Tuyển tập các báo cáo nghiên cứu về bệnh học thý y được đăng trên tạp chí Acta Veterinaria Scandinavica cung cấp cho các bạn kiến thức về bệnh thú y đề tài: Herd and cow characteristics affecting the odds of veterinary treatment for disease – a multilevel analysis. | Acta Veterinaria Scandinavica BioMed Central Research Herd and cow characteristics affecting the odds of veterinary treatment for disease - a multilevel analysis Marie Jansson Mork 1 Ulf Emanuelson1 Ann Lindberg2 Ivar Vâgsholm1 2 and Agneta Egenvall1 Open Access Address Department of Clinical Sciences Swedish University of Agricultural Sciences PO Box 7019 SE-750 07 Uppsala Sweden and 2National Veterinary Institute SE-751 89 Uppsala Sweden Email Marie Jansson Mork - Ulf Emanuelson - Ann Lindberg - Ivar Vâgsholm - Agneta Egenvall - Corresponding author Published 22 August 2009 Received 5 May 2009 . . J. . Accepted 22 August 2009 Acta Veterinaria Scandinavica 2009 51 34 doi 1751-0147-51-34 This article is available from http content 51 1 34 2009 Mork et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background Research has indicated that a number of different factors affect whether an animal receives treatment or not when diseased. The aim of this paper was to evaluate if herd or individual animal characteristics influence whether cattle receives veterinary treatment for disease and thereby also introduce misclassification in the disease recording system. Methods The data consisted mainly of disease events reported by farmers during 2004. We modelled odds of receiving veterinary treatment when diseased using two-level logistic regression models for cows and young animals calves and heifers respectively. Model parameters were estimated using three procedures because these procedures have been shown under some conditions to produce biased estimates for multi-level models with
