Detection of Vaccine Adverse Events Before Package Insert Revisions Using a Japanese Spontaneous Reporting System.

Japanese Adverse Drug Event Report database disproportionality analysis drug safety pharmacovigilance vaccine

Journal

Journal of clinical pharmacology
ISSN: 1552-4604
Titre abrégé: J Clin Pharmacol
Pays: England
ID NLM: 0366372

Informations de publication

Date de publication:
08 2023
Historique:
received: 24 02 2023
accepted: 06 04 2023
medline: 11 7 2023
pubmed: 13 4 2023
entrez: 12 4 2023
Statut: ppublish

Résumé

The usefulness of disproportionality analysis for the pharmacovigilance of vaccines in the Japanese Adverse Drug Event Report (JADER) database is yet to be proven. This study aimed to verify whether significant disproportionality could be detected before adding new vaccine adverse event information to package inserts. Information on package insert revisions related to vaccine adverse drug events from January 2013 to March 2023 was extracted from the Pharmaceuticals and Medical Devices Agency website. This period was set as the maximum period for which early disproportionalities could be detected by the latest JADER database (April 2004 to December 2022). From JADER data, 15 revision histories (10 types of vaccines) of package inserts were identified, and 823,662 cases were obtained. Of the 15, 12 (80%) adverse events were identified as significant disproportionalities before package insert revisions were made. Nine of the 15 (60%) events were identified as significant disproportionalities earlier than at least 12 months. These findings suggest that the JADER database may detect vaccine adverse events earlier than package insert revisions, indicating its usefulness for the safety surveillance of vaccines.

Identifiants

pubmed: 37042319
doi: 10.1002/jcph.2243
doi:

Substances chimiques

Vaccines 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

903-908

Commentaires et corrections

Type : CommentIn
Type : CommentIn

Informations de copyright

© 2023 The Authors. The Journal of Clinical Pharmacology published by Wiley Periodicals LLC on behalf of American College of Clinical Pharmacology.

Références

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The Centers for Disease Control and Prevention (CDC), the US Food and Drug Administration (FDA). Vaccine Adverse Event Reporting System. Accessed June 10, 2022. https://vaers.hhs.gov/index.html
Shimabukuro TT, Nguyen M, Martin D, DeStefano F. Safety monitoring in the vaccine adverse event reporting system (VAERS). Vaccine. 2015;33(36):4398-4405.
Asatryan A, Pool V, Chen RT, et al. Live attenuated measles and mumps viral strain-containing vaccines and hearing loss: vaccine adverse event reporting system (VAERS), United States, 1990-2003. Vaccine. 2008;26:1166-1172.
Haber P, DeStefano F, Angulo FJ, et al. Guillain-Barré syndrome following influenza vaccination. JAMA. 2004;292(20):2478-2481.
Yamaguchi T, Iwagami M, Ishiguro C, et al. Safety monitoring of COVID-19 vaccines in Japan. Lancet Reg Health West Pac. 2022;23:100442.
Rothman KJ, Lanes S, Sacks ST. The reporting odds ratio and its advantages over the proportional reporting ratio. Pharmacoepidemiol Drug Saf. 2004;13(8):519-523.
Evans SJW, Waller PC, Davis S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol Drug Saf. 2001;10(6):483-486.

Auteurs

Shimon Suzuki (S)

Division of Drug Informatics, Keio University Faculty of Pharmacy, Minato-ku, Tokyo, Japan.

Shungo Imai (S)

Division of Drug Informatics, Keio University Faculty of Pharmacy, Minato-ku, Tokyo, Japan.

Satoru Mitsuboshi (S)

Division of Drug Informatics, Keio University Faculty of Pharmacy, Minato-ku, Tokyo, Japan.
Department of Pharmacy, Kaetsu Hospital, Niigata, Japan.

Hayato Kizaki (H)

Division of Drug Informatics, Keio University Faculty of Pharmacy, Minato-ku, Tokyo, Japan.

Masayuki Hashiguchi (M)

Division of Drug Informatics, Keio University Faculty of Pharmacy, Minato-ku, Tokyo, Japan.

Satoko Hori (S)

Division of Drug Informatics, Keio University Faculty of Pharmacy, Minato-ku, Tokyo, Japan.

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Classifications MeSH