The latest edition of the Journal of Evaluation in Clinical Practice (Volume 24, Issue 6, December 2018) is rather exciting for me, as it is devoted to the topic of resilience and its related complexities. It also contains the recent paper my colleagues and I published on the complex dynamics of comorbid depression and physical health.
THE IMPORTANCE OF RESILIENCEAs Carmel Martin, the editor of this special edition states:
This special forum on resilience explores particular worldviews of resilience—clinical, psychosocial, sociological, complexity science, organizational, and political economy through eight papers. This forum aims to open up the wealth of understandings and implications in health care by taking a transdisciplinary overview (CLICK FOR MORE).As Martin also points out:
Interest in resilience in relation to overcoming adversity is growing exponentially in health‐related literature.... (See Figure 1 from Martin) Resilience is now recognized as an important phenomenon for understanding how ecosystems, coral reefs, and individuals overcome stressors and challenges. Resilience is sometimes seen as an individual human trait, other times as a process, and yet other times as an outcome, moving from static models to a dynamic models over time. Interest has broadened to groups, teams, organizations, communities, and political and economic systems.
RESILIENCE AND ALLOSTATIC LOADAt the level of individuals, resilience is a rather complex phenomenon that has presented a number of theoretical and methodological challenges, given that it emerges out of a complex interaction between people (including health behaviors, genetics, and family health history) and the wider socio-ecological settings in which they live.
It is for this reason, as we explain in a recent 2015 publication, Allostatic load as a complex clinical construct, that simultaneous with the rise in the study of reslience has been a turn to the concept of allostatic load. And for good reason. As we explain:
Allostatic load (AL) is a highly useful framework—introduced by McEwen and colleagues 1-5, 7—for understanding the cumulative health costs (“wear and tear”) associated with stress, particularly short‐term‐intense or chronic distress.
The theoretical framework for AL follows a complex, multidimensional and multilevel trajectory: situated within a wider set of intersecting socioecological systems (i.e., poverty traps, high‐stress workplaces, combat, etc), an individual's perceived distress (i.e., stress overload, lack of control, etc) causes many of the body's key allostatic systems—a complex, nonlinear network of interactive and adaptive mediators (e.g., blood pressure, cardiovascular, metabolic, etc)—to shift into a state of relative disequilibrium to maintain wellbeing 6.
Often times, particularly when distress is short‐term‐intense or chronic, this sustained disequilibrium can lead to dysregulation, which can cause significant dysfunction/damage to these allostatic systems; which, in turn, can lead to significant, negative health outcomes (e.g., heart disease, cancer, depression, alcoholism, PTSD) 1-7.And, the key for us and, increasingly, an expanding network of researchers, is the realisation that:
Given its theoretical complexity, AL has shown great potential as an interdisciplinary tool for assessing cumulative health risk 7-11. For example, as Gallo et al. state, “In contrast to the common practice of examining risk factors within a single physiological system, the allostatic load framework provides an integrative approach that may better characterize the cumulative impact of dynamic and nonlinear influences across major biological regulatory systems.”12
In this way, AL links to a variety of fields (from medical sociology and medicine to human biology and public health) focused on the negative impact that stress events have on health and wellbeing; particularly across the life‐course and across different antecedent socioecological factors such as gender, age, ethnicity, mental status, psychological trauma, residence, occupation and—a current major focus—health disparities 8-11, 13-19.In other words, similar to intersectionality theory, allostatic load does not assume that, for example, everyone in a poor urban community will be equally affected by their social conditions, or that they will all have the same negative health outcomes. As we explain:
For example, regarding health disparities, Beckie 19 states, “The theoretical constructs of allostasis and allostatic load (AL) have contributed to our understanding of how constantly changing social and environmental factors impact physiological functioning and shape health and aging disparities, particularly along socioeconomic, gendered, racial, and ethnic lines” (p. 311).
METHODOLOGICAL CHALLENGEThe methodological challenge, however, is to get to such a sophisticated understanding of resilience and allostatic load, one needs to embrace a wider set of methodological techniques that go far beyond the conventions of linear statistics and its bell-shaped curve view of the distribution of health and disease. In short, one needs the computational methods of complexity science.
THE IMPORTANCE OF COMPLEXITY AND COMPUTATIONAL MODELLINGFor example, in our study on allostatic load, we employed the computational modelling, mixed-methods framework of the SACS Toolkit, which draws upon a wide variety of computational and statistical techniques, for the current study, we used four: the Kohonen self‐organizing map (SOM), k‐means cluster analysis, principle components analysis (PCA), and logistic regression. (A brief overview of how we employed PCA, k‐means and the SOM is provided HERE.)
- As a result -- as shown here in this topographical neural net -- we were able to arrive at a very sophisticated model of the multiple trends/profiles along which allostatic load manifests itself in people's lives.
- And, in turn -- as shown here in these radar maps -- we could link these allostatic trends/profiles to the numerous and different ways in which it impact health outcomes later in life.
RESULTS:Using our approach, we made four important results that could otherwise not had been attained: (1) we developed a multisystem, 7‐factor (20 biomarker) model of AL's network of allostatic systems; (2) used it to create a catalog of nine different clinical AL profiles (causal pathways); (3) linked each clinical profile to a typology of 23 health outcomes; and (4) explored our results (post hoc) as a function of gender, a key socioecological factor.
In terms of highlights, (a) the Healthy clinical profile had few health risks; (b) the pro‐inflammatory profile linked to high blood pressure and diabetes; (c) Low Stress Hormones linked to heart disease, TIA/Stroke, diabetes, and circulation problems; and (d) high stress hormones linked to heart disease and high blood pressure. Post hoc analyses also found that males were overrepresented on the High Blood Pressure (61.2%), Metabolic Syndrome (63.2%), High Stress Hormones (66.4%), and High Blood Sugar (57.1%); while females were overrepresented on the Healthy (81.9%), Low Stress Hormones (66.3%), and Low Stress Antagonists (stress buffers) (95.4%) profiles.