The SF-36 summary scales were valid, reliable, and equivalent in a Chinese population

OBJECTIVES: To find out whether the SF-36 physical and mental health summary (PCS and MCS) scales are valid and equivalent in the Chinese population in Hong Kong (HK). STUDY DESIGN AND SETTING: The SF-36 data of a cross-sectional study on 2,410 Chinese adults randomly selected from the general population in HK were analyzed. RESULTS: The hypothesized two-factor structure of the physical and mental health summary scales (PCS and MCS) was replicated and the expected differences in scores between known morbidity groups were shown. The internal reliability coefficients of the PCS and MCS scales ranged from 0.85 to 0.87. The effect size differences between the U.S. standard and HK-specific PCS and MCS scores were mostly <0.5. The effect size differences in the standard PCS and MCS scores of specific groups between the U.S. and H.K. populations were all <0.5. CONCLUSION: The PCS and MCS scales were applicable to the Chinese population in HK. The high level of measurement equivalence of the scales between the U.S. and H.K. populations suggests that data pooling between the two populations could be possible. To our knowledge, this is the first study to show that the SF-36 summary scales are valid and equivalent in an Asian population.     

The SF-36 scales are not accurately summarised by independent physical and mental component scores

Objectives The Short Form 36 Health Status Questionnaire (SF-36) has eight scales that can be condensed into two components: physical component summary (PCS) and mental component summary (MCS). This paper investigates: (1) the assumption that PCS and MCS are orthogonal, (2) the applicability of a single model to different condition-specific subgroups, and (3) a reduced five-scale model. Study design and setting We performed a secondary analysis of two large-scale data sets that utilised the SF-36: the Health Survey for England 1996 and the Welsh Health Survey 1998. We used confirmatory factor analysis to compare hypothetical orthogonal and oblique factor models, and exploratory factor analysis to derive data-driven models for condition-specific subgroups. Results Oblique models gave the best fit to the data and indicated a considerable correlation between PCS and MCS. The loadings of the eight scales on the two component summaries varied significantly by disease condition. The choice of model made an important difference to norm-referenced scores for large minorities, particularly patients with a mental illness or mental–physical comorbidity. Conclusions We recommend that users of the SF-36 adopt the oblique model for calculating PCS and MCS. An oblique five-scale model provides a more universal factor structure without loss of predictive power or reliability.     

A Study of the Construct Validity of the Health Utilities Index Mark 3 (HUI3) in Patients with Schizophrenia

This study assessed the construct validity of the Health Utilities Index Mark 3 (HUI3) in patients with schizophrenia. Patients with schizophrenia recruited from a tertiary mental hospital in Singapore completed the HUI3, the Short-Form 36 Health Survey (SF-36) and the Schizophrenia Quality of Life Scale (SQLS). Patients were assessed for presence and absence of 22 common psychiatric symptoms. Construct validity was assessed using 6 a priori hypotheses. Two hundred and two patients (mean age: 37.8 years, female: 52%) completed the survey. As hypothesized, overall HUI3 utility scores were correlated with SF-36 measures (Spearman’s rho: 0.19 to 0.51), SQLS scales (Spearman’s rho: −0.56 to −0.36), and the number of psychiatric symptoms (Spearman’s rho: −0.49). The HUI3 emotion attribute was moderately correlated with SF-36 mental health (Spearman’s rho: 0.45) and SQLS psychosocial scales (Spearman’s rho: −0.43), and HUI3 pain attribute was strongly correlated with SF-36 bodily pain scale (Spearman’s rho: 0.58). The mean HUI3 overall, emotion, cognition, and speech scores for patients with schizophrenia were 0.07, 0.09, 0.04 and 0.04 points lower than respective age-, sex- and ethnicity-adjusted population norms (p<0.001 for all, ANCOVA). This study provides evidence for the construct validity of the HUI3 in patients with schizophrenia.     

Iron deficiency, general health and fatigue: Results from the Australian Longitudinal Study on Women's Health

Associations between self-reported ‘low iron’, general health and well-being, vitality and tiredness in women, were examined using physical (PCS) and mental (MCS) component summary and vitality (VT) scores from the MOS short-form survey (SF-36). 14,762 young (18–23 years) and 14,072 mid-age (45–50 years) women, randomly selected from the national health insurance commission (Medicare) database, completed a baseline mailed self-report questionnaire and 12,328 mid-age women completed a follow-up questionnaire 2 years later. Young and mid-age women who reported (ever) having had ‘low iron’ reported significantly lower mean PCS, MCS and VT scores, and greater prevalence of ‘constant tiredness’ at baseline than women with no history of iron deficiency [Differences: young PCS = −2.2, MCS = −4.8, VT = −8.7; constant tiredness: 67% vs. 45%; mid-age PCS = −1.4, MCS = −3.1, VT = −5.9; constant tiredness: 63% vs. 48%]. After adjusting for number of children, chronic conditions, symptoms and socio-demographic variables, mean PCS, MCS and VT scores for mid-age women at follow-up were significantly lower for women who reported recent iron deficiency (in the last 2 years) than for women who reported past iron deficiency or no history of iron deficiency [Means: PCS – recent = 46.6, past = 47.8, never = 47.7; MCS – recent = 45.4, past = 46.9, never = 47.4; VT – recent = 54.8, past = 57.6, never = 58.6]. The adjusted mean change in PCS, MCS and VT scores between baseline and follow-up were also significantly lower among mid-age women who reported iron deficiency only in the last 2 years (i.e. recent iron deficiency) [Mean change: PCS = −3.2; MCS = −2.1; VT = −4.2]. The results suggest that iron deficiency is associated with decreased general health and well-being and increased fatigue. This revised version was published online in June 2006 with corrections to the Cover Date.     

Comparison of WHOQOL-BREF and SF-36 in patients with HIV infection

The purpose of the study was to evaluate the reliability and validity of the two generic instruments, the WHOQOL and the SF-36, for assessing health-related quality of life in 224 patients with HIV infection. The internal consistency ranged from 0.75 to 0.86 across the WHOQOL-BREF domains and from 0.72 to 0.93 across the SF-36 scales. The scores of all WHOQOL-BREF domains and SF-36 scales correlated positively with the measure of happiness, Sat-HRQOL and self-perceived health status, and correlated negatively with the number and intensity of symptoms. Patients with higher CD4 cell counts scored significantly higher on G4 (general health), three WHOQOL-BREF domains, seven SF-36 scales, and PCS (physical component summary). Patients with fewer symptoms and with less intensity of symptoms had significantly higher scores on all four domains of WHOQOL-BREF, eight scales, PCS, and MCS (mental component summary) of the SF-36 scale. The correlations between the physical, psychological, and social domains of the WHOQOL-BREF and PF (physical functioning), MH (mental health), and SF (social functioning) of the SF-36 were 0.51, 0.75, and 0.54, respectively. There is also good correlation between PCS of the SF-36 and the physical domain of the WHOQOL-BREF (r = 0.48), and between MCS and all four domains of the WHOQOL-BREF (r range = 0.60–0.75). The WHOQOL-BREF domains showed fewer floor or ceiling effect than the SF-36 scales. We concluded that both the WHOQOL-BREF and the SF-36 are reliable and valid health related quality-of-life instruments in patients with HIV infection.     

Do SF-36 summary component scores accurately summarize subscale scores?

Standard scoring algorithms were recently made available for aggregating scores from the eight SF-36 subscales in two distinct, higher-order summary scores: Physical Component Summary (PCS) and Mental Component Summary (MCS). Recent studies have suggested, however, that PCS and MCS scores are not independent and may in part be measuring the same constructs. The aims of this paper were to examine and illustrate (1) relationships between SF-36 subscale and PCS/MCS scores, (2) relationships between PCS and MCS scores, and (3) their implications for interpreting research findings. Simulation analyses were conducted to illustrate the contributions of various aspects of the scoring algorithm to potential discrepancies between subscale profile and summary component scores. Using the Swedish SF-36 normative database, correlation and regression analyses were performed to estimate the relationship between the two components, as well as the relative contributions of the subscales to the components. Discrepancies between subscale profile and component scores were identified and explained. Significant correlations (r = −0.74, −0.67) were found between PCS and MCS scores at their respective upper scoring intervals, indicating that the components are not independent. Regression analyses revealed that in these ranges PCS primarily measures aspects of mental health (57% of variance) and MCS measures physical health (65% of variance). Implications of the findings were discussed. It was concluded that the current PCS/MCS scoring procedure inaccurately summarizes subscale profile scores and should therefore be revised. Until then, component scores should be interpreted with caution and only in combination with profile scores. This revised version was published online in June 2006 with corrections to the Cover Date.     

Do the SF-36 and WHOQOL-BREF Measure the Same Constructs? Evidence from the Taiwan Population*

Background: The SF-36 and WHOQOL-BREF are available for international use, but it is not clear if they measure the same constructs. We compared the psychometric properties and factor structures of these two instruments. Methods: Data were collected from a national representative sample (n=11,440) in the 2001 Taiwan National Health Interview Survey, which included Taiwan versions of the SF-36 and WHOQOL-BREF. We used Cronbach’s alpha coefficient to estimate scale reliability. We conducted exploratory factor analysis to determine factor structure of the scales, and applied multitrait analysis to evaluate convergent and discriminant validity. We used standardized effect size to compare known-groups validity for health-related variables (including chronic conditions and health care utilization) and self-reported overall quality of life. Structural equation modeling was used to analyze relationships among the two SF-36 component scales (PCS and MCS) and the four WHOQOL subscales (physical, psychological, social relations, and environmental). Results: Cronbach’s alpha coefficients were acceptable (⩾0.7) for all subscales of both instruments. The factor analysis yielded two unique factors: one for the 8 SF-36 subscales and a second for the 4 WHOQOL subscales. Pearson correlations were weak (<0.3) among subscales of both instruments. Correlations for subscales hypothesized to measure similar constructs differed little from those measuring heterogeneous subscales. Effect sizes suggested greater discrimination by the SF-36 for health status and services utilization known groups, but greater discrimination by the WHOQOL for QOL-defined groups. Structural equation modeling suggested that the SF-36 PCS and MCS were weakly associated with WHOQOL. Conclusions: In this Taiwan population sample, the SF-36 and WHOQOL-BREF appear to measure different constructs: the SF-36 measures health-related QOL, while the WHOQOL-BREF measures global QOL. Clinicians and researchers should carefully define their research questions related to patient-reported outcomes before selecting which instrument to use. * Presented in part at (1) 11th Annual Conference of the International Society for Quality of Life Research. Hong Kong, China, 2004. (2) 2004 Quality of Life Symposium – Conceptualization and Measurement issues in QOL. Tai-Chuan, Taiwan, 2004     

A comparison of SF-36 summary measures of physical and mental health for women across the life course

Background

Physical and mental component summary scores (PCS and MCS, respectively) are often used to summarise SF-36 quality of life subscales. This paper investigates PCS and MCS across the life course and compares the trajectories obtained from two different methods of calculation.

Methods

The Australian Longitudinal Study on Women’s Health is a population-based study with three cohorts of women and SF-36 surveys taken at multiple time points. Scoring coefficients for each component score were determined using factor analysis with uncorrelated (orthogonal) and correlated (oblique) rotation at the baseline survey, which were then used to compute correlated and uncorrelated PCS and MCS scores at each survey (scaled to have mean of 50 and standard deviation of 10 at baseline).

Results

For both methods, PCS declined progressively across the lifespan, while MCS rose in young and mid-age women to a peak and subsequently declined in later life. Differences were apparent between correlated and uncorrelated scores, most notably for MCS in the older cohort, where correlated MCS reached 54.6 but still less than uncorrelated MCS, with a random effects model indicating 1.63 (95 % confidence intervals 1.58–1.67) units difference; it then declined to a score of 51.2 by the last survey and the difference widened to 3.44 (3.38–3.50) units compared with the uncorrelated MCS.

Conclusions

PCS and MCS have distinct trajectories through life, with differences in results from correlated and uncorrelated component summary scores. The divergence is most notable with MCS, especially for older women, suggesting that correlated MCS and PCS should be used when examining change in health over time in this age group.     

Interpreting SF&-36 summary health measures: A response

In response to questions raised about the “accuracy” of SF-36 physical (PCS) and mental (MCS) component summary scores, particularly extremely high and low scores, we briefly comment on: how they were developed, how they are scored, the factor content of the eight SF-36 subscales, cross-tabulations between item-level responses and extreme summary scores, and published and new tests of their empirical validity. Published cross-tabulations between SF-36 items and PCS and MCS scores, reanalyses of public datasets (N = 5919), and preliminary results from the Medicare Health Outcomes Survey (HOS) (N = 172,314) yielded little or no evidence in support of Taft's hypothesis that extreme scores are an invalid artifact of some negative scoring weights. For example, in the HOS, those (N = 432) with “unexpected” PCS scores worse than 20 (which, according to Taft, indicate better mental health rather than worse physical health) were about 25% more likely to die within two years, in comparison with those scoring in the next highest (21– 30)␣category. In this test and in all other empirical tests, results of predictions supported the validity of extreme PCS and MCS scores. We recommend against the interpretation of average differences smaller than one point in studies that seek to detect “false” measurement and we again repeat our 7-year-old recommendation that results based on summary measures should be thoroughly compared with the SF-36 profile before drawing conclusions. To facilitate such comparisons, scoring utilities and user-friendly graphs for SF-36 profiles and physical and mental summary scores (both orthogonal and oblique scoring algorithms) have been made available on the Internet at www.sf-36.com/test. This revised version was published online in June 2006 with corrections to the Cover Date.     

The SF-36 summary scales: problems and solutions

Summary To determine the accuracy of the SF-36 summary mental and physical health scales in reflecting their underlying subscales using the traditional method of scoring based on factor coefficients derived through principle components analysis and orthogonal rotation. A representative Australian population survey containing the SF-36 was used to obtain factor coefficients from principle components analysis and orthogonal rotation for scoring the physical component summary (PCS) and the mental component summary (MCS) of the SF-36 in the traditional way. In addition two other methods were used to produce coefficients. The first method used maximum likelihood extraction and oblique rotation. The second method fit a structural equation model to the data in a confirmatory factor analysis. The coefficients derived by each of the methods were applied to the data of a second representative population survey. This survey also provided data on physical and mental health status which allowed comparison of the summary scores and underlying subscales according to various health states. Neither of the scoring methods based on the exploratory factor analyses methods (orthogonal and oblique) produced summary scale scores, by age group, that adequately reflected the underlying subscales. When coefficients derived using structural equation modeling were fit to the data in a confirmatory factor analysis the MCS and PCS accurately reflected their underlying subscale scores. They also produced MCS and PCS scores for the various health states as would be expected from the underlying subscales. The traditional methods of scoring the SF-36 summary scales produce results that would not be expected from the underlying subscales. The problem was only corrected by fitting a structural equation model to the data in a confirmatory factor analysis. The results advise caution in the use of the SF-36 summary scales and suggests that alternative methods of developing factor coefficients need to be employed in studies using the SF-36 summary scales.

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Die Summenskalen des SF-36: Probleme und Lösungen

Zusammenfassung Die zur Berechnung der Summenskalen der physischen (PCS) und psychischen Dimension (MCS) des SF-36 nötigen Faktorwerte wurden in einer repräsentativen australischen Bevölkerungsbefragung nach dem Standardverfahren durch eine Hauptkomponentenanalyse mit orthogonaler Rotation ermittelt. Zusätzlich wurden zwei weitere Verfahren zur Berechnung der Koeffizienten angewendet: eine Faktorenextraktion nach Maximum-Likelihood mit anschliessender schiefwinkliger Rotation und die anpassung eines Strukturgleichungsmodells an die Daten in einer konfirmatorischen Faktoranalyse. Die so berechneten Faktorwerte wurden in einer zweiten repräsentativen Bevölkerungsbefragung verwendet. In dieser Erhebung wurden zusätzlich verschiedene Masse zur physischen und psychischen Gesundheit erhoben, die einen Vergleich der Summenskalen und der zugrunde liegenden Subskalen in Gruppen mit unterschiedlichem Gesundheitsstatus erlaubt. Keine der auf Basis explorativer Faktoranalysen (orthogonale oder schiefwinklige Rotation) berechneten Summenskalen bildet die zugrunde liegenden Subskalen in verschiedenen Altersgruppen adäquat ab. Werden die Faktorwerte in einer konfirmatorischen Faktoranalyse mit einem Strukturgleichungs-modell ermittelt, entsprechen die Summenskalen MCS und PCS den zugrunde liegenden Subskalen besser. Auch die aufgrund der Subskalen erwarteten Unterschiede in Gruppen mit unterschiedlichem Gesund-heitsstatus konnten reproduziert werden. Die Standardverfahren zur Berechnung der Summenskalen des SF-36 zeigen Ergebnisse, die aufgrund der zugrunde liegenden Subskalen nicht zu erwarten sind. Eine bessere Entsprechung konnte in einer konfirmatorischen Faktoranalyse durch die Anpassung eines Strukturgleichungsmodell an die Daten erzielt werden. Die Ergebnisse weisen darauf hin, dass die Interpretation der Summenskalen des SF-36 mit Vorsicht zu erfolgen hat und dass alternative Verfahren zur Berechnung der Faktorwerte angewendet werden sollten.

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Les scores synthétiques du SF-36: Problèmes et solutions

Résumé Pour déterminer si les échelles synthétiques de santé mentale et physique du SF-36 reflètént correctement les huit échelles sous-jacentes lorsqu'on utilise la méthode traditionnelle de scorage basée sur des coefficients dérivés de l'analyse des composantes principales suivie d'une rotation orthogonale. Use enquête représentative de la population australienne utilisant le SF-36 a été mise à profit pour calculer les scores synthétiques physique (PCS) et mental (MCS) du SF-36 selon la méthode traditionnelle, à partir des coefficients issus d'une analyse factorielle exploratoire de composantes principales suivie de rotation orthogonale des huit scores SF-36 initiaux. De plus, deux autres méthodes furent utilisées pour générer les coefficients. La première méthode utilisait la maximisation de la vraisemblance et la rotation oblique. La seconde méthode appliquait un modele d'équations structurale aux données dans une analyse factorielle confirmatoire. Les coefficients dérivés dans chacune des méthodes furent appliqués aux données d'une deuxième. enquête représentative de population. Cette enquête fournit également des données sur la santé physique et mentale qui permettent de comparer les scores synthétiques aux échelles sous-jacentes selon différents états de santé. Aucune des méthodes de scorage basée sur les analyses factorielles exploratoires (orthogonale et oblique) n'a produit des scores synthétiques, par groupes d'âge qui reflétait de façon adéquate ces échelles sous-jacentes. Lorsque les coefficients dérivés des équations structurales furent appliqués aux données dans une analyse factorielle confirmatoire, le MCS et PCS reflétaient correctement les échelles sousjacentes. Ils produisirent aussi des scores de MCS et PCS pour les différents états de santé que l'on aurait pu attendre avec les échelles sous-jacentes. Les méthodes traditionnelles de scorage des échelles synthétiques du SF-36 produisent des résultats qui n'auraient pas été attendus avec les échelles sous-jacentes. Ce problème peut être corrigé par l'application d'un modèle d'équations structurale aux données dans une analyse factorielle confirmatoire. Ces résultats suggèrent que les échellès synthétiques du SF-36 devraient utilisés avec prudence et que les méthodes alternatives pour développer les coefficients factoriels devraient utilisés dans ce genre d'étude.

     

Psychometric evaluation of a Chinese (Taiwanese) version of the SF-36 health survey amongst middle-aged women from a rural community

To test the psychometric properties of the Chinese (Taiwanese) version of the short form 36 health survey (SF-36), 1439 women, aged 40–54 years and living in Kinmen (a Taiwanese island reflecting a predominantly rural community) were recruited to participate in this survey. The rate of unavailable data points for the 36 tested items remained consistently low, and item-discriminate validity was high (95%) for all subscales. Cronbach's α coefficient remained above the 0.70 threshold criterion for all scales except for social functioning and bodily pain. Principal components analysis supported the two major dimensions of health, physical and mental, in the internal structure of the SF-36 scales, although the dimensions did not match the hypothesized association very well. Poorer health profiles were associated with physical and mental conditions. The mental health subscores in the SF-36 test correlated highly with the associated hospital anxiety and depression score (Spearman rank correlation coefficient = −0.62). In conclusion, the reliability and validity tests performed on the data collected support the cross-cultural application of the Chinese (Taiwanese) version of the SF-36 test. This revised version was published online in June 2006 with corrections to the Cover Date.     

Outcome measurement in functional somatic syndromes: SF-36 summary scores and some scales were not valid

Objective

This study aimed to test the validity of the 36-item Short-Form Health Survey (SF-36) scales and summaries in patients with severe functional somatic syndromes (FSS), such as fibromyalgia and irritable bowel syndrome.

Study Design and Setting

One hundred twenty patients with severe FSS enrolled in a randomized controlled trial filled in the SF-36 questionnaire. We tested for data quality, central scaling assumptions, and agreement with the conceptual model.

Results

Most SF-36 scales were found to be valid; however, three scales (role physical, role emotional, and general health) did not satisfy predefined criteria for construct validity, internal consistency, or targeting to the sample. The correlations between SF-36 scales differed considerably from those reported in the general population. As a consequence, the SF-36 summaries, physical component summary (PCS) and mental component summary (MCS), did not accurately reflect their underlying scales and were negatively correlated (r = −0.46, 95% CI [−0.60 to −0.31]).

Conclusion

Although the SF-36 is a valuable instrument to assess perceived health in patients with severe FSS, there are problems with some of the scales and with the scoring procedure of the summaries. The SF-36 PCS may, therefore, not accurately measure the physical health status of these patients. Alternative summary measures are needed.     

Dimensionality and Summary Measures of the SF-36 v1.6: Comparison of Scale- and Item-Based Approach Across ECRHS II Adults Population

ObjectivesThe objective of this study was twofold: 1) to confirm the hypothetical eight scales and two-component summaries of the questionnaire Short Form 36 Health Survey (SF-36), and 2) to evaluate the performance of two alternative measures to the original physical component summary (PCS) and mental component summary (MCS).MethodsWe performed principal component analysis (PCA) based on 35 items, after optimal scaling via multiple correspondence analysis (MCA), and subsequently on eight scales, after standard summative scoring. Item-based summary measures were planned. Data from the European Community Respiratory Health Survey II follow-up of 8854 subjects from 25 centers were analyzed to cross-validate the original and the novel PCS and MCS.ResultsOverall, the scale- and item-based comparison indicated that the SF-36 scales and summaries meet the supposed dimensionality. However, vitality, social functioning, and general health items did not fit data optimally. The novel measures, derived a posteriori by unit-rule from an oblique (correlated) MCA/PCA solution, are simple item sums or weighted scale sums where the weights are the raw scale ranges. These item-based scores yielded consistent scale-summary results for outliers profiles, with an expected known-group differences validity.ConclusionsWe were able to confirm the hypothesized dimensionality of eight scales and two summaries of the SF-36. The alternative scoring reaches at least the same required standards of the original scoring. In addition, it can reduce the item-scale inconsistencies without loss of predictive validity.     

Results from several population studies show that recommended scoring methods of the SF-36 and the SF-12 may lead to incorrect conclusions and subsequent health decisions

Purpose

To compare the measurement properties of the physical component summary (PCS) and mental component summary (MCS) scores of the SF-36 and SF-12 based on the traditional orthogonal scoring algorithms with the performance of the PCS and MCS scored based on structural equation model coefficients from a correlated model.

Methods

This study used three large-scale representative population studies to compare the measurement properties of the PCS and MCS scores of the SF-36 and SF-12 with the performance of the PCS and MCS scores based on structural equation models producing coefficients from a correlated model. We assessed the relationships of these scores with selected important mental health measures and chronic conditions from three representative Australian population studies that address clinical conditions of high prevalence and health service importance.

Results

Structural equation model scoring methods produced summary scores with higher correlations than the recommended orthogonal methods across a range of disease and health conditions. The problem experienced in using the orthogonal methods is that negative scoring coefficients are applied to negative z-scores for sub-scales, inflating the resulting summary scores. Effect sizes over a half of a standard deviation were common.

Conclusions

If health policy or investment decisions are made based on the results of studies employing the recommended orthogonal scoring methods then the expected outcome of such decisions or investments may not be achieved.     

Development and testing of the UK SF-12 (short form health survey)

OBJECTIVES: The 36 item short form health survey (SF-36) has proved to be of use in a variety of settings where a short generic health measure of patient-assessed outcome is required. This measure can provide an eight dimension profile of health status, and two summary scores assessing physical function and mental well-being. The developers of the SF-36 in America have developed algorithms to yield the two summary component scores in a questionnaire containing only one-third of the original 36 items, the SF-12. This paper documents the construction of the UK SF-12 summary measures from a large-scale dataset from the UK in which the SF-36, together with other questions on health and lifestyles, was sent to randomly selected members of the population. Using these data we attempt here to replicate the findings of the SF-36 developers in the UK setting, and then to assess the use of SF-12 summary scores in a variety of clinical conditions. METHODS: Factor analytical methods were used to derive the weights used to construct the physical and mental component scales from the SF-36. Regression methods were used to weight the 12 items recommended by the developers to construct the SF-12 physical and mental component scores. This analysis was undertaken on a large community sample (n = 9332), and then the results of the SF-36 and SF-12 were compared across diverse patient groups (Parkinson's disease, congestive heart failure, sleep apnoea, benign prostatic hypertrophy). RESULTS: Factor analysis of the SF-36 produced a two factor solution. The factor loadings were used to weight the physical component summary score (PCS-36) and mental component summary score (MCS-36). Results gained from the use of these measures were compared with results gained from the PCS-12 and MCS-12, and were found to be highly correlated (PCS: rho = 0.94, p < 0.001; MCS: rho = 0.96, p < 0.001), and produce remarkably similar results, both in the community sample and across a variety of patient groups. CONCLUSIONS: The SF-12 is able to produce the two summary scales originally developed from the SF-36 with considerable accuracy and yet with far less respondent burden. Consequently, the SF-12 may be an instrument of choice where a short generic measure providing summary information on physical and mental health status is required.     

Validity and Reliability of the Fear-Avoidance Beliefs Questionnaire (FABQ) in Workers with Upper Extremity Injuries

Introduction Fear Avoidance Beliefs (FAB) have been associated with increased pain, dysfunction and difficulty returning to work in Upper Extremity (UE) injures. The FABQ is used to assess FAB, but its measurement properties have not been established in UE. The purpose of this study is to evaluate the reliability and validity of the FABQ to screen UE compensated injured workers for FAB. Methods Consenting workers attending a specialty clinic completed a modified FABQ, QuickDASH (Disability), SPADI Pain Score and von Korff Chronic Pain Grade (Pain), SF-36v2 (General Health), and Work Instability Scale (Job Instability). A sub-sample of workers (n = 48) completed the FABQ 2 weeks later for test–retest reliability. Results 187 workers; 54.0% male; mean age 45.2 (sd 9.68); 56% were currently working. Mean subscale scores (FABQ-Work [FABQ-W]/FABQ-Physical Activity [FABQ-PA]) were 35/42 and 20/24. Ceiling effects (23%/38%) existed in both subscales. Cronbach’s alphas were 0.75/0.78. Test–retest analysis (ICC(2,1)) was lower than desired (0.52/0.59). Construct validation was supported by a moderate correlation between FABQ-W/FABQ-PA and QuickDASH Work Module (0.51/0.42) and WIS (0.46/0.38) in those currently working. Low correlations were found between the subscales measures of pain (SPADI: 0.24/0.23; Chronic Pain Grade: 0.25/0.25), and SF-36 MCS (−0.25/−0.30). Conclusions Although FAB is an important concept to measure in compensated UE injured workers, the FABQ had limitations in this population as there was a high ceiling effect, and lower than desired reliability for individual discrimination. A priori hypotheses around construct validity were rejected for 16/22 concepts tested.     

Predicting an SF-6D Preference-Based Score Using MCS and PCS Scores from the SF-12 or SF-36

Background:  The SF-6D preference-based scoring system was developed several years after the SF-12 and SF-36 instruments. A method to predict SF-6D scores from information in previous reports would facilitate backwards comparisons and the use of these reports in cost-effectiveness analyses.
Methods:  This report uses data from the 2001–2003 Medical Expenditures Panel Survey (MEPS), the Beaver Dam Health Outcomes Survey, and the National Health Measurement Study. SF-6D scores were modeled using age, sex, mental component summary (MCS) score, and physical component summary (PCS) score from the 2002 MEPS. The resulting SF-6D prediction equation was tested with the other datasets for groups of different sizes and groups stratified by age, MCS score, PCS score, sum of MCS and PCS scores, and SF-6D score.
Results:  The equation can be used to predict an average SF-6D score using average age, proportion female, average MCS score, and average PCS score. Mean differences between actual and predicted average SF-6D scores in out-of-sample tests was −0.001 (SF-12 version 1), −0.013 (SF-12 version 2), −0.007 (SF-36 version 1), and −0.010 (SF-36 version 2). Ninety-five percent credible intervals around these point estimates range from ±0.045 for groups with 10 subjects to ±0.008 for groups with more than 300 subjects. These results were consistent for a wide range of ages, MCS scores, PCS scores, sum of MCS and PCS scores, and SF-6D scores. SF-6D scores from the SF-36 and SF-12 from the same data set were found to be substantially different.
Conclusions:  Simple equation predicts an average SF-6D preference-based score from widely published information.     

Psychometric evaluation of the SF-36 health survey in Medicare managed care

Data quality and scoring assumptions for the SF-36 Health Survey were evaluated among the elderly and disabled, using 1998 Cohort I baseline Medicare HOS data (n=177,714). Missing data rates were low, and scoring assumptions were met. Internal consistency reliability was 0.83 to 0.93 for the eight scales and 0.94 and 0.89, respectively, for the physical (PCS) and mental (MCS) component summary measures. Results declined with increased risk factors (e.g., older age, more chronic conditions), but were well above accepted standards for all subgroups. These findings support using standard algorithms for scoring the SF-36 in the HOS and subgroup analyses of HOS data.     

Reliability and validity of the SF-12v2 in the medical expenditure panel survey

Objective  Evaluate the reliability and validity of the Medical Outcomes Study Short-Form version 2 (SF-12v2) in the 2003–2004 Medical Expenditure Panel Survey (MEPS). Research design  Data were collected in the self-administered mail-out questionnaire and face-to-face interviews of the MEPS (n = 20,661). Internal consistency and test–retest reliability and construct, discriminate, predictive and concurrent validity were tested. The EQ-5D, perceived health and mental health questions were used to test construct and discriminate validity. Self-reported work, physical and cognitive limits tested predictive validity and number of chronic conditions assessed concurrent validity. Results  Both Mental Component Summary Scores (MCS) and Physical Component Summary Scores (PCS) were shown to have high internal consistency reliability (α > .80). PCS showed high test–retest reliability (ICC = .78) while MCS demonstrated moderate reliability (ICC = .60). PCS had high convergent validity for EQ-5D items (except self-care) and physical health status (r > .56). MCS demonstrated moderate convergent validity on EQ-5D and mental health items (r > .38). PCS distinguish between groups with different physical and work limitations. Similarly, MCS distinguished between groups with and without cognitive limitations. The MCS and PCS showed perfect dose response when variations in scores were examined by participant’s chronic condition status. Conclusions  Both component scores showed adequate reliability and validity with the 2003–2004 MEPS and should be suitable for use in a variety of proposes within this database.     

Responsiveness of the Adult Attention-Deficit/Hyperactivity Disorder Quality of Life Scale (AAQoL)

Aims This study examined responsiveness of the Adult Attention-Deficit/Hyperactivity Disorder Quality of Life Scale (AAQoL), which was developed to assess health-related quality of life (HRQL) among adults with attention-deficit/hyperactivity disorder (ADHD). Methods Adults with ADHD completed the AAQoL, Conners’ Adult ADHD Rating Scale (CAARS), SF-36, and Endicott Work Productivity Scale (EWPS) at baseline and week 8 of a randomized, placebo-controlled trial of atomoxetine. Clinicians rated symptom severity and improvement (CGI-ADHD-S, CGI-ADHD-I). Responsiveness was examined through effect sizes and association with change in the measures listed previously (Spearman correlations, GLMs). Results Analyses included 328 patients (58.8% male; mean age = 36.9 years). All AAQoL scales reflected significant improvement from baseline to week 8 (P < 0.0001). AAQoL change scores were significantly correlated with change in the CGI-ADHD-S (r = −0.37 to −0.50), EWPS (r = −0.43 to −0.63), and CAARS (r = −0.35 to −0.62) (all P < 0.001). AAQoL change scores significantly discriminated among patients with various levels of symptom improvement. AAQoL effect sizes (−0.67 to −1.11) were larger than effect sizes for the SF-36 (0.15 to −0.39). Conclusions The AAQoL was responsive to change in symptoms of ADHD, and it appears to be a useful outcome measure for treatments of ADHD in adults.