How final year high school students’ depression develop during COVID-19 in China? A latent class growth modeling analysis

Abstract

Depression increased sharply during the initial months of COVID-19, but how it developed over time is rarely explored, especially for adolescents. The current study measured depression of 605 final year high school students in China over 11 months in 4 waves. The latent growth curve modeling (LGCM) was used to examine overall trends in depression and latent class growth modeling (LCGM) was used to identify potential subgroups of adolescents' depressive trajectories. At the same time, gender, life events, and rumination were included as time-invariant covariates. Overall, the development of depression in the final year of high school students showed a slight downward trend. Meanwhile, the depression trajectories showed heterogeneity, and three categories of depression trajectories were identified, which were low-stable (24.3%), depression-risk (67.9%), and high-stable (7.8%). Neuroticism, rumination, and life events such as punishment and loss were found to significantly predict these trajectories of depression. This study helps to characterize differential depression trajectories among adolescents throughout the COVID-19 pandemic and establish several related predictors of the trajectory of depression.