BACKGROUND: To describe an organizing framework, Population Impact Analysis, for applying the findings of systematic reviews of public health literature to estimating the impact on a local population, with the aim of implementing evidence-based decision-making. METHODS: A framework using population impact measures to demonstrate how resource allocation decisions may be influenced by using evidence-based medicine and local data. An example of influenza vaccination in the over 65s in Trafford to reduce hospital admissions for chronic obstructive pulmonary disease (COPD) is used. RESULTS: The number of COPD admissions due to non-vaccination of the over 65 in Trafford was 16.4 (95% confidence interval: 13.5; 19.5) and if vaccination rates were taken up to 90%, 11.5 (95% confidence interval: 9.3; 13.8) admissions could have been prevented. A total of 705 (95% confidence interval: 611; 861) people would have to be vaccinated against influenza to prevent one hospital admission. CONCLUSIONS: Population Impact Analysis can help the 'implementation' aspect of evidence for population health. It has been developed to support public health policy makers at both local and national/international levels in their role of commissioning services.
BACKGROUND: To describe an organizing framework, Population Impact Analysis, for applying the findings of systematic reviews of public health literature to estimating the impact on a local population, with the aim of implementing evidence-based decision-making. METHODS: A framework using population impact measures to demonstrate how resource allocation decisions may be influenced by using evidence-based medicine and local data. An example of influenza vaccination in the over 65s in Trafford to reduce hospital admissions for chronic obstructive pulmonary disease (COPD) is used. RESULTS: The number of COPD admissions due to non-vaccination of the over 65 in Trafford was 16.4 (95% confidence interval: 13.5; 19.5) and if vaccination rates were taken up to 90%, 11.5 (95% confidence interval: 9.3; 13.8) admissions could have been prevented. A total of 705 (95% confidence interval: 611; 861) people would have to be vaccinated against influenza to prevent one hospital admission. CONCLUSIONS: Population Impact Analysis can help the 'implementation' aspect of evidence for population health. It has been developed to support public health policy makers at both local and national/international levels in their role of commissioning services.
Authors: Mirko S Winkler; Samuel Fuhrimann; Phuc Pham-Duc; Guéladio Cissé; Jürg Utzinger; Hung Nguyen-Viet Journal: Int J Public Health Date: 2016-08-30 Impact factor: 3.380