Dependant variables
Total (N=181) Patients (n=79) Controls (n=102) HRSD a
WHOQOL-Bref b HRSD
WHOQOL-Bref HRSD
WHOQOL-Bref
Independent variables
Neuroticism 1 0.74** -0.70** 0.58** -0.44** 0.37** -0.42**
Extraversion 1 -0.46** 0.53** -0.13 0.32** -0.16 0.24*
Openness 1 -0.24** 0.37** 0.13 0.18 -0.05 0.22*
Agreeableness 1 -0.02 0.06 -0.08 0.09 0.02 0.10
Conscientiousness 1 -0.37** 0.46** -0.13 0.37** -0.05 0.15 Age (continuous) 1 -0.09 -0.06 -0.56** 0.08 0.20* -0.13
Gender 3 0.07 -0.01 -0.10 0.11 0.14 0.02
Education 1 -0.23** 0.31** 0.15 0.07 0.04 0.09
Marital status c 3 0.08 -0.03 -0.11 0.01 0.08 0.11
Dependency 2 0.14 -0.20* -0.21 -0.01 -0.10 -0.07
Social support 1 -0.33** 0.41** 0.03 0.25* -0.11 0.17
SRRS d 1 0.12 -0.19** 0.05 -0.06 0.34** -0.32**
Impact of stressors 1 -0.56** 0.57** -0.15 0.28* -0.27** 0.23*
CIRS e 1 0.36** -0.49** -0.12 -0.25* 0.29** -0.34**
** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)
1 Pearson, 2 Spearman, 3 Kendall
a HRSD=Hamilton Rating Scale of Depression, b WHOQOL-Bref= World Health Organization Quality of Life, c 4 categories= single, married, widowed, divorced, d SRRS=Social Readjustment Rating Scale total score, e CIRS=Cumulative Illness Rating Scale
Objective I: Predictors of depression
An exploratory hierarchical multiple regression analysis assessed the association between depressive symptoms (HRSD scores) and all independent variables in the full sample (N=181, Model A, Table 7). In model A, age was introduced as continuous variable, and its interactions with the other predictor variables (education, CIRS, social support, SRRS, impact of stressors) were accounted for.
After the variables classically associated with depressive symptoms (as well as their interactions with age) had been accounted for in a first block of predictors, the second block added the five personality dimensions (and their interactions with age).
Table 7: Model A: Symptom severity (HRSD) prediction by socio-demographics, physical health, life
a age as continuous variable, b 1=male, 2=female, c 1=non-couple, 2=couple, d CIRS = Cumulative Illness Rating Scale, e SRRS=Social Readjustment Rating Scale
The first block explained 58% of the depressive symptoms variance, while personality factors explained an additional 16%, reflecting a significant improvement in prediction compared to the smaller model as indicating by the significant Incremental R2 changes.
When the same regression model is repeated with the significant predictors only, the R2 of the first block is 0.49 and after adding the second block (R2=0.16), the R2 of the full model equals 0.68.
The interpretation of the results of this hierarchical multiple regression needs to be moderated by the fact that the HRSD total scores do not follow a normal distribution (half of the sample are never-depressed control subjects, resulting into a positive skewness, Shapiro-Wilk p<0.001).
Regarding the individual predictors, in the model with the significant predictors only, amongst the socio-demographic variables (age, gender, level of education), only age emerged as a significant factor, playing a protective role. Physical illness (CIRS score) enhanced the risk for depressive symptoms, emerging as the less important significant predictor. Regarding psychosocial predictors (marital status, subjective social support as defined by the number of trustworthy persons, lack of autonomy as expressed by the number of hours of home care, and stressful life events), interestingly, it was not the amount of experienced stressful life events in the past 12 months that was related to depressive symptoms, but the subjectively perceived impact of these events. Less negative impact (or rather more positive emotional impact) showed a positive influence on the HRSD score.
Amongst the personality factors, only Neuroticism presented a significant positive relationship to depression severity. Contrary to expectations, none of the interactions between age and the other independent variables revealed to be a significant predictor, neither for socio-demographic, nor psychosocial or personality variables.
Neuroticism refers to a tendency to experience negative affect, and has a high degree of overlap with depression, as confirmed by the strong positive correlation (r=0.75) between Neuroticism and HRSD scores in the current sample. An additional analysis was performed to address the critic that Neuroticism is merely a contaminant of the depressive state, rather than a personality dimension that has both depressive mood-state dependent and trait-like properties. A longitudinal design would have been needed to address the mood-state dependency of Neuroticism. In the absence of such as design, in the present cross-sectional design, the residuals of a linear regression with Neuroticism scores as dependent variable and HRSD scores as independent variables were used to reflect the part of Neuroticism that is not explained by the mood state. HRSD scores explain 55% of the variance of Neuroticism, showing that both scales do not assess the same construct. These residuals were introduced as an alternative to Neuroticism scores together with the other significant independent variables to predict depression in regression model A (Table 8). Results reveal Neuroticism only predicts depression, when it is used as a personality dimension that includes both mood-state dependant and trait-like properties. The mood-independent
variance of Neuroticism does not significantly predict depression. A closer look at the lower-order facets of the Neuroticism dimension of personality confirm that it is more precisely the participants tendency to experience feelings of guilt, sadness, despondency and loneliness (N3) that puts them at risk for depression, while their ease in communicating their distress, and their absence of social shyness or anxiety (N4) protects them from depression.
Table 8: Model A bis: Symptom severity (HRSD) prediction by age, stress impact, physical illness and Neuroticism respectively mood-independent residuals of Neuroticism only (N=181) Self-consciousness (N4) -0.25 0.10 0.010 Impulsiveness (N5) -0.11 0.09 0.237 Vulnerability (N6) 0.05 0.10 0.618
a age as continuous variable, b CIRS = Cumulative Illness Rating Scale
Another alternative of model A is the prediction of depression outcome as a binary variable (case/control) by the same independent variables by means of a logistic regression model.
This alternative is illustrated in Appendix 4. The application of this binary depression outcome sacrificed the information related to the severity of the depressive symptoms, and did not allow for including all independent variables given the limited sample size. However, it compensated the skewed distribution of the HRSD score. With this binary dependent variable, the first block explained 58% of the pseudo-variance the depression diagnosis.
(The pseudo-variance is defined as the proportion in terms of the log likelihood obtained by a logistic regression, similar to the R2 found in linear regression). Neuroticism again
explained an additional 18% of pseudo-variance and showed a significant improvement of the model (LRT: χ2=(146-96)=50, 1df, p<0.001). It confirmed the positive prediction of depression by the severity of physical illness as well as protective impact of a lower level of subjective impact of life stressors. Interestingly, the use of this simplified and methodologically purer binary outcome model, levelled out the significant influence of age.
Age significantly predicted depressive symptom severity, but not depression diagnosis.
As mentioned, results of the relationship between personality factors and depression, and more precisely after accounting for the influence of physical illness and dependency have been published in November 2011 in the Aging & Mental Health Journal (see Appendix 6a). I autonomously composed and wrote the article and it was corrected and approved by the thesis director prior to publication. Reviewers had stressed the need to repeat the model with the significant predictors only after they had been identified by a first run of a full model with all predictors. Note that in the article, which used the binary depression outcome and focused on a smaller model without considering all independent variables, and especially not the variable of stressful life events, the level of dependency (hours of home care) had emerged as a significant predictor. In the larger picture considered in the present manuscript, the influence of the dependency variable no longer emerged as having a significant role (see table 7).
Regarding stressful life events, several authors (Kessler, 1997; Hammer, 2005; Liu & Alloy, 2010) had stressed that it is neither the impact of the stressful event nor the personality factor in itself that predict depression, but rather the interaction between these two variables.
Table 9: Model A ter: Symptom severity (HRSD) prediction by age, stress impact, physical illness, Neuroticism and Neuroticism X stress interaction (N=181)
Regression coefficients
Predictors B SE p R2
Age a -0.10 0.03 0.001 0.67
CIRS b 0.46 0.18 0.010
Impact of stressors -0.53 0.08 <0.001
Neuroticism 0.18 0.02 <0.001
Impact * Neuroticism 0.00 0.00 0.629
a age as continuous variable, b CIRS = Cumulative Illness Rating Scale
Table 9 explores this additional issue (after centring the Neuroticism scores to account for multicollinearity). Interestingly, in the present study, the prediction was significant only when
both variables are treated independently; the interaction between stress impact and Neuroticism was not a significant predictor variable.
The results on the relationship between stressful life events, personality and depression have been submitted for publication in International Journal of Geriatric Psychiatry in November 2011. After the editors of the journal replied that they were not interested in reviewing the article, it was submitted to Psychogeriatrics in June 2012 (Appendix 6b). I also autonomously composed and wrote this article and it was corrected and approved by the director as well as all members of the PhD commission prior to publication.