Estimating individual-level risk in spatial epidemiology using spatially aggregated information on the population at risk

Peter J. Diggle, Yongtao Guan, Anthony C. Hart, Fauzia Paize, Michelle Stanton

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

We propose a novel alternative to case-control sampling for the estimation of individual-level risk in spatial epidemiology. Our approach uses weighted estimating equations to estimate regression parameters in the intensity function of an inhomogeneous spatial point process, when information on risk-factors is available at the individual level for cases, but only at a spatially aggregated level for the population at risk. We develop data-driven methods to select the weights used in the estimating equations and show through simulation that the choice of weights can have a major impact on efficiency of estimation. We develop a formal test to detect non-Poisson behavior in the underlying point process and assess the performance of the test using simulations of Poisson and Poisson cluster point processes. We apply our methods to data on the spatial distribution of childhood meningococcal disease cases in Merseyside, U.K. between 1981 and 2007.

Original languageEnglish (US)
Pages (from-to)1394-1402
Number of pages9
JournalJournal of the American Statistical Association
Volume105
Issue number492
DOIs
StatePublished - Dec 2010

Keywords

  • Estimating equations
  • Inhomogeneous spatial-point processes
  • Meningococcal disease

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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