Mental health profiles and the related socio-demographic predictors in Hong Kong university students under the COVID-19 pandemic: A latent class analysis

Abstract

While the COVID-19 pandemic has brought about significant challenges to mental health of university students, there is limited research in this area. Particularly, few studies examined the person-centered mental health symptom profiles such as depression and anxiety and the related socio-demographic predictors. Using Latent Class Analysis (LCA), this study investigated the symptom profiles of depression and anxiety in university students in Hong Kong under the COVID-19 pandemic and the socio-demographic predictors. A total of 978 undergraduate students completed an online questionnaire including socio-demographic factors and measures of depression and anxiety during the summer of 2022. The LCA identified three latent classes: “normal” group, “moderate comorbid depression and anxiety” group and “severe comorbid depression and anxiety” group. Multinominal logistic regression showed that comparing with the “normal” group and the “moderate symptom” group, the “severe symptom” group had higher personal financial difficulties and individual/family member unemployment during the pandemic. In contrast, other socio-demographic factors (age, gender, year of study, living status, and COVID-19 infection status) had no significant association with group status. The study contributes to understanding of person-centered depression and anxiety symptom profiles and the risk role of personal financial difficulty in mental health of university students under the pandemic.