e-epidemiology – adapting epidemiological data collection to the 21st century (4 cr3 1100...
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April 12, 2023Christin Bexelius 1
Bexelius C et al.: e-epidemiology – adapting epidemiological data collection to the 21st century This slideshow, presented at Medicine 2.0’08, Sept 4/5th, 2008, in Toronto, was
uploaded on behalf of the presenter by the Medicine 2.0 team Do not miss the next Medicine 2.0 congress on 17/18th Sept 2009
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e-epidemiology – adapting epidemiological data collection to the 21st century
Christin Bexelius, PhD-studentJan-Eric Litton, ProfessorDepartment of Medical Epidemiology and BiostatisticsKarolinska Institutet, Sweden
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Outline
Epidemiology
LifeGene
e-epidemiology
Examples Web Cell phones Interactive Voice Response
Conclusion
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Epidemiology
Cured
Dead
Chronic/currentdisease
time
DiagnosisHealthy
Exposuree.g., smoking, genetics, diet
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Cohort
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Prospective cohort with at least 500,000 individuals Genetically informative sample Entire country
Collection of genetic samples at start
Rapid, repeated collection of environmental and life-style information
A national resource Open to all Harmonized with other international cohorts
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e-epidemiology
e-epidemiology
Web
Digital paper
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Web-based questionnaires
Women’s LifeStyle and Health study 50,000 women Aged 40-59 years Mixed mode; paper and web Response rate 71%
The twin study 43,000 twins, men and women Aged 20-45 years Response rate 50% web only
HPV-study 25,000 women Aged 18-45 years Mixed mode; paper and web Response rate 62%
Prostate cancer 7,000 men with history of
prostate cancer, 55-70 years Genomic and environmental
data Data collection during 1.5 years Mixed mode; paper and web Response rate 77%
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Web-based hearing test
Hearing test in home environment
Java-based
Requires headphones and calibration from reference person
Sensitivity 75% Specificity 96%
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Cell phones – text messaging
Pilot study testing the feasibility of using text messaging in collection of data on influenza vaccination
2400 individuals in the Swedish population, 0-100 years
Low response rate
Feasible for data collection on vaccination status
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Cell phones – Java based questionnaire
Real-time measures of physical activity levels via Java-based questionnaire on cell phones
22 women aged 19-45 years
Comparison to Double Labeled Water (gold standard)
High agreement compared to paper questionnaires
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Real-time data collection through web and IVR Surveillance of acute respiratory
infection through self-report Web-based questionnaires Interactive Voice Response (IVR)
3,500 individuals living in Stockholm county
October 2007 – May 2008
1/3 IVR 2/3 Web
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Conclusion
To conduct large scale epidemiological studies, new effective methods for data collection is needed
Electronic techniques have this potential
The science underlying e-epidemiology adapts the epidemiological data collection to the 21th century
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e-epidemiology – adapting epidemiological data collection to the 21st century
Thank you!
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Questionnaire 2 Mobile average 14 Mobile day 15
Bland Altman plot
Quest 2
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Introduction
Internet and telephone access, Sweden 2007
87%
71%
94% 94%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Internet inhousehold
Broad band Mobilephones
Landlinephones
Source: Statistics’ Sweden and PTS
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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
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Text messaging vs. Telephone Interview
Age group Level of education