Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Searching socialmediaforadverseevents


Published on

Dr. Su Golder, NIHR Research Fellow at the University of York, presents findings from her recent publication: “Systematic review on the prevalence, frequency and comparative value of adverse events data in social media”.

Published in: Health & Medicine
  • Be the first to comment

  • Be the first to like this

Searching socialmediaforadverseevents

  1. 1. Does searching social media for adverse event data improve patient outcomes? Su Golder, Yoon Loke, Gill Norman
  2. 2. Conflict of Interest This presentation is based on independent research arising from a Postdoctoral Research Fellowship, Su Golder PDF-2014- 07-041 supported by the National Institute for Health Research. The views expressed in this presentation are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health.
  3. 3. Why adverse effects matter? Often serious Hospitalisation, disability, death USA: 4th-6th leading cause of death (Lazarou 1998) Unpleasant, worsen quality of life Make people stop treatment Cost UK NHS costs estimated at £2 billion per year (Compass 2008)
  4. 4. Definitions Adverse event A harmful or undesirable outcome that occurs during or after the use of a drug or intervention but is not necessarily caused by it. Adverse effect A harmful or undesirable outcome that occurs during or after the use of a drug or intervention for which there is at least a reasonable possibility of a causal relation. (Chou 2010)
  5. 5. Adverse event case reports Pharmacovigilance data Under-reporting by public, clinicians and other health care professionals Access Duplicate records Lack of detail Published case reports Unlikely representative (Loke 2004) Few published
  6. 6. Popularity of social media “Social media” refers to a set of web-based services that enable users to share content with each other. Most viewed websites: 1st 2nd 3rd 74% of online adults use social media and 52% of online adults use two or more sites (PewResearchCenter 2014 and Duggan 2014) > 1.59 billion Facebook users and > 305 million Twitter users
  7. 7. 4
  8. 8. Social Media
  9. 9. Quiz Time Q:What percentage of internet users say they looked online for health information within the past year? A: 22% B: 43% C: 80%
  10. 10. Social media usage by the public 80% of internet users say they looked online for health information within the past year 34% say they read or watched someone else’s experience about health or medical issues in the last 12 months 16% of internet users say they went online in the last year to find others who might share the same health concerns
  11. 11. Social media usage by researchers Dissemination of research findings Recruitment to studies Online focus groups Polling/surveying online groups Surveillance/data mining Public health monitoring (e.g. disease outbreaks, health behaviours) Identifying patient views/experiences (e.g. treatments)
  12. 12. Systematic review Objective To summarize the prevalence, frequency and comparative value of information on the adverse events of healthcare interventions from user comments and videos in social media.
  13. 13. Methods Search 18 databases, handsearching, reference checking, internet searches and contacting experts Inclusion criteria (PICOS) Population: Any Intervention: Social media Comparator(s): Any (e.g. literature or pharmacovigilance or drug labels) or none Outcome(s): AEs information of any treatment Study design: Any Quality assessment tool Created in-house
  14. 14. Results 3045 records retrieved (4457 before duplicates removed) 51 studies (64 publications) included 174 social media sites evaluated Characteristics of included studies Any adverse events (90%) Drug interventions (86%) Discussion forums (71%) DailyStrength, AskaPatient, ehealthform etc
  15. 15. Quality Assessment 1. Search strategy to identify posts Search strategy (18 studies) Automation (‘scrapes’/text mining) with dictionaries (such as Consumer health vocabularies, MedDRA ) (11 studies) Browsing (11 studies)
  16. 16. Example post Works to calm mania or depression but zonks me and scares me about the diabetes issues reported Leaman et al 2010 Adverse event: somnolence Other: diabetes Indication: depression Indication: mania
  17. 17. Example post ARGH! Got me nicely hypomanic for two weeks, then pooped out on me and just made me gain a half pound a day so I had to stop. Leaman et al 2010 Beneficial effect: hypomania Adverse event: tolerance Adverse event: weight gain
  18. 18. Quality Assessment 2. Selection of relevant posts Manual (22 studies), automation (such as co- occurrence of terms) (12 studies) 3. Definition of a report of an adverse event FDA definition (5 studies) (FDA criteria = identifiable reporter, identifiable patient, reaction or event, and suspected medicinal product) 4. Duplicate data Excluded duplicate data (6 studies)
  19. 19. Prevalence of AEs reports Social Media % AEs posts from all posts % AEs posts from posts related to intervention/illness Facebook 4% 0.7 - 2% Blogs, Facebook, Twitter and forums 0.3 - 8% Twitter 2% - 4% 0.02% - 11.5% General forums 0.2% - 1.42% 18.2% - 35% General and disease specific forums 12% -58% Disease specific forums 12% - 62% Disease specific forums and blogs 12.4% YouTube 40% - 78%
  20. 20. Results Adverse events from social media already documented in data sources, such as, pharmacovigilance data, published trials and drug labels 57% to 99% More rapid identification of adverse events More detail on patient perspective
  21. 21. Quiz Time Q: Were more adverse event reports identified on social media than from other sources? A: Yes, for all adverse events B: Only for mild adverse events (e.g. weight gain) C: Only for serious adverse events (e.g. death)
  22. 22. Results Overall agreement in rank order of adverse events Higher frequency ‘mild’ or ‘symptom related’ adverse events Lower frequency ‘serious’ adverse events or laboratory abnormalities
  23. 23. Limitations Genuine/duplicate posts Biased sample population Decipher ‘true’ adverse effects Insufficient clinical detail False positive signal generation Difficult to search social media Language, noise - It’s a huge pile of coal filled with diamonds” No denominator
  24. 24. Conclusions Social media may be useful: As a signal-generating source, particularly for ‘mild’AEs To gain patient perspective and identifyAEs most important to patients To help formulate and prioritise questions on AEs for future research
  25. 25. Next steps Systematic review of the ethical considerations of social media research Submitted a grant with University of Arizona on using social media for identifying adverse events for systematic reviews