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Med Care. 1998 Jun;36(6):778-92.

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

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

Abstract

OBJECTIVES:

The authors developed an "off-the-shelf" source of health-related quality of life (HRQL) scores for cost-effectiveness analysts unable to collect primary data.

METHODS:

The authors derived and conducted preliminary validation on a set of health-related quality of life scores for chronic conditions using nationally representative data from the National Health Interview Survey (NHIS) and the Healthy People 2000 Years of Healthy Life measure developed to monitor the health (longevity and health-related quality of life) of Americans during this decade. The measure comprises two domains, role function and self-rated health, and is scaled from 0 (death) to 1 (best health state). Health-related quality of life scores for chronic conditions were calculated using the Years of Healthy Life scores associated with chronic conditions reported in the 1987-1992 National Health Interview Survey. Preliminary validation was examined by comparing the health-related quality of life scores with those obtained in two other studies.

RESULTS:

Tables provide health-related quality of life scores for persons with and without conditions. The scores had reasonable face validity, ranging from 0.87 for allergic rhinitis to 0.27 for hemiplegia. Correlations of the health-related quality of life condition weight scores with those from two other studies were 0.78 and 0.86.

CONCLUSIONS:

These condition weights may prove useful to investigators conducting cost-effectiveness analyses using secondary data, where community ratings of health-related quality of life for chronic conditions are required. Use of a standard set of health-related quality of life weights gathered from a national sample can enhance the comparability of cost-effectiveness analyses. Improvements in national data collection techniques, with empirical gathering of preferences, will further strengthen this measure.

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PMID:
9630120
[PubMed - indexed for MEDLINE]
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