title: Population Impact Analysis: a framework for assessing the population impact of a risk or intervention.
abstract: 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.affiliation: Manchester Urban Collaboration on Health, School of Translational Medicine, Manchester Academic Health Science Centre, University of Manchester, Manchester M14 7PH, UK. arpana.verma@manchester.ac.uk
authors.forename: Arpana
authors.initials: A
authors.lastname: Verma
authors.forename: Perihan
authors.initials: P
authors.lastname: Torun
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Harris
authors.forename: Richard
authors.initials: R
authors.lastname: Edwards
authors.forename: Isla
authors.initials: I
authors.lastname: Gemmell
authors.forename: Roger A
authors.initials: RA
authors.lastname: Harrison
authors.forename: Iain E
authors.initials: IE
authors.lastname: Buchan
authors.forename: Lisa
authors.initials: L
authors.lastname: Davies
authors.forename: Lesley
authors.initials: L
authors.lastname: Patterson
authors.forename: Richard F
authors.initials: RF
authors.lastname: Heller
mesh_terms: D000368:Aged; D003362:Cost-Benefit Analysis; D003627:Data Interpretation, Statistical; D003657:Decision Making; D018450:Disease Progression; D019317:Evidence-Based Medicine / Q000191:economics* / Q000379:methods / Q000592:standards; D006291:Health Policy; D006801:Humans; D007252:Influenza Vaccines / Q000191:economics / Q000627:therapeutic use; D007251:Influenza, Human / Q000150:complications / Q000191:economics / Q000517:prevention & control; D017063:Outcome Assessment, Health Care / Q000191:economics* / Q000379:methods / Q000592:standards; D010343:Patient Admission / Q000191:economics / Q000706:statistics & numerical data; D011634:Public Health / Q000191:economics* / Q000379:methods / Q000592:standards; D029424:Pulmonary Disease, Chronic Obstructive / Q000191:economics / Q000517:prevention & control; D040841:Resource Allocation; D012196:Review Literature as Topic; D006113:United Kingdom
chemical_list: D007252:Influenza Vaccines