e-epidemiology – adapting epidemiological data collection to the 21st century (4 Cr3 1100 Bexelius)

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  • e-epidemiology – adapting epidemiological data collection to the 21st century (4 Cr3 1100 Bexelius)

    1. 1. Bexelius C et al.: e-epidemiology – adapting epidemiological data collection to the 21st century <ul><li>This slideshow, presented at Medicine 2.0’08 , Sept 4/5 th , 2008, in Toronto, was uploaded on behalf of the presenter by the Medicine 2.0 team </li></ul><ul><li>Do not miss the next Medicine 2.0 congress on 17/18th Sept 2009 ( www.medicine20congress.com ) </li></ul><ul><li>Order Audio Recordings (mp3) of Medicine 2.0’08 presentations at http://www.medicine20congress.com/mp3.php </li></ul>
    2. 2. e-epidemiology – adapting epidemiological data collection to the 21st century Christin Bexelius, PhD-student Jan-Eric Litton, Professor Department of Medical Epidemiology and Biostatistics Karolinska Institutet, Sweden
    3. 3. Outline <ul><li>Epidemiology </li></ul><ul><li>LifeGene </li></ul><ul><li>e-epidemiology </li></ul><ul><li>Examples </li></ul><ul><ul><li>Web </li></ul></ul><ul><ul><li>Cell phones </li></ul></ul><ul><ul><li>Interactive Voice Response </li></ul></ul><ul><li>Conclusion </li></ul>
    4. 4. Epidemiology Cured Dead Chronic/current disease time Diagnosis Healthy Exposure e.g., smoking, genetics, diet
    5. 5. Cohort
    6. 6. <ul><li>Prospective cohort with at least 500,000 individuals </li></ul><ul><ul><li>Genetically informative sample </li></ul></ul><ul><ul><li>Entire country </li></ul></ul><ul><li>Collection of genetic samples at start </li></ul><ul><li>Rapid, repeated collection of environmental and life-style information </li></ul><ul><li>A national resource </li></ul><ul><ul><li>Open to all </li></ul></ul><ul><ul><li>Harmonized with other international cohorts </li></ul></ul>
    7. 7. e-epidemiology e-epidemiology Web Digital paper
    8. 8. Web-based questionnaires <ul><li>Women’s LifeStyle and Health study </li></ul><ul><ul><li>50,000 women </li></ul></ul><ul><ul><li>Aged 40-59 years </li></ul></ul><ul><ul><li>Mixed mode; paper and web </li></ul></ul><ul><ul><li>Response rate 71% </li></ul></ul><ul><li>The twin study </li></ul><ul><ul><li>43,000 twins, men and women </li></ul></ul><ul><ul><li>Aged 20-45 years </li></ul></ul><ul><ul><li>Response rate 50% web only </li></ul></ul><ul><li>HPV-study </li></ul><ul><ul><li>25,000 women </li></ul></ul><ul><ul><li>Aged 18-45 years </li></ul></ul><ul><ul><li>Mixed mode; paper and web </li></ul></ul><ul><ul><li>Response rate 62% </li></ul></ul><ul><li>Prostate cancer </li></ul><ul><ul><li>7,000 men with history of prostate cancer, 55-70 years </li></ul></ul><ul><ul><li>Genomic and environmental data </li></ul></ul><ul><ul><li>Data collection during 1.5 years </li></ul></ul><ul><ul><li>Mixed mode; paper and web </li></ul></ul><ul><ul><li>Response rate 77% </li></ul></ul>
    9. 9. Web-based hearing test <ul><li>Hearing test in home environment </li></ul><ul><li>Java-based </li></ul><ul><li>Requires headphones and calibration from reference person </li></ul><ul><li>Sensitivity 75% </li></ul><ul><li>Specificity 96% </li></ul>
    10. 10. Cell phones – text messaging <ul><li>Pilot study testing the feasibility of using text messaging in collection of data on influenza vaccination </li></ul><ul><li>2400 individuals in the Swedish population, 0-100 years </li></ul><ul><li>Low response rate </li></ul><ul><li>Feasible for data collection on vaccination status </li></ul>
    11. 11. Cell phones – Java based questionnaire <ul><li>Real-time measures of physical activity levels via Java-based questionnaire on cell phones </li></ul><ul><li>22 women aged 19-45 years </li></ul><ul><li>Comparison to Double Labeled Water (gold standard) </li></ul><ul><li>High agreement compared to paper questionnaires </li></ul>
    12. 12. Real-time data collection through web and IVR <ul><li>Surveillance of acute respiratory infection through self-report </li></ul><ul><ul><li>Web-based questionnaires </li></ul></ul><ul><ul><li>Interactive Voice Response (IVR) </li></ul></ul><ul><li>3,500 individuals living in Stockholm county </li></ul><ul><li>October 2007 – May 2008 </li></ul><ul><li>1/3 IVR </li></ul><ul><li>2/3 Web </li></ul>
    13. 13. Conclusion <ul><li>To conduct large scale epidemiological studies, new effective methods for data collection is needed </li></ul><ul><li>Electronic techniques have this potential </li></ul><ul><li>The science underlying e-epidemiology adapts the epidemiological data collection to the 21th century </li></ul>
    14. 14. e-epidemiology – adapting epidemiological data collection to the 21st century <ul><li>Thank you! </li></ul><ul><li>[email_address] </li></ul>
    15. 15. Questionnaire 2 Mobile average 14 Mobile day 15 Bland Altman plot Quest 2
    16. 16. Introduction Source: Statistics’ Sweden and PTS
    17. 17. 1636 (76%) 1192 (57%) 1454 (68%) 1055 (44%) 14 (1%) 176 (7%) 344 (14%) 868 (36%) SMS-group TI-group Phone number found Contacted participants Contact established Aborted contacts Drop-outs 2150 (100%) 2400 (100%) Original sample 1009 (47%) Participants 154 (6%) 7 (<1%) 176 (8%) Aborted contacts Drop-outs 187 (8%) Declined contact 183 (9%) Declined contact 1192 (55%) landline 444 (21%) mobile
    18. 18. Text messaging vs. Telephone Interview Age group Level of education

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