LongBench v2 / 66ed2c87821e116aacb1f149 / From the table 1, with the increase of current annual income, people who is LGBT…

Original Paper
Effects of Bullying on Anxiety, Depression, and Posttraumatic
Stress Disorder Among Sexual Minority Youths: Network Analysis
1School of Psychology, South China Normal University, Guangzhou, China
2Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, China
3Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
4Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
5College of Education for the Future, Beijing Normal University, Beijing, China
6Northeast Asian Research Center, Jilin University, Changchun, China
7Department of Population, Resources and Environment, Jilin University, Changchun, China
8Faculty of Health and Life Sciences, De Montfort University, De Montfort, United Kingdom
*these authors contributed equally
School of Psychology
South China Normal University
55 West Zhongshan Avenue
Abstract
Background: Bullying victimization is highly prevalent among sexual minority youths, particularly in educational settings,
negatively affecting their mental health. However, previous studies have scarcely explored the symptomatic relationships among
anxiety, depression, and posttraumatic stress disorder (PTSD) among sexual minority youths who experienced bullying on college
campuses.
Objective: The objectives of our study were to (1) characterize the anxiety-depression-PTSD network structures of gay or
lesbian, bisexuals, and other sexual minority youths previously bullied on college campuses; and (2) compare symptomatic
associations in the anxiety-depression-PTSD networks among bullied sexual minority youths and heterosexual youths’ groups.
Methods: This cross-sectional study recruited college participants from Jilin Province, China. Data were analyzed using a subset
of the data extracted after screening for sexual orientation and history of bullying victimization. Sexual minority youths were
then divided into 3 subgroups: gay or lesbian (homosexual), bisexual, and other. Mental health symptom severity was assessed
using scales: the 7-item Generalized Anxiety Disorder Scale measuring anxiety, the 9-item Patient Health Questionnaire measuring
depression, and the 10-item Trauma Screening Questionnaire measuring PTSD symptoms. Combining the undirected and Bayesian
network analyses, the anxiety-depression-PTSD networks were compared among sexual minority youths subgroups, and the
difference between heterosexual youths and sexual minority youths was investigated. Chi-square tests were used to compare the
difference in categorical variables, while independent-sample t tests were run on continuous variables.
Results: In this large-scale sample of 89,342 participants, 12,249 identified as sexual minority youths, of which 1603 (13.1%,
95% CI 12.5%-13.7%) reported being bullied on college campuses in the past year. According to the expected influence (EI) and
bridge expected influence (bEI) index, in the global network structure of anxiety, depression, and PTSD, sad mood (EI=1.078,
bEI=0.635) and irritability (EI=1.077, bEI=0.954) were identified as central and bridge symptoms; emotional cue reactivity
(EI=1.015) was a central symptom of PTSD in this global network. In the anxiety-depression-PTSD Bayesian network, anhedonia
had the highest prediction priority for activating other symptoms; and feeling afraid linked symptoms from anxiety to the PTSD
community. Compared to their heterosexual counterparts, sexual minority youths exhibited a stronger association between
JMIR Public Health Surveill 2023 | vol. 9 | e47233 | p. 1
https://publichealth.jmir.org/2023/1/e47233
(page number not for citation purposes)
Li et al
JMIR PUBLIC HEALTH AND SURVEILLANCE
XSL•FO
RenderX


difficulty concentrating and appetite. The “sad mood-appetite” edge was strongest in the gay or lesbian network; the
“irritability-exaggerated startle response” edge was strongest in the bisexual network.
Conclusions: For the first time, this study identified the most central and bridge symptoms (sad mood and irritability) within
the depression-anxiety-PTSD network of sexual minority youths with past bullying-victim experiences on college campuses.
Emotional cue reactivity, anhedonia, and feeling afraid were other vital symptoms in the comorbid network. Symptomatic
relationships existed showing heterogeneity in bullied heterosexual youths and sexual minority youth networks, which also was
present within the sexual minority youth subgroups. Consequently, refined targeted interventions are required to relieve anxiety,
depression, and PTSD symptoms.
(JMIR Public Health Surveill 2023;9:e47233) doi: 10.2196/47233
KEYWORDS
sexual minority youths; bullying victimization; anxiety; depression; posttraumatic stress disorder; network analysis; Bayesian
network
Introduction
Bullying is defined as the intent or behavior that seeks to harm
another person repeatedly, with an imbalance of power [1,2].
It is reported in the literature that the rate of bullying in higher
education (on college or university campuses) varies across
cultures, ranging from 1.7% to 25.2% [3-5]. Particularly, sexual
minority youths, who identify their sexual orientations as
nonheterosexual, such as lesbian, gay, or bisexual, are vulnerable
to discrimination and bullying [5,6], and have higher odds of
experiencing bullying on campus than their heterosexual
counterparts [7-10]. A national survey in the United States
further reported that sexual minority youths experience a high
rate of verbal harassment (76.1%), sexual harassment (53.7%),
physical harassment (31.2%), and physical assault (12.5%) [11].
In addition, after being bullied, sexual minority youths may
experience adverse physical health and persistent negative
effects on their mental health [12].
A considerable amount of literature has reported that sexual
minority youths exhibit higher rates of anxiety, depression, and
posttraumatic stress disorder (PTSD) than their heterosexual
counterparts [13-16]. As a recent meta-analysis noted [17],
compared to heterosexual counterparts, lesbian or gay, as well
as bisexual individuals yielded a higher risk of anxiety and
depression. In addition, sexual minority individuals were found
to be vulnerable to adverse experiences, such as being harassed
and even bullied in school, increasing the risk for anxiety,
depression, and PTSD than those participants who have not
experienced adverse life events [18].
Past research has found differences in mental health problems
among subgroups of sexual minorities [17,19]. For example,
the result from a longitudinal study suggests that bullying
victimization is connected to stronger depressive symptoms
among bisexual women, and stronger anxiety symptoms among
bisexual men [20]. Bisexual individuals are also more likely to
report PTSD than their gay or lesbian counterparts [21].
Pansexual women appear to be more open about their sexuality
but with a higher consciousness in regard to their sexual
orientation-based stigma than bisexual women, which is linked
to poorer mental health [22]. Further, asexual men are suggested
to have higher scores of depression and psychiatric disturbance
than their homosexual and bisexual counterparts [23]. The above
studies show a high heterogeneity of mental health outcomes
among sexual minority subgroups. However, whether these
differences remain consistent among sexual minority youths
who are being bullied on college campuses requires further
exploration.
Minority stress theory provides a conceptual model that the
mental health issues of sexual minorities are partially derived
from minority stressors, which are defined as the excess stress
to which individuals from stigmatized social categories are
exposed to due to their minority position [24], such as higher
rates of bullying and discrimination. Furthermore, minority
stress can cause biological responses that are related to mental
health symptoms [25]. For example, exposure to general
minority stress can affect one’s gene expression, which is
connected to cardiovascular function [26] and exerts an
interactional effect between anxiety, depression, and PTSD
[27-29]. Meanwhile, it may increase the dysregulation of the
hypothalamus-pituitary-adrenal axis, directly adding to the risk
of depression, anxiety, and PTSD [30,31]. Nevertheless, the
symptomatic relationship between anxiety, depression, and
PTSD among sexual minority youths who are bullied on college
campuses lacks investigation.
In recent years, several published studies have adopted network
analysis in both clinical psychology or psychiatric fields to
explore the relationship between symptoms of various disorders
[32-34]. The undirected network analysis depicts the interactions
between individual symptoms, with a symptom being termed
a “node,” and the partial correlation coefficient between 2 nodes
determining their “edge” [35]. According to the expected
influence (EI) index and the bridge expected influence (bEI)
index, the central and bridge symptoms of the network structure
could be identified, respectively [36,37]. Furthermore, in this
study, Bayesian network analysis was applied to clarify the
directed relationship between symptoms further. Based on the
completed partially directed acyclic graph (CPDAG), Bayesian
network analysis can unclose the putative directions of the
potential causal relationship between 2 nodes [38], and it could
help to discover activating paths among symptoms of anxiety,
depression, and PTSD network [33].
Given these premises, this study was implemented using
undirected and Bayesian network analyses to achieve 2 main
objectives: first, to characterize the symptomatic relationships
in the anxiety-depression-PTSD network among bullied sexual
JMIR Public Health Surveill 2023 | vol. 9 | e47233 | p. 2
https://publichealth.jmir.org/2023/1/e47233
(page number not for citation purposes)
Li et al
JMIR PUBLIC HEALTH AND SURVEILLANCE
XSL•FO
RenderX


minority youths and sexual minority youth subgroups; second,
to compare the network structures among bullied sexual minority
youth subgroups. For aim 1, based on previous results
[34,39,40], the researchers hypothesized that depressive and
anxious symptoms would be the most central and bridge among
all symptoms. For aim 2, considering sexual minority youth
subgroups have various prevalence and symptomatic
manifestations of anxiety, depression, and PTSD [14,21], the
researchers hypothesized that different strengths of symptomatic
associations would exist among sexual minority youth subgroups
who had disclosed experiences of being bullied. In addition,
the researchers aimed to enrich the field, as well as help offer
targeted suggestions and interventions for specific symptoms
associated with poor mental health.
Methods
Participants and Settings
This study was designed with a cross-sectional approach,
recruiting College students from 63 Universities or Colleges in
Jilin Province, China, from October to November 2021. Students
completed an electronic questionnaire with a Quick Response
code, delivered by their teachers with their classmates. The
inclusion criteria for recruitment were (1) being 15 years old or
older; (2) studying at universities or colleges in Jilin Province,
China; (3) understanding the Chinese questionnaire and having
the ability to provide informed consent.
Ethical Considerations
Ethical approval for this study was granted by the Ethics
Committee of Jilin University (NO20210929 [11 October
2021]), following the 1964 Helsinki Declaration and its
amendments in 2013. Electronic informed consent was provided
by all participants, with consent that their answers could be
applied to any secondary analyses.
Measurements
Participants’sexual orientation was measured by a single-option
question with 6 items: “Which of the following better describes
your sexual orientation?” Except for heterosexuality, all other
sexual orientations are combined to form the sexual minority
youth group [41,42], which was then further divided into 3
subgroups, including homosexuality (gay or lesbian),
bisexuality, and others (asexuality, pansexuality, and uncertain).
Anxiety was measured by the 7-item Generalized Anxiety
Disorder Questionnaire [43], depression by the 9-item Patient
Health Questionnaire [44], and PTSD by the 10-item Trauma
Screening Questionnaire [45]. The experience of being bullied
during higher education was measured by a single question:
“Have you ever been bullied on campus in the past year?”
Details of measurements are presented in the Section S1 in
Multimedia Appendix 1, Additional information for networks.
Statistical Analysis
Descriptive Analysis
In total, 117,769 participants were recruited and 89,342 were
included according to the criteria and completion of the
questionnaire. A descriptive analysis was run on the subdata
extracted from screening for sexual orientation and history of
bullying victimization. In the data set of those who had been
bullied on college campuses in the past, they were divided into
groups of heterosexual and sexual minority youth groups.
Sociodemographic variables included participants’ age, sex,
residence, ethnicity, family type, current annual income, and
whether they were an only child. The categorical variables in
heterosexual or sexual minority youth groups were compared
by chi-square tests, while continuous variables were compared
by two-tailed independent-sample t tests.
Undirected Network Estimation
This study used R programming [46] to structure the undirected
network, with the R package “qgraph” (R Foundation for
Statistical Computing) to visualize the Graphical Gaussian
Model [35]. The R package “mgm” assessed the predictability
of a node, referring to the variance of a node that could be
explained by all others [47]. The differences between the sexual
minority youths and heterosexual network structures and among
sexual minority youth subgroups were compared using the R
package “Network Comparison Test” with 1000 permutations
[48]. For more detail on the method, see Section S2 in
Multimedia Appendix 1, Additional information for networks.
Bayesian Network Estimation
In this study, DAG was modelled by the R package “bnlearn,”
with the hill-climbing algorithm [49]. To further obtain a clear
direction among symptoms, the Markov equivalence classes of
the DAG were drawn, which can be described uniquely by a
CPDAG [50]. In CPDAG, all arrows of undirected edges can
be invertible, while all directed edges cannot be converted into
undirected edges. Finally, in the determined CPDAG, the direct
activating paths in the Bayesian network are more conclusive.
More details are shown in Section S2 in Multimedia Appendix
1, Additional information for networks.
Results
Descriptive Statistics
Table 1 presents the sociodemographic characteristics of
participants. Within groups, 5063 (6%) heterosexual participants
and 1603 (13.1%) sexual minority youth participants reported
being bullied on college campuses in the past year. The
prevalence of bullying among heterosexual youths was 6%
(95% CI 5.9%-6.2%), and 13.1% (95% CI 12.5%-13.7%) among
sexual minority youths. Furthermore, among gay participants,
the prevalence of bullying was 19.9% (95% CI 17.1%-23.1%),
12.2% (95% CI 9.8%-15.1%) among lesbians, 15.6% (95% CI
13.6%-17.9%) among bisexual males, and 12% (95% CI
11.2%-12.9%) among bisexual females. The odds of being
bullied among sexual minority males was 14.4% (95% CI
12.7%-16.2%), and 11.8% (95% CI 10.6%-13%) among sexual
minority females.
JMIR Public Health Surveill 2023 | vol. 9 | e47233 | p. 3
https://publichealth.jmir.org/2023/1/e47233
(page number not for citation purposes)
Li et al
JMIR PUBLIC HEALTH AND SURVEILLANCE
XSL•FO
RenderX


Table 1. Sociodemographic characteristics of participants who had been bullied on college campuses.
P value
Degrees
of free-
dom (df)
Χ²a
Others
(n=569), n
(%)
Bisexuals
(n=815), n
(%)
Gay
(n=142), n
(%)
Lesbian
(n=77), n
(%)
Sexual minority
youths
(n=1603), n (%)
Heterosexuals
(n=5063), n
(%)
<.001b
1
94.2
Sex
234 (41.1)
176 (21.6)
142 (100)
N/Ac
552 (34.4)
2444 (48.3)
Male
335 (58.9)
639 (78.4)
N/A
77 (100)
1051 (65.6)
2619 (51.7)
Female
<.001b
1
44.6
Residence
333 (58.5)
494 (60.6)
71 (50)
51 (66.2)
949 (59.2)
2513 (49.6)
City
236 (41.5)
321 (39.4)
71 (50)
26 (33.8)
654 (40.8)
2550 (50.4)
Town and county
.31
1
1.0
Ethnicity
503 (88.4)
728 (89.3)
119 (83.8)
74 (96.1)
1424 (88.8)
4543 (89.7)
Han
66 (11.6)
87 (10.7)
23 (16.2)
3 (3.9)
179 (11.2)
520 (10.3)
Others
<.001b
2
17.2
Family type
375 (65.9)
502 (61.6)
86 (60.6)
42 (54.5)
1005 (62.7)
3311 (65.4)
Nuclear family
93 (16.3)
143 (17.5)
26 (18.3)
12 (15.6)
274 (17.1)
951 (18.8)
Above 3 genera-
tion
101 (17.8)
170 (20.9)
30 (21.1)
23 (29.9)
324 (20.2)
801 (15.8)
Others
<.001b
3
20.0
Current annual income (US $)
208 (36.6)
234 (28.7)
47 (33.1)
27 (35)
516 (32.2)
1659 (32.8)
<930
167 (29.3)
247 (30.3)
33 (23.2)
16 (20.8)
463 (28.9)
1688 (33.3)
930-2169
67 (11.8)
143 (17.5)
27 (19)
14 (18.2)
251 (15.6)
763 (15.1)
2170-3565
127 (22.3)
191 (23.4)
35 (24.6)
20 (26)
373 (23.3)
953 (18.8)
≥3565
<.001b
1
19.4
Only-child status
320 (56.2)
408 (50.1)
72 (50.7)
42 (54.5)
842 (52.5)
2340 (46.2)
Yes
249 (43.8)
407 (49.9)
70 (49.3)
35 (45.5)
761 (47.5)
2723 (53.8)
No
.49
6659
–0.7d
19.63 (1.73)
19.51 (1.63)
19.74 (1.70)
19.55 (1.66)
19.57 (1.67)
19.54 (1.72)
Age (years), mean (SD)
<.001b
6664
–11.2d
7.69 (5.80)
7.93 (5.15)
7.61 (5.30)
8.06 (5.90)
7.82 (5.44)
6.25 (474)
GAD-7e, mean (SD)
<.001b
6664
–13.1d
10.23 (6.85)
10.16 (5.96)
9.45 (5.42)
10.74 (7.52)
10.15 (6.33)
8.06 (5.30)
PHQ-9f, mean (SD)
<.001b
6664
–9.7d
5.55 (3.27)
5.91 (2.97)
5.58 (3.20)
5.94 (3.38)
5.75 (3.12)
4.88 (3.16)
PTSD-10g, mean (SD)
aХ²: used to compare differences between heterosexual and sexual minority youth groups among specified demographic variables.
bThere was a significant difference existing with P<.001.
cN/A: Not applicable.
dT: used to compare differences between heterosexual and sexual minority youth groups among specified demographic variables.
eGAD-7: the 7-item Generalized Anxiety Disorders Scale.
fPHQ-9: the 9-item Patient Health Questionnaire.
gPTSD-10: measured by the 10-item Trauma Screening Questionnaire.
Compared to their heterosexual counterparts, more sexual
minority females, and more sexual minority youths living in
urban residences reported being bullied on the campuses. In
addition, experiences of being bullied were more likely to occur
among participants from nuclear families, with lower annual
family income, or among those who had siblings. Bullied sexual
minority youths also reported higher rates of anxiety, depression,
and PTSD symptoms than bullied heterosexual youths.
Undirected Network Structure
As seen in Figure 1, according to the centrality index EI, GAD2
(Generalized Anxiety Disorders Scale; “control worry,”
EI=1.134) had the highest EI, followed by PHQ4 (Patient Health
Questionnaire; “energy,” EI=1.116), GAD3 (“worry a lot,”
EI=1.085), PHQ2 (“sad mood,” EI=1.078), GAD6 (“irritability,”
EI=1.077), and PTSD4 (“energy,” EI=1.015). These 6 symptoms
played the most central role in the global network of anxiety,
depression, and PTSD symptoms among all bullied sexual
JMIR Public Health Surveill 2023 | vol. 9 | e47233 | p. 4
https://publichealth.jmir.org/2023/1/e47233
(page number not for citation purposes)
Li et al
JMIR PUBLIC HEALTH AND SURVEILLANCE
XSL•FO
RenderX


minority youths. The central network structures of the 3 sexual
minority youth subgroups and their centrality index EI are shown
in Table S1 and Figures S1, S2, S3, and S4 in Multimedia
Appendix 1, Additional information for networks.
As seen in Figure 2, according to bEI, GAD6 (“irritability,”
bEI=0.954), PHQ8 (“motor,” bEI=0.883), GAD7 (“feeling
afraid,” bEI=0.758), GAD5 (“restless,” bEI=0.718), GAD1
(“nervous,” bEI=0.694), and PHQ2 (“sad mood,” bEI=0.635)
had crucial bridging roles in connecting to the other symptoms
of anxiety, depression, or PTSD, which may possibly transform
from 1 community to another. The bridge network structures
of the 3 sexual minority youth subgroups and their bridge bEI
index are shown in Table S1 and Figures S5, S6, S7, and S8 in
Multimedia Appendix 1, Additional information for networks.
Figure 1. The global network structure of central symptoms among all bullied sexual minority youths (N=1603). GAD: the 7-item Generalized Anxiety
Disorders Scale; LGB: homosexuality (gay or lesbian), bisexuality and other sexual minority youths; PHQ: the 9-item Patient Health Questionnaire;
PTSD: posttraumatic stress disorder measured by the 10-item Trauma Screening Questionnaire.
Figure 2. The global network structure of bridge symptoms among all bullied sexual minority youths (N=1603). GAD: the 7-item Generalized Anxiety
Disorders Scale; LGB: homosexuality (gay or lesbian), bisexuality and other sexual minority youths; PHQ: the 9-item Patient Health Questionnaire;
PTSD: posttraumatic stress disorder measured by the 10-item Trauma Screening Questionnaire.
The average predictability of nodes was 48.67%, which means
that, on average, the variance of 48.67% per node could be
explained by its neighbors. In addition, all networks show good
stability in this study (see in Figures S9, S10, S11, and S12 in
Multimedia Appendix 1, Additional information for networks).
Bayesian Network Structure
Figure 3 shows the CPDAG results, indicating that PHQ1
(“anhedonia”) had the highest predictive priority for other
symptoms occurring in the anxiety-depression-PTSD network.
PHQ2 (“sad mood”), GAD6 (“irritability”), and GAD7 (“feeling
afraid”) were identified as 3 crucial bridge symptoms, whose
downstream symptoms had more branches and could activate
more pathways. In addition, there were reversible connections
between PHQ1 (“anhedonia”), PHQ2 (“sad mood”), and PHQ4
(“appetite”). Whereas, the edges between PHQ2 (“sad mood”)
and PHQ6 (“guilt”), between GAD6 (“irritability”), PHQ8
(“motor”), and PTSD7 (“irritability or anger”), and between
JMIR Public Health Surveill 2023 | vol. 9 | e47233 | p. 5
https://publichealth.jmir.org/2023/1/e47233
(page number not for citation purposes)
Li et al
JMIR PUBLIC HEALTH AND SURVEILLANCE
XSL•FO
RenderX


GAD7 (“feeling afraid”) to PTSD9 (“hypervigilance”) were
unidirectional and irreversible. These results indicate the most
likely direction from both depression and anxiety symptoms to
PTSD symptoms, rather than vice versa. More results of CPDAG
can be found in Figure S13 in Multimedia Appendix 1,
Additional information for networks.
Figure 3. The Bayesian network of anxiety, depression, and posttraumatic stress disorder among bullied sexual minority youths, based on a completed
partially directed acyclic graph. GAD: the 7-item Generalized Anxiety Disorders Scale; PHQ; the 9-item Patient Health Questionnaire; PTSD: posttraumatic
stress disorder measured by the 10-item Trauma Screening Questionnaire. The edges without directions refer to reversiblity. The solid edges with
direction refer to irreversiblity. The dotted edges with direction refer to irreversibility and arrowing to a node in another symptom community.
Network Comparison Structures
Figure 4 shows the results when comparing the network
structures between the sexual minority youths and heterosexual
groups. The edge of PTSD8 (“difficulty concentrating”)-PHQ5
(“appetite”) was significantly stronger in the sexual minority
youth network structure than in the heterosexual.
Among the 3 subgroups of sexual minority youths, there was
no significant difference in edges between the gay or lesbian
network and the other sexual minority youth networks. However,
the edge of PHQ2 (“sad mood”)-PHQ5 (“appetite”) was
strongest in the gay or lesbian network and the edge of PTSD7
(“irritability”)-PTSD10 (“exaggerated startle response”) was
strongest in the bisexual sexual minority youth networks (Figure
S14 in Multimedia Appendix 1, Additional information for
networks).
JMIR Public Health Surveill 2023 | vol. 9 | e47233 | p. 6
https://publichealth.jmir.org/2023/1/e47233
(page number not for citation purposes)
Li et al
JMIR PUBLIC HEALTH AND SURVEILLANCE
XSL•FO
RenderX


Figure 4. The comparison of network structure between sexual minority and heterosexual youths. GAD: the 7-item Generalized Anxiety Disorders
Scale; PHQ: the 9-item Patient Health Questionnaire; PTSD: posttraumatic stress disorder measured by the 10-item Trauma Screening Questionnaire.
Discussion
Principal Findings
This is the first study based on a large-scale sample to
investigate the symptomatic relationship of anxiety, depression,
and PTSD among sexual minority youths bullied on college
campuses in the past. The results partly supported hypothesis
1: sad mood and irritability were identified as the most
frequently crucial activating symptoms of poor mental health,
where emotional cue reactivity was the most central symptom
of the PTSD community. In the Bayesian network, anhedonia
had the highest predictive priority to activate the occurrence of
all other symptoms; feeling afraid played a bridging role in the
anxiety-depression-PTSD network. The results supported
hypothesis 2: compared to heterosexuals, sexual minority youths
exhibited a stronger association between difficulty concentrating
and appetite. The “sad mood-appetite” edge was strongest in
the gay or lesbian network, and the “irritability-exaggerated
startle response” edge was strongest in the bisexual network.
Sexual minority youths has been frequently reported to
experience discrimination, prejudice, and stigma-bias because
of their sexual orientation [6,51], associated with bullying
victimization of sexual minority youths [52,53]. Our study
further reported a high prevalence of being bullied on college
campuses among sexual minority youths, which was 13.3%
(95% CI 12.5%-13.7%, n=1603). Consistent with previous
findings, exposure to bullying on college campuses was found
to be related to the onset of mental health problems among
sexual minority youths [53]. As a meta-analysis supported,
individuals who experience bullying victimization had increased
odds of depression, anxiety, and PTSD [54].
Sad mood and irritability were the most crucial symptoms (both
central and bridge) in all anxiety-depression-PTSD network
models. Previous network analyses have consistently identified
sad mood as the central and bridge symptom in the structure of
the anxiety-depression network [36,55]. This result may be
explained by the external stressors that sexual minority youths
face, making them feel sad [51,56]. According to a US national
survey on youths’risk behavior, high rates of in-school bullying
victimization for homosexuals (28.22%, n=91) and bisexuals
(34.03%, n=318) occurred because of external discrimination
[7]. When under stressful conditions, if those bullied students
experience insufficient support from teachers, they could feel
abandoned and hopeless [57]. In terms of irritability, 2 prior
studies also noted that irritability had a high betweenness index
(another bridge centrality index) [34], and was identified as one
of the central symptoms in the depression-anxiety-PTSD
network [58]. Research has indicated that, once bullied, an
inability to find a coping strategy or resist bullying behaviors
can make individuals feel frustrated, which may trap them in
rumination anger [9]. Moreover, traumatic memories of bullying
victimization could generate severe distress and irritability as
well [59]. Our results showed irritability also had a high
predictive priority of the occurrence of PTSD symptoms.
Findings indicate that for those who do withstand cumulative
bullying victimization, their subjective aggression can be
triggered, potentially increasing their irritable mood and PTSD
symptoms simultaneously [60]. Notably, emotional cue
reactivity (in this study referring to feeling upset by reminders
of the events) was the most central symptom in the PTSD
network. This may be explained by the influences of
autobiographical memories (a mixed memory of complicated
life events) [61]. Experiences of being bullied incur negative
emotional cues, such as insults and physical aggression [62],
and these negative memories would, in turn, cause further
distress.
In the Bayesian network, anhedonia had the highest predictive
priority of activating other symptoms. It parallels a previous
network analysis that identified anhedonia as a central symptom
in the anxiety-depression network [36]. Previous findings
reported that bullying behaviors could lead to poorer mental
JMIR Public Health Surveill 2023 | vol. 9 | e47233 | p. 7
https://publichealth.jmir.org/2023/1/e47233
(page number not for citation purposes)
Li et al
JMIR PUBLIC HEALTH AND SURVEILLANCE
XSL•FO
RenderX


health for bullying victims [63]. After being bullied, sexual
minority youths may form a negative schema about an
institution, which influences their interaction with others,
decreasing their interest in participating in activities with their
peers [64]. Another possible explanation could be a lack of
support from teachers and families after exposure to being
bullied, which further hinders sexual minority youths’ mental
health [65] and also makes it harder for them to perceive
pleasure [66]. Additionally, being bullied may negatively
influence the release of dopamine, a chemical produced by
neuronal receptors that relate to feeling good, in the case of
bullying, this causes a lack of pleasure [67]. Feeling afraid also
played a significant bridging role in the Bayesian network,
pointing to the occurrence of hypervigilance in the PTSD
community. On the one hand, past bullying victimization could
lead sexual minority youths to be afraid of reoccurring bullying
[68]; on the other hand, without efficient support from others,
exposing perpetrators of bullying could trigger their fear of
retaliation [69]. In addition, feeling afraid is linked to the
symptoms of PTSD in this study. As early findings stated, those
bullied could incur idiopathic nightmares and sleep problems,
leading to hypervigilance [70]. From the neuropsychological
perspective, fear-related threats increase the steady-state visual
evoked amplitudes, thus heightening individuals’vigilance [71].
Based on this fear of repeated bullying, sexual minority youths’
visual attention to possible danger may present with
over-heightened reactions.
Compared to the heterosexual network structure, difficulty
concentrating was found to be more closely associated with
appetite in the sexual minority youth network structure. As
previous findings stated, sexual minority youths were more
vulnerable to weight-based bullying by peers than heterosexual
youths [72,73]. Moreover, a motivation to avoid or prevent
bullying may be a key driver for those who seek treatment for
obesity [74]. Thus, the ideal body image and weight bias may
result in more sexual minority youths choosing to diet in order
to lose weight or engage in harmful weight-control strategies,
such as purging, to maintain a satisfactory appearance [75]. It
should be noted that over-preoccupation with body shape might
impair concentration in other areas, supported by a previous
study in which those who experienced bullying reported that
they perceived their attention was distracted due to the fear
associated with the experience, which led to difficulty
concentrating when learning and during academic achievement,
further hindering their state of mental well-being [76].
Differently, compared to bullied bisexual youths, sad mood had
a stronger correlation with appetite among bullied gay or lesbian
youths. Researchers consistently report more disordered eating
behaviors in gay or lesbian youths than in bisexuals [77]. In
addition, gay or lesbian youths were reported to have more body
dissatisfaction, driven by an ideal body shape [78,79]. Negative
mood also relates to subjective appetite, causing poor appetite
or overeating [80]. Since discrimination and bullying incur a
sad mood for gay or lesbian youths, they may choose binge
eating to elevate their mood momentarily [81]. Furthermore,
compared to other sexual minority youths, bisexual youths
exhibited a stronger correlation between irritability and
exaggerated startle response. This may be due to bisexual youths
experiencing dual discrimination from homosexual and
heterosexual communities [82]. Since bullying shapes a
threatening context for bisexual youths, they may become
irritable and exhibit a high startle reactivity [83]. Meanwhile,
higher irritability can alter attention bias, with decreased
functional connectivity between the left inferior frontal gyrus
and a periaqueductal grey area, which may increase the response
to frustrating stimuli [84].
Limitations
Several limitations should be noted in this study. First, as a
cross-sectional study, it is hard to explore the dynamic process
of psychopathological networks [85], thus, further longitudinal
research is required to obtain causal conclusions. Second, the
replicability and generalizability of network structures are still
limited across various samples [86]. In the future, neuroimaging
studies are required to explore the organic effects on the brain.
Third, measurements were limited in several aspects: (1) the
minority stressors, such as lacking peers or family support, were
not directly measured, and the results could be biased because
of other potential influences. (2) There may be a part of the
youths who have hesitancy toward or concealment of being
identified as sexual minority youths for fear of being exposed
in the context. (3) In addition, the interaction of gender identity
and sexual orientation should be considered in future
investigations. For example, a transgender man attracted to a
cisgender man could also consider himself gay. Further studies
should be conducted covering diverse gender identities in detail.
(4) Because the experience of being bullied on college campuses
in the past was measured by a self-report as well, in which the
standard to determine the conceptualization of bullying varies
across individuals, it was inevitable to include samples with
different severities of bullying victimization. Future research
should provide more detailed standards to define bullying and
explore the underlying mechanism between various types or
levels of bullying behaviors and the impact this has on the
mental health of sexual minority populations. Bullying
off-campus perpetrated by peers should be measured, and its
impacts must be explored. (5) The bias of recall errors or social
desirability effects is unavoidable for data from subjective
reporting scales. Finally, other psychiatric disorders, such as
obsessive-compulsive disorder or eating disorders, were not
measured directly, which may affect the results.
Conclusions
This cross-sectional study based on a large-scale sample,
combined the undirected network analysis and Bayesian network
analysis, for the first time, identifying the most central and
bridge symptoms (sad mood and irritability), as well as the
central role of emotional cue reactivity within the
depression-anxiety-PTSD network of sexual minority youths
who were bullied on college campuses in the past. CPDAG
results also indicated the vital roles of anhedonia and feeling
afraid in the global Bayesian network in this study. In addition,
the comparisons of networks supported three findings: (1)
compared 
to 
heterosexuals, 
the 
edge 
of 
“difficulty
concentrating” was stronger than in the global sexual minority
youth network, (2) the edge of “sad mood-appetite” was
strongest in the gay or lesbian network, and (3) the edge of
JMIR Public Health Surveill 2023 | vol. 9 | e47233 | p. 8
https://publichealth.jmir.org/2023/1/e47233
(page number not for citation purposes)
Li et al
JMIR PUBLIC HEALTH AND SURVEILLANCE
XSL•FO
RenderX


“irritability-exaggerated startle response” was strongest in the
bisexual network. These findings should be considered when
offering targeted support for improving the physical health and
mental well-being of sexual minority youths. Consequently,
refined targeted interventions could be formulated [6] to relieve
the symptoms of anxiety, depression, and PTSD comorbidity
among sexual minority youths.
Acknowledgments
The conception and design of this study was done by YW and SX. SX and XS did the data collection. Data quality control was
done by SX. Data analysis and all figures were done by YJ, CC, XLuo, YL, and XLing. This study’s write-up was performed by
JL, YJ, and SX. Critical comments were made by YW and AW. This study was supervised by YW and SX. We thank all the
participants and staff involved in this study. This study was supported by the National Natural Science Foundation of China
(82201708).
Data Availability
The data set for this specific paper is available from the corresponding author upon reasonable request.
Conflicts of Interest
None declared.
Multimedia Appendix 1
Additional information for networks.
[DOCX File , 7006 KB-Multimedia Appendix 1]
References
1.
Smith PK. Bullying: definition, types, causes, consequences and intervention. Soc Personal Psychol Compass
2016;10(9):519-532 [doi: 10.1111/spc3.12266]
2.
Younan B. A systematic review of bullying definitions: how definition and format affect study outcome. J Aggress Confl
Peace Res 2019;11(2):109-115 [doi: 10.1108/jacpr-02-2018-0347]
3.
Lund EM, Ross SW. Bullying perpetration, victimization, and demographic differences in college students: a review of the
literature. Trauma Violence Abuse 2017 Jul;18(3):348-360 [doi: 10.1177/1524838015620818] [Medline: 26759417]
4.
Pörhölä M, Cvancara K, Kaal E, Kunttu K, Tampere K, Torres MB. Bullying in university between peers and by personnel:
cultural variation in prevalence, forms, and gender differences in four countries. Soc Psychol Educ 2019;23(1):143-169
[FREE Full text] [doi: 10.1007/s11218-019-09523-4]
5.
Sinkkonen HM, Puhakka H, Meriläinen M. Bullying at a university: students' experiences of bullying. Studies in Higher
Education 2012;39(1):153-165 [doi: 10.1080/03075079.2011.649726]
6.
Smith TE, Bauerband LA, Aguayo D, McCall CS, Huang FL, Reinke WM, et al. School bullying and gender minority
youth: victimization experiences and perceived prevalence. School Psych Rev 2022:1-14 [doi:
10.1080/2372966x.2021.2002123]
7.
Angoff HD, Barnhart WR. Bullying and cyberbullying among LGBQ and heterosexual youth from an intersectional
perspective: findings from the 2017 national youth risk behavior survey. J Sch Violence 2021;20(3):274-286 [doi:
10.1080/15388220.2021.1879099]
8.
Wensley K, Campbell M. Heterosexual and nonheterosexual young university students' involvement in traditional and
cyber forms of bullying. Cyberpsychol Behav Soc Netw 2012;15(12):649-654 [doi: 10.1089/cyber.2012.0132] [Medline:
23078337]
9.
Zsila Á, Urbán R, Demetrovics Z. Anger rumination and unjust world beliefs moderate the association between cyberbullying
victimization and psychiatric symptoms. Psychiatry Res 2018;268:432-440 [FREE Full text] [doi:
10.1016/j.psychres.2018.08.001] [Medline: 30130710]
10.
Kann L, McManus T, Harris WA, Shanklin SL, Flint KH, Queen B, et al. Youth risk behavior surveillance - United States,
2017. MMWR Surveill Summ 2018;67(8):1-114 [FREE Full text] [doi: 10.15585/mmwr.ss6708a1] [Medline: 29902162]
11.
Kosciw JG, Clark CM, Menard L. The 2021 National School Climate Survey: The experiences of LGBTQ+ youth in our
nation's schools. New York: GLSEN; 2022. URL: https://healtheducationresources.unesco.org/library/documents/
2021-national-school-climate-survey-experiences-lgbtq-youth-our-nations-schools [accessed 2023-09-22]
12.
Russell ST, Fish JN. Mental health in lesbian, gay, bisexual, and transgender (LGBT) youth. Annu Rev Clin Psychol
2016;12:465-487 [FREE Full text] [doi: 10.1146/annurev-clinpsy-021815-093153] [Medline: 26772206]
13.
Mittleman J. Sexual minority bullying and mental health from early childhood through adolescence. J Adolesc Health
2019;64(2):172-178 [doi: 10.1016/j.jadohealth.2018.08.020] [Medline: 30392862]
JMIR Public Health Surveill 2023 | vol. 9 | e47233 | p. 9
https://publichealth.jmir.org/2023/1/e47233
(page number not for citation purposes)
Li et al
JMIR PUBLIC HEALTH AND SURVEILLANCE
XSL•FO
RenderX


14.
Ross LE, Salway T, Tarasoff LA, MacKay JM, Hawkins BW, Fehr CP. Prevalence of depression and anxiety among
bisexual people compared to gay, lesbian, and heterosexual individuals: a systematic review and meta-analysis. J Sex Res
2018;55(4-5):435-456 [FREE Full text] [doi: 10.1080/00224499.2017.1387755] [Medline: 29099625]
15.
Poteat VP, O'Brien MD, Rosenbach SB, Finch EK, Calzo JP. Depression, anxiety, and interest in mental health resources
in school-based gender-sexuality alliances: implications for sexual and gender minority youth health promotion. Prev Sci
2021;22(2):237-246 [FREE Full text] [doi: 10.1007/s11121-020-01196-2] [Medline: 33410118]
16.
Burton CM, Marshal MP, Chisolm DJ, Sucato GS, Friedman MS. Sexual minority-related victimization as a mediator of
mental health disparities in sexual minority youth: a longitudinal analysis. J Youth Adolesc 2013;42(3):394-402 [FREE
Full text] [doi: 10.1007/s10964-012-9901-5] [Medline: 23292751]
17.
Wittgens C, Fischer MM, Buspavanich P, Theobald S, Schweizer K, Trautmann S. Mental health in people with minority
sexual orientations: A meta-analysis of population-based studies. Acta Psychiatr Scand 2022;145(4):357-372 [FREE Full
text] [doi: 10.1111/acps.13405] [Medline: 35090051]
18.
Schnarrs PW, Stone AL, Bond MA, Salcido R, Dorri AA, Nemeroff CB. Development and psychometric properties of the
sexual and gender minority adverse childhood experiences (SGM-ACEs): Effect on sexual and gender minority adult mental
health. Child Abuse Negl 2022;127:105570 [FREE Full text] [doi: 10.1016/j.chiabu.2022.105570] [Medline: 35231816]
19.
Borgogna NC, McDermott RC, Aita SL, Kridel MM. Anxiety and depression across gender and sexual minorities: implications
for transgender, gender nonconforming, pansexual, demisexual, asexual, queer, and questioning individuals. Psychol Sex
Orientat Gend Divers 2019;6(1):54-63 [doi: 10.1037/sgd0000306]
20.
Katz-Wise SL, Rosario M, Calzo JP, Scherer EA, Sarda V, Austin SB. Associations of timing of sexual orientation
developmental milestones and other sexual minority stressors with internalizing mental health symptoms among sexual
minority young adults. Arch Sex Behav 2017;46(5):1441-1452 [FREE Full text] [doi: 10.1007/s10508-017-0964-y] [Medline:
28271349]
21.
Carey FR, LeardMann CA, Lehavot K, Jacobson IG, Kolaja CA, Stander VA, et al. Health disparities among lesbian, gay,
and bisexual service members and veterans. Am J Prev Med 2022;63(4):521-531 [FREE Full text] [doi:
10.1016/j.amepre.2022.04.034] [Medline: 35794031]
22.
Morandini J, Strudwick J, Menzies R, Dar-Nimrod I. Differences between Australian bisexual and pansexual women: an
assessment of minority stressors and psychological outcomes. Psychol Sex 2022;14(1):233-251 [doi:
10.1080/19419899.2022.2100717]
23.
Yule MA, Brotto LA, Gorzalka BB. Mental health and interpersonal functioning in self-identified asexual men and women.
Psychol Sex 2013;4(2):136-151 [doi: 10.1080/19419899.2013.774162]
24.
Meyer IH. Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and
research evidence. Psychol Bull 2003;129(5):674-697 [FREE Full text] [doi: 10.1037/0033-2909.129.5.674] [Medline:
12956539]
25.
Flentje A, Heck NC, Brennan JM, Meyer IH. The relationship between minority stress and biological outcomes: a systematic
review. J Behav Med 2020;43(5):673-694 [FREE Full text] [doi: 10.1007/s10865-019-00120-6] [Medline: 31863268]
26.
Flentje A, Kober KM, Carrico AW, Neilands TB, Flowers E, Heck NC, et al. Minority stress and leukocyte gene expression
in sexual minority men living with treated HIV infection. Brain Behav Immun 2018;70:335-345 [FREE Full text] [doi:
10.1016/j.bbi.2018.03.016] [Medline: 29548994]
27.
Dedert EA, Calhoun PS, Watkins LL, Sherwood A, Beckham JC. Posttraumatic stress disorder, cardiovascular, and metabolic
disease: a review of the evidence. Ann Behav Med 2010;39(1):61-78 [FREE Full text] [doi: 10.1007/s12160-010-9165-9]
[Medline: 20174903]
28.
Gustad LT, Bjerkeset O, Strand LB, Janszky I, Salvesen Ø, Dalen H. Cardiac function associated with previous, current
and repeated depression and anxiety symptoms in a healthy population: the HUNT study. Open Heart 2016;3(1):e000363
[FREE Full text] [doi: 10.1136/openhrt-2015-000363] [Medline: 26925243]
29.
Johnson AK, Hayes SN, Sawchuk C, Johnson MP, Best PJ, Gulati R, et al. Analysis of posttraumatic stress disorder,
depression, anxiety, and resiliency within the unique population of spontaneous coronary artery dissection survivors. J Am
Heart Assoc 2020;9(9):e014372 [FREE Full text] [doi: 10.1161/JAHA.119.014372] [Medline: 32342736]
30.
Almeida FB, Pinna G, Barros HMT. The role of HPA axis and allopregnanolone on the neurobiology of major depressive
disorders and PTSD. Int J Mol Sci 2021;22(11):5495 [FREE Full text] [doi: 10.3390/ijms22115495] [Medline: 34071053]
31.
Hinds JA, Sanchez ER. The role of the Hypothalamus–Pituitary–Adrenal (HPA) axis in test-induced anxiety: assessments,
physiological responses, and molecular details. Stresses 2022;2(1):146-155 [doi: 10.3390/stresses2010011]
32.
Amore M, Murri MB, Calcagno P, Rocca P, Rossi A, Aguglia E, et al. The association between insight and depressive
symptoms in schizophrenia: Undirected and Bayesian network analyses. Eur Psychiatry 2020;63(1):1-21 [FREE Full text]
[doi: 10.1192/j.eurpsy.2020.45] [Medline: 32372731]
33.
McNally RJ, Heeren A, Robinaugh DJ. A Bayesian network analysis of posttraumatic stress disorder symptoms in adults
reporting childhood sexual abuse. Eur J Psychotraumatol 2017;8(sup3):1341276 [FREE Full text] [doi:
10.1080/20008198.2017.1341276] [Medline: 29038690]
JMIR Public Health Surveill 2023 | vol. 9 | e47233 | p. 10
https://publichealth.jmir.org/2023/1/e47233
(page number not for citation purposes)
Li et al
JMIR PUBLIC HEALTH AND SURVEILLANCE
XSL•FO
RenderX


34.
Price M, Legrand AC, Brier ZMF, Hébert-Dufresne L. The symptoms at the center: examining the comorbidity of
posttraumatic stress disorder, generalized anxiety disorder, and depression with network analysis. J Psychiatr Res
2019;109:52-58 [FREE Full text] [doi: 10.1016/j.jpsychires.2018.11.016] [Medline: 30502492]
35.
Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: a tutorial paper. Behav Res
Methods 2018;50(1):195-212 [FREE Full text] [doi: 10.3758/s13428-017-0862-1] [Medline: 28342071]
36.
Beard C, Millner AJ, Forgeard MJC, Fried EI, Hsu KJ, Treadway MT, et al. Network analysis of depression and anxiety
symptom relationships in a psychiatric sample. Psychol Med 2016;46(16):3359-3369 [FREE Full text] [doi:
10.1017/S0033291716002300] [Medline: 27623748]
37.
Jones PJ, Ma R, McNally RJ. Bridge centrality: a network approach to understanding comorbidity. Multivariate Behav Res
2021;56(2):353-367 [doi: 10.1080/00273171.2019.1614898] [Medline: 31179765]
38.
Pearl J, Russell S. Bayesian networks. 2011. URL: https://escholarship.org/uc/item/53n4f34m [accessed 2023-09-22]
39.
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(2):71-83 [FREE Full text] [doi: 10.1159/000497425]
[Medline: 30889609]
40.
Jin Y, Xu S, Hu Z, Li J, Li H, Wang X, et al. Co-occurrence of PTSD and affective symptoms in a large sample with
childhood trauma subtypes: a network analysis. Front Public Health 2023;11:1093687 [FREE Full text] [doi:
10.3389/fpubh.2023.1093687] [Medline: 36960378]
41.
Rice K. Pansexuality. Int Encyclopedia Human Sexuality 2015:861-1042 [doi: 10.1002/9781118896877.wbiehs328]
42.
Hsieh N, Shuster SM. Health and health care of sexual and gender minorities. J Health Soc Behav 2021;62(3):318-333
[doi: 10.1177/00221465211016436] [Medline: 34528481]
43.
Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7.
Arch Intern Med 2006;166(10):1092-1097 [FREE Full text] [doi: 10.1001/archinte.166.10.1092] [Medline: 16717171]
44.
Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med
2001;16(9):606-613 [FREE Full text] [doi: 10.1046/j.1525-1497.2001.016009606.x] [Medline: 11556941]
45.
Brewin CR, Rose S, Andrews B, Green J, Tata P, McEvedy C, et al. Brief screening instrument for post-traumatic stress
disorder. Br J Psychiatry 2018;181(2):158-162 [FREE Full text] [doi: 10.1192/bjp.181.2.158]
46.
Ihaka R, Gentleman R. R: a language for data analysis and graphics. J Comput Graph Stat 1996;5(3):299-314 [doi:
10.1080/10618600.1996.10474713]
47.
Haslbeck J, Waldorp LJ. mgm: estimating time-varying mixed graphical models in high-dimensional data. J Stat Soft
2020;93(8):1-8 [doi: 10.18637/jss.v093.i08]
48.
van Borkulo CD, van Bork R, Boschloo L, Kossakowski JJ, Tio P, Schoevers RA, et al. Comparing network structures on
three aspects: a permutation test. Psychol Methods 2022 [doi: 10.1037/met0000476] [Medline: 35404628]
49.
Scutari M. Learning bayesian networks with the bnlearn R package. J Stat Softw 2010;35(3):1-22 [doi:
10.48550/arXiv.0908.3817]
50.
Castelletti F, Consonni G, Della Vedova ML, Peluso S. Learning markov equivalence classes of directed acyclic graphs:
an objective bayes approach. Bayesian Anal 2018;13(4):1235-1260 [doi: 10.1214/18-ba1101]
51.
Ayhan CHB, Bilgin H, Uluman OT, Sukut O, Yilmaz S, Buzlu S. A systematic review of the discrimination against sexual
and gender minority in health care settings. Int J Health Serv 2020;50(1):44-61 [FREE Full text] [doi:
10.1177/0020731419885093] [Medline: 31684808]
52.
D'Urso G, Pace U. Homophobic bullying among adolescents: the role of insecure-dismissing attachment and peer support.
J LGBT Youth 2019;16(2):173-191 [doi: 10.1080/19361653.2018.1552225]
53.
Zhao M, Xiao D, Wang W, Wu R, Zhang W, Guo L, et al. Association among maltreatment, bullying and mental health,
risk behavior and sexual attraction in chinese students. Acad Pediatr 2021;21(5):849-857 [doi: 10.1016/j.acap.2020.11.024]
[Medline: 33279736]
54.
Moore SE, Norman RE, Suetani S, Thomas HJ, Sly PD, Scott JG. Consequences of bullying victimization in childhood
and adolescence: a systematic review and meta-analysis. World J Psychiatry 2017;7(1):60-76 [FREE Full text] [doi:
10.5498/wjp.v7.i1.60] [Medline: 28401049]
55.
Kaiser T, Herzog P, Voderholzer U, Brakemeier EL. Unraveling the comorbidity of depression and anxiety in a large
inpatient sample: network analysis to examine bridge symptoms. Depress Anxiety 2021;38(3):307-317 [FREE Full text]
[doi: 10.1002/da.23136] [Medline: 33465284]
56.
Bettis AH, Liu RT. Population-based analysis of temporal trends in the prevalence of depressed mood among sexual minority
and heterosexual youths from 1999 through 2017. JAMA Pediatr 2019;173(12):1201-1203 [FREE Full text] [doi:
10.1001/jamapediatrics.2019.3804] [Medline: 31633768]
57.
Tholander M, Lindberg A, Svensson D. “A freak that no one can love”: difficult knowledge in testimonials on school
bullying. Res Pap 2019;35(3):359-377 [FREE Full text] [doi: 10.1080/02671522.2019.1568534]
58.
Ma S, Yang J, Xu J, Zhang N, Kang L, Wang P, et al. Using network analysis to identify central symptoms of college
students' mental health. J Affect Disord 2022;311:47-54 [FREE Full text] [doi: 10.1016/j.jad.2022.05.065] [Medline:
35588909]
JMIR Public Health Surveill 2023 | vol. 9 | e47233 | p. 11
https://publichealth.jmir.org/2023/1/e47233
(page number not for citation purposes)
Li et al
JMIR PUBLIC HEALTH AND SURVEILLANCE
XSL•FO
RenderX


59.
Källmén H, Hallgren M. Bullying at school and mental health problems among adolescents: a repeated cross-sectional
study. Child Adolesc Psychiatry Ment Health 2021;15(1):74 [FREE Full text] [doi: 10.1186/s13034-021-00425-y] [Medline:
34906157]
60.
Kircanski K, White LK, Tseng WL, Wiggins JL, Frank HR, Sequeira S, et al. A latent variable approach to differentiating
neural mechanisms of irritability and anxiety in youth. JAMA Psychiatry 2018;75(6):631-639 [FREE Full text] [doi:
10.1001/jamapsychiatry.2018.0468] [Medline: 29625429]
61.
Sheldon S, Williams K, Harrington S, Otto AR. Emotional cue effects on accessing and elaborating upon autobiographical
memories. Cognition 2020;198:104217 [FREE Full text] [doi: 10.1016/j.cognition.2020.104217] [Medline: 32044616]
62.
Solomon DT, Combs EM, Allen K, Roles S, DiCarlo S, Reed O, et al. The impact of minority stress and gender identity
on PTSD outcomes in sexual minority survivors of interpersonal trauma. Psychol Sex 2019;12(1-2):64-78 [doi:
10.1080/19419899.2019.1690033]
63.
Evans CBR, Smokowski PR, Rose RA, Mercado MC, Marshall KJ. Cumulative bullying experiences, adolescent behavioral
and mental health, and academic achievement: an integrative model of perpetration, victimization, and bystander behavior.
J Child Fam Stud 2018;27(9):2415 [FREE Full text] [doi: 10.1007/s10826-018-1078-4] [Medline: 30174382]
64.
Braun A, Santesteban-Echarri O, Cadenhead KS, Cornblatt BA, Granholm E, Addington J. Bullying and social functioning,
schemas, and beliefs among youth at clinical high risk for psychosis. Early Interv Psychiatry 2022;16(3):281-288 [doi:
10.1111/eip.13157] [Medline: 33938145]
65.
Day JK, Fish JN, Grossman AH, Russell ST. Gay-straight alliances, inclusive policy, and school climate: LGBTQ youths'
experiences of social support and bullying. J Res Adolesc 2020;30 Suppl 2(Suppl 2):418-430 [FREE Full text] [doi:
10.1111/jora.12487] [Medline: 30861243]
66.
Luk JW, Gilman SE, Haynie DL, Simons-Morton BG. Sexual orientation and depressive symptoms in adolescents. Pediatrics
2018;141(5):e20173309 [FREE Full text] [doi: 10.1542/peds.2017-3309] [Medline: 29661939]
67.
Ke T, De Simoni S, Barker E, Smith P. The association between peer-victimisation and structural and functional brain
outcomes: a systematic review. JCPP Adv 2022;2(2):e12081 [FREE Full text] [doi: 10.1002/jcv2.12081] [Medline: 37431463]
68.
Barnett AP, Molock SD, Nieves-Lugo K, Zea MC. Anti-LGBT victimization, fear of violence at school, and suicide risk
among adolescents. Psychol Sex Orientat Gend Divers 2019;6(1):88-95 [FREE Full text] [doi: 10.1037/sgd0000309]
[Medline: 37383950]
69.
Bjereld Y, Daneback K, Mishna F. Adults’ responses to bullying: the victimized youth’s perspectives. Res Pap Educ
2019;36(3):257-274 [FREE Full text] [doi: 10.1080/02671522.2019.1646793]
70.
Nielsen T. The stress acceleration hypothesis of nightmares. Front Neurol 2017;8:201 [FREE Full text] [doi:
10.3389/fneur.2017.00201] [Medline: 28620339]
71.
Kastner-Dorn AK, Andreatta M, Pauli P, Wieser MJ. Hypervigilance during anxiety and selective attention during fear:
using steady-state visual evoked potentials (ssVEPs) to disentangle attention mechanisms during predictable and unpredictable
threat. Cortex 2018;106:120-131 [FREE Full text] [doi: 10.1016/j.cortex.2018.05.008] [Medline: 29929061]
72.
Puhl RM, Lessard LM. Weight stigma in youth: prevalence, consequences, and considerations for clinical practice. Curr
Obes Rep 2020;9(4):402-411 [doi: 10.1007/s13679-020-00408-8] [Medline: 33079337]
73.
Bucchianeri MM, Gower AL, McMorris BJ, Eisenberg ME. Youth experiences with multiple types of prejudice-based
harassment. J Adolesc 2016;51:68-75 [doi: 10.1016/j.adolescence.2016.05.012] [Medline: 27310725]
74.
Reece LJ, Bissell P, Copeland RJ. 'I just don't want to get bullied anymore, then I can lead a normal life'; insights into life
as an obese adolescent and their views on obesity treatment. Health Expect 2016;19(4):897-907 [FREE Full text] [doi:
10.1111/hex.12385] [Medline: 27403849]
75.
Himmelstein MS, Puhl RM, Watson RJ. Weight-based victimization, eating behaviors, and weight-related health in sexual
and gender minority adolescents. Appetite 2019;141:104321 [FREE Full text] [doi: 10.1016/j.appet.2019.104321] [Medline:
31202917]
76.
Al-Raqqad HK, Al-Bourini ES, Al Talahin FM, Aranki RME. The impact of school bullying on students' academic
achievement from teachers point of view. Int Educ Stud 2017;10(6):44-50 [doi: 10.5539/ies.v10n6p44]
77.
Cao Z, Cini E, Pellegrini D, Fragkos KC. The association between sexual orientation and eating disorders-related eating
behaviours in adolescents: a systematic review and meta-analysis. Eur Eat Disord Rev 2023;31(1):46-64 [FREE Full text]
[doi: 10.1002/erv.2952] [Medline: 36367345]
78.
Yean C, Benau EM, Dakanalis A, Hormes JM, Perone J, Timko CA. The relationship of sex and sexual orientation to
self-esteem, body shape satisfaction, and eating disorder symptomatology. Front Psychol 2013;4:887 [FREE Full text] [doi:
10.3389/fpsyg.2013.00887] [Medline: 24348441]
79.
Cella S, Iannaccone M, Ascione R, Cotrufo P. Body dissatisfaction, abnormal eating behaviours and eating disorder attitude
in homo- and heterosexuals. Eat Weight Disord 2010;15(3):e180-e185 [doi: 10.3275/6866] [Medline: 20160471]
80.
Hepworth R, Mogg K, Brignell C, Bradley BP. Negative mood increases selective attention to food cues and subjective
appetite. Appetite 2010;54(1):134-142 [doi: 10.1016/j.appet.2009.09.019] [Medline: 19815043]
81.
Svaldi J, Werle D, Naumann E, Eichler E, Berking M. Prospective associations of negative mood and emotion regulation
in the occurrence of binge eating in binge eating disorder. J Psychiatr Res 2019;115:61-68 [doi:
10.1016/j.jpsychires.2019.05.005] [Medline: 31121393]
JMIR Public Health Surveill 2023 | vol. 9 | e47233 | p. 12
https://publichealth.jmir.org/2023/1/e47233
(page number not for citation purposes)
Li et al
JMIR PUBLIC HEALTH AND SURVEILLANCE
XSL•FO
RenderX


82.
Doan Van EE, Mereish EH, Woulfe JM, Katz-Wise SL. Perceived discrimination, coping mechanisms, and effects on health
in bisexual and other non-monosexual adults. Arch Sex Behav 2019;48(1):159-174 [FREE Full text] [doi:
10.1007/s10508-018-1254-z] [Medline: 30105618]
83.
Radoman M, Akinbo FD, Rospenda KM, Gorka SM. The impact of startle reactivity to unpredictable threat on the relation
between bullying victimization and internalizing psychopathology. J Psychiatr Res 2019;119:7-13 [FREE Full text] [doi:
10.1016/j.jpsychires.2019.09.004] [Medline: 31520836]
84.
Tseng WL, Deveney CM, Stoddard J, Kircanski K, Frackman AE, Yi JY, et al. Brain mechanisms of attention orienting
following frustration: associations with irritability and age in youths. Am J Psychiatry 2019;176(1):67-76 [FREE Full text]
[doi: 10.1176/appi.ajp.2018.18040491] [Medline: 30336704]
85.
Ebner-Priemer UW, Eid M, Kleindienst N, Stabenow S, Trull TJ. Analytic strategies for understanding affective (in)stability
and other dynamic processes in psychopathology. J Abnorm Psychol 2009;118(1):195-202 [doi: 10.1037/a0014868]
[Medline: 19222325]
86.
Forbes MK, Wright AGC, Markon KE, Krueger RF. Evidence that psychopathology symptom networks have limited
replicability. J Abnorm Psychol 2017;126(7):969-988 [FREE Full text] [doi: 10.1037/abn0000276] [Medline: 29106281]
Abbreviations
bEI: bridge expected influence
CPDAG: completed partially directed acyclic graph
EI: expected influence
GAD: Generalized Anxiety Disorder Scale
PHQ: Patient Health Questionnaire
PTSD: posttraumatic stress disorder
Edited by A Mavragani, T Sanchez; submitted 13.03.23; peer-reviewed by E Fedele, M Scutari; comments to author 17.05.23; revised
version received 06.06.23; accepted 05.09.23; published 01.11.23
Please cite as:
Li J, Jin Y, Xu S, Wilson A, Chen C, Luo X, Liu Y, Ling X, Sun X, Wang Y
Effects of Bullying on Anxiety, Depression, and Posttraumatic Stress Disorder Among Sexual Minority Youths: Network Analysis
JMIR Public Health Surveill 2023;9:e47233
URL: https://publichealth.jmir.org/2023/1/e47233
doi: 10.2196/47233
PMID: 37910159