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Area of Science:

  • Epidemiology
  • Geographic Information Systems (GIS)
  • Biostatistics

Background:

  • Spatial disease risk monitoring is crucial in public health, particularly cancer epidemiology.
  • Kernel density estimation (KDE) is a common method for estimating spatial relative risk functions (sRRF).
  • This study evaluates fixed versus adaptive bandwidth KDE for detecting cancer risk areas.

Purpose of the Study:

  • To compare the performance of fixed and adaptive bandwidth KDE methods in identifying spatial disease risk areas.
  • To assess the sensitivity and specificity of these methods in detecting known 'true risk areas' derived from population data.
  • To investigate the impact of bandwidth selection on the accuracy of sRRF estimation.

Main Methods:

  • Estimated sRRF using locational data of cancer cases and spatial controls.
  • Defined 'true risk areas' based on population underestimation in urban centers.
  • Conducted sensitivity and specificity analyses by overlaying 'true risk areas' with sRRF-derived p-contour lines.

Main Results:

  • Fixed bandwidth KDE exhibited lower sensitivity but higher specificity due to oversmoothing in urban areas.
  • Adaptive bandwidth KDE demonstrated higher sensitivity and stabilized variance, with equal specificity compared to fixed bandwidth.
  • Halving bandwidths improved both sensitivity and specificity for adaptive KDE, but reduced specificity for fixed KDE.

Conclusions:

  • Fixed bandwidth KDE tends to oversmooth in urban areas and overestimate risk in rural areas.
  • Adaptive bandwidth KDE mitigated some of these issues but increased false positives in this study's urban-centric design.
  • Further research is needed to optimize bandwidth selection methods, especially for adaptive KDE in spatial epidemiology.