Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach
Background: Healthcare workers are at high risk for developing mental health problems during the COVID-19 pandemic. There is an urgent need to identify vulnerability and protective factors related to the severity of psychiatric symptoms among healthcare workers to implement targeted prevention and i...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-01-01
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| Series: | Frontiers in Psychiatry |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2021.752870/full |
| _version_ | 1818968751207350272 |
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| author | Liana C. L. Portugal Liana C. L. Portugal Camila Monteiro Fabricio Gama Raquel Menezes Gonçalves Mauro Vitor Mendlowicz Fátima Smith Erthal Izabela Mocaiber Konstantinos Tsirlis Eliane Volchan Isabel Antunes David Mirtes Garcia Pereira Leticia de Oliveira |
| author_facet | Liana C. L. Portugal Liana C. L. Portugal Camila Monteiro Fabricio Gama Raquel Menezes Gonçalves Mauro Vitor Mendlowicz Fátima Smith Erthal Izabela Mocaiber Konstantinos Tsirlis Eliane Volchan Isabel Antunes David Mirtes Garcia Pereira Leticia de Oliveira |
| author_sort | Liana C. L. Portugal |
| collection | DOAJ |
| description | Background: Healthcare workers are at high risk for developing mental health problems during the COVID-19 pandemic. There is an urgent need to identify vulnerability and protective factors related to the severity of psychiatric symptoms among healthcare workers to implement targeted prevention and intervention programs to reduce the mental health burden worldwide during COVID-19.Objective: The present study aimed to apply a machine learning approach to predict depression and PTSD symptoms based on psychometric questions that assessed: (1) the level of stress due to being isolated from one's family; (2) professional recognition before and during the pandemic; and (3) altruistic acceptance of risk during the COVID-19 pandemic among healthcare workers.Methods: A total of 437 healthcare workers who experienced some level of isolation at the time of the pandemic participated in the study. Data were collected using a web survey conducted between June 12, 2020, and September 19, 2020. We trained two regression models to predict PTSD and depression symptoms. Pattern regression analyses consisted of a linear epsilon-insensitive support vector machine (ε-SVM). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r), the coefficient of determination (r2), and the normalized mean squared error (NMSE) to evaluate the model performance. A permutation test was applied to estimate significance levels.Results: Results were significant using two different cross-validation strategies to significantly decode both PTSD and depression symptoms. For all of the models, the stress due to social isolation and professional recognition were the variables with the greatest contributions to the predictive function. Interestingly, professional recognition had a negative predictive value, indicating an inverse relationship with PTSD and depression symptoms.Conclusions: Our findings emphasize the protective role of professional recognition and the vulnerability role of the level of stress due to social isolation in the severity of posttraumatic stress and depression symptoms. The insights gleaned from the current study will advance efforts in terms of intervention programs and public health messaging. |
| first_indexed | 2024-12-20T14:09:40Z |
| format | Article |
| id | doaj.art-9cbd9f9a87414d3a9ba659d6b3ef044a |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-12-20T14:09:40Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-9cbd9f9a87414d3a9ba659d6b3ef044a2022-12-21T19:38:10ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402022-01-011210.3389/fpsyt.2021.752870752870Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning ApproachLiana C. L. Portugal0Liana C. L. Portugal1Camila Monteiro Fabricio Gama2Raquel Menezes Gonçalves3Mauro Vitor Mendlowicz4Fátima Smith Erthal5Izabela Mocaiber6Konstantinos Tsirlis7Eliane Volchan8Isabel Antunes David9Mirtes Garcia Pereira10Leticia de Oliveira11Neurophysiology Laboratory, Department of Physiological Sciences, Roberto Alcantara Gomes Biology Institute, Biomedical Center, State University of Rio de Janeiro, Rio de Janeiro, BrazilLaboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Federal Fluminense University, Rio de Janeiro, BrazilLaboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Federal Fluminense University, Rio de Janeiro, BrazilLaboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Federal Fluminense University, Rio de Janeiro, BrazilDepartment of Psychiatry and Mental Health, Fluminense Federal University, Rio de Janeiro, BrazilLaboratory of Neurobiology, Institute of Biophysics Carlos Chagas Filho, Rio de Janeiro, BrazilLaboratory of Cognitive Psychophysiology, Department of Natural Sciences, Institute of Humanities and Health, Federal Fluminense University, Rio de Janeiro, BrazilCentre for Medical Image Computing, University College London, London, United KingdomLaboratory of Neurobiology, Institute of Biophysics Carlos Chagas Filho, Rio de Janeiro, BrazilLaboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Federal Fluminense University, Rio de Janeiro, BrazilLaboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Federal Fluminense University, Rio de Janeiro, BrazilLaboratory of Neurophysiology of Behavior, Department of Physiology and Pharmacology, Biomedical Institute, Federal Fluminense University, Rio de Janeiro, BrazilBackground: Healthcare workers are at high risk for developing mental health problems during the COVID-19 pandemic. There is an urgent need to identify vulnerability and protective factors related to the severity of psychiatric symptoms among healthcare workers to implement targeted prevention and intervention programs to reduce the mental health burden worldwide during COVID-19.Objective: The present study aimed to apply a machine learning approach to predict depression and PTSD symptoms based on psychometric questions that assessed: (1) the level of stress due to being isolated from one's family; (2) professional recognition before and during the pandemic; and (3) altruistic acceptance of risk during the COVID-19 pandemic among healthcare workers.Methods: A total of 437 healthcare workers who experienced some level of isolation at the time of the pandemic participated in the study. Data were collected using a web survey conducted between June 12, 2020, and September 19, 2020. We trained two regression models to predict PTSD and depression symptoms. Pattern regression analyses consisted of a linear epsilon-insensitive support vector machine (ε-SVM). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r), the coefficient of determination (r2), and the normalized mean squared error (NMSE) to evaluate the model performance. A permutation test was applied to estimate significance levels.Results: Results were significant using two different cross-validation strategies to significantly decode both PTSD and depression symptoms. For all of the models, the stress due to social isolation and professional recognition were the variables with the greatest contributions to the predictive function. Interestingly, professional recognition had a negative predictive value, indicating an inverse relationship with PTSD and depression symptoms.Conclusions: Our findings emphasize the protective role of professional recognition and the vulnerability role of the level of stress due to social isolation in the severity of posttraumatic stress and depression symptoms. The insights gleaned from the current study will advance efforts in terms of intervention programs and public health messaging.https://www.frontiersin.org/articles/10.3389/fpsyt.2021.752870/fullCOVID-19PTSDdepressionhealthcare worker (HCW)machine learning |
| spellingShingle | Liana C. L. Portugal Liana C. L. Portugal Camila Monteiro Fabricio Gama Raquel Menezes Gonçalves Mauro Vitor Mendlowicz Fátima Smith Erthal Izabela Mocaiber Konstantinos Tsirlis Eliane Volchan Isabel Antunes David Mirtes Garcia Pereira Leticia de Oliveira Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach Frontiers in Psychiatry COVID-19 PTSD depression healthcare worker (HCW) machine learning |
| title | Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach |
| title_full | Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach |
| title_fullStr | Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach |
| title_full_unstemmed | Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach |
| title_short | Vulnerability and Protective Factors for PTSD and Depression Symptoms Among Healthcare Workers During COVID-19: A Machine Learning Approach |
| title_sort | vulnerability and protective factors for ptsd and depression symptoms among healthcare workers during covid 19 a machine learning approach |
| topic | COVID-19 PTSD depression healthcare worker (HCW) machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2021.752870/full |
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