Abstract
Major depression disorder (MDD) is a complex neuropsychiatric disorder and an increasing number of genetic risk variants are being identified. Investigation of their influence in the general population requires accurate and efficient assessment of depressive symptoms. Here, clinical interviews conducted by clinicians are the gold standard. We investigated whether valid and reliable clinical phenotypes can be obtained efficiently using self-administered instruments. Lifetime depressive symptoms and lifetime MDD diagnosis were assessed in 464 population-based individuals using a clinical interview and a structured, self-administered checklist. Analyses were carried out of the following: (i) intraclass correlations (ICC) between checklist and interview;(ii) sensitivity/specificity of the checklist;and (iii) the association of interview and checklist with a positive family history of MDD (FH-MDD +). The correspondence of the self-administered checklist with the clinical interview was good for most depressive symptoms (ICC = 0.60-0.80) and moderate for MDD diagnosis (ICC = 0.45). With the consecutive inclusion of MDD diagnostic criteria, sensitivity decreased from 0.67 to 0.46, whereas specificity remained high (0.95). For checklist and interview, strong associations were found between FH-MDD + and most depressive symptoms and MDD diagnosis (all odds ratio >= 1.83). The self-administered checklist showed high reliability for both the assessment of lifetime depressive symptoms and screening for individuals with no lifetime diagnosis of MDD. However, attention is warranted when the aim is to identify MDD cases. The positive association between depressive symptomatology and FH-MDD + indicates the usefulness of both instruments to assess patients in genetic studies. Our data suggest that the more time-efficient and cost-efficient self-administered instruments also allow for the assessment of depressive symptoms accurate enough to investigate the influence of MDD genetic risk variants in the general population. Copyright (C) 2017 The Author(s). Published by Wolters Kluwer Health, Inc.
Item Type: | Journal article |
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Faculties: | Medicine |
Subjects: | 600 Technology > 610 Medicine and health |
ISSN: | 0955-8829 |
Language: | English |
Item ID: | 51652 |
Date Deposited: | 14. Jun 2018, 09:47 |
Last Modified: | 04. Nov 2020, 13:29 |