The Vaccine Adverse Event Reporting System (VAERS) received thousands of reports of adverse events that occurred after vaccine administrations from the post-marketing vaccine safety surveillance. However, the causality between vaccines and reported adverse events cannot be taken for granted. In this report several data mining methods were applied to VAERS database that is coded in MedDRA terms to discover possible associations between vaccines and adverse events. Efforts were devoted to identify events that are reported more frequently after administering one vaccine than other vaccines using the following data mining techniques: relative ratio (RR), statistical significance (LogP), proportional reporting ratio (PRR), and screened PRR (SPRR).
The vaccine-event combinations that ranked top in each method varied substantially among the methods. RR and PRR gave excessive weight to small counts of vaccine-event pairs, but SPRR was able to correct this weakness. There are only 33 vaccine-event pairs that were shared among the top 1,000 ranked in each method. Evaluating the properties of these data mining methods and exploring other methods will help improve vaccine safety surveillance. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2011-05-3293 |
| Date | 05 August 2011 |
| Creators | Ye, Na, 1983- |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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