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Related Experiment Videos

Toward consistency in cost-utility analyses: using national measures to create condition-specific values

M R Gold1, P Franks, K I McCoy

  • 1Department of Community Health and Social Medicine, City University of New York Medical School, New York 10031, USA.

Medical Care
|June 18, 1998
PubMed
Summary

Researchers created health-related quality of life (HRQL) scores for chronic conditions using national data. These scores aid cost-effectiveness analyses when primary data collection is not feasible.

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Area of Science:

  • Health Economics
  • Public Health
  • Outcomes Research

Background:

  • Cost-effectiveness analysis (CEA) is crucial for healthcare decision-making.
  • Primary data collection for health-related quality of life (HRQL) can be resource-intensive.
  • A standardized, readily available source of HRQL scores is needed for CEA.

Purpose of the Study:

  • To develop an "off-the-shelf" source of HRQL scores for chronic conditions.
  • To provide HRQL scores for use in CEA by analysts unable to collect primary data.

Main Methods:

  • Derived HRQL scores using nationally representative data from the National Health Interview Survey (NHIS).
  • Utilized the Healthy People 2000 Years of Healthy Life measure, assessing role function and self-rated health.

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  • Calculated condition-specific HRQL scores and conducted preliminary validation against two other studies.

    • Main Results:

    • Provided HRQL scores for various chronic conditions, ranging from 0.87 (allergic rhinitis) to 0.27 (hemiplegia).
    • Demonstrated reasonable face validity for the derived scores.
    • Achieved strong correlations (0.78 and 0.86) between condition weights and those from two independent studies.

    Conclusions:

    • The developed condition weights are valuable for CEA using secondary data, particularly when community ratings of HRQL are required.
    • Standardized HRQL weights from a national sample enhance the comparability of CEA studies.
    • Future improvements in national data collection, including empirical preference gathering, will strengthen these measures.