Validity of predictive power of the Adjusted Morbidity Groups (AMG) with respect to others population stratification tools

e202007079

Authors

  • Carmen Arias-López Subdirección General de Calidad e Innovación. Ministerio de Sanidad. Madrid. España.
  • Mª Pilar Rodrigo Val Servicio de Evaluación y Acreditación. Dirección General de Asistencia Sanitaria. Departamento de Sanidad. Gobierno de Aragón. Zaragoza. España.
  • Laura Casaña Fernández Servicio de Evaluación y Acreditación. Dirección General de Asistencia Sanitaria. Departamento de Sanidad. Gobierno de Aragón. Zaragoza. España.
  • Lydia Salvador Sánchez Servicio de Coordinación Asistencial, Sociosanitaria y Salud Mental. Dirección General de Asistencia Sanitaria. Gerencia Regional de Salud de Castilla y León. Valladolid. España.
  • Ana Dorado Díaz Servicio de Estudios, Documentación y Estadística. Secretaría General. Consejería de Sanidad de Castilla y León. Valladolid. España.
  • Marcos Estupiñán Ramírez Sección de Evaluación y Sistemas de Información. Servicio de Atención Primaria. Dirección General de Programas Asistenciales. Servicio Canario de la Salud. Las Palmas de Gran Canaria. España.

Keywords:

Risk groups, Chronic disease, Software, Morbidity, Severity of illness, Health outcomes, Health resources, Mortality, Emergencies, Primary health care

Abstract

Background: 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).
Methods: 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.
Results: 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.
Conclusions: GMA® is a population stratification tool adequate and as useful as others existing morbidity groupers.
Key words: Risk groups, Chronic disease, Software, Morbidity, Severity of illness, Health outcomes, Health resources, Mortality, Emergencies, Primary health care.

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Published

2020-07-03

How to Cite

1.
Arias-López C, Rodrigo Val MP, Casaña Fernández L, Salvador Sánchez L, Dorado Díaz A, Estupiñán Ramírez M. Validity of predictive power of the Adjusted Morbidity Groups (AMG) with respect to others population stratification tools: e202007079. Rev Esp Salud Pública [Internet]. 2020 Jul. 3 [cited 2024 Nov. 16];94:9 páginas. Available from: https://ojs.sanidad.gob.es/index.php/resp/article/view/817