The causal systems approach to prolonged grief: Recent developments and future directions

Abstract

The network theory of prolonged grief posits that causal interactions among symptoms of prolonged grief play a significant role in their coherence and persistence as a syndrome. Drawing on recent developments in the broader network approach to psychopathology, we argue that advancing our understanding of the causal system that gives rise to prolonged grief will require that we (a) strengthen our assessment of each component of the grief syndrome, (b) investigate intra-individual relationships among grief components as they evolve over time within individuals, (c) incorporate biological and social components into network studies of grief, and (d) generate formal theories that posit precisely how these biological, psychological, and social components interact with one another to give rise to prolonged grief disorder.

Keywords: Causal system; Formal theory; Idiographic models; Network approach; Prolonged grief disorder.

Figure 1. The Network (or Causal Systems) Theory of Prolonged Grief

Note. The network theory of prolonged grief posits that causal interactions among the components of grief (G) lead them to persist over time. This figure depicts the structure of three different causal systems (left column) and the stability landscapes (middle column) and trajectories of response (right column) that follow from those systems. In each causal system, grief components are represented as nodes and the causal effects between them are represented as arrows between the nodes. Darker nodes signify greater severity of that component of grief at 6-months post-loss. Thicker arrows signify stronger causal effects. The stability landscapes provide a visualization of the system’s dynamics. In each landscape, the x-axis represents grief severity. The ball on top of the landscape indicates the current level of grief severity (in this case, at 6 months post-loss). The further along the x-axis, the greater one’s grief severity. The topography of the landscape along the y-axis describes the rate of change in grief severity over time. The steeper the landscape, the greater the rate of change. Where the landscape is flat (e.g., in the basins of the landscape) the rate of change is zero. In the absence of perturbations, the ball will always move ‘downhill’ into the nearest basin, where it will remain. For that reason, the basins in the landscape are also referred to as ‘stable states:’ states the system will move toward following perturbation. In a weakly-connected system, there is a single basin in the landscape located at a low level of grief severity. Bereavement may perturb the system, pushing it toward a higher level of grief. However, as bereavement becomes less proximal, its effect on the system dissipates and the system returns to a stable state of low grief severity. In a moderately-connected system, there are more and stronger causal relationships among components of grief. Here, the stability landscape is shallower, signifying a slower rate of change. Consequently, the system takes longer to recover from bereavement (see trajectory plot). However, because there is still only a single stable state of low grief severity, the system will eventually return to that stable state. Finally, in a strongly-connected system, causal relationships are sufficiently strong that the system can become self-perpetuating. This is reflected in the stability landscape by the formation of a new basin: an alternative stable state of high grief severity in which the system can fall. If bereavement is sufficient to push the system beyond the tipping point in the landscape, the causal relationships among the symptoms of grief will lead the system to remain in a stable state of high grief symptoms that does not remit with time (see trajectory plot). In a precisely-defined system, the stability landscapes can be calculated and the presence and location of stable states in the system can be determined. Here, the stability landscapes are used simply as a metaphor to illustrate how the structure of a causal system shapes the systems dynamics and, in turn, the trajectory of its response to bereavement. For further discussion of the relationship between causal system structure and the trajectory of response to bereavement, see Malgaroli, Maccallum, and Bonano’s work on computational approaches to grief in this issue [5].

Figure 2. A Biopsychosocial Framework for Prolonged Grief

Note. This figure depicts the impact of bereavement on the social network and the interplay of that impact with the psychobiological response to bereavement for individuals within that social network. Panel A depicts a social network. Nodes represent people (P) and edges represent the social relationships between them, with thicker edges signifying stronger relationships. Panel B depicts the death of a member of this social network (P7); an individual who was strongly-connected within the social network and whose death significantly disrupts the structure of that network. As a result of the death, significant connections are lost (e.g., between P7 and P1) and some individuals (e.g., P5) become peripheral to the network. Within each individual is a network of biological (B) and psychological (i.e., grief; G) components. Overall severity of grief is indicated by the node color for each individual, with darker grey indicating greater grief severity. Panel C depicts the same social network one year following the death. For P1, there have been significant changes to the network, with existing connections strengthened and new connections formed. Together with the low connectivity in their psychobiological system, this adapted social network structure has led to a decrease in the severity of their grief over time. In contrast, P5 remains socially disconnected, which both affects and is affected by the persistent activity in their strongly inter-connected network of grief-related thoughts, emotions, and behaviors. Accordingly, in this biopsychosocial framework, prolonged grief is affected not only by the causal relationships among components of grief, but also one’s relationship to the deceased, one’s position in the social network, and the grief experienced by those to whom one is connected. Adaption to loss is, thus, not only determined by the network of grief components, but also by the ability to adapt to the altered structure of the social network that arises from the death of a loved one. For a related discussion of the social consequences of bereavement, see Maciejewski, Falzarano, She, Lichtenthal & Prigerson’s contribution to this issue [56].