One Health, One Data: Reimagining Pet Health Surveillance

What if the case notes from your clinic could forecast tomorrow’s outbreak? We sit down with epidemiologist Dr. Lauren Grant to unpack a One Health vision that connects veterinary, human, and environmental data so we can spot risks sooner, act faster, and guide smarter decisions in practice.

We start by clarifying what “integrated companion animal health surveillance” really means and why Canada needs it. Today’s networks rely on selective reporting and expert panels, which are invaluable but miss the power of routine primary care records at scale. Lauren explains how systems like the UK’s VETCOMPASS and SAVSNET turn everyday consultations into population-level insight, building baselines and detecting anomalies that trigger timely investigation. The payoff is concrete: regional trend context to refine differentials, better testing choices, targeted client advice, and earlier alerts for zoonotic and reverse zoonotic threats.

The conversation gets real about barriers to data sharing: policy constraints, privacy, commercial concerns, and a cultural gap where clinicians don’t always see their notes as public health assets. We explore practical solutions—clear governance, de-identified pipelines, minimal viable data fields, and feedback loops that return value to contributing practices through dashboards and timely briefs. Lauren walks through a compelling example from the UK where an unusual spike in canine vomiting was picked up, investigated, and traced to a canine enteric coronavirus, illustrating how strong baselines and near real-time data can change outcomes.

If you’re a veterinarian, public health professional, or data-minded pet owner, this is a roadmap for making companion animals true sentinels of community health. Learn how a Canadian system could start with dogs and cats, build interoperability and trust, and ultimately help both pets and people. Enjoy the episode, share it with a colleague, and if it resonates, subscribe and leave a review so more listeners can find conversations like this.

JAVMA article: https://doi.org/10.2460/javma.25.09.0575

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Transcript

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Lisa Fortier 0:35

Welcome to Veterinary Vertex, the AVMA Journals podcast where we delve into behind-the-scenes look with manuscript authors. I'm editor-in-chief Lisa Fortier, joined by Associate Editor Sarah Wright. Today we're discussing a One Health Integrated Companion Health Survey System with our guest, Lauren Grant. Lauren, thank you so much for taking time out of this blizzardy February day to be with us here today.

Sarah Wright 0:58

Thanks for joining us, Lauren.

Defining One Health Integration

Lauren Grant 0:59

Yeah, thanks so much for having me.

Lisa Fortier 1:01

Lauren, for listeners who might not know what is meant by a One Health Integrated Companion Animal Health Surveillance System and why is it important for today's veterinarian public health landscape?

Scope: What Counts As Companion Animals

How Surveillance Works In Canada Today

Lauren Grant 1:12

Yeah, well, that's definitely a bit of a mouthful to say. But I'm sure that like listeners are already familiar with the concept of One Health. So, it's really this idea around sort of the interconnection of human, animal, and environmental health and really trying to break down the silos between sectors that are responsible for each of those areas. And then when we talk about sort of integration, what we're really trying to recognize there is that there are so many diverse data sources, both within human, animal, and environmental health. But commonly these data sources aren't compatible with one another, which makes it difficult to integrate those data sources across sectors. And the potential for integration is that if we could bring them together in a way that would support new analyses, we can ask more complex questions. We can try and model diseases more closely to what's happening in the real world. And so integration is sort of a step towards that goal. And so when we sort of bring this all together and sort of this idea around this one health integrated companion animal health surveillance system, the idea there is really trying to bring together diverse data sources that we would find within human, animal, and environmental health in a way that would help us to support enhance surveillance of companion animal health outcomes. And the notion with a surveillance system is really the end goal of doing all of that work, is really trying to support effective and timely risk mitigation action. And this is really important in today's landscape, given sort of how interconnected veterinary and public health outcomes can be. For example, when we're considering zoonotic or reverse zoonotic pathogens, or when we're considering shared environmental exposures that we know can influence both veterinary and public health outcomes, the onus or the potential for using these diverse data sources to help us understand the epidemiology of these diseases or help us to conduct more effective surveillance becomes that much more important. What a great collaborative opportunity. Yeah.

Lisa Fortier 3:42

Lauren, when you say companion animal, uh, what is the breadth of that? That can be defined as a lot of different species.

The Gap: Missing Population-Level Data

Reluctance And Barriers To Data Sharing

Lauren Grant 3:48

That's very true. And there isn't really one sort of holistic definition of what a companion animal is. And so in this work and in related work that we were doing, we were mainly focused on dogs and cats, mostly because that's you know the most common companion animals that, um, at least in North America, that that people own, um, but also recognizing that companion animals can refer to other types of species as well. Um, when it comes, and we're talking about this a bit later, but when it comes to sort of these first steps around trying to conduct surveillance, um, we wanted to keep a bit of a smaller scope while still kind of maximizing impact for the majority of pet owners in in Canada and and North America more broadly. Yeah, it's a great clarification because my mind also is like, oh, what about like companion zoo? So that's good to know for this particular study. Right, exactly. So, how does companion animal health surveillance currently operate in Canada and what gaps were you trying to address with this study? Yeah, so at present in Canada, and I believe the situation is also quite similar in the United States as well. Um, companion animal health surveillance is primarily reliant on data that's submitted by veterinary professionals. And this happens when veterinarians are prompted to submit information about specific diseases or syndromes. Um, some of this might be because those diseases are reportable and they're sort of required by law to report. Um, other diseases are under more of this like enhanced passive surveillance, where we try and collect this information through regular survey distribution. And we have a few sort of organizing networks that try and do some of this work. So nationally, there's the Canadian Animal Health Surveillance System, or CAS. And this really acts to function as a network of networks, which helps to bring together species and topic-specific networks of experts who meet regularly to share project and animal health information. And CAS then also functions to act as a distributor of information as well, both online and through email. And then we also have regional networks. So examples of these would be the Western Canadian Animal Health Network or the Ontario Animal Health Network, which are structured quite similarly. They also contain species-specific networks. Both also include a network for companion animals. And using the Ontario Network as an example, here experts would come together to review and discuss information from veterinary practices that we would collect through quarterly syndromic surveys of veterinarians, as well as laboratory data. And then that information then gets disseminated back out through the network as well. However, internationally, we see examples of these larger population-level companion animal health surveillance systems. And so some of the largest ones and perhaps best known are ones like the Veterinary Companion Animal Surveillance System or VETCOMPAS or the Small Animal Veterinary Surveillance Network or SAVSNet, both of which originated in the UK and have grown since then. And the main difference between what we see operating in Canada and systems like these are that these systems actually use routinely collected veterinary consultation data from primary care practices for surveillance and research activities. And so the gap that we were trying to address with this study and as part of a larger project was really the lack of a similar system in Canada. And what that does is really limits our ability to monitor the health status of companion animals at a population level, effectively mitigating risks that are relevant to companion animal health and human health as well in some cases. And also being able to evaluate the effectiveness of interventions at a population level as well, whether that's different policies or programs or regulations that are put in place. It's difficult to understand the value add of some of those interventions when we don't have this sort of larger scale population level information.

Lisa Fortier 8:41

One of the themes, Lauren, that you and your team identified was reluctance around data sharing. What factors might contribute to this hesitation and what things are you trying to implement that might improve that?

Value To Veterinary And Human Health

Early Detection And Baseline Trends

Lauren Grant 8:53

Yeah. So with respect to data sharing, participants mainly discussed two mechanisms. And that was really an inability to share data and then an unwillingness to share data. And so some individuals or organizations may be unable to share data, even if they are willing, even if they see the value in sharing their data. And this could be because of a multitude of reasons. So this could be because of organizational bureaucracy or red tape that makes data sharing quite difficult. It might be due to existing policies or regulations that restrict data sharing, that are, of course, often in place for very good reasons, but perhaps haven't necessarily been updated to potentially explore increased data sharing, or sometimes just a lack of time to be able to share data. On the other hand, there may be an unwillingness to share data, even if you have the ability to do so. And in our study, participants really related this primarily to attitudinal factors. So this might be in relation to reducing commercial value for an organization, concerns related to privacy, or professional perceptions related to the role of veterinarians in companion animal health and disease surveillance, which I find actually really quite interesting. I think when we sort of look at current veterinary education, education around sort of epidemiology and surveillance may be quite limited. And so veterinarians might not sort of appreciate the value of their data, the value of what they're seeing in their individual practices, to sort of informing these sort of broader questions around disease trends that are happening within regional populations. And for me, that's actually one of the more, as an epidemiologist, one of the more exciting perceptions to potentially change would be to help veterinarians really to see themselves actually as part of this interconnected network of people who can supply data that allows us to ask these much broader questions around sort of what's happening at a population level. Fascinating information. Thank you. So, how do you think this kind of integrated system could add value for both animal and human health? So, in our interviews for animal health specifically, participants described the value of a surveillance system really around supporting clinical practice, which goes back to what I was speaking to a little bit earlier around veterinarians really being part of this much broader network of information that allows us to glean new information that a veterinarian in sort of a single practice might not be able to have access to. And more specifically, what I'm referring to there is when we have sort of this higher-level surveillance information, what we're able to do is provide veterinarians with sort of contextual information about population-level trends that are happening sort of regionally or broader nationally at a state or provincial level. And what this information can really do is help to inform differential diagnoses. It might inform the testing that's being conducted by the veterinarian, or even the information that's being communicated to clients to help reduce sort of regional risk of exposure, for example, or helping clients to be more aware of symptoms or diseases that they should be on the lookout for. And so rather than sort of veterinarians sort of operating in sort of their own kind of individual practice silos, what this does is it gives kind of timely information from this like 30,000-foot view that can really help in their sort of day-to-day decision making. And then for human health, there's really value in relation to potential early warnings of emerging or re-emerging zoonotic pathogens that we might see first in companion animals before we see impacts on human health. Certainly the idea of companion animals as sentinel species has been explored by others, but having this kind of integrated system could certainly help to realize this potential further.

Lisa Fortier 13:45

Thanks, Lauren. You really teed up my next question nicely. Are there some examples you can give to listeners how this sort of system has uh supported early detection and response to emerging zoonotic diseases, either forward or reverse?

Case Study: UK Vomiting Outbreak In Dogs

Closing And Where To Read The Study

Lauren Grant 13:59

Yeah. And so maybe I'll sort of talk a little bit about um how that might happen more generally. And then I can give kind of more specific examples that make me really optimistic that this kind of activity can happen. Um and so what we're talking about here is really when we have kind of a this integrated system with these informative data sources from sort of multiple sectors, what we first have is sort of more robust data that allows us to construct baseline trends around what we would normally expect in regards to the incidence of a particular disease over a given time period and area. Once we have that baseline trend established, what an integrated system allows us to do would really be to support detection of aberrations or departures from that trend that would warrant further monitoring or attention and possibly action as well. And if this data is in real time or near real time, we are able to support sort of earlier detection of those sort of departures from the norm as well as a response. And so for emerging diseases that we've seen examples of in the literature, early detection is actually more heavily dependent on increased occurrence of symptoms as opposed to like diagnoses, which sometimes these symptoms can be quite nonspecific. And so, one example internationally was um there was a paper by Alan Radford and colleagues in 2021, um where SAVSNet, which was one of the surveillance systems I mentioned in the UK, they were able to successfully detect an outbreak of severe vomiting in dogs. And that sort of prompted sort of further investigation, and they were eventually able to identify the causal pathogen as a canine enteric coronavirus. And so that was just a really nice example where we had sort of sort of baseline trends. We were able to detect a departure from that trend, and then that sort of warranted further specific investigation that was then able to result in risk mitigation actions.

Sarah Wright 16:33

Wow, what a cool example. Well, thank you, Lauren, so much for joining us. We learned so much from you today, and this has just been an amazing topic to share with our listeners. Great. Thanks so much for having me. And for our listeners and viewers, you can read Lauren's article in Javma. I'm Sarah Wright here with Lisa Fortier. Be sure to tune in next week for another episode of Veterinary Vertex, and don't forget to leave us a rating and review on Apple Podcasts or wherever you listen.