Open Access Research article

Assessing risk profiles for Salmonella serotypes in breeding pig operations in Portugal using a Bayesian hierarchical model

Carla Correia-Gomes12*, Theodoros Economou3, Denisa Mendonça12, Madalena Vieira-Pinto4 and João Niza-Ribeiro12

Author affiliations

1 Instituto de Ciências Biomédicas Abel Salazar – Universidade do Porto (ICBAS-UP), Population Studies Department, Largo Prof. Abel Salazar, Porto, 2, 4099-003, PORTUGAL

2 Instituto de Saúde Pública da Universidade do Porto (ISPUP), Rua das Taipas, Porto, 135, 4050-600, PORTUGAL

3 CEMS, University of Exeter, Harrison Building, North Park Road, Exeter, EX4 4QF, UK

4 UTAD, Veterinary Science Department, Apartado, Vila Real, 1013, 5001-801, PORTUGAL

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Citation and License

BMC Veterinary Research 2012, 8:226  doi:10.1186/1746-6148-8-226

Published: 21 November 2012

Abstract

Background

The EU Regulation No 2160/2003 imposes a reduction in the prevalence of Salmonella in pigs. The efficiency of control programmes for Salmonella in pigs, reported among the EU Member States, varies and definitive eradication seems very difficult. Control measures currently recommended for Salmonella are not serotype-specific. Is it possible that the risk factors for different Salmonella serotypes are different? The aim of this study was to investigate potential risk factors for two groups of Salmonella sp serotypes using pen faecal samples from breeding pig holdings representative of the Portuguese pig sector.

Methods

The data used come from the Baseline Survey for the Prevalence of Salmonella in breeding pigs in Portugal. A total of 1670 pen faecal samples from 167 herds were tested, and 170 samples were positive for Salmonella. The presence of Salmonella in each sample (outcome variable) was classified in three categories: i) no Salmonella, ii) Salmonella Typhimurium or S. Typhimurium-like strains with the antigenic formula: 1,4,5,12:i:-, , and iii) other serotypes. Along with the sample collection, a questionnaire concerning herd management and potential risk factors was utilised. The data have a “natural” hierarchical structure so a categorical multilevel analysis of the dataset was carried out using a Bayesian hierarchical model. The model was estimated using Markov Chain Monte Carlo methods, implemented in the software WinBUGS.

Results

The significant associations found (when compared to category “no Salmonella”), for category “serotype Typhimurium or S. Typhimurium-like strains with the antigenic formula: 1,4,5,12:i:-” were: age of breeding sows, size of the herd, number of pigs/pen and source of semen. For the category “other serotypes” the significant associations found were: control of rodents, region of the country, source of semen, breeding sector room and source of feed.

Conclusions

The risk factors significantly associated with Salmonella shedding from the category “serotype Typhimurium or serotype 1,4,5,12:i:-“ were more related to animal factors, whereas those associated with “other serotypes” were more related to environmental factors. Our findings suggest that different control measures could be used to control different Salmonella serotypes in breeding pigs.