TY - JOUR
T1 - Linkages between animal and human health sentinel data
AU - Scotch, Matthew
AU - Odofin, Lynda
AU - Rabinowitz, Peter
N1 - Funding Information:
The authors would like to thank Daniel Chudnov for his programming work on the Canary Database and Martin Slade for his assistance with the statistical analysis of our study. This project is supported in part by The National Library of Medicine (NLM) grants TI5 LM007056 and K99 LM009825 to Matthew Scotch and G08 LM07881 to Peter Rabinowitz. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Library Of Medicine or the National Institutes of Health.
PY - 2009/4/23
Y1 - 2009/4/23
N2 - Introduction: In order to identify priorities for building integrated surveillance systems that effectively model and predict human risk of zoonotic diseases, there is a need for improved understanding of the practical options for linking surveillance data of animals and humans. We conducted an analysis of the literature and characterized the linkage between animal and human health data. We discuss the findings in relation to zoonotic surveillance and the linkage of human and animal data.Methods: The Canary Database, an online bibliographic database of animal-sentinel studies was searched and articles were classified according to four linkage categories.Results: 465 studies were identified and assigned to linkage categories involving: descriptive, analytic, molecular, or no human outcomes of human and animal health. Descriptive linkage was the most common, whereby both animal and human health outcomes were presented, but without quantitative linkage between the two. Rarely, analytic linkage was utilized in which animal data was used to quantitatively predict human risk. The other two categories included molecular linkage, and no human outcomes, which present health outcomes in animals but not humans.Discussion: We found limited use of animal data to quantitatively predict human risk and listed the methods from the literature that performed analytic linkage. The lack of analytic linkage in the literature might not be solely related to technological barriers including access to electronic database, statistical software packages, and Geographical Information System (GIS). Rather, the problem might be from a lack of understanding by researchers of the importance of animal data as a 'sentinel' for human health. Researchers performing zoonotic surveillance should be aware of the value of animal-sentinel approaches for predicting human risk and consider analytic methods for linking animal and human data. Qualitative work needs to be done in order to examine researchers' decisions in linkage strategies between animal and human data.
AB - Introduction: In order to identify priorities for building integrated surveillance systems that effectively model and predict human risk of zoonotic diseases, there is a need for improved understanding of the practical options for linking surveillance data of animals and humans. We conducted an analysis of the literature and characterized the linkage between animal and human health data. We discuss the findings in relation to zoonotic surveillance and the linkage of human and animal data.Methods: The Canary Database, an online bibliographic database of animal-sentinel studies was searched and articles were classified according to four linkage categories.Results: 465 studies were identified and assigned to linkage categories involving: descriptive, analytic, molecular, or no human outcomes of human and animal health. Descriptive linkage was the most common, whereby both animal and human health outcomes were presented, but without quantitative linkage between the two. Rarely, analytic linkage was utilized in which animal data was used to quantitatively predict human risk. The other two categories included molecular linkage, and no human outcomes, which present health outcomes in animals but not humans.Discussion: We found limited use of animal data to quantitatively predict human risk and listed the methods from the literature that performed analytic linkage. The lack of analytic linkage in the literature might not be solely related to technological barriers including access to electronic database, statistical software packages, and Geographical Information System (GIS). Rather, the problem might be from a lack of understanding by researchers of the importance of animal data as a 'sentinel' for human health. Researchers performing zoonotic surveillance should be aware of the value of animal-sentinel approaches for predicting human risk and consider analytic methods for linking animal and human data. Qualitative work needs to be done in order to examine researchers' decisions in linkage strategies between animal and human data.
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U2 - 10.1186/1746-6148-5-15
DO - 10.1186/1746-6148-5-15
M3 - Editorial
C2 - 19389228
AN - SCOPUS:66149140268
SN - 1746-6148
VL - 5
JO - BMC Veterinary Research
JF - BMC Veterinary Research
M1 - 15
ER -