PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 17019675

Data mining for signals in spontaneous reporting databases: proceed with caution.

Wendy P Stephenson·Manfred Hauben
View original source ↗Export metadata ↓

Abstract

PURPOSE: To provide commentary and points of caution to consider before incorporating data mining as a routine component of any Pharmacovigilance program, and to stimulate further research aimed at better defining the predictive value of these new tools as well as their incremental value as an adjunct to traditional methods of post-marketing surveillance. METHODS/RESULTS: Commentary includes review of current data mining methodologies employed and their limitations, caveats to consider in the use of spontaneous reporting databases and caution against over-confidence in the results of data mining. CONCLUSIONS: Future research should focus on more clearly delineating the limitations of the various quantitative approaches as well as the incremental value that they bring to traditional methods of pharmacovigilance.

Explore related subjects

Adverse Drug Reaction Reporting SystemsData CollectionData Interpretation, StatisticalDatabases as TopicDrug-Related Side Effects and Adverse ReactionsHumansPharmacoepidemiologyRisk AssessmentJournal ArticleReview

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wendy P Stephenson, Manfred Hauben. 2007. Data mining for signals in spontaneous reporting databases: proceed with caution.. https://doi.org/10.1002/pds.1323

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

Interrater agreement with a standard scheme for classifying medication errors.

PURPOSE: The interrater agreement for and reliability of the National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP) index for categorizing medication errors were determined. METHODS: A letter was sent by the U.S. Pharmacopeia to all 550 contacts in the MEDMARX system user database. Participants were asked to categorize 27 medication scenarios using the NCC MERP index and were randomly assigned to one of three tools (the index alone, a paper-based algorithm, or a computer-based algorithm) to assist in categorization. Because the NCC MERP index accounts for harm and cost, and because categories could be interpreted as substantially similar, study results were analyzed after the nine error categories were collapsed to six. The interrater agreement was measured using Cohen's kappa value. RESULTS: Of 119 positive responses, 101 completed surveys were returned for a response rate of 85%. There were no significant differences in baseline demographics among the three groups. The overall interrater agreement for the participants, regardless of group assignment, was substantial at 0.61 (95% confidence interval [CI], 0.41-0.81). There was no difference among the kappa values of the three study groups and the tools used to aid in medication error classification. When the index was condensed from nine categories to six, the interrater agreement increased with a kappa value of 0.74 (95% CI, 0.56-0.90). CONCLUSION: Overall interrater agreement for the NCC MERP index for categorizing medication errors was substantial. The tool provided to assist with categorization did not influence overall categorization. Further refining of the scale could improve the usefulness and validity of medication error categorization.

Adverse Drug Reaction Reporting Systems↗
PubMed2007

Post Launch Monitoring of food products: what can be learned from pharmacovigilance.

Post Launch Monitoring (PLM) is one of the new approaches that are used in assessing the safety of novel foods or ingredients. It shares a close resemblance with procedures applied in the field of medicines, where Post Marketing Surveillance (PMS) has been carried out since the beginning of the 1960s. For this reason, Unilever and the Netherlands Pharmacovigilance Centre Lareb, maintaining the national reporting scheme in the Netherlands for adverse drug reactions, have been working together to optimize the Unilever's Post Launch Monitoring service. As a result of this cooperation a practical model for conducting PLM for food products has been developed. This model is also applicable for consumer products in general. The system allows for coding and assessing reports and the early detection of 'signals' of unintended health reactions. The methodological issues surrounding reporting of possible health reactions and practical issues surrounding coding and assessment of the reports that were encountered in the first period of this partnership are discussed. In addition, similarities and differences concerning PMS and PLM are described.

Adverse Drug Reaction Reporting Systems↗