Hosar Rannei, Steinsbekk Aslak
Department of Public Health and Nursing, Norwegian University of Science and Technology (NTNU), Norway.
Scand J Public Health. 2024 Jul;52(5):607-615. doi: 10.1177/14034948231166974. Epub 2023 Apr 23.
This study aimed to present the Johns Hopkins Adjusted Clinical Groups (ACG) System risk stratification profile of a total adult population of somatic health-care users when using data from either general practitioners (GPs) or hospital services and to compare the number and characteristics of individuals identified as having complex and long-term health-care needs in each data source.
This was a registry-based study that included all adult residents (=168,285) in four municipalities in Central Norway who received somatic health care during 2013. Risk profiles were generated using the ACG System based on age, sex and diagnoses registered by GPs or the local hospital. ACG output variables on number of chronic conditions, frailty and concurrent resource utilisation were chosen as indicators of complexity.
Nearly nine out of 10 (83.9%) of the population had been in contact with a GP, and 35.4% with the hospital. The mean number of diagnoses (3.0) was equal in both sources. A larger proportion of the population had higher risk scores in all variables except frailty when comparing hospital data to GP data. This was also found when comparing individuals identified as having complex and long-term health-care needs. A similar proportion of the population was found to have complex and long-term health-care needs (hospital 6.7%, GP 6.3%), but only one in five (21.5%) were identified in both data sets.
As data from GPs and hospitals identified mostly different individuals with complex and long-term health-care needs, combining data sources is likely to be the best option for identifying those most in need of special attention.
本研究旨在利用全科医生(GP)或医院服务的数据,呈现成年躯体健康护理使用者总体人群的约翰霍普金斯调整临床组(ACG)系统风险分层概况,并比较每个数据源中被确定为有复杂和长期医疗需求的个体数量及特征。
这是一项基于登记处的研究,纳入了挪威中部四个市镇在2013年接受躯体健康护理的所有成年居民(=168,285人)。基于年龄、性别以及全科医生或当地医院登记的诊断信息,使用ACG系统生成风险概况。选择慢性病数量、虚弱程度和并发资源利用等ACG输出变量作为复杂性指标。
近十分之九(83.9%)的人群曾与全科医生接触,35.4%的人群曾与医院接触。两个数据源的平均诊断数量(3.0)相等。与全科医生数据相比,在除虚弱程度外的所有变量中,医院数据中有更高比例的人群具有更高的风险评分。在比较被确定为有复杂和长期医疗需求的个体时也发现了这一点。发现有相似比例的人群有复杂和长期医疗需求(医院6.7%,全科医生6.3%),但两个数据集中仅五分之一(21.5%)的个体被共同识别出。
由于全科医生和医院的数据识别出的有复杂和长期医疗需求的个体大多不同,合并数据源可能是识别最需要特别关注个体的最佳选择。