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, 19 (32), 4627-34

Data Mining in the US Vaccine Adverse Event Reporting System (VAERS): Early Detection of Intussusception and Other Events After Rotavirus Vaccination

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Data Mining in the US Vaccine Adverse Event Reporting System (VAERS): Early Detection of Intussusception and Other Events After Rotavirus Vaccination

M T Niu et al. Vaccine.

Abstract

The Vaccine Adverse Event Reporting System (VAERS) is the US passive surveillance system monitoring vaccine safety. A major limitation of VAERS is the lack of denominator data (number of doses of administered vaccine), an element necessary for calculating reporting rates. Empirical Bayesian data mining, a data analysis method, utilizes the number of events reported for each vaccine and statistically screens the database for higher than expected vaccine-event combinations signaling a potential vaccine-associated event. This is the first study of data mining in VAERS designed to test the utility of this method to detect retrospectively a known side effect of vaccination-intussusception following rotavirus (RV) vaccine. From October 1998 to December 1999, 112 cases of intussusception were reported. The data mining method was able to detect a signal for RV-intussusception in February 1999 when only four cases were reported. These results demonstrate the utility of data mining to detect significant vaccine-associated events at early date. Data mining appears to be an efficient and effective computer-based program that may enhance early detection of adverse events in passive surveillance systems.

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