Bovine Viral Diarrhoea Virus (BVDV) in Dairy Cattle: A Matched Case-Control Study. Machado, G., Egocheaga, R., Hein, H., Miranda, I., Neto, W., Almeida, L., Canal, C., Stein, M., & Corbellini, L. Transboundary and Emerging Diseases, 2016.
abstract   bibtex   
© 2014 Blackwell Verlag GmbH. Bovine viral diarrhoea virus (BVDV) causes one of the most important diseases of cattle in terms of economic costs and welfare. The aims were to estimate herd prevalence and to investigate the factors associated with antibodies in bulk tank milk (BTM) in dairy herds through a matched case-control study. To estimate herd prevalence, BTM samples were randomly selected (n = 314) from a population (N = 1604). The true prevalence of BVDV was 24.3% (CI 95% = 20.1-29.3%). For the case-control study, BVDV antibody-positive herds (high antibody titres) were classified as cases (n = 21) and matched (n = 63) by milk production with herds presenting low antibody titres (ratio of 1 : 3). Three multivariable models were built: 1) full model, holding all 21 variables, and two models divided according to empirical knowledge and similarity among variables; 2) animal factor model; and 3) biosecurity model. The full model (model 1) identified: age as a culling criteria (OR = 0.10; CI 95% = 0.02-0.39; P < 0.01); farms that provided milk to other industries previously (OR = 4.13; CI 95% = 1.17-14.49; P = 0.02); and isolation paddocks for ill animals (OR = 0.14; CI 95% = 0.01-0.26; P = 0.02). The biosecurity model revealed a significant association with the use of natural mating (OR = 9.03; CI 95% = 2.14-38.03; P < 0.01); isolation paddocks for ill animals (OR = 0.06; CI 95% = 0.05-0.83; P = 0.03); years providing milk for the same industry (OR = 0.94; CI 95% = 0.91-0.97; P = 0.02); and direct contact over fences among cattle of neighbouring farms (OR = 5.78; CI 95% = 1.41-23.67; P = 0.04). We recommend the application of grouping predictors as a good choice for model building because it could lead to a better understanding of disease-exposure associations.
@article{
 title = {Bovine Viral Diarrhoea Virus (BVDV) in Dairy Cattle: A Matched Case-Control Study},
 type = {article},
 year = {2016},
 identifiers = {[object Object]},
 keywords = {BVDV,Bulk tank milk,Epidemiology,Model building,Risk factor},
 volume = {63},
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 created = {2018-06-14T19:29:00.976Z},
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 last_modified = {2018-06-14T19:29:00.976Z},
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 abstract = {© 2014 Blackwell Verlag GmbH. Bovine viral diarrhoea virus (BVDV) causes one of the most important diseases of cattle in terms of economic costs and welfare. The aims were to estimate herd prevalence and to investigate the factors associated with antibodies in bulk tank milk (BTM) in dairy herds through a matched case-control study. To estimate herd prevalence, BTM samples were randomly selected (n = 314) from a population (N = 1604). The true prevalence of BVDV was 24.3% (CI 95% = 20.1-29.3%). For the case-control study, BVDV antibody-positive herds (high antibody titres) were classified as cases (n = 21) and matched (n = 63) by milk production with herds presenting low antibody titres (ratio of 1 : 3). Three multivariable models were built: 1) full model, holding all 21 variables, and two models divided according to empirical knowledge and similarity among variables; 2) animal factor model; and 3) biosecurity model. The full model (model 1) identified: age as a culling criteria (OR = 0.10; CI 95% = 0.02-0.39; P  <  0.01); farms that provided milk to other industries previously (OR = 4.13; CI 95% = 1.17-14.49; P = 0.02); and isolation paddocks for ill animals (OR = 0.14; CI 95% = 0.01-0.26; P = 0.02). The biosecurity model revealed a significant association with the use of natural mating (OR = 9.03; CI 95% = 2.14-38.03; P  <  0.01); isolation paddocks for ill animals (OR = 0.06; CI 95% = 0.05-0.83; P = 0.03); years providing milk for the same industry (OR = 0.94; CI 95% = 0.91-0.97; P = 0.02); and direct contact over fences among cattle of neighbouring farms (OR = 5.78; CI 95% = 1.41-23.67; P = 0.04). We recommend the application of grouping predictors as a good choice for model building because it could lead to a better understanding of disease-exposure associations.},
 bibtype = {article},
 author = {Machado, G. and Egocheaga, R.M.F. and Hein, H.E. and Miranda, I.C.S. and Neto, W.S. and Almeida, L.L. and Canal, C.W. and Stein, M.C. and Corbellini, L.G.},
 journal = {Transboundary and Emerging Diseases},
 number = {1}
}

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