The European Journal of Health Economics

, Volume 15, Issue 6, pp 599–610 | Cite as

Association between fee-for-service expenditures and morbidity burden in primary care

  • Troels KristensenEmail author
  • Kim Rose Olsen
  • Henrik Schroll
  • Janus Laust Thomsen
  • Anders Halling
Original Paper



In primary care, fee-for-services (FFS) tariffs are often based on political negotiation rather than costing systems. The potential for comprehensive measures of patient morbidity to explain variation in negotiated FFS expenditures has not previously been examined.


To examine the relative explanatory power of morbidity measures and related general practice (GP) clinic characteristics in explaining variation in politically negotiated FFS expenditures.


We applied a multilevel approach to consider factors that explain FFS expenditures among patients and GP clinics. We used patient morbidity characteristics such as diagnostic markers, multimorbidity casemix adjustment based on resource utilisation bands (RUB) and related GP clinic characteristics for the year 2010. Our sample included 139,527 patients visiting GP clinics.


Out of the individual expenditures, 31.6 % were explained by age, gender and RUB, and around 18 % were explained by RUB. Expenditures increased progressively with the degree of resource use (RUB0–RUB5). Adding more patient-specific morbidity measures increased the explanatory power to 44 %; 3.8–9.4 % of the variation in expenditures was related to the GP clinic in which the patient was treated.


Morbidity measures were significant patient-related FFS expenditure drivers. The association between FFS expenditure and morbidity burden appears to be at the same level as similar studies in the hospital sector, where fees are based on average costing. However, our results indicate that there may be room for improvement of the association between politically negotiated FFS expenditures and morbidity in primary care.


General practice Expenditure variation Resource utilisation band (RUB) Fee-for-services (FFS) The Johns Hopkins Adjusted Clinical Groups® (ACG®) system 

JEL Classification

D61 H51 I120 I180 



We are grateful for comments from two anonymous referees and from participants at the Nordic Health Economist’s Study Group Meeting, NHSG2012, Kuopio, Finland and the PCSI 2012 Avignon 28th Patient Classification System International Conference.


  1. 1.
    Moth, G., Olesen, F., Vedsted, P.: Reasons for encounter and disease patterns in Danish primary care: changes over 16 years. Scand. J. Prim. Health Care 30(2), 70–75 (2012)PubMedCentralPubMedCrossRefGoogle Scholar
  2. 2.
    Moth, G., Vestergaard, M., Vedsted, P.: Chronic care management in Danish general practice—a crosssectional study of workload and multimorbidity. BMC Fam. Pract. 13(1), 52 (2012)PubMedCentralPubMedCrossRefGoogle Scholar
  3. 3.
    Starfield, B., Kinder, K.: Multimorbidity and its measurement. Health Policy 103(1), 3–8 (2011)PubMedCrossRefGoogle Scholar
  4. 4.
    Weiner, J.P., Starfield, B.H., Steinwachs, D.M., Mumford, L.M.: Development and application of a population-oriented measure of ambulatory care case-mix. Med. Care 29(5), 452–472 (1991)PubMedCrossRefGoogle Scholar
  5. 5.
    Starfield, B., Weiner, J., Mumford, L., Steinwachs, D.: Ambulatory care groups—a categorization of diagnoses for research and management. Health Serv. Res. 26(1), 53–74 (1991)PubMedCentralPubMedGoogle Scholar
  6. 6.
    Lee, W.C., Huang, T.P.: Explanatory ability of the ACG system regarding the utilization and expenditure of the National Health Insurance population in Taiwan—a 5-year analysis. J. Chin. Med. Assoc. 71(4), 191–199 (2008)PubMedCrossRefGoogle Scholar
  7. 7.
    Starfield, B., Lemke, K.W., Bernhardt, T., Foldes, S.S., Forrest, C.B., Weiner, J.P.: Comorbidity: implications for the importance of primary care in ‘case’ management. Ann. Fam. Med. 1(1), 8–14 (2003)PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Aguado, A., Guino, E., Mukherjee, B., Sicras, A., Serrat, J., Acedo, M., et al.: Variability in prescription drug expenditures explained by adjusted clinical groups (ACG) case-mix: a cross-sectional study of patient electronic records in primary care. BMC Health Serv. Res. 8, 53 (2008). doi: 10.1186/1472-6963-8-53. Available at PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Hennig-Schmidt, H., Selten, R., Wiesen, D.: How payment systems affect physicians’ provision behaviour—an experimental investigation. J. Health Econ. 30(4), 637–646 (2011)PubMedCrossRefGoogle Scholar
  10. 10.
    Carlsson, L., Börjesson, U., Edgren, L.: Patient based ‘burden-of-illness’ in Swedish Primary Health Care. Applying the Johns Hopkins ACG Case-mix System in a retrospective study of electronic patient records. Int. J. Health Plann. Manage. 17, 269–282 (2002)PubMedCrossRefGoogle Scholar
  11. 11.
    Zielinski, A., Kronogard, M., Lenhoff, H., Halling, A.: Validation of ACG case-mix for equitable resource allocation in Swedish primary health care. BMC Public Health 9, 347 (2009). doi: 10.1186/1471-2458-9-347. Available at PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Schroll, H., Christensen, B., Andersen, J.S., Sondergaard, J.: Danish General Medicine Database—future tool! The Danish Society of General Medicine. Ugeskr. Laeger 170(12), 1013 (2008)PubMedGoogle Scholar
  13. 13.
    Schroll, H.: Data collection perspectives from patient care in general practice. Ugeskr. Laeger 171(20), 1681–1684 (2009)PubMedGoogle Scholar
  14. 14.
    Pedersen, K., Andersen, J., Søndergaard, J.: General practice and primary health care in Denmark. J. Am. Board Fam. Med. 25, S34–S38 (2012)PubMedCrossRefGoogle Scholar
  15. 15.
    Olejaz, M., Juul Nielsen, A., Rudkjøbing, A., Okkels Birk, H., Krasnik, A., Hernández-Ouevedo, C.: Denmark: health system review. Health Syst. Transit. 14(2), 1–192 (2012)Google Scholar
  16. 16.
    Rigsrevisionen: Beretning til Statsrevisorerne om aktiviteter og udgifter i praksis-sektoren. Ministeriet for Sundhed og Forebyggelse. August 2012,1030)/17-2011.pdf
  17. 17.
    Krasnik, A., Groenewegen, P.P., Pedersen, P.A., von Scholten, P., Mooney, G., Gottschau, A., Flierman, H.A., Damsgaard, M.T.: Changing remuneration systems: effects on activity in general practice. Br. Med. J. 300(6741), 1698–1701 (1990)CrossRefGoogle Scholar
  18. 18.
    Kristensen, T., Laudicella, M., Ejersted, C., Street, A.: Cost variation in diabetes care delivered in English hospitals. Diabet. Med. 27(8), 949–957 (2010)PubMedCrossRefGoogle Scholar
  19. 19.
    Davis, P., Gribben, B., Scott, A., Lay-Yee, R.: The “supply hypothesis” and medical practice variation in primary care: testing economic and clinical models of inter-practitioner variation. Soc. Sci. Med. 50(3), 407–418 (2000)PubMedCrossRefGoogle Scholar
  20. 20.
    Rice, N., Jones, A.: Multilevel models and health economics. Health Econ. 6(6), 561–575 (1997)PubMedCrossRefGoogle Scholar
  21. 21.
    Laudicella, M., Olsen, K.R., Street, A.: Examining cost variation across hospital departments—a two-stage multi-level approach using patient-level data. Soc. Sci. Med. 71(10), 1872–1881 (2010)PubMedCrossRefGoogle Scholar
  22. 22.
    The Health Services Research & Development Center at The Johns Hopkins University. The Johns Hopkins ACG® system, Technical Reference Guide Version 9.0. The Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD (December 2009)Google Scholar
  23. 23.
    Orueta, J.F., Lopez-De-Munain, J., Báez, K., Aiarzaguena, J.M., Aranguren, J.I., Pedrero, E.: Application of the ambulatory care groups in the primary care of European national health care system: does it work? Med. Care 37(3), 238–248 (1999)PubMedCrossRefGoogle Scholar
  24. 24.
    WONCA: International classification of primary care. ICPC-2-R, 2nd edn. Oxford University Press, New York (2005)Google Scholar
  25. 25.
    Soler, J.K., Okkes, I., Wood, M., Lamberts, H.: The coming of age of ICPC: celebrating the 21st birthday of the International Classification of Primary Care. Fam. Pract. 25(4), 312–317 (2008)PubMedCrossRefGoogle Scholar
  26. 26.
    Berger, A.N., DeYong, R., Udell, G.: Efficiency barriers to the consolidation of the European financial services industry. Eur. Financ. Manag. 7(1), 117–130 (2001)CrossRefGoogle Scholar
  27. 27.
    Watson, D.E., Katz, A., Reid, R.J., Bogdanovic, B., Roos, N., Heppner, P.: Family physician workload and access to care in Winnipeg 1991 to 2001. Can. Med. Assoc. J. 171(4), 339–342 (2004)CrossRefGoogle Scholar
  28. 28.
    Britt, H., Bhasale, A., Miles, D.A., Meza, A., Sayer, G.P., Angelis, M.: The sex of the general practitioner: a comparison of characteristics, patients, and medical conditions managed. Med. Care 34(5), 403–415 (1996)PubMedCrossRefGoogle Scholar
  29. 29.
    Sayer, G.P., Britt, H.: Sex differences in morbidity: a case of discrimination in general practice. Soc. Sci. Med. 42(2), 257–264 (1996)PubMedCrossRefGoogle Scholar
  30. 30.
    Harrison, C.M., Britt, H.C., Charles, J.: Sex of the GP—20 years on. Med. J. Aust. 195(4), 192–196 (2011)PubMedGoogle Scholar
  31. 31.
    Verhaak, P.F., Schellevis, F.G., Nuijen, J., Volkers, A.C.: Patients with a psychiatric disorder in general practice: determinants of general practitioners’ psychological diagnosis. Gen. Hosp. Psychiatry 28(2), 125–132 (2006)PubMedCrossRefGoogle Scholar
  32. 32.
    Hutton, C., Gunn, J.: Do longer consultations improve the management of psychological problems in general practice? A systematic literature review. BMC Health Serv. Res. 7, 71 (2007)PubMedCentralPubMedCrossRefGoogle Scholar
  33. 33.
    Long, J.S., Ervin, L.H.: Using heteroscedasticity consistent standard errors in the linear regression model. Am. Stat. 54, 217–224 (2000)Google Scholar
  34. 34.
    Schroll, H., Christensen, R.D., Thomsen, J.L., Andersen, M., Friborg, S., Sondergaard, J.: The Danish model for improvement of diabetes care in general practice: impact of automated collection and feedback of patient data. Int. J. Fam. Med. 2012, 208123 (2012)Google Scholar
  35. 35.
    Sicras-Mainar, A., Velasco–Velasco, S., Navarro-Artieda, R., Prados-Torres, A., Bolibar-Ribas, B., Violan-Fors, C.: Adaptive capacity of the Adjusted Clinical Groups Case-Mix System to the cost of primary healthcare in Catalonia (Spain): a observational study. BMJ Open. 2, e000941 (2012). doi: 10.1136/bmjopen-2012-000941
  36. 36.
    Engstrom, S.G., Carlsson, L., Ostgren, C.J., Nilsson, G.H., Borgquist, L.A.: The importance of comorbidity in analysing patient costs in Swedish primary care. BMC Public Health 6, 36 (2006). doi: 10.1186/1471-2458-6-36. Available at PubMedCentralPubMedCrossRefGoogle Scholar
  37. 37.
    Schokkaert, E., Van de Voorde, C.: Risk selection and the specification of the conventional risk adjustment formula. J. Health Econ. 23, 1237–1259 (2004)PubMedCrossRefGoogle Scholar
  38. 38.
    Sibley, L.M., Glazier, R.H.: Evaluation of the equity of age-sex adjusted primary care capitation payments in Ontario, Canada. Health Policy 104(2), 186–192 (2012)PubMedCrossRefGoogle Scholar
  39. 39.
    Rosen, A.K., Reid, R., Broemeling, A.M., Rakovski, C.C.: Applying a risk-adjustment framework to primary care: can we improve on existing measures? Ann. Fam. Med. 1(1), 44–51 (2003)PubMedCentralPubMedCrossRefGoogle Scholar
  40. 40.
    Horner, R.D.: Risk-adjusted capitation in an era of personalized medicine: a dangerous opportunity to bend the health care cost curve. Med. Care 50(8), 633–634 (2012)PubMedCrossRefGoogle Scholar
  41. 41.
    Ash, A.S., Ellis, R.P.: Risk-adjusted payment and performance assessment for primary care. Med. Care 50(8), 643–653 (2012)PubMedCentralPubMedCrossRefGoogle Scholar
  42. 42.
    Wennberg, J.E., Barnes, B.A., Zubkoff, M.: Professional uncertainty and the problem of supplier-induced demand. Soc. Sci. Med. 16(7), 811–824 (1982)PubMedCrossRefGoogle Scholar
  43. 43.
    Schroll, H., Stovring, H., Kragstrup, J.: Differences in general practitioners’ use of International Classification for Primary Care diagnoses. The interobserver and intraobserver variation. Ugeskr. Laeger 165(43), 4104–4107 (2003)PubMedGoogle Scholar
  44. 44.
    Jegers, M., Kesteloot, K., De, G.D., Gilles, W.: A typology for provider payment systems in health care. Health Policy 60(3), 255–273 (2002)PubMedCrossRefGoogle Scholar
  45. 45.
    De Lusignan, S.: The barriers to clinical coding in general practice: a literature review. Med. Inform. Internet Med. 30(2), 89–97 (2005)PubMedCrossRefGoogle Scholar
  46. 46.
    Shen, J., Andersen, R., Brook, R., Kominski, G., Albert, P.S., Wenger, N.: The effects of payment method on clinical decision-making—physician responses to clinical scenarios. Med. Care 42(3), 297–302 (2004)PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Troels Kristensen
    • 1
    • 2
    Email author
  • Kim Rose Olsen
    • 1
    • 2
  • Henrik Schroll
    • 3
  • Janus Laust Thomsen
    • 2
    • 3
  • Anders Halling
    • 2
  1. 1.Faculty of Health Sciences, COHERE-Centre of Health Economics Research, Institute of Public HealthUniversity of Southern DenmarkOdense CDenmark
  2. 2.Faculty of Health Sciences, Research Unit for General Practice, Institute of Public HealthUniversity of Southern DenmarkOdense CDenmark
  3. 3.DAK-E Danish Quality Unit of General PracticeOdense CDenmark

Personalised recommendations