Version 1
: Received: 12 September 2023 / Approved: 13 September 2023 / Online: 13 September 2023 (07:51:40 CEST)
How to cite:
Antonov, L.; Kirilov, B.; Dokova, K. Modeling Adverse Events Counts in the Bulgarian Pharmacovigilance Database. Preprints2023, 2023090849. https://doi.org/10.20944/preprints202309.0849.v1
Antonov, L.; Kirilov, B.; Dokova, K. Modeling Adverse Events Counts in the Bulgarian Pharmacovigilance Database. Preprints 2023, 2023090849. https://doi.org/10.20944/preprints202309.0849.v1
Antonov, L.; Kirilov, B.; Dokova, K. Modeling Adverse Events Counts in the Bulgarian Pharmacovigilance Database. Preprints2023, 2023090849. https://doi.org/10.20944/preprints202309.0849.v1
APA Style
Antonov, L., Kirilov, B., & Dokova, K. (2023). Modeling Adverse Events Counts in the Bulgarian Pharmacovigilance Database. Preprints. https://doi.org/10.20944/preprints202309.0849.v1
Chicago/Turabian Style
Antonov, L., Bogdan Kirilov and Klara Dokova. 2023 "Modeling Adverse Events Counts in the Bulgarian Pharmacovigilance Database" Preprints. https://doi.org/10.20944/preprints202309.0849.v1
Abstract
We tested the possibility of using “adverse events count” (AEC) as a drug-risk indicator and side-effect severity indicator. Data from 3938 adverse event (AE) reports for COVID-19 vaccines (Comirnaty, Moderna, Vaxzevria, and Janssen) and 6869 AE reports for other medicines were collected from the Bulgarian Drug Agency database (01.01.2018–31.03.2022). AEC was modeled with zero-adjusted negative binomial (ZANBI) and zero-adjusted Poisson inverse Gaussian (ZAPIG) regression models, which account for zero absence and overdispersion. The models’ fit was checked with residual diagnostic plots and parametric correspondence to normality. Explanatory variables were: age, sex, sequence number (order of submission), severity of AE, and vaccine type. Average AEC was higher in severe vs. non-severe AE, and in females vs. males; it decreased with age and was lower in Comirnaty than other vaccines. Variability of AEC for COVID-19 data decreased with sequence number in severe AE and increased in non-severe AE. Full ZANBI models had greater sensitivity than parametric ZANBI or ZAPIG models. Results showed correlation between AEC and AE severity, suggesting AEC as a simple and reliable measure of drug-risk and side-effect severity for clinicians and regulators, especially for COVID-19 vaccines.
Keywords
COVID-19 vaccines; SARS-CoV-2; vaccine safety; pharmacovigilance; vaccination campaigns; adverse event; negative binomial distribution; Poisson-inverse Gaussian distribution; model diagnostics; real-world data
Subject
Medicine and Pharmacology, Medicine and Pharmacology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
