Cargando…
Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab
INTRODUCTION: Adverse drug reactions (ADRs) are associated with significant health-related and financial burden, and multiple sources are currently utilized to actively discover them. Social media has been proposed as a potential resource for monitoring ADRs, but drug-specific analytical studies com...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223697/ https://www.ncbi.nlm.nih.gov/pubmed/30167992 http://dx.doi.org/10.1007/s40264-018-0707-6 |
_version_ | 1783369447181910016 |
---|---|
author | Smith, Karen Golder, Su Sarker, Abeed Loke, Yoon O’Connor, Karen Gonzalez-Hernandez, Graciela |
author_facet | Smith, Karen Golder, Su Sarker, Abeed Loke, Yoon O’Connor, Karen Gonzalez-Hernandez, Graciela |
author_sort | Smith, Karen |
collection | PubMed |
description | INTRODUCTION: Adverse drug reactions (ADRs) are associated with significant health-related and financial burden, and multiple sources are currently utilized to actively discover them. Social media has been proposed as a potential resource for monitoring ADRs, but drug-specific analytical studies comparing social media with other sources are scarce. OBJECTIVES: Our objective was to develop methods to compare ADRs mentioned in social media with those in traditional sources: the US FDA Adverse Event Reporting System (FAERS), drug information databases (DIDs), and systematic reviews. METHODS: A total of 10,188 tweets mentioning adalimumab collected between June 2014 and August 2016 were included. ADRs in the corpus were extracted semi-automatically and manually mapped to standardized concepts in the Unified Medical Language System. ADRs were grouped into 16 biologic categories for comparisons. Frequencies, relative frequencies, disproportionality analyses, and rank ordering were used as metrics. RESULTS: There was moderate agreement between ADRs in social media and traditional sources. “Local and injection site reactions” was the top ADR in Twitter, DIDs, and systematic reviews by frequency, ranked frequency, and index ranking. The next highest ADR in Twitter—fatigue—ranked fifth and seventh in FAERS and DIDs. CONCLUSION: Social media posts often express mild and symptomatic ADRs, but rates are measured differently in scientific sources. ADRs in FAERS are reported as absolute numbers, in DIDs as percentages, and in systematic reviews as percentages, risk ratios, or other metrics, which makes comparisons challenging; however, overlap is substantial. Social media analysis facilitates open-ended investigation of patient perspectives and may reveal concepts (e.g. anxiety) not available in traditional sources. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s40264-018-0707-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6223697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-62236972018-11-18 Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab Smith, Karen Golder, Su Sarker, Abeed Loke, Yoon O’Connor, Karen Gonzalez-Hernandez, Graciela Drug Saf Original Research Article INTRODUCTION: Adverse drug reactions (ADRs) are associated with significant health-related and financial burden, and multiple sources are currently utilized to actively discover them. Social media has been proposed as a potential resource for monitoring ADRs, but drug-specific analytical studies comparing social media with other sources are scarce. OBJECTIVES: Our objective was to develop methods to compare ADRs mentioned in social media with those in traditional sources: the US FDA Adverse Event Reporting System (FAERS), drug information databases (DIDs), and systematic reviews. METHODS: A total of 10,188 tweets mentioning adalimumab collected between June 2014 and August 2016 were included. ADRs in the corpus were extracted semi-automatically and manually mapped to standardized concepts in the Unified Medical Language System. ADRs were grouped into 16 biologic categories for comparisons. Frequencies, relative frequencies, disproportionality analyses, and rank ordering were used as metrics. RESULTS: There was moderate agreement between ADRs in social media and traditional sources. “Local and injection site reactions” was the top ADR in Twitter, DIDs, and systematic reviews by frequency, ranked frequency, and index ranking. The next highest ADR in Twitter—fatigue—ranked fifth and seventh in FAERS and DIDs. CONCLUSION: Social media posts often express mild and symptomatic ADRs, but rates are measured differently in scientific sources. ADRs in FAERS are reported as absolute numbers, in DIDs as percentages, and in systematic reviews as percentages, risk ratios, or other metrics, which makes comparisons challenging; however, overlap is substantial. Social media analysis facilitates open-ended investigation of patient perspectives and may reveal concepts (e.g. anxiety) not available in traditional sources. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s40264-018-0707-6) contains supplementary material, which is available to authorized users. Springer International Publishing 2018-08-30 2018 /pmc/articles/PMC6223697/ /pubmed/30167992 http://dx.doi.org/10.1007/s40264-018-0707-6 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Research Article Smith, Karen Golder, Su Sarker, Abeed Loke, Yoon O’Connor, Karen Gonzalez-Hernandez, Graciela Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab |
title | Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab |
title_full | Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab |
title_fullStr | Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab |
title_full_unstemmed | Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab |
title_short | Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab |
title_sort | methods to compare adverse events in twitter to faers, drug information databases, and systematic reviews: proof of concept with adalimumab |
topic | Original Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6223697/ https://www.ncbi.nlm.nih.gov/pubmed/30167992 http://dx.doi.org/10.1007/s40264-018-0707-6 |
work_keys_str_mv | AT smithkaren methodstocompareadverseeventsintwittertofaersdruginformationdatabasesandsystematicreviewsproofofconceptwithadalimumab AT goldersu methodstocompareadverseeventsintwittertofaersdruginformationdatabasesandsystematicreviewsproofofconceptwithadalimumab AT sarkerabeed methodstocompareadverseeventsintwittertofaersdruginformationdatabasesandsystematicreviewsproofofconceptwithadalimumab AT lokeyoon methodstocompareadverseeventsintwittertofaersdruginformationdatabasesandsystematicreviewsproofofconceptwithadalimumab AT oconnorkaren methodstocompareadverseeventsintwittertofaersdruginformationdatabasesandsystematicreviewsproofofconceptwithadalimumab AT gonzalezhernandezgraciela methodstocompareadverseeventsintwittertofaersdruginformationdatabasesandsystematicreviewsproofofconceptwithadalimumab |