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Discrete versus continuous domain models for disease mapping.

Garyfallos Konstantinoudis1, Dominic Schuhmacher2, Håvard Rue3

  • 1Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.

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|February 3, 2020
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Summary

Log-Gaussian Cox processes (LGCPs) offer superior disease mapping compared to the Besag-York-Mollié (BYM) model, especially when precise geocodes are available. This spatial epidemiology approach enhances risk identification and surface recovery.

Keywords:
Gaussian Markov random fields (GMRF)Geographical analysisICARModifiable areal unit problem (MAUP)Spatial smoothing

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Area of Science:

  • Spatial Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS)

Background:

  • Disease mapping aims to estimate risk and identify high-risk areas.
  • Limited geographical resolution of data often hampers spatial analyses.
  • The Besag-York-Mollié (BYM) model is a common approach for count data per spatial unit.

Purpose of the Study:

  • To compare Log-Gaussian Cox processes (LGCPs) with the BYM model for disease mapping.
  • To evaluate model performance in recovering risk surfaces and identifying high-risk areas.
  • To assess the utility of LGCPs in spatial epidemiology using childhood leukaemia data.

Main Methods:

  • A simulation study mimicking childhood leukaemia incidence in Zürich, Switzerland.
  • Utilized actual residential locations of children for precise geocoding.
  • Compared LGCPs against the traditional BYM model for spatial disease analysis.

Main Results:

  • Log-Gaussian Cox processes (LGCPs) demonstrated superior performance over BYM models.
  • LGCPs excelled in recovering risk surfaces and identifying high-risk areas in most scenarios.
  • The study used actual childhood leukaemia incidence data from Zürich (1985-2015).

Conclusions:

  • LGCPs provide significant advantages for spatial epidemiology compared to BYM models.
  • Precise geocodes enable more accurate disease risk estimation and mapping.
  • Adoption of LGCPs can lead to substantial improvements in spatial epidemiological studies.