Abstract
The coronavirus disease 2019 (COVID-19) emergency has led to numerous attempts to assess the impact of the pandemic on population mental health. The findings indicate an increase in depression and anxiety but have been limited by the lack of specificity about which aspects of the pandemic (e.g. viral exposure or economic threats) have led to adverse mental health outcomes. Network analyses were conducted on data from wave 1 (N = 2025, recruited 23 March-28 March 2020) and wave 2 (N = 1406, recontacts 22 April-1 May 2020) of the COVID-19 Psychological Research Consortium Study, an online longitudinal survey of a representative sample of the UK adult population. Our models included depression (PHQ-9), generalized anxiety (GAD-7) and trauma symptoms (ITQ); and measures of COVID-specific anxiety, exposure to the virus in self and close others, as well as economic loss due to the pandemic. A mixed graphical model at wave 1 identified a potential pathway from economic adversity to anxiety symptoms via COVID-specific anxiety. There was no association between viral exposure and symptoms. Ising network models using clinical cut-offs for symptom scores at each wave yielded similar findings, with the exception of a modest effect of viral exposure on trauma symptoms at wave 1 only. Anxiety and depression symptoms formed separate clusters at wave 1 but not wave 2. The psychological impact of the pandemic evolved in the early phase of lockdown. COVID-related anxiety may represent the mechanism through which economic consequences of the pandemic are associated with psychiatric symptoms.
Highlights
From its beginning, it was recognized that the coronavirus disease 2019 (COVID-19) pandemic would likely create a burden on mental ill-health in the general population (Holmes et al, 2020)
The mgm estimation procedure employs a penalty approach that aims to control for potential spurious associations which would lead to false-positive findings, namely, the least absolute shrinkage and selection operator (LASSO) (Tibshirani, 1996)
Our models revealed that various aspects of the pandemic were differentially related to psychopathology symptoms
Summary
It was recognized that the coronavirus disease 2019 (COVID-19) pandemic would likely create a burden on mental ill-health in the general population (Holmes et al, 2020). The expectation is that this approach will lead to the identification of symptoms which are important in the genesis of psychological disturbance or which ‘bridge the gap’ between different syndromes, thereby leading to comorbidity between disorders (Beard et al, 2016; McNally, 2016) Until recently, these tools have been largely used inductively, to make data-driven, hypothesis-generating inferences about possible causal relations between symptoms. Recent studies have sought to use these methods in a hypothesis-driven manner Such studies, for instance, have included trauma-related variables in psychopathology networks (to infer pathways from environmental adversity to severe mental illness; Isvoranu et al, 2017); used measures of neighbourhood deprivation and trust (to show how harsh living environments may lead to paranoid symptoms; McElroy et al, 2019); and have included theoretically relevant variables in psychopathology networks to adjudicate between different theories (De Beurs et al, 2019)
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