Infodemiology, Infoveillance, Twitter- and Google-based Surveillance: The Infovigil System

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Seminar talk at the University of Twente, presenting preliminary infoveillance results, using Twitter and Google

Seminar talk at the University of Twente, presenting preliminary infoveillance results, using Twitter and Google

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  • 1. Associate Professor  Department of Health Policy, Management and Evaluation, University of Toronto; Senior Scientist ,  Centre for Global eHealth Innovation, Division of Medical Decision Making and Health Care Research;  Toronto General Research Institute of the UHN, Toronto General Hospital, Canada [email_address] Gunther Eysenbach MD MPH Gunther Eysenbach MD MPH Infodemiology and Infoveillance Infodemiology and Infoveillance
  • 2. CDC http://www.webcitation.org/5h4wOBpC1 Swine Flu / H1N1 Posible caso de influenza, (C) Hello32020, licensed under CC-by license
  • 3. Count what is countable, measure what is measurable. What is not measurable, make measurable. [Galileo Galilei]
  • 4. The premise
    • “ The Internet has made measurable what was previously immeasurable: The distribution of health information in a population, tracking (in real time) health information trends over time, and identifying gaps between information supply and demand. “
    Eysenbach G. Infodemiology. Proc AMIA Fall Symp 2006
  • 5. “ Infodemiology” the epidemiology of information Describing and analyzing health information & communication patterns (e.g. on the Web) for public health purposes Demand Metrics (search & navigation) Supply Metrics (what’s published) Gunther Eysenbach J Med Internet Res 2009;11(1):e11
  • 6. “ Infodemiology” the epidemiology of information Describing and analyzing information & communication patterns and its relationship to population health status The science of distribution and determinants of disease in populations Epidemiology Public Health Professionals Policy Makers Public Health Interventions Policy Decisions Population Health Status The notion of “infodemiology” G. Eysenbach. Infodemiology. American Journal of Medicine , 2002;113(0):763-765 Information & Communication patterns
  • 7. Infoveillance = “using infodemiology data for surveillance purposes” Eysenbach G Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet J Med Internet Res 2009;11(1):e11 URL: http://www.jmir.org/2009/1/e11 doi: 10.2196/jmir.1157
  • 8. Infovigil - an infoveillance prototype Centre for Global eHealth Innovation, Toronto, in collaboration with OAHPP Infovigil Aggregator/ Datamining/ Vizualisation Public, Clinicians, Epidemiologists Websites Filter Keywords / Concepts of Interest Online Questionnaires
  • 9. http://www.jmir.org/2009/1/e11/
  • 10. Infodemiology * Application Areas
    • detecting and quantifying disparities in health information availability
    • identifying and monitoring of public health relevant publications on the Internet
    • measure information diffusion and knowledge translation, and tracking the effectiveness of health marketing campaigns.
    • Monitoring health-related behavior
    • Syndromic surveillance
    * science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy.
  • 11. “ Infodemiology” the epidemiology of information Describing and analyzing health information & communication patterns (e.g. on the Web) Demand Metrics Supply Metrics Gunther Eysenbach Infodemiology: the epidemiology of (mis)information American Journal of Medicine , 2002;113(0):763-765
  • 12. Information prevalence (plotted against disease incidence)
  • 13. Information occurance ratios: Linguistic/cultural differences and inequalities in health information availability
  • 14. Co-occurance ratios as metric for knowledge translation/uptake
  • 15. Methodological Challenges
    • Search engines such as Google provide estimates (vary from day-to-day or hour-to-hour)
    • “ hits” (word occurrences) instead of semantic occurrences
    • => Infovigil infodemiology system: Dedicated system to collect metrics across different search engines and continuously
  • 16. http://www.webcitation.org/5h43hTpxz
  • 17. Swineflu vs H1N1 terminology on Twitter
  • 18. “ Infodemiology” the epidemiology of information Describing and analyzing health information & communication patterns (e.g. on the Web) Demand Metrics Supply Metrics Gunther Eysenbach Infodemiology: the epidemiology of (mis)information American Journal of Medicine , 2002;113(0):763-765
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  • 22. Drawback of surveys
    • Not real-time data
    • Measure what people say they do (rather than measuring what people actually do)
    • Biases (social desirability bias etc.)
    • Expensive
  • 23. A “demand metric”: Health-related searches on the web Eysenbach G, Köhler C. What is the Prevalence of Health-related Searches on the World Wide Web? Qualitative and Quantitative Analysis of Search Engine Queries on the Internet. Proc AMIA Annu Fall Symp ; 2003: 225-229 Eysenbach G, Köhler C. Health-Related Searches on the Internet JAMA , Jun 2004; 291: 2946. Method also used by e.g.: Cobb NK, Graham AL Characterizing Internet Searchers of Smoking Cessation Information J Med Internet Res 2006;8(3):e17 <URL: http://www.jmir.org/2006/3/e17/>
  • 24. Breakdown of health-related search engine queries by category Eysenbach G, Köhler C. Health-Related Searches on the Internet JAMA 2004; 291:2946
  • 25. Daily searches on Google.ca for “flu” or “flu symptoms” 2003/2004 2007/2008 2004/2005 2005/2006 2006/2007 Impressions (searches)
  • 26. Why Syndromic Surveillance?
    • “ The fundamental objective of syndromic surveillance is to identify illness clusters early, before diagnoses are confirmed and reported to public health agencies, and to mobilize a rapid response, thereby reducing morbidity and mortality.” (Kelly J. Henning, MMWR 2004, http://www. webcitation .org/5fde6JBKx )
  • 27. “ before diagnoses are confirmed” In reality, many syndromic surveillance systems tap into clinical data - we know little what is going on at people’s home when they first notice symptoms, before they see/call a doctor
  • 28. “ I have an idea -- let’s start thinking outside the box”
  • 29. What is the first thing many people do before they see a doctor (or call Telehealth Ontario), or take OTC medicines?
  • 30. Before seeing/calling a doctor many people do this:
  • 31. Confirmed diagnoses Preliminary diagnoses Orders Laboratory tests Physician office visits EMS activity ED visits, Hospitalizations Incubation Period Exposure Symptom onset Health behavior Information seeking (web clickstream, etc.) Over the counter and prescription medications School and work absenteeism Nurse triage telephone calls Health and healthcare behaviors Healthcare encounter Surveillance of citizen self-assessments & status updates Initial findings Data types Timeline Biological sensors Syndromic Surveillance Diagnostics Final diagnosis Medical evaluation Additional evaluation Adapted from Mandl 2007
  • 32. Two examples Demand-based infoveillance: Google Supply-based infoveillance: Twitter
  • 33. Demand-based infoveillance Google searches
  • 34. Infodemiology: Tracking demand for health information for syndromic surveillance Eysenbach G. Infodemiology. Proc AMIA Fall Symp 2006
  • 35. What is the correlation between
    • Traditional flu surveillance metrics (per week)
      • ILI: Influenza-like illness (ILI) consultation rates (rate per 1.000 visits)
      • Total number of influenza tests performed in testing laboratories
      • Number of positive lab tests (“cases”)
    • Google metrics (searches + ad clicks)
      • Impressions (ad views, correlates with number of flu-related search queries entered)
      • Click-thru-rate
      • Clicks on “do you have the flu?” ad
    Eysenbach G. Infodemiology. Proc AMIA Fall Symp 2006
  • 36. Correlation clicks vs next wk cases Eysenbach G. Infodemiology. Proc AMIA Fall Symp 2006
  • 37. Correlation sentinel physicians ILI reports vs next wk cases Eysenbach G. Infodemiology. Proc AMIA Fall Symp 2006
  • 38. Eysenbach G. Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet. J Med Internet Res 2009;11(1):e11 URL: http://www.jmir.org/2009/1/e11 doi: 10.2196/jmir.1157
  • 39. J Ginsberg et al. Nature 2009 doi:10.1038/nature07634 A comparison of model estimates for the mid-Atlantic region (black) against CDC-reported ILI percentages (red), including points over which the model was fit and validated.
  • 40. Wilson K, Brownstein JS. Early detection of disease outbreaks using the Internet. CMAJ 2009. DOI: 10.1503/cmaj.1090215
  • 41. Supply-based infoveillance Example: Twitter status updates
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  • 55. The next step: Involving consumers, guiding consumer actively to questionnaires to solicit additional information
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  • 59. Syndromic Surveillance 2.0 Actively involving the public
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  • 64. Internet (Twitter/Google)-based surveillance esp. appropriate for
    • Symptoms which are noticable for the consumer (and bothersome enough to microblog about it or search information)
    • Conditions with long lag time between preclinical symptoms and presentation at Emergency Department / Physician
    • Symptoms in younger people (18-55 yrs)
    • Can obtain further information (ask about other symptoms) through rapid online questionnaire and/or direct consumer to interventions
  • 65. Real outbreak or “epidemic of fear”? Public Health response to suspected outbreak Public Health response to surge in information needs Other traditional surveillance data Infoveillance Data
    • Even in the absence of any predictive value
    • for outbreaks data can be used:
    • -to improve health communication
    • Learn about population behavior + knowledge
    • Build consumer health vocabulary
    Real epidemic Epidemic of fear
  • 66. Infoveillance
    • Applicable for infectious diseases
    • But also for chronic conditions (monitor misinformation, consumer information needs, information gaps)
  • 67. Questions I have
    • Partners? (you?)
    • Funders?
    • Application Areas: For which conditions (concepts) is “infoveillance” useful?
      • Which keywords/syndromes should we monitor?
      • What further information should we gather (rapid questionnaires)?
      • What are the interventions we should consumers guide to?
  • 68. “ Your proposal is innovative. Unfortunately, we won’t be able to use it because we never tried it’s something before.”
  • 69. Infodemiology * Application Areas
    • detecting and quantifying disparities in health information availability
    • identifying and monitoring of public health relevant publications on the Internet
    • measure information diffusion and knowledge translation, and tracking the effectiveness of health marketing campaigns.
    • Monitoring health-related behavior
    • Syndromic surveillance
    * science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy.
  • 70. Thanks for your attention !
  • 71. Associate Professor  Department of Health Policy, Management and Evaluation, University of Toronto; Senior Scientist ,  Centre for Global eHealth Innovation, Division of Medical Decision Making and Health Care Research;  Toronto General Research Institute of the UHN, Toronto General Hospital, Canada [email_address] www.jmir.org Twitter:eysenbach Gunther Eysenbach MD MPH Gunther Eysenbach MD MPH Thank you! Thank you !