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.JEL classification: I10

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.JEL classification: I10

SummaryAs the number of preference-based instruments grows, it becomes increasingly important to compare different preference-based measures of health in order to inform an important debate on the choice of instrument. This paper presents a comparison of two of them, the EQ-5D and the SF-6D (recently developed from the SF-36) across seven patient/population groups (chronic obstructive airways disease, osteoarthritis, irritable bowel syndrome, lower back pain, leg ulcers, post menopausal women and elderly). The mean SF-6D index value was found to exceed the EQ-5D by 0.045 and the intraclass correlation coefficient between them was 0.51. Whilst this convergence lends some support for the validity of these measures, the modest difference at the aggregate level masks more significant differences in agreement across the patient groups and over severity of illness, with the SF-6D having a smaller range and lower variance in values. There is evidence for floor effects in the SF-6D and ceiling effects in the EQ-5D. These discrepancies arise from differences in their health state classifications and the methods used to value them. Further research is required to fully understand the respective roles of the descriptive systems and the valuation methods and to examine the implications for estimates of the impact of health care interventions.

BackgroundPatient complaints have been identified as a valuable resource for monitoring and improving patient safety. This article critically reviews the literature on patient complaints, and synthesises the research findings to develop a coding taxonomy for analysing patient complaints.MethodsThe PubMed, Science Direct and Medline databases were systematically investigated to identify patient complaint research studies. Publications were included if they reported primary quantitative data on the content of patient-initiated complaints. Data were extracted and synthesised on (1) basic study characteristics; (2) methodological details; and (3) the issues patients complained about.Results59 studies, reporting 88 069 patient complaints, were included. Patient complaint coding methodologies varied considerably (eg, in attributing single or multiple causes to complaints). In total, 113 551 issues were found to underlie the patient complaints. These were analysed using 205 different analytical codes which when combined represented 29 subcategories of complaint issue. The most common issues complained about were ‘treatment’ (15.6%) and ‘communication’ (13.7%). To develop a patient complaint coding taxonomy, the subcategories were thematically grouped into seven categories, and then three conceptually distinct domains. The first domain related to complaints on the safety and quality of clinical care (representing 33.7% of complaint issues), the second to the management of healthcare organisations (35.1%) and the third to problems in healthcare staff–patient relationships (29.1%).ConclusionsRigorous analyses of patient complaints will help to identify problems in patient safety. To achieve this, it is necessary to standardise how patient complaints are analysed and interpreted. Through synthesising data from 59 patient complaint studies, we propose a coding taxonomy for supporting future research and practice in the analysis of patient complaint data.

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The aim of this study was to examine the effect of playing formation on high-intensity running and technical performance during elite soccer matches. Twenty English FA Premier League games were analysed using a multiple-camera computerized tracking system (n = 153 players). Overall ball possession did not differ (P < 0.05) between 4-4-2, 4-3-3 and 4-5-1 formations (50%, s = 7 vs. 49%, s = 8 vs. 44%, s = 6). No differences were observed in high-intensity running between 4-4-2, 4-3-3 and 4-5-1 formations. Compared with 4-4-2 and 4-3-3 formations, players in a 4-5-1 formation performed less very high-intensity running when their team was in possession (312 m, s = 196 vs. 433 m, s = 261 vs. 410 m, s = 270; P 5 0.05) but more when their team was not in possession (547 m, s = 217 vs. 461 m, s = 156 vs. 459 m, s = 169; P < 0.05). Attackers in a 4-3-3 performed ~30% more (P < 0.05) high-intensity running than attackers in 4-4-2 and 4-5-1 formations. However, the fraction of successful passes was highest in a 4-4-2 (P < 0.05) compared with 4-3-3 and 4-5-1 formations. The results suggest that playing formation does not influence the overall activity profiles of players, except for attackers, but impacts on very high-intensity running activity with and without ball possession and some technical elements of performance.

Background: Mapping from health status measures onto generic preference-based measures is becoming a common solution when health state utility values are not directly available for economic evaluation. However the accuracy and reliability of the models employed is largely untested, and there is little evidence of their suitability in patient datasets. This paper examines whether mapping approaches are reliable and accurate in terms of their predictions for a large and varied UK patient dataset.

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