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Identification of Salmonella high risk pig-herds in Belgium by using semiparametric quantile regression

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Abstract:

Summary. 

Consumption of pork that is contaminated with Salmonella is an important source of human salmonellosis world wide. To control and prevent salmonellosis, Belgian pig-herds with high Salmonella infection burden are encouraged to take part in a control programme supporting the implementation of control measures. The Belgian government decided that only the 10% of pig-herds with the highest Salmonella infection burden (denoted high risk herds) can participate. To identify these herds, serological data reported as sample-to-positive ratios (SP-ratios) are collected. However, SP-ratios have an extremely skewed distribution and are heavily subject to confounding seasonal and animal age effects. Therefore, we propose to identify the 10% high risk herds by using semiparametric quantile regression with P-splines. In particular, quantile curves of animal SP-ratios are estimated as a function of sampling time and animal age. Then, pigs are classified into low and high risk animals with high risk animals having an SP-ratio that is larger than the corresponding estimated upper quantile. Finally, for each herd, the number of high risk animals is calculated as well as the beta–binomial p-value reflecting the hypothesis that the Salmonella infection burden is higher in that herd compared with the other herds. The 10% pig-herds with the lowest p-values are then identified as high risk herds. In addition, since high risk herds are supported to implement control measures, a risk factor analysis is conducted by using binomial generalized linear mixed models to investigate factors that are associated with decreased or increased Salmonella infection burden. Finally, since the choice of a specific upper quantile is to a certain extent arbitrary, a sensitivity analysis is conducted comparing different choices of upper quantiles.

Keywords: Generalized linear mixed models; Pig-herds; Risk factors; Salmonella; Semiparametric quantile regression

Document Type: Research Article

DOI: https://doi.org/10.1111/j.1467-985X.2007.00525.x

Affiliations: 1: Hasselt University, Diepenbeek, Belgium 2: Ghent University, Merelbeke, Belgium 3: Veterinary and Agrochemical Research Center, Brussels, Belgium

Publication date: 2008-04-01

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