less clear but could include allergic sensitization.36,41,42 In the absence of an established animal model for asthma, the possible mechanisms of long-term exposure to air pollutants on asthma risk remain unclear, and may require a combination of epidemiologic and toxicologic approaches.
ACKNOWLEDGMENTS
The coordination of ECRHS II was supported by the European Commission, as part of their Quality of Life program. The following bodies funded the local studies in ECRHS II included in this paper—Albacete: Fondo de Investigaciones Sanitarias (FIS) (grant code: 97/0035-01, 99/0034-01 and 99/0034-02), Hospital Universitario de Albacete, Consejería de Sanidad; Antwerp: FWO (Fund for Scientific Research)-Flanders Belgium (grant code: G.0402.00), University of Antwerp, Flemish Health Ministry; Barcelona: SEPAR, Public Health Service (grant code: R01 HL62633-01), Fondo de Investigaciones Santarias (FIS) (grant code: 97/0035-01, 99/0034-01 and 99/0034-02) CIRIT (grant code: 1999SGR 00241) “Instituto de Salud Carlos III” Red de Centros RCESP, C03/09 and Red RESPIRA, C03/011; Basel: Swiss National Science Foundation, Swiss Federal Office for Education & Science, Swiss National Accident Insurance Fund (SUVA); Bergen: Norwegian Research Council, Norwegian Asthma & Allergy Association (NAAF), Glaxo Wellcome AS, Norway Research Fund; Bordeaux: Institut Pneumologique d'Aquitaine; Erfurt: GSF-National Research Centre for Environment & Health, Deutsche Forschungsgemeinschaft (DFG) (grant code FR 1526/1-1); Galdakao: Basque Health Dept; Göteborg: Swedish Heart Lung Foundation, Swedish Foundation for Health Care Sciences & Allergy Research, Swedish Asthma & Allergy Foundation, Swedish Cancer & Allergy Foundation; Grenoble: Programme Hospitalier de Recherche Clinique-DRC de Grenoble 2000 no. 2610, Ministry of Health, Direction de la Recherche Clinique, Ministère de l'Emploi et de la Solidarité, Direction Générale de la Santé, CHU de Grenoble, Comite des Maladies Respiratoires de l'Isère; Hamburg: GSF-National Research Centre for Environment & Health, Deutsche Forschungsgemeinschaft (DFG) (grant code MA 711/4-1); Ipswich and Norwich: National Asthma Campaign (UK); Huelva: Fondo de Investigaciones Sanitarias (FIS) (grant code: 97/0035-01, 99/0034-01 and 99/0034-02); Montpellier: Programme Hospitalier de Recherche Clinique-DRC de Grenoble 2000 no. 2610, Ministry of Health, Direction de la Recherche Clinique, CHU de Grenoble, Ministère de l'Emploi et de la Solidarité, Direction Générale de la Santé, Aventis (France), Direction Régionale des Affaires Sanitaires et Sociales Languedoc-Roussillon; Oviedo: Fondo de Investigaciones Santarias (FIS) (grant code: 97/0035-01, 99/0034-01 and 99/0034-02); Paris: Ministère de l'Emploi et de la Solidarité, Direction Générale de la Santé, UCB-Pharma (France), Aventis (France), Glaxo France, Programme Hospitalier de Recherche Clinique-DRC de Grenoble 2000 no. 2610, Ministry of Health, Direction de la Recherche Clinique, CHU de Grenoble; Pavia: Glaxo-SmithKline Italy, Italian Ministry of University and Scientific and Technological Research (MURST), Local University Funding for research 1998 & 1999 (Pavia, Italy); Reykjavik: Icelandic Research Council, Icelandic University Hospital Fund; Tartu: Estonian Science Foundation; Turin: ASL 4 Regione Piemonte (Italy), AO CTO/ICORMA Regione Piemonte (Italy), Ministero dell'Università e della Ricerca Scientifica (Italy), Glaxo Wellcome spa (Verona, Italy); Umeå: Swedish Heart Lung Foundation, Swedish Foundation for Health Care Sciences & Allergy Research, Swedish Asthma & Allergy Foundation, Swedish Cancer & Allergy Foundation; Uppsala: Swedish Heart Lung Foundation, Swedish Foundation for Health Care Sciences & Allergy Research, Swedish Asthma & Allergy Foundation, Swedish Cancer & Allergy Foundation; Verona: University of Verona; Italian Ministry of University and Scientific and Technological Research (MURST); Glaxo-SmithKline Italy.
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