Assessment of anthelmintic efficacy against cattle gastrointestinal nematodes in western France and southern Italy.

Our objective was to measure the efficacy of ivermectin (IVM) and benzimidazoles (BZ, i.e. fenbendazole and albendazole) in 15 cattle farms in western France and southern Italy. A total of 11 groups were treated with IVM and 11 with BZ. Efficacy was assessed by calculating the percentage of faecal egg count reduction (%FECR) using the pre- and post-treatment arithmetic means. Anthelmintic resistance was considered to be present when the %FECR was <95% and the lower limit of the 95% confidence interval <90%. For IVM, the percentages of FECR ranged from 73% to 100%. Lack of efficacy to IVM was detected in two farms out of four in France, but was not detected in any of the seven farms in Italy. For BZ, the percentages of FECR ranged from 95% to 100%. No case of BZ resistance was detected in the five farms in France and the six farms in Italy.

Economic efficacy of anthelmintic treatments in dairy sheep naturally infected by gastrointestinal strongyles.

The aim of the present paper was to assess benefit of strategic anthelmintic treatments on milk production in six commercial dairy sheep farms, located in southern Italy, whose animals were naturally infected with gastrointestinal strongyles. On each farm, two similar groups were formed, one untreated control group and one treated group. In all the treated groups, the strategic anthelmintic schemes were based on: (i) only one treatment with moxidectin in the periparturient period (February, Farm No. 6), or; (ii) two treatments, i.e. the first with moxidectin performed in the periparturient period (February, Farms Nos. 1, 2, 3 and 4) or in the postparturient period (April, Farm No. 5), and the second with netobimin at the mid/end of lactation (June, Farms Nos. 1, 2, 3, 4 and 5). Faecal egg count reduction (FECR) tests were performed on each farm in order to asses the anthelmintic efficacy of the drugs used. In addition, milk yield measurements for each animal fortnightly in each farm for the lactation period were performed. In terms of FECR, both moxidectin and netobimin were effective in all the 6 studied farms. Regarding milk production, overall in the 6 study farms the mean daily milk productions of the treated groups were higher than those of the control group. However, there were important differences between the 6 farms, i.e. the increase of milk production in the treated groups versus the control groups was as follows: +18.9% (Farm 1), +30.4% (Farm 2), +4.0% (Farm 3), +37.0% (Farm 4), +5.5% (Farm 5) and +40.8% (Farm 6). The results of the study showed that the economic efficacy of an anthelmintic treatment is not a cause-effect issue, but is a multifactorial issue which depends upon the quali-quantitative parasitological status of the animals, the pathogenesis of the species of parasites, the virulence of the strains of parasites, the local epidemiology, the timing of treatment, the breed of animal in terms of genetics and production types, nutrient supply.

Disease mapping and risk assessment in veterinary parasitology: some case studies.

Disease mapping and risk assessment are important tasks in the area of medical and veterinary epidemiology. The development of methods for mapping diseases has progressed considerably in recent years. Geographical Information Systems (GIS), Remote Sensing (RS), and Spatial Analysis represent new tools for the study of epidemiology, and their application to parasitology has become more and more advanced, in particular to study the spatial and temporal patterns of diseases. The present review highlights the usefulness of GIS and RS in veterinary parasitology in order to better know the epidemiology of parasite organisms, causing either snail/arthropod borne diseases or direct transmissible diseases, mostly in small areas with a strong impact by man. It demonstrates the potential of these technologies to serve as effective tools for: data capture, mapping and analysis for the development of descriptive parasitological maps; studying the environmental features that influence the distribution of parasites; predicting parasite occurrence/seasonality based on their environmental requirements and as decision support for disease intervention; and surveillance and monitoring of animal diseases.

Geographical Information Systems and on-line GIServices for health data sharing and management.

Integrating Geographical Information Systems (GIS) technology and public health experience may represent a solution for a better comprehension of spatial and temporal trends of phenomena. Useful applications can be built that support practitioners in their daily tasks, from risk assessment to prevention programmes. Also, making available data on the Internet through GIServices represents an important goal. Institutions and public health practitioners may benefit from the technological integration of GIS, the Web, handheld and mobile global positioning systems (GPS) devices. Expert users may be supported in deriving thematic maps which represent a spatial synthesis documentation starting from an analytic study expressed in terms of numbers and features. In this paper we show an example of an on-line data sharing and processing application, emphasizing ways GIS can provide added value to health research and management.

[Statistical models for spatial analysis in parasitology]. / Modelli statistici per l'analisi spaziale in parassitologia.

The simplest way to study the spatial pattern of a disease is the geographical representation of its cases (or some indicators of them) over a map. Maps based on raw data are generally "wrong" since they do not take into consideration for sampling errors. Indeed, the observed differences between areas (or points in the map) are not directly interpretable, as they derive from the composition of true, structural differences and of the noise deriving from the sampling process. This problem is well known in human epidemiology, and several solutions have been proposed to filter the signal from the noise. These statistical methods are usually referred to as Disease Mapping. In geographical analysis a first goal is to evaluate the statistical significance of the heterogeneity between areas (or points). If the test indicates rejection of the hypothesis of homogeneity the following task is to study the spatial pattern of the disease. The spatial variability of risk is usually decomposed into two terms: a spatially structured (clustering) and a non spatially structured (heterogeneity) one. The heterogeneity term reflects spatial variability due to intrinsic characteristics of the sampling units (e.g. igienic conditions of farms), while the clustering term models the association due to proximity between sampling units, that usually depends on ecological conditions that vary over the study area and that affect in similar way breedings that are close to each other. Hierarchical bayesian models are the main tool to make inference over the clustering and heterogeneity components. The results are based on the marginal posterior distributions of the parameters of the model, that are approximated by Monte Carlo Markov Chain methods. Different models can be defined depending on the terms that are considered, namely a model with only the clustering term, a model with only the heterogeneity term and a model where both are included. Model selection criteria based on a compromise between degree of complexity and goodness of fit are then needed to discriminate among them, because each specification has a different biological meaning. Our aim is to demonstrate that these techniques can be used to study the geographical distribution of a parasite infection. Our analyses are based on data collected in 142 farms of the province of Latina. In each breeding a fixed number of sheeps has been sampled (20) and checked for the presence of C. daubneyi. We have specified a Binomial model for the proportion of infected animals in each breeding. The heterogeneity component is modelled in a standard way, while we have used different prior specifications for the clustering term to show how they affect the results. When we use the usual specification also for clustering, the two models show a completely different spatial pattern of infection, probably because the intrinsic spatial structure of the clustering term tend to bias our inferences. The selection criterion indicates in this case the heterogeneity model as the "best" one. However, if we modify the prior so that a lower degree of spatial interaction is assumed, the clustering model is less complex and its goodness of fit better and it should be preferred.