Relationship between three palliative care outcome scales
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Relationship between three palliative care outcome scales Irene J Higginson* and Nora Donaldson Address: Department of Palliative Care and Policy, King's College London, Weston Education Centre, Cutcombe Road, London SE5 9RJ, UK Email: Irene J Higginson* - [email protected]; Nora Donaldson - [email protected] * Corresponding author
Published: 29 November 2004 Health and Quality of Life Outcomes 2004, 2:68
doi:10.1186/1477-7525-2-68
Received: 06 October 2004 Accepted: 29 November 2004
This article is available from: http://www.hqlo.com/content/2/1/68 © 2004 Higginson and Donaldson; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract Background: Various scales have been used to assess palliative outcomes. But measurement can still be problematic and core components of measures have not been identified. This study aimed to determine the relationships between, and factorial structure of, three widely used scales among advanced cancer patients. Methods: Patients were recruited who received home or hospital palliative care services in the south of England. Hope, quality of life and palliative outcomes were assessed by patients in face to face interviews, using three previously established scales – a generic measure (EQoL), a palliative care specific measure (POS) and a measure of hope (Herth Hope Index). Analysis comprised: exploratory factor analysis of each individual scale, and all scales combined, and confirmatory factor analysis for model building and validation. Results: Of 171 patients identified, 140 (81%) consented and completed first interviews; mean age was 71 years, 54% were women, 132 had cancer. In exploratory analysis of individual means, three out of the five factors in the EQoL explained 75% of its variability, four out of the 10 factors in POS explained 63% of its variability, and in the Hope Index, nine out of the 12 items explained 69% of its variability. When exploring the relative factorial structure of all three scales, five factors explained 56% of total combined variability. Confirmatory analysis reduced this to a model with four factors – self-sufficiency, positivity, symptoms and spiritual. Removal of the spiritual factor left a model with an improved goodness of fit and a measure with 11 items. Conclusion: We identified three factors which are important outcomes and would be simple to measure in clinical practice and research.
Background Measurement of the effect of illness and its treatment on patients is now an accepted part of clinical trial design [1]. Such measurement is also proposed as an aid to improve clinical practice and decision making [2,3]. However, as the illness becomes more advanced the value of many
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