Affiliations: [a] UC3M Medialab, Department of Communication and Media Studies, University Carlos III of Madrid, Madrid, Spain | [b] Eu2P Programme, University of Bordeaux, Bordeaux, France | [c] Health Research Centre, University of Almeria, Almeria, Spain
Correspondence:
[*]
Address for correspondence: Revati Pathak, UC3M Medialab, Department of Communication and Media Studies, University Carlos III of Madrid, Madrid 28903, Spain; and Eu2P Programme, University of Bordeaux, Bordeaux 33000, France. E-mail: revatig123@gmail.com
Abstract: BACKGROUND:As Twitter has gained significant popularity, tweets can serve as large pool of readily available data to estimate the adverse events (AEs) of medications. OBJECTIVE:This study evaluated whether tweets were an early indicator for potential safety warnings. Additionally, the trend of AEs posted on Twitter was compared with AEs from the Yellow Card system in the United Kingdom. METHODS:English Tweets for 35 drug-event pairs for the period 2017–2019, two years prior to the date of EMA Pharmacovigilance Risk Assessment Committee (PRAC) meeting, were collected. Both signal and non-signal AEs were manually identified and encoded using the MedDRA dictionary. AEs from Yellow Card were also gathered for the same period. Descriptive and inferential statistical analysis was conducted using Fisher’s exact test to assess the distribution and proportion of AEs from the two data sources. RESULTS:Of the total 61,661 English tweets, 1,411 had negative or neutral sentiment and mention of at least one AE. Tweets for 15 out of the 35 drugs (42.9%) contained AEs associated with the signals. On pooling data from Twitter and Yellow Card, 24 out of 35 drug-event pairs (68.6%) were identified prior to the respective PRAC meetings. Both data sources showed similar distribution of AEs based on seriousness, however, the distribution based on labelling was divergent. CONCLUSION:Twitter cannot be used in isolation for signal detection in current pharmacovigilance (PV) systems. However, it can be used in combination with traditional PV systems for early signal detection, as it can provide a holistic drug safety profile.
Keywords: Safety signal, Twitter, social media, pharmacovigilance, adverse events
