ArticleFrontiers in veterinary science2019
Exploring Uncertainty in Canine Cancer Data Sources Through Dasymetric Refinement.
Article in Frontiers in veterinary science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Spontaneous tumors in dogs and cats in Algeria: a dual-source descriptive study of practitioner-reported patterns and histopathology-submitted cases.BMC veterinary research · 2026Article
- The epidemiological landscape of canine tumors in central Italy: animal cancer registry insights, distribution patterns, and incidence estimates.Frontiers in veterinary science · 2026Article
- Epidemiology of canine mammary tumours on the Canary Archipelago in Spain.BMC veterinary research · 2022Article
- Application of Bayesian Regression for the Identification of a Catchment Area for Cancer Cases in Dogs and Cats.Frontiers in veterinary science · 2022Article
- A text-mining based analysis of 100,000 tumours affecting dogs and cats in the United Kingdom.Scientific data · 2021Article
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Authors and funding
5 authors.
Funding
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Abstract
In spite of the potentially groundbreaking environmental sentinel applications, studies of canine cancer data sources are often limited due to undercounting of cancer cases. This source of uncertainty might be further amplified through the process of spatial data aggregation, manifested as part of the modifiable areal unit problem (MAUP). In this study, we explore potential explanatory factors for canine cancer incidence retrieved from the Swiss Canine Cancer Registry (SCCR) in a regression modeling framework. In doing so, we also evaluate differences in statistical performance and associations resulting from a dasymetric refinement of municipal units to their portion of residential land. Our findings document severe underascertainment of cancer cases in the SCCR, which we linked to specific demographic characteristics and reduced use of veterinary care. These explanatory factors result in improved statistical performance when computed using dasymetrically refined units. This suggests that dasymetric mapping should be further tested in geographic correlation studies of canine cancer incidence and in future comparative studies involving human cancers.
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