The presentation discusses using social media data to supplement traditional signal detection methods in pharmacovigilance. It describes how Google developed a model with the CDC to detect influenza outbreaks using Google search queries, which provided faster detection than traditional surveillance. However, the model overestimated flu incidence due to increased searches after media coverage of the Google-used query. The presentation argues that social media could be similarly used to detect drug safety signals if cases are anonymized and confirmed by healthcare professionals.

1. Slide 1pharmaSOL - All Rights Reserved
Dr Marc A. Zittartz, pharmaSOL
Designing an efficient Pharmacovigilance System
02. April 2014, München, Germany

2. • Signal Detection has become the focus of
Pharmacovigilance activities in the last years. Computer
models have been developed to automatically detect signals
based on Adverse Event Report Systems, like FDA AERS.
However, the vast amount of information available on social
media has not been fully used so far, as this information
does not fit in the traditional Pharmacovigilance model.
• In 2008 Google, together with the Centers for Disease
Control and Prevention (CDC), developed a computer model
using Google search queries to detect outbreaks of the flu
across the world.
• This presentation explains the model developed by Google
Flu Trends, the benefits and challenges experienced so far,
and how it could supplement traditional signal detection
methods in Pharmacovigilance.
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4. • Disruption of the pharma business model by the
internet so far is minor.
• Exchange with or usage of social media is limited
• Patients use the Internet
• Prior to a doctor visit to seek information and/or to decide
whether they need professional help;
• After the doctor visit for reassurance or because of
dissatisfaction with the information provided.
Patient Educ Couns. 2006 Oct;63(1-2):24-8. Epub 2006 Jan 6.
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6. • From the guidance:
“Example 4: A sales representative acting on behalf of
a firm posts comments about the innovative release
mechanism of the firm’s product on an independent
third-party site. Because the sales representative is
acting on behalf of the firm, the firm is responsible for
submitting the comments to FDA to meet the
postmarketing submission requirements.”
pharmaSOL - All Rights Reserved Slide 6

7. • Several unknowns about the safety profile of a new
drug exist following Clinical Development
• Rare adverse events
• Specific populations (paediatric, pregnancies, elderly)
• Interactions with other drugs
• Long time exposure
pharmaSOL - All Rights Reserved Slide 7

8. • Qualitative
• Individual Case Processing
• Case Series Review
• PSUR/DSUR Review
• Literature
• Health Authorities
• Quantitative
• Frequency analysis
• Disproportionality analysis
• Origin
• Public Database
• FDA AERS
• WHO Vigibase
• (EudraVigilance)
• Company Database
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9. • Company Database
• Case Volume
• Diversity
• Public Database
• Outdated information
(delay of 6 months or more)
• Duplicates
• Uneven Quality
• Reporting Bias (e.g.
seriousness, time on
market, media coverage)
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Under-reporting estimated more than 90%
Under-reporting of adverse drug reactions : a systematic review, Drug Saf. 2006;29(5):385-96.

10. Conservative approach to information from social media
• No identifiable patient
• No identifiable reporter
• Not confirmed by a Health Care Professional
pharmaSOL - All Rights Reserved Slide 10
Invalid
case

11. • USA: Centers for Disease Control and
Prevention (CDC)
• Collects data from thousands of Health
Care Providers
• Publishes data on a 1 weekly basis
• Typical Reporting lag: 1-2 weeks
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12. • In 2008 Google.org started a collaboration with the
CDC
• Google provides data from Google Search and the
algorithm.
• keywords and phrases related to the flu, including
thermometer, flu symptoms, muscle aches, chest congestion.
• CDC provides historic influenza data collected in the
USA
Detecting influenza epidemics using search engine query data (Nature 457, 1012-1014)
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13. • Use historic data (CNC) Identify a suitable search query
• Provide up to date trends and warnings
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16. • New York Times published an
example query actually used in the
model.
 Traffic increase on that query
• Google considerably
overestimated the incidence of flu
in the U.S.
• Root Cause Analysis
“People react to heightened media
coverage, which lead to an up rise
in searches.”
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17. • Google.org shows that Signal Detection on internet
search data significantly reduces the time delay
experienced in traditional influenza surveillance
• Current development focuses on predictive
algorithms to detect peaks in the future
• Google expanded to identify Dengue Fever trends
• The influence of media coverage must not be
underestimated and needs to be taken into account for
the algorithm
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The House
of Signal Detection
Quantitative Qualitative
Social
Media
Trends