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Related Experiment Videos

Defining normality--art or science?

D Robinson1, E A Bevan

  • 1BUPA Medical Research and Development Ltd., London, U.K.

Methods of Information in Medicine
|April 1, 1993
PubMed
Summary
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Defining health normality and reference ranges is subjective. This study proposes "How Often That Happens" (HOTH) graphs as an objective alternative for analyzing health data and population changes.

Area of Science:

  • Biostatistics
  • Medical Informatics
  • Population Health

Background:

  • Establishing reliable reference ranges for biological and medical parameters is crucial for defining normality.
  • Current methods for determining 'normal' ranges often involve subjective choices, limiting their objectivity.
  • The aging population presents challenges in maintaining accurate health parameter distributions.

Purpose of the Study:

  • To critically evaluate existing methods for defining normality and reference ranges.
  • To introduce and explore the utility of "How Often That Happens" (HOTH) graphs as an alternative to conventional reference ranges.
  • To investigate the application of HOTH graphs and simulation models in understanding age-related changes in health parameter distributions.

Main Methods:

Related Experiment Videos

  • Review and critique of conventional methods for establishing reference ranges.
  • Development and application of "How Often That Happens" (HOTH) graphs to visualize data distributions.
  • Utilizing computer simulation models to analyze changes in parameter frequency distributions across aging populations.

    • Main Results:

    • Analysis indicates that all current methods for defining 'normal' ranges are ultimately subjective.
    • HOTH graphs provide a novel visualization of result frequencies, offering an alternative to traditional reference ranges.
    • Simulation models demonstrate the potential of HOTH graphs in describing age-related shifts in population health parameters.

    Conclusions:

    • The definition of normality in health and disease is more nuanced and subjective than typically acknowledged.
    • HOTH graphs offer a promising, data-driven approach to characterizing health states and population dynamics.
    • Further research into non-linear dynamic systems may provide deeper insights into health, illness, and death states.