Open Access Open Badges Research article

Effects of multiple chronic conditions on health care costs: an analysis based on an advanced tree-based regression model

Hans-Helmut König1*, Hanna Leicht1, Horst Bickel2, Angela Fuchs3, Jochen Gensichen4, Wolfgang Maier5, Karola Mergenthal6, Steffi Riedel-Heller7, Ingmar Schäfer8, Gerhard Schön9, Siegfried Weyerer10, Birgitt Wiese11, Hendrik van den Bussche8, Martin Scherer8, Matthias Eckardt1 and for the MultiCare study group

Author Affiliations

1 Department of Health Economics and Health Services Research, Hamburg Center for Health Economics, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg, 20246, Germany

2 Department of Psychiatry, Technical University of Munich, Ismaninger Straße 22, Munich, 81675, Germany

3 Department of General Practice, University of Düsseldorf Medical Center, Moorenstraße 5, Düsseldorf, 40225, Germany

4 Department of General Practice, Jena University Hospital, Bachstraße 18, 07743, Jena, Germany

5 Department of Psychiatry and Psychotherapy, University of Bonn, Sigmund-Freud-Straße 25, Bonn, 53105, Germany

6 Institute of General Practice, Goethe-University Frankfurt am Main, Theodor-Stern-Kai 7, Frankfurt am Main, 60590, Germany

7 Institute of Social Medicine, Occupational Health and Public Health, University of Leipzig, Ph.-Rosenthal-Str. 55, Leipzig, 04103, Germany

8 Department of Primary Medical Care, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg, 20246, Germany

9 Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, Hamburg, 20246, Germany

10 Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, D6, 68159, Germany

11 Institute for Biometry, Hannover Medical School, Carl-Neuberg-Straße 1, Hannover, 30625, Germany

For all author emails, please log on.

BMC Health Services Research 2013, 13:219  doi:10.1186/1472-6963-13-219

Published: 15 June 2013



To analyze the impact of multimorbidity (MM) on health care costs taking into account data heterogeneity.


Data come from a multicenter prospective cohort study of 1,050 randomly selected primary care patients aged 65 to 85 years suffering from MM in Germany. MM was defined as co-occurrence of ≥3 conditions from a list of 29 chronic diseases. A conditional inference tree (CTREE) algorithm was used to detect the underlying structure and most influential variables on costs of inpatient care, outpatient care, medications as well as formal and informal nursing care.


Irrespective of the number and combination of co-morbidities, a limited number of factors influential on costs were detected. Parkinson’s disease (PD) and cardiac insufficiency (CI) were the most influential variables for total costs. Compared to patients not suffering from any of the two conditions, PD increases predicted mean total costs 3.5-fold to approximately € 11,000 per 6 months, and CI two-fold to approximately € 6,100. The high total costs of PD are largely due to costs of nursing care. Costs of inpatient care were significantly influenced by cerebral ischemia/chronic stroke, whereas medication costs were associated with COPD, insomnia, PD and Diabetes. Except for costs of nursing care, socio-demographic variables did not significantly influence costs.


Irrespective of any combination and number of co-occurring diseases, PD and CI appear to be most influential on total health care costs in elderly patients with MM, and only a limited number of factors significantly influenced cost.

Trial registration

Current Controlled Trials ISRCTN89818205

Multiple chronic conditions; Multimorbidity; Co-morbidity; Health care costs; Conditional inference trees; Statistical learning