Climate-sensitive companion animal zoonoses and their implications for public health: A scoping review protocol
Bibliographic record
Abstract
Companion animals, though important, remain an underappreciated source of zoonoses. Through direct and indirect transmission routes, companion animals can potentially cause more than 70 human diseases, varying from mild to severe manifestations and even death (Weese & Fulford, 2011). In the absence of a standardized definition for a companion animal, companion animals are limited to dogs and cats for the purpose of this review (Weese & Fulford, 2011). With over 40% of American and Canadian households owning at least one dog or cat, there is an inherent risk of companion animal-associated zoonotic transmission (Agriculture and Agri-Food Canada, 2011; American Veterinary Medical Association, 2018). As such, companion animal zoonoses, spanning the four major taxonomic groups (i.e., parasites, bacteria, viruses, and fungi), are of public health concern. In the near term, the incidence of zoonoses is expected to increase due to changing climates (Rees et al., 2019; Rupasinghe et al., 2022). Several converging factors influence this phenomenon, including the link between spatiotemporal climatic changes in temperature, precipitation, and extreme weather events and survival, abundance, and geographic range of zoonotic pathogens (Omazic et al., 2019). Through these mechanisms, even modest changes in temperature and precipitation have enabled many disease vectors and reservoirs to establish in previously unoccupied areas (i.e., emerge) or propagate in already inhabited areas (i.e., re-emerge), increasing the likelihood of human exposure to zoonoses (Omazic et al., 2019). As a contemporary example, over the last 25 years in Canada, exponential increases in Lyme disease cases have been observed, driven in part by warmer and wetter climates facilitating the range expansion of black-legged (i.e., Ixodes scapularis) tick vectors and the etiological agent, Borrelia burgdorferi (Otten et al., 2020). While companion animals do not directly transmit Lyme disease to humans, they can carry black-legged ticks near people, potentially elevating the risk of exposure (Stull et al., 2015). Despite companion animals playing a role in human disease, they have received limited attention in the context of changing climates relative to wildlife and livestock species. Presently, there is disparate evidence surrounding climate-sensitive companion animal zoonoses, the meteorological factors associated with each zoonosis, and the anticipated impacts of changing climates on human diseases potentially acquired from dogs or cats. This scoping review’s general aim is to map the international evidence on potential climate-sensitive companion animal zoonoses in human populations. The specific objectives are to (1) describe spatial patterns of companion animal zoonoses in the context of varying climates, (2) describe the seasonality of companion animal zoonoses in the context of varying climates, (3) summarize meteorological factors associated with the risk of companion animal zoonoses morbidity and mortality, and (4) evaluate the projected impacts of future climate change (or emission) scenarios on morbidity and mortality risk of companion animal zoonoses. Such results could be used to identify knowledge gaps and inform subsequent research directions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.061 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".