Preventive veterinary medicine最新文献

This study explores the implementation of biosecurity measures by farm workers through daily work routines on dairy cattle farms in Spain. The implementation of biosecurity measures on dairy cattle farms is mainly decided by farmers and veterinarians, but it is carried out by both farmers and farm workers. However, farm workers may be affected by socio-employment factors such as the precariousness of their work such that implementation of biosecurity measures may be context dependent and may differ from official recommendations. An ethnographic approach was used through observations and conversations on four farms in two regions of Spain, two in Galicia (north-west) and two in Catalonia (north-east) to explore these factors. The profiles of participants were farmer-family workers, internal worker-employees and external worker-employees. Results showed that there were social differences, particularly communicative and hierarchical differences, between workers and farmers that influenced the implementation of biosecurity measures. Workers implemented biosecurity practices incompletely, differently or incorrectly from their supervisors' instructions. Workers also relied on what the authors called an anthropomorphic approach to implementing biosecurity measures, which deviated from farm guidelines. In order to improve the implementation of biosecurity measures on dairy cattle farms, it is necessary to consider workers as key stakeholders in biosecurity. Such consideration could also help to professionalise workers, reduce their turnover and increase their permanence on these farms.

Adult dogs and cats in overweight or obese condition are common, but prevalence data for different life stages, especially growth, are limited, and may help inform when preventative measures may be most effective. In this retrospective observational study, prevalences of overweight and obese condition were determined from the electronic medical records of dogs and cats of all life stages visiting Banfield Pet Hospital in the USA between 2020 and 2023. Animals were identified either by body condition score (BCS; overweight 6-7; obese 8-9) or from a clinical diagnosis of overweight condition or obesity when recorded. Life stages (early growth, late growth, young adult, adult, mature, and senior) were defined by age range, adjusted for species and breed size in dogs. Individuals could only be included once within each life stage, with the maximum BCS used. Prevalence was determined for the 4-year period and for each calendar year. The evolution of BCS was also assessed for animals with multiple records. In total, 4933,916 unique dogs and 1341,118 unique cats were included. In dogs, prevalences of overweight or obese condition were: 0.9 % and < 0.0 % (early growth), 9.5 % and 0.3 % (late growth), 24.4 % and 1.9 % (young adult); 44.5 % and 8.4 % (adult), 50.1 % and 12.6 % (mature); 46.4 % and 11.3 % (senior). In cats, prevalences of overweight or obese condition were: 0.8 % and < 0.0 % (early growth); 10.7 % and 0.4 % (late growth); 36.2 % and 3.6 % (young adult); 47.2 % and 13.9 % (adult); 44.8 % and 21.7 % (mature); and 32.0 % and 12.6 % (senior). From 2020-2021 and 2021-2022 prevalences of overweight and obese condition in dogs and overweight condition in cats increased in most life stages. The prevalence of overweight condition in dogs and obese condition in cats and dogs significantly decreased between 2022 and 2023 for some life stages. The odds ratio of an overweight or obese condition in adulthood was 1.85 (95 % confidence interval [CI]: 1.81, 1.86); P ≤ 0.001) for dogs and 1.52 (95 % CI: 1.48, 1.56; P ≤ 0.001) for cats where an overweight or obese condition was recorded during growth. In conclusion, both overweight and obese condition are prevalent throughout adult life, peaking during the mature life stage in dogs and cats, with overweight or obese condition during growth persisting into adulthood in most affected animals. Veterinarian-led prevention strategies are recommended from growth onwards, including the use of growth standard charts.

African Swine Fever (ASF) is a viral disease affecting both wild and domestic swine, with the potential for major lethality rates. In addition to direct losses for producers, its notification in a free country or zone leads to international trade restrictions. The disease has spread globally at concerning levels, with outbreaks reported in recent years across five continents. Time series analysis of ASF outbreak notifications indicates an increasing trend in Europe and Asia. For Europe, including both domestic pigs and wild boar, seasonality was pronounced in the summer and autumn (July, August, and October). Wild boar represented 78.00 % (25,017) of the reported outbreaks in the continent, with pronounced seasonality in winter (December, January and February) and a peak in summer (July). In domestic pigs, seasonality was pronounced mainly in the summer (July and August) and autumn (October). Poland and Romania were the countries with the highest number of reported outbreaks on the continent, representing 35.34 % and 22.50 % of the total in Europe, respectively. In Asia, analysis including both domestic pigs and wild boar showed pronounced seasonality in February and March. For domestic animals, a higher number of outbreaks occur in the early months of the year (mainly February, and March), in the third quarter and early fourth (August, September, October and November), with a decrease in the middle (July) and at the end of the year (December). In China, the notifications are predominantly in domestic swine, with 97.21 % (209) of the reported outbreaks in the country. For wild boars, South Korea accounts for 96.46 % (1690) of the notifications in Asia. Seasonality in Europe may be related to increased human movement during these periods and wild boar behavior. In Asia, seasonality coincides with the period immediately following the Chinese New Year, probably related to the increased national demand for pork and the movement of people and by-products in the country. Recent notifications in 2021 and 2022 in the Caribbean region have raised concerns across the Americas.

Salmon lice (Lepeophtheirus salmonis) are parasites on salmonid fish and a density-dependent constraint to the sustainable farming of salmonids in open net pens. To control the parasites, fish farmers in Norway are required to count the number of salmon lice in different developmental stages on a subset of the fish each week. Furthermore, they must ensure that the number of adult female lice per fish does not increase beyond a specified threshold level. Here we present a model that may assist farmers in the salmon lice management. The model can predict the numbers of salmon lice in different developmental stages in each cage in a farm one to two weeks ahead. Input variables are current-week lice counts, a lice infestation pressure index, sea temperature, mean weight of the fish and presence or absence of wrasses (family Labridae) as cleaner fish. Count data for three parasitic stage groups (adult females, other motiles and sessile) are analysed jointly in one statistical model. The model predicted a large part of the variance, e.g. 50 % of the farm-level variance in adult female lice two weeks ahead. At farm-level, but not at cage-level, the numbers of other motile and sessile lice were, however, similarly well predicted by assuming "next week is the same as this week". The model also quantifies uncertainty and shows what range of outcomes is likely given the observations to that date. By using this model as decision support, fish farmers may more accurately assess the risk of exceeding lice limits.

Bovine besnoitiosis, a disease caused by the tissue cyst-forming apicomplexan Besnoitia besnoiti, is re-emerging in Europe, leading to significant impairment of health and production, as well as economic losses. The early detection of the disease is of the utmost importance for the implementation of effective control measures, yet this is a challenge due to the lack of specific early clinical signs. The objectives of our study were 1) to estimate the diagnostic accuracy of three tests to detect B. besnoiti in naturally exposed cattle (histopathology-skin (HIS-SK); PCR-skin (PCR-SK); and parallel PCR of nasal and scleroconjunctival swabs (PCR-NS-SC)) using a Bayesian latent class model (BLCM) and 2) to describe the clinical presentation of besnoitiosis in the studied animals. The study involved 54 adult Limousin cattle. Biosecurity measures were assessed and scored as medium. At clinical examination, a sire was diagnosed with a form of besnoitiosis between the end of the acute phase and the beginning of the chronic phase. Furthermore, 29 animals displaying a subclinical infection, characterized by the presence of scleroconjunctival cysts, were identified. The PCR-SK and PCR-NS-SC were able to detect B. besnoitia. The diagnostic performance of PCR-SK, PCR-NS-SC and HIS-SK was evaluated. The BLCM indicated that HIS-SK had the highest specificity (99.1 %, 95 % posterior probability interval PI: 96-100 %), while PCR-SK and PCR-NS-SC demonstrated higher sensitivities (91.0 %, 95 % PI: 68-100 %, and 85.0 %, 95 % PI: 67-100 %, respectively). The study concludes that the use of a parallel PCR-NS-SC could represent a viable alternative for the early detection of B. besnoiti, providing a less invasive method to monitor and control bovine besnoitiosis at the herd level.

The use of ectoparasiticides is a major concern in the control of parasites. In this study, we examined the trends and patterns of veterinary medicines use comparing between a high-risk epidemiological zone (HRZ) and a low-risk epidemiological zone (LRZ) for ectoparasites over a four-year period (2017-2020) at country level data. The objective of this study was to analyze the patterns of ectoparasiticide use in Uruguayan cattle, using the Anatomical Therapeutic Chemical Classification System for Veterinary Drugs (ATCvet) and dose indicators to consider regional variations in the animal population and production intensity. A total of 222 registered products classified as acaricides and/or insecticides based on 21 different active ingredients were grouped into 8 ATCvet level 4 categories. The annual consumption of active ingredients in ectoparasiticides averaged 40.6 tons. The total volume in the HRZ was 693 % higher than in the LRZ. The most sold ATCvet groups were Amidines (55.9 %), Pyrethrins/Pyrethroids (20.7 %), and Organophosphate Compounds (7 %). We calculated four different indicators with the resulting median values for entire country: 89.3 Technical Units per 1000 kg (IQR range of 10.5), 0.15 mg/kg of dosing biomass, 45.7 mg/ha of dose grazing area, and 3.09e+ 10 mg/LU/ha, respectively. Amidines were the most used group in the HRZ across all indicators, while Carbamates and Pyrethrins/Pyrethroids dominated in the LRZ. Cypermethrin, Amitraz, and Ethion were predominant in the HRZ, while Cypermethrin, Carbaryl, and Diazinon were common in the LRZ. The analysis of the four mixed Generalized Linear Models revealed significant differences in the use of veterinary medicines between zones with varying levels of epidemiological risk for parasitic diseases, with certain categories showing consistent patterns between indicators and zones. These results highlight the complexity of a veterinary medicine usage and the need for specialized strategies in veterinary medicine to address regional differences in the use of parasitic agents.

Antimicrobial resistance is considered a global One Health threat. Controlling selection pressure by reducing antibiotic use in livestock is a significant component of the response to this threat. The science concerning use and resistance is complicated and affected by time from antibiotic exposure, changing bacterial fitness, and varies by drug and pathogen. From May 2020 through October 2023, we collected intestinal (substandard and sick pigs) and fecal swab (healthy pig) samples at breed-to-wean (BTW) and wean-to-market (WTM) swine production sites and isolated E. coli bacteria. Antibiotic susceptibility testing was performed on these isolates to determine minimum inhibitory concentrations (MIC) for ceftiofur and enrofloxacin. Monthly antibiotic purchase data were used to calculate the active milligrams of drug purchased and these were divided by the kilograms of pigs produced from a farm site to provide a mass-adjusted proxy metric for farm-level antibiotic use. The relationship between use and MIC was then evaluated using a variety of multivariable statistical models. Across multiple modeling approaches, both farm type (i.e., BTW versus WTM) and farm-level antibiotic use maintained statistically significant relationships relative to E. coli MIC values for each respective drug. Use of ceftiofur and enrofloxacin can lead to increased MIC values among E. coli over time. The reasons for antibiotic purchases were not tracked as part of this project. Future work should evaluate the age of the individual pig and the time from last treatment when sampling these animals to separate out the group from individual-level effects of antibiotic use.

We conducted a citizen science survey on winter honey bee colony losses in Poland. A total of 2169 beekeepers, possessing 77 867 colonies, reported valid loss rates from all regions of the country between 2017 and 2022. We identified five beekeeping management-related factors and three types of apiaries (small-scale apiaries, medium-scale apiaries, and large-scale apiaries) and analysed their impact on winter bee colony losses. In large-scale apiaries, migration, replacement of queens, and replacement of brood combs were practiced more often than in others. Monitoring and treatment of varroosis were practiced with equal frequency in all apiary types. In total, beekeepers reported 9466 lost colonies, accounting for 12.2 % of the overall winter bee colony loss rate (95 % confidence interval (CI 95 %): 11.4 %-12.8 %). The highest overall winter bee colony losses were reported from the small-scale apiaries (14.8 %, CI 95 %: 13.2 %-16.7 %), followed by large-scale apiaries (11.6 %, CI 95 %: 10.4 %-12.8 %) and medium-scale apiaries (11.4 %, CI 95 %: 10.4 %-12.5 %). The primary category of losses was characterised by the presence of "dead colonies", with symptoms that could be linked to either colony depopulation syndrome or starvation. All management-related factors contributed to the lower winter bee colony loss rates, but the relationships were mainly mild, complex, and highly dependent on the type of apiary.

Supervised machine-learning (SML) algorithms are potentially powerful tools that may be used for screening cows for infectious diseases such as bovine leukemia virus (BLV) infection. Here, we compared six different SML algorithms to identify the most important risk factors for predicting BLV seropositivity in dairy cattle in Florida. We used a dataset of 1279 dairy blood sample records from the Bronson Animal Disease Diagnostic Laboratory that were submitted for BLV antibody testing from 2012 to 2022. The SML algorithms that we used were logistic regression (LR), decision tree (DT), gradient boosting (GB), random forest (RF), neural network (NN), and support vector machine (SVM). A total of 312 serum samples were positive for BLV with corrected seroprevalence of 26.0 %. Subject to limitations of the analyzed retrospective data, the RF model was the best model for predicting BLV seropositivity in dairy cattle indicated by the highest Kolmogorov-Smirnov (KS) statistic of 0.75, area under the receiver operating characteristic (AUROC) of 0.93, gain of 2.6; and lowest misclassification rate of 0.10. The LR model was the worst. The RF model showed that the best predictors for BLV seropositivity were age (dairy cows of age ≥ 5 years) and geographic location (southern Florida). We concluded that the RF and other SML algorithms hold promise for predicting BLV seropositivity in dairy cattle and that dairy cattle 5 years of age or older raised in southern Florida have a higher likelihood of testing positive for BLV. This study makes an important methodological contribution to the needed development of predictive tools for effective screening for BLV infection and emphasizes the importance of collecting and using representative data in such predictive models.

Paratuberculosis (Johne's disease), caused by Mycobacterium avium subsp. paratuberculosis (MAP), is a common, economically-important and potentially zoonotic contagious disease of cattle, with worldwide distribution. Disease management relies on identification of animals which are at high-risk of being infected or infectious. The disease is chronic in nature, and infected animals may be infectious in the absence of overt clinical signs. Coupled with limited sensitivity of available diagnostic tests, this creates difficulties in identifying high-risk animals. In some disease-control programmes, dairy cows are classified with regards to risk according to the results of serial tests which quantify MAP antibodies in milk samples. Such classification systems are limited by the influence of non-disease factors on test results, dichotomisation of continuous results into "positive" or "negative" according to an imperfect threshold, and subjectivity in defining which patterns of serial test results indicate different risk-categories. An unsupervised learning (clustering) approach was applied to paratuberculosis test results and milk-recording data collated from 47 farms over an approximately ten-year period between 2010 and 2021. Paratuberculosis test results were first adjusted according to influential non-disease factors using linear models. Continuous-time hidden Markov models were fit to the adjusted test results. The final model revealed four distinct latent states (clusters). Examination of the distribution of adjusted test results associated with each latent state suggested that states were ordinal and aligned with disease progression. Model transition probabilities demonstrated that the probability of an animal progressing to the highest state was dependent on its current state. Of particular note was the existence of a latent state, characterised by paratuberculosis test results below the conventional test-positive threshold, which was associated with a relatively high probability of progression to the highest cluster. This research has led to objective classification of animals according to serial test results, and furthermore suggests the presence of groups of different disease risk amongst animals whose test results fall below the routinely used test-positive threshold. Identification of such groups could be used to better manage disease on farms, through implementation of management practices which limit disease transmission from high-risk animals.