Depression, post-traumatic stress, anxiety, and fear of COVID-19 in the general population and health-care workers: prevalence, relationship, and explicative model in Peru

David Villarreal-Zegarra, Anthony Copez-Lonzoy, Ana L. Vilela-Estrada, Jeff Huarcaya-Victoria

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

Resumen

Background: This study has two aims. First, determine the fit of the fear model to COVID-19, anxiety, and post-traumatic stress in the general population and health-care workers. Second, determine which model best explains the relationship between depression and the triad of fear, anxiety, and post-traumatic stress in both groups. Method: A cross-sectional study was conducted using self-reported questionnaires for anxiety, fear of COVID-19, depression, and post-traumatic stress. Information was collected from adults living in Lima, the capital and the most populous city in Peru. The explanatory models were evaluated using a structural equation model. Results: A total of 830 participants were included, including general population (n = 640) and health-care workers (n = 190). A high overall prevalence of depressive symptoms (16%), anxiety (11.7%), and post-traumatic stress (14.9%) were identified. A higher prevalence of depressive, anxious, or stress symptoms was identified in the general population (28.6%) compared to health-care workers (17.9%). The triad model of fear of COVID-19, anxiety, and stress presented adequate goodness-of-fit indices for both groups. A model was identified that manages to explain depressive symptoms in more than 70% of the general population and health-care workers, based on the variables of the triad (CFI = 0.94; TLI = 0.94; RMSEA = 0.06; SRMR = 0.06). In the general population post-traumatic stress mediated the relationship between anxiety and depression (β = 0.12; 95%CI = 0.06 to 0.18) which was significant, but the indirect effect of post-traumatic stress was not significant in health care workers (β = 0.03; 95%CI = − 0.11 to 0.19). Limitations: The prevalence estimates relied on self-reported information. Other variables of interest, such as intolerance to uncertainty or income level, could not be evaluated. Conclusions: Our study proposes and tests one model that explains more than 70% of depressive symptoms. This explanatory model can be used in health contexts and populations to determine how emotional factors can affect depressive symptoms.

Idioma originalInglés
Número de artículo455
PublicaciónBMC Psychiatry
Volumen21
N.º1
DOI
EstadoPublicada - dic 2021
Publicado de forma externa

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