Neighbourhood and Mortality Table 3. Hazard Ratios (95% Confidence Intervals) from Survival Analyses for Non-Cardiovascular Chronic Disease Mortality by Neighbourhood SES Category Quintiles for CAD Patients (n = 485) After 13.3 Years Follow-Up Time Survival model without covariates Survival model with covariates: Step 1 Step 2 Covariates in Models SES quintile Age (years) Male sex BMI Diabetes Smoking (ever or current) Alcohol consumption SES quintile Education 1.20 (1.02–1.42)* 1.07 (1.04–1.10) { 0.92 (0.49–1.71) 1.02 (0.96–1.08) 1.76 (0.99–3.13) 2.21 (1.13–4.31)* 1.06 (0.68–1.39) 1.20 (1.02–1.42)* Unemployment 1.23 (1.01–1.50)* 1.07 (1.04–1.11) { 0.95 (0.51–1.77) 1.02 (0.97–1.08) 1.92 (1.08–3.43)* 2.12 (1.08–4.13)* 0.89 (0.61–1.31) 1.30 (1.06–1.60)* Median Family Income 1.24 (1.03–1.48)* 1.07 (1.04–1.10) { 0.97 (0.52–1.82) 1.03 (0.97–1.08) 1.82 (1.03–3.22)* 2.13 (1.10–1.16)* 0.93 (0.63–1.36) 1.21 (1.01–1.46)* Hazard ratios, 95% confidence intervals, and significance values for each quintile increase in SES indices are given from Cox regression models for risk of non- cardiovascular chronic disease mortality. Covariates listed were force-entered in adjusted Cox regression models *p#0.05 {p#0.01 SES = socioeconomic status; BMI = body mass index doi:10.1371/journal.pone.0004120.t003 cancer mortality from the highest to lowest quintile of neighbour- hood median family income (95% CI 1.47–18.5, p = 0.01). No significant relationship was observed for education. Despite the limited number of covariates included, the AUC value for the cancer mortality model with age, sex, BMI and smoking, improved from 0.651 to 0.720 and 0.712 for SES indices of unemployment and income, respectively. HL tests indicated no significant deviation from model calibration with SES variables. Covariate-adjusted model HL X2 test statistics were 4.09 (p = 0.85), and became 9.17 (p = 0.33), 4.57 (p = 0.80), and 8.09 (p = 0.43) with the addition of indices for education, unemploy- ment, and family income, respectively, confirming no loss of model calibration with the addition of SES indices. Discussion We investigated whether neighbourhood socioeconomic condi- tions predicted mortality among 485 stable coronary artery disease patients living in British Columbia, after .13 years of follow-up time. Based on the extensive literature documenting social gradients in morbidity and mortality from CAD [1–7,9,10], we expected that patients with adverse neighbourhood socioeconomic conditions would be especially prone to cardiac death. However, we did not find a gradient in overall or cardiovascular mortality attributable to neighbourhood characteristics. Instead we observed a marked gradient in mortality from non-cardiovascular chronic diseases. For each quintile increase in neighbourhood SES deprivation, estimated risks for non-CAD chronic disease deaths increased between 21–30%, leading to an average 2.4-fold increase between highest and lowest neighbourhood SES quintiles. Although the number of cancer deaths were small, profound effects were observed for rates of cancer mortality; estimated risks for cancer death increased 42% and 62% for each quintile decrease in neighbourhood SES family income and employment, respectively. Although it would not be appropriate to use neighbourhood SES solely to distinguish which CAD patients should be screened for other chronic diseases, we demonstrate that neighbourhood SES indices improved risk prediction for chronic disease mortality in these patients. Areas under time-adjusted receiver operator curves for non-cardiovascular and cancer deaths increased when neighbourhood SES was added to a risk model that included age, sex, BMI, diabetes, smoking, and alcohol consumption. Also, calibration tests well suited to assess cohorts of this size [28] show that good model calibration was achieved across range of risk for covariate-adjusted models. Alongside numerous studies demonstrating increased incidence of CAD and cardiovascular death in neighbourhoods with lower SES, there is extensive literature documenting higher total mortality among CAD patients living in lower SES neighbour- hoods. However, few studies have investigated which specific types of mortality are increased among CAD patients living in lower SES neighbourhoods. We distinguish cardiovascular mortality from deaths due to other chronic diseases, and add new information regarding the nature of these increased risks. It is not clear why a social gradient in cardiovascular deaths did not emerge in our data. Two other studies have found that CAD patients from low SES neighborhoods are at increased all-cause mortality risk [10,11]. These studies had much larger sample sizes than ours, so reduced statistical power may have masked these effects in our study. The patients in the other studies had recently been hospitalized for myocardial infarction, so these subjects may have had more advanced disease or been less medically stable in comparison to patients in our study. Also, patients in the other studies were drawn from many regional acute care centers, while our cohort patients were investigated by selective coronary angiography and treated in cardiology services at major teaching hospitals, thus similarities of care may have eliminated—or at least diminished—existing social disparities in prognosis. Disparities in care that associate with neighbourhood SES could explain why these studies observed associations between neighbourhood and cardiovascular mortality and we did not. But as we can presume care was cardiac-specific, it would not have had the same effects for other chronic diseases such as cancer. Future research is needed to explore this possibility, and to determine the applicability of our findings to other cardiac populations. Our findings are consistent with published evidence showing a gradient in health across neighbourhoods, however it remains unclear whether our findings are attributable to community versus individual SES, as the former could simply be acting as a proxy measure for variations in the latter. Both of these factors predict mortality from a variety of causes, and in many cases they do so independently of one another [8,9,29,30]. Our findings that non- CAD chronic disease death rates increase with neighbourhood SES deprivation in CAD patients suggests neighbourhood conditions may independently instigate longer term health effects which persist in the context of a medical system designed to provide care regardless of individual-level SES. Health status of PLoS ONE | www.plosone.org 5 January 2009 | Volume 4 | Issue 1 | e4120