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Cancer mapping: why not use absolute scales?

¹German Cancer Research Center, Division of Epidemiology, Heidelberg.

European Journal of Cancer (Oxford, England : 1990)

|January 1, 1994

Summary

Cancer atlases can be improved using absolute scaling, a method that preserves the true mortality magnitudes across different cancer sites and sexes. This approach enhances comparability and interpretation compared to traditional relative scaling methods.

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Area of Science:

Epidemiology
Biostatistics
Medical Cartography

Background:

Cancer atlases commonly employ relative scaling for mortality rates, comparing regional data to national means or medians.
This relative scaling presents limitations in comparability and interpretation of cancer mortality data.
Existing methods hinder a clear understanding of absolute differences in cancer incidence across regions and demographics.

Purpose of the Study:

To introduce and evaluate absolute scaling as an alternative to relative scaling in cancer atlases.
To demonstrate how absolute scaling can improve the comparability and interpretation of cancer mortality data.
To apply absolute scaling to the German Cancer Atlas data from 1984.

Main Methods:

Absolute scaling, analogous to altitude mapping in geographic atlases, was implemented.
A consistent color scheme with a wide, common range was utilized across maps.
The method was applied to the mortality data of the German Cancer Atlas (1984).

Main Results:

Absolute scaling preserves the actual magnitudes of mortality for various cancer types and sexes.
This technique allows for the use of different ranges within a common color scheme, reflecting true variations.
The application demonstrated that absolute scaling overcomes limitations of relative scaling methods.

Conclusions:

Absolute scaling offers a superior method for presenting cancer mortality data in atlases.
This approach enhances the interpretability and comparability of cancer risk across different populations and cancer sites.
The findings suggest a significant improvement in visualizing epidemiological data through absolute scaling techniques.

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