Rajandran, Puvaindran
(2022)
Post licensure surveillance of Human Papillomavirus vaccine, vaccine adverse event reporting system, 2010-2017.
MPhil thesis, University of Nottingham.
Abstract
Currently, there are three approved and commercially available Human Papillomavirus (HPV) vaccines. These are Gardasil®, a quadrivalent HPV, Cervarix®, a bivalent vaccine, and Gardasil®9, a nonvalent HPV and they were first approved in 2006, 2009, and 2014, respectively for use in females. Approvals for use in males followed a few years later for Gardasil® and Gardasil®9. The acceptance of HPV vaccination has been a challenge, including cost, cultural views, parent’s acceptance, safety, and adverse events of the vaccine. Vaccination acceptance is mainly influenced by safety reports and, unfortunately, also by misinformation from social media. The United States (US) Food and Drug Administration (FDA) collects and maintains post-marketing products’ safety data, including vaccines. These safety data or adverse event data are collected through several methods, including chart reviews and reporting systems. The use of data mining has shown to be useful in extracting critical information from a large dataset. The adverse event datasets associated with commercially available HPV vaccines for years 2010-2017 from the Vaccine Adverse Event Reporting System (VAERS) were analyzed using SAS Text Analytics. The results showed that most of the detected adverse events were commonly reported, and many are non-serious associated with teenage and young adult patients. In the 7-year data analyzed, authors found that most adverse events terms were associated with nervous system disorders (n=1251) followed with general disorders and administration site conditions (n=1192). In addition, death, and serious terms (Guillain-Barre syndrome, seizure, anaphylactic shock) were also identified. In conclusion, this study did not detect safety signals associated with HPV vaccines between 2010 to 2017. Big data analysis will serve as a baseline for analysis of this ongoing surveillance in the pharmacovigilance field in the future.
| Item Type: |
Thesis (University of Nottingham only)
(MPhil)
|
| Supervisors: |
Ting, Kang Nee |
| Keywords: |
data mining, predictive text analytics, human papillomavirus vaccine, vaccine, vaccination, adverse events, pharmacovigilance |
| Subjects: |
R Medicine > RC Internal medicine |
| Faculties/Schools: |
University of Nottingham, Malaysia > Faculty of Science and Engineering — Science > Division of Biomedical Sciences |
| Item ID: |
69495 |
| Depositing User: |
Rajandran, Puvaindran
|
| Date Deposited: |
24 Jul 2022 04:40 |
| Last Modified: |
24 Jul 2022 04:40 |
| URI: |
https://eprints.nottingham.ac.uk/id/eprint/69495 |
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