Share This Article:

Development and Validation of an Objective Risk Scoring System for Assessing the Likelihood of Virus Introduction in Porcine Reproductive and Respiratory Syndrome Virus-Free Sow Farms in the US

Full-Text HTML Download Download as PDF (Size:448KB) PP. 168-175
DOI: 10.4236/ojvm.2013.32026    3,118 Downloads   4,815 Views   Citations

ABSTRACT

The lack of validated tools to predict how long sow farms will remain PRRS virus-free following successful elimination of the virus has deterred veterinarians and producers from attempting to eliminate the PRRS virus from sow farms. The aim of this study was to use the database of PRRS Risk Assessments for the Breeding Herd in PADRAP to develop and validate an objective risk scoring system for predicting the likelihood of virus introduction in PRRS virus-free sow farms in the US. To overcome the challenges of dealing with a large number of variables, group lasso for logistic regression (GLLR) was applied to a retrospective dataset of PRRS Risk Assessment for the Breeding Herd surveys completed for 704 farms to develop the risk scoring system. The validity of the GLLR risk scoring system was then evaluated by testing its predictive ability on a dataset from a long-term prospective study of 196 sow farms to assess risk factors associated with how long PRRS virus-free sow farms remained PRRS virus-free. Receiver operator characteristic(ROC) curves were estimated to compare the performance of the GLLR risk scoring system to the risk scoring system based on expert opinion (EO), currently used in the PRRS Risk Assessment for the Breeding Herd, for predicting whether herds remained PRRS virus-free for 130 weeks. The GLLR risk scoring system (AUC, 0.76; 95% CI, 0.67 - 0.84) performed significantly better than the EO risk scoring system (AUC, 0.36; 95% CI, 0.27 - 0.46) for predicting whether to sow farms in the prospective study survived for 130 weeks (p < 0.001). Dividing farms into 3 risk groups (low, medium and high) using a low and high cutoff values for the GLLR risk score was informative as the differences in the KM survival curves for the 3 groups were both clinically meaningful and statistically significant. The GLLR risk scoring system used in conjunction with the PRRS Risk Assessment for the Breeding Herd survey delivered through PADRAP appears to have the potential to help veterinarians predict the likelihood of virus introduction in PRRS virus-free sow farms in the US.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

D. Holtkamp, H. Lin, C. Wang and D. Polson, "Development and Validation of an Objective Risk Scoring System for Assessing the Likelihood of Virus Introduction in Porcine Reproductive and Respiratory Syndrome Virus-Free Sow Farms in the US," Open Journal of Veterinary Medicine, Vol. 3 No. 2, 2013, pp. 168-175. doi: 10.4236/ojvm.2013.32026.

References

[1] D. J. Holtkamp, et al., “Assessment of the Economic Impact of Porcine Reproductive and Respiratory Syndrome Virus on United States Pork Producers,” Journal of Swine Health and Production, Vol. 21, No. 2, 2013, pp. 72-84.
[2] Y. Li, et al., “Emergence of a Highly Pathogenic Porcine Reproductive and Respiratory Syndrome Virus in the Mid-Eastern Region of China,” The Veterinary Journal, Vol. 174, No. 3, 2007, pp. 577-584. doi:10.1016/j.tvjl.2007.07.032
[3] S. Dee, H. Joo, B. Park, T. Molitor and G. Bruna, “Attempted Elimination of Porcine Reproductive and Respiratory Syndrome Virus from a Seedstock Farm by Vaccination of the Breeding Herd and Nursery Depopulation,” Veterinary Record, Vol. 142, No. 21, 1998, pp. 569-572. doi:10.1136/vr.142.21.569
[4] S. Dee, M. Bierk, J. Deen and T. Molitor, “An Evaluation of Test and Removal for the Elimination of Porcine Reproductive and Respiratory Syndrome Virus from Five Swine Farms,” Canadian Journal of Veterinary Research, Vol. 63, No. 1, 2001, pp. 22-27.
[5] T. Gillespie and A. Carroll, “Methods of Control and Elimination of Porcine Reproductive and Respiratory Syndrome Virus Using Modified Live Vaccine in a Two-Site Production System,” Journal of Swine Health and Production, Vol. 11, No. 6, 2003, pp. 291-295.
[6] C. A. Corzo, E. Mondaca, S. Wayne, M. Torremorell, S. Dee, P. Davies and R. B. Morrison, “Control and Elimination of Porcine Reproductive and Respiratory Syndrome Virus,” Virus Research, Vol. 154, No. 1-2, 2010, pp. 185-192. doi:10.1016/j.virusres.2010.08.016
[7] D. J. Holtkamp, et al., “Terminology for Classifying Swine Herds by Porcine Reproductive and Respiratory Syndrome Virus Status,” Journal of Swine Health and Production, Vol. 19, No. 1, 2011, pp. 44-56.
[8] R. A. Rockar, K. S. Drobatz and F. S. Shofer, “Development of a Scoring System for the Veterinary Trauma Patient,” Journal of Veterinary Emergency and Critical Care, Vol. 4, No. 2, 1994, pp. 77-83. doi:10.1111/j.1476-4431.1994.tb00118.x
[9] American Association of Swine Veterinarians, “Production Animal Disease Risk Assessment Program,” 2012. http://www.padrap.org
[10] L. Meier, S. van de Geer and P. Buhlmann, “The Group Lasso for Logistic Regression,” Journal of the Royal Statistical Society: Series B, Vol. 70, 2008, pp. 53-71. doi:10.1111/j.1467-9868.2007.00627.x
[11] E. Barranger, et al., “An Axilla Scoring System to Predict Non-Sentinel Lymph Node Status in Breast Cancer Patients with Sentinel Lymph Node Involvement,” Breast Cancer Research and Treatment, Vol. 91, No. 2, 2005, pp. 113-119. doi:10.1007/s10549-004-5781-z
[12] A. Alert and J. A. Anderson, “On the Existence of Maximum Likelihood Estimates in Logistic Regression Models,” Biometrika, Vol. 71, No. 1, 1984, pp. 1-10. doi:10.1093/biomet/71.1.1
[13] H. Lin, C. Wang, P. Liu and D. J. Holtkamp, “Construction of Disease Risk Scoring Systems Using Logistic Group Lasso: Application to Porcine Reproductive and Respiratory Syndrome Survey Data,” Journal of Applied Statistics, Vol. 40, No. 4, pp. 736-746.
[14] L. Meier, “Grplasso: Fitting User Specified Models with Group Lasso Penalty,” R Package Version 0 4-2, 2009. http://CRAN.R-project.org/package=grplasso
[15] “R: A Language and Environment for Statistical Computing [Computer Program],” Vienna, R Foundation for Statistical Computing, 2011.
[16] R Development Core Team, “R Package Survival,” R Package Version 2 35-8, 2009. http://CRAN.R-project.org/package=survival
[17] J. J. Zimmerman, et al., “Porcine Reproductive and Respiratory Virus (Porcine Aterivirus),” In: J. J. Zimmerman, L. A. Karriker, A. Ramirez, K. J. Schwartz and G. W. Stevenson, Eds., Diseases of Swine, 10th Edition, Wiley-Blackwell, Hoboken, 2012, pp. 461-486.

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.