Skip to main content
SpringerLink
Log in

Spatial Analysis of Disease

  • Chapter
Biostatistical Applications in Cancer Research

Part of the book series: Cancer Treatment and Research ((CTAR,volume 113))

Abstract

Cancer rate comparisons around the world suggest clear geographic differences that have only recently been appreciated and evaluated by statistical methods. The goal of this chapter is to briefly review the progression of the spatial analysis of disease from simple dot maps and crude rate comparisons to the complex hierarchical spatial models used today. After providing a historical background and necessary epidemiologic fundamentals, we summarize available methods for the exploration, hypothesis testing, and modeling of spatial data. Although the focus here is on methods appropriate for cancer research, other related methods will be mentioned.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Alexander F. E., Cuzick J. (1992). Methods for the assessment of disease clusters. In: Elliott P., Cuzick J., English D., Stern R., editors. Geographical & Environmental Epidemiology: Methods for Small-Area Studies. New York: Oxford University Press; p 238–50.

    Google Scholar 

  • Astakhova L. N., Anspaugh L. R., Beebe G. W., Bouville A., Drozdovitch V. V., Garber V., Gavrilin Y. I., Khrouch V. T., Kuvshinnikov A. V., Kuzmenkov Y. N., Minenko V. P., Moschik K. V., Nalivko A. S., Robbins J., Shemiakina E. V., Shinkarev S. (1998). Chernobyl-related thyroid cancer in children of Belarus: A case-control study. Radiation Research 150 (3): 349–56.

    Article  PubMed  CAS  Google Scholar 

  • Barrett F. A. (2000). Finke’s 1792 map of human diseases: the first world disease map? Social Science & Medicine 50 (78): 915–21.

    Article  CAS  Google Scholar 

  • Beebe G.W., Kato H., Land C. E. (1971). Studies of the mortality of A-bomb survivors. 4. Mortality and radiation dose, 1950–1966. Radiat Res 48 (3): 613–49.

    Article  PubMed  CAS  Google Scholar 

  • Bernardinelli L., Pascutto C., Best N. G., Gilks W. R. (1997). Disease mapping with errors in covariates. Statistics in Medicine 16 (7): 741–52.

    Article  PubMed  CAS  Google Scholar 

  • Besag J. (1974). Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, Series B 36: 192–236.

    Google Scholar 

  • Besag J. (1975). Statistical analysis of non-lattice data. The Statistician 24 (No.3): 179–95.

    Article  Google Scholar 

  • Besag J., Kooperberg C. (1995). On conditional and intrinsic autoregression. Biometrika 82 (4): 733–46.

    Google Scholar 

  • Besag J., Newell J. (1991). The detection of clusters in rare diseases. Journal of the Royal Statistical Society, Ser A 154: 143–55.

    Google Scholar 

  • Blot W. J., Fraumeni Jr. J. F. (1976). Geographic patterns of lung cancer: Industrial correlations. American Journal of Epidemiology 103 (6): 539–50.

    PubMed  CAS  Google Scholar 

  • Blot W. J., Harrington J. M., Toledo A., Hoover R., Heath C. W., Jr., Fraumeni J. F., Jr. (1978). Lung cancer after employment in shipyards during World War II. N Engl J Med 299 (12): 620–4.

    Article  PubMed  CAS  Google Scholar 

  • Bonetti M., Pagano M. (2001). Detecting clustering in regional counts. American Statistical Association Proceedings of the Survey Research Methods Section.

    Google Scholar 

  • Brillinger D. R. (1986). The natural variability of vital rates and associated statistics. Biometrics 42: 693–734.

    Article  PubMed  CAS  Google Scholar 

  • Bross I. D. J. (1954). A confidence interval for a percentage increase. Biometrics 10: 245–50.

    Article  Google Scholar 

  • Burbank F. (1972). A Sequential space-time cluster analysis of Cancer mortality in the United States: Etiologic Implications. Journal of Epidemiology 95 (5): 393–417.

    CAS  Google Scholar 

  • Carlin B. P., Louis T. A. (2000). Bayes and Empirical Bayes Methods for Data Analysis. Second ed. New York: Chapman & Hall/CRC.

    Google Scholar 

  • Carr D. B., Pierson S. M. (1996). Emphasizing statistical summaries and showing spatial context with micromaps. Statistical Computing & Statistical Graphics Newsletter 7 (3): 16–23.

    Google Scholar 

  • Carroll R. J., Ruppert D., Stefanski L. A. (1995). Measurement Error in Nonlinear Models. 1st ed. ed. London: Chapman & Hall.

    Google Scholar 

  • Clayton D., Kaldor J. (1987). Empirical bayes estimates for age-standardized relative risks for use in disease mapping. Biometrics 43: 671–81.

    Article  PubMed  CAS  Google Scholar 

  • Cleveland W. S., Devlin S. J. (1988). Locally weighted regression–An approach to regression analysis by local fitting. Journal of the American Statistical Association 83 (403): 596–610.

    Article  Google Scholar 

  • Cornfield J. (1951). A method of estimating comparative rates from clinical data. Journal of the National Cancer Institute 11: 1269–75.

    PubMed  CAS  Google Scholar 

  • Cox D. R. (1970). The Analysis of Binary Data. 1st ed. London: Methuen & Co. Ltd. Cressie N. A. C. (1993). Statistics for Spatial Data. Revised ed. New York: J. Wiley.

    Google Scholar 

  • Cressie N. A. C., Read T. R. C. (1989). Spatial data-analysis of regional counts. Biometrical Journal 31 (6): 699–719.

    Article  Google Scholar 

  • Cubbin C., Pickle L. W., Fingerhut L. (2000). Social Context and Geographic Patterns of Homicide Among U.S. Black and White Males. American Journal of Public Health 90 (4): 579–87.

    Article  PubMed  CAS  Google Scholar 

  • Cuzick J., Edwards R. (1990). Spatial clustering for inhomogeneous populations. Journal of the Royal Statistical Society, Series B 52: 73–104.

    Google Scholar 

  • Cuzick J., Edwards R. (1996). Cuzick-Edwards one-sample and inverse two-sampling statistics. In: Alexander F. E., Boyle P., editors. Methods for investigating localized clustering of disease. Lyon: International Agency for Research on Cancer; p 200–2.

    Google Scholar 

  • Devesa S., Grauman D. J., Blot W. J., Pennello G. A., Hoover R. N., Fraumeni Jr J. F. (1999). Atlas of Cancer Mortality in the United States: 1950–94. Bethesda, MD: National Cancer Institute.

    Google Scholar 

  • Devine O. J., Annest J. L., Kirk M. L., Holmgreen P, Emrich S. S. (1991). Injury Mortality Atlas. Atlanta, GA: Centers for Disease Control, U.S. Department of Health and Human Services.

    Google Scholar 

  • Diggle P. (1983). Statistical Analysis of Spatial Point Patterns. London: Academic Press.

    Google Scholar 

  • Diggle P. J. (2000). Overview of statistical methods for disease mapping and its relationship to cluster detection. In: Elliott P., Wakefield J. C., Best N. G., Briggs D. J., editors. Spatial Epidemiology: Methods and Applications. Oxford: Oxford University Press; p 87–103.

    Google Scholar 

  • Draper G. J., Stiller C. A., Cartwright R. A., Craft A. W., Vincent T. J. (1993). Cancer in Cumbria and in the vicinity of the Sellafield nuclear installation, 196–390. British Medical Journal 306 (6870): 89–94.

    Article  PubMed  CAS  Google Scholar 

  • Fleiss J. L. (1981). Statistical Methods for Rates and Proportions. Second ed. New York: John WIley & Sons.

    Google Scholar 

  • Geary R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician 5: 115–45.

    Article  Google Scholar 

  • Ghosh M., Natarajan K., Stroud T. W. F., Carlin B. P. (1998). Generalized linear models for small-area estimation. Journal of the American Statistical Association 93 (441): 273–82.

    Article  Google Scholar 

  • Ghosh M., Rao J. N. K. (1994). Small-area estimation–an appraisal. Statistical Science 9 (1): 55–76.

    Article  Google Scholar 

  • Haining R. P. (1990). Spatial Data Analysis in the Social and Environmental Sciences. Cambridge England: Cambridge University Press.

    Book  Google Scholar 

  • Hansen K. M. (1991). Head-banging: robust smoothing in the plane. IEEE Transactions on Geoscience and Remote Sensing 29 (3): 369–78.

    Article  Google Scholar 

  • Haviland A. (1875). The geographical distribution of heart disease and dropsy, cancer in females and phthisis in females in England and Wales. London: Swan Schonnenschein.

    Google Scholar 

  • Howe G. M. (1963). National Atlas of Disease Mortality in the United Kingdom. London: Nelson.

    Google Scholar 

  • Howe G. M. (1989). Historical evolution of disease mapping in general and specifically of cancer mapping. In: Boyle P., Muir C. S., Grundmann E., editors. Cancer Mapping. New York: Springer-Verlag; p 1–21.

    Chapter  Google Scholar 

  • Janerich D. T., Burnett W. S., Feck G., Hoff M., Nasca P., Polednak A. P., Greenwald P., Vianna N. (1981). Cancer incidence in the Love Canal area. Science 212 (4501): 1404–7.

    Article  PubMed  CAS  Google Scholar 

  • Kafadar K. (1994). Choosing among two-dimensional smoothers in practice. Computational Statistics & Data Analysis 18: 419–39.

    Article  Google Scholar 

  • Kaluzny S. P., Vega S. C., Cardoso T. P., Shelly A. A. (1998). S+ Spatial Stats: User’s manual for Windows ® and UNIX ®. New York: Springer.

    Google Scholar 

  • Kemp I., Boyle P., Smans M., Muir C. S. (1985). Atlas of Cancer in Scotland, 1975–1980, Incidence and Epidemiological Perspective. Lyon: International Agency for Research on Cancer.

    Google Scholar 

  • Knox E. (1989). Detection of clusters. In: Elliott P., editor. Methodology of Enquiries into Disease Clustering. London: Small Area Health Statistics Unit.

    Google Scholar 

  • Knox G. (1964). Detection of space-time interactions. Appl Stat 13: 25–9.

    Article  Google Scholar 

  • Kulldorff M. (1997). A spatial scan statistic. Communications in Statistics-Theory and Methods 26: 1481–96.

    Article  Google Scholar 

  • Lawson A., Biggeri A., Bohning D., Lesaffre E., Viel J.-F., Bertollini R. (1999). Disease Mapping and Risk Assessment For Public Health. Chichester: Wiley.

    Google Scholar 

  • Le N. D., Marrett L. D., Robson D. L., Semenciw R. M., Turner D., Walter S. D. (1996). Canadian Cancer Incidence Atlas. Ottawa: Government of Canada.

    Google Scholar 

  • LeClere F. B., Rogers R. G., Peters K. (1998). Neighborhood social context and racial differences in women’s heart disease mortality. J Health Soc Behav 39 (2): 91–107.

    Article  PubMed  CAS  Google Scholar 

  • Littell R. C., Milliken G. A., Stroup W. W., Wolfinger R. D. (1996). SAS ® System for Mixed Models. Cary, NC: SAS Institute Inc.

    Google Scholar 

  • Mantel N. (1967). The detection of disease clustering and a generalized regression approach. Cancer Research 27: 209–20.

    PubMed  CAS  Google Scholar 

  • Manton K. G., Woodbury M. A., Stallard E., et al. (1989). Empirical Bayes procedures for stabilizing maps of U.S. cancer mortality rates. Journal of the American Statistical Association 84: 637–50.

    Article  PubMed  CAS  Google Scholar 

  • Mason T. J., McKay F. W., Hoover R., Blot W. J., Fraumeni Jr J. F. (1975). Atlas of Cancer Mortality for U.S. Counties: 1950–1969. Washington, D.C.: USGPO (DHEW pub no (NIH) 75–780 ).

    Google Scholar 

  • MathSoft. (1999). S-Plus 2000 User’s Guide. Seattle, WA: Data Analysis Products Division, MathSoft.

    Google Scholar 

  • McCullagh P., Nelder J. A. (1983). Generalized Linear Models. Second ed. London: Chapman and Hall.

    Google Scholar 

  • McLeod K. S. (2000). Our sense of Snow: the myth of John Snow in medical geography. Social Science & Medicine 50 (7–8): 923–35.

    Article  CAS  Google Scholar 

  • Moran P. A. P. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society, Series B 10: 243–51.

    Google Scholar 

  • Mungiole M., Pickle L. W., Simonson K. H. (1999). Application of a weighted head- banging algorithm to mortality data maps. Statistics in Medicine 18 (23): 3201–9.

    Article  PubMed  CAS  Google Scholar 

  • Najem R. G., Cappadona J. L. (1991). Health effects of hazardous chemical waste disposal sites in New Jersey and in the United States: A review. Statistics in Medicine 7 (6): 352–62.

    CAS  Google Scholar 

  • Nelson D. E., Holtzman D., Waller M., Leutzinger C. L., Condon K. (1998). Objectives and design of the Behavioral Risk Factor Surveillance System. American Statistical Association Proceedings of the Survey Research Methods Section.: 214–8.

    Google Scholar 

  • Neutra R. R. (1990). Counterpoint from a cluster buster. American Journal of Epidemiology 132: 1–8.

    PubMed  CAS  Google Scholar 

  • Neyman J., Scott E. L. (1958). Statistical approach to problems of cosmology. Journal of the Royal Statistical Society, Series B 20: 1–29.

    Google Scholar 

  • Ohno Y., Aoki K. (1981). Cancer deaths by city and county in Japan (1959–1971): A test of significance for geographic clusters of disease. Social Science & Medicine 15: 251–8.

    Google Scholar 

  • Openshaw S., Charlton M., Wymer C., Craft A. W. (1987). A mark I geographical analysis machine for the automated analysis of point data sets. International Journal of Geographical Information Systems 1: 335–58.

    Article  Google Scholar 

  • Peterman AH. 1852. Cholera map of the British Isles showing the districts attacked in 1831, 1832, and 1833. Constructed from official documents. London: Betts.

    Google Scholar 

  • Pickle LW, Herrmann D. 1995. Cognitive Aspects of Statistical Mapping. Hyattsville, MD: National Center for Health Statistics; Working Paper Series Report no 18.

    Google Scholar 

  • Pickle L. W., Mason T. J., Howard N., Hoover R., Fraumeni Jr. J. F. (1987). Atlas of U.S. Cancer Mortality Among Whites: 1950–1980. Washington, DC: USGPO (DHHS pub no (NIH) 87–2900).

    Google Scholar 

  • Pickle L. W., Mason T. J., Howard N., Hoover R., Fraumeni J. F. (1990). Atlas of U.S. Cancer Mortality Among Nonwhites: 1950–1980. Washington D.C.: USGPO (DHHS pub no (NIH) 90–1582 ).

    Google Scholar 

  • Pickle L. W., Mungiole M., Jones G. K., White A. A. (1996). Atlas of United States Mortality. Hyattsville, MD: National Center for Health Statistics, USGPO (DHHS pub no (PHS) 97–1015 ).

    Google Scholar 

  • Pickle L. W., Su Y. (2002). Geographic patterns of health insurance coverage and health risk factors for U.S. counties. American Journal of Preventive Medicine 22 (2): 75–83.

    Article  PubMed  Google Scholar 

  • Pickle L. W., White A. A. (1995). Effects of the choice of age-adjustment method on maps of death rates. Statistics in Medicine 14 (5–7): 615–27.

    Article  PubMed  CAS  Google Scholar 

  • Potthoff R. F., Whittinghill M. (1996). Testing for homogeneity in the Poisson distribution. Biometrika 53: 183–90.

    Google Scholar 

  • Rasbash J., Browne W., Goldstein H., Yang M., Plewis I., Healy M., Woodhouse G., Draper D, Langford I., Lewis T. (2000). A User’s Guide to MLwiN. London: Multilevel Models Project, Institute of Education, University of London.

    Google Scholar 

  • Riggan W. B., Creason J. P., Nelson W. C., Manton K. G., Woodbury M. A., Stallard E., Pellom A. C., Beaubier J. (1987). U.S. Cancer Mortality Rates and Trends, 1950–1979. Research Triangle Park, NC: U.S. Environmental Protection Agency.

    Google Scholar 

  • Snow J. (1855). On the Mode of Communication of Cholera. 2nd ed. London: John Churchill.

    Google Scholar 

  • Spiegelhalter D., Thomas A., Best N., Gilks W. (1995). BUGS: Bayesian inference using GIBBS Sampling, Version 0.50. Cambridge: MRC Biostatistics Unit.

    Google Scholar 

  • Stocks P. 1928. On the evidence for a regional distribution of cancer prevalence in England and Wales. London: British Empire Cancer Campaign.

    Google Scholar 

  • Stocks P. 1936. Distribution in England and Wales of cancer of various organs. London: British Cancer Campaign; Report no 13.

    Google Scholar 

  • Tango T. (1995). A class of tests for detecting ‘general’ and ’focused’ clustering of rare diseases. Statistics in Medicine 14: 2323–34.

    Article  PubMed  CAS  Google Scholar 

  • Tango T. (1999). Comparison of general tests for spatial clustering. In: Lawson A., Biggeri A., Bohning D., Lesaffre E., Viel J.-F., Bertollini R., editors. Disease Mapping and Risk Assessment for Public Health. Chichester: John Wiley and Sons, Ltd; p 111–7.

    Google Scholar 

  • Tango T. (2000). A test for spatial disease clustering adjusted for multiple testing. Statistics in Medicine 19 (2): 191–204.

    Article  PubMed  CAS  Google Scholar 

  • Tukey J. W. (1977). Exploratory Data Analysis. Reading, Mass.: Addison-Wesley Pub. Co.

    Google Scholar 

  • Tukey P. A., Tukey J. W. (1981). Graphical display of data sets in 3 or more dimensions. In: Barnett V., editor. Interpreting Multivariate Data. New York: John Wiley and Sons.

    Google Scholar 

  • Turnbull B., Iwano E. J., Burnett W. S. (1990). Monitoring for clusters of disease: application to leukemia incidence in upstate New York. Am J Epidemiol 132: S136 - S143.

    PubMed  CAS  Google Scholar 

  • Varon E. 1998 Sep 7. Connecting the dots: NCI hopes advanced GIS tools will help trace causes of breast cancer. Federal Computer Week;1, 76.

    Google Scholar 

  • Verdecchia A., Capocaccia R., Egidi V., Golini A. (1989). A method for the estimation of chronic disease morbidity and trends from mortality data. Statistics in Medicine 8 (2): 201–16.

    Article  PubMed  CAS  Google Scholar 

  • Wakefield J. C., Kelsall J. E., Morris S. E. (2000). Clustering, cluster detection, and spatial variation in risk. In: Elliott P., Wakefield J. C., Best N. G., Briggs D. J., editors. Spatial Epidemiology: Methods and Applications. Oxford: Oxford University Press; p 128–52.

    Google Scholar 

  • Walker S. H., Duncan D. B. (1967). Estimation of the probability of an event as a function of several independent variables. Biometrika 54: 167–79.

    PubMed  CAS  Google Scholar 

  • Waller, L. A. Hierarchical models for disease mapping. Encyclopedia of Environmetrics, Vol 2. Chichester: John Wiley and Sons; p 1004–7.

    Google Scholar 

  • Whittemore A. S., Fiend N., Brown Jr B. W., Holly E. (1987). A test to detect clusters of disease. Biometrika 74 (3): 631–5.

    Article  Google Scholar 

  • Woolf B. (1955). On estimating the relation between blood group and disease. Annals of Human Genetics 19: 251–3.

    Article  PubMed  CAS  Google Scholar 

  • Xiang H. Y., Nuckols J. R., Stallones L. (2000). A geographic information assessment of birth weight and crop production patterns around mother’s residence. Environmental Research 82 (2): 160–7.

    Article  PubMed  CAS  Google Scholar 

  • Zhu L., Carlin B. P. (2000). Comparing hierarchical models for spatio-temporally misaligned data using the deviance information criterion. Statistics in Medicine 19 (17–18): 2265–78.

    Article  PubMed  CAS  Google Scholar 

  • Zoellner I. K., Schmidtmann I. M. (1999). Empirical studies of cluster detection–Different cluster tests in application to German cancer maps. In: Lawson A., Biggeri A., Bohning D., Lesaffre E., Viel J.-F., Bertollini R., editors. Disease Mapping and Risk Assessment for Public Health. Chichester: John Wiley and Sons, Ltd.; p 169–78.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Cancer Institute, USA

    Linda Williams Pickle

Authors
  1. Linda Williams Pickle
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. H. Lee Moffitt Cancer Center & Research Institute, University of South Florida College of Medicine, Tampa, FL, USA

    Craig Beam Ph.D.

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer Science+Business Media New York

About this chapter

Cite this chapter

Pickle, L.W. (2002). Spatial Analysis of Disease. In: Beam, C. (eds) Biostatistical Applications in Cancer Research. Cancer Treatment and Research, vol 113. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3571-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4757-3571-0_7

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-5310-0

  • Online ISBN: 978-1-4757-3571-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation