Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter

Abstract

BackgroundTraditional 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.ObjectiveThe 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.MethodsWe 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).ResultsOf the 6.9 million Twitter posts collected, 4,401 Proto-AEs were identified out of 60,000 examined. Automated, dictionary-based symptom classification had 72 % recall and 86 % precision. 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.ConclusionPatients 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.

Pharmacovigilance for a Revolving World: Prospects of Patient-Generated Data on the Internet

“…In a similar vein, the pooled analyses of internet search patterns and spontaneous reports was found to outperform separate analyses of each in recalling established adverse drug reactions [11]. In the context of initial data preparation, Freifeld et al [9] reported that information extracted from product labels improved accuracy, which is another way that joint consideration of distinct information sources may bring benefit.…”

“…In a paper recently published in Drug Safety, Freifeld et al [9] described an analysis of Twitter microblog posts for references to drugs and adverse events, with comparison to reporting patterns in the US FDA Adverse Event Reporting System (FAERS). Other researchers have explored online discussion fora and internet search patterns for similar purposes [10][11][12].…”

“…This will enable their inclusion in regular pharmacovigilance signal detection and analysis. If the descriptions do not fulfil reporting requirements, they might perhaps still be captured in E2B format to facilitate analysis but be kept separate; their appropriate use requires careful consideration [7][8][9]. In turn, the longitudinal data shared by patients on dedicated social network sites represent a form of patient-maintained electronic medical record that could be subjected to analytics developed for other sources of longitudinal observational data [13].…”

“…Freifeld et al [9] described several of the key challenges, most notably the analysis of unstructured free text. The detection of references to drugs and medical events is a first step but not straightforward at that, given the multitude of ways in which we may refer to the same adverse event or drug.…”

Zoo or Savannah? Choice of Training Ground for Evidence-Based Pharmacovigilance

“…There are individual case reports [1], longitudinal health records [2], internet search patterns [3] and social media [4]. There is disproportionality analysis [1], regression [5,6], adjustment by propensity scores [7,8], self-controlled designs [2,9] and more.…”