Central Index Research Articles

A dynamic evaluation of an underground transportation system using image processing and centrality index computation

ABSTRACT The study of transportation networks covers a wide range of topics related to managing services and maintaining structures. In order to represent the topological structure of such a network, we associated a graph to a network. Then some different properties or attributes of the network can be managed as weights to be applied to the graph itself. In addition, different centrality indices can be computed in order to identify nodes or edges that are more important or more exposed to capacity changes (such as, for example, disruptive events). The purpose of this research is to provide a methodology for dynamically evaluating the significance of stations in a transportation network. This is accomplished through two distinct phases of activity. Firstly, we propose a dynamic analysis of the underground transportation system of the city of Milan, Italy. This transportation system is the mobility backbone of the city and counts four lines with 110 links and 107 stations, including 21 junctions (the transfer stations that connect the lines). Two data sets about passenger flows (both entering and leaving stations) are used to calculate the flows on links with a resolution of 1 min. The data sets refer to a week in 2018, without the changes in demand due to the pandemic scenario. Data are processed through an ad hoc written assignment procedure. Secondly, a centrality index is calculated by using passenger flows (entering, exiting, and on-segments) as weights of the underground graph. After maps reporting the outcomes of those calculations are drawn, an image comparison is carried out by using image processing tools and different aggregation intervals, in order to investigate how the importance or exposure of each station changes over time. The findings demonstrate that over time, indices vary by station, with junction stations naturally having the highest values. By increasing the observation interval from 1 min up to 30 min, index changes become progressively smoother, like an application of a low pass filter, suggesting that for certain applications (e.g. those concerning security), aggregating data could lead to misleading conclusions.

Multilateral collaborative governance of Arctic shipping safety: Examining the impact of the Arctic Council via collaborative network analysis

Global climate change has dramatically reduced Arctic sea ice, opening new potential shipping routes, which has garnered international attention. This study explores the role of the Arctic Council in the collaborative governance network focused on Arctic shipping safety. For this purpose, a collection of 49 collaborative events initiated by international organisations and the Arctic Council was used as the data sample. These events are divided into two groups, one containing all the events and the other excluding those initiated by the Arctic Council. As a result, two collaborative networks are developed and evaluated based on network density, average path length, network transitivity, centrality indices (degree, closeness, betweenness), and clustering analysis. The results demonstrate that the Arctic Council significantly enhances the network density and transitivity of the international collaboration network, and reshapes core-periphery dynamics and cohesive subgroup structures. For instance, under the influence of the Arctic Council, Canada, France, and Japan surpass the United Kingdom in centrality (degree, closeness, betweenness). Results of the clustering coefficients reveal the Arctic Council's impact on connections among states, leading to shifts in collaboration patterns. Additionally, subgroup cohesion and density coefficients confirm the Arctic Council's role in promoting collaboration among states and restructuring governance networks.

Links between Vaccination Fear-, Anxiety-, Alexithymia-, and Type D Personality-Related Vaccination Decisions: A Network Analysis in a Multicultural Sample.

This study examines the links between vaccination status, fear of vaccination (cognitive and somatic symptoms), anxiety, alexithymia, and type D personality (negative affect and social inhibition), to propose policies to increase vaccination rates. A sample of university students (n = 2535; mean age = 20.59, SD = 2.04; male: 26.75%, female: 73.25%) from Spain, Italy, Lebanon, Nigeria, Turkey, and Ukraine completed the Vaccination Fear Scale (VFS-6), the Generalised Anxiety Disorder scale (GAD-7), the Perth Alexithymia Questionnaire-Short Form (PAQ-S), the Type D Scale (DS14), and also a question on vaccination status. Correlation, regression, and network analyses were applied. Cognitive symptoms of fear of vaccination and negative affect were the most significant in the correlation and regression analyses. In the network analysis, negative affect showed the highest values in all centrality indices and positive relationships with other nodes. Vaccination status showed negative relationships with fear of vaccination, alexithymia, and social inhibition. The network structure is similar between the sexes but varies between cultures and sexes within cultures. The relationship between vaccination status and cognitive symptoms of fear of vaccination was the most consistent, allowing for interventions at this level to be advised across cultures. For more specific interventions, cultural context must be considered for optimal results.

A latent profile analysis and network analysis of anxiety and depression symptoms in Chinese widowed elderly

BackgroundWidowhood, as a traumatic event in the aging process, may lead to adverse psychological consequences such as anxiety and depression. However, the heterogeneity of anxiety and depression comorbidity patterns in widowed elderly and the interrelationships between symptoms have not been adequately studied. Method10,239 elderly aged 65 years and older were screened from the Chinese Longitudinal Healthy Longevity Survey (CLHLS 2017–2018), to assess depression and anxiety using the Center for Epidemiologic Studies Depression Scale (CESD) and the 7-item Generalized Anxiety Disorder Questionnaire (GAD). The subgroups of widowed elderly with similar patterns of symptoms were identified by latent profile analysis (LPA). The structure of anxiety-depressive comorbidity network was characterized using “bridge expected influence” as centrality indices. Network stability was tested using a case drop bootstrap program. A network comparison test (NCT) was performed to examine the differences in network characteristics across LPA subgroups. ResultLPA identified dichotomous profiles: low comorbid (n = 4457) and high comorbid (n = 692). NCT revealed a significant difference in the global strength between networks (S = 0.631, p < 0.001). GAD1 (Nervousness or anxiety) is the common bridging symptom for both networks, while the bridging symptom for the high comorbidity network also includes GAD3 (Generalized worry). LimitationsCross-sectional methods are unable to verify causal relationships, and further randomized controlled trials are warranted. ConclusionAnxiety-depressive pattern in Chinese widowed elderly can be categorized into a low comorbid or a high comorbid group. GAD3 (Generalized worry) can be used as the core intervention target during intervention.

ASSESSMENT OF PORT CITY SERVICE CENTER ALIGNMENT IN THE TERRAIN

Medan, in North Sumatra, is growing rapidly, especially in the sub-districts of Medan Marelan and Medan Belawan, where urban activities and population movement are increasing. As a result, North Medan has been designated as the City Service Centre (CSC) of Medan City, with CSC Labuhan in Medan Labuhan sub-district playing a major role. This highlights the importance of Medan Labuhan in providing essential services and infrastructure to support the growth of the area. In order to effectively serve the community, it is important to ensure that CSC Labuhan meets the area's needs and standards for a city service center. Aims: The objective of this research is twofold: first, to determine the status of the service center system in North Medan; and second, to evaluate the suitability of CSC Labuhan's functions in serving the North Medan region. Methodology and results: The research method is descriptive analysis using both quantitative data and qualitative information. This is achieved through the use of scalogram analysis, centrality index analysis and rank-size rule analysis. Conclusion, significance and implication: The results indicate that the optimal locations for urban service centers are in Sub-District Besar (Medan Labuhan subdistrict), Subdistrict Rengas Pulau (Medan Marelan subdistrict), and Subdistrict Belawan I (Medan Belawan subdistrict). Conversely, Medan City RTRW 2022-2042 designates Labuhan CSC as Subdistrict Pekan Labuhan.

Arm symptom pattern among breast cancer survivors with and without lymphedema: a contemporaneous network analysis.

Arm symptoms commonly endure in post-breast cancer period and persist into long-term survivorship. However, a knowledge gap existed regarding the interactions among these symptoms. This study aimed to construct symptom networks and visualize the interrelationships among arm symptoms in breast cancer survivors (BCS) both with and without lymphedema (LE). We conducted a secondary analysis of 3 cross-sectional studies. All participants underwent arm circumference measurements and symptom assessment. We analyzed 17 symptoms with a prevalence >15%, identifying clusters and covariates through exploratory factor and linear regression analysis. Contemporaneous networks were constructed with centrality indices calculated. Network comparison tests were performed. 1116 cases without missing data were analyzed, revealing a 29.84% prevalence of LE. Axillary lymph node dissection [ALND] (vs sentinel lymph node biopsy [SLNB]), longer post-surgery duration, and radiotherapy significantly impacted overall symptom severity (P < .001). "Lymphatic Stasis," "Nerve Injury," and "Movement Limitation" symptom clusters were identified. Core symptoms varied: tightness for total sample network, firmness for non-LE network, and tightness for LE network. LE survivors reported more prevalent and severe arm symptoms with stronger network connections than non-LE group (P = .010). No significant differences were observed among different subgroups of covariates (P > .05). Network structures were significantly different between ALND and SLNB groups. Our study revealed arm symptoms pattern and interrelationships in BCS. Targeting core symptoms in assessment and intervention might be efficient for arm symptoms management. Future research is warranted to construct dynamic symptom networks in longitudinal data and investigate causal relationships among symptoms.

Understanding the Associations between Executive Function and Psychological Variables in Fibromyalgia Syndrome: A Network Analysis Approach.

The aim of this study was to quantify the multivariate relationships between clinical, cognitive performance, executive functioning, and psychological outcomes in women with fibromyalgia (FMS) using network analyses. Demographic (age, height, weight), clinical (pain history, pain intensity, and related disability), neurocognitive (D2 Attention test, Rey-Osterrieth Complex Figure for visual perception, "Digits D/R/I" tests of the WAIS-IV battery for working memory, the 5-Digit Test for mental inhibition, the Symbol Search for processing speed and the Zoo Test for planning/decision making) and psychological (depressive symptoms, anxiety levels, sleep quality, pain hypervigilance) variables were collected in 129 women with FMS and 111 healthy women. Network analyses were conducted separately for each group to quantify the adjusted correlations between the modeled variables and to assess their centrality indices (i.e., connectivity with other symptoms in the network and their importance in the network). The network identified 74 associations in FMS women and 46 associations in controls with small differences. The strongest correlations in both groups were found between different attention variables: d2_CON with d2_C, d2_O with d_2TR, and d2_CON with d2_TA. The most central variables were d2_TA, d2_C, and d2_CON (highest strength centrality in both groups) and anxiety levels and pain hypervigilance (highest harmonic centrality in FMS women). The strength centrality of the network was stable for women with FMS (CScor0.7: 0.68) but not for healthy women (CScor0.7: 0.28). This study found that attention variables are most relevant within a neurocognitive network and that psychological variables are most important for the treatment of women with FMS. The clinical implications of the current findings, such as the development of treatments targeting these variables, are discussed.

Contemporaneous symptom networks analysis in lymphoma patients during chemotherapy: protocol for a single-centre prospective cross-sectional study

BackgroundSymptom networks offer a theoretical basis for developing personalised and precise symptom management strategies. However, symptom networks in lymphoma patients during chemotherapy have been rarely reported. This study intends to...

Unraveling the core and bridge menopausal symptoms of perimenopausal women: a network analysis.

Perimenopause is the period from the early menopausal transition to 12 months after the final menstrual period. The clustering of menopausal symptoms poses a challenge for perimenopausal management. Core symptoms are targets for interventions that can alleviate other related symptoms. Bridge symptoms are connectors that link related symptom clusters and can improve the effectiveness of interventions. This study aims to construct a network structure of menopausal symptoms and to identify core and bridge symptoms as a reference for future management. Two hundred forty-two Chinese perimenopausal women were included in the survey. The structure and associations of the menopausal symptoms assessed by the Greene Climacteric Scale were analyzed using a network analysis. We generated the network structure graph using R software and checked its accuracy and stability. In the menopausal transition, the most prevalent symptoms were feeling tired or lacking in energy, excitability, and irritability. Sexual dysfunction was common among early postmenopausal women. Irritability (S = 7.16, C = 0.0167, B = 8) was a core symptom of the network. The depressive symptom cluster was a core symptom cluster, most of which have high centrality indices. Excitability (B = 6) was a bridge symptom connecting the anxiety and depressive symptom clusters. Our study has highlighted the crucial significance of irritability and excitability in perimenopausal management. Overcoming the challenges of perimenopausal management requires the public to ameliorate the prejudice and stigma associated with emotional symptoms.

Node Centrality Based on Edge Dynamics in a Chaotic Network

This paper introduces a novel node centrality index based on edge dynamics in a complex network: adjacent edge index. Through the analysis of network dynamics and topology, we provide a neat index to qualify node importance, which is calculated by the eigenvector components corresponding to the Fiedler value of the network. Notably, it identifies nodes whose adjacent edge sets play a significant role in network synchronization. The index indicates that not only a node’s degree but also its location should be considered. To demonstrate the effectiveness of the proposed index, consider a double-star network and a Jazz Musicians network in which chaotic Chua’s circuits as nodes are coupled. By factoring in the dynamic information exchange between nodes and their neighbors, this index offers valuable insights for optimizing strategies in engineering areas such as efficient information dissemination.

Evaluation of robustness in underground networks

Since the development of urban public mass transport in cities, transport infrastructures are a milestone for the economies of the cities and those of their respective regions. The construction of underground systems has seen impressive growth in many transport networks in the past few years, becoming the hinge of mobility in many of the principal cities of the world. Due to the latter and because of the possibility of disruptive events (natural disasters, random failures, and intentional attacks), many scholars and governments have worried about how robust the networks are. For this reason, in this paper we reviewed the robustness of networks by using graph theory tools to analyze and measure them through a new centrality index, called Sum of Weighted Distances Index (SWDI). The index measures the percentage of changes in traveled distances in the event that the node and all edges containing it are disrupted. To evaluate the robustness of a network, we compare the outcomes of SWDI at a standard working configuration and at a degraded one, modeled as a reduction of performance at a certain edge, edge by edge. SWDI is able to show at what edges and nodes of the network the degradation will have the highest impact. Summing up the normalized SWDI outcomes for all the nodes and edges, the final evaluation of the robustness of the network is obtained. Applications to the two case studies of Boston and Barcelona are developed, compared, and discussed, limited to their topology.

Pathways to experienced coercion during psychiatric admission: a network analysis

BackgroundIn mental health care, experienced coercion, also known as perceived coercion, is defined as the patient’s subjective experience of being submitted to coercion. Besides formal coercion, many other factors have been identified as potentially affecting the experience of being coerced. This study aimed to explore the interplay between these factors and to provide new insights into how they lead to experienced coercion.MethodsCross-sectional network analysis was performed on data collected from 225 patients admitted to six psychiatric hospitals. Thirteen variables were selected and included in the analyses. A Gaussian Graphical Model (GGM) using Spearman’s rank-correlation method and EBICglasso regularisation was estimated. Centrality indices of strength and expected influence were computed. To evaluate the robustness of the estimated parameters, both edge-weight accuracy and centrality stability were investigated.ResultsThe estimated network was densely connected. Formal coercion was only weakly associated with both experienced coercion at admission and during hospital stay. Experienced coercion at admission was most strongly associated with the patients’ perceived level of implication in the decision-making process. Experienced humiliation and coercion during hospital stay, the most central node in the network, was found to be most strongly related to the interpersonal separation that patients perceived from staff, the level of coercion perceived upon admission and their satisfaction with the decision taken and the level of information received.ConclusionsReducing formal coercion may not be sufficient to effectively reduce patients’ feeling of being coerced. Different factors seemed indeed to come into play and affect experienced coercion at different stages of the hospitalisation process. Interventions aimed at reducing experienced coercion and its negative effects should take these stage-specific elements into account and propose tailored strategies to address them.

Structural parameters are superior to eigenvector centrality in detecting progressive supranuclear palsy with machine learning & multimodal MRI

Progressive supranuclear palsy (PSP) is an atypical Parkinsonian syndrome characterized initially by falls and eye movement impairment. This multimodal imaging study aimed at eliciting structural and functional disease-specific brain alterations. T1-weighted and resting-state functional MRI were applied in multi-centric cohorts of PSP and matched healthy controls. Midbrain, cerebellum, and cerebellar peduncles showed severely low gray/white matter volume, whereas thinner cortical gray matter was observed in cingulate cortex, medial and temporal gyri, and insula. Eigenvector centrality analyses revealed regionally specific alterations. Multivariate pattern recognition classified patients correctly based on gray and white matter segmentations with up to 98 % accuracy. Highest accuracies were obtained when restricting feature selection to the midbrain. Eigenvector centrality indices yielded an accuracy around 70 % in this comparison; however, this result did not reach significance. In sum, the study reveals multimodal, widespread brain changes in addition to the well-known midbrain atrophy in PSP. Alterations in brain structure seem to be superior to eigenvector centrality parameters, in particular for prediction with machine learning approaches.

Psychopathology symptoms of frontline nurses under sudden public health crisis: A network analysis.

Public health crises can significantly impact the emotional well-being of healthcare workers. Network analysis is a novel approach to exploring interactions between mental disorders at the symptom level. This study aimed to elucidate the characteristics of post-traumatic stress disorder (PTSD), anxiety, depression, and insomnia symptoms network among frontline nurses under sudden public health crisis. A cross-sectional survey was conducted online among 556 frontline nurses through convenience sampling in Hubei Province, China, from 21 February 2020, to 10 March 2020. Symptoms of PTSD, anxiety, depression, and insomnia were assessed by the Post-Traumatic Stress Disorder Checklist (PCL-5), Generalized Anxiety Disorder scale (GAD-7), Patient Health Questionnaire (PHQ-9) and Insomnia Severity Index, respectively. Central symptoms (the most important symptoms, activation has the strongest influence the other nodes) and bridge symptoms (nodes where deactivation can prevent activation from spreading from one disorder to another) were identified via centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. We found that the correlation between PHQ-9 item 9 'suicidal thoughts' and PCL-5 item 16 'reckless or self-destructive behaviour' was the strongest. Moreover, 'reckless or self-destructive behaviour' was the strongest central symptom, and PHQ-9 item 3 'sleep problems' was the most important bridge symptom. Other major symptoms included GAD-7 item 6 'uncontrollable anxiety' and PHQ-9 item 2 'depressed or sad mood'. Timely, systemic targeting interventions on central symptoms and bridge symptoms may effectively alleviate co-occurring experiences of psychopathological symptoms among frontline nurses.

Identifying critical nodes in interdependent networks by GA-XGBoost

Once a critical node is destroyed, the interdependent network is prone to experience severe cascading failure. Due to the coupling, traditional methods are challenging to apply to interdependent networks. Here, we propose a novel comprehensive model based on machine learning. The main work of this data-driven approach is to train the model on a small set of nodes (5 % of the graph) and do the critical node identification on the rest. We collect node centrality indicators to describe the node features and provide informative input data from different dimensions. The uniform node sampling is improved to cluster oversampling, which combines K-means and Synthetic Minority Over-sampling Technique (SMOTE) to select and recreate uniformly distributed training samples. We optimize the XGBoost based on the Genetic algorithm (GA) to overcome the instability of manual parameters. Kendall's τ correlation coefficient, Jaccard similarity coefficient, R2, and RMSE are used as the model performance evaluation metrics. Experiment results confirm that the proposed GA-XGBoost model outperforms others, demonstrating higher adaptability and stability in various situations. The heuristic algorithm-optimized machine learning model offers a viable solution for identifying critical nodes in interdependent networks, which is of great significance for controlling virus propagation and preventing failures.

Spatial food webs in the Barents Sea: atlantification and the reorganization of the trophic structure.

Climate change affects ecosystems at several levels: by altering the spatial distribution of individual species, by locally rewiring interspecific interactions, and by reorganizing trophic networks at larger scales. The dynamics of marine food webs are becoming more and more sensitive to spatial processes and connections in the seascape. As a case study, we study the atlantification of the Barents Sea: we compare spatio-temporal subsystems at three levels: the identity of key organisms, critically important interactions and the entire food web. Network analysis offers quantitative measurements, including centrality indices, trophic similarity indices, a topological measure of interaction asymmetry and network-level measures. We found that atlantification alters the identity of key species (boreal demersals becoming hubs), results in strongly asymmetric interactions (dominated by haddock), changes the dominant regulation regime (from bottom-up to wasp-waist control) and makes the food web less modular. Since the results of food web analysis may be quite sensitive to network construction, the aggregation of food web data was explicitly studied to increase the robustness of food web analysis. We found that an alternative, mathematical aggregation algorithm better preserves some network properties (e.g. density) of the original, unaggregated network than the biologically inspired aggregation into functional groups. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Understanding shoppers’ cross-channel analysis of influencing factors of online and offline channels: Evidence from clothing product

As the online to offline (O2O) retail market develops rapidly, most consumers become O2O shoppers who shop online and offline. However, researchers seldom focus on O2O retailers collaborating online and offline channels to better satisfy consumers' needs. To fill the gap, this study explores the neglected problem from the shoppers’ cross-channel analysis perspective. In this paper, we used text data mining to capture shoppers' online comments on Suning.com, where we selected T-shirts, skirts and trousers as products to be analyzed. After determining the influencing factors, we used social network analysis to construct the two relation networks based on the semantic set of shoppers' comments. This study reveals the relations between the influencing factors of online and offline channels and finds shoppers' cross-channel analysis behavior characteristics by analyzing the density and centrality indicators of the rebuilt relation network. The research results demonstrate that the impacts of the influencing factors on shoppers' purchase intentions are different. Offline product experience, online product comments, online product price, and offline product price significantly influence and control their purchase intention more than the other factors when shoppers cross-channel analyze the influencing factors. Moreover, this study uses the Shapley model to quantitatively analyze the impact of influencing factors on shoppers' purchase intention by building collaborative alliances. The Shapley values confirm that shoppers attach the highest importance to offline product experience, followed by online product comments, online product price and so on. The findings can improve O2O retailers' collaborative marketing strategies.

Identification of the Core Characteristics of Vulnerable/Hypersensitive Narcissism and its Association with the Dark Triad in a Large International Sample: A Network Analysis Study.

The current paper aimed to investigate the network structure and centrality indexes of hypersensitive narcissism using the hypersensitive Narcissism Scale (HSN). Additionally, we aimed to explore its relationships with dark triad personality aspects. A globally diverse sample of "53,981" participants (47.9% non-United States responders) completed the HSN and Dark Triad Dirty Dozen scale (DTD). We estimated the network structure across genders to determine the core characteristics of hypersensitive narcissism. Additionally, bridge and central nodes (characteristics) were identified. All analyses were performed using R-Studio programming software. The network comparison test indicated significant differences in the network structures between males and females (Network-Invariance: 0.0489, P < 0.01; Global Strength Invariance: 0.101, P < 0.01). In the network of HSN for male participants, characteristics with the highest strength centrality were "Highly affected by criticism" (HSN.2, strength = 1.08) and "Self-absorbed in personal pursuits" (HSN.8, strength = 1.28). For female participants, "Self-absorbed in personal pursuits" (HSN.8, strength = 1.32) and "privately annoyed by others' needs" (HSN.10, strength = 1.21) were the highest central characteristics. The assessment of bridge strength indicated that nodes HSN.2 (Highly sensitive to criticism), scoring 0.42, and DTD.1 (Tendency to manipulate for gain, a component of Machiavellianism), scoring 0.428, showed the highest bridge strength values. The current study identified core characteristics of hypersensitive narcissism and its correlation with dark triad personality, revealing gender-specific patterns and bridging symptoms between the two constructs. These findings showed that focusing on these core characteristics may be advantageous in treating individuals exhibiting elevated levels of narcissism.

Evidence-based practice in traditional persian medicine (TPM): a stakeholder and social network analysis

BackgroundThe utilization of complementary and alternative medicine (CAM) is experiencing a global surge, accompanied by the adoption of national CAM policies in numerous countries. Traditional Persian medicine (TPM) is highly used as CAM in Iran, and the ongoing scientific evaluation of its interventions and the implementation of evidence-based medicine (EBM) encounters various barriers. Therefore, comprehending the characteristics and interactions of stakeholders is pivotal in advancing EBM within TPM policies. In this study, we utilized both classical stakeholder analysis and social network analysis to identify key stakeholders and potential communication patterns, thereby promoting EBM in TPM policy-making.MethodsA cross-sectional nationwide stakeholder analysis was conducted in 2023 using snowball sampling. The interviews were carried out using a customized version of the six building blocks of health. Data were collected through semi-structured interviews. Stakeholders were assessed based on five factors (power, interest, influence, position, and competency). The connections and structure of the network were analyzed using degree, betweenness, closeness centrality, and modularity index to detect clusters of smaller networks.ResultsAmong twenty-three identified stakeholders, the Ministry of Health and Medical Education (MOHME) and the Public were the most powerful and influential. The Iranian Academy of Medical Sciences was the most competent stakeholder. Social network analysis revealed a low density of connections among stakeholders. Pharmaceutical companies were identified as key connectors in the network, while the Public, supreme governmental bodies, and guilds acted as gatekeepers or brokers. The MOHME and Maraji were found to be high-ranking stakeholders based on four different centrality measures.ConclusionThis study identifies powerful stakeholders in the network and emphasizes the need to engage uninterested yet significant stakeholders. Recommendations include improving competence through education, strengthening international relations, and fostering stronger relationships. Engaging key connectors and gatekeepers is essential for bridging gaps in the network.

A network analysis of the interrelationships between depression, anxiety, insomnia and quality of life among fire service recruits.

Research on the mental health and quality of life (hereafter QOL) among fire service recruits after the end of the COVID-19 restrictions is lacking. This study explored the network structure of depression, anxiety and insomnia, and their interconnections with QOL among fire service recruits in the post-COVID-19 era. This cross-sectional study used a consecutive sampling of fire service recruits across China. We measured the severity of depression, anxiety and insomnia symptoms, and overall QOL using the nine-item Patient Health Questionnaire (PHQ-9), seven-item Generalized Anxiety Disorder scale (GAD-7), Insomnia Severity Index (ISI) questionnaire, and World Health Organization Quality of Life-brief version (WHOQOL-BREF), respectively. We estimated the most central symptoms using the centrality index of expected influence (EI), and the symptoms connecting depression, anxiety and insomnia symptoms using bridge EI. In total, 1,560 fire service recruits participated in the study. The prevalence of depression (PHQ-9 ≥ 5) was 15.2% (95% CI: 13.5-17.1%), while the prevalence of anxiety (GAD-7 ≥ 5) was 11.2% (95% CI: 9.6-12.8%). GAD4 ("Trouble relaxing") had the highest EI in the whole network model, followed by ISI5 ("Interference with daytime functioning") and GAD6 ("Irritability"). In contrast, PHQ4 ("Fatigue") had the highest bridge EI values in the network, followed by GAD4 ("Trouble relaxing") and ISI5 ("Interference with daytime functioning"). Additionally, ISI4 "Sleep dissatisfaction" (average edge weight = -1.335), which was the central symptom with the highest intensity value, had the strongest negative correlation with QOL. Depression and anxiety were important mental health issues to address among fire service recruits in the post-COVID-19 era in China. Targeting central and bridge symptoms identified in network analysis could help address depression and anxiety among fire service recruits in the post-COVID-19 era.