Subgroups of depressive Symptoms determined by a Latent Class Analysis in a Chinese College Students Population during COVID-19

Abstract

Background: The ongoing epidemic continues to exert a lasting impact on the physical and mental health of students. Therefore, it is crucial to identify the related risk factors and target populations with a high risk of mental health issues. Method: A sample of 6309 participants from 6 colleges in Guangdong Province, China was recruited from June 10 to 18, 2021. Depressive symptoms were assessed using the Patient Health Questionnaire and risk factors were assessed using the Intolerance of Uncertainty (IU) scale and demographics. Data analysis consisted of chi-square test, latent class analysis, and Multinomial Logistic Regression. Result: (1) Four latent subgroups were identified: “low-symptom group (2244, 35.57%),” “moderate-symptom group (1323, 20.97%),” “major-symptom with low-risk of suicide group (1128, 17.88%),” and “major-symptom with high-risk of suicide group (1614, 25.58%).” (2) Students with high IU were more likely to report severe suicidal ideation (OR=14.14, p<0.001,). (3) Although females were more likely to have depressive symptoms (OR=1.55, p<0.001,), but that of male may be server once they had depressive symptoms (OR=0.73, p<0.001,). (4) The students with severe depressive symptoms were likely to be non-exercise, uninfected area, unvaccinated and severer impact on family economy Conclusion: Our findings offer valuable data support for identifying subgroups that are particularly susceptible to developing depressive symptoms during the epidemic. These results have important implications for mental health interventions and the formulation of public health policies.