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KMID : 1011320200120020091
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Journal of Pharmacoepidemiology and Risk Management
2020 Volume.12 No. 2 p.91 ~ p.109
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A Class-Effect Study of Vaccine Signal Detection Using Korea Adverse Event Reporting System Database
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Kim Moon-Jung
Kim Bo-Ra
Han Soon-Young
Chung Soo-Youn
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Abstract
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Objective: This study was aimed to identify the class-effect of vaccine using spontaneous adverse event reporting system database.
Methods: The vaccines to be analyzed were defined as 22 vaccines based on the Anatomical Therapeutic Chemical code and the adverse events to be analyzed were selected as 16 preferred terms in World Health Organization - Adverse Reaction Terminology 092 based on the particular adverse events following immunization (AEFIs) listed in Korea regulations. We used the vaccine dataset and full drug dataset from 1989 to 2018 of Korea Adverse Event Reporting System. Statistically significant vaccines were detected as signals by observing quantitative proportional reporting ratio for the 16 adverse events.
Results: The number of significant vaccines in the vaccine dataset/ the number of significant vaccines in the full drug dataset for each adverse event were arthritis 3/3, convulsions 5/10, encephalopathy 5/8, neuritis 2/5, lymphadenopathy 2/10, anaphylactic shock 2/2, anaphylactoid reaction 2/4, sepsis 4/0, hyperpyrexia 2/7, osteomyelitis 1/1, neuropathy peripheral 1/0, purpura thrombocytopenic 3/7, infection Bacille Calmette-Guerin 1/1, osteitis 1/1, injection site infection 2/12, and anaphylactic reaction 2/0.
Conclusion: Our study suggests that the vaccine with higher contribution to AEFI compared to the all other vaccines can be identified at the vaccine class level using vaccine dataset.
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KEYWORD
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Vaccine, Adverse event following immunization, Class-effect, Signal detection
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