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Latent class analysis of depression and anxiety among medical students during COVID-19 epidemic
OBJECTIVE: The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. This study aims to identify subgroups of medical students based on depressio...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8506472/ https://www.ncbi.nlm.nih.gov/pubmed/34641795 http://dx.doi.org/10.1186/s12888-021-03459-w |
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author | Liu, Zhuang Liu, Rongxun Zhang, Yue Zhang, Ran Liang, Lijuan Wang, Yang Wei, Yange Zhu, Rongxin Wang, Fei |
author_facet | Liu, Zhuang Liu, Rongxun Zhang, Yue Zhang, Ran Liang, Lijuan Wang, Yang Wei, Yange Zhu, Rongxin Wang, Fei |
author_sort | Liu, Zhuang |
collection | PubMed |
description | OBJECTIVE: The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. This study aims to identify subgroups of medical students based on depression and anxiety and explore the influencing factors during the COVID-19 epidemic in China. METHODS: A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Depression and anxiety symptoms were assessed using Patient Health Questionnaire 9 (PHQ9) and Generalized Anxiety Disorder 7 (GAD7) respectively. Latent class analysis was performed based on depression and anxiety symptoms in medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. RESULTS: In this study, three distinct subgroups were identified, namely, the poor mental health group, the mild mental health group and the low symptoms group. The number of medical students in each class is 4325, 9321 and 16,017 respectively. The multinomial logistic regression results showed that compared with the low symptoms group, the factors influencing depression and anxiety in the poor mental health group and mild mental health group were sex, educational level, drinking, individual psychiatric disorders, family psychiatric disorders, knowledge of COVID-19, fear of being infected, and participate in mental health education on COVID-19. CONCLUSIONS: Our findings suggested that latent class analysis can be used to categorize different medical students according to their depression and anxiety symptoms during the outbreak of COVID-19. The main factors influencing the poor mental health group and the mild mental health group are basic demographic characteristics, disease history, COVID-19 related factors and behavioural lifestyle. School administrative departments can carry out targeted psychological counseling according to different subgroups to promote the physical and mental health of medical students. |
format | Online Article Text |
id | pubmed-8506472 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85064722021-10-12 Latent class analysis of depression and anxiety among medical students during COVID-19 epidemic Liu, Zhuang Liu, Rongxun Zhang, Yue Zhang, Ran Liang, Lijuan Wang, Yang Wei, Yange Zhu, Rongxin Wang, Fei BMC Psychiatry Research OBJECTIVE: The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. This study aims to identify subgroups of medical students based on depression and anxiety and explore the influencing factors during the COVID-19 epidemic in China. METHODS: A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Depression and anxiety symptoms were assessed using Patient Health Questionnaire 9 (PHQ9) and Generalized Anxiety Disorder 7 (GAD7) respectively. Latent class analysis was performed based on depression and anxiety symptoms in medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. RESULTS: In this study, three distinct subgroups were identified, namely, the poor mental health group, the mild mental health group and the low symptoms group. The number of medical students in each class is 4325, 9321 and 16,017 respectively. The multinomial logistic regression results showed that compared with the low symptoms group, the factors influencing depression and anxiety in the poor mental health group and mild mental health group were sex, educational level, drinking, individual psychiatric disorders, family psychiatric disorders, knowledge of COVID-19, fear of being infected, and participate in mental health education on COVID-19. CONCLUSIONS: Our findings suggested that latent class analysis can be used to categorize different medical students according to their depression and anxiety symptoms during the outbreak of COVID-19. The main factors influencing the poor mental health group and the mild mental health group are basic demographic characteristics, disease history, COVID-19 related factors and behavioural lifestyle. School administrative departments can carry out targeted psychological counseling according to different subgroups to promote the physical and mental health of medical students. BioMed Central 2021-10-12 /pmc/articles/PMC8506472/ /pubmed/34641795 http://dx.doi.org/10.1186/s12888-021-03459-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Liu, Zhuang Liu, Rongxun Zhang, Yue Zhang, Ran Liang, Lijuan Wang, Yang Wei, Yange Zhu, Rongxin Wang, Fei Latent class analysis of depression and anxiety among medical students during COVID-19 epidemic |
title | Latent class analysis of depression and anxiety among medical students during COVID-19 epidemic |
title_full | Latent class analysis of depression and anxiety among medical students during COVID-19 epidemic |
title_fullStr | Latent class analysis of depression and anxiety among medical students during COVID-19 epidemic |
title_full_unstemmed | Latent class analysis of depression and anxiety among medical students during COVID-19 epidemic |
title_short | Latent class analysis of depression and anxiety among medical students during COVID-19 epidemic |
title_sort | latent class analysis of depression and anxiety among medical students during covid-19 epidemic |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8506472/ https://www.ncbi.nlm.nih.gov/pubmed/34641795 http://dx.doi.org/10.1186/s12888-021-03459-w |
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