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      Herd-level risk factors for Cryptosporidium infection in dairy-goat kids in western France

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          Abstract

          We conducted a cross-sectional study of risk factors for herd-level kid positivity for Cryptosporidium parvum oocysts in dairy-goat farms (Deux-Sèvres, western France). From January to March 2003, faeces from a convenient sample of 879 5- to 30-day-old goat kids from 60 herds were examined microscopically after staining with carbol fuschin. Oocyst shedding was scored semi-quantitatively (0 to 4+) allowing us to obtain a cumulative score per herd. Standardized questionnaires with information about management practices were collected in each farm. We found positive kids in 32 of 60 herds (53.3%) and in 142 animals out of 879 (16.2%). We used logistic regression for two risk-factor model: (1) simple positive (case: herd score ≥1+, at least one positive kid in the herd, versus control: herd score = 0), (2) strongly positive (case: overall herd score ≥3+ versus control: herd score <3+). Risk factors associated with simple positive herds were period of sampling compared to the peak of births (After versus Before, OR = 4.2, 95% CI 1.2, 15.3) and practice of kid grouping by age or weight (Yes versus No, OR = 4.4, 95% CI 1.0, 19.1). Risk factors associated with strongly positive herds were period of investigation (February/March versus January, OR = 12.7, 95% CI 2.1, 76.6), exposure to graminaceous plants in forage (OR = 11.6, 95% CI 1.7, 81.0) and type of ventilation in the goat premises (Vertical versus Wind effect, OR = 14.7, 95% CI 2.1, 106.1). No important association was found between kid-management practices and herd positivity. These results suggest a major role of the environment of kids during their first hours of life in the adult-goat premises regarding the transmission of C. parvum infection.

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          A review of the importance of cryptosporidiosis in farm animals

          Cryptosporidium species are coccidian parasites with a large capacity to reproduce and to disseminate. Several species are known to infect farm animals, although the economic importance of cryptosporidiosis is highly host species dependent. This paper reviews the impact of cryptosporidial infections in livestock and poultry. For different farm animals, the Cryptosporidium spp. that occur, as well as their clinical and pathological features, and their interactions with other pathogens, are described. In addition, data concerning the prevalence, the transmission and the epidemiology of the disease are mentioned and a description of the economic losses associated with cryptosporidiosis in each of the hosts is given. Cryptosporidiosis seems to be mainly a problem in neonatal ruminants. Cryptosporidium parvum is considered to be an important agent in the aetiology of the neonatal diarrhoea syndrome of calves, lambs and goat kids, causing considerable direct and indirect economic losses. Avian cryptosporidiosis is an emerging health problem in poultry, associated with respiratory disease in chickens and other Galliformes, and with intestinal disease in turkeys and quails. Because of limited availability of effective drugs, the control of cryptosporidiosis relies mainly on hygienic measures and good management.
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            A new look at statistical model identification

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              An overview of techniques for dealing with large numbers of independent variables in epidemiologic studies.

              Many studies of health and production problems in livestock involve the simultaneous evaluation of large numbers of risk factors. These analyses may be complicated by a number of problems including: multicollinearity (which arises because many of the risk factors may be related (correlated) to each other), confounding, interaction, problems related to sample size (and hence the power of the study), and the fact that many associations are evaluated from a single dataset. This paper focuses primarily on the problem of multicollinearity and discusses a number of techniques for dealing with this problem. However, some of the techniques discussed may also help to deal with the other problems identified above. The first general approach to dealing with multicollinearity involves reducing the number of independent variables prior to investigating associations with the disease. Techniques to accomplish this include: (1) excluding variables after screening for associations among independent variables; (2) creating indices or scores which combine data from multiple factors into a single variable; (3) creating a smaller set of independent variables through the use of multivariable techniques such as principal components analysis or factor analysis. The second general approach is to use appropriate steps and statistical techniques to investigate associations between the independent variables and the dependent variable. A preliminary screening of these associations may be performed using simple statistical tests. Subsequently, multivariable techniques such as linear or logistic regression or correspondence analysis can be used to identify important associations. The strengths and limitations of these techniques are discussed and the techniques are demonstrated using a dataset from a recent study of risk factors for pneumonia in swine. Emphasis is placed on comparing correspondence analysis with other techniques as it has been used less in the epidemiology literature.
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                Author and article information

                Contributors
                Journal
                Prev Vet Med
                Prev. Vet. Med
                Preventive Veterinary Medicine
                Elsevier B.V.
                0167-5877
                1873-1716
                5 September 2006
                17 November 2006
                5 September 2006
                : 77
                : 1
                : 109-121
                Affiliations
                [a ]AFSSA Site de NIORT, Laboratoire d’Etudes et de Recherches Caprines, 60 rue de Pied de Fond, B.P. 3081, 79012 Niort Cedex, France
                [b ]Groupement de Défense Sanitaire de l’Orne, B.P. 138, 61004 Alençon, France
                [c ]Laboratorio de Parasitología, Departamento de Microbiología y Parasitología, Facultad de Farmacia, Universidad de Santiago de Compostela, Avda. de Vigo s/n, 15782 Santiago de Compostela, La Coruña, Galicia, España
                Author notes
                [* ]Correspondence at: AFSSA Site de NIORT, Laboratoire d’Etudes et de Recherches Caprines, 60 rue de Pied de Fond, B.P. 3081, 79012 Niort Cedex, France. Fax: +33 5 4979 4219. c.chartier@ 123456niort.afssa.fr
                Article
                S0167-5877(06)00160-7
                10.1016/j.prevetmed.2006.07.001
                7132478
                16911839
                c1a5f407-26a9-4705-b5cd-e1f3493f64fb
                Copyright © 2006 Elsevier B.V. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 10 September 2004
                : 2 June 2006
                : 5 July 2006
                Categories
                Article

                Veterinary medicine
                cryptosporidium parvum,dairy-goat farms,france,logistic regression
                Veterinary medicine
                cryptosporidium parvum, dairy-goat farms, france, logistic regression

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