Shervin Assari, M.D., M.P.H.
Research Assistant Professor
University of Michigan
Department of Psychiatry
Dr. Assari is a Research Assistant Professor with the Department of Psychiatry, with joint appointments at the Center for Research on Ethnicity, Culture and Health (CRECH), Poverty Solutions, and the Institute for Healthcare Policy and Innovation (IHPI). His interest lies in the intersection of community mental health and social epidemiology. As a community mental health researcher, he is interested in mood disorders due to fundamental causes such as ethnicity, gender, class, and place.
Dr. Assari is an M.D. with post-graduate training in Public Health (M.P.H.) and postdoctoral training in health disparities. He has authored over 200 peer reviewed papers and is awarded a fellow status by the Society of Behavioral Medicine (SBM), New York Academy of Medicine (NYAM) and American Academy of Health Behavior (AAHB). With more than 400 scientific peer reviews, he was recently ranked in the top ten scientists who perform peer review in publons.com
He is an Associate Editor for three psychiatry and medical journals (Frontiers in Psychiatry, Frontiers in Public Health, and Archives in Medical Science), and has served on the Board of Directors of the American College of Epidemiology (ACE) and American Academy of Health Behavior (AAHB).
Dr. Assari’s findings have shown that the links between socioeconomic status, stress, mood disorders, and chronic disease depend on the intersections of ethnicity, gender, and class. For example, his research has consistently documented stronger health gain associated with socioeconomic resources and psychological assets for Whites than Blacks, also known as minorities diminished return. He has shown that high socioeconomic status is positively associated with depression and suicidality among African American men, and environmental stress better predicts depression and obesity for men and women, respectively.
Dr. Assari has worked with large scale national mental health surveys and cohorts. He is currently using Prechter longitudinal data to explain the mechanisms by which the intersections of ethnicity, gender, and class influence trajectories of bipolar disorder. He applies structural equation modeling and latent growth curve modeling to longitudinal data to better understand trajectories of bipolar disorders over time.