Diagnostic categories or dimensions? A question for the Diagnostic And Statistical Manual Of Mental Disorders--fifth edition.
ABSTRACT The question of whether mental disorders are discrete clinical conditions or arbitrary distinctions along dimensions of functioning is a long-standing issue, but its importance is escalating with the growing recognition of the frustrations and limitations engendered by the categorical model. The authors provide an overview of some of the dilemmas of the categorical model, followed by a discussion of research that addresses whether mental disorders are accurately or optimally classified categorically or dimensionally. The authors' intention is to document the importance of this issue and to suggest that future editions of the Diagnostic and Statistical Manual of Mental Disorders give more recognition to dimensional models of classification. They conclude with a dimensional mental disorder classification that they suggest provides a useful model.
Full-textDOI: · Available from: Douglas B Samuel, Nov 24, 2014
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ABSTRACT: The course-heterogeneity of Major Depressive Disorder (MDD) hampers development of better prognostic models. Although latent class growth analyses (LCGA) have been used to explain course-heterogeneity, such analyses have failed to also account for symptom-heterogeneity of depressive symptoms. Therefore, the aim was to identify more specific data-driven subgroups based on patterns of course-trajectories on different depressive symptom domains. In primary care MDD patients (n=205), the presence of the MDD criterion symptoms was determined for each week during a year. Weekly 'mood/cognition' (MC) and 'somatic' (SOM) scores were computed and parallel processes-LCGA (PP-LCGA) was used to identify subgroups based on the course on these domains. The classes׳ associations with baseline predictors and 2-/3-year outcomes were investigated. PP-LCGA identified four classes: quick recovery, persisting SOM, persisting MC, and persisting SOM+MC (chronic). Persisting SOM was specifically predicted by higher baseline somatic symptomatology and somatization, and was associated with more somatic depressive symptomatology at long-term follow-up. Persisting MC was specifically predicted by higher depressive severity, thinking insufficiencies, neuroticism, loneliness and lower self-esteem, and was associated with lower mental health related quality of life and more mood/cognitive depressive symptomatology at follow-up. The sample was small and contained only primary care MDD patients. The weekly depression assessments were collected retrospectively at 3-month intervals. The results indicate that there are two specific prototypes of depression, characterized by either persisting MC or persisting SOM, which have different sets of associated prognostic factors and long-term outcomes, and could have different etiological mechanisms. Copyright © 2015 Elsevier B.V. All rights reserved.Journal of Affective Disorders 03/2015; 179:38-46. DOI:10.1016/j.jad.2015.03.029 · 3.71 Impact Factor
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ABSTRACT: Atypical response behavior on depression questionnaires may invalidate depression severity measurements. This study aimed to identify and investigate atypical profiles of depressive symptoms using a data-driven approach based on the item response theory (IRT). A large cohort of participants completed the Inventory of Depressive Symptomatology self-report (IDS-SR) at baseline (n=2329) and two-year follow-up (n=1971). Person-fit statistics were used to quantify how strongly each patient׳s observed symptom profile deviated from the expected profile given the group-based IRT model. Identified atypical profiles were investigated in terms of reported symptoms, external correlates and temporal consistency. Compared to others, atypical responders (6.8%) showed different symptom profiles, with higher 'mood reactivity' and 'suicidal ideation' and lower levels of mild symptoms like 'sad mood'. Atypical responding was associated with more medication use (especially tricyclic antidepressants: OR=1.5), less somatization (OR=0.8), anxiety severity (OR=0.8) and anxiety diagnoses (OR=0.8-0.9), and was shown relatively stable (29.0%) over time. This is a methodological proof-of-principal based on the IDS-SR in outpatients. Implementation studies are needed. Person-fit statistics can be used to identify patients who report atypical patterns of depressive symptoms. In research and clinical practice, the extra diagnostic information provided by person-fit statistics could help determine if respondents׳ depression severity scores are interpretable or should be augmented with additional information. Copyright © 2015 Elsevier B.V. All rights reserved.Journal of Affective Disorders 07/2015; 180:36-34. DOI:10.1016/j.jad.2015.03.043 · 3.71 Impact Factor