Long-term outcomes in dogs with elbow dysplasia, assessed using the canine orthopaedic index

BackgroundElbow dysplasia (ED) is an important cause of lameness in dogs. This study aimed to report long-term outcomes in dogs with elbow osteoarthritis. MethodsDemographic data, medical management, and scores from The American College of Veterinary Surgeons' Canine Orthopaedic Index (COI) were collected from owners of dogs radiographically screened for ED, graded as normal, mild, or moderate. Telephone interviews were performed in 2017 (Q1), followed by an email survey in 2020 (Q2). The association between ED grade and deterioration in COI scores over time was evaluated with logistic regression. ResultsA total of 765 replies were collected for Q1 and 293 for Q2. At Q2, 222 dogs (76%) were alive, with a median age of 8 years (range 5-12 years). No association was found between ED and changes in COI score over time or between ED and survival (p = 0.071). Dogs with mild and moderate ED were treated with analgesic medications to a higher degree than dogs without ED (p < 0.05). LimitationsOnly owner-assed data were assessed; no clinical orthopaedic examination or follow-up radiographic evaluation was performed. ConclusionsNo association was found between the grade of ED and the worsening of clinical signs in dogs with elbow osteoarthritis

Long-Term Prognosis of Quality of Life in Dogs Diagnosed With Mild to Moderate Elbow Dysplasia in Sweden

Objective: The objective of this study was to increase knowledge regarding long-term prognosis of mild to moderate elbow dysplasia (ED) using a canine orthopedic index.Study Design: Cross-sectional observational study.Sample Population: Sixty dogs randomly selected from each of five different breeds and three ED groups: ED0 (control), ED1, and ED2, based on the Kennel Club's screening results. The total number of selected dogs was 900 (60*5*3).Methods: Questionnaires were administered to owners by telephone interview. Bayesian network modeling was used to assess the relation between ED grade, treatment options, dog demographics, and quality-of-life indicators.Results: Seven hundred sixty-five questionnaires were collected (85% response rate), of which 61 concerned dogs euthanized due to osteoarthritis. There was no direct association between ED grade and owner's perceived quality of life, but ED1 and ED2 dogs were more likely to receive veterinary care and subsequent NSAID treatment compared to ED0 dogs. A significant association was found between the occurrence of euthanasia due to orthopedic disease and ED scores 1 and 2 in the sample (p &lt; 0.001).Conclusion: The degree of osteoarthritis was not directly associated with the canine orthopedic index, except for ED2 and lameness score. It can be speculated that owners who paid closer attention to orthopedic symptoms and perceived them as impairing their dogs' lives were also more likely to seek veterinary care and get treatment, irrespective of the ED grading.Impact: ED1-graded dogs had a lower risk than might be expected to develop visible clinical symptoms and showed a similar quality of life as dogs with ED0. ED2-graded dogs were more likely than ED0-graded dogs to have their lives impaired by lameness, according to the owners' perception

Transmission Dynamics of Low Pathogenicity Avian Influenza Infections in Turkey Flocks

Low pathogenicity avian influenza (LPAI) viruses of H5 and H7 subtypes have the potential to mutate into highly pathogenic strains (HPAI), which can threaten human health and cause huge economic losses. The current knowledge on the mechanisms of mutation from LPAI to HPAI is insufficient for predicting which H5 or H7 strains will mutate into an HPAI strain, and since the molecular changes necessary for the change in virulence seemingly occur at random, the probability of mutation depends on the number of virus replicates, which is associated with the number of birds that acquire infection. We estimated the transmission dynamics of LPAI viruses in turkeys using serosurveillance data from past epidemics in Italy. We fitted the proportions of birds infected in 36 flocks into a hierarchical model to estimate the basic reproduction number (R0) and possible variations in R0 among flocks caused by differences among farms. We also estimated the distributions of the latent and infectious periods, using experimental infection data with outbreak strains. These were then combined with the R0 to simulate LPAI outbreaks and characterise the resulting dynamics. The estimated mean within-flock R0 in the population of infected flocks was 5.5, indicating that an infectious bird would infect an average of more than five susceptible birds. The results also indicate that the presence of seropositive birds does not necessarily mean that the virus has already been cleared and the flock is no longer infective, so that seropositive flocks may still constitute a risk of infection for other flocks. In light of these results, the enforcement of appropriate restrictions, the culling of seropositive flocks, or pre-emptive slaughtering may be useful. The model and parameter estimates presented in this paper provide the first complete picture of LPAI dynamics in turkey flocks and could be used for designing a suitable surveillance program

Additive Bayesian Network Modeling with the R Package abn

The R package abn is designed to fit additive Bayesian models to observational datasets. It contains routines to score Bayesian networks based on Bayesian or information theoretic formulations of generalized linear models. It is equipped with exact search and greedy search algorithms to select the best network. It supports a possible blend of continuous, discrete and count data and input of prior knowledge at a structural level. The Bayesian implementation supports random effects to control for one-layer clustering. In this paper, we give an overview of the methodology and illustrate the package's functionalities using a veterinary dataset about respiratory diseases in commercial swine production

Additive Bayesian Network Modeling with the R Package abn

The R package abn is designed to fit additive Bayesian network models to observational datasets and contains routines to score Bayesian networks based on Bayesian or information theoretic formulations of generalized linear models. It is equipped with exact search and greedy search algorithms to select the best network, and supports continuous, discrete and count data in the same model and input of prior knowledge at a structural level. The Bayesian implementation supports random effects to control for one-layer clustering. In this paper, we give an overview of the methodology and illustrate the package's functionality using a veterinary dataset concerned with respiratory diseases in commercial swine production

Stochastic partial budget analysis of strategies to reduce the prevalence of lung lesions in finishing pigs at slaughter

Ante- and post-mortem inspections of food-producing animals at slaughter are mandatory activities carried out in many countries to ensure public health, animal health, and meat quality. In finishing pigs, lung lesions are the most frequent defects found in meat inspections. It is possible to implement managerial strategies on-farm to reduce the occurrence and spread of respiratory diseases, but such strategies come with additional costs that could impede implementation. This study assessed the economic impact of two strategies aimed at reducing the prevalence of lung lesions in finishing pigs at slaughter by improving the health conditions of the animals during the production cycle. First, a farrow-to-finish pig farm with 355 sows was modeled based on the current standard practice for finishing pig production in Sweden, using economic data, meat inspection data and biological variables from the literature and expert opinions. A partial budget analysis was then performed in which the baseline farm was compared with two hypothetical strategies aimed at reducing the occurrence and spread of respiratory diseases during pig production: (S1) avoiding mixing of litters after weaning and (S2) keeping purchased pregnant gilts separated from sows during gestation, farrowing and lactation. Both these strategies intended to reduce the occurrence of respiratory disease in finishing pigs at slaughter gave an average gain in annual net income (33,805 SEK in S1 and 173,160 SEK in S2, equal to 3,146€ and 16,113€, respectively, at the time of analysis), indicating that both were economically sustainable under the assumed conditions. The impact analysis of the two strategies revealed that the reduced prevalence of lung lesions when adopting one of the strategies was the most influential factor in net benefit change on the farm. Overall, the results suggest that with the increasing prevalence of lung lesions in Swedish pig production (as also observed worldwide in recent years), adopting an effective strategy to decrease respiratory infections will become more relevant and economically beneficial

Can we use meat inspection data for animal health and welfare surveillance?

Ante- and post-mortem inspections at abattoir were originally introduced to provide assurance that animal carcasses were fit for human consumption. However, findings at meat inspection can also represent a valuable source of information for animal health and welfare surveillance. Yet, before making secondary use of meat inspection data, it is important to assess that the same post-mortem findings get registered in a consistent way among official meat inspectors across abattoirs, so that the results are as much independent as possible from the abattoir where the inspection is performed. The most frequent findings at official meat inspections of pigs and beef cattle in Sweden were evaluated by means of variance partitioning to quantify the amount of variation in the probabilities of these findings due to abattoir and farm levels. Seven years of data (2012-2018) from 19 abattoirs were included in the study. The results showed that there was a very low variation between abattoirs for presence of liver parasites and abscesses, moderately low variation for pneumonia and greatest variation for injuries and nonspecific findings (e.g., other lesions). This general pattern of variation was similar for both species and implies that some post-mortem findings are consistently detected and so are a valuable source of epidemiological information for surveillance purposes. However, for those findings associated with higher variation, calibration and training activities of meat inspection staff are necessary to enable correct conclusions about the occurrence of pathological findings and for producers to experience an equivalent likelihood of deduction in payment (independent of abattoir)

Additive Bayesian Network Modelling with the R Package abn

The R package abn is designed to fit additive Bayesian models to observational datasets. It contains routines to score Bayesian networks based on Bayesian or information theoretic formulations of generalized linear models. It is equipped with exact search and greedy search algorithms to select the best network. It supports a possible blend of continuous, discrete and count data and input of prior knowledge at a structural level. The Bayesian implementation supports random effects to control for one-layer clustering. In this paper, we give an overview of the methodology and illustrate the package's functionalities using a veterinary dataset about respiratory diseases in commercial swine production.Comment: 37 pages, 14 figures and 2 table

Questionnaire study suggests grave consequences of infectious laryngotracheitis, infectious coryza and mycoplasmosis in small chicken flocks

BackgroundA growing number of people in western countries keep small chicken flocks. In Sweden, respiratory disease is a common necropsy finding in chickens from such flocks. A respiratory real-time polymerase chain reaction (PCR) panel was applied to detect infectious laryngotracheitis virus (ILTV), Avibacterium paragallinarum (A. paragallinarum) and Mycoplasma gallisepticum (M. gallisepticum) in chickens from small flocks which underwent necropsy in 2017-2019 and had respiratory lesions. Owners (N = 100) of PCR-positive flocks were invited to reply to a web-based questionnaire about husbandry, outbreak characteristics and management.ResultsResponse rate was 61.0%. The flocks were from 18 out of Sweden's 21 counties indicating that respiratory infections in small chicken flocks are geographically widespread in Sweden. Among participating flocks, 77.0% were coinfected by 2-3 pathogens; 91.8% tested positive for A. paragallinarum, 57.4% for M. gallisepticum and 50.8% for ILTV. Larger flock size and mixed-species flock structure were associated with PCR detection of M. gallisepticum (P = 0.00 and P = 0.02, respectively). Up to 50% mortality was reported by 63.9% of respondents. Euthanasia of some chickens was carried out in 86.9% of the flocks as a result of the outbreaks. Full clinical recovery was reported by 39.3% of owners suggesting chronic infection is a major challenge in infected flocks. Live birds had been introduced in many flocks prior to outbreaks, which suggested these as an important source of infection. Following the outbreaks, 36.1% replaced their flocks with new birds and 9.8% ceased keeping chickens.ConclusionsThis study highlights the severity of respiratory outbreaks in small non-commercial chicken flocks and points to the need for more research and veterinary assistance to prevent and manage respiratory infections in small chicken flocks

Evaluating Surveillance Strategies for the Early Detection of Low Pathogenicity Avian Influenza Infections

In recent years, the early detection of low pathogenicity avian influenza (LPAI) viruses in poultry has become increasingly important, given their potential to mutate into highly pathogenic viruses. However, evaluations of LPAI surveillance have mainly focused on prevalence and not on the ability to act as an early warning system. We used a simulation model based on data from Italian LPAI epidemics in turkeys to evaluate different surveillance strategies in terms of their performance as early warning systems. The strategies differed in terms of sample size, sampling frequency, diagnostic tests, and whether or not active surveillance (i.e., routine laboratory testing of farms) was performed, and were also tested under different epidemiological scenarios. We compared surveillance strategies by simulating within-farm outbreaks. The output measures were the proportion of infected farms that are detected and the farm reproduction number (Rh). The first one provides an indication of the sensitivity of the surveillance system to detect within-farm infections, whereas Rh reflects the effectiveness of outbreak detection (i.e., if detection occurs soon enough to bring an epidemic under control). Increasing the sampling frequency was the most effective means of improving the timeliness of detection (i.e., it occurs earlier), whereas increasing the sample size increased the likelihood of detection. Surveillance was only effective in preventing an epidemic if actions were taken within two days of sampling. The strategies were not affected by the quality of the diagnostic test, although performing both serological and virological assays increased the sensitivity of active surveillance. Early detection of LPAI outbreaks in turkeys can be achieved by increasing the sampling frequency for active surveillance, though very frequent sampling may not be sustainable in the long term. We suggest that, when no LPAI virus is circulating yet and there is a low risk of virus introduction, a less frequent sampling approach might be admitted, provided that the surveillance is intensified as soon as the first outbreak is detected