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Keep it Simple? Predicting Primary Health Care Costs with Measures of Morbidity and Multimorbidity

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  • Samuel L Brilleman

    (Academic Unit of Primary Care, University of Bristol)

  • Hugh Gravelle

    (Centre for Health Economics, University of York, UK)

  • Sandra Hollinghurst

    (Academic Unit of Primary Care, University of Bristol)

  • Sarah Purdy

    (Academic Unit of Primary Care, University of Bristol)

  • Chris Salisbury

    (Academic Unit of Primary Care, University of Bristol)

  • Frank Windmeijer

    (Department of Economics, University of Bristol)

Abstract

In this paper we investigate the relationship between patients’ primary care costs (consultations, tests, drugs) and their age, gender, deprivation and alternative measures of their morbidity and multimorbidity. Such information is required in order to set capitation fees or budgets for general practices to cover their expenditure on providing primary care services. It is also useful to examine whether practices’ expenditure decisions vary equitably with patient characteristics. Electronic practice record keeping systems mean that there is very rich information on patient diagnoses. But the diagnostic information (with over 9000 possible diagnoses) is too detailed to be practicable for setting capitation fees or practice budgets. Some method of summarizing such information into more manageable measures of morbidity is required. We therefore compared the ability of eight measures of patient morbidity and multimorbidity to predict future primary care costs using data on 86,100 individuals in 174 English practices. The measures were derived from four morbidity descriptive systems (17 chronic diseases in the Quality and Outcomes Framework (QOF), 17 chronic diseases in the Charlson scheme, 114 Expanded Diagnosis Clusters (EDCs), and 68 Adjusted Clinical Groups (ACGs)). We found that, in general, for a given disease description system, counts of diseases and sets of disease dummy variables had similar explanatory power and that measures with more categories did better than those with fewer. The EDC measures performed best, followed by the QOF and ACG measures. The Charlson measures had the worst performance but still improved markedly on models containing only age, gender, deprivation and practice effects. Allowing for individual patient morbidity greatly reduced the association of age and cost. There was a pro-deprived bias in expenditure: after allowing for morbidity, patients in areas in the highest deprivation decile had costs which were 22% higher than those in the lowest deprivation decile. The predictive ability of the best performing morbidity and multimorbidity measures was very good for this type of individual level cross section data, with R2 ranging from 0.31 to 0.46. The statistical method of estimating the relationship between patient characteristics and costs was less important than the type of morbidity measure. Rankings of the morbidity and multimorbidity measures were broadly similar for generalised linear models with log link and Poisson errors and for OLS estimation. It would be currently feasible to combine the results from our study with the data on the number of patients with each QOF disease, which is available on all practices in England, to calculate budgets for general practices to cover their primary care costs.

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File URL: http://www.york.ac.uk/media/che/documents/papers/researchpapers/CHERP72_predicting_primary_health_care_costs.pdf
File Function: First version, 2011
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Bibliographic Info

Paper provided by Centre for Health Economics, University of York in its series Working Papers with number 072cherp.

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Length: 29 pages
Date of creation: Dec 2011
Date of revision:
Handle: RePEc:chy:respap:72cherp

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Keywords: multimorbidity; primary care; utilisation; costs; deprivation; budgets;

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References

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  1. Manning, Willard G. & Basu, Anirban & Mullahy, John, 2005. "Generalized modeling approaches to risk adjustment of skewed outcomes data," Journal of Health Economics, Elsevier, vol. 24(3), pages 465-488, May.
  2. Buntin, Melinda Beeuwkes & Zaslavsky, Alan M., 2004. "Too much ado about two-part models and transformation?: Comparing methods of modeling Medicare expenditures," Journal of Health Economics, Elsevier, vol. 23(3), pages 525-542, May.
  3. Erik Schokkaert & Geert Dhaene & Carine Van De Voorde, 1998. "Risk adjustment and the trade-off between efficiency and risk selection: an application of the theory of fair compensation," Health Economics, John Wiley & Sons, Ltd., vol. 7(5), pages 465-480.
  4. 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.
  5. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
  6. Teresa Bago d’Uva & Andrew M. Jones & Eddy van Doorslaer, 2007. "Measurement of horizontal inequity in health care utilisation using European Panel data," Health, Econometrics and Data Group (HEDG) Working Papers 07/17, HEDG, c/o Department of Economics, University of York.
  7. Matthew Sutton, 2002. "Vertical and horizontal aspects of socio-economic inequity in general practitioner contacts in Scotland," Health Economics, John Wiley & Sons, Ltd., vol. 11(6), pages 537-549.
  8. Van de ven, Wynand P.M.M. & Ellis, Randall P., 2000. "Risk adjustment in competitive health plan markets," Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 14, pages 755-845 Elsevier.
  9. Morris, Stephen & Sutton, Matthew & Gravelle, Hugh, 2005. "Inequity and inequality in the use of health care in England: an empirical investigation," Social Science & Medicine, Elsevier, vol. 60(6), pages 1251-1266, March.
  10. Blough, David K. & Madden, Carolyn W. & Hornbrook, Mark C., 1999. "Modeling risk using generalized linear models," Journal of Health Economics, Elsevier, vol. 18(2), pages 153-171, April.
  11. Willard G. Manning & John Mullahy, 1999. "Estimating Log Models: To Transform or Not to Transform?," NBER Technical Working Papers 0246, National Bureau of Economic Research, Inc.
  12. Mullahy, John, 1998. "Much ado about two: reconsidering retransformation and the two-part model in health econometrics," Journal of Health Economics, Elsevier, vol. 17(3), pages 247-281, June.
  13. Hugh Gravelle & Mark Dusheiko & Steve Martin & Pete Smith & Nigel Rice & Jennifer Dixon, 2011. "Modelling Individual Patient Hospital Expenditure for General Practice Budgets," Working Papers 073cherp, Centre for Health Economics, University of York.
  14. Teresa Bago d'Uva, 2005. "Latent class models for use of primary care: evidence from a British panel," Health Economics, John Wiley & Sons, Ltd., vol. 14(9), pages 873-892.
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