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Syndromic Surveillance System

Syndromic Surveillance System

All countries require sensitive disease surveillance systems capable of detecting diseases
early and monitor outbreaks. One such system is syndromic surveillance that has been developed
recently. It is a complex technology tool developed for recording data from several sources with
an aim of identifying the probability of a disease outbreak. It goes ahead to focus on non clinical
information that indicates an outbreak. The information from various relevant sources is
collected and analyzed to detect bioterrorism and alert the public on the same. Syndromic
surveillance is influenced by the emerging threats of bioterrorism and the advancements in

technology that allows the public health officials to analyze data from various sources for
detecting cases of disease outbreak on time (Chen, Zeng & Yan, 2010).
Syndromic surveillance includes collection of information from various sources for
analysis. One of these sources is monitoring of employees reporting absence from their work.
Employee absence from their work is a useful tool of early outbreak identification or disease
clusters such as influenza. The main advantage of this source of data for syndromic surveillance
is its timeliness. For instance, in Netherlands, the start of influenza and respiratory syncytial
virus was detected much earlier than laboratory confirmation. However, this method may capture
seasonal aspects of employees’ absence that are not related to any infectious disease. This means
that it may have low specificity (National Academy of Sciences (U.S.) et al, 2011).
School absenteeism is another source of syndromic surveillance data. It requires use of
school records in order to identify absence of students. This method of data collection has been
reported to help in early outbreak identification. It helps in understanding epidemiology of
influenza in various communities by monitoring trends of school absenteeism. It has shown to be
an effective method of timely detection of influenza prior one week to other systems. It has also
shown a correlation between other traditional methods of surveillance such as validity of
respiratory pathogens. This method has the ability to combine clinical data on students’ illnesses
to make informed decisions on disease control measures, school closures, suspension of classes,
and communication with parents. When this method is automated, collection of data on
absenteeism requires minimal surveillance and allows public health officials to make use of
limited resources effectively. Automated self reporting of diseases in university students
provides an opportunity to avail information on self care and timing for return to schools.
Although this method of data collection may be effective in some cases, it may be inconsistent in

some schools and participation may vary from time to time. Likewise, the criteria for
determining the absence of school absenteeism may greatly vary. This method may prove to be
ineffective in schools that do not present the reasons for students’ absence. Therefore, reacting to
every indication based on students’ absence would cause unacceptable cases of false alarms and
hence inefficient use of public health resources (M’ikanatha, 2007).
The other source of information for syndromic surveillance is emergency department
chief complaints. ED chief complaints refer to the records of patients’ reported symptoms and
signs of disease at presentation. It typically consists of a brief statement entered into the
electronic system in short phrases. It has been used in early identification of outbreaks of
diseases at the start and the end of seasons of pathogens like influenza. This method is also used
in mass gatherings and in monitoring and identification of novel threats. In some cases it is used
to supplement data from other sources such as laboratory testing and sentinel physician consults.
It has shown to be better than most methods of surveillances as it gives the real picture of the
situation, and it detects clusters of diseases much earlier. For instance, in the case of H1N1
pandemic, alerts from ED data was detected about 7 days prior laboratory confirmation.
However, this method may not be effective since not all patients use ED when receiving
treatment. Additionally, data entry for ED chief is unformatted thereby reducing opportunities of
automation for efficiency and effectiveness. This method relies on the specificity and sensitivity
of the system and a variety of cases involved. Emergency and non emergence health records,
pharmacy sales, and online resources are other source of data for syndromic surveillance that
help in early detection of cluster of diseases or outbreaks (Zeng, 2011).
M’ikanatha (2007) confirms that syndromic surveillance is an overhaul of traditional
surveillance system, which entails compulsory reporting of certain diseases to central health

authority. This shows that traditional surveillance system relies solely on laboratory results to
ascertain the probability of a disease outbreak. In case of a communicable disease such as
influenza, the public health officials monitor the disease to detect any case of an outbreak. The
traditional method is simple, but very slow. Although it can help in detecting a disease outbreak,
it is not as effective as syndromic surveillance that detects outbreaks much earlier. Therefore,
despite that syndromic disease surveillance is quite expensive and uses a complex of technology,
it has proved to be more effectual than the traditional method. It can be used to detect outbreaks
of certain infectious diseases on time and allow public health care to react. Early detection of
outbreaks helps in saving many lives and managing outbreaks of communicable diseases.


Chen, H., Zeng, D., & Yan, P. (2010). Infectious disease informatics: Syndromic surveillance for
public health and biodefense. New York: Springer..
M’ikanatha, N. M. (2007). Infectious disease surveillance. Malden, MA: Blackwell Pub.
National Academy of Sciences (U.S.)., Institute of Medicine (U.S.)., National Research Council
(U.S.)., National Research Council (U.S.)., & National Academies Press (U.S.). (2011).

BioWatch and public health surveillance: Evaluating systems for the early detection of
biological threats. Washington, D.C: National Academies Press.
Zeng, D. (2011). Infectious disease informatics and biosurveillance. New York: Springer.

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