Skip to main content

Spatial Analysis of Disease — Applications

  • Chapter
Biostatistical Applications in Cancer Research

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

  • 220 Accesses

Abstract

The objective of this chapter is to provide useful information for taking the spatial analysis of health data “from the lab to the clinic.” The preceding chapter reviewed the history and theory of spatial statistics as applied to health data. This chapter provides examples of how this theory can be used in practice. Emphasis is placed on the tools and resources available to enable a statistical analyst to perform a spatial statistical analysis. Because the methods and software are constantly improving, the author advises the reader to review the latest literature as a first step in embarking on a spatial analysis.

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

Access this chapter

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

  • Armstrong M. P., Rushton G., Zimmerman D. L. (1999). Geographically masking health data to preserve confidentiality. Statistics in Medicine 18 (5): 497–525.

    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., York J., Mollie A. (1991). Bayesian image restoration with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics 43 (1): 1–59.

    Article  Google Scholar 

  • Breslow N. E., Clayton D. G. (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88 (421): 9–25.

    Google Scholar 

  • Maptitude® Geographic Information System for Windows (2000). Version 4.2. Newton, MA:

    Google Scholar 

  • Clayton D., Bernardinelli L. (1992). Bayesian methods for mapping disease risk. In: Elliott P., Cuzick J., English D., Stern R., editors. Geographical and Environmental Epidemiology: Methods for Small Area Studies. New York: Oxford University Press; p 205–20.

    Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  • Cressie N. A. C. (1993). Statistics for Spatial Data. Revised ed. New York: J. Wiley.

    Google Scholar 

  • Cressie N. A. C., Chan N. H. (1989). Spatial modeling of regional variables. Journal of the American Statistical Association 84 (406): 393–401.

    Article  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 

  • Department of Health and Human Services. (1999). Standards for privacy of individually identifiable health information. Office of the Assistant Secretary for Planning and Evaluation, DHHS. Proposed rule. Federal Register 64 (212): 59918–60065.

    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 

  • ArcView Spatial Analyst (2000). Version 2. 0a. Redlands, CA: Environmental Systems Research Institute, Inc.

    Google Scholar 

  • Frisbie W. P., Biegler M., de Turk P., Forbes D., Pullum S. G. (1997). Racial and ethnic differences in determinants of intrauterine growth retardation and other compromised birth outcomes. American Journal of Public Health 87 (12): 1977–83.

    Article  PubMed  CAS  Google Scholar 

  • Goldstein H., Rasbash J. (1996). Improved approximations for multilevel models with binary responses. Journal of the Royal Statistical Society, Series A 159: 505–13.

    Google Scholar 

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

    Google Scholar 

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

    Book  Google Scholar 

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

    Article  Google Scholar 

  • Kulldorff M., Nagarwalla N. (1995). Spatial disease clusters: detection and inference. Statistics in Medicine 14 (8): 799–810.

    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 

  • Manton K. G., Woodbury M. A., Stallard E., Riggan W. B., Creason J. P., Pellom A. C. (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 

  • Mapinfo Professional® (2000). Troy, NY: Mapinfo Corporation

    Google Scholar 

  • Meade M. S., Florin J. W., Gesler W. M. (1988). Medical Geography. New York: Guilford Press.

    Google Scholar 

  • O’Campo P., Xue X., Wang M. C., Caughy M. (1997). Neighborhood risk factors for low birthweight in Baltimore: a multilevel analysis. American Journal of Public Health 87 (7): 1113–8.

    Article  PubMed  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.

    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 

  • 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 

  • Raudenbush S. W., Yang M. L., Yosef M. (2000). Maximum likelihood for generalized linear models with nested random effects via high-order, multivariate Laplace approximation. Journal of Computational and Graphical Statistics 9 (1): 141–57.

    Google Scholar 

  • Rushton G., Krishnamurthy R., Krishnamurti D., Lolonis P., Song H. (1996). The spatial relationship between infant mortality and birth defect rates in a U.S. city. Statistics in Medicine 15 (17–18): 1907–19.

    Article  PubMed  CAS  Google Scholar 

  • Rushton G., Lolonis P. (1996). Exploratory spatial analysis of birth defect rates in an urban population. Statistics in Medicine 15 (7–9): 717–26.

    Article  PubMed  CAS  Google Scholar 

  • Showstack J. A., Budetti P. P., Minkler D. (1984). Factors associated with birthweight: an exploration of the roles of prenatal care and length of gestation. American Journal of Public Health 74 (9): 1003–8.

    Article  PubMed  CAS  Google Scholar 

  • Snijders T. A. B., Bosker R. J. (1999). Multilevel Analysis: An introduction to basic and advanced multilevel modeling. London: Sage Publications.

    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 

  • Spiegelhalter D., Thomas A., Best N., Gilks W. (1996). BUGS 0.5* Examples Volume 2 (version ii). Cambridge: MRC Biostatistics Unit, Institute of Public Health.

    Google Scholar 

  • Stehman S. V., Overton W. S. (1996). Spatial Sampling. In: Arlinghaus S. L., editor. Practical Handbook of Spatial Statistics. New York: CRC Press; p 31–63.

    Google Scholar 

  • Usher R., McLean F. (1969). Intrauterine growth of live-born Caucasian infants at sea level: standards obtained from measurements in 7 dimensions of infants born between 25 and 44 weeks of gestation. Journal of Pediatrics 74 (6): 901–10.

    Article  PubMed  CAS  Google Scholar 

  • Waller L. A., Carlin B. P., Xia H., Gelfand A. E. (1997). Hierarchical spatiotemporal mapping of disease rates. Journal of the American Statistical Association 92 (438): 607–17.

    Article  Google Scholar 

  • Xia H., Carlin B. P. (1998). Spatio-temporal models with errors in covariates: mapping Ohio lung cancer mortality. Statistics in Medicine 17 (18): 2025–43.

    Article  PubMed  CAS  Google Scholar 

  • Zakos-Feliberti A. 2000. [Personal Communication to B.Sue Bell].

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer Science+Business Media New York

About this chapter

Cite this chapter

Bell, B.S. (2002). Spatial Analysis of Disease — Applications. 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_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-4757-3571-0_8

  • 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