Background: This study identified differences between individuals with and without depression regarding demographic and socioeconomic variables, health behavior, health status, health care utilization, and self-rated health (SRH) to identify the depressed group's SRH trajectories.
Methods: Data of individuals with (n = 589) and without (n = 6856) depression aged ≥20 from the 2013-2017 Korean Health Panel were analyzed. A chi-square test and t-tests examined differences in demographic and socioeconomic variables, health behaviors, health status, health care utilization, and the mean of SRH. Latent Growth Curve and Latent Class Growth Modeling identified SRH development trajectories and the most suitable latent classes explaining the trajectories, respectively. Multinomial logistic regression determined the predicting factors that classified latent classes.
Results: The depressed group had a lower mean SRH than the non-depressed group among most variables. Three latent classes were identified, each showing different SRH trajectories. Body-mass index and pain/discomfort were predicting factors for the "poor" classes compared with the "moderate-stable" class; older age, less national health insurance, less physical activity, more pain/discomfort, and more hospitalization were predictors for the "poor-stable" class. The depressed group's mean SRH was "poor."
Limitations: Latent Class Growth Modeling in individuals with depression was based on experimental data; however, it needed to review other sample data to identify similar types of latent classes to those suggested in the current study.
Conclusions: Predictors of the "poor-stable" class that were identified in this study can contribute to the formulation of intervention plans for the health and welfare of individuals with depression.
Keywords: Depression; Latent class growth modeling; Latent growth curve modeling; Self-rated health.
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