title: Geostatistical Analysis of County-Level Lung Cancer Mortality Rates in the Southeastern United States.
abstract: The analysis of health data and putative covariates, such as environmental, socioeconomic, demographic, behavioral, or occupational factors, is a promising application for geostatistics. Transferring methods originally developed for the analysis of earth properties to health science, however, presents several methodological and technical challenges. These arise because health data are typically aggregated over irregular spatial supports (e.g., counties) and consist of a numerator and a denominator (i.e., rates). This article provides an overview of geostatistical methods tailored specifically to the characteristics of areal health data, with an application to lung cancer mortality rates in 688 U.S. counties of the southeast (1970-1994). Factorial Poisson kriging can filter short-scale variation and noise, which can be large in sparsely populated counties, to reveal similar regional patterns for male and female cancer mortality that correlate well with proximity to shipyards. Rate uncertainty was transferred through local cluster analysis using stochastic simulation, allowing the computation of the likelihood of clusters of low or high cancer mortality. Accounting for population size and rate uncertainty led to the detection of new clusters of high mortality around Oak Ridge National Laboratory for both sexes, in counties with high concentrations of pig farms and paper mill industries for males (occupational exposure) and in the vicinity of Atlanta for females.
authors.affiliation: BioMedware Inc., Ann Arbor, MI.
authors.forename: Pierre
authors.initials: P
authors.lastname: Goovaerts
references.citation: J Geogr Syst. 2005 May;7(1):137-159
references.pmid: 16710441
references.citation: Math Geol. 2009;41:243-264
references.pmid: 19412347
references.citation: Int J Health Geogr. 2006 Feb 09;5:7
references.pmid: 16469095
references.citation: J Natl Cancer Inst. 1999 Jun 16;91(12):1040-50
references.pmid: 10379967
references.citation: Int J Health Geogr. 2004 Jul 23;3(1):14
references.pmid: 15272930
references.citation: Int J Health Geogr. 2006 Nov 30;5:52
references.pmid: 17137504
references.citation: Int J Health Geogr. 2003 Dec 19;2(1):9
references.pmid: 14687424
references.citation: J Epidemiol Community Health. 2004 Jan;58(1):6-10
references.pmid: 14684719
references.citation: J Geogr Syst. 2005 May;7(1):67-84
references.pmid: 18509516
references.citation: Int J Health Geogr. 2006 Aug 03;5:32
references.pmid: 16887016
references.citation: Stat Med. 1995 Jan 15;14(1):17-26
references.pmid: 7701154
references.citation: J Natl Cancer Inst. 1978 Feb;60(2):295-8
references.pmid: 621749
references.citation: Int J Health Geogr. 2005 Dec 14;4:31
references.pmid: 16354294
references.citation: IMA J Math Appl Med Biol. 1998 Sep;15(3):279-97
references.pmid: 9773520
references.citation: Int J Health Geogr. 2008 Feb 04;7:6
references.pmid: 18248676
references.citation: Int J Health Geogr. 2006 Oct 13;5:45
references.pmid: 17038192
references.citation: Stat Med. 1999 Aug 30;18(16):2147-62
references.pmid: 10441770
references.citation: N Engl J Med. 1978 Sep 21;299(12):620-4
references.pmid: 683235
references.citation: Stat Med. 1996 Apr 15-May 15;15(7-9):887-92
references.pmid: 8861157
references.citation: Geogr Anal. 2005 Apr;37(2):152-182
references.pmid: 16915345
references.citation: Environ Ecol Stat. 2008 Dec;15(4):421-446
references.pmid: 19023455
references.citation: Am J Ind Med. 2000 May;37(5):512-21
references.pmid: 10723045
references.citation: Math Geol. 2008;40(1):101-128
references.pmid: 18725997
references.citation: Environ Health Perspect. 2004 Jun;112(9):1016-25
references.pmid: 15198922
references.citation: Epidemiology. 1995 Nov;6(6):584-90
references.pmid: 8589088