Brief reportThe continuity of depression symptoms: Use of cluster analysis for profile identification in patient and student samples
Section snippets
Participants
The clinically depressed sample consisted of 101 adult outpatients with a primary DSM-IV (APA, 1994) diagnosis of major depressive disorder (59 females, 42 males; mean age=44.39 years; S.D.=14.05). All patients were experiencing a DSM-IV-defined major depressive episode and were non-psychotic. Dysthymia (29%), panic disorder (21%) and social phobia (17%) were the most common comorbid conditions.
The non-clinical sample comprised 351 undergraduate university students. The analogue depressed group
Statistical recovery of clinically depressed patients and non-distressed students
It is important to establish the sensitivity of the clustering algorithm (i.e., its ability to identify known cases of depression) if cluster assignments are to be useful. Therefore, the DDS symptom profiles of the 101 clinically depressed patients were analyzed with the clustering procedure, to determine if a high percentage could be ‘recovered’. This requirement was met, with 96.0% being correctly classified. An identical procedure was employed with the non-distressed college students and
Discussion
The marked similarity of the prototypical cluster profiles of the non-depressed, analogue, and clinical samples is reflective of the qualitative similarity previously identified (Cox et al., 1999). The present results shed light on the nature of the quantitative differences in depression symptom continuity in non-clinical and clinical samples. The close match in the DSM-IV depression symptom profiles of the more conservatively selected analogue groups and their clinical counterparts is apparent
Acknowledgements
This research was supported by an operating grant from the Manitoba Health Research Council. The authors are grateful to Sharon Borger for assistance with data collection and analysis, and to Drs. Harvey Keselman and Keith Wilson for their helpful comments on an earlier version of this paper.
References (14)
- et al.
Subthreshold depressions: clinical and polysomnographic validation of dysthymic residual and masked forms
J. Affect. Dis.
(1997) - et al.
The nature of the depressive experience in analogue and clinically depressed samples
Behav. Res. Ther.
(1999) - et al.
Prevalence, correlates, and course of minor depression and major depression in the national comorbidity survey
J. Affect. Dis.
(1997) Diagnostic and Statistical Manual of Mental Disorders
(1994)- et al.
Cognitive Therapy of Depression
(1979) - et al.
An inventory for measuring depression
Arch. Gen. Psychiatry
(1961) - et al.
Structured Clinical Interview for DSM-IV axis I Disorders
(1995)
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