BACKGROUND: Those responsible for planning and commissioning health services require a method of assessing the benefits and costs of interventions. Quality-adjusted life years, based on health-related quality of life (HRQoL) estimates, can be used as part of this commissioning process. The purpose of this study was to generate nationally representative HRQoL estimates for cardiovascular disease (heart attack, angina and stroke) and predisposing conditions (diabetes, hypertension and obesity) and assess differential impacts by socio-economic position using data from the Health Survey for England. METHODS: Regression modelling was used to estimate the relationship of EQ-5D index scores with each condition independently and differentially by socio-economic position. RESULTS: Of the cardiovascular conditions/risk factors considered, having doctor-diagnosed stroke, heart attack or angina were each associated with the greatest decreases in EQ-5D. With the exception of heart attack, the reduction in EQ-5D associated with the condition/risk factor was greater for those occupying lower socio-economic positions, statistically significantly so for obesity, hypertension and diabetes. CONCLUSION: The estimates calculated provide nationally representative baseline data for England, which can be used for modelling the impact of interventions on HRQoL. They illustrate the importance of socio-economic circumstances for the association between a given condition/risk factor and HRQoL.
BACKGROUND: Those responsible for planning and commissioning health services require a method of assessing the benefits and costs of interventions. Quality-adjusted life years, based on health-related quality of life (HRQoL) estimates, can be used as part of this commissioning process. The purpose of this study was to generate nationally representative HRQoL estimates for cardiovascular disease (heart attack, angina and stroke) and predisposing conditions (diabetes, hypertension and obesity) and assess differential impacts by socio-economic position using data from the Health Survey for England. METHODS: Regression modelling was used to estimate the relationship of EQ-5D index scores with each condition independently and differentially by socio-economic position. RESULTS: Of the cardiovascular conditions/risk factors considered, having doctor-diagnosed stroke, heart attack or angina were each associated with the greatest decreases in EQ-5D. With the exception of heart attack, the reduction in EQ-5D associated with the condition/risk factor was greater for those occupying lower socio-economic positions, statistically significantly so for obesity, hypertension and diabetes. CONCLUSION: The estimates calculated provide nationally representative baseline data for England, which can be used for modelling the impact of interventions on HRQoL. They illustrate the importance of socio-economic circumstances for the association between a given condition/risk factor and HRQoL.
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