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Measuring health inequality using qualitative data.

R Andrew Allison1, James E Foster

  • 1Kansas Health Institute, Topeka, KS, USA.

Journal of Health Economics
|May 4, 2004
PubMed
Summary
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This study introduces a new method to analyze health inequality using self-reported health status (SRHS) data. The approach allows for the evaluation of health distribution and changes, even with qualitative health data.

Area of Science:

  • Health Economics
  • Biostatistics
  • Public Health Policy

Background:

  • Assessing health status distribution is crucial for health policy, but objective data is often limited.
  • Self-reported health status (SRHS) is increasingly used, despite its qualitative nature.
  • Traditional inequality metrics like the Lorenz curve are unsuitable for categorical SRHS data.

Purpose of the Study:

  • To develop a methodology for evaluating health inequality using qualitative SRHS data.
  • To adapt distributional analysis tools for categorical health status indicators.
  • To assess health distribution and policy impacts with non-numeric health data.

Main Methods:

  • Defined a partial inequality ordering for qualitative data to assess distribution spread.

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  • Utilized first-order dominance to evaluate overall shifts in health levels.
  • Applied the methodology to National Health Interview Survey (NHIS) State Data Files (1994).

    • Main Results:

    • The proposed methodology effectively analyzes inequality in SRHS data without relying on numerical scaling.
    • Demonstrated the application of partial orderings for understanding health distribution variations.
    • Illustrated how SRHS data can inform health policy by showing distributional changes.

    Conclusions:

    • A novel approach for health inequality analysis with qualitative SRHS data has been presented.
    • The methodology offers a robust way to evaluate health distribution and changes, applicable to policy interventions.
    • This work facilitates a more nuanced understanding of health disparities using commonly available self-reported data.