Hair cortisol concentration in finishing pigs on commercial farms: variability between pigs, batches, and farms

Hair cortisol is a stress indicator and could be used to assess the pigs’ exposure to stressors in the weeks/months prior to non-invasive hair sampling. The main aim of this study was to describe the hair cortisol concentration (HCC) variability between individuals within a batch, between farms and between batches within a farm. The secondary aim was to determine how the number of sampled pigs influences the characterization of HCC within a batch. Twenty farrow-to-finish pig farms were recruited considering the diversity of their management practices and health status (data collected). Hair was sampled in two separate batches, 8 months apart. The necks of 24 finishing pigs were clipped per batch the week prior to slaughter. To describe the variability in HCC, an analysis of the variance model was run with three explanatory variables (batch, farm and their interaction). To identify farm clusters, a principal component analysis followed by a hierarchical clustering was carried out with four active variables (means and standard deviations of the two batches per farm) and 17 supplementary variables (management practices, herd health data). We determined how the number of sampled pigs influenced the characterization of HCC within a batch by selecting subsamples of the results. HCC ranged from 0.4 to 121.6 pg/mg, with a mean of 25.9 ± 16.2 pg/mg. The variability in HCC was mainly explained by differences between pigs (57%), then between farms (24%), between batches within the same farm (16%) and between batches (3%). Three clusters of farms were identified: low homogeneous concentrations (n = 3 farms), heterogeneous concentrations with either higher (n = 7) or lower (n = 10) HCC in batch 2 than in batch 1. The diversity of management practices and health statuses allowed to discuss hypotheses explaining the HCC variations observed. We highlighted the need to sample more than 24 pigs to characterize HCC in a pig batch. HCC differences between batches on six farms suggest sampling pigs in more than one batch to describe the HCC at the farm level. HCC variations described here confirm the need to study its links with exposure of pigs to stressors

Factor modelling of multiple time series to detect temporal variations in common dynamics

International audienc

Méthodes statistiques en épidémiologie animale

International audienceThe main aim of veterinary epidemiology is to increase knowledge on both i) the dynamics and impact ofdiseases on animal production, health and welfare and on ii) the risk of animal-acquired (i.e. zoonoses) and food-bornediseases in humans. Because animals and animal products are the source of income for a large sector of society, theemphasis on economic aspects is much more important in veterinary epidemiology than in human epidemiology.Typical research questions deal with the identification of risk factors for a disease, the estimation of the impact of adisease on production, the evaluation of the efficacy of a treatment or the timely identification of disease occurrence. Forthese purposes, a wide range of statistical techniques are used and several types of data sources exist. One peculiarityin animal productions is the collection of large production related data such as daily milk productions, live weightsor reproduction data for example. These extremely large databases are both an asset and a challenge for statisticalmodelling. To illustrate the data sources and statistical methods used in veterinary epidemiology, we present part of thework conducted following the emergence of the bluetongue virus in cattle in 2006. First, we show how the impacts ofthe disease on milk production and reproduction were estimated. Then, in order to improve the timeliness of detectionof such emergences, the application of syndromic surveillance methods to the bluetongue emergence is presented.Finally, some knowledge gaps and directions for future work are presented.L’objectif principal de l’épidémiologie animale est de faire progresser les connaissances à la fois sur i) ladynamique et l’impact des maladies sur les productions, la santé et le bien-être des animaux ii) les risques pour lasanté humaine associés aux maladies animales transmissibles à l’homme (zoonoses) et aux toxi-infections alimentaires.Parce que les animaux et les produits animaux sont une source de revenus majeure pour une partie de la population, uneplace plus importante est accordée aux aspects économiques en épidémiologie animale qu’en épidémiologie humaine.Pour un trouble de santé, des questions de recherche classiques auront trait à l’identification de facteurs de risquede survenue du trouble, l’estimation de son impact sur la production, l’évaluation de l’efficacité d’un traitement ouencore la détection précoce de sa survenue. A ces fins, un large éventail de méthodes statistiques est utilisé et denombreuses sources de données existent. Une particularité des productions animales consiste en la collecte de grandsvolumes de données en lien avec la production tels que des productions laitières quotidiennes par vache, des poidsvifs ou des données de reproduction. Ces grands volumes de données disponibles représentent à la fois un avantageet une difficulté pour la modélisation statistique. Pour illustrer les sources de données et les méthodes utilisables enépidémiologie animale, nous présentons des travaux effectués suite à l’émergence de la fièvre catarrhale ovine en 2006.Dans un premier temps, nous montrons comment les impacts de la maladie sur la production laitière et la reproductionont été estimés. Puis, dans l’objectif d’améliorer la précocité de la détection de telles émergence, l’application desméthodes de surveillance syndromique est présentée. Enfin des besoins de connaissances et des perspectives pour defutures recherches sont présentés

Rearing system with nurse cows and risk factors for Cryptosporidium infection in Organic dairy calves

Rearing dairy calves with nurse cows has been increasingly adopted by French farmers especially in organic farming and is characterized by a fostering of two to four calves during the first month of life by an unmilked lactating cow. This type of rearing remains poorly documented regarding its impact on calf health, such as cryptosporidiosis. The objectives of our study were to describe practices related to rearing dairy calves with nurse cows and to evaluate the prevalence, intensity and risk factors for Cryptosporidium infection in calf neonates. Between January and September 2019, the rearing practices of calves were described in 20 organic French farms and faeces were sampled once from 611 animals aged between 5 and 21 days. Cryptosporidium oocyst shedding was identified by modified Ziehl-Neelsen technique and scored semi-quantitatively (score 0–4). The risk of excretion (score 0 versus 1–4) was analysed using multivariate logistic regression models.This cow-calf rearing system usually consisted of a first phase with the dam, followed by an optional phase of artificial milk feeding (calves being fed with whole milk of the farm) and a final phase of fostering by a nurse cow. Each nurse was suckled from one to five calves of close age with a fostering age of 8 days on average. The oocyst shedding prevalence was 40.2 % and similar to classically reared calves, but the intensity of shedding and the prevalence of diarrhoea appeared to be lower. The identified six risk factors for oocyst shedding were: born in the last two thirds of the birth order, born between January and July versus August and September, calf with its dam in the barn versus on pasture, having an artificial milk feeding phase versus being with the dam only, and contact between peer calves and notably the presence of an oocyst excretory calf fostered by the same nurse. These results emphasize the role of the environment for the direct and indirect contamination, particularly that related to the accumulation of oocysts from previous or peer calves facilitating the faecal-oral route of transmission. This highlights the crucial role of the premises used intensively during the winter and spring months with higher densities of calves in the barn compared to outdoor situations promoted by this rearing

Evaluation of a continuous indicator for syndromic surveillance through simulation. application to vector borne disease emergence detection in cattle using milk yield.

Two vector borne diseases, caused by the Bluetongue and Schmallenberg viruses respectively, have emerged in the European ruminant populations since 2006. Several diseases are transmitted by the same vectors and could emerge in the future. Syndromic surveillance, which consists in the routine monitoring of indicators for the detection of adverse health events, may allow an early detection. Milk yield is routinely measured in a large proportion of dairy herds and could be incorporated as an indicator in a surveillance system. However, few studies have evaluated continuous indicators for syndromic surveillance. The aim of this study was to develop a framework for the quantification of both disease characteristics and model predictive abilities that are important for a continuous indicator to be sensitive, timely and specific for the detection of a vector-borne disease emergence. Emergences with a range of spread characteristics and effects on milk production were simulated. Milk yields collected monthly in 48 713 French dairy herds were used to simulate 576 disease emergence scenarios. First, the effect of disease characteristics on the sensitivity and timeliness of detection were assessed: Spatio-temporal clusters of low milk production were detected with a scan statistic using the difference between observed and simulated milk yields as input. In a second step, the system specificity was evaluated by running the scan statistic on the difference between observed and predicted milk yields, in the absence of simulated emergence. The timeliness of detection depended mostly on how easily the disease spread between and within herds. The time and location of the emergence or adding random noise to the simulated effects had a limited impact on the timeliness of detection. The main limitation of the system was the low specificity i.e. the high number of clusters detected from the difference between observed and predicted productions, in the absence of disease

Parameters used for the simulation of the 8 selected scenarios representing 8 disease groups.

<p>The abbreviations used and the milk loss scenarios are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073726#pone-0073726-t001" target="_blank">Tables 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073726#pone-0073726-t002" target="_blank">2</a>.</p

Groups generated using k-means algorithm from the scenarios 2 principal components.

<p>On the left-hand side plot, each dot represents a scenario and each color represents one of the 8 retained scenario groups. The plot on the right-hand side represents the mean quantity of milk lost per cow in each of 8 geographical area and during the first 8 weeks after emergence. The same colors are used on both sides.</p