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      Data Mining of the Public Version of the FDA Adverse Event Reporting System

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          Abstract

          The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS, formerly AERS) is a database that contains information on adverse event and medication error reports submitted to the FDA. Besides those from manufacturers, reports can be submitted from health care professionals and the public. The original system was started in 1969, but since the last major revision in 1997, reporting has markedly increased. Data mining algorithms have been developed for the quantitative detection of signals from such a large database, where a signal means a statistical association between a drug and an adverse event or a drug-associated adverse event, including the proportional reporting ratio (PRR), the reporting odds ratio (ROR), the information component (IC), and the empirical Bayes geometric mean (EBGM). A survey of our previous reports suggested that the ROR provided the highest number of signals, and the EBGM the lowest. Additionally, an analysis of warfarin-, aspirin- and clopidogrel-associated adverse events suggested that all EBGM-based signals were included in the PRR-based signals, and also in the IC- or ROR-based ones, and that the PRR- and IC-based signals were in the ROR-based ones. In this article, the latest information on this area is summarized for future pharmacoepidemiological studies and/or pharmacovigilance analyses.

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          Most cited references91

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          Under-reporting of adverse drug reactions : a systematic review.

          The purpose of this review was to estimate the extent of under-reporting of adverse drug reactions (ADRs) to spontaneous reporting systems and to investigate whether there are differences between different types of ADRs. A systematic literature search was carried out to identify studies providing a numerical estimate of under-reporting. Studies were included regardless of the methodology used or the setting, e.g. hospital versus general practice. Estimates of under-reporting were either extracted directly from the published study or calculated from the study data. These were expressed as the percentage of ADRs detected from intensive data collection that were not reported to the relevant local, regional or national spontaneous reporting systems. The median under-reporting rate was calculated across all studies and within subcategories of studies using different methods or settings. In total, 37 studies using a wide variety of surveillance methods were identified from 12 countries. These generated 43 numerical estimates of under-reporting. The median under-reporting rate across the 37 studies was 94% (interquartile range 82-98%). There was no significant difference in the median under-reporting rates calculated for general practice and hospital-based studies. Five of the ten general practice studies provided evidence of a higher median under-reporting rate for all ADRs compared with more serious or severe ADRs (95% and 80%, respectively). In comparison, for five of the eight hospital-based studies the median under-reporting rate for more serious or severe ADRs remained high (95%). The median under-reporting rate was lower for 19 studies investigating specific serious/severe ADR-drug combinations but was still high at 85%. This systematic review provides evidence of significant and widespread under-reporting of ADRs to spontaneous reporting systems including serious or severe ADRs. Further work is required to assess the impact of under-reporting on public health decisions and the effects of initiatives to improve reporting such as internet reporting, pharmacist/nurse reporting and direct patient reporting as well as improved education and training of healthcare professionals.
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            Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports.

            The process of generating 'signals' of possible unrecognized hazards from spontaneous adverse drug reaction reporting data has been likened to looking for a needle in a haystack. However, statistical approaches to the data have been under-utilised. Using the UK Yellow Card database, we have developed and evaluated a statistical aid to signal generation called a Proportional Reporting Ratio (PRR). The proportion of all reactions to a drug which are for a particular medical condition of interest is compared to the same proportion for all drugs in the database, in a 2 x 2 table. We investigated a group of newly-marketed drugs using as minimum criteria for a signal, 3 or more cases, PRR at least 2, chi-squared of at least 4. The database was used to examine retrospectively 15 drugs newly-marketed in the UK, with the highest levels of ADR reporting. The method identified 481 signals meeting the minimum criteria during the period 1996-8. Further evaluation of these showed that 70% were known adverse reactions, 13% were events which were likely to be related to the underlying disease and 17% were signals requiring further evaluation. Proportional reporting ratios are a valuable aid to signal generation from spontaneous reporting data which are easy to calculate and interpret, and various refinements are possible.
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              VigiBase, the WHO Global ICSR Database System: Basic Facts

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                Author and article information

                Journal
                Int J Med Sci
                Int J Med Sci
                ijms
                International Journal of Medical Sciences
                Ivyspring International Publisher (Sydney )
                1449-1907
                2013
                25 April 2013
                : 10
                : 7
                : 796-803
                Affiliations
                1. Center for Integrative Education in Pharmacy and Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan;
                2. Kyoto Constella Technologies Co., Ltd., Kyoto 604-8156, Japan;
                3. Department of Systems Biosciences for Drug Discovery, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan.
                Author notes
                ✉ Corresponding author: Toshiyuki Sakaeda, Ph.D., Center for Integrative Education in Pharmacy and Pharmaceutical Sciences, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan, Tel: +81-75-753-9560, Fax: +81-75-753-9253, e-mail: sakaedat@ 123456pharm.kyoto-u.ac.jp ; Yasushi Okuno, Ph.D., Department of Systems Biosciences for Drug Discovery, Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan, Tel&Fax: +81-75-753-4559, e-mail: okuno@ 123456pharm.kyoto-u.ac.jp .

                Competing Interests: The authors have declared that no competing interest exists.

                Article
                ijmsv10p0796
                10.7150/ijms.6048
                3689877
                23794943
                e7a2ae44-eb17-4712-9f6c-2e75a69fda40
                © Ivyspring International Publisher. This is an open-access article distributed under the terms of the Creative Commons License (http://creativecommons.org/licenses/by-nc-nd/3.0/). Reproduction is permitted for personal, noncommercial use, provided that the article is in whole, unmodified, and properly cited.
                History
                : 7 February 2013
                : 16 April 2013
                Categories
                Review

                Medicine
                adverse event,adverse event reporting system,faers,database,data mining,signal,signal detection,proportional reporting ratio,reporting odds ratio,information component,empirical bayes geometric mean,pharmacoepidemiology,pharmacovigilance.

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