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”.

1. Does searching social media
for adverse event data
improve patient outcomes?
Su Golder, Yoon Loke,
Gill Norman

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. 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. 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. 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. 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. 4

8. Social Media

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. 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
http://www.pewinternet.org/2013/01/15/health-online-2013/

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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