Jerrett et al. analysis. Including these conditions may have overcontrolled for confounding because some of the variables may be on the causal pathway from pollution to mortality. For example, preclinical markers of IHD have been associ- ated with TRAP or PM partly arising from traffic (Künzli et al. 2005). There is also some indication that COPD may associate with TRAP (Schikowski et al. 2005) and with dia- betes (Brook et al. 2008). In such instances, inclusion of the preexisting conditions would remove some of the true effect attributable to air pollution. Even with this potential for overcontrol, we found only a small impact on the risk estimates for NO2 on all-cause and circulatory mortality. We subsequently addressed effect modification by disease status, but we found no significant effect modifiers identified. Although this cohort is enhanced with individuals with preexisting conditions, the results are generally insensitive to inclu- sion of these conditions as either confound- ers or effect modifiers. One explanation for the lack of influence of chronic disease sta- tus as a confounder or modifier may arise from the role of air pollution in disease for- mation. All of the chronic conditions tested were independently and positively associated with mortality in this cohort. If air pollution did contribute to disease formation, and these diseases were associated with elevated mortal- ity, these disease states may not modify or confound the association between mortality and air pollution. In this case, the effects of air pollution would be represented in elevated mortality related to chronic disease status. This cohort lacked the statistical power to use structural equation models to assess the path- way model directly, but a larger cohort is cur- rently under construction that will allow for structural models to address this hypothesis. The exposure assessment used in this study relied on extensive field measurements and models capable of predicting fine-scale variation in TRAP. Few other studies have used such an extensive network of field meas- urements to characterize the likely ambient exposures. Although we took the measure- ments over short periods, the spatial pattern appears relatively stable over time. For the 43 sites where the NO2 was available in both rounds of measurement, the correlation coef- ficient r-value was 0.82, and both seasons had a similar spatial structure [see Supplemental Material, Appendix, for details (http://www. ehponline.org/members/2009/11533/suppl. pdf)]. The relative stability over time of the spatial pattern of pollutant concentrations derived from short-term saturation moni- toring has been reported by others (Lebret et al. 2000; Sahsuvaroglu et al. 2006). Thus, although the measurement period for NO2 was at or beyond the end of mortality follow- up, there is evidence suggesting spatial 776 exposure contrast observed from these shorter periods captures the essential aspects of the chronic exposure experience for the cohort. With the accurate predictions from the LUR models, the assigned exposures appear to reduce measurement error, which may have contributed to the large effect size for circu- latory mortality. The road distance buffers tested here and used in comparable studies (Finkelstein et al. 2004) did produce elevated risks, but with less certainty than with the predicted NO2 concentrations, as would be expected from a less precise exposure estima- tor (Molitor et al. 2007). The exposure assess- ment appears to diminish measurement error and produce more certain estimates, but we are unable to comment directly on which spe- cific constituents of the pollutant mixture are linked to the health effects. Research efforts are under way to measure speciated PM and volatile organic compounds to better under- stand what constituents of the traffic mixture are most closely related to the observed risks. We based NO2 estimates on two simul- taneous 2-week monitoring periods. This method obviates the need to account for tem- poral effects in different measurements, but for most of the year we had no monitoring at these locations. This approach may have resulted in invalid estimates of the annual average. To assess this possibility, we com- piled 2-week averages for the downtown Toronto central monitoring site operated by the Ontario MOE. We compared the 2-week averages with the annual averages based on daily data. The annual average was 23.2 ppb, with a minimum of 18.9 ppb and maximum of 30.9 ppb. The mean difference in absolute values between the 2-week average and the annual average is 2.39 ppb, with a maximum difference of 7.72 ppb and a minimum dif- ference of 0.014 ppb. On average, then, we found about a 10% difference between the annual mean and the 2-week averages. In an earlier publication documenting the exposure modeling (Jerrett et al. 2007), we compared the 2-week average values to the monthly aver- age and 5-year averages at the three govern- ment monitors where co-located measurements were available. The NO2 levels were within 4.1–27.7% of the 5-year average. These analy- ses suggest that the two simultaneous 2-week samples probably reflect the annual and 5-year average values well. Similar studies in Southern California indicated high interclass correlations > 0.9 between the annual mean and two or three 2-week average measurements of NO2 (Gilliland et al. 2005). Other risk factors near roadways may also have contributed to the elevated mortality. Traffic noise is a particular concern given simi- lar source contributions compared with NO2 and the previous research linking traffic noise to elevated myocardial infarctions (Babisch et al. 2005). In this study, we are unable to rule out confounding by noise, and there may be residual influence from this unmeasured risk factor. Studies combining noise and air pollu- tion exposure are a priority for future research. Because we used existing clinic data rather than prospectively collected information, we did not obtain some valuable information on individual risk factors. For example, socio- economic status was not available at the indi- vidual level, although neighborhood data did appear to be a useful surrogate. There is thus the possibility of residual confounding by individual factors that could not be measured with this study design. Although this study benefited from a well- characterized clinical cohort, generalizability of the results could be questioned given the high prevalence of chronic diseases relative to the general population: Nearly half the sub- jects had been diagnosed with IHD, and 30% had COPD. The high prevalence of cardio- pulmonary disease in the cohort probably lim- its inference of the findings to populations with a substantial proportion of susceptible indi- viduals. The results must also be viewed with the caveat that the sample size was smaller than in other cohort studies of mortality, and with small samples there is a higher possibility of chance findings. In follow-up research, a larger more representative sample of patients from general-practice clinics will be investigated. The reported findings may be influenced by the inclusion of susceptible individuals, but the large risks uncovered here and in the few other studies focused on traffic impli- cate TRAP as a risk to public health. When viewed in the context of increasing miles trav- eled (Frumkin et al. 2004) and the rapid rise in automobile sales in newly industrialized nations such as China and India (Molina and Molina 2004), the health effects observed here may have growing relevance in relation to the prevention of disease and premature mortality. Ref Rencs Babisch W, Beule B, Schust M, Kersten N, Ising H. 2005. Traffic noise and risk of myocardial infarction. Epidemiology (16):33–40. Beckerman B, Jerrett M, Brook JR, Verma DK, Arain MA, Finkelstein MM. 2008. Correlation of nitrogen dioxide with other traffic pollutants near a major expressway. Atmos Environ 42(2):275–290. Beelen R, Hoek G, van den Brandt PA, Goldbohm RA, Fischer P, Schouten LJ, et al. 2007. Long-term effects of traffic-related air pollution on mortality in a Dutch cohort (NLCS-AIR Study). Environ Health Perspect 116:196–202. Brook R, Jerrett M, Brook J, Finkelstein MM. 2008. The rela- tionship between diabetes mellitus and traffic-related air pollution. J Occup Environ Med 50(1):32–38. Burrough PA, McDonnell R. 1998. Principles of Geographical Information Systems. Oxford, UK:Oxford University Press. Cyrys J, Hochadel M, Gehring U, Hoek G, Diegmann V, Brunekreef B, et al. 2005. GIS-based estimation of expo- sure to particulate matter and NO2 in an urban area: stochastic versus dispersion modeling. Environ Health Perspect 113:987–992. ovlume 117 | number 5 | May 2009 • Environmental Health Perspectives