Table 3 summarizes lung cancer OR with exposure to PM2.5, NO2, and O3 derived from the national spatiotemporal models. Adjusted for all individual and geographic variables, the OR for a 10 µg/m3 increase in PM2.5 was 1.29 (95% CI = 0.95–1.76), and for a 10 ppb increase in NO2 and O3 was 1.11 (1.00–1.24) and 1.09 (0.85–1.39), respectively. For NO2, all exposure quintiles were elevated relative to the lowest (<7.1 ppb), but there was no dose-response relationship. Although variance inflation factors for all three pollutant exposures were less than 2.5, the high positive correlation between PM2.5 and NO2 exposures (r = 0.73) and the complex spatial patterns of these pollutant relationships limit the interpretation of joint models. We did, however, examine joint models for NO2 and O3 to explore the independent associations between each pollutant and lung cancer incidence because O3 is typically decreased in high NO2 locations. In the joint national model, the NO2 OR was slightly increased to 1.14 (1.02–1.28) and the O3 OR doubled to 1.20 (0.92–1.56).
We also examined the influence of urban residence using a community-size category based on the longest residence during the exposure period. A community-size variable was not included in the national models due to high correlation with NO2 (r = 0.73) and to a lesser degree with PM2.5 (r = 0.55). When the urban-size category was included in the national models, the fully adjusted OR per 10 unit increase in NO2 was 1.14 (0.99–1.31) and for PM2.5 was 1.26 (0.90–1.77). No change was seen when average population density within 5 and 10 km of residential postal codes (over the 20-year exposure period) was added. There were weak associations between population density within 5 and 10 km and lung cancer incidence (ORs of 1.06 [0.83–1.15] and 1.10 [0.86–1.40] for the highest vs. lowest population density categories).
Table 4 presents stratified models for smoking status, smoking pack years, educational attainment, and sex. No consistent patterns were observed for any of the national PM2.5, NO2, and O3 exposures. For example, compared with current smokers, larger ORs for lung cancer were seen among former smokers for PM2.5 and O3, but smaller ORs for NO2. The small number of never smokers in this study makes interpretation of these models difficult. For all three pollutants, higher ORs were seen in men.
Urban Fixed-site Monitor Subanalyses
The urban analyses, based on exposures derived from the closest monitor within 50 km, are summarized in Table 5. In the fully adjusted model, a 10 µg/m3 increase in TSP was associated with an OR of 1.04 (0.95–1.13). The largest difference from the national analysis was seen for NO2: a 10 ppb increase in the monitor-based analysis was associated with an OR of 1.34 (1.07–1.69). It is likely that NO2 exposures derived for the urban monitors are also capturing a component of PM2.5, due to the correlation between the two pollutants. Figure 1 in the eAppendix (http://links.lww.com/EDE/A678) illustrates the relationship between exposures derived from measured NO2 and TSP (as PM2.5 measurements were available only after 1984 and had poor spatial coverage).
Proximity to Vehicle Emissions
Table 6 summarizes ORs per 10 years living in proximity (50, 100, or 300 m) to a highway or major road, as well as weighted proximity measures that capture the decrease in vehicle emissions over the exposure period. Few study participants lived within 50 m of highways, but increased ORs were observed for these participants, as well those living within 100 m of highways. No associations were seen for those residing near major roads.
The present study aimed to enhance current understanding of the risks posed by air pollution to lung cancer incidence. We attempted to reduce exposure misclassification by conducting extensive spatiotemporal air pollution exposure assessments that incorporate long-term residential histories, and we examined associations with various pollutants and sources of exposure. We were also able to control for a comprehensive set of potential individual and geographic confounding factors.
Overall, our results support previous literature showing that ambient PM2.5 air pollution is associated with increased lung cancer risk. In our national analysis, we found that a 10 µg/m3 increase in PM2.5 was associated with an OR of 1.29 (0.95–1.76). This estimate is similar to the effect size reported in a 2008 meta-analysis, with a pooled relative risk (RR) of 1.21 (1.10–1.32) per 10 µg/m3 increase in PM2.5.1 An extended follow-up of the Harvard six cities study from 1974 to 2009 also found a 37% (7–75%) increase,5 and a recent analysis of never smokers in the Cancer Prevention Study-II cohort based on 26 years of follow-up found a RR of 1.19 (0.97–1.47) (both for a 10 µg/m3 increase in PM2.5).27
Unlike the relatively robust literature on PM2.5 and lung cancer, there are fewer studies on the associations of the gaseous pollutants NO2 and O3 with lung cancer. We found an OR for a ten-unit increase in NO2 of 1.11 (1.00–1.24) in the national analysis and a substantially larger OR [1.34 (1.07–1.69)] in the urban monitor-based analysis. This higher estimate may be due to restricting the study to large urban areas, more accurate exposure assessment, or exposure assessment that captured both NO2 and PM2.5 influences (due to the high correlation between PM2.5 and NO2 and the lack of PM2.5 monitoring data before 1984). Studies of NO2 and lung cancer risk generally show positive associations ranging from 5 to 30% increases in risk per 10 ppb increases in NO2;2,8,9,26 however, negative associations have also been observed (RR 0.86 [0.70–1.07] per 30 µg/m3).10
In addition to NO2, a number of studies have examined NOx air pollution (primarily as a marker of traffic air pollution) with most reporting positive associations with lung cancer.4,7,8,28 When we considered proximity to highways and major roads as a surrogate for traffic air pollution exposure, we found elevated risk of lung cancer incidence associated with living within 100 m of highways (OR 1.10 [0.83–1.46] per 10-year residence), but not for major roads. Our results are similar to those from a Danish cohort (incidence rate ratio of 1.21 [0.95–1.55] for lung cancer associated with living within 50 m of a major road [>10,000 vehicles per day])4 as well as those from a Dutch cohort (RR of 1.10 [0.74–1.62] for living within 100 m of a motorway or 50 m of a road with >10,000 vehicles/day).10 Major roads in urban locations of Canada have similar traffic volumes; however, we did not see any associations between living near major roads and lung cancer incidence.
We found a trend of increasing lung cancer incidence with increasing O3 concentrations (OR 1.09 [0.85–1.39]) for a 10 ppb increase in the national models) with similar results in the urban analysis. In multipollutant models incorporating NO2 and O3, the O3 OR increased substantially to 1.20 (0.92–1.56), suggesting that accounting for areas with low O3 but high NO2 may be important to further understand the association between long-term O3 exposure and lung cancer risk. There are no other large studies we are aware of to compare with these findings.
Lastly, we did not observe clear patterns between air pollution exposures and specific histological subtypes. Generally, PM2.5 exposure was most strongly associated with small cell and adenocarcinoma; NO2 with adenocarcinoma; and O3 with squamous cell carcinoma. The most persuasive association was for NO2 and adenocarcinoma (OR 1.17 [1.01–1.35]). Adenocarcinoma is the most common histological subtype among never smokers, but there is no consensus in the literature as to whether air pollution is associated more strongly with adenocarcinoma or other histological subtypes. Some studies have found air pollution to be more strongly associated with adenocarcinoma,12,14,29 whereas others have found the strongest associations with other histological subtypes.11,13,28
This study relies on the accuracy of historical exposure assessments. A number of sensitivity analyses were conducted to examine how the ORs change with different historical exposure assessment methods (summarized in Figure 2). These methods included the spatiotemporal models (used in national models and described in methods); spatiotemporal models developed with a national ratio of historical pollutant concentrations to current levels (for PM2.5 only); historical regression models that use satellite data, population density, and a time trend to predict historical concentrations;18 the satellite or chemical transport model spatial surfaces without temporal adjustments; and exposures estimated only from fixed-site monitoring data within 50 km. Figure 2 demonstrates a relatively small degree of variability in the PM2.5 and O3 OR estimates, whereas the NO2 urban monitor exposure assessment has a higher OR than the two national NO2 models incorporating spatial and temporal variability. For all pollutant models, the a priori national spatiotemporal exposure assessments had the smallest standard errors.
Strengths and Limitations
This study has a number of strengths that address important limitations in the current air pollution and lung cancer literature. First, we estimated long-term historical air pollution levels at six-digit residential postal codes. To reduce exposure misclassification, exposures were derived from 20 years of residential histories. This time-period was selected because, before 1975, cases tended to report more addresses than population controls, which would have incorporated bias into the study.18 To further reduce bias, only people with complete 20-year residential histories were included in the final analyses. We were able to examine the influence of residential history completeness and found that including study subjects with missing residential histories resulted in substantial attenuation of the OR estimates. For example, including subjects with 18 years (90%) of complete exposures in the national models resulted in ORs per 10 unit increase in PM2.5, NO2, and O3 of 1.23 (0.92–1.65), 1.11 (1.00–1.22), and 1.05 (0.83–1.33). Attenuation was greater when subjects with 15 years (75%) of complete exposures were included. Unlike other studies that assume participants have lived at their home residence for a certain amount of time, missing data in this study likely represent substantial exposure error as study participants self-reported their addresses and missing periods represent addresses they could not recall or residential locations outside of Canada.
Second, unlike most studies, which are restricted to single pollutants and city locations, we developed national models for multiple pollutants and were able to include participants in all areas of Canada. This type of exposure assessment has also been used in a recent national Canadian cohort analysis of PM2.5 and nonaccidental and cardiovascular mortality.30 Third, unlike many prior studies, we had a large sample size (n = 2,390 incident lung cancer cases and 3,507 population controls), which allowed us to examine the associations between air pollution and lung cancer histology. Fourth, a comprehensive set of individual and geographic-level information was available for modeling important confounding variables. The inclusion of smoking information in particular had a large influence on study results. Smoking variables in the adjusted models substantially increased ORs, due to the small negative spatial association between smoking prevalence and air pollution exposures.31 The inclusion of ecological radon exposures was also important, specifically in the NO2 and PM2.5 models, as high radon concentrations in Canada are located in areas that generally have lower NO2 and PM2.5 concentrations.
A number of study limitations also need to be considered. First, although this study has a relatively high response rate for cases (62%) and population controls (67%), response and recall bias cannot be ruled out. No difference in the completeness of self-reported residential histories was present between cases and controls when restricted to the 1975–1994 exposure period. Second, it is essential to note that populations are not distributed evenly across geographic communities, and thus, a random sample of the population may not be a random sample of all places. The national enhanced cancer surveillance system was designed so each provincial cancer agency would sample and recruit study participants. A province variable was therefore included in the fully adjusted models to capture any differences between sampling strategies (health insurance plans were used in five provinces, random digit dialing in two, and property assessment data in one). This is not ideal, in that the province variable likely captured a portion of the air pollution variance. The province variable also had a large influence on histology results, suggesting possible classification or recruitment differences by province. In addition, a large portion of our study population was located in and around Toronto, Ontario (see Figure 1A), which had the highest PM2.5 exposures. Any response bias or exposure assessment error in this geographic area would have a large influence on our study results. A sensitivity analysis including all provinces but Ontario (1,399 cases and 2,050 controls) indicated that results changed only slightly for NO2 (OR 1.12 [0.97–1.31] per 10 ppb increase) and O3 (OR 1.12 [0.80–1.56] per 10 ppb increase), but were reduced for PM2.5 (OR 1.15 [0.77–1.72] per 10 µg/m3 increase). The reduction for PM2.5 is presumably due to the exclusion of the highest exposed (those living in Southern Ontario), which greatly reduced exposure variation in the analysis. The sensitivity to geographic variables is not as pronounced for NO2 because those with the highest NO2 exposure quintile lived in various large cities across Canada, rather than clustered in one region. We also included a random effect based on the census division of longest residence to account for unmeasured spatial structure in the data.
Third, the models were sensitive to subanalyses, as seen with the monitor-based exposure assessment results, which were substantially higher than the national NO2 results. The difference in NO2 results may be due to the various exposure assessment approaches, with the national models capturing inter- and intraurban variation and the urban monitor-based assessment capturing predominantly intraurban differences. NO2 exposures derived from urban monitors may also be capturing a component of PM2.5 because monitoring data for PM2.5 were not available before 1984. Fourth, the OR estimates, primarily for PM2.5, changed slightly with various coding schemes for smoking variables. For example, when a continuous smoking-pack-years-squared variable was included in the national model to account for nonlinear associations between smoking and lung cancer, the OR associated with a 10 unit increase in PM2.5 decreased to 1.23 (0.91–1.67). Fifth, all model results did not show dose-response gradients. This may have been due to the relatively small sample size and range of exposures for study participants, particularly in the urban monitor-based analyses. Sixth, due to privacy concerns, residential history locations were limited to six-digit postal codes, which are accurate in urban areas but can cover much larger regions in rural areas. Proximity analyses were therefore restricted to urban areas of Canada. Lastly, although we were able to estimate exposure from residential history, no information was available for other important microenvironments such as work locations.
In sum, we found increased risks of lung cancer incidence with residential exposures to ambient PM2.5, NO2, and O3, as well as living within 100 m of highways. Results were most robust for NO2 and PM2.5. More research is needed to establish whether O3 exposure is an independent risk factor for lung cancer.
We thank the Canadian Cancer Registries Epidemiologic Research Group for their contributions to the collection of the lung cancer case-control data; the National Air Pollution Surveillance program for the historical pollution monitoring data; Jeff Brook, Qian Li, Ilan Levy, Aaron van Donkelaar, Lok Lamsal, and Randall Martin for their contributions to the retrospective air pollution exposure assessments; and XiaoHong Jiang at the Public Health Agency of Canada for preparing the National Enhanced Cancer Surveillance System analysis files.
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