Ibargoyen-Roteta Nora, Gutierrez-Ibarluzea Iñaki, Benguria-Arrate Gaizka, Galnares-Cordero Lorea, Asua José
Osteba, Basque Office for Health Technology Assessment, Basque Health Department, Olaguibel 38, Vitoria- Gasteiz, Spain.
Int J Technol Assess Health Care. 2009 Jul;25(3):367-73. doi: 10.1017/S0266462309990183.
The aim of this study was to analyze the EuroScan Database and to describe and compare the characteristics of the included technologies and participating agencies.
Data of interest were exported from the EuroScan Database to Excel and to SPSS. A descriptive analysis depending on the agency, type of technology, stage of diffusion, and technology purpose was conducted. A frequency distribution analysis of the diffusion stage for different technology types and assigned purposes was made with the EpiCalc 2000 statistical calculator. A p value of less than .05 was considered to be statistically significant.
Four agencies introduced the great majority of the technologies (81 percent), with drugs representing the 46.26 percent of the total, followed by devices (21.21 percent). The purpose of 24.45 percent of the identified technologies was not specified, and 34.58 percent of them were identified at the investigational or phase III stage. The frequency distribution of diffusion stage at identification was found to be similar for devices and diagnostics (p = .543), whereas drugs were identified earlier than devices (p <.001). Some agencies were found to focus their work on drugs, whereas others focused mainly on devices. Interagency differences were also observed with regard to the stage of diffusion at which technologies were identified.
This is the first analysis of one of the most important databases on new and emerging health technologies. Our study suggests that more active strategies should be designed to provide an earlier identification, mainly in the case of devices.
本研究旨在分析欧洲扫描数据库，并描述和比较所纳入技术及参与机构的特征。
将感兴趣的数据从欧洲扫描数据库导出到Excel和SPSS。根据机构、技术类型、传播阶段和技术目的进行描述性分析。使用EpiCalc 2000统计计算器对不同技术类型和指定目的的传播阶段进行频率分布分析。p值小于0.05被认为具有统计学意义。
绝大多数技术（81%）由四个机构引入，其中药物占总数的46.26%，其次是设备（21.21%）。24.45%的已识别技术目的未明确说明，其中34.58%是在研究或三期阶段识别的。发现设备和诊断技术在识别时传播阶段的频率分布相似（p = 0.543），而药物比设备更早被识别（p < 0.001）。发现一些机构专注于药物工作，而另一些机构主要专注于设备。在技术被识别的传播阶段方面也观察到了机构间差异。
这是对最重要的新兴健康技术数据库之一的首次分析。我们的研究表明，应设计更积极的策略以实现更早的识别，主要是针对设备而言。
