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发出“ping”声的机器:经合组织国家的医疗技术与医疗支出

Machines that Go 'Ping': Medical Technology and Health Expenditures in OECD Countries.

作者信息

Willemé Peter, Dumont Michel

机构信息

Federal Planning Bureau, Brussels, Belgium.

Ghent University, Ghent, Belgium.

出版信息

Health Econ. 2015 Aug;24(8):1027-41. doi: 10.1002/hec.3089. Epub 2014 Jul 28.

Abstract

Technology is believed to be a major determinant of increasing health spending. The main difficulty to quantify its effect is to find suitable proxies to measure medical technological innovation. This paper's main contribution is the use of data on approved medical devices and drugs to proxy for medical technology. The effects of these variables on total real per capita health spending are estimated using a panel model for 18 Organisation for Economic Co-operation and Development (OECD) countries covering the period 1981-2012. The results confirm the substantial cost-increasing effect of medical technology, which accounts for almost 50% of the explained historical growth of spending. Despite the overall net positive effect of technology, the effect of two subgroups of approvals on expenditure is significantly negative. These subgroups can be thought of as representing 'incremental medical innovation', whereas the positive effects are related to radically innovative pharmaceutical products and devices. A separate time series model was estimated for the USA because the FDA approval data in fact only apply to the USA, while they serve as proxies for the other OECD countries. Our empirical model includes an indicator of obesity, and estimations confirm the substantial contribution of this lifestyle variable to health spending growth in the countries studied.

摘要

技术被认为是医疗支出不断增加的一个主要决定因素。量化其影响的主要困难在于找到合适的指标来衡量医疗技术创新。本文的主要贡献在于利用获批医疗设备和药物的数据来代表医疗技术。使用涵盖1981 - 2012年期间18个经济合作与发展组织(经合组织)国家的面板模型,估计这些变量对实际人均医疗总支出的影响。结果证实了医疗技术具有显著的成本增加效应,这一效应几乎占已解释的历史支出增长的50%。尽管技术总体上具有净正向效应,但两类获批产品对支出的影响显著为负。这两类获批产品可被视为代表“渐进式医疗创新”,而正向效应则与根本性创新的药品和设备相关。由于美国食品药品监督管理局(FDA)的获批数据实际上仅适用于美国,而它们被用作其他经合组织国家的指标,因此针对美国估计了一个单独的时间序列模型。我们的实证模型包含一个肥胖指标,估计结果证实了这一生活方式变量对所研究国家医疗支出增长的重大贡献。

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