Global losses due to dairy cattle diseases: A comorbidity-adjusted economic analysis.

An economic simulation was carried out over 183 milk-producing countries to estimate the global economic impacts of 12 dairy cattle diseases and health conditions: mastitis (subclinical and clinical), lameness, paratuberculosis (Johne's disease), displaced abomasum, dystocia, metritis, milk fever, ovarian cysts, retained placenta, and ketosis (subclinical and clinical). Estimates of disease impacts on milk yield, fertility, and culling were collected from the literature, standardized, meta-analyzed using a variety of methods ranging from simple averaging to random-effects models, and adjusted for comorbidities to prevent overestimation. These comorbidity-adjusted disease impacts were then combined with a set of country-level lactational incidence and/or prevalence estimates, herd characteristics, and price estimates within a series of Monte Carlo simulations that estimated and valued the economic losses due to these diseases. It was estimated that total annual global losses are USD 65 billion (B). Subclinical ketosis, clinical mastitis, and subclinical mastitis were the costliest diseases modeled, resulting in mean annual global losses of approximately USD 18B, USD 13B, and USD 9B, respectively. Estimated global annual losses due to clinical ketosis, displaced abomasum, dystocia, lameness, metritis, milk fever, ovarian cysts, paratuberculosis, and retained placenta were estimated to be USD 0.2B, 0.6B, 0.6B, 6B, 5B, 0.6B, 4B, 4B, and 3B, respectively. Without adjustment for comorbidities, when statistical associations between diseases were disregarded, mean aggregate global losses would have been overestimated by 45%. Although annual losses were greatest in India (USD 12B), the USA (USD 8B), and China (USD 5B), depending on the measure of losses used (losses as a percent of GDP, losses per capita, losses as a percent of gross milk revenue), the relative economic burden of these dairy cattle diseases across countries varied markedly.

Multidrug-Resistant Commensal and Infection-Causing Staphylococcus spp. Isolated from Companion Animals in the Valencia Region.

The emergence of antimicrobial resistance (AMR) and multidrug resistance (MDR) among microorganisms to commonly used antibiotics is a growing concern in both human and veterinary medicine. Companion animals play a significant role in the epidemiology of AMR, as their population is continuously increasing, posing a risk of disseminating AMR, particularly to strains of public health importance, such as methicillin-resistant Staphylococcus strains. Thus, this study aimed to investigate the prevalence of AMR and MDR in commensal and infection-causing Staphylococcus spp. in dogs and cats in Valencia region. For this purpose, 271 samples were taken from veterinary centers to assess antimicrobial susceptibility against 20 antibiotics, including some of the most important antibiotics for the treatment of Staphylococcus infections, including the five last resort antibiotics in this list. Of all the samples, 187 Staphylococcus spp. strains were recovered from asymptomatic and skin-diseased dogs and cats, of which S. pseudintermedius (≈60%) was more prevalent in dogs, while S. felis (≈50%) was more prevalent in cats. In the overall analysis of the isolates, AMR was observed for all antibiotics tested, including those crucial in human medicine. Furthermore, over 70% and 30% of the strains in dogs and cats, respectively, exhibited MDR. This study highlights the significance of monitoring the trends in AMR and MDR among companion animals. The potential contribution of these animals to the dissemination of AMR and its resistance genes to humans, other animals, and their shared environment underscores the necessity for adopting a One Health approach.

Identification and distribution of species of Neofusicoccum that cause blueberry stem blight in Chile.

Stem blight is a destructive woody disease of blueberry (Vaccinium corymbosum) caused by several species of the family Botryosphaeriaceae. A field survey was conducted in the mayor blueberry production area of Chile, comprising latitudes 32°49'S to 40°55'S, to determine the occurrence and distribution of Botryosphaeriaceae in the region. Together, a multilocus analysis, morphological characterization, and phytopathogenicity testing were used to identify 51 Neofusicoccum isolates belonging to N. nonquaesitum (28 strains), N. parvum (22 strains), and N. australe (1 strain). Of these, N. parvum and N. nonquaesitum were the most commonly found, with N. parvum most frequent from latitude 37°40'S to the north and N. nonquaesitum predominantly located from the same latitude toward the south. Morphological traits of the isolates were consistent with the species identified by molecular techniques, despite the overlapping of conidial size of some isolates among species. Pathogenicity trials showed that the three species were pathogenic to blueberry plants and revealed that N. parvum and N. nonquaesitum were the most aggressive species, although variability in virulence was observed among isolates of N. parvum and N. nonquaesitum.

Estimating the burden of multiple endemic diseases and health conditions using Bayes' Theorem: A conditional probability model applied to UK dairy cattle.

The Global Burden of Animal Diseases (GBADs) is an international collaboration aiming, in part, to measure and improve societal outcomes from livestock. One GBADs objective is to estimate the economic impact of endemic diseases in livestock. However, if individual disease impact estimates are linearly aggregated without consideration for associations among diseases, there is the potential to double count impacts, overestimating the total burden. Accordingly, the authors propose a method to adjust an array of individual disease impact estimates so that they may be aggregated without overlap. Using Bayes' Theorem, conditional probabilities were derived from inter-disease odds ratios in the literature. These conditional probabilities were used to calculate the excess probability of disease among animals with associated conditions, or the probability of disease overlap given the odds of coinfection, which were then used to adjust disease impact estimates so that they may be aggregated. The aggregate impacts, or the yield, fertility, and mortality gaps due to disease, were then attributed and valued, generating disease-specific losses. The approach was illustrated using an example dairy cattle system with input values and supporting parameters from the UK, with 13 diseases and health conditions endemic to UK dairy cattle: cystic ovary, disease caused by gastrointestinal nematodes, displaced abomasum, dystocia, fasciolosis, lameness, mastitis, metritis, milk fever, neosporosis, paratuberculosis, retained placenta, and subclinical ketosis. The diseases and conditions modelled resulted in total adjusted losses of £ 404/cow/year, equivalent to herd-level losses of £ 60,000/year. Unadjusted aggregation methods suggested losses 14-61% greater. Although lameness was identified as the costliest condition (28% of total losses), variations in the prevalence of fasciolosis, neosporosis, and paratuberculosis (only a combined 22% of total losses) were nearly as impactful individually as variations in the prevalence of lameness. The results suggest that from a disease control policy perspective, the costliness of a disease may not always be the best indicator of the investment its control warrants; the costliness rankings varied across approaches and total losses were found to be surprisingly sensitive to variations in the prevalence of relatively uncostly diseases. This approach allows for disease impact estimates to be aggregated without double counting. It can be applied to any livestock system in any region with any set of endemic diseases, and can be updated as new prevalence, impact, and disease association data become available. This approach also provides researchers and policymakers an alternative tool to rank prevention priorities.

A methodology to assess indirect economic impacts of animal disease outbreaks: A case of hypothetical African swine fever outbreak in Switzerland.

Indirect costs of animal disease outbreaks often significantly exceed the direct costs. Despite their importance, indirect costs remain poorly characterized due to their complexity. In this study, we developed a framework to assess the indirect costs of a hypothetical African swine fever outbreak in Switzerland. We collected data through international and national stakeholder interviews, analysis of national disease control regulations and industry data. We developed a framework to capture the resulting qualitative and quantitative data, categorize the impacts of these regulations, and rank the impacts in order of importance. We then developed a spreadsheet model to calculate the indirect costs of one category of control measure for an individual group of stakeholders. We developed a decision tree model to guide the most economically favourable implementation plan for a given control measure category, under different outbreak scenarios. Our results suggest that the most important measure/impact categories were 'Transport logistics', 'Consumer demand', 'Prevention of wild boar and domestic pig contact' and 'Slaughter logistics'. In our hypothetical scenario, the greatest costs associated with 'Prevention of wild boar and domestic pig contact' were due to assumed partial or total depopulation of fattening pig farms in order to reduce herd size to comply with the simulated control regulations. The model also provides suggestions on the most economically favourable strategy to reduce contact between wild boar and domestic pigs in control areas. Our approach provides a new framework to integrate qualitative and quantitative data to guide disease control strategy. This method could be useful in other countries and for other diseases, including in data- and resource-poor settings, or areas with limited experience of animal disease outbreaks.

Key Learnings During the Development of a Generic Data Collection Tool to Support Assessment of Freedom of Infection in Cattle Herds.

Various European Member States have implemented control or eradication programmes for endemic infectious diseases in cattle. The design of these programmes varies between countries and therefore comparison of the outputs of different control programmes is complex. Although output-based methods to estimate the confidence of freedom resulting from these programmes are under development, as yet there is no practical modeling framework applicable to a variety of infectious diseases. Therefore, a data collection tool was developed to evaluate data availability and quality and to collect actual input data required for such a modeling framework. The aim of the current paper is to present the key learnings from the process of the development of this data collection tool. The data collection tool was developed by experts from two international projects: STOC free (Surveillance Tool for Outcome-based Comparison of FREEdom from infection, www.stocfree.eu) and SOUND control (Standardizing OUtput-based surveillance to control Non-regulated Diseases of cattle in the EU, www.sound-control.eu). Initially a data collection tool was developed for assessment of freedom of bovine viral diarrhea virus in six Western European countries. This tool was then further generalized to enable inclusion of data for other cattle diseases i.e., infectious bovine rhinotracheitis and Johne's disease. Subsequently, the tool was pilot-tested by a Western and Eastern European country, discussed with animal health experts from 32 different European countries and further developed for use throughout Europe. The developed online data collection tool includes a wide range of variables that could reasonably influence confidence of freedom, including those relating to cattle demographics, risk factors for introduction and characteristics of disease control programmes. Our results highlight the fact that data requirements for different cattle diseases can be generalized and easily included in a data collection tool. However, there are large differences in data availability and comparability across European countries, presenting challenges to the development of a standardized data collection tool and modeling framework. These key learnings are important for development of any generic data collection tool for animal disease control purposes. Further, the results can facilitate development of output-based modeling frameworks that aim to calculate confidence of freedom from disease.

To Sample or Not to Sample? An Analysis of the Need for Salmonella Sampling of Smaller Poultry Processors.

Within the European Union (EU), microbiological criteria (MC) sampling for Salmonella in poultry was introduced in 2005. In particular, processors had to meet a target of fewer than seven positive samples out of 50. However, processors producing small amounts of poultry meat did not have to sample if national authorities determined this was an acceptable risk. The U.K. Food Standards Agency (FSA) thus has a sampling regime based on throughput that allows smaller processors not to sample. In 2011, the limit of 7/50 was reduced to 5/50. Given the current uncertainty regarding U.K. trade relations with the EU, the U.K. FSA decided to conduct a new risk assessment of the risks of Salmonella produced by smaller processors, to determine whether sampling was now necessary. Current evidence suggests that an MC sampling regime in smaller slaughterhouses is not warranted from a national public health perspective. Because of the insensitivities of the MC sampling scheme, the introduction of MC sampling into smaller slaughterhouses would only be necessary if the suspected carcass prevalence was 15% or more. While our analysis is prone to uncertainty, we estimated that the carcass prevalence in smaller processors is below this. Thus, we recommended that the current sampling framework, allowing smaller processors not to sample, was still applicable.