Literature DB >> 26798054

Social Media Listening for Routine Post-Marketing Safety Surveillance.

Gregory E Powell1, Harry A Seifert2, Tjark Reblin3, Phil J Burstein4, James Blowers5, J Alan Menius5, Jeffery L Painter5, Michele Thomas6, Carrie E Pierce7, Harold W Rodriguez7, John S Brownstein7, Clark C Freifeld7, Heidi G Bell8, Nabarun Dasgupta7.   

Abstract

INTRODUCTION: Post-marketing safety surveillance primarily relies on data from spontaneous adverse event reports, medical literature, and observational databases. Limitations of these data sources include potential under-reporting, lack of geographic diversity, and time lag between event occurrence and discovery. There is growing interest in exploring the use of social media ('social listening') to supplement established approaches for pharmacovigilance. Although social listening is commonly used for commercial purposes, there are only anecdotal reports of its use in pharmacovigilance. Health information posted online by patients is often publicly available, representing an untapped source of post-marketing safety data that could supplement data from existing sources.
OBJECTIVES: The objective of this paper is to describe one methodology that could help unlock the potential of social media for safety surveillance.
METHODS: A third-party vendor acquired 24 months of publicly available Facebook and Twitter data, then processed the data by standardizing drug names and vernacular symptoms, removing duplicates and noise, masking personally identifiable information, and adding supplemental data to facilitate the review process. The resulting dataset was analyzed for safety and benefit information.
RESULTS: In Twitter, a total of 6,441,679 Medical Dictionary for Regulatory Activities (MedDRA(®)) Preferred Terms (PTs) representing 702 individual PTs were discussed in the same post as a drug compared with 15,650,108 total PTs representing 946 individual PTs in Facebook. Further analysis revealed that 26 % of posts also contained benefit information.
CONCLUSION: Social media listening is an important tool to augment post-marketing safety surveillance. Much work remains to determine best practices for using this rapidly evolving data source.

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Year:  2016        PMID: 26798054     DOI: 10.1007/s40264-015-0385-6

Source DB:  PubMed          Journal:  Drug Saf        ISSN: 0114-5916            Impact factor:   5.606


  45 in total

Review 1.  Aims and approaches of Web-RADR: a consortium ensuring reliable ADR reporting via mobile devices and new insights from social media.

Authors:  Rajesh Ghosh; David Lewis
Journal:  Expert Opin Drug Saf       Date:  2015-10-05       Impact factor: 4.250

Review 2.  Utilizing social media data for pharmacovigilance: A review.

Authors:  Abeed Sarker; Rachel Ginn; Azadeh Nikfarjam; Karen O'Connor; Karen Smith; Swetha Jayaraman; Tejaswi Upadhaya; Graciela Gonzalez
Journal:  J Biomed Inform       Date:  2015-02-23       Impact factor: 6.317

3.  Drug related problems with Antiparkinsonian agents: consumer Internet reports versus published data.

Authors:  Sabrina Schröder; York Francis Zöllner; Marion Schaefer
Journal:  Pharmacoepidemiol Drug Saf       Date:  2007-10       Impact factor: 2.890

4.  Social media and networks in pharmacovigilance: boon or bane?

Authors:  I Ralph Edwards; Marie Lindquist
Journal:  Drug Saf       Date:  2011-04-01       Impact factor: 5.606

5.  Design and validation of an automated method to detect known adverse drug reactions in MEDLINE: a contribution from the EU-ADR project.

Authors:  Paul Avillach; Jean-Charles Dufour; Gayo Diallo; Francesco Salvo; Michel Joubert; Frantz Thiessard; Fleur Mougin; Gianluca Trifirò; Annie Fourrier-Réglat; Antoine Pariente; Marius Fieschi
Journal:  J Am Med Inform Assoc       Date:  2012-11-29       Impact factor: 4.497

6.  Leveraging social networks for toxicovigilance.

Authors:  Michael Chary; Nicholas Genes; Andrew McKenzie; Alex F Manini
Journal:  J Med Toxicol       Date:  2013-06

7.  Pharmacovigilance for a revolving world: prospects of patient-generated data on the internet.

Authors:  G Niklas Norén
Journal:  Drug Saf       Date:  2014-10       Impact factor: 5.606

8.  Analysis of patients' narratives posted on social media websites on benfluorex's (Mediator® ) withdrawal in France.

Authors:  M Abou Taam; C Rossard; L Cantaloube; N Bouscaren; G Roche; L Pochard; F Montastruc; A Herxheimer; J L Montastruc; H Bagheri
Journal:  J Clin Pharm Ther       Date:  2013-10-21       Impact factor: 2.512

Review 9.  Adverse Drug Reaction Identification and Extraction in Social Media: A Scoping Review.

Authors:  Jérémy Lardon; Redhouane Abdellaoui; Florelle Bellet; Hadyl Asfari; Julien Souvignet; Nathalie Texier; Marie-Christine Jaulent; Marie-Noëlle Beyens; Anita Burgun; Cédric Bousquet
Journal:  J Med Internet Res       Date:  2015-07-10       Impact factor: 5.428

10.  Breaking the news or fueling the epidemic? Temporal association between news media report volume and opioid-related mortality.

Authors:  Nabarun Dasgupta; Kenneth D Mandl; John S Brownstein
Journal:  PLoS One       Date:  2009-11-18       Impact factor: 3.240

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  25 in total

1.  Validation of New Signal Detection Methods for Web Query Log Data Compared to Signal Detection Algorithms Used With FAERS.

Authors:  Susan Colilla; Elad Yom Tov; Ling Zhang; Marie-Laure Kurzinger; Stephanie Tcherny-Lessenot; Catherine Penfornis; Shang Jen; Danny S Gonzalez; Patrick Caubel; Susan Welsh; Juhaeri Juhaeri
Journal:  Drug Saf       Date:  2017-05       Impact factor: 5.606

2.  Detection of Adverse Drug Reactions using Medical Named Entities on Twitter.

Authors:  Andrew MacKinlay; Hafsah Aamer; Antonio Jimeno Yepes
Journal:  AMIA Annu Symp Proc       Date:  2018-04-16

Review 3.  Social Media and the Adolescent and Young Adult (AYA) Patient with Cancer.

Authors:  Miguel-Angel Perales; Emily K Drake; Naveen Pemmaraju; William A Wood
Journal:  Curr Hematol Malig Rep       Date:  2016-12       Impact factor: 3.952

4.  Serendipity-A Machine-Learning Application for Mining Serendipitous Drug Usage From Social Media.

Authors:  Boshu Ru; Dingcheng Li; Yueqi Hu; Lixia Yao
Journal:  IEEE Trans Nanobioscience       Date:  2019-04-04       Impact factor: 2.935

Review 5.  Using social media in safety signal management: is it reliable?

Authors:  Sue Rees; Sadiqa Mian; Neal Grabowski
Journal:  Ther Adv Drug Saf       Date:  2018-08-09

6.  Patient-Reported Safety Information: A Renaissance of Pharmacovigilance?

Authors:  Linda Härmark; June Raine; Hubert Leufkens; I Ralph Edwards; Ugo Moretti; Viola Macolic Sarinic; Agnes Kant
Journal:  Drug Saf       Date:  2016-10       Impact factor: 5.606

7.  Social Media Impact of the Food and Drug Administration's Drug Safety Communication Messaging About Zolpidem: Mixed-Methods Analysis.

Authors:  Michael S Sinha; Clark C Freifeld; John S Brownstein; Macarius M Donneyong; Paula Rausch; Brian M Lappin; Esther H Zhou; Gerald J Dal Pan; Ajinkya M Pawar; Thomas J Hwang; Jerry Avorn; Aaron S Kesselheim
Journal:  JMIR Public Health Surveill       Date:  2018-01-05

8.  Filtering Entities to Optimize Identification of Adverse Drug Reaction From Social Media: How Can the Number of Words Between Entities in the Messages Help?

Authors:  Redhouane Abdellaoui; Stéphane Schück; Nathalie Texier; Anita Burgun
Journal:  JMIR Public Health Surveill       Date:  2017-06-22

9.  A method for data-driven exploration to pinpoint key features in medical data and facilitate expert review.

Authors:  Kristina Juhlin; Kristina Star; G Niklas Norén
Journal:  Pharmacoepidemiol Drug Saf       Date:  2017-08-16       Impact factor: 2.890

10.  Using Social Listening Data to Monitor Misuse and Nonmedical Use of Bupropion: A Content Analysis.

Authors:  Laurie S Anderson; Heidi G Bell; Michael Gilbert; Julie E Davidson; Christina Winter; Monica J Barratt; Beta Win; Jeffery L Painter; Christopher Menone; Jonathan Sayegh; Nabarun Dasgupta
Journal:  JMIR Public Health Surveill       Date:  2017-02-01
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