Modelling risk factors for depressive symptoms in seizure disorders
TL;DR
Exploratory factor analysis elucidates the intricate relationships between variables that impact depressive symptoms in individuals with seizure disorders, providing insight into how depressive symptoms develop in this population of adults.
Abstract
Background: Depression is a common mental health comorbidity among individuals with seizure disorders. The emergence of depression results from the complex interplay of clinical, psychological and social factors. Objectives: To identify clusters of risk factors associated with depressive symptoms in adults with seizure disorders. This study uses exploratory factor analysis (EFA) to model a range of variables and investigate their interactions and influence on depressive symptoms. Design: Cross-sectional observational study. Methods: A retrospective examination of medical records identified 121 patients with seizures and moderate depressive symptoms. Demographic, clinical and psychosocial data were extracted and modelled using EFA. Factor structure was determined by adhering to standard fit criteria: CFI ⩾0.90, TFI ⩾0.90, RMSEA ⩽0.08 and latent factor loadings ⩾0.4. Results: The mean age of the cohort was 31.3 ± 10.0 years (range of 18–64), comprising 68 males (56.2%) and 53 married individuals (43.8%). Qataris constituted the largest nationality at 48 (39.7%), and the most prevalent diagnosis was right temporal lobe epilepsy (28.9%). Moderate depressive symptoms were the most common PHQ-9 classification (42.1%), with an average score of 14. EFA yielded a three-factor solution (χ2 = 71.9, df = 52, RMSEA = 0.06, CFI = 0.96, TFI = 0.92) with the first latent factor incorporating age and social variables, the second encompassing medications and cognitive abilities, and a final factor encapsulating seizure-related items. Conclusion: Exploratory factor analysis elucidates the intricate relationships between variables that impact depressive symptoms in individuals with seizure disorders. It highlights how clusters of variables emerge, providing insight into how depressive symptoms develop in this population. These findings offer new potential avenues for the management and treatment of depression in people experiencing these debilitating events.