An adaptive spatiotemporal smoothing model for estimating trends and step changes in disease risk

Alastair Rushworth, Duncan Lee, Christophe Sarran

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)
146 Downloads (Pure)


Statistical models used to estimate the spatiotemporal pattern in disease risk from areal unit data represent the risk surface for each time period with known covariates and a set of spatially smooth random effects. The latter act as a proxy for unmeasured spatial confounding, whose spatial structure is often characterized by a spatially smooth evolution between some pairs of adjacent areal units whereas other pairs exhibit large step changes. This spatial heterogeneity is not consistent with existing global smoothing models, in which partial correlation exists between all pairs of adjacent spatial random effects. Therefore we propose a novel space–time disease model with an adaptive spatial smoothing specification that can identify step changes. The model is motivated by a new study of respiratory and circulatory disease risk across the set of local authorities in England and is rigorously tested by simulation to assess its efficacy. Results from the England study show that the two diseases have similar spatial patterns in risk and exhibit some common step changes in the unmeasured component of risk between neighbouring local authorities.

Original languageEnglish
Pages (from-to)141-157
Number of pages17
JournalJournal of the Royal Statistical Society: Series C
Issue number1
Early online date4 May 2016
Publication statusPublished - 31 Jan 2017


  • adaptive smoothing
  • Gaussian Markov random fields
  • spatiotemporal disease mapping
  • step change detection


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