Application of the PyHasse program features: Sensitivity,Similarity, and Separability for environmental health data
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It has been evident for decades that many environmental chemicals pose an enormous risk to the
environment as well as to humans. There is increasing pressure to intensify the research and to
more efficiently evaluate the data on persistent and bioaccumulative chemicals in the environment
as well as in human bodies. An appropriate data analysis method is based on the theory of partially
ordered sets. The program PyHasse, developed by the third author, provides several features which
are useful for gaining information out of the data and drawing conclusions concerning the impact
of those chemicals and their prevention. In our data analysis approach we investigated data sets of
breast milk samples of women in Denmark and Finland which contained measurable levels of 32
persistent organic pollutants (POPs). Three important features of the PyHasse program are used:
The Sensitivity Analysis, the Similarity Analysis and the Separability Analysis. The aim of this discrete
mathematical approach is to find differences in the chemicals’ contamination between the
healthy boys and those boys who were suffering from congenital malformations (cryptorchidism).
Keywords: Environmental Health Data, Persistent Organic Pollutants (POPs), Cryptorchidism, Partial Order, PyHasse Program. |
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