ResearchOpen Access

Ambient Air Pollution and Chronic Bronchitis in a Cohort of U.S. Women

    Published:CID: 027005



    Limited evidence links air pollution exposure to chronic cough and sputum production. Few reports have investigated the association between long-term exposure to air pollution and classically defined chronic bronchitis.


    Our objective was to estimate the association between long-term exposure to particulate matter (diameter <10μm, PM10; <2.5μm, PM2.5), nitrogen dioxide (NO2), and both incident and prevalent chronic bronchitis.


    We estimated annual average PM2.5, PM10, and NO2 concentrations using a national land-use regression model with spatial smoothing at home addresses of participants in a prospective nationwide U.S. cohort study of sisters of women with breast cancer. Incident chronic bronchitis and prevalent chronic bronchitis, cough and phlegm, were assessed by questionnaires.


    Among 47,357 individuals with complete data, 1,383 had prevalent chronic bronchitis at baseline, and 647 incident cases occurred over 5.7-y average follow-up. No associations with incident chronic bronchitis were observed. Prevalent chronic bronchitis was associated with PM10 [adjusted odds ratio (aOR) per interquartile range (IQR) difference (5.8μg/m3)=1.07; 95% confidence interval (CI): 1.01, 1.13]. In never-smokers, PM2.5 was associated with prevalent chronic bronchitis (aOR=1.18 per IQR difference; 95% CI: 1.04, 1.34), and NO2 was associated with prevalent chronic bronchitis (aOR=1.10; 95%CI=1.01,1.20), cough (aOR=1.10; 95% CI: 1.05, 1.16), and phlegm (aOR=1.07; 95% CI: 1.01, 1.14); interaction p-values (nonsmokers vs. smokers) <0.05.


    PM10 exposure was related to chronic bronchitis prevalence. Among never-smokers, PM2.5 and NO2 exposure was associated with chronic bronchitis and component symptoms. Results may have policy ramifications for PM10 regulation by providing evidence for respiratory health effects related to long-term PM10 exposure.


    Chronic bronchitis is a common clinical condition defined by chronic cough and sputum production for at least 3 mo in 2 or more consecutive years (American Thoracic Society 1995). Prevalence estimates in the general population of adults range from 3.5 to 27% (Kim et al. 2011; Martinez et al. 2014; Montes De Oca et al. 2012). This wide range may reflect, in part, variability in case definitions. Chronic bronchitis is a phenotype of chronic obstructive pulmonary disease (COPD) (Kim and Criner 2013). Among persons with COPD, chronic bronchitis portends increased frequency and severity of exacerbations (Burgel et al. 2009; Kim et al. 2011). Among persons without COPD, chronic bronchitis symptoms predict an increased risk of developing COPD, lower health-related quality-of-life scores, and increased risk for all-cause mortality (de Marco et al. 2007; Guerra et al. 2009; Lindberg et al. 2005; Martinez et al. 2014).

    Smoking is the primary risk factor for chronic bronchitis, but exposure to ambient air pollution may also contribute (Kim and Criner 2013). The relationship between short-term air pollution exposure and acute respiratory symptoms and hospitalizations is well established (Peacock et al. 2011; Peel et al. 2005; Sunyer 2001), but limited data suggest a relationship between long-term ambient pollution exposure and COPD (Schikowski et al. 2014). There is a paucity of data on the possible relationship between classically defined chronic bronchitis and long-term exposure to the criteria pollutants PM2.5, PM10 (particulate matter <2.5μm and <10μm in diameter, respectively), and nitrogen dioxide (NO2). The sparse existing data provide inconsistent support for an association between PM10 and chronic cough and phlegm, and between NO2 and chronic cough (Bentayeb et al. 2010b; Cai et al. 2014; Schikowski et al. 2005; Zemp et al. 1999).

    To address these relationships in a larger study, using specific outcome definitions and advanced exposure assessments, we investigated the association between residential exposure to PM2.5, PM10, and NO2 and both incident and prevalent chronic bronchitis in a prospective nationwide cohort of more than 50,000 U.S. women participating in the National Institute of Environmental Health Sciences (NIEHS) Sister Study. We estimated exposure at individuals’ residential addresses. Taking advantage of the comprehensive survey, we uniformly classified cases of chronic bronchitis using the classical clinical definition.


    Study Population

    The NIEHS Sister Study is a longitudinal cohort study of U.S. women with a sister diagnosed with breast cancer, but no personal breast cancer diagnosis at time of baseline interview (n=50,884). Women were enrolled between August 2003 and March 2009, and completed a baseline computer-assisted telephone survey. Follow-up telephone surveys were performed every 2 to 3 y. We analyzed data through the second follow-up survey (data release 4, data available through August 2014). Baseline and follow-up surveys queried participants on a wide range of health diagnoses and symptoms.

    Of the 50,884 women participating in the NIEHS Sister Study, 1,234 (2.4%) were excluded for missing exposure data due to residential locations outside the modeling region or addresses that could not be geocoded (Figure 1). After excluding those missing baseline data on cough and phlegm, 47,357 individuals remained for analysis of prevalent outcomes. Of the 45,955 participants without chronic bronchitis symptoms at baseline, 6,111 (12.3%) were missing data on cough or phlegm for at least one of the two follow-up questionnaires, leaving 39,844 individuals for analysis of incident outcomes.

    Flowchart showing NIEHS Sister Study cohort for women participants

    Figure 1. Study population with excluded/missing participants.

    *Total number of participants with nonmissing covariates for prevalence analyses is 44,158.

    †Total number of participants with nonmissing covariates for incidence analysis is 38,006.

    The Institutional Review Boards of the University of Washington and the NIEHS approved this study; all participants provided written informed consent.

    Outcome Assessment

    Chronic bronchitis was defined according to the classical symptom-based definition of chronic cough productive of phlegm for at least 3 mo out of a year for a minimum of 2 consecutive years (American Thoracic Society 1995). Participants were asked about the presence of cough and phlegm independently, and the duration of each symptom was specified using questions derived from the British Medical Research Council adult respiratory symptom standardized questionnaire. Women with cough and phlegm symptoms, both present for at least 3 mo per year out of the previous 2 y, were considered to have chronic bronchitis. Prevalent chronic bronchitis was determined by meeting symptom-based criteria at the baseline questionnaire. In a sensitivity analysis, we included history of physician diagnosis of chronic bronchitis in the case definition. Incident chronic bronchitis was defined by satisfying the case definition at either the second follow-up survey, or both the first and second follow-ups among participants who did not have chronic bronchitis at baseline. Participants whose symptoms did not persist from first through second follow-up were not considered cases.

    Secondary outcomes were chronic cough (3 or more months of cough for at least 2 consecutive years, regardless of phlegm production), chronic phlegm (3 or more months of phlegm production for at least 2 consecutive years, regardless of cough), and chronic cough or phlegm. Both prevalent chronic cough and chronic phlegm were defined by being present at baseline.

    Ambient Air Pollution Exposure Assessment

    Air pollution exposure was estimated using annual average PM2.5, PM10, and NO2 levels at each participant’s current primary residence. Home addresses of participants were geocoded using ArcGIS (version 10; Esri). We estimated long-term exposure using year 2000 annual mean concentration levels for all pollutants. Measurements of PM2.5, PM10, and NO2 concentrations from monitors using federal reference methods were obtained from the U.S. Environmental Protection Agency (EPA) Air Quality System database. After excluding locations with only seasonal coverage or large amounts of missing data, the observations were aggregated into annual averages. The annual averages were used to fit a universal kriging regression model for predicting at points within the contiguous United States. The models for PM2.5 (Sampson et al. 2013) and NO2 (Young et al. 2016) have been previously described in detail, and the model for PM10 was fit in the same manner as the PM2.5 model. Partial least squares, a dimension reduction technique, was used to select linear combinations of land use, roadway proximity, and other geographic covariates. The NO2 prediction model additionally incorporated satellite data (Young et al. 2016). Spatial smoothing was included via an exponential covariance function. This model therefore incorporated land-use regression and spatial smoothing of values observed in the monitoring network. Model performance was evaluated using 10-fold cross-validation and for the year 2000. The cross-validated R2 was 0.85 for NO2, 0.53 for PM10, and 0.77 for PM2.5. Exposure modeling was limited to the continental United States; participants from Alaska, Hawaii, and Puerto Rico were excluded (n=1,234).

    Statistical Analysis

    To estimate the association between outcomes and pollutant exposures, we used multivariable logistic regression. Covariates were selected a priori based on plausible relationships and review of existing literature. Potential confounders, measured at baseline, were age (continuous), ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other), body mass index (continuous), education (high school or less, some college, associate or technical degree, bachelor’s degree, graduate degree), household income (continuous), occupational exposure to dust (ever/never) or vapors/fumes (ever/never), smoking status (never, former, current), tobacco pack-years (continuous), and years of secondhand smoke exposure since age 19 (continuous). After exclusion of individuals missing any of these covariates, 44,158 individuals were available for the analysis of prevalent outcomes. For incident outcomes, among those without chronic bronchitis at baseline, 38,006 individuals were available after excluding missing covariates.

    A model adjusted for age alone was first performed followed by a fully adjusted model including all a priori identified covariates. PM2.5, PM10, and NO2 were modeled separately. Given potential bias by length of follow-up time for the incident chronic bronchitis outcome, adjustment was made for duration of follow-up time (from baseline to second follow-up survey) using restricted cubic splines with four knots (Dinse and Lagakos 1983). We performed analyses stratified by baseline smoking status (ever/never) and tested for interaction using product terms for smoking status and pollutant exposure. A two-pollutant model was performed by including remaining copollutants in the fully adjusted model. In all instances, a p-value of <0.05 was considered significant.

    Sensitivity analyses were prespecified and performed on the following subgroups in independent analyses: a) prevalent chronic bronchitis that included either symptom-based criteria or report of physician diagnosis; b) excluding baseline asthmatics [defined by history of physician diagnosis, recent (within 12 mo) asthma medication use, and self-reported current asthma], given clinical overlap between asthma and chronic bronchitis; and c) participants who lived at least 10 y at their primary residence. In the asthma sensitivity analysis, asthmatics who reported either current smoking or history of 10 pack-years at baseline were not excluded given possibility of smoking-related symptoms leading to asthma misdiagnosis. Given concern that seasonal variation may affect results, an additional sensitivity analysis was performed by adjusting for season at the time of baseline and follow-up questionnaires.

    Statistical analysis was performed using the statistical program Stata (version 11; StataCorp).


    Participants were, on average, 55.4 y old at baseline [standarddeviation(SD)=8.9], 84.8% were white, 52.6% had a bachelor’s degree or higher level of education, 56.4% had never smoked cigarettes, and only 8% were current smokers. The proportion of black and Hispanic participants increased across tertiles of PM2.5, PM10, and NO2 (Table 1). The distributions of occupational exposures, smoking history, and cumulative tobacco smoke exposure did not vary materially by ambient air pollution exposure levels (Table 1).

    Table 1 Participant characteristics at baseline by exposure tertiles for particulate matter PM2.5, PM10, and NO2.

    Table 1 lists characteristics in the first column; the corresponding values of exposure tertiles for PM sub 2.5 in micrograms per cubic meter, N O sub 2 in parts per billion, and PM sub 10 in micrograms per cubic meter are listed in the other columns.
    CharacteristicPM2.5 (μg/m3)NO2 (ppb)PM10 (μg/m3)
    Age (years)55.9±8.955.4±954.9±8.955.7±8.855.3±8.955.3±955.7±8.755.3±8.955.3±9
    Race/ethnicity (%)         
     White (non-Hispanic)90.785.977.789.285.479.790.185.578.6
     Black (non-Hispanic)2.48.716.85.69.412.95.69.412.9
    Education (%)         
     HS or less14.715.114.817.614.812.315.115.114.5
     Some college35.233.532.136.833.330.833.732.734.3
    Household income (USD)41,218±25,84943,570±26,91443,743±28,29339,995±24,81743,064±26,33945,472±29,53342,473±25,99943,104±26,89242,953±28,242
    Occupational Exposures (%)         
     Vapors/fumes (ever)24.924.32425.724.123.524.424.424.4
     Dust (ever)22.222.723.823.522.422.922.722.623.5
    Smoking status (%)         
    Pack-years among ever-smokers14.5±15.314.7±15.214.8±15.515.1±15.614.3±15.114.5±15.214.8±15.514.5±15.214.6±15.2
    Packs per day among current smokers0.7±0.40.7±0.40.7±0.50.7±0.50.7±0.40.6±0.40.7±0.50.7±0.40.7±0.4
    Adult secondhand smoke (years)10.9±12.911.3±13.311.4±13.211.5±13.211.1±13.111.1±13.111.3±13.111.1±13.111.3±13.3
    Number of years at primary residence12.8±10.913.5±11.313.6±11.512.7±1112.7±10.714.6±11.813.4±1113.2±11.213.3±11.5
    Lived at primary residence 10y (%)50.753.553.950.150.857.153.652.252.4
    Asthma at baseline (%)
    Physician diagnosis of COPD (%)
    Physician diagnosis of chronic bronchitis (%)7.488.488.17.87.588.4

    Note: BMI, body mass index; COPD, chronic obstructive pulmonary disease; HS, high school; PM, particulate matter.

    The mean follow-up time was 5.7 y from enrollment to the second follow-up survey. During the follow-up period, there were 638 incident cases of chronic bronchitis, giving an estimated incidence rate of 2.8 cases per 1,000 person-years. At baseline, 1,351 (3.1%) women met symptom-based criteria for chronic bronchitis, whereas 4,698 (10.6%) participants reported ever having had a physician diagnosis of chronic bronchitis. Prevalent chronic cough was reported by 3,749 (8.5%) and chronic phlegm by 2,776 (6.3%) participants at baseline.

    The median estimated exposure concentrations were 12.4μg/m3[interquartilerange(IQR)=4.4μg/m3] for PM2.5, 2.16μg/m3 (5.8μg/m3) for PM10, and 11.7ppb (7.3ppb) for NO2. The results of the age-adjusted and fully adjusted regression analyses are presented in Table 2. No statistically significant associations were found between incident chronic bronchitis and any of the air pollution exposures. Limiting the incidence analysis to long-term residents (>10y) did not appreciably alter the effect estimates (Table S1).

    Table 2 Odds ratios per interquartile range (IQR) increase in particulate matter PM2.5 (4.4μg/m3), NO2 (7.3ppb), and PM10 (5.8μg/m3).

    Table 2 lists exposure and outcomes in the first and second columns, respectively. The odds ratio (95 percent confidence interval) and p values for age-adjusted and fully adjusted regression analyses are listed in the other columns.
    Exposure and outcomeCasesAge adjustedFully adjusted
    OR (95% CI)p-ValueOR (95% CI)p-Value
     Incident chronic bronchitis6380.94 (0.84, 1.05)0.2560.94 (0.83, 1.06)0.289
     Prevalent (at baseline)     
      Chronic bronchitis1,3511.04 (0.97, 1.13)0.2761.04 (0.96, 1.13)0.318
      Chronic cough3,7491.03 (0.98, 1.08)0.2131.04 (0.99, 1.10)0.103
      Chronic phlegm2,7761.07 (1.02, 1.13)0.0101.04 (0.98, 1.10)0.213
      Chronic cough or phlegm5,2711.05 (1.01, 1.10)0.0151.04 (1.00, 1.09)0.067
     Incident chronic bronchitis6380.95 (0.87, 1.03)0.1981.00 (0.92, 1.09)0.974
     Prevalent (at baseline)     
      Chronic bronchitis1,3511.00 (0.95, 1.06)0.9231.05 (0.99, 1.11)0.136
      Chronic cough3,7491.02 (0.99, 1.06)0.2151.06 (1.02, 1.10)0.002
      Chronic phlegm2,7761.01 (0.97, 1.05)0.7301.02 (0.98, 1.07)0.266
      Chronic cough or phlegm5,2711.02 (0.99, 1.05)0.1991.04 (1.01, 1.08)0.008
     Incident chronic bronchitis6380.92 (0.85, 1.01)0.0660.98 (0.90, 1.08)0.745
     Prevalent (at baseline)     
      Chronic bronchitis1,3511.06 (1.01, 1.12)0.0271.07 (1.01, 1.13)0.019
      Chronic cough3,7491.04 (1.00, 1.07)0.0451.04 (1.00, 1.08)0.030
      Chronic phlegm2,7761.07 (1.03, 1.12)<0.0011.07 (1.02, 1.11)0.002
      Chronic cough or phlegm5,2711.05 (1.01, 1.08)0.0011.05 (1.02, 1.08)0.003

    Note: Each outcome was compared to all participants without that outcome. The total number of participants with nonmissing data on all covariates was 38,006 for the analysis of incident outcomes, 44,158 for prevalent outcomes. Fully adjusted model includes age, race/ethnicity, body mass index, education, household income, occupational exposure to vapors/fumes or dust (ever), smoking status, total pack-years, and environmental tobacco smoke exposure. Primary outcome (incident chronic bronchitis) analysis additionally adjusted for length of follow-up time. CI, confidence interval; IQR, interquartile range; OR, odds ratio; PM, particulate matter.

    For prevalent chronic bronchitis, a statistically significant positive association was seen with PM10 [odds ratio (OR) per IQR increase in PM10=1.07; 95% confidence interval (CI): 1.01, 1.13] (Table 2). Similar magnitudes of association with prevalent chronic bronchitis were seen for NO2 (OR=1.05; 95% CI: 0.99, 1.11) and PM2.5 (OR=1.04; 95% CI: 0.96, 1.13), but were not statistically significant. PM10 was also statistically significantly associated with chronic cough (OR=1.04; 95% CI: 1.00, 1.08), chronic phlegm (OR=1.07; 95% CI: 1.02, 1.11), and chronic cough or phlegm (OR=1.05; 95% CI: 1.02, 1.08); coadjustment for PM2.5 did not alter these effect estimates (Table S2). Adjustment of the PM10 model for NO2 resulted in a general attenuation of associations between prevalent symptoms and PM10. This attenuation is likely due in part to the strong correlation between NO2 and PM10 (Pearson’s r: 0.59).

    NO2 showed a significant positive association with chronic cough (OR = 1.06; 95% CI: 1.02, 1.10) and chronic cough or phlegm (OR=1.04; 95% CI: 1.01, 1.08). In the NO2 model, ORs were robust to coadjustment for PM2.5 (Table S2). Coadjustment for PM10 in the NO2 model resulted in a loss of precision for the association between NO2 and chronic cough or phlegm, and an overall decrease in size of effect estimates across all prevalent outcomes. The significant association with chronic cough was preserved (OR=1.05; 95% CI: 1.01, 1.10). ORs for all pollutants and outcomes were generally very similar between age-adjusted and fully adjusted models.

    For prevalent chronic bronchitis, sensitivity analyses incorporating additional case requirements into the classical symptom-based definition showed comparable effect estimates in association with PM10 (Table 3). For example, PM10 was significantly associated with prevalent chronic bronchitis defined either by symptoms or including participant-reported physician diagnosis (OR per IQR increase=1.06; 95% CI: 1.02, 1.09). In an analysis of prevalent chronic bronchitis excluding baseline asthmatics, the effect estimate was similar but less precise, commensurate with the smaller sample size (OR for IQR increase in PM10=1.06; 95% CI: 0.99, 1.13). Similarly, exclusion of the 47% of participants who lived at their residence less than 10 y largely preserved the estimated association, but with loss of precision reflecting the smaller sample size (OR per IQR increase in PM10=1.07; 95% CI: 0.99, 1.16). Comparable sensitivity analyses involving PM2.5 or NO2 and prevalent chronic bronchitis yielded ORs that were similar in magnitude and direction to the primary models (Table 3). Effect estimates for all three pollutants were essentially unchanged by seasonal adjustment (Table S3).

    Table 3 Sensitivity analyses evaluating case definitions with additional inclusion or exclusion criteria for association of prevalent (baseline) chronic bronchitis with ambient air pollutants: odds ratios per interquartile range (IQR) increase in particulate matter PM2.5 (4.4μg/m3), NO2 (7.3ppb), and PM10 (5.8μg/m3).

    Table 3 lists exposure and case definitions (primary and sensitivity analyses) in the first column; the corresponding N values, cases, adjusted odds ratio (95 percent confidence interval), and p values are listed in the other columns.
    Exposure and case definitions (primary and sensitivity analyses)naCasesAdjusted OR (95% CI)p-Value
     Prevalent chronic bronchitis (primary case definition)44,1581,3511.04 (0.96, 1.13)0.318
     Including physician diagnosis44,0994,6981.04 (0.99, 1.09)0.104
     Excluding asthma at baseline41,4881,1040.99 (0.90, 1.08)0.798
     Excluding those living at residence <10y23,2737200.97 (0.87, 1.09)0.643
     Prevalent chronic bronchitis (primary case definition)44,1581,3511.05 (0.99, 1.11)0.136
     Including physician diagnosis44,0994,6981.02 (0.99, 1.06)0.191
     Excluding asthma at baseline41,4881,1041.02 (0.96, 1.09)0.492
     Excluding those living at residence <10y23,2737201.03 (0.95, 1.11)0.444
     Prevalent chronic bronchitis (primary case definition)44,1581,3511.07 (1.01, 1.13)0.019
     Including physician diagnosis44,0994,6981.06 (1.02, 1.09)0.001
     Excluding asthma at baseline41,4881,1041.06 (0.99, 1.13)0.077
     Excluding those living at residence <10y23,2737201.07 (0.99, 1.16)0.093

    Note: For each case definition, the comparison group was all individuals without that outcome. Each analysis was performed independently for each case definition. Adjusted for age, race/ethnicity, body mass index, education, household income, occupational exposure to vapors/fumes or dust (ever), smoking status, total pack-years, and secondhand smoke exposure. CI, confidence interval; OR, odds ratio; PM, particulate matter.

    aTotal number of individuals with nonmissing data on all covariates for analysis.

    In smoking-stratified analyses, we found evidence for stronger associations between all three air pollutants and prevalent outcomes in never-smokers (Table 4). PM2.5 was strongly associated with prevalent chronic bronchitis among never-smokers (OR per IQR difference=1.18; 95% CI: 1.04, 1.34), and the difference by smoking status was statistically significant (pinteraction=0.013). Similarly with NO2, in never-smokers, significant associations were seen for all four prevalent outcomes: chronic bronchitis (OR=1.10; 95% CI: 1.01, 1.20), chronic cough (OR=1.10; 95% CI: 1.05, 1.16), chronic phlegm (OR=1.07; 95% CI: 1.01, 1.14), and chronic cough or phlegm (OR=1.09; 95% CI: 1.04, 1.13), and the differences by smoking status were statistically significant for both cough (pinteraction=0.020), phlegm (pinteraction=0.017), and cough or phlegm (pinteraction=0.004). Corresponding ORs for PM2.5 and NO2 were close to null for ever-smokers. For PM10, results did not differ significantly by smoking status, although the same pattern of stronger associations in never-smokers was seen (Table 4).

    Table 4 Chronic bronchitis in relation to air pollutants [particulate matter PM2.5, NO2, and PM10] by smoking status (never/ever): odds ratios per interquartile range (IQR) increase in PM2.5 (4.4μg/m3), NO2 (7.3ppb), and PM10 (5.8μg/m3).

    Table 4 lists exposure and outcome in the first column; the corresponding number of cases, odds ratio (95 percent confidence interval), and p values for never-smoker and ever-smoker are listed in the other columns. The p interaction values are listed in the last column.
    Exposure and outcomeNever-smokerEver-smoker 
    CasesOR (95% CI)p-ValueCasesOR (95% CI)p-ValuepInteraction
     Incident chronic bronchitis2710.92 (0.77, 1.10)0.3823670.95 (0.81, 1.11)0.5160.815
     Prevalent (at baseline)       
      Chronic bronchitis5801.18 (1.04, 1.34)0.0117710.96 (0.86, 1.07)0.4270.013
      Chronic cough1,8021.07 (1.00, 1.15)0.0531,9471.02 (0.95, 1.10)0.5450.345
      Chronic phlegm1,3621.08 (1.00, 1.17)0.0591,4141.00 (0.92, 1.09)0.9560.189
      Chronic cough or phlegm2,6321.06 (0.99, 1.12)0.0772,6391.03 (0.97, 1.10)0.2920.638
     Incident chronic bronchitis2711.03 (0.91, 1.17)0.6093670.97 (0.86, 1.10)0.6600.498
     Prevalent (at baseline)       
      Chronic bronchitis5801.10 (1.01, 1.20)0.0297711.00 (0.92, 1.08)0.9550.097
      Chronic cough1,8021.10 (1.05, 1.16)<0.0011,9471.01 (0.96, 1.06)0.6420.020
      Chronic phlegm1,3621.07 (1.01, 1.14)0.0141,4140.97 (0.92, 1.03)0.3590.017
      Chronic cough or phlegm2,6321.09 (1.04, 1.13)<0.0012,6390.99 (0.95, 1.04)0.8060.004
     Incident chronic bronchitis2711.04 (0.91, 1.18)0.5873670.95 (0.83, 1.08)0.4580.359
     Prevalent (at baseline)       
      Chronic bronchitis5801.09 (1.00, 1.20)0.0557711.06 (0.98, 1.14)0.1310.597
      Chronic cough1,8021.07 (1.01, 1.12)0.0151,9471.02 (0.97, 1.08)0.3910.260
      Chronic phlegm1,3621.10 (1.04, 1.17)0.0021,4141.04 (0.98, 1.10)0.1790.189
      Chronic cough or phlegm2,6321.08 (1.03, 1.13)<0.0012,6391.02 (0.98, 1.07)0.3520.081

    Note: For each case definition, the comparison group was all individuals without that outcome. The total number of participants with nonmissing data on all covariates was 38,006 (21,527 never-smokers and 16,479 ever-smokers) for the analysis of incident outcomes and 44,158 (24,894 never-smokers and 19,264 ever-smokers) for prevalent outcomes. Adjusted for age, race/ethnicity, body mass index, education, household income, occupational exposure to vapors/fumes or dust (ever), total pack-years, and environmental tobacco smoke exposure. Incident analysis additionally adjusted for length of follow-up time. CI, confidence interval; IQR, interquartile range; OR, odds ratio; PM, particulate matter.


    To our knowledge, this is the largest study to investigate the association between classically defined chronic bronchitis and long-term ambient air pollution exposure using a validated national exposure model. We did not find an association between incident chronic bronchitis and any of the three air pollution measures. However, exposure to higher concentrations of PM10 was significantly associated with all prevalent outcomes: chronic bronchitis, chronic cough, chronic phlegm, and chronic cough or phlegm. These findings were robust to coadjustment for PM2.5 in a two-pollutant model (Table S2). We also found NO2 exposure was significantly associated with chronic cough and chronic cough or phlegm. To the best of our knowledge, no other study has shown an association between PM10 and classically defined chronic bronchitis. These findings provide evidence that long-term ambient air pollution exposure, particularly PM10, is a risk factor for chronic bronchitis and the chronic respiratory symptoms of cough and phlegm that define it.

    Incident chronic bronchitis should be superior to prevalent chronic bronchitis for making causal inference regarding observed associations with air pollution. However, the relatively short follow-up duration (mean: 5.7 y) limited our power to detect an association between ambient air pollutants and incident chronic bronchitis. With the much larger number of cases of prevalent conditions, we had substantially higher power than for the incident analyses. One smaller study of nonsmoking Seventh Day Adventists in California has shown an association between incident chronic bronchitis and long-term exposure to PM2.5; however, levels were in excess of 20μg/m3, a concentration almost double that observed in our study (Abbey et al. 1995).

    Comparison to previous studies is limited due to substantial variability in defining chronic bronchitis and exposure estimation methods. The observed incidence rate of 2.5 cases per 1,000 person-years and prevalence of 2.9% are at the low end of the range reported in the literature (Cai et al. 2014; Cerveri et al. 2001; Huchon et al. 2002; Kim et al. 2011; Sobradillo et al. 1999). However, our study population was more than half nonsmoking women, and our estimates are in agreement with study populations with similar demographics (Montes De Oca et al. 2012; Sunyer et al. 2006). National prevalence and incidence figures for chronic bronchitis are lacking because they rely on physician diagnosis rather than the classical symptom-based diagnostic criteria (American Lung Association 2013). Including participant-reported physician diagnosis greatly increases the prevalence of chronic bronchitis in this study and likely elsewhere (Schikowski et al. 2005).

    Our study provides evidence that PM10 exposure is a risk factor for chronic bronchitis, while the existing literature suggests associations between PM10 and various respiratory symptoms. A large cross-sectional study in Switzerland found an association between increased prevalence of chronic cough and phlegm with PM10 exposure among never-smokers (Zemp et al. 1999). The European Study of Cohorts for Air Pollution Effects (ESCAPE) meta-analysis of five European cohorts similarly showed an association between PM10 and prevalent chronic phlegm, but not chronic bronchitis, in never-smokers (Cai et al. 2014). A French study of elderly adults demonstrated increased prevalence of chronic cough associated with PM10 exposure (Bentayeb et al. 2010a). Furthermore, in the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA) cohort, decline in PM10 over time was associated with a reduction in chronic cough and phlegm (Schindler et al. 2009). Our study suggests long-term PM10 exposure is associated with prevalent chronic bronchitis, the distinct clinical entity, as well as the associated symptoms that define it.

    In contrast to PM2.5, which deposits within the distal alveoli, the preferential deposition of coarse particles within the conducting airways of the tracheobronchial tree provides biologic plausibility for the association between PM10 and chronic bronchitis (Carvalho et al. 2011). Chronic airway epithelial inflammation and mucus metaplasia are the pathologic bases of chronic bronchitis (Kim and Criner 2013). Chronic bronchitis is associated with narrowing and mucus plugging of the nonalveolated conducting airways (Matsuba and Thurlbeck 1973). PM has been frequently implicated in triggering pro-inflammatory cascades within airway epithelial cells (Øvrevik et al. 2015). Certain PM10 components, including transition metals and endotoxins, have been shown to drive airway inflammation and mucus hypersecretion via upregulation of transcription factors and generation of reactive oxygen species and oxidative stress (Longphre et al. 2000; Øvrevik et al. 2015).

    For NO2, we saw no associations with prevalent chronic bronchitis or chronic phlegm in the main analyses; however, there were positive associations with both chronic cough and chronic cough or phlegm. These associations were robust to coadjustment for PM2.5. Adjustment for PM10, in an attempt to isolate the effect of NO2, was limited by the strong correlation between these copollutants. Previous studies have shown inconsistent associations between respiratory symptoms and NO2. The ESCAPE meta-analysis showed no significant association between NO2 and chronic bronchitis, cough, or phlegm (Cai et al. 2014). The German Study on the Influence of Air Pollution on Lung, Inflammation and Aging (SALIA) cohort analysis showed association with cough, but not chronic bronchitis, while the Swiss SAPALDIA study showed no association overall (Schikowski et al. 2005; Zemp et al. 1999). In both the SALIA and SAPALDIA studies, conducted in the 1990s, the annual mean NO2 levels were more than double those in our study.

    Among never-smokers, associations between prevalent symptoms and exposures were stronger for all pollutants. If we limit our interpretation of the stratified analyses to those with statistically significant interactions, we find stronger evidence for associations between both NO2 and PM2.5 and our prevalent outcomes among never-smokers. The finding of stronger associations with these two pollutants and outcomes in never-smokers is consistent with some previous literature findings. In the Swiss SAPALDIA study, NO2 was related to chronic bronchitis only among nonsmokers. For PM2.5, the ESCAPE meta-analysis found a positive association between chronic cough and PM2.5 only among never-smokers (Cai et al. 2014). NO2 was associated with chronic cough in a study of about 4,700 women in Germany, 74% of whom were never-smokers (Schikowski et al. 2005).

    The reason for stronger associations with NO2 in never-smokers in our study and others is unclear. Perhaps the airways of smokers are more tolerant to the irritant effects of ambient NO2 than nonsmokers who, as a result, are dissuaded from smoking because of greater sensitivity to these effects. Alternatively, the effects of compounds in cigarette smoke might swamp the effects of ambient NO2 exposure. Stronger associations with PM2.5 in never-smokers could reflect, in part, the high dose of fine particles inhaled by smokers. The biologic effects of cigarette smoke may overwhelm the effects from long-term, low-level ambient air pollution and thus mask any association. For PM10, we saw clear associations with chronic bronchitis in all subjects, possibly related to PM10 favoring deposition in the conducting airways that are responsible for producing bronchitic symptoms. This association is apparent in the aggregate sample. In contrast, the associations between chronic bronchitis and PM2.5 and NO2 were only apparent in never-smokers. Theses pollutants have distribution patterns that tend to bypass the conducting airways for deposition and adsorption in the distal alveoli.

    Outcome misclassification was reduced by using the symptom-defined definition of chronic bronchitis. However, the symptom-based questionnaire still has limitations; due to recall bias, it likely results in inclusion of cases with recent, but not necessarily chronic, symptoms. Overlap with asthma remains possible given the clinical similarities of these conditions. Sensitivity analyses excluding subjects with a physician diagnosis of asthma and active asthma symptoms at baseline attenuated the association with all pollutants. However, the exclusion of these participants with overlapping chronic bronchitis symptoms may have eliminated true cases of chronic bronchitis and thus reduced power.

    The sensitivity analysis including individuals reporting doctor diagnosis of chronic bronchitis showed preserved associations with increased precision. Self-report of doctor diagnosis is likely to include more individuals with symptom duration shorter than the 2-y minimum required for the chronic bronchitis definition (i.e., may include participants who have received a diagnosis of acute bronchitis in the past). Given that chronic bronchitis is defined by duration of symptoms, directly asking subjects questions on cough and phlegm is preferable to asking about physician diagnosis, which requires accurate reporting by both parties. However, the association between PM10 and prevalent chronic bronchitis remains robust, even with the less strict definition, suggesting that presence of symptoms is driving the relationship, rather than the specific duration of symptoms.

    The objection has been raised that chronic bronchitis prevalence as reported on questionnaires may reflect recent symptoms and that prevalence and/or severity might then vary by season. Therefore, we undertook a sensitivity analysis adjusting for season of both baseline and follow-up questionnaire (Table S3). No change was observed in the effect estimates for outcomes associated with PM2.5 or NO2. The associations between prevalent chronic bronchitis and chronic cough and PM10 were no longer statistically significant after adjusting for season, but the effect estimates remained largely unchanged and in the anticipated direction. Season of questionnaire administration does not seem to contribute significant bias in reporting of chronic bronchitis.

    Our air pollution exposure estimates are based on a validated national model using land-use regression and spatial smoothing to capture within- and between-region air pollution variability and minimize exposure misclassification. This model is a considerable improvement over road proximity, regional fixed-monitor averages, and simple land-use regression models employed in prior chronic bronchitis investigations (Keller et al. 2014; Young et al. 2016). In addition, seasonal bias in the exposure should be mitigated by using annual averages and a chronic outcome whose case definition dictates that symptoms must span a minimum of 2 consecutive years. Air pollution estimates used year 2000 annual averages, predating baseline enrollment for all participants. While concentrations of criteria pollutants are declining nationally, spatial differences of annual average pollution concentrations account for the majority of variability in PM2.5 measurement and were relatively stable across the study period (Kim et al. 2017). However, it is acknowledged that variability in the decline in pollution levels may contribute to exposure misclassification, and the resulting biases are difficult to predict. It is plausible that our observed lack of association for incident outcomes and positive effects for prevalent symptoms could be related to variable change in pollution levels; e.g., if the most polluted regions experienced more dramatic declines in levels than the cleaner areas.

    Additional limitations exist. This study is limited to women, and the findings may not be broadly applicable to men. Outdoor ambient pollutant concentrations may not reflect the indoor exposures. Exposure measurement error owing to our use of residential addresses to characterize exposure owing both to subjects’ residential mobility, time spent away from home or indoors, and spatiotemporal trends is a limitation both of this study and epidemiological studies of air pollution health effects in general. Exposure measurement of this nature error is generally expected to bias associations toward the null rather than producing false positive associations.

    In 2006, the EPA revoked the National Ambient Air Quality Standard for annual PM10 due to insufficient data on health risks associated with long-term exposure to PM10 as opposed to the finer PM2.5 fraction (U.S. EPA 2006). The preceding long-term PM10 exposure standard was an annual average of 50μg/m3, roughly double the mean concentration experienced by participants in this study. This study provides evidence that chronic respiratory health effects occur with long-term exposure to PM10 at levels below the previous national standards. These results add to a limited body of evidence relating morbidity to long-term PM10 exposure and consequently may have policy implications both nationally and globally.


    The authors thank A. Gassett and C. Sack for data management and support with this analysis, as well as the staff at Social and Scientific Systems, Inc. and Westat, Inc. for overall study support and management of Sister Study data acquisition. The authors also thank the University of Washington Center for Clean Air Research (UW CCAR) and the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) for providing the infrastructure and development of the exposure models. MESA Air is conducted and supported by the U.S. EPA through grant RD831697 to the UW. This publication has not been formally reviewed by the U.S. EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the agency. U.S. EPA does not endorse any products or commercial services mentioned in this publication. The authors also thank the thousands of women who participate in the Sister Study.

    This work was supported by the University of Washington Pulmonary and Critical Care Medicine Training Grant (T32 HL007287), the Intramural Research Program of the National Institutes of Health (NIH), and the National Institute of Environmental Health Sciences (NIEHS) (Z01 ES044005 and ES043012). Exposure modeling was supported by the U.S. EPA (RD831697 and R834796), and Biostatistics, Epidemiologic, and Bioinformatic Training in Environmental Health Training Grant was from NIEHS (T32ES015459).


    • Abbey DE, Ostro BE, Petersen F, Burchette RJ. 1995. Chronic respiratory symptoms associated with estimated long-term ambient concentrations of fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) and other air pollutants. J Expo Anal Environ Epidemiol 5(2):137–159, PMID: 7492903. MedlineGoogle Scholar
    • American Lung Association.2013. Trends in COPD (Chronic Bronchitis and Emphysema): Morbidity and Mortality. [accessed 9 May 2017]. Google Scholar
    • American Thoracic Society. 1995. Standards for the diagnosis and care of patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 152(5 Part 2):S77–121, PMID: 7582322. MedlineGoogle Scholar
    • Bentayeb M, Helmer C, Raherison C, Dartigues JF, Tessier JF, Annesi-Maesano I. 2010. Bronchitis-like symptoms and proximity air pollution in French elderly. Respir Med 104(6):880–888, PMID: 20129767, doi:10.1016/j.rmed.2010.01.004. Crossref, MedlineGoogle Scholar
    • Burgel PR, Nesme-Meyer P, Chanez P, Caillaud D, Carré P, Perez T, et al.2009. Cough and sputum production are associated with frequent exacerbations and hospitalizations in COPD subjects. Chest 135(4):975–982, PMID: 19017866, doi:10.1378/chest.08-2062. Crossref, MedlineGoogle Scholar
    • Cai Y, Schikowski T, Adam M, Buschka A, Carsin AE, Jacquemin B, et al.2014. Cross-sectional associations between air pollution and chronic bronchitis: an ESCAPE meta-analysis across five cohorts. Thorax 69(11):1005–1014, PMID: 25112730, doi:10.1136/thoraxjnl-2013-204352. Crossref, MedlineGoogle Scholar
    • Carvalho TC, Peters JI, Williams RO. 2011. Influence of particle size on regional lung deposition–what evidence is there?Int J Pharm 406(1–2):1–10, PMID: 21232585, doi:10.1016/j.ijpharm.2010.12.040. Crossref, MedlineGoogle Scholar
    • Cerveri I, Accordini S, Verlato G, Corsico A, Zoia MC, Casali L, et al.2001. Variations in the prevalence across countries of chronic bronchitis and smoking habits in young adults. Eur Respir J 18(1):85–92, PMID: 11510810. Crossref, MedlineGoogle Scholar
    • de Marco R, Accordini S, Cerveri I, Corsico A, Antó JM, Künzli N, et al.2007. Incidence of chronic obstructive pulmonary disease in a cohort of young adults according to the presence of chronic cough and phlegm. Am J Respir Crit Care Med 175(1):32–39, PMID: 17008642, doi:10.1164/rccm.200603-381OC. Crossref, MedlineGoogle Scholar
    • Dinse GE, Lagakos SW. 1983. Regression analysis of tumour prevalence data. J R Stat Soc Ser C Appl Stat 32(3):236, doi:10.2307/2347946. CrossrefGoogle Scholar
    • Guerra S, Sherrill DL, Venker C, Ceccato CM, Halonen M, Martinez FD. 2009. Chronic bronchitis before age 50 years predicts incident airflow limitation and mortality risk. Thorax 64(10):894–900, PMID: 19581277, doi:10.1136/thx.2008.110619. Crossref, MedlineGoogle Scholar
    • Huchon GJ, Vergnenègre A, Neukirch F, Brami G, Roche N, Preux PM. 2002. Chronic bronchitis among French adults: high prevalence and underdiagnosis. Eur Respir J 20(4):806–812, PMID: 12412668, doi:10.1183/09031936.02.00042002. Crossref, MedlineGoogle Scholar
    • Keller JP, Olives C, Kim SY, Sheppard L, Sampson PD, Szpiro AA, et al.2014. A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution. Environ. Health Perspect 123(4):301–309, PMID: 25398188, doi:10.1289/ehp.1408145. LinkGoogle Scholar
    • Kim SY, Olives C, Sheppard L, Sampson PD, Larson TV, Keller JP, et al.2017. Historical prediction modeling approach for estimating long-term concentrations of PM2.5 in cohort studies before the 1999 implementation of widespread monitoring. Environ Health Perspect 125(1):38–46, PMID: 27340825, doi:10.1289/EHP131. LinkGoogle Scholar
    • Kim V, Criner GJ. 2013. Chronic bronchitis and chronic obstructive pulmonary disease. Am J Respir Crit Care Med 187(3):228–237, PMID: 23204254, doi:10.1164/rccm.201210-1843CI. Crossref, MedlineGoogle Scholar
    • Kim V, Han MK, Vance GB, Make BJ, Newell JD, Hokanson JE, et al.2011. The chronic bronchitic phenotype of COPD: an analysis of the COPDGene Study. Chest 140(3):626–633, PMID: 21474571, doi:10.1378/chest.10-2948. Crossref, MedlineGoogle Scholar
    • Lindberg A, Jonsson AC, Rönmark E, Lundgren R, Larsson LG, Lundbäck B. 2005. Ten-year cumulative incidence of COPD and risk factors for incident disease in a symptomatic cohort. Chest 127(5):1544–1552, PMID: 15888826, doi:10.1378/chest.127.5.1544. Crossref, MedlineGoogle Scholar
    • Longphre M, Li D, Li J, Matovinovic E, Gallup M, Samet JM, et al.2000. Lung mucin production is stimulated by the air pollutant residual oil fly ash. Toxicol Appl Pharmacol 162(2):86–92, PMID: 10637131, doi:10.1006/taap.1999.8838. Crossref, MedlineGoogle Scholar
    • Martinez CH, Kim V, Chen Y, Kazerooni EA, Murray S, Criner GJ, et al.2014. The clinical impact of non-obstructive chronic bronchitis in current and former smokers. Respir Med 108(3):491–499, PMID: 24280543, doi:10.1016/j.rmed.2013.11.003. Crossref, MedlineGoogle Scholar
    • Matsuba K, Thurlbeck WM. 1973. Disease of the small airways in chronic bronchitis. Am Rev Respir Dis 107(4):552–558, PMID: 4697664, doi:10.1164/arrd.1973.107.4.552. Crossref, MedlineGoogle Scholar
    • Montes de Oca M, Halbert RJ, Lopez MV, Perez-Padilla R, Tálamo C, Moreno D, et al.2012. The chronic bronchitis phenotype in subjects with and without COPD: the PLATINO study. Eur Respir J 40(1):28–36, PMID: 22282547, doi:10.1183/09031936.00141611. Crossref, MedlineGoogle Scholar
    • Øvrevik J, Refsnes M, Låg M, Holme J, Schwarze PE. 2015. Activation of proinflammatory responses in cells of the airway mucosa by particulate matter: oxidant- and non-oxidant-mediated triggering mechanisms. Biomolecules 5(3):1399–1440, PMID: 26147224, doi:10.3390/biom5031399. Crossref, MedlineGoogle Scholar
    • Peacock JL, Anderson HR, Bremner SA, Marston L, Seemungal TA, Strachan DP, et al.2011. Outdoor air pollution and respiratory health in patients with COPD. Thorax 66(7):591–596, PMID: 21459856, doi:10.1136/thx.2010.155358. Crossref, MedlineGoogle Scholar
    • Peel JL, Tolbert PE, Klein M, Metzger KB, Flanders WD, Todd K, et al.2005. Ambient air pollution and respiratory emergency department visits. Epidemiology 16(2):164–174, PMID: 15703530, doi:10.1097/01.ede.0000152905.42113.db. Crossref, MedlineGoogle Scholar
    • Sampson PD, Richards M, Szpiro AA, Bergen S, Sheppard L, Larson TV, et al.2013. A regionalized national universal kriging model using Partial Least Squares regression for estimating annual PM2.5 concentrations in epidemiology. Atmos Environ (1994) 75:383–392, PMID: 24015108, doi:10.1016/j.atmosenv.2013.04.015. Crossref, MedlineGoogle Scholar
    • Schikowski T, Mills IC, Anderson HR, Cohen A, Hansell A, Kauffmann F, et al.2014. Ambient air pollution: a cause of COPD?Eur Respir J 43(1):250–263, PMID: 23471349, doi:10.1183/09031936.00100112. Crossref, MedlineGoogle Scholar
    • Schikowski T, Sugiri D, Ranft U, Gehring U, Heinrich J, Wichmann HE, et al.2005. Long-term air pollution exposure and living close to busy roads are associated with COPD in women. Respir Res 6:152, PMID: 16372913, doi:10.1186/1465-9921-6-152. Crossref, MedlineGoogle Scholar
    • Schindler C, Keidel D, Gerbase MW, Zemp E, Bettschart R, Brändli O, et al.2009. Improvements in PM10 exposure and reduced rates of respiratory symptoms in a cohort of Swiss adults (SAPALDIA). Am J Respir Crit Care Med 179(7):579–587, PMID: 19151198, doi:10.1164/rccm.200803-388OC. Crossref, MedlineGoogle Scholar
    • Sobradillo V, Miravitlles M, Jiménez CA, Gabriel R, Viejo JL, Masa JF, et al.1999. [Epidemiological study of chronic obstructive pulmonary disease in Spain (IBERPOC): prevalence of chronic respiratory symptoms and airflow limitation]. Arch. Bronconeumol 35(4):159–166, PMID: 10330536. Crossref, MedlineGoogle Scholar
    • Sunyer J. 2001. Urban air pollution and chronic obstructive pulmonary disease: a review. Eur Respir J 17(5):1024–1033, PMID: 11488305, doi:10.1183/09031936.01.17510240. Crossref, MedlineGoogle Scholar
    • Sunyer J, Jarvis D, Gotschi T, Garcia-Esteban R, Jacquemin B, Aguilera I, et al.2006. Chronic bronchitis and urban air pollution in an international study. Occup Environ Med 63(12):836–843, PMID: 16847030, doi:10.1136/oem.2006.027995. Crossref, MedlineGoogle Scholar
    • U.S. EPA (Environmental Protection Agency). 2006. National Ambient Air Quality Standards for Particulate Matter. Final Rule. [accessed 29 December 2017]. Google Scholar
    • Young MT, Bechle MJ, Sampson PD, Szpiro AA, Marshall JD, Sheppard L, et al.2016. Satellite-based NO2 and model validation in a national prediction model based on universal kriging and land-use regression. Environ Sci Technol 50(7):3686–3694, PMID: 26927327, doi:10.1021/acs.est.5b05099. Crossref, MedlineGoogle Scholar
    • Zemp E, Elsasser S, Schindler C, Künzli N, Perruchoud AP, Domenighetti G, et al.1999. Long-term ambient air pollution and respiratory symptoms in adults (SAPALDIA study). The SAPALDIA Team. Am J Respir Crit Care Med 159(4 Pt 1):1257–1266, PMID: 10194174, doi:10.1164/ajrccm.159.4.9807052. Crossref, MedlineGoogle Scholar