| アブストラクト | INTRODUCTION: There are few studies assessing how data on adverse drug events from consumers on social media compare with other sources. AIM: The aim of this study was to assess the consistency of adverse event data of statin medications from social media as compared with other sources. METHODS: We collected data on the adverse events of statins from Twitter, the US FDA Adverse Event Reporting System (FAERS), the UK Medicines and Healthcare products Regulatory Agency (MHRA), drug information databases (DIDs) and systematic reviews. We manually annotated 12,649 tweets collected between June 2013 and August 2018. We collected 45,447 reports from FAERS, 10,415 from MHRA, identified 17 systematic reviews with relevant data and extracted data from Facts and Comparisons((R)) and Clinical Pharmacology((R)). We compared the proportion, relative frequencies and rank of each category of adverse event from each source using MedDRA((R)) primary System Organ Class codes. RESULTS: Compared with other sources, patients on social media are proportionally far more likely to complain about musculoskeletal symptoms than other adverse events. Most adverse events showed a high level of agreement between Twitter and regulatory data. DIDs tend to demonstrate similar patterns but not as strongly. Systematic reviews tend to examine pre-specified adverse events or those reported by trial investigators. CONCLUSIONS: Combining the data from multiple sources, albeit challenging, may provide a broader safety profile of any medication. Systematically collected social media reports may be able to contribute information on the most pertinent adverse effects to patients. |
|---|
| ジャーナル名 | Drug safety |
|---|
| 投稿日 | 2020/10/2 |
|---|
| 投稿者 | Golder, Su; Smith, Karen; O'Connor, Karen; Gross, Robert; Hennessy, Sean; Gonzalez-Hernandez, Graciela |
|---|
| 組織名 | NIHR Postdoctoral Research Fellow, Department of Health Sciences, University of;York, York, YO10 5DD, UK. su.golder@york.ac.uk.;Regis University School of Pharmacy, Denver, CO, USA.;Department of Biostatistics, Epidemiology and Informatics, Perelman School of;Medicine, University of Pennsylvania, Philadelphia, PA, USA. |
|---|
| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/33001380/ |
|---|
