Use of a voluntary testing program to study the spatial epidemiology of Johne’s disease affecting dairy herds in Minnesota: a cross sectional study
Abstract Background One of the key steps in the management of chronic diseases in animals including Johne’s disease (JD), caused by Mycobacterium avium subsp. paratuberculosis (MAP), is the ability to track disease incidence over space and time. JD surveillance in the U.S. dairy cattle is challengin...
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doaj-51eba2ae832042c08a9a0180c97547bb2020-12-06T12:18:22ZengBMCBMC Veterinary Research1746-61482019-12-0115111110.1186/s12917-019-2155-7Use of a voluntary testing program to study the spatial epidemiology of Johne’s disease affecting dairy herds in Minnesota: a cross sectional studyK. S. T. Kanankege0G. Machado1L. Zhang2B. Dokkebakken3V. Schumann4S. J. Wells5A. M. Perez6J. Alvarez7Department of Population Medicine, College of VeterinaryMedicine, University of MinnesotaDepartment of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State UniversityDivision of Biostatistics, School of Public Health, University of MinnesotaMinnesota Dairy Herd Improvement AssociationMinnesota Dairy Herd Improvement AssociationDepartment of Population Medicine, College of VeterinaryMedicine, University of MinnesotaDepartment of Population Medicine, College of VeterinaryMedicine, University of MinnesotaCentro de Vigilancia Sanitaria Veterinaria (VISAVET), Departamento de Sanidad Animal, Facultad de Veterinaria, Universidad ComplutenseAbstract Background One of the key steps in the management of chronic diseases in animals including Johne’s disease (JD), caused by Mycobacterium avium subsp. paratuberculosis (MAP), is the ability to track disease incidence over space and time. JD surveillance in the U.S. dairy cattle is challenging due to lack of regulatory requirements, imperfect diagnostic tests, and associated expenses, including time and labor. An alternative approach is to use voluntary testing programs. Here, data from a voluntary JD testing program, conducted by the Minnesota Dairy Herd Improvement Association, were used to: a) explore whether such a program provides representative information on JD-prevalence in Minnesota dairy herds, b) estimate JD distribution, and, c) identify herd and environmental factors associated with finding JD-positive cows. Milk samples (n = 70,809) collected from 54,652 unique cows from 600 Minnesota dairy herds between November 2014 and April 2017 were tested using a MAP antibody ELISA. Participant representativeness was assessed by comparing the number of JD-tested herds with the number of herds required to estimate the true disease prevalence per county based on official statistics from the National Agricultural Statistical Services. Multivariable logistic regression models, with and without spatial dependence between observations, were then used to investigate the association between herd status to JD (positive/negative), as indicated by milk ELISA results, and available covariates at the herd level. Results Within the study population, at least one test-positive cow was found in 414 of 600 (69%) herds. Results indicated that large herds that test frequently and herds located in loamy or silt soils are more likely to have at least one MAP test-positive cow. After adjusting for herd size, testing frequency, and soil type, there was no spatial dependence in JD risk between neighboring dairies within 5 to 20 km. Furthermore, the importance of collecting data on herd management, feed, and biosecurity for insightful interpretations was recognized. The study suggested that, although limited, the voluntary testing database may support monitoring JD status. Conclusions Results presented here help elucidate the spatial characteristics of JD in Minnesota and the study may ultimately contribute to the design and implementation of surveillance programs for the disease.https://doi.org/10.1186/s12917-019-2155-7ParatuberculosisMilk ELISASpatial epidemiologyCAR modelRegressionPassive surveillance |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
K. S. T. Kanankege G. Machado L. Zhang B. Dokkebakken V. Schumann S. J. Wells A. M. Perez J. Alvarez |
spellingShingle |
K. S. T. Kanankege G. Machado L. Zhang B. Dokkebakken V. Schumann S. J. Wells A. M. Perez J. Alvarez Use of a voluntary testing program to study the spatial epidemiology of Johne’s disease affecting dairy herds in Minnesota: a cross sectional study BMC Veterinary Research Paratuberculosis Milk ELISA Spatial epidemiology CAR model Regression Passive surveillance |
author_facet |
K. S. T. Kanankege G. Machado L. Zhang B. Dokkebakken V. Schumann S. J. Wells A. M. Perez J. Alvarez |
author_sort |
K. S. T. Kanankege |
title |
Use of a voluntary testing program to study the spatial epidemiology of Johne’s disease affecting dairy herds in Minnesota: a cross sectional study |
title_short |
Use of a voluntary testing program to study the spatial epidemiology of Johne’s disease affecting dairy herds in Minnesota: a cross sectional study |
title_full |
Use of a voluntary testing program to study the spatial epidemiology of Johne’s disease affecting dairy herds in Minnesota: a cross sectional study |
title_fullStr |
Use of a voluntary testing program to study the spatial epidemiology of Johne’s disease affecting dairy herds in Minnesota: a cross sectional study |
title_full_unstemmed |
Use of a voluntary testing program to study the spatial epidemiology of Johne’s disease affecting dairy herds in Minnesota: a cross sectional study |
title_sort |
use of a voluntary testing program to study the spatial epidemiology of johne’s disease affecting dairy herds in minnesota: a cross sectional study |
publisher |
BMC |
series |
BMC Veterinary Research |
issn |
1746-6148 |
publishDate |
2019-12-01 |
description |
Abstract Background One of the key steps in the management of chronic diseases in animals including Johne’s disease (JD), caused by Mycobacterium avium subsp. paratuberculosis (MAP), is the ability to track disease incidence over space and time. JD surveillance in the U.S. dairy cattle is challenging due to lack of regulatory requirements, imperfect diagnostic tests, and associated expenses, including time and labor. An alternative approach is to use voluntary testing programs. Here, data from a voluntary JD testing program, conducted by the Minnesota Dairy Herd Improvement Association, were used to: a) explore whether such a program provides representative information on JD-prevalence in Minnesota dairy herds, b) estimate JD distribution, and, c) identify herd and environmental factors associated with finding JD-positive cows. Milk samples (n = 70,809) collected from 54,652 unique cows from 600 Minnesota dairy herds between November 2014 and April 2017 were tested using a MAP antibody ELISA. Participant representativeness was assessed by comparing the number of JD-tested herds with the number of herds required to estimate the true disease prevalence per county based on official statistics from the National Agricultural Statistical Services. Multivariable logistic regression models, with and without spatial dependence between observations, were then used to investigate the association between herd status to JD (positive/negative), as indicated by milk ELISA results, and available covariates at the herd level. Results Within the study population, at least one test-positive cow was found in 414 of 600 (69%) herds. Results indicated that large herds that test frequently and herds located in loamy or silt soils are more likely to have at least one MAP test-positive cow. After adjusting for herd size, testing frequency, and soil type, there was no spatial dependence in JD risk between neighboring dairies within 5 to 20 km. Furthermore, the importance of collecting data on herd management, feed, and biosecurity for insightful interpretations was recognized. The study suggested that, although limited, the voluntary testing database may support monitoring JD status. Conclusions Results presented here help elucidate the spatial characteristics of JD in Minnesota and the study may ultimately contribute to the design and implementation of surveillance programs for the disease. |
topic |
Paratuberculosis Milk ELISA Spatial epidemiology CAR model Regression Passive surveillance |
url |
https://doi.org/10.1186/s12917-019-2155-7 |
work_keys_str_mv |
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