Journal of Traumatic Stress
February 2020, 33, 106–115
A Network Approach to Understanding Quality of Life Impairments
in Prolonged Grief Disorder
Fiona Maccallum
1,2
and Richard A. Bryant
2
1
2
School of Psychology, University of Queensland, St. Lucia, Australia
School of Psychology, University of New South Wales, Sydney, Australia
Prolonged grief (PGD) is a potentially debilitating consequence of bereavement that is experienced by 7%–10% of bereaved individuals.
In recent years, PGD has been the focus of increasing interest as it is associated with a range of significant negative physical and
mental health outcomes. To date, however, there is little understanding of how impairment is associated with individual PGD symptom
interactions. Network analysis is an innovative statistical approach that has been productively applied to examine how symptoms of
psychopathology influence and reinforce each other. In this study, we examined the association between PGD symptoms and quality of
life (QoL) impairments. Data from 215 bereaved individuals were used to construct networks comprising PGD symptoms and different
facets of QoL. The results showed that PGD symptoms of meaninglessness and role confusion were linked with reduced psychological
QoL, trust difficulties were linked with reduced social QoL, and bitterness was linked with reduced environmental QoL. These results are
consistent with models that highlight the importance of self-identity and loss of meaning in PGD. By elucidating pathways of dysfunction,
these findings offer clinical implications that may help to improve outcomes for persons with PGD.
Prolonged grief (PG) is a potentially debilitating consequence of bereavement that 7%–10% of bereaved
individuals experience (Lundorff, Holmgren, Zachariae,
Farver-Vestergaard, & O’Connor, 2017; Maciejewski, Maercker, Boelen, & Prigerson, 2016; Prigerson et al., 2009). The
syndrome is characterized by intense and prolonged yearning
for the deceased, emotional pain, difficulty accepting the death,
avoidance of reminders, emotional numbness, bitterness, loss
of trust, meaninglessness or a sense that life lacks purpose, and
a difficulty reengaging in activities, which persist for at least 6
months postloss and are associated with functional impairment
(Prigerson et al., 2009). Prolonged grief represents a significant
public health issue. Cross-sectional and longitudinal studies
have shown that the syndrome is independently associated with
elevated risk for a variety of mental and physical health conditions, including increased suicidal ideation and reduced quality
of life (QoL; Boelen & Prigerson, 2007; Chen et al., 1999;
Latham & Prigerson, 2004; Maciejewski et al., 2016; Prigerson
et al., 1995, 1997). For this reason, prolonged grief disorder
(PGD) was introduced as a new diagnosis in the 11th revision
of the International Classification of Diseases (ICD-11; World
Health Organization [WHO], 2018). A variant of the condition, persistent complex bereavement disorder (PCBD), was
introduced as a “condition for further study” in the fifth edition
of the Diagnostic and Statistical Manual for Mental Disorders
(DSM-5; American Psychiatric Association [APA], 2013).
Much of the empirical focus on PGD has been directed toward establishing the extent to which the syndrome as a whole
is distinct from depression and anxiety and is associated with
negative outcomes (Boelen & Prigerson, 2007; Boelen, van
de Schoot, van den Hout, de Keijser, & van den Bout, 2010;
Boelen & van den Bout, 2002; Golden & Dalgleish, 2010;
Maciejewski et al., 2016; Silverman et al., 2000). This approach
has been critical in establishing an evidence base for PGD (see
also Shear et al., 2011). However, a question that has yet to be
addressed is the extent to which the negative outcomes associated with PGD may be differentially associated with individual
symptoms. Given the potential for significant heterogeneity in
how individuals may present with psychological disorders (see
Galatzer-Levy & Bryant, 2013), an understanding of how individual PGD symptoms are related to negative life outcomes
will help advance the field’s understanding of important phenomenology and pathways to dysfunction.
Complex systems network analysis is a methodology that
is gaining increasing popularity in terms of studying interrelations between symptoms of psychopathology (Borsboom &
Cramer, 2013; Borsboom et al., 2016; Hofmann, Curtiss, &
McNally, 2016; McNally, 2016). This approach is based on
This study was supported by a grant (568970) from the National Health and
Medical Research Council of Australia, awarded to Richard A Bryant; and a
grant (1053997) from the National Health and Medical Research Council of
Australia, awarded to Fiona Maccallum.
Correspondence concerning this article should be addressed to Fiona
Maccallum, School of Psychology, University of Queensland, St Lucia QLD
4072 Australia. E-mail: f.maccallum@uq.edu.au
C 2019 International Society for Traumatic Stress Studies. View this article
online at wileyonlinelibrary.com
DOI: 10.1002/jts.22383
106
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Prolonged Grief and Quality of Life
the proposition that symptoms of mental health disorders arise
and cluster together as a result of direct causal associations between the symptoms themselves (Borsboom & Cramer, 2013).
This is in contrast to traditional views of psychopathology,
which propose that individual symptoms are not informative of
themselves except in so far as they represent observable markers of an underlying process or pathology (for discussion, see
Borsboom & Cramer, 2013, and McNally, 2016). From a network perspective, depression is not a latent entity that independently gives rise to insomnia, fatigue, and concentration
and decision-making difficulties; rather, the symptoms directly
cause and reinforce each other. For example, insomnia results
in fatigue, fatigue results in concentration difficulties, concentration difficulties result in decision-making difficulties, and
decision-making difficulties feed back into insomnia. Over
time, such associations settle into a pathological equilibrium
that gives rise to a syndrome we call “depression” (McNally,
2016). Fried and colleagues (2015) compared these different approaches to conceptualizing psychopathology by modeling the
impact of bereavement on symptoms of depression using both
network analysis and a latent variable approach. Providing support for the network conceptualization, Fried et al. (2015) found
that the network approach, which estimated direct relations between bereavement and symptoms of depression, provided a
better fit for the data than the latent approach, which modeled
an indirect association between bereavement and symptoms via
a latent variable.
Within a network, individual symptoms are represented as
“nodes,” and the interrelations between symptoms are represented as “edges” that connect these nodes. Previous studies
have shown that PGD is amenable to modeling via network analysis; symptoms of PGD have been found to cluster together in
meaningful ways and cluster separately from symptoms of depression (Maccallum, Malgaroli, & Bonanno, 2017; Malgaroli,
Maccallum & Bonanno, 2018; Robinaugh, LeBlanc, Vuletich,
& McNally, 2014). Moreover, these analyses have found that
certain symptoms are more connected within the network and,
thus, potentially more influential on symptom pattern and overall severity than others. For example, emotional pain has been
found to have stronger connections within the PGD network
than avoidance of reminders (Maccallum et al., 2017). A key
implication of network analysis is that by identifying and targeting influential nodes, it should be possible to disrupt the
network to reduce or increase overall levels of psychopathology (Hofmann et al., 2016). To date, most network analyses
have focused simply on modeling associations between symptoms of psychopathology. More recently, Heeren and McNally
(2016) extended the approach by including network nodes that
indexed attentional bias to explore hypothesized causal associations between attention and social anxiety disorder (see also
Jones, Heeren, & McNally, 2017). We adapted this expanded
network approach to explore the association between influential
nodes and negative outcomes in PGD.
Previous studies of PGD have identified a range of negative
life outcomes associated with more severe symptomatology,
including worse general mental health, sleep disturbance, social
dysfunction, increased substance use, somatic symptoms, and
hospital admissions (see Boelen & Prigerson, 2007; Chen et al.,
1999; Prigerson et al., 1999). Such QoL indicators have also
been identified as predictors of bereavement outcomes (Lobb
et al., 2010). An understanding of the interrelations between
symptoms of psychopathology and QoL deficits therefore has
important implications for understanding dysfunction in PGD.
It is important to recognize that QoL is a multidimensional construct (WHOQOL Group, 1996). The WHO has divided QoL
into four domains: physical health, psychological health, social relationships, and environmental health (WHOQOL Group,
1996). Although it is likely that functional impairment across
these domains is related, it is also possible that functioning
varies across the four domains and that each is differentially related to symptom presence. For example, in PGD, trust difficulties may have a greater direct impact on social functioning than
on other domains whereas emotional pain or shock may link
more strongly with psychological health QoL. Accordingly, in
this study, we constructed networks composed of nodes that
represent the symptoms of PGD and the four QoL domains.
Based on prior network analyses of PGD, we expected emotional pain and meaninglessness to emerge as the most central
and well-connected nodes (Maccallum et al., 2017; Robinaugh
et al., 2014; Robinaugh, Millner, & McNally, 2016). For similar reasons, we expected that avoidance of reminders would
be relatively peripheral. In terms of the association between
PGD and QoL node clusters, we predicted an overall negative
association; that is, we hypothesized that higher scores on PGD
nodes would be associated with lower scores on QoL nodes and
vice versa. Given the nature of PGD symptoms and the difficulties associated with the condition, we predicted that PGD
nodes would be more strongly connected with the psychological health and social functioning nodes than with the physical
and environmental health QoL nodes. We speculated that it
would be the PGD nodes which were most central to PGD that
would show the strongest associations with these QoL nodes.
In particular, given that efficacious treatments for PGD focus
on rebuilding a sense of meaning and purpose in the bereaved
person’s life (e.g., Bryant et al., 2014; Shear et al., 2016), we
expected that meaninglessness would be the PGD node with
the strongest link to QoL.
Method
Participants and Procedure
The sample comprised 215 bereaved adults (82.4% women)
with a mean age of 49.24 years (SD = 13.87). Participants
were recruited through advertisements placed in major newspapers in Sydney, Australia, and online recruitment websites
seeking bereaved individuals interested in participating in
a research project focused on understanding adaptation to
bereavement. All participants attended a clinical assessment
conducted by a masters-level clinical psychologist and
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Maccallum & Bryant
Table 1
Participant Characteristics and Mean Scores for Prolonged
Grief–13 (PG-13) Items and World Health Organization Quality
of Life (WHOQOL-BREF) Subscales
%
Age (years)
Women
Relationship of deceased
Partner
Child
Parent
Sibling
Other
Type of death
Medical
Accident
Suicide
Homicide
Years since loss
Years of education
Total PG-13 score
PGD diagnosis
PG-13 itemsa
Yearning
Emotional pain
Avoidance
Shocked and stunned
Role confusion
Difficulty accepting loss
Difficulty trusting
Bitterness and anger
Difficulty reengaging
Numbness
Loss of meaning
WHOQOL-BREF subscales
Psychological Health
Social Functioning
Physical Health
Environmental Health
Mean
SD
Range
49.24 13.87 19–80
82.4
30.2
22.3
36.3
9.8
1.4
76.7
13.0
8.8
1.4
3.72 3.71 0.5–20
14.04 3.11 7–24
32.39 11.51 11–54
64.2
87.7
82.9
75.4
79.6
66.4
66.4
56.4
64.9
72.9
71.6
72.5
3.36
3.11
3.15
2.94
2.72
2.72
2.44
2.70
2.98
2.76
2.99
1.34
1.29
1.52
1.36
1.49
1.43
1.47
1.48
1.47
1.40
1.47
46.07
50.19
58.29
67.08
20.74
23.07
18.60
15.61
0–94
6–100
0–100
31–100
Note. N = 215. PG = prolonged grief; PGD = prolonged grief disorder.
a Range for PG-13 individual items is 1–5. Percentages for PG-13 indiviudal items
represent proportion of participants who were scored as 2 or above.
completed self-report questionnaires. Participant characteristics are presented in Table 1. The most common loss was loss of
a parent (36.3%), and the most common type of death was due
to a medical condition (76.7%). Participants who were actively
suicidal or experiencing current psychotic symptoms were
excluded from participation. Participants were also required to
be proficient in English. Participants provided written informed
consent. The study was approved by Human Research Ethics
Committee of Western Sydney Local Health District.
Measures
Prolonged grief. Prolonged grief was assessed using a
semistructured interview based on the PG-13 self-report measure (Prigerson et al., 2009). The PG-13 assesses for the presence of yearning, emotional distress at the lost relationship,
difficulty accepting the death, shock, avoidance of reminders,
numbness, bitterness, difficulty engaging in life, identity disturbance, and a sense of purposelessness and meaninglessness
and functional impairment. Items on the PG-13 are scored by
clinicians on a 5-point Likert scale ranging from 1 (not at all)
to 5 (several times a day/overwhelmingly). As the item relating
to functional impairment does not reflect a discrete symptom,
but rather the combined impact of the other symptoms, we did
not include it in the network analysis. Similarly, we did not
include the item that indexes symptom duration. This resulted
in the inclusion of 11 PGD items in the network. In the present
sample, Cronbach’s alpha was .91.
QoL. The WHOQOL-BREF (Harper, Power, & WHOQOL Group, 1998; WHOQOL Group, 1996) is a WHO
instrument that assesses QoL across four domains: physical
health (daily living, pain, and work capacity), psychological
health (mood, self-esteem, and concentration), social relationships (personal relationships, social support, and sexual
activity), and environmental health (financial resources, health
care, and home environment). Items are scored on a scale of 1
to 5, where higher scores indicate better QoL. Domain scores
are summed and transformed into percentile scores (0–100).
The WHOQOL-BREF has demonstrated good discriminant
validity, content validity, internal consistency, and test–retest
reliability (Hawthorne, Herman, & Murphy, 2006). Cronbach’s
alpha values for the Physical Health, Psychological Health,
Social Relationships, and Environmental Health subscales
were .84, .89, .72, and .81, respectively, in this study.
Data Analysis
A network of regularized partial correlation coefficients was
computed using the GLASSO procedure from the R package qgraph (Epskamp, Cramer, Waldorp, Schmittmann, &
Borsboom, 2012; R Core Team, 2016). Four participants had
missing responses on the WHOQOL (< 1.4% of responses).
Pair-wise deletion of missing responses was implemented. In
a regularized partial correlation network, each edge represents
the partial correlation between two nodes after controlling for
all other variables in the dataset. The weight of the edge represents the strength of the partial correlation. When the partial
correlation is zero, no edge is drawn between the nodes. The
GLASSO procedure employs a “least absolute shrinkage and
selection operator” (LASSO) correction to shrink very small
connections to zero. The degree of correction is determined by
minimizing the extended Bayesian information criteria (EBIC).
The result is a more parsimonious network in which fewer edges
are used to explain variation in the data (Epskamp, Borsboom,
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Prolonged Grief and Quality of Life
& Fried 2018; Epskamp & Fried, 2018; Friedman, Hastie, &
Tibshirani, 2008).
Several methods were used to explore the importance of individual nodes. First, we calculated three indices of node centrality: strength, closeness, and betweenness. Strength centrality
represents the sum of absolute edge weights attached to a node.
The higher a node’s strength centrality, the greater it’s combined association with other nodes in the network. Closeness
represents the inverse of the sum of the distances of the node
from all other nodes in the network. A closeness-central node
is one that is likely to be quickly affected by changes in other
nodes either directly or indirectly. Betweenness represents the
number of times in which a given node lies on the shortest
path between two other nodes. Nodes with high levels of betweenness are directly connected to many other nodes within
the network.
Next, we modelled PGD and QoL nodes as separate clusters,
or communities of nodes and used the bridge function from
the R package networktools (Jones, 2018) to calculate bridge
centrality. The bridge centrality calculations provided an index of the strength of connections between individual nodes
and all nodes in the other cluster (i.e., PGD or QoL, respectively). Nodes with high bridge centrality values are thought
to play an important role in the spreading of activation between clusters of nodes: When a bridge node is activated, the
risk of activation spreading to the adjoining cluster is increased
(Jones, Ma, & McNally, 2017). Where clusters are connected
via negative edges, as was expected in the current analysis,
networktools calculates two types of bridge centrality indices:
bridge strength and bridge expected influence (EI; Jones, 2018).
Importantly, bridge strength and bridge EI represent somewhat
similar indices; however, bridge EI retains information about
the direction of the association and, in contrast, bridge strength
is a sum of absolute edge strengths (see Robinaugh et al., 2016).
For this reason, we focused on bridge EI in the current study.
The PGD cluster had nearly 3 times as many the nodes as the
QoL cluster. To increase the comparability of findings across
clusters, bridge EI values for each node were divided by the total number of nodes in the other cluster. A node with a positive
bridge EI value is one that has an overall positive association
with the other cluster whereas a node with a negative EI value
has an overall negative association with the other cluster. For
cases in which a node has equally strong positive and negative
connections with nodes in the other cluster, its bridge EI may
be zero, as the positive and negative associations cancel each
other out.
Finally, we assessed the accuracy and stability of the network using the R package bootnet (Epskamp et al., 2018).
This analysis provided an indication of the extent to which
apparent differences in the produced network were stable and
thus interpretable. To do this, bootnet computes 95% confidence intervals (CI) around each edge and tests for significant
differences in edge weights and node strength centrality. The
package also calculates a measure of network stability known
at the correlation stability coefficient (CS coefficient). The CS
coefficient provides an indication of the stability of the produced centrality coefficients: The larger the CS coefficient, the
more stable and thus interpretable the order of that centrality
index. From an analytic perspective, it represents the maximum proportion of cases that can be dropped from the sample
and while retaining a correlation of .7 (with 95% certainty)
with the produced indices. It is recommended that when a CS
coefficient is above .50, the index can be interpreted with a
degree of confidence; however, if a CS coefficient falls below .20, the index should not be interpreted (Epskamp et al.,
2018).
Results
Table 1 presents participant characteristics and the mean
scores for each of the PGD items and WHOQOL subscales
as well as the proportion of the sample who endorsed the symptom at least once or with a rating of slightly. The mean number
of PGD items participants experienced to at least some degree
in the previous month was 7.97 (SD = 3.14). The items reported with the highest frequency were yearning (87.7%) and
emotional pain (82.1%). The items with the lowest frequency
were bitterness (64.9%) and trust difficulties (56.4%). Approximately two-thirds of participants (n = 138, 64.2%) were diagnosed by clinicians as meeting criteria for PGD on the basis
of PG-13 responses (Prigerson et al., 2009). Notably, mean
scores on each of the WHOQOL subscales were 1–2 standard
deviations below the mean norms for the Australian population
(Hawthorne et al., 2006).
Networks of PGD and QoL
Figure 1 presents the regularized partial correlation network
of PGD and WHOQOL nodes. Solid edges indicate a positive association between nodes, and dashed edges indicate a
negative association. As can be seen in Figure 1, PGD nodes
formed a network of positive interrelations. Strong positive
edges were observed between yearning and emotional pain,
avoidance and shock, and meaninglessness and reengagement
difficulties. These edges were significantly stronger than most
other edges in the network, p < .050 (see supplementary
Figure S2). The four WHOQOL nodes also clustered together
with positive links. The links between psychological health
and physical health as well as psychological health and social
functioning were among the strongest in the network (see Supplementary Figure S2). Four of the 11 PGD nodes evidenced
edges with a WHOQOL node. As can be seen in Figure 1,
these edges were all negative. Role confusion and meaninglessness were linked with worse psychological health, trust
difficulties was linked with a lower level of social functioning,
and bitterness was linked with lower ratings of environmental
health.
Network centrality indices are presented in Figure 2. Strength
is an index of a node’s (absolute) sum of connections with all
other nodes, closeness is an index of the distance between each
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Maccallum & Bryant
PGD symptoms
QoL domains
Figure 1. Regularized partial correlation network of prolonged grief disorder (PGD) symptoms and quality of life. Solid lines indicate positive associations,
and dashed lines indicate negative associations. Yearn = yearning; E Pain = emotional pain; Avoid = avoidance of reminders; Shock = shock or dazed; Role
= role confusion; Accept = acceptance difficulties; Trust = trust difficulties; Bitter = bitterness since loss; Engage = difficulties reengaging in activities and
relationships; Numb = emotional numbness; Meaning = life is meaningless or unfulfilling; Psychol = psychological health; Physical = physical health; Social =
social functioning; Environmental = environmental health.
node and all other nodes, and betweenness is an index of the
frequency with which each node lies on the shortest path between any two other nodes. The PGD nodes with the highest
strength centrality were emotional pain and meaninglessness.
Meaninglessness was also the node with the highest closeness
and betweenness centrality. In terms of QoL nodes, psychological health demonstrated high centrality values across all three
centrality indices. In contrast, physical health and environmental health showed the lowest centrality of all nodes within the
network. The network CS coefficients indicated that the relative
order of strength centrality, CS = .61, was interpretable with
a degree of confidence whereas some caution was indicated in
interpreting the order of the closeness index, CS = .34, and
the betweenness index, CS = .20, should not be interpreted
(Epskamp et al., 2018).
Figure 3 presents results of the bridge EI analysis. Values
represent the sum of edge strengths divided by the number
of nodes within the other cluster. As can be seen, no nodes
showed a positive bridge EI. That is, all PGD nodes had
a negative or zero-sum association with the QoL nodes to
which they were connected. Trust difficulties evidenced the
strongest bridge EI among the PGD nodes. Difficulties reengaging with life, meaninglessness, role confusion, and bitterness were the next most influential PGD nodes on QoL.
Of the QoL nodes, psychological health had the strongest
bridge EI. The remaining QoL nodes had relatively weak
bridge EI.
Discussion
Prolonged grief disorder has been associated with a range of
mental health, physical health, and social impairments. However, little is known about the associations between individual
symptoms of PGD and life outcomes. This study used network
analysis to explore this issue. Consistent with previous studies,
we observed that the symptoms of PGD formed a network of
nodes connected by positive edges (Maccallum et al., 2017;
Robinaugh et al., 2014, 2016). This indicated that the node
values were positively associated, independent of other nodes
within the network. The edge weight indicated the strength of
that association. Positive edges were also observed between
the four QoL nodes, which indicated that psychological, social,
physical, and environmental health were positively associated.
As expected, however, the edges between PGD and QoL nodes
were negative; higher values on PGD nodes were associated
with lower values on QoL nodes. These findings are consistent
with previous studies that have reported negative associations
among PGD as a whole and mental, physical, and social outcomes (Boelen & Prigerson, 2007; Maciejewski et al., 2016).
However, the current analysis extended these findings by delineating associations between individual PGD symptoms and
QoL domains. Specifically, we found that the PGD nodes representing role confusion, meaninglessness, trust difficulties, and
bitterness were the nodes most strongly (and negatively) linked
to a QoL domain: Role confusion and meaninglessness with
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Prolonged Grief and Quality of Life
Betweenness
Closeness
Strength
Yearn
Trust
Social
Shock
Role
Psychol
Physical
Numb
Meaning
Environ
Engage
E_Pain
Bier
Avoid
Accept
Figure 2. Node betweenness, closeness, and strength centrality indices. Values on the x-axis represent standardized scores. Yearn = yearning; E Pain = emotional
pain; Avoid = avoidance of reminders; Shock = shock or dazed; Role = role confusion; Accept = acceptance difficulties; Trust = trust difficulties; Bitter =
bitterness since loss; Engage = difficulties reengaging in activities and relationships; Numb = emotional numbness; Meaning = life is meaningless or unfulfilling;
Psychol = psychological health; Physical = physical health; Social = social functioning; Environmental = environmental health.
psychological health, trust difficulties with social functioning,
and bitterness with environmental health. These PGD nodes
also had relatively higher bridge EI values than other PGD
nodes. This is does not mean that the remaining PGD nodes had
no association with QoL but rather that the pattern of findings
suggests that any impact from other nodes, such as emotional
pain, was indirect in nature.
To date, network approaches to understanding psychopathology have focussed largely on mapping the association between the symptoms of disorders (e.g., Bringmann, Lemmens,
Huibers, Borsboom, & Tuerlinckx, 2015; Maccallum et al.,
2017; McNally et al., 2015). In extending this methodology
to explore the relation between symptoms and QoL, this study
advanced our understanding of PGD in several ways. The development of new sources of meaning and purpose in life is held
to be a key task in successful adaption to loss (Maccallum &
Bryant, 2013; Neimeyer, 2016). As predicted, meaninglessness
emerged as a key node in the network, sharing edges with five
other PGD nodes and demonstrating high strength and closeness centrality. The former indicated meaninglessness had a
strong association with other nodes, and the latter suggested
that meaninglessness was likely to more quickly affect or be
affected by changes in other nodes. Importantly, meaninglessness was also linked directly with lower levels of psychological
health. This is perhaps not surprising given that lack of meaning and purpose in one form or another is associated with a
range of negative psychological outcomes following bereavement (Folkman, 2001). Moreover, effective treatments for PGD
include strategies that focus on building meaning and purpose
in life (Bryant et al., 2014; Shear et al., 2016). The finding that
role confusion was linked with lower levels of psychological
health is also consistent with this finding and with propositions from theoretical models of PGD (Maccallum & Bryant,
2013) and general psychological distress (Conway & PleydellPearce, 2000; Higgins, Bond, Klein, & Strauman, 1986; Strauman, 1992). However, it is also important to recognize that
psychological health may have impacted PGD nodes. For example, higher levels of psychological functioning may act as a
protective factor in the context of bereavement, facilitating attempts at meaning-making and role adjustment in the wake of
loss. The cross-sectional nature of the present study precluded
conclusions about the direction of the observed associations;
indeed, the association may be bidirectional. Nonetheless, the
current findings provide some support for the application of
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Yearn
E_Pain
Avoid
Shock
Role
Accept
Trust
Bier
Engage
Numb
Meaning
Physical
Psychol
Social
Environ
-0.08
-0.06
-0.04
-0.02
0.0
Figure 3. Direct (one step) and indirect (two step) bridge expected influence
(EI) for nodes in prolonged grief disorder and quality of life clusters. Values on
the x-axis represent the sum of regularized partial correlations. Yearn = yearning; E Pain = emotional pain; Avoid = avoidance of reminders; Shock = shock
or dazed; Role = role confusion; Accept = acceptance difficulties; Trust =
trust difficulties; Bitter = bitterness since loss; Engage = difficulties reengaging in activities and relationships; Numb = emotional numbness; Meaning =
life is meaningless or unfulfilling; Psychol = psychological health; Physical =
physical health; Social = social functioning; Environmental = environmental
health.
clinical strategies designed to assist PGD clients in establishing
new roles and sources of purpose and meaning in life.
Whereas a number of PGD symptoms were highly correlated with social functioning, trust difficulties was the only
PGD node to share a direct edge with this QoL domain. Trust
was also the PGD node with the highest bridge EI. The finding
that difficulty trusting others since the loss was independently
related to low social functioning (and difficulties reengaging
in life) is perhaps not surprising; there is much evidence that
poor adjustment to bereavement is associated with difficulties
relying on others and fears of abandonment (Boelen & Klugkist, 2011; Fraley & Bonanno, 2004). There is also substantial
evidence that access to quality social support is a strong predictor of positive bereavement outcomes (Burke, Neimeyer, &
McDevitt-Murphy, 2010; Lobb et al., 2010). Trust, or distrust,
has received comparatively little empirical attention in PGD
research. The extent to which higher levels of distrust negatively impact social functioning and/or social functioning acts
as a protective factor against distrust following bereavement
awaits further investigation. Importantly, however, the social
relationships node was also strongly linked to psychological
health, underscoring that there are multiple pathways by which
the nodes may impact each other.
Bereavement has the potential to impact many areas of an
individual’s life, including finances, housing, and employment.
However, few studies have investigated how PGD symptoms
relate to environmental health. We found that low ratings of
environmental health (home, financial resources, accessibility
to work, health care, and leisure opportunities) were associated
with higher ratings of bitterness about the loss. It is feasible
that negative life circumstances following the death of a close
loved one may generate a stronger sense of bitterness about the
loss. Alternatively, individuals who report more bitterness due
to their loss may appraise their postloss living circumstances
in a more negative way. We note, however, that this edge was
significantly weaker than the links between PGD nodes and
psychological health and social functioning (see Supplementary Figure S2). No links were observed between PGD nodes
and physical health. However, the WHOQOL measure assessed
health-related functioning rather than specific physical health
conditions (e.g., cancer and high blood pressure; Prigerson
et al., 1997) that have been associated with PGD.
A key motivation of network analysis is the potential it offers
for identifying influential nodes (symptoms) within a network.
By identifying these nodes, it may be possible to modify and
change the functioning of the network to reduce (or increase)
activation of other nodes (McNally, 2016). Interestingly, with
the exception of meaninglessness, the nodes linked with QoL
impairment in this analysis showed only low-to-moderate centrality; role confusion was the least-central PGD node. This
observation raises the possibility that important nodes may not
necessarily be the most central or that the most central nodes
may not be directly associated with impairment. For example, a
node may be central to a network because it is activated by many
other nodes rather than being a cause of activation. Emotional
pain and psychological health, which had the strongest overall associations (as indexed by strength centrality) are likely
to be core features of distress-related syndromes. Overall, the
pattern of findings for bridge centrality was consistent with network centrality findings. Bridge centrality indices represent the
sum of the edges between an individual node with all nodes in
the other cluster (either PGD or QoL, respectively). However,
whereas methods exist for assessing the reliability of network
centrality (Epskamp et al., 2018), we await the development
Journal of Traumatic Stress DOI 10.1002/jts. Published on behalf of the International Society for Traumatic Stress Studies.
113
Prolonged Grief and Quality of Life
of methods to assess bridge centrality reliability. Therefore, we
recommend some caution in interpreting order and magnitude
differences in bridge EI values. We note the mean time since
loss in this study was greater than 3 years. Future longitudinal
research that maps the onset of PGD and examines dynamic
interrelations between nodes (e.g., Bringmann et al., 2015) will
assist in further clarifying the relative importance of nodes in
terms of development, maintenance, and clinical intervention.
Notwithstanding these issues, the results of this analysis share
features with previous network analyses of PGD (Maccallum
et al., 2017) and PCBD (Malgaroli et al., 2018; Robinaugh
et al., 2014). There was a strong positive edge between emotional pain and yearning, and links between meaninglessness
and numbness and meaninglessness and difficulties reengaging
were found to be stronger than many other in the network (see
Supplementary Figure S1). There were also a number of differences between the current and previous analyses. In contrast to
predictions, avoidance showed a higher level of strength centrality in this study as compared to previous analyses. Although
it remained on the edge of the network, the link between avoidance and shock was one of the strongest edges (see Figure 1).
Notably, both the proportion of individuals who endorsed at
least some avoidance of reminders during the past month and
the mean avoidance score was higher than in previous studies
(Maccallum et al., 2017; Malgaroli et al, 2018; Robinaugh et al.,
2014). Shock was also somewhat more present in this sample
than has been reported in the previous samples; this may reflect
the higher levels of PGD observed in this study as compared
to previous analyses. The strong link between avoidance and
feeling stunned, shocked, or dazed by the loss is consistent
with cognitive models of PGD (Boelen, van den Hout, & van
den Bout, 2006; Maccallum & Bryant, 2013). These models
propose that avoidance of reminders contributes to ongoing
symptoms by inhibiting integration of the loss into the person’s
autobiographical memory database, which may contribute to an
ongoing sense of shock or unreality about the loss.
Several methodological issues need to be recognized. First,
as noted, this study was cross-sectional. This precluded conclusions about the direction of any of the observed associations.
Second, although a strength of our study was the high levels
of PGD in our sample, our participants were predominantly
women who had experienced the loss of a close loved one due
to medical reasons. They had also volunteered to participate
in a bereavement study. In addition, this study was undertaken
prior to the publication of ICD-11 criteria for PGD (WHO,
2018). We indexed PGD using a validated self-report measure
of prolonged grief; however, it is of note that there are some
minor differences in symptoms between the ICD-11 criteria
and the PG-13. The extent to which findings are generalizable
to other bereaved samples awaits further investigation. Fourth,
our sample size was toward the lower end of the acceptable
range (Epskamp et al., 2016). This precluded splitting the sample to undertake investigation of potential factors that may have
impacted network associations, such as relationship of the deceased, nature of the loss, or gender, or including additional
nodes that represented common comorbid conditions, such as
depression. Prior studies have suggested that differences based
on relationship to the deceased may be minimal (Maccallum
et al., 2017). Future research undertaken with larger samples
sizes will enable further exploration of this these issues.
In summary, this study presented the first network analysis
to examine associations between individual symptoms of PGD
and QoL. As expected, we found that PGD was associated with
lower levels of QoL. The finding that specific nodes representing disruptions in meaning, role, and trust were negatively
associated with QoL outcomes is consistent with theoretical
models of PGD and sheds light on the pathways by which
PGD may be linked with impaired functioning. Future studies
that employ longitudinal designs and larger sample sizes will
assist in identifying temporal associations between symptoms
and outcomes. There are now a growing number of analytic
techniques available to researchers interested in modeling psychopathology and patterns of heterogeneity. Each comes with
comes with its own theoretical rational, aims, and limitations
(Borsboom et al., 2016). Network analysis seeks to simultaneously map complex associations between symptoms to identify
probable causal pathways and points of intervention. In doing so, this emerging field offers significant potential to better
understand the mechanisms underlying PG and thus improve
adaptation of people adversely affected by bereavement.
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