Dijital epidemiyoloji

Year 2020, Volume: 18 Issue: 2, 192 - 203, 31.08.2020

Gamze Bayın Donar

https://doi.org/10.20518/tjph.656035

Abstract

Dijital epidemiyoloji, bilimsel bilgiye ve dijital araçlara daha fazla erişim ile gelişen yenilikçi bir bilimsel disiplindir. Belirli toplumlardaki sağlıkla ilgili durumların dağılımının ve sağlığın belirleyicilerinin çevrimiçi platformlar aracılığı ile elde edilmesi ve bu bilginin sağlığı geliştirmek ve hastalığı önlemek için kullanılmasıdır. Bu yeni yaklaşım, halk sağlığıyla ilgili bilgilerin, sağlık hizmeti sistemine dahil olmaları gerekmeden, doğrudan halk tarafından çevrimiçi hizmetleri kullanmaları yoluyla üretildiği fikrine dayanmaktadır. Web arama kayıtları, sohbet odaları, sosyal ağlar, bloglar ve çevrimiçi haber medyası; bu çevrimiçi araçlardandır. Bazı bulaşıcı ve kronik hastalık verileri, bu veri kaynakları aracılığıyla tespit edilebilmekte ve sağlık davranış ve tutumlarının değerlendirilmesi, hastalık salgınlarının erken tespiti gibi birçok epidemiyolojik amaç için kullanılabilmektedir. Yapılan araştırmalar, dünyadaki gerçek hastalık epidemiyolojisine paralel olarak birçok hastalığı ve tedaviyi tahmin etmek için güvenilir bir araç olabileceğine dair artan sonuçlar sunmaktadır. Bu çalışma kapsamında dijital epidemiyoloji kavramına, uygulamalarına, fırsat ve zorluklarına değinilerek, bu alanda yapılan araştırmaların bulgularının sunulması amaçlanmıştır. Böylelikle, bu yeni alanın sağlık politikacılarına, halk sağlığı uzmanlarına, araştırmacılara sağladığı fırsatlar ve getirdiği varsayım ve zorluklar hakkında bir temel oluşturulmaya çalışılmıştır.

Keywords

Epidemiyoloji, dijital epidemiyoloji, halk sağlığı

Digital epidemiology

Abstract

Digital epidemiology is an innovative scientific discipline that develops with greater access to scientific knowledge and digital tools. The distribution of health related situations in certain societies and the determinants of health are obtained through online platforms and is used to improve health and prevent disease. This new approach is based on the idea that public health information is produced directly by the public using online services without having to be included in the health care system. Web search records, chat rooms, social networks, blogs and online news media are among these online tools. Some infectious and chronic disease data can be identified through these data sources and can be used for many epidemiological purposes such as assessment of health behaviors and attitudes, early detection of disease outbreaks. Researches has shown increasing parallel evidence that it can be a reliable tool for predicting many diseases and treatments in line with actual disease epidemiology in the world. In this study, it is aimed to present the findings of the researches in this field by addressing the concept of digital epidemiology, its applications, opportunities and difficulties. In this way, it has been tried to provide a basis for the opportunities and assumptions and difficulties that this new field provides for health politicians, public health experts and researchers.

Keywords

Epidemiology, digital epidemiology, public health

References

• 1. Bailey, L. A., Gordis, L., Green, M. (1994). Reference guide on epidemiology. Reference Manual on Scientific Evidence.
• 2. Eysenbach, G. (2009). Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. Journal of medical Internet research, 11(1), e11.
• 3. International Telecommunication Union. (2011). Measuring the information society 2011. International Telecommunication Union.
• 4. Salathé, M., Bengtsson, L., Bodnar, T. J., Brewer, D. D., Brownstein, J. S., Buckee, C., ... & Vespignani, A. (2012). Digital epidemiology. PLoS computational biology, 8(7), e1002616.
• 5. Brownstein, J. S., Freifeld, C. C., Madoff, L. C. (2009). Digital disease detection—harnessing the Web for public health surveillance. New England Journal of Medicine, 360:21, 2153-2157.
• 6. Hay, S. I., George, D. B., Moyes, C. L., & Brownstein, J. S. (2013). Big data opportunities for global infectious disease surveillance. PLoS medicine, 10(4), e1001413.
• 7. Ekman, A., & Litton, J. E. (2007). New times, new needs; e-epidemiology. European journal of epidemiology, 22(5), 285-292.
• 8. Jessop, L. S. (2015). Use of web-based epidemiology in the investigation of risk factors for common mental disorder (Doctoral dissertation, Cardiff University).
• 9. Salathé, M. (2018). Digital epidemiology: what is it, and where is it going?. Life sciences, society and policy, 14(1), 1.
• 10. Vayena, E. (2015). Ethical Challenges of Big Data in Public HealthEffy Vayena. European Journal of Public Health, 25(suppl_3).
• 11. Roth, J. A., Battegay, M., Juchler, F., Vogt, J. E., & Widmer, A. F. (2018). Introduction to machine learning in digital healthcare epidemiology. Infection Control & Hospital Epidemiology, 39(12), 1457-1462.
• 12. D’Ambrosio, A., Tozzi, A., Gesualdo, F. (2016). Digital epidemiology. Using the internet for population health. How to listen and what can we discoverAngelo D'Ambrosio. European Journal of Public Health, 26:1.
• 13. Salathé, M., & Khandelwal, S. (2011). Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control. PLoS computational biology, 7(10), e1002199.
• 14. Salathé, M., Freifeld, C. C., Mekaru, S. R., Tomasulo, A. F., & Brownstein, J. S. (2013). Influenza A (H7N9) and the importance of digital epidemiology. The New England journal of medicine, 369(5), 401.
• 15. Höhle, M. (2017). A statistician’s perspective on digital epidemiology. Life sciences, society and policy, 13(1), 17.
• 16. Velasco, E. (2018). Disease detection, epidemiology and outbreak response: the digital future of public health practice. Life sciences, society and policy, 14(1), 7.
• 17. Anema, A., Kluberg, S., Wilson, K., Hogg, R. S., Khan, K., Hay, S. I. et al. (2014). Digital surveillance for enhanced detection and response to outbreaks. The Lancet Infectious Diseases, 14:11, 1035-1037.
• 18. White, R. W., Tatonetti, N. P., Shah, N. H., Altman, R. B., & Horvitz, E. (2013). Web-scale pharmacovigilance: listening to signals from the crowd. Journal of the American Medical Informatics Association, 20(3), 404-408.
• 19. Yu, V. L., & Madoff, L. C. (2004). ProMED-mail: an early warning system for emerging diseases. Clinical infectious diseases, 39(2), 227-232.
• 20. Chan, E. H., Brewer, T. F., Madoff, L. C., Pollack, M. P., Sonricker, A. L., Keller, M., et al. (2010). Global capacity for emerging infectious disease detection. Proceedings of the National Academy of Sciences, 107:50, 21701-21706.
• 21. Bengtsson, L., Lu, X., Thorson, A., Garfield, R., Von Schreeb, J. (2011). Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti. PLoS medicine, 8:8, e1001083.
• 22. Signorini, A., Segre, A. M., & Polgreen, P. M. (2011). The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic. PloS one, 6(5), e19467.
• 23. Heymann, D. L., & Rodier, G. (2004). Global surveillance, national surveillance, and SARS. Emerging infectious diseases, 10(2), 173.
• 24. Brownstein, J. S., Freifeld, C. C., Reis, B. Y., Mandl, K. D. (2008). Surveillance Sans Frontieres: Internet-based emerging infectious disease intelligence and the HealthMap project. PLoS medicine, 5:7, e151.
• 25. Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012.
• 26. Barrett-Connor, E., Ayanian, J. Z., Brown, E. R., Coultas, D. B., Francis, C. K., Goldberg, R. et al. (2011). A nationwide framework for surveillance of cardiovascular and chronic lung diseases, Institute of Medicine, Washington DC, USA.
• 27. Freifeld, C. C., Mandl, K. D., Reis, B. Y., & Brownstein, J. S. (2008). HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports. Journal of the American Medical Informatics Association, 15(2), 150-157.
• 28. Google: Google Trends. Available: <http://www.google.com/trends/
• 29. Nuti, S. V., Wayda, B., Ranasinghe, I., Wang, S., Dreyer, R. P., Chen, S. I., & Murugiah, K. (2014). The use of google trends in health care research: a systematic review. PloS one, 9(10), e109583.
• 30. Cervellin, G., Comelli, I., Lippi, G. (2017). Is Google Trends a reliable tool for digital epidemiology? Insights from different clinical settings. Journal of epidemiology and global health, 7:3, 185-189.
• 31. Seifter, A., Schwarzwalder, A., Geis, K., & Aucott, J. (2010). The utility of “Google Trends” for epidemiological research: Lyme disease as an example. Geospatial health, 135-137.
• 32. Bakker, K. M., Martinez-Bakker, M. E., Helm, B., Stevenson, T. J. (2016). Digital epidemiology reveals global childhood disease seasonality and the effects of immunization. Proceedings of the National Academy of Sciences, 113:24, 6689-6694.
• 33. Scheres, L. J. J., Lijfering, W. M., Middeldorp, S., & Cannegieter, S. C. (2016). Influence of World Thrombosis Day on digital information seeking on venous thrombosis: a Google Trends study. Journal of Thrombosis and Haemostasis, 14(12), 2325-2328.
• 34. Hassid, B. G., Day, L. W., Awad, M. A., Sewell, J. L., Osterberg, E. C., & Breyer, B. N. (2017). Using search engine query data to explore the epidemiology of common gastrointestinal symptoms. Digestive diseases and sciences, 62(3), 588-592.
• 35. Google: Google Flu Trends. https://www.google.org/flutrends/about/
• 36. Olson, D. R., Baer, A., Coletta, M. A., Deyneka, L., Gentry, R., Ising, A., ... & Konty, K. J. (2009). Searching for better flu surveillance? A brief communication arising from Ginsberg et al. Nature 457, 1012-1014 (2009).
• 37. Olson, D. R., Konty, K. J., Paladini, M., Viboud, C., & Simonsen, L. (2013). Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales. PLoS computational biology, 9(10), e1003256.
• 38. Cook, S., Conrad, C., Fowlkes, A. L., Mohebbi, M. H. (2011). Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PloS one, 6:8, e23610.
• 39. Malik, M. T., Gumel, A., Thompson, L. H., Strome, T., & Mahmud, S. M. (2011). “Google flu trends” and emergency department triage data predicted the 2009 pandemic H1N1 waves in Manitoba. Canadian Journal of Public Health, 102(4), 294-297.
• 40. Dugas, A. F., Hsieh, Y. H., Levin, S. R., Pines, J. M., Mareiniss, D. P., Mohareb, A., ... & Rothman, R. E. (2012). Google Flu Trends: correlation with emergency department influenza rates and crowding metrics. Clinical infectious diseases, 54(4), 463-469.
• 41. HealthMap. http://www.healthmap.org/
• 42. Brownstein, J. S., Freifeld, C. C. (2007). HealthMap: the development of automated real-time internet surveillance for epidemic intelligence. European communicable disease bulletin, 12:11, E071129-5.
• 43. Dodson, S., Klassen, K. M., McDonald, K., Millard, T., Osborne, R. H., Battersby, M. W., ... & Roney, J. (2016). HealthMap: a cluster randomised trial of interactive health plans and self-management support to prevent coronary heart disease in people with HIV. BMC infectious diseases, 16(1), 114.
• 44. Millard, T., Dodson, S., McDonald, K., Klassen, K. M., Osborne, R. H., Battersby, M. W., ... & Elliott, J. H. (2018). The systematic development of a complex intervention: HealthMap, an online self-management support program for people with HIV. BMC infectious diseases, 18(1), 615.
• 45. InfluenzaNet. http://influenzanet.info/
• 46. Paolotti, D., Carnahan, A., Colizza, V., Eames, K., Edmunds, J., Gomes, G., ... & Van Noort, S. (2014). Web‐based participatory surveillance of infectious diseases: the Influenzanet participatory surveillance experience. Clinical Microbiology and Infection, 20(1), 17-21.
• 47. van Noort, S. P., Codeco, C. T., Koppeschaar, C. E., Van Ranst, M., Paolotti, D., & Gomes, M. G. M. (2015). Ten-year performance of Influenzanet: ILI time series, risks, vaccine effects, and care-seeking behaviour. Epidemics, 13, 28-36.
• 48. Koppeschaar, C. E., Colizza, V., Guerrisi, C., Turbelin, C., Duggan, J., Edmunds, W. J., ... & Paolotti, D. (2017). Influenzanet: citizens among 10 countries collaborating to monitor influenza in Europe. JMIR public health and surveillance, 3(3), e66.
• 49. ProMED-mail. http://www.promedmail.org
• 50. Woodall, J., & Calisher, C. H. (2001). ProMED-mail: background and purpose. Emerging infectious diseases, 7(3 Suppl), 563.
• 51. Madoff, L. C., & Woodall, J. P. (2005). The internet and the global monitoring of emerging diseases: lessons from the first 10 years of ProMED-mail. Archives of medical research, 36(6), 724-730.
• 52. Herman Tolentino, M., Raoul Kamadjeu, M. D., Michael Matters PhD, M. P. H., Marjorie Pollack, M. D., & Larry Madoff, M. D. (2007). Scanning the emerging infectious diseases horizon-visualizing ProMED emails using EpiSPIDER. Adv Dis Surveil, 2, 169.
• 53. Klein, A. Z., Sarker, A., Weissenbacher, D., & Gonzalez-Hernandez, G. (2019). Towards scaling Twitter for digital epidemiology of birth defects. NPJ digital medicine, 2(1), 1-9.
• 54. Hamer, S. A., Curtis-Robles, R., & Hamer, G. L. (2018). Contributions of citizen scientists to arthropod vector data in the age of digital epidemiology. Current opinion in insect science, 28, 98-104.
• 55. Phillips, C. A., Leahy, A. B., Li, Y., Schapira, M. M., Bailey, L. C., & Merchant, R. M. (2018). Relationship between state-level Google online search volume and cancer incidence in the United States: retrospective study. Journal of medical Internet research, 20(1), e6.
• 56. Xie, T., Yang, Z., Yang, S., Wu, N., & Li, L. (2014). Correlation between reported human infection with avian influenza A H7N9 virus and cyber user awareness: what can we learn from digital epidemiology?. International Journal of Infectious Diseases, 22, 1-3.
• 57. Dalla Costa, G., Giordano, A., Romeo, M., Sangalli, F., Comi, G., & Martinelli, V. (2018). Digital epidemiology confirms a latitude gradient of MS in France. Multiple sclerosis and related disorders, 20, 129-131.
• 58. Shariatpanahi, S. P., Jafari, A., Sadeghipour, M., Azadeh-Fard, N., Majidzadeh-A, K., Farahmand, L., & Ansari, A. M. (2017). Assessing the effectiveness of disease awareness programs: Evidence from Google Trends data for the world awareness dates. Telematics and Informatics, 34(7), 904-913.
• 59. Klembczyk, J., Jalalpour, M., Levin, S., Washington, R., Pines, J. M., Rothman, R., & Dugas, A. (2015). Google flu trends: spatial correlation with influenza emergency department visits. Online journal of public health informatics, 7(1).
• 60. Xu, C., Bai, Y., Yang, H., Cao, X., Jia, P., & Wang, Y. (2019). Detecting Lung Cancer Trends Leveraging Real World and Internet Based Data: Digital Epidemiology Study. Available at SSRN 3444431.