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Internet2 and Public Health Surveillance

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International Partnerships and Public Health update to the Internet2 Fall Member meeting, Atlanta, GA USA on Sunday October 31st, 2010.

International Partnerships and Public Health update to the Internet2 Fall Member meeting, Atlanta, GA USA on Sunday October 31st, 2010.


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  • Photo credit: Unknown!
  • >30 new infectious diseases identified since 1973Figure 1. Geographical distribution of a selected subset of outbreaks confirmed by the World Health Organization (WHO) and reported in the “Disease Outbreak News” reports, 1996-2009. Points mark the reported origin of the outbreak, or if unknown, where there were the highest reported morbidity and mortality rates.
  • (1) Delay at any point will delay response(2) Infectious diseases can travel at the speed of an airlinerSensitivity analysis was conducted where the hazard after a certain cutoff year relative to the hazard before that cutoff year. Was computed This was done for each year from 1997 to 2008, and found that the hazard ratio actually already starts climbing in 2003, which is the year that SARS broke out. So the experience from SARS alone may have provided the stimulus to bring about changes in reporting diligence. In fact, it was SARS that finally put into action long talked of plans for revising the IHR.
  • It is not necessarily lack of information… we have a lot of information… rather, can we put the information into intelligence (or context) in a timely manner?Multiple streams include the following- say something about why you need to stitch multiple sources together...How do you put an event into context? And, where is the next disease is going to emerge from... that is the holly grail in this business... Dead crows on the streets of NYCPepto-bismol disappearing from the shelves of grocery storesPhone calls from citizens and the media to the health department about increased absenteeism from schools and businessesIncreased Internet search hits on certain terms per weekSidebar: 5/50 rule, in 5 years time, 50% of all content will be user-generated: (Reference: The Podshowby Ron Bloom (http://www.ronbloom.com/?p=11) 60% content has geo-spatial and temporal aspects… Image Sources:Wikipedia: http://www.citris-uc.org/system/files/imce-u10/Wikipedia-logo.png Blogger: http://z.about.com/d/weblogs/1/5/V/-/-/-/BloggerHomePage.PNG OpenMRS: http://ruddzw.files.wordpress.com/2007/05/openmrs_osx.png Remote Sensing: http://www.medscape.com/content/2000/00/41/47/414717/art-e0603.01.fig2.jpg Cell phone/iPhone; http://healthinformaticsblog.files.wordpress.com/2008/03/iphone-denticon-patient-thumb.jpgWhoIsSick.org: http://gmapsmania.googlepages.com/whosickgmm.JPG
  • Automated aggregation of data from multiple sourcesApplying artificial intelligence to data filtering/selectionLeverage non-professional networksAllow members of community to populate data> 600 alerts per day from 22 sources from over 20,000 websitesAlerts in 201 countries & territories and 175 disease categoriesSeven languages – English, French, Spanish, Russian, Chinese, Portuguese, Arabic
  • Data can also be leverage for epidemiological studiesInternationalization  LocalizationUseful because of a new context for public health issuesFurther supports English report biasgreater population, numbers of media outlets, public health resources, and availability of electronic communication infrastructure.
  • In 2002, mobile subscribers overtook fixed line subscribers worldwide and this occurred across geographic regions, socio-demographic criteria (gender, income, age) or economic criteria such as GDP per capita1FeldmannV: Mobile overtakes Internet: Implications for Policy and Regulation. International Telecommjunications Union 2003:1-39 [http://www.itu.int/osg/spu/ni/mobileovertakes/Resources/Mobileovertakes_Paper.pdf]. last accessed 13 March 2009.Access to mobile networks available to over 90% of world population 143 countries offer 3G serviceshttp://www.itu.int/net/pressoffice/press_releases/2010/39.aspx
  • Platform for collecting information and communicating via short message service (SMS). The system allows a central data hub to be deployed using a laptop and an inexpensive cell phone. Users can then send “broadcast” messages to groups of people, including basic forms requesting information, and collect the responses.
  • Transcript

    • 1. Office of Surveillance, Epidemiology, and Laboratory Services Public Health Surveillance Program Office Digital Disease Detection: Novel Approaches in Public Health Surveillance Taha A. Kass-Hout, MD, MS Deputy Director for Information Science and BioSense Program Manager Division of Healthcare Information (DHI) Public Health Surveillance Program Office (PHSPO) Office of Surveillance, Epidemiology, and Laboratory Services (OSELS) Centers for Disease Control & Prevention (CDC) Any views or opinions expressed here do not necessarily represent the views of the CDC, HHS, or any other entity of the United States government. Furthermore, the use of any product names, trade names, images, or commercial sources is for identification purposes only, and does not imply endorsement or government sanction by the U.S. Department of Health and Human Services. The International meeting of Internet2 Renaissance Waverly Hotel Sunday October 30th, 2010: 13:30–14:15 Atlanta, GA (USA)
    • 2.   The Public Health Surveillance Challenge   Public Health Surveillance and the Internet– the State of the Art   The Possibilities for Internet2
    • 3.   Surveillance is a global challenge that knows no borders   The importance of timely detection   Limitations of traditional reporting systems   Hierarchical lines of reporting   Variance across different countries   Multitude of potential data sources   Real-world lessons from SARS and H1N1
    • 4. WHO reported outbreaks, 1996-2009 n=398  
    • 5.   Median of 12 [95% CI, 9 —18] days between suspected and confirmed reports   Countries with a high GDP tended to have shorter lags (Pearson correlation -0.4; 95% CI, -0.6 — -0.2) Brownstein JS, Freifeld CC, Chan EH, Keller M, Sonricker AL, Mekaru SR, Buckeridge DL. Information Technology and Global Surveillance of Cases of 2009 H1N1 Influenza. New England Journal of Medicine 2010;362(18):1731-1735.
    • 6.   Event-based   ad-hoc unstructured reports issued by formal or informal sources…   Indicator-based   number of cases, rates, proportion of strains… Adapted from T. Kass-Hout, 2009
    • 7.   Waiting for confirmed laboratory results is potentially hazardous   Surveillance is increasingly relying on informal sources of data   This creates a data deluge with lots of noise   Fusion of multiple channels of data which varying levels of confidence   Collaboration and expert opinion is as important as “pure” data   Existing tools may generate “false alarms”Adapted from J. Brownstein, 2010
    • 8. J. Brownstein et al. 2010. New England Journal of Medicine.
    • 9.   Professional Social Networks   GPHIN   ProMed   Geo-Sentinel   Mechanical Turk   Crawling, Aggregation, Crowd Sourcing and Networking   HealthMap   Ushahidi   Facebook   Mobile Applications   EpiSurveyor   Frontline SMS   Geo Alerting   Outbreak MD
    • 10. Automated  Extrac.on  Automated  Feed   Community  Input     Added Value (1) Discovery of new content (2) Two-way contextualization (3) Validation studies
    • 11. www.healthmap.org
    • 12. www.flu.gov
    • 13. Adapted from Dzenowagi, WHO, 2005 and T. Kass-Hout, 2009 |   ITU estimates two billion people online by end 2010   Access to mobile networks available to over 90% of world population   143 countries offer 3G services http://www.itu.int/net/pressoffice/press_releases/2010/39.aspx
    • 14. http://www.mission4636.org
    • 15. www.frontlinesms.com Outbreaks Near Me App: H1N1: iPhone submissions vs CDC sentinel surveillance
    • 16.   Can’t mine   all possible sources   in all languages   all data types   Delay required for searching, curating and processing   Massive bandwidth and processing requirements   Resource limited process (both machine and human)
    • 17. Automated healthcare data (laboratory, immunization, notifiable conditions, syndromic, personal health records, …)
    • 18.   Enlisting current Internet2 partners to collaborate on surveillance efforts   Explore new data types and analytical methods enabled by Internet2   Use Internet2 Infrastructure to increase   Bandwidth and processing capabilities   Performance and Operational Reliability   Support collaborative Public Health Surveillance Research   Increase the power of existing public health networks
    • 19.   John Brownstein, PhD   Clark Freifeld, MS   Seth Foldy, MD, MPH   Sam Groseclose, DVM, MPH   James Buehler, MD   Walton (John) Page, BS, BA
    • 20. Taha A. Kass-Hout, MD, MS Deputy Director for Information Science and BioSense Program Manager Division of Healthcare Information (DHI) Public Health Surveillance Program Office (PHSPO) Office of Surveillance, Epidemiology, and Laboratory Services (OSELS) Centers for Disease Control & Prevention (CDC) Any views or opinions expressed here do not necessarily represent the views of the CDC, HHS, or any other entity of the United States government. Furthermore, the use of any product names, trade names, images, or commercial sources is for identification purposes only, and does not imply endorsement or government sanction by the U.S. Department of Health and Human Services. Thank You!