Spatial scale effects in environmental risk-factor modelling for diseases

Ram K. Raghavan, Karen M. Brenner, John A. Jr. Harrington, James J. Higgins, Kenneth R. Harkin
  • Ram K. Raghavan
    Kansas State Veterinary Diagnostic Laboratory, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States | rkraghavan@vet.k-state.edu
  • Karen M. Brenner
    Department of Clinical Sciences, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States
  • John A. Jr. Harrington
    Department of Geography, College of Arts and Sciences, Kansas State University, Manhattan, KS, United States
  • James J. Higgins
    Department of Statistics, College of Arts and Sciences, Kansas State University, Manhattan, KS, United States
  • Kenneth R. Harkin
    Department of Clinical Sciences, College of Veterinary Medicine, Kansas State University, Manhattan, KS, United States

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.

Keywords

spatial extent, modifiable areal unit problem, geographical information system, leptospirosis, canine.

Full Text:

PDF
Submitted: 2014-12-15 11:39:32
Published: 2013-05-01 00:00:00
Search for citations in Google Scholar
Related articles: Google Scholar
Abstract views:
492

Views:
PDF
358

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM


Copyright (c) 2013 Ram K. Raghavan, Karen M. Brenner, John A. Jr. Harrington, James J. Higgins, Kenneth R. Harkin

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
 
© PAGEPress 2008-2017     -     PAGEPress is a registered trademark property of PAGEPress srl, Italy.     -     VAT: IT02125780185