title: Evidence-based commissioning: using population impact measures to help primary care trusts estimate the benefit of interventions in diabetes and heart failure.
abstract: OBJECTIVE
  To test the feasibility of applying population impact measures utilising local population data on established interventions for heart failure and diabetes mellitus.
  
  
  METHODS
  Modelling study. Setting Registered general practitioner (GP) population in a primary care trust (PCT) Data sources Local data sources included the quality and outcomes framework, chronic disease registers for coronary heart disease and diabetes, hospital episode statistics and a range of published risk data in heart failure and diabetes.
  
  
  METHODS
  Number of events prevented in the population (NEPP) by increasing the uptake of established interventions expressed as the number of deaths, hospitalisations and cardiovascular events prevented.
  
  
  RESULTS
  Data from 17 GP practices (representing 55% of the PCT GP registered population) were used to derive the NEPP. A 10% increase in the number of eligible patients receiving ACE inhibitors (n = 191) could result in at least 18 fewer deaths (95% CI 9.8 to 27.1) and 32 fewer hospitalisations (95% CI 24.9 to 40.7) 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 (95% CI 15.6 to 73.1) fewer cardiovascular disease (CVD) events over 5 years. Similarly, more rigorous blood pressure control in an additional 662 diabetic patients could result in 26 (95% CI -2.7 to 55.6) 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).
  
  
  CONCLUSIONS
  Local data and published literature estimates can be successfully combined to produce the number of events prevented within a locally defined PCT population (NEPP). Commissioners have shown interest in the utility of such a measure in identifying and quantifying areas for improvement.
authors.affiliation: Department of Health Sciences, School of Medicine, University of Leicester, Leicester, UK. david.spence2@northants.nhs.uk
authors.forename: D T
authors.initials: DT
authors.lastname: Spence
authors.forename: G
authors.initials: G
authors.lastname: Bandesha
authors.forename: S
authors.initials: S
authors.lastname: Horsley
authors.forename: R F
authors.initials: RF
authors.lastname: Heller
mesh_terms: D002318:Cardiovascular Diseases / Q000517:prevention & control* / Q000628:therapy; D003920:Diabetes Mellitus / Q000517:prevention & control* / Q000628:therapy; D019317:Evidence-Based Medicine / Q000458:organization & administration*; D005194:Family Practice / Q000592:standards; D006801:Humans; D010043:Outcome and Process Assessment, Health Care / Q000379:methods*; D011159:Population Surveillance; D011320:Primary Health Care / Q000592:standards*