Mini-SymposiumPredicting the impact of population level risk reduction in cardio-vascular disease and stroke on acute hospital admission rates over a 5 year period—a pilot study
Introduction
This study explores the extent to which a payer in the health system could estimate the potential benefit of investing in public health interventions to reduce risk of cardiovascular disease (CVD) in a population. The aim would be to reduce the cost of acute hospital admissions for conditions such as heart attacks, strokes and heart failure over a 5-year period.
Most guidelines for the prevention of coronary heart disease (CHD) recommend preventative measures to asymptomatic individuals at high risk. Risk assessment is performed using one of several risk tools that combine values for different risk factors into a global risk estimate. These tools are generally based upon multivariate risk prediction equations derived from large prospective cohort studies or randomized trials that estimate a patient's risk of having a CVD event over 5–10 years. The most commonly used risk prediction equations are based on the Framingham Heart Study. There are several concerns about the generalizability of Framingham data to European populations.1 These include the following:
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Framingham possibly overestimates absolute risk in populations with lower coronary heart disease rates.2, 3
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Framingham has been shown to overestimate CVD risk in British (middle aged male), Danish, German and Italian populations.4, 5, 6, 7
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The definition of end-points used in Framingham differs from those used in most other studies including clinical trials.
The hypothesis for this study is straightforward. There is a proven link between the level of cardiovascular risk in an individual and the likelihood of them having a heart attack, stroke or other CVD related acute event. If an individual has certain levels of risk factors, i.e. systolic blood pressure, body mass index (BMI), cholesterol level, smoking status. then it is possible, using the Framingham equation, to estimate their risk of having a heart attack, stroke etc over the next 5 years. If they have a 10% chance of suffering a CVD related event in the next 5 years, it could be assumed that if a hundred people have the same mean level of risk then 10 of them would have a CVD event in the next 5 years.
Section snippets
Methodology
This study uses a ‘cost offset’ modelling approach. It brings together data and evidence from health promotion, epidemiology, clinical practice, accountancy, health policy and hospital coding systems, in a systematic way to attempt to understand the impact on future hospital costs of a systematic, evidence based approach to CVD reduction at a population level. It is in this combination of data that the greatest strength of modelling, and the greatest opportunity for error, probably lies. There
Predicted events vs. actual events
To test or validate the model, an exercise was carried out whereby the model was populated with data from five South Yorkshire PCTs. The predicted number of CVD-related admissions based on current risk factor levels was compared with the actual number of admissions for these conditions in 2004/2005. The result is shown in Fig. 1.
Clearly the predictive power of the model is reasonably accurate in high-risk populations such as PCT1, PCT 2, and PCT 3 and slightly overestimating the lower risk
Conclusion
‘Measures to tackle rising public health problems are being scrapped or postponed because the funds have not materialized, public health chiefs say: An extra £211 m was to be ploughed into projects in England this year to tackle smoking, obesity and sexual health. But public health directors say just a fraction was coming through because of the deficits crisis and the push risked ‘never getting off the ground’’.20 It is very difficult to argue for significant investment in health improvement
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