Crouse et al. Table 2 (doi:10.1289/ehp.1002221)]. The two methods of historical extrapolation produced almost identical associations with the risk of developing invasive breast cancer (Table 3; see also Supplemental Material, Table 2). Sensitivity analyses. Adjustment for indi- vidual-level educational status had the stron- gest effect on the size of the ORs. For example, in a reduced model using estimates of exposure for 2006, adjusted only for age and individual- level educational status, the OR5ppb was 1.21 (95% CI, 0.94–1.57). None of the other cova- riates included in the fully adjusted models contributed > 3% to the increase in the ORs. We also computed ORs for the 408 women (195 cases, 213 controls) who reported that they had been residents at the same address for at least 10 years before diagnosis. These ORs were slightly larger than those pro- duced with the full study population, although because of the smaller sample size, the CIs were wider (Table 3). For example, in the fully adjusted models, the OR5ppb ranged from a low of 1.23 (95% CI, 0.87–1.74) using esti- mates for 1985 to a high of 1.52 (95% CI, 0.82–2.81) using estimates for 2006. Similar to the findings with the full data set, the ORs calculated per interquartile range were much less variable between exposure periods, varying from 1.31 (95% CI, 0.82–2.09) to 1.35 (95% CI, 0.84–2.18) [see Supplemental Material, Table 2 (doi:10.1289/ehp.1002221)]. We also computed ORs for subsets that excluded proxy respondents, whose interviews could have elicited less accurate information (362 cases, 362 controls), and controls diag- nosed with bladder cancer, a cancer linked to diesel exhaust fumes exposure (383 cases, 392 controls). In both analyses, we found associa- tions very similar to those found with the full study population [see Supplemental Material, Table 3 (doi:10.1289/ehp.1002221)]. Discussion We found evidence of an association between exposure to outdoor concentrations of NO2 and the incidence of postmenopausal breast cancer. Although the size of effect varied somewhat using estimates of exposure from different periods, we found an increased risk of approximately 25% for every increase of 5 ppb in exposure. Several methodological strengths of our study suggest that the effect sizes are unbiased and perhaps even conservative. Response rates among cases and controls were relatively high, and there is no reason to believe that catchment areas of different hospitals would differ for cases and controls by level of exposure to traffic- related pollution, thus limiting the likelihood of selection bias. The selection of cancer patients as controls reduces the possibility of recall bias for the covariates, and we estimated concentrations of NO2 using independent data sources. The cancer sites in the control group are not known or suspected to be associated with air pollution, with the possible exception of bladder cancer. However, should any of the control series can- cer sites be shown subsequently to be causally associated with air pollution, this would have the effect of attenuating the risks demonstrated in our study. The exclusion of bladder cancer, which has been associated with diesel exhaust fumes, did not substantially change the results. Additionally, we found that individual-level educational attainment was the only covariate to affect the estimate of risk by ≥ 5%. We acknowledge that the present analysis provides only partial information on personal exposure to air pollution. Total personal expo- sure relates to a number of factors, including daily activity patterns and amount of time spent indoors and outdoors, among others. Two limitations of using the home address as a surrogate of exposure are related to population Table 3. Associations between ambient concentrations of NO2 (per 5 ppb) and postmenopausal breast cancer, Island of Montreal, Canada. Limited to subjects who were residents at the same address for Full data set at least 10 years before interview (n = 799; 383 cases, 416 controls) (n = 408; 195 cases, 213 controls) Age adjusted Fully adjusteda Age adjusted Fully adjusteda Exposure surface OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) 2006 1.15 (0.89–1.48) 1.35 (0.94–1.94) 1.08 (0.75–1.56) 1.52 (0.82–2.81) 1996b 1.14 (0.91–1.42) 1.36 (0.99–1.88) 1.05 (0.76–1.45) 1.42 (0.81–2.48) Mean of 1996 and 1985b 1.10 (0.90–1.34) 1.25 (0.94–1.65) 1.06 (0.80–1.41) 1.34 (0.83–2.16) 1985b 1.07 (0.90–1.27) 1.17 (0.91–1.50) 1.06 (0.83–1.36) 1.28 (0.84–1.93) 1996c 1.10 (0.91–1.32) 1.31 (1.00–1.71) 1.02 (0.78–1.34) 1.34 (0.84–2.14) Mean of 1996 and 1985c 1.07 (0.91–1.27) 1.22 (0.97–1.54) 1.04 (0.82–1.32) 1.28 (0.86–1.91) 1985c 1.05 (0.91–1.22) 1.16 (0.94–1.42) 1.04 (0.85–1.29) 1.23 (0.87–1.74) aAdjusted for hospital of diagnosis, mother or sister with breast cancer, oophorectomy, years of education, ethnicity, age at menarche, age at first full-term pregnancy, breast-feeding history, oral contraceptive use, hormone replacement therapy use, body mass index, exposure to tobacco smoke, respondent/proxy status, alcohol consumption, history of benign breast disease, and occupational exposures to solvents with reactive metabolites, extremely low magnetic fields, carbon monoxide, and PAHs; and two neighborhood ecologic covariates: median household income and percent- age of adults without a high school diploma. See Table 1. bExtrapolated using observed concentrations of NO2 at each fixed-site monitoring station. cExtrapolated using predicted concentrations of NO2 derived from the land-use regression in 2006 at each fixed-site monitoring station. mobility: People do not necessarily live in the same home over the course of their lifetime, nor do they spend all of their time at home. Although it is true that many subjects may spend their days away from home, a study by Leech et al. (2002) found that Canadian adults spend on average approximately 67–68% of their time at home (indoors and outdoors combined). The finding of higher risks among subjects who lived for ≥ 10 years at the same address before diagnosis suggests that our risk estimates may be conservative. Last, given the inherent imprecision associ- ated with geocoded addresses and other geo- graphic data, as well as the fact that we used spatially derived exposures as surrogates for per- sonal exposures, the risk estimates presented here are likely subject to nondifferential misclas- sification bias. Our results thus probably under- estimate the true estimates of the relative risk of postmenopausal breast cancer associated with exposure to air pollution in this population. Our findings should not be interpreted as meaning that NO2 is a causal factor; it is more likely a marker of the complex mixture that is derived from combustion (Goldberg 2007). We found an association between expo- sures to traffic-related air pollution and the incidence of postmenopausal breast cancer in a city that by international standards is relatively unpolluted. Our findings are qualitatively simi- lar to those reported in the two other studies that examined the hypothesis that breast cancer may be associated with exposure to air pollu- tion (Bonner et al. 2005; Nie et al. 2007). Our results differ somewhat, however, from those studies. Among postmenopausal women, those authors found associations between early-life exposures to markers of air pollution and the incidence of breast cancer, but no associations with exposures 10 and 20 years before diag- nosis. We could not assess associations with early-life exposures, nor do we know what age periods may be critical in the induction of can- cer. Our analysis of occupational exposures (Labrèche et al. 2010) suggests that exposures to some compounds before 36 years of age may be more important. If this is the case, it is possible that the risks observed here are underestimated. Studies are needed to verify whether these results represent true associations or whether they are attributable to chance or to undetected bias. If these associations are veri- fied, additional studies should explore potential critical periods of exposure to air pollution in relation to the development of breast cancer. Ref Rencs American Cancer Society. 2009. Cancer Facts and Figures 2009. Atlanta, GA:American Cancer Society. Bako G, Dewar R, Hanson J, Hill G. 1984. Population density as an indicator of urban-rural differences in cancer incidence, Alberta, Canada, 1969–73. Can J Public Health 75(2):152–156. Beckerman B, Jerrett M, Brook JR, Verma DK, Arain MA, Finkelstein MM. 2008. Correlation of nitrogen dioxide with 1582 ovlume 118 | number 11 | November 2010 • Environmental Health Perspectives