Understanding the impact of exposure to adverse socioeconomic conditions on chronic stress from a complexity science perspective

Abstract

Background Chronic stress increases chronic disease risk and may underlie the association between exposure to adverse socioeconomic conditions and adverse health outcomes. The relationship between exposure to such conditions and chronic stress is complex due to feedback loops between stressor exposure and psychological processes, encompassing different temporal (acute stress response to repeated exposure over the life course) and spatial (biological/psychological/social) scales. We examined the mechanisms underlying the relationship between exposure to adverse socioeconomic conditions and chronic stress from a complexity science perspective, focusing on amplifying feedback loops across different scales. Methods We developed a causal loop diagram (CLD) to interpret available evidence from this perspective. The CLD was drafted by an interdisciplinary group of researchers. Evidence from literature was used to confirm/contest the variables and causal links included in the conceptual framework and refine their conceptualisation. Our findings were evaluated by eight independent researchers. Results Adverse socioeconomic conditions imply an accumulation of stressors and increase the likelihood of exposure to uncontrollable childhood and life course stressors. Repetition of such stressors may activate mechanisms that can affect coping resources and coping strategies and stimulate appraisal of subsequent stressors as uncontrollable. We identified five feedback loops describing these mechanisms: (1) progressive deterioration of access to coping resources because of repeated insolvability of stressors; (2) perception of stressors as uncontrollable due to learned helplessness; (3) tax on cognitive bandwidth caused by stress; (4) stimulation of problem avoidance to provide relief from the stress response and free up cognitive bandwidth; and (5) susceptibility to appraising stimuli as stressors against a background of stress. Conclusions Taking a complexity science perspective reveals that exposure to adverse socioeconomic conditions implies recurrent stressor exposure which impacts chronic stress via amplifying feedback loops that together could be conceptualised as one vicious cycle. This means that in order for individual-level psychological interventions to be effective, the context of exposure to adverse socioeconomic conditions also needs to be addressed.

Lower Socioeconomic Position Is Associated with Greater Activity in and Integration within an Allostatic-Interoceptive Brain Network in Response to Affective Stimuli

“…This is interesting given that greater global efficiency may confer some potential advantages in cognitive function (Li et al, 2009;Kesler et al, 2018), suggesting that lower SEP may shape the efficiency of brain networks that specifically help identify salient information in the environemt and respond accordingly. However, enhanced efficiency among AIN and ECN nodes may be useful for individuals lower in SEP, who may experience greater chronic unpredictable threats (Baum et al, 1999;Crielaard et al, 2021). Greater efficiency between these two networks may be adaptive in helping individuals lower in SEP to quickly detect salient information in the environment and make decisions about how to regulate responses to such information.…”

Chronic stress-induced immune dysregulation in breast cancer: Implications of psychosocial factors

“…[79,80] At the theoretical level, considering the unusual complexity of these intermediary markers and systems, it may be necessary to test and model their relationships in a hierarchical model. [81] Future studies should examine the effect of these biomarkers on the prognosis of breast cancer patients along with their related psychosocial factors and stress. [82,83] At the same time, we should not only explore the biological significance of mediators but also their clinical significance for patient health.…”

Refining the causal loop diagram: A tutorial for maximizing the contribution of domain expertise in computational system dynamics modeling.

“…Direct structure tests assess whether the structure of the model is valid by individually comparing each causal link, both in terms of the causal relationship it represents and its functional form, to knowledge about the true system (Barlas, 1996). This could, for instance, be achieved by presenting the model to independent experts that were not involved in the model building process and assessing whether they agree with this representation of the system (Crielaard et al, 2021). This ensures that the information used to validate the model is independent of the information used to develop the model.…”

“…The aCLD is then ideally accompanied by a reference matrix that includes each proposed variable, its causal links, and metadata. Metadata are in the form of a verbal statement of the causation narrative (see section “Recording Intermediary Variables” and the supplementary material of Kenzie et al, 2018 and of Crielaard et al, 2021 for examples).…”

“…This is in line with the recently developed network theory of psychiatric disorders, which conceptualizes disorders as (relatively stable) networks of symptoms that causally interact (Borsboom, 2017; Borsboom et al, 2019). Furthermore, to apply the complexity science perspective, conceptual models have been built for example for depression (Wittenborn et al, 2016) and for stress (Crielaard et al, 2021).…”