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Evaluation of threshold selection methods for adaptive kernel density estimation in disease mapping.

Warangkana Ruckthongsook1, Chetan Tiwari2, Joseph R Oppong3

  • 1Department of Biological Sciences, University of North Texas, Denton, TX, USA.

International Journal of Health Geographics
|May 10, 2018
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Summary
This summary is machine-generated.

Choosing the right smoothing parameter is crucial for reliable disease mapping. Automatic bandwidth selectors can guide this choice, balancing map resolution and statistical accuracy for different population groups.

Keywords:
Bandwidth selectionDisease mappingKernel density estimationMonte Carlo simulationThreshold

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems

Background:

  • Disease mapping requires careful consideration of population distribution to avoid the small numbers problem, which can lead to unreliable rate estimations.
  • Kernel Density Estimation (KDE) is a smoothing method used to address this by controlling the population basis for spatial support in rate calculations.
  • The selection of a smoothing parameter (bandwidth or threshold) critically impacts disease map resolution and the reliability of computed rates.

Purpose of the Study:

  • To assess the relative performance of automatic smoothing parameter selection methods for disease mapping.
  • To evaluate the utility of methods like normal scale, plug-in, and smoothed cross-validation for controlling the spatial resolution and reliability of disease maps.
  • To understand how different parameter choices affect disease rate estimations across various population subgroups.

Main Methods:

  • Utilized a simulated dataset of heart disease mortality for males aged 35 and older in Texas.
  • Assessed multiple automatic methods for selecting smoothing parameters in Kernel Density Estimation.
  • Compared the spatial resolution and reliability of disease rates generated by different parameter selection strategies.

Main Results:

  • All tested parameter selection methods accurately estimated overall state disease rates.
  • Significant variations in spatial resolution were observed among the different parameter choices.
  • Parameter settings optimal for one population subgroup (e.g., a specific age group) were not necessarily suitable for other groups.

Conclusions:

  • The optimal smoothing parameter threshold is data-dependent, highlighting the need for informed selection.
  • Bandwidth selector algorithms provide valuable guidance for choosing appropriate mapping parameters.
  • Unguided parameter selection can lead to disease maps that compromise the balance between spatial resolution and statistical reliability.