National Institute for Public Health and the Environment (RIVM), Expertise Centre for Methodology and Information Services, Bilthoven, the Netherlands.
Pharmacoeconomics. 2011 Mar;29(3):175-87. doi: 10.2165/11586130-000000000-00000.
A shortcoming of many economic evaluations is that they do not include all medical costs in life-years gained (also termed indirect medical costs). One of the reasons for this is the practical difficulties in the estimation of these costs. While some methods have been proposed to estimate indirect medical costs in a standardized manner, these methods fail to take into account that not all costs in life-years gained can be estimated in such a way. Costs in life-years gained caused by diseases related to the intervention are difficult to estimate in a standardized manner and should always be explicitly modelled. However, costs of all other (unrelated) diseases in life-years gained can be estimated in such a way. We propose a conceptual model of how to estimate costs of unrelated diseases in life-years gained in a standardized manner. Furthermore, we describe how we estimated the parameters of this conceptual model using various data sources and studies conducted in the Netherlands. Results of the estimates are embedded in a software package called 'Practical Application to Include future Disease costs' (PAID 1.0). PAID 1.0 is available as a Microsoft® Excel tool (available as Supplemental Digital Content via a link in this article) and enables researchers to 'switch off' those disease categories that were already included in their own analysis and to estimate future healthcare costs of all other diseases for incorporation in their economic evaluations. We assumed that total healthcare expenditure can be explained by age, sex and time to death, while the relationship between costs and these three variables differs per disease. To estimate values for age- and sex-specific per capita health expenditure per disease and healthcare provider stratified by time to death we used Dutch cost-of-illness (COI) data for the year 2005 as a backbone. The COI data consisted of age- and sex-specific per capita health expenditure uniquely attributed to 107 disease categories and eight healthcare provider categories. Since the Dutch COI figures do not distinguish between costs of those who die at a certain age (decedents) and those who survive that age (survivors), we decomposed average per capita expenditure into parts that are attributable to decedents and survivors, respectively, using other data sources.
许多经济评估的一个缺点是，它们没有将所有与获得的生命年相关的医疗成本（也称为间接医疗成本）纳入其中。造成这种情况的一个原因是，这些成本的估计存在实际困难。虽然已经提出了一些方法来以标准化的方式估计间接医疗成本，但这些方法没有考虑到并非所有获得的生命年的成本都可以以这种方式估计。与干预相关的疾病所导致的获得的生命年的成本难以以标准化的方式进行估计，因此应该始终明确建模。然而，获得的生命年中所有其他（不相关）疾病的成本可以以这种方式进行估计。我们提出了一个概念模型，用于以标准化的方式估计获得的生命年中与疾病无关的成本。此外，我们描述了如何使用各种数据源和在荷兰进行的研究来估计这个概念模型的参数。估计结果被嵌入到一个名为“实用方法以纳入未来疾病成本”（PAID 1.0）的软件包中。PAID 1.0 是一个 Microsoft®Excel 工具（可通过本文中的链接作为补充数字内容获得），使研究人员能够“关闭”他们自己的分析中已经包含的那些疾病类别，并估计所有其他疾病的未来医疗保健成本，以便纳入他们的经济评估。我们假设总医疗保健支出可以用年龄、性别和死亡时间来解释，而成本与这三个变量的关系因疾病而异。为了估计每种疾病的特定年龄和性别的人均卫生支出以及按死亡时间分层的医疗保健提供者的价值，我们使用了 2005 年荷兰疾病成本（COI）数据作为基础。COI 数据包括按年龄和性别划分的、唯一归因于 107 种疾病类别和 8 种医疗保健提供者类别的人均卫生支出。由于荷兰的 COI 数据无法区分在特定年龄死亡的人（死者）和在该年龄幸存的人（幸存者）的成本，因此我们使用其他数据源将平均人均支出分解为分别归因于死者和幸存者的部分。
