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Assessing spatial patterns in disease rates

S D Walter1

  • 1McMaster University, Department of Clinical Epidemiology & Biostatistics, Hamilton, Ontario, Canada.

Statistics in Medicine
|October 1, 1993
PubMed
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This study evaluates spatial autocorrelation indices for cancer data. Standard methods are biased by population heterogeneity, but Moran's I shows higher power for detecting disease patterns.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Geographic Information Systems (GIS)

Background:

  • Regional cancer incidence data analysis requires accurate spatial autocorrelation measures.
  • Heterogeneity in regional populations (size, age) affects rate precision and statistical analysis.
  • Existing methods for spatial autocorrelation indices may not account for this heterogeneity.

Purpose of the Study:

  • To empirically assess the performance of Moran's I, Geary's c, and a rank adjacency statistic (D) for regional cancer data.
  • To investigate the impact of population heterogeneity on these spatial autocorrelation indices.
  • To estimate the power of these indices in detecting disease spatial patterns.

Main Methods:

  • Empirical performance evaluation of Moran's I, Geary's c, and statistic D.

Related Experiment Videos

  • Simulation studies to estimate the power of indices under various disease patterns.
  • Analysis of regional cancer incidence data, including preliminary work on the Ontario cancer registry.

    • Main Results:

    • Standard methods for assessing spatial autocorrelation indices (I, c, D) are liberally biased when ignoring population heterogeneity, particularly for c and D.
    • The power to detect disease patterns varies significantly among indices, with Moran's I generally demonstrating higher power.
    • Null distributions of the indices are robust in small samples, even with zero observed cases in some regions.

    Conclusions:

    • Population heterogeneity significantly biases standard spatial autocorrelation analyses of cancer data.
    • Moran's I is a more powerful index for detecting spatial disease patterns compared to Geary's c and statistic D.
    • Variations in case registration rates or missing data have minimal impact on spatial analysis.