Author: Huijskens, E. G. W.; Smit, L. A. M.; Rossen, J. W. A.; Heederik, D.; Koopmans, M.
Title: Evaluation of Patients with Communityâ€Acquired Pneumonia Caused by Zoonotic Pathogens in an Area with a High Density of Animal Farms Cord-id: elwfiima Document date: 2015_7_27
ID: elwfiima
Snippet: Intensive animal farming could potentially lead to outbreaks of infectious diseases. Clinicians are at the forefront of detecting unusual diseases, but the lack of specificity of zoonotic disease symptoms makes this a challenging task. We evaluated patients with communityâ€acquired pneumonia (CAP) with known and unknown aetiology in an area with a high livestock density and a potential association with animal farms in the proximity. Between 2008 and 2009, a period coinciding with a large Q feve
Document: Intensive animal farming could potentially lead to outbreaks of infectious diseases. Clinicians are at the forefront of detecting unusual diseases, but the lack of specificity of zoonotic disease symptoms makes this a challenging task. We evaluated patients with communityâ€acquired pneumonia (CAP) with known and unknown aetiology in an area with a high livestock density and a potential association with animal farms in the proximity. Between 2008 and 2009, a period coinciding with a large Q fever outbreak in the Netherlands, patients with CAP were tested for the presence of possible respiratory pathogens. The presence and number of farm animals within 1 km of the patients' home address were assessed using geographic information system (GIS) and were compared between cases and ageâ€matched control subjects. Of 408 patients with CAP, pathogens were detected in 275 (67.4%) patients. The presence of sheep and the number of goats were associated with CAP caused by Coxiella burnetii in a multiple logistic regression model (P < 0.05). CAP with unknown aetiology was not associated with the presence of animal farms (P > 0.10). The use of GIS in combination with aetiology of CAP could be potentially used to target diagnostics and to identify outbreaks of rare zoonotic disease.
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