Use of Herd Information for Predicting Salmonella Status in Pig Herds

• Published in: Zoonoses and public health 2010-11, Vol.57 (s1), p.49-59
• Main Authors: Baptista, F. M., Alban, L., Nielsen, L. R., Domingos, I., Pomba, C., Almeida, V.
• Format: Article
• Language: English
• Subjects: Abattoirs; Animal diseases; Animal Husbandry; Animals; Antibodies, Bacterial - analysis; biosecurity; Cross-Sectional Studies; Discriminant analysis; Enzyme-Linked Immunosorbent Assay - methods; Enzyme-Linked Immunosorbent Assay - veterinary; factor analysis; Factor Analysis, Statistical; Feces - microbiology; herd classification; Hogs; Livestock industry; Logistic Models; pig; Portugal; Predictive Value of Tests; Risk Assessment; Risk Factors; Salmonella; Salmonella - classification; Salmonella - immunology; Salmonella Infections, Animal - epidemiology; Salmonella Infections, Animal - microbiology; Salmonella Infections, Animal - transmission; Sentinel Surveillance - veterinary; Serotyping; Surveys and Questionnaires; Swine; Swine Diseases - epidemiology; Swine Diseases - microbiology
• ISSN: 1863-1959; 1863-2378
• DOI: 10.1111/j.1863-2378.2010.01354.x

Summary Salmonella surveillance‐and‐control programs in pigs are highly resource demanding, so alternative cost‐effective approaches are desirable. The aim of this study was to develop and evaluate a tool for predicting the Salmonella test status in pig herds based on herd information collected from 108 industrial farrow‐to‐finish pig herds in Portugal. A questionnaire including known risk factors for Salmonella was used. A factor analysis model was developed to identify relevant factors that were then tested for association with Salmonella status. Three factors were identified and labelled: general biosecurity (factor 1), herd size (factor 2) and sanitary gap implementation (factor 3). Based on the loadings in factor 1 and factor 3, herds were classified according to their biosecurity practices. In total, 59% of the herds had a good level of biosecurity (interpreted as a loading below zero in factor 1) and 37% of the farms had good biosecurity and implemented sanitary gap (loading below zero in factor 1 and loading above zero in factor 3). This implied that they, among other things, implemented preventive measures for visitors and workers entering the herd, controlled biological vectors, had hygiene procedures in place, water quality assessment, and sanitary gap in the fattening and growing sections. In total, 50 herds were tested for Salmonella. Logistic regression analysis showed that factor 1 was significantly associated with Salmonella test status (P = 0.04). Herds with poor biosecurity had a higher probability of testing Salmonella positive compared with herds with good biosecurity. This study shows the potential for using herd information to classify herds according to their Salmonella status in the absence of good testing options. The method might be used as a potentially cost‐effective tool for future development of risk‐based approaches to surveillance, targeting interventions to high‐risk herds or differentiating sampling strategies in herds with different levels of infection.