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

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• 作者：Hans-Helmut K?nig (1)
  Hanna Leicht (1)
  Horst Bickel (2)
  Angela Fuchs (3)
  Jochen Gensichen (4)
  Wolfgang Maier (5)
  Karola Mergenthal (6)
  Steffi Riedel-Heller (7)
  Ingmar Sch?fer (8)
  Gerhard Sch?n (9)
  Siegfried Weyerer (10)
  Birgitt Wiese (11)
  Hendrik van den Bussche (8)
  Martin Scherer (8)
  Matthias Eckardt (1)
  
• 关键词：Multiple chronic conditions ; Multimorbidity ; Co ; morbidity ; Health care costs ; Conditional inference trees ; Statistical learning
• 刊名：BMC Health Services Research
• 出版年：2013
• 出版时间：December 2013
• 年：2013
• 卷：13
• 期：1
• 全文大小：319KB
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• 作者单位：Hans-Helmut K?nig (1)
  Hanna Leicht (1)
  Horst Bickel (2)
  Angela Fuchs (3)
  Jochen Gensichen (4)
  Wolfgang Maier (5)
  Karola Mergenthal (6)
  Steffi Riedel-Heller (7)
  Ingmar Sch?fer (8)
  Gerhard Sch?n (9)
  Siegfried Weyerer (10)
  Birgitt Wiese (11)
  Hendrik van den Bussche (8)
  Martin Scherer (8)
  Matthias Eckardt (1)
  
  1. Department of Health Economics and Health Services Research, Hamburg Center for Health Economics, University Medical Center Hamburg-Eppendorf, Martinistra?e 52, Hamburg, 20246, Germany
  2. Department of Psychiatry, Technical University of Munich, Ismaninger Stra?e 22, Munich, 81675, Germany
  3. Department of General Practice, University of Düsseldorf Medical Center, Moorenstra?e 5, Düsseldorf, 40225, Germany
  4. Department of General Practice, Jena University Hospital, Bachstra?e 18, 07743, Jena, Germany
  5. Department of Psychiatry and Psychotherapy, University of Bonn, Sigmund-Freud-Stra?e 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, Martinistra?e 52, Hamburg, 20246, Germany
  9. Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Martinistra?e 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-Stra?e 1, Hannover, 30625, Germany
  

文摘

Background To analyze the impact of multimorbidity (MM) on health care costs taking into account data heterogeneity. Methods 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 ? 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. Results 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. Conclusions 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