Literature DB >> 15319619

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

Nishan Sengupta1, Michael B Nichol, Joanne Wu, Denise Globe.   

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.

Entities:  

Mesh:

Year:  2004        PMID: 15319619     DOI: 10.1097/01.mlr.0000135812.52570.42

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  25 in total

1.  Estimating utilities for chronic kidney disease, using SF-36 and SF-12-based measures: challenges in a population of veterans with diabetes.

Authors:  Mangala Rajan; Kuan-Chi Lai; Chin-Lin Tseng; Shirley Qian; Alfredo Selim; Lewis Kazis; Leonard Pogach; Anushua Sinha
Journal:  Qual Life Res       Date:  2012-03-06       Impact factor: 4.147

2.  Measuring preferences for cost-utility analysis: how choice of method may influence decision-making.

Authors:  Christine M McDonough; Anna N A Tosteson
Journal:  Pharmacoeconomics       Date:  2007       Impact factor: 4.981

3.  Estimating utility values for health states of overweight and obese individuals using the SF-36.

Authors:  Michael A Kortt; Philip M Clarke
Journal:  Qual Life Res       Date:  2005-12       Impact factor: 4.147

4.  Mapping the eight-item Parkinson's Disease Questionnaire (PDQ-8) to the EQ-5D utility index.

Authors:  Y B Cheung; L C S Tan; P N Lau; W L Au; N Luo
Journal:  Qual Life Res       Date:  2008-09-20       Impact factor: 4.147

5.  Predicting preference-based SF-6D index scores from the SF-8 health survey.

Authors:  P Wang; A Z Fu; H L Wee; J Lee; E S Tai; J Thumboo; N Luo
Journal:  Qual Life Res       Date:  2012-10-10       Impact factor: 4.147

6.  Predicting EQ-5D-US and SF-6D societal health state values from the Osteoporosis Assessment Questionnaire.

Authors:  C M McDonough; M R Grove; A D Elledge; A N A Tosteson
Journal:  Osteoporos Int       Date:  2011-04-12       Impact factor: 4.507

7.  Quality-adjusted life years lost to road crash injury: updating the injury impairment index.

Authors:  Rebecca S Spicer; Ted R Miller; Delia Hendrie; Lawrence J Blincoe
Journal:  Ann Adv Automot Med       Date:  2011

8.  Quality-of-life loss of people admitted to burn centers, United States.

Authors:  Ted Miller; Soma Bhattacharya; William Zamula; Dennis Lezotte; Karen Kowalske; David Herndon; James Fauerbach; Loren Engrav
Journal:  Qual Life Res       Date:  2012-12-08       Impact factor: 4.147

9.  Mapping CushingQOL scores to EQ-5D utility values using data from the European Registry on Cushing's syndrome (ERCUSYN).

Authors:  X Badia; M Roset; E Valassi; H Franz; A Forsythe; S M Webb
Journal:  Qual Life Res       Date:  2013-03-29       Impact factor: 4.147

10.  Predicting an SF-6D preference-based score using MCS and PCS scores from the SF-12 or SF-36.

Authors:  Janel Hanmer
Journal:  Value Health       Date:  2009-03-24       Impact factor: 5.725

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