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A computer program for analyses of vital statistics-based occupational mortality data.

J A Singleton1, J J Beaumont, G Doebbert

  • 1Northern California Occupational Health Center, Division of Occupational and Environmental Medicine, University of California, Davis 95616.

Computers and Biomedical Research, an International Journal
|October 1, 1989
PubMed
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This study introduces a Fortran program to analyze occupational mortality using vital statistics and census data. The tool aids in identifying occupational hazards and mortality differences linked to lifestyle factors.

Area of Science:

  • Public Health
  • Epidemiology
  • Occupational Health

Background:

  • Vital statistics mortality data, including occupation and industry, are crucial for hypothesis generation regarding occupational hazards.
  • These data also help identify mortality differentials potentially linked to socioeconomic status and lifestyle choices.

Purpose of the Study:

  • To present a Fortran program designed for the comprehensive analysis of occupational mortality data.
  • To enable researchers to explore occupational mortality patterns using vital statistics and census information.

Main Methods:

  • The program utilizes routinely collected vital statistics (death certificates) and census data.
  • It allows flexible grouping of demographic variables (age, race), occupational/industry codes, and cause of death codes.

Related Experiment Videos

  • Stratification by social class or lifestyle factors (e.g., smoking behavior) is supported.
  • Main Results:

    • The program facilitates the formation of custom data groupings for detailed occupational mortality analysis.
    • Output is structured for subsequent Poisson regression analysis, enabling robust statistical modeling of mortality rates.

    Conclusions:

    • The developed Fortran program provides a valuable tool for occupational mortality research.
    • It enhances the ability to investigate occupational hazards and associated socioeconomic and lifestyle factors through flexible data analysis.