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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Estimating individual-level risk in spatial epidemiology using spatially aggregated information on the population at risk. / Diggle, Peter J.; Guan, Yongtao; Hart, Anthony C. et al.

In: Journal of the American Statistical Association, Vol. 105, No. 492, 12.2010, p. 1394-1402.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Diggle, PJ, Guan, Y, Hart, AC, Paize, F & 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, vol. 105, no. 492, pp. 1394-1402. https://doi.org/10.1198/jasa.2010.ap09323

APA

Diggle, P. J., Guan, Y., Hart, A. C., Paize, F., & 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), 1394-1402. https://doi.org/10.1198/jasa.2010.ap09323

Vancouver

Diggle PJ, Guan Y, Hart AC, Paize F, Stanton M. Estimating individual-level risk in spatial epidemiology using spatially aggregated information on the population at risk. Journal of the American Statistical Association. 2010 Dec;105(492):1394-1402. doi: 10.1198/jasa.2010.ap09323

Author

Diggle, Peter J. ; Guan, Yongtao ; Hart, Anthony C. et al. / Estimating individual-level risk in spatial epidemiology using spatially aggregated information on the population at risk. In: Journal of the American Statistical Association. 2010 ; Vol. 105, No. 492. pp. 1394-1402.

Bibtex

@article{33ccd441a0624b1eadf0a08f72d1b88f,
title = "Estimating individual-level risk in spatial epidemiology using spatially aggregated information on the population at risk",
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.",
keywords = "Estimating equations, Inhomogeneous spatial-point processes, Meningococcal disease, MENINGOCOCCAL DISEASE, ECOLOGICAL INFERENCE, SOCIAL DEPRIVATION, ENGLAND, INFLUENZA, WALES",
author = "Diggle, {Peter J.} and Yongtao Guan and Hart, {Anthony C.} and Fauzia Paize and Michelle Stanton",
year = "2010",
month = dec,
doi = "10.1198/jasa.2010.ap09323",
language = "English",
volume = "105",
pages = "1394--1402",
journal = "Journal of the American Statistical Association",
issn = "0162-1459",
publisher = "Taylor and Francis Ltd.",
number = "492",

}

RIS

TY - JOUR

T1 - Estimating individual-level risk in spatial epidemiology using spatially aggregated information on the population at risk

AU - Diggle, Peter J.

AU - Guan, Yongtao

AU - Hart, Anthony C.

AU - Paize, Fauzia

AU - Stanton, Michelle

PY - 2010/12

Y1 - 2010/12

N2 - 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.

AB - 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.

KW - Estimating equations

KW - Inhomogeneous spatial-point processes

KW - Meningococcal disease

KW - MENINGOCOCCAL DISEASE

KW - ECOLOGICAL INFERENCE

KW - SOCIAL DEPRIVATION

KW - ENGLAND

KW - INFLUENZA

KW - WALES

UR - http://www.scopus.com/inward/record.url?scp=78651320529&partnerID=8YFLogxK

U2 - 10.1198/jasa.2010.ap09323

DO - 10.1198/jasa.2010.ap09323

M3 - Journal article

VL - 105

SP - 1394

EP - 1402

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

SN - 0162-1459

IS - 492

ER -