Disease Surveillance Systems - Essay Example

Disease Surveillance Systems Name Institution Date Introduction Infectious diseases are a major source of medical burden globally, causing about 15 million deaths annually (Paolotti et al., 2014). The challenge of disease is currently globalized hence the surveillance of infectious diseases is a significant public health practice worldwide, with web-based platforms being increasingly adopted due to their wider reach (Board on Global Health, 2007). While traditionally surveillance was limited to public records, epidemiological databases and laboratory databases, presently the scope is expanding to accommodate internet-based data (Schwind et al., 2014). Online disease surveillance systems for have various pros and cons that make it necessary to carefully evaluate their applicability. This paper evaluates the use of online-based surveillance systems in managing infectious diseases, with reference to HealthMap, a web-based disease surveillance tool. The HealthMap Surveillance System Digital surveillance involves gathering information from mobile phone or web-based sources and using it to establish the incidence, distribution and risk that relate to a disease (Schwind et al., 2014). HealthMap is one of the applications that are used in the effort. According to Kloby (2012), it is a multi-stream system that aggregates various online data sources such as Really Simple Syndication (RSS) feeds, alerts from the WHO, EuroSurveillance reports and mailing lists by ProMED. The automatically generated information in HealthMap is acquired by text mining, Natural Language Interpretation, Screen Scraping and parsing in order to identify the locations and geocodes of disease outbreaks. HealthMap is freely available as a mobile app named ‘outbreaks near me’ and on the website 'healthmap.org (HealthMap, 2015). Freifeld et al. (2008) explain that it automatically queries, filters, integrates and visualizes online reports and applies Automated Text Processing Algorithms in classifying alerts basing on disease and location, processing about 30 disease alerts a day, with a default time window of 30 days and capability of displaying about 1,000 alerts any time in a simplified visualized manner. According to Morain & Budge (2012), it continuously updates itself and provides information in nine languages. Evaluation of the System in Influenza Tracking Considering that web-based technology can revolutionize the surveillance of emerging infectious diseases in Australia (Milinovich et al., 2013), HealthMap is among the applicable measures in disease and specifically Influenza surveillance. The country’s National Notifiable Diseases Surveillance System (NNDSS) which according to Parrella et al. (2009) is the main body handling the responsibility has a lot to benefit from the system’s functions. One of the pros of the system is its greater efficiency in detection of disease unlike traditional methods, in what Schwind et al. (2014), describes as near real-time dissemination and acquisition of information, and also expansion of the amount of information that would otherwise not be accessed through traditional methods. This is arguably an important aspect because the earlier any disease is detected, the easier it becomes to manage or control it. Another major strength of the system is in its scope of population and disease. It is a global application and therefore would offer a broader span of data, apart from handling not only influenza, but also other diseases. In comparing it with other systems such as FluTracking in monitoring of Influenza, Parrella et al. (2009) for instance observes that in FluTracking there is a targeted respondent group and it ends up monitoring their health against immunization status unlike in HealthMap where anyone can report and therefore even emergent health aspects are accessed. It is therefore definitely a better approach, considering that FluTracking is probably the best alternative that one can think of for Influenza. HealthMap is arguably cheap as a system. According to Morain & Budge (2012), it brings with it relatively low operational costs when compared to the other tedious approaches, a feature that is amplified further by the real-time nature of its information and therefore efficiency. The greater number of people is a major factor defining this advantage. Paolotti et al. (2014) explains that computers and smartphones link over 2 billion people worldwide to the Internet hence facilitating sharing of such health-related information faster. One can only imagine the potential of a system such as HealthMap within such a large reporting population. The argument is valid because the other patient-targeting approaches in Influenza surveillance obviously involve more activity and processes and cannot have the kind of reach and variety of information that HealthMap avails. In spite of the various strengths, the system however has weaknesses. One of these is lack of representativeness in the information provided. Public Health Ontario (2012) explains that lack of objectivity arises for instance because syndromic data gathered through the tracking of influenza reports online may be influenced by age, gender or search trends at the moment. In addition, many people tend to reduce their usage of the internet while ill. The overall implication of the weakness stated by Public Health Ontario is that there could be an observed trend that is not reported simply because the people witnessing it have not reported it. There are also no guidelines that would ensure such representativeness is attained. A second weakness in the system is absence of standards that would ensure ethics in information gathering. According to Kloby (2012), the process only involves the mining of information relating to them through search tools and websites. The implication of the process reported by Kloby is that there cannot be universal standards to guide the capture, report, processing, sharing and interpretation of structured data acquired as it remains quite informal, raising an ethical challenge because the process amounts to the gathering of information about a person’s health without seeking their permission. It is deductible that there will not be issues such as seeking consent of participants or even allowing them to properlydrop out of data gathering. There is the challenge of accuracy in the information that is gathered. This is acknowledged by the data collection site itself which states that it is entirely dependent on publicly available third-party information, implying that the information online is often not peer-reviewed and in the process of aggregation therefore, both scientific and non-scientific reports are put together (HealthMap, 2015). It would be expected that anything relating to human health should be availed and analyzed by experts, so that this shortcoming appears a great disappointment. One can only hope that it does not distort the practicality of data. Lastly, although it covers a large geographical span, the system does not consider special needs of some regions. For instance, according to Schwind et al. (2014), there are many places where internet connectivity and access is limited so that it is difficult to raise accurate by use of a digital disease database, with globally significant health events only being reported in print media, local television and radio. This weakness is significant. This is because it ends up locking out some places yet a complete global picture is needed for covering a disease such as influenza that has a global dimension. The system cannot therefore be said to be effective if not supplemented probably with local media monitoring. Conclusion Infectious diseases are a great challenge to human health hence need for their early detection and preemption. Web-based technologies help in this effort through enabling the gathering of disease-related intelligence either from practitioners or the general public. HealthMap is among the effective systems used and is a great improvement on traditional approaches, for instance in Influenza surveillance. From the discussion, application of health information technology tools such as HealthMap enables an increase in the quantity, quality, timeliness and capacity of modern health surveillance systems. Its early detection for example helps in the containment or reduction of epidemics while alerting health authorities to ensure more effective strategies for risk management. While it has its strengths and weaknesses, it ultimately offers an effective way of acquiring cheap, timely and widespread data. Recommendations HealthMap’s effectiveness could be improved if some kind of standards were developed for reporting. This is because it will make it possible to apply outcomes across computational systems. The system could also adopt an open editing feature. I believe that having such would make information easy to alter so as to reflect the reality at a given moment in time. It would be useful if the system included the user editing function. However, because a health issue is beyond being determined by any member of the public, the function can be provided only to a limited number of experts. Technology such as the system used in HealthMap normally appears to be influenced by the need to innovate rather than to tackle existing problems. This is a challenge that might be limiting HealthMap’s application. It would therefore be wise for the public health community to try and be more in touch with the people’s health needs then collaborate more closely with technologists to ensure more appropriate solutions to the public’s health needs. Lastly, more studies on the equity and ease of accessing the Internet, encouragement of people to self-report online when ill and evaluation of HealthMap’s cost-effectiveness will also help to improve surveillance further. References Australian Department of Health. 2015. Introduction to the National Notifiable Diseases Surveillance System. Retrieved on 14 march 2015 from Board on Global Health. (2007).Global Infectious Disease Surveillance and Detection: Assessing the Challenges-Finding Solutions. Washington, DC: National Academies Press Parrella, A, Dalton, C, Pearce, R and Litt, J. (2009). ASPREN Surveillance System for Influenza-like Illness: A Comparison with FluTracking and the National Notifiable Diseases Surveillance System. Australian Family Physician. Vol. 38 (11): 932-935 Freifeld, C, Mandl, K and Reis, B. (2008). HealthMap: Global Infectious Disease Monitoring through Automated Classification and Visualization of Internet Media Reports. Journal of American Medical Informatics Association. Vol. 15(2): 150–157 HealthMap. (2015). About. Retrieved on 13 March 2015 from Kloby, K. (2012). Citizen 2.0: Public and Governmental Interaction through Web 2.0 Technologies. Hershey: IGI Global Milinovich, G, Williams, G, Clements, A and Hu, W. (2013). Internet-based Surveillance Systems for Monitoring Emerging Infectious Diseases. The Lancet Infectious Diseases. DOI: 10.1016/S1473-3099(13)70244-5 Morain, S and Budge, A. (2012). Environmental Tracking for Public Health Surveillance. London: CRC Press Paolotti, D et al. (2014). Web-based Participatory Surveillance of Infectious Diseases: the Influenzanet Participatory Surveillance Experience. Clinical Microbiology and Infection. Vol. 20 (1): 17–21 Public Health Ontario. (2012). Syndromic Surveillance Discussion Paper. Ontario: Provincial Infectious Diseases Advisory Committee (PIDAC) Schwind, J and Mazet, J et al. (2014) Evaluation of Local Media Surveillance for Improved Disease Recognition and Monitoring in Global Hotspot Regions. Plos One. Vol. 9 (10): e110236 Read More