Arias-López Carmen, Rodrigo Val Mª Pilar, Casaña Fernández Laura, Salvador Sánchez Lydia, Dorado Díaz Ana, Estupiñán Ramírez Marcos
Subdirección General de Calidad e Innovación. Ministerio de Sanidad. Madrid. España.
Servicio de Evaluación y Acreditación. Dirección General de Asistencia Sanitaria. Departamento de Sanidad. Gobierno de Aragón. Zaragoza. España.
Rev Esp Salud Publica. 2020 Jul 3;94:e202007079.
This work was performed in order to get objective elements of judgment that support the improvement of a national population morbidity grouper based in the Adjusted Morbidity Groups (AMG). The study compared the performance in terms of predictive power on certain health and resource outcomes, in between the AMG and several existing morbidity groupers (ACG®, Adjusted Clinical Groups and CRG®, Clinical Risk Group) used in some Autonomous Regions in Spain (Aragón, Canarias y Castilla y León).
Cross-sectional analytical study in entitled/insured population with respect to rights of healthcare. Predictive capacity of the complexity weight obtained with the different stratification tools in the first year of the study period was evaluated using a simple classification method that compares the areas under the curves ROC for the following outcomes that occurred in the second year of the study period: Probability of death; probability of having at least one urgent hospital admission; total number of visits to hospital emergencies; total number of visits to primary care; total number of visits to hospital care and spending in pharmacy.
The results showed that AMG complexity weight were good predictors for almost all the analyzed outcomes (AUC ROC>0.7; p<0.05), for the different Autonomous Regions and compared to ACG® or CRG®. Only for the outcome of visits to hospital emergencies in Aragon and Canarias; and visits to specialized care in Aragon, the predictive power was weak for all the compared stratification tools.
GMA® is a population stratification tool adequate and as useful as others existing morbidity groupers.
开展本研究以获取客观的判断要素，支持基于调整后发病组（AMG）改进国家人口发病分组器。该研究比较了AMG与西班牙部分自治区（阿拉贡、加那利群岛和卡斯蒂利亚-莱昂）使用的几种现有发病分组器（ACG®，调整后临床分组；CRG®，临床风险分组）在某些健康和资源结果方面的预测能力。
对享有医疗保健权利的参保人群进行横断面分析研究。在研究期的第一年，使用一种简单的分类方法评估不同分层工具获得的复杂性权重的预测能力，该方法比较研究期第二年出现的以下结果的ROC曲线下面积：死亡概率；至少有一次紧急住院的概率；医院急诊就诊总数；初级保健就诊总数；医院护理就诊总数以及药房支出。
结果表明，对于不同的自治区，与ACG®或CRG®相比，AMG复杂性权重几乎是所有分析结果的良好预测指标（AUC ROC>0.7；p<0.05）。仅对于阿拉贡和加那利群岛的医院急诊就诊结果；以及阿拉贡的专科护理就诊结果，所有比较的分层工具的预测能力都较弱。
GMA®是一种合适的人群分层工具，与其他现有发病分组器一样有用。
