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A Review of the Development and Application of Generic Preference-Based Instruments with the Older Population

  • Jenny Cleland
  • Claire Hutchinson
  • Jyoti Khadka
  • Rachel Milte
  • Julie RatcliffeEmail author
Review Article

Abstract

Older people (aged 65 years and over) are the fastest growing age cohort in the majority of developed countries, and the proportion of individuals defined as the oldest old (aged 80 years and over) living with physical frailty and cognitive impairment is rising. These population changes put increasing pressure on health and aged care services, thus it is important to assess the cost effectiveness of interventions targeted for older people across health and aged care sectors to identify interventions with the strongest capacity to enhance older peoples’ quality of life and provide value for money. Cost-utility analysis (CUA) is a form of economic evaluation that typically uses preference-based instruments to measure and value health-related quality of life for the calculation of quality-adjusted life-years (QALYS) to enable comparisons of the cost effectiveness of different interventions. A variety of generic preference-based instruments have been used to measure older people’s quality of life, including the Adult Social Care Outcomes Toolkit (ASCOT); Health Utility Index Mark 2 (HUI2); Health Utility Index Mark 3 (HUI3); Short-Form-6 Dimensions (SF-6D); Assessment of Quality of Life-6 dimensions (AQoL-6D); Assessment of Quality of Life-8 dimensions (AQoL-8D); Quality of Wellbeing Scale-Self-Administered (QWB-SA); 15 Dimensions (15D); EuroQol-5 dimensions (EQ-5D); and an older person specific preference-based instrument—the Investigating Choice Experiments Capability Measure for older people (ICECAP-O). This article reviews the development and application of these instruments within the older population and discusses the issues surrounding their use with this population. Areas for further research relating to the development and application of generic preference-based instruments with populations of older people are also highlighted.

Notes

Author Contributions

JC contributed to the design of the study, extracted, interpreted and analysed the data for review, led the drafting of the manuscript, and approved the final version. CH, JK and RM contributed to the conception and design of the study, analysis of the data, critical revision of the draft manuscript, and approved the final version. JR substantially contributed to the conception and design of the study, interpretation of data for the review, drafting of the manuscript, critical revision of the draft manuscript, and approved the final version.

Compliance with Ethical Standards

Funding

Jenny Cleland is supported by a PhD scholarship awarded from the College of Nursing and Health Sciences, Flinders University. This study was supported in part by funding provided by the Australian Research Council Linkage Grant Scheme (Grant no. LP170100664).

Conflict of interest

Jenny Cleland, Claire Hutchinson, Jyoti Khadka, Rachel Milte and Julie Ratcliffe have no conflicts of interest to declare.

Supplementary material

40258_2019_512_MOESM1_ESM.docx (28 kb)
Supplementary material 1 (DOCX 29 kb)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Health and Social Care Economics Group, College of Nursing and Health SciencesFlinders UniversityAdelaideAustralia
  2. 2.Healthy Ageing Research Consortium, Registry of Older South Australians (ROSA)South Australian Health and Medical Research Institute (SAHMRI)AdelaideAustralia

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