Seidl Astrid, Danner Marion, Wagner Christoph J, Sandmann Frank G, Sroczynski Gaby, Stürzlinger Heidi, Zsifkovits Johannes, Schwalm Anja, Lhachimi Stefan K, Siebert Uwe, Gerber-Grote Andreas
Institute for Quality and Efficiency in Health Care (IQWiG), Cologne, Germany (A Seidl, A Schwalm).
Institute for Health Economics and Clinical Epidemiology, Cologne University Hospital, Cologne, Germany (MD, CJW).
MDM Policy Pract. 2018 Jan 10;3(1):2381468317751923. doi: 10.1177/2381468317751923. eCollection 2018 Jan-Jun.
Estimating input costs for Markov models in health economic evaluations requires health state-specific costing. This is a challenge in mental illnesses such as depression, as interventions are not clearly related to health states. We present a hybrid approach to health state-specific cost estimation for a German health economic evaluation of antidepressants. Costs were determined from the perspective of the community of persons insured by statutory health insurance ("SHI insuree perspective") and included costs for outpatient care, inpatient care, drugs, and psychotherapy. In an additional step, costs for rehabilitation and productivity losses were calculated from the societal perspective. We collected resource use data in a stepwise hierarchical approach using SHI claims data, where available, followed by data from clinical guidelines and expert surveys. Bottom-up and top-down costing approaches were combined. Depending on the drug strategy and health state, the average input costs varied per patient per 8-week Markov cycle. The highest costs occurred for agomelatine in the health state first-line treatment (FT) ("FT relapse") with €506 from the SHI insuree perspective and €724 from the societal perspective. From both perspectives, the lowest costs (excluding placebo) were €55 for selective serotonin reuptake inhibitors in the health state "FT remission." To estimate costs in health economic evaluations of treatments for depression, it can be necessary to link different data sources and costing approaches systematically to meet the requirements of the decision-analytic model. As this can increase complexity, the corresponding calculations should be presented transparently. The approach presented could provide useful input for future models.
在健康经济评估中，估计马尔可夫模型的投入成本需要针对特定健康状态进行成本核算。这在抑郁症等精神疾病中是一项挑战，因为干预措施与健康状态之间的关系并不明确。我们提出了一种混合方法，用于对德国抗抑郁药物的健康经济评估进行特定健康状态的成本估计。成本是从法定健康保险所涵盖人群的角度（“法定健康保险参保者角度”）确定的，包括门诊护理、住院护理、药物和心理治疗的成本。在额外的步骤中，从社会角度计算了康复成本和生产力损失。我们采用逐步分层的方法，利用法定健康保险理赔数据（如有）收集资源使用数据，随后收集临床指南和专家调查的数据。自下而上和自上而下的成本核算方法相结合。根据药物策略和健康状态，每个患者每8周的马尔可夫周期的平均投入成本各不相同。从法定健康保险参保者角度来看，阿戈美拉汀在“一线治疗（FT）（‘FT复发’）”健康状态下成本最高，为506欧元，从社会角度来看为724欧元。从两个角度来看，在“FT缓解”健康状态下，选择性5-羟色胺再摄取抑制剂的成本最低（不包括安慰剂），为55欧元。在抑郁症治疗的健康经济评估中估计成本时，可能需要系统地链接不同的数据源和成本核算方法，以满足决策分析模型的要求。由于这可能会增加复杂性，相应的计算应透明呈现。所提出的方法可为未来的模型提供有用的输入。
