Incorporation of multilevel ontologies of adverse events and vaccines for vaccine safety surveillance PROJECT SUMMARY Vaccines face tougher safety standards than most pharmaceutical products because they are given to healthy people, often children. Effective and rigorous analyses of post-vaccination adverse events (AEs) is critical to ensure the safety of vaccines. The Vaccine Adverse Event Reporting System (VAERS) is a national vaccine safety surveillance program which contains spontaneous reports from 1990 to present. Statistical approaches have been used on VAERS to extract important signals hidden in this large, complex database and offer a hypothesis-free view of the safety characteristics in the underlying data. However, existing methods may miss detecting serious AEs due to modeling under the false assumption of independence between different types of AEs. In response to the FOA, PA-18-873, this proposal addresses the specific objective: ?creation/evaluation of statistical methodologies for analyzing data on vaccine safety, including data available from existing data sources such as passive reporting systems or healthcare databases.? We propose to develop a series of methods for vaccine safety surveillance while incorporating adverse event ontology as well as vaccine ontology. Specifically, we will use the Medical Dictionary for Regulatory Activities (MedDRA) and the vaccine ontology (VO) to form the basis of our models for systematically mining and monitoring safety signals. To the best of our knowledge, this is the first attempt to directly incorporate AE and vaccine ontologies in the signal detection method. Multiple AEs may individually be rare enough to go undetected, but if they are related, they can borrow strength from each other to increase the chance of being flagged. Furthermore, borrowing strength induces shrinkage of related AEs, thereby also reducing headline-grabbing false positives. Additionally, multiple AEs may collectively point to an underlying adverse cause, combined with additional expert knowledge from the vaccine ontology, such as vaccine components, we will be able to understand the root cause of different types of AEs. 1

Public Health Relevance

Vaccination safety is critical for individual and public health. Currently, vaccine trust is at an all-time low. The proposed ontology-based methods will provide important insights into the underlying mechanism of safety issues. Their development and applications to the VAERS dataset will contribute to public health by enabling earlier identification and prevention of potentially serious safety problems, reassuring the public that vaccines are safe, and recovering the public trust.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
Project #
Application #
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Lapham, Cheryl K
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Michigan Ann Arbor
Biostatistics & Other Math Sci
Schools of Public Health
Ann Arbor
United States
Zip Code