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Long-term Residential Exposure to Air Pollution and Lung Cancer Risk

Hystad, Perrya; Demers, Paul A.b; Johnson, Kenneth C.c; Carpiano, Richard M.d; Brauer, Michaela

doi: 10.1097/EDE.0b013e3182949ae7
Air Pollution
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Background: There is accumulating evidence that air pollution causes lung cancer. Still, questions remain about exposure misclassification, the components of air pollution responsible, and the histological subtypes of lung cancer that might be produced.

Methods: We investigated lung cancer incidence in relation to long-term exposure to three ambient air pollutants and proximity to major roads, using a Canadian population-based case-control study. We compared 2,390 incident, histologically confirmed lung cancer cases with 3,507 population controls in eight Canadian provinces from 1994 to 1997. We developed spatiotemporal models for the whole country to estimate annual residential exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) over a 20-year exposure period. We carried out a subanalysis in urban centers, using exposures derived from fixed-site air pollution monitors, and also examined traffic proximity measures. Hierarchical logistic regression models incorporated a comprehensive set of individual and geographic covariates.

Results: The increase in lung cancer incidence (expressed as fully adjusted odds ratios [ORs]) was 1.29 (95% confidence interval = 0.95–1.76) with a ten-unit increase in PM2.5 (μg/m3), 1.11 (1.00–1.24) with a ten-unit increase in NO2 (ppb), and 1.09 (0.85–1.39) with a ten-unit increase in O3 (ppb). The urban monitor-based subanalyses generally supported the national results, with larger associations for NO2 (OR = 1.34; 1.07–1.69) per 10 ppb increase. No dose-response trends were observed, and no clear relationships were found for specific histological cancer subtypes. There was the suggestion of increased risk among those living within 100 m of highways, but not among those living near major roads.

Conclusions: Lung cancer incidence in this Canadian study was increased most strongly with NO2 and PM2.5 exposure. Further investigation is needed into possible effects of O3 on development of lung cancer.

Supplemental Digital Content is available in the text.

From the aSchool of Population and Public Health, University of British Columbia, Vancouver, BC, Canada; bOccupational Cancer Research Centre, Cancer Care Ontario, Ontario, Canada; cScience Integration Division, Centre for Chronic Disease Prevention and Control, Public Health Agency of Canada, Ottawa, Canada; and dDepartment of Sociology, University of British Columbia, Vancouver, BC, Canada.

Original funding for the National Enhanced Cancer Surveillance System was provided through the Government of Canada’s Action Plan on Health and the Environment. The Michael Smith Foundation for Health Research (MSFHR) and Canadian Institute for Health Research (CIHR) supported P.H. through a UBC Bridge Strategic Training Fellowship, a Senior Graduate Studentship, and a Frederick Banting and Charles Best Canada Graduate Scholarship. MSFHR and CIHR supported R.M.C. through Investigator Awards.

The authors declare no conflicts of interest.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). This content is not peer-reviewed or copy-edited; it is the sole responsibility of the author.

Correspondence: Perry Hystad, School of Population and Public Health, University of British Columbia, 2206 East Mall Vancouver, BC, V6T 1Z3 Canada. E-mail: phystad@gmail.com.

Received September 8, 2012

Accepted January 25, 2013

Evidence is accumulating for a causal association between exposure to ambient air pollution and lung cancer;1–5 however, several uncertainties remain. Air pollution exposure misclassification is a particular concern, due to the long latency period for lung cancer, temporal changes in air pollution levels, and the likelihood of substantial residential mobility during biologically relevant exposure periods. To date, few studies of lung cancer have incorporated historical exposure assessments4,6–9 or examined different air pollutants and emission sources6–9 beyond urban settings.9,10 In addition, little research has examined air pollution exposure and lung cancer risk by histological subtypes,11–14 due to the need for large sample sizes. Given the variation in risks associated with cigarette smoking and lung cancer histology,15 as well as evidence from occupational16 and animal studies,17 it is probable that risks associated with air pollution also vary by histological subtype.

The present study builds upon prior work to partially address these uncertainties by identifying associations between three ambient air pollutants and proximity to traffic emissions, and lung cancer incidence. Specifically, we use a Canadian population-based case-control study that includes comprehensive individual and geographic information on potential confounding factors such as cigarette smoking, second-hand smoke exposure, occupational hazards, and residential radon exposures, as well as complete 20-year residential histories from 1975 to 1994. Spatiotemporal models were developed and applied to annual residential histories in both urban and rural locations to estimate long-term exposures to fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3).18 An urban subanalysis was also conducted using exposures derived from the nearest fixed-site air pollution monitors within 50 km, as well as proximity measures to highways and major roads.

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METHODS

Study Design

The National Enhanced Cancer Surveillance System is a population-based, multicancer-site case-control study that includes 3,280 histologically confirmed lung cancer cases, and 5,073 population controls collected between 1994 and 1997 in eight of Canada’s ten provinces. Johnson et al19 describe the recruitment methodology and study design of the overall National Enhanced Cancer Surveillance System project. Between 1994 and 1997 cases were identified and randomly sampled for inclusion in the study by provincial cancer registries within 1–3 months of initial diagnosis. Population controls were selected from a random sample of people within each province, frequency matched on sex and 5-year age categories to the overall collection of National Enhanced Cancer Surveillance System cancer cases (∼20,000 cases including 19 types of cancer). Recruitment methods for controls depended on data availability and accessibility by province and included provincial health insurance plans in five provinces, random digit dialing in two, and property assessment data in one. A research questionnaire was mailed to selected cases and controls and active follow-up was conducted. The response rate was 62% for contacted lung cancer cases and 67% for population controls. The research questionnaire collected comprehensive information regarding personal characteristics, lifetime occupational exposures, and residential histories. Residential histories were geo-coded to 6-digit postal codes and are the basis of the air pollution exposure assessment. Due to residential mobility, postal codes were located in all provinces of Canada, requiring national-level exposure assessment.

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National Air Pollution Exposure Assessment

Long-term exposures to ambient PM2.5, NO2, and O3, and proximity to highways and major roads, were estimated from residential histories from 1975 to 1994. Residential histories were available before 1975; however, few air pollution measurements and geographic data were available for these years, and recall bias was present for residential histories before 1975 (cases tended to report more residences than controls).18 To ensure reliable exposure assessment, only persons with complete 20-year residential histories in Canada during this period were included in the final analysis, which reduced the study to 2,390 cases and 3,507 controls. Various exposure periods were examined (eg, 1975–1980/85/90), but ambient pollution exposures for all periods were highly correlated with the 1975–1994 period (r ≥ 0.96).

The spatiotemporal air pollution exposure assessment approach is described in detail elsewhere.18 Briefly, a multistaged approach was used to assign annual concentrations of PM2.5 and NO2, and summer (May to September) O3, to residential histories. First, national spatial surface estimates of each pollutant were created from recent satellite-based estimates at a 10x10 km resolution (for PM2.520 and NO221) and from a 25x25 km resolution chemical transport model (for O322). Next, all fixed-site National Air Pollution Surveillance monitoring data were formatted to annual averages for the study period. Since PM2.5 measurements were not available before 1984, a random effects linear regression model was used to estimate pre-1986 PM2.5 based on total suspended particulate (TSP) measurements (as these were measured beginning in 1974) and metropolitan variables (Model R2 = 0.67, root mean square error = 2.31 µg/m3). This approach is similar to others studies that have estimated PM2.5 from TSP.2,23 Finally, yearly calibration of the national spatial pollutant surfaces was conducted by calculating a ratio of measured to surface estimates at each National Air Pollution Surveillance monitoring station. Smoothed inverse distance–weighted interpolation was conducted using the ratios, and the resulting surface applied to adjust the spatial pollutant surface for each year in the 1975–1994 study period.

Figure 1 illustrates the average spatiotemporal pollutant surfaces from 1975 to 1994 and the location of study participants’ residential histories (sum of residential postal code locations within a 50-km grid). These maps represent pollution concentrations that would be assigned if there were no residential mobility; in practice, the exposure assessment was conducted using yearly pollutant concentrations and residential histories.

FIGURE 1

FIGURE 1

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Urban Fixed-site Monitor Exposure Assessment

An urban subanalysis was conducted using air pollution exposures derived solely from fixed-site National Air Pollution Surveillance measurements. As mentioned, the spatial and temporal coverage of PM2.5 monitors is limited before 1986, and TSP measurements and modeled PM2.5 are thus examined in the urban analysis. Annual average pollutant concentrations were calculated for postal codes using the nearest National Air Pollution Surveillance monitor (within 50 km) with at least 6 months of complete measurements and 1 month per season for TSP and NO2 and at least 3 summer months for O3. Cumulative averages were calculated for people with at least 18 years of complete monitor coverage from 1975 to 1994.

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Proximity Measures to Highways and MajorRoads

Proximity measures to major roads were used to estimate exposure to vehicle emissions. The 1996 (DMTI Spatial, Inc.) road network was applied to derive proximity measures for all residential years, due to the lack of historical national road networks. We calculated the number of years residing within 50, 100, and 300 m of a highway or major road. Because emissions from vehicles have decreased significantly over the study period, proximity indicators were weighted to account for these changes using annual motor vehicle emission estimates.18 Analyses of proximity to highways and major roads were also restricted to participants residing in urban areas (defined as >30,000 residents) due to large spatial errors associated with rural postal code locations.

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Outcomes

Histologically confirmed lung cancer incidence is the primary outcome variable of this study. We also examined specific histological subtypes, which for the 2,390 lung cancer cases with complete residential histories included: 669 (28%) squamous cell carcinoma, 756 (32%) adenocarcinoma, 363 (15%) small cell carcinoma, 213 (9%) large cell carcinoma, and 389 (16%) other or unspecified carcinomas (which are not included in subsequent analyses due to the heterogeneity of this category).

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Covariates

We include a comprehensive set of individual and geographic-level variables in the multivariate models. Individual-level covariates include age, sex, educational attainment, average household income during the 5 years before study interview, smoking pack years, years since quitting smoking, person-years of residential and occupational second-hand smoke exposure (defined by the number of smokers in the home multiplied by number of residential years and the number of smokers in the immediate work environment multiplied by number of occupational years), average alcohol and meat consumption per week, years working with daily or weekly exposure to dust, odors, and hazardous substances, and exposure to specific occupational lung hazards (arsenic, asbestos, asphalt, benzene, mustard gas, welding, and wood dust). Geographic covariates included study province (to account for the study design), ecological radon risk (defined using mean residential radon concentrations by Health Regions),24 and neighborhood contextual deprivation variables (described in the eAppendix; http://links.lww.com/EDE/A678). Coding for all individual and geographic variables is provided in the eAppendix, eTable1 (http://links.lww.com/EDE/A678).

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Statistical Analyses

Analyses were conducted using two-level random intercept logistic regression models (GLIMMIX, SAS version 9.3; SAS Institute, INC, Cary, NC). The random intercept was defined from Statistics Canada 1986 census division boundaries (n = 188), representing regional areas in Canada, and assigned to each person’s longest residential location to account for residual geographic patterns. We report odds ratios (ORs) and 95% confidence intervals (95% CIs) for ten-unit increases in ambient pollutant concentrations and for exposure quintiles. Only the national models were stratified by major lung cancer histological subtypes, given the reduced sample sizes for the urban subset analysis. National models were also stratified to examine pollutant interactions by a priori variables (smoking status, education, and sex) that may modify the relationship between air pollution and lung cancer.4,10,25,26

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RESULTS

Characteristics of Case and Control Subjects

Table 1 provides descriptive statistics and ORs (adjusted for age, sex, and study province) for selected subject characteristics (descriptive statistics for all individual and geographic variables are shown in eAppendix, eTable 1; http://links.lww.com/EDE/A678). Study subjects were approximately evenly divided by sex, and lung cancer cases were slightly older than population controls. Cases had a higher number of smoking pack years, less education, lower income, higher alcohol and meat consumption, higher residential and occupational second-hand smoke exposures, and more occupational exposures to dust, odors, and hazardous substances. Only 130 (6%) of lung cancer cases were never smokers compared with 1,337 (38%) of population controls. Cases lived in regions with higher average indoor radon measurements and resided longer in the most socioeconomic-deprived neighborhoods. Table 2 summarizes study participant air pollution exposures from the national spatiotemporal models and correlations between pollutants.

TABLE 1

TABLE 1

TABLE 2

TABLE 2

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National Analyses

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).

TABLE 3

TABLE 3

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.

TABLE 4

TABLE 4

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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).

TABLE 5

TABLE 5

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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.

TABLE 6

TABLE 6

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DISCUSSION

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.

FIGURE 2

FIGURE 2

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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.

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ACKNOWLEDGMENTS

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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