Journal of Affective Disorders 84 (2005) 53 – 62
www.elsevier.com/locate/jad
Research report
Stability and change of emotional functioning in late life:
modelling of vulnerability profiles
Edwin de Beursa,*, Hannie Comijsb, Jos W.R. Twiskc,d, Caroline Sonnenbergb,
Aartjan T.F. Beekmanb, Dorly Deegb
a
Department of Psychiatry, Leiden University Medical Center, PO Box 9600, 2300 RC Leiden, The Netherlands
b
Department of Psychiatry, Vrije Universiteit, Amsterdam, The Netherlands
c
EMGO Institute, Vrije Universiteit, Amsterdam, The Netherlands
d
Department of Clinical Epidemiology and Biostatistics, Vrije Universiteit, Amsterdam, The Netherlands
Received 23 July 2004; received in revised form 21 September 2004; accepted 27 September 2004
Abstract
Background and aims: The present study investigated stability and change in emotional well-being in a prospective study of a
large sample of community-dwelling older adults (z55 years). Emotional functioning was conceptualized according to the
tripartite model distinguishing three aspects: general negative affect (NA), depression, and anxiety. The study tested models for
the decline of mental health in late life based on the diathesis–stress model. In previous studies, support has been found for the
diathesis–stress model (for an overview, see [Goldberg, D.P., Huxley, P., 1992. Common mental disorders: a biosocial model.
Routledge, London; Zuckerman, M., 1999. Vulnerability to psychopathology. American Psychological Association,
Washington, DC.]). The predictive ability of vulnerability factors (the personality characteristics mastery and neuroticism)
and stressful life events and their interaction was tested for an increase in general negative affect, decreased positive affect (PA),
and increased anxiety. More specifically, we tested the hypothesis that loss leads to decreased positive affect in subjects with
low mastery, whereas threat leads to anxiety in subjects with high neuroticism.
Methods: Data from the Longitudinal Aging Study Amsterdam (LASA) were used. LASA is a longitudinal study in a large
representative sample of adults aged 55 to 85 (N=1837). Self-report data on depression, anxiety, and negative affect were
collected from adults over a 6-year period in three waves. The data were analyzed using multilevel analysis.
Results: The findings revealed an association between low mastery, high neuroticism, and an increase in negative affect, lack of
positive affect, and anxiety. Furthermore, high mastery protected against the negative impact of loss events, but neuroticism did
not augment the negative impact of threat events on emotional health.
Conclusion: Partial support was found for a diathesis–stress model of change in emotional functioning in late life. Furthermore,
support was found for distinguishing between symptoms of negative affect, depression, and anxiety.
D 2004 Published by Elsevier B.V.
Keywords: Anxiety; Depression; Risk factors; Vulnerability; Life events; Multilevel analysis
* Corresponding author. Tel.: +31 71 526 3416; fax: +31 71 524 8156.
E-mail address: e.de_beurs@lumc.nl (E. de Beurs).
0165-0327/$ - see front matter D 2004 Published by Elsevier B.V.
doi:10.1016/j.jad.2004.09.006
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E. de Beurs et al. / Journal of Affective Disorders 84 (2005) 53–62
1. Introduction
Anxiety and depression are the most prevalent
psychological problems in late life. Traditionally,
research into emotional aspects of the aging process
focused on depression. However, recently, studies
have been undertaken investigating anxiety in late
life, and knowledge regarding the prevalence and
comorbidity patterns of anxiety in older persons is
growing (Flint, 1994; Fuentes and Cox, 1997). These
studies show that anxiety disorders are quite prevalent among older persons. In an overview, the 6
months prevalence of anxiety in older persons was
estimated at 10% (Flint, 1994). Although prevalence
rates for major depression among older persons are
generally lower (ranging between 0.5 and 3%), an
estimated 10% to 15% suffers from sufficient
depressive symptoms to have a significant diminished quality of life and increased medical consumption (Beekman et al., 1999). Similar findings
regarding the impact of anxiety disorders in late life
have been reported (de Beurs et al., 1999; Flint,
1994).
Commonly, a strong association between depression and anxiety is reported; correlation coefficients
between self-report measures for depression and
anxiety usually range from r=0.50 to r=0.70.
Although this may in part be explained by lack of
specificity of these measures for depression and
anxiety (Clark and Watson, 1991), there is also
considerable true overlap in symptoms between both
disorders. Furthermore, there is a high comorbidity
of anxiety and depression at the disorder or caseness
level (Barbee, 1998), especially in late life (Kirby et
al., 1997). Among elderly, about 50% of the
clinically depressed suffers from comorbid anxiety
disorders, and 25% of patients with anxiety suffers
from major depression (Beekman et al., 2000).
Finally, depression and anxiety respond equally well
to similar therapeutic interventions, such as Selective
Serotonin Reuptake Inhibitors, further blurring a
distinction between both disorders. Current knowledge on the interplay of depression and anxiety in
late life is limited. The scarce available data suggest
that distinctive features of depression and anxiety
become even less pronounced with rising age
(MacKinnon et al., 1994). Findings such as these
raise doubt on the usefulness of distinguishing
between both conditions (Tyrer, 1989) and made
some researchers suggest that a dimensional
approach to affective psychopathology would do
more justice to the state of affairs (Andrews, 1996;
Ormel et al., 1995).
To explain the high comorbidity and high
concurrence of symptoms in anxiety and depression,
Clark and Watson (1991) proposed a tripartite model,
postulating three dimensions for symptoms commonly
found in patients suffering from depression and/or
anxiety disorders. The model states that anxiety and
depression share a common component, negative
affectivity (NA) with presumably a genetic/temperamental background (Clark and Watson, 1991; Watson and Clark, 1984). This general dimension can be
considered as a core component of anxiety disorders
and depressive disorders (Brown et al., 1998) and
consists of symptoms such as feeling depressed,
hopeless, sad, afraid, and nervous. The second
dimension comprises symptoms specific to depression, which they named positive affect (PA). Relatively unique to depression is a lack of emotional
states such as feeling good about oneself and feeling
optimistic or successful; the third dimension consists
of symptoms specific to anxiety, predominantly
somatic symptoms of anxiety and therefore named
physiological hyperarousal or Somatic Anxiety (SA)
(Keogh and Reidy, 2000).
An important aspect on which these dimensions of
anxious and depressive symptomatology may be
distinguished are the risk profiles they are associated
with. Some vulnerability factors or life events may be
uniquely associated with negative affect, while others
rather predict lack of positive affect or somatic
anxiety symptoms. The predominant model for the
etiology of psychopathology is the diathesis–stress
model (Zuckerman, 1999). Initially formulated to
describe risk factors of schizophrenia, the model has
been applied with success to study etiological factors
in depression and anxiety, where it became known as
the vulnerability–stress model (Brown and Harris,
1978; Goldberg and Huxley, 1992). Basically, the
model states that destabilization (getting symptoms) is
the result of long-lasting vulnerability factors, acting
in concert with exposure to environmental stressors,
usually one or more highly stressful events. Regarding the question why some persons develop depression whereas others develop anxiety, Finlay-Jones and
E. de Beurs et al. / Journal of Affective Disorders 84 (2005) 53–62
Brown (Finlay-Jones and Brown, 1981) suggested
that the type of event may be a decisive factor:
stressful life events involving loss (e.g., death of a
loved one, retirement) are more likely to lead to
depression, whereas stressful events involving threat
(e.g., being victimized by crime) are more likely to
lead to anxiety. Furthermore, interactions of these life
events and specific vulnerability factors are suggested. Events involving loss would be especially
troublesome for subjects with a low sense of mastery,
whereas threat events would have a stronger impact
on subjects high in neuroticism. Put differently, the
model proposes a moderator effect of mastery and
neuroticism on the destabilizing effect of stressful life
events. The notion that low mastery or an external
locus of control is important for the development of
depression is founded in the work of Rotter (1975)
and can also be related to Seligman’s concept of
learned helplessness in animal models of depression
(Seligman, 1978). Also from other theoretical perspectives, a relation is proposed between depression
on the one hand and low mastery, low perceived
control, or dependency on the other (Beck, 1996;
Blatt and Zuroff, 1992). Experiencing adverse events
increases the risk for depression (Paykel, 2003),
especially in those persons who feel limited control
over their life’s course (Brown and Siegel, 1988).
High neuroticism is a notorious risk factor for both
depression and anxiety (Clark et al., 1994; Eysenck
and Eysenck, 1991; Zuckerman, 1999). The importance of threat operationalized as the perception of
looming vulnerability for the development of anxiety
has been underlined by Riskind (1997).
In the present study, the prognostic value of the
risk factors is assessed for change in negative affect,
positive affect, and somatic anxiety symptoms. Risk
profiles for these three groups of symptoms are
compared, evaluating main effects of risk factors
and their interactions. The main research questions are
the following: which factors predict a significant
increase of emotional problems in late life and what
decides whether the outcome of destabilization is
predominantly negative affect, lack of positive affect,
or somatic anxiety? These research questions are
broken down into the following:
(1)
Are different long-standing vulnerability factors
involved for the three dimensions?
(2)
(3)
55
Are certain types of stressful life events (loss vs.
threat events) specific for developing negative
affect, decreased positive affect, or somatic
anxiety symptoms?
Do stressful events and vulnerability factors
(mastery*loss and neuroticism*threat) interact
in the predicted manner, or do they rather have
an additive effect on destabilization?
Data from the Longitudinal Aging Study Amsterdam (LASA; Deeg et al., 1993) were used to
investigate risk factors for changes in negative
affect, positive affect, and somatic anxiety in the
elderly. LASA is a longitudinal community-based
study in a large representative sample of adults aged
55 to 85. In a previous cross-sectional study
(Beekman et al., 2000), clear differences were found
between subjects with a diagnosis of major depression and subjects with anxiety disorders. Major
depression was associated only with age and an
external locus of control, whereas having an anxiety
disorder was associated with a wide range of
vulnerability factors and stressors. Subjects with a
comorbid condition had again a quite distinct risk
profile from those with pure depression or anxiety
(Beekman et al., 2000). We also investigated the
development of depression and anxiety in longitudinal data from two waves of the LASA study. We
used cutoff scores on symptom measures to delineate adults as either anxious, depressed, or both.
Results revealed that depression and anxiety have
many risk factors in common, but specific risk
factors were also found, especially in subjects with
both depression and anxiety (de Beurs et al., 2001).
The present study builds on these findings by using
longitudinal data from three waves of LASA. A
different data analytic strategy is used, no longer
dichotomizing patients as disordered (yes–no) but
using the full range of scores on assessment
instruments. This approach is more suitable to test
the dimensional approach to psychopathology.
Moreover, this way of analyzing the data yields
more statistical power by preserving the continuous
character of the symptom scores. Emotional problems were conceptualized according to the tripartite
model for negative emotional states, distinguishing
negative affect, lack of positive affect, and somatic
anxiety.
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E. de Beurs et al. / Journal of Affective Disorders 84 (2005) 53–62
2. Method
Table 1
Descriptives of the sample at baseline and 6 years later
2.1. Sample and procedure
Data for LASA were collected by interviewing
participants in their homes by trained and intensively
supervised interviewers. All interviews were audiotaped to allow random quality checks. In 1992, a
random sample of older (55–85 years) men and
women stratified for age and sex was drawn from
the population registers of 11 municipalities in three
regions of The Netherlands. Older men were initially
oversampled to ensure sufficient participants in these
strata for a later phase of the study. In the first cycle of
LASA (T 1), 3107 participants were interviewed. This
sample has been described extensively in previous
publications of LASA (Beekman et al., 1995; de
Beurs et al., 1999; Deeg and Westendorp-de Seriere,
1994). Three years later (T 2), the participants were
contacted again, and 2302 (74%) were willing and
still able to be interviewed again. When we contacted
the participants again in 1998–1999, 2076 (66.8%) of
the initial sample could be interviewed again. Of the
1031 nonrespondents, 761 (24.5% of the T 1 sample)
had died, 81 (2.6%) were too ill or cognitively
impaired to be interviewed, 160 (5.2%) indicated that
they were no longer interested in participating in the
study, and 29 (0.9%) could not be contacted. Due to
item nonresponse on measures for depression and
anxiety at T 1, T 2, or T 3, a further 239 subjects were
lost, leaving 1837 subject for whom scores on
emotional functioning were available at all time
points (60.1% of the T 1 participants). The present
analysis was performed on this sample.
The 1270 nonrespondents were compared with the
1837 participants on key variables to check for
selective attrition. No significant difference was found
between participants and nonrespondents regarding
anxiety symptoms, but lower positive affect [t(3105)=
7.43; pb0.001] and higher negative affect [t(3105)=
6.59; pb0.001] at T 1 did make attrition more likely.
For other variables, we also found an association with
attrition: nonrespondents were more likely to be male
[v 2(1)=22.7; pb0.001], unmarried [v 2(1)=32.3; pb
0.001], older [t(3105)=21.8; pb0.001], had lower socioeconomic status (SES; t=9.05; pb0.001), less education [v 2(2)=60.7; pb0.001], and a lower score on
the Mini Mental State Exam (MMSE) [t(3105)=23.5;
N
Negative affect, mean (S.D.)
(Lack of) Positive affect,
mean (S.D.)
Somatic anxiety, mean (S.D.)
Sex
Male
Female
Marital status
Married
Unmarried
Age, mean (S.D.)
SES, mean (S.D.)
Education
Low
Middle
High
MMSE, mean (S.D.)
T1
T3
3107
1.72 (2.87)
3.28 (3.04)
1837
1.80 (2.74)
3.53 (3.02)
2.53 (3.30)
3.12 (3.58)
1506 (48.5%)
1601 (51.5%)
816 (44.4%)
1021 (55.6%)
1942
1165
70.8
33.7
(62.7%)
(37.3%)
(8.8)
(19.3)
1236
601
71.1
29.9
(67.3%)
(32.7%)
(8.1)
(19.0)
1376
1370
353
26.6
(44.3%)
(44.1%)
(11.4%)
(3.8)
713
908
216
27.4
(38.8%)
(49.4%)
(11.8%)
(2.9)
pb0.001]. Thus, the subjects who were lost from T 1 to
T 3 comprised the less healthy and worse functioning
part of the initial participants at T 1. Baseline
characteristics of the baseline sample and the final
study sample are presented in Table 1.
3. Measures
3.1. Negative affect, lack of positive affect, and
somatic anxiety symptoms
In LASA, depressive symptoms were measured
with the Center for Epidemiological Studies Depression scale (CES-D; Radloff, 1977). This 20-item
scale comprises Likert-type items which describe
feelings such as depression, hopelessness, and the
blues. The respondent is asked to indicate whether
these feelings were experienced in the past week on
a four-point scale, ranging from 0=brarely or neverQ
to 3=balways or almost always.Q Two subscales of
the CES-D were used. To assess negative affect, we
used a subscale of seven items: having the blues,
feeling depressed, life is a failure, feeling fearful.
Lack of positive affect was assessed with four items:
feeling as good as others, hopeful about the future,
being happy, enjoying life. Thus, we limited use of
the available CES-D data to two of the four subscales of this instrument. These two subscales mea-
E. de Beurs et al. / Journal of Affective Disorders 84 (2005) 53–62
sure the closest intended concepts of negative affect
and lack of positive affect.
Anxiety was measured with the anxiety subscale of
the Hospital Anxiety and Depression Scale (HADS-A;
Zigmond and Snaith, 1983). The HADS-A comprises
seven Likert-type items in which the respondent is
asked to indicate whether he/she has experienced
feelings such as restlessness, tenseness, or panic on a
scale scoring from 0=bseldom or neverQ to 3=balways
or almost always.Q The anxiety subscale score has a
theoretical range from 0 to 21.
57
of the events. Weights for various life events were
derived from Tennant and Andrews (Tennant and
Andrews, 1976). As both composites had a skewed
and peaked distribution, they were log-transformed to
approach a normal distribution.
All assessment instruments used in the study had
been previously validated in The Netherlands or their
psychometric properties had been evaluated for their
use in the older population in LASA pilot studies
(Deeg et al., 1993).
3.4. Statistical analysis
3.2. Risk factors
Two stable psychological characteristics of the
participants were assessed: mastery and neuroticism.
Mastery was assessed in the interview with a fiveitem scale adapted from Pearlin and Scooler (1978).
A higher score means a more external locus of control
or less mastery. Neuroticism (15 items) was measured
through the abbreviated subscale of the Dutch
Personality Inventory (Luteijn et al., 1985). This
self-report scale was completed after the interview
and mailed in by the respondent. Not all participants
complied: 138 of the 1837 participants (8%) failed to
return fully completed questionnaires. Nonresponse
on the self-report data was not related to sex of the
participant [v 2(1)=0.92, p=0.34] but was related to
higher age [more nonresponse in older participants,
t(3105)=7.69; p=0.002].
3.3. Life events
In the T 2 and T 3 interviews, it was assessed
retrospectively whether stressful life events had
occurred in the 3-year time interval prior to the
interview. The following stressful events were
assessed: illness of one’s partner, death of one’s
partner, illness of a relative, death of a relative, a
major conflict with others, income loss (of at least $50
a month), victimized by crime, and relocation. To
investigate the impact of these life events, we
constructed two composites: one for events associated
with loss (e.g., death of a partner or relative) and one
for events associated with threat (e.g., illness of a
partner or relative, crime). Composites scores were
made by differentially weighting life events and
combining them in one score, representing the impact
To examine the association between neuroticism,
mastery, and the outcome variables, multivariate
multilevel analysis were carried out. The general idea
behind using multilevel analysis for multivariate
problems is that the measurements of the different
outcome variables can be seen as dclusteredT within
the subject (Goldstein, 1995). As this was a longitudinal study, three levels of analysis can be distinguished: (1) the outcome variables negative affect,
lack of positive affect, and anxiety, (2) the observations at different time points, and (3) the participants.
This multivariate multilevel analysis is basically a
multivariate linear regression analysis. There is,
however, an additional level because we have
repeated observations within each participant. In
addition to the main effects for neuroticism and
mastery, the interaction between neuroticism*threat
and mastery*loss were added to the models. In a
second analysis, the association between the predictor
variables and the three outcome variables were
examined in separate multilevel analyses. Figs. 1
and 2 present the associations of main interest for a
simple model with one outcome variable and for the
Fig. 1. Model for the association between predictors and change in
emotional functioning.
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E. de Beurs et al. / Journal of Affective Disorders 84 (2005) 53–62
Fig. 2. Hypothesized model for the associations among predictors and three dependent variables.
complete model. All analyses were adjusted for
gender, age, socioeconomic status, and health (number of somatic diseases) and were performed with
MLwiN (version 1.10.0007; Centre for Multilevel
Modeling, Institute of Education, London, UK). For
socioeconomic status, we used a weighted score
composed of level of education, occupation, and
income (range 0–100; van Tilburg et al., 1995).
4. Results
First, the complete model was analyzed. Results
are presented in Table 2. Overall, adjustment for
control variables (age, sex, and SES) did not change
the results of the analyses in a meaningful way. The
results of analysis of the entire model revealed main
Table 2
Results of multivariate multilevel analyses (main effects)
Adjusteda
Unadjusted
95% CI
B
Mastery
0.15
Neuroticism
0.10
p
0.13 to b0.001
0.17
0.09 to b0.001
0.11
B
95% CI
0.13
0.09
p
0.11 to b0.001
0.15
0.08 to b0.001
0.11
a
Adjusted for sex, age, socioeconomic status, and health of the
respondent.
effects of mastery ( pb0.001) and neuroticism
( pb0.001) on deterioration of emotional functioning
(the overall outcome, not distinguishing negative
affect, lack of positive affect and anxiety). Less
mastery and higher neuroticism were significantly
associated with decreased emotional well-being (see
Table 2). Furthermore, a significant interaction effect
of mastery and loss events (B=0.002; CI=0.001–
0.003; p=0.002) was found. Apparently, loss events
have a greater impact for participants who report less
mastery. This finding is in accordance with our
prediction. However, the predicted interaction between neuroticism and threat events is not supported
by the data since this interaction failed to reach
significance (B=0.001, p=0.44).
Finding significant effects in the overall model
justifies a closer look into the associations by
analyzing three models separately, one for each
dependent variable. Table 3 presents the results for
main effects in the separate models for general
negative affect, lack of positive affect, and somatic
anxiety.
Regarding main effects, the results reveal that
general negative affect has a positive association with
neuroticism and an equally strong but negative association with mastery (B=0.09 and B= 0.14, respectively). Lack of positive affect (specific to depression)
was associated with low mastery. Neuroticism was also
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E. de Beurs et al. / Journal of Affective Disorders 84 (2005) 53–62
Table 3
Results of multilevel analyses for three outcomes (main effects and interaction effects)
Adjusteda
Unadjusted
B
95% CI
B
p
Negative affect
Mastery
0.14
0.11 to 0.16
b0.001
Neuroticism
0.09
0.07 to 0.10
b0.001
Interaction effects
Significant interaction between loss and mastery ( pb0.011)
No significant interaction between threat and neuroticism ( p=0.90)
Lack of positive affect
Mastery
0.17
0.14 to 0.19
b0.001
Neuroticism
0.09
0.08 to 0.11
b0.001
Interaction affects
No significant interaction between loss and mastery ( p=0.34)
No significant interaction between threat and neuroticism ( p=0.25)
Somatic anxiety
Mastery
0.14
0.11 to 0.17
b0.001
Neuroticism
0.13
0.11 to 0.15
b0.001
Interaction affects
Significant interaction between loss and mastery ( pb0.001)
No significant interaction between threat and neuroticism ( p=0.98)
a
95% CI
p
0.12
0.09
0.10 to 0.15
0.07 to 0.10
b0.001
b0.001
0.15
0.09
0.12 to 0.17
0.07 to 0.10
b0.001
b0.001
0.13
0.13
0.10 to 0.16
0.11 to 0.15
b0.001
b0.001
Adjusted for sex, age, socioeconomic status, and health of the respondent.
associated with lack of positive affect but according to
the regression coefficients (B) to a somewhat lesser
extent than mastery (B=0.09 vs. B= 0.17). In contrast,
high somatic anxiety was equally strongly associated
with high neuroticism and low mastery (B=0.13 and B=
0.14). Controlling for sex, age, education, SES, and
health did not alter these results in a meaningful way.
Regarding interaction affects of personality characteristics and both type of events, the results reveal some
significant associations. No modifying effect of mastery or neuroticism on events leading to decreased
positive affect is apparent in the data. Mastery
moderates the impact of loss on somatic anxiety ( pb
0.001), and the same interaction effect is found with
negative affect as outcome variable ( p=0.011). The
predicted interaction effect of threat events and neuroticism on somatic anxiety was not found in the data.
In general, the findings of the overall model and
the three separate analyses converge: of the two
predictors, mastery has the strongest association with
the outcome variables. However, when comparing the
strength of the associations of mastery and neuroticism for the three dimensions of psychopathology
according to the tripartite model, neuroticism has the
strongest association with somatic anxiety, a finding
in accordance with the hypothesized distinctness of
these three dimensions.
The interaction of neuroticism and threat events is
not found in the overall model and neither in the
analyses of dependent variables separately. Instead,
mastery modifies the effects of loss events on somatic
anxiety ( pb0.001) and, to a lesser extent, on negative
affect ( p=0.011). The direction of the interaction
effect of mastery and loss events on anxiety is as one
would expect: high mastery protects for the effect of
loss events.
5. Discussion
Overall, support has been found for the proposed
model predicting different pathways for symptoms of
depression, anxiety, and general negative affect.
Increases in anxiety and in negative affect were both
associated with an interaction effect of life events
involving loss and low mastery. Contrary to our
hypothesis, no significant interaction effect was found
between neuroticism and negative life events involving
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E. de Beurs et al. / Journal of Affective Disorders 84 (2005) 53–62
threat. What do the present finding reveal about the
validity of the tripartite model for anxiety and
depression in late life? The findings suggest clearly
explanatory power for the present model. By demonstrating different effects for risk factors on depression,
anxiety, and general negative affect, the findings
underline the validity of separating out these distinct
groups of symptoms according to the tripartite model.
Furthermore, distinguishing vulnerability in lack of
mastery and high neuroticism proved fruitful given the
distinct outcomes for these variables: mastery appeared
a better predictor of decreased negative affect than
neuroticism. The same holds for distinguishing
between loss and threat events.
In spite of the general support for the proposed
model, differences among predicting variables for
distinct outcomes were modest (e.g., B= 0.09 vs.
B=0.14). In interpreting this outcome, one should also
bear in mind that the present model for developing
emotional problems or destabilization in late life is far
from complete. The decisive factor for whether the
outcome of life events and vulnerability is depression
or anxiety may very well be of a biological (e.g.,
genetic (Kendler et al., 2001)) or social nature.
Regarding the latter, the buffering effect of an
extensive social network on the impact of negative
life event is well documented, in part by studies on
data from LASA (Penninx et al., 1998). Regarding the
former, we have previously reported on the profound
impact of physical health on anxiety (de Beurs et al.,
2000). Other biological factors, such as HPA-axis
functioning in depression, are currently under study.
These factors were not included in the present
analysis. More conclusive evidence regarding the
utility of the tripartite model for a dimensional
conceptualization of psychopathology will come from
studies linking the dimensions to biological factors or
to response to interventions in clinical populations.
The present findings may have been influenced by
a potential lack of variance in our composite scores
for events. Particularly, the composite for threat events
yielded low scores. For instance, very few subjects
reported being victimized by crime. Furthermore, by
their very nature, loss events may have a stronger
impact than stress events. For instance, mourning a
close relative has a greater impact on emotional
functioning as compared to the same relative falling
ill (a threat event mirroring losing a relative).
Furthermore, life events were assessed by putting
information from different sections of the interview
together. Potentially, a more thorough assessment of
events would have yielded different results. (e.g., the
life history calendar approach to assessing life events
(Caspi et al., 1996).
Furthermore, the lack of support for some of our
predictions, especially those regarding distinct predictions for somatic anxiety and lack of positive affect
may be due to the fact that the present study was
undertaken in a nonclinical elderly population. The
present sample was population-based. Moreover, as
time progressed, it comprised predominantly of those
with the best chances of survival. The hypotheses may
get more support when studied in a patient group,
where symptom profiles will be more clearly distinct
and where potentially more aggravating life events
will be reported.
Another limitation to the present study was the use
of subscales of the CES-D and the HADS-A to
measure symptoms according to the tripartite model.
Although both instruments are well researched and
valid, they are not the best choice for an optimum
distinction between the symptoms of anxiety and
depression. The subscales of the MASQ (Watson et
al., 1995) or the Beck Depression Inventory and the
Beck Anxiety Inventory (Beck et al., 1961; Beck and
Steer, 1990) show less concordance between anxiety
and depression and may have yielded more pronounced differences between correlates of depression
and anxiety than the subscales of the CES-D and
HADS-A. Future research with a dependent variable
that is better suited for distinguishing the three factors
of the tripartite model may yield more clear-cut results.
In addition, the predictive power of the model might be
improved by incorporating additional predictors.
Promising predictors could be found in the biological
realm (Charney, 2004) or in other psychological traits,
such as attributional style or cognitive biases, that
produce vulnerability for the development of psychopathology. Currently, in The Netherlands, three universities have started a collaborative effort to study
(determinants of) the course of depression and anxiety
in primary and secondary care patients in a longitudinal design (NESDA). A large number of patients will
be repeatedly assessed over a 10-year time span. Data
will be gathered on diagnostic status, symptoms of
anxiety and depression, biological parameters, such as
E. de Beurs et al. / Journal of Affective Disorders 84 (2005) 53–62
cortisol, and psychological parameters (personality
traits and other vulnerability factors such as anxiety
sensitivity). Availability of these data will allow testing
a more comprehensive model for different age groups.
6. Conclusion
The present findings lend partial support to the
diathesis–stress model. Low mastery and neuroticism
are both associated with a decrease in emotional wellbeing over time, mastery most strongly with depression, and neuroticism with anxiety. The predicted
modifying effect of neuroticism on the association
between threat events and anxiety was not corroborated by the data. However, the significant interaction
between mastery and loss events delineates those
specifically at risk: individuals who experience
negative life events and tend not to feel in control
of their lives. A possible clinical implication of this
finding is that efforts to increase perceived control,
e.g., through cognitive behavior therapy, may enhance
the outcome of pharmacological treatment for depression in late life and may prevent relapse after
successful therapy. Finally, the study findings support
distinguishing three types of symptoms: negative
affect, lack of positive affect, and anxiety.
References
Andrews, G., 1996. Comorbidity and the general neurotic syndrome. Br. J. Psychiatry 168, 76 – 84.
Barbee, J.G., 1998. Mixed symptoms and syndromes of anxiety and
depression: diagnostic, prognostic, and etiologic issues. Ann.
Clin. Psychiatry 10, 15 – 29.
Beck, A.T., 1996. Beyond belief: a theory of modes, personality and
psychopathology. In: Salkovscis, P.M. (Ed.), Frontiers of
Cognitive Therapy. Guilford Press, New York, pp. 265 – 290.
Beck, A.T., Steer, R.A., 1990. Manual for the Beck Anxiety
Inventory. Psychological, San Antonio, TX.
Beck, A.T., Ward, C.H., Mendelson, M., Mock, J.E., Erbaugh, J.,
1961. An inventory for measuring depression. Arch. Gen.
Psychiatry 4, 561 – 571.
Beekman, A.T.F., Deeg, D.J.H., van Tilburg, T., Smith, J.H.,
Hooijer, C., van Tilburg, W., 1995. Major and minor depression
in later life: a study of prevalence and risk factors. J. Affect.
Disord. 34, 41 – 49.
Beekman, A.T., Copeland, J.R., Prince, M.J., 1999. Review of
community prevalence of depression in later life. Br. J.
Psychiatry 174, 307 – 311.
61
Beekman, A.T.F., de Beurs, E., van Balkom, A.J.L.M., Deeg,
D.J.H., Van Dyck, R., van Tilburg, W., 2000. Anxiety and
depression in later life: co-occurrence and communality of risk
factors. Am. J. Psychiatry 157, 89 – 95.
Blatt, S.J., Zuroff, D.C., 1992. Interpersonal relatedness and selfdefinition: two prototypes for depression. Clin. Psychol. Rev.
12, 527 – 562.
Brown, G.W., Harris, T.O., 1978. Social Origins of Depression.
Tavistock, London.
Brown, J.D., Siegel, J.M., 1988. Attributions for negative life events
and depression: the role of perceived control. J. Pers. Soc.
Psychol. 54, 316 – 322.
Brown, T.A., Chorpita, B.F., Barlow, D.H., 1998. Structural
relationships among dimensions of the DSM-IV anxiety and
mood disorders. J. Abnorm. Psychology 107, 179 – 192.
Caspi, A., Moffitt, T.E., Thornton, A., Freedman, D., Amell, J.W.,
Harrington, H., Smeijers, J., Silva, P.A., 1996. The life history
calendar: a research and clinical assessment method for
collecting retrospective event-history data. Int. J. Methods
Psychiatr. Res. 6, 101 – 114.
Charney, D.S., 2004. Psychobiological mechanisms of resilience
and vulnerability: implications for successful adaptation to
extreme stress. Am. J. Psychiatry 161, 195 – 216.
Clark, L.A., Watson, D., 1991. A tripartite model of anxiety and
depression: psychometric evidence and taxonomic implications.
J. Abnorm. Psychology 100, 316 – 336.
Clark, L.A., Watson, D., Mineka, S., 1994. Temperament, personality, and the mood and anxiety disorders. J. Abnorm.
Psychology 103, 103 – 116.
de Beurs, E., Beekman, A.T., van Balkom, A.J., Deeg, D.J., Van
Dyck, R., van Tilburg, W., 1999. Consequences of anxiety in
older persons: its effect on disability, well-being and use of
health services. Psychol. Med. 29, 583 – 593.
de Beurs, E., Beekman, A.T., Deeg, D.J., Van Dyck, R., van
Tilburg, W., 2000. Predictors of change in anxiety symptoms of
older persons: results from the Longitudinal Aging Study
Amsterdam. Psychol. Med. 30, 515 – 527.
de Beurs, E., Beekman, A., Geerlings, S., Deeg, D., Van Dyck, R.,
van Tilburg, W., 2001. On becoming depressed or anxious in
late life: similar vulnerability factors but different effects of
stressful life events. Br. J. Psychiatry 179, 426 – 431.
Deeg, D.J.H., Westendorp-de Seriere, M., 1994. Autonomy and
Well-Being in the Aging Population: I. Report from the
Longitudinal Aging Study Amsterdam 1992–1993. VU University Press, Amsterdam.
Deeg, D.J.H., Knipscheer, C.P.M., van Tilburg, W., 1993.
Autonomy and well-being in the aging population: concepts
and design of the Longitudinal Aging Study Amsterdam. NIGOverzichtstudies, 7 edn. Nederlands Instituut voor de Gerontologie, Bunnik.
Eysenck, H.J., Eysenck, S.B.G., 1991. Eysenck Personality Scales.
Hodder and Staughton, London.
Finlay-Jones, R., Brown, G.W., 1981. Types of stressful life event
and the onset of anxiety and depressive disorders. Psychol. Med.
11, 803 – 815.
Flint, A.J., 1994. Epidemiology and comorbidity of anxiety
disorders in the elderly. Am. J. Psychiatry 151, 640 – 649.
62
E. de Beurs et al. / Journal of Affective Disorders 84 (2005) 53–62
Fuentes, K., Cox, B.J., 1997. Prevalence of anxiety disorders in
elderly adults: a critical analysis. J. Behav. Ther. Exp. Psychiatry
28, 269 – 279.
Goldberg, D.P., Huxley, P., 1992. Common Mental Disorders: A
Bio-Social Model. Routledge, London.
Goldstein, H., 1995. Multilevel Statistical Models. Wiley, New
York.
Kendler, K.S., Thornton, L.M., Prescott, C.A., 2001. Gender
differences in the rates of exposure to stressful life events and
sensitivity to their depressogenic effects. Am. J. Psychiatry 158,
587 – 593.
Keogh, E., Reidy, J., 2000. Exploring the factor structure of the
mood and anxiety symptom questionnaire (MASQ). J. Pers.
Assess. 74, 106 – 125.
Kirby, M., Bruce, I., Radic, A., Coakley, D., Lawlor, B.A., 1997.
Mental disorders among the community-dwelling elderly in
Dublin. Br. J. Psychiatry 171, 369 – 372.
Luteijn, F., Starren, J., van Dijk, H., 1985. Manual for the Dutch
Personality Quaestionnaire [Handleiding bij de NPV]. Swets
and Zeitlinger, Lisse, The Netherlands.
MacKinnon, A.J., Christensen, H., Jorm, A.F., Henderson, A.S.,
Scott, R., Korten, A.E., 1994. A latent trait analysis of an
inventory designed to detect symptoms of anxiety and depression using an elderly community sample. Psychol. Med. 24,
977 – 986.
Ormel, J., Oldehinkel, A.J., Goldberg, D.P., Hodiamont, P.P.,
Wilmink, F.W., Bridges, K., 1995. The structure of common
psychiatric symptoms: how many dimensions of neurosis?
Psychol. Med. 25, 521 – 530.
Paykel, E.S., 2003. Life events and affective disorders. Acta
Psychiatr. Scand., Suppl., 61 – 66.
Pearlin, L.J., Scooler, C., 1978. The structure of coping. J. Health
Soc. Behav. 19, 2 – 21.
Penninx, B.W., Tilburg, T., Boeke, A.J.P., Deeg, D.J.H., Kriegsman,
D.M.W., Eijk, J.T.M., 1998. Effects of social support and
personal coping resources on depressive symptoms: different for
various chronic diseases? Health Psychol. 17, 551 – 558.
Radloff, L.S., 1977. The CES-D scale: a self-report depression scale
for research in the general population. Appl. Psychol. Meas. 1,
385 – 401.
Riskind, J.H., 1997. Looming vulnerability to threat: a cognitive
paradigm for anxiety. Behav. Res. Ther. 35, 685 – 702.
Rotter, J.B., 1975. Some problems and misconceptions related to the
construct of internal versus external control of reinforcement.
J. Consult. Clin. Psychol. 43, 56 – 67.
Seligman, M.E., 1978. Learned helplessness as a model of
depression. Comment and integration. J. Abnorm. Psychology
87, 165 – 179.
Tennant, C., Andrews, G., 1976. A scale to measure the stress of life
events. Aust. N. Z. J. Psychiatry 10, 27 – 32.
Tyrer, P., 1989. General neurotic syndrome and mixed anxietydepressive disorders. In: Tyrer, P. (Ed.), Classification of
Neurosis. Wiley, New York, NY, pp. 132 – 164.
van Tilburg, T., Dykstra, P., Liefbroer, A.C., Broese van Groenou,
M., 1995. Sourcebook of Living Arrangements and Social
Networks of Older Adults in The Netherlands. VU University
Press, Amsterdam.
Watson, D., Clark, L.A., 1984. Negative affectivity: the disposition
to experience aversive emotional states. Psychol. Bull. 96,
465 – 490.
Watson, D., Weber, K., Assenheimer, J.S., Clark, L.A., Strauss,
M.E., McCormick, R.A., 1995. Testing a tripartite model: I.
Evaluating the convergent and discriminant validity of anxiety
and depression symptom scales. J. Abnorm. Psychology 104,
3 – 14.
Zigmond, A.S., Snaith, R.P., 1983. The hospital anxiety and
depression scale. Acta Psychiatr. Scand. 67, 361 – 370.
Zuckerman, M., 1999. Vulnerability to Psychopathology. American
Psychological Association, Washington, DC.