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      Value of syndromic surveillance within the Armed Forces for early warning during a dengue fever outbreak in French Guiana in 2006

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          Abstract

          Background

          A dengue fever outbreak occured in French Guiana in 2006. The objectives were to study the value of a syndromic surveillance system set up within the armed forces, compared to the traditional clinical surveillance system during this outbreak, to highlight issues involved in comparing military and civilian surveillance systems and to discuss the interest of syndromic surveillance for public health response.

          Methods

          Military syndromic surveillance allows the surveillance of suspected dengue fever cases among the 3,000 armed forces personnel. Within the same population, clinical surveillance uses several definition criteria for dengue fever cases, depending on the epidemiological situation. Civilian laboratory surveillance allows the surveillance of biologically confirmed cases, within the 200,000 inhabitants.

          Results

          It was shown that syndromic surveillance detected the dengue fever outbreak several weeks before clinical surveillance, allowing quick and effective enhancement of vector control within the armed forces. Syndromic surveillance was also found to have detected the outbreak before civilian laboratory surveillance.

          Conclusion

          Military syndromic surveillance allowed an early warning for this outbreak to be issued, enabling a quicker public health response by the armed forces. Civilian surveillance system has since introduced syndromic surveillance as part of its surveillance strategy. This should enable quicker public health responses in the future.

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          Most cited references20

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          Implementing syndromic surveillance: a practical guide informed by the early experience.

          Syndromic surveillance refers to methods relying on detection of individual and population health indicators that are discernible before confirmed diagnoses are made. In particular, prior to the laboratory confirmation of an infectious disease, ill persons may exhibit behavioral patterns, symptoms, signs, or laboratory findings that can be tracked through a variety of data sources. Syndromic surveillance systems are being developed locally, regionally, and nationally. The efforts have been largely directed at facilitating the early detection of a covert bioterrorist attack, but the technology may also be useful for general public health, clinical medicine, quality improvement, patient safety, and research. This report, authored by developers and methodologists involved in the design and deployment of the first wave of syndromic surveillance systems, is intended to serve as a guide for informaticians, public health managers, and practitioners who are currently planning deployment of such systems in their regions.
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            Evaluation of reporting timeliness of public health surveillance systems for infectious diseases

            Background Timeliness is a key performance measure of public health surveillance systems. Timeliness can vary by disease, intended use of the data, and public health system level. Studies were reviewed to describe methods used to evaluate timeliness and the reporting timeliness of National Notifiable Diseases Surveillance System (NNDSS) data was evaluated to determine if this system could support timely notification and state response to multistate outbreaks. Methods Published papers that quantitatively measured timeliness of infectious disease surveillance systems operating in the U.S. were reviewed. Median reporting timeliness lags were computed for selected nationally notifiable infectious diseases based on a state-assigned week number and various date types. The percentage of cases reported within the estimated incubation periods for each disease was also computed. Results Few studies have published quantitative measures of reporting timeliness; these studies do not evaluate timeliness in a standard manner. When timeliness of NNDSS data was evaluated, the median national reporting delay, based on date of disease onset, ranged from 12 days for meningococcal disease to 40 days for pertussis. Diseases with the longer incubation periods tended to have a higher percentage of cases reported within its incubation period. For acute hepatitis A virus infection, which had the longest incubation period of the diseases studied, more than 60% of cases were reported within one incubation period for each date type reported. For cryptosporidiosis, Escherichia coli O157:H7 infection, meningococcal disease, salmonellosis, and shigellosis, less than 40% of cases were reported within one incubation period for each reported date type. Conclusion Published evaluations of infectious disease surveillance reporting timeliness are few in number and are not comparable. A more standardized approach for evaluating and describing surveillance system timeliness should be considered; a recommended methodology is presented. Our analysis of NNDSS reporting timeliness indicated that among the conditions evaluated (except for acute hepatitis A infection), the long reporting lag and the variability across states limits the usefulness of NNDSS data and aberration detection analysis of those data for identification of and timely response to multistate outbreaks. Further evaluation of the factors that contribute to NNDSS reporting timeliness is warranted.
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              Using laboratory-based surveillance data for prevention: an algorithm for detecting Salmonella outbreaks.

              By applying cumulative sums (CUSUM), a quality control method commonly used in manufacturing, we constructed a process for detecting unusual clusters among reported laboratory isolates of disease-causing organisms. We developed a computer algorithm based on minimal adjustments to the CUSUM method, which cumulates sums of the differences between frequencies of isolates and their expected means; we used the algorithm to identify outbreaks of Salmonella Enteritidis isolates reported in 1993. By comparing these detected outbreaks with known reported outbreaks, we estimated the sensitivity, specificity, and false-positive rate of the method. Sensitivity by state in which the outbreak was reported was 0%(0/1) to 100%. Specificity was 64% to 100%, and the false-positive rate was 0 to 1.
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                Author and article information

                Journal
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central
                1472-6947
                2008
                2 July 2008
                : 8
                : 29
                Affiliations
                [1 ]Institut Pasteur de la Guyane, Cayenne 97306, French Guiana
                [2 ]Université de la Méditerranée, Marseille 13385, France
                [3 ]Institut de Médecine Tropicale du Service de santé des armées, Marseille 13998, France
                [4 ]Cellule Inter Régionale d'Epidémiologie Antilles-Guyane, 97306, French Guiana
                [5 ]Direction de la Santé et du Développement Social de la Guyane, 97306, French Guiana
                [6 ]Ecole du Val-de-Grâce, Paris 75230, France
                [7 ]Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK
                Article
                1472-6947-8-29
                10.1186/1472-6947-8-29
                2459153
                18597694
                e16312d0-a6ca-4ee7-b655-ef6ee72391b5
                Copyright © 2008 Meynard et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 December 2007
                : 2 July 2008
                Categories
                Research Article

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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