title: How are population-based funding formulae for healthcare composed? A comparative analysis of seven models.
abstract: BACKGROUND
  Population-based funding formulae act as an important means of promoting equitable health funding structures. To evaluate how policy makers in different jurisdictions construct health funding formulae and build an understanding of contextual influences underpinning formula construction we carried out a comparative analysis of key components of funding formulae across seven high-income and predominantly publically financed health systems: New Zealand, England, Scotland, the Netherlands, the state of New South Wales in Australia, the Canadian province of Ontario, and the city of Stockholm, Sweden.
  
  
  METHODS
  Core components from each formula were summarised and key similarities and differences evaluated from a compositional perspective. We categorised approaches to constructing funding formulae under three main themes: identifying factors which predict differential need amongst populations; adjusting for cost factors outside of needs factors; and engaging in normative correction of allocations for 'unmet' need.
  
  
  RESULTS
  We found significant congruence in the factors used to guide need and cost adjustments. However, there is considerable variation in interpretation and implementation of these factors.
  
  
  CONCLUSIONS
  Despite broadly similar frameworks, there are distinct differences in the composition of the formulae across the seven health systems. Ultimately, the development of funding formulae is a dynamic process, subject to availability of data reflecting health needs, the influence of wider socio-political objectives and health system determinants.
authors.affiliation: Centre for Health Systems, Department of Preventive and Social Medicine, University of Otago, P,O, Box 913, Dunedin 9054, New Zealand. erin.penno@otago.ac.nz.
authors.forename: Erin
authors.initials: E
authors.lastname: Penno
authors.forename: Robin
authors.initials: R
authors.lastname: Gauld
authors.forename: Rick
authors.initials: R
authors.lastname: Audas
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D003695:Delivery of Health Care / Q000191:economics / Q000458:organization & administration; D004739:England; D005260:Female; D017048:Health Care Costs / Q000706:statistics & numerical data; D006291:Health Policy; D006301:Health Services Needs and Demand / Q000191:economics / Q000458:organization & administration / Q000706:statistics & numerical data; D061889:Healthcare Financing; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D018803:Models, Economic; D009426:Netherlands; D009517:New South Wales; D009520:New Zealand; D009864:Ontario; D040841:Resource Allocation / Q000191:economics / Q000458:organization & administration; D012606:Scotland; D012737:Sex Factors; D013548:Sweden; D055815:Young Adult