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ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

4.5. Social media

 

4.5.1. Definition

 

Technological advances have dramatically increased the range of data sources that can be used to complement traditional ones and may provide compelling insights into effectiveness and safety of interventions. Such data include digital media that exist in a computer-readable format as websites, web pages, blogs, vlogs, social networking sites, internet forums, chat rooms, health portals. A recent addition to this list is represented by the biomedical data collected through wearable technology (e.g., heart rate, physical activity and sleep pattern, dietary patterns). This data is unsolicited and generated in real time.

 

Social media is considered as a sub-set of digital media. The European Commission’s Digital Single Market Glossary defines social media as “a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0 and that allow the creation and exchange of user-generated content. It employs mobile and web-based technologies to create highly interactive platforms via which individuals and communities share, co-create, discuss, and modify user-generated content.

 

4.5.2. Use in pharmacovigilance

 

Social media has been used to provide insights into the patient’s perception of the effectiveness of drugs and for the collection of patient reported outcomes, as discussed in Web-based patient-reported outcomes in Drug Safety and risk management: challenges and opportunities? (Drug Saf 2012;35(6):437-46).

 

Another possible use of social media is in the signal detection process. In this setting, it would add value only if more issues are identified or they are identified faster, but there is currently no evidence this is the case. Using Social Media Data in Routine Pharmacovigilance: A Pilot Study to Identify Safety Signals and Patient Perspectives (Pharm Med 2017;31(3): 167-74) explores whether analysis of social media data could identify new signals, known signals from routine pharmacovigilance, known signals sooner, and specific issues (i.e., quality issues and patient perspectives). This study also tried to determine the quantity of posts with resemblance to adverse events and the types and characteristics of products that would benefit from social media analysis. It concludes that, although analysis of data from social media did not identify new safety signals, it can provide unique insight into the patient perspective. Assessment was limited by numerous factors, such as data acquisition, language, and demographics. Further research is deemed necessary to determine the best uses of social media data to augment traditional pharmacovigilance surveillance.

 

From a regulatory perspective, social media is a source of potential reports of suspected adverse drug reactions and marketing authorisation holders are legally obliged to screen web sites under their management and assess whether reports of adverse reactions qualify for spontaneous reporting (see Good Pharmacovigilance practice Module VI- (Rev. 2), Chapter VI.B.1.1.4).

 

4.5.3. Challenges

 

While offering the promise of new research models and approaches, the rapidly evolving social media environment presents many challenges including the need for strong and systematic processes for selection, validation and study implementation. Articles which detail associated challenges are: Evaluating Social Media Networks in Medicines Safety Surveillance: Two Case Studies (Drug Saf 2015; 38(10): 921-30.) and Social media and pharmacovigilance: A review of the opportunities and challenges (Br J Clin Pharmacol 2015; 80(4): 910-20).

 

There is currently no defined strategy or framework in place in order to meet the standards around data validity, generalisability for this type of data, and their regulatory acceptance may therefore be lower than for traditional sources. However, more tools and solutions for analysing unstructured data are becoming available, especially for pharmacoepidemiology and Drug Safety research, as in Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts (J Am Med Inform Assoc 2017 Feb 22) and Social Media Listening for Routine Post-Marketing Safety Surveillance (Drug Saf 2016;39(5):443-54).

 

4.5.4. Data protection

 

The EU General Data Protection Regulation (GDPR) introduces EU-wide legislation on personal data and security. It specifies that the impact of data protection at the time of study design concept should be assessed and reviewed periodically. Other technical documents may also be applicable such as Smartphone Secure Development Guidelines (2011) published by the European Network and Information Security Agency (ENISA), which advises on design and technical solutions. The principles of these security measures are found in the European Data Protection Supervisor (EDPS) opinion on mobile health (Opinion 1/2015 Mobile Health-Reconciling technological innovation with data protection).

 

 

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