Network analysis of persistent complex bereavement disorder in conjugally bereaved adults.

Abstract

Persistent complex bereavement disorder (PCBD) is a bereavement-specific syndrome characterized by prolonged and impairing grief. Most research on this syndrome rests on the traditional latent variable model whereby symptoms reflect an underlying entity. The network (or causal system) approach offers an alternative framework for understanding PCBD that does not suffer from limitations inherent in the latent entity approach. The network approach to psychopathology conceptualizes the relation between symptoms and disorder as mereological, not reflective. That is, symptoms do not reflect an inferred, unobservable category or dimension, but rather are themselves constitutive of the disorder. Accordingly, we propose that PCBD constitutes a causal system of mutually reinforcing symptoms that arise following the death of a loved one and settle into a pathological equilibrium. In this study, we used data from the Changing Lives of Older Couples (CLOC) database to identify symptoms central to PCBD, distinguish the PCBD network from an overlapping but distinct network of depression symptoms, and examine how previously identified risk factors may contribute to the maintenance or development of PCBD. Together, these findings provide an important first step toward understanding the nature and etiology of the PCBD network.

Prolonged grief disorder for ICD-11: the primacy of clinical utility and international applicability

“…Network analysis examined the relationship between proposed symptoms of PCBD to isolate and identify the key symptoms central to the disorder (Robinaugh et al, 2014). The most highly correlated symptoms were found to be preoccupation, emotional pain, yearning and feeling that life is empty.…”

“…The symptoms of PGD are found to be distinct from often comorbid near neighbours such as depression, PTSD and separation anxiety (Boelen, van de Schoot, van den Hout, de Keijser, & van den Bout, 2010; Boelen, 2013). Several different statistical methods have been used to confirm the distinct nature of PGD, including confirmatory factor analysis (Boelen, van den Hout, & van den Bout, 2008), latent class analysis (Boelen, Reijntjes, J. Djelantik, & Smid, 2016) and network analysis (Robinaugh, LeBlanc, Vuletich, & McNally, 2014). In light of recent research (Cozza et al, 2016; Mauro et al, 2017), it should be noted that different results have been reported in terms of the diagnostic performance of the PGD-2009 criteria, presumably depending on the study population sample.…”

The interplay between cognitive risk and resilience factors in remitted depression: A network analysis

“…In line with previous literature (Costantini et al, 2015a;McNally et al, 2014), we relied on different types of network models to obtain a more comprehensive representation of factors related to remission from depression. First, we examined simple correlational patterns (i.e, association network).…”

A Bayesian network analysis of posttraumatic stress disorder symptoms in adults reporting childhood sexual abuse

“…Indeed, much of the early work applying network analysis to stress-related syndromes characterized them as potentially causal systems of interacting symptoms (e.g. PTSD in earthquake survivors, McNally et al, 2015; complicated grief following spousal bereavement, Robinaugh, LeBlanc, Vuletich, & McNally, 2014; Robinaugh, Millner, & McNally, 2016). …”