Abstract
Syndromic Surveillance is an approach that aims at detecting an outbreak earlier as compared to traditional disease surveillance. To realize this goal, this approach expounds on situational awareness that focuses on the characteristics of the affected population by monitoring outbreak distribution and spread. The key features of this approach include the analysis of pre-diagnostic data, automated data acquisition and analysis with the use of statistical algorithms, and the use of computerized tools for validating the data.
Introduction
In the contemporary world, it is critical for nations to have effective and efficient systems of detecting outbreaks to safeguard the overall health status of their populations (Petti, 2006). While traditional surveillance approaches have been able to reduce the overall impact of outbreaks, the introduction of syndromic surveillance has made the control and prevention of outbreaks to be much more efficient. This approach uses pre-diagnostic data and statistical algorithms that aim at identifying an epidemic before it occurs as compared to traditional approaches that relied on laboratory and hospital reports to detect the presence of an epidemic or disease outbreak. This report will thus focus on the identification, collection, analysis, and validation data for syndromic surveillance and compare the same under traditional disease surveillance.
Identification
Traditional disease surveillance relies mainly on laboratory and health care reports. In this respect, an outbreak will only be identified after it has occurred and cases have already been reported within health institutions and have been confirmed through laboratory analyses (Bravata et al., 2004). However, syndromic surveillance uses a different approach. It focuses on pre-diagnostic data such as medication sales, absenteeism from work or school, and patient chief complaints (Chen, 2004). This approach thus makes it possible to identify illness clusters before conclusive diagnoses are made hence reducing morbidity and mortality within the population.
Collection and Analyzing of Data
Under the syndromic approach, data is collected through automated electronic reporting from databases such as drug sale records, attendance records for institutions, and medical records of patients in various health institutions (Mandi, 2004). This data is then subjected to several statistical algorithms for analysis. The extent to which this data will be analyzed is subjective and depends on the nature of the disease at hand, its characteristics, and the impact it has on the population. Thus, analyses vary from simple to complex but do not need to be highly computerized or technical (Petti, 2006). Therefore, near-real-time data acquisition and analyses are conducted under this approach. However, because syndromic surveillance resources might be limited to developing states, it is advised that these nations should use prior work and apply simple syndromic surveillance techniques. To assist with this, the World Health Organization (WHO) has made its open-source systems available to be used in such instances.
Validation
Validation of data under traditional disease surveillance was mainly through laboratory testing. On the other hand, syndromic surveillance has several automated testing tools that can be used to validate the data that has been collected and analyzed under this approach. These tools are set to adhere to specific rules, standards, and guidelines to ensure that the results are not only valid but reliable as well. This ensures that the epidemic/outbreak in hand is put under control to reduce morbidity and mortality within the population.
Conclusion
This report has expounded on syndromic surveillance identification, collecting, analysis, and validation of data and compared it with the traditional disease surveillance approach. From the discussion that has been presented, it is evident that there is a huge disparity between these two approaches in the sense that syndromic surveillance uses near-real-time identification and analysis procedures before the occurrence of an outbreak, unlike the traditional disease surveillance approach that conducts tests and analyses after an outbreak has occurred. In this respect, syndromic surveillance is a much more efficient approach to reducing the morbidity and mortality that might result due to poor identification, prevention, and control of an outbreak within a population.
References
Bravata, D., McDonald, K. and Smith, M. (2004). Systematic review: surveillance systems for early detection of bioterrorism-related diseases. Ann Intern Med, 140(1), 910-922
Chen, L. (2004). Human resources for health: Overcoming the crisis. Lancet, 364(1), 1984-90
Mandi, K. (2004). Implementing syndromic surveillance: A practical guide informed by the early experience. J Am Med Inform Assoc, 11(1), 141-50.
Petti, C. (2006). Laboratory medicine in Africa: A barrier to effective health care. Clin Infect Dis, 42(1), 377-82