B Mezuk1, K S Kendler. 1. Department of Epidemiology and Community Health, Virginia Commonwealth University, Richmond, VA 23298, USA. bmezuk@vcu.edu
Abstract
BACKGROUND: Older adults have the lowest prevalence and incidence of major depressive disorder, although it has been hypothesized that this finding is due in part to differences in expression of psychopathology in later life. The aim of this study was to examine variation in depressive symptomatology in the general population across the lifespan. METHOD: Data came from three sites of the Epidemiologic Catchment Area (ECA) Project (n=10 529). Depressive symptoms during the past 6 months were assessed using the Diagnostic Interview Schedule (DIS). Latent class analysis (LCA) was used to identify homogeneous groups of depressive symptomatology based on 16 individual symptoms, and to examine variation in the prevalence and composition of depression classes across age groups. RESULTS: The DIS symptoms fit a four-class model composed of non-depressed (83.2%), mild depression (11.6%), severe depression (1.9%), and despondent (3.2%) groups. Relative to the non-depressed class, older age was inversely associated with being in the mild or severe depression class. The profile of the latent classes was similar across age groups with the exception of the despondent class, which was not well differentiated among the youngest adults and was not inversely associated with age. CONCLUSIONS: The symptom profiles of depression are similar across age with the exception of the despondent class, which is more differentiated from severe depression among older adults. The findings demonstrate the benefit of examining individual symptoms rather than broad symptom groups for understanding the natural history of depression over the lifespan.
BACKGROUND: Older adults have the lowest prevalence and incidence of major depressive disorder, although it has been hypothesized that this finding is due in part to differences in expression of psychopathology in later life. The aim of this study was to examine variation in depressive symptomatology in the general population across the lifespan. METHOD: Data came from three sites of the Epidemiologic Catchment Area (ECA) Project (n=10 529). Depressive symptoms during the past 6 months were assessed using the Diagnostic Interview Schedule (DIS). Latent class analysis (LCA) was used to identify homogeneous groups of depressive symptomatology based on 16 individual symptoms, and to examine variation in the prevalence and composition of depression classes across age groups. RESULTS: The DIS symptoms fit a four-class model composed of non-depressed (83.2%), mild depression (11.6%), severe depression (1.9%), and despondent (3.2%) groups. Relative to the non-depressed class, older age was inversely associated with being in the mild or severe depression class. The profile of the latent classes was similar across age groups with the exception of the despondent class, which was not well differentiated among the youngest adults and was not inversely associated with age. CONCLUSIONS: The symptom profiles of depression are similar across age with the exception of the despondent class, which is more differentiated from severe depression among older adults. The findings demonstrate the benefit of examining individual symptoms rather than broad symptom groups for understanding the natural history of depression over the lifespan.
Authors: Ronald C Kessler; Patricia Berglund; Olga Demler; Robert Jin; Doreen Koretz; Kathleen R Merikangas; A John Rush; Ellen E Walters; Philip S Wang Journal: JAMA Date: 2003-06-18 Impact factor: 56.272
Authors: Megan S Schuler; Stephen E Gilman; Rachel M Burns; Elizabeth Roth; Joshua Breslau Journal: J Affect Disord Date: 2021-03-11 Impact factor: 4.839
Authors: Monica Salazar-Villanea; Edward Liebmann; Mauricio Garnier-Villarreal; Esteban Montenegro-Montenegro; David K Johnson Journal: J Depress Anxiety Date: 2015-09-27
Authors: A Starnawska; Q Tan; M Soerensen; M McGue; O Mors; A D Børglum; K Christensen; M Nyegaard; L Christiansen Journal: Transl Psychiatry Date: 2019-09-02 Impact factor: 6.222
Authors: Dawid Gondek; David Bann; Praveetha Patalay; Alissa Goodman; Eoin McElroy; Marcus Richards; George B Ploubidis Journal: Psychol Med Date: 2021-01-21 Impact factor: 10.592