Population impact measures (PIMs) have been devised to assess the impact of an intervention at a population level. In this study, researchers tested the feasibility of using locally derived population data from primary care to:

• Calculate the number of deaths or hospitalisations that could be prevented among heart failure patients with increased ACE inhibitor provision.

• Calculate the number of cardiovascular disease (CVD) events that could be prevented with improved lipid and blood pressure control in persons with diabetes.

• Illustrate the potential of PIMs to assist with the PCT commissioning process.

Data from 17 GP practices (55% PCT GP registered population) were used to derive the number of events prevented in the population (NEPP) and the following findings reported:

• A 10% increase in the number of eligible patients receiving ACE inhibitors (n = 191) could result in at least 18 fewer deaths and 32 fewer hospitalisations for heart failure every year.

• Only 45% of persons with diabetes with an above target total cholesterol were receiving a statin; increasing this to 75% (additional 921) could lead to 44 fewer CVD events over 5 years.

• Similarly, more rigorous blood pressure control in an additional 662 diabetic patients could result in 26 fewer CVD events over 5 years.

• There were differences in the potential impact of these interventions according to subgroups within the PCT, as defined by age and geography (locality).

The researchers conclude that their study “demonstrates the pragmatic use of PIMs to quantify the benefits of more rigorous implementation of established evidence-based practice. Despite practicalities and some modelling limitations, the use of local data to calculate the eligible population and the NEPP in a specific PCT population makes the impact of “local commissioning decisions” more realistic and relevant. The further development of locally derived NEPPs across a range of diseases seems a logical next step and offers a means of assessing quality improvement in commissioning decisions based on outcomes avoided, which is transparent and evidence-based.”