Assessment of depression in epilepsy: the utility of common and disease-specific self-report depression measures. Issue 4 (19th May 2018)

• Record Type: Journal Article
• Authors: Strober, Lauren B.; Chapin, Jessica; Spirou, Angela; Tesar, George; Viguera, Adele; Najm, Imad; Busch, Robyn M.
• Is Part Of: Clinical neuropsychologist. Volume 32:Issue 4(2018)
• Page Start: 681
• Page End: 699
• Publication Date: 2018-05-19
• Subjects: Epilepsy -- depression -- assessment -- measurement -- quality of life -- psychological functioning; Neuropsychology -- Periodicals; Neuropsychology -- Periodicals; Mental Disorders -- Periodicals; 612.805
• DOI: 10.1080/13854046.2017.1400107
• Languages: English
• ISSNs: 1385-4046

Abstract

Abstract: Objectives: Depression is common in epilepsy, with rates ranging from 20 to 55% in most samples and reports as high as 70% in patients with intractable epilepsy. However, some contend that depression may be over- and/or under-reported and treated in this population. This may be due to the use of common self-report depression measures that fail to take into account the overlap of disease and depressive symptoms and also the host of side effects associated with antiepileptic medication, which may also be construed as depression.Methods: The present study examined the utility of common self-report depression measures and those designed specifically for the medically ill, including a proposed new measure, to determine which may be more appropriate for use among people with epilepsy.Results: We found that common self-report depression measures are useful for screening depression in epilepsy, particularly with a raised cutoff for one, with sensitivities ranging from .91 to .96. A measure designed for the medically ill obtained the greatest specificity of .91, suggesting its use as a diagnostic tool with a slightly raised cutoff. The positive likelihood ratio of this latter measure was 8.76 with an overall classification accuracy of 88%.Conclusions: Assessment of depression in epilepsy can be improved when utilizing self-report measures that better differentiate disease symptoms from neurovegetative symptoms of depression (e.g. fatigue, sleep disturbance). This was demonstrated in the present study. Clinical implications are discussed.