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BACKGROUND AND OBJECTIVES: During the COVID-19 pandemic, Chinese international students (CISs) experienced distress associated with both unique and universal stressors, among which everyday discrimination may be especially harmful. DESIGN: Cross-sectional design. METHODS: We compared distress between CISs (N = 381) and Chinese students in Chinese colleges (CSCCs; N = 305) and examined correlates of distress including the association between everyday discrimination and distress as well as moderators on this link. RESULTS: Compared to CSCCs, CISs reported greater depression and anxiety. Sensitivity analyses - multiple regressions controlling for covariates and coarsened exact matched (CEM) comparisons - replicated the results. 28.6% CISs reported suicidal ideation (PHQ-9 item 9) at least several days during the past two weeks. Within CISs, depression was associated with being older, female, non-heterosexual, increased everyday discrimination, decreased self-esteem, coping flexibility, perceived social support, and satisfaction with online learning. Anxiety was associated with being in undergraduate years, female, increased discrimination, decreased self-esteem, coping flexibility, and satisfaction with online learning. High perceived social support and being heterosexual weakened the association between discrimination and anxiety and depression, while high self-esteem strengthened the association between discrimination and anxiety. CONCLUSIONS: Our study underscored the distress experienced by CISs and highlighted risk/protective factors that may warrant attention.
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Background: Post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) are two highly comorbid psychological outcomes commonly studied in the context of stress and potential trauma. In Hubei, China, of which Wuhan is the capital, residents experienced unprecedented stringent lockdowns in the early months of 2020 when COVID-19 was first reported. The comorbidity between PTSD and MDD has been previously studied using network models, but often limited to cross-sectional data and analysis. Objectives: This study aims to examine the cross-sectional and longitudinal network structures of MDD and PTSD symptoms using both undirected and directed methods. Methods: Using three types of network analysis – cross-sectional undirected network, longitudinal undirected network, and directed acyclic graph (DAG) – we examined the interrelationships between MDD and PTSD symptoms in a sample of Hubei residents assessed in April, June, August, and October 2020. We identified the most central symptoms, the most influential bridge symptoms, and causal links among symptoms. Results: In both cross-sessional and longitudinal networks, the most central depressive symptoms included sadness and depressed mood, whereas the most central PTSD symptoms changed from irritability and hypervigilance at the first wave to difficulty concentrating and avoidance of potential reminders at later waves. Bridge symptoms showed similarities and differences between cross-sessional and longitudinal networks with irritability/anger as the most influential bridge longitudinally. The DAG found feeling blue and intrusive thoughts the gateways to the emergence of other symptoms. Conclusions: Combining cross-sectional and longitudinal analysis, this study elucidated central and bridge symptoms and potential causal pathways among PTSD and depression symptoms. Clinical implications and limitations are discussed. HIGHLIGHTS This study examined the cross-sectional and longitudinal network structures of depression and post-traumatic disorder symptoms using undirected and directed methods. The most central depressive symptoms included sadness and depressed mood, whereas the most central post-traumatic disorder symptoms changed from irritability and hypervigilance to difficulty concentrating and avoidance of reminders over time. Bridge symptoms showed similarities and differences between cross-sessional and longitudinal networks with irritability/anger as the most influential bridge longitudinally.
ABSTRACT
Background: Post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) are two highly comorbid psychological outcomes commonly studied in the context of stress and potential trauma. In Hubei, China, of which Wuhan is the capital, residents experienced unprecedented stringent lockdowns in the early months of 2020 when COVID-19 was first reported. The comorbidity between PTSD and MDD has been previously studied using network models, but often limited to cross-sectional data and analysis. Objectives: This study aims to examine the cross-sectional and longitudinal network structures of MDD and PTSD symptoms using both undirected and directed methods. Methods: Using three types of network analysis - cross-sectional undirected network, longitudinal undirected network, and directed acyclic graph (DAG) - we examined the interrelationships between MDD and PTSD symptoms in a sample of Hubei residents assessed in April, June, August, and October 2020. We identified the most central symptoms, the most influential bridge symptoms, and causal links among symptoms. Results: In both cross-sessional and longitudinal networks, the most central depressive symptoms included sadness and depressed mood, whereas the most central PTSD symptoms changed from irritability and hypervigilance at the first wave to difficulty concentrating and avoidance of potential reminders at later waves. Bridge symptoms showed similarities and differences between cross-sessional and longitudinal networks with irritability/anger as the most influential bridge longitudinally. The DAG found feeling blue and intrusive thoughts the gateways to the emergence of other symptoms. Conclusions: Combining cross-sectional and longitudinal analysis, this study elucidated central and bridge symptoms and potential causal pathways among PTSD and depression symptoms. Clinical implications and limitations are discussed.
Antecedentes: El trastorno de estrés postraumático (TEPT) y el trastorno depresivo mayor (TDM) son dos resultados psicológicos altamente comórbidos que se estudian comúnmente en el contexto del estrés y trauma potencial. En Hubei, China, de la cual Wuhan es la capital, los residentes experimentaron cuarentenas estrictas sin precedentes en los primeros meses de 2020 cuando se informó por primera vez del COVID-19. La comorbilidad entre TEPT y TDM se ha estudiado previamente utilizando modelos de red, pero a menudo se limita a datos y análisis transversales.Objetivos: Este estudio tiene como objetivo examinar las estructuras de red transversales y longitudinales de los síntomas de TDM y TEPT utilizando métodos dirigidos y no dirigidos.Métodos: Mediante el uso de tres tipos de análisis de red: red no dirigido transversal, red no dirigido longitudinal y gráfico acíclico dirigido (DAG), examinamos las interrelaciones entre los síntomas de TDM y TEPT en una muestra de residentes de Hubei evaluados en abril, junio, agosto y octubre de 2020. Identificamos los síntomas centrales, los síntomas puente más influyentes y los vínculos causales entre los síntomas.Resultados: Tanto en redes transversales como longitudinales, los síntomas depresivos más centrales incluyeron tristeza y estado de ánimo deprimido, mientras que los síntomas de TEPT más centrales cambiaron de irritabilidad e hipervigilancia en la primera ola a dificultad para concentrarse y evitar posibles recordatorios en las oleadas posteriores. Los síntomas puente, mostraron similitudes y diferencias entre las redes transversales y longitudinales con irritabilidad/ira como el puente más influyente longitudinalmente. El DAG descubrió que la tristeza y los pensamientos intrusivos son las puertas de entrada a la aparición de otros síntomas.Conclusiones: Al combinar los análisis transversal y longitudinal, este estudio elucidó los síntomas centrales y puente y las posibles vías causales entre los síntomas de TEPT y de depresión. Se discuten las implicaciones clínicas y las limitaciones.
Subject(s)
COVID-19 , Depressive Disorder, Major , Stress Disorders, Post-Traumatic , COVID-19/epidemiology , Communicable Disease Control , Cross-Sectional Studies , Depression/epidemiology , Depressive Disorder, Major/epidemiology , Humans , Stress Disorders, Post-Traumatic/epidemiologyABSTRACT
In Hubei, China, where the coronavirus disease (COVID-19) epidemic first emerged, the government has enforced strict quarantine and lockdown measures. Longitudinal studies suggest that the impact of adverse events on psychological adjustment is highly heterogenous. To better understand protective and risk factors that predict longitudinal psychopathology and resilience following strict COVID-19 lockdowns, this study used unsupervised machine learning to identify half-year longitudinal trajectories (April, June, August, and October, 2020) of three mental health outcomes (depression, anxiety, and posttraumatic stress disorder [PTSD]) among a sample of Hubei residents (N = 326), assessed a broad range of person- and context-level predictors, and applied least absolute shrinkage and selection operator (LASSO) logistic regression, a supervised machine learning approach, to select best predictors for trajectory memberships of resilience and chronic psychopathology. Across outcomes, most individuals remained resilient. Models with both person- and context-level predictors showed excellent predictive accuracy, except for models predicting chronic anxiety. The person-level models showed either good or excellent predictive accuracy. The context-level models showed good predictive accuracy for depression trajectories but were only fair in predicting trajectories of anxiety and PTSD. Overall, the most critical person-level predictors were worry, optimism, fear of COVID, and coping flexibility, whereas important context-level predictors included features of stressful life events, community satisfaction, and family support. This study identified clinical patterns of response to COVID-19 lockdowns and used a combination of risk and protective factors to accurately differentiate these patterns. These findings have implications for clinical risk identifications and interventions in the context of potential trauma. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Subject(s)
COVID-19 , Resilience, Psychological , Anxiety/epidemiology , Anxiety Disorders , China/epidemiology , Communicable Disease Control , Humans , Longitudinal StudiesABSTRACT
The coronavirus disease 2019 (COVID-19) has disrupted multiple domains of life including sleep. The present study used a longitudinal dataset (N = 671) and a person-centered analytic approach - latent profile analysis (LPA) - to elucidate the relationship between sleep and depression. We used LPA to identify profiles of sleep patterns assessed by Pittsburg Sleep Quality Index (PSQI) at the beginning of the study. The profiles were then used as a predictor of depression magnitude and variability over time. Three latent profiles were identified (medicated insomnia sleepers [MIS], inefficient sleepers [IS], and healthy sleepers [HS]). MIS exhibited the highest level of depression magnitude over time, followed by IS, followed by HS. A slightly different pattern emerged for the variability of depression: While MIS demonstrated significantly greater depression variability than both IS and HS, IS and HS did not differ in their variability of depression over time. Medicated insomnia sleepers exhibited both the greatest depression magnitude and variability than inefficient sleepers and healthy sleepers, while the latter two showed no difference in depression variability despite inefficient sleepers' greater depression magnitude than healthy sleepers. Clinical implications and limitations are discussed.
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COVID-19 , Sleep Initiation and Maintenance Disorders , Depression/epidemiology , Humans , SARS-CoV-2 , Sleep , Sleep Initiation and Maintenance Disorders/epidemiologyABSTRACT
Research on traumatic events often emphasizes the importance of posttraumatic growth (PTG) and resilience, yet few studies have explored their trends and their relationship throughout the progression of traumatic events. This paper explores the longitudinal relationship between resilience and PTG, as well as the role of job burnout in this relationship, among frontline healthcare workers during the COVID-19 pandemic, who have been exposed to high-risk work environments over extraordinarily long workdays. In Study 1, 134 Chinese frontline healthcare workers completed a three-wave survey (Time 1, Time 2, and Time 3) in February-May 2020. In Study 2, 401 frontline healthcare workers completed a cross-sectional survey. The cross-lagged analysis suggested that resilience at Time 1 positively predicted PTG at Time 2, which in turn positively predicted resilience at Time 3. PTG at Time 1 also positively predicted resilience at Time 2 (Study 1). However, job burnout was negatively related to both resilience and PTG; in particular, emotional exhaustion moderated the link between PTG and resilience (Study 2). Our findings support a cycle of reinforcement between resilience and PTG over time. The positive effect of PTG on resilience, however, is undermined by emotional exhaustion. Implications for future intervention research and workplace support are discussed.
Subject(s)
COVID-19 , Posttraumatic Growth, Psychological , Resilience, Psychological , Adaptation, Psychological , China , Cross-Sectional Studies , Health Personnel , Humans , Pandemics , SARS-CoV-2ABSTRACT
Background: The Coronavirus Disease 2019 (COVID-19) pandemic has led to overwhelming levels of distress as it spread rapidly from Wuhan, Hubei province to other regions in China. To contain the transmission of COVID-19, China has executed strict lockdown and quarantine policies, particularly in provinces with the highest severity (i.e., Hubei). Although the challenges faced by individuals across provinces may share some similarities, it remains unknown as to whether and how the severity of COVID-19 is related to elevation in depression. Methods: The present study compared depression among individuals who lived in mildly, moderately, and severely impacted provinces in China following the lockdown (N = 1,200) to norm data obtained from a representative sample within the same provinces in 2016 (N = 950), and examined demographic correlates of depression in 2020. Results: Residents in 2020, particularly those living in more heavily impacted provinces, reported increased levels of depression than the 2016 sample. Subsequent analyses of sub-dimensions of depression replicated the findings for depressed mood but not for positive affect, as the latter only declined among residents in the most severely impacted area. Increased depressed mood was associated with female, younger age, fewer years of education, and being furloughed from work, whereas reduced positive affect was associated with younger age and fewer years of education only. Conclusions: This study underscored the impact of COVID-19 on depression and suggested individual characteristics that may warrant attention.
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Amid the global outbreak of COVID-19, resilience is likely to be one of the many possible outcomes. Studies pertaining to resilience following potentially traumatic events including disease outbreak have shown that the vast majority of individuals are resilient, and that outcomes depend on a combination of resilience factors including exposure severity, individual differences, family context, and community characteristics. To better understand psychological dysfunction and resilience during the global outbreak of COVID-19, researchers are encouraged to investigate long-term patterns of mental health rather than cross-sectional prevalence rates, adopt prospective designs and analyses, integrate multiple risk and resilience factors to enhance outcome prediction, and consider the importance of flexibility as the situation unfolds. (PsycInfo Database Record (c) 2021 APA, all rights reserved).