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Heterogeneity in symptom profiles among older adults diagnosed with major depression

Published online by Cambridge University Press:  18 January 2011

Celia F. Hybels*
Affiliation:
Department of Psychiatry and Behavioral Sciences, Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, North Carolina, U.S.A.
Dan G. Blazer
Affiliation:
Department of Psychiatry and Behavioral Sciences, Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, North Carolina, U.S.A.
Lawrence R. Landerman
Affiliation:
Department of Medicine, Division of Geriatrics, Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, North Carolina, U.S.A.
David C. Steffens
Affiliation:
Department of Psychiatry and Behavioral Sciences, Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, North Carolina, U.S.A.
*
Correspondence should be addressed to: Dr. Celia F. Hybels, Department of Psychiatry and Behavioral Sciences, Center for the Study of Aging and Human Development, Box 3003, Duke University Medical Center, Duke South, Durham, NC 27710, U.S.A. Phone: +1 (919) 660-7546; Fax: +1 (919) 668-0453. Email: cfh@geri.duke.edu.

Abstract

Background: Late-life depression may be undiagnosed due to symptom expression. These analyses explore the structure of depressive symptoms in older patients diagnosed with major depression by identifying clusters of patients based on their symptom profiles.

Methods: The sample comprised 366 patients enrolled in a naturalistic treatment study. Symptom profiles were defined using responses to the Center for Epidemiologic Studies Depression Scale (CES-D), the Hamilton Rating Scale for Depression (HAM-D) and the depression section of the Diagnostic Interview Schedule (DIS) administered at enrollment. Latent class analysis (LCA) was used to place patients into homogeneous clusters. As a final step, we identified a risk profile from representative items across instruments selected through variable reduction techniques.

Results: A model with four discrete clusters provided the best fit to the data for the CES-D and the DIS depression module, while three clusters best fit the HAM-D. Using LCA to identify clusters of patients based on their endorsement of seventeen representative symptoms, we found three clusters of patients differing in ways other than severity. Age, sex, education, marital status, age of onset, functional limitations, level of perceived stress and subjective social support were differentially distributed across clusters.

Conclusions: We found considerable heterogeneity in symptom profiles among older adults with an index episode of major depression. Clinical indicators such as depression history may play less of a role differentiating clusters of patients than variables such as stress, social support, and functional limitations. These findings can help conceptualize depression and potentially reduce misdiagnosis for this age group.

Type
Research Article
Copyright
Copyright © International Psychogeriatric Association 2011

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