Frequency and network analysis of depressive symptoms in patients with cancer compared to the general population

Mapping of the network connection between sleep quality symptoms, depression, generalized anxiety, and burnout in the general population of Peru and El Salvador

BACKGROUND

A meta-analysis of randomized controlled trials has suggested a bidirectional relationship between sleep problems and mental health issues. Despite these findings, there is limited conclusive evidence on the relationship between sleep quality, depression, anxiety, and burnout.

OBJECTIVE

The current study aimed to evaluate the relationships between sleep quality symptoms, anxiety, depression, and burnout in samples of adult individuals from two Latin American countries, Peru and El Salvador, through network analysis and to identify key symptoms that reinforce the correlation and intensify the syndromes.

METHODS

A total of 1012 individuals from El Salvador and Peru participated, with an average age of 26.5 years (SD = 9.1). Symptom networks were constructed for both countries based on data from the Jenkins Sleep Scale, Patient Health Questionnaire-2, General Anxiety Disorder-2, and a single burnout item.

RESULTS

The results indicated that Depressed Mood, Difficulty Falling Asleep, and Nervousness were the most central symptoms in a network in the participating countries. The strongest conditional associations were found between symptoms belonging to the same construct, which were similar in both countries. Thus, there is a relationship between Nervousness and Uncontrollable Worry, Anhedonia and Depressed Mood, and Nighttime Awakenings and Difficulty in Staying Asleep. It was observed that burnout is a bridge symptom between both countries and presents stronger conditional associations with Tiredness on Awakening, Depressed Mood, and Uncontrollable Worry. Other bridge symptoms include a Depressed Mood and Nervousness. The network structure did not differ between the participants from Peru and El Salvador.

CONCLUSION

The networks formed by sleep quality, anxiety, depression, and burnout symptoms play a prominent role in the comorbidity of mental health problems among the general populations of Peru and El Salvador. The symptom-based analytical approach highlights the different diagnostic weights of these symptoms. Treatments or interventions should focus on identifying central and bridge symptoms.

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

OBJECTIVE

Both 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).

METHOD

The 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 consisting 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.

RESULT

The 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.

CONCLUSION

Although 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.

Emotional Distress Symptoms, Coping Efficacy, and Social Support: A Network Analysis of Distress and Resources in Persons With Cancer

BACKGROUND

The study's main aim was to analyze the structure and configuration of distress symptoms and resource factors.

PURPOSE

Common methods of assessing distress symptoms in cancer patients (i) do not capture the configuration of individual distress symptoms and (ii) do not take into account resource factors (e.g., social support, coping, caring health professionals). Network analysis focuses on the configuration and relationships among symptoms that can result in tailored interventions for distress. Network analysis was used to derive a symptom-level view of distress and resource factors.

METHODS

Nine hundred and ninety-two cancer patients (mixed diagnoses) completed an abridged Distress Screening Schedule that included 24 items describing symptoms related to distress (depression, anxiety) and resource factors (social support, coping, caring health professionals).

RESULTS

In network analysis, the centrality strength index (CSI) is the degree to which an item is connected to all other items, thus constituting an important focal point in the network. A depression symptom had the highest CSI value: felt lonely/isolated (CSI = 1.30). In addition, resource factors related to coping efficacy (CSI = 1.20), actively seeking support (CSI = 1.10), perceiving one's doctor as caring (CSI = 1.10), and receiving social support (CSI = 1.10) also all had very high CSI scores.

CONCLUSIONS AND IMPLICATIONS

These results emphasize the integral importance of the social symptoms of loneliness/isolation in distress. Thus, distress symptoms (loneliness) and resource factors (coping efficacy, seeking social support, and perceiving medical professionals as caring) should be integral aspects of distress management and incorporated into assessment tools and interventions to reduce distress.

Symptom networks in glioma patients: understanding the multidimensionality of symptoms and quality of life

PURPOSE

To comprehend the complex relationship between symptoms and health-related quality of life (HRQoL) in patients with diffuse glioma, we applied symptom network analysis to identify patterns of associations between depression, cognition, brain tumor-related symptoms, and HRQoL. Additionally, we aimed to compare global strength between symptom networks to understand if symptoms are more tightly connected in different subgroups of patients.

METHODS

We included 256 patients and stratified the sample based on disease status (preoperative vs. postoperative), tumor grade (grade II vs. III/IV), and fatigue status (non-fatigued vs. fatigued). For each subgroup of patients, we constructed a symptom network. In these six networks, each node represented a validated subscale of a questionnaire and an edge represented a partial correlation between two nodes. We statistically compared global strength between networks.

RESULTS

Across the six networks, nodes were highly correlated: fatigue severity, depression, and social functioning in particular. We found no differences in GS between the networks based on disease characteristics. However, global strength was lower in the non-fatigued network compared to the fatigued network (5.51 vs. 7.49, p < 0.001).

CONCLUSIONS

Symptoms and HRQoL are highly interrelated in patients with glioma. Interestingly, nodes in the network of fatigued patients were more tightly connected compared to non-fatigued patients.

IMPLICATIONS FOR CANCER SURVIVORS

We introduce symptom networks as a method to understand the multidimensionality of symptoms in glioma. We find a clear association between multiple symptoms and HRQoL, which underlines the need for integrative symptom management targeting fatigue in particular.

How do men and women differ in their depressive symptomatology? A gendered network analysis of depressive symptoms in a French population-based cohort

BACKGROUND

The experience of depressive manifestations and the presentation of symptoms in clinical settings may differ in men and women. Despite the extensive literature, it remains unclear how depressive manifestations interact at symptom levels in men and women. First, we aimed to describe and compare depressive networks by sex. Second, we examined symptom connections to Clinical depression and Functional Limitations as a proxy of self-recognition of a depressive episode.

METHODS

We estimated networks from the 20 CES-D items in men and women from a large population-based French cohort. We computed centrality measures and ran comparisons. Then, we re-estimated two networks in men and women separately, adding, on the one hand, Clinical Depression and, on the other hand, Limitations due to a depressive episode.

RESULTS

Over 200,000 participants were included in this study. Women were twice as likely to have a previous diagnosis of depression. Sex-ratio was less pronounced (1,7:1) for Limitations due to depression. Centrality measures revealed similar symptom patterns. However, network structures differed between men and women. We found some symptom connections to Clinical depression and Limitations to be non-invariant according to sex.

LIMITATIONS

Cross-sectional data does not capture the direction of the connections between symptoms and an eventual diagnosis. We lacked data about the diagnosis's context and could not account for other factors influencing depressive symptomatology.

CONCLUSIONS

Network structures differed, suggesting gender-specific mechanisms in activating symptoms and depressive states. Addressing central symptoms evoking depressed moods with tailored interventions may serve to tackle depressive states in men and women.

Looking at individual symptoms: the dynamic network structure of depressive symptoms in cancer survivors and their preferences for psychological care

PURPOSE

The majority of depressed cancer survivors do not receive psychological care, possibly because offered care does not align with their experiences and preferences. We examined (1) which depressive symptoms cancer survivors would like to receive psychological care for; (2) how distinct depressive symptoms are related to each other in the contemporaneous and temporal network of depressive symptoms; and (3) whether survivors' care needs correspond to the interconnectedness of these specific symptoms.

METHOD

Fifty-two cancer survivors suffering from at least mild depressive symptoms and were not receiving psychological care filled out a baseline questionnaire about their care needs for distinct depressive symptoms, followed by ecological momentary assessments (EMA) assessing depressive symptoms (14 days, five times a day). Multi-level vector autoregression analysis was used to estimate associations between distinct depressive symptoms as well as their centrality within the network.

RESULTS

Cancer survivors most strongly preferred to receive care for fatigue, feeling down, little enjoyment, and sleep problems. Fatigue, together with worry and lack of concentration, most strongly predicted the onset of other symptoms. Little enjoyment and feeling down were two of the most central symptoms (i.e., strongly connected to other symptoms) in the contemporaneous network and were most strongly influenced by other symptoms in the temporal network.

CONCLUSIONS

Clinicians can offer specific interventions that target fatigue, as these played an important role in the onset of symptoms and would align with survivors' needs.

IMPLICATIONS FOR CANCER SURVIVORS

Offering such symptom-specific care may increase the uptake of psychological interventions in cancer survivors.

Network analysis used to investigate the interplay among somatic and psychological symptoms in patients with cancer and cancer survivors: a scoping review

PURPOSE

Patients with cancer often experience multiple somatic and psychological symptoms. Somatic and psychological symptoms are thought to be connected and may reinforce each other. Network analysis allows examination of the interconnectedness of individual symptoms. The aim of this scoping review was to examine the current state of knowledge about the associations between somatic and psychological symptoms in patients with cancer and cancer survivors, based on network analysis.

METHODS

This scoping review followed the five-stage framework of Arksey and O'Malley. The literature search was conducted in May, 2023 in PubMed, APA PsycINFO, Embase Cochrane central, and CINAHL databases.

RESULTS

Thirty-two studies were included, with eleven using longitudinal data. Seventeen studies reported on the strength of the associations: somatic and psychological symptoms were associated, although associations among somatic as well as among psychological symptoms were stronger. Other findings were the association between somatic and psychological symptoms was stronger in patients experiencing more severe symptoms; associations between symptoms over time remained rather stable; and different symptoms were central in the networks, with fatigue being among the most central in half of the studies.

IMPLICATIONS FOR CANCER SURVIVORS

Although the associations among somatic symptoms and among psychological symptoms were stronger, somatic and psychological symptoms were associated, especially in patients experiencing more severe symptoms. Fatigue was among the most central symptoms, bridging the somatic and psychological domain. These findings as well as future research based on network analysis may help to untangle the complex interplay of somatic and psychological symptoms in patients with cancer.

Centrality measures in psychological networks: A simulation study on identifying effective treatment targets

The network theory of psychopathology suggests that symptoms in a disorder form a network and that identifying central symptoms within this network might be important for an effective and personalized treatment. However, recent evidence has been inconclusive. We analyzed contemporaneous idiographic networks of depression and anxiety symptoms. Two approaches were compared: a cascade-based attack where symptoms were deactivated in decreasing centrality order, and a normal attack where symptoms were deactivated based on original centrality estimates. Results showed that centrality measures significantly affected the attack's magnitude, particularly the number of components and average path length in both normal and cascade attacks. Degree centrality consistently had the highest impact on the network properties. This study emphasizes the importance of considering centrality measures when identifying treatment targets in psychological networks. Further research is needed to better understand the causal relationships and predictive capabilities of centrality measures in personalized treatments for mental disorders.

Screening for depression in the Serbian general population sample: an alternative to the traditional patient health questionnaire-9 cut-off score

BACKGROUND

The Patient Health Questionnaire (PHQ-9) score ≥ 10 balances best sensitivity and specificity when detecting probable depression in patients. In the general population, different cut-offs are suggested. European studies on general populations validating the PHQ-9 against a diagnostic interview to detect depression are rare.

METHODS

This was a cross-sectional observational epidemiological survey using multistage household probabilistic sampling to recruit a representative adult sample (N = 1203; age = 43.7 ± 13.6; 48.7% male). Mental disorders including current major depressive episode (MDE) were observer-rated (Mini International Neuropsychiatric Interview). The PHQ-9, quality of life (QoL), and loneliness were self-assessed. We performed validity and reliability tests of the PHQ-9 and receiver operating curve (ROC) analysis.

RESULTS

The Serbian PHQ-9 was internally consistent and correlated in the expected directions with QoL and loneliness. At the cut-off score ≥ 8, sensitivity was .85 and specificity was .91. ROC analysis showed that the area under the curve was .95, indicating that the Serbian PHQ-9 can discriminate very well between persons with/without MDE.

CONCLUSIONS

When the PHQ-9 is assessed against the structured diagnostic interview in the general population to detect depression, the cut-off of ≥8 balances best sensitivity and specificity.

Network analysis of frontal lobe alpha asymmetry confirms the neurophysiological basis of four subtypes of depressive behavior

Introduction

Although depression is widespread carries a major disease burden, current treatments remain non-universally effective, arguably due to the heterogeneity of depression, and leading to the consideration of depressive "subtypes" or "depressive behavior subtypes." One such model of depressive behavior (DB) subtypes was investigated for its associations with frontal lobe asymmetry (FLA), using a different data analytic procedure than in previous research in this field.

Methods

100 community volunteers (54 males, 46 females) aged between 18 yr. and 75 years (M = 32.53 yr., SD = 14.13 yr) completed the Zung Self-rating Depression Scale (SDS) and underwent 15 min of eyes closed EEG resting data collection across 10 frontal lobe sites. DB subtypes were defined on the basis of previous research using the SDS, and alpha-wave (8-13 Hz) data produced an index of FLA. Data were examined via network analysis.

Results

Several network analyses were conducted, producing two models of the association between DB subtypes and FLA, confirming unique neurophysiological profiles for each of the four DB subtypes.

Discussion

As well as providing a firm basis for using these DB subtypes in clinical settings, these findings provide a reasonable explanation for the inconsistency in previous FLA-depression research.

Prevalence of depression and its association with quality of life among guardians of hospitalized psychiatric patients during the COVID-19 pandemic: a network perspective

Background

The COVID-19 pandemic has greatly affected treatment-seeking behaviors of psychiatric patients and their guardians. Barriers to access of mental health services may contribute to adverse mental health consequences, not only for psychiatric patients, but also for their guardians. This study explored the prevalence of depression and its association with quality of life among guardians of hospitalized psychiatric patients during the COVID-19 pandemic.

Methods

This multi-center, cross-sectional study was conducted in China. Symptoms of depression and anxiety, fatigue level and quality of life (QOL) of guardians were measured with validated Chinese versions of the Patient Health Questionnaire - 9 (PHQ-9), Generalized Anxiety Disorder Scale - 7 (GAD-7), fatigue numeric rating scale (FNRS), and the first two items of the World Health Organization Quality of Life Questionnaire - brief version (WHOQOL-BREF), respectively. Independent correlates of depression were evaluated using multiple logistic regression analysis. Analysis of covariance (ANCOVA) was used to compare global QOL of depressed versus non-depressed guardians. The network structure of depressive symptoms among guardians was constructed using an extended Bayesian Information Criterion (EBIC) model.

Results

The prevalence of depression among guardians of hospitalized psychiatric patients was 32.4% (95% CI: 29.7-35.2%). GAD-7 total scores (OR = 1.9, 95% CI: 1.8-2.1) and fatigue (OR = 1.2, 95% CI: 1.1-1.4) were positively correlated with depression among guardians. After controlling for significant correlates of depression, depressed guardians had lower QOL than non-depressed peers did [F_{(1, 1,101)} = 29.24, p < 0.001]. "Loss of energy" (item 4 of the PHQ-9), "concentration difficulties" (item 7 of the PHQ-9) and "sad mood" (item 2 of the PHQ-9) were the most central symptoms in the network model of depression for guardians.

Conclusion

About one third of guardians of hospitalized psychiatric patients reported depression during the COVID-19 pandemic. Poorer QOL was related to having depression in this sample. In light of their emergence as key central symptoms, "loss of energy," "concentration problems," and "sad mood" are potentially useful targets for mental health services designed to support caregivers of psychiatric patients.

Network analysis of depression in prostate cancer patients: Implications for assessment and treatment

OBJECTIVES

Many prostate cancer patients also suffer from depression, which can decrease their life satisfaction and also impede recovery from their cancer. This study described the network structure of depressive symptomatology in prostate cancer patients, with a view to providing suggestions for clinical interventions for depressed patients.

METHODS

Using a cross-sectional design, 555 prostate cancer patients completed the Patient Health Questionnaire-9 (PHQ-9).

RESULTS

Network analysis and multidimensional scaling indicated that anhedonia was the most central symptom for these men, and that several sets of depression symptoms were closely associated with each other. These included anhedonia-depressed mood; sleeping problems-fatigue/lethargy; and suicidal ideation-low self-worth-depressed mood. Other depression symptoms such as appetite problems, concentration problems, and motor problems, were less well-related with the remainder of the network. Patients receiving treatment for reocurring prostate cancer (PCa) had significantly higher PHQ9 scores than patients undergoing their initial treatment, but no major differences in their network structures. Implications for clinical practice were derived from the relationships between individual depression symptoms and the overall depression network by examining node predictability.

CONCLUSIONS

The use of total depression scores on an inventory does not reflect the underlying network structure of depression in PCa patients. Identification and treatment of the central symptom of anhedonia in PCa patients suggests the need to adopt specific therapies that are focussed upon this symptom.

The facets of psychopathology in patients with cancer: Cross-sectional and longitudinal network analyses

OBJECTIVE

Cancer patients display heterogeneous psychopathology, comprising depressive, anxiety, hostility, and somatic symptoms. Often, clinical pictures evolve over time deteriorating the individual functioning and prognosis. Network models can reveal the relationships between symptoms, thus providing clinical insights.

METHOD

This study examined data of the Brief Symptom Inventory and the Distress Thermometer, from 1108 cancer outpatients. Gaussian Graphical Models were estimated using regularized and non-regularized Bayesian methods. In addition, we used community detection methods to identify the most relevant symptom groupings, and longitudinal network analyses on 515 participants to examine the connections between symptoms over three months.

RESULTS

The network models derived from baseline data suggested symptoms clustered into three main complexes (depression/anxiety, hostility, and somatic symptoms). Symptoms related to depression and hostility were highly connected with suicidal and death thoughts. Faintness, weakness, chest pain, and dyspnoea, among somatic symptoms, were more strongly connected with psychopathological features. Longitudinal analyses revealed that sadness, irritability, nervousness, and tension predicted each other. Panic and death thoughts predicted fearfulness and faintness.

CONCLUSIONS

Somatic symptoms, sadness, irritability, chronic and acute anxiety interact between each other, shaping the heterogeneous clinical picture of distress in cancer. This study, strengthened by robust methods, is the first to employ longitudinal network analyses in cancer patients. Further studies should evaluate whether targeting specific symptoms might prevent the onset of chronic distress and improve clinical outcomes in cancer patients.

Depression and quality of life among Macau residents in the 2022 COVID-19 pandemic wave from the perspective of network analysis

Background

In the summer of 2022, Macau experienced a surge of COVID-19 infections (the 618 COVID-19 wave), which had serious effects on mental health and quality of life (QoL). However, there is scant research on mental health problems and QoL among Macau residents during the 618 COVID-19 wave. This study examined the network structure of depressive symptoms (hereafter depression), and the interconnection between different depressive symptoms and QoL among Macau residents during this period.

Method

A cross-sectional study was conducted between 26th July and 9th September 2022. Depressive symptoms were measured with the 9-item Patient Health Questionnaire (PHQ-9), while the global QoL was measured with the two items of the World Health Organization Quality of Life-brief version (WHOQOL-BREF). Correlates of depression were explored using univariate and multivariate analyses. The association between depression and QoL was investigated using analysis of covariance (ANCOVA). Network analysis was used to evaluate the structure of depression. The centrality index "Expected Influence" (EI) was used to identify the most central symptoms and the flow function was used to identify depressive symptoms that had a direct bearing on QoL.

Results

A total 1,008 participants were included in this study. The overall prevalence of depression was 62.5% (n = 630; 95% CI = 60.00-65.00%). Having depression was significantly associated with younger age (OR = 0.970; p < 0.001), anxiety (OR = 1.515; p < 0.001), fatigue (OR = 1.338; p < 0.001), and economic loss (OR = 1.933; p = 0.026). Participants with depression had lower QoL F (1, 1,008) =5.538, p = 0.019). The most central symptoms included PHQ2 ("Sad Mood") (EI: 1.044), PHQ4 ("Fatigue") (EI: 1.016), and PHQ6 ("Guilt") (EI: 0.975) in the depression network model, while PHQ4 ("Fatigue"), PHQ9 ("Suicide"), and PHQ6 ("Guilt") had strong negative associations with QoL.

Conclusion

Depression was common among Macao residents during the 618 COVID-19 wave. Given the negative impact of depression on QoL, interventions targeting central symptoms identified in the network model (e.g., cognitive behavioral therapy) should be developed and implemented for Macau residents with depression.

Network Structure of Comorbidity Patterns in U.S. Adults with Depression: A National Study Based on Data from the Behavioral Risk Factor Surveillance System

Background

People with depression are at increased risk for comorbidities; however, the clustering of comorbidity patterns in these patients is still unclear.

Objective

The aim of the study was to identify latent comorbidity patterns and explore the comorbidity network structure that included 12 chronic conditions in adults diagnosed with depressive disorder.

Methods

A cross-sectional study was conducted based on secondary data from the 2017 behavioral risk factor surveillance system (BRFSS) covering all 50 American states. A sample of 89,209 U.S. participants, 29,079 men and 60,063 women aged 18 years or older, was considered using exploratory graphical analysis (EGA), a statistical graphical model that includes algorithms for grouping and factoring variables in a multivariate system of network relationships.

Results

The EGA findings show that the network presents 3 latent comorbidity patterns, i.e., that comorbidities are grouped into 3 factors. The first group was composed of 7 comorbidities (obesity, cancer, high blood pressure, high blood cholesterol, arthritis, kidney disease, and diabetes). The second pattern of latent comorbidity included the diagnosis of asthma and respiratory diseases. The last factor grouped 3 conditions (heart attack, coronary heart disease, and stroke). Hypertension reported higher measures of network centrality.

Conclusion

Associations between chronic conditions were reported; furthermore, they were grouped into 3 latent dimensions of comorbidity and reported network factor loadings. The implementation of care and treatment guidelines and protocols for patients with depressive symptomatology and multimorbidity is suggested.

Associations between depressive symptoms and quality of life among residents of Wuhan, China during the later stage of the COVID-19 pandemic: A network analysis

BACKGROUND

Various populations have experienced significant increases in depression and decreased quality of life (QOL) during the coronavirus disease 2019 (COVID-19) pandemic. This network analysis study was designed to elucidate interconnections between particular depressive symptoms and different aspects of QOL and identify the most clinically important symptoms in this network among adults in Wuhan China, the initial epicenter of the COVID-19 pandemic.

METHODS

This cross-sectional, convenience-sampling study (N = 2459) was conducted between May 25 to June 18, 2020, after the lockdown policy had been lifted in Wuhan. Depressive symptoms and QOL were measured with the Patient Health Questionnaire-9 (PHQ-9) and first two items of the World Health Organization Quality of Life Questionnaire - brief version (WHOQOL-BREF), respectively. A network structure was constructed from the extended Bayesian Information Criterion (EBIC) model. Network centrality strength and bridge strength were evaluated along with the stability of the derived network model.

RESULTS

Loss of energy (DEP-4) and Guilt feelings (DEP-6) were the two central symptoms with the highest strength as well as the two most prominent bridge symptoms connecting the clusters of depression and quality of life (QOL) in tandem with the two nodes from the QOL cluster. Network structure and bridge strengths remained stable after randomly dropping 75 % of the sample.

CONCLUSION

Interventions targeting "Loss of energy" and "Guilt feelings" should be evaluated as strategies for reducing depressive symptoms and promoting improved QOL in COVID-19-affected populations.

Symptom Structure of Depression in Older Adults on the Qinghai-Tibet Plateau: A Network Analysis

Previous studies have confirmed that depression among residents in high-altitude areas is more severe, and that depression may be more persistent and disabling in older adults. This study aims to identify the symptom structure of depression among older adults on the Qinghai-Tibet Plateau (the highest plateau in the world) from a network perspective. This cross-sectional study enrolled 507 older adults (ages 60-80 years old) from the Yushu Prefecture, which is on the Qinghai-Tibet Plateau, China. Depressive symptoms were self-reported using the shortened Center for Epidemiological Studies-Depression Scale (CES-D-10). Then, a Gaussian graphical model (GGM) of depression was developed. Poor sleep, fear, and hopelessness about the future exhibited high centrality in the network. The strongest edge connections emerged between unhappiness and hopelessness about the future, followed by hopelessness about the future and fear; hopelessness about the future and poor sleep; fear and unhappiness; and then poor sleep and unhappiness in the network. The findings of this current study add to the small body of literature on the network structure and complex relationships between depressive symptoms in older adults in high-altitude areas.

The backbone symptoms of depression: a network analysis after the initial wave of the COVID-19 pandemic in Macao

Background

The coronavirus disease 2019 (COVID-19) pandemic disrupted the working lives of Macau residents, possibly leading to mental health issues such as depression. The pandemic served as the context for this investigation of the network structure of depressive symptoms in a community sample. This study aimed to identify the backbone symptoms of depression and to propose an intervention target.

Methods

This study recruited a convenience sample of 975 Macao residents between 20th August and 9th November 2020. In an electronic survey, depressive symptoms were assessed with the Patient Health Questionnaire-9 (PHQ-9). Symptom relationships and centrality indices were identified using directed and undirected network estimation methods. The undirected network was constructed using the extended Bayesian information criterion (EBIC) model, and the directed network was constructed using the Triangulated Maximally Filtered Graph (TMFG) method. The stability of the centrality indices was evaluated by a case-dropping bootstrap procedure. Wilcoxon signed rank tests of the centrality indices were used to assess whether the network structure was invariant between age and gender groups.

Results

Loss of energy, psychomotor problems, and guilt feelings were the symptoms with the highest centrality indices, indicating that these three symptoms were backbone symptoms of depression. The directed graph showed that loss of energy had the highest number of outward projections to other symptoms. The network structure remained stable after randomly dropping 50% of the study sample, and the network structure was invariant by age and gender groups.

Conclusion

Loss of energy, psychomotor problems and guilt feelings constituted the three backbone symptoms during the pandemic. Based on centrality and relative influence, loss of energy could be targeted by increasing opportunities for physical activity.

Application of Network Analysis to Uncover Variables Contributing to Functional Recovery after Stroke

To estimate network structures to discover the interrelationships among variables and distinguish the difference between networks. Three hundred and forty-eight stroke patients were enrolled in this retrospective study. A network analysis was used to investigate the association between those variables. A Network Comparison Test was performed to compare the correlation of variables between networks. Three hundred and twenty-five connections were identified, and 22 of these differed significantly between the high- and low-Functional Independence Measurement (FIM) groups. In the high-FIM network structure, brain-derived neurotrophic factor (BDNF) and length of stay (LOS) had associations with other nodes. However, there was no association with BDNF and LOS in the low-FIM network. In addition, the use of amantadine was associated with shorter LOS and lower FIM motor subscores in the high-FIM network, but there was no such connection in the low-FIM network. Centrality indices revealed that amantadine use had high centrality with others in the high-FIM network but not the low-FIM network. Coronary artery disease (CAD) had high centrality in the low-FIM network structure but not the high-FIM network. Network analysis revealed a new correlation of variables associated with stroke recovery. This approach might be a promising method to facilitate the discovery of novel factors important for stroke recovery.

Fear of Recurrence in Young Adult Cancer Patients—A Network Analysis

Simple Summary

Fear of cancer recurrence is a main concern for the majority of cancer patients during their disease. Young adults with cancer may experience fear of recurrence throughout their lives, given their relatively long potential survival time. More research is needed to identify evidence-based interventions that can adequately address this fear. Investigating the underlying mechanisms that trigger and sustain fear of cancer recurrence is an important step toward this goal. Network analysis is a useful tool to study symptoms and their structural relationships. The aim of this study is to apply the network analysis approach in a sample of young cancer patients to comprehend their specific symptomatology and define the optimal structure of a questionnaire to assess fear of recurrence in this patient group. Future studies may seek to replicate our findings among different age group samples to identify network structures and potential targets for clinical intervention.

Abstract

Due to the high survival rates of many young cancer patients and a high risk of second tumors, fear of cancer recurrence (FCR) can cause serious impairment for adolescent and young adult (AYA) cancer patients. The aim of this study is to analyze the structure of the Fear of Disease Progression Questionnaire (FoP-Q-12) to better understand the construct of FCR. We performed a cross-sectional survey on a sample of AYA patients aged 15–39 years with different tumor entities. FCR was measured using the FoP-Q-12, and a network analysis was conducted to examine the relationship of FCR symptoms. The importance of individual items in the network was determined using centrality analyses. A total of 247 AYA patients (81.8% female, median age 31.0 years) participated in the study. The mean FCR score in the sample was 35.9 (SD = 9.9). The majority of patients reported having high FCR (59.5%), according to the established cut-off. The two questionnaire items with the strongest association related to fears about work, and the most central symptom was the fear of serious medical interventions. The centrality of emotional issues in the sample indicates that these symptoms should be prioritized in the development of interventions targeting FCR. Further research should address this topic with larger samples of patients in other age groups and in longitudinal studies.

The network structure of depressive symptomatology in Peruvian adults with arterial hypertension

Background: Globally, arterial hypertension (AH) has increased by 90% over the last four decades, and has increased by 1.6% in Peru over the previous four years. Scientific evidence indicates the prevalence of depressive symptoms in patients with AH and its importance in the comprehensive evaluation of the adult for adherence to clinical treatment. Previous studies carried out in the Peruvian population with AH mostly report the prevalence and associations, but do not indicate which depressive symptoms are more relevant in patients with AH. This study involved a network analysis of depressive symptomatology in Peruvian patients with AH using network estimation. Network analysis is used in this study for analysis, control, and monitoring purposes.

Method: A representative cross-sectional study at the national level, using secondary data from 2019 Demographic and Family Health Survey (ENDES) was performed. The sample used included men and women of age over 17 years diagnosed with AH and was able to respond to Patient Health Questionnaire-9 (PHQ-9).

Results: The symptoms of depressive mood (bridging force and centrality) and energy fatigue or loss (bridge centrality) play an essential role in the network structure, as does the feeling of uselessness in terms of closeness and intermediation.

Conclusion: The study highlighted the symptoms related to depressive mood and energy fatigue or loss as bridging symptoms, which could trigger a depressive episode in patients diagnosed with AH. The results will contribute to developing personalized treatments aimed at patients with specific depressive symptoms who have also been diagnosed with AH. The study analysis presents statistical coefficients of effect size (≤ 0,1 = small; > 0,1 to < 0,5 = moderate; ≥ 0,5 = large) to determine network connections.

Network Analysis of the Brief ICF Core Set for Schizophrenia

BACKGROUND

The International Classification of Functioning, Disability, and Health Core Sets (ICF-CSs) for schizophrenia are a set of categories for assessing functioning in persons with this health condition. This study aimed to: a) estimate the network structure of the Brief ICF-CS for schizophrenia, b) examine the community structure (categories strongly clustered together) underlying this network, and c) identify the most central categories within this network.

METHODS

A total of 638 health professionals from different backgrounds and with a significant role in the treatment of individuals with schizophrenia participated in a series of Delphi studies. Based on their responses we used the Ising model to estimate the network structure of the 25-category Brief ICF-CS, and then estimated the degree of centrality for all categories. Finally, the community structure was detected using the walktrap algorithm.

RESULTS

The resulting network revealed strong associations between individual categories within components of the ICF (i.e., Body functions, Activities and participation, and Environmental factors). The results also showed three distinct clusters of categories corresponding to the same three components. The categories e410 Individual attitudes of immediate family members, e450 Individual attitudes of health professionals, d910 Community life, and d175 Solving problems were among the most central categories in the Brief ICF-CS network.

CONCLUSION

These results demonstrate the utility of a network approach for estimating the structure of the ICF-CSs. Implications of these results for clinical interventions and development of new instruments are discussed.

Examining the impact of the COVID-19 pandemic through the lens of the network approach to psychopathology: Analysis of the Brazilian Longitudinal Study of Health (ELSA-Brasil) cohort over a 12-year timespan

Cohort studies have displayed mixed findings on changes in mental symptoms severity in 2020, when the COVID-19 pandemic outbreak started. Network approaches can provide additional insights by analyzing the connectivity of such symptoms. We assessed the network structure of mental symptoms in the Brazilian Longitudinal Study of Health (ELSA-Brasil) in 3 waves: 2008-2010, 2017-2019, and 2020, and hypothesized that the 2020 network would present connectivity changes. We used the Clinical Interview Scheduled-Revised (CIS-R) questionnaire to evaluates the severity of 14 common mental symptoms. Networks were graphed using unregularized Gaussian models and compared using centrality and connectivity measures. The predictive power of centrality measures and individual symptoms were also estimated. Among 2011 participants (mean age: 62.1 years, 58% females), the pandemic symptom 2020 network displayed higher overall connectivity, especially among symptoms that were related to general worries, with increased local connectivity between general worries and worries about health, as well as between anxiety and phobia symptoms. There was no difference between 2008 and 2010 and 2017-2019 networks. According to the network theory of mental disorders, external factors could explain why the network structure became more densely connected in 2020 compared to previous observations. We speculate that the COVID-19 pandemic and its innumerous social, economical, and political consequences were prominent external factors driving such changes; although further assessments are warranted.

Network Analysis of Depressive Symptomatology in Underweight and Obese Adults

BACKGROUND

Depressive symptoms can affect people's quality of life and social environment. In addition, in severe situations, they can lead to suicidal behaviors.

OBJECTIVE

This study aimed to analyze the differences in depressive symptoms in underweight and obese Peruvian adults.

METHODS

A cross-sectional study was carried out based on secondary data obtained from the Instituto Nacional de Estadística e Informática (INEI), Lima, Peru. A sample of 10 053 participants was considered, of which 55.96% were women. Two Gaussian plot models were estimated and the levels of depressive symptomatology were compared between the 2 groups (adults with underweight and obese).

RESULTS

A total of 1510 (15.02%) were underweight adults and 8543 (84.98%) were obese adults. There were differences in the reporting of depressive symptoms in the underweight group; the most central items were "Depressed mood" (PH2), "Tiredness/low energy" (PH4), and "Psychomotor difficulties" (PH8).

CONCLUSION

This study provides new evidence on the dynamic relationship between depressive symptoms according to the body mass index categories (underweight and obese) assessed.

Network analysis of depressive symptoms in Hong Kong residents during the COVID-19 pandemic

In network theory depression is conceptualized as a complex network of individual symptoms that influence each other, and central symptoms in the network have the greatest impact on other symptoms. Clinical features of depression are largely determined by sociocultural context. No previous study examined the network structure of depressive symptoms in Hong Kong residents. The aim of this study was to characterize the depressive symptom network structure in a community adult sample in Hong Kong during the COVID-19 pandemic. A total of 11,072 participants were recruited between 24 March and 20 April 2020. Depressive symptoms were measured using the Patient Health Questionnaire-9. The network structure of depressive symptoms was characterized, and indices of "strength", "betweenness", and "closeness" were used to identify symptoms central to the network. Network stability was examined using a case-dropping bootstrap procedure. Guilt, Sad Mood, and Energy symptoms had the highest centrality values. In contrast, Concentration, Suicide, and Sleep had lower centrality values. There were no significant differences in network global strength (p = 0.259), distribution of edge weights (p = 0.73) and individual edge weights (all p values > 0.05 after Holm-Bonferroni corrections) between males and females. Guilt, Sad Mood, and Energy symptoms were central in the depressive symptom network. These central symptoms may be targets for focused treatments and future psychological and neurobiological research to gain novel insight into depression.

Exploring Depressive Symptoms Among Healthcare Professionals and the General Population During the COVID-19 Pandemic in Brazil

Our study aimed to examine the symptoms that might play a role in the co-occurrence of 9 DSM-5 symptom criteria of major depression among Brazil's adult population and healthcare professionals after three months of detecting the new coronavirus in Brazil. We estimated regularized Gaussian graphical models for both samples and compared the network structures. Depressed mood was the most central symptom in the general population network compared to the healthcare professional network. The findings revealed some individual symptoms showed a differential association between the general population and healthcare professionals. Those symptoms may be valuable targets for future research and treatment.

Comparison of DASS-21, PHQ-8, and GAD-7 in a virtual behavioral health care setting

Background

Validated depression and anxiety symptom screeners are commonly used in clinical settings. How results from different brief depression and anxiety symptom assessment tools compare to each other is not well established, especially in real world healthcare settings. This study aimed to compare the Depression Anxiety Stress Scales 21 Depression scale (DASS-Depression) and Anxiety (DASS-Anxiety) scale to the Patient Health Questionnaire 8 (PHQ-8) and Generalized Anxiety Disorder 7 (GAD-7) respectively, in a real-world virtual behavioral healthcare setting.

Methods

This was a retrospective comparison study of clinical data from a population of adults who completed a consultation via telephone or secure video with a licensed therapist as part of a standardized, evidence-based, virtual behavioral therapy program for individuals with comorbid medical and behavioral health conditions. The joint distributions and correlations between scores yielded by each depression and anxiety scale were assessed using descriptive and Spearman correlation statistics.

Results

The DASS-Depression and PHQ-8 were highly correlated (r = .71; p=<.001); the DASS-Anxiety and GAD-7 correlation was also high (r = .61; p=<.001). The PHQ-8 categorized more individuals as having above-threshold depression scores versus the DASS-Depression (71.5% vs. 43.5%; p < .001). The GAD-7 categorized more individuals as having above-threshold anxiety scores versus the DASS-Anxiety (59.0% vs. 45.0%; p < .001).

Limitations

This study compared results yielded by validated screeners, precluding conclusions related to the validity of screener results.

Conclusions

The DASS-Depression and PHQ-8 and the DASS-Anxiety and GAD-7 similarly ranked symptom severity. The PHQ-8 and GAD-7 were more likely than the DASS-21 Depression or Anxiety scales to classify individuals as having above-threshold symptom severity.

Relationship communication and the course of psychological outcomes among couples coping with localised prostate cancer

OBJECTIVE

How couples communicate about cancer is an important predictor of psychological outcomes for men diagnosed with localised prostate cancer and their spouses. We examined the predictive role of disclosure, responsiveness, mutual avoidance, and holding back on depressive symptoms, psychological adjustment, cancer-specific distress, and cancer concerns.

METHODS

Eighty-one prostate cancer patients and their spouses completed measures of communication at baseline and measures of four psychological outcomes at baseline, five, 12, and 26 weeks after baseline. Dyadic growth models tested the effects of time and role on each outcome over time.

RESULTS

Higher disclosure and responsiveness predicted better psychological outcomes. Less mutual avoidance and holding back predicted poorer psychological outcomes. Across communication variables, individuals who engaged in poorer communication initially had poorer psychological outcomes that improved over time, whereas individuals who engaged in better communication initially maintained their more positive standing without change or changed in the positive direction. For all outcomes, those with better communication still had better psychological outcomes at six months.

CONCLUSION

Couples' cancer-specific relationship communication predicts their psychological outcomes. More research is needed to identify effective interventions, including a longer therapy course, individual communication training, or greater focus on addressing barriers to sharing and responsiveness.

Phenotype Network and Brain Structural Covariance Network of Major Depression

Phenotype networks enable clinicians to elucidate the patterns of coexistence and interactions among the clinical symptoms, negative cognitive styles , neurocognitive performance, and environmental factors in major depressive disorder (MDD). Results of phenotype network approach could be used in finding the target symptoms as these are tightly connected or associated with many other phenomena within the phenotype network of MDD specifically when comorbid psychiatric disorder(s) is/are present. Further, by comparing the differential patterns of phenotype networks before and after the treatment, changing or enduring patterns of associations among the clinical phenomena in MDD have been deciphered.Brain structural covariance networks describe the inter-regional co-varying patterns of brain morphologies, and overlapping findings have been reported between the brain structural covariance network and coordinated trajectories of brain development and maturation. Intra-individual brain structural covariance illustrates the degrees of similarities among the different brain regions for how much the values of brain morphological features are deviated from those of healthy controls. Inter-individual brain structural covariance reflects the degrees of concordance among the different brain regions for the inter-individual distribution of brain morphologic values. Estimation of the graph metrics for these brain structural covariance networks uncovers the organizational profile of brain morphological variations in the whole brain and the regional distribution of brain hubs.

On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology

BACKGROUND

In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms' causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis).

METHODS

Here, we summarize three types of evidence pertaining to the centrality hypothesis in psychopathology. First, we discuss the validity of the theoretical assumptions underlying the centrality hypothesis in psychopathology. We then summarize the methodological aspects of extant studies using centrality measures as predictors of symptom change following treatment, while delineating their main findings and several of their limitations. Finally, using a specific dataset of 710 treatment-seeking patients with posttraumatic stress disorder (PTSD) as an example, we empirically examine node centrality as a predictor of therapeutic change, replicating the approach taken by previous studies, while addressing some of their limitations. Specifically, we investigated whether three pre-treatment centrality indices (strength, predictability, and expected influence) were significantly correlated with the strength of the association between a symptom's change and the change in the severity of all other symptoms in the network from pre- to post-treatment (Δnode-Δnetwork association). Using similar analyses, we also examine the predictive validity of two simple non-causal node properties (mean symptom severity and infrequency of symptom endorsement).

RESULTS

Of the three centrality measures, only expected influence successfully predicted how strongly changes in nodes/symptoms were associated with change in the remainder of the nodes/symptoms. Importantly, when excluding the amnesia node, a well-documented outlier in the phenomenology of PTSD, none of the tested centrality measures predicted symptom change. Conversely, both mean symptom severity and infrequency of symptom endorsement, two standard non-network-derived indices, were found to be more predictive than expected influence and remained significantly predictive also after excluding amnesia from the network analyses.

CONCLUSIONS

The centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks.

The Differential Role of Central and Bridge Symptoms in Deactivating Psychopathological Networks

The network model of psychopathology suggests that central and bridge symptoms represent promising treatment targets because they may accelerate the deactivation of the network of interactions between the symptoms of mental disorders. However, the evidence confirming this hypothesis is scarce. This study re-analyzed a convenience sample of 51 cross-sectional psychopathological networks published in previous studies addressing diverse mental disorders or clinically relevant problems. In order to address the hypothesis that central and bridge symptoms are valuable treatment targets, this study simulated five distinct attack conditions on the psychopathological networks by deactivating symptoms based on two characteristics of central symptoms (degree and strength), two characteristics of bridge symptoms (overlap and bridgeness), and at random. The differential impact of the characteristics of these symptoms was assessed in terms of the magnitude and the extent of the attack required to achieve a maximum impact on the number of components, average path length, and connectivity. Only moderate evidence was obtained to sustain the hypothesis that central and bridge symptoms constitute preferential treatment targets. The results suggest that the degree, strength, and bridgeness attack conditions are more effective than the random attack condition only in increasing the number of components of the psychopathological networks. The degree attack condition seemed to perform better than the strength, bridgeness, and overlap attack conditions. Overlapping symptoms evidenced limited impact on the psychopathological networks. The need to address the basic mechanisms underlying the structure and dynamics of psychopathological networks through the expansion of the current methodological framework and its consolidation in more robust theories is stressed.