Research
Targeted versus universal prevention. a resource allocation model to prioritize cardiovascular prevention
1 Centre for Prevention and Health Services Research, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
2 Department of Epidemiology, University Medical Centre Groningen, Groningen, the Netherlands
3 Institute for Medical Technology Assessment, Erasmus University Rotterdam, Rotterdam, the Netherlands
4 Expertise Centre for Methodology and Information Services, RIVM, Bilthoven, The Netherlands
5 Centre for Public Health Forecasting, RIVM, Bilthoven, the Netherlands
6 EMGO Institute for Health and Care Research, VU University Amsterdam, Amsterdam, the Netherlands
Cost Effectiveness and Resource Allocation 2011, 9:14 doi:10.1186/1478-7547-9-14
Published: 6 October 2011Abstract
Background
Diabetes mellitus brings an increased risk for cardiovascular complications and patients profit from prevention. This prevention also suits the general population. The question arises what is a better strategy: target the general population or diabetes patients.
Methods
A mathematical programming model was developed to calculate optimal allocations for the Dutch population of the following interventions: smoking cessation support, diet and exercise to reduce overweight, statins, and medication to reduce blood pressure. Outcomes were total lifetime health care costs and QALYs. Budget sizes were varied and the division of resources between the general population and diabetes patients was assessed.
Results
Full implementation of all interventions resulted in a gain of 560,000 QALY at a cost of €640 per capita, about €12,900 per QALY on average. The large majority of these QALY gains could be obtained at incremental costs below €20,000 per QALY. Low or high budgets (below €9 or above €100 per capita) were predominantly spent in the general population. Moderate budgets were mostly spent in diabetes patients.
Conclusions
Major health gains can be realized efficiently by offering prevention to both the general and the diabetic population. However, a priori setting a specific distribution of resources is suboptimal. Resource allocation models allow accounting for capacity constraints and program size in addition to efficiency.
