Syndromic Surveillance for Influenzalike Illness in Ambulatory Care Setting
| Autor: | William T. M. Dunsmuir, Richard Danila, Heidi Kassenborg, Benjamin Miller, James D. Nordin, Mansour Hadidi, Jayne Griffith |
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| Rok vydání: | 2004 |
| Předmět: |
Microbiology (medical)
medicine.medical_specialty Epidemiology Minnesota lcsh:Medicine Surveillance Methods Disease lcsh:Infectious and parasitic diseases Disease Outbreaks Ambulatory care Influenza Human Ambulatory Care medicine Humans lcsh:RC109-216 Intensive care medicine Disease surveillance Surveillance business.industry Research lcsh:R Health Maintenance Organizations Outbreak Syndrome Models Theoretical medicine.disease Bioterrorism United States Infectious Diseases Infectious disease (medical specialty) Population Surveillance Health maintenance Seasons Medical emergency business Information Systems ICD-9 |
| Zdroj: | Emerging Infectious Diseases Emerging Infectious Diseases, Vol 10, Iss 10, Pp 1806-1811 (2004) |
| ISSN: | 1080-6059 1080-6040 |
| DOI: | 10.3201/eid1010.030789 |
| Popis: | Detection algorithm using proxy data for a bioterrorism agent release and historical data for influenza was effective. Conventional disease surveillance mechanisms that rely on passive reporting may be too slow and insensitive to rapidly detect a large-scale infectious disease outbreak; the reporting time from a patient's initial symptoms to specific disease diagnosis takes days to weeks. To meet this need, new surveillance methods are being developed. Referred to as nontraditional or syndromic surveillance, these new systems typically rely on prediagnostic data to rapidly detect infectious disease outbreaks, such as those caused by bioterrorism. Using data from a large health maintenance organization, we discuss the development, implementation, and evaluation of a time-series syndromic surveillance detection algorithm for influenzalike illness in Minnesota. |
| Databáze: | OpenAIRE |
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