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Pre-event psychiatric states predict trajectories of post-traumatic stress symptoms during the COVID-19 pandemic (preprint)
researchsquare; 2024.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-3865066.v1
ABSTRACT
Recent time-dependent analyses of stress-related disorders have identified heterogeneity of trajectories and their modifying factors. While psychiatric patients are vulnerable to stress events, it is unclear how psychiatric conditions in the general population modulate subsequent stress responses. Using our longitudinal online survey from before the COVID-19 pandemic to post-pandemic follow-ups (n = 3815 Japanese adults), here we identified four trajectories of post-traumatic stress symptoms (PTSS) a latent growth mixture model; resilient, chronic, mild chronic, and early response. The depression/anxiety were identified as specific risk factors for the early-response trajectory. In contrast, general psychiatric burden and social withdrawal were identified as common risk/protective factors. Further, we estimated “baseline” PTSS to determine the predictability of the PTSS prognosis from pre-pandemic states. The chronic group showed significantly higher baseline PTSS scores than the mild-chronic and early-response groups, both of which were significantly higher than the resilient group. We concluded that prior psychiatric conditions significantly affect the PTSS trajectory. These results suggest that prior psychiatric conditions may be considered for the prevention and treatment of maladaptive stress responses.
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Full text: Available Collection: Preprints Database: PREPRINT-RESEARCHSQUARE Main subject: Anxiety Disorders / Fractures, Stress / Stress Disorders, Traumatic / Depressive Disorder / COVID-19 / Mental Disorders Language: English Year: 2024 Document Type: Preprint

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Full text: Available Collection: Preprints Database: PREPRINT-RESEARCHSQUARE Main subject: Anxiety Disorders / Fractures, Stress / Stress Disorders, Traumatic / Depressive Disorder / COVID-19 / Mental Disorders Language: English Year: 2024 Document Type: Preprint