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Long-term air pollution exposure is associated with increased severity of rhinitis in 2 European cohorts

Published:January 23, 2020DOI:https://doi.org/10.1016/j.jaci.2019.11.040

      Background

      Very few studies have examined the association between long-term outdoor air pollution and rhinitis severity in adults.

      Objective

      We sought to assess the cross-sectional association between individual long-term exposure to air pollution and severity of rhinitis.

      Methods

      Participants with rhinitis from 2 multicenter European cohorts (Epidemiological Study on the Genetics and Environment on Asthma and the European Community Respiratory Health Survey) were included. Annual exposure to NO2, PM10, PM2.5, and PMcoarse (calculated by subtracting PM2.5 from PM10) was estimated using land-use regression models derived from the European Study of Cohorts for Air Pollution Effects project, at the participants’ residential address. The score of rhinitis severity (range, 0-12), based on intensity of disturbance due to symptoms reported by questionnaire, was categorized into low (reference), mild, moderate, and high severity. Polytomous logistic regression models with a random intercept for city were used.

      Results

      A total of 1408 adults with rhinitis (mean age, 52 years; 46% men, 81% from the European Community Respiratory Health Survey) were included. The median (1st quartile-3rd quartile) score of rhinitis severity was 4 (2-6). Higher exposure to PM10 was associated with higher rhinitis severity (adjusted odds ratio [95% CI] for a 10 μg/m3 increase in PM10: for mild: 1.20 [0.88-1.64], moderate: 1.53 [1.07-2.19], and high severity: 1.72 [1.23-2.41]). Similar results were found for PM2.5. Higher exposure to NO2 was associated with an increased severity of rhinitis, with similar adjusted odds ratios whatever the level of severity. Adjusted odds ratios were higher among participants without allergic sensitization than among those with, but interaction was found only for NO2.

      Conclusions

      People with rhinitis who live in areas with higher levels of pollution are more likely to report more severe nasal symptoms. Further work is required to elucidate the mechanisms of this association.

      Key words

      Abbreviations used:

      ECRHS (European Community Respiratory Health Survey), EGEA (Epidemiological Study on the Genetics and Environment on Asthma), ESCAPE (European Study of Cohorts for Air Pollution Effects), OR (Odds ratio), PM (Particulate matter), PM2.5 (Airborne particulate matter with an aerodynamic diameter of less than or equal to 2.5 μm), PM10 (Airborne particulate matter with an aerodynamic diameter of less than or equal to 10 μm), PMcoarse (Airborne particulate matter with an aerodynamic diameter ranging from 2.5 to 10 μm, calculated by subtracting PM2.5 from PM10)
      Rhinitis is a very frequent disease affecting between 20% and 50% of the population according to countries and definitions.
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      it is a genuine question to wonder about the effects of air pollution on rhinitis.
      There are very few studies focusing on the association between air pollution and rhinitis in adults. Short-term exposure to air pollution has been associated with exacerbation of rhinitis, leading to more daily clinical examinations.
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      However, association between long-term air pollution and rhinitis severity has scarcely been studied. One large French study assessing the link between grass pollen counts, air pollution levels, and severity of seasonal allergic rhinitis found a positive but not statistically significant association between air pollutant level and severe allergic rhinitis.
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      However, this study only considered seasonal allergic rhinitis and no other phenotypes. Recently, an American study examined the relationship between PM2.5 (airborne particulate matter with an aerodynamic diameter of ≤2.5 μm) and black carbon and rhinitis in 125 patients with chronic rhinosinusitis with and without polyps.
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      The association of air pollutants and allergic and nonallergic rhinitis in chronic rhinosinusitis.
      They found significantly higher exposure levels of PM2.5 and black carbon among participants without allergic sensitization compared with those with allergic sensitization, and also found an association between black carbon and nonallergic symptoms of rhinitis. In a previous study, we found no consistent evidence for an association between long-term exposure to air pollution and incidence of rhinitis, whether allergic or nonallergic.
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      • et al.
      Association between air pollution and rhinitis incidence in two European cohorts.
      We hypothesized that air pollution may not induce rhinitis development, but may still be associated with an increase in severity of the disease.
      In the present study, we aimed to examine the association between long-term exposure to air pollution and severity of allergic and nonallergic rhinitis in 2 European studies.

      Methods

       Study design and settings

      Participants included in the analysis were those suffering from rhinitis at the second follow-up (2011-2013) of 2 large multicenter epidemiological European studies: the European Community Respiratory Health Survey (ECRHS) and the Epidemiological Study on the Genetics and Environment on Asthma (EGEA).
      The EGEA
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      EGEA—descriptive characteristics.
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      • Kauffmann F.
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      • Pin I.
      • Paty E.
      • Gormand F.
      • Vervloet D.
      • et al.
      Epidemiological study of the genetics and environment of asthma, bronchial hyperresponsiveness, and atopy: phenotype issues.
      (https://egeanet.vjf.inserm.fr/) is a French cohort of 2047 participants (patients with asthma—adults or children—enrolled from hospital chest clinics, their first-degree relatives, and controls who were recruited from other hospital wards or from electoral lists) enrolled between 1991 and 1995 from 5 French cities. The first follow-up was conducted between 2003 and 2007 (EGEA2, N = 2121
      • Kauffmann F.
      • Dizier M.H.
      • Pin I.
      • Paty E.
      • Gormand F.
      • Vervloet D.
      • et al.
      Epidemiological study of the genetics and environment of asthma, bronchial hyperresponsiveness, and atopy: phenotype issues.
      ,
      • Siroux V.
      • Boudier A.
      • Bousquet J.
      • Bresson J.-L.
      • Cracowski J.-L.
      • Ferran J.
      • et al.
      Phenotypic determinants of uncontrolled asthma.
      ) and the second between 2011 and 2013 (EGEA3, N = 1558
      • Bouzigon E.
      • Nadif R.
      • Le Moual N.
      • Dizier M.-H.
      • Aschard H.
      • Boudier A.
      • et al.
      Facteurs génétiques et environnementaux de l’asthme et de l’allergie : synthèse des résultats de l’étude EGEA.
      ).
      The ECRHS
      • Burney P.G.
      • Luczynska C.
      • Chinn S.
      • Jarvis D.
      The European Community Respiratory Health Survey.
      is a population-based cohort of young adults, enriched with participants with respiratory symptoms, recruited from 1992 to 1994 in 28 western European cities (ECRHS I, N = 17,880; http://www.ecrhs.org/) and followed up twice: between 2000 and 2002 (ECRHS II, N = 10,933
      European Community Respiratory Health Survey II Steering Committee
      The European Community Respiratory Health Survey II.
      ) and between 2011 and 2013 (ECRHS III, N = 7,040).
      Participants of both studies were extensively characterized with regard to their respiratory health and risk factors using similar standardized protocols and questionnaires. Ethical approval was obtained for each study from the appropriate institutional ethics committees, and written informed consent was obtained from each participant.

       Definition of rhinitis

      In this cross-sectional analysis, report of ever rhinitis was defined by a positive response to the question “Have you ever had a problem with sneezing, or a runny or a blocked nose when you did not have a cold or the flu?” in EGEA3 and ECRHS III.
      Among those who had reported ever rhinitis, rhinitis in the last 12 months was defined by a positive response to the question “Have you ever had a problem with sneezing, or a runny or a blocked nose when you did not have a cold or the flu in the last 12 months?” in EGEA3 and ECRHS III.

       Definition of score of severity of rhinitis (based on symptoms of rhinitis)

      A numeric score of severity of rhinitis was assessed at EGEA3 and ECRHSIII, adapted from the Symptomatic Global Score for seasonal allergic rhinitis.
      • Rouve S.
      • Didier A.
      • Demoly P.
      • Jankowski R.
      • Klossek J.M.
      • Annesi-Maesano I.
      Numeric score and visual analog scale in assessing seasonal allergic rhinitis severity.
      This score was calculated on the basis of the answers to question on severity of the 4 main symptoms of rhinitis (watery runny nose, blocked nose, itchy nose, and sneezing). In case of missing data for severity of 1 or more symptoms, no imputation was done and these participants were not included in the analyses. For each of the 4 items, participants had to indicate how the symptom had hampered their daily life in the last 12 months:
      • 0.
        No problem (symptom not present)
      • 1.
        A problem that is/was present but not disturbing
      • 2.
        A disturbing problem but not hampering daytime activities or sleep
      • 3.
        A problem that hampers certain activities or sleep
      Each question scored from 0 to 3 and thus summing the answers to these 4 questions, the overall score ranged from 0 to 12, with a higher score indicating a higher severity. The overall score was further categorized into 4 levels according to the quartiles of the distribution: low severity (score ≤2), mild severity (score = 3 or 4), moderate severity (score = 5 or 6), and high severity (score ≥7). Low severity was considered as the reference in the analyses.
      In addition, severity was analyzed symptom by symptom, using the following classification to approximate closely the Allergic Rhinitis and its Impact on Asthma guidelines
      • Katelaris C.H.
      • Lee B.W.
      • Potter P.C.
      • Maspero J.F.
      • Cingi C.
      • Lopatin A.
      • et al.
      Prevalence and diversity of allergic rhinitis in regions of the world beyond Europe and North America.
      : The category 0 was considered as the reference compared with mild rhinitis (1), and moderate/severe rhinitis (2/3 pooled together).

       Definition of allergic sensitization

      In EGEA2, allergic sensitization was defined using skin prick test for 12 aeroallergens (mean wheal diameter 3 mm ≥ than that with the negative control to at least 1 of the following allergens: cat, Dermatophagoides pteronyssinus, Blattela germanica, olive, birch, Parieteria judaica, timothy grass, ragweed pollen, Aspergillus, Cladosporium herbarum, and Alternaria tenuis).
      In ECRHS II, allergic sensitization was defined using specific IgE to 4 allergens (specific IgE ≥ 35 kU/mL to at least 1 of the following allergens: cat, D pteronyssinus, Cladosporium, and timothy grass).

       Definition of asthma

      Ever asthma was defined by a positive response to the question “Have you ever had asthma?” in ECRHS III and by a positive response to 1 of the following questions “Have you ever had attacks of breathlessness at rest with wheezing?” or “Have you ever had asthma attacks?” at EGEA1, EGEA2, or EGEA3 or by being recruited as asthmatic cases in EGEA 1.
      • Siroux V.
      • Basagaña X.
      • Boudier A.
      • Pin I.
      • Garcia-Aymerich J.
      • Vesin A.
      • et al.
      Identifying adult asthma phenotypes using a clustering approach.

       Estimation of air pollution exposure

      Long-term exposure to pollutants was estimated using land-use regression models derived from the European Study of Cohorts for Air Pollution Effects (ESCAPE; www.escapeproject.eu
      • Beelen R.
      • Hoek G.
      • Vienneau D.
      • Eeftens M.
      • Dimakopoulou K.
      • Pedeli X.
      • et al.
      Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe—the ESCAPE project.
      ,
      • Eeftens M.
      • Beelen R.
      • De Hoogh K.
      • Bellander T.
      • Cesaroni G.
      • Cirach M.
      • et al.
      Development of land use regression models for PM2.5, PM 2.5 absorbance, PM10 and PMcoarse in 20 European study areas: results of the ESCAPE project.
      ) project. The home addresses of the participants at EGEA2 and ECRHS II, living in ESCAPE cities, were geocoded. Because no exposure data at the same year of EGEA2 or ECRHS II were available, the exposure at the closest year was used. Therefore, geocodes were linked with ambient concentrations of air pollutants estimated using land-use regression models between 2009 and 2010. Available air pollutants were NO2 (nitrogen dioxide), PM10 (airborne particulate matter with an aerodynamic diameter of ≤10 μm), PM2.5, and PMcoarse (airborne particulate matter with an aerodynamic diameter ranging from 2.5 to 10 μm, calculated by subtracting PM2.5 from PM10). Estimates of NO2 were available for all 17 cities (Umeå, Norwich, Ipswich, Antwerp, Erfurt, Paris, Lyon, Grenoble, Marseille, Verona, Pavia, Turin, Oviedo, Galdakao, Barcelona, Albacete, and Huelva) and estimates of all particulate matter (PM) metrics for 7 cities (Norwich, Ipswich, Antwerp, Paris, Grenoble, Turin, and Barcelona). Data on 2 traffic exposure indicators—traffic intensity (on the nearest road) and traffic load (in a 100-m buffer)—were also available.

       Statistical analysis

      Associations between long-term exposure to air pollutants (estimated at participants’ residential addresses at the first follow-up of each study, thus between 2000 and 2007) and the severity score of rhinitis (assessed at the second follow-up of each study, thus between 2011 and 2013) were analyzed using polytomous logistic regression. To account for between-city heterogeneity, a random intercept for city was used (GLLAMM procedure in STATA14).
      Models were first carried out without any adjustment, and then adjusted on preselected variables based on previous literature: age, sex, smoking status, asthma, and allergic sensitization. Analyses with traffic density or traffic load were further adjusted for NO2 background level as described in the ESCAPE protocol. Results are presented as odds ratio (OR) with the associated 95% CI. Estimates were reported for an increase of 10 μg/m3 for NO2 and PM10, 5 μg/m3 for PM2.5 and PMcoarse, and 4,000,000 vehicles × m/d for traffic load on all major roads in a 100-m buffer and 5,000 vehicles/d for traffic density on the nearest road, following ESCAPE protocol.
      Considering that air pollution may act differently according to allergic sensitization, a stratified analysis on allergic sensitization status was carried out. Because air pollution is known to increase asthma severity and because asthma and rhinitis are strongly related, a stratified analysis on asthma status was also carried out. Smokers have a continual bombardment of the nasal cavities with PM and irritant gases from cigarette smoke, which could affect pollutant response, and thus a stratified analysis on smoking status (current smokers vs ex- or never smokers) was carried out. Finally, because the design of ECRHS and EGEA differed, a stratified analysis on study was also carried out on the study. The interactions between each pollutant and each factor of stratification were tested by likelihood ratio tests in separate models.
      To test whether association between air pollution and severity of rhinitis differs according to the type of symptom, the association between air pollutants and severity of each of the 4 symptoms separately was estimated.
      Sensitivity analyses were performed: because treatment may have lowered severity, analysis excluding participants with medication in the last 12 months (use of medication—pills or spray—to treat nasal disorder in the last 12 months) was performed. Given that the methods used to take into account the within-city (or center) correlation is a complex issue,
      • Basagaña X.
      • Pedersen M.
      • Barrera-Gómez J.
      • Gehring U.
      • Giorgis-Allemand L.
      • Hoek G.
      • et al.
      Analysis of multicentre epidemiological studies: contrasting fixed or random effects modelling and meta-analysis.
      and to ensure robustness of our results, simple polytomous regressions without adding a city-level intercept were also performed. Because participants may have moved between the first and the second follow-ups of both studies, we also performed the fully adjusted analysis among nonmovers only to ensure robustness of the results. Because only half the population has PM data and to ensure comparability of the results, analyses with NO2 were also performed in the restricted population with PM exposure data.
      Bipollutant models including NO2 and each one of the PM metrics were also used to test the independence of the results for each of the pollutant.
      All analyses were carried out by Stata (Stata 14; StataCorp, College Station, Tex).

      Results

      This study included 1408 participants from EGEA3 and ECRHS III with symptoms of rhinitis in the last 12 months, having available data on rhinitis severity score and individual air pollution estimates (Flowchart available in Fig 1).
      A detailed description of the characteristics of the 1408 participants is reported in Table I, for all participants and according to the 4 levels of severity of rhinitis. ECRHS contributed with 81% of the study population. Participants were on average 52.3 years old, 54.4% were women and 28% had asthma. The median severity score of rhinitis was 4 (1st quartile-3rd quartile = 2-6). When increasing in the severity category, participants were younger, more often from the EGEA study, and more often had asthma and allergic sensitization (from 18% in low severity to 39% in high severity and from 35% in low severity to 64% in high severity, respectively). Participants with higher severity also reported allergic rhinitis or hay fever more often (from 34% for low severity to 81% for high severity) as well as more frequent symptoms of rhinitis.
      Table ICharacteristics of the participants, overall and by levels of score of severity
      VariableAll (N = 1,408)Low severity (N = 418)Mild severity (N = 417)Moderate severity (N = 251)High severity (N = 322)P value
      Score of severity, median (q1-q3)4 (2-6)2 (1-2)4 (3-4)6 (5-6)8 (7-9)
      Age (y), mean ± SD52.3 ± 10.354.2 ± 9.5352.8 ± 10.350.5 ± 10.850.5 ± 10.6<.001
      Study, % EGEA19.012.018.523.125.5<.001
      Sex: women, %54.451.251.860.257.5.059
      Smoking status, %
       Current18.319.917.323.813.0.004
       Ex-smoker38.340.341.530.737.8
       Never43.439.841.245.649.2
      Educational level, %.386
       Low21.421.817.822.225.2
       Medium29.729.831.227.829.0
       High48.948.451.050.045.9
      Asthma ever, %28.218.028.430.938.9<.001
      Asthma age of onset (y), mean ± SD16.9 ± 13.917.1 ± 13.918.5 ± 14.716.2 ± 13.813.8 ± 14.7.43
      Report of allergic rhinitis or hay fever ever, %58.834.658.769.981.6<.001
      Allergic sensitization, %47.235.446.246.764.3<.001
      Frequency of the symptoms, persistent, %29.421.9263043.3<.001
      Medication for rhinitis in the last 12 mo, yes, %42.016.737.052.768.8<.001
      NO2 (μg/m3), mean ± SD29.9 ± 14.628.2 ± 14.130.5 ± 14.930.5 ± 15.130.8 ± 14.2.047
      PM10 (μg/m3), mean ± SD25.2 ± 6.9524.1 ± 6.3424.8 ± 7.0926.1 ± 7.0926.7 ± 7.21.0009
      PM2.5 (μg/m3), mean ± SD15.3 ± 3.7914.6 ± 3.3715.3 ± 4.1015.7 ± 3.7415.9 ± 3.81.0018
      PMcoarse (μg/m3), mean ± SD10.1 ± 3.919.77 ± 3.809.85 ± 3.6710.4 ± 3.9510.8 ± 4.27.0231
      Traffic load, mean1,600,6271,460,0001,510,0001,790,0001,750,000.52
      Traffic intensity, mean5,6244,3285,4967,2276,439.0109
      Severity of runny nose<.001
       No26.657.723.011.62.80
       Mild37.137.859.535.137.1
       Moderate/severe36.24.6017.553.488.2
      Severity of blocked nose<.001
       No32.572.726.614.32.20
       Mild25.321.844.420.39.00
       Moderate/severe42.25.5029.065.388.8
      Severity of itchy nose<.001
       No44.782.546.827.96.20
       Mild31.616.549.245.017.7
       Moderate/severe23.71.004.1027.176.1
      Severity of sneezing<.001
       No30.460.828.816.73.70
       Mild37.836.457.138.714.0
       Moderate/severe31.82.8714.244.682.3
      q1, Quartile 1; q3, quartile 3.
      P value overall is the P value of the overall difference between the 4 categories of severity of rhinitis.
      An increase in air pollution exposure was associated with an increase in the severity of rhinitis (Fig 2; for exact ORs, see Table E1 in this article’s Online Repository at www.jacionline.org). Increased levels of PM10 and PM2.5 were associated with higher levels of severity, with an exposure-response association. A similar pattern was found for PMcoarse, with a slightly lower effect and reaching statistical significance only for high severity. For NO2, there was no exposure-response relationship. ORs for mild, moderate, and high severity were similarly estimated at around 1.15. No association was found between traffic load or traffic intensity and score of severity.
      Figure thumbnail gr2
      Fig 2Associations between air pollutant metrics and severity of rhinitis. Reference: low severity, OR adjusted for age, sex, smoking status, asthma, allergic sensitization, and NO2 background (for traffic load and traffic intensity), with city as a random intercept. Number reported below the pollutants corresponds to the number of patients included in the adjusted analysis.

       Stratified analyses

       Stratification by allergic sensitization

      Among participants with no allergic sensitization, increase in NO2, PM metrics, and traffic intensity exposure was associated with an increased severity score of rhinitis, with an exposure-response relationship (Fig 3; see Table E1). Among participants with allergic sensitization, increase in air pollution exposure was associated with an increased severity score of rhinitis only for PM2.5. No association was found for the other pollutants. A statistically significant interaction was found between allergic sensitization and NO2 (Pinteraction of the likelihood ratio test = .02), at borderline statistical significance for traffic load (Pinteraction = .05) and traffic intensity (Pinteraction = .08), and not statistically significant for PM10 (Pinteraction = .26), PM2.5 (Pinteraction = .21), and PMcoarse (Pinteraction = .24).
      Figure thumbnail gr3
      Fig 3Associations between air pollutant metrics and levels of severity score of rhinitis, among participants without allergic sensitization (A) and with allergic sensitization (B). Reference: low severity, OR adjusted for age, sex, smoking status, asthma, and NO2 background (for traffic load and traffic intensity), with city as a random intercept. Number reported below the pollutants corresponds to the number of patients included in the adjusted analysis.

       Stratification by asthma status

      Among the participants without asthma, an increase in air pollution exposure was associated with an increased severity score of rhinitis, similar to the results of the participants without allergic sensitization (see Table E1). In contrast, in the participants with asthma, an increase in air pollution exposure was not associated with an increased severity score of rhinitis, similar to the results of the participants with allergic sensitization. An interaction was found for NO2 (Pinteraction = .007) and for traffic load (Pinteraction = .03), and no interaction was found between other pollutants and asthma (Pinteraction > .11).

       Stratification by smoking status

      Among nonsmokers, a higher air pollution exposure was associated with an increased severity score of rhinitis (see Table E2 in this article’s Online Repository at www.jacionline.org). In contrast, higher exposure was not associated with an increased severity score in smokers although all interaction tests were below conventional levels of significance (Pinteraction < .14)

       Stratification by study

      Among participants from the ECRHS, higher air pollution exposure was associated with an increased severity score of rhinitis (see Table E2). In contrast, a higher exposure was not associated with an increased severity score in participants from the EGEA although all interaction tests were below conventional levels of significance (Pinteraction < .40).

       Analyses of the association between air pollutant exposure and severity of each symptom of rhinitis

      The associations between air pollutant exposure and the symptoms composing the score are presented in Table E3 in this article’s Online Repository at www.jacionline.org. In summary, PM10 and PM2.5 exposure increased the severity of blocked nose, itchy nose, and sneezing, with an exposure-response relationship. NO2 exposure increased the severity of runny and blocked nose when compared with no symptoms, with a similar effect size for moderate/severe or mild symptoms. No association was found between NO2 exposure and severity of itchy nose and sneezing. No association was found between PMcoarse exposure and severity of any of the 4 symptoms. No association was found between traffic load or traffic intensity and severity of any of the 4 symptoms.

       Sensitivity analyses

      When considering only those participants who did not take medicine for rhinitis in the last 12 months, results were similar to those from the main analysis, with a dose-response relationship for PM (see Table E4 in this article’s Online Repository at www.jacionline.org). Analyses among nonmovers only showed similar results to those from the main analysis, with even higher ORs for NO2 and PM metrics (see Table E5 in this article’s Online Repository at www.jacionline.org).
      In the bipollutant models (see Table E6 in this article’s Online Repository at www.jacionline.org), results remained consistent for PM metrics, with higher OR but wider CIs, leading to only 2 statistically significant ORs: for high level of severity for PM10 and PM2.5 (Pearson correlation between NO2 and PM2.5 = 0.60, between NO2 and PM10 = 0.70, and between NO2 and PMcoarse = 0.72). Interestingly, estimates for NO2 in the bipollutant models with PM10 and PMcoarse were higher than those in the main model and were reaching statistical significance for almost all levels of severity.
      Unadjusted models or models without taking city level into account gave very similar results as those using adjusted model with a random intercept for city, without changing the statistical significance of the results (results not shown).
      Analyses with NO2 in the restricted population having PM exposure data gave similar results (results not shown).

       Discussion

      In 1408 participants from 2 European studies with detailed characterization of rhinitis, we investigated the association between individual air pollution exposure and severity of rhinitis. An increase in PM10 and PM2.5 exposure was associated with an increased severity of rhinitis. To a lesser extent, PMcoarse and NO2 were also associated with severity of rhinitis, with no exposure-response relationship for NO2. No association was found between traffic load or traffic intensity and severity of rhinitis.
      Our study is one of the first to examine the long-term effects of air pollution on the severity of rhinitis, considering allergic and nonallergic rhinitis separately. Previously, a French study among 17,567 children and adults has modeled the risk of suffering from severe seasonal allergic rhinitis as a function of both grass pollen count and outdoor air pollution evaluated by daily mean exposure over a period of a few months.
      • Annesi-Maesano I.
      • Rouve S.
      • Desqueyroux H.
      • Jankovski R.
      • Klossek J.-M.
      • Thibaudon M.
      • et al.
      Grass pollen counts, air pollution levels and allergic rhinitis severity.
      It found a positive but not statistically significant association between NO2 or PM10 exposure and seasonal allergic rhinitis high severity, with a trend for PM10, and with adjustment for pollen count. Findings from this study cannot be directly compared with findings from our study given the differences in the phenotype studied, in the definition of severity as they considered high versus no, low, or moderate severity, in the estimation of exposure to air pollution, and given the lack of results without adjustment on grass pollen. Nevertheless, our results for PM10 and PM2.5 among participants with allergic sensitization seem to be in line with results of this previous study. Unfortunately, we did not have data on grass pollen to take into account their interrelation with air pollutants in the study of allergic rhinitis.
      One of the weaknesses of our study is the time discrepancy between ESCAPE measurement and the follow-up dates of the 2 studies: individual addresses were collected between 2000 and 2007, air pollution was measured and modeled between 2009 and 2010, and severity of rhinitis data were collected between 2011 and 2013. Although the temporality—exposure assessment before severity assessment—is respected, we did not have the annual exposure corresponding to the year before questionnaire completion. This is a common problem when dealing with estimation of long-term annual air pollution. We assume that spatial variability of that specific year also represents the spatial patterns during previous years.
      • de Hoogh K.
      • Gulliver J.
      • Donkelaar A van
      • Martin R.V.
      • Marshall J.D.
      • Bechle M.J.
      • et al.
      Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data.
      Our results among nonmovers were similar to those from the main analysis, with even higher ORs, strengthening the robustness of our results.
      One of the strength of the present analysis is the appraisal of allergic and nonallergic rhinitis phenotypes, of rhinitis severity as well as the consideration of different types of symptoms. We found differences in the association between air pollution exposure and severity of rhinitis according to the phenotype studied. After stratification by allergic sensitization, the effect of pollutants seemed higher among participants without allergic sensitization than among those with, although that interaction was statistically significant only for NO2. The interactions between PM metrics and allergic sensitization were not statistically significant. However, we may not have enough power to find an interaction, because the sample size is almost half for PM analyses compared with NO2 analyses. The higher association among participants without allergic sensitization could be partly supported by the fact that allergic sensitization is already a risk factor for high severity: there are twice as many participants with allergic sensitization with a high severity of rhinitis compared with those with low severity. The effect of pollutants may have a lower impact on those with already severe rhinitis. However, as discussed before, no association was found in the study by Annesi-Maesano et al
      • Annesi-Maesano I.
      • Rouve S.
      • Desqueyroux H.
      • Jankovski R.
      • Klossek J.-M.
      • Thibaudon M.
      • et al.
      Grass pollen counts, air pollution levels and allergic rhinitis severity.
      between air pollution levels and score of allergic rhinitis. In the study by Mady et al,
      • Mady L.J.
      • Schwarzbach H.L.
      • Moore J.A.
      • Boudreau R.M.
      • Kaffenberger T.M.
      • Willson T.J.
      • et al.
      The association of air pollutants and allergic and nonallergic rhinitis in chronic rhinosinusitis.
      a positive correlation was found between exposure to black carbon and some symptoms of rhinitis, regardless of allergic sensitization status, and no results were available for PM2.5. Furthermore, this study was based on a small selected sample of patients with disease, without formal statistical analyses. We had no data on the seasonality of the symptoms, and thus we were not able to assess whether air pollution exposure had a different impact on severity of rhinitis, depending on seasonal or “throughout the year” symptoms. We had data on the long-term annual exposure of air pollution, and this study was not designed to investigate short-term/seasonal variation in nasal symptoms according to air pollutant levels.
      Accounting for asthma when assessing the association between air pollution and rhinitis is not trivial because rhinitis and asthma are strongly related, and air pollution is known to increase asthma severity and incidence. Our results were similar when adjusting or not adjusting for asthma. Stratified analyses on asthma showed a higher effect of air pollutants among participants without asthma, but the interaction was statistically significant only for NO2, as for allergic sensitization. The similarity of results according to allergic sensitization and according to asthma is probably because allergic sensitization is strongly interrelated to asthma. Anyway, the association between air pollution exposure and rhinitis severity is most likely not confused by asthma status.
      We found that a higher exposure to air pollution was associated with an increased severity of rhinitis among nonsmokers but not among smokers. However, the estimates were of comparable magnitude or even higher in smokers than in nonsmokers, and no interaction was found. These results are probably because less than 20% of individuals were current smokers and therefore the sample size was probably small for such an evaluation. Similarly, we found that a higher exposure to air pollution was associated with an increased severity in ECRHS but not in EGEA, which represents around 20% of the individuals of our study. However, because EGEA is originally a case-control study on asthma and thus has a high proportion of participants with asthma, results from the stratified analyses by study are in line with those from the analyses stratified by asthma.
      We found similar results whether or not taking into account the city or including a random intercept for the city level, suggesting that adding the city level did not provide more information for the model and thus that the association between air pollution and severity of rhinitis does not change according to the city. Generally, our results were quite robust because estimates were similar in crude and adjusted analysis, whether taking the city into account or not.
      The Allergic Rhinitis and its Impact on Asthma classification on severity was initially built for allergic rhinitis, but it may be extended to other phenotypes of rhinitis such as nonallergic rhinitis. Indeed, questions used to define severity are not specifically related to the allergic facet of the disease. Rhinitis is usually not defined by only 1 symptom, but by a combination of several symptoms characterizing the disease as a whole.
      • Bousquet J.
      • Khaltaev N.
      • Cruz A.A.
      • Denburg J.
      • Fokkens W.J.
      • Togias A.
      • et al.
      Allergic Rhinitis and its Impact on Asthma (ARIA) 2008*.
      We have therefore considered the score of severity to appraise the general effect of long-term air pollution on rhinitis severity. However, some symptoms may be more frequent in allergic rhinitis or nonallergic rhinitis,
      • Quillen D.M.
      • Feller D.B.
      Diagnosing rhinitis: allergic vs. nonallergic.
      and the effect of air pollutants on rhinitis severity may depend on the type of symptom of rhinitis. In our study, results differed according to the symptom and even if results were slightly stronger for “blocked nose,” no clear allergic or nonallergic pattern stood out.
      Our results also differed according to the pollutant studied. Association between PM metrics and rhinitis severity increased gradually with levels of severity, whereas in the association between NO2 exposure, effect size was the same whatever the levels. Both NO2 and PM metrics are pollutants related to traffic, but their size and mechanisms of action are different, as well as how they can interact with pollen.
      • D’Amato G.
      • Pawankar R.
      • Vitale C.
      • Lanza M.
      • Molino A.
      • Stanziola A.
      • et al.
      Climate change and air pollution: effects on respiratory allergy.
      • D’Amato M.
      • Cecchi C.
      • Annesi-Maesano I.
      • D’Amato G.
      News on climate change, air pollution and allergic trigger factors of asthma.
      • Diaz-Sanchez D.
      • Penichet-Garcia M.
      • Saxon A.
      Diesel exhaust particles directly induce activated mast cells to degranulate and increase histamine levels and symptom severity.
      The interaction between allergic sensitization and asthma status and NO2 also supports this hypothesis. The potential mechanisms of action suggested to explain the effects of air pollutants are related to oxidative stress,
      • Bates J.T.
      • Fang T.
      • Verma V.
      • Zeng L.
      • Weber R.J.
      • Tolbert P.E.
      • et al.
      Review of acellular assays of ambient particulate matter oxidative potential: methods and relationships with composition, sources, and health effects.
      reactive oxygen species, apoptosis, and inflammation.
      • Jang A.-S.
      • Jun Y.J.
      • Park M.K.
      Effects of air pollutants on upper airway disease.
      In our case, gaseous and particulate pollutants seem to both have a distinct effect on rhinitis severity, and this was confirmed by our bipollutant model. It is somehow surprising that associations were weaker or null for PMcoarse, given that this PM fraction would be expected to have a higher nasal fractional deposition than PM2.5. Particulates of different aerodynamic diameters may lead to different inflammatory responses in the respiratory tract,
      • Huang K.L.
      • Liu S.Y.
      • Chou C.C.K.
      • Lee Y.H.
      • Cheng T.J.
      The effect of size-segregated ambient particulate matter on Th1/Th2-like immune responses in mice.
      and the mechanisms underlying the interaction between PM and the immune system still need to be elucidated and addressed clinically.
      • Wu J.-Z.
      • Ge D.-D.
      • Zhou L.-F.
      • Hou L.-Y.
      • Zhou Y.
      • Li Q.-Y.
      Effects of particulate matter on allergic respiratory diseases.
      Furthermore, the biological effects of particulates are based on their chemical compositions, which may depend on the diameter of particulates. We found no clear association between traffic metrics and severity of rhinitis, a result consistent with previous ESCAPE articles reporting associations of specific pollutants with asthma and lung function, but not with traffic metrics.
      • Adam M.
      • Schikowski T.
      • Carsin A.E.
      • Cai Y.
      • Jacquemin B.
      • Sanchez M.
      • et al.
      Adult lung function and long-term air pollution exposure. ESCAPE: a multicentre cohort study and meta-analysis.
      ,
      • Jacquemin B.
      • Siroux V.
      • Sanchez M.
      • Carsin A.-E.
      • Schikowski T.
      • Adam M.
      • et al.
      Ambient air pollution and adult asthma incidence in six European cohorts (ESCAPE).
      All these results bring up the hypothesis that biological mechanisms by which air pollution may affect rhinitis are not the same depending on the pollutant as well as on the phenotype of rhinitis studied, and in particular according to allergic sensitization. Further studies filling the gap between air pollution exposure, biological markers of inflammation, and phenotype of rhinitis are needed to better understand the underlying mechanisms of the general association.

       Conclusions

      Using data from the 1408 adults with rhinitis from 2 European studies on respiratory health, the present study showed that annual air pollution exposure was associated with increased severity of rhinitis, in particular for PM metrics. These results bring new insights into the management of rhinitis, a hidden major public health challenge, associated with substantial daily impairment and high cost to society. Finally, our results contribute to a better understanding of the environmental risk factors of this disease and reemphasize the evidence that air pollution needs to be better controlled.
      Key messages
      • Very little is known about air pollution as a risk factor for rhinitis and its phenotypes in adults.
      • Air pollution and particularly particulate matter are associated with an increase in rhinitis severity.
      • Air pollution needs to be controlled.
      ECRHS: The ECRHS data incorporated in this analysis would not have been available without the collaboration of the following individuals and their research teams:
      ECRHS Coordinating center: P. Burney, D. Jarvis, S. Chinn, and J. Knox (ECRHS II), C. Luczynska+, and J Potts.
      Principal investigators and senior scientific teams for ECRHS III: Belgium: South Antwerp and Antwerp City (J. Weyler, H. Bentouhami, and V. Nelen); France: Grenoble (I. Pin, V. Siroux, J. Ferran, and J.L. Cracowski) and Paris (B. Leynaert, D. Soussan, D. Courbon, C. Neukirch, L. Alavoine, X. Duval, and I. Poirier); Germany: Erfurt (J. Heinrich, E. Becker, G. Woelke, and O. Manuwald); Italy: Pavia (I. Cerveri, A. Corsico, A. Grosso, F. Albicini, E. Gini, E.M Di Vincenzo, V. Ronzoni, S. Villani, F. Campanella, M. Gnesi, F. Manzoni, L. Rossi, and O. Ferraro), Turin: (M. Bugiani, R. Bono, P. Piccioni, R. Tassinari, V. Bellisario, and G. Trucco), and Verona (R. de Marco†, S. Accordini, L. Calciano, L. Cazzoletti, M. Ferrari, A.M. Fratta Pasini, F. Locatelli, P. Marchetti, A. Marcon, E. Montoli, G. Nguyen, M. Olivieri, C. Papadopoulou, C. Posenato, G. Pesce, P. Vallerio, G. Verlato, and E. Zanolin); Norway (C. Svanes, E. Omenaas, A. Johannessen, T. Skorge, and F. Gomez Real); Spain: Barcelona (J.-M. Antó, J.P. Zock, J. Garcia-Aymerich, M. Kogevinas, X. Basagaña, A.E. Carsin, F. Burgos, C. Sanjuas, S. Guerra, B. Jacquemin, and P. Davdand), Galdakao (N. Muñozguren, I. Urrutia, U. Aguirre, and S. Pascual), Huelva (J. Antonio Maldonado, A. Pereira, J. Luis Sánchez, and L. Palacios), and Oviedo (F. Payo, I. Huerta, N. Sánchez, M. Fernández, and B. Robles); Sweden: Umeå (B. Forsberg, L. Braback, L. Modig, B. Järvholm, H. Bertilsson, K.A. Franklin, and C. Wahlgreen); and UK: Ipswich (N. Innes) and Norwich: (A. Wilson).
      EGEA:
      Coordination: V. Siroux (epidemiology, PI since 2013); F. Demenais (genetics); I. Pin (clinical aspects); R. Nadif (biology); F. Kauffmann (PI 1992-2012). Respiratory epidemiology: Inserm ex-U 700, Paris: M. Korobaeff (EGEA1), F. Neukirch (EGEA1); Inserm ex-U 707, Paris: I. Annesi-Maesano (EGEA1-2); Inserm ex-U 1018, Villejuif: F. Kauffmann and M.P. Oryszczyn (EGEA1-2); Inserm U 1168, Villejuif: N. Le Moual, R. Nadif, and R. Varraso; Inserm U 1209 Grenoble: V. Siroux. Genetics: Inserm ex-U 393, Paris: J. Feingold; Inserm U 946, Paris: E. Bouzigon, F. Demenais, and M.H. Dizier; CNG, Evry: I. Gut (now CNAG, Barcelona, Spain), M. Lathrop (now University of McGill, Montreal, Canada). Clinical centers: Grenoble: I. Pin and C. Pison; Lyon: D. Ecochard (EGEA1), F. Gormand, and Y. Pacheco; Marseille: D. Charpin (EGEA1) and D. Vervloet (EGEA1-2); Montpellier: J. Bousquet; Paris Cochin: A. Lockhart (EGEA1) and R. Matran (now in Lille); Paris Necker: E. Paty (EGEA1-2) and P. Scheinmann (EGEA1-2); Paris-Trousseau: A. Grimfeld (EGEA1-2) and J. Just. Data and quality management: Inserm ex-U155 (EGEA1): J. Hochez; Inserm U 1168, Villejuif: N. Le Moual; Inserm ex-U780: C. Ravault (EGEA1-2); Inserm ex-U794: N. Chateigner (EGEA1-2); and Grenoble: J. Quentin (EGEA1-2).
      We are indebted to the participants of EGEA3 and ECRHS III for their willingness to help with the collection of data.

      Appendix

      Table E1OR (95% CI) of the associations between NO2, PM10, PM2.5, PMcoarse, traffic load, and traffic intensity and the 4 levels of severity of rhinitis among all participants, and according to allergic sensitization and asthma
      PollutantLevel of severity of rhinitisAll, OR (95% CI)Stratified on allergic sensitization, OR (95% CI)Stratified on asthma, OR (95% CI)
      Results not adjusted on asthma.
      No allergic sensitizationAllergic sensitizationNo asthmaAsthma
      NO2Mild1.15 (1.02-1.30)1.24 (1.06-1.44)1.06 (0.90-1.26)1.14 (1.00-1.29)1.19 (0.95-1.48)
      Moderate1.17 (1.02-1.34)1.29 (1.09-1.53)1.05 (0.87-1.28)1.37 (1.17-1.59)0.93 (0.72-1.20)
      High1.14 (1.00-1.3)1.38 (1.16-1.64)0.95 (0.80-1.13)1.19 (1.03-1.39)1.08 (0.86-1.35)
      PM10Mild1.20 (0.88-1.64)1.54 (0.97-2.44)0.94 (0.61-1.44)1.19 (0.83-1.72)1.25 (0.69-2.29)
      Moderate1.53 (1.07-2.19)2.10 (1.24-3.55)1.15 (0.70-1.89)1.72 (1.13-2.62)1.17 (0.58-2.34)
      High1.72 (1.23-2.41)2.18 (1.27-3.75)1.40 (0.91-2.16)1.81 (1.19-2.76)1.51 (0.83-2.75)
      PM2.5Mild1.42 (1.08-1.87)1.73 (1.17-2.56)1.16 (0.77-1.73)1.46 (1.05-2.04)1.24 (0.71-2.17)
      Moderate1.73 (1.25-2.40)2.46 (1.53-3.94)1.33 (0.83-2.12)2.06 (1.39-3.05)1.12 (0.59-2.12)
      High1.91 (1.40-2.60)2.16 (1.32-3.54)1.70 (1.13-2.56)2.15 (1.45-3.18)1.43 (0.82-2.47)
      PMcoarseMild1.11 (0.83-1.49)1.30 (0.84-2.01)0.95 (0.65-1.39)1.04 (0.75-1.45)1.32 (0.76-2.32)
      Moderate1.28 (0.91-1.79)1.73 (1.06-2.83)0.96 (0.61-1.52)1.34 (0.91-1.96)1.15 (0.59-2.24)
      High1.47 (1.07-2.00)2.00 (1.22-3.29)1.15 (0.78-1.69)1.44 (0.99-2.10)1.52 (0.86-2.69)
      Traffic loadMild1.00 (0.78-1.26)0.90 (0.62-1.3)1.08 (0.75-1.55)1.07 (0.78-1.47)0.82 (0.54-1.26)
      Moderate1.12 (0.88-1.43)1.16 (0.83-1.63)1.13 (0.75-1.72)1.39 (1.01-1.90)0.67 (0.38-1.15)
      High1.13 (0.89-1.45)1.35 (0.97-1.87)0.89 (0.6-1.33)1.35 (0.98-1.87)0.82 (0.53-1.27)
      Traffic intensityMild1.12 (0.97-1.3)1.10 (0.87-1.39)1.12 (0.95-1.32)1.14 (0.97-1.35)1.05 (0.84-1.31)
      Moderate1.21 (1.05-1.41)1.24 (0.97-1.58)1.21 (1.02-1.43)1.29 (1.09-1.53)1.01 (0.79-1.30)
      High1.12 (0.97-1.31)1.30 (1.02-1.67)1.04 (0.87-1.25)1.10 (0.90-1.34)1.07 (0.86-1.34)
      Reference: low severity. OR adjusted for age, sex, smoking status, asthma, and NO2 background (for traffic load and traffic intensity), with city as a random intercept. Estimates are presented for an increase of 10 μg/m3 for NO2 and PM10 and 5 μg/m3 for PM2.5 and PMcoarse, and of 4,000,000 vehicles × m/d for traffic load on all major roads in a 100-m buffer and 5,000 vehicles/d for traffic density on the nearest road. Boldface indicates statistically significant ORs.
      Results not adjusted on asthma.
      Table E2OR of the associations between NO2, PM10, PM2.5, PMcoarse, traffic load, and traffic intensity and the 4 levels of severity of rhinitis according to smoking status and study
      PollutantLevel of severity of rhinitisAll, OR (95% CI)Stratified on smoking status, OR (95% CI)
      Results not adjusted on smoking status.
      Stratified on study, OR (95% CI)
      NonsmokersSmokersEGEAECRHS
      NO2Mild1.15 (1.02-1.30)1.17 (1.03-1.34)1.05 (0.78-1.41)1.05 (0.77-1.42)1.18 (1.05-1.33)
      Moderate1.17 (1.02-1.34)1.17 (1.01-1.35)1.20 (0.88-1.64)0.97 (0.70-1.35)1.22 (1.07-1.41)
      High1.14 (1.00-1.3)1.12 (0.97-1.29)1.31 (0.94-1.81)0.84 (0.61-1.17)1.24 (1.08-1.41)
      PM10Mild1.20 (0.88-1.64)1.29 (0.91-1.81)0.81 (0.35-1.85)1.17 (0.36-3.85)1.17 (0.86-1.59)
      Moderate1.53 (1.07-2.19)1.56 (1.05-2.33)1.42 (0.63-3.23)1.78 (0.49-6.40)1.46 (1.02-2.09)
      High1.72 (1.23-2.41)1.76 (1.22-2.56)1.33 (0.56-3.17)1.65 (0.51-5.28)1.73 (1.23-2.43)
      PM2.5Mild1.42 (1.08-1.87)1.43 (1.07-1.93)1.27 (0.55-2.93)0.92 (0.25-3.33)1.41 (1.06-1.86)
      Moderate1.73 (1.25-2.40)1.67 (1.17-2.37)2.61 (1.07-6.39)2.67 (0.66-10.77)1.65 (1.18-2.30)
      High1.91 (1.40-2.60)1.86 (1.34-2.59)2.02 (0.80-5.11)1.28 (0.36-4.62)1.93 (1.41-2.65)
      PMcoarseMild1.11 (0.83-1.49)1.13 (0.82-1.56)0.98 (0.49-1.97)2.00 (0.74-5.36)1.03 (0.77-1.39)
      Moderate1.28 (0.91-1.79)1.32 (0.91-1.91)1.09 (0.52-2.30)1.60 (0.53-4.82)1.25 (0.88-1.76)
      High1.47 (1.07-2.00)1.45 (1.03-2.04)1.48 (0.71-3.09)1.38 (0.51-3.76)1.53 (1.11-2.11)
      Traffic loadMild1.00 (0.78-1.26)1.02 (0.76-1.36)0.94 (0.57-1.53)1.27 (0.68-2.38)0.96 (0.73-1.27)
      Moderate1.12 (0.88-1.43)1.10 (0.80-1.52)1.18 (0.80-1.73)1.13 (0.58-2.20)1.15 (0.88-1.52)
      High1.13 (0.89-1.45)1.17 (0.86-1.58)1.00 (0.58-1.73)0.91 (0.45-1.86)1.20 (0.92-1.57)
      Traffic intensityMild1.12 (0.97-1.3)1.20 (1.02-1.41)0.97 (0.75-1.26)1.20 (0.88-1.64)1.08 (0.94-1.25)
      Moderate1.21 (1.05-1.41)1.32 (1.12-1.55)0.94 (0.65-1.36)1.08 (0.77-1.51)1.22 (1.06-1.42)
      High1.12 (0.97-1.31)1.22 (1.03-1.44)0.90 (0.65-1.26)1.06 (0.76-1.47)1.13 (0.97-1.31)
      Reference: low severity. OR adjusted for age, sex, smoking status, asthma, and NO2 background (for traffic load and traffic intensity), with city as a random intercept.
      Estimates are presented for an increase of 10 μg/m3 for NO2 and PM10 and 5 μg/m3 for PM2.5 and PMcoarse, and of 4,000,000 vehicles × m/d for traffic load on all major roads in a 100-m buffer and 5,000 vehicles/d for traffic density on the nearest road. Boldface indicates statistically significant ORs.
      Results not adjusted on smoking status.
      Table E3OR of the associations between NO2, PM10, PM2.5, PMcoarse, traffic load, and traffic intensity and the severity of rhinitis by symptom


      Pollutant
      Level of severity of rhinitisOutcome, OR (95% CI)
      Runny noseBlocked noseItchy noseSneezing
      NO2Mild1.13 (1.01-1.27)1.13 (1.00-1.27)1.00 (0.90-1.12)1.06 (0.96-1.16)
      Moderate/severe1.12 (1.00-1.26)1.17 (1.04-1.30)0.94 (0.83-1.06)1.07 (0.97-1.18)
      PM10Mild1.04 (0.78-1.39)1.09 (0.77-1.54)1.02 (0.79-1.33)1.73 (1.01-2.97)
      Moderate/severe1.20 (0.89-1.61)1.55 (1.13-2.12)1.40 (1.04-1.87)2.59 (1.52-4.40)
      PM2.5Mild0.95 (0.73-1.24)1.16 (0.85-1.57)0.92 (0.72-1.18)1.15 (0.63-2.12)
      Moderate/severe1.26 (0.97-1.63)1.67 (1.26-2.20)1.40 (1.08-1.82)1.87 (1.05-3.31)
      PMcoarseMild1.15 (0.88-1.51)1.03 (0.75-1.41)0.98 (0.77-1.25)1.03 (0.69-1.53)
      Moderate/severe1.28 (0.97-1.68)1.32 (0.99-1.77)1.14 (0.86-1.49)1.25 (0.83-1.86)
      Traffic loadMild1.03 (0.82-1.28)0.95 (0.73-1.22)1.00 (0.82-1.21)1.06 (0.86-1.29)
      Moderate/severe1.10 (0.89-1.36)1.14 (0.92-1.40)0.98 (0.78-1.22)1.06 (0.85-1.31)
      Traffic intensityMild1.13 (1.00-1.28)1.09 (0.97-1.23)1.00 (0.92-1.10)1.03 (0.92-1.14)
      Moderate/severe1.10 (0.97-1.25)1.09 (0.97-1.22)1.03 (0.94-1.13)1.09 (0.98-1.21)
      Reference: no problem (symptom not present) for the symptoms. OR adjusted for age, sex, smoking status, asthma, allergic sensitization, and NO2 background (for traffic load and traffic intensity), with city as a random intercept. Estimates are presented for an increase of 10 μg/m3 for NO2 and PM10 and 5 μg/m3 for PM2.5 and PMcoarse, and of 4,000,000 vehicles × m/d for traffic load on all major roads in a 100-m buffer and 5000 vehicles/d for traffic density on the nearest road. Boldface indicates statistically significant ORs.
      Table E4OR of the associations between NO2, PM10, PM2.5, PMcoarse, traffic load, and traffic intensity and the levels of severity of rhinitis among participants who did not take medication for rhinitis in the last 12 mo (n = 639)
      PollutantLevel of severity of rhinitisOR (95% CI)
      NO2Mild1.12 (0.97-1.30)
      Moderate1.21 (1.02-1.45)
      High1.16 (0.97-1.39)
      PM10Mild1.03 (0.72-1.47)
      Moderate1.77 (1.13-2.77)
      High2.22 (1.43-3.45)
      PM2.5Mild1.26 (0.92-1.74)
      Moderate1.88 (1.25-2.84)
      High2.35 (1.58-3.48)
      PMcoarseMild0.94 (0.68-1.31)
      Moderate1.29 (0.85-1.95)
      High1.62 (1.09-2.41)
      Traffic loadMild1.02 (0.73-1.44)
      Moderate0.94 (0.60-1.49)
      High1.34 (0.89-2.01)
      Traffic intensityMild1.10 (0.95-1.26)
      Moderate1.15 (0.97-1.36)
      High0.97 (0.77-1.22)
      Reference: low severity. OR adjusted for age, sex, smoking status, asthma, allergic sensitization, and NO2 background (for traffic load and traffic intensity), with city as a random intercept. Estimates are presented for an increase of 10 μg/m3 for NO2 and PM10 and 5 μg/m3 for PM2.5 and PMcoarse, and of 4,000,000 vehicles × m/d for traffic load on all major roads in a 100-m buffer and 5,000 vehicles/d for traffic density on the nearest road. Boldface indicates statistically significant ORs.
      Table E5OR of the associations between NO2, PM10, PM2.5, PMcoarse, traffic load, and traffic intensity and the levels of severity of rhinitis among nonmover participants (n = 840)
      PollutantLevel of severity of rhinitisOR (95% CI)
      NO2Mild1.12 (0.98-1.29)
      Moderate1.23 (1.06-1.43)
      High1.24 (1.07-1.43)
      PM10Mild1.28 (0.91-1.79)
      Moderate1.78 (1.21-2.60)
      High1.96 (1.37-2.81)
      PM2.5Mild1.24 (0.91-1.69)
      Moderate1.73 (1.21-2.48)
      High1.84 (1.31-2.58)
      PMcoarseMild1.11 (0.82-1.50)
      Moderate1.44 (1.03-2.00)
      High1.64 (1.20-2.23)
      Traffic loadMild0.93 (0.69-1.24)
      Moderate1.17 (0.89-1.54)
      High1.20 (0.92-1.56)
      Traffic intensityMild1.04 (0.92-1.19)
      Moderate1.15 (1.02-1.31)
      High1.12 (0.99-1.27)
      Reference: low severity. OR adjusted for age, sex, smoking status, asthma, and NO2 background (for traffic load and traffic intensity), with city as a random intercept. Estimates are presented for an increase of 10 μg/m3 for NO2 and PM10 and 5 μg/m3 for PM2.5 and PMcoarse, and of 4,000,000 vehicles × m/d for traffic load on all major roads in a 100-m buffer and 5,000 vehicles/d for traffic density on the nearest road. Boldface indicates statistically significant ORs.
      Table E6OR of the associations between NO2, PM10, PM2.5, and PMcoarse and the levels of severity of rhinitis in the bipollutant models (NO2 and PM10, NO2 and PM2.5, NO2 and PMcoarse)
      Bipollutant modelsLevels of severity of rhinitisOR (95% CI)
      NO2Mild1.48 (0.97-1.19)
      Moderate1.71 (1.04-1.33)
      High1.25 (0.77-0.98)
      PM10Mild1.45 (0.63-0.96)
      Moderate1.68 (0.62-1.02)
      High2.87 (1.14-1.81)
      NO2Mild1.29 (0.88-1.06)
      Moderate1.48 (0.96-1.19)
      High1.19 (0.77-0.96)
      PM2.5Mild1.87 (0.94-1.33)
      Moderate2.15 (0.95-1.43)
      High2.88 (1.35-1.97)
      NO2Mild1.56 (1.02-1.26)
      Moderate1.96 (1.18-1.52)
      High1.96 (1.18-1.52)
      PMcoarseMild1.22 (0.57-0.84)
      Moderate1.17 (0.45-0.73)
      High2.11 (0.91-1.38)
      Reference: low severity. OR adjusted for age, sex, smoking status, asthma, and allergic sensitization, with city as a random intercept. Estimates are presented for an increase of 10 μg/m3 for NO2 and PM10 and 5 μg/m3 for PM2.5 and PMcoarse. Boldface indicates statistically significant ORs.

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