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A geographic regression model for medical statistics.

S Kennedy1

  • 1Department of Geography, University of California, Santa Barbara 93106.

Social Science & Medicine (1982)
|January 1, 1988
PubMed
Summary

Male lung cancer mortality shows spatial patterns, unlike female lung cancer. This suggests neighborhood factors influence male lung cancer, while regional factors affect female lung cancer.

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Area of Science:

  • Environmental epidemiology
  • Geographic information systems
  • Biostatistics

Background:

  • Lung cancer mortality rates vary geographically.
  • Understanding spatial patterns is crucial for public health interventions.
  • Geographic data analysis can reveal environmental and social determinants of disease.

Purpose of the Study:

  • To introduce a method for modeling geographic processes using census-type data.
  • To analyze spatial patterns in male and female lung cancer mortality rates.
  • To investigate the relationship between mortality rates in adjacent counties.

Main Methods:

  • Spatially autoregressive modeling was employed.
  • Census-type data were utilized for analysis.
  • The study focused on counties bordering the Gulf of Mexico and southeast Atlantic Coast.

Main Results:

  • Male lung cancer mortality exhibited significant spatial autocorrelation.
  • Female lung cancer mortality did not show spatial autocorrelation.
  • Female lung cancer data displayed a spatial trend, while male data did not.

Conclusions:

  • Neighborhood-scale factors are more influential in male lung cancer etiology.
  • Regional-scale factors play a more significant role in female lung cancer etiology.
  • Geographic modeling provides insights into sex-specific lung cancer determinants.

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