Effects of multiple chronic conditions on health care costs: an analysis based on an advanced tree-based regression model
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
Citation and License
BMC Health Services Research 2013, 13:219 doi:10.1186/1472-6963-13-219Published: 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.
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