Svansdottir et al. BMC Public Health 2012, 12:42 http://www.biomedcentral.com/1471-2458/12/42 depression models and excluded from further analysis in those models. Underlying assumptions were inspected for each regression model and indicated no problems. The effect size for differences in anxiety, depression, and perceived stress scores by Type D personality were estimated using Cohen’s d calculations. Linear regres- sion analysis was used to test the unique and shared predictive power of both Type D components for anxi- ety, depression and stress, where continuous and SI scale scores (0-28) were included as predictors instead of Type D personality. Each linear regression model was run twice, first with inserted at the first step and SI at the second step, and then with SI inserted at the first step and at the second step, in order to assess the unique explained variance of and SI. Furthermore, a logistic regression, which incorporated the same covari- ates as the linear regression, was conducted to assess the odds ratio associated with Type D patients for mani- festation of increased symptoms of anxiety, depression, and stress. For this analysis, all predictors were inserted into the model simultaneously using the enter method. Finally, a re-analysis was conducted for all significant associations where the Type D/non-Type D categoriza- tion (≥ 10 on and SI) was substituted with continu- ous and SI scale scores [37]. Inter-quartile ranges were used to rescale and SI scores and the by SI interaction term, so that a one unit difference repre- sented a clinically relevant metric. Within these inter-quartile distribution, 70% (N = 49) of Type Ds fell within the 4th quartile, and 30% (N = 21) within the 3rd quartile. For SI 49% (N = 34) of Type Ds fell within the 4th quartile and 51% (N = 36) within the 3rd quar- tile. In the inter-quartile by SI scores, 89% (N = 62) of Type Ds were within the 4th quartile and 11% (N = 8) within the 3rd quartile. Linear regression models for anxiety, depression, and stress were re-executed, with NA, SI and the by SI interaction term entered at the first step and covariates at the second. For health- related risk markers, binary logistic regression analyses (stepwise procedure) were used with NA, SI and the by SI interaction term as predictors. All analyses were two-tailed and alpha < 0.05 was used to indicate statistical significance. The SPSS 17 statistical software for windows was used for the analysis (Statisti- cal Package for Social Sciences, Chicago, IL, USA). Results Demographical and clinical variables Mean age in the sample was 62.9 years (SD 10.5) and males were more prevalent (74%, N = 199) than females. A total of 26% of patients were defined as having Type D personality, which is in line with previous research [3,19-21]. Baseline characteristics of Type D and non- Type D individuals are presented in Table 1. Type D Page 4 of 10 patients were on average younger than non-Type D patients, but no differences emerged in gender distribu- tion, family status or educational level between groups. Likewise, prevalence of traditional CAD risk factors was similar across groups, except that Type D patients were less likely to be on hypertension treatment compared to their non-Type D counterparts. No difference was found in disease severity, as measured by the number of ves- sels affected by CAD, nor previous cardiac history (for- mer PCI, MI and/or CABG) between Type D and non- Type D participants. The association of Type D personality with anxiety, depression and stress Type D patients had significantly higher anxiety, depres- sion and perceived stress scores compared to their non- Type D counterparts (M = 9.7 (SD 2.6) versus M = 7.7 (SD 2.2), t(262) = 5.92, p < 0.001 for anxiety; M = 6.0 (SD 2.3) versus M = 4.7 (SD 1.4), t(86.2) = 5.54, p < 0.001 for depression; M = 21.8 (SD 6.4) versus M = 15.9 (SD 5.7), t(250) = 7.02, p < 0.001 for perceived stress; see Figure 1). Further analysis with multiple linear regressions showed that the association between Type D personality and higher scores on anxiety, depression and perceived stress was independent of age, gender, family status, education, disease severity and cardiac history. In all cases, Type D personality had a strong association at the first step, (explaining 13%, 11% and 16% of variance in anxiety, depression and stress scores, respectively), and the association remained when covariates were inserted into the model at the second step. The inclu- sion of covariates contributed to a 6% increase in explained variance of anxiety scores, but did not signifi- cantly improve model fit for depression or perceived stress (see Table 2). The effect sizes associated with Type D personality were high (Cohen’s d = 0.78, 0.74, and 0.93 for anxiety, depression, and perceived stress respectively). Analysis of the unique and shared predictive power of both Type D subcomponents revealed that the associa- tion between Type D and anxiety was primarily driven by (31% of the variance), while the total variance explained by both factors was 34%. SI did not signifi- cantly contribute to this model of anxiety; the shared variance of both factors was 2.5%. Conversely, both and SI contributed to the association with depression and perceived stress, with 9% shared variance for both measures. The unique effect of was larger in both cases, with and SI explaining 9% and 4% of depres- sion scores and 17% and 1% in perceived stress scores, respectively. Multivariate logistic regression analyses replicated these findings, indicating that Type D patients had about three to four times greater odds of experiencing