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Spatio-Temporal Methods in Environmental Epidemiology

Chapter 8 - Spatial Patterns in Disease

Summary

This chapter introduces disease mapping and contains the theory for spatial lattice processes and models for performing
smoothing of risks over space. From this chapter, the reader will have gained an understanding of the following topics:

  • Disease mapping, where we have seen how to improve estimates of risk by borrowing strength from adjacent
    regions which can reduce the instability inherent in risk estimates (SMRs) based on small expected numbers.
  • Seen how smoothing can be performed using either the empirical Bayes or fully Bayesian approaches.
  • Been introduced to computational methods for handling areal data.
  • Learned about Besag’s seminal contributions to the field of spatial statistics including the very important concept
    of a Markov random field.
  • Explored approaches to modelling a real data including the conditional autoregressive models.
  • Seen how Bayesian spatial models for lattice data use WinBUGS, R and R–INLA.

  • R CODE

    Example 8.1

    Example 8.3

    Example 8.4

    Example 8.5


    DATA

    COPD Expected Mortality

    COPD Observed Mortality

    England Local Authority Shape File

    England Local Authority Details File

    • ©Gavin Shaddick and James V. Zidek 2015