Using health state utility values from the general population to approximate baselines in decision analytic models when condition specific data are not available
AbstractDecision analytic models in healthcare require baseline health related quality of life (HRQoL) data to accurately assess the benefits of interventions. The use of inappropriate baselines such as assuming the value of perfect health (EQ-5D = 1) for not having a condition may overestimate the benefits of some treatment and thus distort policy decisions informed by cost per QALY thresholds. The primary objective was to determine if data from the general population are appropriate for baseline health state utility values (HSUVs) when condition-specific data are not available. Methods: Data from four consecutive Health Surveys for England were pooled. Self-reported health status and EQ-5D data were extracted and used to generate mean HSUVs for cohorts with or without prevalent health conditions. These were compared with mean HSUVs from all respondents irrespective of health status. Results: Over 45% of respondents (n=41,174) reported at least one health condition and almost 20% reported at least two. Our results suggest that data from the general population could be used to approximate baseline HSUVs in some analyses but not all. In particular, HSUVs from the general population would not be an appropriate baseline for cohorts who have just one health condition. In these instances, if condition-specific data are not available, data from respondents who report they do not have a prevalent health condition may be more appropriate. Exploratory analyses suggest the decrement on HRQoL may not be constant across ages for all conditions and these relationships may be condition-specific. Additional research is required to validate our findings.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 29946.
Date of creation: 2010
Date of revision:
health state utility values; baseline; quality of life; EQ-5D; age-adjusted;
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