Literature DB >> 19250046

Development of a syndromic surveillance system for detection of disease among livestock entering an auction market.

David C Van Metre1, Daniel Q Barkey, M D Salman, Paul S Morley.   

Abstract

OBJECTIVE: To develop a syndromic surveillance system based on visual inspection from outside the livestock pens that could be used for detection of disease among livestock entering an auction market.
DESIGN: Cross-sectional study. ANIMALS: All livestock (beef and dairy cattle, sheep, goats, horses, and pigs) entering a single auction market in Colorado during 30 business days. Procedures-Livestock were enumerated and visually inspected for clinical signs of disease by a veterinarian outside the pens, and clinical signs that were observed were categorized into 12 disease syndromes. Frequency of clinical signs and disease syndromes was then calculated.
RESULTS: Data were recorded for a total of 29,371 animal observation days. For all species combined, the most common disease syndrome was respiratory tract disease (218.9 observations/10,000 animal observation days), followed by thin body condition and abnormal ambulation or posture (80.7 and 27.2 observations/10,000 animal observation days, respectively). Together, these 3 disease syndromes accounted for 92.8% of all clinical signs of disease observed. The syndromes least commonly identified were non-injury-related hemorrhage, death, and injury-related hemorrhage (0.0, 0.3, and 0.7 observations/10,000 animal observation days, respectively). CONCLUSIONS AND CLINICAL RELEVANCE: Results suggested that a syndromic surveillance system based on visual inspection alone could be developed to identify possible disease conditions among livestock at an auction market. Further studies are needed to determine the sensitivity and specificity of visual observation in detecting disease.

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Mesh:

Year:  2009        PMID: 19250046     DOI: 10.2460/javma.234.5.658

Source DB:  PubMed          Journal:  J Am Vet Med Assoc        ISSN: 0003-1488            Impact factor:   1.936


  12 in total

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10.  Comparison of covariate adjustment methods using space-time scan statistics for food animal syndromic surveillance.

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