Roy Perlis, MD MSc, Director of the Center for Quantitative Health at MGH, is interviewed in a two-part series about deaths of despair during the pandemic on the podcast EPIDEMIC with Dr. Celine Gounder. Dr. Perlis discusses the findings of the national COVID States survey of mental health among Americans during the pandemic. The pandemic had a substantial impact on the mental health of all of the subgroups examined; however, those experiencing economic stress, such as loss of income or housing insecurity, have higher levels of depression, anxiety, and suicidal thoughts.
“This is not like a lot of the other disasters that people have studied. It looks a lot more like what you’d expect to see in people who have lived through a war. ” Roy Perlis, MD MSc
Most alarming is the finding that there has been a 10-fold increase in the prevalence of suicidal thoughts among young adults. Dr. Perilis emphasizes the importance of bringing mental health services to those affected by the pandemic, noting that even before the pandemic, we have had serious problems with access to mental health care in the United States, “COVID just laid bare where the cracks are in our mental health system. The big crack in mental health is access.”
A Perfect Storm for Depression – Deaths of Despair Part II / Anne Case and Roy Perlis on EPIDEMIC with Dr. Celine Gounder.
Read More:
THE STATE OF THE NATION: A 50-STATE COVID-19 SURVEY REPORT #23: DEPRESSION AMONG YOUNG ADULTS
Roy Perlis, MD, MSc is the Director of the Center for Quantitative Health at MGH and Associate Chief for Research in the Department of Psychiatry. He is the Ronald I. Dozoretz, MD Endowed Professor of Psychiatry at Harvard Medical School and Associate Editor (Neuroscience) at JAMA's new open-access journal, JAMA Network - Open. His research is focused on identifying predictors of treatment response in brain diseases, and using these biomarkers to develop novel treatments. He directs two complementary laboratory efforts, one focused on patient-derived cellular models and one applying machine learning to large clinical databases.
