Exploring Efficacious FDA Approved Drugs and Their Subgroups in a Large Claims Database for Protection Against COVID-19

• Activity Number: 341 - Contributed Poster Presentations: Section on Statistical Learning and Data Science
• Type: Contributed
• Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Joshua W Lambert*
• Companies: University of Cincinnati
• Keywords: Subgroup; Interaction; Claims; COVID

Abstract

Coronavirus Disease 2019 (COVID-19) is a global public health emergency. Thus, there is an urgent need for efficacious therapeutics against the disease. One potential strategy is to identify and repurpose already existing drugs to treat the disease. We utilize a retrospective health care claims database to explore drug and their subgroups for protection against adverse outcomes of COVID-19 on a large scale. The Change Healthcare Database, a part of the COVID-19 Research Database, contains the health insurance claims data for more than 5 million Americans diagnosed with COVID-19 in 2020. While adjusting for more than 10 known confounders and risk factors, we utilize logistic regression and a novel statistical algorithm called the Feasible Solutions Algorithm (FSA) to explore potentially efficacious FDA approved drugs and their demographic and clinical subgroups (biological sex, age, obesity, and history of other related comorbidities) against adverse outcomes of COVID-19. The findings and their potential benefit are presented and discussed.