Neighbourhood and Mortality Table 2. Baseline Variable Means6Standard Deviations and Numbers (%) for CAD Patients (n = 485) and CAD Patients Alive or Deceased After 13.3 Years Follow-up Time Variable Age Sex Ethnicity Smoking status Hypertension Diabetes Alcohol consumption (drinks per week) BMI Categories or Units Years Male Female European Chinese or South Asian Ever or current Other Never No Yes No Yes Never Occasional (1–5) Moderate (6–10) Heavy (.10) kg/m2 All CAD Patients Alive 61.0610.5 59.3610.1 383 (79) 102 (21) 392 (81) 48 (10) 45 (9) 122 (25) 348 (72) 183 (40) 273 (60) 393 (81) 92 (19) 118 (24) 268 (55) 75 (15) 12 (3) 28.164.5 270 (71) 67 (66) 266 (68) 40 (83) 31 (69) 91 (75) 236 (68) 193 (71) 126 (69) 284 (72) 53 (58) 86 (73) 183 (68) 53 (71) 9 (75) 27.864.1 Deceased 64.9610.3 113 (29) 35 (34) 126 (32) 8 (17) 14 (31) 31 (25) 112 (32) 80 (29) 57 (31) 109 (28) 39 (42) 32 (32) 85 (32) 22 (29) 3 (25) 28.765.3 p-value* ,0.01 0.35 0.09 0.16 0.67 ,0.01 0.81 0.08 *p#0.05 for differences in baseline variables between alive and deceased patients, from Mann-Whitney U tests and Pearson chi-square tests for continuous and categorical variables, respectively, and Manzel-Haenszel X2 tests for alcohol consumption. CAD = coronary artery disease; BMI = body mass index doi:10.1371/journal.pone.0004120.t002 1.11 (1.00–1.23, p = 0.05) for education, 1.08 (0.95–1.22, p = 0.24) for unemployment, and 1.10 (0.98–1.24, p = 0.10) for income. The next wave of analyses examined SES disparities in cause- specific mortality. None of the indices of neighborhood disadvan- tage was associated with mortality from cardiovascular disease over the follow-up time. Unadjusted hazard ratios (95% CI, and significance values) were 1.03 (0.87–1.21, p = 0.75) for education, 0.96 (0.79–1.17, p = 0.69) for unemployment, and 1.02 (0.86–1.23, p = 0.80) for income. However, there were significant disparities in non-cardiovascu- lar chronic disease mortality rates by SES. Each one quintile increase in neighbourhood unemployment was associated with a 30% greater risk of non-cardiovascular chronic disease death, following adjustment for risk factors (age, gender, BMI, diabetes, smoking, and alcohol consumption). Quintiles of median family income and neighbourhood education levels were both associated with 21% increases in risk, following adjustment for the same risk factors. Linearity of SES effects for education, income and unemployment were confirmed in covariate-adjusted models as described above for all neighbourhood SES indices (p,0.05 for all SES measures). Results from unadjusted survival models, and forced-entry covariate adjusted survival models, are displayed in Table 3. Survival curves from covariate adjusted models are depicted graphically in the panels of Figure 1. Time-adjusted AUC values for non-cardiovascular chronic disease mortality with and without SES indicators were generated from adjusted Cox regression survival analyses. AUC values obtained were 0.600 for covariates age, sex, BMI, diabetes, smoking, and alcohol consumption without SES indicators. The AUC values improved to 0.712, 0.715, and 0.720 for the model with addition of indices for education, unemployment, and income, respectively. HL tests of model calibration, for which p#0.05 indicates poor model calibration across a range of risk, show good risk model fit for prediction of non-cardiovascular mortality for the covariate- adjusted adjusted models tested above. Models including ; ; with SES variables education, unemployment and income, yielded HL chi-square test statistics of 9.17 (p = 0.33), 8.16 (p = 0.42), and 6.94 (p = 0.54), respectively, and the basic model statistic was 5.71 (p = 0.68). Disease Specific Survival Analyses To discern which causes of death were underlying these associations, we carried out secondary analyses of mortality from specific non-cardiovascular chronic diseases. Cancer was the most common cause of death in this category, with 31 patients (6.4% of the cohort) having died from neoplastic diseases. We did not carry out further analyses for death from other types of chronic diseases, due to insufficient numbers in each category. As Table 4 shows, Cox survival analyses revealed significant neighborhood disparities in cancer mortality for unemployment and family income. These relationships withstood adjustment for the covariates age, sex, BMI, and smoking history. For neighbourhood unemployment, risk of death from neoplastic causes increased 62% per quintile (p,0.01). This relationship is displayed graphically in Figure 2. A parallel finding was evident for median family income, with a 42% increase in cancer mortality per quintile (p = 0.01). Linearity of SES effects for unemployment and income were confirmed in covariate-adjusted models (p = 0.01 for unemployment, p = 0.04 for income). Comparing patients in the lowest versus highest quartiles of SES, there was a 10-fold disparity in cancer mortality from the highest to the lowest quintile of neighbourhood unemployment (95% CI 1.31–76.33, p = 0.03). There was a 5.2–fold increase in PLoS ONE | www.plosone.org 4 January 2009 | Volume 4 | Issue 1 | e4120