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Introduction: Quality of life is included in the economic evaluation of health services by measuring the preference for health states, i.e. health state utilities. However, most intervention studies include a disease-specific, not a utility, instrument. Consequently, there has been increasing use of statistical mapping algorithms which permit utilities to be estimated from a disease-specific instrument. The present paper provides such algorithms between the MacNew Heart Disease Quality of Life Questionnaire (MacNew) instrument and six multi-attribute utility (MAU) instruments, the Euroqol (EQ-5D), the Short Form 6D (SF-6D), the Health Utilities Index (HUI) 3, the Quality of Wellbeing (QWB), the 15D (15 Dimension) and the Assessment of Quality of Life (AQoL-8D). Methods: Heart disease patients and members of the healthy public were recruited from six countries. Non-parametric rank tests were used to compare subgroup utilities and MacNew scores. Mapping algorithms were estimated using three separate statistical techniques. Results: Mapping algorithms achieved a high degree of precision. Based on the mean absolute error and the intra class correlation the preferred mapping is MacNew into SF-6D or 15D. Using the R squared statistic the preferred mapping is MacNew into AQoL-8D. Implications for research: The algorithms reported in this paper enable MacNew data to be mapped into utilities predicted from any of six instruments. This permits studies which have included the MacNew to be used in cost utility analyses which, in turn, allows the comparison of services with interventions across the health system. [ABSTRACT FROM AUTHOR]

The UK has long experienced a shortage of social workers and has recruited internationally to meet demand. There have been few specific data quantifying the scale of social work mobility to the UK through which such experiences can be set in context. The analysis reported in this article uses data from October 2008, relating to registered social workers working in England. As part of a wider study of migrant social care workers in England, the article reports analysis of data records of over 7,000 non-UK social workers registered to work in England and compares their characteristics to UK-qualified social workers. These analyses are supplemented by analysis of more recent application and registration data from the General Social Care Council pertaining to social workers qualified within and outside the European Union during 2008 and 2009. The findings highlight several important observations in terms of non-UK-qualified social workers' profile as well as some possible trends in migration and variations in rates of qualification verification. Over half of all international social workers in England were trained in four countries: Australia, South Africa, India and the USA. Findings are contextualised with qualitative data obtained from the wider study and policy debates. [ABSTRACT FROM PUBLISHER]