TY - GEN
T1 - Network analysis of possible anaphylaxis cases reported to the US vaccine adverse event reporting system after H1N1 influenza vaccine
AU - Botsis, Taxiarchis
AU - Ball, Robert
PY - 2011/1/1
Y1 - 2011/1/1
N2 - The identification of signals from spontaneous reporting systems plays an important role in monitoring the safety of medical products. Network analysis (NA) allows the representation of complex interactions among the key elements of such systems. We developed a network for a subset of the US Vaccine Adverse Event Reporting System (VAERS) by representing the vaccines/adverse events (AEs) and their interconnections as the nodes and the edges, respectively; this subset we focused upon included possible anaphylaxis reports that were submitted for the H1N1 influenza vaccine. Subsequently, we calculated the main metrics that characterize the connectivity of the nodes and applied the island algorithm to identify the densest region in the network and, thus, identify potential safety signals. AEs associated with anaphylaxis formed a dense region in the 'anaphylaxis' network demonstrating the strength of NA techniques for pattern recognition. Additional validation and development of this approach is needed to improve future pharmacovigilance efforts.
AB - The identification of signals from spontaneous reporting systems plays an important role in monitoring the safety of medical products. Network analysis (NA) allows the representation of complex interactions among the key elements of such systems. We developed a network for a subset of the US Vaccine Adverse Event Reporting System (VAERS) by representing the vaccines/adverse events (AEs) and their interconnections as the nodes and the edges, respectively; this subset we focused upon included possible anaphylaxis reports that were submitted for the H1N1 influenza vaccine. Subsequently, we calculated the main metrics that characterize the connectivity of the nodes and applied the island algorithm to identify the densest region in the network and, thus, identify potential safety signals. AEs associated with anaphylaxis formed a dense region in the 'anaphylaxis' network demonstrating the strength of NA techniques for pattern recognition. Additional validation and development of this approach is needed to improve future pharmacovigilance efforts.
KW - H1N1
KW - Network Analysis
KW - Spontaneous Reporting System
KW - VAERS
UR - http://www.scopus.com/inward/record.url?scp=83055161783&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=83055161783&partnerID=8YFLogxK
U2 - 10.3233/978-1-60750-806-9-564
DO - 10.3233/978-1-60750-806-9-564
M3 - Conference contribution
C2 - 21893812
AN - SCOPUS:83055161783
SN - 9781607508052
T3 - Studies in Health Technology and Informatics
SP - 564
EP - 568
BT - User Centred Networked Health Care - Proceedings of MIE 2011
PB - IOS Press
T2 - 23rd International Conference of the European Federation for Medical Informatics, MIE 2011
Y2 - 28 August 2011 through 31 August 2011
ER -