ABSTRACT

Background/objective: To describe the design of ‘DepMod,’ a health-economic Markov model for assessing cost-effectiveness and budget impact of user-defined preventive interventions and treatments in depressive disorders.

Methods: DepMod has an epidemiological layer describing how a cohort of people can transition between health states (sub-threshold depression, first episode of mild, moderate or severe depression (partial) remission, recurrence, death). Superimposed on the epidemiological layer, DepMod has an intervention layer consisting of a reference scenario and alternative scenario comparing the effectiveness and cost-effectiveness of a user-defined package of preventive interventions and psychological and pharmacological treatments of depression. Results are presented in terms of quality-adjusted life years (QALYs) gained and healthcare expenditure. Costs and effects can be modeled over 5 years and are subjected to probabilistic sensitivity analysis.

Results: DepMod was used to assess the cost-effectiveness of scaling up preventive interventions for treating people with subclinical depression, which showed that there is an 82% probability that scaling up prevention is cost-effective given a willingness-to-pay threshold of €20,000 per QALY.

Conclusion: DepMod is a Markov model that assesses the cost-utility and budget impact of different healthcare packages aimed at preventing and treating depression and is freely available for academic purposes upon request at the authors.

摘要

背景/目标：描述“DepMod”的设计，这是一种健康经济马尔可夫模型，用于评估用户定义的抑郁症预防干预和治疗的成本效益和预算影响。

方法：DepMod 有一个流行病学层，描述了一群人如何在健康状态（亚阈值抑郁、轻度、中度或重度抑郁（部分）缓解、复发、死亡）之间转换。叠加在流行病学层上，DepMod 有一个干预层，由参考情景和替代情景组成，比较用户定义的预防干预措施以及抑郁症心理和药物治疗包的有效性和成本效益。结果以获得的质量调整生命年 (QALY) 和医疗保健支出表示。成本和影响可以建模超过 5 年，并进行概率敏感性分析。

结果：DepMod 用于评估扩大预防性干预措施治疗亚临床抑郁症患者的成本效益，结果表明，鉴于支付意愿门槛为 €，扩大预防措施具有成本效益的可能性为 82%每个 QALY 20,000。

结论：DepMod 是一种马尔可夫模型，用于评估旨在预防和治疗抑郁症的不同医疗保健方案的成本效用和预算影响，可应作者要求免费用于学术目的。