Dr. C. Hendricks Brown is Director of the Social Systems Informatics focus area in the Center for Computational Science, a program that he founded in 2011.  He holds the rank of Professor of Epidemiology and Public Health, at the Miller School of Medicine, University of Miami, and is Interim Director of the Prevention Science and Community Health Division in that department.Originally trained in theoretical chemistry and statistics, Brown’s recent work involves computational methods to model and test how one’s behaviors and health interact with one’s social, physical, and virtual environments, and how system-level interventions can improve health and behavior.  These computational methods include social network analysis, agent-based modeling, and intelligent data analysis.  An example is the modeling and testing of an intervention to prevent youth suicide.  Suicidal youth are often isolated from most youth; those they are close to are often suicidal themselves, and these factors compound the risk and make them more difficult to reach with standard interventions in school.  With colleagues at the University of Rochester and USC, Brown is examining how messages are transmitted through peer and adult social networks, and how interventions can be designed to reach and influence the most isolated and vulnerable individuals.

Brown and colleagues are also examining the role that antidepressants may play in causing suicide, especially for youth and young adults.  The U.S. Food and Drug Administration (FDA) has placed a major warning label on antidepressants in these groups because some data indicate that those who take antidepressants report higher rates of suicide attempts.  Other data show protective effects of antidepressants, and our task has been to address these disparate findings and provide clarity for the field.   The causal processes involved, in which more depressed youth are prescribed antidepressants but may also be more likely to report suicide attempt, are being studied through complex mediational modeling across multiple datasets.  Another example where the findings of suicide have been in conflict has been the question of whether veterans are more at risk for suicide than non-veterans, when matched for age and other characteristics.  Here we have found clear evidence of much higher risk among more recent, younger veterans, and a modest but consistent 50% elevation in suicide risk for veterans over 25.

One focus area of research involves the young field of implementation science, which discerns how scientific findings and interventions can be implemented in communities and organizations to improve health of our population.  For years, federal institutes such as NIH (National Institutes of Health) have funded research to develop programs and practices that can improve physical and mental health or prevent or treat illnesses.  But the vast majority of these advanced have not been implemented widely in our society.  This new challenge area requires sophisticated computational methods for modeling and testing complex system-level implementations in schools, social service agencies, and medical settings.  To this end, Brown directs the Center for Prevention Implementation Methodology (Ce-PIM), which is funded by the National Institute on Drug Abuse (NIDA/NIH) to support moving evidence-based prevention programs into practice to prevent drug abuse and HIV/AIDS.

A recent example of this work involves a randomized trial of 51 counties in California and Ohio to test the dissemination of an evidence-based foster care program.  This CAL-OH project randomizes counties to one of two implementation strategies, then tests how quickly and completely implementation of this complex foster care program takes place.

Brown has been a member of the recent National Academy of Sciences/Institute of Medicine Committee on Prevention Science, and serves on numerous federal panels, advisory boards, and editorial boards.