مقاله انگلیسی رایگان در مورد ارزیابی افسردگی در صرع – تیلور و فرانسیس 2017

Depression is common in epilepsy with prevalence rates ranging from 20 to 55% in most samples (Kanner, 2003) and reports as high as 70% in patients whose seizures are refractory to medications (Devinsky, 2003). In general, individuals with epilepsy are thought to have nearly a four- to fivefold higher incidence of depression than the general population (Kanner & Palac, 2000). Depression in epilepsy is known to significantly impact a patient’s quality of life, may impede treatment compliance and efficacy, and most importantly, may contribute to a high suicide risk, which is nearly five to 10 times greater in patients with epilepsy (PWE) than in the general population (Kanner, 2003; Kanner & Palac, 2000). Given the suspected high prevalence, the overall grave impact that depression may have on individuals, and the availability of effective treatments, proper detection of depression among PWE is paramount. However, it has been suggested that depression frequently goes unrecognized and undertreated among PWE (Boylan et al., 2004; Wiegartz, Seidenberg, Woodard, Gidal, & Hermann, 1999). Conversely, others contend that depression may be over-diagnosed in epilepsy, particularly when utilizing self-report screening measures, which are heavily weighted with neurovegetative items such as questions concerning fatigue, sleep disturbances, and appetite changes that may overlap with symptoms related to seizure activity and/or side effects of antiepileptic medications (AEDs; Gilliam et al., 2006). Possible explanations for the inaccurate detection of depression (either under- or over-diagnosing) by practitioners include: (a) the host of positive and negative effects of AEDs on mood; (b) difficulty in appreciating the difference between normal adjustment to a medical diagnosis and clinical depression, including the so called ‘understandability phenomenon’ (Blanchard, 1992), which asserts that depressed mood in epilepsy is understandable given the limitations imposed by seizures and the unpredictability of the disease; (c) differences in symptom complaint by patients; and/or (d) failure to consider the overlap of disease symptoms and somatic complaints of depression due to the use of diagnostic criteria and self-report depression screening measures designed for the general, non-medical population. This latter hurdle may be one of the greatest culprits for the inaccurate detection of depression and thus is the focus of the present investigation. Common and widely used self-report depression screening measures such as the Beck Depression Inventory—Second Edition (BDI-II; Beck, Steer, & Brown, 1996) and the Center for Epidemiological Studies Depression Scale (CES-D; Radloff, 1977) are based on Diagnostic and Statistical Manual for Mental Disorders (DSM) criteria for major depression. Criteria for a major depressive disorder includes the endorsement of five of nine of the following symptoms (with at least one from the first two): depressed mood, loss of interest, sleep changes, appetite changes, fatigue, psychomotor retardation, concentration difficulties, feelings of guilt/worthlessness, and suicidal ideation. Individuals with a medical illness such as epilepsy can easily endorse five out of nine of these symptoms by virtue of their illness and not as a result of experiencing major depression. While these measures have been well validated in the general population, they have not been standardized for use with medical populations. Due to this lack of medical standardization, the BDI-II and CES-D may have limited validity when used in this context, given the significant overlap of neurovegetative symptoms of depression and disease symptoms. Further, a review of nine common self-report depression screening measures used in primary care settings involving the normal population revealed only an average sensitivity of 84% and specificity of 72% (Mulrow et al., 1995). With the report of common depression screening measures yielding a nearly 30% false positive rate (Mulrow et al., 1995), it has been recommended that measures be designed specifically for the medically ill, or even more optimally, for the particular disease of interest.