Objective: To identify the frequency and manifestations of depression after traumatic brain injury (TBI) and the factors that contribute to developing this mood disorder.
Design: A prospective, nationwide, multicenter study; 17 centers supplied data from medical records and patient responses on a standardized criterion instrument.
Setting: Traumatic Brain Injury Model Systems programs.
Participants: A demographically diverse sample of 666 outpatients with TBI was evaluated 10 to 126 months after injury.
Interventions: Not applicable.
Main outcome measures: Depressive symptoms were characterized with the Neurobehavioral Functioning Inventory by using the Diagnostic and Statistical Manual of Mental Disorders (4th ed; DSM-IV) diagnostic framework. Analysis of variance and Pearson correlations were used to identify factors that were significantly related to depression.
Results: Fatigue (29%), distractibility (28%), anger or irritability (28%), and rumination (25%) were the most commonly cited depressive symptoms in the sample. Twenty-seven percent of patients with TBI met the prerequisite number (>/=5) of criterion A symptoms for a DSM-IV diagnosis of major depressive disorder. Feeling hopeless, feeling worthless, and difficulty enjoying activities were the 3 symptoms that most differentiated depressed from nondepressed patients. Patients who were unemployed at the time of injury and who were impoverished were significantly more likely to report DSM-IV criterion A symptoms than patients who were employed, were students, or were retired due to age. Time after injury, injury severity, and postinjury marital status were not significantly related to depression.
Conclusions: Patients with TBI are at great risk for developing depressive symptoms. Findings provide empirical support for the inclusion of depression evaluation and treatment protocols in brain injury programs. Unemployment and poverty may be substantial risk factors for the development of depressive symptoms. Future research should develop biopsychosocial predictive models to identify high-risk patients and examine the efficacy of treatment interventions.