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      Gender differences in health-related quality-of-life are partly explained by sociodemographic and socioeconomic variation between adult men and women in the US: evidence from four US nationally representative data sets

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          Abstract

          Purpose

          The purpose of this study was to describe gender differences in self-reported health-related quality-of-life (HRQoL) and to examine whether differences are explained by sociodemographic and socioeconomic status (SES) differentials between men and women.

          Methods

          Data were from four US nationally representative surveys: US Valuation of the EuroQol EQ-5D Health States Survey (USVEQ), Medical Expenditure Panel Survey (MEPS), National Health Measurement Study (NHMS) and Joint Canada/US Survey of Health (JCUSH). Gender differences were estimated with and without adjustment for sociodemographic and SES indicators using regression within and across data sets with SF-6D, EQ-5D, HUI2, HUI3 and QWB-SA scores as outcomes.

          Results

          Women have lower HRQoL scores than men on all indexes prior to adjustment. Adjusting for age, race, marital status, education and income reduced but did not remove the gender differences, except with HUI3. Adjusting for marital status or income had the largest impact on estimated gender differences.

          Conclusions

          There are clear gender differences in HRQoL in the United States. These differences are partly explained by sociodemographic and SES differentials.

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          Most cited references25

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          The estimation of a preference-based measure of health from the SF-12.

          The SF-12 is a multidimensional generic measure of health-related quality of life. It has become widely used in clinical trials and routine outcome assessment because of its brevity and psychometric performance, but it cannot be used in economic evaluation in its current form. We sought to derive a preference-based measure of health from the SF-12 for use in economic evaluation and to compare it with the original SF-36 preference-based index. The SF-12 was revised into a 6-dimensional health state classification (SF-6D [SF-12]) based on an item selection process designed to ensure the minimum loss of descriptive information. A sample of 241 states defined by the SF-6D (of 7500) have been valued by a representative sample of 611 members of the UK general population using the standard gamble (SG) technique. Models are estimated of the relationship between the SF-6D (SF-12) and SG values and evaluated in terms of their coefficients, overall fit, and the ability to predict SG values for all health states. The models have produced significant coefficients for levels of the SF-6D (SF-12), which are robust across model specification. The coefficients are similar to those of the SF-36 version and achieve similar levels of fit. There are concerns with some inconsistent estimates and these have been merged to produce the final recommended model. As for the SF-36 model, there is evidence of over prediction of the value of the poorest health states. The SF-12 index provides a useful tool for researchers and policy makers wishing to assess the cost-effectiveness of interventions.
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            The estimation of a preference-based measure of health from the SF-36.

            This paper reports on the findings of a study to derive a preference-based measure of health from the SF-36 for use in economic evaluation. The SF-36 was revised into a six-dimensional health state classification called the SF-6D. A sample of 249 states defined by the SF-6D have been valued by a representative sample of 611 members of the UK general population, using standard gamble. Models are estimated for predicting health state valuations for all 18,000 states defined by the SF-6D. The econometric modelling had to cope with the hierarchical nature of the data and its skewed distribution. The recommended models have produced significant coefficients for levels of the SF-6D, which are robust across model specification. However, there are concerns with some inconsistent estimates and over prediction of the value of the poorest health states. These problems must be weighed against the rich descriptive ability of the SF-6D, and the potential application of these models to existing and future SF-36 data set.
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              Multiattribute and single-attribute utility functions for the health utilities index mark 3 system.

              The Health Utilities Index Mark 3 (HUI3) is a generic multiattribute preference-based measure of health status and health-related quality of life that is widely used as an outcome measure in clinical studies, in population health surveys, in the estimation of quality-adjusted life years, and in economic evaluations. HUI3 consists of eight attributes (or dimensions) of health status: vision, hearing, speech, ambulation, dexterity, emotion, cognition, and pain with 5 or 6 levels per attribute, varying from highly impaired to normal. The objectives are to present a multiattribute utility function and eight single-attribute utility functions for the HUI3 system based on community preferences. Two preference surveys were conducted. One, the modeling survey, collected preference scores for the estimation of the utility functions. The other, the direct survey, provided independent scores to assess the predictive validity of the utility functions. Preference measures included value scores obtained on the Feeling Thermometer and standard gamble utility scores obtained using the Chance Board. A random sample of the general population (> or =16 years of age) in Hamilton, Ontario, Canada. Estimates were obtained for eight single-attribute utility functions and an overall multiattribute utility function. The intraclass correlation coefficient between directly measured utility scores and scores generated by the multiattribute function for 73 health states was 0.88. The HUI3 scoring function has strong theoretical and empirical foundations. It performs well in predicting directly measured scores. The HUI3 system provides a practical way to obtain utility scores based on community preferences.
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                Author and article information

                Contributors
                +310-393-0411 , dcherepanov@ucla.edu
                Journal
                Qual Life Res
                Quality of Life Research
                Springer Netherlands (Dordrecht )
                0962-9343
                1573-2649
                23 May 2010
                23 May 2010
                October 2010
                : 19
                : 8
                : 1115-1124
                Affiliations
                [1 ]Department of Health Services, University of California Los Angeles School of Public Health, P.O. Box 90095-1772, Los Angeles, CA USA
                [2 ]The RAND Corporation, 1776 Main Street. M5S, Santa Monica, CA 90407-2138 USA
                [3 ]Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, 707 WARF Building, 610 North Walnut Street, Madison, WI 53726 USA
                Article
                9673
                10.1007/s11136-010-9673-x
                2940034
                20496168
                b9fce3bf-2bad-442f-9308-b9e777b004c7
                © The Author(s) 2010
                History
                : 29 April 2010
                Categories
                Article
                Custom metadata
                © Springer Science+Business Media B.V. 2010

                Public health
                men’s health,health status,outcome assessment,gender differences,hui3,sf-6d,qwb-sa,quality of life,population study,sex differences,women’s health,hui2,eq-5d

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