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NabarunDasgupta,CarriePierce,ChristopherMenone,KateO’Brien,MelissaJordan,CarlyWinokur,
MichaelGilbert,Clark Freifeld,HaroldRodriguez,ChiBahk,JohnBrownstein
August25, 2015 –ICPEBoston
Slidesavailablenow @epidemico
Does Direct-to-ConsumerAdvertising LeadtoStimulated
ReportingofAdverse Eventsin SocialMedia?
01. USA
A public-privatepartnershipenteringYear 2.
TheWEB-RADR projectis supportedbythe
InnovativeMedicinesInitiativeJoint
Undertaking(IMI JU)undergrant agreementn°
115632,resourcesof which arecomposedof
financialcontributionfrom theEuropean
Union'sSeventhFrameworkProgramme
(FP7/2007-2013)andEFPIAcompanies’in kind
contribution.
02. EUROPE
GSK hasbeena development
partnerto extendthetechnology
platformto meet the standardsof
regulatedsoftwarefor thelastyear
andhalf.
03. INDUSTRY
Disclosures Funding Sources
FDA hasbeenfunding theresearch
intothe technologyfor3 years
throughthe Office of the Chief
Scientist.
EPIDEMICO, Inc.+ EPIDEMICO, Ltd.
Authorsare employeesorconsultants.Epidemico iscommercializingaspectsof thissoftwareplatform.
Epidemicois a whollyownsubsidiaryof BoozAllenHamilton.
Dyspareunia
Influenza
Diabetes
Weight loss
Atrial fibrillation
Psoriasis
COPD
COPD
IBS
Smoking cessation
Diabetes
Depression
Arthritis
Arthritis
Fibromyalgia
Arthritis
Depression
ED
Atrial fibrillation
ED
2014 Direct-to-Consumer Spending
Includesbroadcast, print, outdoor, and B2B
Source: Medical Marketing & Media DTC Report 2015
Osphena
Tamiflu
Invokana
Belviq
Xarelto
Humira
Symbicort
Breo Ellipta
Linzess
Chantix
Lyrica
Abilify
Celebrex
Humira
Lyrica
Xeljanz
Latuda
Viagra
Eliquis
Cialis
$0M $50M $100M $150M $200M $250M
SCIENTISTS: TAVARES G, FAISAL A. PLOS ONE. 2013 PMID: 23843945
JOURNALISTS: REYES J SIMMONS L, FALLON S FOR WIRED, OCTOBER 2013
ARTISTS: KEIFER C & POELL J, ONFORMATIVE
Remove false
positives
(optional)
Hypothesis
generation
Automated processes
to identifyevents and
clean data
Collect public
social mediadata
Synthesis of
information from
other sources
Acquire Curate Causation
StatisticsFilter
D A T A P R O C E S S O V E R V I E W
SCRANTONB,DEVRIESJ,BERINATOS,BORTZC.HARVARDBUSINESSREVIEW,JUNE2013
Geotag
Geographyidentified
if availableusing
GoogleAPI
4
3
2
1
Consolidate
Multiplicatecopies&
postsfrom sameuser
consolidated
(PIIremoved)
Translate
Internetvernacularto
MedDRAv18 preferred
terms
Identify Events
AutomatedNLP Bayesian
classifier
Machinelearning
A U T O M A T E D P R O C E S S E S
Spelling VariationsCombined or invented wordsImplied phrasesInternet Vernacular Translation
lost their eyesight
seeing weird color
seeing weird colour
doublevision
uldn’t see
double vision
blind
googley eyed
blurry vision
changes in viscross eyed
seeing weird
vision change
blindness
cross visionvisual snow
googly eyed
seeing doub
making me eat like a mouse
anorexic
st appetite
#notevenhungry
appetite is nonexistent
apetite surpressed
didn’t get hungry
dont want to eat
killed my apetite
miss feeling hung
killed my appetite
can’t eat
lost apetite
lost my appetite
no appetitey
lack of apetite
stomach small
lost teh appetite
never hnever want to eat
cant eat
coucrosseyed
blurry
apetite surpressed
killed my apetite
lost apetite
lack of apetite
lost teh appetite
Typos
seeing weird color
seeing weird colour
googley eyed
googly eyed
#notevenhungry
no appetitey
making me eat like a mouse
never want to eat
Visual impairment
MedDRA 10047571
Visual impairment
SNOMED 397540003
Decreased appetite
MedDRA 10061428
Loss of appetite
SNOMED 79890006
making me eat like a mouse
anorexic
st appetite
#notevenhungry
appetite is nonexistent
apetite surpressed
didn’t get hungry
dont want to eat
miss feeling hung
killed my appetite
can’t eat
lost apetite
lost my appetite
no appetitey
lack of apetite
stomach small
lost teh appetite
never hnever want to eat
cant eat
lost their eyesight
seeing weird color
seeing weird colour
doublevision
uldn’t see
double vision
blind
googley eyed
blurry vision
changes in viscross eyed
seeing weird
vision change
blindness
cross visionvisual snow
googly eyed
seeing doub
coucrosseyed
blurry
killed my apetite
P r o t o - A E
P o s t s w i t h R e s e m b l a n c e t o A d v e r s e E v e n t s
Manually classified
to identifyAEs
Precision
-
Positive predictive
value
Recall
-
Sensitivity
Posts mentioning
medical products
57M 380k 68% 88%
S Y S T E M P E R F O R M A N C E
Methods
- PublicTwitterposts
- 58products
(see appendix)
- Feb’14toJul ‘15(18 months)
- Geotagged
- Englishlanguage
DataSource
- Time ofdaypatterns
- USvs.AU/CA/UK
- DTCRxproducts
(withTV ads)
Design
- RateofProto-AEsper1000
Mentions
- RateofProto-AEsper1M Twitter
users
- Poissonmodelsbycountry
Statistics
- Effectofnon-TV advertising
uniformbetweencountries
Assumptions
M E T H O D S
RemoveSpam, news, coupons
67% of posts were spam
Consolidate multiplicate copies
35% Excludingliteral duplicates and retweets
Keep postswith in-scope geography
Keep posts from US, UK, CA, AU
Identifiable geographyfor 55% of Proto-AEs
Import Twitter Posts
All DTCproducts
LimittoEnglish Language
Only6 posts with unknownlanguage
Identify Postswith Proto-AEs
Automated classifier
Exclude Chantix/Champix
DTCadvertising allowed in non-UScountries
1,245,530
408,867
338,263
218,512
11,265
5,535
4,499
D A T A P R O C E S S I N G
Tweetsper15MinuteIncrement
0
5,000
10,000
15,000
20,000
25,000
30,000
All Public Twitter Posts
Mentions (spam & bots removed)
Proto-AEs
0:00 4:00 8:00 12:00 16:00 20:00 24:00
Hour of the Day (local time)
Daytime Television Prime TimeEvening News
Tweetsper15MinuteIncrement
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000All Mentions
TV Discussions
Rate of Proto-AEs per 100 Mentions
LOESS - Proto-AE Rate per 100 Mentions
0:00 4:00 8:00 12:00 16:00 20:00 24:00
Hour of the Day (local time)
Daytime Television Prime TimeEvening News
All mentions
Television
Tweetsper15MinuteIncrement
0
50
100
150
200
250
300
350
0:00
4:00
8:00
12:00
16:00
20:00
Proto-AEs
0:00 24:00
Hour of the Day (local time)
Daytime Television Prime Time
Results
64.8 (60.2, 69.7)
83.7 (72.8, 96.3)
111.5 (92.8, 134.0)
89.0 (78.9, 100.3)
Canada
Australia
United States
United Kingdom
R A T E S p e r 1 M T w i t t e r U s e r s
18 month study period
Results
31.6 (30.0, 33.2)
31.7 (27.5, 36.4)
39.2 (32.0, 48.0)
25.5 (22.7, 28.6)
Canada
Australia
United States
United Kingdom
R A T E S p e r 1 0 0 0 M e n t i o n s
18 month study period
Missing Geotags
Only used half of Mentions.
Causality
Can patients assess reliably?
Falsepositives
Can we improve precision?
DifferingPopulations
Tweeters vs. Disease vs. Rx
ChoiceofDenominators
How mature is our understanding?
Whatis the bias amongthosewho allow location disclosure?
Define methods to measureprobability of
realworld significance
Thepopulations to whomthe ads are targeted are likely to be older than
most Twitter users.
Manualcurationcanimprove positive predictive value.
Use different denominatorsto understand
the nuances.
L I M I T A T I O N S
CarrieMike Aranka Ciara Nabarun Wenjie
Harold John Robin Chi Sue
Kelly Kate Clark Chris Robin
Derek
Matt
Essam
Melissa
Kyra
Nico
Lou
Carly
Lucas
@epidemico @WEBRADR demo.medwatcher.org nabarun@epidemico.com
C O N T A C T U S
No DTC tv ads
juxtapid|amyvid|seretide|incivek|incivo|copaxone|saphris|sycrest|avonex
gleevec|doxil|aptiom|viread|firmagon|tarceva|kapvay|zmax|aranesp|xenazine
tetrabenazine|vantas|vascepa|natroba
All products with DTC
Cialis Lyrica Gardasil Cymbalta Chantix Champix Abilify Viagra Humira Botox Celebrex
Xarelto Enbrel Latuda Advair Seretide Zostavax Victoza Orencia Pradaxa Symbicort
Crestor Stelara Breo Ellipta Restasis Januvia Spiriva Xeljanz Incivek Incivo Axiron
Copaxone Eliquis Prolia Invokana Farxiga Intuniv Saphris Avonex Anoro Ellipta Gleevec
Exelon Patch Exelon Doxil Aptiom Viread Firmagon Tarceva Kapvay Zmax Aranesp
Xenazine Tetrabenazine Vantas Vascepa Juxtapid Amyvid Natroba

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Does Direct-to-Consumer Advertising Lead To Stimulated Reporting of Adverse Events in Social Media?

  • 1. NabarunDasgupta,CarriePierce,ChristopherMenone,KateO’Brien,MelissaJordan,CarlyWinokur, MichaelGilbert,Clark Freifeld,HaroldRodriguez,ChiBahk,JohnBrownstein August25, 2015 –ICPEBoston Slidesavailablenow @epidemico Does Direct-to-ConsumerAdvertising LeadtoStimulated ReportingofAdverse Eventsin SocialMedia?
  • 2. 01. USA A public-privatepartnershipenteringYear 2. TheWEB-RADR projectis supportedbythe InnovativeMedicinesInitiativeJoint Undertaking(IMI JU)undergrant agreementn° 115632,resourcesof which arecomposedof financialcontributionfrom theEuropean Union'sSeventhFrameworkProgramme (FP7/2007-2013)andEFPIAcompanies’in kind contribution. 02. EUROPE GSK hasbeena development partnerto extendthetechnology platformto meet the standardsof regulatedsoftwarefor thelastyear andhalf. 03. INDUSTRY Disclosures Funding Sources FDA hasbeenfunding theresearch intothe technologyfor3 years throughthe Office of the Chief Scientist. EPIDEMICO, Inc.+ EPIDEMICO, Ltd. Authorsare employeesorconsultants.Epidemico iscommercializingaspectsof thissoftwareplatform. Epidemicois a whollyownsubsidiaryof BoozAllenHamilton.
  • 3. Dyspareunia Influenza Diabetes Weight loss Atrial fibrillation Psoriasis COPD COPD IBS Smoking cessation Diabetes Depression Arthritis Arthritis Fibromyalgia Arthritis Depression ED Atrial fibrillation ED 2014 Direct-to-Consumer Spending Includesbroadcast, print, outdoor, and B2B Source: Medical Marketing & Media DTC Report 2015 Osphena Tamiflu Invokana Belviq Xarelto Humira Symbicort Breo Ellipta Linzess Chantix Lyrica Abilify Celebrex Humira Lyrica Xeljanz Latuda Viagra Eliquis Cialis $0M $50M $100M $150M $200M $250M
  • 4. SCIENTISTS: TAVARES G, FAISAL A. PLOS ONE. 2013 PMID: 23843945 JOURNALISTS: REYES J SIMMONS L, FALLON S FOR WIRED, OCTOBER 2013 ARTISTS: KEIFER C & POELL J, ONFORMATIVE
  • 5.
  • 6. Remove false positives (optional) Hypothesis generation Automated processes to identifyevents and clean data Collect public social mediadata Synthesis of information from other sources Acquire Curate Causation StatisticsFilter D A T A P R O C E S S O V E R V I E W
  • 9. Spelling VariationsCombined or invented wordsImplied phrasesInternet Vernacular Translation lost their eyesight seeing weird color seeing weird colour doublevision uldn’t see double vision blind googley eyed blurry vision changes in viscross eyed seeing weird vision change blindness cross visionvisual snow googly eyed seeing doub making me eat like a mouse anorexic st appetite #notevenhungry appetite is nonexistent apetite surpressed didn’t get hungry dont want to eat killed my apetite miss feeling hung killed my appetite can’t eat lost apetite lost my appetite no appetitey lack of apetite stomach small lost teh appetite never hnever want to eat cant eat coucrosseyed blurry apetite surpressed killed my apetite lost apetite lack of apetite lost teh appetite Typos seeing weird color seeing weird colour googley eyed googly eyed #notevenhungry no appetitey making me eat like a mouse never want to eat
  • 10. Visual impairment MedDRA 10047571 Visual impairment SNOMED 397540003 Decreased appetite MedDRA 10061428 Loss of appetite SNOMED 79890006 making me eat like a mouse anorexic st appetite #notevenhungry appetite is nonexistent apetite surpressed didn’t get hungry dont want to eat miss feeling hung killed my appetite can’t eat lost apetite lost my appetite no appetitey lack of apetite stomach small lost teh appetite never hnever want to eat cant eat lost their eyesight seeing weird color seeing weird colour doublevision uldn’t see double vision blind googley eyed blurry vision changes in viscross eyed seeing weird vision change blindness cross visionvisual snow googly eyed seeing doub coucrosseyed blurry killed my apetite
  • 11. P r o t o - A E P o s t s w i t h R e s e m b l a n c e t o A d v e r s e E v e n t s
  • 12. Manually classified to identifyAEs Precision - Positive predictive value Recall - Sensitivity Posts mentioning medical products 57M 380k 68% 88% S Y S T E M P E R F O R M A N C E
  • 13. Methods - PublicTwitterposts - 58products (see appendix) - Feb’14toJul ‘15(18 months) - Geotagged - Englishlanguage DataSource - Time ofdaypatterns - USvs.AU/CA/UK - DTCRxproducts (withTV ads) Design - RateofProto-AEsper1000 Mentions - RateofProto-AEsper1M Twitter users - Poissonmodelsbycountry Statistics - Effectofnon-TV advertising uniformbetweencountries Assumptions M E T H O D S
  • 14. RemoveSpam, news, coupons 67% of posts were spam Consolidate multiplicate copies 35% Excludingliteral duplicates and retweets Keep postswith in-scope geography Keep posts from US, UK, CA, AU Identifiable geographyfor 55% of Proto-AEs Import Twitter Posts All DTCproducts LimittoEnglish Language Only6 posts with unknownlanguage Identify Postswith Proto-AEs Automated classifier Exclude Chantix/Champix DTCadvertising allowed in non-UScountries 1,245,530 408,867 338,263 218,512 11,265 5,535 4,499 D A T A P R O C E S S I N G
  • 15. Tweetsper15MinuteIncrement 0 5,000 10,000 15,000 20,000 25,000 30,000 All Public Twitter Posts Mentions (spam & bots removed) Proto-AEs 0:00 4:00 8:00 12:00 16:00 20:00 24:00 Hour of the Day (local time) Daytime Television Prime TimeEvening News
  • 16. Tweetsper15MinuteIncrement 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000All Mentions TV Discussions Rate of Proto-AEs per 100 Mentions LOESS - Proto-AE Rate per 100 Mentions 0:00 4:00 8:00 12:00 16:00 20:00 24:00 Hour of the Day (local time) Daytime Television Prime TimeEvening News All mentions Television
  • 18.
  • 19.
  • 20. Results 64.8 (60.2, 69.7) 83.7 (72.8, 96.3) 111.5 (92.8, 134.0) 89.0 (78.9, 100.3) Canada Australia United States United Kingdom R A T E S p e r 1 M T w i t t e r U s e r s 18 month study period
  • 21. Results 31.6 (30.0, 33.2) 31.7 (27.5, 36.4) 39.2 (32.0, 48.0) 25.5 (22.7, 28.6) Canada Australia United States United Kingdom R A T E S p e r 1 0 0 0 M e n t i o n s 18 month study period
  • 22. Missing Geotags Only used half of Mentions. Causality Can patients assess reliably? Falsepositives Can we improve precision? DifferingPopulations Tweeters vs. Disease vs. Rx ChoiceofDenominators How mature is our understanding? Whatis the bias amongthosewho allow location disclosure? Define methods to measureprobability of realworld significance Thepopulations to whomthe ads are targeted are likely to be older than most Twitter users. Manualcurationcanimprove positive predictive value. Use different denominatorsto understand the nuances. L I M I T A T I O N S
  • 23. CarrieMike Aranka Ciara Nabarun Wenjie Harold John Robin Chi Sue Kelly Kate Clark Chris Robin Derek Matt Essam Melissa Kyra Nico Lou Carly Lucas
  • 24. @epidemico @WEBRADR demo.medwatcher.org nabarun@epidemico.com C O N T A C T U S
  • 25. No DTC tv ads juxtapid|amyvid|seretide|incivek|incivo|copaxone|saphris|sycrest|avonex gleevec|doxil|aptiom|viread|firmagon|tarceva|kapvay|zmax|aranesp|xenazine tetrabenazine|vantas|vascepa|natroba All products with DTC Cialis Lyrica Gardasil Cymbalta Chantix Champix Abilify Viagra Humira Botox Celebrex Xarelto Enbrel Latuda Advair Seretide Zostavax Victoza Orencia Pradaxa Symbicort Crestor Stelara Breo Ellipta Restasis Januvia Spiriva Xeljanz Incivek Incivo Axiron Copaxone Eliquis Prolia Invokana Farxiga Intuniv Saphris Avonex Anoro Ellipta Gleevec Exelon Patch Exelon Doxil Aptiom Viread Firmagon Tarceva Kapvay Zmax Aranesp Xenazine Tetrabenazine Vantas Vascepa Juxtapid Amyvid Natroba

Editor's Notes

  1. A total of 18,906 ads appeared in the 504-hour sample, including 907 OTC drug ads (4.8%) and 428 prescription (Rx) drug ads (2.3%), which together occupied about 8% of all commercial airtime. Although OTC drug ads were more common, Rx drug ads on average were significantly longer. Our findings suggest that Americans who watch average amounts of television may be exposed to more than 30 hours of direct-to-consumer drug advertisements each year, far surpassing their exposure to other forms of health communication. For every one pharma ad, you will see 6 car ads.
  2. Direct-to-consumer drug ads appeared most frequently during news programs and soap operas and during the middle-afternoon and early-evening hours. J Health Commun. 2004 Nov-Dec;9(6):491-7. Summer 2001 Direct-to-consumer drug advertisements on network television: an exploration of quantity, frequency, and placement. Brownfield ED1, Bernhardt JM, Phan JL, Williams MV, Parker RM.
  3. Mike, Matt, Nico, Melissa, Carly, Essam, Derek, Lucas, Kyra, Lou