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Modelling supply and demand influences on the use of health care: implications for deriving a needs-based capitation formula

  • Hugh Gravelle

    (National Primary Care Research and Development Centre, Centre for Health Economics, University of York, UK)

  • Matthew Sutton

    (General Practice and Primary Care, Community-Based Sciences, University of Glasgow and Primary Care Information Group, ISDScotland, Edinburgh, UK)

  • Stephen Morris

    (The Business School, Imperial College London, UK)

  • Frank Windmeijer

    (Institute for Fiscal Studies, London, UK)

  • Alastair Leyland

    (MRC Social and Public Health Sciences Unit, University of Glasgow, UK)

  • Chris Dibben

    (Department of Social Policy and Social Work, University of Oxford, UK)

  • Mike Muirhead

    (Consultancy Team, ISDScotland, Edinburgh, UK)

Many health-care systems allocate funding according to measures of need. The utilisation approach for measuring need rests on the assumptions that use of health care is determined by demand and supply and that need is an important element of demand. By estimating utilisation models which allow for supply it is possible to isolate the socio-economic and health characteristics which affect demand. A subset of these variables can then be identified by a combination of judgement and further analysis as needs variables to inform funding allocations. We estimate utilisation models using newly assembled data on admissions to acute hospitals, measures of supply, morbidity and socio-economic characteristics for 8414 small geographical areas in England. We make a number of methodological innovations including deriving additional measures of specific morbidities at small area level from individual level survey data. We compare models with different specifications for the effect of waiting times and provider characteristics, with total, planned and unplanned hospital admissions, and estimated at small area (ward) and primary care organisation (general practice) level. After allowing for waiting times, distance, capacity and the availability of private health care, measures of mortality, self-reported morbidity, low education and low income increase the use of health care. We find evidence of horizontal inequity with respect to ethnicity and employment and suggest a method for reducing its effects when deriving a needs-based allocation formula. Copyright © 2003 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/hec.830
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Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

Volume (Year): 12 (2003)
Issue (Month): 12 ()
Pages: 985-1004

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Handle: RePEc:wly:hlthec:v:12:y:2003:i:12:p:985-1004
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/5749

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  1. Wagstaff, Adam & van Doorslaer, Eddy, 2000. "Chapter 34 Equity in health care finance and delivery," Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 34, pages 1803-1862 Elsevier.
  2. Erik SCHOKKAERT & Carine VAN DE VOORDE, 2000. "Risk Selection and the Specification of the Conventional Risk Adjustment Formula," Center for Economic Studies - Discussion papers ces0011, Katholieke Universiteit Leuven, Centrum voor Economische Studiën.
  3. Katharina Hauck & Rebecca Shaw & Peter C. Smith, 2002. "Reducing avoidable inequalities in health: a new criterion for setting health care capitation payments," Health Economics, John Wiley & Sons, Ltd., vol. 11(8), pages 667-677.
  4. Derek Bond and Prof. Denis Conniffe, 2002. "Cross-Regional Equity in Health Care Funding," NIRSA Working Paper Series 3, National Institute for Regional and Spatial Analysis (NIRSA), NUI Maynooth, Ireland..
  5. Hugh Gravelle & Peter C. Smith & Ana Xavier, . "Waiting Times and Waiting Lists: A Model of the Market for Elective Surgery," Discussion Papers 00/27, Department of Economics, University of York.
  6. Roy Carr-Hill & Geoffrey Hardman & Stephen Martin & Stuart Peacock & Trevor Sheldon & Peter Smith, 1994. "A formula for distributing NHS revenues based on small area use of hospital beds," Working Papers 022cheop, Centre for Health Economics, University of York.
  7. Nigel Rice & Paul Dixon & David Lloyd & David Roberts, 1999. "Derivation of a needs based capitation formula for allocation prescribing budgets," Working Papers 034cheop, Centre for Health Economics, University of York.
  8. Richard Blundell & Frank Windmeijer, 2000. "Identifying demand for health resources using waiting times information," IFS Working Papers W00/03, Institute for Fiscal Studies.
  9. Peter C. Smith & Nigel Rice & Roy Carr-Hill, 2001. "Capitation funding in the public sector," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(2), pages 217-257.
  10. Manning, Willard G., 1998. "The logged dependent variable, heteroscedasticity, and the retransformation problem," Journal of Health Economics, Elsevier, vol. 17(3), pages 283-295, June.
  11. Peter Zweifel & Stefan Felder & Markus Meiers, 1999. "Ageing of population and health care expenditure: a red herring?," Health Economics, John Wiley & Sons, Ltd., vol. 8(6), pages 485-496.
  12. Gravelle, Hugh & Dusheiko, Mark & Sutton, Matthew, 2002. "The demand for elective surgery in a public system: time and money prices in the UK National Health Service," Journal of Health Economics, Elsevier, vol. 21(3), pages 423-449, May.
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