Talk to the Veterans Crisis Line now
U.S. flag
An official website of the United States government

Health Services Research & Development

Veterans Crisis Line Badge
Go to the ORD website
Go to the QUERI website

2011 HSR&D National Meeting Abstract

Printable View

2011 National Meeting

1074 — Estimating Quality-Adjusted Life Years Using SF-36 Questionnaire Responses: Challenges in a Population of Veterans with Diabetes

Sinha A (REAP - East Orange, NJ), Rajan M (REAP - East Orange, NJ), Pogach L (REAP - East Orange, NJ)

Objectives:
Health economic analyses of new medical interventions require preference-weighted measures of quality of life, utilities to estimate quality-adjusted life years (QALYs). Using existing quality of life data, e.g. SF-36, to estimate utilities has the potential to efficiently provide investigators with utility information, based on large samples of responses. Our objective was to compare three previously published methods of transforming SF-36 data into utilities, using available survey responses from veterans with recent-onset diabetes.

Methods:
We utilized the Large Veterans Health Survey (LVHS) data from 1999 to select respondents with recent-onset diabetes. We used three previously published regression-based transformations (A = Nichol et al 2001, B = Brazier et al 2004, C = Brazier et al 2002) to estimate utilities, on a scale of 0 to 1, associated with specific health states and combinations of health states.

Results:
47,779 LVHS respondents had recent-onset diabetes. Utilities estimated by method A were systematically found to be less than utilities estimated by B or C. For example, among patients with chronic renal failure, utility [U, mean(sd)] was 0.596 (0.149), 0.694 (0.100), 0.649 (0.106) using A, B, and C respectively. The rank order remained consistently U(B) > U(C) > U(A) when patients were stratified by GI, pulmonary, musculoskeletal, neurological, or substance abuse conditions. Pearson’s correlation showed that U(B) was modestly correlated to U(A) and to U(C) (r = 0.51, 0.49 respectively). U(A) and U(C) were highly correlated (r = 0.84) for respondents with < = five health conditions. U(A) and U( C) were moderately correlated (r = 0.73) for those with > = six health conditions.

Implications:
Because utilities factor into the denominator of cost-effectiveness ratios, small differences in utility values can have a large impact on cost-effectiveness results. Among diabetic veterans, we found systematic differences in the utilities estimated using three transformations of SF-36 data. In particular, method B may not capture all available SF-36 information, resulting in inconsistent utility estimates relative to methods A and C.

Impacts:
The use of SF-36 transformations to estimate utilities needs further development using empirically collected data before routine incorporation into cost-effectiveness analyses.


Questions about the HSR&D website? Email the Web Team.

Any health information on this website is strictly for informational purposes and is not intended as medical advice. It should not be used to diagnose or treat any condition.