title: Effects of multiple chronic conditions on health care costs: an analysis based on an advanced tree-based regression model.
abstract: 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 ≥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.
  
  
  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.
  
  
  BACKGROUND
  Current Controlled Trials ISRCTN89818205.
authors.forename: Hans-Helmut
authors.initials: HH
authors.lastname: König
authors.forename: Hanna
authors.initials: H
authors.lastname: Leicht
authors.forename: Horst
authors.initials: H
authors.lastname: Bickel
authors.forename: Angela
authors.initials: A
authors.lastname: Fuchs
authors.forename: Jochen
authors.initials: J
authors.lastname: Gensichen
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Maier
authors.forename: Karola
authors.initials: K
authors.lastname: Mergenthal
authors.forename: Steffi
authors.initials: S
authors.lastname: Riedel-Heller
authors.forename: Ingmar
authors.initials: I
authors.lastname: Schäfer
authors.forename: Gerhard
authors.initials: G
authors.lastname: Schön
authors.forename: Siegfried
authors.initials: S
authors.lastname: Weyerer
authors.forename: Birgitt
authors.initials: B
authors.lastname: Wiese
authors.forename: Hendrik van den
authors.initials: Hv
authors.lastname: Bussche
authors.forename: Martin
authors.initials: M
authors.lastname: Scherer
authors.forename: Matthias
authors.initials: M
authors.lastname: Eckardt
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000553:Ambulatory Care / Q000191:economics; D002908:Chronic Disease / Q000191:economics* / Q000453:epidemiology; D015897:Comorbidity; D016527:Drug Costs; D005260:Female; D005858:Germany / Q000453:epidemiology; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006699:Home Care Services / Q000191:economics; D006760:Hospitalization / Q000191:economics; D006801:Humans; D008297:Male; D011446:Prospective Studies; D012044:Regression Analysis