Eysenbach: Infodemiology and Infoveillance

Eysenbach: Infodemiology and Infoveillance - Presentation Transcript

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

“ 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

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

“ Infodemiology” the epidemiology of information Describing and analyzing health information & communication patterns (e.g. on the Web) for public health purposes Demand Metrics Supply Metrics Gunther Eysenbach Infodemiology: the epidemiology of (mis)information American Journal of Medicine , 2002;113(0):763-765

Gunther Eysenbach Infodemiology: the epidemiology of (mis)information American Journal of Medicine , 2002;113(0):763-765

What is published in the medical peer-reviewed literature What is published on the Internet Comparison: Gaps indicate knowledge translation problems

Supply Metrics in Public Health Policy Objectives http://www.healthypeople.gov

What is published on the Internet / in public media What is published on the Internet / in public media time Identify emerging disease outbreaks

Global Public Health Intelligence Network (GPHIN) GPHIN monitors global media sources (such as news wires and web sites ), then gathers and disseminates relevant information on such topics as disease outbreaks, infectious diseases, contaminated food and water, bio-terrorism and exposure to chemical and radio-nuclear agents, and natural disasters. It also monitors issues related to the safety of products, drugs and medical devices.

“ 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

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

Canadians don’t use the web for xxx

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/>

Breakdown of health-related search engine queries by category Eysenbach G, Köhler C. Health-Related Searches on the Internet JAMA 2004; 291:2946

Daily searches on Google.ca for “flu” or “flu symptoms” 2003/2004 2007/2008 2004/2005 2005/2006 2006/2007 Impressions (searches) Eysenbach G. Infodemiology. Proc AMIA Fall Symp 2006

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

Infodemiology: Tracking demand for health information for syndromic surveillance Eysenbach G. Infodemiology. Proc AMIA Fall Symp 2006

Correlation clicks vs next wk cases Eysenbach G. Infodemiology. Proc AMIA Fall Symp 2006

Correlation sentinel physicians ILI reports vs next wk cases Eysenbach G. Infodemiology. Proc AMIA Fall Symp 2006

Real outbreak or “epidemic of fear”? Public Health response to suspected outbreak Public Health response to surge in information needs Other traditional surveillance data (ED data etc.) 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

Infovigil project An infoveillance prototype system at the the Centre for Global eHealth Innovation

Infovigil - an infoveillance prototype Centre for Global eHealth Innovation, Toronto % Increase Search Activity for Flu-Related Information over last week

Help About Infovigil

Technologies involved

Concepts of interest (COIs) need to be selected (e.g. influenza)

Construct search queries for COIs [multilingual] or : Natural Language Processing (map free text user queries to concepts)

IP geolocation software: Longitude, Latitude of searcher

Visualization: GIS, GeoBoost Mashup, Google Earth KML: %increase/decrease in searches over time, animated with timeline

Spatio-temporal analysis to detect anomalies

Accuracy of IP geolocation

98% accuracy on country-level

Within-country accuracy:

Source: GeoIP, http://www.webcitation.org/5TIPdsZ5m

Data sources

Search engines

Problem: Where to get search data from? (Search engines not forthcoming with data) Gonzales vs Google Inc. Motion to Compel. Jan 18, 2006 http://www.webcitation.org/1142396222389927 The U.S. Department of Justice filed a motion in federal court seeking a court order that would compel search engine company Google, Inc. to turn over “a multi-stage random sample of one million URL’s” from Google’s database, and a computer file with “the text of each search string entered onto Google’s search engine over a one-week period (absent any information identifying the person who entered such query.”

Data sources

Search engines

Let users (volunteers) install browser extensions, reporting search queries to data analysis centre

Privacy issues

Volunteer bias

Hawthorne effect

Data sources

Search engines

Let users (volunteers) install browser extensions, reporting search queries to data analysis centre

Privacy issues

Volunteer bias

Hawthorne effect

Feed data from collaborating websites into a data center

Infovigil consortium - an infoveillance prototype Centre for Global eHealth Innovation, Toronto within-site search engines Infovigil™ Aggregator/ Datamining/ Vizualisation Interested to participate with your site? Email geysenba@gmail.com XML (aggregate search reports, log files) http://www.google.com/search? q=flu &hl=en &start=10 &num=10 &output=xml &client=google-csbe &cx=00255077836266642015:u-scht7a-8i http://www.infovigil.com/search? q=flu (…)

Medicine 2.0 Congress Toronto, Sept 4-5 th , 2008 www.medicine20congress.com

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 Interested to participate in Infovigil with your site? Email geysenba@gmail.com [email_address] Gunther Eysenbach MD MPH Gunther Eysenbach MD MPH Thank you! Thank you !

Questions / Discussion points