AUTHOR=Gomez Rapson , Skilbeck Clive , Thomas Matt , Slatyer Mark 

TITLE=Growth Mixture Modeling of Depression Symptoms Following Traumatic Brain Injury

JOURNAL=Frontiers in Psychology

VOLUME=Volume 8 - 2017

YEAR=2017

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2017.01320

DOI=10.3389/fpsyg.2017.01320

ISSN=1664-1078

ABSTRACT=Latent Class Growth Analysis (LCGA) was used to investigate the longitudinal trajectory of groups (classes) of depression symptoms, and how these groups were predicted by the covariates of age, sex, severity, and length of hospitalisation following Traumatic Brain Injury(TBI). The study also examined the associations between groups and TBI cause.  Participants were 1074 patients (696 male) with TBI. Symptoms of depression were assessed using the Hospital Anxiety and Depression Scale (HADS) within 3 weeks of injury, and at 1, 3, 6, 12, and 24 months post-injury. The results revealed three groups: low, high, and delayed depression. The low groups depression scores remained below the clinical cut-off at all assessments during the 24-months post-TBI, and the high group produced scores above the clinical cut-off at all assessments.  The delayed group showed an increase in depression symptoms to 12 months after injury, followed by a return to initial assessment level during the following 12 months.   Covariates were found to be differentially associated with the 3 groups.  For example, relative to the low group, high depression was associated with more severe TBI, being female, and a shorter period of hospitalisation.  The delayed group also had a shorted period of hospitalisation, were younger, and sustained less severe TBI. The clinical implications of the findings are discussed.

Key Words: latent class growth modelling, traumatic brain injury, depression, outcome.