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Exploring uncertainty in canine cancer data sources through dasymetric refinement

Exploring uncertainty in canine cancer data sources through dasymetric refinement
Exploring uncertainty in canine cancer data sources through dasymetric refinement

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.

Cancer underascertainment, Canine cancer incidence, Dasymetric refinement, Geographic correlation studies, Spatial data aggregation
45
Boo, Gianluca
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Leyk, Stefan
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Fabrikant, Sara I.
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Graf, Ramona
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Pospischil, Andreas
9d5d0e71-7d0a-4f5a-b7c3-77edcdd74a24
Boo, Gianluca
d49f7aaa-6d95-4e36-b9be-e469911c4a3d
Leyk, Stefan
00f91399-4d02-488d-833f-c3cd53538139
Fabrikant, Sara I.
10e73f2e-3343-4ef1-9a07-b0fe43acc96c
Graf, Ramona
8c76754f-e3a7-4d88-b952-cb8a4165d359
Pospischil, Andreas
9d5d0e71-7d0a-4f5a-b7c3-77edcdd74a24

Boo, Gianluca, Leyk, Stefan, Fabrikant, Sara I., Graf, Ramona and Pospischil, Andreas (2019) Exploring uncertainty in canine cancer data sources through dasymetric refinement. Frontiers in Veterinary Science, 6 (FEB), 45. (doi:10.3389/fvets.2019.00045).

Record type: Article

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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More information

Accepted/In Press date: 4 February 2019
Published date: 26 February 2019
Keywords: Cancer underascertainment, Canine cancer incidence, Dasymetric refinement, Geographic correlation studies, Spatial data aggregation

Identifiers

Local EPrints ID: 429095
URI: https://eprints.soton.ac.uk/id/eprint/429095
PURE UUID: 28ce5ad8-647b-488b-835d-3ccfe00efb11
ORCID for Gianluca Boo: ORCID iD orcid.org/0000-0002-4078-8221

Catalogue record

Date deposited: 21 Mar 2019 17:30
Last modified: 20 Jul 2019 00:22

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