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Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab

Author

Listed:
  • Karen Smith

    (Regis University)

  • Su Golder

    (University of York)

  • Abeed Sarker

    (University of Pennsylvania)

  • Yoon Loke

    (University of East Anglia)

  • Karen O’Connor

    (University of Pennsylvania)

  • Graciela Gonzalez-Hernandez

    (University of Pennsylvania)

Abstract

Introduction Adverse drug reactions (ADRs) are associated with significant health-related and financial burden, and multiple sources are currently utilized to actively discover them. Social media has been proposed as a potential resource for monitoring ADRs, but drug-specific analytical studies comparing social media with other sources are scarce. Objectives Our objective was to develop methods to compare ADRs mentioned in social media with those in traditional sources: the US FDA Adverse Event Reporting System (FAERS), drug information databases (DIDs), and systematic reviews. Methods A total of 10,188 tweets mentioning adalimumab collected between June 2014 and August 2016 were included. ADRs in the corpus were extracted semi-automatically and manually mapped to standardized concepts in the Unified Medical Language System. ADRs were grouped into 16 biologic categories for comparisons. Frequencies, relative frequencies, disproportionality analyses, and rank ordering were used as metrics. Results There was moderate agreement between ADRs in social media and traditional sources. “Local and injection site reactions” was the top ADR in Twitter, DIDs, and systematic reviews by frequency, ranked frequency, and index ranking. The next highest ADR in Twitter—fatigue—ranked fifth and seventh in FAERS and DIDs. Conclusion Social media posts often express mild and symptomatic ADRs, but rates are measured differently in scientific sources. ADRs in FAERS are reported as absolute numbers, in DIDs as percentages, and in systematic reviews as percentages, risk ratios, or other metrics, which makes comparisons challenging; however, overlap is substantial. Social media analysis facilitates open-ended investigation of patient perspectives and may reveal concepts (e.g. anxiety) not available in traditional sources.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:drugsa:v:41:y:2018:i:12:d:10.1007_s40264-018-0707-6
    DOI: 10.1007/s40264-018-0707-6
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    References listed on IDEAS

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    1. Stella Stergiopoulos & Carrie A. Brown & Thomas Felix & Gustavo Grampp & Kenneth A. Getz, 2016. "A Survey of Adverse Event Reporting Practices Among US Healthcare Professionals," Drug Safety, Springer, vol. 39(11), pages 1117-1127, November.
    2. Huiqin Yang & Dawn Craig & David Epstein & Laura Bojke & Kate Light & Ian Bruce & Mark Sculpher & Nerys Woolacott, 2012. "Golimumab for the Treatment of Psoriatic Arthritis," PharmacoEconomics, Springer, vol. 30(4), pages 257-270, April.
    3. 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.
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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. Ruixue Hu & Su Golder & Guoyan Yang & Xun Li & Di Wang & Liqiong Wang & Ruyu Xia & Nanqi Zhao & Sainan Fang & Baoyong Lai & Jianping Liu & Yutong Fei, 2019. "Comparison of drug safety data obtained from the monitoring system, literature, and social media: An empirical proof from a Chinese patent medicine," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-14, November.

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