Description
There has been a significant increase in suicide rates in the United States (U.S.) over the past two decades. Studies have highlighted the need for further exploration of suicide risk factors, particularly combinations of factors. In this study, a pharmacovigilance analysis was conducted to better understand drugs and indications as risk factors for suicide using data from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and Adverse Event Open Learning through Universal Standardization (AEOLUS), a standardized version of FAERS. Association rule mining techniques were applied to 85,071 cases involving suicide-related adverse reactions and demographic subsets of these cases. Preliminary results reveal combinations of drugs and indications that may increase the likelihood of suicide, with certain combinations potentially affecting some demographic groups more than others. Further work is needed to validate the initial findings, explore subpopulations, and determine the broader implications for suicide prevention.
Learning Objective: Use data from a spontaneous reporting system and data mining techniques to characterize combinations of drugs and indications associated with adverse reactions.
Authors:
Tiffany Ding (Presenter)
Brown University
Elizabeth Chen, Brown University
Presentation Materials: