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Drug Safety

, Volume 42, Issue 10, pp 1199–1201 | Cite as

The Impact of Litigation-Associated Reports on Signal Identification in the US FDA’s Adverse Event Reporting System

  • Monica A. MuñozEmail author
  • Gerald J. Dal Pan
Research Letter
  • 224 Downloads

Dear Editor,

Prescription drug litigation can result in concentrated influxes of reports submitted to the US FDA Adverse Event Reporting System (FAERS). Rogers et al. recently demonstrated lawyer-submitted reports did not meaningfully distort signals of disproportionate reporting (SDRs) for isotretinoin and atorvastatin [1]; however, we are aware of examples in which litigation can have significant impacts on SDRs. In this letter, we demonstrate the impact of litigation-associated reports (LARs) on the FAERS overall and using metoclopramide as an example. A boxed warning communicating the risk of tardive dyskinesia (TD) was added to the labeling of metoclopramide in 2009; subsequently, in 2011 the FAERS received almost 10,000 cases of TD listing metoclopramide as a suspect drug [2].

We used an algorithm to identify LARs received in the FAERS through 31 December 2018. Reports were classified as litigation-associated if either the reporter’s occupation equaled lawyer or if the report’s...

Notes

Compliance with Ethical Standards

Conflicts of Interest

Monica A. Muñoz and Gerald J. Dal Pan have no conflicts of interest to declare.

Funding

No funding was used for the preparation of this letter.

Disclaimer

The views expressed are those of the authors and do not necessarily represent the position of, nor imply endorsement from, the US FDA or the US Government.

References

  1. 1.
    Rogers JR, Sarpatwari A, Desai RJ, Bohn JM, Khan NF, Kesselheim AS, et al. Effect of lawyer-submitted reports on signals of disproportional reporting in the food and drug administration’s adverse event reporting system. Drug Saf. 2019;42(1):85–93.CrossRefPubMedGoogle Scholar
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    US FDA Adverse Event Reporting System (FAERS) Public Dashboard. Available at: https://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/ucm070093.htm. Accessed 4 Apr 2019.
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    DuMouchel W. Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system. Am Stat. 1999;53:177–90.Google Scholar
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    Harinstein L, Kalra D, Kortepeter CM, Munoz MA, Wu E, Dal Pan GJ. Evaluation of postmarketing reports from industry-sponsored programs in drug safety surveillance. Drug Saf. 2019;42(5):649–55.CrossRefPubMedGoogle Scholar
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    Klein K, Scholl JHG, De Bruin ML, van Puijenbroek EP, Leufkens HGM, Stolk P. When more is less: an exploratory study of the precautionary reporting bias and its impact on safety signal detection. Clin Pharmacol Ther. 2018;103(2):296–303.CrossRefPubMedGoogle Scholar

Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2019

Authors and Affiliations

  1. 1.Office of Surveillance and Epidemiology, Center for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringUSA

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