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

Pierce, Carrie E.; Bouri, Khaled; Pamer, Carol; Proestel, Scott; Rodriguez, Harold; Le, Hoa Van; Freifeld, Clark C.; Brownstein, John S.; Walderhaug, Mark; Edwards, I. Ralph; Dasgupta, Nabarun

doi:10.1007/s40264-016-0491-0

Cited by 108 publications

(115 citation statements)

References 39 publications

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“…The EMA’s Eudravigilance database and the World Health Organization’s Vigibase contain vast numbers of drug reaction reports that can be screened to detect signals. Informal social media reports may uncover novel adverse drug reactions14 and help to understand serious reactions from the patient’s perspective 15…”

Section: How Are New Adverse Drug Reactions Uncovered?mentioning

confidence: 99%

Adverse drug reactions

Ferner

McGettigan

2018

BMJ

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Section: How Are New Adverse Drug Reactions Uncovered?mentioning

confidence: 99%

Adverse drug reactions

Ferner

McGettigan

2018

BMJ

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“…The examination of SM utility in identifying AEs and SDRs has been previously performed by multiple groups, using various approaches. Some authors have concluded that SM should be further considered as a potential SD tool, 17,18 while others concluded that SM shows a poor performance in AE and SDR identification. 19,20 Although we count this work among the former group, we do not disagree with the core conclusions of the latter.…”

Section: Discussionmentioning

confidence: 99%

Comparison of text processing methods in social media–based signal detection

Gavrielov‐Yusim¹,

Kürzinger

Nishikawa

et al. 2019

Pharmacoepidemiology and Drug

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Purpose Adverse event (AE) identification in social media (SM) can be performed using various types of natural language processing (NLP) and machine learning (ML). These methods can be categorized by complexity and precision level. Co‐occurrence‐based ML methods are rather basic, as they identify simultaneous appearance of drugs and clinical events in a single post. In contrast, statistical learning methods involve more complex NLP and identify drugs, events, and associations between them. We aimed to compare the ability of co‐occurrence and NLP to identify AEs and signals of disproportionate reporting (SDR) in patient‐generated SM. We also examined the performance of lift in SM‐based signal detection (SD). Methods Our examination was performed in a corpus of SM posts crawled from open online patient forums and communities, using the spontaneously reported VigiBase data as reference data set. Results We found that co‐occurrence and NLP produce AEs, which are 57% and 93% consistent with VigiBase AEs, respectively. Among the SDRs identified both in SM and in VigiBase, up to 55.3% were identified earlier in co‐occurrence, and up to 32.1% were identified earlier in NLP‐processed SM. Using lift in SM SD provided performance similar to frequentist methods, both in co‐occurrence and in NLP‐processed AEs. Conclusion Our results indicate that using SM as a data source complementary to traditional pharmacovigilance sources should be considered further. Various levels of SM processing may be considered, depending on the preferred policies and tolerance for false‐positive to false‐negative balance in routine pharmacovigilance processes.

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“…18 Data from patient registries and social media are also expected to be utilized in the regulatory process worldwide in the near future. [19][20][21] Information available at the postmarket stage will be changed in line with the social healthcare situation. We need to prepare in response to such changes by utilizing multiple sources of information and data, which depends not mainly on SAERs, to assure drug safety at the postmarket stage.…”

Section: Discussionmentioning

confidence: 99%

Lower Proportion of Spontaneous Adverse Event Reports for Generic Drugs by Comparison With Original Branded Drugs at the Postmarket Stage in Japan

Takami

Hirata

Ishiguro

et al. 2019

Clin Pharma and Therapeutics

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We investigated impacts of increased generic drug use on spontaneous adverse event reports (SAERs), because SAERs have been a major source of data for drug safety assessment at the postmarket stage. Reporting proportion of SAERs for the generic drugs was consistently and significantly lower than that for the original branded drugs. The reporting proportion targeting for 55 active product ingredients, which had the longest follow-up period after generic drug marketed, gradually decreased for the original branded drugs and increased for the generic drugs. However, these transitions did not parallel the changes in market share over the same period. These results suggest that the reporting proportion of SAERs for generic drugs may not keep pace with growth in market share. When generic drugs account for the majority of market share, utilization of multiple sources of information and data, in addition to SAERs, may be a key to assuring drug safety at the postmarket stage.

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

Cited by 108 publications

References 39 publications

Adverse drug reactions

Adverse drug reactions

Comparison of text processing methods in social media–based signal detection

Lower Proportion of Spontaneous Adverse Event Reports for Generic Drugs by Comparison With Original Branded Drugs at the Postmarket Stage in Japan

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