Abstract
Rationale: Ambient air pollution has been suggested as a risk factor for chronic obstructive pulmonary disease (COPD). However, there is a lack of longitudinal studies to support this assertion.
Objectives: To investigate the associations of long-term exposure to elevated traffic-related air pollution and woodsmoke pollution with the risk of COPD hospitalization and mortality.
Methods: This population-based cohort study included a 5-year exposure period and a 4-year follow-up period. All residents aged 45–85 years who resided in Metropolitan Vancouver, Canada, during the exposure period and did not have known COPD at baseline were included in this study (n = 467,994). Residential exposures to traffic-related air pollutants (black carbon, particulate matter <2.5 μm in aerodynamic diameter, nitrogen dioxide, and nitric oxide) and woodsmoke were estimated using land-use regression models and integrating changes in residences during the exposure period. COPD hospitalizations and deaths during the follow-up period were identified from provincial hospitalization and death registration databases.
Measurements and Main Results: An interquartile range elevation in black carbon concentrations (0.97 × 10−5/m, equivalent to 0.78 μg/m3 elemental carbon) was associated with a 6% (95% confidence interval, 2–10%) increase in COPD hospitalizations and a 7% (0–13%) increase in COPD mortality after adjustment for covariates. Exposure to higher levels of woodsmoke pollution (tertile 3 vs. tertile 1) was associated with a 15% (2–29%) increase in COPD hospitalizations. There were positive exposure–response trends for these observed associations.
Conclusions: Ambient air pollution, including traffic-related fine particulate pollution and woodsmoke pollution, is associated with an increased risk of COPD.
Ambient air pollution has been suggested as a risk factor for chronic obstructive pulmonary disease (COPD). However, the findings from previous epidemiologic studies are not consistent, and there is a lack of longitudinal studies to support this assertion.
This study found that long-term exposure to elevated traffic-related fine particulate air pollution, indicated by black carbon, was associated with an increased risk of COPD hospitalization and mortality; long-term exposure to elevated woodsmoke pollution was associated with an increased risk of COPD hospitalization. This study provides evidence to suggest that ambient air pollution, including woodsmoke and traffic-related fine particulate pollution, is associated with an increased risk of COPD.
Chronic obstructive pulmonary disease (COPD), a leading cause of morbidity and mortality worldwide (1), is characterized by progressive airflow obstruction related to a chronic inflammatory response in the lung (2). Although smoking has been regarded as the most important risk factor for COPD (3, 4), accumulating evidence has demonstrated that many COPD cases cannot be explained by smoking history. COPD is also a common chronic disease among never-smokers; worldwide, about 25–45% of patients with COPD are never-smokers (5). In most studies, the population-attributable fraction for smoking as a cause of COPD is less than 80% (6). It has been suggested that nonsmoking risk factors may also play important roles in the development and progression of COPD (5, 6).
There is some evidence that ambient air pollution is associated with COPD (4–7), but existing evidence is mostly limited to cross-sectional studies, and there is a lack of longitudinal studies to support this assertion (8). A recent Danish cohort study reported that long-term exposure to elevated traffic-related air pollution was associated with increased hospital admissions for COPD (9). This study provides strong longitudinal evidence that traffic-related air pollution is a risk factor for COPD hospitalization. However, in the Danish study, nitrogen dioxide (NO2) and nitrogen oxides (NOx) were used as surrogates for traffic-related air pollution, leaving unclear which pollutants were responsible for the observed associations. Identifying specific air pollutants responsible for COPD outcomes is important for evidence-based policy making and cost-effective air pollution interventions. In addition, although there is strong evidence linking indoor biomass smoke to COPD in developing countries (6), it is unknown whether exposure to outdoor woodsmoke pollution is associated with COPD outcomes. Therefore, we conducted a large population-based cohort study in Metropolitan Vancouver, Canada, to investigate the relationships between long-term exposure to ambient air pollution, including four major traffic-related air pollutants and woodsmoke, and the risk of COPD hospitalization and mortality.
In British Columbia, Canada, the provincial health insurance program provides universal health care coverage for all residents. We linked various administrative databases by specific personal health insurance numbers to assemble a population-based cohort. We have previously reported the associations of coronary heart disease (CHD) with traffic noise and traffic-related air pollution in this cohort (10–12).
This study included a 5-year exposure period (January 1994 to December 1998) and a 4-year follow-up period (January 1999 to December 2002). All Metropolitan Vancouver residents who met the following inclusion criteria at baseline (January 1999) were included in this cohort: (1) registered with the provincial health insurance plan; (2) resided in the study region during the 5-year exposure period; (3) aged 45–85 years; and (4) had no previous physician diagnosis of COPD.
During the 5-year exposure period, individual exposures to ambient air pollutants were estimated at each person’s residential postal code centroid using land use regression models (13–16). During the 4-year follow-up period, COPD hospitalization cases and death cases were identified from the linked health databases. The associations between each air pollutant and COPD outcomes were examined using the Cox proportional hazards regression model. The study was approved by the Behavioral Research Ethics Board of The University of British Columbia (Certificate # H08-00185).
The air pollution exposure assessment method has been described in detail elsewhere (13–16). Briefly, we used high spatial resolution land use regression models to estimate residential exposure to traffic-related air pollutants including black carbon, particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5), NO2, and nitric oxide (NO) in 2003 (13, 16). These estimates were combined with temporally resolved air quality monitoring data to calculate monthly concentrations of each pollutant for each residential postal code area during the 5-year exposure period from 1994 to 1998 (14). These air pollution data were assigned to study subjects through their residential postal codes. After integrating changes in residences, we calculated 5-year average concentrations of each pollutant for each study subject.
Exposure to woodsmoke was assessed using mobile monitoring of ambient PM2.5 and fixed-location measurements of a specific woodsmoke tracer, levoglucosan (15, 16). Because of greater uncertainties in spatial patterns and absolute levels of woodsmoke compared with traffic-related air pollutants, woodsmoke levels were divided into tertiles, representing low, medium, and high woodsmoke pollution areas (15). The woodsmoke exposures were assigned to study subjects through their residential postal codes.
In the study region, the size of an area covered by a postal code depends on its population density. In urban areas, a residential postal code typically represents a high-rise building or one side of a city block. However, in rural areas it represents a larger area. On average, a residential postal code includes approximately 35 persons.
We used International Statistical Classification of Diseases, 9th Revision (ICD-9) codes 490–492 and 496 or 10th Revision (ICD-10) codes J40–J44 to identify COPD cases during the 4-year follow-up period.
COPD hospitalization case: a hospitalization record with COPD as the principal diagnosis (the most responsible diagnosis) for a hospital admission in the provincial hospitalization database.
COPD death case: a death certificate record with COPD as the cause of death in the provincial death registration database.
A broader definition was used to identify prior COPD cases that were subsequently excluded from the analyses. Subjects who had a hospitalization record with COPD as the principal or primary diagnosis (secondary diagnosis that had a substantial influence on hospital length of stay) in the 8 years before baseline (based on data from January 1991 to December 1998) were regarded as prior COPD cases.
We included age, sex, preexisting comorbid conditions, and neighborhood socioeconomic status (SES) as covariates in the data analysis.
Preexisting comorbid conditions included asthma (ICD-9, 493; ICD-10, J45–J46); diabetes (ICD-9, 250; ICD-10, E10–E14); CHD (ICD-9, 410–414 and 429.2; ICD-10, I20–I25); and hypertensive heart disease (ICD-9, 401–404; ICD-10, I10–I13). To sufficiently control for the influence of these comorbid conditions, all diagnoses (up to 16 diagnoses) in a hospitalization record were used to identify subjects with these conditions. The presence of at least one hospitalization record with the diagnosis of a specified disease during January 1991 to December 1998 was defined as the presence of that comorbid condition.
Neighborhood SES reflects neighborhood characteristics and is a risk factor for chronic diseases (17). Furthermore, individual SES is a known risk factor for COPD (18, 19). Individual SES data were not available for the present study. Therefore, we used neighborhood SES to approximate individual SES (20, 21). We have previously demonstrated that neighborhood SES is strongly associated with individual household income, educational attainment, marital status, and daily fruit and vegetable intakes in a subgroup of this cohort (11). Neighborhood-income quintiles from the 2001 Census were assigned to study subjects through their residential postal codes (12).
The baseline characteristics were compared between subjects who were hospitalized or died from COPD and subjects who did not have COPD during the follow-up period using a chi-square test for categorical variables and t test for continuous variables. Correlations between these air pollutants were examined using Spearman rank correlation.
We used the Cox proportional hazards regression model to determine the associations between air pollutants and COPD outcomes. Person-days of follow-up were calculated for each person from baseline to the date of the first COPD hospitalization, COPD death, or end of follow-up. For those who died from other diseases or those who moved out of the province, person-days were calculated from baseline to the date of death or the last known date in the province. We calculated relative risks (RRs) for COPD outcomes in relation to an interquartile range (IQR) elevation in the concentrations of each pollutant using bivariable and multivariable models. In the multivariable analyses, we adjusted for age, sex, asthma, diabetes, CHD, hypertensive heart disease, neighborhood income quintiles, and copollutants.
For those pollutants that were associated with COPD hospitalization and mortality, we further examined exposure–response trends using natural cubic spline functions with four degrees of freedom. Furthermore, we performed stratified analyses to examine effect modification by age, sex, preexisting comorbid conditions, and neighborhood SES.
At baseline, 467,994 subjects met the inclusion criteria and were included in the cohort. Of these subjects, 47% were male; the average age (standard deviation) was 60 (11) years (range, 45–83 yr). During the 4-year follow-up period, 38,377 (8%) subjects were lost to follow-up because of moving out of the province or dying from other diseases.
Compared with the subjects without COPD, subjects who experienced COPD hospitalization or mortality were older and more likely to be male; live in lower SES neighborhoods; and have preexisting comorbid conditions including asthma, diabetes, CHD, and hypertensive heart disease, especially for death cases (Table 1).
| Subjects without COPD (n = 465,360) | Hospitalization Cases (n = 2,299)† | Mortality Cases (n = 541)† | |
| Men, % | 47.1 | 48.0‡ | 62.3 |
| Age (mean ± SD), yr | 59.4 ± 10.7 | 69.9 ± 9.1 | 74.6 ± 6.8 |
| Comorbid conditions, % | |||
| Asthma | 0.8 | 9.5 | 6.8 |
| Diabetes | 2.8 | 6.2 | 7.8 |
| Coronary heart disease | 4.9 | 14.8 | 14.4 |
| Hypertensive heart disease | 5.4 | 15.6 | 15.7 |
| Any of the above | 10.2 | 31.0 | 32.2 |
| Neighborhood income quintiles, % | |||
| 1 (lowest) | 18.3 | 29.2 | 32.0 |
| 2 | 19.0 | 20.3 | 22.2 |
| 3 | 19.5 | 19.1 | 16.3 |
| 4 | 20.5 | 17.0 | 13.1 |
| 5 (highest) | 22.7 | 14.5 | 16.5 |
Descriptive statistics for these air pollutants are summarized in Table 2. Overall, the concentrations of these air pollutants are weakly correlated with each other, with the exception of the high correlation between NO2 and NO (r = 0.66).
| Correlation Coefficient | ||||||||
| Pollutant | Mean ± SD | Median | IQR | Range | BC | PM2.5 | NO2 | NO |
| BC, 10−5/m | 1.50 ± 1.10* | 1.02 | 0.83–1.80 | 0–4.98 | 1.00 | — | — | — |
| PM2.5, μg/m3 | 4.10 ± 1.64 | 4.03 | 3.22–4.80 | 0–10.24 | 0.13 | 1.00 | — | — |
| NO2, μg/m3 | 32.2 ± 8.1 | 30.7 | 26.7–35.1 | 15.3–57.7 | 0.39 | 0.47 | 1.00 | — |
| NO, μg/m3 | 32.1 ± 12.0 | 29.3 | 24.3–37.5 | 8.8–126.0 | 0.42 | 0.43 | 0.66 | 1.00 |
During the follow-up period, 2,299 subjects were hospitalized for COPD (hospitalization rate, 13.0 per 10,000 person-years), and 541 subjects died from COPD (mortality rate, 3.1 per 10,000 person-years). Exposure to black carbon was strongly associated with the risk of COPD hospitalization and mortality. Adjustment for age, sex, preexisting comorbid conditions (asthma, diabetes, CHD, and hypertensive heart disease), and neighborhood SES greatly attenuated the risk estimates, whereas additional adjustment for copollutants (PM2.5 and NO2) did not change the risk estimates. In the final model, an IQR elevation in black carbon concentrations (0.97 × 10−5/m, equivalent to 0.78 μg/m3 elemental carbon) was associated with a 6% (95% confidence interval [CI], 2–10%) increase in COPD hospitalization and a 7% (95% CI, 0–14%) increase in COPD mortality (Table 3).
| Black Carbon (0.97 × 10−5/m)* | PM2.5 (1.58 μg/m3)* | NO2 (8.40 μg/m3)* | NO (13.2 μg/m3)* | |
| Hospitalization | ||||
| Model 1: unadjusted single pollutant model | 1.14 (1.10–1.17) | 1.15 (1.11–1.19) | 1.17 (1.12–1.21) | 1.11 (1.07–1.16) |
| Model 2: + age, sex, SES | 1.06 (1.02–1.09) | 1.02 (0.98–1.06) | 1.00 (0.96–1.05) | 1.03 (0.98–1.07) |
| Model 3: + asthma, diabetes, CHD, HHD | 1.06 (1.02–1.09) | 1.02 (0.98–1.06) | 1.00 (0.96–1.05) | 1.03 (0.98–1.08) |
| Model 4: + two other pollutants† | 1.06 (1.02–1.10) | 1.02 (0.98–1.07) | 0.98 (0.93–1.03) | 0.98 (0.93–1.04) |
| Mortality | ||||
| Model 1: unadjusted single pollutant model | 1.17 (1.09–1.24) | 1.17 (1.08–1.26) | 1.24 (1.15–1.33) | 1.15 (1.06–1.25) |
| Model 2: + age, sex, SES | 1.07 (1.00–1.14) | 1.02 (0.94–1.10) | 1.04 (0.95–1.12) | 1.06 (0.97–1.16) |
| Model 3: + asthma, diabetes, CHD, HHD | 1.07 (1.00–1.14) | 1.02 (0.94–1.10) | 1.04 (0.96–1.13) | 1.06 (0.97–1.16) |
| Model 4: + two other pollutants† | 1.07 (1.00–1.14) | 1.00 (0.92–1.09) | 1.03 (0.93–1.13) | 1.02 (0.91–1.13) |
In unadjusted single-pollutant models, PM2.5, NO2, and NO were associated with COPD hospitalization and mortality, with a similar strength of association to that of black carbon. However, after adjustment for covariates, these air pollutants were not significantly associated with COPD hospitalization and mortality (Table 3).
Based on natural cubic spline models with four degrees of freedom, there was a positive exposure–response relationship between black carbon and COPD hospitalization (Figure 1A) and mortality (Figure 1B). When stratified by quintiles of black carbon concentrations, the linear trend was stronger for COPD hospitalization (P < 0.001) compared with COPD mortality (P = 0.084) (see Table E1 in the online supplement). In contrast, there were no discernible exposure–response trends for PM2.5, NO2, and NO.
Figure 1. Exposure–response relationships of black carbon with chronic obstructive pulmonary disease (COPD) hospitalization (A) and mortality (B). Adjusted for age; sex; preexisting comorbid conditions (asthma, diabetes, coronary heart disease, and hypertensive heart disease); neighborhood income quintiles; and copollutants (particulate matter <2.5 μm in aerodynamic diameter, nitrogen dioxide). The vertical lines along x-axes indicate the minimum, 25th percentile, median, 75th percentile, and maximum of black carbon concentrations.
[More] [Minimize]Stratified analyses showed that the risk of COPD hospitalization in relation to an IQR elevation in black carbon concentrations was higher for subjects without diabetes and for subjects living in lower SES neighborhoods (Table 4). The risk of COPD mortality was higher for subjects younger than 65 years, with CHD, or living in lower SES neighborhoods. Nevertheless, the differences were not statistically significant, with the exception of higher COPD mortality for subjects younger than 65 years compared with those 65 years and older (Table 4).
| Hospitalization | Mortality | |||||
| No. of Cases | RR (95% CI) | P for Interaction | No. of Cases | RR (95% CI) | P for Interaction | |
| Age | 0.501 | 0.003 | ||||
| <65 yr | 592 | 1.07 (1.00–1.15) | 49 | 1.31 (1.07–1.60) | ||
| ≥65 yr | 1,707 | 1.06 (1.02–1.10) | 492 | 1.04 (0.97–1.12) | ||
| Sex | 0.260 | 0.880 | ||||
| Men | 1,104 | 1.07 (1.02–1.13) | 337 | 1.06 (0.97–1.15) | ||
| Women | 1,195 | 1.05 (1.00–1.10) | 204 | 1.08 (0.97–1.20) | ||
| Asthma | 0.899 | 0.872 | ||||
| No | 2,080 | 1.06 (1.02–1.10) | 504 | 1.06 (0.99–1.14) | ||
| Yes | 219 | 1.07 (0.96–1.19) | 37 | 1.08 (0.83–1.40) | ||
| Diabetes | 0.224 | 0.740 | ||||
| No | 2,157 | 1.06 (1.03–1.10) | 499 | 1.07 (0.99–1.14) | ||
| Yes | 142 | 0.99 (0.86–1.14) | 42 | 1.07 (0.83–1.37) | ||
| CHD | 0.469 | 0.322 | ||||
| No | 1,959 | 1.06 (1.02–1.10) | 463 | 1.05 (0.97–1.13) | ||
| Yes | 340 | 1.04 (0.95–1.14) | 78 | 1.17 (0.99–1.39) | ||
| Hypertensive disease | 0.602 | 0.879 | ||||
| No | 1,941 | 1.06 (1.02–1.10) | 456 | 1.07 (0.99–1.15) | ||
| Yes | 358 | 1.06 (0.98–1.16) | 85 | 1.07 (0.90–1.27) | ||
| Comorbid conditions† | 0.211 | 0.509 | ||||
| No | 1,587 | 1.06 (1.02–1.11) | 367 | 1.04 (0.96–1.13) | ||
| Yes | 712 | 1.04 (0.98–1.11) | 174 | 1.11 (0.99–1.25) | ||
| Income quintiles | 0.561 | 0.103 | ||||
| 1–3 (low) | 1,576 | 1.08 (1.03–1.12) | 381 | 1.11 (1.03–1.20) | ||
| 4–5 (high) | 723 | 1.04 (0.98–1.11) | 160 | 0.98 (0.85–1.14) | ||
The risk of COPD and CHD in relation to an IQR elevation in black carbon concentrations is presented in Figure E1. For the two diseases, risk estimates for hospitalization are more precise than that for mortality, whereas risk estimates for mortality are greater than that for hospitalization. Preexisting CHD had little influence on COPD hospitalization, but increased COPD mortality. This is also the case for preexisting COPD on CHD outcomes.
There were 467,994 (96%) subjects with woodsmoke data, from which we identified 400,254 subjects (86%) who did not change their woodsmoke exposure status during the 5-year exposure period. Compared with subjects living in lower woodsmoke areas (tertile 1), subjects living in higher woodsmoke areas (tertile 3) were 15% (95% CI, 2–29%) more likely to be hospitalized for COPD. Potential confounding variables including copollutants (black carbon, PM2.5, and NO2) had little influence on the risk estimate. However, there was no discernible effect of exposure to woodsmoke on COPD mortality (Table 5). For this subgroup, black carbon, PM2.5, NO2, and NO had almost the same risk estimates for COPD outcomes as those presented in Table 3 for the whole cohort; further adjustment for woodsmoke tertiles did not change the risk estimates, indicating that in the whole cohort woodsmoke exposure would not affect the associations between the traffic-related air pollutants and COPD outcomes.
| Tertile 1 (n = 116,230) | Tertile 2 (n = 144,289) | Tertile 3 (n = 139,735) | |
| Hospitalization | |||
| No. of cases | 523 | 730 | 734 |
| Model 1: unadjusted single pollutant model | 1.00 | 1.12 (1.00–1.25) | 1.17 (1.04–1.31) |
| Model 2: + age, sex, SES | 1.00 | 1.07 (0.96–1.20) | 1.18 (1.05–1.32) |
| Model 3: + asthma, diabetes, CHD, HHD | 1.00 | 1.07 (0.96–1.20) | 1.18 (1.05–1.32) |
| Model 4: + three other pollutants† | 1.00 | 1.08 (0.96–1.20) | 1.15 (1.02–1.29) |
| Mortality | |||
| No. of cases | 156 | 174 | 146 |
| Model 1: unadjusted single pollutant model | 1.00 | 0.89 (0.72–1.11) | 0.78 (0.62–0.98) |
| Model 2: + age, sex, SES | 1.00 | 0.84 (0.68–1.04) | 0.81 (0.65–1.02) |
| Model 3: + asthma, diabetes, CHD, HHD | 1.00 | 0.84 (0.67–1.04) | 0.80 (0.64–1.01) |
| Model 4: + three other pollutants† | 1.00 | 0.84 (0.67–1.04) | 0.81 (0.64–1.03) |
In this large population-based cohort study, we found that long-term exposure to higher levels of black carbon was associated with an increased risk of COPD hospitalization and mortality. We also found that living in areas exposed to elevated woodsmoke pollution was associated with an increased risk of COPD hospitalization. There were positive exposure–response trends for these observed associations. In metropolitan areas, black carbon can be regarded as an indicator of traffic-related fine particulate air pollution (22–24). In this study region, the main source of ambient woodsmoke is residential wood burning for heating during the winter months (15).
We did not find significant associations of COPD with PM2.5, NO2, and NO. For these pollutants, such as PM2.5, in addition to road traffic, there are numerous other sources; they are also produced in the atmosphere through physical-chemical transformation (25). Their spatial distributions are thus more homogeneous (e.g., coefficient of variation was 40% for PM2.5, 25% for NO2, and 37% for NO). The null associations might reflect the lack of spatial variability of these pollutants in this intraurban study. However, black carbon is a primary particulate pollutant. In metropolitan areas, it is emitted directly from diesel- and gasoline-powered motor vehicles (22). The concentrations of black carbon are strongly dependent on traffic exhaust emissions and the distance from major roadways (25). Therefore, its spatial distribution is more heterogeneous (coefficient of variation is 73%); study subjects exposed to different levels of black carbon are more likely to exhibit different health outcomes.
Most previous studies have used NO2 as an indicator of traffic-related air pollution to examine the associations with COPD, and the findings are not fully consistent. In a recent population-based cohort study of 57,053 Danish adults, Andersen and coworkers (9) found that long-term exposure to higher levels of NO2 was associated with an 8% (95% CI, 2–14%) increase in COPD hospitalization. This finding was consistent with the result of a case-control study conducted in Athens, Greece, when recent 5-year (but not 20-yr) average NO2 levels were used in exposure assessment (26). In a 6-year cohort study including all residents aged 51–90 years in Oslo, Norway, Naess and coworkers (27) found significant associations of COPD mortality with NO2, PM2.5, and PM10. A national cohort study of US trucking industry workers found positive but nonsignificant associations of COPD mortality with NO2, PM10, and SO2 (28). A cross-sectional study of 4,757 women in the Ruhr Valley, Germany, found that the prevalence of COPD (Global Initiative for Chronic Obstructive Lung Disease stage 1 or higher) increased 79% (95% CI, 6–202%) for women living close to a major road (<100 m) compared with those who lived 100 m or more away (29). A study in Southern Sweden reported a similar strength of cross-sectional association of self-reported COPD with residential proximity to road traffic and residential exposure to NOx (30). In contrast, a cross-sectional study in Nottingham, United Kingdom, did not find any associations of spirometry-defined COPD with residential proximity to road traffic or residential exposure to NO2 (31). This null association was replicated using a large nationally representative sample in England (32).
Although these findings are not fully consistent, the present study and most previous studies suggest that traffic-related air pollution, especially traffic-related fine particulate air pollution, is associated with an increased risk of COPD. Meanwhile, there is sufficient evidence that exposure to indoor woodsmoke, originating from household cooking in developing countries, is associated with COPD morbidity and mortality (6). To our knowledge, this is the first study to show that long-term exposure to outdoor woodsmoke pollution was associated with COPD hospitalization in a developed country setting.
There is convincing pathophysiologic evidence to support the associations between particulate air pollution and COPD outcomes. A study of 64 healthy children found that exposure to elevated particulate air pollution was associated with increased black carbon content in airway macrophages, which was further associated with the decrease in lung function (33). Controlled exposure studies in healthy human volunteers have shown that exposure to diesel exhaust can induce pronounced airway inflammatory response as exemplified by increased neutrophils and lymphocytes in sputum (34, 35), bronchoalveolar lavage (36, 37), and bronchial biopsies (36). Concentrated ambient particles can also lead to similar inflammatory response in the lungs (38). Furthermore, there is some evidence that diesel exhaust may up-regulate gene expression to increase the production of proinflammatory mediators, such as interleukin-8, in the lower respiratory tract (39). In addition, oxidative stress caused by excessive reactive oxygen species in particulates may also play an important role in the observed associations (40–43).
The present study has some limitations that should be considered. First, this cohort was assembled using health insurance administrative databases; some individual risk factors, such as active or passive smoking status and individual SES, were not available and thus could not be accounted for in the data analysis. To partially control for these unmeasured risk factors, we adjusted for neighborhood SES and preexisting comorbid conditions. Because these conditions and COPD share some common behavioral risk factors, adjusting for these comorbidities was presumably able to reduce the influence of unmeasured factors on the risk estimates (11, 44).
Second, cigarette smoking is the single most important risk factor for COPD (2, 4, 7). However, as a third factor, cigarette smoking is not necessarily a confounder for the association between air pollution and COPD. In a subgroup analysis of the study subjects (n = 2,824) who participated in the Canadian Community Health Survey (2001) including detailed individual behavioral risk factors, we examined the associations between smoking status (current, former, and never-smokers) and the concentrations of these traffic-related air pollutants. Descriptive statistics and analysis of variance showed that there was no significant difference in air pollution levels by smoking status (45). Furthermore, we conducted a literature review of the associations between long-term exposure to air pollution and COPD outcomes. We found four studies that provided stratified analyses by smoking status. Two cohort studies showed that the associations were stronger for never-smokers compared with current smokers (9, 46). The results from the two cross-sectional studies were not consistent, but the effect estimates were not statistically different (30, 47). There were two other studies that did not perform stratified analyses but specified that smoking was not a confounder and including smoking-related variables did not alter the effect estimates (26, 29). According to these findings, lack of smoking information was unlikely to substantially affect the associations between air pollution and COPD outcomes in our study. Similarly, we did not find any evidence that unmeasured occupational exposure might substantially affect the risk estimates (9, 28).
Third, because of privacy protection, we were unable to evaluate the accuracy of COPD diagnosis (e.g., use of spirometry) in the provincial hospitalization database. For each hospitalization record, there were up to 16 diagnoses during 1991–2000 and up to 25 diagnoses during 2001–2002. To reduce the possibility of misdiagnoses, we used only the principal diagnosis (the most responsible diagnosis for a hospital admission) to identify hospitalization cases. This stringent definition might improve the accuracy of the COPD diagnosis in our study; however, we might likely lose some hospitalization cases for which COPD was not the principal diagnosis. Furthermore, these hospitalization cases likely represent more severe cases rather than all cases of COPD in Metropolitan Vancouver. These factors might lead to underestimation of the true risk of COPD associated with ambient air pollution. Similarly, the provincial death registry had only one cause of death for each death case. Because COPD was less likely to be cited as the underlying cause of death, the mortality data might substantially underestimate COPD as a cause of death (7). This scenario might cause underestimation of the true risk of COPD mortality; also, this situation might partly explain the nonsignificant inverse association between woodsmoke exposure and COPD mortality in the present study.
Finally, exposure assessment was based on residential postal codes to estimate residential exposure to ambient air pollution. This method cannot precisely reflect individual exposure because many environmental factors, such as specific housing characteristics, wind direction, and individual activities, might substantially affect individual exposure to ambient air pollution. Nevertheless, these factors would be more likely to cause nondifferential exposure misclassification, leading to underestimation of the true risk of COPD (48).
In conclusion, this large population-based cohort study demonstrated that long-term exposure to elevated traffic-related fine particulate air pollution, indicated by black carbon, was associated with an increased risk of COPD hospitalization and mortality. Exposure to elevated woodsmoke pollution was associated with an increased risk of COPD hospitalization. This study provides new evidence to support the assertion that ambient air pollution, including traffic-related fine particulate pollution and woodsmoke pollution, is associated with an increased risk of COPD. Given the substantial social and economic burden of COPD, ambient air pollution as a widespread environmental risk factor deserves more attention for prevention and control of COPD.
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Supported in part by Health Canada by an agreement with the British Columbia Centre for Disease Control to the Border Air Quality Study. Additional support was provided by the Centre for Health and Environment Research at The University of British Columbia, funded by the Michael Smith Foundation for Health Research, and the Canadian Institutes of Health Research.
Author Contributions: All authors substantially contributed to the study conception and design. W.Q.G. performed the statistical analyses; all other authors participated in the statistical analyses. W.Q.G. wrote the first draft of the manuscript; all other authors critically revised the manuscript for important intellectual content. All authors have read and approved the final version of the manuscript.
This article has an online supplement, which is accessible from this issue's table of contents at www.atsjournals.org
Originally Published in Press as DOI: 10.1164/rccm.201211-2004OC on February 7, 2013
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