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Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project

Author

Listed:
  • Ola Caster

    (Uppsala Monitoring Centre)

  • Juergen Dietrich

    (Bayer AG)

  • Marie-Laure Kürzinger

    (Sanofi, Epidemiology and Benefit-Risk Evaluation)

  • Magnus Lerch

    (Lenolution GmbH)

  • Simon Maskell

    (University of Liverpool)

  • G. Niklas Norén

    (Uppsala Monitoring Centre)

  • Stéphanie Tcherny-Lessenot

    (Sanofi, Epidemiology and Benefit-Risk Evaluation)

  • Benoit Vroman

    (UCB Pharma)

  • Antoni Wisniewski

    (AstraZeneca Global Regulatory Affairs)

  • John Stekelenborg

    (Janssen R&D)

Abstract

Introduction and Objective Social media has been proposed as a possibly useful data source for pharmacovigilance signal detection. This study primarily aimed to evaluate the performance of established statistical signal detection algorithms in Twitter/Facebook for a broad range of drugs and adverse events. Methods Performance was assessed using a reference set by Harpaz et al., consisting of 62 US Food and Drug Administration labelling changes, and an internal WEB-RADR reference set consisting of 200 validated safety signals. In total, 75 drugs were studied. Twitter/Facebook posts were retrieved for the period March 2012 to March 2015, and drugs/events were extracted from the posts. We retrieved 4.3 million and 2.0 million posts for the WEB-RADR and Harpaz drugs, respectively. Individual case reports were extracted from VigiBase for the same period. Disproportionality algorithms based on the Information Component or the Proportional Reporting Ratio and crude post/report counting were applied in Twitter/Facebook and VigiBase. Receiver operating characteristic curves were generated, and the relative timing of alerting was analysed. Results Across all algorithms, the area under the receiver operating characteristic curve for Twitter/Facebook varied between 0.47 and 0.53 for the WEB-RADR reference set and between 0.48 and 0.53 for the Harpaz reference set. For VigiBase, the ranges were 0.64–0.69 and 0.55–0.67, respectively. In Twitter/Facebook, at best, 31 (16%) and four (6%) positive controls were detected prior to their index dates in the WEB-RADR and Harpaz references, respectively. In VigiBase, the corresponding numbers were 66 (33%) and 17 (27%). Conclusions Our results clearly suggest that broad-ranging statistical signal detection in Twitter and Facebook, using currently available methods for adverse event recognition, performs poorly and cannot be recommended at the expense of other pharmacovigilance activities.

Suggested Citation

  • Ola Caster & Juergen Dietrich & Marie-Laure Kürzinger & Magnus Lerch & Simon Maskell & G. Niklas Norén & Stéphanie Tcherny-Lessenot & Benoit Vroman & Antoni Wisniewski & John Stekelenborg, 2018. "Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project," Drug Safety, Springer, vol. 41(12), pages 1355-1369, December.
  • Handle: RePEc:spr:drugsa:v:41:y:2018:i:12:d:10.1007_s40264-018-0699-2
    DOI: 10.1007/s40264-018-0699-2
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    Cited by:

    1. Bissan Audeh & Florelle Bellet & Marie-Noëlle Beyens & Agnès Lillo-Le Louët & Cédric Bousquet, 2020. "Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med Project," Drug Safety, Springer, vol. 43(9), pages 835-851, September.
    2. Lucie M. Gattepaille & Sara Hedfors Vidlin & Tomas Bergvall & Carrie E. Pierce & Johan Ellenius, 2020. "Prospective Evaluation of Adverse Event Recognition Systems in Twitter: Results from the Web-RADR Project," Drug Safety, Springer, vol. 43(8), pages 797-808, August.
    3. Cedric Bousquet & Bissan Audeh & Florelle Bellet & Agnès Lillo-Le Louët, 2018. "Comment on “Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project”," Drug Safety, Springer, vol. 41(12), pages 1371-1373, December.

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