Abstract
The analysis of health data and putative covariates, such as environmental, socio-economic, behavioral or demographic factors, is a promising application for geostatistics. However, it presents several methodological challenges that arise from the fact that data is typically aggregated over irregular spatial supports and consists of a numerator and a denominator (e.g., population size). This paper presents an overview of recent developments in the field of health geostatistics, with an emphasis on three main steps in the analysis of areal health data: (1) estimation of the underlying disease risk, (2) detection of areas with significantly higher risk, and (3) analysis of relationships with putative risk factors. The analysis is illustrated by using age-adjusted cervix cancer mortality rates recorded from 1970 to 1994 of 118 counties in four Western USA states. Poisson kriging allows the filtering of noisy mortality rates computed from small population sizes, enhancing the correlation with two putative explanatory variables: percentage of habitants living below the federally defined poverty line, and percentage of Hispanic females. Area-to-point kriging formulation creates continuous maps of mortality risk, reducing the visual bias associated with the interpretation of choropleth maps. Stochastic simulation is used to generate realizations of cancer mortality maps, which allows one to quantify how uncertainty of the spatial distribution of health outcomes translates into uncertainty of the location of clusters of high values or the correlation with covariates. Finally, geographically-weighted regression highlights the non-stationarity in the explanatory power of covariates; the higher mortality values along the coast are better explained by the two covariates than the lower risk recorded in Utah.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Anselin L (1995) Local indicators of spatial association—LISA. Geogr Anal 27:93–115
Avruskin GA, Jacquez GM, Meliker JR, Slotnick MJ, Kaufmann AM, Nriagu JO (2004) Visualization and exploratory analysis of epidemiologic data using a novel space time information system. Int J Health Geogr 3(26). doi:10.1186/1476-072X-3-26
Bellier E, Monestiez P, Guinet C (2009) Geostatistical modeling of wildlife populations: a non-stationary hierarchical model for count data. In: Atkinson P (ed) geoENV VII—Geostatistics for environmental applications. Springer, Berlin (in press)
Besag J, York J, Mollie A (1991) Bayesian image restoration with two applications in spatial statistics. Ann Inst Stat Math 43:1–59
Christakos G, Lai J (1997) A study of the breast cancer dynamics in North Carolina. Soc Sci Med 45(10):1503–1517
Congdon P (2006) A model for non-parametric spatially varying regression effects. Comput Stat Data Anal 50(2):422–445
Cressie N (1993) Statistics for spatial data. Wiley, New York
Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley, New York
Friedel GH, Tucker TC, McManmon E, Moser M, Hernandez C, Nadel M (1992) Incidence of dysplasia and carcinoma of the uterine cervix in an Appalachian population. J Natl Cancer Inst 84:1030–1032
Goovaerts P (1999) Using elevation to aid the geostatistical mapping of rainfall erosivity. Catena 34:227–242
Goovaerts P (2005a) Simulation-based assessment of a geostatistical approach for estimation and mapping of the risk of cancer. In: Leuangthong O, Deutsch CV (eds) Geostatistics banff 2004, vol 2. Kluwer Academic, Dordrecht, pp 787–796
Goovaerts P (2005b) Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging. Int J Health Geogr 4(31). doi:10.1186/1476-072X-4-31
Goovaerts P (2005c) Detection of spatial clusters and outliers in cancer rates using geostatistical filters and spatial neutral models. In: Renard Ph, Demougeot-Renard H, Froidevaux R (eds) geoENV V—Geostatistics for environmental applications. Springer, Berlin, pp 149–160
Goovaerts P (2005d) Analysis and detection of health disparities using geostatistics and a space-time information system. The case of prostate cancer mortality in the United States, 1970–1994. In: Proceedings of GIS Planet 2005, Estoril, May 30–June 2. http://home.comcast.net/~pgoovaerts/Paper148_PierreGoovaerts.pdf
Goovaerts P (2006a) Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using Poisson kriging and p-field simulation. Int J Health Geogr 5(7). doi:10.1186/1476-072X-5-7
Goovaerts P (2006b) Geostatistical analysis of disease data: accounting for spatial support and population density in the isopleth mapping of cancer mortality risk using area-to-point Poisson kriging. Int J Health Geogr 5(52). doi:10.1186/1476-072X-5-52
Goovaerts P (2008a) Kriging and semivariogram deconvolution in presence of irregular geographical units. Math Geosci 40(1):101–128
Goovaerts P (2008b) Accounting for rate instability and spatial patterns in the boundary analysis of cancer mortality maps. Environ Ecol Stat 15(4):421–446
Goovaerts P (2009a) Geostatistical analysis of county-level lung cancer mortality rates in the Southeastern US. Geogr Anal (in review)
Goovaerts P (2009b) Application of geostatistics in cancer studies. In: Atkinson P (ed) geoENV VII—Geostatistics for environmental applications. Springer, Berlin (in press)
Goovaerts P, Gebreab S (2008) How does Poisson kriging compare to the popular BYM model for mapping disease risks? Int J Health Geogr 7(6). doi:10.1186/1476-072X-7-6
Goovaerts P, Jacquez GM (2004). Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York. Int J Health Geogr 3(14). doi:10.1186/1476-072X-3-14
Gotway CA, Young LJ (2002) Combining incompatible spatial data. J Am Stat Assoc 97(459):632–648
Gotway CA, Young LJ (2005) Change of support: an inter-disciplinary challenge. In: Renard Ph, Demougeot-Renard H, Froidevaux R (eds) geoENV V—Geostatistics for environmental applications. Springer, Berlin, pp 1–13
Gotway CA, Young LJ (2007) A geostatistical approach to linking geographically-aggregated data from different sources. J Comput Graph Stat 16(1):115–135
Jacquez GM, Greiling DA (2003) Local clustering in breast, lung and colorectal cancer in Long Island, New York. Int J Health Geogr 2(3). doi:10.1186/1476-072X-2-3
Journel AG (1983) Nonparametric estimation of spatial distributions. Math Geol 15(3):445–468
Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic Press, San Diego
Kyriakidis P (2004) A geostatistical framework for area-to-point spatial interpolation. Geogr Anal 36(2):259–289
Lajaunie C (1991) Local risk estimation for a rare noncontagious disease based on observed frequencies. Note N-36/91/G. Centre de Geostatistique, Fontainebleau, Ecole des Mines de Paris
Liu XH, Kyriakidis PC, Goodchild MF (2008) Population density estimation using regression and area-to-point residual kriging. Int J Geogr Inf Sci 22(4):431–447
Mockus A (1998) Estimating dependencies from spatial averages. J Comput Graph Stat 7(4):501–513
Monestiez P, Dubroca L, Bonnin E, Durbec JP, Guinet C (2005) Comparison of model based geostatistical methods in ecology: application to fin whale spatial distribution in northwestern Mediterranean Sea. In: Leuangthong O, Deutsch CV (eds) Geostatistics banff 2004, vol 2. Kluwer Academic, Dordrecht, pp 777–786
Monestiez P, Dubroca L, Bonnin E, Durbec JP, Guinet C (2006) Geostatistical modelling of spatial distribution of Balenoptera physalus in the Northwestern Mediterranean Sea from sparse count data and heterogeneous observation efforts. Ecol Model 193(3–4):615–628
Oliver MA, Lajaunie C, Webster R, Muir KR, Mann JR (1993) Estimating the risk of childhood cancer. In: Soares A (ed) Geostatistics Troia 1992, vol 2. Kluwer Academic, Dordrecht, pp 899–910
Oliver MA, Webster R, Lajaunie C, Muir KR, Parkes SE, Cameron AH, Stevens MCG, Mann JR (1998) Binomial cokriging for estimating and mapping the risk of childhood cancer. IMA J Math Appl Med 15(3):279–297
Rushton G, Peleg I, Banerjee A, Smith G, West M (2004) Analyzing geographic patterns of disease incidence: rates of late-stage colorectal cancer in Iowa. J Med Syst 28(3):223–236
Wackernagel H (1998) Multivariate geostatistics, 2nd completely revised edition. Springer, Berlin
Waller LA, Gotway CA (2004) Applied spatial statistics for public health data. Wiley, New York
Webster R, Oliver MA, Muir KR, Mann JR (1994) Kriging the local risk of a rare disease from a register of diagnoses. Geogr Anal 26:168–185
Webster T, Vieira V, Weinberg J, Aschengrau A (2006) Method for mapping population-based case-control studies: an application using general additive models. Int J Health Geogr 5(26). doi:10.1186/1476-072X-5-26
Yoo E-H, Kyriakidis PC (2006) Area-to-point Kriging with inequality-type data. J Geogr Syst 8(4):357–390
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Goovaerts, P. Medical Geography: A Promising Field of Application for Geostatistics. Math Geosci 41, 243–264 (2009). https://doi.org/10.1007/s11004-008-9211-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11004-008-9211-3