Which traits predict elevated distress during the Covid-19 pandemic? Results from a large, longitudinal cohort study with psychiatric patients and healthy controls

  1. Katharina Brosch
  2. Tina Meller
  3. Julia-Katharina Pfarr
  4. Frederike Stein
  5. Simon Schmitt
  6. Kai G. Ringwald
  7. Lena Waltemate
  8. Hannah Lemke
  9. Katharina Thiel
  10. Elisabeth Schrammen
  11. Carina Hülsmann
  12. Susanne Meinert
  13. Katharina Dohm
  14. Elisabeth J. Leehr
  15. Nils Opel
  16. Axel Krug
  17. Udo Dannlowski
  18. Igor Nenadić
  19. Tilo Kircher
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Abstract

Background

The Covid-19 pandemic resulted in repeated, prolonged restrictions in daily life. Social distancing policies as well as health anxiety are thought to lead to mental health impairment. However, there is lack of longitudinal data identifying at-risk populations particularly vulnerable for elevated Covid-19-related distress.

Methods

We collected data of N=1268 participants (n=622 healthy controls (HC), and n=646 patients with major depression, bipolar disorder, schizophrenia or schizoaffective disorder) at baseline (2014-2018) and during the first lockdown in Germany (April-May 2020). We obtained information on Covid-19 restrictions (number and subjective impact of Covid-19 events), and Covid-19-related distress (i.e., subjective fear and isolation). Using multiple linear regression models including trait variables and individual Covid-19 impact, we sought to predict Covid-19-related distress.

Results

HC and patients reported similar numbers of Covid-19-related events, and similar subjective impact rating. They did not differ in Covid-19-related subjective fear. Patients reported significantly higher subjective isolation. 30.5% of patients reported worsened self-rated symptoms since the pandemic. Subjective fear in all participants was predicted by four variables: trait anxiety (STAI-T), conscientiousness (NEO-FFI), Covid-19 impact, and sex. Subjective isolation in HC was predicted by social support (FSozu), Covid-19 impact, age, and sex; in patients, it was predicted by social support and Covid-19 impact.

Conclusion

Our data shed light on differential effects of the pandemic in psychiatric patients and HC. They identify relevant, easy-to-obtain variables for risk profiles related to interindividual differences in Covid-19-related distress for direct translation into clinical practice.

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