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O2‐02‐03: Development of DEMQOL‐U and DEMQOL‐Proxy‐U: Generation of preference‐based indices from DEMQOL and DEMQOL‐Proxy for use in economic evaluation
Author(s) -
Banerjee Sube
Publication year - 2012
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2012.05.630
Subject(s) - proxy (statistics) , eq 5d , valuation (finance) , population , econometrics , statistics , medicine , mathematics , economics , environmental health , finance , disease , pathology , health related quality of life
Background:What is needed to enable cost-effectiveness evaluation in dementia? The currently available brief generic measures are problematic due to the error inherent in their use. We generated a preference-based single index for DEMQOL and DEMQOL-Proxy for use in economic evaluation. Objectives were: 1) to derive health state classification systems amenable to valuation from DEMQOL and DEMQOL-Proxy, 2) to generate utility values for every health state defined by the health state classification systems developed, and 3) to examinewhether utility values elicited from the general population differ from utility values elicited from patients and carers for dementia.Methods: 1) Derivation of the health state classification system – using item analysis and five separate Rasch models on DEMQOL (n 1⁄4 1189) and DEMQOL-Proxy (n 1⁄4 1223) data; 2) General population valuation survey and modelling to produce values for every health state – a general population survey with 600 subjects; 3) Patient/carer valuation survey – 71 people with dementia and 71 carers. Results: The factor structures were robust. Using stringent criteria we generated five items for DEMQOL and four for DEMQOL-Proxy. These new instruments were named DEMQOL-U and DEMQOL-Proxy -U. These descriptive systems were amenable to valuation. The population data were subject to multiple regression, from this two preference-based algorithms were estimated, one each for DEMQOL-U and the DEMQOL-Proxy-U, for valuing all dementia related states defined by these classifications. Themodels fitted to the data performed favourably compared to similar models estimated for other preference-based measures in terms of fit, prediction and the consistency of the coefficients. Conclusions: This means we can generate health state utility values from any dataset where one or both of DEMQOL and DEMQOL-Proxy have been completed. This is the first such dementia-specific system to have been developed and the first time that the values of the general population have been compared to patients and carers in this way. The values obtained from people with dementia and carers were systematically different to those from the general public, who tended to undervalue the impact of dementia. The population used to produce dementia health state utility values could impact upon the results of cost-effectiveness analysis and potentially affect resource allocation decisions.