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D.G. O’Neill1, B.N. Bonnett2
1Royal Veterinary College, Pathobiology and Population Health, Petts Wood- Kent, United Kingdom 2International Partnership for Dogs, Epidemiology, Ontario, Canada
How do we get valid data on diseases that affect breeding? And how can we use such data to ensure healthy dogs and cats?
Dr Dan G O’Neill1 & Dr Brenda N. Bonnett2
1 MVB BSc(hons) GPCert(SAP) GPCert(FelP) GPCert(Derm) GPCert(B&PS) MSc(VetEpi) PhD MRCVS
Veterinary Epidemiology, Economics and Public Health, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hat eld, Hertfordshire AL9 7TA, United Kingdom 2 DVM, PhD
Epidemiologist, B Bonnett Consulting and CEO, International Partnership for Dogs (IPFD); Georgian Bluffs, Ontario, Canada N0H 2T0;
Have you ever asked a question about how common a condition is in dogs or cats, or in a speci c breed? Have you ever wondered why some breeds are more likely to get speci c conditions than others? These questions seem basic to understanding and improving the health of pets and yet clear answers are often not available. It is increasingly recognised that radical improvements
are needed on the quality and quantity of population- based data on dogs and cats if we are to make real gains to animal health nationally and internationally. As we try to understand similarities and differences in health issues across regions; as breeding advisors and animal breeders try to make the best decisions to improve health and welfare; as veterinarians try to understand and explain risk to their clients; as any stakeholders try
to design and monitor the effect of health interventions, there are calls for more and better information quantifying the occurrence of disease across various populations and sub-populations. This presentation explores why we do not have all the answers and how we might start to get them by highlighting the basis of the evidence and the gaps underpinning a more quantitative approach
to understanding health and disease, existing and developing sources of data and the need for collaborative development across stakeholder groups, internationally and the roles each of us has to play.
Historically, many belief systems in companion animal health were propagated with heavy reliance on personal experience and expert opinion. The memory and perception of, e.g. private practitioners or even the most experienced breeders cannot be expected to produce accurate estimates of disease occurrence that relate to a wide population of animals. Expert opinion is sometimes called eminence based veterinary medicine and relies
on the personal opinion of recognised experts or self- appointed commentators, often with minimal explicit critical appraisal applied to the quality of this opinion. Although expert opinion has been widely accepted as highly persuasive and reliable, it too is primarily anecdotal and, therefore, among the weakest type of evidence unless it is underpinned by a solid and stated evidence base (1). This is because many cognitive biases are inevitably inherent within the belief systems of any individual; and these explain why opinions across experts or interest groups so often disagree. However, we are now seeing increasing demands to challenge existing beliefs and position statements with the call for evidence: ‘Show me the numbers’.
As we embrace this new enlightenment of evidence based veterinary medicine (EBVM) (2), however, there are new and exciting challenges that include but are not limited to:
• Better data collection and curation methods
• Identifying and comparing data sources
• Linking databases / Collaborative research projects / ‘Jigsaw’ projects
• Understanding the uses and limitations of various data collection methods and sources – from research to veterinary practice data to breed health surveys
• Standardised terminology (e.g. SnoMed, VeNom, PETscan, Agria)
• Knowledge of analytical methods
• Dissemination of information
• Inclusivity for all stakeholders
• Awareness of opportunities missed
• Prioritisation of data requirements
• Understanding of the risks from Poor data versus No data
The adoption of a more quantitative, evidence based approach requires not only knowledge but also a change in attitudes and approach. Understanding breed-speci c data such as prevalence and risk is both a science and an art. The science is the generation of appropriate
data of good quality from a representative population and the extraction of reliable meaning from these data. The art comes in the communication and application
of these data to improve animal health given both the
An Urban Experience

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