Studies of individual differences in bereavement have revealed prototypical patterns of outcome. However, many of these studies were conducted prior to the advent of sophisticated contemporary data analytic techniques. For example, Bonanno et al. (2002) used rudimentary categorization procedures to identify unique trajectories of depression symptomatology from approximately 3 years prior to 4 years following conjugal loss in a representative sample of older American adults. In the current study, we revisited these same data using Latent Class Growth Analysis (LCGA) to derive trajectories and test predictors. LCGA is a technique well-suited for modeling empirically- and conceptually-derived heterogeneous longitudinal patterns while simultaneously modeling predictors of those longitudinal patterns. We uncovered four discrete trajectories similar in shape and proportion to the previous analyses: Resilience (characterized by little or no depression; 66.3%), Chronic Grief (characterized by depression following loss, alleviated by 4 years post-loss; 9.1%), _Pre-existing Chronic Depression (ongoing high pre- through post-loss depression; 14.5%), and Depressed-Improved (characterized by high pre-loss depression that decreases following loss; 10.1%). Using this analytic strategy, we were able to examine multiple hypotheses about bereavement simultaneously. Health, financial stress, and emotional stability emerged as strong predictors of variability in depression only for some trajectories, indicating that depression levels do not have a common etiology across all the bereaved. As such, we find that identifying distinct patterns informs both the course and etiology of depression in response to bereavement.
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