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Distance and risk measures for the analysis of spatial data: A study of childhood cancers

Author

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  • Selvin, S.
  • Schulman, J.
  • Merrill, D. W.

Abstract

Three statistical approaches, used to detect spatial clusters of disease associated with a point source exposure, are applied to childhood cancer data for the city of San Francisco (1973-1988). The distributions of incident cases of leukemia (51 cases), brain cancer (35 cases), and lymphatic cancer (37 cases) among individuals less than 21 years of age are described using three measures of clustering: distance on a geopolitical map, distance on a density equalized transformed map, and relative risk. The point source of exposure investigated is a large microwave tower located southwest of the center of the city (Sutro Tower). The three analytic approaches indicate that the patterns of the major childhood cancers are essentially random with respect to the point source. These results and a statistical model for spatial clustering are used to explore distance and risk measures in the analysis of spatial data. Both types of measures of spatial clustering are shown to perform similarly when a specific area of exposure can be defined.

Suggested Citation

  • Selvin, S. & Schulman, J. & Merrill, D. W., 1992. "Distance and risk measures for the analysis of spatial data: A study of childhood cancers," Social Science & Medicine, Elsevier, vol. 34(7), pages 769-777, April.
    • Handle: RePEc:eee:socmed:v:34:y:1992:i:7:p:769-777
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    Cited by:

    1. Ronald E. Gangnon & Murray K. Clayton, 2000. "Bayesian Detection and Modeling of Spatial Disease Clustering," Biometrics, The International Biometric Society, vol. 56(3), pages 922-935, September.

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