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Exploring Uncertainty in Canine Cancer Data Sources Through Dasymetric Refinement
Gianluca Boo1,2,3, Stefan Leyk4, Sara I Fabrikant1,5
1Department of Geography, University of Zurich, Zurich, Switzerland.
Frontiers in Veterinary Science
|March 14, 2019
Summary
Canine cancer data often undercounts cases, especially in spatial studies. Refining data with dasymetric mapping improves accuracy, revealing links to demographics and veterinary care use.
Area of Science:
- Veterinary epidemiology
- Geographic information systems (GIS)
- Environmental health
Background:
- Canine cancer data is crucial for environmental health studies but suffers from undercounting.
- Spatial data aggregation can worsen uncertainty through the modifiable areal unit problem (MAUP).
Purpose of the Study:
- To explore factors influencing canine cancer incidence using Swiss Canine Cancer Registry (SCCR) data.
- To evaluate the impact of dasymetric refinement on statistical performance and associations.
Main Methods:
- Regression modeling framework applied to SCCR data.
- Comparison of statistical models using standard municipal units versus dasymetrically refined residential land units.
Main Results:
- Severe underascertainment of canine cancer cases in the SCCR was identified.
- Factors linked to undercounting include specific demographics and reduced veterinary care utilization.
- Dasymetric refinement improved statistical performance and associations.
Conclusions:
- Dasymetric mapping enhances the accuracy of canine cancer incidence studies.
- This methodology should be further explored for canine and comparative human cancer research.
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