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- 5 June 2013
See also Bioethics | Finding medical / health care statistics online | Mashups in medicine | Privacy in social networks | Text-mining | Use of social media in sexual partner notification
Clinical surveillance (also known as syndromic surveillance) is the monitoring and examination of changes, big or small, in populations of people as seen through online health data. Syndromic surveillance is the analysis of medical datasets to detect or anticipate disease outbreaks (or pandemics) within a given population. According to the US Centers for Disease Control "...'syndromic surveillance' applies to health-related data that precedes diagnosis and signals a sufficient probability of a case or an outbreak to warrant further public health response... though historically syndromic surveillance has been utilized to target investigation of potential cases, its utility for detecting outbreaks associated with bioterrorism is increasingly being explored by public health officials." Surveillance of clinical information can also be used in economic analysis, and adverse event or drug reporting. Any information that provides predictive value in the clinical is potentially useful for evidence-based decision-making, policy development and health services delivery. Traditional techniques for detecting changes in a given population have focussed on data stored in laboratory or epidemiological databases, public records and other health information sources. In 2006, Canadian informatician Gunther Eysenbach, University of Toronto, suggested that the use of online search queries can be used as indicators of increased flu incidence, and expanded on the idea of infodemiology and infoveillance in the Journal of Medical Internet Research in 2009. Social media tools such as Facebook and Twitter are increasingly used around the world to follow trends in health, and to track emerging diseases.
Tracking diseases via web
- FluDetector was developed by the Intelligent Systems Laboratory at the University of Bristol, and analyzes the content of twitter feeds. It provides time-sensitive information regarding flu rates in the United Kingdom. In 2011, Eysenbach analyzed Twitter content during the H1N1 outbreak in 2009 and to assess its utility in clinical surveillance. Eke has used Twitter to track dental pain.
- Google Flu Trends identifies flu-related searches on Google as a measure of flu activity in a specific geographic area. It was reported in Nature, that Google's flu findings preceded the Centers for Disease Control and Prevention (CDC) data (collected through traditional methods) by 1 to 2 weeks. An in-depth analysis of influenza-related blog content identified related findings.
- Influenzanet is a surveillance system that finds self-reports of flu symptoms via online questionnaires. In 2011, ten countries participated in a European survey via each country’s partner site e.g., Gripenet in Portugal, Influweb in Italy and Flusurvey in the United Kingdom.
- Sickweather.com is the first consumer-facing product to take a stab at tracking diseases through social media.
Pros and cons
There are a number of advantages of Internet-based surveillance methods such as easier accessibility, timeliness and low costs associated with data collection. The challenges of using online surveillance are the potential for bias and concerns with the ethics of gathering information about someone's health without their knowledge. Participants are also unable to opt out of data gathering as it often involves mining information about them from their use of online websites and search tools. Researchers are debating whether traditional statistical methods should be used in social media or whether new approaches should be developed. Even though the analysis of social media content has been happening for a while now, there are a number of challenges in collating “unstructured texts” in blogs and Twitter, especially with respect to its validity and reliability.
Mashups in surveillance
Mashups are hybrid tools that take the functionality of two information sources, merge certain aspects of each, to create a novel third source (Cho, 2007). The ability to create mashups has provided some interesting opportunities for clinical surveillance. Mapping data is combined (using the Google maps API) with epidemiological information from the Internet and social media sources. Healthmap tracks emerging diseases in real time using multiple data sources, mapping them online. This information is also shared via the organization’s Twitter feed. Other examples include the Avian flu mashup (from Nature) that tracks spread of diseases and WhoisSick? for tracking symptoms by area.
Another possibility for social media is the ability to rapidly share information with large numbers of people following identification of disease outbreaks by clinical surveillance. The US CDC reported on the use of social media as a communication tool during the 2009 H1N1 epidemic.
Surveillance information & websites
- Bavington B. Core competencies for public health in Canada: an assessment and comparison of determinants of health content. St. Francis Xavier University. National Collaborating Centre for Determinants of Health (NCCDH), 2012.
- Brownstein JS, Freifeld CC, Madoff LC. Digital disease detection--harnessing the web for public health surveillance. N Engl J Med. 2009;360(21):2153-57.
- Centers for Disease Control and Prevention. H1N1 web and social media metrics: cumulative data report. 2009.
- Chew C, Eysenbach G. Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PLoS One. 2010;5(11):e14118.
- Cho A. An introduction to mashups for health librarians. JCHLA / JABSC. 2007;28(1):19-21.
- Corley CD, Cook DJ, Mikler AR, Singh KP. Using web and social media for influenza surveillance. Adv Exp Med Biol. 2010;680:559-64.
- Dreesman J, Denecke K. Challenges for signal generation from medical social media data. Stud Health Technol Inform. 2011;169:639-41.
- Dugas AF, et al. Influenza forecasting with Google Flu trends. OJPHI. 2013;5(1): e56176.
- Eke PI. Using social media for research and public health surveillance. J Dent Res. 2011;90(9):1045-6.
- Eysenbach G. Infodemiology: tracking flu-related searches on the web for syndromic surveillance. AMIA Annu Symp Proc. 2006:244–248.
- Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res. 2009;11(1):e11.
- Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457(7232):1012–4.
- Giustini D. Social media trends for health librarians: a primer on using social media for clinical disease surveillance. JCHLA / JABSC. 2012;33:92-4.
- Hanfling D, Altevogt BM, Viswanathan K, Gostin LO. Crisis standards of care: a systems framework for catastrophic disaster response. Institute of Medicine (IOM), 2012.
- Khan S, Maclean CD, Littenberg B. The effect of the Vermont Diabetes Information System on inpatient and emergency room use: results from a randomized trial. Health outcomes research in medicine. 2010;1(1):e61-6.
- Kilbridge PM, Campbell UC. Automated surveillance for adverse drug events at a community hospital and an academic medical center. J Am Med Inform Assoc. 2006;13(4):372-7.
- Kraft M. Social media tracking illnesses. KraftyLibrarian blog. January 7, 2013.
- Reynolds BJ. Building trust through social media. CDC's experience during the H1N1 influenza response. Mark Health Serv. 2010;30(2):18-21.
- Scanfeld D, Scanfeld V, Larson EL. Dissemination of health information through social networks: Twitter and antibiotics. Am J Infect Control. 2010;38(3):182-8.