Skip to main content
Article thumbnail
Location of Repository

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

By Samuel L Brilleman, Hugh Gravelle, Sandra Hollinghurst, Sarah Purdy, Chris Salisbury and Frank Windmeijer


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.multimorbidity; primary care; utilisation; costs; deprivation; budgets

OAI identifier:

Suggested articles


  1. (2002). 1040-1048.Keep it simple? Predicting primary heath care costs with measures of morbidity and multimorbidity
  2. (2007). A comparative analysis of claims based tools for health risk assessment.
  3. (2008). A model based on age, sex, and morbidity to explain variation in UK general practice prescribing: cohort study.
  4. (1987). A new method of classifying prognostic co-morbidity in longitudinal-studies - development and validation.
  5. (1992). Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases.
  6. (1991). Ambulatory care groups - A categorization of diagnoses for research and management.
  7. (1997). An R-squared measure of goodness of fit for some common nonlinear regression models.
  8. (2010). Association and Royal Pharmaceutical Society.
  9. (2005). Case-mix and variation in specialist referrals in general practice.
  10. (2007). Communities and Local Government. The English Indices of Deprivation
  11. (2006). Dealing with skewed data on costs and expenditures.
  12. (1991). Development and application of a population-oriented measure of ambulatory care case-mix.
  13. (2011). Epidemiology and impact of multimorbidity in primary care: a retrospective cohort study.
  14. (2010). Equity and excellence: Liberating the NHS. Cm 7881.
  15. (1978). Estimating dimension of a model.
  16. (2001). Estimating log models: to transform or not to transform?
  17. (2005). Generalised modelling approaches to risk adjustment of skewed outcomes data.
  18. (2005). Inequity and inequality in the use of health care in England: an empirical investigation.
  19. (2007). Is the British National Health Service equitable? The evidence on socioeconomic differences in utilization.
  20. (2005). Latent class models for use of primary care: evidence from a British panel.
  21. (2009). Measurement of horizontal inequity in health care utilisation using European panel data,
  22. (2012). Measures of multimorbidity and morbidity burden for use in primary care settings: a systematic review and guide. Annals of Family Medicine. (In press) Johns Hopkins Bloomberg School of Public Health.
  23. (1999). Modeling risk using generalized linear models.
  24. (2011). Modelling individual patient hospital expenditure for general practice budgets. CHE Research Paper 72. Centre for Health Economics,
  25. (1998). Much ado about two: reconsidering retransformation and the two-part model in health econometrics.
  26. (1993). Primary non-compliance with prescribed medication in primary care.
  27. (1978). Regression and ANOVA with zero-one data: measures of residual variation.
  28. (2011). Resource Allocation: Weighted Capitation Formula. 7 th Edition. df.
  29. (2007). Review of the General Medical Services global sum formula.
  30. (1998). Risk adjustment and the trade-off between efficiency and risk selection: an application of the theory of fair compensation.
  31. (2000). Risk adjustment in competitive health plan markets,
  32. (2008). Service (NHS) Information Centre.
  33. (2009). Stata Statistical Software: Release 11. College Station, TX: StataCorp LP.
  34. (1998). The logged dependent variable, heteroskedasticity, and the retransformation problem.
  35. (2004). Too much ado about two-part models and transformation? Comparing methods of modelling Medicare expenditures.
  36. (2008). Unit costs of health and social care
  37. (2006). Validating the Johns Hopkins ACG case-mix system of the elderly in Swedish primary health care. Bmc Public Health, 6., available from: ISI:000239803600001 Hippisley-Cox
  38. (2002). Vertical and horizontal aspects of socio-economic inequity in general practitioner contacts in Scotland.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.