Improving Diseases Early Detection Using Syndromic Surveillance

摘要

To enhance traditional passive surveillance systems, methods based on the analysis of pre-diagnostic and unspecific routinely collected data were developed at the beginning of the 21st century in human health applications, when the bioterrorist anthrax attacks of 2001 in the USA drew attention to the need for early detection of pathogen ab:. Such approaches, referred to as syndromic surveillance systems (SyS), aim to identify the early, often weak, signal of an outbreak in the absence of (or prior to) an accurate identification of the disease by medical practitioners or laboratories. Indeed, traditional passive surveillance is poorly effective in detecting rare or emerging diseases due to the limited ability of clinicians to recognize the signs of unknown, or poorly known, diseases. First developed to enhance the early detection of new pathogens, SyS are also applied in the monitoring of well-known diseases such as human flu in order to implement protective measures early and limit the impact of the disease. SyS offers great opportunities for diseases surveillance but it also comes with challenges. Because it relies on health-related indicators, syndromic surveillance usually has a low specificity and the variations in the indicator might be due to disease or to another event. In addition, classical approaches used in syndromic surveillance are not able to take into account other epidemiological information available for a disease, such as environmental risk factors. This limits the development of integrated surveillance systems able to combine in unique output all the information available on a disease. The development of syndromic surveillance quantitative outputs, which are more objective, flexible and easily interpretable, is thus a promising area of research.