Comparing Methods for Classification of Hospital Visits in the Danish National Patient Registry (DNPR): DNPR3 Versus DNPR2.

IF 3.4 2区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Clinical Epidemiology Pub Date : 2025-03-21 eCollection Date: 2025-01-01 DOI:10.2147/CLEP.S499822

Kirsten Skjærbæk Duch, Bergur Magnussen, Flemming Skjøth, Rasmus Westermann, Lene Wohlfahrt Dreyer

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引用次数: 0

Abstract

Background: The Danish National Patient Registry (DNPR) is a central source of information on hospital contacts for the Danish population and is a key data source for health-related Danish registry studies. The data structure of DNPR was updated from DNPR2 to DNPR3 in 2019, where a key patient-type variable for classification of inpatient, outpatient, or emergency wards was removed. This affects how hospital contacts can be defined and compared across different calendar years.

Aim: To present and compare different algorithms to determine the type of hospital visit (inpatient, outpatient, or emergency) for all hospital visits in Denmark from 2006 to 2021 across DNPR2 and DNPR3.

Methods: The monthly number of hospital visits per 1000 citizens was presented for four different algorithms: 1) a validated approach suggested by Skjøth et al, 2) an approach suggested by the Danish Ministry of Health and Elderly, 3) the latter combined with patient type variables available in DNPR2 only, and 4) a consensus-driven algorithm introduced by Gregersen et al.

Results: Using the same algorithm for DNPR2 and DNPR3 yielded the most similar results across calendar years. The least variation across calendar years was observed for the approach suggested by the Danish Ministry of Health and Elderly, whereas the validated approach suggested by Skjøth et al was more in line with the patient-type variable previously used in DNPR2. When comparing the algorithms, the main difference in the number of hospital visits was observed for inpatient and emergency visits.

Conclusion: We recommend using the same algorithm across DNPR2 and DNPR3. The choice of algorithm should be based on the disease or patient group being studied and by considering how the approaches reflect reality and need in the actual study. We recommend the algorithm suggested by Skjøth et al for the specific clinical situations presented in this study.

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丹麦国家患者登记（DNPR）中医院就诊分类方法的比较：DNPR3与DNPR2

背景：丹麦国家患者登记处（DNPR）是丹麦人口医院接触信息的中心来源，也是丹麦健康相关登记研究的关键数据来源。2019年，DNPR的数据结构从DNPR2更新为DNPR3，其中删除了用于住院、门诊或急诊病房分类的关键患者类型变量。这影响了如何定义和比较不同日历年的医院联系人。目的：通过DNPR2和DNPR3，介绍并比较2006年至2021年丹麦所有医院就诊类型（住院、门诊或急诊）的不同算法。方法：采用四种不同的算法给出了每1000名公民的每月就诊次数：1)Skjøth等人提出的经过验证的方法，2)丹麦卫生和老年人部提出的方法，3)后者与仅在DNPR2中可用的患者类型变量相结合，以及4)Gregersen等人提出的共识驱动算法。结果：对DNPR2和DNPR3使用相同的算法产生了跨日历年最相似的结果。丹麦卫生和老年人部建议的方法在历年之间的变化最小，而Skjøth等人建议的经过验证的方法更符合先前在DNPR2中使用的患者类型变量。当比较这些算法时，主要的差异是住院和急诊就诊的次数。结论：我们建议在DNPR2和DNPR3中使用相同的算法。算法的选择应基于所研究的疾病或患者群体，并考虑方法在实际研究中如何反映现实和需要。针对本研究的具体临床情况，我们推荐Skjøth等人提出的算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical Epidemiology Medicine-Epidemiology

CiteScore

6.30

自引率

5.10%

发文量

169

审稿时长

16 weeks

期刊介绍： Clinical Epidemiology is an international, peer reviewed, open access journal. Clinical Epidemiology focuses on the application of epidemiological principles and questions relating to patients and clinical care in terms of prevention, diagnosis, prognosis, and treatment. Clinical Epidemiology welcomes papers covering these topics in form of original research and systematic reviews. Clinical Epidemiology has a special interest in international electronic medical patient records and other routine health care data, especially as applied to safety of medical interventions, clinical utility of diagnostic procedures, understanding short- and long-term clinical course of diseases, clinical epidemiological and biostatistical methods, and systematic reviews. When considering submission of a paper utilizing publicly-available data, authors should ensure that such studies add significantly to the body of knowledge and that they use appropriate validated methods for identifying health outcomes. The journal has launched special series describing existing data sources for clinical epidemiology, international health care systems and validation studies of algorithms based on databases and registries.