The prevalence of major depression among older adults in community samples can be as high as 5% (1
), and in primary care settings, home health care populations, and institutional samples can range from 4–14% (4
). In longitudinal studies, depression has been shown to often have a chronic course, and to be associated with adverse outcomes such as disability, cognitive decline, and mortality (8
). The current nomenclature may not adequately reflect depression as observed in older adults, suggesting the true prevalence of clinically significant depressive symptoms may be even higher.
Across psychiatry in general, there have been recent discussions about the validity of psychiatric diagnoses as the field moves toward a revised nomenclature. For example, Krishnan suggested a dual classification system – one reflecting clinical manifestations and the other reflecting etiology, a suggestion particularly applicable to geriatric psychiatry where patients with late onset disorders may differ from those with an earlier onset (9
). Others support the importance of moving beyond categorical diagnoses while noting establishing causality in the midst of comorbid conditions as often observed among older adults may present additional challenges to geriatric psychiatry (10
). There has also been discussion about the necessity and advantages of focusing on or adding a dimensional approach to complement the existing categorical criteria (11
). Kraemer recently reviewed the history and context of this potential enhancement to the nomenclature, pointing out within a categorical diagnosis there can be variation in etiology, clinical characteristics, symptomatology and adverse consequences, and the necessity of identifying important sources of heterogeneity among those patients with a categorical diagnosis (12
Recent work has utilized latent class cluster analysis to examine the structure of various psychiatric syndromes, including posttraumatic stress disorder (13
), hypochondriasis (14
), Alzheimer’s disease (15
), chronic fatigue and fibromyalgia (16
), borderline personality disorder (17
), psychosis (18
), and eating disorders (19
In cluster analysis, individuals are grouped into clusters based on their personal data such that individuals in one group share similar characteristics and differ from those in other groups. This technique differs from other classification techniques such as factor analysis, where the multivariate relationship among variables is of interest. For example, a factor analysis of depressive symptoms in a scale would show how different symptoms covary due to their dependence on underlying latent constructs (factors) that suggest a subdimension of depression (e.g., negative affect). In a cluster analysis, the study participants are placed into groups or clusters based on their characteristics such as depressive symptoms. Cluster analysis characterizes individuals into subtypes (i.e., individuals are assigned to groups based on their symptom profiles). Latent class cluster analysis has potential advantages over more traditional clustering techniques in that latent class cluster analysis utilizes a model-based approach and assigns individuals to clusters based on their posterior membership probabilities (21
). As in traditional clustering methods, the number of groups is unknown a priori and is specified as k. The dependent variable for these cluster models, therefore, is a k-category latent variable where k represents the number of clusters derived from the data. In latent class cluster analysis, each individual is assigned a probability of class membership for each of the identified clusters based on both measured and unmeasured characteristics. These types of models therefore have much to offer the field of psychiatry in their ability to identify sources of heterogeneity within samples of individuals.
The structure of depressive symptoms has been studied in community samples across all age groups using latent class analysis. Eaton et al. fit a three-class model to the Baltimore and Duke ECA data and found one class resembled DSM-III major depression. A second class was an intermediate disorder and the last group was a group of ‘normals’ (23
). Sullivan et al. using latent class analysis identified six classes of depressive symptoms among participants in the National Comorbidity Survey who reported a lifetime history of depressive symptoms. The classes were severe typical, mild typical, severe atypical, mild atypical, intermediate, and minimal symptoms (24
). Older adults, however, were not included in this sample. Similar work was done using the Virginia Twin Registry in which seven classes of depression were identified, again with the classes generally on a gradient (25
). Using data from the National Survey of American Life, Lincoln et al. recently examined profiles of depressive symptoms among African Americans and Caribbean Blacks, and identified a high symptom and low symptom class (26
). Two decades ago, using data from the Duke Epidemiologic Catchment Area (ECA) Survey and grade-of-membership analyses, Blazer et al. identified five subtypes of depression among those 18 or older with depressive symptoms (27
). One profile closely resembled major depression, while other subtypes included a premenstrual syndrome among younger women, a mixed anxiety/depression group, a mildly dysphoric group, and a group with cognitive difficulties.
Little is known about the structure of depressive symptoms specific to older adults. With the number of older adults expected to increase over the coming decades, the public health impact of depressive symptoms in this population may be substantial, and a greater understanding of the structure underlying symptom presentation as a potential source of heterogeneity is critical. The structure of depressive symptoms in older adults may differ from that observed in younger adults since depression in older adults like other psychiatric syndromes can be more heterogeneous and affected by variables such as age of onset, number of lifetime episodes, and particularly, comorbidity, which can contribute to, be associated with, or result from psychopathology. Work is needed to identify symptom profiles in both community and clinical populations of older adults.
As noted, studies of depressive symptoms in community samples, like those for other psychiatric syndromes, typically identify one or two classes that resemble DSM disorders. Whether or not there is heterogeneity within groups of individuals with a categorical diagnosis is not known, but relevant to the field of psychiatry. The purpose of this analysis was to identify latent clusters or discrete groups of individuals within a sample of older adults diagnosed with major depression based on symptom scores at the time of study enrollment. These clusters or subtypes are therefore derived from the actual symptoms older adults reported to clinicians, and can lead to other studies focusing on etiologic and treatment variables associated with these subtypes as well as outcomes and course of depression over time. We hypothesized that we would find more than one cluster of patients within this group with a categorical diagnosis, that is, that symptomatology would be an important source of heterogeneity. A secondary objective was to examine if demographic, clinical and social variables known to be associated with late life depression were differentially associated with the clusters identified.