Abstract
AIM: The current study aims to characterize the longitudinal patterns of depression subtypes and investigate the associations among the stability of depression subtypes, COVID-19-related stressors, and depression severity. METHODS: The study utilized data from the Canadian Longitudinal Study on Aging, which is a national, long-term study of Canadian adults aged 45 and older (n = 12,957). Latent profile analysis was used to identify latent depression subtypes. Latent transition analysis was then applied to assess the stability of these subtypes over time. Hierarchical multivariate linear regression was used to explore the relationships among these identified depression subtypes, COVID-19-related stressors, and depression severity among males and females, respectively. RESULTS: Distinct depression subtypes were identified. Except for atypical depression, other depression subtypes showed greater stability over time. We also found that melancholic depression (B = 9.432) and typical depression (B = 6.677) were strongly associated with depression severity during the pandemic. Health-related stressors (B = 0.840), conflict (B = 3.639), difficulties accessing resources (B = 0.927), separation from family (B = 0.840), and caregiving experience (B = 0.764), were significantly associated with increased depression severity. Sex-specific analyses also revealed differences in the associations between stressors and depression severity between males and females. CONCLUSIONS: This study contributes valuable insights into the latent clustering of depression subtypes and their stability. Stressors were associated with increased depression severity, with distinct associations observed among males and females. These findings have implications for targeted early interventions and integrated clinical management strategies by providing the evidence base for tailored mental health care during and after the pandemic.