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      Development of physical and mental health summary scores from the patient-reported outcomes measurement information system (PROMIS) global items

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          Abstract

          Background

          The use of global health items permits an efficient way of gathering general perceptions of health. These items provide useful summary information about health and are predictive of health care utilization and subsequent mortality.

          Methods

          Analyses of 10 self-reported global health items obtained from an internet survey as part of the Patient-Reported Outcome Measurement Information System (PROMIS) project. We derived summary scores from the global health items. We estimated the associations of the summary scores with the EQ-5D index score and the PROMIS physical function, pain, fatigue, emotional distress, and social health domain scores.

          Results

          Exploratory and confirmatory factor analyses supported a two-factor model. Global physical health (GPH; 4 items on overall physical health, physical function, pain, and fatigue) and global mental health (GMH; 4 items on quality of life, mental health, satisfaction with social activities, and emotional problems) scales were created. The scales had internal consistency reliability coefficients of 0.81 and 0.86, respectively. GPH correlated more strongly with the EQ-5D than did GMH ( r = 0.76 vs. 0.59). GPH correlated most strongly with pain impact ( r = −0.75) whereas GMH correlated most strongly with depressive symptoms ( r = −0.71).

          Conclusions

          Two dimensions representing physical and mental health underlie the global health items in PROMIS. These global health scales can be used to efficiently summarize physical and mental health in patient-reported outcome studies.

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          Most cited references21

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          US valuation of the EQ-5D health states: development and testing of the D1 valuation model.

          The EQ-5D is a brief, multiattribute, preference-based health status measure. This article describes the development of a statistical model for generating US population-based EQ-5D preference weights. A multistage probability sample was selected from the US adult civilian noninstitutional population. Respondents valued 13 of 243 EQ-5D health states using the time trade-off (TTO) method. Data for 12 states were used in econometric modeling. The TTO valuations were linearly transformed to lie on the interval [-1, 1]. Methods were investigated to account for interaction effects caused by having problems in multiple EQ-5D dimensions. Several alternative model specifications (eg, pooled least squares, random effects) also were considered. A modified split-sample approach was used to evaluate the predictive accuracy of the models. All statistical analyses took into account the clustering and disproportionate selection probabilities inherent in our sampling design. Our D1 model for the EQ-5D included ordinal terms to capture the effect of departures from perfect health as well as interaction effects. A random effects specification of the D1 model yielded a good fit for the observed TTO data, with an overall R of 0.38, a mean absolute error of 0.025, and 7 prediction errors exceeding 0.05 in absolute magnitude. The D1 model best predicts the values for observed health states. The resulting preference weight estimates represent a significant enhancement of the EQ-5D's utility for health status assessment and economic analysis in the US.
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            Correlated physical and mental health summary scores for the SF-36 and SF-12 Health Survey, V.1

            Background The SF-36 and SF-12 summary scores were derived using an uncorrelated (orthogonal) factor solution. We estimate SF-36 and SF-12 summary scores using a correlated (oblique) physical and mental health factor model. Methods We administered the SF-36 to 7,093 patients who received medical care from an independent association of 48 physician groups in the western United States. Correlated physical health (PCSc) and mental health (MCSc) scores were constructed by multiplying each SF-36 scale z-score by its respective scoring coefficient from the obliquely rotated two factor solution. PCSc-12 and MCSc-12 scores were estimated using an approach similar to the one used to derive the original SF-12 summary scores. Results The estimated correlation between SF-36 PCSc and MCSc scores was 0.62. There were far fewer negative factor scoring coefficients for the oblique factor solution compared to the factor scoring coefficients produced by the standard orthogonal factor solution. Similar results were found for PCSc-12, and MCSc-12 summary scores. Conclusion Correlated physical and mental health summary scores for the SF-36 and SF-12 derived from an obliquely rotated factor solution should be used along with the uncorrelated summary scores. The new scoring algorithm can reduce inconsistent results between the SF-36 scale scores and physical and mental health summary scores reported in some prior studies. (Subscripts C = correlated and UC = uncorrelated)
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              A comparative review of generic quality-of-life instruments.

              The assessment of health-related quality of life (HR-QOL) is an essential element of healthcare evaluation. Hundreds of generic and specific HR-QOL instruments have been developed. Generic HR-QOL instruments are designed to be applicable across a wide range of populations and interventions. Specific HR-QOL measures are designed to be relevant to particular interventions or in certain subpopulations (e.g. individuals with rheumatoid arthritis). This review examines 7 generic HR-QOL instruments: (i) the Medical Outcomes Study 36-Item Short Form (SF-36) health survey; (ii) the Nottingham Health Profile (NHP); (iii) the Sickness Impact Profile (SIP); (iv) the Dartmouth Primary care Cooperative Information Project (COOP) Charts; (v) the Quality of Well-Being (QWB) Scale; (vi) the Health Utilities Index (HUI); and (vii) the EuroQol Instrument (EQ-5D). These instruments were selected because they are commonly used and/or cited in the English language literature. The 6 characteristics of an instrument addressed by this review are: (i) conceptual and measurement model; (ii) reliability; (iii) validity; (iv) respondent and administrative burden; (v) alternative forms; and (vi) cultural and language adaptations. Of the instruments reviewed, the SF-36 health survey is the most commonly used HR-QOL measure. It was developed as a short-form measure of functioning and well-being in the Medical Outcomes Study. The Dartmouth COOP Charts were designed to be used in everyday clinical practice to provide immediate feedback to clinicians about the health status of their patients. The NHP was developed to reflect lay rather than professional perceptions of health. The SIP was constructed as a measure of sickness in relation to impact on behaviour. The QWB, HUI and EQ-5D are preference-based measures designed to summarise HR-QOL in a single number ranging from 0 to 1. We found that there are no uniformly 'worst' or 'best' performing instruments. The decision to use one over another, to use a combination of 2 or more, to use a profile and/or a preference-based measure or to use a generic measure along with a targeted measure will be driven by the purpose of the measurement. In addition, the choice will depend on a variety of factors including the characteristics of the population (e.g. age, health status, language/culture) and the environment in which the measurement is undertaken (e.g. clinical trial, routine physician visit). We provide our summary of the level of evidence in the literature regarding each instrument's characteristics based on the review criteria. The potential user of these instruments should base their instrument selection decision on the characteristics that are most relevant to their particular HR-QOL measurement needs.
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                Author and article information

                Contributors
                hays@rand.org
                Journal
                Qual Life Res
                Quality of Life Research
                Springer Netherlands (Dordrecht )
                0962-9343
                1432-1203
                19 June 2009
                September 2009
                : 18
                : 7
                : 873-880
                Affiliations
                [1 ]Department of Medicine, UCLA, Los Angeles, CA USA
                [2 ]QualityMetric Incorporated, 640 George Washington Highway, Suite 201, Lincoln, RI 02865 USA
                [3 ]Center for Health Outcomes Research, United BioSource Corporation, 7101 Wisconsin Ave., Suite 600, Bethesda, MD 20814 USA
                [4 ]Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL USA
                Article
                9496
                10.1007/s11136-009-9496-9
                2724630
                19543809
                be0fa67b-4774-4c98-b14d-13b00566b842
                © The Author(s) 2009
                History
                : 15 October 2008
                : 28 May 2009
                Categories
                Article
                Custom metadata
                © Springer Science+Business Media B.V. 2009

                Public health
                promis,eq-5d,global health,item response theory
                Public health
                promis, eq-5d, global health, item response theory

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