Preventive veterinary medicine最新文献

This study describes the association of non-esterified fatty acids (NEFA) and calcium concentrations at calving with early lactation disease, reproductive performance and culling in 646 dairy cows from 13 commercial grazing dairy herds in Uruguay. During one year, health events were recorded from calving to 30 days in milk (DIM). The first author visited each farm every 20 days. During each visit, body condition score (BCS) was recorded (scale 1–5), defining BCS < 3 as suboptimal and BCS > 3 as optimal, and a blood sample was taken from cows between 0 and 4 DIM for metabolite determination. To evaluate the association between health events (i.e., retained placenta-metritis and clinical mastitis) and risk factors (parity, BCS, high NEFA (> 0.6 mmol/L) and subclinical hypocalcemia (SCH) (< 2.10 mM)) data were analysed using multivariable logistic regression models. To evaluate the association of health events and risk factors with reproductive performance and culling, data were analysed using Cox proportional hazard regression models. A risk factor and an outcome of interest were assumed to be associated at P < 0.05 and a tendency to be associated was defined at P < 0.10. Overall, 47 % (n = 303) of the cows showed elevated NEFA concentration and 77 % (n = 499) had SCH. In addition, 21.5 % (n = 139) of the cows recorded at least one clinical disease. Cumulative incidence was 17 % (n = 109) for clinical mastitis, 4.2 % (n = 27) for retained placenta (RP)-metritis and 1.4 % (n = 7) for lameness. Clinical mastitis was associated with parity, with lower odds in primiparous (PP) cows (OR = 0.42, P < 0.01). Cows in an optimal BCS also tended to have lower odds (OR = 0.66, P = 0.07). Moreover, high NEFA and SCH cows had higher odds of CM (OR = 4.5, P = 0.01 and OR = 1.75, P = 0.04, respectively). Retained placenta-metritis tended to be associated with high NEFA concentration (OR = 2.2, P = 0.06). Primiparous cows with suboptimal BCS showed an increased first insemination rate (HR = 2.34; P < 0.01). The risk of culling was lower in PP cows (HR = 0.19; P < 0.01) and in cows with optimal BCS and low NEFA concentration (HR = 0.38; P = 0.03). Our data show that metabolic challenge (defined as peripartum suboptimal BCS, high NEFA or SCH) is associated with increased odds of clinical mastitis and RP-metritis, decreased probability of insemination and increased hazard of culling. Under grazing conditions, we suggest that farm management to improve the metabolic adaptation to lactation represents an opportunity to enhance cow performance in terms of health, fertility and longevity.

Antibiotic resistance is one of the major concerns in veterinary and human medicine and poses a considerable threat to both human and animal health. It has been shown that over- or misuse of antibiotics is one of the primary drivers of antibiotic resistance. To develop the surveillance of antibiotic use, Switzerland introduced the "Informationssystem Antibiotika in der Veterinärmedizin" (IS ABV) in 2019, mandating electronic registration of antibiotic prescriptions by all veterinarians in Switzerland. However, initial data analysis revealed a considerable amount of implausible data entries, potentially compromising data quality and reliability. These anomalies may be caused by input errors, inaccuracies, incorrect or aberrant master data or data transmission and make analysis impossible. To address this issue efficiently, we propose a two-stage anomaly detection framework utilizing machine learning algorithms. In this study, our primary focus was on cattle treatments with either single or group therapy, as they were the species with the highest prescription volume. However, not all outliers are necessarily incorrect; some may be legitimate but unusual antibiotic treatments. Thus, expert review plays a crucial role in distinguishing outliers, that are correct from actual errors. Initially, relevant prescription variables were extracted and pre-processed with a custom-built scaler. A set of unsupervised algorithms calculated the probability of each data point and identified the most likely outliers. In collaboration with experts, we annotated anomalies and established anomaly thresholds for each production type and active substance. These expert-annotated labels were then used to fine-tune the final supervised classification algorithms. With this methodology, we identified 22,816 anomalies from a total of 1,994,170 prescriptions in cattle (1.1 %). Cattle with no further specified production type had the most (2 %) anomalies with 7758 out of 379,995. The anomalies were consistently identified and comprised prescriptions with too high and too low dosages. Random Forest achieved a ROC-AUC score of 0.994, (95 % CI: 0.992, 0.995) and a F1-Score of 0.962 (95 % CI: 0.958, 0.966) for single treatments. The versatility of this framework allows its adaptation to other species within IS ABV and potentially to other prescription-based surveillance systems. If applied regularly to uploaded prescriptions, it should reduce input errors over time, improving the validity of the data in the long term.

In September 2023, bluetongue virus serotype 3 (BTV-3) emerged in the Netherlands, infecting over five thousand livestock farms. In sheep, high morbidity and mortality rates were reported that were unlike previously described bluetongue outbreaks. This study aimed to quantify the impact of BTV-3 in the small ruminant population in the Netherlands in 2023. Sheep and goat movement census data and BTV-3 notification data were available from 2020 until the end of 2023. Data were aggregated to farm and week level and mortality indicators were calculated for lambs (<1 year) and adult animals (≥1 year). Population averaged GEE models with a Negative-binomial distribution and a log-link function correcting for repeated measures per farm in time were used to quantify the association between BTV-3 and mortality. In 2023, 2994 sheep farmers and 89 goat farmers notified clinical signs of BTV-3 to the NVWA. During this BTV-3 outbreak period, an additional 55,000 sheep died compared to the same period in 2020–2022. At flock level a high variety in mortality was observed, with a clear increase in mortality in both flocks that were not notified but that were located in infected areas and in flocks of which the farmer notified clinical signs. During the BTV-3 outbreak period, mortality in infected areas increased 4.2 (95 % CI: 4.0–4.3) times in sheep lambs (<1 year) and 4.6 (95 % CI: 4.4–4.8) times in sheep (≥1 year) compared to BTV-3 free areas. Flocks with a confirmed BTV-3 infection that were notified in September showed a 12.8 (95 % CI: 11.4–14.3) times higher mortality in lambs and a 15.1 (95 % CI: 13.7–16.6) times higher mortality in sheep compared to flocks in BTV-3 areas. In flocks of which the farmer notified clinical signs after September, mortality was 4.6 (95 % CI: 4.2–5.0) and 5.6 (95 % CI: 5.1–6.0) times higher in lambs and sheep compared BTV-3 areas respectively. In goats, around 4000 additional deaths were recorded during the BTV-3 outbreak period. In farms that were notified, mortality of goats (≥1 year) was 1.8 (95 % CI: 1.2–2.8) times higher compared to BTV-3 free areas. Since May 2024, multiple BTV-3 vaccines are available in the Netherlands. In June 2024, the first new infections of BTV-3 were confirmed in Dutch sheep flocks. Hopes are that with the possibility to vaccinate, the spread and impact of BTV-3 in the Netherlands will rapidly decline and that losses as observed in 2023 will no longer be seen.

Limiting the use of antimicrobial agents in intensive livestock production is a challenge due to different perceptions between the short-term benefits of antimicrobial use (AMU) by livestock producers and the long-term social costs of antimicrobial resistance (AMR) and its impact on human, animal, and environmental health. Pig farmers are key stakeholders supporting Thailand’s national strategic plan on AMR which aims to reduce antimicrobial consumption in the livestock sector. Consequently, this study explored the knowledge, perceptions, attitudes, and practices of pig farmers towards AMU and AMR, expecting to provide information that would help guide policymakers in the development of a proper interventional program for antimicrobial stewardship. The qualitative approach utilized individual, face-to-face, in-depth, semi-structured interviews with the 20 owners of middle- and large-scale pig farms in eastern Thailand. The major themes identified in this finding were: (1) the use of antimicrobials on farms and the knowledge of antimicrobials; (2) AMU on pig farms (the purpose of AMU, antimicrobial prescription practices, and performing antimicrobial susceptibility testing); (3) attitudes towards prescribing practices and record keeping; (4) farm animal husbandry practices and AMU; (5) factors influencing AMU; and (6) awareness regarding AMR. Most of the pig farm owners recognized that achieving Good Agricultural Practices certification required following the legislation and regulations for AMU. Other observations were that the respondents had positive attitudes towards prescribing practices, performing antimicrobial susceptibility testing before antimicrobial therapy, and recording drug use on farms; however, there was low awareness of AMR. In addition, alternative substances, such as medicinal plants, and proper biosecurity practices were mentioned. Factors influencing AMU on pig farms were intensive enforcement of legislation and regulations, the implementation of farm management practices, the provision of information and consultancy services by veterinary professionals, farmer experience, and consumer trends.

Influenza A virus (IAV) is an important pathogen in Brazilian swine herds, and monitoring the viral circulation is essential to control and reduce the transmission. Surveillance programs for IAV are often based on individual piglets level sampling, making the evaluation of the available diagnostic tools crucial to assessing IAV circulation in herds. Thus, two sample collection methodologies were compared in pig herds in southern Brazil to detect IAV by RT-qPCR: nasal swab (NS) and nasal wipe (NW). A Bayesian latent class model (BLCM) was set for two tests and two populations. The NW and NS used are more specific (higher than 95 % for both) than sensitive. The sensitivity for NW was lower than the NS, 84.14 % (70 % – 95 %; posterior probability interval (PPI): 95 %) and 87.15 % (73 % – 97 %; PPI: 95 %), respectively, and the specificity was 95 % (90 % – 99 %; PPI: 95 %) and 99 % (96 % – 100 %; PPI: 95 %), respectively. Although the wipe sample collection loses both sensitivity and specificity compared with nasal swab, differences in test performance were very limited and PPIs largely overlapped. Therefore NW can also be considered a valuable tool. The decision about the use of both techniques should be based on the trade-off between their performance limitations and feasibility in routine monitoring.

This study aimed to describe the attitudes and personalities of farm managers (FMs) in large Estonian dairy herds and analyse the potential associations with calf mortality. The study included FMs from 114 free-stall farms with at least 100 cows. Each participant completed a questionnaire that comprised questions about the respondent and various statements to reveal their attitudes towards calves, calf mortality, and farming in general. A 7-point Likert scale was used to record the responses. The data on the number of live births and deaths and animal movement data were gathered from farm records and the Estonian Agricultural Registers and Information Board. The yearly calf mortality risk (%) during the first 21 days (YAG) and mortality rate between 22–90 days of age (OAG) adjusted for the animal time-at-risk were then calculated for each herd. Univariate negative binomial regression analysis was used to identify associations between calf mortality risk/rate, and the studied statements and variables with a p-value < 0.25 were included in a k-modes clustering analysis. The mean calf mortality risk was determined to be 5.9 % (range 0.0–26.8 %) during the first 21 days and mean calf mortality rate was 1.8 (range 0.0–9.2) deaths per 100 calf-months during 22–90 days of age. In both age group analyses, two FMs´ clusters formed based on 17 pre-selected statements. The FMs of the high-mortality cluster were found to be dissatisfied with the calf mortality levels. In the YAG analysis, FMs from high-mortality cluster gave lower priority to the issue of calf mortality, placed high importance on the influence of workers on calf mortality, and were more satisfied with the staff's performance compared to FMs of the cluster of herds with lower calf mortality. They were additionally less satisfied with their own performance and felt less recognized by the farm staff. They were also more inclined to try new products and practices on the farm and demonstrated greater empathy towards cattle. In the OAG analysis, the FMs from the higher-mortality cluster viewed reducing calf mortality more costly, had a less ambitious and target-driven management style, and rated their self-performance lower. This study determined that FMs working in herds with high calf mortality were dissatisfied and did not prioritize addressing calf mortality compared to managers working in farms with lower calf mortality. FMs' attitudes and management styles were associated with calf mortality, while the respondents' personality traits had little influence.

Toxoplasma gondii is a zoonotic pathogen that can infect farm animals, companion animals, and humans, sometimes causing public health issues. In Taiwan, the pig industry is a vital agricultural industry, with a self-sufficiency rate of 91 %, and pigs are also food-producing animal reservoirs of Toxoplasma gondii. Infected pigs are usually asymptomatic, and abortions and death may occur in severe cases. We combined an enzyme-linked immunosorbent assay (ELISA) and an indirect fluorescence assay (IFA) to investigate the seroprevalence of Toxoplasma gondii among pig populations in Taiwan. A stratified sampling approach to determine the number of sample farms proportional to the number of pig farms in each county was employed, with 15 blood samples collected at each farm between July and September 2017. With the tested results, empirical Bayesian smoothing was utilized to assess the proportion of Toxoplasma-positive farms at the county level. Bayesian mixed-effects logistic regression models, incorporating farm and county as random effects, were employed to investigate associations between Toxoplasma test results and potential risk factors. A total of 930 serum samples from 62 pig farms were collected and tested. An overall herd prevalence of 27.4 % was shown with the seroprevalence in northern Taiwan being greater than that in southern Taiwan. The sampling month and companion dog density in 2017 were significantly associated with Toxoplasma infections in pigs. With every increase in the number of companion dogs per km² at the county level, the odds of Toxoplasma infection in pigs increased by 4.7 % (95 % CI: 1.7–8.9 %). This study demonstrated that combining ELISA for screening with IFA for confirmation is a cost-effective and time-saving method for conducting a large-scale sample investigation. This was also the first nationwide, cross-sectional study in Taiwanese pig herds to investigate Toxoplasma gondii infection.

The research addresses zoonotic sporotrichosis in Brazil, particularly caused by Sporothrix brasiliensis, highlighting its epidemiological severity. Transmission occurs through contact with sick animals, especially felines, and diagnosis in humans is challenging due to the low fungal load in the lesions. The study analyzed data from Information System for Notifiable Diseases (SINAN) and Zoonosis Surveillance Unit (UVZ) from January 2017 to March 2023, carried out in Contagem, Minas Gerais. Geospatial tools and statistical analysis revealed a significant increase in cases, peaking in 2021 for felines and 2022 for humans. The geospatial analysis highlighted areas of higher incidence, suggesting a correlation between human and feline populations. The research contributes to the understanding of sporotrichosis in Contagem, emphasizing the importance of integrated approaches for surveillance and control strategies, aiming to mitigate impacts on the local community.

Foot-and-mouth disease (FMD) is an important endemic disease in livestock in Southeast Asia. Transboundary movement of animals may result in the transnational disease spread. A major cattle market is located at the Thailand-Myanmar border, where most cattle imported from Myanmar are traded. In this study, we built a stochastic susceptible-exposed-infectious-recovered (SEIR) model to investigate the effectiveness of a private animal quarantine service center in preventing FMDV from entering the major cattle market. We computed with different parameters and found that, with 50 % vaccine effectiveness, the risk of releasing infected cattle to the market per batch was generally low during the quarantine period of 21 and 28 days, with the risk ranging from 0.071 to 0.078 and 0.032 to 0.036, respectively. Despite the best scenario, the zero-risk state is difficult to attain. The sensitivity analysis highlights that the percentage of immune animals before entering the quarantine centers and the vaccine effectiveness are important factors. In conclusion, the 21-day quarantine period mitigates the risk of FMDV introduction into the cattle market. This control measure should be rigorously maintained to sustainably prevent FMDV outbreaks through transboundary animal movements, especially among countries in FMD-endemic regions.

Background

The representation of wildlife-livestock interface (WLI) at an accurate spatial resolution poses several challenges. Furthermore, there is a lack of published material providing detailed descriptions of geospatial techniques for the purpose of producing visual results that are interpretable and contrastable for epidemiological analysis.

Objectives

Our aim is to develop a standardized, applicable, and scalable methodological framework for describing and characterizing the WLI across a large spatial extent. Subsequently, we aim to employ this framework to depict specific WLI based on different epidemiological scenarios determined by the abundance of wild boar (Sus scrofa) and domestic ungulates as an illustrative case, specifically focusing on mainland Spain.

Methods

To establish a methodological framework, we merged data from both wild and domestic sources into a hexagonal grid. We utilized data on wild boar hunting and the locations of pig, cattle, sheep, and goat farms in mainland Spain. New variables were derived from this combined dataset to illustrate the overlapping abundance between wild boar and domestic species. Finally, a cluster analysis of the generated variables was carried out, with the aim of distinguishing and characterizing various scenarios of the wild boar-domestic ungulate interface in mainland Spain.

Results

The hexagonal grid proved appropriate to represent and evaluate the WLI at fine spatial resolution over such broad extent. Despite the inability to ascribe a dominant livestock type and production system to a specific region, we were able to identify fifteen main areas of interest in terms of overlap. As for extensive livestock, normally at the highest risk of interaction with wild boar, the primary regions in Spain were those with dehesa agroecosystem and the Atlantic areas. Certain scenarios were particularly relevant in terms of risk for interaction and subsequent transmission of disease, namely, the case of extensive pig production in south western Spain (dehesa agroecosystem), which is especially concerned about the potential introduction of African Swine fever (ASF) in the Country.

Discussion and conclusions

We provide a basis for visualizing and understanding of different WLI scenarios, which is extensible to other regions and interfaces, and automatable where precise source of data from wildlife and livestock are available. This spatial statistics framework enables the utilization of high-resolution data, ensuring consistency on uniform grids. This aligns with the needs of high-resolution disease dissemination models based on wildlife behaviour. Such aspects are crucial for developing risk assessment and improving strategies for the prevention, control, and eradication of shared priority emerging diseases at national and international levels, such as ASF.