A network analysis of risk factors for suicide in Iraq/Afghanistan-era veterans

Network analysis of smoking-related sleep characteristics in Chinese adults

The associations between multiple sleep characteristics and smoking behavior are inconsistent, and it is unclear which sleep characteristics are most crucial for tobacco prevention. This study aimed to explore the associations between smoking status/intensity and multiple sleep characteristics and to identify the potential core domain of smoking-related sleep using network analysis. Data were obtained from a survey of cancer-related risk factors among Chinese adults. Logistic regression models were used to quantify the associations between sleep characteristics and smoking status/intensity. Network analyses were employed to identify the core sleep characteristics. A total of 5,228 participants with a median age of 44 years old were included in the study. Current smoking was significantly positively associated with long nap time, difficulty falling asleep, late bedtime, getting up after 7 am, and waking up earlier than expected. There was significant positive association between current smoking and short sleep duration in young adults under 45 years old. Late bedtime and getting up after 7 am were only associated with current heavy smoking, but not current light smoking. Network analyses showed that multiple smoking-related sleep characteristics were interconnected, with difficulty falling asleep and late bedtime as central characteristics in the network. The study found that the associations between sleep characteristics and smoking varied by age and smoking intensity and highlights the potential benefits of sleep health promotion in smoking cessation, with a particular focus on difficulty falling asleep and late bedtime.

Examination of PTSD symptom networks over the course of cognitive processing therapy

OBJECTIVES

Cognitive processing therapy (CPT) is an evidence-based psychotherapy for posttraumatic stress disorder (PTSD); however, little is known about how interrelationships between PTSD symptoms change over the course of treatment. The current study examined baseline, midtreatment, and posttreatment PTSD symptom networks during CPT for PTSD.

METHOD

Adults with PTSD (n = 107) received 12 sessions of CPT as part of a randomized trial. Self-reported PTSD symptoms were assessed at pretreatment, midtreatment, and posttreatment, and network analysis was used to examine the interrelationships between symptoms at these three timepoints. Linear regression was conducted to examine whether any baseline symptoms or midpoint symptoms predicted overall treatment change.

RESULTS

In the baseline PTSD network, feelings of detachment and feeling upset at reminders of the trauma were central to the symptom network. These symptoms were no longer central at midtreatment, possibly suggesting that CPT quickly reduces the importance of these symptoms. These findings were consistent with regression results that, after accounting for multiple comparisons, high baseline scores of feeling upset at trauma reminders predicted later treatment change. At the conclusion of treatment, strong negative emotions were the most central symptom and may be most important in maintaining or lowering other PTSD symptoms at the conclusion of treatment.

CONCLUSIONS

Though replication is necessary, these findings offer insights into identifying which symptoms may be most predictive of treatment outcomes and the course by which CPT reduces PTSD symptoms. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Violent behavior and the network properties of psychopathological symptoms and real-life functioning in patients with schizophrenia

Objective

To assess the interplay among psychopathological symptoms and real-life functioning, and to further detect their influence with violent behavior in patient with schizophrenia.

Methods

A sample of 1,664 patients with post-violence assessments and their propensity score-matched controls without violence from a disease registration report system of community mental health service in Guangdong, China, were studied by network analysis. Ising-Model was used to estimate networks of psychopathological symptoms and real-life functioning. Then, we tested whether network properties indicated the patterns of interaction were different between cases and controls, and calculated centrality indices of each node to identify the central nodes. Sensitivity analysis was conducted to examine the difference of interaction patterns between pre-violence and post-violence assessments in violence cases.

Results

Some nodes in the same domain were highly positive interrelations, while psychopathological symptoms were negatively related to real-life functioning in all networks. Many symptom-symptom connections and symptom-functioning connections were disconnected after the violence. The network density decreased from 23.53% to 12.42% without statistical significance (p = 0.338). The network structure, the global network strength, and the global clustering coefficient decreased significantly after the violence (p < 0.001, p = 0.019, and p = 0.045, respectively). Real-life functioning had a higher node strength. The strength of sleeping, lack of spontaneity and flow of conversation, and preoccupation were decreased in post-violence network of patients.

Conclusion

The decreasing connectivity may indicate an increased risk of violence and early warning for detecting violence. Interventions and improving health state based on nodes with high strength might prevent violence in schizophrenia patients.

Suicide risk profiles among service members and veterans exposed to suicide

OBJECTIVES

Rates of suicide exposure are high among service members and Veterans and are especially concerning given the link between suicide exposure and subsequent suicide risk. However, to date, it is unclear which individuals who are exposed to suicide are subsequently at high risk for suicide. Latent profile analysis (LPA) can provide information on unique risk profiles and subgroups of service members and Veterans who have higher suicide risk after suicide exposure, which has not yet been empirically studied. The purpose of this study was to utilize LPA to identify subgroups of service members and Veterans who are at the highest risk for suicidal thoughts and behaviors following suicide exposure.

METHODS

We analyzed data using LPA from 2570 service members and Veterans (82.1% male, 69.5% White, and 12.1% Latino/a/x) who completed the Military Suicide Research Consortium's Common Data Elements, a battery of self-report suicide-related measures. Psychopathology, substance use, mental health service utilization, interpersonal theory of suicide, and suicide exposure variables were used to validate classes.

RESULTS

Three latent classes emerged from analyses, one low-risk class and two-high risk classes with differing profile compositions (one primarily differentiated by anxiety symptoms and one differentiated by substance use).

CONCLUSION

Class-specific recommendations for suicide prevention efforts will be discussed.

Bridge symptoms between parenting styles and proximal psychological risk factors associated with adolescent suicidal thoughts: a network analysis

BACKGROUND

Parenting styles and the associated proximal psychological factors are suggested to increase suicidal risks in adolescents. However, how the two factors interact and confer risks on the emergence of adolescent suicidal thoughts remains unclear. Herein, we used a network approach to investigate their interrelationship and explore whether the network properties predict adolescent suicidal thoughts.

METHODS

Self-report questionnaires were completed by 1171 students aged 12-16. Network analyses were performed by Gaussian graphical models estimating the adolescent psychosocial network structure of parenting styles and psychological variables including depression, anxiety, affective lability, rumination, and resilience. Furthermore, we re-examined the network by adding a variable measuring active suicidal thoughts. Moreover, we conducted linear regressions to examine the predictive utility of bridge symptoms for adolescent suicidal thoughts.

RESULTS

Resilience, Afraid, Rumination, Concentration, and affective lability (Anger) had the highest bridge strengths in the adolescent psychosocial network. Among the identified bridge symptoms, Resilience was negatively correlated with active suicidal thoughts (regularized edge weights = -0.181, bootstrapped 95% CIs: [-0.043, -0.155]), whereas affective lability (from Anxiety to Depression, Anger), Rumination, and Afraid were positively correlated with active suicidal thoughts, with edge weights (bootstrapped 95% CIs) ranging from 0.057 (0.001, 0.112) to 0.081(0.026, 0.136). Regression analysis showed that bridge strength was significantly correlated with active suicidal thoughts (R2 = 0.432, P = 0.001).

CONCLUSION

Negative parenting styles may drive and maintain suicidal thoughts by modifying the key proximal psychological variables. Our findings highlight the important role of bridge symptoms, which may serve as vital targets for triggering adolescent suicide.

Exploring psychological and psychosocial correlates of non-suicidal self-injury and suicide in college students using network analysis

BACKGROUND

Non-suicidal self-injury (NSSI) and suicide are a serious health concern in young people. Remarkable progress has been made in understanding the correlates and risk factors for suicidality. However, it remains unclear the complex interplay between different factors and which factors are most saliently associated with NSSI and suicide risk and should be targeted for interventions.

METHODS

This study utilized network analysis to examine the interrelationship between NSSI, suicide and a variety of psychological (e.g., depression, psychotic-like experiences) and psychosocial (e.g., childhood maltreatment, family dysfunction, being bullied and social support) correlates in a sample of Chinese first year college students (n = 2328).

RESULTS

The severity of depressive symptoms was the only factor connected to both NSSI and suicide. Other psychiatric factors like psychotic experiences could only act in an indirect way via the bridging effect of depression. Emotional abuse, compared with other forms of childhood adversities, was found to be the most influential maltreatment form associated with suicidal ideation and attempts. Finally, social support showed the potential to lower the risk of self-injury and suicide in young people.

LIMITATIONS

Only a subset of risk and resilience factors of NSSI and suicide were included. Causal inference was impossible due to the cross-sectional design.

CONCLUSIONS

Our findings highlighted the importance of identifying risk groups with mental illnesses or negative childhood events, and providing additional support during the key time of transitioning into higher education.

Exploring Risk Factors in Suicidal Ideation and Attempt Concept Cooccurrence Networks

Suicide is a significant and rising threat to public health. In the United States, 47,500 people died from suicide in 2019, a 10-year increase of 30%. Many researchers are interested in studying the risk factors associated with suicidal ideation and suicide attempt to help inform clinical screening, intervention, and prevention efforts. Many suicide risk factor analyses draw from clinical subdomains and quantify risk factors independently. While traditional modeling approaches might assume independence between risk factors, current suicide research suggests that the development of suicidal intent is a complex, multifactorial process. Thus, it may be beneficial to how suicide risk-factors interact with one another. In this study, we used network analysis to generate visual suicidality risk relationship diagrams. We extract medical concepts from free-text clinical notes and generate cooccurrence-based risk networks for suicidal ideation and suicide attempt. In addition, we generate a network of risk factors for suicidal ideation which evolves into a suicide attempt. Our networks were able to replicate existing risk factor findings and provide additional insight into the degree to which risk factors behave as independent morbidities or as interacting comorbidities with other risk factors. These results highlight potential avenues for risk factor analyses of complex outcomes using network analysis.

Risk factors of suicidal behaviors in a high-risk longitudinal veteran sample: A network analysis

INTRODUCTION

Suicide is a substantial public health burden, particularly among veterans. Risk factors have been delineated for suicide; however, the dynamic interrelations between risk factors have not been fully examined. Such research has the potential to elucidate processes that contribute to suicide risk between individuals with a past suicide attempt (attempters) and those without a past suicide attempt (nonattempters).

METHODS

In the current study, network analysis was used to compare networks between attempters and nonattempters in a high-risk veteran sample (N = 770; M_age  = 32.3 years, SD = 6.8; 326 with a past suicide attempt) who were followed over 1 year. Networks were estimated to examine (1) concurrent relations of suicide risk factors at baseline and (2) predictability of prospective suicidal behavior (SB).

RESULTS

There were no differences in the overall connectivity of attempter and nonattempter networks. Perceived burdensomeness and posttraumatic stress disorder (PTSD) symptoms were most central in the attempters' network, whereas PTSD symptoms and insomnia were most central in the nonattempters' network. The risk factors prospective SB in either network. However, attempters were more likely to engage in SB over the course of the study.

CONCLUSION

These findings highlight the difficulty in predicting who will attempt suicide.

Correlates of Incarceration History Among Military Veterans

Veterans with histories of incarceration are at greater risk for poor physical and mental health outcomes, yet prior research in this population has focused on specific subsets of veterans or a narrow range of predictors. We utilized the Bronfenbrenner Socioecological Model as the framework to evaluate correlates of incarceration history in a large sample of Iraq and Afghanistan-era veterans at four levels: demographic, historical, clinical, and contextual. Participants were 2,904 veterans (76.9% male; 49.5% White and 46.5% Black; mean age 38.08, SD = 10.33), 700 of whom reported a history of incarceration. Four logistic regression models predicting history of incarceration were tested, adding demographic, historical, clinical, and contextual variables hierarchically. In the final model, younger age (OR=0.99, 95% CI=0.98-1.00), male gender (OR of being female =0.28, 95% CI=0.21-0.38), belonging to a historically marginalized group (OR of being White =0.69, 95% CI=0.56-0.84), family history of incarceration (OR=1.47, 95% CI=1.10-1.94), adult interpersonal trauma (OR=1.39, 95% CI=1.28-1.51), problematic alcohol use (OR=1.03, 95% CI=1.02-1.05), drug abuse (OR=1.15, 95% CI=1.11-1.19), and unemployment (OR for being employed=0.76, 95% CI=0.62-0.92) were significantly associated with a history of incarceration. Implications of these findings for developing interventions and supporting systems to effectively target this high-risk population of veterans are discussed.

Toward an integrative model of transdiagnostic risk factors and suicide: A network comparison of psychiatric outpatients

Predictive models using traditional statistical methods have largely failed to describe suicide etiology. Network theory, which conceptualizes factors as mutually interacting, reinforcing elements of a complex outcome, can model relationships between transdiagnostic and neurocognitive vulnerability factors. The present study used a network approach to produce an atheoretical model of psychological factors and their interrelationships within a population of ideators and non-ideators. We developed two network models (i.e., suicidal ideators and psychiatric controls) describing the relationships between a diverse set of risk factors and symptom measures for a population of psychiatric outpatients. We compared networks using three measures of network structure (i.e., network structure invariance, global strength invariance, edge invariance) and described the differences. Network structures for ideators (N = 229) and non-ideators (N = 454) were stable and accurate. In non-ideators, cognitive-affective depression symptoms (Expected Influence [EI]: 2.06), trauma avoidance (EI: 1.08), and negative affect (EI: 0.81) were most influential to the psychological network. In ideators, cognitive-affective depression symptoms (EI: 1.77), intolerance of uncertainty-negative self-referent implications (EI: 1.29), and negative affect (EI: 1.19) were most influential. Invariance testing did not indicate significant differences in overall network structure between ideators and non-ideators (p = .111), but did indicate significant differences in node strength (p = .013). Significant differences in node EI were detected for intolerance of uncertainty-negative self-referent implications, anxiety sensitivity physical concerns, thwarted belongingness, worry, and negative affect. These findings indicated differences in network structures for suicidal psychiatric outpatients and provide crucial directions for future research on therapeutic targets for suicidal thoughts and behaviors.

Cannabis use and suicide risk among Gulf War veterans

Cannabis use has been indicated as a risk factor for suicide in veterans. This study of Gulf War veterans tested the relationship between self-report past year cannabis use and (a) past year suicidal ideation and (b) risk for suicidal behavior. Data were from a national sample (N = 1126) of Gulf War veterans. Logistic regression models indicated cannabis use was associated with past year suicidal ideation and elevated risk for suicidal behavior, independent of key covariates. In corroboration with research on other military populations, this study indicates a potentially concerning association between cannabis use and suicide risk in Gulf War veterans.

The First Year After Military Service: Predictors of U.S. Veterans' Suicidal Ideation

INTRODUCTION

Little is known about predictors of military veterans' suicidal ideation as they transition from service to civilian life, a potentially high-risk period that represents a critical time for intervention. This study examined factors associated with veterans' suicidal ideation in the first year after military separation.

METHODS

A national sample of U.S. veterans (N=7,383) from The Veterans Metrics Initiative Study reported on their mental health, psychosocial well-being, and demographic/military characteristics in an online survey at 3 and 9 months after separation. Cross-validated random forest models and mean decrease in accuracy values were used to identify key predictors of suicidal ideation. Bivariate ORs were calculated to examine the magnitude and direction of main effects associations between predictors and suicidal ideation. Data were collected in 2016/2017 and analyzed in 2021.

RESULTS

In the first year after separation, 15.1% of veterans reported suicidal ideation. Endorsing depression symptoms and, to a lesser extent, identifying oneself as experiencing depression, were most predictive of suicidal ideation. Other psychopathology predictors included higher anxiety and posttraumatic stress disorder symptoms. Psychosocial well-being predictors included higher health satisfaction and functioning, community satisfaction and functioning, and psychological resilience. Logistic models performed similarly to random forest models, suggesting that relationships between predictors and suicidal ideation were better represented as main effects than interactions.

CONCLUSIONS

Results highlight the potential value of bolstering key aspects of military veterans' mental health and psychosocial well-being to reduce their risk for suicidal ideation in the first year after separation. Findings can inform interventions aimed at helping veterans acclimate to civilian life.