NIH Public Access

Department of Psychiatry. University of Pittsburgh School of Medicine, Pittsburgh, USA.
International Psychogeriatrics (Impact Factor: 1.93). 05/2008; 20(2):221-36. DOI: 10.1017/S1041610207006667
Source: PubMed


The aim of this study is to understand the long-term course and outcomes of depressive symptoms among older adults in the community by examining trajectories of depressive symptoms over time and identifying profiles of depressive symptoms predicting different trajectories.
We measured depressive symptoms biennially for up to 12 years, using the modified Center for Epidemiological Studies-Depression (mCES-D) scale, in 1260 community-based adults aged 65+ years. We determined latent trajectories of total mCES-D scores over time. We identified symptom profiles based on subgroups of baseline depressive symptoms derived from factor analysis, and examined their associations with the different trajectories.
Six trajectories were identified. Two had one or no depressive symptoms at baseline and flat trajectories during follow-up. Two began with low baseline symptom scores and then diverged; female sex and functional disability were associated with future increases in depressive symptoms. Two trajectories began with high baseline scores but had different slopes: the higher trajectory was associated with medical burden, higher overall baseline score, and higher baseline scores on symptom profiles including low self-esteem, interpersonal difficulties, neurovegetative symptoms, and anhedonia. Mortality was higher among those in the higher trajectories.
In the community at large, those with minimal depressive symptoms are more likely to experience future increases in symptoms if they are women and have functional disability. Among those with higher current symptom levels, depression is more likely to persist over time in individuals who have greater medical burden and specific depressive symptoms.

6 Reads
  • Source
    • "A critical difference from GoM is that in groupbased trajectory modeling, each individual belongs to a distinct qualitative trajectory group, whereas in GoM, individual-level trajectories are determined by a linear combination of extreme types. Some applications of group-based trajectory modeling have concentrated on such health outcomes as body mass index, depression, and cognition (e.g., Andreescu et al. 2008), and recent applications have focused on disability (Connor 2006; Dodge et al. 2006; Gill et al. 2010; Liang et al. 2010; Taylor 2005). None of these have jointly modeled the outcome of interest and the dropping out of observations, which in the case of late-life disability is often associated with mortality. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This article uses a group-based modeling approach to jointly estimate disability and mortality trajectories over time based on data from the population aged 80 and older in China, and explores relations of demographic, socioeconomic, and early-life characteristics to membership in gender-specific trajectory groups. A three-group model best fits the data for both males and females. For most groups, predicted numbers of limitations in activities of daily living (ADLs) increase with age, but the pace is gradual in some cases and rapid in others. For each gender, the estimated mortality probability trajectories for the three groups follow a hierarchy that is related to the predicted ADL counts at age 80. Only a few characteristics predict trajectory-group membership. Prior nonagricultural occupation is associated with less favorable disability trajectories for both genders. For females, rural residence, a greater number of children ever born, and having a father who did not work in agriculture are associated with more favorable trajectories. For a small group of males who received education, disability is moderate but changes little with age. Findings may reflect heterogeneity of survival among the least advantaged, as well as a possible expansion of morbidity among a small advantaged group.
    Demography 02/2012; 49(1):291-314. DOI:10.1007/s13524-011-0075-7 · 1.93 Impact Factor
  • Source
    • "This is in line with reports of 8% to 16% of the prevalence of significant depressive symptoms among communitydwelling older adults (Blazer, 2003). The six distinct trajectories identified by us are similar to those observed by Andreescu et al. (2008) in 1,260 older adults with low income and education in rural southwestern Pennsylvania over a 12-year period. Our findings have extended the generalizability of the previously observed trajectories of depressive symptoms to a nationally representative sample of middle aged and older Americans with significant representation of racial/ethnic minorities. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This research aims to identify distinct courses of depressive symptoms among middle-aged and older Americans and to ascertain how these courses vary by race/ethnicity. Data came from the 1995-2006 Health and Retirement Study which involved a national sample of 17,196 Americans over 50 years of age with up to six repeated observations. Depressive symptoms were measured by an abbreviated version of the Center for Epidemiologic Studies Depression scale. Semiparametric group based mixture models (Proc Traj) were used for data analysis. Six major trajectories were identified: (a) minimal depressive symptoms (15.9%), (b) low depressive symptoms (36.3%), (c) moderate and stable depressive symptoms (29.2%), (d) high but decreasing depressive symptoms (6.6%), (e) moderate but increasing depressive symptoms (8.3%), and (f) persistently high depressive symptoms (3.6%). Adjustment of time-varying covariates (e.g., income and health conditions) resulted in a similar set of distinct trajectories. Relative to White Americans, Black and Hispanic Americans were significantly more likely to be in trajectories of more elevated depressive symptoms. In addition, they were more likely to experience increasing and decreasing depressive symptoms. Racial and ethnic variations in trajectory groups were partially mediated by SES, marital status, and health conditions, particularly when both interpersonal and intrapersonal differences in these variables were taken into account.
    Psychology and Aging 08/2011; 26(4):761-77. DOI:10.1037/a0023945 · 2.73 Impact Factor
  • Source
    • "These findings are consistent with the literature on late-life depression (Katona and Shankar, 2004). Our study also showed that the presence of depression is significantly associated with attrition from the study, confirming previous findings that mortality is often preceded by depression or drop in mood (Jorm et al., 1991; Katona and Shankar, 2004; Andreescu et al., 2008). This drop parallels observations regarding a decrease in cognitive function before death, the phenomenon referred to as " terminal decline " (Wilson et al., 2007; Lavery et al., 2008). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Attrition from mortality is common in longitudinal studies of the elderly. Ignoring the resulting non-response or missing data can bias study results. 1260 elderly participants underwent biennial follow-up assessments over 10 years. Many missed one or more assessments over this period. We compared three statistical models to evaluate the impact of missing data on an analysis of depressive symptoms over time. The first analytic model (generalized mixed model) treated non-response as data missing at random. The other two models used shared parameter methods; each had different specifications for dropout but both jointly modeled both outcome and dropout through a common random effect. The presence of depressive symptoms was associated with being female, having less education, functional impairment, using more prescription drugs, and taking antidepressant drugs. In all three models, the same variables were significantly associated with depression and in the same direction. However, the strength of the associations differed widely between the generalized mixed model and the shared parameter models. Although the two shared parameter models had different assumptions about the dropout process, they yielded similar estimates for the outcome. One model fitted the data better, and the other was computationally faster. Dropout does not occur randomly in longitudinal studies of the elderly. Thus, simply ignoring it can yield biased results. Shared parameter models are a powerful, flexible, and easily implemented tool for analyzing longitudinal data while minimizing bias due to nonrandom attrition.
    International Psychogeriatrics 04/2009; 21(5):869-78. DOI:10.1017/S104161020900876X · 1.93 Impact Factor
Show more