Network Structure Of Symptoms Research Articles

The network structures of depressive and insomnia symptoms among cancer patients using propensity score matching: Findings from the Health and Retirement Study (HRS)

ObjectiveBoth depression and insomnia are found to be more prevalent in cancer patients compared to the general population. This study compared the network structures of depression and insomnia among cancer patients versus cancer-free participants (controls hereafter). MethodThe 8-item Center for Epidemiological Studies Depression Scale (CESD-8) and the 4-item Jenkins Sleep Scale (JSS-4) were used to measure depressive and insomnia symptoms, respectively. Propensity score matching (PSM) was used to construct the control group using data from the Health and Retirement Study (HRS). In total, a sample consisted of 2216 cancer patients and 2216 controls was constructed. Central (influential) and bridge symptoms were estimated using the expected influence (EI) and bridge expected influence (bridge EI), respectively. Network stability was assessed using the case-dropping bootstrap method. ResultThe prevalence of depression (CESD-8 total score ≥ 4) in cancer patients was significantly higher compared to the control group (28.56 % vs. 24.73 %; P = 0.004). Cancer patients also had more severe depressive symptoms relative to controls, but there was no significant group difference for insomnia symptoms. The network structures of depressive and insomnia symptoms were comparable between cancer patients and controls. “Felt sadness” (EI: 6.866 in cancer patients; EI: 5.861 in controls), “Felt unhappy” (EI: 6.371 in cancer patients; EI: 5.720 in controls) and “Felt depressed” (EI: 6.003 in cancer patients; EI: 5.880 in controls) emerged as the key central symptoms, and “Felt tired in morning” (bridge EI: 1.870 in cancer patients; EI: 1.266 in controls) and “Everything was an effort” (bridge EI: 1.046 in cancer patients; EI: 0.921 in controls) were the key bridge symptoms across both groups. ConclusionAlthough cancer patients had more frequent and severe depressive symptoms compared to controls, no significant difference was observed in the network structure or strength of the depressive and insomnia symptoms. Consequently, psychosocial interventions for treating depression and insomnia in the general population could be equally applicable for cancer patients who experience depression and insomnia.

Mapping prodromal symptoms in patients with bipolar disorder: A network perspective

Bipolar disorder (BD) is a major mental disorder that significantly impairs behavior and social functioning. This study assessed the network structure of prodromal symptoms in patients with BD prior to their index mood episode. Semi-structured interviews were conducted with the Bipolar Prodrome Symptom Scale-Retrospective (BPSS-R) to examine patients’ prodromal symptoms. Network analysis was conducted to elucidate inter-relations between prodromal symptoms. A total of 120 eligible patients participated in this study. Network analysis indicated that the observed model was stable. The edge Mania3-Depression9 (‘Racing thoughts’ - ‘Thinking about suicide’, edge weight = 14.919) showed the strongest positive connection in the model, followed by the edge Mania1-depression1 (‘Extremely energetic/active’ - ‘Depressed mood’, edge weight = 14.643). The only negative correlation in the model was for Mania7-depression2 (‘Overly self-confident’ - ‘Tiredness or lack of energy’, edge weight = -1.068). Nodes Mania3 (‘Racing thoughts’), Depression9 (‘Thinking about suicide’), Mania1 (‘Extremely energetic/active’), and Depression1 (‘Depressed mood’) were the most central symptoms. Both depressive and manic or hypomanic symptoms appeared in the prodromal phase. Symptoms reflecting ‘Racing thoughts’, ‘Thinking about suicide’, ‘Extremely energetic/active’, and ‘Depressed mood’ should be thoroughly assessed and targeted as crucial prodromal symptoms in interventions to reduce the risk of BD episodes.

Network analysis of suicide ideation and depression–anxiety symptoms among Chinese adolescents

BackgroundThe co-occurrence of depression and anxiety among adolescents is typically associated with suicide ideation.AimsThe study aimed to investigate the symptom-level relationship between suicide ideation and the comorbidity of depression and...

Sex differences in symptom network structure of depression, anxiety, and self-efficacy among people with diabetes: a network analysis.

The present study aims to explore the relations between symptoms of depression and anxiety and self-efficacy among people with diabetes. At the same time, we also examined the sex difference between network structures. This study recruited 413 participants with diabetes, and they completed Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire (PHQ-9), and the Self-efficacy for Diabetes (SED). Symptom network analysis and network comparison test were used to construct and compare the depression-anxiety symptom network models of the female and male groups. Finally, we conducted flow diagrams to explore the symptoms directly or indirectly related to self-efficacy. The strongest edges in the depression-anxiety symptom networks are the edge between "GAD3" (Excessive worry) and "GAD4" (Trouble relaxing) and the edge between "PHQ1" (Anhedonia) and "PHQ4" (Energy) in the female and male groups, respectively. Most of the symptoms with the highest EI and bridge EI are related to worry and nervousness. Additionally, in the flow diagram of the female group, "PHQ6" (Guilt) has a high negative association with self-efficacy. Females with diabetes are more vulnerable to depression and anxiety. Interventions targeting key symptoms in the network may be helpful in relieving the psychological problems among people with diabetes.

Depression and anxiety symptom network structure among patients with coronary heart disease and association with quality of life: protocol for a multicentre cross-sectional and prospective longitudinal study

BackgroundAnxiety and depression are critical mental health problems among persons with coronary heart disease (CHD). The range of symptoms is an important stressor for adverse cardiovascular events, and these symptoms...

Network analysis of depression, cognitive functions, and suicidal ideation in patients with diabetes: an epidemiological study in Iran.

The main aim of this study was to assess the prevalence of suicidal ideation and previous suicide attempts among Iranian patients diagnosed with Type-1 diabetes (T1D) and Type-2 diabetes (T2D). Additionally, the study sought to estimate the network structure of depressive symptoms and cognitive functions. 1073 patients participated in the current study. We used Patient Health Questionnaire-9 (PHQ-9), Ask Suicide-Screening Questionnaire, diabetes-related factors, and a battery of cognitive functions tasks to estimate network structures. Also, suicidal ideations and suicide attempts prevalence have been estimated. Statistical analyses were performed using R-studio software, including mixed-graphical models (MGMs) for undirected effects and Directed Acyclic Graphs (DAGs) for directed effects. The prevalence of suicidal ideation was 29.97% in T1D and 26.81% in T2D (p < 0.05). The history of suicide attempts was higher in T1D (10.78%) compared to T2D (8.36%) (p < 0.01). In the MRF networks for T1D, suicidal ideation was directly linked to 'feeling guilt (PHQ.6)', 'Suicide (PHQ.9)', HbA1c, and FBS, while the Inhibition node was directly related to suicidal ideation. The DAGs suggested connections between 'depression', HbA1c, and 'inhibition' with suicidal ideation, along with a link between the current family history of suicide attempts and the patient's history of suicide attempts. For T2D, the MRF networks indicated direct links between suicidal ideation and 'anhedonia (PHQ.1)', 'suicide (PHQ.9)', age, being female, and BMI, with inhibition also being directly related to suicidal ideation. The DAGs revealed connections between 'depression', age, and 'inhibition' with suicidal ideation, as well as links between being female or single/divorced and the patient's history of suicide attempts. The findings suggest that suicide ideation is highly prevalent in patients with diabetes, and these symptoms should be carefully monitored in these patients.

Pandemic Grief and Suicidal Ideation in Latin American Countries: A Network Analysis.

This study aimed to characterize the network structure of pandemic grief symptoms and suicidal ideation in 2174 people from eight Latin American countries. Pandemic grief and suicidal ideation were measured using the Pandemic Grief Scale and a single item, respectively. Network analysis provides an in-depth characterization of symptom-symptom interactions within mental disorders. The results indicated that, "desire to die," "apathy" and "absence of sense of life" are the most central symptoms in a pandemic grief symptom network; therefore, these symptoms could be focal elements for preventive and treatment efforts. Suicidal ideation, the wish to die, and the absence of meaning in life had the strongest relationship. In general, the network structure did not differ among the participating countries. It identifies specific symptoms within the network that may increase the likelihood of their co-occurrence and is useful at the therapeutic level.

Depression, anxiety and suicidality among Chinese mental health professionals immediately after China's dynamic zero-COVID policy: A network perspective

BackgroundUsing network analysis, the interactions between mental health problems at the symptom level can be explored in depth. This study examined the network structure of depressive and anxiety symptoms and suicidality among mental health professionals after the end of China's Dynamic Zero-COVID Policy. MethodsA total of 10,647 mental health professionals were recruited nationwide from January to February 2023. Depression and anxiety were assessed using the 9-item Patient Health Questionnaire (PHQ-9) and 7-item Generalized Anxiety Disorder Scale (GAD-7), respectively, while suicidality was defined by a ‘yes’ response to any of the standard questions regarding suicidal ideation (SI), suicide plan (SP) and suicide attempt (SA). Expected Influence (EI) and Bridge Expected Influence (bEI) were used as centrality indices in the symptom network to characterize the structure of the symptoms. ResultsThe prevalence of depression, anxiety, and suicidality were 45.99 %, 28.40 %, and 7.71 %, respectively. The network analysis identified GAD5 (“Restlessness”) as the most central symptom, followed by PHQ4 (“Fatigue”) and GAD7 (“Feeling afraid”). Additionally, PHQ6 (“Guilt”), GAD5 (“Restlessness”), and PHQ8 (“Motor disturbance”) were bridge nodes linking depressive and anxiety symptoms with suicidality. The flow network indicated that the strongest connections of S (“Suicidality”) was with PHQ6 ("Guilt"), GAD7 (“Feeling afraid”), and PHQ2 ("Sad mood"). ConclusionsDepression, anxiety, and suicidality among mental health professionals were highly prevalent after China's Dynamic Zero-COVID Policy ended. Effective measures should target central and bridge symptoms identified in this network model to address the mental health problems in those at-risk.

Personality traits and depressive symptoms among Chinese older people: A network approach

BackgroundThe present study aims to investigate the network structure of depressive symptoms, the interrelationships between individual personality traits and depressive symptoms, and gender differences among Chinese older people aged 60 and above. MethodWe performed network analyses with a regularized Graphical Gaussian Model and a case-dropping bootstrap approach. A sample of 4876 older Chinese people aged 60+ was included in the analyses. We investigated the central symptoms in the depression network and the bridge nodes that connect personality facets and depressive symptoms. Gender differences were investigated by testing the global strength, network invariance, and edge weights. ResultsSadness and depressed mood were the most central depressive symptoms, while somatic symptoms such as restless sleep were the least central. Neurotic facets, particularly “worry a lot” and “get nervous easily”, played significant bridging roles in the web of personality traits and depressive symptoms. Gender differences were observed in three edges among different personality traits (rude-worried, original-worried, and forgiving-nervous). LimitationThe study adopts a cross-sectional dataset, and therefore, cannot track the network changes over time or conclude a causal relationship. DiscussionThe study calls for more focus and prioritization on sadness, depressed mood and neurotic traits in the identification of depression among older Chinese people. Future researchers and practitioners should better understand of older Chinese adults' worry and nervousness to develop appropriate practices and policies.

The relations of psychotic-like experiences (PLEs) and depressive symptoms and the bias of depressive symptoms during the clustering among Chinese adolescents: Findings from the network perspective

BackgroundThere are rare studies about the network structure of psychotic-like experiences (PLEs) and depressive symptoms among adolescents. Studies have widely acknowledged that PLEs in adolescents confer a higher risk of depressive symptoms, but the complex interactions remain inadequately understood. Our study aimed to examine the hierarchy and inter-associations of PLEs and depressive symptoms in a large adolescent sample from the network analysis perspective. MethodsA total of 5008 Chinese adolescents were enrolled in our sample. Community Assessment of Psychic Experiences-42 (CAPE-42) was applied to build the network. Centrality indexes were calculated to represent the significance of nodes in the network. Community detection was conducted to figure out the specific clustering of nodes. Demographic information was collected for the sub-network comparisons. Accuracy and stability of the network were also tested. Results“Failure”, “External control”, and “Lack of activity” were the most central nodes. The main bridge nodes linking PLEs and depressive symptoms were “Failure”, “Guilty”, and “No future”. Positive PLE “Odd looks” and negative PLE “Unable to terminate” are the two PLEs that were most relevant to depressive nodes. Community detection further demonstrated the bias of depressive nodes in the data-driven clustering. Comparative sub-network analysis suggested that age was the only demographic factor related to the current network. ConclusionIn this study of a large adolescent sample, we first demonstrated the network structure and specific clustering preference of PLEs and depressive symptoms.Our findings may enhance the understanding of the relationship between PLE and depressive symptoms.

Identifying central symptom clusters and correlates in patients with lung cancer post-chemotherapy: A network analysis

ObjectiveThis study aims to investigate the network structures of symptoms and symptom clusters in patients with lung cancer post-chemotherapy, with a focus on identifying the central symptom cluster. Understanding the central cluster is crucial for targeted and effective symptom management. MethodsSymptom occurrence and severity were assessed using the Memorial Symptom Assessment Scale (MSAS). Principal component analysis (PCA) was employed to explore symptom clusters, while network analysis unveiled the network structure and pinpointed the central symptom cluster. ResultsThe study included 512 patients with lung cancer. Four distinct symptom clusters emerged: sickness behavior, psychological, lung cancer-specific, and epithelial. The sickness behavior symptom cluster was identified as the central symptom cluster. ConclusionsThis research designates the sickness behavior symptom cluster as central in post-chemotherapy patients with lung cancer, offering valuable insights for clinical nurses in devising more effective symptom management strategies. Trial registrationChiCTR2300070944 (Chinese Clinical Trial Register).

Network Analysis of Depression Levels in Adolescents with Non-suicidal Self-injury

Adolescence is a critical developmental period characterized by a notable increase in the prevalence of depression and self-harming behaviors. Thus, understanding their interplay is crucial for intervention and prevention strategies. This study uses network analysis to investigate the characteristics of the depressive symptom network in non-suicidal self-injury among adolescents and identify potential intervention targets. Between June 2023 and October 2023, a total of 171 non-suicidal self-injury adolescents were recruited. The Chinese version of the Center for Epidemiologic Studies Depression Scale was used to assess depressive symptoms. Statistical analysis and visualization of the network were conducted using the R software. In order to make quantitative research on the depressive symptom network structure, indicators of closeness, strength, and betweenness were utilized to identify symptoms that play a central role in the net work. Network analysis unveiled a group of central nodes with significant influence, assuming pivotal roles in the network. The symptoms of felt sad, thought life as a failure, could not get going, felt that everything I did was an effort were central to the network. Furthermore, this network exhibited good stability and accuracy of edge weights. The findings from this study not only advance peoples comprehension of the connection between non-suicidal self-injury and depression in adolescents but also provide new insights for future intervention strategies. This research underscores the need for comprehensive approaches to address this intricate issue, offering guidance for precise interventions and clinical practices.

Understanding Comorbidity Between Non-Suicidal Self-Injury and Depressive Symptoms in a Clinical Sample of Adolescents: A Network Analysis.

Non-suicidal self-injury (NSSI) and depression often co-occur among adolescents with more severe clinical symptoms. This study examined the network structures of NSSI and depressive symptoms in adolescents. Participants were recruited in the psychiatric outpatient clinics of three tertiary hospitals between April 10 and July 10, 2023. All participants been already found with self-injury behaviors in outpatient when enrolled. NSSI diagnostic criteria and Patient Health Questionnaire-9 (PHQ-9) were utilized to collect NSSI and depressive symptoms separately. We performed a network analysis to visualize the correlation between each symptom and to identify core and bridging symptoms in comorbidities. A total of 248 patients were enrolled in the study, with a mean age of 15.48 (SD = 1.62). Based on the PHQ-9 scores and grades, our results showed that the incidence of depression in adolescents with non-suicidal self-injury behavior was relatively high (N=235, 94.76%), with the majority having severe depression. The network analysis revealed that nodes D-6 "feeling bad, failing or letting yourself or your family down", D-1 "little interest or pleasure" and D-4 "feeling tired" were the most vital and most central symptoms. The most crucial bridging symptom is the node NSSI-8 "frequent thinking about self-injury", which connects the NSSI to the depression comorbid network. This study offers a significant symptom-level conceptualization of the association between NSSI and depressive symptoms in a clinical sample of adolescents, which not only enhances our understanding of the comorbid but also identifies potential treatment targets to prevent and treat comorbidity between adolescent NSSI and depression.

Are central eating disorder network symptoms sensitive to item selection and sample? Implications for conceptualization of eating disorder psychopathology from a network perspective.

Item selection is a critical decision in modeling psychological networks. The current preregistered two-study research used random selections of 1,000 symptom networks to examine which eating disorder (ED) and co-occurring symptoms are most central in longitudinal networks among individuals with EDs (N = 71, total observations = 6,060) and tested whether centrality changed based on which items were included in the network. Participants completed 2 weeks of ecological momentary assessment (five surveys/day). In Study 1, we obtained initial strength centrality values by estimating an a priori network using eight items with the highest means. We then estimated 1,000 networks and their centrality from a random selection of unique eight-item symptom combinations. We compared the strength centrality from the a priori network to the distribution of strength centrality estimates from the random-item networks. In Study 2, we repeated this procedure in an independent longitudinal dataset (N = 41, total observations = 4,575) to determine if our results generalized across samples. Shame, guilt, worry, and fear of losing control were consistently central across networks, regardless of items included in the network or sample. Results suggest that these symptoms may be important to the structure of ED psychopathology and have implications for how we understand the structure of ED psychopathology. Existing methods for item inclusion in psychological networks may distort the structure of ED symptom networks by either under- or overestimating strength centrality, or by omitting consistently central symptoms that are nontraditional ED symptoms. Future research should consider including these symptoms in models of ED psychopathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Understanding of Depressive Symptomatology across Major Depressive Disorder and Bipolar Disorder: A Network Analysis.

Background and Objectives: Depressive symptoms are prominent in both major depressive disorder (MDD) and bipolar disorder (BD). However, comparative research on the network structure of depressive symptoms in these two diagnostic groups has been limited. This study aims to compare the network structure of depressive symptoms in MDD and BD, providing a deeper understanding of the depressive symptomatology of each disorder. Materials and Methods: The Zung Self-Rating Depressive Scale, a 20-item questionnaire, was administered to assess the depressive symptoms in individuals with MDD (n = 322) and BD (n = 516). A network analysis was conducted using exploratory graph analysis (EGA), and the network structure was analyzed using regularized partial correlation models. To validate the dimensionality of the Zung SDS, principal component analysis (PCA) was adopted. Centrality measures of the depressive symptoms within each group were assessed, followed by a network comparison test between the two groups. Results: In both diagnostic groups, the network analysis revealed four distinct categories, aligning closely with the PCA results. "Depressed affect" emerged as the most central symptom in both MDD and BD. Furthermore, non-core symptoms, "Personal devaluation" in MDD and "Confusion" in BD, displayed strong centrality. The network comparison test did not reveal significant differences in the network structure between MDD and BD. Conclusions: The absence of significant differences in the network structures between MDD and BD suggests that the underlying mechanisms of depressive symptoms may be similar across these disorders. The identified central symptoms, including "Depressed affect", in both disorders and the distinct non-core symptoms in each highlight the complexity of the depressive symptomatology. Future research should focus on validating these symptoms as therapeutic targets and incorporate various methodologies, including non-metric dimension reduction techniques or canonical analysis.

Eating disorders symptoms and depressive symptoms in Chinese Tibetan University students: a network analysis

BackgroundDepression is being increasingly acknowledged as a global public health concern, and following this trend, attention towards eating disorders (EDs) has surged within China’s national consciousness. EDs symptoms frequently coexist with various mental health conditions, including depression. However, research focusing on EDs symptoms and depressive symptoms among Tibetan students in China remains scant. This study employs network analysis to estimate the relational network between EDs and depressive symptoms.MethodsTibetan (n = 2,582) and Han (n = 1,743) students from two universities in the Xizang Autonomous Region, China, completed the Eating Attitude Test-26 (EAT-26) and the Patient Health Questionnaire-9 (PHQ-9). We estimated the network structure of EDs symptoms and depressive symptoms, identified central and bridge symptoms, and examined whether network characteristics differed by gender and ethnic.ResultsThe core symptoms identified within this study were Calorie_awareness, Desire_to_thin and Fatigue. Conversely, bridge symptoms included Appetite, Suicide, Anhedonia, Guilty, Body_fat_awareness, and Food_preoccupation. The study also revealed no significant gender differences within the network model. However, disparities among ethnic groups were observed within the network structure.ConclusionsOur study examined the correlation between EDs symptoms and depressive symptoms in Tibetan college students. Focusing on the individual's quest for the perfect body shape and some Tibetan students' appetite problems – potentially stemming from transitioning to a new university environment, adapting to the school canteen's diet, or being away from their hometown – could aid in the prevention and management of EDs and depression symptoms. It could reduce the incidence of complications by helping students maintain good physical and mental health. Concurrently, our research provides insights into the relatively higher levels of depression triggered by the unique plateau environment.

Prevalence and network structure of post-traumatic stress symptoms and their association with suicidality among Chinese mental health professionals immediately following the end of China’s Dynamic Zero-COVID Policy: a national survey

Studies on post-traumatic stress symptoms (PTSS) among mental health professionals (MHPs) are limited, particularly since restrictions due to coronavirus disease (COVID-19) have been lifted such as the recent termination of China’s Dynamic Zero-COVID Policy. The current study filled this gap by exploring the prevalence, correlates, and network structure of PTSS as well as its association with suicidality from a network analysis perspective. A cross-sectional, national survey was conducted using a convenience sampling method on MHPs between January 22 and February 10, 2023. PTSS were assessed using the Post-Traumatic Stress Disorder Checklist-Civilian version, while suicidality was assessed using standardized questions related to ideation, plans, and attempts. Univariate and multivariate analyses examined correlates of PTSS. Network analysis explored the structure of PTSS and suicidality. The centrality index of “Expected influence” was used to identify the most central symptoms in the network, reflecting the relative importance of each node in the network. The “flow” function was adopted to identify specific symptoms that were directly associated with suicidality. A total of 10,647 MHPs were included. The overall rates of PTSS and suicidality were 6.7% (n = 715; 95% CI = 6.2–7.2%) and 7.7% (n = 821; 95% CI = 7.2–8.2%), respectively. Being married (OR = 1.523; P < 0.001), quarantine experience (OR = 1.288; P < 0.001), suicidality (OR = 3.750; P < 0.001) and more severe depressive symptoms (OR = 1.229; P < 0.001) were correlates of more PTSS. Additionally, higher economic status (e.g., good vs. poor: OR = 0.324; P = 0.001) and health status (e.g., good vs. poor: OR = 0.456; P < 0.001) were correlates of reduced PTSS. PCL6 (“Avoiding thoughts”; EI = 1.189), PCL7 (“Avoiding reminders”; EI = 1.157), and PCL11 (“Feeling emotionally numb”; EI = 1.074) had the highest centrality, while PCL12 (“Negative belief”), PCL 16 (“Hypervigilance”) and PCL 14 (“Irritability”) had the strongest direct, positive associations with suicidality. A high prevalence of lingering PTSS was found among MHPs immediately after China’s “Dynamic Zero-COVID Policy” was terminated. Avoidance and hyper-arousal symptoms should be monitored among at-risk MHPs after the COVID-19 pandemic and serve as potential targets for the prevention and treatment of PTSS in this population.

A Network Analysis Study to Evaluate Obsessive-Compulsive Beliefs/Dimensions and Personality Beliefs in Patients with Obsessive-Compulsive Disorder (OCD): A Cross-Sectional Study in Two Common OCD Subtypes.

Objective: Obsessive-Compulsive Disorder (OCD) has been considered a heterogeneous psychiatric disorder. Identifying this heterogeneity can lead to better diagnosis and treatment. The two most common OCD subtypes include contamination/cleaning and danger/checking. This study aimed to identify the network structure of OCD symptoms, personality, and obsessive beliefs in these OCD subtypes. Method : 333 patients with OCD were included in the study (156 patients with the danger/check subtype and 177 patients with the contamination/cleaning subtype). In addition, 276 healthy individuals participated in this study. We used a network analysis approach to determine the OCD central symptoms [nodes]. The Personality Beliefs Questionnaire-Short Form [PBQ-SF], Obsessive-Compulsive Inventory-Revised [OCI-R], and Obsessional Beliefs Questionnaire-44 [OBQ-44] were used for network estimation. Results: The results indicated that the network structure in healthy individuals was different from the network structure in patients with obsessive-compulsive disorder [PwOCD]. In healthy individuals, "obsessive" personality belief and "danger" obsessive belief were the most strength nodes and formed the core of the central communication between symptoms in these subjects. Regarding the contamination/cleaning subtype, the most central symptoms include "washing" obsessive symptom, "danger" obsessive belief, and "wash" obsessive symptom. However, "perfect" obsessive beliefs, "check" obsessive symptoms, and "avoidant" personality beliefs were the most central symptoms in the danger/check subtype. These results demonstrated that each of the studied groups had a unique network structure, which is consistent with the heterogeneous nature of OCD. Conclusion: Different OCD subtypes have different cognitive-behavioral network structures. According to the results of this study, these symptoms should be given priority in OCD theoretical models and treatment strategies.

Subjective dry eye symptoms and associated factors among the national general population in China during the COVID-19 pandemic: A network analysis.

The coronavirus disease 2019 (COVID-19) pandemic has presented numerous challenges to public health, including the emergence of subjective dry eye symptoms among individuals. However, there is currently a dearth of comprehensive knowledge regarding the associated factors with subjective dry eye symptoms in the general population. A nationwide survey in China was carried out from 20 June to 31 August 2022, involving 21 916 participants. Subjective dry eye symptoms were assessed using the Ocular Surface Disease Index-6. We employed random forest classification to select potential associated factors based on the socioecological model. We also conducted univariate and multivariable logistic regression analyses to explore the factors associated with subjective dry eye symptoms. Network analyses were employed to explore the network structure of subjective dry eye symptoms and associated factors. The item "watching TV (or similar task)" displayed the highest node strength and exhibited the highest predictability within the network. The results of the multivariate logistic regression analysis demonstrated significant associations between subjective dry eye symptoms and several factors, including loneliness (odds ratio (OR) = 1.29; 95% confidence interval (CI) = 1.26 to 1.32), problematic Internet use (OR = 1.08; 95% CI = 1.08 to 1.09), family communication (OR = 1.01; 95% CI = 1.00 to 1.01), the presence of depression (OR = 1.53; 95% CI = 1.39 to 1.68) and anxiety (OR = 1.31; 95% CI = 1.17 to 1.47) symptoms, older age (OR = 1.01; 95% CI = 1.01 to 1.01), self-rated health status (OR = 0.99; 95% CI = 0.98 to 0.99), family health (OR = 0.97; 95% CI = 0.96 to 0.97), health literacy (OR = 0.98; 95% CI = 0.97 to 0.99) and the agreeableness personality trait (OR = 0.97; 95% CI = 0.95 to 0.99). These findings have important implications for public health interventions targeting the prevention and management of subjective dry eye symptoms in the general population. Strategies addressing individual risk factors and supporting psychological well-being may prove beneficial in mitigating the burden associated with subjective dry eye symptoms.

Clinical and psychological implications of post-traumatic stress in firefighters: a moderated network study

ABSTRACT Firefighters are frequently exposed to trauma and may experience a unique symptom presentation of post-traumatic stress. Prior research has identified stronger associations between certain post-traumatic stress symptoms (e.g. detachment, intrusions, physiological reactivity) using network analysis. However, little is known about the effects of symptom severity and emergency work-related trauma on symptom networks. The present study probed the network structure of post-traumatic stress symptoms in trauma-exposed firefighters (N = 871) to model the dynamic interactions of psychological symptoms. We developed a network of post-traumatic stress symptoms and a network of post-traumatic stress with clinical covariates and used moderated network modelling to assess the effects of having PTSD and experiencing work-related trauma on the networks. We identified high edge correlations between several nodes (e.g. startle/hypervigilance, internal/external cue avoidance, detachment/lack of interest) and high centrality of detachment, external cue avoidance, and flashbacks. Additionally, having PTSD moderated positive network associations between risk-taking and suicidality and between distorted blame and post-traumatic cognitions. Work-related trauma moderated negative associations between appetite gain and loss and appetite loss and suicidality. Findings suggest that targeting specific symptoms of detachment, external cue avoidance, and flashbacks could allow for the development of effective trauma-informed interventions for these populations.