Regulatory Definitions and Good Pharmacovigilance Practices in Social Media: Challenges and Recommendations

Naik, Paulami; Umrath, Thomas; Stekelenborg, John van; Ruben, Regina; Abdul-Karim, Nasiba; Boland, Robert A.; Ibara, Michael; Ekuta, Jethro; Washburn, Dana; Stergiopoulos, Stella

doi:10.1177/2168479015587362

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…WEB-RADR seeks to analyze these requirements, and whether they need to evolve to include requirements to monitor other media or by other stakeholders. As highlighted by Naik et al, 24 social media present new challenges for conducting pharmacovigilance activities. Based on the outcome of our survey as well as current experience, there are certain areas that remain to be addressed or further clarified, despite existing guidelines on the monitoring of digital media and the identification and reporting of suspected ADRs.…”

Section: Discussionmentioning

confidence: 99%

Social Media Monitoring and Adverse Drug Reaction Reporting in Pharmacovigilance: An Overview of the Regulatory Landscape

Lengsavath

Pra

Ferran

et al. 2017

Ther Innov Regul Sci

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In the context of the European Union's Innovative Medicines Initiative (IMI) project titled Web-Recognizing Adverse Drug Reactions (WEB-RADR; http://web-radr.eu ), which focuses on the assessment of new data sources and the optimization of the collection of information on suspected adverse reactions in pharmacovigilance, a survey was performed in 182 countries/jurisdictions in 2014 to 2015. The goal was to gather information on existing practices, guidance, and legal requirements on social media monitoring to identify potential safety issues related to medicines. The survey response rate was 100%. The results revealed that 80% of the surveyed countries do not have such necessities despite the fact that 63% of these countries have an established national pharmacovigilance system. Among the countries having an established pharmacovigilance system, only 29% have specific requirements, most countries do have similar provisions as set out in the EU guidelines on Good Pharmacovigilance Practices (GVP). A small subset of countries within the European Economic Area (EEA) have requirements that exceed those stated in GVP, namely, Italy, France, Sweden, and the UK. Outside the EEA, Turkey and the United States have also developed further guidance. The outcome of the survey will inform the development of a future policy framework on the further use of social media as new pharmacovigilance data source in the EEA. In addition, this paper elaborates on some current practical case management issues encountered by companies based on the existing regulatory guidance.

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Section: Discussionmentioning

confidence: 99%

Social Media Monitoring and Adverse Drug Reaction Reporting in Pharmacovigilance: An Overview of the Regulatory Landscape

Lengsavath

Pra

Ferran

et al. 2017

Ther Innov Regul Sci

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“…Social media technology is defined as a web-based application that allows for the creation and exchange of user-generated content. This includes, but is not limited to: websites, web pages, blogs, vlogs, social networks, internet forums, chat rooms, wikis and smartphone applications, where users have the ability to generate content (typically by providing posts and comments, often in an anonymous fashion or with limited identifying information) and are able to view/exchange content from and with others in an interactive digital environment 30. Crowd sourcing is the practice of obtaining needed services, ideas or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers 31.…”

Section: Methodsmentioning

confidence: 99%

Utility of social media and crowd-sourced data for pharmacovigilance: a scoping review protocol

et al. 2017

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IntroductionAdverse events associated with medications are under-reported in postmarketing surveillance systems. A systematic review of published data from 37 studies worldwide (including Canada) found the median under-reporting rate of adverse events to be 94% in spontaneous reporting systems. This scoping review aims to assess the utility of social media and crowd-sourced data to detect and monitor adverse events related to health products including pharmaceuticals, medical devices, biologics and natural health products.Methods and analysisOur review conduct will follow the Joanna Briggs Institute scoping review methods manual. Literature searches were conducted in MEDLINE, EMBASE and the Cochrane Library from inception to 13 May 2016. Additional sources included searches of study registries, conference abstracts, dissertations, as well as websites of international regulatory authorities (eg, Food and Drug Administration (FDA), the WHO, European Medicines Agency). Search results will be supplemented by scanning the references of relevant reviews. We will include all publication types including published articles, editorials, websites and book sections that describe use of social media and crowd-sourced data for surveillance of adverse events associated with health products. Two reviewers will perform study selection and data abstraction independently, and discrepancies will be resolved through discussion. Data analysis will involve quantitative (eg, frequencies) and qualitative (eg, content analysis) methods.DisseminationThe summary of results will be sent to Health Canada, who commissioned the review, and other relevant policymakers involved with the Drug Safety and Effectiveness Network. We will compile and circulate a 1-page policy brief and host a 1-day stakeholder meeting to discuss the implications, key messages and finalise the knowledge translation strategy. Findings from this review will ultimately inform the design and development of a data analytics platform for social media and crowd-sourced data for pharmacovigilance in Canada and internationally.Registration detailsOur protocol was registered prospectively with the Open Science Framework (https://osf.io/kv9hu/).

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“…Regulators have become increasingly interested in mining such data from support group websites and social media postings as a potential new source for pharmacovigilance data [ 8 , 9 ]. In 2013, the Association of the British Pharmaceutical Industry published guidance on the management of adverse events (AE) and product complaints sourced from digital media [ 10 ]. Although such guidance regarding the use of social media data for pharmacovigilance is lacking in the USA, the US FDA issued related regulations for publishing promotional material and risk/benefit information on social media [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Combining Social Media and FDA Adverse Event Reporting System to Detect Adverse Drug Reactions

2020

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Introduction Adverse drug reactions (ADRs) are unintended reactions caused by a drug or combination of drugs taken by a patient. The current safety surveillance system relies on spontaneous reporting systems (SRSs) and more recently on observational health data; however, ADR detection may be delayed and lack geographic diversity. The broad scope of social media conversations, such as those on Twitter, can include health-related topics. Consequently, these data could be used to detect potentially novel ADRs with less latency. Although research regarding ADR detection using social media has made progress, findings are based on single information sources, and no study has yet integrated drug safety evidence from both an SRS and Twitter. Objective The aim of this study was to combine signals from an SRS and Twitter to facilitate the detection of safety signals and compare the performance of the combined system with signals generated by individual data sources. Methods We extracted potential drug–ADR posts from Twitter, used Monte Carlo expectation maximization to generate drug safety signals from both the US FDA Adverse Event Reporting System and posts from Twitter, and then integrated these signals using a Bayesian hierarchical model. The results from the integrated system and two individual sources were evaluated using a reference standard derived from drug labels. Area under the receiver operating characteristics curve (AUC) was computed to measure performance. Results We observed a significant improvement in the AUC of the combined system when comparing it with Twitter alone, and no improvement when comparing with the SRS alone. The AUCs ranged from 0.587 to 0.637 for the combined SRS and Twitter, from 0.525 to 0.534 for Twitter alone, and from 0.612 to 0.642 for the SRS alone. The results varied because different preprocessing procedures were applied to Twitter. Conclusion The accuracy of signal detection using social media can be improved by combining signals with those from SRSs. However, the combined system cannot achieve better AUC performance than data from FAERS alone, which may indicate that Twitter data are not ready to be integrated into a purely data-driven combination system.

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Regulatory Definitions and Good Pharmacovigilance Practices in Social Media: Challenges and Recommendations

Cited by 7 publications

References 2 publications

Social Media Monitoring and Adverse Drug Reaction Reporting in Pharmacovigilance: An Overview of the Regulatory Landscape

Social Media Monitoring and Adverse Drug Reaction Reporting in Pharmacovigilance: An Overview of the Regulatory Landscape

Utility of social media and crowd-sourced data for pharmacovigilance: a scoping review protocol

Combining Social Media and FDA Adverse Event Reporting System to Detect Adverse Drug Reactions

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