Spatial scale effects in environmental risk-factor modelling for diseases

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dc.contributor.author Raghavan, Ram K.
dc.contributor.author Brenner, Karen M.
dc.contributor.author Harrington, John A., Jr.
dc.contributor.author Higgins, James J.
dc.contributor.author Harkin, Kenneth R.
dc.date.accessioned 2013-07-16T20:24:34Z
dc.date.available 2013-07-16T20:24:34Z
dc.date.issued 2013-05-01
dc.identifier.uri http://hdl.handle.net/2097/15987
dc.description.abstract Studies attempting to identify environmental risk factors for diseases can be seen to extract candidate variables from remotely sensed datasets, using a single buffer-zone surrounding locations from where disease status are recorded. A retrospective case-control study using canine leptospirosis data was conducted to verify the effects of changing buffer-zones (spatial extents) on the risk factors derived. The case-control study included 94 case dogs predominantly selected based on positive polymerase chain reaction (PCR) test for leptospires in urine, and 185 control dogs based on negative PCR. Land cover features from National Land Cover Dataset (NLCD) and Kansas Gap Analysis Program (KS GAP) around geocoded addresses of cases/controls were extracted using multiple buffers at every 500 m up to 5,000 m, and multivariable logistic models were used to estimate the risk of different land cover variables to dogs. The types and statistical significance of risk factors identified changed with an increase in spatial extent in both datasets. Leptospirosis status in dogs was significantly associated with developed high-intensity areas in models that used variables extracted from spatial extents of 500-2000 m, developed medium-intensity areas beyond 2,000 m and up to 3,000 m, and evergreen forests beyond 3,500 m and up to 5,000 m in individual models in the NLCD. Significant associations were seen in urban areas in models that used variables extracted from spatial extents of 500-2,500 m and forest/woodland areas beyond 2,500 m and up to 5,000 m in individual models in Kansas gap analysis programme datasets. The use of ad hoc spatial extents can be misleading or wrong, and the determination of an appropriate spatial extent is critical when extracting environmental variables for studies. Potential work-arounds for this problem are discussed. en_US
dc.language.iso en_US en_US
dc.relation.uri http://www.geospatialhealth.unina.it/summary.php?ida=204 en_US
dc.relation.uri http://doi.org/10.4081/gh.2013.78
dc.rights Permission to archive granted by Geospatial Health, June 18, 2013. en_US
dc.subject Spatial extent en_US
dc.subject Modifiable Areal Unit Problem (MAUP) en_US
dc.subject Geographical information system en_US
dc.subject GIS en_US
dc.subject Leptospirosis en_US
dc.subject Canine en_US
dc.title Spatial scale effects in environmental risk-factor modelling for diseases en_US
dc.type Article (publisher version) en_US
dc.date.published 2013 en_US
dc.citation.doi 10.4081/gh.2013.78
dc.citation.epage 182 en_US
dc.citation.issue 2 en_US
dc.citation.jtitle Geospatial Health en_US
dc.citation.spage 169 en_US
dc.citation.volume 7 en_US
dc.contributor.authoreid rkraghavan en_US
dc.contributor.authoreid jharrin en_US
dc.contributor.authoreid jhiggins en_US
dc.contributor.authoreid harkin en_US


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