Access to this full-text is provided by Frontiers.
Content available from Frontiers in Public Health
This content is subject to copyright.
TYPE Original Research
PUBLISHED 01 December 2022
DOI 10.3389/fpubh.2022.1067646
OPEN ACCESS
EDITED BY
Alberto Sardella,
University of Messina, Italy
REVIEWED BY
Maria Devita,
University of Padua, Italy
Robert Sigström,
University of Gothenburg, Sweden
*CORRESPONDENCE
Xufeng Liu
lxf_fmmu@126.com
Shengjun Wu
wushj@fmmu.edu.cn
†These authors have contributed
equally to this work
SPECIALTY SECTION
This article was submitted to
Aging and Public Health,
a section of the journal
Frontiers in Public Health
RECEIVED 12 October 2022
ACCEPTED 14 November 2022
PUBLISHED 01 December 2022
CITATION
Yang T, Guo Z, Cao X, Zhu X, Zhou Q,
Li X, Wang H, Wang X, Wu L, Wu S and
Liu X (2022) Network analysis of
anxiety and depression in the
functionally impaired elderly.
Front. Public Health 10:1067646.
doi: 10.3389/fpubh.2022.1067646
COPYRIGHT
©2022 Yang, Guo, Cao, Zhu, Zhou, Li,
Wang, Wang, Wu, Wu and Liu. This is
an open-access article distributed
under the terms of the Creative
Commons Attribution License (CC BY).
The use, distribution or reproduction
in other forums is permitted, provided
the original author(s) and the copyright
owner(s) are credited and that the
original publication in this journal is
cited, in accordance with accepted
academic practice. No use, distribution
or reproduction is permitted which
does not comply with these terms.
Network analysis of anxiety and
depression in the functionally
impaired elderly
Tianqi Yang1†, Zhihua Guo1†, Xiaoqin Cao2† , Xia Zhu1,
Qin Zhou2, Xinhong Li3, Hui Wang1, Xiuchao Wang1, Lin Wu1,
Shengjun Wu1*and Xufeng Liu1*
1Department of Military Medical Psychology, Air Force Medical University, Shaanxi, China, 2Xijing
Hospital, Air Force Medical University, Shaanxi, China, 3Tangdu Hospital, Air Force Medical
University, Shaanxi, China
Background: Evidence from previous studies has confirmed that functionally
impaired elderly individuals are susceptible to comorbid anxiety and
depression. Network theory holds that the comorbidity emerges from
interactions between anxiety and depression symptoms. This study aimed
to investigate the fine-grained relationships among anxiety and depression
symptoms in the functionally impaired elderly and identify central and
bridge symptoms to provide potential targets for intervention of these two
comorbid disorders.
Methods: A total of 325 functionally impaired elderly individuals from five
communities in Xi’an, China, were recruited for our investigation. The GAD-7
and PHQ-9 were used to measure anxiety and depression, respectively. SPSS
22.0 software was used for descriptive statistics, and R 4.1.1 software was used
for network model construction, expected influence (EI) evaluation and bridge
expected influence (BEI) evaluation.
Results: In the network, there were 35 edges (indicating partial correlations
between symptoms) across the communities of anxiety and depression,
among which the strongest edge was A1 “Nervousness or anxiety”-D2
“Depressed or sad mood.” A2 “Uncontrollable worry” and D2 “Depressed or
sad mood” had the highest EI values in the network, while A6 “Irritable” and
D7 “Concentration diculties” had the highest BEI values of their respective
community. In the flow network, the strongest direct edge of D9 “Thoughts of
death” was with D6 “Feeling of worthlessness.”
Conclusion: Complex fine-grained relationships exist between anxiety and
depression in functionally impaired elderly individuals. “Uncontrollable worry,”
“depressed or sad mood,” “irritable” and “concentration diculties” are
identified as the potential targets for intervention of anxiety and depression.
Our study emphasizes the necessity of suicide prevention for functionally
impaired elderly individuals, and the symptom “feeling of worthlessness” can
be used as an eective target.
KEYWORDS
anxiety, depression, elderly, network analysis, functional impairment
Frontiers in Public Health 01 frontiersin.org
Yang et al. 10.3389/fpubh.2022.1067646
Introduction
Functionally impaired elderly individuals are those who
cannot independently perform any activities of daily living, such
as bathing, transfers from bed and chair, toileting, dressing,
feeding, etc., due to their old age, weakness, disability, illness,
mental retardation, etc. (1,2). According to a report issued by
the China Aging Scientific Research Center and Social Sciences
Academic Press (3), there are more than 40 million functionally
impaired elders in China as of 2015, accounting for 18.3% of
the elderly population. The aging world population has brought
escalating social and economic burdens (4). Functionally
impaired elders not only fail to undertake basic personal care
tasks but are also susceptible to psychiatric comorbidities, such
as anxiety and depression (5–10).
Anxiety and depression are closely associated with a range of
negative consequences and possible dangers such as Parkinson’s
disease and cardiovascular disease (11,12). Furthermore, anxiety
and depression contribute to the increasing likelihood of suicidal
ideation and suicide attempts for both adolescents (13) and
functionally impaired elders (14,15). The high prevalence and
undesirable consequences of these two disorders make them a
large global burden of disease (16), contributing substantially to
the increasing health care costs and low quality of life for the
functionally impaired elderly. Therefore, anxiety and depression
in the functionally impaired elderly population warrant more
attention. For clinical applications, efforts are needed to
determine effective intervention and treatment methods for the
two psychiatric disorders.
It is widely known that anxiety and depression are frequently
comorbid disorders (17–20), with co-occurrence rates ranging
from 10 to 90% (21,22). Patients with comorbid anxiety and
depression are often more severely ill than patients with only one
disorder. This population does not respond well to treatment,
has a longer duration of illness, and experiences poor prognosis
(22). It has been revealed that anxiety and depression trigger
each other and are bidirectional risk factors for one another (23,
24). Therefore, simply targeting only one disorder may not be
effective because anxiety and depression are mutually enhanced
and facilitated, like coinfections (25), making it difficult to
treat them separately. Additionally, the majority of previous
studies focus on anxiety and depression based on sum-scores,
masking the heterogeneity between different symptoms and
obscuring the symptom-level relationships (26–28). Analysis
Abbreviations: COVID-19, Coronavirus disease 2019; GAD-7, Generalized
Anxiety Disorder-7; PHQ-9, Patient Health Questionnaire-9; DSM-
IV, Diagnostic Manual Of Mental Disorders-IV; GGM, Gaussian
graphical model; LASSO, Least absolute shrinkage and selection
operator; EBIC, Extended Bayesian information criterion; EI, Expected
influence; BEI, Bridge expected influence; CS-coecient, Correlation
stability coecient.
of individual symptoms and their interactions provides a
promising way forward. In summary, high comorbidity of
anxiety and depression emphasizes the need for identifying
underlying mechanisms of these two co-occurring disorders
and targeted symptoms at a fine-grained level for more
effective treatment, for example, the symptoms critical to linking
anxiety and depression or symptoms playing important roles in
developing both anxiety and depression.
Network analysis, an emerging data-driven approach to
psychopathology and comorbidity, is ideally suited for the
purpose of this study (29–31). In the network model,
psychiatric disorders are constructed as networks emerging
from interactions between symptoms, which means that the
symptoms and their interactions actively contribute to the
development and maintenance of disorders rather than passively
reflecting the latent variable (30,31). The network is composed
of two basic elements, namely, the nodes (symptoms) and
the node-to-node edges (associations between symptoms)
(32). It helps to investigate the characteristics of comorbid
systems and the fine-grained relationships between individual
symptoms from the perspective of mathematics and visualize
them intuitively (33–35). Network analysis offers important
new perspectives on comorbidity (36). This approach involves
assessing the important edges that may shed light on the
psychiatric pathways between comorbidities via edge weights.
The approach also makes it possible to identify central nodes
that activate all other symptom nodes, exert great influence on
the overall network and determine bridge symptoms that play
significant roles in maintaining the co-occurrence of mental
disorders and facilitating the spread of comorbidity; these
identified symptoms represent promising and effective targets
for intervention and treatment (35,37–39).
To date, several studies have examined the network structure
of anxiety and depression in a joint framework in different
populations, including patients with major depressive disorder
(40), patients with anxiety disorders (41,42), epilepsy patients
(43,44), and nursing students (45,46). However, the anxiety
and depression symptom interactions of functionally impaired
elderly individuals have not been researched via network
analysis. Considering the different self-reported scales used,
various study populations, and the data-driven nature of the
network methodology, the findings are specific to the samples
included in these studies and can hardly be generalized to
functionally impaired elderly individuals. Consequently, studies
are necessary to examine the relationships between anxiety
and depression in functionally impaired elderly individuals and
determine promising targets for intervention and treatment.
To address this research gap, the current study is the first to
construct a symptom-level structure of anxiety and depression in
functionally impaired elderly individuals using network analysis.
Herein, we wanted to examine the strongest edges between
anxiety and depression symptoms, the most influential nodes
that maintain the whole anxiety-depression network, and the
Frontiers in Public Health 02 frontiersin.org
Yang et al. 10.3389/fpubh.2022.1067646
critical nodes that bridge anxiety and depression communities.
The purpose of the study is to advance our understanding of
the fine-grained relationships between anxiety and depression
and to determine effective therapeutic targets for these two
comorbid disorders.
Materials and methods
Participants
From March to August 2022, a total of 325 functionally
impaired elderly individuals from 5 communities in Xi’an,
China, were recruited by the convenience sampling method.
The inclusion criteria were: age 60 years or older, Barthel Index
score <100, elderly care at home for more than 6 months,
clear awareness, barrier-free communication, and informed
and voluntary participation in the survey. Those who could not
cooperate with the investigation, such as functionally impaired
elderly with severe mental illness, cognitive dysfunction and
severe physical disease, were excluded. The systematically
trained investigators conducted face-to-face household
questionnaires in the 5 communities which have continuous
nursing cooperation with us. The Generalized Anxiety Disorder-
7 (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9)
were used to investigate the functionally impaired elderly after
making them clear about the purpose and method of the study
by using unified guidelines. A total of 335 questionnaires were
distributed, and 325 valid questionnaires were collected with an
effective recovery rate of 97.01%. The current study followed
the Helsinki Declaration and was approved by the Ethics
Committee of Xijing Hospital, Air Force Medical University
(No. KY20222194-C-1).
Measurements
Generalized anxiety disorder-7 (GAD-7)
According to DSM-IV (47), GAD-7 (48) is used to assess the
most important diagnostic symptoms of anxiety within the last 2
weeks. It contains 7 items, such as “Feeling nervous, anxious or
on edge.” The items are rated on a 4-point Likert-type scale with
“not at all,” “on some days,” “on more than half of the days” and
“almost every day” scored 0–3, respectively. Higher scores on the
GAD-7 indicate more severe symptoms of anxiety. In the present
study, the Cronbach’s αcoefficient of the GAD-7 was 0.87.
Patient health questionnaire-9 (PHQ-9)
The PHQ-9 (49) is a widely used evaluation tool for
depression symptoms recommended by DSM-IV (47). It
consists of nine items, such as “Thoughts that you would be
better off dead or of hurting yourself in some way.” The items
are rated on a 4-point Likert-type scale with “not at all,” “on
some days,” “on more than half of the days” and “almost every
day” scored 0–3, respectively. Higher scores on the PHQ-9
suggest more severe symptoms of depression. The Cronbach’s α
coefficient of the PHQ-9 in the present study was 0.84.
Data analysis
SPSS 22.0 software was used to calculate the means,
standard deviations and Cronbach’s αcoefficients on the GAD-
7 and PHQ-9. R 4.1.1 software was used for network model
construction, expected influence (EI) evaluation and bridge
expected influence (BEI) evaluation.
Network model construction
We used the R package qgraph (50) to construct a Gaussian
graphical model (GGM) of the anxiety-depression network in
functionally impaired elderly individuals (32). Least absolute
shrinkage and selection operator (LASSO) (51) regularization
and the extended Bayesian information criterion (EBIC) (52)
were applied to shrink all edges and set small spurious edges
exactly to zero (32). The tuning parameter of the EBIC keeps the
balance between including false edges and removing true edges
(33), and it was set to 0.5 according to the recommendation of
Foygel and Drton (53). The Spearman rho correlation method
was used in the network construction. The nodes in the network
represented items of GAD-7 and PHQ-9 and were divided into
two communities according to their theoretical sources, namely,
the anxiety community and the depression community. The
correlations of symptoms were represented by edges, and the
calculation of the correlation between two nodes was conducted
after statistical control for the influence of all the other nodes
included in the network (54). Blue edges and red edges represent
positive and negative correlations, respectively, and thicker
edges represent higher correlations. The Fruchterman-Reingold
algorithm was used to arrange the network layout; in this
layout, strong correlations are placed in the center of the
network, and weak correlations are placed in the periphery of
the network (55). The graphics function “flow” of the R package
qgraph was used to display clearly which symptoms of anxiety
and depression are directly or indirectly correlated with the
depression symptom “thoughts of death.”
We used the R package bootnet (32) to test the significance
of the difference in edge weights of different node pairs and
estimate the accuracy of edge weights. The difference in edge
weights of different node pairs was tested by using bootstrapping
(1,000 bootstrapped samples, α=0.05). The accuracy of
edge weights was evaluated by estimating the 95% confidence
interval using nonparametric bootstrapping (1,000 bootstrapped
samples). According to Epskamp et al. (32), a good accuracy of
the edge weights is represented by a narrow confidence interval.
Frontiers in Public Health 03 frontiersin.org
Yang et al. 10.3389/fpubh.2022.1067646
EI evaluation
We used the R package qgraph (50) to evaluate the EIs
of nodes in the anxiety-depression network of the functionally
impaired elderly. As one of the centrality indices, the EI of a
specific node is defined as the sum of all the edges that extend
from this given node (56). EI reflects the importance of the given
node in the network (46) and was selected in our study because
it is particularly suitable for networks containing positive and
negative edges (30).
We used the R package bootnet (32) to test the stability
of EI and the significance of difference on EIs of nodes. The
stability of EI was tested by using case-dropping bootstrapping
(1,000 bootstrapped samples) and quantitatively estimated by
the correlation stability (CS) coefficient. A CS coefficient >0.5
indicates that the stability is ideal (32). The difference in EIs
of different nodes was tested by using bootstrapping (1,000
bootstrapped samples, α=0.05).
BEI evaluation
We used the R package networktools (35) to evaluate
the BEIs of nodes in the anxiety-depression network of the
functionally impaired elderly. BEI is defined as the sum of the
cross-community edge weights of the given node. A higher value
of BEI indicates stronger connections with nodes of the other
community (57).
We used the R package bootnet (32) to test the stability
of BEI and the significance of difference on BEIs of nodes.
The stability of the BEI was tested by using case-dropping
bootstrapping (1,000 bootstrapped samples) and quantitatively
estimated by the correlation stability (CS) coefficient. A CS
coefficient >0.5 indicates that the stability is ideal (32). The
difference in BEIs of different nodes was tested by using
bootstrapping (1,000 bootstrapped samples, α=0.05).
Results
Descriptive statistics
The demographic characteristics of the participants are
shown in Table 1. The means, standard deviations, EIs and
BEIs of the symptoms in the anxiety-depression network of the
functionally impaired elderly are shown in Table 2.
Network analysis
Structure of the anxiety-depression network
The anxiety-depression network of functionally impaired
elderly individuals is displayed in Figure 1A. Theoretically,
there are up to 120 possible edges in this network, while our
study found only 78 non-zero edges (edge weight ranging
from −0.09 to 0.55). Among these edges, 35 edges (44.87%)
TABLE 1 Demographic characteristics of the functionally impaired
elderly (n=325).
Variables Mean (SD), range, %
Age 75.03 (9.74), 60–97
Gender
Male 180, 55.38%
Female 145, 44.62%
Educational background
Education without schooling 37, 11.38%
Primary school education 54, 16.62%
Junior high school education 91, 28.00%
High school education 73, 22.46%
University education 70, 21.54%
Marital status
Unmarried 2, 0.62%
Married 250, 76.92%
Divorced 5, 1.54%
Widowed 68, 20.92%
were across communities, and 43 edges (55.13%) were within
communities. Most of the cross-community edges were positive,
of which the 10 edges with the highest edge weight were A1
“Nervousness or anxiety”-D2 “Depressed or sad mood” (edge
weight =0.23), A6 “Irritable”-D7 “Concentration difficulties”
(edge weight =0.20), A1 “Nervousness or anxiety”-D3 “Sleep
difficulties” (edge weight =0.11), A4 “Trouble relaxing”-
D7 “Concentration difficulties” (edge weight =0.10), A7
“Afraid something will happen”-D9 “Thoughts of death” (edge
weight =0.10), A3 “Worry too much”-D8 “Psychomotor
agitation/retardation” (edge weight =0.09), A5 “Restlessness”-
D4 “Fatigue” (edge weight =0.09), A6 “Irritable”-D5 “Appetite
changes” (edge weight =0.08), A7 “Afraid something will
happen”-D6 “Feeling of worthlessness” (edge weight =0.08)
and A2 “Uncontrollable worry”-D5 “Appetite changes” (edge
weight =0.07). It is worth mentioning that there were 2 negative
cross-community edges, namely, A1 “Nervousness or anxiety”-
D8 “Psychomotor agitation/retardation” (edge weight = −0.09)
and A4 “Trouble relaxing”-D5 “Appetite changes” (edge weight
= −0.06). Supplementary Table 1 in the Supplementary material
shows more detailed information on the network structure.
As shown in Supplementary Figure 1 in the Supplementary
material, the accurate estimation of edge weights was proven by
the narrow bootstrapped 95% confidence interval. The results of
the bootstrapped difference test for edge weights are shown in
Supplementary Figure 2 in the Supplementary material.
The central symptoms
As shown in Figure 1B, A2 “Uncontrollable worry” and D2
“Depressed or sad mood” had the highest EI values (1.04, 1.30)
in the anxiety-depression network of the functionally impaired
Frontiers in Public Health 04 frontiersin.org
Yang et al. 10.3389/fpubh.2022.1067646
TABLE 2 The means, standard deviations, expected influences and bridge expected influences of the symptoms in the anxiety-depression network.
Symptoms M SD EI BEI
Anxiety symptoms
A1: Nervousness or anxiety 1.06 0.90 0.88 0.35
A2: Uncontrollable worry 0.61 0.81 1.04 0.18
A3: Worry too much 0.46 0.73 0.81 0.17
A4: Trouble relaxing 0.52 0.75 0.75 0.07
A5: Restlessness 0.44 0.70 0.99 0.27
A6: Irritable 0.54 0.76 0.87 0.41
A7: Afraid something will happen 0.59 0.80 0.95 0.29
Depression symptoms
D1: Anhedonia 1.30 0.90 0.95 0.07
D2: Depressed or sad mood 1.28 0.92 1.30 0.31
D3: Sleep difficulties 1.26 1.04 0.54 0.21
D4: Fatigue 0.97 0.84 0.87 0.10
D5: Appetite changes 0.68 0.81 0.51 0.12
D6: Feeling of worthlessness 0.86 0.88 0.94 0.24
D7: Concentration difficulties 0.61 0.80 1.00 0.48
D8: Psychomotor agitation/retardation 0.56 0.80 0.56 0.04
D9: Thoughts of death 0.42 0.71 0.79 0.17
The maximum possible value on the items is 3.
elderly and were identified as central symptoms. In addition, the
EIs of A2 “Uncontrollable worry” and D2 “Depressed or sad
mood” were significantly larger than those of 33 to 100% of all
the other symptoms in the network (see Supplementary Figure 3
in the Supplementary material,P<0.05). The CS coefficient of
EI was 0.60, which indicated ideal stability of EI estimation (see
Supplementary Figure 4 in the Supplementary material).
The bridge symptoms
The bridge symptoms of the anxiety-depression network
of functionally impaired elderly individuals are shown in
Figure 2A. As shown in Figure 2B, A6 “Irritable” (0.41) and D7
“Concentration difficulties” (0.48) were the symptoms with the
highest BEIs in their own community and were identified as
bridge symptoms. In addition, the BEIs of A1 “Nervousness or
anxiety”, A6 “Irritable” and D7 “Concentration difficulties” were
significantly larger than those of 33% to 73% of all the other
symptoms in the network (see Supplementary Figure 5 in the
Supplementary material,P<0.05). The CS coefficient of BEI
was 0.52, which indicated ideal stability of BEI estimation (see
Supplementary Figure 6 in the Supplementary material).
Flow network of death thoughts
The flow network of D9 “Thoughts of death” is shown
in Figure 3. There were 10 symptoms directly related to D9
“Thoughts of death” and 5 symptoms indirectly related to
D9 “Thoughts of death.” Among the direct edges with D9
“Thoughts of death”, the strongest edges were with D6 “Feeling
of worthlessness” (edge weight =0.35), D2 “Depressed or sad
mood” (edge weight =0.11) and A7 “Afraid something will
happen” (edge weight =0.10).
Discussion
In the current study, we built a network model to explore
the fine-grained relationships between anxiety and depression
in functionally impaired elderly individuals. We estimated the
EI and BEI indices to find the potential intervention targets
for anxiety and depression. We constructed a flow network
to intuitively display the anxiety and depression symptoms
directly or indirectly related to death thoughts to provide
reliable suggestions for suicide prevention. Considering that
no studies have investigated the relationships between anxiety
and depression symptoms among functionally impaired elderly
individuals, our study is largely exploratory and only provides
initial insights into this question.
The fine-grained relationships between
anxiety and depression
The cross-community edges displayed in the network
structure represented the fine-grained relationships
Frontiers in Public Health 05 frontiersin.org
Yang et al. 10.3389/fpubh.2022.1067646
FIGURE 1
The anxiety-depression network structure and the expected influence indices in the functionally impaired elderly. (A) The anxiety-depression
network structure of the functionally impaired elderly. The nodes in the network represent symptoms of anxiety and depression, and the edges
represent correlations of symptoms. Blue edges and red edges represent positive and negative correlations, respectively, and thicker edges
represent higher correlations. (B) The expected influence indices in the anxiety-depression network of the functionally impaired elderly (raw
score). A1, Nervousness or anxiety; A2, Uncontrollable worry; A3, Worry too much; A4, Trouble relaxing; A5, Restlessness; A6, Irritable; A7, Afraid
something will happen; D1, Anhedonia; D2, Depressed or sad mood; D3, Sleep diculties; D4, Fatigue; D5, Appetite changes; D6, Feeling
worthlessness; D7, Concentration diculties; D8, Psychomotor agitation/retardation; D9, Thoughts of death.
between anxiety and depression, and these relationships
help maintain comorbidity and may reflect the potential
interactions of anxiety and depression in the functionally
impaired elderly (58,59). Therefore, the crucial edges
connecting the anxiety and depression communities
are discussed.
In the present study, A1 “Nervousness or anxiety” was
positively correlated with D2 “Depressed or sad mood” and
D3 “Sleep difficulties” and negatively correlated with D8
“Psychomotor agitation/retardation.” The correlation between
“nervousness or anxiety” and “depressed or sad mood” has
been confirmed by previous studies (33). As the core diagnostic
symptoms of anxiety and depression, respectively, “Nervousness
or anxiety” and “Depressed or sad mood” built the strongest
correlation pathway to connect anxiety and depression. Sleep
problems and anxiety were proven to deteriorate interactively
(60,61). Neuroimaging studies suggest that total sleep loss
amplifies activities in brain regions related to anxiety, such
as the limbic system (62). Molecular imaging posits that
neurotransmitter mechanisms underlying sleep-wake regulation
are involved in anxiety (63). In addition, psychomotor agitation
is a potentially violent syndrome characterized by uncontrollable
motor increases and psychological and emotional activities
(64,65); as a marker of depression severity, psychomotor
retardation is characterized by persistent slowness of cognitive
and motor processing in speech, thinking and movements
(66,67). In fact, psychomotor retardation may be more
common in functionally impaired elderly individuals, and the
inhibition and poverty of thoughts caused by psychomotor
retardation may relieve the nervousness of functionally impaired
elderly individuals.
A4 “Trouble relaxing” was positively correlated with D7
“Concentration difficulties” and negatively correlated with D5
“Appetite changes.” Concentration is the ability to use cognitive
resources to selectively inhibit the interference of irrelevant
stimuli (68). Individuals with difficulties relaxing tend to
consume more cognitive resources, which may lead to difficulty
in concentration maintenance. Moreover, the functionally
impaired elderly may improve their appetite if difficulties in
relaxing is relieved.
A6 “Irritable” was positively correlated with D5 “Appetite
changes” and D7 “Concentration difficulties.” Irritability
Frontiers in Public Health 06 frontiersin.org
Yang et al. 10.3389/fpubh.2022.1067646
FIGURE 2
The anxiety-depression network structure highlighting the bridge symptoms and the bridge expected influence indices in the functionally
impaired elderly. (A) The anxiety-depression network structure highlighting the bridge symptoms. The nodes in the network represent
symptoms of anxiety and depression, and bridge symptoms are highlighted in orange. The edges represent correlations of symptoms, blue
edges and red edges represent positive and negative correlations, respectively, and thicker edges represent higher correlations. (B) The bridge
expected influence indices in the anxiety-depression network of the functionally impaired elderly (raw score). A1, Nervousness or anxiety; A2,
Uncontrollable worry; A3, Worry too much; A4, Trouble relaxing; A5, Restlessness; A6, Irritable; A7, Afraid something will happen; D1,
Anhedonia; D2, Depressed or sad mood; D3, Sleep diculties; D4, Fatigue; D5, Appetite changes; D6, Feeling worthlessness; D7, Concentration
diculties; D8, Psychomotor agitation/retardation; D9, Thoughts of death.
represents an increased tendency to anger relative to peers
(69), and a change in food intake can be a way to vent
anger. Irritability is frequently comorbid with attention-
deficit/hyperactivity disorder (70), and the impairments and
dysfunction in individuals with irritability may be partially
caused by concentration difficulties (71).
A7 “Afraid something will happen” was positively correlated
with D9 “Thoughts of death.” In the functionally impaired
elderly, a large part of the fear of the unknown is death
anxiety. Existential theories point out that death anxiety is an
inevitable process that is experienced before entering the level
of consciousness (72); it describes the functionally impaired
elderly’s fear of death and is characterized by threat and
discomfort (73).
The intervention targets of anxiety and
depression
A high value of EI indicates that a given symptom has
a strong positive correlation with other symptoms in the
network (74). Therefore, EI may play an important role in
identifying potential intervention targets. In the present study,
A2 “Uncontrollable worry” had the highest EI values in the
network. Previous studies have found that “fatigue” has the
highest centrality value in the network of nursing students,
family workers and patients with major depressive disorder (40,
46,75). This finding, which is inconsistent with previous studies,
reflects the uniqueness of the anxiety and depression symptoms
of functionally impaired elderly individuals. According to the
cognitive avoidance theory of worry, imagery is prone to be
more emotionally evocative than verbal-based thoughts; thus,
for functionally impaired elderly people, the transition from
threatening imagery to verbal-based worry can inhibit negative
physiological arousal, and as a coping strategy, uncontrollable
worry is negatively reinforced (76,77). This negative coping
strategy may lead to the interactive deterioration of anxiety
and depression symptoms in functionally impaired elderly
individuals. In addition, D2 “depressed or sad mood” also has a
highest EI value and may provide a psychological intervention
target for decreasing the severity of depression and anxiety.
Depressed mood was found in 20% of free-living old people (78),
Frontiers in Public Health 07 frontiersin.org
Yang et al. 10.3389/fpubh.2022.1067646
FIGURE 3
The flow network of death thoughts in the functionally impaired
elderly. The nodes in the network represent symptoms of
anxiety and depression, and the edges represent correlations of
symptoms. Blue edges and red edges represent positive and
negative correlations, respectively, and thicker edges represent
higher correlations.
and the proportion may be higher in the functionally impaired
elderly. Moreover, depressed mood may be a predictor of daily
living disability (79).
According to Jones et al. (35), bridge symptoms increase
the risk of interaction between different disorders. The
identification of bridge symptoms helps to clarify the complexity
of comorbidity and provide potential intervention targets for
comorbid disorders (80). In the present study, A6 “Irritable”
and D7 “Concentration difficulties” were identified as bridge
symptoms. At least 50% of major depressive disorder patients
show irritability during the depressive episode (81), and
irritability predicts a greater severity of major depressive
disorder (82). Shared risk factors, including genetic risk,
common temperamental characteristics and negative parenting
styles, are used to explain the association between irritability
and depression (83,84). Similarly, concentration difficulties are
common in anxiety disorders (85), and previous experimental
evidence has confirmed the detrimental influence of anxiety on
concentration (86).
Suicide prevention for the functionally
impaired elderly
As shown in the flow network, D9 “Thoughts of death”
was directly related to most of the anxiety and depression
symptoms. As one of the diagnostic symptoms of major
depression (47), “thoughts of death” contains a series of
contents, ranging from a passive wish of death to active suicide
plans. The close relationship of death thoughts with anxiety and
depression has been confirmed in many studies (87–91), and the
fine-grained associations of death thoughts with distinct anxiety
and depression symptoms revealed by network analysis reflect
the complexity of death thoughts (46). The strongest direct
edge of D9 “Thoughts of death” was with D6 “Feeling of
worthlessness.” This finding was similar to the research of Wei
et al. (44), who observed that “thoughts of death” were most
correlated with “feeling of worthlessness” in the network of
patients with epilepsy. However, according to Garabiles et al.
(75) and Ren et al. (46), the strongest edge of “thoughts
of death” was with “psychomotor agitation/retardation” in
domestic workers and nursing students. This difference may
be related to the health status of the participants; the need for
long-term care due to illness or weakness leads to a feeling
of worthlessness, and the feeling of worthlessness has been
suggested as an indicator of an increased risk of suicide (92).
Although previous studies revealed that “thoughts of death”
ranked the lowest centrality in the anxiety-depression network
(33,44), the centrality index of D9 “Thoughts of death”
was not the lowest in the present study, which indicates
the importance of death thoughts in the anxiety-depression
network of the functionally impaired elderly. This result
emphasizes the necessity of suicide prevention for functionally
impaired elderly individuals. In addition to “thoughts of death,”
“feeling of worthlessness” can be used as a potential target for
suicide prevention.
Limitations
Several limitations should be noted in this study. First, our
research was based on cross-sectional data, and consequently,
the temporal causality of anxiety and depression cannot be
ascertained. Second, self-assessment scales were used in our
study, and the results were susceptible to socially desirable
responses. Third, although the difference tests of edge weight,
centrality and bridge centrality have been widely used in
network analysis (93–95), according to Epskamp et al. (32),
the methods commonly used in correction for multiple
comparison at present are not feasible in network analysis, and
new method development is still a topic of future research.
Fourth, considering the comorbidity of cognitive impairment,
anxiety and depression among the functionally impaired elderly,
network analysis including these three may be the direction of
future research. Finally, the identification of intervention targets
was based on network analysis theory; thus, testing these targets
in practice is needed.
Conclusion
In summary, the present study represents the first
application of network analysis to comprehend the comorbidity
of anxiety and depression in functionally impaired elderly
Frontiers in Public Health 08 frontiersin.org
Yang et al. 10.3389/fpubh.2022.1067646
individuals. The network structure helps to build a clear
understanding of the fine-grained relationships between
anxiety and depression in functionally impaired elderly
individuals. The EI and BEI help to identify the central
and bridge symptoms in the network and provide potential
psychological intervention targets for efficiently reducing
anxiety and depression. The flow network intuitively displays
the relationship of death thoughts with other anxiety and
depression symptoms and provides valid suggestions for suicide
prevention. Our research enriches the theory of comorbidity
of anxiety and depression and is of great significance to the
practice of psychological intervention for functionally impaired
elderly individuals.
Data availability statement
The raw data supporting the conclusions of this article will
be made available by the authors, without undue reservation.
Ethics statement
The studies involving human participants were reviewed
and approved by the Ethics Committee of Xijing Hospital,
Air Force Medical University (No. KY20222194-C-1). The
patients/participants provided their written informed consent to
participate in this study.
Author contributions
Concept and design: TY, SW, and XLiu. Acquisition of the
data: XC, QZ, HW, and LW. Analysis and interpretation of
the data: TY, XLi, and XW. Drafting of the manuscript: TY
and ZG. Critical revision of the manuscript: XLiu, XZ, and
SW. All authors contributed to the article and approved the
submitted version.
Funding
This study was funded by Air Force Medical University
(BKJ19J021, BKJ21J013, and BWS16J012).
Acknowledgments
We thank the participants who contributed to our research.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed
or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpubh.
2022.1067646/full#supplementary-material
References
1. Jagger C, Arthur AJ, Spiers NA, Clarke M. Patterns of onset of disability
in activities of daily living with age. J Am Geriatr Soc. (2001) 49:404–
9. doi: 10.1046/j.1532-5415.2001.49083.x
2. Fong JH, Feng J. Patterns of functional disability in the oldest adults in China.
J Am Geriatr Soc. (2016) 64:1890–4. doi: 10.1111/jgs.14264
3. Dang WJ, Wei YY, Liu NN. Blue Book of Aging: Survey Report on The
Living Conditions of China’s Urban and Rural Older Persons. Beijing, China: Social
Sciences Academic Press (2018).
4. Chakravarti D, LaBella KA, DePinho RA. Telomeres: history, health, and
hallmarks of aging. Cell. (2021) 184:306–22. doi: 10.1016/j.cell.2020.12.028
5. Gureje O, Ogunniyi A, Kola L, Afolabi E. Functional disability in elderly
Nigerians: results from the Ibadan study of aging. J Am Geriatr Soc. (2006)
54:1784–9. doi: 10.1111/j.1532-5415.2006.00944.x
6. Lee H, Joo NH. Correlates of spirituality, death anxiety and depression
among community-dwelling disabled elderly. J Rehabil Res. (2021) 25:1–
18. doi: 10.16884/JRR.2021.25.2.1
7. Li M, Yu Y, Wang L, Yin Z. Anxiety, depression and influencing
factors of the elderly disabled under the influence of COVID-19 outbreak:
evidence from China. Psychiatr Danub. (2021) 33:604–10. doi: 10.24869/psyd.20
21.604
8. Zhang L, Lu D, Chen X. Prevalence and influencing factors of depression
symptoms among disabled elderly in Henan province: a cross-sectional study.
China J Public Health. (2021) 37:1602–6. doi: 10.11847/zgggws1130653
9. Xin Y, Ren X. Predicting depression among rural and urban disabled
elderly in China using a random forest classifier. BMC Psychiatry. (2022)
22:118. doi: 10.1186/s12888-022-03742-4
10. Tang T, Jiang J, Tang X. Prevalence of depression among older adults living in
care homes in China: a systematic review and meta-analysis. Int J Nurs Stud. (2022)
125:104114. doi: 10.1016/j.ijnurstu.2021.104114
11. Forbes EJ, Byrne GJ, O’Sullivan JD, Yang J, Marsh R, Dissanayaka NN.
Defining atypical anxiety in Parkinson’s disease. Mov Disord Clin Pract. (2021)
8:571–81. doi: 10.1002/mdc3.13193
Frontiers in Public Health 09 frontiersin.org
Yang et al. 10.3389/fpubh.2022.1067646
12. Roseman A, Morton L, Kovacs AH. Health anxiety among
adults with congenital heart disease. Curr Opin Cardiol. (2021)
36:98–104. doi: 10.1097/hco.0000000000000811
13. Husky MM, Olfson M, He JP, Nock MK, Swanson SA, Merikangas
KR. Twelve-month suicidal symptoms and use of services among adolescents:
results from the National Comorbidity Survey. Psychiatr Serv. (2012) 63:989–
96. doi: 10.1176/appi.ps.201200058
14. Su JJ, Kyung LH, A. study on the factors influencing on the suicidal ideation
of the disabled elderly. Ment Health Soc Work. (2016) 44:34–63.
15. Kim Y-S, Nam Y-H. Analysis of factors related to suicidal ideation among
people with and without disabilities in elderly living alone. J Korean Soc Sch
Community Health Educ. (2021) 22:69–81. doi: 10.35133/kssche.20210831.06
16. GBD 2017 Disease and Injury Incidence and Prevalence Collaborators.
Global, regional, and national incidence, prevalence, and years lived with disability
for 354 diseases and injuries for 195 countries and territories, 1990–2017: a
systematic analysis for the global burden of disease study 2017. Lancet. (2018)
392:1789–858. doi: 10.1016/s0140-6736(18)32279-7
17. Brady EU, Kendall PC. Comorbidity of anxiety and depression
in children and adolescents. Psychol Bull. (1992) 111:244–
55. doi: 10.1037/0033-2909.111.2.244
18. Lamers F, van Oppen P, Comijs HC, Smit JH, Spinhoven P, van Balkom AJ,
et al. Comorbidity patterns of anxiety and depressive disorders in a large cohort
study: the Netherlands study of depression and anxiety (NESDA). J Clin Psychiatry.
(2011) 72:341–8. doi: 10.4088/JCP.10m06176blu
19. Winer ES, Bryant J, Bartoszek G, Rojas E, NadorffMR, Kilgore J. Mapping the
relationship between anxiety, anhedonia, and depression. J Affect Disord. (2017)
221:289–96. doi: 10.1016/j.jad.2017.06.006
20. Schniering CA, Einstein D, Kirkman JJL, Rapee RM. Online treatment of
adolescents with comorbid anxiety and depression: a randomized controlled trial.
J Affect Disord. (2022) 311:88–94. doi: 10.1016/j.jad.2022.05.072
21. Cummings CM, Caporino NE, Kendall PC. Comorbidity of anxiety and
depression in children and adolescents: 20 years after. Psychol Bull. (2014) 140:816–
45. doi: 10.1037/a0034733
22. Gorman JM. Comorbid depression and anxiety
spectrum disorders. Depress Anxiety. (1996) 4:160–
8. doi: 10.1002/(sici)1520-6394(1996)4:4<160::aid-da2>3.0.co;2-j
23. Wittchen HU, Kessler RC, Pfister H, Lieb M. Why do people with anxiety
disorders become depressed? A prospective-longitudinal community study. Acta
Psychiatr Scand Suppl. (2000) 3:14–23. doi: 10.1111/j.0065-1591.2000.acp29-03.x
24. Jacobson NC, Newman MG. Anxiety and depression as bidirectional risk
factors for one another: a meta-analysis of longitudinal studies. Psychol Bull. (2017)
143:1155–200. doi: 10.1037/bul0000111
25. De Rycker M, Baragaña B, Duce SL, Gilbert IH. Challenges and recent
progress in drug discovery for tropical diseases. Nature. (2018) 559:498–
506. doi: 10.1038/s41586-018-0327-4
26. Fried EI. Problematic assumptions have slowed down depression research:
why symptoms, not syndromes are the way forward. Front Psychol. (2015)
6:309. doi: 10.3389/fpsyg.2015.00309
27. Fried EI, Epskamp S, Nesse RM, Tuerlinckx F, Borsboom D. What are
’good’ depression symptoms? Comparing the centrality of DSM and non-DSM
symptoms of depression in a network analysis. J Affect Disord. (2016) 189:314–
20. doi: 10.1016/j.jad.2015.09.005
28. Fried EI, Nesse RM. Depression sum-scores don’t add up: why
analyzing specific depression symptoms is essential. BMC Med. (2015)
13:72. doi: 10.1186/s12916-015-0325-4
29. Borsboom D, Cramer AO. Network analysis: an integrative approach
to the structure of psychopathology. Annu Rev Clin Psychol. (2013) 9:91–
121. doi: 10.1146/annurev-clinpsy-050212-185608
30. McNally RJ. Can network analysis transform psychopathology? Behav Res
Ther. (2016) 86:95–104. doi: 10.1016/j.brat.2016.06.006
31. Borsboom D, A. network theory of mental disorders. World Psychiatry.(2017)
16:5–13. doi: 10.1002/wps.20375
32. Epskamp S, Borsboom D, Fried EI. Estimating psychological
networks and their accuracy: a tutorial paper. Behav Res. (2018)
50:195–212. doi: 10.3758/s13428-017-0862-1
33. Beard C, Millner AJ, Forgeard MJ, Fried EI, Hsu KJ, Treadway MT,
et al. Network analysis of depression and anxiety symptom relationships in a
psychiatric sample. Psychol Med. (2016) 46:3359–69. doi: 10.1017/s00332917160
02300
34. Galderisi S, Rucci P, Kirkpatrick B, Mucci A, Gibertoni D,
Rocca P, et al. Interplay among psychopathologic variables, personal
resources, context-related factors, and real-life functioning in individuals
with schizophrenia: a network analysis. JAMA Psychiatry. (2018)
75:396–404. doi: 10.1001/jamapsychiatry.2017.4607
35. Jones PJ, Ma R, McNally RJ. Bridge centrality: a network
approach to understanding comorbidity. Multivariate Behav Res. (2021)
56:353–67. doi: 10.1080/00273171.2019.1614898
36. Cramer AOJ, Waldorp LJ, van der Maas HLJ, Borsboom D. Complex realities
require complex theories: Refining and extending the network approach to mental
disorders. Behav Brain Sci. (2010) 33:178–93. doi: 10.1017/S0140525X10000920
37. Contreras A, Nieto I, Valiente C, Espinosa R, Vazquez C. The study of
psychopathology from the network analysis perspective: a systematic review.
Psychother Psychosom. (2019) 88:71–83. doi: 10.1159/000497425
38. Byrne ME, Tanofsky-KraffM, L avenderJM, Parker MN, Shank LM, Swanson
TN, et al. Bridging executive function and disinhibited eating among youth: a
network analysis. Int J Eat Disord. (2021) 54:721–32. doi: 10.1002/eat.23476
39. Haws JK, Brockdorf AN, Gratz KL, Messman TL, Tull MT, DiLillo
D. Examining the associations between PTSD symptoms and aspects of
emotion dysregulation through network analysis. J Anxiety Disord. (2022)
86:102536. doi: 10.1016/j.janxdis.2022.102536
40. Park SC, Kim D. The centrality of depression and anxiety symptoms in major
depressive disorder determined using a network analysis. J Affect Disord. (2020)
271:19–26. doi: 10.1016/j.jad.2020.03.078
41. Heeren A, Jones PJ, McNally RJ. Mapping network connectivity among
symptoms of social anxiety and comorbid depression in people with social anxiety
disorder. J Affect Disord. (2018) 228:75–82. doi: 10.1016/j.jad.2017.12.003
42. Dobson ET, Croarkin PE, Schroeder HK, Varney ST, Mossman SA, Cecil K,
et al. Bridging anxiety and depression: a network approach in anxious adolescents.
J Affect Disord. (2021) 280:305–14. doi: 10.1016/j.jad.2020.11.027
43. Gauld C, Bartolomei F, Micoulaud-Franchi JA, McGonigal A. Symptom
network analysis of anxiety and depression in epilepsy. Seizure. (2021) 92:211–
5. doi: 10.1016/j.seizure.2021.09.013
44. Wei Z, Ren L, Wang X, Liu C, Cao M, Hu M, et al. Network of
depression and anxiety symptoms in patients with epilepsy. Epilepsy Res. (2021)
175:106696. doi: 10.1016/j.eplepsyres.2021.106696
45. Bai W, Xi HT, Zhu Q, Ji M, Zhang H, Yang BX, et al. Network analysis of
anxiety and depressive symptoms among nursing students during the COVID-19
pandemic. J Affect Disord. (2021) 294:753–60. doi: 10.1016/j.jad.2021.07.072
46. Ren L, Wang Y, Wu L, Wei Z, Cui LB, Wei X, et al. Network structure
of depression and anxiety symptoms in Chinese female nursing students. BMC
Psychiatry. (2021) 21:279. doi: 10.1186/s12888-021-03276-1
47. American Psychiatric Association. Diagnostic and Statistical Manual of
Mental Disorders, DSM-5. Virginia, US: American Psychiatric Association (2013).
48. Spitzer RL, Kroenke K, Williams JB, Löwe B, A. brief measure for assessing
generalized anxiety disorder: the GAD-7. Arch Intern Med. (2006) 166:1092–
7. doi: 10.1001/archinte.166.10.1092
49. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity
of a brief depression severity measure. J Gen Intern Med. (2001)
16:606–13. doi: 10.1046/j.1525-1497.2001.016009606.x
50. Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D.
qgraph: network visualizations of relationships in psychometric data. J Stat Softw.
(2012) 48:367–71. doi: 10.18637/jss.v048.i04
51. Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc B
(Methodol). (1996) 58:267–88. doi: 10.1111/j.2517-6161.1996.tb02080.x
52. Chen J, Chen Z. Extended Bayesian information criteria
for model selection with large model spaces. Biometrika. (2008)
95:759–71. doi: 10.1093/biomet/asn034
53. Foygel R, Drton M. Extended Bayesian information criteria for
Gaussian graphical models. Adv Neural Inf Process Syst. (2010) 23:604–12.
doi: 10.48550/arXiv.1011.6640
54. Epskamp S, Fried EI, A. tutorial on regularized partial correlation networks.
Psychol Methods. (2018) 23:617–34. doi: 10.1037/met0000167
55. Fruchterman TMJ, Reingold EM. Graph drawing by force-directed
placement. Softw Pract Exp. (1991) 21:1129–64. doi: 10.1002/spe.4380211102
56. Robinaugh DJ, Millner AJ, McNally RJ. Identifying highly influential
nodes in the complicated grief network. J Abnorm Psychol. (2016) 125:747–
57. doi: 10.1037/abn0000181
57. Guineau MG, Jones PJ, Bellet BW, McNally RJ, A. network analysis of DSM-
5 posttraumatic stress disorder symptoms and event centrality. J Trauma Stress.
(2021) 34:654–64. doi: 10.1002/jts.22664
58. Nelson JD, Cuellar AE, Cheskin LJ, Fischer S. Eating disorders and
posttraumatic stress disorder: a network analysis of the comorbidity. Behav Ther.
(2022) 53:310–22. doi: 10.1016/j.beth.2021.09.006
Frontiers in Public Health 10 frontiersin.org
Yang et al. 10.3389/fpubh.2022.1067646
59. Calma-Birling D, Zelazo PD, A. network analysis of high school and
college students’ COVID-19-related concerns, self-regulatory skills, and affect. Am
Psychol. (2022) 77:727–42. doi: 10.1037/amp0001023
60. Johnson EO, Roth T, Breslau N. The association of insomnia with anxiety
disorders and depression: exploration of the direction of risk. J Psychiatr Res.(2006)
40:700–8. doi: 10.1016/j.jpsychires.2006.07.008
61. Seo J, Pace-Schott EF, Milad MR, Song H, Germain A. Partial and
total sleep deprivation interferes with neural correlates of consolidation of fear
extinction memory. Biol Psychiatry Cogn Neurosci Neuroimaging. (2021) 6:299–
309. doi: 10.1016/j.bpsc.2020.09.013
62. Yoo SS, Gujar N, Hu P, Jolesz FA, Walker MP. The human emotional
brain without sleep–a prefrontal amygdala disconnect. Curr Biol. (2007) 17:R877–
8. doi: 10.1016/j.cub.2007.08.007
63. Elmenhorst D, Elmenhorst EM, Hennecke E, Kroll T, Matusch A, Aeschbach
D, et al. Recovery sleep after extended wakefulness restores elevated A(1) adenosine
receptor availability in the human brain. Proc Natl Acad Sci U S A. (2017)
114:4243–8. doi: 10.1073/pnas.1614677114
64. Manzano-Bort Y, Mir-Abellán R, Via-Clavero G, Llopis-Cañameras J, Escuté-
Amat M, Falcó-Pegueroles A. Experience of mental health nurses regarding
mechanical restraint in patients with psychomotor agitation: a qualitative study.
J Clin Nurs. (2022) 31:2142–53. doi: 10.1111/jocn.16027
65. Vieta E, Garriga M, Cardete L, Bernardo M, Lombraña M, Blanch J, et al.
Protocol for the management of psychiatric patients with psychomotor agitation.
BMC Psychiatry. (2017) 17:328. doi: 10.1186/s12888-017-1490-0
66. Janzing JGE, Birkenhäger TK, van den Broek WW, Breteler LMT, Nolen
WA, Verkes RJ. Psychomotor retardation and the prognosis of antidepressant
treatment in patients with unipolar psychotic depression. J Psychiatr Res. (2020)
130:321–6. doi: 10.1016/j.jpsychires.2020.07.020
67. Schrijvers D, Hulstijn W, Sabbe BG. Psychomotor symptoms in depression:
a diagnostic, pathophysiological and therapeutic tool. J Affect Disord. (2008)
109:1–20. doi: 10.1016/j.jad.2007.10.019
68. Najmi S, Amir N, Frosio KE, Ayers C. The effects of cognitive load on
attention control in subclinical anxiety and generalised anxiety disorder. Cogn
Emot. (2015) 29:1210–23. doi: 10.1080/02699931.2014.975188
69. Brotman MA, Kircanski K, Stringaris A, Pine DS, Leibenluft E.
Irritability in youths: a translational model. Am J Psychiatry. (2017) 174:520–
32. doi: 10.1176/appi.ajp.2016.16070839
70. Galera C, Orri M, Vergunst F, Melchior M, Van der Waerden J, Bouvard
MP, et al. Developmental profiles of childhood attention-deficit/hyperactivity
disorder and irritability: association with adolescent mental health, functional
impairment, and suicidal outcomes. J Child Psychol Psychiatry. (2021) 62:232–
43. doi: 10.1111/jcpp.13270
71. Bell E, Boyce P, Porter RJ, Bryant RA, Malhi GS. Irritability in mood disorders:
neurobiological underpinnings and implications for pharmacological intervention.
CNS Drugs. (2021) 35:619–41. doi: 10.1007/s40263-021-00823-y
72. Geçtan E. Existence and Psychiatry. Istanbul, Turkey: Remzi
Bookstore. (1990).
73. Neimeyer RA, Moser RP, Wittkowski J. Assessing attitudes toward dying
and death: psychometric considerations. Omega J Death Dying. (2003) 47:45–
76. doi: 10.2190/EP4R-TULM-W52G-L3EX
74. Forrest LN, Perkins NM, Lavender JM, Smith AR. Using network analysis
to identify central eating disorder symptoms among men. Int J Eat Disord. (2019)
52:871–84. doi: 10.1002/eat.23123
75. Garabiles MR, Lao CK, Xiong Y, Hall BJ. Exploring comorbidity between
anxiety and depression among migrant Filipino domestic workers: a network
approach. J Affect Disord. (2019) 250:85–93. doi: 10.1016/j.jad.2019.02.062
76. Borkovec TD, Alcaine O, Behar ES. Avoidance theory of worry and
generalized anxiety disorder. In: Heimberg R, Mennin D, Turk C, editors.
Generalized Anxiety Disorder: Advances in Research and Practice. New York:
Guilford (2004). p. 77-108.
77. Borkovec TD. The nature, functions, and origins of worry. In: Davey GCL,
Tallis F, editors. Worrying: Perspectives on Theory, Assessment and Treatment. New
York, NJ: Wiley (1994). p. 5-33.
78. Unützer J, Patrick DL, Simon G, Grembowski D, Walker E, Rutter C,
et al. Depressive symptoms and the cost of health services in HMO patients
aged 65 years and older. A 4-year prospective study. JAMA. (1997) 277:1618–
23. doi: 10.1001/jama.1997.03540440052032
79. Armenian HK, Pratt LA, Gallo J, Eaton WW. Psychopathology as a predictor
of disability: a population-based follow-up study in Baltimore, Maryland. Am J
Epidemiol. (1998) 148:269–75. doi: 10.1093/oxfordjournals.aje.a009635
80. Wang Y, Hu Z, Feng Y, Wilson A, Chen R. Changes in network centrality of
psychopathology symptoms between the COVID-19 outbreak and after peak. Mol
Psychiatry. (2020) 25:3140–9. doi: 10.1038/s41380-020-00881-6
81. Perlis RH, Fava M, Trivedi MH, Alpert J, Luther JF, Wisniewski SR, et al.
Irritability is associated with anxiety and greater severity, but not bipolar spectrum
features, in major depressive disorder. Acta Psychiatr Scand. (2009) 119:282–
9. doi: 10.1111/j.1600-0447.2008.01298.x
82. Judd LL, Schettler PJ, Coryell W, Akiskal HS, Fiedorowicz JG. Overt
irritability/anger in unipolar major depressive episodes: past and current
characteristics and implications for long-term course. JAMA Psychiatry. (2013)
70:1171–80. doi: 10.1001/jamapsychiatry.2013.1957
83. Stringaris A, Zavos H, Leibenluft E, Maughan B, Eley TC. Adolescent
irritability: phenotypic associations and genetic links with depressed
mood. Am J Psychiatry. (2012) 169:47–54. doi: 10.1176/appi.ajp.2011.101
01549
84. Vidal-Ribas P, Stringaris A. How and why are irritability
and depression linked? Child Adolesc Psychiatr Clin N Am. (2021)
30:401–14. doi: 10.1016/j.chc.2020.10.009
85. Nail JE, Christofferson J, Ginsburg GS, Drake K, Kendall PC,
McCracken JT, et al. Academic impairment and impact of treatments
among youth with anxiety disorders. Child Youth Care Forum. (2015)
44:327–42. doi: 10.1007/s10566-014-9290-x
86. Robinson OJ, Vytal K, Cornwell BR, Grillon C. The impact of anxiety upon
cognition: perspectives from human threat of shock studies. Front Hum Neurosci.
(2013) 7:203. doi: 10.3389/fnhum.2013.00203
87. Walker J, Magill N, Rosenstein DL, Frost C, Sharpe M. Suicidal thoughts
in patients with cancer and comorbid major depression: findings from a
depression screening program. J Acad Consult Liaison Psychiatry. (2022) 63:251–
9. doi: 10.1016/j.jaclp.2021.09.003
88. Bauer AM, Chan YF, Huang H, Vannoy S, Unützer J. Characteristics,
management, and depression outcomes of primary care patients who endorse
thoughts of death or suicide on the PHQ-9. J Gen Intern Med. (2013) 28:363–
9. doi: 10.1007/s11606-012-2194-2
89. Bogers IC, Zuidersma M, Boshuisen ML, Comijs HC, Oude Voshaar RC.
Determinants of thoughts of death or suicide in depressed older persons. Int
Psychogeriatr. (2013) 25:1775–82. doi: 10.1017/s1041610213001166
90. Brown LA, Denis CM, Leon A, Blank MB, Douglas SD, Morales KH, et al.
Number of opioid overdoses and depression as a predictor of suicidal thoughts.
Drug Alcohol Depend. (2021) 224:108728. doi: 10.1016/j.drugalcdep.2021.
108728
91. Strömberg A, Jaarsma T. Thoughts about death and perceived health
status in elderly patients with heart failure. Eur J Heart Fail. (2008) 10:608–
13. doi: 10.1016/j.ejheart.2008.04.011
92. Wakefield JC, Schmitz MF. Feelings of worthlessness during a single
complicated major depressive episode predict postremission suicide attempt. Acta
Psychiatr Scand. (2016) 133:257–65. doi: 10.1111/acps.12521
93. Ross J, Armour C, Kerig PK, Kidwell MC, Kilshaw RE, A. network analysis
of posttraumatic stress disorder and dissociation in trauma-exposed adolescents. J
Anxiety Disord. (2020) 72:102222. doi: 10.1016/j.janxdis.2020.102222
94. Campbell S, Osborn TL. Adolescent psychopathology and psychological
wellbeing: a network analysis approach. BMC Psychiatry. (2021)
21:333. doi: 10.1186/s12888-021-03331-x
95. Wei X, Jiang H, Wang H, Geng J, Gao T, Lei L, et al. The
relationship between components of neuroticism and problematic
smartphone use in adolescents: a network analysis. Pers Individ Differ. (2022)
186:111325. doi: 10.1016/j.paid.2021.111325
Frontiers in Public Health 11 frontiersin.org
Available via license: CC BY
Content may be subject to copyright.
