Evaluation of Threshold Selection Methods for Adaptive Kernel Density Estimation in Disease Mapping

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This article assesses the relative performance of threshold selection methods in terms of resolution and reliability for disease mapping.

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13 p.

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Ruckthongsook, Warangkana; Tiwari, Chetan; Oppong, Joseph R. & Natesan, Prathiba May 8, 2018.

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This article assesses the relative performance of threshold selection methods in terms of resolution and reliability for disease mapping.

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13 p.

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Abstract
Background

Maps of disease rates produced without careful consideration of the underlying population distribution may be unreliable due to the well-known small numbers problem. Smoothing methods such as Kernel Density Estimation (KDE) are employed to control the population basis of spatial support used to calculate each disease rate. The degree of smoothing is controlled by a user-defined parameter (bandwidth or threshold) which influences the resolution of the disease map and the reliability of the computed rates. Methods for automatically selecting a smoothing parameter such as normal scale, plug-in, and smoothed cross validation bandwidth selectors have been proposed for use with non-spatial data, but their relative utilities remain unknown. This study assesses the relative performance of these methods in terms of resolution and reliability for disease mapping.
Results

Using a simulated dataset of heart disease mortality among males aged 35 years and older in Texas, we assess methods for automatically selecting a smoothing parameter. Our results show that while all parameter choices accurately estimate the overall state rates, they vary in terms of the degree of spatial resolution. Further, parameter choices resulting in desirable characteristics for one sub group of the population (e.g., a specific age-group) may not necessarily be appropriate for other groups.
Conclusion

We show that the appropriate threshold value depends on the characteristics of the data, and that bandwidth selector algorithms can be used to guide such decisions about mapping parameters. An unguided choice may produce maps that distort the balance of resolution and statistical reliability.

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  • International Journal of Health Geographics, 17(10), BioMed Central Ltd., May 8, 2018, pp. 1-13

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  • Publication Title: International Journal of Health Geographics
  • Volume: 17
  • Issue: 10
  • Peer Reviewed: Yes

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UNT Scholarly Works

Materials from the UNT community's research, creative, and scholarly activities and UNT's Open Access Repository. Access to some items in this collection may be restricted.

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  • May 8, 2018

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  • June 15, 2018, 10:41 p.m.

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  • Dec. 5, 2023, 10:04 a.m.

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Ruckthongsook, Warangkana; Tiwari, Chetan; Oppong, Joseph R. & Natesan, Prathiba. Evaluation of Threshold Selection Methods for Adaptive Kernel Density Estimation in Disease Mapping, article, May 8, 2018; London, United Kingdom. (https://digital.library.unt.edu/ark:/67531/metadc1164532/: accessed April 21, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Science.

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