IDEAS home Printed from https://ideas.repec.org/a/spr/drugsa/v40y2017i4d10.1007_s40264-016-0491-0.html
   My bibliography  Save this article

Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts

Author

Listed:
  • Carrie E. Pierce

    (Epidemico, Inc.)

  • Khaled Bouri

    (US Food and Drug Administration)

  • Carol Pamer

    (US Food and Drug Administration)

  • Scott Proestel

    (US Food and Drug Administration)

  • Harold W. Rodriguez

    (Epidemico, Inc.)

  • Hoa Le

    (Epidemico, Inc.)

  • Clark C. Freifeld

    (Epidemico, Inc.
    Northeastern University College of Computer and Information Science)

  • John S. Brownstein

    (Epidemico, Inc.)

  • Mark Walderhaug

    (US Food and Drug Administration)

  • I. Ralph Edwards

    (WHO Collaborating Centre for International Drug Monitoring)

  • Nabarun Dasgupta

    (Epidemico, Inc.)

Abstract

Introduction The rapid expansion of the Internet and computing power in recent years has opened up the possibility of using social media for pharmacovigilance. While this general concept has been proposed by many, central questions remain as to whether social media can provide earlier warnings for rare and serious events than traditional signal detection from spontaneous report data. Objective Our objective was to examine whether specific product–adverse event pairs were reported via social media before being reported to the US FDA Adverse Event Reporting System (FAERS). Methods A retrospective analysis of public Facebook and Twitter data was conducted for 10 recent FDA postmarketing safety signals at the drug–event pair level with six negative controls. Social media data corresponding to two years prior to signal detection of each product–event pair were compiled. Automated classifiers were used to identify each ‘post with resemblance to an adverse event’ (Proto-AE), among English language posts. A custom dictionary was used to translate Internet vernacular into Medical Dictionary for Regulatory Activities (MedDRA®) Preferred Terms. Drug safety physicians conducted a manual review to determine causality using World Health Organization-Uppsala Monitoring Centre (WHO-UMC) assessment criteria. Cases were also compared with those reported in FAERS. Findings A total of 935,246 posts were harvested from Facebook and Twitter, from March 2009 through October 2014. The automated classifier identified 98,252 Proto-AEs. Of these, 13 posts were selected for causality assessment of product–event pairs. Clinical assessment revealed that posts had sufficient information to warrant further investigation for two possible product–event associations: dronedarone–vasculitis and Banana Boat Sunscreen--skin burns. No product–event associations were found among the negative controls. In one of the positive cases, the first report occurred in social media prior to signal detection from FAERS, whereas the other case occurred first in FAERS. Conclusions An efficient semi-automated approach to social media monitoring may provide earlier insights into certain adverse events. More work is needed to elaborate additional uses for social media data in pharmacovigilance and to determine how they can be applied by regulatory agencies.

Suggested Citation

  • Carrie E. Pierce & Khaled Bouri & Carol Pamer & Scott Proestel & Harold W. Rodriguez & Hoa Le & Clark C. Freifeld & John S. Brownstein & Mark Walderhaug & I. Ralph Edwards & Nabarun Dasgupta, 2017. "Evaluation of Facebook and Twitter Monitoring to Detect Safety Signals for Medical Products: An Analysis of Recent FDA Safety Alerts," Drug Safety, Springer, vol. 40(4), pages 317-331, April.
  • Handle: RePEc:spr:drugsa:v:40:y:2017:i:4:d:10.1007_s40264-016-0491-0
    DOI: 10.1007/s40264-016-0491-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40264-016-0491-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s40264-016-0491-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gianluca Trifirò & Janet Sultana & Andrew Bate, 2018. "From Big Data to Smart Data for Pharmacovigilance: The Role of Healthcare Databases and Other Emerging Sources," Drug Safety, Springer, vol. 41(2), pages 143-149, February.
    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. Yiqing Zhao & Yue Yu & Hanyin Wang & Yikuan Li & Yu Deng & Guoqian Jiang & Yuan Luo, 2022. "Machine Learning in Causal Inference: Application in Pharmacovigilance," Drug Safety, Springer, vol. 45(5), pages 459-476, May.
    4. Ying Li & Antonio Jimeno Yepes & Cao Xiao, 2020. "Combining Social Media and FDA Adverse Event Reporting System to Detect Adverse Drug Reactions," Drug Safety, Springer, vol. 43(9), pages 893-903, September.
    5. Camille Goyer & Genaro Castillon & Yola Moride, 2022. "Implementation of Interventions and Policies on Opioids and Awareness of Opioid-Related Harms in Canada: A Multistage Mixed Methods Descriptive Study," IJERPH, MDPI, vol. 19(9), pages 1-12, April.
    6. Karen Smith & Su Golder & Abeed Sarker & Yoon Loke & Karen O’Connor & Graciela Gonzalez-Hernandez, 2018. "Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab," Drug Safety, Springer, vol. 41(12), pages 1397-1410, December.
    7. Juergen Dietrich & Lucie M. Gattepaille & Britta Anne Grum & Letitia Jiri & Magnus Lerch & Daniele Sartori & Antoni Wisniewski, 2020. "Adverse Events in Twitter-Development of a Benchmark Reference Dataset: Results from IMI WEB-RADR," Drug Safety, Springer, vol. 43(5), pages 467-478, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:drugsa:v:40:y:2017:i:4:d:10.1007_s40264-016-0491-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com/economics/journal/40264 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.