Literature DB >> 29253683

A standardized stepwise drug treatment algorithm for depression reduces direct treatment costs in depressed inpatients - Results from the German Algorithm Project (GAP3).

Roland Ricken1, Katja Wiethoff2, Thomas Reinhold3, Thomas J Stamm4, Thomas C Baghai5, Robert Fisher6, Florian Seemüller7, Peter Brieger8, Joachim Cordes9, Gerd Laux10, Iris Hauth11, Hans-Jürgen Möller12, Andreas Heinz2, Michael Bauer13, Mazda Adli14.   

Abstract

BACKGROUND: In a previous single center study we found that a standardized drug treatment algorithm (ALGO) was more cost effective than treatment as usual (TAU) for inpatients with major depression. This report aimed to determine whether this promising initial finding could be replicated in a multicenter study.
METHODS: Treatment costs were calculated for two time periods: the study period (from enrolment to exit from study) and time in hospital (from enrolment to hospital discharge) based on daily hospital charges. Cost per remitted patient during the study period was considered as primary outcome.
RESULTS: 266 patients received ALGO and 84 received TAU. For the study period, ALGO costs were significantly lower than TAU (ALGO: 7 848 ± 6 065 €; TAU: 10 033 ± 7 696 €; p = 0.04). For time in hospital, costs were not different (ALGO: 14 734 ± 8 329 €; TAU: 14 244 ± 8 419 €; p = 0.617). Remission rates did not differ for the study period (ALGO: 57.9%, TAU: 50.0%; p=0.201). Remission rates were greater in ALGO (83.3%) than TAU (66.2%) for time in hospital (p = 0.002). Cost per remission was lower in ALGO (13 554 ± 10 476 €) than TAU (20 066 ± 15 391 €) for the study period (p < 0.001) and for time in hospital (ALGO: 17 582 ± 9 939 €; TAU: 21 516 ± 12 718 €; p = 0.036). LIMITATIONS: Indirect costs were not assessed. Different dropout rates in TAU and ALGO complicated interpretation.
CONCLUSIONS: Treatment algorithms enhance the cost effectiveness of the care of depressed inpatients, which replicates our prior results in an independent sample.
Copyright © 2017. Published by Elsevier B.V.

Entities:  

Keywords:  Cost effectiveness analysis; Depression; Health economy; Treatment algorithm

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Year:  2017        PMID: 29253683     DOI: 10.1016/j.jad.2017.11.051

Source DB:  PubMed          Journal:  J Affect Disord        ISSN: 0165-0327            Impact factor:   4.839


  1 in total

1.  Concordance of the treatment patterns for major depressive disorders between the Canadian Network for Mood and Anxiety Treatments (CANMAT) algorithm and real-world practice in China.

Authors:  Lu Yang; Yousong Su; Sijia Dong; Tao Wu; Yongjing Zhang; Hong Qiu; Wenjie Gu; Hong Qiu; Yifeng Xu; JianLi Wang; Jun Chen; Yiru Fang
Journal:  Front Pharmacol       Date:  2022-08-31       Impact factor: 5.988

  1 in total

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