Introduction: Standard methods of economic analysis may not be suitable for local decision making that is specific to a particular population.
Background: We describe a new three-step methodology, termed 'population cost-impact analysis', which provides a population perspective to the costs and benefits of alternative interventions. The first two steps involve calculating the population impact and the costs of the proposed interventions relevant to local conditions. This involves the calculation of population impact measures (which have been previously described but are not currently used extensively) - measures of absolute risk and risk reduction, applied to a population denominator. In step three, preferences of policy-makers are obtained. This is in contrast to the QALY approach in which quality weights are obtained as a part of the measurement of benefit.
Methods: We applied the population cost-impact analysis method to a comparison of two interventions - increasing the use of beta-adrenoceptor antagonists (beta-blockers) and smoking cessation - after myocardial infarction in a scaled-back notional local population of 100,000 people in England. Twenty-two public health professionals were asked via a questionnaire to rank the order in which they would implement four interventions. They were given information on both population cost impact and QALYs for each intervention.
Results: In a population of 100,000 people, moving from current to best practice for beta-adrenoceptor antagonists and smoking cessation will prevent 11 and 4 deaths (or gain of 127 or 42 life-years), respectively. The cost per event prevented in the next year, or life-year gained, is less for beta-adrenoceptor antagonists than for smoking cessation. Public health professionals were found to be more inclined to rank alternative interventions according to the population cost impact than the QALY approach.
Discussion: The use of the population cost-impact approach allows information on the benefits of moving from current to best practice to be presented in terms of the benefits and costs to a particular population. The process for deciding between alternative interventions in a prioritisation exercise may differ according to the local context. We suggest that the valuation of the benefit is performed after the benefits have been quantified and that it takes into account local issues relevant to prioritisation. It would be an appropriate next step to experiment with, and formalise, this part of the population cost-impact analysis to provide a standardised approach for determining willingness to pay and provide a ranking of priorities.
Conclusion: Our method adds a new dimension to economic analysis, the ability to identify costs and benefits of potential interventions to a defined population, which may be of considerable use for policy makers working at the local level.