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Abstract: Enzootic pneumonia (EP) is responsible for considerable economic losses in pig production. This study analyses temporal variations of pneumonic lesions present in slaughtered finishing pigs utilising a novel analytical tool – STL decomposition. Using data collected over a 6-year period starting in July 2005, time-series analyses were conducted to identify trend and the presence of seasonal variations to support industry led measures to monitor and control this important respiratory disease. In England, the BPEX Pig Health Scheme monitors the occurrence of EP in slaughtered finished pigs by identifying its gross pathology, enzootic pneumonia-like (EP-like) lesions. For visual analytics, the monthly prevalence for EP-like lesions was modelled using STL, a seasonal-trend decomposition method based on locally-weighted regression. A binomial generalised linear mixed-effects model (GLMM), accounting for clustering at batch level, was used to test the significance of the trend and seasonality. A mean of 12,370 pigs was assessed per month across 12 pig abattoirs over the study period. A trend toward reduction in prevalence of EP-like lesions during the first 3 years of BPHS, followed by an increasing trend, was identified with STL. This feature was consistent with the presence of a statistically significant positive quadratic term (“U” shape) as identified using the GLMM inference model. November and December appeared in the STL explorations as higher seasonal peaks of the occurrence of EP-like lesions. These 2 months had a significantly higher risk of this disease (OR=1.38, 95% CI: 1.24–1.54 and OR=1.4, 95% CI: 1.25–1.58, respectively, with July taken as baseline). The results were reported back to the pig industry as part of the national monitoring investigations. [Copyright &y& Elsevier]

Traditional indicator-based livestock surveillance has been focused on case definitions, definitive diagnoses and laboratory confirmation. The use of syndromic disease surveillance would increase the population base from which animal health data are captured and facilitate earlier detection of new and re-emerging threats to animal health. Veterinary practitioners could potentially play a vital role in such activities. In a pilot study, specialist private veterinary practitioners (PVP) working in the English pig industry were asked to collect and transfer background data and disease incident reports for pig farms visited during the study period. Baseline data from 110 pig farms were received, along with 68 disease incident reports. Reports took an average of approximately 25 minutes to complete. Feedback from the PVPs indicated that they saw value in syndromic surveillance. Maintenance of anonymity in the outputs would be essential, as would timely access for the PVPs to relevant information on syndromic trends. Further guidance and standardisation would also be required. Syndromic surveillance by PVPs is possible for the pig industry. It has potential to fill current gaps in the collection of animal health data, as long as the engagement and participation of data providers can be obtained and maintained., Competing Interests: Competing interests: None declared., (© British Veterinary Association 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

The paper analyses the impact of a priori determinants of biosecurity behaviour of farmers in Great Britain. We use a dataset collected through a stratified telephone survey of 900 cattle and sheep farmers in Great Britain (400 in England and a further 250 in Wales and Scotland respectively) which took place between 25 March 2010 and 18 June 2010. The survey was stratified by farm type, farm size and region. To test the influence of a priori determinants on biosecurity behaviour we used a behavioural economics method, structural equation modelling (SEM) with observed and latent variables. SEM is a statistical technique for testing and estimating causal relationships amongst variables, some of which may be latent using a combination of statistical data and qualitative causal assumptions. Thirteen latent variables were identified and extracted, expressing the behaviour and the underlying determining factors. The variables were: experience, economic factors, organic certification of farm, membership in a cattle/sheep health scheme, perceived usefulness of biosecurity information sources, knowledge about biosecurity measures, perceived importance of specific biosecurity strategies, perceived effect (on farm business in the past five years) of welfare/health regulation, perceived effect of severe outbreaks of animal diseases, attitudes towards livestock biosecurity, attitudes towards animal welfare, influence on decision to apply biosecurity measures and biosecurity behaviour. The SEM model applied on the Great Britain sample has an adequate fit according to the measures of absolute, incremental and parsimonious fit. The results suggest that farmers' perceived importance of specific biosecurity strategies, organic certification of farm, knowledge about biosecurity measures, attitudes towards animal welfare, perceived usefulness of biosecurity information sources, perceived effect on business during the past five years of severe outbreaks of animal diseases, membership in a cattle/sheep health scheme, attitudes towards livestock biosecurity, influence on decision to apply biosecurity measures, experience and economic factors are significantly influencing behaviour (overall explaining 64% of the variance in behaviour). Three other models were run for the individual regions (England, Scotland and Wales). A smaller number of variables were included in each model to account for the smaller sample sizes. Results show lower but still high levels of variance explained for the individual models (about 40% for each country). The individual models' results are consistent with those of the total sample model. The results might suggest that ways to achieve behavioural change could include ensuring increased access of farmers to biosecurity information and advice sources., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Ascaris suum is the most important internal parasite in farmed pigs world-wide. In England, the BPEX Pig Health Scheme (BPHS) monitors the prevalence of ascariosis in slaughtered finished pigs by identifying milk spots - the healing lesions caused by A. suum larvae migration through the liver. This study investigates the trend of milk spot lesions from July 2005 to December 2010 to identify the progress made by the industry in controlling this parasitic disease. For visual explorations, the monthly prevalence for milk spots was modelled using "STL", a seasonal-trend decomposition method based on locally weighted regression. Random effects binomial modelling accounting for clustering at batch level was used to test the significance of the trend and seasonality. Additionally, the differences in the milk spot prevalence trends for BPHS members (those that joined the scheme) and non-members were investigated and tested. A mean of 12,442 pigs was assessed per month (in 290 batches) across 12 pig abattoirs over the study period, from which a monthly mean of 7102 pigs (159 batches) came from BPHS members. A mild overall decrease in prevalence of milk spots over the monitored period was identified as well as a seasonal variation which showed peaks in summer and at the beginning of autumn. BPHS members maintained a lower prevalence than non-members. The results from this work illustrate ascariosis as a persistent problem in current farm production., (© 2011 Elsevier B.V. All rights reserved.)