personal health insurance numbers (HINs) were introduced to replace the previous insur- ance numbers, which were family based. We collected the HINs and other identifiers for all patients treated at the clinic between 1992 and the end of 1999. Members of the cohort were linked to numerous administrative databases using the HIN as the identifier. These databases include the OHIP physician billing database, 1992–1999; the Ontario Hospital Discharge Database, 1992–1999; and the Ontario Mortality Registry, 1992–2002. In total, 2,414 patients with Toronto postal codes were identified. We extracted height, weight, results of lung function testing, and smoking history from the clinic’s database. Fifty-four subjects were missing data necessary to com- pute the body mass index (BMI). Because BMI is an important predictor of mortality, and potentially a major confounder, we chose to exclude subjects with missing BMI data, rather than to impute values. Each claim for payment submitted to OHIP by physicians must include a diag- nosis code. We used the OHIP linkage to classify underlying respiratory disease sta- tus by using diagnostic codes submitted to OHIP by specialist physicians. Diagnoses included chronic obstructive pulmonary dis- ease (COPD) [International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM 2007) codes 491, 492, 496] and asthma (ICD-9 493). We classified subjects diagnosed with both asthma and COPD as having COPD (ICD-9-CM 2007). We also searched the OHIP billing file and Ontario hospital discharge database for diagnoses of diabetes (ICD-9-CM 250) and chronic ischemic heart disease (IHD) (ICD-9-CM 412–414). Subjects were classified with these disorders if the diagnosis had been made in two or more claims submissions by a general practitioner, one claim submission by a spe- cialist, or in any hospitalization. Neighborhood contextual confounders. Evidence from social epidemiology suggests that neighborhood context may affect health independently beyond individual risk factors (Diez-Roux 2002). In Quebec, a deprivation index (DI) derived from the Canadian cen- sus was correlated with premature mortality due to tobacco-associated causes (Pampalon and Raymond 2000). Because a relationship may exist between neighborhood context and traffic exposures, we used a similar index to adjust mortality risks across the region of our study to control for confounding of the TRAP effect. We used data from the 1996 Census of Canada at the enumeration area (EA) level to derive a contextual variable. EAs are the smallest unit of aggregation in the Canadian census, with an average population of about 1,000. In a small number of cases (< 60), we assigned census tract or metro- politan area census data because EA data were suppressed by Statistics Canada to protect privacy because of small population counts in the EA. We employed principal components to generate the DI. Analysis showed that only the first principal component, accounting for 72% of the variance, was related to mortality. We thus retained only the first component, and we defined the DI as [–0.62 × log(mean income) + 0.58 × unemployment rate + 0.53 × proportion of residents who did not com- plete high school]. Higher scores on the DI indicate a less favorable combination of income, education, and employment. Exposure assessment. We collected NO2 samples using two-sided Ogawa passive dif- fusion samplers at 150 locations across Toronto. We selected sampling locations with a location-allocation model based on pollu- tion variability over space and on residential population density summarized elsewhere (Kanaroglou et al. 2005). We measured NO2 levels across Toronto for two 2-week periods, one in early fall 2002 and the other in spring 2004. To assess the reliability of the samplers, we deployed two samplers in tandem at each location during the first round of monitor- ing, yielding a total of four separate samples at each location. After determining low varia- tion between the duplicates from the 2002 monitoring, we deployed one single two-sided sampler at each location, but in about 15% of locations we used two two-sided samplers for the spring 2004 sampling. During each sampling period, we deployed 100 Ogawa samplers simultaneously. This deployment strategy eliminated the need for tempo- ral adjustments to the measurements before developing land-use regression (LUR) models as has been necessary in other studies (Cyrys et al. 2005; Hochadel et al. 2006). During the second round, we kept 50 locations the same as those in the first round and added 50 new locations (a total 150 spatial locations and 143 valid measurements over the two sampling periods). We chose these new sites using the same location-allocation approach to improve spatial coverage of the model, whereas the 50 that were the same during both deploy- ments provided an assessment of interseasonal correlation and hence the stability of the NO2 spatial pattern, which we subsequently used to assign exposure across the cohort. We sent the Ogawa filters to an Environment Canada laboratory for analysis, where NO2 levels were determined by ion chromatography (Jerrett et al. 2007). We derived variables employed in subse- quent LUR models as predictors of NO2 with the ArcGIS 9 geographic information system (GIS) software (ESRI, Redlands, CA, USA). LUR employs the measured pollutant levels as the dependent variable and an array of land Environmental Health Perspectives • volume 117 | number 5 | May 2009 Traffic pollution and mortality use, traffic, physical geography, and popu- lation variables as predictors. The detailed modeling framework and results of the fall, first season of measurement are described by Jerrett et al. (2007), and the models used to predict exposures for the second, spring sea- son are discussed by Finkelstein and Jerrett (2007). Briefly, we implemented a manual forward-selection procedure to select variables that explained the largest proportion of varia- tion in NO2. We screened > 90 variables for each season, representing different potential predictors of exposure. In both models, R2 values were nearly 0.7. Each model contained variables representing roadways, population density, land use, and the geographic x-coor- dinate to represent a west-to-east trend of higher to lower NO2 across the city. We also derived interpolated surfaces for PM2.5 and ozone. For the PM2.5 surface, we computed the annual geometric average for 2002 from 14 Ministry of the Environment of Ontario (MOE) stations, equipped with tapered element oscillating microbalance monitors (hourly measurements). This was the first year when a spatially extensive cov- erage of PM2.5 was available for the study area. For O3, we used 16 MOE sites and one Environment Canada site to generate annual averages. We computed annual averages for O3 for 1992, 1997, and 2002 (the beginning, middle, and end of the follow-up period). After interpolation, we averaged the three surfaces and assigned them to the subjects. The spatial domain of the samplers extended beyond our immediate study area to increase the stability of the interpolations, but the study area was approximately at the center of the interpolation surface. We performed interpolations using an inverse distance weighting algorithm in ArcGIS 9 (Burrough and McDonnell 1998). Statistical model. The analysis relied on Cox proportional hazards regression mod- els run in STATA version 9.0 (StataCorp., College Station, TX, USA).We ran models for all nonaccidental causes, circulatory (ICD- 9-CM 400–440), respiratory (ICD-9-CM 460–519), and lung cancer (ICD-9-CM 162) and, as a negative control, for all nonac- cidental causes less circulatory, respiratory, and lung cancer deaths. For each outcome, we began with a model adjusted only for age and sex. We then added variables to control for confounding: clinical measures (BMI, lung function), smoking, and the DI. As a sensitivity analysis, we added as explanatory variables diagnoses of COPD, diabetes, and IHD, diseases probably on the causal pathway between pollution exposures and mortality, but also possibly representative of unmeas- ured confounders such as diet or occupational exposure. We adjusted SEs for clustering in census tracts. We tested deviations from the 773