Latent class of depressive symptoms of and its determinants: A cross-sectional study among Iranian University students

抑郁症状的潜在类别及其决定因素:一项针对伊朗大学生的横断面研究

阅读:2

Abstract

BACKGROUND: According to the report of the World Health Organization, mental disorders are one of the 10 most important causes of disability in the world. This study was conducted with the aim of determining the number and frequency of latent classes of depression and its determinants in Isfahan university of medical students. MATERIALS AND METHODS: A total of 1408 medical students from Isfahan University of Medical Sciences, Iran, were enrolled in the study in 2017. The symptoms and severity of depression were assessed using the standard Hospital Anxiety and Depression scale questionnaire. Latent class analysis was applied to seven symptoms of depression, all of which had four levels. Latent class subgroups were compared using the Chi-square test and analysis of variance test. The regression model was used to check the relationship between identified classes and related factors. Analyzes were done using SPSS-21 and Mplus7 software. RESULTS: In this study, three latent classes were identified, that is, the group of healthy people, the group of borderline people, and the group of people suspected of depression. The prevalence of identified latent classes among medical students is 0.52, 0.32, and 0.16%, respectively. The regression results showed that compared to the healthy group, the factors affecting depression in the borderline and suspicious group were increasing age, female gender, interest in the field of study, physical activity, history of depression, and history of anxiety. CONCLUSION: The three classes that were identified based on the students' answers to the depression symptoms questions differed only based on severity. The history of depression and anxiety were the strongest predictors of latent classes of depression.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。