| アブストラクト | 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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| 投稿日 | 2001/9/6 |
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| 投稿者 | Niu, M T; Erwin, D E; Braun, M M |
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| ジャーナル名 | Vaccine |
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| 組織名 | Vaccine Safety Branch, Division of Epidemiology, Office of Biostatistics and;Epidemiology, Center for Biologic Evaluation and Research, US Food and Drug;Administration, 1401 Rockville Pike, HFM-210, Rockville, MD 20852-1448, USA.;niu@cber.fda.gov |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/11535310/ |
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