Depression and PTSD in the aftermath of strict COVID-19 lockdowns: a cross-sectional and longitudinal network analysis

Interactive changes in depression and loneliness symptoms prior to and during the COVID-19 pandemic: A longitudinal network analysis

OBJECTIVES

Depression and loneliness co-occur frequently. This study examined interactive changes between depression and loneliness among older adults prior to and during the COVID-19 pandemic from a longitudinal network perspective.

METHODS

This network study was based on data from three waves (2016-2017, 2018-2019, and 2020) of the English Longitudinal Study of Ageing (ELSA). Depression and loneliness were measured with the eight-item version of the Center for Epidemiologic Studies Depression Scale (CESD-8) and three item version of the University of California Los Angeles (UCLA) Loneliness Scale, respectively. A network model was constructed using an Ising Model while network differences were assessed using a Network Comparison Test. Central symptoms were identified via Expected Influence (EI).

RESULTS

A total of 4,293 older adults were included in this study. The prevalence and network of depression and loneliness did not change significantly between the baseline and pre-pandemic assessments but increased significantly from the pre-pandemic assessment to during COVID-19 assessment. The central symptom with the strongest increase from pre-pandemic to pandemic assessments was "Inability to get going" (CESD8) and the edge with the highest increase across depression-loneliness symptom communities was "Lack companionship" (UCLA1) - "Inability to get going" (CESD8). Finally, "Feeling depressed" (CESD1) and "Everything was an effort" (CESD2) were the most central symptoms over the three assessment periods.

CONCLUSIONS

The COVID-19 pandemic was associated with significant changes in the depression-loneliness network model. The most changed symptoms and edges could be treatment targets for reducing the risk of depression and loneliness in older adults.

Longitudinal Association Between Posttraumatic Stress Symptoms and Depression Symptoms Among Chinese Adolescents During COVID-19: Evidence from Network Perspective

Although there are an increasing number of studies that have explored the mental health consequences of COVID-19 focusing on revealing risk factors, the longitudinal research examining the potential mechanism of the co-occurrence of posttraumatic stress symptoms (PTSS) and depression symptoms among adolescents were scarce. The present study identified the important comorbidity symptoms and explored longitudinal relationship of PTSS and depression symptoms from the network perspective. A two-wave investigation (4 months interval; T1 and T2) was conducted with a sample of 1225 Chinese adolescents. Cross-sectional network and cross-lagged panel network (CLPN) analyses were adopted. Results showed that comorbidity symptoms consisted of both overlapping and non-overlapping symptoms, including "Future foreshortening" at T1 and T2 from PTSS, and "Hard to get started" at T1 and "Not sleep well" at T2 from depression symptoms. Strong longitudinal pathways appeared from all PTSS to depression symptoms, among which the pathway from "Difficulty concentrating" to "Hard to get started" was the strongest. These findings suggest that the possible comorbidity between PTSS and depression symptoms is caused by their independent and related structures, and their longitudinal association. Clinical intervention for these symptoms may alleviate adolescents' psychological problems in the aftermath of traumatic events.

On the path to recovery: traumatic stress research during the COVID-19 pandemic 2021-2023

This Special Issue of the European Journal of Psychotraumatology (EJPT) presents 51 articles published between 2021 and 2023 and follows the Special Issue on pandemic-related traumatic stress research published in 2021 (O'Donnell, M. L., & Greene, T. [2021]. Understanding the mental health impacts of COVID-19 through a trauma lens. European Journal of Psychotraumatology, 12(1), 1982502). Research on traumatic stress during the pandemic has cast the spotlight on vulnerable populations and groups, notably front-line healthcare workers; people faced with major losses including the deaths of loved ones; those who personally survived debilitating and often life-threatening viral infection; and students who were isolated and experienced profound delays in their education, relationships, and emerging independence. The papers in this collection underscore the associations between COVID-19 related stressors and a plethora of adverse mental health sequelae, including posttraumatic stress reactions, and draw attention to the ubiquity of grief and moral injury and their wide-ranging and detrimental impact. Currently, there is a paucity of evidence on interventions to enhance resources, self-efficacy, and hope for affected groups and individuals through societal, organisational, and healthcare systems; however early research on the prevention of COVID-related traumatic stress disorders provides a basis for both hope and preparedness for the future.

Intensity and Network Structure of Insomnia Symptoms and the Role of Mental Health During the First Two Waves of the COVID-19 Pandemic

Purpose

Insomnia, being a mental disorder, is best conceived as a network of symptoms. With the important increase in insomnia prevalence during the COVID-19 pandemic, our aim was to investigate how the structure of insomnia symptoms in the general population has changed due to the pandemic. We also looked at the directional dependencies of nightmares and of covid- and lockdown-related stress/anxiety and depression in insomnia.

Patients and Methods

5986 persons replied to our online questionnaire for the first wave and 2843 persons to our second wave questionnaire. Both questionnaires included the Insomnia Severity Index (ISI). Regularized Gaussian Graphical Models (GGM) and Bayesian Directed Acyclic Graphs (DAG) were estimated.

Results

The pre- and peri-lockdown networks were equally strongly connected (first wave: S = 0.13, p = 0.39; second wave: S = 0.03, p = 0.67), but differed for the first lockdown regarding only six edges (M = 0.13, p < 0.001) and for the second lockdown only five edges (M = 0.16, p < 0.001). These symptoms all worsened during the lockdowns in comparison to before the pandemic (p < 0.001). The diurnal items of the ISI had the highest predictability and centrality values in the GGMs. Lockdown-related stress/anxiety influenced indirectly nightmares through covid-related stress/anxiety, lockdown-related depressive affect and mental fatigue. These reported feelings of stress/anxiety and depression showed an indirect impact on insomnia symptoms through mental and physical fatigue.

Conclusion

Though the lockdown slightly intensified insomnia symptoms, it did not alter their network structure. Despite their differences, both GGMs and DAGs agree that the diurnal symptoms of the ISI, play an essential role in the network structure. Both methods confirm the need for emphasizing the cognitive/affective component in the treatment of insomnia (ie cognitive behavioral therapy).

PTSD Symptom dynamics after the great east japan earthquake: mapping the temporal structure using Dynamic Time Warping

Background: After the Great East Japan Earthquake [GEJE], approximately 70,000 Japan Ground Self Defense Force [JGSDF] personnel were deployed, risking Post-Traumatic Stress Disorder [PTSD]. The network approach to psychopathology suggests that symptoms may cause and exacerbate each other, resulting in the emergence and maintenance of disorders, including PTSD. It is therefore important to further explore the temporal interplay between symptoms. Most studies assessing the factor structure of the Impact of Event Scale-Revised [IES-R] have used cross-sectional designs. In this study, the structure of the IES-R was re-evaluated while incorporating the temporal interplay between symptoms.Methods: Using Dynamic Time Warping [DTW] the distances between PTSD symptoms on the IES-R were modelled in 1120 JGSDF personnel. Highly correlated symptoms were clustered at the group level using Distatis three-way principal component analyses of the distance matrices. The resulting clusters were compared to the original three subscales of the IES-R using a Confirmatory Factor Analysis (CFA).Results: The DTW analysis yielded four symptom clusters: Intrusion (five items), Hyperarousal (six items), Avoidance (six items), and Dissociation (five items). CFA yielded better fit estimates for this four-factor solution (RMSEA = 0.084, CFI = 0.918, TLI = 0.906), compared to the original three subscales of the IES-R (RMSEA = 0.103, CFI = 0.873, TLI = 0.858).Conclusions: DTW offers a new method of modelling the temporal relationships between symptoms. It yielded four IES-R symptom clusters, which may facilitate understanding of PTSD as a complex dynamic system.