Roy Perlis, MD MSc: The time has come for over-the-counter antidepressants (STAT)

April 8, 2024
Roy Perlis, MD MSc
Mental health is a public health crisis in the United States, where access to evidence-based treatment a huge challenge. What can we do to fix this problem?
The need for accessible depression treatment has never been greater.  Multiple national surveys, including those administered as part of the COVID States Project, led by Roy Perlis, MD MSc, Director of the Center for Quantitative Health at MGH, have reported high levels of depression, especially among young adults. More than 1 in 10 adults in a U.S. Census survey reported needing therapy for mental health issues yet being unable to get it — including 1 in 4 adults who reported current depression or anxiety.

In an article, Dr. Perlis discusses the risks and benefits of making antidepressants available without a prescription.  Over the last decades, we have seen many medications, including medications for acid reflux and oral contraceptives become available over the counter.  When access to psychiatric care is so limited, what would it mean to offer antidepressants as an over-the-counter medication?  We are obviously concerned about adverse events associated with the unsupervised use of antidepressants, but at the same time we must consider the adverse consequences of untreated depression in millions of Americans.

Read more on STAT.

The time has come for over-the-counter antidepressants  STAT

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.

 

 

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