Increase in Suicidal Thinking During COVID-19
- PMID: 38602997
- PMCID: PMC7967020
- DOI: 10.1177/2167702621993857
Increase in Suicidal Thinking During COVID-19
Abstract
There is concern that the COVID-19 pandemic may cause increased risk of suicide. In the current study, we tested whether suicidal thinking has increased during the COVID-19 pandemic and whether such thinking was predicted by increased feelings of social isolation. In a sample of 55 individuals recently hospitalized for suicidal thinking or behaviors and participating in a 6-month intensive longitudinal smartphone monitoring study, we examined suicidal thinking and isolation before and after the COVID-19 pandemic was declared a national emergency in the United States. We found that suicidal thinking increased significantly among adults (odds ratio [OR] = 4.01, 95% confidence interval [CI] = [3.28, 4.90], p < .001) but not adolescents (OR = 0.84, 95% CI = [0.69, 1.01], p = .07) during the onset of the COVID-19 pandemic. Increased feelings of isolation predicted suicidal thinking during the pandemic phase. Given the importance of social distancing policies, these findings support the need for digital outreach and treatment.
Keywords: interpersonal interaction; longitudinal methods; suicide prevention.
© The Author(s) 2021.
Conflict of interest statement
Declaration of Conflicting Interests: J. W. Smoller is an unpaid member of the Bipolar/Depression Research Community Advisory Panel of 23andMe, a member of the Leon Levy Foundation Neuroscience Advisory Board, and received an honorarium for an internal seminar at Biogen, Inc. M. K. Nock is an unpaid member of the TalkLife Advisory Board. The author(s) declared that there were no other potential conflicts of interest with respect to the authorship or the publication of this article.
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References
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- Ammerman B. A., Burke T. A., Jacobucci R., McClure K. (2020). Preliminary investigation of the association between COVID-19 and suicidal thoughts and behaviors in the U.S. PsyArXiv. 10.31234/osf.io/68djp - DOI - PubMed
-
- Bates D., Machler M., Bolker B. M., Walker S. C. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. 10.18637/jss.v067.i01 - DOI
-
- Christensen R. H. B. (2019). Regression models for ordinal data (Version 2019.12-10) [Computer software]. Comprehensive R Archive Network. https://CRAN.R-project.org/package=ordinal
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