title: [Cost predictors of depressive inpatient episodes in Germany. The health insurer's point of view].
abstract: BACKGROUND
  Inpatient treatment is the most costly sector of treatment for depressive disorders in Germany. However, little is known about which patient and hospital characteristics contribute to costs of inpatient episodes.
  
  
  METHODS
  To take part in this study, patients had to fullfill criteria for ICD-10 diagnosis of F31.3-F31.5, F32, F33, F34.1, F43.20, or F43.21. Episodes were recorded between September 9 2001 and March 3 2003 in ten hospitals in three German states. Inpatient records of 1,202 persons were analysed. Multiple regression analysis was performed to identify significant patient predictors of cost per inpatient episode, and the predictive function of hospital characteristics was analysed by applying hierarchical linear modeling.
  
  
  RESULTS
  Patient characteristics at admission could not explain a substantial part of the variance in episode costs. Better prediction was possible including variables from the whole treatment process. Also, conditions for admission and patient-related factors did not well explain cost differences between hospitals, but characteristics of the whole treatment were.
  
  
  CONCLUSIONS
  For predicting costs of inpatient depressive episodes, the complete course treatment has to be considered. As in the physiologic sector, therapeutic and diagnostic procedures have a great effect on cost prediction.
authors.affiliation: Zentralinstitut für Seelische Gesundheit, J 5, 68159, Mannheim. stamm@zi-mannheim.de
authors.forename: K
authors.initials: K
authors.lastname: Stamm
authors.forename: H J
authors.initials: HJ
authors.lastname: Salize
authors.forename: M
authors.initials: M
authors.lastname: Härter
authors.forename: S
authors.initials: S
authors.lastname: Brand
authors.forename: P
authors.initials: P
authors.lastname: Sitta
authors.forename: M
authors.initials: M
authors.lastname: Berger
authors.forename: W
authors.initials: W
authors.lastname: Gaebel
authors.forename: F
authors.initials: F
authors.lastname: Schneider
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003365:Costs and Cost Analysis; D003866:Depressive Disorder / Q000191:economics* / Q000628:therapy; D017050:Episode of Care; D005260:Female; D005858:Germany; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006295:Health Resources / Q000191:economics*; D006760:Hospitalization / Q000191:economics*; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D009313:National Health Programs / Q000191:economics*; D013223:Statistics as Topic; D017749:Total Quality Management / Q000191:economics