Causality Assessment of Adverse Events for Covid-19 Vaccine in Comparison Between Racial Classification Using Naranjo Algorithm

DOI

10.2991/978-94-6463-202-6_5How to use a DOI?

Keywords

Adverse events; Causality assessment; Naranjo algorithm

Abstract

In pharmacovigilance, causality assessment remains an important approach to analyze the causal relationship between adverse events and vaccine application. In 2020, the rapid development of COVID-19 vaccines became a global imperative. Vaccine development typically takes decades before it is approved. However, due to the severity of the pandemic, clinical trials have been cut short.

Objective: The Naranjo algorithm was used to compare the causality assessment of adverse events for the COVID-19 vaccine across racial classifications.

Methods: This was a descriptive type of quantitative observational research. Naranjo algorithm was used as a probability scale to standardize causality assessments for adverse events. Respondent data were obtained in the form of numbers which will then be classified.

Results: For each racial classification, the majority of the causality assessments of adverse events following COVID-19 vaccination were in the “Probable” (30.0%) and “Possible” (41.4%) groups. In the “Probable” group, the percentage of the Caucasian race is 42.1%, Asian is 29.7%, Black/African is 33.3%, and another race is 9.1%. While in the “Possible” group, the Caucasian, Asian, Black/African and other races were 31.6%, 37.8%, 66.7%, and 63.6%, respectively.

Conclusion: The “Probable” category includes adverse events that (1) had a reasonable time-related continuity after vaccine application, (2) had a recognized response to the suspected vaccine, (3) were confirmed by withdrawal but not by exposure to the vaccine application, and (4) could not be reasonably exposed by known clinical characteristics of the patient. The “possible” group indicates that the adverse events (1) had a time-related continuity after vaccine application, (2) possibly exhibited a recognized pattern to the suspected vaccine, and (3) could be explained by the patient's disease characteristics.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rachma Dessidianti
AU  - Fuad Muzakky
AU  - Karima Samlan
PY  - 2023
DA  - 2023/06/26
TI  - Causality Assessment of Adverse Events for Covid-19 Vaccine in Comparison Between Racial Classification Using Naranjo Algorithm
BT  - Proceedings of the 2nd International Conference of Health Innovation and Technology (ICHIT 2022)
PB  - Atlantis Press
SP  - 23
EP  - 36
SN  - 2468-5739
UR  - https://doi.org/10.2991/978-94-6463-202-6_5
DO  - 10.2991/978-94-6463-202-6_5
ID  - Dessidianti2023
ER  -