Approximating the global economic (market) value of farmed animals.

Understanding the global economic importance of farmed animals to society is essential as a baseline for decision making about future food systems. We estimated the annual global economic (market) value of live animals and primary production outputs, e.g., meat, eggs, milk, from terrestrial and aquatic farmed animal systems. The results suggest that the total global market value of farmed animals ranges between 1.61 and 3.3 trillion USD (2018) and is expected to be similar in absolute terms to the market value of crop outputs (2.57 trillion USD). The cattle sector dominates the market value of farmed animals. The study highlights the need to consider other values of farmed animals to society, e.g., finance/insurance value and cultural value, in decisions about the sector's future.

Epidemiology of companion animal AMR in the United States of America: filling a gap in the one health approach.

Introduction: Antimicrobial resistance (AMR) is a global health concern that affects all aspects of the One Health Triad, including human, animal, and environmental health. Companion animals, such as cats and dogs, may contribute to the spread of AMR through their close contact with humans and the frequent prescription of antimicrobials. However, research on AMR in companion animals is limited, and there are few surveillance measures in place to monitor the spread of resistant pathogens in the United States. Methods: This study aims to explore the practicality of using data from commercial laboratory antimicrobial susceptibility testing (AST) services for epidemiological analyses of AMR in companion animals in the United States. Results: The study analyzed 25,147,300 individual AST results from cats and dogs submitted to a large commercial diagnostic laboratory in the United States between 2019 and 2021, and found that resistance to certain antimicrobials was common in both E. coli and S. pseudintermedius strains. Conclusion: There has been a paucity of information regarding AMR in companion animals in comparison to human, environmental and other animal species. Commercial AST datasets may prove beneficial in providing more representation to companion animals within the One Health framework for AMR.

Isolation of Elizabethkingia spp. from Diagnostic Specimens from Dogs and Cats, United States, 2019-2021.

We retrospectively reviewed Elizabethkingia spp. culture and susceptibility results from 86 veterinary diagnostic laboratory results from US dogs and cats. We noted 26 E. menigoseptica, 1 E. miricola, and 59 unspeciated Elizabethkingia isolates from 9 US states (2-22 isolates per state). Elizabethkingia infections in animals might increase risks to humans.

Morbidity of insured Swedish cats between 2011 and 2016: Comparing disease risk in domestic crosses and purebreds.

BACKGROUND: Sources of population-based cat health information are scarce. The objective of this study was to determine disease frequency in cats using pet insurance data to inform health promotion efforts. METHODS: A descriptive analysis of cats insured with Agria Pet Insurance in Sweden (2011-2016) was performed. Incidence rates of broad disease categories were calculated based on veterinary care events and an exact denominator consisting of cat-years-at-risk. Rate ratios were calculated, comparing domestic crosses to all purebreds and specific purebreds to all other purebreds combined. RESULTS: The study included over 1.6 million cat-years-at-risk (78.5% were domestic crosses), 18 breeds and 24 disease categories. The most common disease categories causing morbidity in purebreds were digestive, whole body, injury, urinary lower, skin and female reproduction. Purebreds had the highest relative risk (compared to domestics crosses) in the female reproduction, heart, operation complication, respiratory lower and immunological disease categories. LIMITATIONS: There are typical limitations of secondary data, but they do not negate the overall value of such a large dataset. CONCLUSION: This study demonstrates how pet insurance data can be used to find breed-specific differences in the incidence of various disease categories in cats. This may be of importance for breeders, cat owners, veterinarians and researchers.

Use of health parameter trends to communicate pet health information in companion animal practice: A mixed methods analysis.

BACKGROUND: Reviewing patient health parameter trends can strengthen veterinarian-client-patient relationships. The objective of this study is to identify characteristics associated with veterinarians' communication of health parameter trends to companion animal clients. METHODS: Using a sequential exploratory mixed methods design, independent pet owner (n = 27) and veterinarian (n = 24) focus groups were conducted and analysed via content analysis to assess perceptions of how health parameter trends are communicated by veterinarians. Subsequently, a quantitative assessment of video recorded veterinary appointments (n = 917) compared characteristics identified in focus groups with health parameter trend discussions in practice. A mixed logistic model was used to assess characteristics associated with the occurrence of weight trend discussions. RESULTS: Fifteen characteristics relating to veterinarians' use of health parameter trends were identified across focus groups. Veterinarians discussed 77 health parameter trends in relation to bodyweight (57/77), blood work (15/77) and other health parameters (5/77), within 73 (73/917) appointments. The odds of a weight trend discussion were higher if the veterinarian identified the pet as overweight or obese compared to an ideal bodyweight (odds ratio (OR) = 2.17; 95% confidence interval (CI) = 1.15-4.09; p = 0.016). CONCLUSION: Mention of a health parameter trend was uncommon and rarely included use of visual aids. Health parameter trends related to bodyweight were discussed reactively, rather than proactively.

From FAIR to FAIRS: Data security by design for the global burden of animal diseases.

Solving complex global problems involving data and data analysis can require data from both the public and private sectors. The sharing of data has traditionally been restricted to open data. To facilitate the use of both open and private data, a new data-sharing framework has been constructed as an extension to the popular Findable-Accessible-Interoperable-Reusable (FAIR) framework. The "Secure by Design" approach has been taken to define the FAIRS data-sharing framework where S stands for Secure. A Cloud infrastructure architecture is proposed that would allow data brokers to implement FAIRS. This architecture is being constructed for the Global Burden of Animal Diseases (GBADs) to facilitate the sharing of livestock data.

Technology-enhanced weight-loss program in multiple-cat households: a randomized controlled trial.

OBJECTIVES: The objectives of this study were to determine whether a technology-enhanced weight-loss program, using a home pet health technology ecosystem, is an effective tool in feline weight-loss management in multiple-cat households and to evaluate its impact on cat behavior. METHODS: The study was a prospective parallel unmasked block-randomized controlled trial comparing two weight loss intervention groups: (1) traditional group with dietary restriction alone (n = 9); (2) technology group that used dietary restriction, digital scales, smart feeders, activity monitors and pet treat cameras (n = 6). A 12-week weight-loss program of client-owned indoor-only two- or three-cat households with at least one overweight cat was conducted in Canada and the USA. Owner impressions of the technology, weight loss rates, smart feeder data, activity monitor data and health-related quality of life (HRQoL) were assessed. RESULTS: The study was completed by 9/15 traditional group and 6/10 technology group cats. Dropouts were mainly due to owner issues unrelated to the study. The pet health technology ecosystem received favorable reviews (six responders). Smart feeders and home scales were perceived as valuable additions, while activity monitors and pet treat cameras were valued lower. The average weekly weight-loss rate (percent loss of initial body weight) was higher (P = 0.036) in the technology group (0.694%) than in the traditional group (0.175%). Although not associated with weight-loss rates, technology group cats trended toward grazing feeding patterns and decreased daily activity counts, while HRQoL increased, on average, for all cats. CONCLUSIONS AND RELEVANCE: This introductory investigation suggests that a technology-enhanced weight-loss program would be accepted by cat owners and may deliver advantageous outcomes in multiple-cat households, providing an effective and practical tool in feline weight-loss strategies that will continue to evolve as new technologies become available. It also illustrates the potential value of data gathered from home monitoring devices and digital diaries, providing deeper insights into pet behavior.

Companion animal veterinarians' and veterinary clients' perceptions of information exchanged while communicating about blood tests.

BACKGROUND: Blood tests play a vital role in veterinary medicine, as they enable veterinarians to make decisions about their patients' medical care and provide opportunities to engage clients in veterinary care. The objective of this study was to explore perspectives of veterinary clients and veterinarians on current reporting practices for blood tests ordered in companion animal practice. The type of information communicated and client information seeking behaviours were examined. METHODS: Two online surveys were distributed using snowball sampling: one targeting veterinary clients and one targeting veterinarians. RESULTS: In total, 529 veterinary client and 416 veterinarian surveys were included in analyses. Significant differences were found between what blood test information veterinarian respondents felt they provided clients and what client respondents perceived they received with regard to their pet's blood tests. Almost 60% of clients somewhat or strongly agreed that they search the Internet if they do not understand why a test was ordered or what the results mean. Half of clients reported that they never receive a written report of their pet's blood work, 81% of whom were interested in receiving a copy. CONCLUSION: Results of this research demonstrate opportunities to improve veterinarians' and clients' communication about blood tests.

Evaluating shared decision-making between companion animal veterinarians and their clients using the Observer OPTION5 instrument.

BACKGROUND: Communication practices used by veterinarians can impact client's involvement in decision-making. The objective of this study was to evaluate the level of client involvement in veterinary care decisions and factors impacting client involvement. METHODS: A random sample of practising companion animal veterinarians and a convenience sample of pet owners were recruited. Each consultation was audio-video recorded and analyzed using the Observer OPTION5 instrument, adapted from human medicine, to assess veterinarians' level of shared decision-making. A mixed linear model was used to assess statistical associations between veterinarian, client and appointment-level factors and OPTION5 scores. RESULTS: Sixty veterinarians and 909 of their clients were audio-video recorded during a consultation, of which 717 interactions included a decision identified for analysis using the OPTION5 instrument. Elements consistent with shared decision-making were uncommon, with a mean OPTION5 score of 22.6 (median = 20.0; sd = 13.05; range = 0-75) out of 100. OPTION5 scores were positively associated with the length of the appointment and negatively associated with the veterinarian's years in practice when controlling for statistical interaction between client income and appointment type. CONCLUSION: Findings suggest an opportunity exists for veterinarians to incorporate more client involvement through increased use of shared decisionmaking behaviours during veterinary consultations.

Day-1 Competencies for Veterinarians Specific to Health Informatics.

In 2015, the American Association of Veterinary Medical Colleges (AAVMC) developed the Competency-Based Veterinary Education (CBVE) framework to prepare practice-ready veterinarians through competency-based education, which is an outcomes-based approach to equipping students with the skills, knowledge, attitudes, values, and abilities to do their jobs. With increasing use of health informatics (HI: the use of information technology to deliver healthcare) by veterinarians, competencies in HI need to be developed. To reach consensus on a HI competency framework in this study, the Competency Framework Development (CFD) process was conducted using an online adaptation of Developing-A-Curriculum, an established methodology in veterinary medicine for reaching consensus among experts. The objectives of this study were to (1) create an HI competency framework for new veterinarians; (2) group the competency statements into common themes; (3) map the HI competency statements to the AAVMC competencies as illustrative sub-competencies; (4) provide insight into specific technologies that are currently relevant to new veterinary graduates; and (5) measure panelist satisfaction with the CFD process. The primary emphasis of the final HI competency framework was that veterinarians must be able to assess, select, and implement technology to optimize the client-patient experience, delivery of healthcare, and work-life balance for the veterinary team. Veterinarians must also continue their own education regarding technology by engaging relevant experts and opinion leaders.

Pet owners' and veterinarians' perceptions of information exchange and clinical decision-making in companion animal practice.

One of the most complex aspects of the veterinarian-client-patient interaction is the clinical decision-making process. Research suggests that the approach to communication used by veterinarians can impact veterinary clients' involvement in the decision-making process and their ultimate satisfaction. Using different approaches to the decision-making process may affect how information is exchanged and consequently how decisions are made. The objective of this study was to determine pet owners' expectations with respect to information exchange and decision-making during veterinarian-client-patient interactions and to compare veterinarians' perceptions of those expectations and the challenges they face in meeting them. Five pet owner focus groups (27 owners) and three veterinarian focus groups (24 veterinarians) were conducted with standardized open-ended questions and follow-up probes. Thematic analysis of the transcribed data was conducted to identify trends and patterns that emerged during the focus groups. Three pet owner-based themes were identified: 1) understanding the client; 2) providing information suitable for the client; and 3) decision-making. In addition, three barriers for veterinarians affecting information exchange and decision-making were identified: 1) time constraints; 2) involvement of multiple clients; and 3) language barriers. Results suggest that pet owners expect to be supported by their veterinarian to make informed decisions by understanding the client's current knowledge, tailoring information and educating clients about their options. Breakdowns in the information exchange process can impact pet owners' perceptions of veterinarians' motivations. Pet owners' emphasis on partnership suggests that a collaborative approach between veterinarians and clients may improve client satisfaction.

Collaborating in the Time of COVID-19: The Scope and Scale of Innovative Responses to a Global Pandemic.

The emergence of COVID-19 spurred the formation of myriad teams to tackle every conceivable aspect of the virus and thwart its spread. Enabled by global digital connectedness, collaboration has become a constant theme throughout the pandemic, resulting in the expedition of the scientific process (including vaccine development), rapid consolidation of global outbreak data and statistics, and experimentation with novel partnerships. To document the evolution of these collaborative efforts, the authors collected illustrative examples as the pandemic unfolded, supplemented with publications from the JMIR COVID-19 Special Issue. Over 60 projects rooted in collaboration are categorized into five main themes: knowledge dissemination, data propagation, crowdsourcing, artificial intelligence, and hardware design and development. They highlight the numerous ways that citizens, industry professionals, researchers, and academics have come together worldwide to consolidate information and produce products to combat the COVID-19 pandemic. Initially, researchers and citizen scientists scrambled to access quality data within an overwhelming quantity of information. As global curated data sets emerged, derivative works such as visualizations or models were developed that depended on consistent data and would fail when there were unanticipated changes. Crowdsourcing was used to collect and analyze data, aid in contact tracing, and produce personal protective equipment by sharing open designs for 3D printing. An international consortium of entrepreneurs and researchers created a ventilator based on an open-source design. A coalition of nongovernmental organizations and governmental organizations, led by the White House Office of Science and Technology Policy, created a shared open resource of over 200,000 research publications about COVID-19 and subsequently offered cash prizes for the best solutions to 17 key questions involving artificial intelligence. A thread of collaboration weaved throughout the pandemic response, which will shape future efforts. Novel partnerships will cross boundaries to create better processes, products, and solutions to consequential societal challenges.

An animal health example of managing and analyzing a large volume of data on a PC: Modeling body weight and age of over 13 million cats for explanatory and predictive purposes.

Large amounts of animal health data are available to researchers, but are often stored in different formats and information silos. Analysis of this existing information can provide new insights into the health and welfare of animals and possibly reduce the need to collect additional data. The objective of this study was to develop a method of managing and analyzing large amounts of data on a personal computer that can be run within 24 h to limit the time and resources spent deploying models on larger servers. This paper describes an overall approach that makes use of existing methods for data acquisition and modeling, but adapts and combines them in a way that allows manipulation and analysis of large volumes of data on a PC. This included a total of five steps: removing errors; removing data points outside the scope of a specific hypothesis; creating descriptive statistics; developing explanatory and/or predictive models; and assessing the fit or accuracy of the models created. The approach was developed using electronic medical records for 19,416,753 feline patients from 3972 anonymized veterinary clinics in the United States and Canada, recorded between January 1981 and June 2016. Data regarding patient signalment (age, sex, breed, reproductive status) and body weight were extracted from the records and used to create linear regression models to describe body weight in cats of different ages, breeds, genders and reproductive status. Ordinary least squares linear regression and stochastic gradient descent linear regression were compared to determine their effectiveness and suitability for creating predictive models with large datasets, using 10 fold cross validation. This approach could be used to build workflows to create models to determine exploratory and predictive properties of health parameters for animals and people. The ability to work with large datasets on a PC or equivalent technology was demonstrated. Significant interactions were present among sex, reproductive status and age. A peak in weight occurred between 6 and 9 years depending on the sex, reproductive status and breed. The predictive ability of the two models was similar, with both producing a root mean square error of 1.45 and a mean absolute error of 1.09, and mean error that was approximately zero on the validation dataset.

The Assessment of Twitter's Potential for Outbreak Detection: Avian Influenza Case Study.

Social media services such as Twitter are valuable sources of information for surveillance systems. A digital syndromic surveillance system has several advantages including its ability to overcome the problem of time delay in traditional surveillance systems. Despite the progress made with using digital syndromic surveillance systems, the possibility of tracking avian influenza (AI) using online sources has not been fully explored. In this study, a Twitter-based data analysis framework was developed to automatically monitor avian influenza outbreaks in a real-time manner. The framework was implemented to find worrisome posts and alerting news on Twitter, filter irrelevant ones, and detect the onset of outbreaks in several countries. The system collected and analyzed over 209,000 posts discussing avian influenza on Twitter from July 2017 to November 2018. We examined the potential of Twitter data to represent the date, severity and virus type of official reports. Furthermore, we investigated whether filtering irrelevant tweets can positively impact the performance of the system. The proposed approach was empirically evaluated using a real-world outbreak-reporting source. We found that 75% of real-world outbreak notifications of AI were identifiable from Twitter. This shows the capability of the system to serve as a complementary approach to official AI reporting methods. Moreover, we observed that one-third of outbreak notifications were reported on Twitter earlier than official reports. This feature could augment traditional surveillance systems and provide a possibility of early detection of outbreaks. This study could potentially provide a first stepping stone for building digital disease outbreak warning systems to assist epidemiologists and animal health professionals in making relevant decisions.

Investigation of relationships between body weight and age among domestic cats stratified by breed and sex.

OBJECTIVE To evaluate mean body weight (BW) over the lifespan of domestic cats stratified by breed and sex (including reproductive status [neutered vs sexually intact]). ANIMALS 19,015,888 cats. PROCEDURES Electronic medical records from veterinary clinics in the United States and Canada from 1981 to 2016 were collected through links to practice management software programs and anonymized. Age, breed, sex and reproductive status, and BW measurements and measurement dates were recorded. Data were cleaned, and descriptive statistics were determined. Linear regression models were created with data for 8-year-old domestic shorthair, medium hair, and longhair (SML) cats to explore changes in BW over 3 decades (represented by the years 1995, 2005, and 2015). RESULTS 9,886,899 of 19,015,888 (52%) cats had only 1 BW on record. Mean BW for cats of the 4 most common recognized breeds (Siamese, Persian, Himalayan, and Maine Coon Cat) peaked between 6 and 10 years of age and then declined. Mean BW of SML cats peaked at 8 years and was subjectively higher for neutered than for sexually intact cats. Mean BW of neutered 8-year-old SML cats increased between 1995 and 2005 but was steady between 2005 and 2015. CONCLUSIONS AND CLINICAL RELEVANCE The large dataset for this study yielded useful information on mean BW over the lifespan of domestic cats. This could be a basis for BW management discussions during veterinary visits. A low frequency of repeated BW measurements suggested a low frequency of repeated veterinary visits, especially after 1 year of age, making engagement of cat owners in the health of their animals particularly relevant.

A scoping review of 'big data', 'informatics', and 'bioinformatics' in the animal health and veterinary medical literature.

Research in big data, informatics, and bioinformatics has grown dramatically (Andreu-Perez J, et al., 2015, IEEE Journal of Biomedical and Health Informatics 19, 1193-1208). Advances in gene sequencing technologies, surveillance systems, and electronic medical records have increased the amount of health data available. Unconventional data sources such as social media, wearable sensors, and internet search engine activity have also contributed to the influx of health data. The purpose of this study was to describe how 'big data', 'informatics', and 'bioinformatics' have been used in the animal health and veterinary medical literature and to map and chart publications using these terms through time. A scoping review methodology was used. A literature search of the terms 'big data', 'informatics', and 'bioinformatics' was conducted in the context of animal health and veterinary medicine. Relevance screening on abstract and full-text was conducted sequentially. In order for articles to be relevant, they must have used the words 'big data', 'informatics', or 'bioinformatics' in the title or abstract and full-text and have dealt with one of the major animal species encountered in veterinary medicine. Data items collected for all relevant articles included species, geographic region, first author affiliation, and journal of publication. The study level, study type, and data sources were collected for primary studies. After relevance screening, 1093 were classified. While there was a steady increase in 'bioinformatics' articles between 1995 and the end of the study period, 'informatics' articles reached their peak in 2012, then declined. The first 'big data' publication in animal health and veterinary medicine was in 2012. While few articles used the term 'big data' (n = 14), recent growth in 'big data' articles was observed. All geographic regions produced publications in 'informatics' and 'bioinformatics' while only North America, Europe, Asia, and Australia/Oceania produced publications about 'big data'. 'Bioinformatics' primary studies tended to use genetic data and tended to be conducted at the genetic level. In contrast, 'informatics' primary studies tended to use non-genetic data sources and conducted at an organismal level. The rapidly evolving definition of 'big data' may lead to avoidance of the term.

An open ecosystem engagement strategy through the lens of global food safety.

The Global Food Safety Partnership (GFSP) is a public/private partnership established through the World Bank to improve food safety systems through a globally coordinated and locally-driven approach. This concept paper aims to establish a framework to help GFSP fully leverage the potential of open models. In preparing this paper the authors spoke to many different GFSP stakeholders who asked questions about open models such as: what is it?what's in it for me?why use an open rather than a proprietary model?how will open models generate equivalent or greater sustainable revenue streams compared to the current "traditional" approaches?  This last question came up many times with assertions that traditional service providers need to see opportunity for equivalent or greater revenue dollars before they will buy-in. This paper identifies open value propositions for GFSP stakeholders and proposes a framework for creating and structuring that value. Open Educational Resources (OER) were the primary open practice GFSP partners spoke to us about, as they provide a logical entry point for collaboration. Going forward, funders should consider requiring that educational resources and concomitant data resulting from their sponsorship should be open, as a public good. There are, however, many other forms of open practice that bring value to the GFSP. Nine different open strategies and tactics (Appendix A) are described, including: open content (including OER and open courseware), open data, open access (research), open government, open source software, open standards, open policy, open licensing and open hardware. It is recommended that all stakeholders proactively pursue "openness" as an operating principle. This paper presents an overall GFSP Open Ecosystem Engagement Strategy within which specific local case examples can be situated. Two different case examples, China and Colombia, are presented to show both project-based and crowd-sourced, direct-to-public paths through this ecosystem.