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. Keywords: Socioeconomic status, Causal loop diagram, Chronic stress, Feedback loops
Abstract
Network analysis is gaining momentum as an accepted practice to identify which factors in causal loop diagrams (CLDs)—mental models that graphically represent causal relationships between a ...system’s factors—are most likely to shift system-level behaviour, known as leverage points. This application of network analysis, employed to quantitatively identify leverage points without having to use computational modelling approaches that translate CLDs into sets of mathematical equations, has however not been duly reflected upon. We evaluate whether using commonly applied network analysis metrics to identify leverage points is justified, focusing on betweenness- and closeness centrality. First, we assess whether the metrics identify the same leverage points based on CLDs that represent the same system but differ in inferred causal structure—finding that they provide unreliable results. Second, we consider conflicts between assumptions underlying the metrics and CLDs. We recognise six conflicts suggesting that the metrics are not equipped to take key information captured in CLDs into account. In conclusion, using betweenness- and closeness centrality to identify leverage points based on CLDs is at best premature and at worst incorrect—possibly causing erroneous identification of leverage points. This is problematic as, in current practice, the results can inform policy recommendations. Other quantitative or qualitative approaches that better correspond with the system dynamics perspective must be explored.
Objectives:
While status anxiety has received attention as a potential mechanism generating health inequalities, empirical evidence is still limited. Studies have been ecological and have largely ...focused on mental and not physical health outcomes.
Methods:
We conducted individual-level analyses to assess status anxiety (feelings of inferiority resulting from social comparisons) and resources (financial difficulties) as mediators of the relationship between socioeconomic status (SES) (education/occupation/employment status) and type 2 diabetes (T2D). We used cross-sectional data of 21,150 participants (aged 18–70 years) from the Amsterdam-based HELIUS study. We estimated associations using logistic regression models and estimated mediated proportions using natural effect modelling.
Results:
Odds of status anxiety were higher among participants with a low SES e.g., OR = 2.66 (95% CI: 2.06–3.45) for elementary versus academic occupation. Odds of T2D were 1.49 (95% CI: 1.12–1.97) times higher among participants experiencing status anxiety. Proportion of the SES–T2D relationship mediated was 3.2% (95% CI: 1.5%–7.0%) through status anxiety and 10.9% (95% CI: 6.6%–18.0%) through financial difficulties.
Conclusion:
Status anxiety and financial difficulties played small but consistent mediating roles. These individual-level analyses underline status anxiety’s importance and imply that status anxiety requires attention in efforts to reduce health inequalities.
Cross-sectional studies are widely prevalent since they are more feasible to conduct compared with longitudinal studies. However, cross-sectional data lack the temporal information required to study ...the evolution of the underlying dynamics. This temporal information is essential to develop predictive computational models, which is the first step towards causal modelling. We propose a method for inferring computational models from cross-sectional data using Langevin dynamics. This method can be applied to any system where the data-points are influenced by equal forces and are in (local) equilibrium. The inferred model will be valid for the time span during which this set of forces remains unchanged. The result is a set of stochastic differential equations that capture the temporal dynamics, by assuming that groups of data-points are subject to the same free energy landscape and amount of noise. This is a 'baseline' method that initiates the development of computational models and can be iteratively enhanced through the inclusion of domain expert knowledge as demonstrated in our results. Our method shows significant predictive power when compared against two population-based longitudinal datasets. The proposed method can facilitate the use of cross-sectional datasets to obtain an initial estimate of the underlying dynamics of the respective systems.
Complexity science and systems thinking are increasingly recognized as relevant paradigms for studying systems where biology, psychology, and socioenvironmental factors interact. The application of ...systems thinking, however, often stops at developing a conceptual model that visualizes the mapping of causal links within a system, e.g., a causal loop diagram (CLD). While this is an important contribution in itself, it is imperative to subsequently formulate a computable version of a CLD in order to interpret the dynamics of the modeled system and simulate "what if" scenarios. We propose to realize this by deriving knowledge from experts' mental models in biopsychosocial domains. This article first describes the steps required for capturing expert knowledge in a CLD such that it may result in a computational system dynamics model (SDM). For this purpose, we introduce several annotations to the CLD that facilitate this intended conversion. This annotated CLD (aCLD) includes sources of evidence, intermediary variables, functional forms of causal links, and the distinction between uncertain and known-to-be-absent causal links. We propose an algorithm for developing an aCLD that includes these annotations. We then describe how to formulate an SDM based on the aCLD. The described steps for this conversion help identify, quantify, and potentially reduce sources of uncertainty and obtain confidence in the results of the SDM's simulations. We utilize a running example that illustrates each step of this conversion process. The systematic approach described in this article facilitates and advances the application of computational science methods to biopsychosocial systems.
Translational Abstract
Systems thinking is essential to study complex problems that arise from many interacting system parts at different levels. An example of a complex problem is depression, related to individual biological and psychological characteristics, but also our society and environment. To schematically describe which system parts are important in explaining a complex problem, it is common to draw a visual representation of the system that produces the problem, i.e., a causal loop diagram (CLD). Even though a CLD can tell us a lot about the origins of a complex problem, it cannot show what the effect would be if a system part were changed. For example, even if the CLD indicates that income inequality is an important system part in explaining depression, we still cannot tell from just this visual representation whether depression rates would change if income inequality were reduced. To answer such questions, we need to develop a computational model reflecting the CLD: a computer program that can calculate what would happen under different conditions. This article describes how to create a CLD that can be converted into a computational system dynamics model. We propose to achieve this conversion by asking experts to communicate their knowledge about individual system parts and storing this information within the CLD. This expert knowledge then helps us calculate what happens when all system parts interact simultaneously. This method can aid in answering questions about the effects of changes in system parts for a broader range of complex problems.
Full text
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CEKLJ, FFLJ, NUK, ODKLJ, PEFLJ
ObjectiveBenign multiple sclerosis (BMS) is often defined by the Expanded Disability Status Scale (EDSS) score of ≤3.0 after ≥15 years of disease duration. This classification’s clinical relevance ...remains unclear as benign patients may suffer other impairments and advance towards a progressive course, prompting our objective to holistically investigate factors associated with BMS and its long-term prognosis.MethodsBenign cases were identified in the Swedish Multiple Sclerosis registry. Baseline clinical data, demographic features and influence of multiple sclerosis (MS) major risk alleles on likelihood of benign course were investigated. Physical disability (EDSS), cognitive function (Symbol Digit Modalities Test; SDMT) and self-reported and socioeconomic differences between benign and non-benign patients were evaluated using generalised estimation equations models.Results11222 patients (2420 benign/8802 non-benign) were included. Benign patients were more likely to be female and younger at MS onset, have fewer relapses within the first two and 5 years from onset and fully recover from the first relapse (p<0.001). No association between human leucocyte antigen (HLA) DRB1*15:01 carriership (OR: 0.97, 95% CI: 0.86 to 1.09) or HLA-A*02:01 lacking (OR: 0.99, 95% CI: 0.87 to 1.11) and benign/non-benign was found. Non-benign patients accumulated an extra 0.04 (95% CI 0.03 to 0.04, p<0.001) EDSS score/year, lost an extra 0.3 (95% CI − 0.39 to − 0.18, p<0.001) SDMT score/year and deteriorated faster in self-reported impact and socioeconomic measures (p<0.001).ConclusionPatients with BMS have a better disease course as they progress more slowly at the group level in all respects. Lack of an association with major genetic risk factors indicates that MS course is most likely influenced by either environmental factor(s) or genetic factors outside the HLA region.
•Chronic psychological stress contributes to the development of type 2 diabetes.•Interactions between multiple variables underlie the link between stress and type 2 diabetes.•The impact of stress on ...metabolic health is driven by non-linear dynamics at different spatiotemporal scales.•Complexity science can help to understand the role of chronic stress in type 2 diabetes onset.
Chronic stress contributes to the onset of type 2 diabetes (T2D), yet the underlying etiological mechanisms are not fully understood. Responses to stress are influenced by earlier experiences, sex, emotions and cognition, and involve a complex network of neurotransmitters and hormones, that affect multiple biological systems. In addition, the systems activated by stress can be altered by behavioral, metabolic and environmental factors.
The impact of stress on metabolic health can thus be considered an emergent process, involving different types of interactions between multiple variables, that are driven by non-linear dynamics at different spatiotemporal scales.
To obtain a more comprehensive picture of the links between chronic stress and T2D, we followed a complexity science approach to build a causal loop diagram (CLD) connecting the various mediators and processes involved in stress responses relevant for T2D pathogenesis. This CLD could help develop novel computational models and formulate new hypotheses regarding disease etiology.
Little is known about how health-related behaviours cluster across different populations and how lifestyle clusters are associated with type 2 diabetes (T2D) risk. We investigated lifestyle clusters ...and their association with T2D in a multi-ethnic population. 4396 Dutch, 2850 South-Asian Surinamese, 3814 African Surinamese, 2034 Ghanaian, 3328 Turkish, and 3661 Moroccan origin participants of the HELIUS study were included (2011–2015). K-medoids cluster analyses were used to identify lifestyle clusters. Logistic and cox regression analyses were performed to investigate the association of clusters with prevalent and incident T2D, respectively. Pooled analysis revealed three clusters: a ‘healthy’, ‘somewhat healthy’, and ‘unhealthy’ cluster. Most ethnic groups were unequally distributed: Dutch participants were mostly present in the ‘healthy’ cluster, Turkish and Moroccan participants in the ‘somewhat healthy’ cluster, while the Surinamese and Ghanaian participants were equally distributed across clusters. When stratified for ethnicity, analysis revealed three clusters per ethnic group. While the ‘healthy’ and ‘somewhat healthy’ clusters were similar to those of the pooled analysis, we observed considerable differences in the ethnic-specific ‘unhealthy’ clusters. Fruit consumption (3–4 days/week) was the only behaviour that was consistent across all ethnic-specific ‘unhealthy’ clusters. The pooled ‘unhealthy’ cluster was positively associated with prediabetes (OR: 1.34, 95%CI 1.21–1.48) and incident T2D (OR: 1.23, 95%CI 0.89–1.69), and negatively associated with prevalent T2D (OR: 0.80, 95%CI 0.69–0.93). Results were similar for most, but not all, ethnic-specific clusters. This illustrates that targeting multiple behaviours is relevant in prevention of T2D but that ethnic differences in lifestyle clusters should be taken into account.
•Ethnic minorities are at higher risk of diabetes compared to majority populations.•Identifying lifestyle clusters can aid the development of prevention strategies.•Stratified analysis identified ethnic-specific ‘unhealthy’ clusters.•‘Unhealthy’ clusters were positively associated with incident diabetes.
Summary
Group‐level obesity can be seen as an emergent property of a complex system, consisting of feedback loops between individual body weight perception, individual weight‐related behaviour and ...group‐level social norms (a product of group‐level ‘normal' body mass index (BMI) and sociocultural ‘ideal' BMI). As overweight becomes normal, the norm might be counteracting health awareness in shaping individual weight‐related behaviour. System dynamics modelling facilitates understanding and simulating this system's emergent behaviour. We constructed six system dynamics models (SDMs) based on an expert‐informed causal loop diagram and data from six sociocultural groups (Dutch, Moroccan and South‐Asian Surinamese men and women). The SDMs served to explore the effect of three scenarios on group‐level BMI: ‘what if' weight‐related behaviour were driven by (1) health awareness, (2) norms or (3) a combination of the two. Median BMI decreased approximately 50% and 30% less in scenarios 2 and 3, respectively, than in 1. In men, the drop in BMI was approximately two times larger in scenario 1 versus 3, whereas in women, the drop was approximately equal in these scenarios. This study indicates that the overweight norm in men holds group‐level BMI close to overweight despite health awareness. Since norms are counteracting health awareness less strongly in women, other drivers of obesity must be more relevant.
Obesity is a major risk factor for the development of type 2 diabetes (T2D), where a sustained weight loss may result in T2D remission in individuals with obesity. To design effective and feasible ...intervention strategies to prevent or reverse T2D, it is imperative to study the progression of T2D and remission together. Unfortunately, this is not possible through experimental and observational studies. To address this issue, we introduce a data-driven computational model and use human data to investigate the progression of T2D with obesity and remission through weight loss on the same timeline. We identify thresholds for the emergence of T2D and necessary conditions for remission. We explain why remission is only possible within a window of opportunity and the way that window depends on the progression history of T2D, individual’s metabolic state, and calorie restrictions. These findings can help to optimize therapeutic intervention strategies for T2D prevention or treatment.
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•A data-driven model for obesity-driven progression of diabetes and remission•The model successfully reproduces clinical trial data for diabetes remission•The model identifies a window of opportunity for remission through weight loss•These findings could help optimize diabetes prevention and treatment strategies
Human metabolism; Bioinformatics; Computational bioinformatics
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