Using Social Media Data in Routine Pharmacovigilance: A Pilot Study to Identify Safety Signals and Patient Perspectives

Bhattacharya, Mondira; Snyder, Scott R; Malin, Murray; Truffa, Melissa; Marinic, Sandy; Engelmann, Rachel; Raheja, Ritu R.

doi:10.1007/s40290-017-0186-6

Cited by 22 publications

(48 citation statements)

References 12 publications

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“…Most other work in this area has focused on identifying and optimising the recognition of single adverse events from social media [ 5 , 7 , 8 , 20 , 21 ], while relatively few papers focused on the actual assessment of utility of social media in providing evidence for ADRs relative to traditional data sources [ 6 , 22 ]. Other studies do focus on the possible uses of social media above and beyond spontaneous reporting systems, but do not provide specific comparisons in performance [ 9 ].…”

Section: Discussionmentioning

confidence: 99%

“…Past investigations into the utility of social media for signal detection have been somewhat limited in the scope of methods, products and events (e.g. abuse or misuse) [ 3 – 6 ], or focused on the recognition of adverse events in single posts [ 7 , 8 ]. In contrast, this work aims to present a comprehensive analysis of the use of social media for the detection of safety signals for a wide range of products using statistical methods.…”

Section: Introductionmentioning

confidence: 99%

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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

et al. 2018

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Introduction and ObjectiveSocial 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.MethodsPerformance 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.ResultsAcross 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%).ConclusionsOur 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.Electronic supplementary materialThe online version of this article (10.1007/s40264-018-0699-2) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…Social media data are increasingly recognised as a valid source of patient perspectives and data on adverse events (AEs) in pharmacovigilance [61]. This information is in abundance and is timely, relevant and often publicly available.…”

Section: Type 2: Unsolicited Reporting (Social Media Monitoring)mentioning

confidence: 99%

“…A recent pilot study by Bhattacharya et al [61] suggests that the use of traditional pharmacovigilance methods to analyse social media data is ineffective. Mao et al [119] note that frequency data should not serve as prevalence of the adverse effects/reactions, but as a measure of which symptoms may be the most salient to patients on a day-to-day basis.…”

Section: Analysis Of Social Datamentioning

confidence: 99%

“…Methods exist to reduce noise and make the data suitable for post-market safety surveillance. However, big data cannot be considered a substitute for traditional data collection and analysis, but rather functions as a supplement to existing methods [61,122]. Abbasi et al [122] stress the importance of developing an understanding of the strengths and limitations of the various social media channels and the capabilities of real-time analytics.…”

Section: Key Insights and Future Directionsmentioning

confidence: 99%

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Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions

Pappa

Stergioulas

2019

Int J Data Sci Anal

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The ever-increasing supply of information combined with the growing knowledge elicitation capabilities of key emerging technologies presents pharmacovigilance with enormous opportunities. Currently, safety monitoring is expanding its evidence base, moving beyond traditional approaches towards sophisticated methods that can identify possible safety signals from multiple information sources, both structured and unstructured. In this context, health information posted online by patients represents a potentially valuable, yet currently left largely unexploited source of post-market safety data that could supplement data from traditional sources of drug safety information. As the use of social media data for pharmacovigilance is still in its infancy, the present paper explores the state of the art in the application of social data to adverse drug reaction detection; provides a thorough review of existing work in the field, highlighting important research efforts and achievements; and finally, discusses the current challenges and promising avenues for future work. Following a literature review methodology, a critical appraisal was conducted of carefully selected work on the use of social data in post-market surveillance, as presented in the recent scientific literature. Out of a sample of more than 1300 articles, which was the result of the literature search, the final selection of articles was made based on their relevance to the applications of social networking sites (SNS) to pharmacovigilance, and a thorough review of this corpus was completed with a total of 100 articles reviewed. The main contributions of this review include the mapping and systematisation of the current knowledge in the field by drawing comparisons of different approaches, types of social data and of relevant sources currently used in the field, and by developing new classifications of social data sources and taxonomies for social data for use in pharmacovigilance, as well as the identification of key challenges and the extraction of new insights in terms of potential for practical applications and future research directions in the area of pharmacovigilance. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Assessment of Beliefs and Attitudes About Statins Posted on Twitter

et al. 2020

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Using Social Media Data in Routine Pharmacovigilance: A Pilot Study to Identify Safety Signals and Patient Perspectives

Cited by 22 publications

References 12 publications

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

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

Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions

Assessment of Beliefs and Attitudes About Statins Posted on Twitter

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