Transdiagnostic symptomatology amidst real-world environmental uncertainty: a cross-sectional and cross-lagged panel network analysis
Uncertainty is a potential driver of poor mental health outcomes, and uncertainty is mounting globally across many domains of daily life. However, it remains unclear how anxiety and depression symptoms emerge in response to uncertainty in these real-life contexts. Capitalising on the COVID-19 pandemic as a naturalistic experiment, we investigate how heightened real-world environmental uncertainty interacts with intolerance of uncertainty (IU) to drive mental health symptoms. We collected self-reported data on symptoms of anxiety, depression and IU from 301 participants for two time points. We performed cross-sectional network analyses to identify influential contributors and established a cross-lagged panel network (CLPN) to investigate longitudinal effects. Anxiety, depression and IU symptoms were higher when real-world uncertainty was higher (t2) compared to when it was lower (t1). Network analyses revealed the same three symptom clusters at both timepoints, however worry became more central to the network at t2. In the CLPN, unlike anxiety and depression symptoms, IU symptoms had the highest autoregressive values, meaning IU at t2 was well predicted by IU at t1. The item "inhibition of behaviour due to doubt" positively predicted both anxiety and anhedonia. This study highlights the relevance of worry during higher real-world uncertainty and the predictive value of predispositional IU. Our findings further suggest behavioural inhibition to be a potential target to alleviate internalising psychopathology. During ever-increasing fluctuations in global uncertainty, we provide novel insights into the temporal relationships of highly prevalent psychopathology, which might inform support strategies for people with existing mental health vulnerabilities.