在丹麦国家患者登记册 (DNPR) 中对医院访问的分类方法进行比较:DNPR3与DNPR2对比
Kirsten Skjærbæk Duch1,2,3, Bergur Magnussen1, Flemming Skjøth4,5
1Center of Rheumatic Research Aalborg (CERRA), Aalborg University Hospital, Aalborg, Denmark.
Clinical epidemiology
|March 26, 2025
概括
对丹麦医院访问分类的比较算法显示,在丹麦国家患者登记 (DNPR) 版本 (DNPR2和DNPR3) 中使用一致的方法对于可靠的健康数据分析至关重要.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 公共卫生 数据科学 数据科学
- 流行病学 流行病学
背景情况:
- 丹麦国家患者登记 (DNPR) 对丹麦的健康研究至关重要.
- 2019年数据结构更新 (DNPR2到DNPR3) 删除了一个关键的患者类型变量,影响了医院访问分类.
- 这种变化需要评估方法,以跨年一致的医院联系定义.
研究的目的:
- 为了比较丹麦医院访问 (住院,门诊,急诊) 的分类算法.
- 评估DNPR2和DNPR3数据结构 (2006-2021) 中这些算法的一致性.
- 为了指导选择适合的算法用于健康登记册研究.
主要方法:
- 评估了四个算法:Skjøth等人. 验证方法,丹麦卫生和老年人部方法,使用DNPR2特定变量的部方法,以及Gregersen等人. 达成共识的算法.
- 对于每个算法,每个1000名公民的月度医院访问量都被计算出来.
- 比较的重点是跨DNPR版本和日历年的一致性.
主要成果:
- 使用相同的算法对DNPR2和DNPR3都产生了最一致的结果随着时间的推移.
- 丹麦卫生部和老年人部的方法在过去几年中变化最小.
- 在 Skjøth 等. 这种方法最符合之前的DNPR2患者类型变量;对于住院和急诊的访问有显著差异.
结论:
- 建议在DNPR2和DNPR3中使用单一,一致的算法进行可靠的分析.
- 算法选择应与特定研究的疾病或患者组及其现实世界的适用性保持一致.
- 在 Skjøth 等. 对于本研究中详细介绍的特定临床情景,建议使用该算法.
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