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. 2014 May;37(5):343-50.
doi: 10.1007/s40264-014-0155-x.

Digital drug safety surveillance: monitoring pharmaceutical products in twitter

Clark C Freifeld  1 , John S Brownstein, Christopher M Menone, Wenjie Bao, Ross Filice, Taha Kass-Hout, Nabarun Dasgupta
Affiliations

Digital drug safety surveillance: monitoring pharmaceutical products in twitter

Clark C Freifeld et al. Drug Saf. 2014 May.

Erratum in

  • Drug Saf. 2014 Jul;37(7):555

Abstract

Background: Traditional adverse event (AE) reporting systems have been slow in adapting to online AE reporting from patients, relying instead on gatekeepers, such as clinicians and drug safety groups, to verify each potential event. In the meantime, increasing numbers of patients have turned to social media to share their experiences with drugs, medical devices, and vaccines.

Objective: The aim of the study was to evaluate the level of concordance between Twitter posts mentioning AE-like reactions and spontaneous reports received by a regulatory agency.

Methods: We collected public English-language Twitter posts mentioning 23 medical products from 1 November 2012 through 31 May 2013. Data were filtered using a semi-automated process to identify posts with resemblance to AEs (Proto-AEs). A dictionary was developed to translate Internet vernacular to a standardized regulatory ontology for analysis (MedDRA(®)). Aggregated frequency of identified product-event pairs was then compared with data from the public FDA Adverse Event Reporting System (FAERS) by System Organ Class (SOC).

Results: Of the 6.9 million Twitter posts collected, 4,401 Proto-AEs were identified out of 60,000 examined. Automated, dictionary-based symptom classification had 86 % recall and 72 % precision [corrected]. Similar overall distribution profiles were observed, with Spearman rank correlation rho of 0.75 (p < 0.0001) between Proto-AEs reported in Twitter and FAERS by SOC.

Conclusion: Patients reporting AEs on Twitter showed a range of sophistication when describing their experience. Despite the public availability of these data, their appropriate role in pharmacovigilance has not been established. Additional work is needed to improve data acquisition and automation.

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Figures

Fig. 1
Fig. 1
Data collection scheme for both Twitter and FAERS reports. API application programming interface, FAERS FDA Adverse Event Reporting System, FDA Food and Drug Administration, MedDRA Medical Dictionary for Regulatory Activities, NLP natural language processing, Proto-AEs posts with resemblance to adverse events
Fig. 2
Fig. 2
Correlation by system organ class between Proto-AEs in Twitter and consumer reports in Food and Drug Administration Adverse Event Reporting System (note log-log scale). AE adverse event, FDA US Food and Drug Administration, Proto-AEs posts with resemblance to adverse events
Fig. 3
Fig. 3
Rank order correlation by system organ class between Proto-AEs in Twitter and consumer reports in Food and Drug Administration Adverse Event Reporting System (note log-log scale). AE adverse event, FDA US Food and Drug Administration, Proto-AEs posts with resemblance to adverse events
See this image and copyright information in PMC

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