D T Spence1, G Bandesha, S Horsley, R F Heller. 1. Department of Health Sciences, School of Medicine, University of Leicester, Leicester, UK. david.spence2@northants.nhs.uk
Abstract
OBJECTIVE: To test the feasibility of applying population impact measures utilising local population data on established interventions for heart failure and diabetes mellitus. DESIGN: 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. MAIN OUTCOME MEASURES: 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.
OBJECTIVE: To test the feasibility of applying population impact measures utilising local population data on established interventions for heart failure and diabetes mellitus. DESIGN: 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. MAIN OUTCOME MEASURES: 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 diabeticpatients 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: Alicia O'Cathain; Fiona Sampson; Mark Strong; Mark Pickin; Elizabeth Goyder; Simon Dixon Journal: BMJ Open Date: 2015-11-06 Impact factor: 2.692