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Spatial scale effects in environmental risk-factor modelling for diseases.

Ram K Raghavan1, Karen M Brenner, John A Harrington

  • 1Kansas State Veterinary Diagnostic Laboratory, College of Veterinary Medicine, Kansas State University, Manhattan, KS 66506, USA. rkraghavan@vet.k-state.edu

Geospatial Health
|June 5, 2013
PubMed
Summary
This summary is machine-generated.

Environmental risk factors for canine leptospirosis depend on the spatial extent used for analysis. Using different buffer zones significantly altered identified risk factors, highlighting the critical need for appropriate spatial extent determination in environmental health studies.

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Area of Science:

  • Environmental epidemiology
  • Geographic Information Systems (GIS)

Background:

  • Identifying environmental risk factors for diseases often relies on remotely sensed data within fixed buffer zones.
  • The choice of buffer zone size can influence the identification of significant environmental risk factors.

Purpose of the Study:

  • To investigate how varying buffer zone sizes (spatial extents) affect the identification of environmental risk factors for canine leptospirosis.
  • To assess the impact of different spatial extents on risk factor analysis using remotely sensed land cover data.

Main Methods:

  • A retrospective case-control study using canine leptospirosis data (94 cases, 185 controls).
  • Extraction of land cover features using multiple buffer zones (500m to 5,000m) from NLCD and KS GAP datasets.
  • Application of multivariable logistic models to assess the association between land cover variables and leptospirosis risk.

Main Results:

  • The types and statistical significance of identified risk factors changed with increasing spatial extent.
  • Canine leptospirosis showed significant associations with developed high-intensity areas (500-2000m), medium-intensity areas (2000-3000m), and evergreen forests (>3500m) in NLCD data.
  • Associations with urban areas (500-2500m) and forest/woodland areas (>2500m) were observed in KS GAP data.

Conclusions:

  • The selection of an appropriate spatial extent is critical and using arbitrary buffer zones can lead to misleading conclusions in environmental health studies.
  • Results underscore the importance of carefully determining spatial extents for accurate environmental risk factor identification.