Syndromic Surveillance for Dengue Fever in the Armed Forces
Author Information
Author(s): Meynard Jean-Baptiste, Chaudet Hervé, Texier Gaetan, Ardillon Vanessa, Ravachol Françoise, Deparis Xavier, Jefferson Henry, Dussart Philippe, Morvan Jacques, Boutin Jean-Paul
Primary Institution: Institut Pasteur de la Guyane
Hypothesis
Can a syndromic surveillance system provide earlier detection of dengue fever outbreaks compared to traditional clinical surveillance?
Conclusion
Syndromic surveillance allowed for an early warning of the dengue fever outbreak, leading to a quicker public health response by the armed forces.
Supporting Evidence
- Syndromic surveillance detected the outbreak several weeks before clinical surveillance.
- The military response included enhanced vector control measures based on syndromic surveillance data.
- 149 suspected cases and 15 confirmed cases were reported among military personnel during the outbreak.
- The civilian population reported 2,500 confirmed cases during the same outbreak.
- Statistical methods used included CPEG and CUSUM for analyzing surveillance data.
- Early warning allowed for quicker public health responses by military authorities.
- Different surveillance systems showed varying timelines in detecting the outbreak.
- Syndromic surveillance has been integrated into civilian surveillance strategies since the outbreak.
Takeaway
The military used a special system to quickly find out if people had dengue fever, which helped them respond faster to an outbreak.
Methodology
The study compared syndromic surveillance with traditional clinical surveillance and laboratory surveillance during a dengue outbreak.
Potential Biases
The analysis of syndromic surveillance used historical data from clinical events, which may have introduced classification bias.
Limitations
The study could not rigorously compare the two surveillance systems due to different disease definition criteria and population characteristics.
Participant Demographics
The study involved 3,000 military personnel, predominantly male (88.2%) with an average age of 34 years.
Digital Object Identifier (DOI)
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