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  • Records of post-vaccination adverse events were queried from the CDC Vaccine Adverse Event Reporting System (VAERS; access date: May 18th, 2011). The query was constructed to retrieve adverse event information of killed inactivated vaccines (TIV group) consisting of Afluria, Fluarix, Fluvirin, and Fluzone, and live attenuated vaccine (LAIV group) FluMist. The names and the numbers of AEs for each type of vaccines were summarized. Some AE symptoms are common in both groups, while a significant number of symptoms are unique to each cohort. Symptoms from both AE record tables differ in rankings (number of occurrences) and the nature of adverse events themselves. We hypothesize that performing a comparative analysis of different physiological responses implied by AE symptoms associated with each vaccine could lead us to understand the underlying response mechanism of the influenza A/B vaccines. AE report signal detection with Proportional Reporting Ratios (PRRs): Each group of reports (TIV and LAIV) was analyzed independently with the PRR method (as introduced by Evans et al. [11]) (Table S1). PRR calculates the proportions of specific AE(s) for a vaccine (or a group of vaccines) of interest where the comparator is all other vaccines in the VAERS database. Therefore, calculations to detect signals from the data pool utilize the total number of reports for each vaccine as a denominator to determine the proportion of all reports that fall in the type of interest (which in this case is the individual AE that was retrieved by each group of vaccine compared against reports of that particular AE in the total VAERS database pool). The PRR score of individual AEs in each group is then used as one of the composite criteria to compare for significant AEs in each group. Chi-square test to identify statistically significant AEs: In parallel with PRR signal detection, the Chi-square significance test for contingency tables was applied to individual AE MedDRA terms that are associated with TIV or LAIV independently [11]. The Chi-square test computes a Chi-square score and probability for each AE in each group using a 2×2 frequency/contingency table. The 2×2 contingency table was composed of four disjoint counts based on the total number of all reports in each group (37,621 TIV cases, 3,707 LAIV cases) against the overall VAERS data (616,215 cases). An AE was called significant when its Chi-square score was greater than 4, which implied P-value of approximately 0.05 or smaller [11]. AE case report frequency as a cutoff for filtering out background noise: Besides the PRR calculation and Chi-square test, the screened PRR method (SPRR) also used a minimal sample size cutoff ([18] [19]). The original SPRR paper uses a minimal sample size cutoff of 3 case reports for each AE to be further considered. Such a constant cutoff does not work for our project since the two groups (TIV and LAIV) of case reports have different case report sizes. In our study, the sample size cutoff threshold of the number of reports for both groups was determined to be 0.2% of the total number of reports of each group. Using this cutoff, the biological implication would mean that at least 2 out of 1000 cases reported the AE of interest. The selection of the cutoff was supported by the report signal curve on the total case reports for each group (Figure S1 A and B). In either TIV or LAIV case, the 0.2% cutoff line was able to cut off many AEs which are in the bottom of the signal curve and considered as “noise”. The cutoff line is located in a similar pattern in both cases (Figure S1 A and B), suggesting that the 0.2% cutoff removes “noise” AEs in each group correspondingly. The number of cases for one AE to get called in for TIV group was evaluated to be 75 (number of reports > = 75), while the cutoff for LAIV group was evaluated at 8 (number of reports > = 8). To determine which AEs were exclusively enriched for TIV or LAIV, we excluded AEs that appeared as common signals in both lists. We also excluded ambiguous AEs such as no adverse event, or those of lab test result normal. We were then left with 48 TIV-enriched AEs and 68 LAIV-enriched AEs. These are AEs that their corresponding PRR score is at least 2, and Chi-square is greater than 4 (approximately of probability value of 0.05 or smaller). Comparison of concept reorganization based on semantic similarity of the Ontology of Adverse Events (OAE): The OAE (http://www.oae-ontology.org/) was previously named the Adverse Event Ontology (AEO) [15]. The change of the name space was applied to avoid a conflict with another ontology. OAE was downloaded from http://sourceforge.net/projects/oae/. OAE was visualized with the Protégé 4.0.2 OWL editor. For better comparison and analysis, related MedDRA terms associated with TIV and LAIV were mapped to corresponding OAE terms. However, the ontological structures of these terms in the two systems are often different. TIV- and LAIV-related AEs were classified based on the OAE structure hierarchy for comparative analyses. Specifically, the TIV- and LAIV-specific AEs and their parent term hierarchies were extracted from the OAE using the OntoFox program [16]. The hierarchical results were visualized using the Protégé-OWL editor and manually studied and compared. To compare the performances of classification using different ontologies, TIV- and LAIV-specific AE terms were also classified using SNOMET-CT, and COSTART/MedDRA.
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