The third objective addressed the age-related specificities of the different predictor variables with respect to young versus old age depression (model C), as well as quality of life in young and old age (model D).
To address model C, depression features between the young and the old age patient groups were compared to allow for comparable clinical background (see Appendix 4 for details).
Besides disease duration and age of onset, the most salient difference between the 38 young and 41 old patients was the stronger depressive symptom severity in younger patients (HRSD, F(1,77)=46.42, p<0.001). In the young age group, more depressive mood was further associated with higher past (χ2=13.07, p<0.001) and current suicide risks (χ2=19.19, p<0.001) and comorbid panic attacks (with and without agoraphobia, χ2=7.04, p=0.008) as assessed with the MINI at study inclusion. When assessed with the HoNOS severity rankings, the group difference in panic attacks no longer remains significant, reflecting the mild nature of these comorbid anxious symptoms. All patients received combined pharmacotherapy-psychotherapy outpatient treatments as usual. Even though severity of mood disorder was stronger in young age, older patients received nevertheless
considerably higher amounts of antidepressant (SSRI, χ2=8.31, p=0.004), benzodiazepine and (χ2=21.43, p<0.001) hypnotic drugs (χ2=11.57, p=0.001) compared to the younger cases. The majority of both groups presented with a recurrent nature of depression and about half of them had been hospitalized at least once in the past without distinction for age group. All patients showed symptoms of major depression for at least 6 months prior to the first contact for this study.
Depression predictors
Logistic regression model C predicted depression in young versus old age and predictors included severity of depressive symptoms (HRSD), which is the most frequently reported difference between depressive episodes in young and in old age, completed by the impact of stressful life events, physical illness and Neuroticism. This model predicted 72% of the occurrence of a depressive diagnosis in old age versus young age. With regard to the predictors, higher depressive symptom severity increases odds for young age depression, while more severe physical illness predicts old age depression. Neither the impact of stressful life events nor Neuroticism emerged as significant independent variables.
Table 11: Model C: Young versus old age depression predicted by depressive mood, impact of life stressors, physical illness and Neuroticism (n=89)
Regression coefficients
Predictors OR SE p * R2
HRSD a 0.69 0.10 <0.001 0.72
Impact of stressors 0.94 0.07 0.378
CIRS b 1.85 0.19 0.001
Neuroticism 1.00 0.02 0.986
OR = odds ratio, * Nagelkerke’s R2
a HRSD=HamiltonRating Scale for Depression, b CIRS = Cumulative Illness Rating Scale score
Quality of life predictors
The final linear regression models D predicted quality of life in all old (n=92) respectively young (n=89) participants. As presented above, age did not significantly predict quality of life in the overall regression model B, neither directly, or indirectly by interacting with any of the other predictors (Table 10). Plus, analysis of variance had revealed no significant main effect of age regarding quality of life (Table 4). Age merely interacted with the sub-sores of the physical and psychological quality of life subdomains. Older controls reported lower physical quality of life than younger controls, contrary to patients. In contrast, older depressed
patients reported higher psychological quality of life than younger patients, contrary to controls.
Model B tested the moderating impact of the socio-demographic, psychosocial and personality variables on the relationship between depression and quality of life. To specifically test this moderation in the two age sub-groups, the same sequential multiple regression model D was performed separately in the young and in the old age groups.
Indeed, depression characteristics differed between both age groups, specifically with respect to symptom severity, disease duration and suicide risk, and might thus have a different impact on quality of life. Physical illness was included as additional variable in model D showing the most important age effect in the above reported analyses (Table 2).
For model D in the OLD age group, depression features predicted 68% of the quality of life variance (Table 12).
Table 12: Model D OLD: Quality of life prediction in OLD age group (n=92)
Regression coefficients Change statistics
Predictors B SE p R2 F df p
Block 1 HRSD a -0.51 0.14 0.001 0.68 60.89 (3,88) <0.001 Years since onset -0.04 0.04 0.322
Suicide risk -3.06 0.98 0.003
+Block 2 CIRS b -0.72 0.19 <0.001 0.73 17.34 (1,87) <0.001 +Block 3 Neuroticism (N) -0.05 0.04 0.182 0.81 3.24 (10,77) 0.001
Extraversion (E) -0.01 0.04 0.739
Openness (O) 0.10 0.04 0.016
Agreeableness (A) 0.10 0.04 0.011
Conscientiousness (C) 0.05 0.03 0.094
N * HRSD 0.00 0.00 0.420
E * HRSD -0.01 0.01 0.203
O * HRSD 0.01 0.01 0.019
A * HRSD 0.00 0.01 0.971
C * HRSD 0.01 0.00 0.023
a HRSD=HamiltonRating Scale for Depression, b CIRS = Cumulative Illness Rating Scale
Incremental R2 changes revealed that the inclusion of physical illness significantly improved the prediction, explaining an additional 5%. The five personality factors added another 8% of explained quality of life variance. Regarding the individual predictors, suicide risk showed the strongest negative effect on quality of life, followed by physical illness and depressive symptoms severity. Both high levels of Openness to experience and Agreeableness had a
significant positive effect on quality of life. Further, Openness and Conscientiousness interacted with symptom severity to significantly lower the impact of depression on quality of life.
Interestingly, the replication of exactly the same model D led to a different pattern in the YOUNG age group (Table 13). The full model with all variables predicted 81% of the quality of life variance (R2). 77% of this variance was already explained by depression severity alone, showing a strong negative effect. In contrast to the old age group, neither the additional inclusion of physical illness nor the inclusion of personality dimensions did significantly improve the prediction for the young age group. Even though it explained the same 81% of the quality of life variance than in the old age group, the same full regression model did not allow for identifying additional explanatory variables besides depressive symptoms.
Table 13: Model D YOUNG: Quality of life prediction in YOUNG age group (n=89)
Regression coefficients Change statistics
Predictors B SE p R2 F df p
Block 1 HRSD a -0.68 0.11 <0.001 0.77 93.48 (3,85) <0.001 Years since onset 0.18 0.12 0.155
Suicide risk -1.01 1.07 0.348
+Block 2 CIRS b -0.39 0.35 0.271 0.77 1.08 (1,84) 0.303 +Block 3 Neuroticism (N) -0.05 0.03 0.097 0.81 1.63 (10,74) 0.114
Extraversion (E) -0.01 0.04 0.758
Openness (O) 0.05 0.04 0.161
Agreeableness (A) -0.03 0.04 0.499
Conscientiousness (C) 0.06 0.03 0.100
N * HRSD 0.00 0.00 0.101
E * HRSD 0.00 0.00 0.678
O * HRSD 0.00 0.00 0.405
A * HRSD 0.00 0.00 0.818
C * HRSD 0.00 0.00 0.422
a HRSD=HamiltonRating Scale for Depression, b CIRS = Cumulative Illness Rating Scale
DISCUSSION
In summary, the present study investigated the relationship between the five main personality dimensions and a major depressive episode in young and old age individuals.
Adopting an integrative approach, it allowed for defining the differential impact of demographic, psychosocial, physical illness and personality factors on acute depressive symptoms in both age groups. In a multiple regression, Neuroticism was the only personality dimension that positively predicted higher intensity of depressive symptoms (R2=0.16) as rated by mental health professionals, once the influences of young age, comorbid physical diseases and negative impact of stressful life events had been accounted for (R2=0.49).
Young age, together with higher levels of Neuroticism, comorbid illness, and negative emotional impact of life stresses emerged as risk factors for depression. In contrast, when the personality-depression relationship was analysed in a univariate analysis, not only higher Neuroticism, but also lower levels of Extraversion, Openness and Conscientiousness distinguished patients from controls.
Further, when the influence of personality was analysed on the relationship between depression and patient’s subjective quality of life, 67% of the quality of life variance was explained by the depressive symptoms, while personality explained an additional 5%, once the protecting influence of gender had been accounted for. Female gender, lower levels of Neuroticism and higher levels of Openness to experience and Conscientiousness revealed a significant positive influence on quality of life. Plus, Neuroticism and Conscientiousness interacted with depressive symptoms to lower their impact on quality of life.
With respect to specific age-related differences in predictor variables of depression and quality of life, intensity of symptom severity and level of comorbid physical diseases were the strongest predictors of old age depression in comparison to depression in younger age. In young age, high quality of life was solely predicted by low severity of depressive mood symptoms. In contrast, in old age high quality of life was best predicted by a multi-factorial approach including low depressive mood and low suicide risk, as well as low levels of physical comorbidity, and high levels of Openness to experience and Agreeableness factors of personality. Openness and Conscientiousness interacted with depression to lower its influence on quality of life in the old age group.
Depression and quality of life outcomes
Not surprisingly, patients had scored significantly higher on the Hamilton Rating Scale for Depression compared to controls. Further, in accordance with previous evidence, severity of depression was significantly stronger in the young age patient group than in the old age patient group. This result confirms existing evidence, which had revealed that older adults predominantly display milder and even sub-threshold forms of depression, and that minor forms of depressive episodes are 2-3 times more prevalent than major depression in this age group (Meeks, 2011). Some caution is advised in interpreting this result. Indeed, some authors have found weak correlations between scores on the Hamilton Rating Scale for Depression and age-specific depression scales (Clayton et al., 1997), even though other studies have shown the opposite (Onega & Abraham 1997). These results stress the importance of using a dimensional approach to depression, which assesses symptom severity, in addition to the categorical depression diagnosis. If depression had been measured only with the diagnostic criteria of the DSM-IV-R, the age-related difference in symptom presentation would not have been captured. Besides the impact of stressful life events, physical illness and Neuroticism, age emerged as an additional significant predictor for depression severity (model A), but not for depression diagnosis (Appendix 4). The utilization of a dimensional versus a categorical dependent variable of depression lead to different results in regression models.
Subjective quality of life was significantly lower in the depressed patients group than in the control group, as assessed by the WHOQOL-Bref total score as well as the four subdomain scores, assessing physical, psychological, social and environmental well-being. Supporting previous studies in younger (Böckerman et al., 2011; Goldberg & Harrow, 2005; Pyne et al., 1997) and in older adults (Chan et al., 2006; Doraiswamy et al., 2002; Naumann et al., 2004), this study solidifies evidence for the strong negative relationship between major depression and well-being. Interestingly, no main age effect was observed for quality of life in the present study, confirming that the strong negative relationship between a depressive episode and overall subjective quality of life holds in clinical populations independently of age. This result extends the study of Masthoff et al. (2007) on quality of life in psychiatric outpatients aged 21-50 years, showing that age played no role in moderating the depression-quality of life relationship. Plus, is this study there was no significant interaction between depression and age, invalidating the hypothesis of an indirect impact of age, which had been shown to act on well-being via lower intensity of depressive symptoms in previous work (Jones et al., 2003). In his literature review in non-clinical populations, Ryff (2008) had explained that hedonistic aspects of well-being (positive affects, life satisfaction) remain
stable or increase with age, while eudaimonic aspects of well-being (meaning in life, personal growth) tend to decrease later in life. Results of the present study revealed that quality of life was not significantly higher for older participants than for younger. They may be explained by the choice of the quality of life assessment tool. Indeed, the WHOQOL-Bref is more likely to measure eudaimonic aspects of well-being that do not increase with age.
Alternatively, the increase of well-being with aging described in literature may hold only in the general population, and vanish as soon as depressed patients are included in the study sample as was the case in the present study. Psychological quality of life subdomain scores were lower in younger compared to older patients, but not in controls, suggesting that age interacts differently with this subdomain according to participants’ mood status.
These data confirm the interest of considering the two outcomes, depression as well as quality of life, when investigating different age groups. While age played a significant role for depressive symptom severity, it did not for quality of life.
Objective I. Does personality predict depression once demographic, psychosocial