Trivalent influenza vaccine adverse symptoms analysis based on MedDRA terminology using VAERS data in 2011. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Du, Jingcheng; Cai, Yi; Chen, Yong; Tao, Cui
• Is Part Of: Journal of biomedical semantics. Volume 7:Issue 1(2016)
• Page Start: 1
• Page End: 7
• Publication Date: 2016-12
• Subjects: Trivalent influenza virus vaccine -- VAERS -- MedDRA; Semantics -- Periodicals; Medicine -- Research -- Periodicals; Biology -- Research -- Periodicals; Computer systems -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1186/s13326-016-0056-2
• Languages: English
• ISSNs: 2041-1480

Abstract

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.