CV Disease Policy Model Using Socioeconomic Deprivation
CV Disease Policy Model Using Socioeconomic Deprivation
Objectives A policy model is a model that can evaluate the effectiveness and cost-effectiveness of interventions and inform policy decisions. In this study, we introduce a cardiovascular disease (CVD) policy model which can be used to model remaining life expectancy including a measure of socioeconomic deprivation as an independent risk factor for CVD.
Design A state transition model was developed using the Scottish Heart Health Extended Cohort (SHHEC) linked to Scottish morbidity and death records. Individuals start in a CVD-free state and can transit to three CVD event states plus a non-CVD death state. Individuals who have a non-fatal first event are then followed up until death. Taking a competing risk approach, the cause-specific hazards of a first event are modelled using parametric survival analysis. Survival following a first non-fatal event is also modelled parametrically. We assessed discrimination, validation and calibration of our model.
Results Our model achieved a good level of discrimination in each component (c-statistics for men (women)—non-fatal coronary heart disease (CHD): 0.70 (0.74), non-fatal cerebrovascular disease (CBVD): 0.73 (0.76), fatal CVD: 0.77 (0.80), fatal non-CVD: 0.74 (0.72), survival after non-fatal CHD: 0.68 (0.67) and survival after non-fatal CBVD: 0.65 (0.66)). In general, our model predictions were comparable with observed event rates for a Scottish randomised statin trial population which has an overlapping follow-up period with SHHEC. After applying a calibration factor, our predictions of life expectancy closely match those published in recent national life tables.
Conclusions Our model can be used to estimate the impact of primary prevention interventions on life expectancy and can assess the impact of interventions on inequalities.
The Global Burden of Disease Study estimated that in 2010 cardiovascular disease (CVD), including rheumatic heart diseases, hypertensive diseases, ischaemic heart diseases, pulmonary heart disease, other forms of heart disease, cerebrovascular diseases (CBVDs), diseases of arteries and diseases of veins, was the cause of death in approximately 30% of all global deaths. In Scotland, although mortality rates have declined steadily for decades (eg, coronary heart disease (CHD) mortality rates for men were 72% lower in 2009 than in 1950), in 2009, Scottish CHD mortality rates for men and women were 39% and 64% higher, respectively, than the average for Western Europe. Furthermore, despite improvements, inequalities persist in Scotland. For example, in 2011, the European age-standardised CHD mortality rates for the most deprived fifth of the Scottish Index of Multiple Deprivation (SIMD) scores were 62% higher than the corresponding rates for the least deprived fifth of SIMD scores.
There are many types of CVD interventions (eg, lifestyle, pharmaceuticals, legislation, etc.) which aim to reduce the disease burden, ranging from those that target the individual to those with population coverage. Reimbursement agencies, such as the National Institute for Health and Care Excellence in England and Wales and the Scottish Medicines Consortium in Scotland, require evidence that interventions are cost-effective prior to adoption. A challenge in generating this evidence is that randomised trials are short term in nature and so often modelling is necessary to predict longer term cost-effectiveness.
A definition of a policy model is a model that can evaluate the effectiveness and cost-effectiveness of interventions and inform policy decisions. There are a wide variety of CVD policy models in existence. A recent example, set in England and Wales, uses Framingham risk equations to build a model which can estimate the impact of potential interventions in terms of CVD events prevented, life years and quality-adjusted life years gained and costs to the National Health Service. A limitation of this model, as the authors acknowledge, is that it uses a limited time frame (10 years) rather than a lifetime perspective.
In developing a model to be used by policy makers, it is important that the approach is consistent with prediction models that are used in clinical practice. The Scottish Intercollegiate Guidelines Network has recommended the use of the ASSIGN score as the clinical prediction tool for prioritising individuals for primary prevention in Scotland. A key feature of this score is the inclusion of an area-based measure of socioeconomic deprivation, SIMD, as an independent risk factor. This has the potential to reduce inequalities because, holding the other risk factors constant, people residing in areas with higher levels of socioeconomic deprivation will be given higher ASSIGN scores. Furthermore, if the ASSIGN score is used in modelling exercises to estimate the impact of new interventions, it also provides an assessment of how the new intervention will affect the level of inequality.
The aim of our study is to develop a CVD policy model for use in Scotland using the same risk factors as ASSIGN but taking a lifetime perspective. The key outputs from this model will be life expectancy, quality-adjusted life expectancy and lifetime healthcare costs. These outputs can be used in decision analysis to assess the cost-effectiveness of interventions and their impact on health inequalities. We will report our model in a comprehensive and transparent manner, following best practice guidelines.
Abstract and Introduction
Abstract
Objectives A policy model is a model that can evaluate the effectiveness and cost-effectiveness of interventions and inform policy decisions. In this study, we introduce a cardiovascular disease (CVD) policy model which can be used to model remaining life expectancy including a measure of socioeconomic deprivation as an independent risk factor for CVD.
Design A state transition model was developed using the Scottish Heart Health Extended Cohort (SHHEC) linked to Scottish morbidity and death records. Individuals start in a CVD-free state and can transit to three CVD event states plus a non-CVD death state. Individuals who have a non-fatal first event are then followed up until death. Taking a competing risk approach, the cause-specific hazards of a first event are modelled using parametric survival analysis. Survival following a first non-fatal event is also modelled parametrically. We assessed discrimination, validation and calibration of our model.
Results Our model achieved a good level of discrimination in each component (c-statistics for men (women)—non-fatal coronary heart disease (CHD): 0.70 (0.74), non-fatal cerebrovascular disease (CBVD): 0.73 (0.76), fatal CVD: 0.77 (0.80), fatal non-CVD: 0.74 (0.72), survival after non-fatal CHD: 0.68 (0.67) and survival after non-fatal CBVD: 0.65 (0.66)). In general, our model predictions were comparable with observed event rates for a Scottish randomised statin trial population which has an overlapping follow-up period with SHHEC. After applying a calibration factor, our predictions of life expectancy closely match those published in recent national life tables.
Conclusions Our model can be used to estimate the impact of primary prevention interventions on life expectancy and can assess the impact of interventions on inequalities.
Introduction
The Global Burden of Disease Study estimated that in 2010 cardiovascular disease (CVD), including rheumatic heart diseases, hypertensive diseases, ischaemic heart diseases, pulmonary heart disease, other forms of heart disease, cerebrovascular diseases (CBVDs), diseases of arteries and diseases of veins, was the cause of death in approximately 30% of all global deaths. In Scotland, although mortality rates have declined steadily for decades (eg, coronary heart disease (CHD) mortality rates for men were 72% lower in 2009 than in 1950), in 2009, Scottish CHD mortality rates for men and women were 39% and 64% higher, respectively, than the average for Western Europe. Furthermore, despite improvements, inequalities persist in Scotland. For example, in 2011, the European age-standardised CHD mortality rates for the most deprived fifth of the Scottish Index of Multiple Deprivation (SIMD) scores were 62% higher than the corresponding rates for the least deprived fifth of SIMD scores.
There are many types of CVD interventions (eg, lifestyle, pharmaceuticals, legislation, etc.) which aim to reduce the disease burden, ranging from those that target the individual to those with population coverage. Reimbursement agencies, such as the National Institute for Health and Care Excellence in England and Wales and the Scottish Medicines Consortium in Scotland, require evidence that interventions are cost-effective prior to adoption. A challenge in generating this evidence is that randomised trials are short term in nature and so often modelling is necessary to predict longer term cost-effectiveness.
A definition of a policy model is a model that can evaluate the effectiveness and cost-effectiveness of interventions and inform policy decisions. There are a wide variety of CVD policy models in existence. A recent example, set in England and Wales, uses Framingham risk equations to build a model which can estimate the impact of potential interventions in terms of CVD events prevented, life years and quality-adjusted life years gained and costs to the National Health Service. A limitation of this model, as the authors acknowledge, is that it uses a limited time frame (10 years) rather than a lifetime perspective.
In developing a model to be used by policy makers, it is important that the approach is consistent with prediction models that are used in clinical practice. The Scottish Intercollegiate Guidelines Network has recommended the use of the ASSIGN score as the clinical prediction tool for prioritising individuals for primary prevention in Scotland. A key feature of this score is the inclusion of an area-based measure of socioeconomic deprivation, SIMD, as an independent risk factor. This has the potential to reduce inequalities because, holding the other risk factors constant, people residing in areas with higher levels of socioeconomic deprivation will be given higher ASSIGN scores. Furthermore, if the ASSIGN score is used in modelling exercises to estimate the impact of new interventions, it also provides an assessment of how the new intervention will affect the level of inequality.
The aim of our study is to develop a CVD policy model for use in Scotland using the same risk factors as ASSIGN but taking a lifetime perspective. The key outputs from this model will be life expectancy, quality-adjusted life expectancy and lifetime healthcare costs. These outputs can be used in decision analysis to assess the cost-effectiveness of interventions and their impact on health inequalities. We will report our model in a comprehensive and transparent manner, following best practice guidelines.
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