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

Diggle, P. and Guan, Y. and Hart, A. and Paize, F. and Stanton, M. (2010) Estimating individual-level risk in spatial epidemiology using spatially aggregated information on the population at risk. Journal of the American Statistical Association, 105 (492). pp. 1394-1402. ISSN 0162-1459

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Official URL: http://pubs.amstat.org/toc/jasa/105/492

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.

Item Type:Article
Subjects:5. Quantitative Data Handling and Data Analysis > 5.9 Spatial Data Analysis
ID Code:2087
Deposited By: L-W-S user
Deposited On:20 Feb 2012 15:10
Last Modified:20 Feb 2012 15:10

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