对算法引导治疗和预定义持续时间的抑郁症治疗计划进行比较分析:利用例行护理数据探索成本效益

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Fang Li¹, Ellen Visser², Maarten Brilman²

¹University of Groningen, Faculty of Science and Engineering, Groningen Research Institute of Pharmacy, Groningen, The Netherlands f.li@rug.nl.

BMJ mental health

|November 15, 2023

概括

算法引导的抑郁症治疗产生了更大的健康益处,但更昂贵,在当前的门下证明不具有成本效益. 这项研究有助于决策者优化治疗分配和提高护理质量.

科学领域:

卫生经济学卫生经济学
心理健康服务研究心理健康服务研究
临床决策临床决策

背景情况:

成本效益数据对于有效的抑郁症治疗分配和改善护理质量至关重要.
了解不同抑郁症管理策略的经济影响至关重要.

研究的目的:

为了比较两个抑郁症治疗方案的实际成本效益:以算法为指导的缓解为重点,与以患者偏好为中心的响应为重点.
从医疗保健系统的角度来看,使用例行护理数据来评估这些计划.

主要方法:

一项涉及6295名参与者的自然主义研究在两年内进行.
以质量调整的生命年 (QALY) 和无抑郁日 (DFD) 被用作结果指标.
计算了增量成本效益比率 (ICER),通过引导和灵敏度分析评估不确定性.

主要成果:

算法引导的程序导致了更多的DFD (12) 和QALY (0.013),但成本更高 (3070欧元).
ICER大约为256欧元/DFD和236154欧元/QALY,这表明与预先定义的持续时间计划相比,成本效益较低.
算法引导的程序在5万欧元/QALY值时具有<10%的成本效益概率;灵敏度分析证实了这些发现.

结论:

虽然算法引导的程序取得了更大的健康收益,但其较高的成本使其在当前荷兰经济门内不具有成本效益.
这两种治疗方案都有不同的好处,取决于患者的偏好和医疗预算.
这项研究为优化治疗分配和提高抑郁症护理成本效益提供了宝贵的见解.

关键词:

抑郁症和情绪障碍抑郁症和情绪障碍

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