Applying systems thinking to unravel the mechanisms underlying orthostatic hypotension related fall risk

Post-COVID dysautonomias: what we know and (mainly) what we don't know

Following on from the COVID-19 pandemic is another worldwide public health challenge that is referred to variously as long COVID, post-COVID syndrome or post-acute sequelae of SARS-CoV-2 infection (PASC). PASC comes in many forms and affects all body organs. This heterogeneous presentation suggests involvement of the autonomic nervous system (ANS), which has numerous roles in the maintenance of homeostasis and coordination of responses to various stressors. Thus far, studies of ANS dysregulation in people with PASC have been largely observational and descriptive, based on symptom inventories or objective but indirect measures of cardiovascular function, and have paid little attention to the adrenomedullary, hormonal and enteric nervous components of the ANS. Such investigations do not consider the syndromic nature of autonomic dysfunction. This Review provides an update on the literature relating to ANS abnormalities in people with post-COVID syndrome and presents a theoretical perspective on how the ANS might participate in common features of PASC.

Using network analysis to identify leverage points based on causal loop diagrams leads to false inference

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.