Trivalent influenza vaccine adverse symptoms analysis based on MedDRA terminology using VAERS data in 2011

Jingcheng Du, Yi Cai, Yong Chen, Cui Tao

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: Trivalent Influenza Virus Vaccine (FLU3) is a traditional flu vaccine to protect people against three different flu viruses, including influenza A H1N1 virus, an influenza A H3N2 virus and one B virus. Methods: We searched Vaccine Adverse Event Reporting System (VAERS) for US reports after FLU3 vaccination in the year of 2011. We conducted descriptive analyses on symptoms from serious reports (i.e., death, life-threatening illness, hospitalization, prolonged hospitalization, or permanent disability). We then further grouped these symptoms to the System Organ Classes (SOC) based on the MedDRA Terminology using NCBO Web Services. We fitted zero-truncated Poisson regression models to estimate the average number of symptoms per subject and compared it across different age groups and between genders. In addition, we compared the risk of occurrence for an SOC across different age groups and between genders by using logistic regression models. Finally, we constructed the pairwise correlation matrix of the SOCs by calculating Spearman's rank correlation coefficients. Results: We identified 638 unique serious FLU3 reports from year 2011. There are 1410 unique symptoms from these reports. Descriptive statistics shows that the most common symptom and symptom pair are Pyrexia and Guillain-Barre syndrome - Hypoesthesia respectively. The estimated average number of symptoms per subject in the study cohort is 8.74 (95 % CI 6.76, 10.73). There are statistically significant differences in number of symptoms among four age groups and between genders. Age category and gender are significantly associated with several individual SOCs. Pairwise correlation matrix shows that "Endocrine disorders" and "Neoplasms benign, malignant and unspecified (incl cysts and polyps)" are strongly correlated. Conclusions: This paper reports a novel method that combining statistical analyses with terminology grouping using VAERS data. The analyses revealed differences of reactions among different age groups and between genders and correlation on both symptoms and System Organ Class level independently. The results may lead to additional studies to uncover factors contributing to the individual differences in susceptibility to influenza infection. This method can also be applied to other vaccine types and conduct similar analysis.

Original languageEnglish (US)
Article number13
JournalJournal of Biomedical Semantics
Volume7
Issue number1
DOIs
StatePublished - Jun 14 2016

Keywords

  • MedDRA
  • Trivalent influenza virus vaccine
  • VAERS

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Health Informatics
  • Computer Networks and Communications

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