of NO2. We found minimal change in the effect estimate when we added the diagnoses of chronic conditions to the model or when we included the road buffer traffic marker (Table 3). The point estimates for respiratory mortal- ity were elevated, but the confidence inter- vals included unity. We attempted to model associations between lung cancer and NO2; however, there were only 35 deaths in the cat- egory, which resulted in positive estimates with very wide confidence intervals (results data not shown). The effect of NO2 exposure on other nonaccidental causes of death, minus cardio- pulmonary and lung cancer mortality, was confounded by the clinical and socioeconomic variables. In the full model, we found no sig- nificant association between these other non- accidental causes of death and NO2 exposures. The hypothesis of proportionality in the hazards was not rejected for any model. We tested interactions between the explanatory variables and NO2, but none were significant. In a neighboring city (Hamilton, Ontario), we found an association between residence locations close to traffic and an increased risk in all-cause and circulatory mortal- ity (Finkelstein et al. 2004). We applied the same definition of traffic proximity (residence within 50 m of a major road or 100 m of a highway) to the Toronto cohort. Table 3 shows a large point estimate and a border- line significant association between residential proximity to traffic and circulatory mortality. In a model that included NO2, the effect of NO2 was significant and the risk attributable to the traffic marker was lower. We found no relation between proximity to traffic and other nonaccidental causes. Discussion In this article we report the association between NO2, a marker for TRAP, and mor- tality in a cohort of subjects drawn from a pulmonology clinic in Toronto. TRAP was associated with significant elevations in mortality from all causes and from circu- latory causes. With control for confound- ing, all-cause mortality remained elevated by 17% and RRs were nonsignificant only where adjusted for chronic diseases and the Table 2. RR (95% CI) for mortality. proximity to traffic exposure marker. Effect sizes over the IQR for all-cause mortality were similar to those reported in the Norwegian study (Nafstad et al. 2004). Circulatory mortality remained sig- nificantly elevated with RRs in the range of 1.4 in models that controlled maximally for confounding and for neighborhood clustering on the census tracts. The HR for circulatory mortality was 2.28 over a 10-ppb contrast, which was present in the data. These effects are comparable in size to those reported in ear- lier European studies (Hoek et al. 2002) and are consistent with findings linking coronary artery calcification to distance from a major road (Hoffmann et al. 2007). More gener- ally, effects on circulatory outcomes have been demonstrated to be larger than those for other mortality outcomes in relation to air pollution (Jerrett et al. 2005; Pope et al. 2004). Respiratory mortality was also elevated in relation to NO2, although models only pro- vided nonsignificant trends. Statistical power to detect the effects of NO2 on lung cancer mortality was limited because of a small num- ber of deaths in this category, and the results were inconclusive. Nonaccidental deaths less circulatory, respiratory, and lung cancer causes were not associated with TRAP in models that controlled for major confounders. In a previous study in Hamilton, Ontario (Finkelstein et al. 2004), we reported a rela- tion between mortality and residence close to traffic, as indicated by a road buffer. In this study, when using the same traffic buffer, we found a nonsignificant effect of about the same size as in the earlier Hamilton study. In Traffic pollution and mortality a model that included both the traffic buffer indicator variable and NO2, the effect of NO2 remained significant, whereas the effect of the traffic indicator was reduced. Mean values of NO2 were about 22 ppb outside the buf- fer and about 25 ppb inside the buffer. This suggests that the traffic indicator is a surro- gate for NO2 and possibly other near-source traffic-related exposures. Other constituents likely to be higher by roadways include car- bon monoxide, ultrafine PM, volatile organic compounds, and elemental carbon (Reponen et al. 2003; Zhu et al. 2002). A recent study from Toronto suggests that the highest gradi- ent in near-road exposure is in ultrafine PM counts (Beckerman et al. 2008). We examined the relation between other exposure variables and mortality in our cohort. Exposure contrasts for PM2.5 and O3 were small in Toronto (IQR of about 1 µg/m3 or 1 ppb, respectively), and we found no signifi- cant associations with mortality in any of the models (data not shown). The combination of a relatively small number of deaths in the cohort and a limited exposure contrast lim- ited the statistical power to detect mortality effects. Consequently, no definitive conclu- sions can be drawn about these associations with PM2.5 and O3. One unique strength of this study was our ability to control for clinically ascer- tained preexisting conditions and lung func- tion. These data allowed us to apply controls based only on self-reports in other studies of chronic mortality (e.g., Hoek et al. 2002; Jerrett et al. 2005; Pope et al. 2002). We included preexisting diseases as a sensitivity Table 3. RR (95% CI) for mortality in relation to residential proximity to traffic and an IQR increase in mean NO2 (2002 and 2004). Model Model 1: traffic marker is the exposure variable Model 2: traffic marker + NO2 Traffic marker NO2 All nonaccidental causes of death (n = 298) 1.19 (0.92–1.53) 1.11 (0.85–1.45) 1.13 (0.97–1.32) Circulatory (n = 80) 1.48 (0.91–2.42) 1.22 (0.74–2.02) 1.39 (1.05–1.85) Each model was adjusted for all of the confounders controlled for in the final model from Table 2. The first result is for the model in which proximity to traffic is the only exposure variable. The second result is for models in which both the traffic marker and NO2 are included together. Model Baseline: age, sex, mean NO2 + BMI, BMI2, FVC (% predicted) + Smoking (current, former, never) + DI Final model clustered on census tract Sensitivity analysis: add diagnoses of COPD, chronic IHD, and diabetes All nonaccidental causes (n = 298) 1.29 (1.13–1.48) 1.24 (1.08–1.42) 1.21 (1.06–1.39) 1.17 (1.01–1.35) 1.17 (1.00–1.36) 1.15 (0.99–1.33) Circulatory (n = 80) Respiratory (n = 75) 1.52 (1.19–1.95) 1.47 (1.14–1.90) 1.44 (1.11–1.86) 1.45 (1.11–1.91) 1.45 (1.10–1.92) 1.40 (1.05–1.86) 1.11 (0.84–1.47) 1.10 (0.83–1.45) 1.10 (0.83–1.46) 1.06 (0.79–1.43) 1.06 (0.67–1.49) 1.09 (0.78–1.54) Data are the adjusted RR per IQR increase in mean NO2 (2002 and 2004). The first line is the baseline model (age, sex, NO2), and the subsequent lines give the adjusted RR as additional confounders are added to the baseline model. Environmental Health Perspectives • volume 117 | number 5 | May 2009 All nonaccidental causes of death less circulatory, respiratory, and lung cancer (n = 109) 1.17 (0.74–1.84) 1.15 (0.71–1.85) 1.03 (0.81–1.31) All nonaccidental causes of death less circulatory, respiratory, and lung cancer (n = 109) 1.23 (0.98–1.53) 1.17 (0.94–1.47) 1.12 (0.89–1.40) 1.06 (0.83–1.35) 1.06 (0.84–1.34) 1.05 (0.83–1.33) 775