Mapping the SF-12 to the HUI3 and VAS in a managed care population

Med Care. 2004 Sep;42(9):927-37. doi: 10.1097/01.mlr.0000135812.52570.42.

Abstract

Background: Transforming generic health-related quality of life (HRQOL) instruments to a summary utility index is useful for deriving quality-adjusted life years (QALY) in any cost/QALY analysis.

Objective: The purpose of this study was to investigate the role of the SF-12 in predicting utility scores derived from Health Utility Index (HUI3) and Visual Analog Scale (VAS).

Method: Data were obtained from a survey of 6923 managed care patients in the United States, aged 18 to 93 years, selected by strata of medication usage (at least 1 medication in target year, 5 or more medications, target medications, and both). The SF-12 was used to assess self-reported HRQOL. Utility was measured by the HUI3 and a VAS. The SF-12 items were used to predict HUI3 and VAS scores using ordinary least square regressions, with sociodemographic covariates. A second model entered each SF-12 item as categorized responses. A third model used the Physical Composite and Mental Composite scores to predict HUI3 and VAS scores.

Results: The SF-12 items and sociodemographic covariates accounted for 35% to 55% of the variations in HUI3 and VAS scores, respectively. Age and most SF-12 items were significantly (P < 0.0001) associated with both utility scores in all 3 models.

Conclusions: This research provides support that an algorithm can be derived from the SF-12 to estimate utility scores based on the HUI3 and VAS for studies in populations where utility has not or cannot be measured directly.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms
  • Female
  • Humans
  • Likelihood Functions
  • Male
  • Managed Care Programs / standards*
  • Managed Care Programs / statistics & numerical data
  • Middle Aged
  • Outcome Assessment, Health Care
  • Quality of Life
  • Quality-Adjusted Life Years*
  • Regression Analysis
  • Sickness Impact Profile*
  • Socioeconomic Factors
  • Surveys and Questionnaires / standards*
  • United States