E-epidemiology - adapting epidemiological data collection to the 21st century
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Abstract
We aim to present the prerequisites to use cell phones and SMS as a means of contact in LifeGene. LifeGene (www.lifegene.se) is a Swedish population-based prospective resource on lifestyle, health and genomics. The cohort will include at least 500.000 Swedes, starting with index person 18-49 years old and their household. This cohort will regularly (annually) be contacted to assess in depth exposure information through electronic means (web, cell phones, etc). There will be regular surveillance of morbidity through population based registers, and study participants will be contacted to assess symptoms for outcomes not typically reported in national health registers or ascertained via hospital discharge (e.g. acute infections, psychiatric disorders and allergy).
The rapid transformation of the society during the last decade has altered the possibilities for data collection in epidemiological studies (1-3). The modern society constantly generate new trends, and people change site and habits more often then twenty and even ten years ago. The traditional approaches, face-to-face interviews and paper questionnaires, are restricted to fixed sites and are costly in terms of time-delay and administration (4, 5). Introduction of new information and communication technologies such as the web, Interactive Voice Response (IVR) and cell phones hold great potential as innovative tools for medical data collections (6-9). E-epidemiology is the science underlying the acquisition, maintenance and application of epidemiological knowledge and information using digital media, and hold the potential to adapt medical data collection to a modern society (10). Using digital technologies, self-reported data through questionnaires can be replaced by objective measurements resembling data gained at health care services. The high access of cell phones gives a possibility to use the device for smaller questionnaires. Many new mobile phones include GPS (Global Positioning System) and JAVA technology, which gives ever more opportunities for real-time data collection.
Sweden has today among the highest Internet and cell phone penetration in the world, making the population suitable for introducing new technologies in data collection (11). The Department of Medical Epidemiology and Biostatistics (MEB) at Karolinska Institutet in Sweden has since 2001 designed and evaluated several studies using e-epidemiology. In 2003, the first large scale web-based study including 47,859 women in the age group 41-60 was conducted (12). Today, the department has used the Internet for several large scale population-based studies, including an Internet based-hearing test and a surveillance system utilizing IVR and a web-based application. Experiences of using cell phones include sending SMS for identifying influenza vaccination coverage among the Swedish population and daily self-report of physical activity level trough a JAVA based questionnaire. In a previous study, we have studied the feasibility of using SMS in a study population between 18-49 years. Our results indicate that no bias is introduced when using SMS compared to telephone interviews and that data collected is comparable, based on socio-economic and demographic features gained from Swedish population based registers.
1. Dillman D. Presidential Address: Navigating the Rapids of Change: Some Observations on Survey Methodology in the Early Twenty-First Century. Public Opin Q 2002;66:473-494.
2. Couper MP. Technology Trends in Survey Data Collection. Social Science Computer Review 2005;23:486-501.
3. Kempf AM, Remington PL. New challenges for telephone survey research in the twenty-first century. Annu Rev Public Health 2007;28:113-26.
4. Siemiatycki J. A comparison of mail, telephone, and home interview strategies for household health surveys. Am J Public Health 1979;69:238-45.
5. Voigt LF, Koepsell TD, Daling JR. Characteristics of telephone survey respondents according to willingness to participate. Am J Epidemiol 2003;157:66-73.
6. Sax LG, SK; Bryant, AN. Assessing response rates and nonresponse bias in Web and Paper surveys. Reaserch in Higher Education 2003;44:409-432.
7. Mundt JC, Moore HK, Bean P. An interactive voice response program to reduce drinking relapse: A feasibility study. J Subst Abuse Treat 2006;30:21-9.
8. Fricker S, Galesic M, Tourangeau R, Yan T. An Experimental Comparison of Web and Telephone Surveys
10.1093/poq/nfi027. Public Opin Q 2005;69:370-392.
9. Brick JM, Brick PD, Dipko S, Presser S, Tucker C, Yuan Y. Cell phone survey feasibility in the U.S.: Sampling and calling cell numbers versus Landline numbers. Public Opin Q 2007;71:23-39.
10. Ekman A, Litton JE. New times, new needs; e-epidemiology. Eur J Epidemiol 2007;22:285-92.
11. ITU/UNCTAD 2007 World Information Society report: Beyond WSIS. In: (ITU) ITU, ed. Geneva: International Telecommunication Union (ITU), 2007:1-175.
12. Ekman A, Dickman PW, Klint A, Weiderpass E, Litton JE. Feasibility of Using Web-based Questionnaires in Large Population-based Epidemiological Studies. Eur J Epidemiol 2006;21:103-11.
The rapid transformation of the society during the last decade has altered the possibilities for data collection in epidemiological studies (1-3). The modern society constantly generate new trends, and people change site and habits more often then twenty and even ten years ago. The traditional approaches, face-to-face interviews and paper questionnaires, are restricted to fixed sites and are costly in terms of time-delay and administration (4, 5). Introduction of new information and communication technologies such as the web, Interactive Voice Response (IVR) and cell phones hold great potential as innovative tools for medical data collections (6-9). E-epidemiology is the science underlying the acquisition, maintenance and application of epidemiological knowledge and information using digital media, and hold the potential to adapt medical data collection to a modern society (10). Using digital technologies, self-reported data through questionnaires can be replaced by objective measurements resembling data gained at health care services. The high access of cell phones gives a possibility to use the device for smaller questionnaires. Many new mobile phones include GPS (Global Positioning System) and JAVA technology, which gives ever more opportunities for real-time data collection.
Sweden has today among the highest Internet and cell phone penetration in the world, making the population suitable for introducing new technologies in data collection (11). The Department of Medical Epidemiology and Biostatistics (MEB) at Karolinska Institutet in Sweden has since 2001 designed and evaluated several studies using e-epidemiology. In 2003, the first large scale web-based study including 47,859 women in the age group 41-60 was conducted (12). Today, the department has used the Internet for several large scale population-based studies, including an Internet based-hearing test and a surveillance system utilizing IVR and a web-based application. Experiences of using cell phones include sending SMS for identifying influenza vaccination coverage among the Swedish population and daily self-report of physical activity level trough a JAVA based questionnaire. In a previous study, we have studied the feasibility of using SMS in a study population between 18-49 years. Our results indicate that no bias is introduced when using SMS compared to telephone interviews and that data collected is comparable, based on socio-economic and demographic features gained from Swedish population based registers.
1. Dillman D. Presidential Address: Navigating the Rapids of Change: Some Observations on Survey Methodology in the Early Twenty-First Century. Public Opin Q 2002;66:473-494.
2. Couper MP. Technology Trends in Survey Data Collection. Social Science Computer Review 2005;23:486-501.
3. Kempf AM, Remington PL. New challenges for telephone survey research in the twenty-first century. Annu Rev Public Health 2007;28:113-26.
4. Siemiatycki J. A comparison of mail, telephone, and home interview strategies for household health surveys. Am J Public Health 1979;69:238-45.
5. Voigt LF, Koepsell TD, Daling JR. Characteristics of telephone survey respondents according to willingness to participate. Am J Epidemiol 2003;157:66-73.
6. Sax LG, SK; Bryant, AN. Assessing response rates and nonresponse bias in Web and Paper surveys. Reaserch in Higher Education 2003;44:409-432.
7. Mundt JC, Moore HK, Bean P. An interactive voice response program to reduce drinking relapse: A feasibility study. J Subst Abuse Treat 2006;30:21-9.
8. Fricker S, Galesic M, Tourangeau R, Yan T. An Experimental Comparison of Web and Telephone Surveys
10.1093/poq/nfi027. Public Opin Q 2005;69:370-392.
9. Brick JM, Brick PD, Dipko S, Presser S, Tucker C, Yuan Y. Cell phone survey feasibility in the U.S.: Sampling and calling cell numbers versus Landline numbers. Public Opin Q 2007;71:23-39.
10. Ekman A, Litton JE. New times, new needs; e-epidemiology. Eur J Epidemiol 2007;22:285-92.
11. ITU/UNCTAD 2007 World Information Society report: Beyond WSIS. In: (ITU) ITU, ed. Geneva: International Telecommunication Union (ITU), 2007:1-175.
12. Ekman A, Dickman PW, Klint A, Weiderpass E, Litton JE. Feasibility of Using Web-based Questionnaires in Large Population-based Epidemiological Studies. Eur J Epidemiol 2006;21:103-11.
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