adjustment for potential confounding variables age, sex, body mass index (BMI), diabetes, smoking status, and alcohol consumption. Model covariates were chosen to represent factors that are associated with risk of mortality, and/or may vary with neighbourhood composition. Separate models were estimated for all-cause mortality, cardiovascular mortality, and for non-cardiovascular chronic disease mortality. Models were also generated for mortality from cancer, but due to the reduced number of deaths from this cause, only age, sex, BMI, and smoking were included as covariates to avoid over-adjustment [22,23]. SES indicators were also entered as categorical variables in covariate adjusted models to permit comparison across quintiles, with the highest level of SES (quintile 1) used as reference. Linearity across SES quintiles were also tested using repeated contrasts to compare each quintile except the first category to the quintile that precedes it. Validity of the proportional hazards assumption for the survival models was verified using Schoenfeld residuals correlated with time, and partial residual plots for all survival models and covariates [24]. To measure survival model improvements offered by SES indices, area under the curve (AUC) values from time-adjusted receiver operator characteristic curves were generated from covariate-adjusted Cox regression models [25], using nearest neighbour kernel smoothing [26]. Models were tested for goodness of fit using Hosmer Lemeshow (HL) tests, which report significance values of p#0.05 for risk models with significant deviation from accurate calibration across a range of risk [27]. Results Differentials between Surviving and Deceased CAD Patients For the current study we focused on 485 patients who had valid postal code data and angiographic evidence of CAD. Of these patients, there were 148 total cases of mortality, 64 of which were attributed to cardiovascular causes, and 66 of which were non- CAD chronic disease deaths. There were also 18 cases of deaths Neighbourhood and Mortality not caused by chronic diseases. Average and total follow-up times were 11.1 and 13.3 years. The group of CAD patients represented all SES quintiles, with roughly equal numbers of patients in each of the 5 quintiles of income, education, and unemployment (Table 1). Table 2 displays patient baseline medical and risk factor covariate characteristics. At recruitment, the mean age of the patients was 61 years. The study group was 79% male, 72% were current or former smokers, and 19% had diabetes. Eighty-one percent of the patients reported European descent, with another 10% reporting either Chinese or South Asian ancestry. A review of angiography reports for this cohort suggested that approximately 90% of the CAD patients presented with stable disease, and the remaining patients presented with acute coronary syndromes (data not shown). As Table 2 shows, patients who were older at study entry were more likely to die over the follow-up, as were patients with diabetes. No other significant differences were observed among baseline variables. Missing response rates were 5% for smoking, 2.5% for alcohol consumption, and 0–2% for all other variables. Relationships between Neighbourhood SES and Patient Characteristics Quintiles of SES variables were evenly distributed across cohort patients. Few significant relationships were observed between SES indices and patient characteristics. However, individuals with lower neighbourhood education levels were more likely to have a higher BMI (correlation of 0.09, p,0.05), and lower alcohol consumption (correlation of 0.11, p = 0.02). No other significant correlations were observed. Survival Analyses The first wave of analyses tested for differences in all-cause mortality according to neighborhood disadvantage among individ- uals with CAD. There was no consistent effect of education, unemployment or family income on mortality. The unadjusted hazard ratios (95% confidence intervals (CI), significance value) were Table 1. Baseline Numbers of Stable CAD Patients (n = 485) and Total Cumulative Mortality Rates after 13.3 Years Follow-Up Time Across Quintiles of Neighbourhood Socioeconomic Indices for Education, Unemployment and Median Family Income Quintile (1 = high SES 5 = low SES) CAD Patients (n) Proportion (%) Deceased (n) Total Cumulative Mortality Rate (%) Neighbourhood SES Category Education Unemployment Median Family Income doi:10.1371/journal.pone.0004120.t001 PLoS ONE | www.plosone.org 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 146 86 65 93 95 63 114 129 91 88 105 86 131 76 87 30% 18% 13% 19% 20% 13% 24% 27% 19% 18% 22% 18% 27% 16% 18% 3 37 38 17 30 36 17 33 41 24 33 29 21 45 18 35 25% 32% 26% 32% 38% 27% 29% 32% 26% 38% 28% 24% 34% 28% 24% January 2009 | Volume 4 | Issue 1 | e4120 p-value* 0.06 0.26 0.10