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

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background

The underreporting of adverse drug reactions (ADRs) through traditional reporting channels is a limitation in the efficiency of the current pharmacovigilance system. Patients’ experiences with drugs that they report on social media represent a new source of data that may have some value in postmarketing safety surveillance.

Objective

A scoping review was undertaken to explore the breadth of evidence about the use of social media as a new source of knowledge for pharmacovigilance.

Methods

Daubt et al’s recommendations for scoping reviews were followed. The research questions were as follows: How can social media be used as a data source for postmarketing drug surveillance? What are the available methods for extracting data? What are the different ways to use these data? We queried PubMed, Embase, and Google Scholar to extract relevant articles that were published before June 2014 and with no lower date limit. Two pairs of reviewers independently screened the selected studies and proposed two themes of review: manual ADR identification (theme 1) and automated ADR extraction from social media (theme 2). Descriptive characteristics were collected from the publications to create a database for themes 1 and 2.

Results

Of the 1032 citations from PubMed and Embase, 11 were relevant to the research question. An additional 13 citations were added after further research on the Internet and in reference lists. Themes 1 and 2 explored 11 and 13 articles, respectively. Ways of approaching the use of social media as a pharmacovigilance data source were identified.

Conclusions

This scoping review noted multiple methods for identifying target data, extracting them, and evaluating the quality of medical information from social media. It also showed some remaining gaps in the field. Studies related to the identification theme usually failed to accurately assess the completeness, quality, and reliability of the data that were analyzed from social media. Regarding extraction, no study proposed a generic approach to easily adding a new site or data source. Additional studies are required to precisely determine the role of social media in the pharmacovigilance system.

Related collections

Most cited references 63

Record: found
Abstract: found
Article: not found

Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies.

J Lazarou, B H Pomeranz, P N Corey (1998)

To estimate the incidence of serious and fatal adverse drug reactions (ADR) in hospital patients. Four electronic databases were searched from 1966 to 1996. Of 153, we selected 39 prospective studies from US hospitals. Data extracted independently by 2 investigators were analyzed by a random-effects model. To obtain the overall incidence of ADRs in hospitalized patients, we combined the incidence of ADRs occurring while in the hospital plus the incidence of ADRs causing admission to hospital. We excluded errors in drug administration, noncompliance, overdose, drug abuse, therapeutic failures, and possible ADRs. Serious ADRs were defined as those that required hospitalization, were permanently disabling, or resulted in death. The overall incidence of serious ADRs was 6.7% (95% confidence interval [CI], 5.2%-8.2%) and of fatal ADRs was 0.32% (95% CI, 0.23%-0.41%) of hospitalized patients. We estimated that in 1994 overall 2216000 (1721000-2711000) hospitalized patients had serious ADRs and 106000 (76000-137000) had fatal ADRs, making these reactions between the fourth and sixth leading cause of death. The incidence of serious and fatal ADRs in US hospitals was found to be extremely high. While our results must be viewed with circumspection because of heterogeneity among studies and small biases in the samples, these data nevertheless suggest that ADRs represent an important clinical issue.

0 comments Cited 475 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

'Global trigger tool' shows that adverse events in hospitals may be ten times greater than previously measured.

David C Classen, Roger Resar, Frances M. Griffin … (2011)

Identification and measurement of adverse medical events is central to patient safety, forming a foundation for accountability, prioritizing problems to work on, generating ideas for safer care, and testing which interventions work. We compared three methods to detect adverse events in hospitalized patients, using the same patient sample set from three leading hospitals. We found that the adverse event detection methods commonly used to track patient safety in the United States today-voluntary reporting and the Agency for Healthcare Research and Quality's Patient Safety Indicators-fared very poorly compared to other methods and missed 90 percent of the adverse events. The Institute for Healthcare Improvement's Global Trigger Tool found at least ten times more confirmed, serious events than these other methods. Overall, adverse events occurred in one-third of hospital admissions. Reliance on voluntary reporting and the Patient Safety Indicators could produce misleading conclusions about the current safety of care in the US health care system and misdirect efforts to improve patient safety.

0 comments Cited 230 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter

Clark C. Freifeld, John Brownstein, Christopher M. Menone … (2014)

Background Traditional 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. Objective The 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. Methods We 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). Results Of 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. Conclusion Patients 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.

0 comments Cited 95 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Jérémy Lardon:

Université Paris 13, Sorbonne Paris Cité, Laboratoire d'Informatique Médicale et d'Ingénieurie des Connaissances en e-Santé (LIMICS), (Unité Mixte de Recherche en Santé, UMR_S 1142), F-93430, Villetaneuse, FranceSorbonne Universités, University of Pierre and Marie Curie (UPMC) Université Paris 06, Unité Mixte de Recherche en Santé (UMR_S) 1142, Laboratoire d'Informatique Médicale et d'Ingénieurie des Connaissances en e-Santé (LIMICS), F-75006Institut National de la Santé et de la Recherche Médicale (INSERM), U1142, Laboratoire d'Informatique Médicale et d'Ingénieurie des Connaissances en e-Santé (LIMICS), F-75006Campus des Cordeliers, Institut National de la Santé et de la Recherche Médicale (INSERM) U 1142 - Laboratoire d'Informatique Médicale et d'Ingénieurie des Connaissances en e-Santé (LIMICS) Esc D - 2ème étage15 rue de l'École de MédecineParis, 75006France33 14427919033 144279192jeremy.lardon@chu-st-etienne.fr

ORCID: http://orcid.org/0000-0002-1914-1685

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J. Med. Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications Inc. (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: July 2015

Publication date (Electronic): 10 July 2015

Volume: 17

Issue: 7

Electronic Location Identifier: e171

Affiliations

[1] ¹Université Paris 13, Sorbonne Paris Cité, Laboratoire d'Informatique Médicale et d'Ingénieurie des Connaissances en e-Santé (LIMICS), (Unité Mixte de Recherche en Santé, UMR_S 1142), F-93430, Villetaneuse, France Sorbonne Universités, University of Pierre and Marie Curie (UPMC) Université Paris 06, Unité Mixte de Recherche en Santé (UMR_S) 1142, Laboratoire d'Informatique Médicale et d'Ingénieurie des Connaissances en e-Santé (LIMICS), F-75006 Institut National de la Santé et de la Recherche Médicale (INSERM), U1142, Laboratoire d'Informatique Médicale et d'Ingénieurie des Connaissances en e-Santé (LIMICS), F-75006 ParisFrance

[2] ²Service de Santé Publique et de l’Information Médicale (SSPIM) Department of Public Health and Medical Informatics Centre Hospitalier Universitaire (CHU) University Hospital of Saint Etienne Saint-EtienneFrance

[3] ³Institut National de la Santé et de la Recherche Médicale (INSERM), Unité Mixte de Recherche en Santé (UMR_S) 1138, équipe 22, Centre de Recherche des Cordeliers, Université Paris Descartes, Sorbonne Paris Cité, F-75006 ParisFrance

[4] ⁴Kappa Santé ParisFrance

[5] ⁵Centre de Pharmacovigilance, Centre Hospitalier Universitaire (CHU) University Hospital of Saint Etienne Saint-EtienneFrance

[6] ⁶Université Paris 13, Sorbonne Paris Cité, Laboratoire d'Informatique Médicale et d'Ingénieurie des Connaissances en e-Santé (LIMICS), (Unité Mixte de Recherche en Santé, UMR_S 1142), F-93430, Villetaneuse, France Sorbonne Universités, University of Pierre and Marie Curie (UPMC) Université Paris 06, Unité Mixte de Recherche en Santé (UMR_S) 1142, Laboratoire d'Informatique Médicale et d'Ingénieurie des Connaissances en e-Santé (LIMICS), F-75006, Paris, Institut National de la Santé et de la Recherche Médicale (INSERM), U1142, Laboratoire d'Informatique Médicale et d'Ingénieurie des Connaissances en e-Santé (LIMICS), F-75006 ParisFrance

[7] ⁷Centre de Pharmacovigilance Centre Hospitalier Universitaire (CHU) University Hospital of Saint Etienne Saint-EtienneFrance

[8] ⁸Institut National de la Santé et de la Recherche Médicale (INSERM), Unité Mixte de Recherche en Santé (UMR_S) 1138, équipe 22 Centre de Recherche des Cordeliers Université Paris Descartes, Sorbonne Paris Cité, F-75006 ParisFrance

[9] ⁹Assistance Publique-Hôpitaux de Paris (AP-HP) Hôpital Européen Georges-Pompidou (HEGP) Department of Medical Informatics ParisFrance

Author notes

Corresponding Author: Jérémy Lardon jeremy.lardon@ 123456chu-st-etienne.fr

Author information

Jérémy Lardon http://orcid.org/0000-0002-1914-1685

Redhouane Abdellaoui http://orcid.org/0000-0002-2938-7478

Florelle Bellet http://orcid.org/0000-0002-0400-2586

Hadyl Asfari http://orcid.org/0000-0002-2540-7400

Julien Souvignet http://orcid.org/0000-0001-7278-4148

Nathalie Texier http://orcid.org/0000-0003-3749-254X

Marie-Christine Jaulent http://orcid.org/0000-0003-4445-7494

Marie-Noëlle Beyens http://orcid.org/0000-0002-5831-8957

Anita Burgun http://orcid.org/0000-0001-6855-4366

Cédric Bousquet http://orcid.org/0000-0001-9775-2476

Article

Publisher ID: v17i7e171

DOI: 10.2196/jmir.4304

PMC ID: 4526988

PubMed ID: 26163365

SO-VID: 833d8dcf-b105-4e10-aa1c-f08b279e049e

Copyright © ©Jérémy Lardon, Redhouane Abdellaoui, Florelle Bellet, Hadyl Asfari, Julien Souvignet, Nathalie Texier, Marie-Christine Jaulent, Marie-Noëlle Beyens, Anita Burgun, Cédric Bousquet. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 10.07.2015.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 30 January 2015

Date revision requested : 27 February 2015

Date revision received : 9 April 2015

Date accepted : 22 April 2015

Comments

Comment on this article

scite_

Cited by 41

See all cited by

Most referenced authors 790

See all reference authors

Submit your digital health research with an established publisher
- celebrating 25 years of open access