The endless quarantine: The impact of the COVID-19 outbreak on healthcare workers after three months of mandatory social isolation in Argentina

• Autores: Giardino, Daniela L.; Huck Iriart, Cristián; Riddick, Maximiliano Luis; Garay, Arturo
• Año de publicación: 2020
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: COVID-19; SOCIAL ISOLATION; INSOMMIA; ANXIETY; HEALTHCARE WORKER; CLUSTERING ANALYSIS
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Objectives: At the end of 2019 the SARS-CoV-2 outbreak spread around the globe with a late arrival to South America. The objective of this study was to evaluate the impact of the long period of mandatory social isolation that took place in Argentina on the general psychological well-being of healthcare workers due to the COVID-19 pandemic.Methods: A survey was conducted during June 2020, in healthcare workers. Pittsburgh Sleep Quality Index, Insomnia Severity Index, Sleepiness-Wakefulness Inability and Fatigue Test, and Goldberg depression and anxiety scale, were used to analyze the effects of the SARS-Cov 2 outbreak after three months of mandatory social isolation. Analyses were performed by logistic regression and a clustering algorithm in order to classify subjects in the function of their outcome's severity.Results: From 1059 surveys, the majority reported symptoms of depression (81.0%), anxiety (76.5%), poor sleep quality (84.7%), and insomnia (73.7%) with 68.9% suffering from nightmares. Logistic regression showed that being in contact with COVID-19 patients, age, gender and the consumption of sleep medication during the mandatory social isolation were relevant predictors for insomnia, anxiety, and depression. Clustering analysis classified healthcare workers in three groups with healthy/mild, moderate, and severe outcomes. The most vulnerable group was composed mainly of younger people, female, non-medical staff, or physicians in training.Conclusion: An extremely high proportion of Argentinian healthcare workers suffered from sleep problems, anxiety, and depression symptoms. The clustering algorithm successfully separates vulnerable from non-vulnerable populations suggesting the need to carry out future studies involving resilience and vulnerability factors.
Fil: Giardino, Daniela L.. Centro de Educaciones Médicas e Investigación Clínica "Norberto Quirno"; Argentina
Fil: Huck Iriart, Cristián. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Riddick, Maximiliano Luis. Universidad Nacional de la Plata. Facultad de Cs.exactas. Centro de Matematica de la Plata.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Garay, Arturo. Centro de Educaciones Médicas e Investigación Clínica "Norberto Quirno"; Argentina