Stamm K, Salize H J, Härter M, Brand S, Sitta P, Berger M, Gaebel W, Schneider F
Zentralinstitut für Seelische Gesundheit, J 5, 68159, Mannheim.
Nervenarzt. 2007 Jun;78(6):665-71. doi: 10.1007/s00115-006-2115-x.
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.
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.
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.
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.
在德国，住院治疗是抑郁症治疗中成本最高的环节。然而，对于哪些患者和医院特征会导致住院治疗费用增加，人们了解甚少。
参与本研究的患者必须符合ICD - 10中F31.3 - F31.5、F32、F33、F34.1、F43.20或F43.21的诊断标准。研究记录了2001年9月9日至2003年3月3日期间德国三个州的十家医院的治疗情况。对1202人的住院记录进行了分析。采用多元回归分析来确定每位住院患者费用的显著预测因素，并通过分层线性模型分析医院特征的预测作用。
入院时的患者特征无法解释住院费用差异的很大一部分。纳入整个治疗过程中的变量能实现更好的预测。此外，入院条件和患者相关因素并不能很好地解释医院之间的费用差异，而整个治疗过程的特征则可以。
为预测住院抑郁症治疗费用，必须考虑整个疗程。与生理领域一样，治疗和诊断程序对费用预测有很大影响。
